The Sounds of Life: How Digital Technology Is Bringing Us Closer to the Worlds of Animals and Plants 9780691240985

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The Sounds of Life

The Sounds of Life How Dig i ta l T e c h nol o g y I s Br i ngi ng Us Cl ose r to t h e Wor l ds of A n i m a l s a n d Pl a n ts

Karen Bakker

pr i nce­t on u n i v e r sit y pr e ss pr i nce­t on a n d ox for d

Copyright © 2022 by Karen Bakker Prince­ton University Press is committed to the protection of copyright and the intellectual property our authors entrust to us. Copyright promotes the pro­gress and integrity of knowledge. Thank you for supporting ­free speech and the global exchange of ideas by purchasing an authorized edition of this book. If you wish to reproduce or distribute any part of it in any form, please obtain permission. Requests for permission to reproduce material from this work should be sent to permissions@press​.­princeton​.­edu Published by Prince­ton University Press 41 William Street, Prince­ton, New Jersey 08540 99 Banbury Road, Oxford OX2 6JX press​.­princeton​.­edu All Rights Reserved ISBN 9780691206288 ISBN (e-­book) 9780691240985 British Library Cataloging-­in-­Publication Data is available Editorial: Alison Kalett and Hallie Schaeffer Production Editorial: Natalie Baan Text Design: Karl Spurzem Jacket/Cover Design: Amanda Weiss Production: Jacquie Poirier Publicity: Kate Farquhar-­Thomson and Sara Henning-­Stout Copyeditor: Jennifer McClain Jacket images from top to bottom: European pond turtle from Deutschlands Amphibien und Reptilien by Bruno Dürigen, 1809. “Bees and Lilies,” illustration from Stories of Insect Life by William J. Claxton, 1912, Private Collection / Bridgeman Images. “Group of Corals,” illustration from Science for All by Robert Brown, 1877. Patulous gorgonia or Flat Gorgonia illustration from The Naturalist’s Miscellany (1789–1813) by George Shaw. Medical botany plant / Raw Pixel. Tomato plant, 1649–1659, Hans-Simon Holtzbecker. Soundwave by Marina / Adobe Stock. This book has been composed in Arno Printed on acid-­free paper. ∞ Printed in the United States of Amer­i­ca 10 ​9 ​8 ​7 ​6 ​5 ​4 ​3 ​2 ​1

For the Peace River

Listening to wild places, we are audience to conversations in a language not our own. —­r obi n wa l l k i m m er er , br a i di ng s w e etgr a s s

Contents

Introduction 1 1. Sounds of Life  11 2. The Singing Ocean  27 3. Quiet Thunder  44 4. Voice of the Turtle  63 5. Reef Lullaby  80 6. Plant Polyphonies  99 7. Bat Banter  119 8. How to Speak Honeybee  138 9. The Internet of Earthlings  158 10. Listening to the Tree of Life  180 Acknowl­edgments and List of Interviewees  205 Appendix A: How to Start Listening  209 Appendix B: Further Reading  213 Appendix C: Brief Overview of Research on Bio-­and Ecoacoustics  215 Notes  221 References  251 Index  345

The Sounds of Life

Introduction

Compared with our cousins on the Tree of Life, ­humans are poor listeners.1 Below the lower end of ­human hearing lies deep infrasound: the realm of thunder and tornadoes, elephants and w ­ hales. Many creatures can sense and communicate in infrasound, which travels long distances with ease, passing through air and w ­ ater, soil and stone. In one of the animal kingdom’s most famous mating rituals, male peacocks transmit power­ful infrasound with their raised tails; what h­ umans perceive to be a visual display is, in fact, a sonic summons.2 The deepest infrasound is generated by our planet itself. If you could tune into the Earth’s infrasound, you might hear the rumblings of calving icebergs, the howl of a volcano, or the roar of a typhoon halfway around the world.3 Lowest of all, the Earth’s periodic infrasonic pulse resonates below our feet and through the air. As ocean waves collide over continental shelves, they vibrate the Earth’s crust in a rhythmic fashion—­the drumming heartbeat of our planet.4 When earthquakes convulse our planet’s surface, they create airborne infrasonic tremors—­ ringing our atmosphere like a quiet bell.5 The planet’s infrasonic chorus is continuously sounding all around you. Many animals—­rock doves and snakes, tigers and mountain beavers—­are able to hear t­ hese low-­frequency sounds, but not h­ umans.6 Our hearing is typically confined to a relatively narrow band of frequencies, between 20 Hz and 20 kHz, a range that narrows as we age. At best, we can sometimes sense infrasound as a throbbing in the chest, or a troubling feeling of unease.7 At the other end of the spectrum, above the upper threshold of ­human hearing, lies the ultrasonic: high-­frequency sounds that vibrate too quickly for us to hear. A surprisingly diverse array of species—­mice 1

2  I n t r o du c t i on

and moths, bats and beetles, corn and corals—­emit ultrasonic sounds imperceptible to ­humans.8 Our ancestors may once have been able to hear ­these high-­pitched sounds, and our smaller primate cousins—­tiny tarsiers and dwarf lemurs—­can still communicate in ultrasound.9 But con­temporary ­humans have lost this ability.10 Still other species use ultrasound to visualize their world: to navigate, find mates, and follow prey. By using what is known as echolocation, bats and toothed w ­ hales create images of their surroundings by sending out beams of ultrasound and analyzing the returning echoes. Biosonar (as echolocation is also known) functions somewhat like an acoustic flashlight, honed by evolution to be as accurate as our finest medical devices. Simpler forms of echolocation are also used by cave swiftlets and oil birds, nocturnal shrews and rats; they, too, see the world through sound.11 Yet although t­ hese calls are some of the loudest ever recorded in the animal kingdom, they are inaudible to us.12 Attuned h­ umans can occasionally hear the subtle clicks at the lower end of animal echolocation; rarely, blind p­ eople even develop the ability to echorange themselves. But for most of us, even the loudest ultrasonic sound blown directly into our ears would feel like nothing more than an empty, ghostly breath of wind. As Blackfoot phi­los­o­pher Leroy L ­ ittle Bear says, “The h­ uman brain is like a station on the radio dial; parked in one spot, it is deaf to all the other stations . . . ​the animals, rocks, trees, si­mul­ta­neously broadcasting across the w ­ hole spectrum of sentience.”13 Our physiologies—­and perhaps our psyches—­limit our capacity to listen to our nonhuman kin. But humanity is beginning to expand its hearing ability. Digital technologies, so often associated with our alienation from nature, are offering us an opportunity to listen to nonhumans in power­ful ways, reviving our connection to the natu­ral world. In recent years, scientists have begun installing digital listening devices in nearly ­every ecosystem on the planet, from the Arctic to the Amazon. Th ­ ese microphones are computerized, automated, and networked with digital sensors, drones, and satellites so power­ful they can hear a ­mother w ­ hale whispering to her calf in the depths of the ocean. Researchers have attached tiny microphones to honeybees and turtles,

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and affixed listening posts to coral reefs and trees. When interconnected, ­these listening networks may stretch across entire continents and ocean basins.14 Amateurs are also listening to nature’s sounds, using inexpensive listening devices, like the AudioMoth (an open-­source device the size of a smartphone); the cheapest build-­it-­yourself version now costs well u­ nder $100.15 Combined, ­these digital devices function like a planetary-­scale hearing aid: enabling ­humans to observe and study nature’s sounds beyond the limits of our own sensory capabilities. This book tells the stories of the scientists who are using ­these digital technologies to decode the hidden world of nonhuman sound, and the surprising sounds they are hearing. Recent scientific breakthroughs have revealed that a vast array of species makes an astonishing assortment of sounds, mostly beyond the range of ­human hearing—­and so, ­until recently, unsuspected and unappreciated. (In writing this book, I surveyed research on more than 1,000 species, a small fraction of the scientific findings on bioacoustics—­the technical term for the science of listening to nonhuman organisms.) Dolphins and belugas, mice and prairie dogs use unique vocalizations (like signature whistles) to refer to one another, much as we do with individual names.16 Baby bats “babble” at their ­mothers, who speak back to their young in “motherese,” just like h­ umans do. Turtle hatchlings—­previously thought to be mute—­ coordinate the moment of their birth by calling to one another through their shells. Animals use sound to warn, protect, and lure one another; to teach, amuse, and name one another. Carefully listening to the nonhuman world reveals complex communication in a broad range of species and challenges the claim that humanity, alone, uniquely possesses language. ­These claims might seem plausible when discussing primates or birds. But what digital technologies reveal is the vast extent of sonic communication across the natu­ral world. Using digital bioacoustics, scientists have documented the ability of species without ears, or any apparent means of hearing, to interpret and respond to complex information conveyed through sound. When dispersed in the open ocean, fish and coral larvae (creatures only a few millimeters in size, with no central ner­vous system) distinguish the sounds of their home reefs from the cacophonous ocean, and then swim

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back home to ­settle. Plants emit distinct ultrasonic noises when dehydrated or distressed. In response to the sound of buzzing bees, flowers flood with sweetened nectar, as if in anticipation. The Earth is in continuous conversation. Now, digital technologies provide a new way for humanity to listen to the vivid soundscapes all around us, opening our ears to the resonant mystery of nonhuman sound.

Resonant Earth The scientific breakthroughs explained in this book primarily occur in two fields of study: bioacoustics and ecoacoustics. Together, ­these scientific disciplines enable ­humans to have digitally mediated access to the hidden conversations ongoing across the natu­ral world, even in the remotest places on Earth. As explored in the chapters that follow, this dramatically enhances our ability to monitor organisms and ecosystems and detect environmental change. Scientists are also experimenting with the use of bioacoustics and ecoacoustics to restore ecosystems; nature’s sounds, they have learned, can be used to regenerate the health of plants and animals, including ourselves. Their research also reveals that environmental noise is an exponentially growing assault on the natu­ral world and a major form of pollution; quieting the ­human din is thus one of the major conservation challenges of our time.17 What, exactly, is bioacoustics? Put simply, bioacoustics is the study of sounds made by living organisms.18 Researchers in this field are ­adept at both the art and science of listening. Imagine a field biologist with the training of an audiologist, the skills of a data scientist, and the sensibility of a musical composer, and you have captured about half of the expertise that con­temporary bioacousticians possess.19 Bioacoustics brings ­great insight to the study of wild places; scientists have discovered entirely new species this way, and even rediscovered species that we thought had gone extinct. A camera only spots the animals walking down the forest path, but a digital recorder hears them hiding in the bushes. Ecoacoustics, also called acoustic ecol­ogy or soundscape studies, entails listening to the environmental sounds generated by entire landscapes.20 Imagine standing in the m ­ iddle of a tropical forest: you might

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hear the rustle of leaves, the cries of birds, the roar of a waterfall. Th ­ ese combined sounds form what is called a soundscape.21 Soundscapes can reveal much about the functional condition of ecosystems. A degraded ecosystem sounds very dif­fer­ent than a healthy one. Like a stethoscope that detects a heart murmur, ecoacoustics can detect the presence or absence of healthy sounds. Each landscape has its own distinct soundscape, like an acoustical calling card that combines animal (including ­human), plant, and even geological sounds.22 Simply by listening, an ecoacoustician can tell you the difference between a tree farm and a forest, or detect early signs of degradation in a seemingly intact ecosystem; using ecoacoustics, we can now map wilderness areas without ever setting foot ­there.23 Ecouacousticians listen to landscapes like a radiologist might look at an MRI scan, discerning the subtlest signs of health and disease. Bio-­and ecoacoustics have recently been transformed by a new generation of digital recording technologies that allow humanity to listen at a distance, in an automated fashion.24 In the early days of analog recordings of nature’s sounds, the technology was bulky, cumbersome, and expensive. T ­ oday, heavy reels of magnetic tape have been replaced by portable, lightweight, inexpensive, and long-­lasting digital recorders. A few de­cades ago, the equipment required to do field recordings could fill a small ­minivan; t­ oday’s digital recorders fit inside a backpack or even your back pocket. ­These digital listening devices can be installed almost anywhere and run continuously, capturing sounds over a larger range than a camera can capture images. This has allowed scientists to listen to the far reaches of the globe, across the Tree of Life. Around the world, both amateurs and experts are tuning in to nature’s sounds. The digitization of any field creates a tsunami of data. In order to deal with this data deluge, scientists have applied new techniques, derived from artificial intelligence, to analyze their digital acoustic recordings.25 Algorithms originally developed for h­ uman use (such as the speech-­to-­ text algorithms in a smartphone) are being adapted to analyze and interpret the voices of other species.26 ­These bioacoustics algorithms have become exponentially more power­ful in the past few years: they can identify species and even individual animals, much like voice recognition

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software.27 It is impor­tant not to exaggerate the current capability of ­these algorithms, which still do not generalize very well and often require some degree of manual verification.28 Challenges with the under­ lying hardware used in the field, such as the power limitations of sensors, are also significant. But if ­these challenges can be addressed, humanity may be on the brink of inventing a zoological version of Google Translate.29 By combining ­these digital listening devices with artificial intelligence, scientists are beginning to decode as well as rec­ord nonhuman sound. Some scientists are using artificial intelligence to build dictionaries in East African Elephant, Southern Australian Dolphin, and Pacific Sperm Whalish. A few researchers have even successfully achieved two-­way communication with nonhumans, mediated by robots and artificial intelligence. Digital technology now allows scientists to approximate an organism’s distinctive pattern of communication: although our vocal cords c­ an’t click like a dolphin or buzz like a bee, our computers and robots can do just that. The same technologies that we use in the Internet of Th ­ ings are now being developed to communicate with other species in fundamentally new ways. ­These technologies have enabled scientific discoveries that revolutionize our understanding of the natu­ral world. In telling the stories of ­these discoveries in the chapters that follow, I emphasize three points: many more nonhumans can make and sense sound than scientists had previously realized; many species have richer, more complex communication and social be­hav­iors than previously understood; and t­ hese findings create new possibilities for both environmental conservation and interspecies communication. Some of t­ hese scientific findings w ­ ere initially met with skepticism. Many researchers initially dismissed the idea that nonhumans could make sounds beyond the range of ­human hearing (although we now know that many species make, and even more species can hear, such sounds). Many researchers also scoffed at the idea that nonhumans could make subtle sounds that carry complex information; ­these qualities, it was thought, w ­ ere reserved for h­ umans (yet we now know the contrary to be true). The scientists whose work is shared in ­these pages often overcame re­sis­tance from their peers

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through painstaking research. Theirs is a collective discovery, de­cades in the making, of the universal importance of sound to the nonhuman world. In offering t­ hese insights, it is impor­tant to acknowledge the primacy of traditional ways of listening. Deep listening is a venerable and ancient art, still practiced as a power­ful method of revealing nature’s truths. Indeed, many of the “discoveries” recounted in this book are often, in fact, merely rediscoveries of older forms of environmental knowledge. As Potawatomi plant ecologist Robin Wall Kimmerer writes, “I smile when my colleagues say ‘I discovered X.’ That’s kind of like Columbus claiming to have discovered Amer­i­ca. Experiments are not about discovery but about listening and translating the knowledge of other beings.”30 Kimmerer reminds us that if we ask clear, open-­minded questions, and patiently pay attention, nature gives us the answers. Much can be learned this way, and traditional ecological knowledge has a ­great deal to teach us in this regard. Deep listening also provides much-­needed guideposts for this new world of digital bioacoustics; it provides an ethics of responsibility and sense of stewardship rooted in place, without which our novel digital tools might enable humanity to further exploit and domesticate rather than protect and connect with other species.

A Globe, Clothing Itself with Ears Over fifty years ago, phi­los­o­pher Pierre Teilhard de Chardin described the f­ uture of computing in a mystical fashion. His poetic meta­phor for the growing ubiquity of computer networks was a prescient description: our planet “clothing itself with a brain.”31 Marshall McLuhan would ­later expand on de Chardin’s description in his best-­selling book The Gutenberg Galaxy.32 De­cades before the invention of the World Wide Web, McLuhan saw on the horizon a digital revolution, in which the interconnection of computer networks was analogous to a planetary ner­vous system. He predicted, moreover, that the emergence of this digital network would give rise to new forms of global consciousness. Technologies, according to McLuhan, are not simply tools that ­people deploy; rather, our inventions alter our be­hav­ior and consciousness, both individually and collectively. The invention of movable type by

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Johannes Gutenberg around 1450, for example, was a pivotal point in the development of a standardized, uniform, and ultimately automated cultural production of knowledge through mass print media, such as books and newspapers. Central to McLuhan’s argument was the interplay between technology and our senses. The rise of movable type, he argued, changed humanity’s perceptual habits. By replacing oral and scribe cultures with print technology, the importance of our visual senses intensified; the salience of oral and aural sensing receded. Information no longer needed to be recalled and remembered; rather, it needed to be collected and or­ga­nized. Gone w ­ ere the recitations of long epic poems, which cultivated the art of memory. ­These ­were replaced by the segmentation of information, which cultivated the art of knowledge specialization. Literacy replaced orality; the Dewey decimal system supplanted Homer’s Odyssey. McLuhan also predicted a resurgence of oral cultures. Whereas print culture separated the storyteller from the audience by interposing a fixed text (a book), he foresaw that digital communication would lead to the return of oral modes of interactive storytelling: interplay between storyteller and audience, call-­and-­response patterns, and mimetic, collaborative evolution of story lines. The rise of internet phenomena like TikTok and interactive computer games arguably prove McLuhan’s point (including his prediction that a renewed tribalism would emerge). What McLuhan and de Chardin failed to predict, however, was the extension of t­ hese digital, networked cultures to include nonhumans. What would they have made of digital bioacoustics and the potential for interspecies communication via the internet? Stories of speaking with animals are as old as h­ uman history. In the Pacific Northwest, Indigenous communities relate how Txeemsim (Raven)—­trickster and shape-­shifter, prankster and shaman—­teaches ­humans about balance and harmony while living within a natu­ral world that both shapes and sustains ­human beings.33 In the Persian epic poem Shahnameh, the phoenix-­god bird Simurgh teaches wisdom to the forsaken Prince Zal, preparing him to rejoin the world of men.34 In the Christian tradition, St. Francis speaks of repentance and love with the

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wolves and the birds. In medieval texts and fables, talking animals abound; medieval bestiaries feature animals ventriloquizing ­human morals, testifying to ­human fallibility, divine grace, and hy­poc­risy in ­humans’ treatment of nature.35 ­These stories remind us that nature is a source of teachings, if we remember to listen. Yet many Western scientists and phi­los­o­phers also espouse the view (defended in a lineage stretching from Aristotle and Augustine, to Aquinas and Descartes, to the pre­sent day), that ­humans “alone among animals possess speech,” and hence uniquely possess the faculty of reason.36 ­These views are now being overturned by a new generation of scientific research. Yet ­human ambivalence about animal language persists and is linked with our uncertainty about ­human status: Are we one animal among ­others, or does something (language, toolmaking, log­os) truly set us apart?37 Debates over animal language are a touchstone for ­human uncertainties about our role in the cosmos. Our uncertainties extend to an ambivalence about our relationship with nature. Although the ability to converse with animals appears in the origin stories of many cultures, our myths also tell us that ­these voices ­were silenced. In Greece, the all-­powerful oracles lived in sacred groves and asked animate Earth deities for advice, yet this did not stop an onslaught of deforestation; as their fellow citizens denuded the islands, Greek poets wrote that felling a tree was akin to committing murder.38 Once, explains Robin Wall Kimmerer, we all spoke the same language—­humans and animals alike; but when colonial settlers came, writes Anishinaabe ­legal scholar John Borrows, nonhuman voices fell ­silent.39 The desire to recover a lost ability to communicate with other species stirs up power­f ul feelings: from fierce skepticism to a yearning for reconnection. The stories told in this book explore this tension. By remembering that sound is more than digital data, I seek to hold multiple truths si­mul­ta­neously: sound as data and information, sound as m ­ usic and meaning, sound as language and the true tongue of places and nonhuman p­ eoples. Listening is both a scientific practice and a form of witnessing that acknowledges our presence as guests on this planet and embraces our kinship with other species across the Tree of Life.

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Digital technologies, allied with science, are often depicted as a method and mindset that distances us from other species. The stories in this book offer another view: the potential for science, enhanced by digital technologies and interwoven with deep listening, to bring us on a journey of rediscovery of the natu­ral world. In this way, we might foster communion rather than dominion, kinship rather than owner­ ship of Earth. We begin by exploring how the Iñupiat shared their traditional knowledge with Western scientists, who used digital technologies to rediscover what Arctic p­ eoples had long known: the vibrancy of w ­ hale song in an ocean once presumed to be ­silent.

1 Sounds of Life

Herbert L. Aldrich discovered ­whale ­music on his deathbed. Riddled with tuberculosis and told he had less than a year to live, he made an impulsive decision: to accompany the New ­England whaling fleet into the Arctic in pursuit of bowhead w ­ hales. Aldrich, a journalist for the New Bedford Eve­ning Standard, had never before embarked on a ship. The year was 1887. He was twenty-­seven years old.1 Two de­c ades e­ arlier, the New York Times had described New Bedford—­the heart of the American whaling industry—as “prob­ably the wealthiest place” in the United States.2 Across the Old and New Worlds, w ­ hale oil fueled industrialization, lighting streetlamps and lubricating pistons, looms, and ball bearings. Whale blubber softened soap, margarine, and lipstick. Baleen ­whalebone curved corsets ­under dresses.3 Even before the gold rush flooded the Yukon’s mountains with miners, a ­whale rush was flooding the Arctic ­waters with boats.4 A single ­whale could be worth (in ­today’s equivalent) over a million dollars; successful ­whalers could retire rich ­after only a few expeditions.5 But by the time Aldrich embarked on his journey, the glory days of whaling ­were over. Whales had been hunted to near extinction in the eastern Arctic; the western Arctic was not far b­ ehind.6 The fleet would exist for only a few more years. Local notables in New Bedford funded the young man’s trip in memoriam: a ­dying man documenting a ­dying industry. Arctic whaling was not for the faint of heart.7 Ships w ­ ere lost e­ very year to pack ice. Ice jams could quickly build higher than the masts of 11

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ships, smashing the wooden hulls to pieces. Aldrich described feeling utterly vulnerable, dependent on a wooden ship “as frail as an egg shell in the clutches of an ice floe.”8 Only a de­cade e­ arlier, an entire fleet of forty vessels had been swallowed by the ice as over a thousand men, ­women, and ­children fled in small rowboats.9 Says Ishmael in Moby Dick, “­There is death in this business of whaling—­a speechlessly quick chaotic bundling of a man into Eternity.”10 Despite the risks, w ­ hales w ­ ere irresistible prey. As the number of ­whales dwindled, ­whalers extended their trips into the furthest, most dangerous recesses of the ocean. H ­ ere, even the Inuit and Iñupiat, master hunters, danced with death. Ittuangat Aksaarjuk describes hunting at Qikiqtaaluk (Baffin Island) along the retreating edge of an ice floe.11 Traveling along the shifting edge of open w ­ ater, the men would haul their open skiffs onto the ice and flip them over for shelter when storms hit, hoping the ice would not break off and carry them away from land. When Aldrich departed on the Young Phoenix in early March, the ice off the Alaska coast was still dense, the sun edging above the horizon. Through heavy fog, the fleet of more than thirty ships followed thin cracks in the ice, which often closed quickly b­ ehind them. If another crack opened up, the ships would move forward. If not, they would wait, watching as the floes pressed in. Moving from one watery prison to the next was like playing a perilous game of chess against an ever-­ shifting opponent. Aldrich’s captain, Edmund Kelley, had lost his ship, the Seneca, only a few years e­ arlier, and the Young Phoenix itself would eventually be lost off the coast of Alaska in a storm that destroyed several ships.12 To pass the time while waiting for the ice to open, sailors kept themselves busy by hunting walrus and seals, gambling, and storytelling. Aldrich boat-­hopped from the Eliza to the Hunter, the Balaena to the Thrasher, photographing the voyage on his Scovill detective cameras.13 But mostly he fretted. Captain Kelley attempted to console him one night by telling tales of “singing” ­whales. At first, Aldrich assumed this was a joke aimed at him, a gullible outsider and the first writer to accompany the whaling fleet into the western Arctic. Then, one day, he too heard the ­whale m ­ usic as the ships followed their prey.

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As Aldrich recounts, Kelley was mocked when he first shared his discovery of ­whale song. But Kelley’s hunting prowess eventually silenced the jibes. Kelley would urge the fleet to follow even the faintest sounds, which he claimed he could hear vibrating through the ship’s hull. “When Captain Kelley took up anchor and set sail,” Aldrich recounts, “­every ship followed him.”14 The captains of the other boats took note and began standing guard for ­whale song, day and night. ­After a w ­ hale had been harpooned, Kelley would put his ear to the taut towline, listening. As Aldrich would ­later describe it: Kelley heard the ­whale that he had struck give a deep, heavy, agonizing groan, like that of a person in pain. With bowhead w ­ hales, the cry is something like the hoo-­oo-oo of the hoot-­owl, although longer drawn out, and more of a humming sound than a hoot. Beginning on F, the tone may rise to G, A, B and sometimes C before slanting back to F. With the humpbacked-­w hale, the tone is much finer, often sounding like the E-­string of a violin.15 As the ­whales traveled through the Bering Strait, the narrow marine gateway to the vast Beaufort Sea, their vocalizations enabled Kelley to hunt the bowheads down. That year was remarkable for the bounty of ­whales caught by the fleet; the w ­ halers’ haul of more than 600,000 pounds of baleen was the largest ever for de­cades.16 For the w ­ halers, the songs w ­ ere a giveaway clue in the hunt. But Aldrich wondered about their purpose.17 Could the songs be “a sort of call, or signal for ­whales when making a passage through the Bering Sea, to notify each other that they are bound north, and perhaps the Straits are clear of ice?”18 Sailors watching from ship mastheads reported that whenever a w ­ hale was struck by a harpoon, other w ­ hales in the vicinity would be frightened by its cries of pain. Two years ­earlier, Kelley’s ship had struck a sperm ­whale and “instantly, the ­whole school, which was three miles or more off, started for their wounded companion, and circling about it huddled together as if to ask ‘what’s the ­matter?’ ”19 Aldrich asked his companions about the meaning of ­w hale song. But caught up in the “picturesque excitement” of the chase, the sailors dismissed his musings about ­whale communication.

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On the last day of August, the Young Phoenix arrived in Point Barrow, the northernmost tip of the United States, over 300 miles north of the Arctic Circle. At this strategic location, the Bering Strait—­only 3 miles wide at its narrowest point—­fans out into the Beaufort Sea. Aldrich went ashore to find the beach still covered in ice. As he gazed t­ oward the Pole, he described the scene: “As far as the eye could reach into the north and eastward, was ice, blue in its solidity, and no more penetrable than so much granite.”20 The local Iñupiat called this place Utqiaġvik, good land for gathering wild roots (utqiq). But their primary source of food was bowheads, which they hunted from sealskin umiat, boats that could slip through the small ice openings where the southerners’ ships ­couldn’t pass. Although no w ­ hales w ­ ere to be seen, the Iñupiat said they would follow the pods further north. But the whaling fleet could go no farther; the ice closed in b­ ehind them as they headed south ­toward home. The voyage of the Young Phoenix was one of the last undertaken by the whaling fleet. The bowheads had been nearly eradicated. Aldrich commented on the devastation: “Before the appearance of the ­whalemen, the natives caught ­whales, seals, and walrus in g­ reat plenty at their very doors. Now hunters must go a long distance, and are rarely successful. The w ­ hales grow more and more shy by the year, to the disadvantage of both natives and ­whalemen.”21 Aldrich—­w ho recovered and lived to the age of eighty-­eight—­ returned home to publish a book about his journey, lecturing widely about whaling and the Arctic. But his account of w ­ hale noise drew l­ ittle attention and was soon entirely forgotten.22 As anthropologist Stefan Helmreich has noted, popu­lar culture framed oceans as quiescent: from Jacques Cousteau’s book The ­Silent World to Kipling’s poetic rendering of the ocean (“There is no sound, no echo of sound, in the deserts of the deep”), oceans ­were presumed to be sonic dead zones, ­silent as the grave.23 By the turn of the twentieth c­ entury, ships’ crews could no longer hear ­whale songs; propellers and engine noise drowned them out. But Captain Kelley’s technique would turn out to be prescient. He was an unintentional pioneer of the science of marine bioacoustics: the study of the sounds made by sea creatures.24

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Pesky Biologicals As soon as microphones w ­ ere in­ven­ted, they began inadvertently picking up squawks and whistles from all sorts of creatures. Few h­ umans paid attention. Research on bioacoustics was largely conducted by quirky scientists like Slovenian biologist Ivan Regen, who played recorded insect sounds to other insects and carefully observed their responses. In one of his best-­known experiments, Regen arranged for a male insect to call a female of the same species using a then brand-­new technology: the telephone. At the time, few ­were interested in ­these esoteric experiments, with one exception: the military.25 ­After World War II, the world’s armies began conducting classified research into marine sound as a foundation for antisubmarine warfare; the military soon realized that underwater acoustics furnished a trove of valuable information. The Navy’s attention was focused on a specific layer of the ocean known as the deep sound channel.26 Through this channel, which is about half a mile ­under the surface of the ocean at midlatitudes, sound waves are able to travel thousands of miles. Discovered in the 1940s, the channel (christened SOFAR—­sound fixing and ranging channel) became the focus of intense military interest, as it could be used to detect sounds from ship sonar, the marine acoustic equivalent of radar.27 The military found that they could drop a hydrophone (an underwater microphone) into the SOFAR channel and hear submarines moving hundreds of miles away. Exploiting the SOFAR channel was a top security priority during the Cold War. Faced with a rapid buildup of the Soviet submarine fleet, the US Navy created a classified global network of fixed ocean floor listening posts, referred to as SOSUS—an acronym for sound surveillance system. The SOSUS network proved its worth: it was able to track Soviet submarines across the oceans, with instruments so sensitive they could detect the sounds of a deployed torpedo or even the whisper of a propeller.28 SOSUS became the American “secret weapon” of antisubmarine warfare. The Navy technicians r­ unning SOSUS frequently complained, however, of low moaning and rumbling background noises that

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interfered with their recordings. Navy scientists ­were perplexed: ­Were the noises from hydrothermal vents? Earthquakes? Some of the sounds could be easily confused with sounds from submarines or other military equipment, raising risks of false alarms in the tense Cold War era. The Navy suspected that some of the sounds w ­ ere made by marine organisms, so they recruited a number of bioacousticians. Among them was the propitiously named Dr. Marie Poland Fish. ­Under contract to the Office of Naval Research for over two de­cades, Dr. Fish experimented on captive marine organisms by poking them with the equivalent of electrical ­cattle prods. She recorded the resulting sounds, so that Navy antisubmarine vessel operators could be trained to distinguish sounds made by living marine organisms from t­ hose of submarines.29 Studying over three hundred species, from mammals to mussels, she reported that a large number of marine species ­were capable of making noise. As biologists l­ ater realized, many of t­ hese sounds w ­ ere produced by violent muscle contractions and w ­ ere not representative of the sounds that the animals produced u­ nder normal conditions.30 Perhaps ­because of this (or perhaps b­ ecause her experiments resembled torture), Dr. Fish’s experiments ­were neither widely replicated nor appreciated.31 Despite t­ hese early experiments, some of the most curious sounds that Navy technicians ­were hearing still evaded explanation. T ­ hese sounds emanated from deep in the ocean: clicks, howls, moans, grunts, and ululations. Curiously, some of the sounds could be detected si­mul­ ta­neously by multiple listening posts, even stationed far apart across entire ocean regions. The technicians labeled the mysterious sounds with the names of machines and fictitious beasts: A-­train, Jezebel (or Jeze) Monster, Commas, and the Barnyard Chorus. Eventually, Navy researchers realized that t­ hese monstrous cries w ­ ere the distinctive sounds of ­whales.32 Diving into the ocean, the ­whales ­were communicating with one another along the SOFAR channel, where their songs traveled unimpeded across hundreds and even thousands of miles. The ­whales had long ago perfected what the military had only just discovered. While the Navy’s knowledge of w ­ hale sounds began expanding rapidly a­ fter World War II, the first publicly available scientific research paper on the subject was published only in 1957. The authors w ­ ere

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inspired by encounters with ­whales while in the military. Originally trained at Harvard as a paleontologist, Bill Schevill had discovered ­whale sounds while working with the US Navy during the war, while he was developing underwater listening techniques for SOSUS. The naval listening posts ­were usually underwater rooms without win­dows, leading military observers to refer to the sounds as mere “fish noises.”33 Intrigued, Schevill abandoned paleontology and talked his way into a job at the prestigious Woods Hole Oceanographic Institution in Cape Cod. He was joined by William Watkins, the self-­taught son of African missionaries who in­ven­ted the first tape recorder designed to rec­ord marine mammals at sea. Watkins, originally hired as a technician, had an abiding interest in linguistics (at the age of fifty-­five, a­ fter mastering over thirty African dialects, he obtained his PhD from the University of Tokyo, where he chose to defend his thesis in Japa­nese).34 Watkins and Schevill traveled the world’s oceans for forty years, sidling up alongside ­whales on sailboats (quieter than motorboats) to inject them with radio tags so they could be tracked.35 Working together with Schevill’s wife, Barbara, the scientists published several hundred hydrophone studies. Watkins’s marine acoustics database—­with over twenty thousand calls of over seventy species of marine mammals—is still used by the military to train sonar technicians.36 Watkins and Schevill’s work laid the groundwork for scientists’ newfound understanding of ­whales as profoundly acoustically oriented creatures. Whales, like other marine creatures, see the world through sound. Over seventy million years ago, the terrestrial ancestors of ­today’s aquatic mammals re­entered the oceans where life began. Th ­ ere, they began to readapt to the underwater world. Beneath the surface, sound trumps sight. Light travels less well underwater than in the air, and few objects can be perceived distinctly beyond a hundred feet. In contrast, sound travels four times faster in w ­ ater than in air. Underwater, marine animals can hear much farther than they can see. Through the pro­cess of evolution, ­whales became finely tuned to the sonic environment of the ocean, and sound became the primary way that they hunt, socialize, and escape predators. Some species evolved the ability to hear in the deep infrasonic, while o­ thers evolved hearing in the high

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ultrasonic. The auditory ganglion (nerve cell) densities of ­whales are double t­ hose of the average land mammal; this exceptionally high auditory nerve fiber count means that ­whales are wired for more complex signal pro­cessing than most land mammals, including h­ umans.37 As Cornell University bioacoustician Chris Clark puts it: “Whales are highly acoustically oriented. Their consciousness and sense of self is based on sound, not sight.”38 Broadly speaking, cetaceans (marine mammals, such as w ­ hales, dolphins, and porpoises) employ three dif­fer­ent types of sound communication.39 The first type of sound—­social calls—­involves vocalizations that fall largely within ­human hearing range. To us, ­these mostly sound like whistles, pulsed squeaks, or squeals, in a variety of patterns. For example, killer ­whale babies babble when they are born, and begin imitating sounds made by their families when they are a few months old. Like ­humans, killer ­whales use sound to identify individuals, exchange information, and negotiate social relationships. Each pod has its own unique dialect of calls that calves learn from their ­mothers over several years, one of the most complex cultural communication repertoires in the animal world.40 As pod members stay together their entire lives, their dialects form part of their identity and indicate a strong cultural bond; killer w ­ hales with dif­fer­ent dialects rarely intermingle for long.41 ­These dialects are so distinct that scientists (and even trained amateurs) can differentiate between pods simply by listening. Some w ­ hale calls are also very loud: sperm ­whales, the loudest animal in the world, can vocalize at over 200 decibels, louder than a rocket launch or a jet engine at takeoff (and loud enough to burst your ear­drums if you ­were swimming nearby). The second type of sound, used by odontocetes (over seventy species of toothed w ­ hales, including dolphins, killer w ­ hales, porpoises, and sperm ­whales), is echolocation, also called biosonar. When an animal uses biosonar (which sounds like a series of fast clicks to us), they visualize their surroundings by projecting high-­frequency sound waves and discerning the distance and direction of objects from the resulting echoes—­much like an ultrasound machine in a doctor’s office. Echolocation enables odontocetes (and other animals, like bats) to “see” and navigate their environment, find prey, and even scan the insides of other

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animals’ bodies. Killer ­whales use sound to continuously scan their environment, just like we do with our eyes; their survival depends on their ability to hear their biosonar echoing back from fast-­moving fish, or from the hulls of approaching ships.42 As Clark puts it: “Their mind’s eye is their mind’s ear.”43 The third type of sound made by some cetaceans is the long, low, rhythmic pattern of sounds produced by baleen w ­ hales (Mysticeti), popularly referred to as ­whale song. The songs, which are thought to be sung exclusively by males and may be related to mating, are some of the most complex sonic displays in the animal kingdom. Some w ­ hales sing in the infrasound range, while ­others sing at frequencies that are audible to h­ umans. Humpback ­whale songs are the best studied, although other species also sing distinct songs. The difference in vocalization patterns reflects a delicate evolutionary balance between habitat and the sound properties of varying ocean depths where dif­fer­ent ­whale species dwell. For deep-­water species, sounds need to be s­ imple, even sparse, in order to reliably broadcast over long distances. But for species in shallower ­waters, where sound cannot travel as far due to the acoustic properties of the ocean, greater variability in patterns and frequencies helps to optimize communication and navigation. Some ­whale songs are longer, ­others shorter; if humpbacks and bowheads recite sonnets, blue and fin ­whales are the marine masters of Zen koans.44 Given the challenge of contending with corrosive salt ­water, storms, and currents, few ­people outside the Navy had the time, money, or savvy to rec­o rd ­w hale sounds. Cumbersome reel-­to-­reel tape recorders—­the size of small suitcases and, notably, not waterproof—­ made the task particularly daunting. Although the initial SOSUS recordings w ­ ere declassified to some degree a­ fter 1991, newer versions remained classified.45 Whale bioacoustics remained ­little known, ­until an unexpected encounter brought ­whale ­music to the world.

Whale ­Music Goes Platinum In 1967, scientists Roger and Katy Payne traveled to Bermuda to go ­whale watching.46 It was a curious quest, born of a tragic encounter. Katy was a classically trained musician. Roger was an acoustic biologist

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who studied bats and owls. His undergraduate teacher at Harvard, Donald Griffin, had discovered that bats echolocate at a frequency that ­humans ­can’t hear. Roger Payne followed in Griffin’s footsteps, completing a PhD at Cornell and building a respectable c­ areer as a scientist. But Payne was troubled by a lack of immediate relevance in his work. “I was ­doing stuff that interested me—­and some other p­ eople—­but it did not serve the purpose of stopping the destruction of the wild world,” he ­later recalled.47 Working late one night in his lab at Tufts University, Payne heard through the radio that a dead ­whale had washed up on a local beach. By the time he got ­there, the ­whale had been mutilated. The flukes had been hacked off, prob­ably as souvenirs. Two p­ eople had carved their initials deeply into its side. Someone had stuck a cigar butt in its blowhole. “I removed the cigar and stood t­ here for a long time with feelings I cannot describe,” Payne l­ ater wrote. “Every­body has some such experience that affects them for life, prob­ably several. That night was one of mine. Although it was at that time more typical than not of what happens to ­whales when they encounter ­humans, that experience was the last straw, and I de­cided to use the first pos­si­ble opportunity to learn enough about ­whales, so that I might have some effect on their fate.”48 Payne’s awakening occurred at a time when commercial whaling was still unrestricted. As industrial fishing boats spread throughout the world’s oceans a­ fter World War II, ­whale populations in previously out-­ of-­reach zones, such as Antarctica, ­were being decimated. Yet l­ ittle was known about where ­whales spent their time. Payne had never seen a live ­whale and had no idea how to find one. Luckily, one of the New York Zoological Society’s trustees, the millionaire and physician Henry Clay Frick II, gave Payne a tip. At one of the society’s meetings, Frick made an offhand remark that his ­family had frequently seen humpback ­whales swimming offshore from their private beachfront estate in Bermuda.49 As soon as they had the chance, the Paynes ­were on a plane to the island. Once they arrived, one of Frick’s contacts introduced the ­couple to a Navy engineer named Frank Watlington. Watlington had been deployed two de­cades ­earlier to run one of the Navy’s listening bases at the Naval Underwater Systems Center in Southhampton, on the

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southern tip of Bermuda. He set up a listening array through which he laboriously gathered data for over thirty years. But he also had a lot of time on his hands, and a love of the ­water; his ancestors, who had settled in Bermuda in the 1600s, ­were ­whalers.50 One day, Watlington headed out in his boat a ­little further than usual and dropped a hydrophone a l­ittle deeper than usual. Over 1,500 feet below the surface, he picked up eerie, haunting sounds. Puzzled, he played the recording for local fishermen, who said the sounds w ­ ere made by w ­ hales. Watlington was captivated; year a­ fter year, he recorded the sounds.51 This was no easy task. He first used a series of drums, which yielded hard copy paper recordings. The system used hot-­wired pens that burned the ocean sounds into the waxy surface of the paper, an acoustic ­etching that had to be carefully stored. ­L ater he began recording on reels of half-­inch magnetic tape; twenty-­four hours of recording took up an entire Ampex reel.52 Sometimes, he would play recordings for ­people he trusted. Mostly, though, he kept them secret out of fear that his superiors would catch wind of his collection or that commercial w ­ halers would misuse the songs to find and kill the ­whales.53 Soon ­after meeting the Paynes, something moved Watlington to show them his secret recordings. Listening to the reels playing in the engine room of Watlington’s boat, Katy ­later recounted that they “had never heard anything like it. Tears flowed from our cheeks. We ­were just completely transfixed and amazed b­ ecause the sounds are so beautiful, so power­ful—so variable. They w ­ ere, as we learned l­ ater, the sounds of just one animal.”54 They knew enough to guess that humpbacks ­were the source. And, as scientists, they had the tools to prove it. Watlington eventually gave them a copy of hundreds of hours of his recordings, which they took home. The gift was accompanied by a request: “Go save the ­whales.” In between taking care of their four young ­children, Katy spent hours listening to the tapes. Playing them over and over again, she began to realize ­there ­were patterns, which she painstakingly annotated. The ­whale songs, she realized, ­were as complex as an intricate piece of ­music. Initially skeptical, Roger was soon convinced. The songs had an internal structure that could be analyzed. But how to do that with hundreds of

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hours of such unfamiliar sounds? To help with the daunting task, the Paynes asked Scott McVay—­a friend at Princeton—­for help. McVay had access to a machine that made ­simple spectrographs, which could parse the recordings into three-­second segments and print out graphs that showed frequency versus time. Katy learned how to run the machine, and paper printouts soon covered the Paynes’ living room floors and walls. Eventually, the research covered thirty-­one years of recordings. Katy made cutouts of the musical lines spewed out by the spectrograph, listening to the songs u­ ntil she could discern the individual voices, as well as the overlapping patterns.55 Individual songs lasted anywhere from six minutes to over half an hour; the ­whales repeated the songs over and over again, sometimes for hours at a time. Like any good musical composition, the songs had shape: phrases, themes, climaxes, resolution, crescendo, and decrescendo. She began memorizing w ­ hale songs at length. Her lifelike renditions sometimes startled visitors: melodious, alien sounds emanating from the body of a suburban American mom. In a landmark paper published in Science, Payne and McVay boldly argued that ­whales did not groan and keen randomly. The intricate sounds had complex structure and rhythm, just like ­music.56 They deliberately used the controversial term songs to describe the “beautiful and varied sounds” that are “repeated with considerable precision.” The analy­sis, which implied that ­whales ­were capable of complex communication, unleashed a furor as scientists debated the bold assertion.57 ­Earlier scientific studies described w ­ hales’ complex sounds but rarely used musical analogies (as this smacked of anthropocentrism, which mainstream scientists eschewed). To assert that cetaceans could make “beautiful ­music” was a step too far for many researchers. Yet scientists found it difficult to avoid musical meta­phors for w ­ hale sounds. In the first paper published on beluga ­whale sounds in northern Canada, researchers referred to whistles and squeals, mewing and chirps, sharp slaps, ticking and clucking sounds that ranged from something like a bell to an orchestra tuning up or “a crowd of ­children shouting in the distance.”58 Although belugas are nicknamed “sea canaries” thanks to their volubility, the researchers ­were careful to offer only descriptions

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of the sounds rather than to describe them as ­music, much less conjecture as to their communicative function. Katy and Roger Payne ­were undeterred by this scientific reticence. They moved with their four young ­children to the coast of Patagonia, Argentina, where they spent fifteen years studying w ­ hale vocalizations. They began to gather evidence that ­whale songs ­were indicative of complex social organ­ization, as well as evidence of cultural transmission within and among ­whale communities. ­Later, Katy made the most striking finding of all: male humpback w ­ hales in specific ocean regions sing the same songs during breeding season. Moreover, ­these songs evolve subtly from one year to the next, so that ­after several years nothing is left of the songs recorded five or ten years ­earlier.59 She also discovered that the longer songs have an internal structure, which she compared to stanzas (or rhymes) repeating at specific intervals or at the end and beginning of phrases; this deeper layer of rhyme-­like repetitions raised the possibility that w ­ hales, like h­ umans, use mnemonic reminders for longer songs.60 De­cades ­after Katy’s discoveries, song transmission among humpback ­whales is now accepted as evidence of the planetary scale of humpback w ­ hale social interactions, vocal learning, and cultural evolution:61 a song originating in one part of the Pacific can gradually spread to other humpback populations across the entire ocean basin. Scientists still do not completely understand the precise mechanism of ­these “song revolutions,” but it is likely due to e­ ither individual ­whales migrating between dif­fer­ent populations or vocal learning on shared or geo­graph­i­cally proximate migratory routes.62 Concerned about ongoing commercial w ­ hale hunting, and keen to draw the attention of the broader public to the plight of w ­ hales, the Paynes took the unusual step of producing their recordings as an a­ lbum. Released in 1970, Songs of the Humpback Whale became a multiplatinum hit and is still the best-­selling natu­ral history recording of all time.63 The liner notes, which describe the ­whale sound as m ­ usic (“exuberant, uninterrupted rivers of sound”), also feature Watlington’s original recording: a track on the ­album simply titled “Solo Whale.” The recordings became a landmark event, changing the way h­ umans perceived the animal world. The sounds of the world’s largest creatures singing through the

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ocean depths captivated public attention.64 The ensuing debate deepened unease about harpooning w ­ hales and using their oil for lipstick and engine transmissions, and galvanized public support for a ban on industrial whaling.65 In 1971, Roger Payne left his academic position at Rocke­fel­ler University and founded the Ocean Alliance, dedicated to protecting ­whales and their environment, and began traveling the world to spread the word about ­whale songs to anyone who would listen. “I figured that if you could build ­these sounds into ­human culture, you could maybe get a movement g­ oing to save the w ­ hales,”66 he l­ ater recalled. Payne’s efforts spurred a global movement. Greenpeace launched its first Save the Whales campaign soon a­ fter the release of the a­ lbum, and at the UN Conference on the ­Human Environment in Stockholm in 1972, participants ­adopted a proposal calling for a ten-­year moratorium on commercial whaling. In 1973, both the initial US listing of endangered species and the newly established Convention on International Trade in Endangered Species identified several ­w hale species as being in danger of extinction.67 ­Under pressure from scientists and the public, the International Whaling Commission (IWC)—­originally created to manage commercial hunting—­implemented a moratorium on commercial whaling in 1982. The IWC ban came just in time, as many w ­ hale species narrowly avoided complete extinction.68 Years l­ater, Roger Payne went on to make an even more far-­fetched claim: songs of some whales—­such as fin and blue ­whales, which are louder than humpbacks—­could travel hundreds or even thousands of miles in the SOFAR channel ­under the right ocean conditions.69 Working with oceanographer Douglas Webb, Payne calculated how far w ­ hale songs could travel underwater, based on the loudness and frequency of their vocalizations. Why would ­whales need to communicate over such long distances? Payne wondered if this ability had evolved due to the lack of mating grounds in certain species, since the ability to communicate over thousands of miles would obviate the need for a designated meeting spot. Or perhaps, he thought, long-­distance calls w ­ ere useful when hunting: schools of krill bloom unpredictably in dif­fer­ent areas

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across the ocean, and perhaps long-­distance calls could help share ­these locations. Most scientists w ­ ere skeptical of the claim of long-­distance communication. Only a few, such as the renowned biologist Peter Marler, even dared to speculate favorably about Payne’s argument. As Marler commented: “Payne believes that by using very low frequency sounds, barely audible to our ears, and placing themselves at an intermediate depth in the ocean, t­ hese w ­ hales may hear one another calling over distances to be mea­sured in hundreds of miles, a phenomenon almost unbelievable ­until we reflect that this is what the navies of the world accomplish with their underwater sound-­signaling systems.”70 Many ­others scientists dismissed Payne as an overly biased activist. As Payne ­later said, “That came closer to destroying my ­whole ­career than anything ­else I did.”71 De­cades l­ater, however, Chris Clark experimentally confirmed Payne’s theory using recently declassified Navy sound recordings. “I was listening to a ­whale singing in Ireland, and I was listening to it off of Bermuda,” Clark says. “The hair still goes up on the back of my neck when I remember thinking, ‘Holy shit, Roger’s right.’ ”72 The Navy data provided irrefutable evidence for Payne’s claim: fin and blue w ­ hales communicate with one another across hundreds of miles of open ocean. Despite growing public awareness, and the passage in 1972 of the Marine Mammal Protection Act, most of the Navy’s recordings remained classified. Scientists began studying other cetaceans, exploring the differences between Odontoceti (toothed ­whales, including dolphins) and Mysticeti (baleen ­whales), and painstakingly parsing their vocalizations into dif­fer­ent categories, such as communication calls, songs, and echolocation. Much of the work involved studies of individual ­whale species that had not yet been systematically recorded; the first documented acoustical recording of minke w ­ hale vocalizations, for example, was from an individual at a breathing hole in the Antarctic in 1972.73 Researchers also began exploring the relationship between ­these sounds and social be­hav­ior. For example, in a landmark playback experiment in the 1980s, Clark and his coauthor showed that southern right w ­ hales

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(Eubalaena australis) respond to vocalizations of members of their own species; the ­whales would approach an underwater speaker if it was emitting sounds made by another southern right w ­ hale, but not if it was emitting humpback ­whale sounds or white noise.74 To scientists, knowledge of marine sounds deepened their understanding of the Earth’s littlest-­known ecosystems: the global oceans. But to the military establishment, knowledge of marine sounds was a strategic asset. Biological underwater noise interfered with the Navy’s ability to properly identify e­ nemy targets and increased the risk of their ships making embarrassing attacks on hapless schools of fish. In one incident, bioacoustics recordings averted a Cold War clash: when the US military went on alert a­ fter hearing low-­frequency blips—­which they thought ­were being used by the Soviets to locate American subs—­researchers demonstrated that the sounds w ­ ere actually produced by fin w ­ hales hunting their prey.75 Despite the obvious practical importance of this knowledge, only in 1992 did the Navy fi­nally sponsor a research proj­ect designed to definitively cata­log acoustic signals from marine mammals.76 For the most part, however, civilian scientists did not have access to the Navy’s underwater listening stations. A few researchers established listening posts in remote parts of the world where ­whales still lingered, but they did not have significant means at their disposal. One of the few exceptions was in the western Arctic, where Aldrich had hunted the bowheads to the brink. A ­century ­after Aldrich’s visit, scientists launched a new study at Point Barrow, in collaboration with the Iñupiat. The results would generate global controversy, and the ensuing debate would reach all the way to the White House. Rather than using sound to hunt the ­whales down, scientists began using sound to try to understand them.

2 The Singing Ocean

A ­century ­after Aldrich’s expedition, southerners returned to Point Barrow in search of ­whales. They arrived in the midst of a heated international ­battle over the ­future of Arctic bowheads.1 In 1978, the International Whaling Commission unilaterally imposed a ban on subsistence whaling in the Arctic. The ban was urgently needed, the commission argued, to save the few remaining bowheads. The Iñupiat disagreed, stating that the w ­ hale population was healthy and rebounding. The Iñupiat argued that southern scientists and politicians w ­ ere punishing Indigenous ­peoples for their own crime: industrial whaling. But the most profound error was the failure, by southern scientists and politicians, to listen to the w ­ hales themselves. For the Iñupiat, much was at stake in their defense of their subsistence hunting rights. One bowhead ­whale could ensure the well-­being of an entire village for a year.2 Bowhead meat and blubber (maktaaq), rich in protein and nutrients like vitamin C, sustained Iñupiat health in a region largely unsuitable for agriculture. Replacing this food source would cost tens or even hundreds of millions of dollars, which the Iñupiat could not afford. And w ­ hales furnished more than food. Whale oil was used for heat and light. Baleen—­malleable when warmed—­was transformed into straps, kayak frames, harpoon lines, and sleds. Whale skin was used to create drums and clothing.3 Rib bones and jawbones ­were used as roof supports and struts; the vertebrae and other bones ­were transformed into tools and amulets. In some communities, the largest ribs w ­ ere rooted in the ground to form an arch, used to demarcate a 27

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portal to a home or community space.4 As Rex Okakok Sr. of Barrow explains: “Our ancestors cultivated kinship with the w ­ hale and their place through our ceremonial relation with the w ­ hales. . . . ​The w ­ hale is our food and ­music, and the ­whale is who we are.”5 ­Music and ceremony; food, tools, and clothing; shelter, heat, and light: Iñupiat life was sustained by the bowheads. Through hunting ceremonies and communal distribution of meat, the bowhead was the focal point of Iñupiat society.6 “In our society, it is always the ­whale that brings us together,” said Mae Ahgeak, a whaling captain’s wife.7 The rituals of preparing for the ­whale hunt, then pro­cessing the ­whales’ bodies, structured the seasonal rhythms of the community. The Iñupiat phrase kiavallakkikput agviq (literally, “into the whaling cycle of life”) embodies what cultural geographer Chie Sakakibara terms cetaceousness, a form of ­w hale consciousness imbued in e­ very aspect of Iñupiat society. ­Earlier generations of anthropologists, with a colonial gaze, referred to a “­whale cult” that governed life in the western Arctic.8 Living intimately with the ­whale for millennia, the Iñupiat claimed, gave them better insight into w ­ hale populations than outsiders. While commission scientists estimated fewer than six hundred ­whales ­were left, the Iñupiat insisted ­there ­were several times that number. The elders remembered the dearth ­after the ­whalers decimated the bowhead population a ­century before. But the ­whale population, they insisted, had rebounded. Government scientists counted the number of ­whales using sporadic visual censuses taken by visiting biologists standing on the shore, supplemented by aerial surveys. The ­whales, the scientists believed, ­were afraid of dangerous ice and restricted their movements to the narrow open w ­ ater channels (called leads), making visual and aerial surveys a precise method for assessing the size of the ­whale population. Local hunters knew differently: hundreds and even thousands of bowhead ­whales, they insisted, passed by Utqiaġvik each year—­many of them ­under the ice rather than through the leads. If the Iñupiat ­were correct in their assertion that w ­ hales passed u­ nder the ice, then the count provided by the government scientists significantly underestimated the size of the bowhead ­whale population.

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The debate between the Iñupiat and the commission ignited a global firestorm. By the mid-1970s, the growing antiwhaling movement was pushing the International Whaling Commission to set stricter quotas on subsistence whaling—­the hunting of ­whales by local communities for food consumption. Pressure groups began calling for quotas on bowhead hunting in the western Arctic and campaigning publicly against the commission. In 1977—­without consulting the Iñupiat—­the commission removed the prior exemption for Indigenous subsistence ­whale hunting from its regulations. The following year, the commission set the quota for the western Arctic bowhead w ­ hale hunt to zero. It based its decision on the assessment that only a few hundred bowhead ­whales remained and that continued subsistence Arctic hunting would likely drive the bowheads to extinction. The hunt, the commission declared from its headquarters in E ­ ngland, was banned. Alarmed and angered, the Iñupiat argued t­ here w ­ ere more bowheads than Western scientists realized, and insisted on their right to continue hunting as they had for millennia.9 The commission refused to budge, and the quota stayed in place. At the core of the dispute ­were two worldviews: traditional, holistic knowledge versus Western, reductionist science.10 The best way to census the w ­ hales, the Iñupiat argued, was by taking a systematic bioacoustics survey over multiple years. This had never been attempted, and scientists and regulators w ­ ere skeptical about its feasibility. They proposed, instead, to use aerial surveys and sonar mapping. But the Iñupiat refused on the grounds that planes and sonar would scare off ­whales.11 Bioacoustics, they insisted, would allow a more accurate census. But the commission refused to entertain the notion of using bioacoustics methods to study the ­whales. Undeterred, the Iñupiat formed their own organ­ization: the Alaska Eskimo Whaling Commission (named as a pointed challenge to the UK-­based International Whaling Commission). Whaling captains from each coastal village w ­ ere named as commissioners, based on traditional Iñupiat governance of the ­whale hunt. Using tax revenues collected from a nearby oil boom, the Eskimo Whaling Commission then launched an ambitious scientific study, the first of its kind in the Arctic: a multiyear

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bioacoustics study of bowheads, the largest ever conducted.12 Point Barrow’s Naval Arctic Research Laboratory—­originally installed to support the Cold War–­era listening posts (christened the Distant Early Warning line) that stretched across the Arctic searching for signs of incoming Soviet bombs—­was converted to a civilian scientific research fa­cil­it­ y. The North Slope Borough—­the newly formed Iñupiat-­r un municipal government—­hired two scientists to oversee the proj­ect. Veterinarian Tom Albert came from Pennsylvania to run the Department of Wildlife Management, and biologist John Craighead “Craig” George, a mountain climber from a ­family of famous naturalists, came from Colorado to lead the fieldwork. The scientists’ quest to listen to the oceans seemed quixotic in a world where popu­lar culture framed oceans as ­silent.13 But Tom Albert was more open-­minded than his peers. He treated the stories of local hunters about sounds made by w ­ hales and other species as scientific hypotheses, and designed novel experiments and equipment to test them. Local Iñupiat hunters w ­ ere key collaborators: experts in bowhead monitoring, with keen powers of observation, accurate memory, tremendous patience, and a finely honed ability to read the land, wind, w ­ ater, and sky. Harry (Kupaaq) Brower Sr., an Iñupiat elder, designed and guided much of the research. Despite the suspicions of some local hunters, Brower insisted that the w ­ hale census be conducted with close collaboration among Western scientists, hunters, and traditional knowledge holders.14 As Albert ­later put it: When I began studying the bowhead w ­ hale in 1978, Harry “took me ­under his wing” and spent many hours teaching me about the bowhead ­w hale. His observations regarding how the w ­ hales move through the ice and his patient explaining of their be­hav­ior during the spring migration off Barrow ­were crucial in our censusing of the bowhead ­whale. Our census study design was in large part based upon information Harry gave to us. We spent years and much money confirming, in a scientific manner, his basic field observations.15 The lead bioacoustician on the study was Cornell University’s Chris Clark, who had been inspired to pivot away from his ­c areer in

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engineering when Roger and Katy Payne invited him to accompany them on a whale-­recording expedition in Patagonia. By the time the Iñupiat invitation arrived, Clark had become an established w ­ hale researcher in his own right; he jumped at the invitation to spend time in the Arctic. Clark knew that the ­waters around Barrow ­were an ideal habitat for ­whales. The village sits at the nexus of one of the world’s g­ reat marine migrations: the northern opening of the Bering Strait, where Siberia and western Alaska nearly touch. This far north, the world is sunless for much of the year, shielded with ice and snow. But as the sun returns in springtime, the ice begins to recede northward. The North Pacific churns as it nears the coast, mixing warm ­waters from the south with cooler Arctic upwellings rich in nutrients. The result is one of the largest population booms on Earth: massive blooms of phytoplankton that feed millions of tiny zooplankton. Many of t­ hese concentrate in Barrow Canyon, a destination for annual migration of the bowheads, who filter the tiny creatures through their long baleen—­one of the world’s largest animals feeding on one of its smallest.16 The zooplankton also feed crustaceans and fish, attracting belugas, walrus, seals, and bears, as well as millions of migrating loons, Arctic terns, and black-­ legged kittiwakes.17 The plenitude of biodiversity is akin to the African savannah. Given this abundance of food, Clark thought it made sense that bowheads should be pre­sent and perhaps even numerous. But he needed a way to test his hypothesis. The ambitious, risky goal of the Iñupiat ­whale census team was to conduct a multiyear experiment to demonstrate that novel observational methods—­combining bioacoustics and traditional knowledge—­were a more accurate way to count the bowheads than a conventional visual census. Guided by the Iñupiat, scientists would head out onto the ice, set up multiple monitoring stations, and listen for whales—­triangulating their recordings in dif­fer­ent places and over several months. The hoped-­for result: an accurate ­whale census. In theory, this sounded ­simple. In practice, ­going out on the ice often felt “like a war” with the ocean, one which the scientists mostly lost.18 The timing of the w ­ hales’ annual migration north meant that recording had to start in April, when daytime temperatures ­were still well below

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zero. The bulky sound recording equipment was h­ oused in enclosed sheds, which w ­ ere mounted on sleds that researchers, led by Iñupiat guides, dragged ­behind snowmobiles over the fractured landscape. As they tried to follow the movement of the ­whales, they ­were blocked by constantly shifting hillocks of shore ice.19 Getting out on the ice was only half the ­battle. The real challenge was to gather sufficient acoustic and visual data to first prove that bioacoustic methods ­were accurate, and then (and only then) begin a census of the w ­ hales. Sitting in “sled sheds” on shifting perches on high-­pressure ice ridges, the teams of visual observers ­were charged with maintaining a twenty-­four-­hour watch for four months straight, meticulously logging any w ­ hale sightings. The acoustic monitoring teams would drop hydrophones into the w ­ ater at the sheer edge of the sea ice, or lower them through holes drilled in thicker, safer ice; their signals w ­ ere relayed by radio frequency to the acoustic observers. Equipment often had to be moved at a moment’s notice as dangerous ice fronts encroached without warning. In the subzero conditions, snowmobiles, generators, theodolites (precise surveying instruments used to measure angles between visible points in horizontal and vertical planes), satellite-linked GPS devices, and the finicky hydrophones all had to be treated with care. The Iñupiat knew their home in all its moods, from calm to treacherous. As the ice melted and shifted, the observers and their perches and sleds w ­ ere constantly u­ nder threat of being submerged. For t­ hose on rotating watches u­ nder the midnight sun, sleep was scarce.20 As one researcher recounted: “Set up the gear, put up a perch, and slam, the sea ice slams into it, and we’d lose all our gear. All of it. It was like a war. We ­were using lead acid batteries. Fifty-­pound batteries. All our clothes ­were ruined with battery acid.” Entire sheds w ­ ere lost. Batteries, rendered brittle by the deep cold, cracked open.21 It took dozens of ­people to build the equipment, maintain the camps, sit on the perches, and keep watch for polar bears and suddenly shifting ice—­which could destroy entire camps in a m ­ atter of minutes, or break off a floe that would carry the observers out into the open ocean. A year’s worth of effort could be lost in a m ­ atter of seconds. And if, against the odds, they

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somehow managed to gather data, the subsequent work was laborious: sound spectrograms—­visual renderings of sound recordings in graphs that display variations in frequency over time—­would need to be matched to the visual sightings, a pro­cess of manual cross-­checking that could take months.22 For many of the scientists, the sound of the ocean singing made it worth the effort. The researchers, who relied on the Iñupiat to select safe listening spots, began to see the landscape in a dif­fer­ent way. At first, the ice seemed full of threats. But something changed a­ fter sitting for days, then weeks, then months out on the ice. As the Iñupiat elder Wesley Aiken once said, sea ice is like a beautiful garden where one gathers food, travels, and feels at home; a place of material, emotional, and spiritual significance.23 As Clark ­later recalled: When you lower the hydrophone into the ocean, it’s a complete cacophony of voices and singers. It’s like . . . ​the twilight zone, a completely dif­fer­ent world—­belugas, bowheads and bearded seals and ice. And ­you’re g­ oing, “oh, my God, it’s a jungle under­neath the ice!”24 Clark was not the first to hear the ­whale’s underwater sounds. When he handed his earphones to his Iñupiat guide, the man already knew which animals ­were making specific sounds. “In their culture, they put the end of an oar up to their jaw, and put the paddle into the ­water, and they listen,” Clark reported.25 Hydrophones provided a dif­fer­ent way of listening than oars: the former mea­sures digital quantities of sound, the latter senses the analog qualities of sound. Well-­positioned arrays of hydrophones could locate precise w ­ hale positions; the Iñupiat derived similar information from their listening methods. They w ­ ere already familiar with the complex sounds of the whales—­purrs and pulses, grunts and moans, staccato bursts interspersed with melodic tones.26 Clark began to realize what the Iñupiat had long known: bowhead ­whales sing, and their songs might rival the better-­studied humpbacks in their complexity. The backdrop of ocean noise made the scientists’ job even more challenging. The sheer variety of sounds defeated any s­ imple analy­sis: noises from ice and wind, as well as seals, walrus, and other animals, w ­ ere

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challenging to decode. It took several attempts before the first hydrophone arrays could successfully detect the precise location of vocalizing ­whales. Then, in 1984, the team had its first significant breakthrough.27 A thick layer of ice that year meant that t­ here w ­ ere no open areas for the ­whales to breathe; only 3 w ­ hales w ­ ere identified by visual means. But the acoustic recordings detected at least 130 ­whales passing under­neath the ice.28 The team found similar results two years l­ater, when they detected 50,552 bowhead calls, even though few w ­ hales ­were spotted by the visual observers.29 The statistical methods used to analyze the bioacoustics data w ­ ere precise enough to follow w ­ hale “tracks” as they moved through the ­water, which ensured they could avoid double-­ counting.30 The scientists thus in­de­pen­dently verified the Iñupiat hypothesis: acoustic data could accurately assess the number of migrating bowhead ­whales, whereas the Western scientists’ visual census was an inaccurate underestimate.31 To Western scientists and the International Whaling Commission, this was a logical impossibility: How could the ­whales swim north when the surface was covered by ice tens of inches thick? The Iñupiat knew the answer. The hunters led the scientists to locations where they could observe ­whales breaking through the ice, using their enormous bow-­ shaped skulls to ram holes through several inches of solid ice. The ­whales, the Iñupiat explained, w ­ ere using visual and acoustic cues to navigate beneath the ice, selecting the best spots to break through in order to breathe.32 The scientists now had an explanation for the bowhead’s curiously s­ haped skull (from which the species derives its name): it functions like a battering ram. The Iñupiat also explained another long-­standing scientific puzzle: the shape of the bowhead’s raised blowholes. The protruding blowhole allowed the ­whales to breathe through even the smallest of open holes in the ice. When it was quiet out on the ice, Iñupiat hunters could hear the ­w hales breathing through the small holes, or even breathing within the air pockets u­ nder the pressure ridges in the ice.33 Inuit hunters in the eastern Arctic, in addition to distinguishing the breathing patterns and sounds of ­whales at ­great distances, ­were able to locate ­whales by spotting the Arctic terns circling above the ice; the terns would feed on the

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small marine animals carried to the surface as ­whales ­rose to breathe.34 Listening to ­whales was a subtle art, honed over centuries. The w ­ hales, noted the Iñupiat, ­were also listening to ­humans. This is why traditional sealskin boats, with sails rather than motors, continued to be used in the hunt. At the time, Western scientific evidence regarding the ability of ­whales to hear was ­limited, and the physiology of ­whale hearing was not well understood. Subsequent studies, however, have demonstrated that bowheads respond to sounds from killer ­whales, diesel engines, approaching icebreakers, distant drill ships, dredging, and long-­distance seismic air guns.35 Again, the Iñupiat ­were proved right. Another area in which the Iñupiat’s knowledge of the w ­ hales outpaced Western science concerned the social lives of the w ­ hales. The hunters insisted that w ­ hales had complex socie­ties of their own, of which songs w ­ ere one cultural marker passed down through generations. One piece of evidence in ­favor of the Iñupiat’s claim was discovered at the old Naval Research Laboratory, where researchers found stone spear tips in ­whales from which they ­were removing tissue samples. The Iñupiat switched to metal harpoons in the 1880s, implying that the ­whales w ­ ere more than a ­century old. This enabled the scientists to calibrate precise age mea­sure­ment techniques, establishing that the bowhead’s normal life span is close to 150 years: the world’s longest-­ living mammal. Results suggested one ­whale was more than 200 years old. The Iñupiat had known this too: through the generations, they passed down information about individual w ­ hales’ distinctive markings, seen many times as the same ­whales traveled past Barrow ­every year; the Iñupiat’s once suspect claim about the longevity of w ­ hales was also proved correct. And for a species this long-­lived, in which calves spend years with their m ­ others, the cultural transmission hypothesis seemed more believable.36 A series of bioacoustics studies ­later revealed that dif­fer­ent groups sing dif­fer­ent songs, the way that h­ uman communities have dif­fer­ent linguistic dialects; thus, the Iñupiat’s claims about cultural be­hav­iors and cultural transmission between generations of bowheads were also confirmed.37 As the scientists listened to the ­whales, they gradually realized the Iñupiat ­were also correct about their most controversial claim: ­there

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­ ere many more ­whales than Western scientists and the International w Whaling Commission had realized.38 Whales w ­ ere not “afraid” of the ice nor confined to the open leads, but instead forged their own paths by swimming ­under the ice and breaking through it to breathe if necessary.39 Bioacoustics was a robust basis for conducting a systematic ­whale census, which proved what the Iñupiat had asserted all along: that ­whale numbers ­were more abundant than visual observations alone had suggested. Indeed, the bowhead population passing Point Barrow was several times larger than Western scientists had previously estimated. ­After years of scientific research—­and hardship, as local hunters suffered years of local food shortages and fears of arrest and imprisonment while low quotas ­were in place—­the Iñupiat ­were vindicated.40 Although the International Whaling Commission was initially dubious, it gradually accepted the weight of evidence. The good news—­that the herd size was increasing—­meant that the Iñupiat ­were able to resume their traditional subsistence hunt. The newfound re­spect for traditional knowledge opened the door to ongoing collaboration with Western scientists and comanagement of quota-­setting with the US government, which changed federal law to recognize the legality of the Iñupiat subsistence bowhead ­whale hunt. Moreover, the acoustic data that was gathered over de­cades enabled scientists to comprehensively track the bowhead ­whale population as it rebounded, and this data continues to help verify bowhead population numbers ­today to ensure the hunt remains sustainable.41 The Iñupiat continued to pursue their sovereignty claims, lobbying the International Whaling Commission to change its regulations, and in 2018 the IWC recognized aboriginal subsistence hunting rights in perpetuity (subject to healthy stock assessments). Equally impor­tant was the IWC’s recognition of the fundamental importance of whaling for Iñupiat culture; ­after more than forty years of strug­gle, the commission acknowledged that “whaling, more than any other activity, fundamentally underlies the total lifeway of ­these communities.”42 Although the Iñupiat won the ­battle with the International Whaling Commission, new threats to the bowheads have appeared. Marine noise pollution from shipping vessels is doubling ­every de­cade.43 Each

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doubling of ocean noise reduces a ­whale’s communication radius by a ­factor of two: over twenty years, for instance, an acoustic radius of 1,000 miles could shrink to just 250 miles, limiting the range over which ­whales can navigate, find food, and locate potential mates.44 In addition to shipping noise, many other ­human activities (such as the deafening noise from the underwater seismic air guns used in oil and gas exploration) are further polluting the ocean’s soundscape.45 The functional acoustic range for w ­ hales is thus shrinking dramatically. Chris Clark’s research has gradually shifted to incorporate the effects of environmental noise pollution, or what he calls “acoustic smog.”46 As environmental noise pollution grows in an increasingly ice-­free Arctic, in which many more ships ply the ­waters, Clark fears that the level of ambient noise ­will overwhelm the w ­ hales.47 ­Those who live in the Arctic are equally worried about climate change. The North is one of the fastest-­changing regions on the planet.48 As the oceans warm, t­ here is less ice cover, and the ice that remains is less predictable, posing a threat to hunters and travelers.49 An increasingly ice-­free Arctic also means more shipping, and vessel traffic through the Bering Strait has increased significantly over the past de­cade.50 The ships bring with them new threats to the ­whales: noise, garbage, entanglement in fishing nets, and ship strikes.51 As the w ­ aters warm, scientists believe that killer w ­ hales are venturing further north, predating on bowheads and disrupting their be­hav­ior; some researchers have argued (somewhat controversially) that an ice-­free Arctic w ­ ill create a new “landscape of fear” for the bowheads.52 In 2019, the warmest summer on rec­ord, with record-­low levels of ice, no bowheads passed by Point Barrow; the w ­ hales stayed well offshore or simply never traveled north.53 Although they once escaped into the ice to avoid industrial ­whalers, climate change left them nowhere to hide.

Digital Whales Over their lifetimes, elders like Harry Brower and George Noongwook, chairman of the Alaska Eskimo Whaling Commission, oversaw the digital transformation of the Iñupiat-­led acoustics research. Each year,

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Brower continued to bring hunters out in the small sealskin umiat and teach them the traditional way of listening and watching for w ­ hales, while the Alaska Eskimo Commission oversaw an increasingly digitized research program, deploying advanced satellite telemetry, three-­ dimensional movement tags, unmanned aerial drones, and passive acoustic monitoring to track ­whales year-­round and across much larger areas of the Arctic Ocean.54 This new phase of digital innovation in Arctic ­whale research was partially spurred by the end of the Cold War and the willingness of the US government to allow dual use of some military assets.55 The US Navy began permitting civilian researchers to use SOSUS, their marine hydrophone network, to track w ­ hales and rec­ord their songs, which provided scientists with the unpre­ce­dented ability to track large w ­ hales across the world’s oceans.56 The success of SOSUS fostered interest in developing a civilian network that was more agile and less costly; researchers developed autonomous acoustic recorders capable of continuous recording for up to a year, which could be deployed across the world’s oceans. The result was an unpre­ce­dented surge of datasets containing w ­ hale song—­more than ­human researchers could analyze. Automated software algorithms ­were developed to analyze the deluge of data, thereby reducing or eliminating the need for manual verification. By the turn of the twenty-­first c­ entury, the heroic efforts of the researchers in Point Barrow ­were rendered largely unnecessary; computers had automated large parts of their jobs.57 ­Today, scientists have developed new types of machine learning algorithms, based on deep neural networks, for automated classification of marine mammal noises. Th ­ ese new methods produce much lower false positive rates while substantially increasing the ability to detect calls, even if trained on relatively small datasets; recordings from a single geographic region recorded over a span of days are sufficient.58 Other innovations include the use of deep learning to improve the accuracy of automated algorithms by “denoising” the recordings—­essentially filtering out the high degree of noise contamination that passive acoustic rec­ords often capture.59 The scope of marine bioacoustics research has also continued to expand. Scientists have begun using acoustic recordings to document

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­ hale be­hav­ior over an entire year rather than a season, and to study the w interplay among w ­ hales, their habitats, and h­ uman threats. Much of the digital bioacoustics deployed to track ­these evolving landscapes is large scale, but some of the most profound insights have come through miniaturizing digital technologies. The digital acoustic recording DTAG—­ also called a “sound and orientation sensor”—is one example.60 Mounted with a tiny hydrophone, an accelerometer, a magnetometer, and a large array of solid-­state memory, the DTAG is delicately attached to a ­whale’s back using noninvasive suction cups and can withstand high pressures in the deep ocean. A single DTAG can ­ride a ­whale’s back a mile below the ocean’s surface, recording the w ­ hale’s vocalizations while tracking its e­ very move, including depth, temperature, orientation, speed, “pitch and roll,” and even individual strokes of the w ­ hale’s tail. The tag also rec­ords sounds in the surrounding environment, enabling scientists to study how marine mammals respond to external, human-­ generated noise.61 The data generated by the tags is combined with data from passive acoustic monitoring and satellite tracking and then analyzed by automated algorithms, which can locate ­whales with a precision of tens of meters.62 In some cases, the algorithms can even identify individual w ­ hales simply on the basis of their unique vocal signatures— in effect, a voice recognition system for ­whales. DTAGs have revealed entirely new be­hav­iors that even dedicated scientists d­ idn’t suspect existed. ­Until recently, for example, scientists ­were only aware of the relatively loud sounds that w ­ hales use to communicate; ­these low-­frequency sounds, which travel over long distances, are easily audible underwater. But Susan Parks, who leads the Bioacoustics and Behavioral Ecol­ogy Lab at Syracuse University, began wondering ­whether ­whales made other, quieter sounds. She de­cided to study North Atlantic right whales—­members of the Balaenida f­ amily and close cousins to the bowheads. Like their bowhead cousins, the right ­whales move slowly and float once killed; hence, the “right” w ­ hale to kill. Their traditional habitat, the east coast of the United States between Boston and Florida, made them easy targets. Adult right w ­ hales, Parks hypothesized, have l­ ittle need to stay quiet, as they have few natu­ral predators. But their calves are vulnerable to

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killer w ­ hales and sharks. In the murky w ­ ater close to shore where right ­whales generally live and feed, killer w ­ hales and sharks are most likely to find ­whale calves by eavesdropping on their vocalizations.63 Did baby ­whales, Parks wondered, make very quiet sounds in order to avoid detection by predators? In one experiment, Parks and her team traveled for several consecutive years to the ­whales’ calving grounds off the coast of Florida and Georgia, a zone where white sharks and killer ­whales are also known to congregate. The team attached DTAGs to mother-­calf pairs, as well as other nearby ­whales without babies. When they analyzed the data from hundreds of hours of recordings, they realized that mother-­calf pairs ­were making sounds entirely unknown to science. The nursing m ­ others ­were making very short, soft, guttural, grunt-­like sounds, which ­were audible only a short distance away.64 “­These sounds can be thought of almost like a ­human whisper,” Parks says. “They allow the m ­ other and calf to stay in touch with each other without advertising their presence to potential predators in the area.”65 Similar studies in bowheads have revealed that they, too, make high-­ pitched sounds, which they incorporate in their songs, and that ­those songs evolve, much like the better-­known humpbacks.66 Unusually, bowheads sing multiple songs within a given year, and their songs are remarkably complex, containing simultaneous, multiple, and harmonically unrelated sounds.67 If humpback w ­ hales are the opera stars of the oceans, bowhead ­whales are the jazz singers. Bowhead songs, frequent and rich with variation, enable scientists not only to count and follow the w ­ hales but also to assess their social structures, health, and be­hav­ iors. Just like songbird diversity is a well-­known index for population viability, the diversity and complexity of songs in bowheads can serve as an indicator of the impact of rapidly encroaching development in the Arctic.68 ­These insights have been made pos­si­ble with the use of digital recording devices, combined with power­ful automated computational techniques based on artificial intelligence. Con­temporary w ­ hale research is a paradigmatic example of the advantages of interweaving supercomputers with ecol­ogy. Cetaceans spend the vast majority of their time

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deep beneath the waves, which poses a particularly tricky challenge for tracking.69 On land, animal researchers can spend years observing the be­hav­ior of gorillas, orangutans, or lions—­whether by watching in secret or familiarizing the animals to their presence. Ocean research is more challenging: boats ­can’t be hidden, and scientists ­can’t follow ­whales on their deep dives, which can be up to 6,000 feet below the surface. ­These challenges w ­ ere, u­ ntil recently, too daunting for scientists to overcome. Devices like DTAGs open up the hidden world of w ­ hale be­hav­ior, and also provide valuable insight into ecosystem conditions in the depths of the ocean where ­humans rarely venture. As the scholar Jennifer Gabrys observes, animal bodies are being incorporated into networks of sensors. “Organisms become computational,” Gabrys writes, “both as carriers of sensors and through the ways in which their sensory ecologies are meant to provide data and information on environmental conditions.” 70 We are now able to perceive the ocean like ­w hales do, through our ears rather than our eyes. Bioacoustics devices thus function like a digital translation device, enabling us to perceive the soundscapes and interpret the songs of t­ hose dwelling ­under the waves.

Dream Whales Digital bioacoustics are useful tools, but they see only at a distance. The digitally mediated relationship with the ­whale is somewhat like that of a spy or voyeur. To r­ eally know a w ­ hale, you must get close up, in person, as the Iñupiat do.71 The attunement of a hunter arises from the intimacy of the hunt. The Iñupiat continue to hunt in small skiffs, ­either skin-­covered umiat or aluminum boats, that are much smaller than the ­whales; the w ­ hale is taken with a harpoon that is no larger to the w ­ hale than a small screwdriver would be to us (although the harpoon is, t­ oday, often armed with an explosive tip). During a hunt, the Iñupiat spend long hours observing ­whales and often slowly approach a ­whale as it gazes at them. Sometimes, the ­whale chooses to position itself in a way that allows it to be safely harpooned; other times, it swims away. The ­whales, they say, are watching ­humans as much as we watch them.72

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The Iñupiat know that w ­ hales have complex socie­ties, communication, and even emotions. This understanding of ­whales as social creatures, akin to h­ umans, governs the hunt. The w ­ hales, Iñupiat elder Harry Brower explained, choose to give themselves to the ­humans—­but only if they are deserving. As the hunters approach in their small boats, they enter into a dialogue with the ­whale. The ­whale is judging, the Iñupiat say, w ­ hether or not the ­humans have been respectful; the ­whale is debating ­w hether or not to give itself to ­humans. The Iñupiat know that ­whales listen, and that if a hunter is disrespectful or selfish, ­whales ­will avoid them. To hunt well, you need quiet and harmony in the boat; even the ­women sewing the sealskin boat must speak g­ ently. The hunter and his wife must intend to share the w ­ hale meat with every­one in the community, with generosity of spirit.73 Hunting is both a prosaic, messy necessity and an act of ceremony. Brower knew the underwater world to be alive with ­whales’ spirits. The w ­ hales, he believed, had biological and intellectual capacities equal to that of h­ umans, and communicated their needs and wishes to h­ umans willing to listen. Digital technologies generate streams of data to verify the Iñupiat’s insights, but the source of their knowledge lies in their tangible connection with the ­whales, formed over many generations—­a more-than-­human cosmology, in which ­whales are understood as kin. Near the end of his life, Harry Brower shared a story. Lying in a hospital bed in Anchorage, he was visited by a baby bowhead. While Brower’s body remained in the hospital, the young ­whale accompanied him more than 600 miles north back home to Utqiaġvik. Brower and the ­w hale traveled together to the edge of the ice and u­ nder the w ­ ater. ­There, Brower saw Iñupiat hunters, including his son, in a sealskin boat. As the hunters approached the calf and its m ­ other, Brower saw the ­faces of the men in the umiat and felt the harpoon enter the body of the calf ’s ­mother. When he spoke, as if in a trance, he told a story about the ­whale kill, detailing how the ­whale had died, which hunter was responsible for the kill, and in which ice cellar the meat was stored. When he was sufficiently recovered from his illness to return home, he learned to his surprise that his dream had been accurate. How did Brower know? The calf, Brower narrates in The Whales, They Give Themselves, “talked to me.

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He told me all the stories about where they had all this trou­ble out on the ice.”74 The ­whale’s story had been shared for a purpose. Brower discussed the dream with the whaling captains, and from t­ hese conversations emerged new rules restricting hunting m ­ other w ­ hales with young offspring. Whales, according to the Iñupiat, share consciousness with ­humans. The concept of cetaceousness refers not only to ­human awareness of ­whales but also to ­whales’ awareness of us. Where does digital bioacoustics fit within this cosmology? Passive acoustic monitoring now allows the Iñupiat and scientists to continuously monitor ­whales anywhere in the world from the comfort of their labs.75 Whereas Captain Kelley once used their songs to hunt w ­ hales indiscriminately, scientists are now using sound to survey, track, understand, and protect them, so that they can be hunted with caution, re­spect, and gratitude. The Point Barrow research studies also nurtured scientific interest in ­whales as social beings. The science of bioacoustics provides a novel means of exploring what the Iñupiat and other traditional whaling cultures have long been saying: creatures other than ­humans are capable of complex communication and possess rich social be­hav­iors, which are intelligible to ­humans who choose to pay attention. As recounted in the chapters that follow, ­these groundbreaking studies in ­whale bioacoustics opened the door to studies of other species—­ including many that ­humans previously thought ­were mute. The early pioneers of b­ ioacoustics opened ears and minds to the lively—­and meaningful—­sounds of the world around us. Scientists began awakening to the idea that, perhaps, the world is not so s­ ilent a­ fter all; perhaps they just needed to learn how to listen. The next astounding breakthrough in bioacoustics came not in the oceans but on land, with the discovery of the unsuspected vocal powers of elephants.

3 Quiet Thunder

When Katy Payne traveled to Africa in the mid-1980s, she arrived in the midst of an elephant genocide. Two centuries e­ arlier, elephants ruled the African continent: islands of p­ eople living in a sea of elephants.1 Less than a ­century ­later, the pattern was reversed. Fueled by the ivory trade, elephant poaching peaked in the 1980s; half of the elephant population was wiped out in a de­cade.2 In K ­ enya, an estimated nine out of ten elephants had been killed by 1990.3 So many elephant bodies littered the landscape that scientists began taking corpse censuses to assess the remaining numbers of living elephants. They also in­ven­ted a grim metric for assessing population health: the carcass ratio (the number of dead elephants divided by the sum of living and dead elephants).4 Journalists labeled it, simply, the “elephant holocaust.”5 Payne had traveled to Africa with the ambitious goal of creating the world’s first elephant dictionary. But as the situation on the ground became evident, Payne realized that the purpose of her research was broader than anticipated. She had come to Africa to document elephants’ acoustic creativity and complexity, but ended up recording the sounds of a species in danger of extinction. She began her work when conservationists ­were just beginning to document the extraordinary intelligence and im­mensely complex social lives of elephants. One of the animals’ most uncanny abilities was their inexplicable capacity to silently or­ga­nize themselves, even across long distances. In Tanzania, conservationist Iain Douglas-­Hamilton marveled at the elephants’ ability to coordinate their be­hav­iors without visual or 44

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audible cues. In K ­ enya, scientists Cynthia Moss and Joyce Poole observed that male and female elephants, living separately and sometimes far apart, found one another with breathtaking speed during the brief, unpredictable win­dow of time when females w ­ ere ready for breeding. In Zimbabwe, Rowan Martin tracked female elephants from dif­fer­ent families for several years, using radio collars to map their movements; without ever meeting, the f­ amily herds coordinated their movements for weeks across long distances and over rough terrain, despite the absence of visual or olfactory cues.6 Folklore from South Asia, where elephants had been domesticated to serve as beasts of burden for thousands of years, was rife with stories of their super­natural abilities.7 Maybe, joked Douglas-­Hamilton, the elephants had extrasensory perception. Payne had a less super­natural hypothesis, which she developed while studying elephants at the Portland Zoo. The zoo had made the headlines two de­cades prior, with the birth of Packy—­the first elephant born in the Western Hemi­sphere in forty-­four years. Packy quickly grew to become the tallest Asian elephant in the United States and became a star attraction. By the time Payne visited the zoo in 1984, it had developed the most successful captive elephant breeding program in the world. Spending time with the largest group of captive elephants on the American continent, Payne hoped, would enable her to learn more about the sounds that elephants make. On her first visit to the zoo, she was approached by a young baby named Sunshine, whose trunk reached for her through the bars. Sunshine’s m ­ other, standing nearby, looked ner­vous. Payne felt a faint throbbing: “like the feeling of thunder, but t­ here had been no thunder.”8 The feeling reminded her of a memory: Singing in the choir in Sage Chapel at Cornell University as a girl, she had marveled at the intensity of the pipe organ as its notes descended t­ oward the deep bass and the entire church seemingly shuddered. “Is that what I was feeling as I sat beside the elephant cage? Sound too low for me to hear, yet so power­ful it caused the air to throb? ­Were the elephants calling to each other in infrasound?”9 From her e­ arlier work with ­whales, Payne knew that fin and blue whales—­the largest mammals in the ocean—­made infrasonic calls; from her training in acoustics, she knew that such sounds could travel

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g­ reat distances through w ­ ater, rock, and air. Could infrasound be the answer to the riddle of elephants’ mysterious powers of communication? Payne returned to Portland a few months l­ ater with recording equipment designed to detect deep infrasounds, below the range of h­ uman hearing. She also brought with her two colleagues: biologist William Langbauer and author Elizabeth Marshall Thomas. Payne planned to rec­ord the elephants and, a­ fter the trip, play the recordings back at a higher speed, raising the frequency; this would reveal any hidden infrasound, like invisible ink u­ nder UV light. Setting up in a grimy barn at the zoo, the three researchers began the tedious pro­cess of daily observation: one person checking the recorder, another meticulously noting the elephants’ be­hav­iors, and another carefully noting the times any audible sounds ­were made. Some nights, the team worked around the clock, taking turns recording, observing, and resting. From time to time, Katy felt the same throbbing sensation she had felt on her first visit; Elizabeth felt it too. But Bill heard and felt nothing, and Katy resolved not to be disappointed if the recordings contained nothing beyond already audible sound. Returning home to Cornell, Payne asked Carl Hopkins, another acoustics scientist, to listen to the recordings with her. She selected a specific episode that had caught her attention during her time at the zoo: two fully grown elephants, separated by a large wall, stood and faced each other through the thick concrete. No audible sound had been made. But with the tape ­running at close to a dozen times its normal speed, the sounds—­condensed and three octaves higher—­revealed themselves. Below the range of ­human hearing, the two elephants had been carry­ing on an extended conversation, a sonorous exchange that sounded a ­little like the lowing and mooing of ­cattle. Enthralled, Katy looked up to see a forlorn look on Carl’s face. “Goddamned infrasound,” he mumbled.10 All ­these years teaching and studying the acoustics of the natu­ral world, and no one had thought to speed up the tapes. It fell to Payne, a classically trained musician, to make the landmark discovery. In the ensuing months, a systematic analy­sis of hundreds of hours of recording revealed an extensive array of elephants calls, some of them within infrasonic range, that seemed to function

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over long distances. Unique calls ­were made in many contexts, such as soothing an upset companion, shooing a stray baby back to its ­mother, sounding the alarm, or shepherding a group from one spot to another. Payne’s discovery generated considerable excitement in conservation circles. The New York Times described her study in lyrical terms: “It is the first evidence that land mammals can produce such infrasonic sounds, and it adds the elephants’ basso calls to the wildlife choir that includes the high-­frequency shrieks of bats, the soprano voices of porpoises, the alto wails of wolves and coyotes and the tenor-­to-­bass-­range songs of humpback ­whales.” 11 Thomas Lovejoy, vice president of the World Wildlife Fund, described the findings as a revelation: “This discovery is like suddenly finding a . . . ​hitherto unknown language.”12 Payne’s recording of the elephants’ conversation through the wall demonstrated the power of infrasound, which differs from the “ordinary” sounds that ­humans can hear in three ways. First, infrasound is too low in frequency for h­ umans to detect, necessitating the use of special technology to observe. Second, infrasound has very long wavelengths—­a highly significant fact for elephants, given how sound waves interact with solid objects. Sounds with short wavelengths—­ such as the high-­pitched sounds that bats use for echolocation—­reflect strongly from even small objects and travel only short distances. By contrast, sounds with long wavelengths pass through and around most objects. Third, while air absorbs high-­frequency sounds very efficiently, low-­frequency sounds travel with l­ ittle loss. Infrasound can thus be used for communication over long distances. It can also penetrate through objects like walls, or vibrate through the earth.13 Elephants w ­ ere having power­ful conversations across long distances and even through buildings—­all without ­humans hearing a ­thing. Within only a few months of publishing her findings in 1986, Payne was on her way to K ­ enya with backing from the World Wildlife Fund and the National Geographic Society. Studying elephant communication, she hoped, could help turn the tide of elephant extinction across the continent. Payne first spent time working with Joyce Poole and Cynthia Moss in ­Kenya. Moss had previously quit her job at Newsweek to become a self-­taught elephant biologist. Her work with an elephant

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herd living in Amboseli National Park, at the foothills of Mount Kilimanjaro, would eventually become the world’s longest-­running study of African savannah elephants.14 ­After reading about Moss in the news, Poole began volunteering with the Amboseli Elephant Research Proj­ect when she was only nineteen years old, and would ­later earn her PhD from Cambridge University.15 Moss and Poole’s intimate knowledge of the savannah elephants—­they could individually recognize hundreds—­ helped make a number of scientific breakthroughs about social be­hav­ior in elephants. Perhaps most importantly, they w ­ ere able to document intricate and sophisticated social networks that extended well beyond the core f­ amily group, encompassing scores of elephants across generations.16 Their findings brought to light the importance of older elephants, particularly matriarchs, as knowledge holders and anchoring personalities for elephant herds. Older females nurtured and taught their elephant calves, who took two de­cades to mature to adulthood.17 The elders’ long memories also provided the herd with valuable information during times of need, such as the location of ephemeral watering areas during a drought, the seasonal availability of food, or the existence of little-­used migration routes. Poole’s research revealed that poaching—­w hich tended to target older elephants with larger tusks—­not only killed individual elephants but also destroyed the social fabric of elephant society. ­These findings ­were instrumental in motivating the international community to fi­nally ban the international ivory trade in 1989. Building on Payne’s discovery, the three researchers began exploring the social contexts of the low-­frequency calls made by savannah elephants. Their findings confirmed that elephants use infrasound to communicate over long distances, coordinate their movements, and search for mates.18 Payne’s deep knowledge of w ­ hales led to another insight: for animals that produce infrasonic calls, communicating over long distances confers a reproductive advantage. Like ­whale species that migrate vast distances to reach their breeding grounds, mature male and female elephants live apart, yet they must find one another and mate during the infrequent, brief periods when a female elephant is in estrus—­a win­dow of time lasting, on average, four days and occurring only once e­ very four years.19 Listening in on female savannah elephants in estrus, Payne

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heard distinctive, low-­frequency calls; in playback experiments, males would walk miles t­ oward loudspeakers playing recordings of the sound.20 Moss and Poole would go on to document the extensive vocal recognition networks of savannah elephants, the precise acoustic cues that could be transmitted over long distances, and evidence that elephants ­were able to teach and learn sounds.21 This research deepened scientific understanding of elephant sociality, which in turn led to further insights on topics about which scientists w ­ ere previously skeptical—­like the notion that elephants could display empathy.22 Payne also knew that infrasound would be useful for transmitting calls in shrubby underbrush or thick forests. The lower the frequency, the more slowly sound attenuates in dense forests. In contrast to their savannah-­dwelling cousins—­who could be studied visually on foot, by plane, or even by satellite—­forest-­dwelling elephants ­were extremely elusive.23 Living deep in the central African forest, the second-­largest rainforest on Earth, ­these elephants ­were still largely a mystery to scientists. Given the constraints on visual signals in the dense tropical forest, Payne speculated that forest elephants ­were likely to be more reliant on low-­frequency sound, even at short distances.24 Bioacoustics could hold the key to studying and saving the forest elephants. Together with scientists at the Bioacoustics Research Center of Cornell University’s Lab of Ornithology, Payne de­cided to launch the Elephant Listening Proj­ect in the heart of the rainforest, on a tributary of the Congo River in the newly founded Dzanga-­Sangha National Park.25 The Elephant Listening Proj­ect’s ambitious goal was to create an “elephant dictionary”: a lexicon of elephant calls and acoustic repertoire associated with be­hav­iors and interactions.26 Working with Andrea Turkalo, who had initiated the world’s longest-­running study of African forest elephants, Payne’s team first laboriously calibrated calling rates by installing autonomous recording units (ARUs) in the forest where elephants ­were thought to hide and linking the number of calls they recorded to the number of elephants making the calls. From 2006 onward, with Cornell acoustician Peter Wrege now leading the Elephant Listening Proj­ect, the technology stack that Payne’s team devised was one of the most impressive bioacoustics networks

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ever built. Wrege divided the rainforest into fifty grids (each about 25 square kilo­meters), and placed a custom ARU at e­ very grid point, about 30 feet up in the treetops—­just higher than an elephant can reach with its trunk while standing on its hind legs. The first ARUs, designed at Cornell, w ­ ere built into PVC sewer pipe with a laptop computer hard drive for data storage and cir­cuit boards that wrote sound files in binary format; each ARU in the first major installation in Gabon required 43 pounds of batteries to run for three months.27 The devices ­were synchronized with remote video cameras, which allowed researchers to ­later watch the videos while listening to the recordings and match sounds to individuals and be­hav­ior. ­Every three months, the researchers would visit each recorder, replace the audio cards, change the batteries, and start all over again. Back in the lab at Cornell, teams of gradu­ate students painstakingly decoded the elephants’ signals. ­After de­cades of study, the team knew how to interpret a wide array of elephants’ signals, and the elephants’ characteristic vocalizations provided information on occupancy, landscape use, population size, and the effects of anthropogenic disturbances.28 Over the years, the Cornell team amassed hundreds of thousands of hours of recordings and observations, enabling them to rec­ord the lives of individual elephants from birth, through adulthood, to death. Combining this bioacoustics data with rare visual sightings, the team was able to discern key aspects of the forest elephants’ life cycles: the sizes and composition of their ­family herds, movements of populations from one place to another, mating, and maternal responses to infants. Distressingly, the recordings also picked up the sounds of gunshots from poachers, chainsaws of illegal loggers, and alarmed elephants in flight.29 Based on this data, the team was the first to sound the alarm on the rapidly dwindling forest elephant population, as they began to fear that they ­were capturing the sounds of a species that might soon be extinct.30 Although one ­earlier study had documented the devastating decline of forest elephants at several key sites in central Africa, l­ittle ­else at the time was known about their numbers across Africa.31 Recent reports concerning their savannah-­dwelling cousins, however, had not been encouraging. When the first-­ever continent-­w ide census of savannah

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elephants was completed in 2016, the results rocked the conservation world.32 When teams of observers overseen by Elephants Without Borders flew small, light planes over much of the continent—­covering 290,000 miles (more than the distance to the moon)—­their findings ­were grim. An estimated 27,000 elephants per year ­were being slaughtered by poachers. Africa’s savannah elephant population had declined by one-­third over seven years. In Cameroon, the researchers counted only 148 living elephants but over six hundred carcasses; with a carcass ratio of over 80 ­percent, the population was in immediate danger of extinction. Even in Tanzania, with its thriving safari tourism businesses, the savannah elephant population had declined by 60 ­percent in the past five years. Forest elephants, unfortunately, ­were excluded from the census, leaving scientists in the dark about the extent to which their population decline mirrored that of their cousins. Payne and her colleagues continued to push for a forest elephant census. In early 2021, the International Union for the Conservation of Nature revealed that forest elephants had lost nearly nine-­tenths of their population in the past ­century, updating its “Red List” assessment and reclassifying forest elephants as critically endangered—­one step away from extinction in the wild.33 Well before this announcement, the Cornell team had pivoted. Given the escalating poaching pressure on forest elephants, the Elephant Listening Proj­ect shifted ­toward developing acoustic-­based approaches to solve practical conservation goals; creating the elephant dictionary was an empty vision if ­there ­were no elephants left. Recording the elephants felt voy­eur­is­tic: in light of the ongoing slaughter, each recording was like a sonic fossil. Rather than merely recording the elephants before they ­were exterminated, Payne wondered what could be done to avert their eradication. Back in 2003, she had suggested that it should be pos­si­ble to design an acoustic monitoring system that could function both as a census device and as an automated tool to warn of threats.34 Such a system would require automated recorders that could be hidden from poachers, as well as a precise, accurate means of identifying both elephant calls and poaching-­related noises, such as gunshots, engines, and chainsaws. Meeting the first requirement was straightforward, given

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recent advances in digital acoustics devices. But the latter proved more challenging, ­because jungles are noisy places. In computer science, this prob­lem has a well-­known analogue in ­human voice research, which is sometimes referred to as the “cocktail party prob­lem”: the need to focus on a specific h­ uman voice while filtering out background noise and other voices. In 2015, researchers in artificial intelligence proposed a novel solution to the cocktail party prob­ lem, which they playfully called Deep Karaoke. To teach computer algorithms how to distinguish between ­human voices and instrumental ­music, they began with a database of songs that contained tracks for ­every specific instrument or voice in the song, as well as the fully mixed version of the song. Each track was converted into a series of spectrograms that represented a unique acoustic fingerprint for each instrument or voice; the fully mixed version of the song was also rendered as a spectrogram, essentially a composite of the other spectrograms added together. The researchers fed this dataset into a neural network: a type of artificial intelligence designed to learn iteratively, just like the ­human brain, by comparing samples and detecting patterns. With a large enough dataset and sufficient computational power, neural networks perform extremely well at identification—­often better, indeed, than ­humans. In this case, the neural network ran through the entire database one hundred times. Then the team fed the neural network a new, unfamiliar song and asked it to pick out the vocal track—­which it successfully did. The algorithm had been designed to recognize a musician’s voice and extract it from the background noise, just like a ­human.35 The Cornell team wondered: Could a similar approach be used to separate elephant vocalizations from the backdrop of a noisy tropical forest? Wrege reached out to computer scientists at Cornell, including Carla Gomes, one of the found­ers of computational sustainability: a new field that applies computer science to environmental and social challenges. They determined that to extract useful data from their acoustic recordings, a new automated approach would need to be devised. Gomes and Wrege reached out to California-­based Conservation Metrics to build a customized neural network: an artificial intelligence algorithm that could automatically identify specific elephant

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vocalizations. When Wrege first handed over the recordings, he asked Conservation Metrics to focus on two tasks: distilling elephant sounds from the background of jungle noise; and distinguishing between dif­ fer­ent types of elephant calls—in par­tic­u­lar, differentiating alarm and distress calls from the rest. The Elephant Listening Proj­ect had accumulated hundreds of thousands of hours of audio data, which provided a sufficiently large training dataset; still, Wrege was uncertain ­whether this untested idea could work. But the results ­were better than expected: not only did the neural network accurately identify elephant noise, it picked up gunshots as well—­even without being asked. To double-­ check their accuracy, the engineers built another version of the neural network—­this time focused on sounds like gunshots and chainsaws, as well as elephant alarm calls. The second algorithm was even more accurate than the first. The team had in­ven­ted what Payne had first suggested nearly twenty years e­ arlier: a real-­time monitoring system for h­ uman threats to elephants, which might fi­nally stop poachers in their tracks.36 As Wrege, Gomes, and their colleagues l­ ater explained in their landmark publication, real-­time threat detection and population monitoring require two difficult tasks to be achieved: first, quickly and accurately detecting both the animals of interest and potential threats to t­ hose animals, such as loggers, hunters, and poachers; and, second, efficiently communicating only the necessary data to the system, to avoid overloading the ­limited bandwidth of the wireless networks. The former is a classic challenge of classification and segmentation of audio data—­ how to distinguish a gunshot from a tree branch cracking—­which they solved by their custom-­built neural network. But to source the acoustic data to feed into the neural network, the latter challenge needed to be solved. This required the team to abandon conventional acoustic algorithms, which typically discount or even eliminate the kind of low-­ frequency sounds where much elephant communication takes place. To do this, they in­ven­ted a new method of audio compression aimed at the neural network—­that is, aimed at a digital rather than ­human audience. This system, the Cornell team realized, could serve as the basis for an early warning signal for park rangers across the continent.37 Building on this research, the team released an open-­source system for automated

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analy­sis of the sounds made by ecosystems (“soundscapes”). By developing a neural network to recognize “acoustic fingerprints” of soundscapes from a variety of ecosystems, the method allows rapid and scalable ecological monitoring that w ­ ill enable researchers in the f­ uture to accurately predict habitat quality and biodiversity and also to automatically identify anomalous sounds, like chainsaws and gunshots.38 What began as a proj­ect to listen to elephants with analog acoustics technology had pivoted to a mission to save them using digital acoustics powered by artificial intelligence. Although ­these digital acoustics tools might be a leap forward in technical terms, they are by no means a magic bullet in the fight to save African elephants from extinction. The tools themselves are not yet sufficiently accurate to detect poaching at a wide scale, and even if poaching was brought u­ nder control, elephants face another, perhaps even more serious threat: habitat loss due to encroachment by ­humans, who are cutting down forests and farming marginal forest lands, generating elephant-­human conflict across the continent. ­Here, too, bioacoustics may offer a solution, while revealing additional, surprising insights about elephant communication.

Honeybee Fences A generation a­ fter Payne first journeyed to Africa, Lucy King began listening to elephants at the foothills of Mount Kilimanjaro. Having grown up in Africa, King had seen the elephant population’s swift decline firsthand. King’s mentor, Iain Douglas-­Hamilton, founded the nonprofit Save the Elephants to advocate for a global ban on the ivory trade. A ­ fter de­cades of lobbying, the ban was fi­nally implemented in 1989 ­under the Convention on International Trade in Endangered Species. But thirty years l­ater, elephants faced a new threat: local farmers. By the time she began her university studies, King had de­cided to dedicate her life to solving elephant-­farmer conflict. King had chosen one of the thorniest challenges in conservation. As ­human populations have grown in Africa, they have encroached on rapidly shrinking elephant habitat. Deprived of bush food, elephants

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increasingly invade farms to feed themselves. A single elephant can destroy acres of crops in an astonishingly short space of time, as elephants uproot and scatter as much as they eat—­several hundred pounds of food per day. Occasionally, elephants kill ­people who get in their way.39 The closer the farms are to wildlife refuges, the greater the likelihood of raiding by wildlife. Elephant deterrence is not a ­simple task. Stone walls and thornbush fences might keep out antelope, but not elephants, so families often rotate crop-­guarding duties throughout the day and night. F ­ athers, ­mothers, and ­children all take turns, attempting to ward off elephants by banging on pots and pans, setting off firecrackers, throwing stones, and even burning hot chili peppers (the smoke deters the elephants, like homemade tear gas).40 ­These methods are not always effective, so farmers sometimes engage in retaliatory killings of elephants. And when park guards are forced to cull “prob­lem elephants,” the surviving members of elephant herds often become even more aggressive t­ oward h­ umans.41 Poachers have learned to leverage this conflict: resentful farmers may be recruited to help hunt down elephants, or they may decide to turn a blind eye to poachers’ movements. And even when deterrence does work, it takes a social toll: c­ hildren, for example, may be kept home from school to safeguard crops.42 From the elephants’ point of view, farmers are the vanguard of a human-­created prob­lem: encroachment on traditional elephant lands and migration routes. A ­ fter the ivory trade was banned, elephant populations slowly began to recover. But remaining elephants across Asia and Africa w ­ ere squeezed into smaller areas as agriculture and h­ uman settlements expanded.43 As their habitats continued to shrink, elephants w ­ ere forced into closer contact with ­people, resulting in more conflict.44 In an attempt to address farmer-­elephant conflict, conservationists and scientists have tried a variety of strategies with l­imited success. Elephants break through electric fences with relative ease; farmers are not terribly satisfied with monetary compensation for losses.45 Blasting recordings of threatening sounds like wild cat growls, h­ uman cries, and calls from other elephant herds is effective only in the short term; elephants quickly learn to ignore ­these sounds, and such systems are often

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too expensive for farmers to maintain.46 King wondered ­whether t­ here might be other, less violent methods to keep elephants away from farmers’ fields. A ­ fter growing up in Africa, she went to Oxford to study animal be­hav­ior, then returned home to found the Human-­Elephant Coexistence program. Her goal: to invent ecofriendly ways of reducing conflicts between farmers and elephants. This was no small task. Her flash of inspiration began with a tip from Maasai beekeepers and honey hunters. Traditional knowledge held that elephants ­were afraid of bees, and the Maasai recounted eyewitness reports of bees chasing elephants over several miles.47 African honeybees (Apis mellifera scutellata) are known to be more aggressive than their Eu­ro­pean counter­ parts: they respond more quickly to threats, send out three to four times more bees, and pursue intruders at much longer distances from the hive (up to a mile, in some reports).48 Aggressive and quick to attack, the bees have killed grown men with hundreds of stings. Bees, the Maasai asserted, could even put ­whole herds of elephants to flight. This might seem surprising, given that thick-­skinned elephants often stand their ground against much larger predators, but African honeybees are ­adept at targeting patches of thin skin on elephant bellies, trunks, ears, and eyes. The tiny honeybee, said the Maasai, is one of the few living beings that can terrify the mighty African elephant. When King’s mentor, Iain Douglas-­Hamilton, began exploring elephant be­hav­ior, he found that the Maasai ­were correct. In one experiment, he hung empty hives in acacia trees; a month l­ ater, over 90 ­percent of surrounding trees had been stripped bare of their leaves by elephants. However, the trees with live beehives ­were untouched, and trees in which he had hung empty beehives suffered less damage than the bee-­ free trees. Elephants, he concluded, are so wary of honeybees that they ­will avoid feeding on acacia trees—­one of their favorite food sources— if a hive is pre­sent. Douglas-­Hamilton then offered a provocative suggestion: rather than expensive fences, farmers could create “honeybee fences,” using strategically placed hives to ward off elephants.49 To test this hypothesis, King’s work began with ­simple experiments: playing recordings of disturbed honeybees on speakers to see how elephants would respond.50 The elephants retreated, shaking their heads

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and dusting themselves—­reactive be­hav­iors that w ­ ere clearly intended to prevent bee stings. Other noises ­were tested as controls, but the elephants ignored them. Bee noise alone—­even in the absence of a­ ctual bees—­was the key to keeping elephants at bay.51 King had proved that acoustic honeybee fences worked as elephant deterrents. But replicating them across small farms was a daunting challenge. Electronic equipment is too expensive and difficult to maintain for the majority of small farmers in Africa. So King’s next step was to build live honeybee fences. For her first live “guardian fence,” King chose a farm on the Laikipia Plateau, northeast of the ­Great Rift Valley. The small two-­acre farm had been experiencing repeated elephant raids. ­After King installed two 100-­foot fences of nine interconnected hives on two sides of the farm, the elephants backed off, while continuing to raid a nearby farm.52 Encouraged, she scaled up her experiment, enclosing seventeen farms with live beehive fences, while leaving another seventeen farms to be protected only by thornbush barriers. Over a period of two years, the beehive fences w ­ ere only broken once (by a per­sis­tent bull elephant), whereas the thornbush barriers w ­ ere easily and frequently breached. The low-­tech live beehive fences worked. The farmers ­were also delighted by the 250 pounds of honey they harvested, which more than covered the cost of building the fences.53 King’s team next collaborated with the K ­ enyan Wildlife Ser­vice to test the beehive fences in one of the highest elephant-­human conflict areas, next to Tsavo East National Park.54 The results ­were so successful that neighboring farmers began requesting beehives, which also enabled them to plant new types of bee-­pollinated crops (like sunflowers) that elephants ­don’t like to eat.55 Beehive fences have since been shown to be effective at warding off elephants from farms in seventeen countries, from Gabon and Mozambique to India and Thailand, although in some cases the elephants appear to habituate to the hives over time.56 Combining longer-­term crop adaptation with the beehive fences, as King originally envisioned, is likely to be the more effective long-­term solution.57 As they spread around the world, King’s ­simple invention is potentially a game changer: a bioacoustics-­based device that may enable peaceful human-­elephant coexistence.58

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How Do You Say “Honeybee” in Elephant? While studying her honeybee fences, King noticed something unusual about the elephants. When confronted with bees (or even just bee noise), elephants produced unusual and distinctive “rumble” vocalizations that ­were very dif­fer­ent from their vocal responses to white noise playback. And they ­were quickly joined by their relatives, some of whom traveled long distances to team up with their alarmed kin. What ­were the elephants saying, and how did the rest of the herd know? King knew that elephants regularly vocalize in the infrasonic range, at frequencies well below the range of normal h­ uman hearing.59 As I discussed e­ arlier in this chapter, ­these vocalizations can travel long distances, and some evidence exists that each elephant has a unique vocal signature; this observation helped scientists understand how elephants can coordinate travel patterns even when miles apart.60 Elephants may also use seismic vibrations to detect ­water sources at long distances.61 To figure out how and what the elephants ­were communicating, King teamed up with Joseph Soltis, a bioacoustician at Disney’s Animal Kingdom. Soltis had spent years studying elephant vocal communication, probing how nuanced variations in their sounds reflected their individual identity, the emotional state of the callers, and social exchange as elephants engaged in antiphonal (call-­and-­response) be­hav­iors.62 Together, Soltis and King devised a series of experiments to test elephant responses to both honeybee and ­human recordings. ­These experiments led to an astonishing finding: elephants’ alarm calls about h­ umans are distinct from their warning calls about honeybees.63 Moreover, only when alarmed by honeybees do the elephants gather and beat a hasty retreat. In response to other types of warning calls, elephants actually disperse. King is not yet sure why elephants show this be­hav­ior, but her initial hypothesis is that banding together reduces the chance that any one elephant ­will be overwhelmed by bee stings: a strategy of sticking together makes sense when dealing with an angry bee swarm, but dispersal is a better strategy when confronted by ­human poachers. ­Were t­ hese distinctive rumbles some sort of elephant signal for the presence of honeybees? Soltis and King played back recordings of t­ hese

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distinctive rumbles to groups of elephants; this produced similar be­hav­ iors (head shaking and further and faster retreat), even in the absence of bees or bee sounds. As King carefully noted: “This suggests that ­these elephant rumbles may function as referential signals, in which a [specific] frequency shift alerts nearby elephants about . . . ​the threat of bees.” King had made a breakthrough discovery: elephants have a specific “word” for “honeybee.”64 To further test the hypothesis that elephants have dif­fer­ent alarm calls for dif­fer­ent threats, Soltis and King set up a new bioacoustics experiment. They recorded the voices of adult male Samburu tribesmen, pastoralists in northern K ­ enya who regularly travel among African elephants and sometimes compete for resources like watering holes. Soltis then played the recordings back to elephants, who displayed typical vigilance be­hav­iors, flight be­hav­iors, and vocalizations. But the rumbling sounds elephants made in response ­were dif­fer­ent from the honeybee rumbling sounds; when Soltis analyzed the recordings, he found distinct differences in the patterns of the “Samburu alarm rumble” and the “bee alarm rumble” (which peaked at a higher pitch). When the team played the Samburu alarm sounds back to elephants, they moved away but did not engage in the head shaking be­hav­iors elicited by the bee alarm calls. Simply put, when making alarm calls, the elephants distinguished between two types of threats, and their calls reflected the relative level of danger.65 King and Soltis thus documented two referential signals used by elephants: honeybee and h­ uman. King went on to demonstrate that elephants distinguished between dif­fer­ent types of ­humans (this is not unique; other wild species, such as magpies, are known to distinguish between familiar and unfamiliar h­ umans).66 In a similar experiment, elephant biologist Karen McComb played recordings of ­human voices to elephants in K ­ enya’s Amboseli National Park; the elephants retreated rapidly when recordings of Maasai male hunters ­were played, but ­were relatively un­perturbed by recordings of Maasai ­women and c­ hildren, or Kamba men (another ethnic group, which does not hunt elephants). McComb’s research provides further evidence that elephants can determine not only ethnicity but also gender and age from acoustic cues in ­human voices.67

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Researchers had previously documented elephants’ ability to discriminate between familiar and unfamiliar alarm calls by other elephants, whose extensive networks of vocal recognition ­were well known.68 Scientists speculated that ­these calls ­were central to elephants’ complex social systems and thus conveyed information about individual elephants or their ­family groups.69 But King and Soltis had documented specific vocal signals that designated nonelephant creatures—­a breakthrough. Moreover, they had demonstrated that ­these sounds function just like words (although scientists prefer to call them “referential signals,” as “words” are associated solely with ­human language). Elephant researchers are now exploring how to incorporate this new knowledge into elephant early warning and monitoring systems. Could elephants learn new signals? Could we teach them specific “words” for threats, to ward them away from harms? Some evidence exists that elephants can learn sounds from ­humans. Mlaika, a semicaptive orphaned elephant in ­Kenya who lived near the Nairobi-­Mombasa Highway, would emit truck-­like sounds around sunset, the best time for transmission of low-­frequency sound across the African savannah.70 In another case, a male African elephant who grew up with two Asian elephants in Switzerland’s Basel Zoo learned to imitate the high-­pitched, repetitive vocalizations of his companions—­sounds that African elephants usually do not make.71 ­These are examples of complex vocal learning: the ability to learn and reproduce sounds heard in one’s environment, rather than innate sounds that arise from an inborn repertoire.72 If elephants can learn how to imitate car noises and the sounds of other species, they can potentially also learn to interpret sounds that discourage them from entering farmers’ fields. Scientists are now testing specific signals to warn elephants away from dif­fer­ent types of danger. This raises a complex set of technical questions: ­whether to use automated analy­sis of acoustic or visual data (or both); how to filter elephant sounds from background noise (the sound of truck engines, for example, can interfere with the low-­frequency signals elephants make to communicate over long distances); and how to coordinate ­these digital systems with the increasingly complex digital poaching detection systems now widely in use in conservation areas.73 The Tusker Alert system developed in Sri Lanka, for example, flashes warning lights and emits

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artificial bee sounds when triggered, and also notifies the wildlife department, railway officials, and police stations through SMS messages and a mobile app.74 Tusker Alert relies on acoustics as well as computer vision: an artificial intelligence algorithm designed to recognize elephants from photos, thereby reducing false alarms. In densely populated areas where elephants coexist with ­humans, ­these types of systems—­ some of which have over 95 ­percent accuracy in elephant recognition in field ­trials—­may enhance public ac­cep­tance of free-­roaming elephants.75 Researchers are experimenting with combining ­these acoustic and visual early warning systems with the use of l­ asers, drones, or honeybee pheromones as deterrents.76 But most researchers agree that adapting local crops and training local farmers in conflict mitigation techniques are also key. Traditional low-­intensive, shifting, subsistence agricultural practices—­which left accessible postharvest zones within forests for elephants to graze, thereby reducing direct conflict—­could also be reinstated.77 If farmers and elephants establish a more peaceful coexistence, then digital conservation tools w ­ ill be more likely to succeed. And as digital bioacoustics continues to help us decode precise animal signals, we can further encode ­these into digital deterrents.

Google Elephant? ­ oday’s acoustic digital deterrents mostly broadcast indiscriminate T sounds, meant to deter animals like elephants through terrifying them. But what w ­ ill tomorrow’s devices do? Consider the translation power of your smartphone, capable of instantaneous translation between hundreds of languages. Could someone one day invent a similar translation device for elephants? ­After forty years of studying elephants, Joyce Poole and her colleagues have recently developed the world’s first digital elephant lexicon: the Elephant Ethogram. An ethogram is a list of be­hav­iors—in this case, vocalizations—­unique to each species that make meaningful distinctions for the animals themselves. By documenting and preserving elephant language through the collaborative Elephant Voices proj­ect, Poole and other elephant scientists hope to advance public awareness, as well as the study of elephant cognition, communication, and social be­hav­ior.78

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Poole’s database is a one-­way tool: viewers can watch dif­fer­ent elephant communication calls and be­hav­ior. Another group of conservationists has created “Hello in Elephant,” a smartphone app that translates h­ uman texts, expressions, and phrases into elephant language. The app, its creators say, is intended to “provide p­ eople with the ability to translate ­simple h­ uman words and emotions into elephant calls that signal similar emotions or intentions.”79 ­These initiatives are more taxonomic than interpretive; a lexicon rather than a true digital dictionary. Developing the latter would require learning more about elephants’ neurobiology, be­hav­ior, cognition, ecol­ogy, and even aesthetics—we would need to learn to take elephants’ perspectives.80 Perhaps, one day, innovations along t­ hese lines w ­ ill evolve into two-­way communication devices; in the near f­ uture, hopefully, they w ­ ill enable acoustic deterrents to include more sophisticated and hence effective signals. The existence of infrasonic communication in elephants seems, in retrospect, self-­evident. How ­else could this highly social species coordinate their be­hav­ior across vast expanses of forest and savannah? But Payne’s discoveries landed like a bombshell, revealing a deeply ­human bias in our scientific understanding of the world. In the absence of sounds we could hear, scientists simply assumed animals ­weren’t making sound. In the absence of evidence that they ­were using sound to convey complex information, scientists assumed an absence of communication. Digital technology helped bridge the technical gap—­but the real leap was cognitive, as we learned to set aside our own biases. A far bigger leap is to extend this insight to the rest of the living world. Given their large brains, it is relatively easy to accept that charismatic megafauna can use vocal communication to convey complex information. But it is more of a leap to argue that species with smaller brains, or perhaps without brains at all, might also be able to listen and communicate. As the following chapters explore, scientists’ insights into ­whales and elephants set the scientific community on a new path: using bioacoustics to decipher nonhuman communication across the Tree of Life. Scientists are learning that a vast array of species is listening and sounding in ways we have only barely begun to understand.

4 Voice of the Turtle

When Camila Ferrara announced the proposed topic of her doctoral research, her professors laughed. She wanted to study Amazonian turtle sounds. “You are crazy! You ­aren’t ­going to get your PhD,” said her supervisor. Another professor told her, “I have been working with turtles for twenty years and never heard them make noise.”1 Ferrara was undeterred. A fellow gradu­ate student—­Jacqueline Giles—­had recently visited Ferrara’s university in Brazil to pre­sent her research on the sounds made by freshwater turtles in Australia. Ferrara was asked to serve as Giles’s guide, traveling up the Rio Trombetas, an isolated tributary of the Amazon in a region with no major roads; ­there, Giles recorded the sounds of the river, including sounds of endangered river turtles. Although Ferrara’s superiors w ­ ere generally dismissive of Giles’s findings, Ferrara was intrigued. If Australian turtles made noise, she reasoned, why would their Amazonian cousins be ­silent? Ferrara stubbornly stuck to her topic, but the path to her PhD was indeed far from straightforward. Ferrara faced skepticism not only from her supervisors but also from the broader scientific community. When she proposed her topic, herpetologists (as turtle biologists are known) had long believed that turtles ­don’t make sounds.2 Christina Davy, a Canadian turtle scientist, describes one moment early in her c­ areer when she picked up a turtle that squawked in her hands. “It’s making noise!” she exclaimed.3 A se­nior colleague standing next to her immediately dismissed the possibility as a ridicu­lous suggestion. Davy was dumbfounded: she was sure the turtle 63

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had made noise. But when she brought it up again, she was politely encouraged to let the ­matter drop. As Davy points out, turtles share common ancestors with birds and crocodiles, which are known to vocalize.4 So why did scientists assume that turtles d­ idn’t make sound? Major reference books on aquatic turtles asserted that they w ­ ere mute and very likely deaf.5 Although rare reports existed of tortoises occasionally making noise, researchers assumed that if turtles did vocalize, they only did so in significant distress, in the throes of mating, or while ­dying. Some argued that aquatic turtles’ anatomy prevented them from making sounds and ­limited their ability to hear sounds. Given their small head size, turtles’ ability to perceive interaural time differences—­the difference in the arrival time of a sound between two ears—­was thought to be low. And their acoustically isolated, small m ­ iddle ear spaces w ­ ere assumed to limit their hearing range, allowing them to do no more than navigate t­ oward or away from the general direction of sound sources.6 As it turns out, however, scientists w ­ ere the ones who ­were hard of hearing. Around the same time as Ferrara began arguing with her supervisors, Giles was defending her PhD on vocalizations of the Australian snake-­ necked river turtle (Chelodina colliei).7 Giles had de­cided to focus her research on turtle sound ­after an unexpected encounter. During her honors research, she undertook a “mark-­recapture” study of the impacts of roads on turtles. The study took place in a wetland where traps ­were set for turtles, so that researchers could track their locations. Marking the nearly seven hundred turtles in her study required paddling in a canoe through the wetlands to each turtle trap. At each stop, Giles carefully hauled the trap into her canoe and extricated the turtles one by one. As her canoe drifted, she weighed, mea­sured, and marked each turtle. Giles then placed each turtle ­gently in the bottom of the boat, collecting several at a time u­ ntil she retraced her steps, paddling back to release the turtles where she had captured them. Depending on the numbers caught in each trap, the turtles might spend quite a bit of time in the bottom of her canoe. One day, Giles remembers, “This par­tic­u­lar turtle was obviously so distressed with being handled, then put in the bottom of the boat. It

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roared like a dinosaur! I just ­couldn’t believe it. It obviously w ­ asn’t a big bellow of a Triceratops, but it was a l­ittle turtle roar.” Giles was utterly amazed: “I ­didn’t know that turtles could make noise. How ignorant of me!” Her next thought was a sense of won­der: “It was like a voice from millions of years ago.” Australia’s long-­necked freshwater turtles are believed to be of Gondwanan origins; the turtles’ calls would have sounded similar in the ancient swamps of the Cretaceous period.8 Back on campus, Giles was astonished to learn that no one had ever conducted a systematic study of turtle noise. Against the advice of her supervisor, she de­cided to devote herself to the study of turtle sounds. “I based my w ­ hole PhD,” Giles marvels, “on one l­ ittle turtle that roared in the bottom of my canoe.” When she announced to her supervisor that she wanted to study the underwater calls of turtles, he told her she was “wasting her time” and “unlikely to hear much more than a few grunts.”9 Without established protocols to guide her, Giles spent months preparing for her fieldwork and crafting her research design. No one in her department knew how to calibrate the hydrophones for her specific needs, but she eventually found an Australian Navy researcher willing to teach her. In order to lug her equipment into the boggy wetlands where the turtles lived, Giles first tried a dolly on wheels—­w hich promptly became stuck in the mud. She then mounted her dolly onto what she fondly termed an “all-­terrain assault vehicle,” complete with wheelbarrow wheels and welded brackets to help carry all the field gear. Thus equipped, Giles hauled her jerry-­rigged hydrophone recorders over sand, long grass, ant trails, and mud. And this was only the prelude: the real work began in the field, walking and sitting for weeks on end at the edges of insect-­filled Australian swamps, listening for what most scientists d­ idn’t think existed. It took Giles 230 days in the field and five hundred hours of recordings to prove conventional scientific wisdom wrong. “Wearing heavy wading gear, hauling equipment 5 miles in 100-­degree heat, then sitting waiting to rec­ord at dawn, noon, dusk, and midnight,” recalls Giles, “almost broke me.”10 But what Giles heard through her underwater microphones made it worthwhile. Not only did the turtles make noise, they made lots of it, day and night. In addition to their calls above the ­water surface, turtles

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used an underwater communication system with a repertoire of complex sounds. Giles recorded clacks, clicks, squawks, hoots, short chirps, long chirps, high calls, cries or wails, whines, grunts, growls, blow bursts, staccatos, a wild howl, drum rolling, and pulsed mating songs. Her results, published in the Journal of the Acoustical Society of Amer­i­ca in 2009, provided the first-­ever academic account of underwater turtle recordings in the wild.11 Her PhD supervisor, initially skeptical, told her it was one of the most in­ter­est­ing doctoral ­theses he had ever supervised. Scientists could perhaps be forgiven for overlooking turtle vocalizations, some of the most subtle sounds in the animal kingdom. Turtles are quiet: they d­ on’t vocalize loudly. The turtles that Giles studied vocalized in a range audible to h­ umans (mostly 1–3 kHz, with clicks in the upper-­frequency range), but their calls are not particularly loud, and many other noises can mask their calls. Turtles are not vocal specialists and do not make many calls; they leave relatively long pauses between one sound and the next, and might wait minutes or even hours before replying to one another.12 Sometimes, Giles waited hours before a single turtle call was heard. In many of her recording sessions, Giles recorded only two or three sounds. If w ­ hales are the opera singers of planet Earth, and birds are the orchestra, then turtles are more like a quiet marimba or a tiny thumb piano: low-­frequency, quiet sounds of relatively short duration that only an attentive ear, and a body held in stillness, might pick up. Giles’s meticulous research was the first systematic scientific study of turtle sound. It opened the door for Ferrara to craft a bold hypothesis: Amazonian turtles produce and use sounds to exchange information and coordinate social be­hav­ior. To the skeptics, the second half of this sentence was even more preposterous than the first. But Ferrara hoped that, if proved correct, her research would solve one of the greatest mysteries of animal be­hav­ior in the Amazon. ­Every year, ­giant Amazon river turtles (Podocnemis expansa), or tartarugas in Portuguese, haul out on the sandy beaches of the enormous forest.13 ­After traveling separately for hundreds of miles, they congregate in groups on specific beaches, lay their eggs, and disperse into the endless river. How do t­ hese turtles know when to gather? How do they

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find one another in the vastness of the Amazon, meeting at precisely the same point in time? Is it pos­si­ble they coordinate their be­hav­ior through acoustic communication? ­These ­were the mysteries that Ferrara hoped to solve. The belief that turtles ­were mute had existed since the colonial era. When the Portuguese arrived in South Amer­ic­ a, the ­waters of the Amazon teemed with turtles so abundant that they posed a ­hazard to navigation. During fall nesting season, female turtles hauled out of the ­water by the millions. Eu­ro­pean travelers w ­ ere astounded by the sight of turtles lining the sandy beaches of the mighty river and its tributaries as far as the eye could see.14 Sent to Brazil to assist with demarcating colonial bound­aries, the Italian astronomer Giovanni Angelo Brunelli wrote a book about the Amazon instead; in De Flumine Amazonum, he marveled at the endless riverbanks on which turtles congregated, and recorded that “­great stretches of land are darkened for many leagues” by their shells. Turtles ­were, in the words of famed naturalist Henry Walter Bates, even more abundant than mosquitoes.15 To the colonial eye, turtles ­were less glamorous than rare Amazon butterflies or colorful birds, and ­were abundant and hence undervalued. In Bates’s best-­selling Naturalist on the River Amazon (1864), praised by Darwin as “the best book of Natu­ral History Travels ever published in E ­ ngland,”16 the meticulous descriptions of insects are a startling contrast to his lackluster descriptions of turtles. Bates’s most memorable comment is perhaps his culinary assessment: The flesh is very tender, palatable, and ­wholesome; but is very cloying: ­every one ends, sooner or ­later, by becoming thoroughly surfeited. I became so sick of turtle in the course of two years that I could not bear the smell of it, although at the same time nothing ­else was to be had.17 Bates’s view was typical of the colonists’ mindset: turtles meant meat and cash. Newly arrived Eu­ro­pe­ans ­were hungry; settlers and soldiers needed food to fuel the expanding empire. But imported ­cattle failed to thrive, and even hardy transplants, like cabbage and kale, succumbed to mysterious fungi. A nutrient-­dense local food source was

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needed, and one par­tic­u­lar species had the misfortune of being the perfect resource. Tartarugas ­were not only abundant but enormous: adults can weigh in over 200 pounds, with shells over 3 feet long and 2 feet wide.18 And they ­were easy prey. Turtle hunters merely rowed up to the nesting beaches and flipped the female turtles over; once on their backs, they w ­ ere unable to right themselves. The hunters then loaded the living turtles onto waiting canoes. One man could easily collect a hundred turtles in a day, enough to feed a thousand soldiers.19 ­After the hunter had finished piling up the turtles, another bounty awaited: turtle eggs, prized as a source of fat. Butter c­ ouldn’t survive the trip from Eu­rope without spoiling, and without vegetable oil, beeswax, or butter, the colonists had no way to fry their foods or light their lamps.20 Turtle fat was a remarkable substitute, shipped to Manaus, Rio, and even Eu­rope. Turtle egg butter (mantiega dos ovos) was, as one colonist put it, “the redemption of this land.”21 Back in the colonial villages, the captive m ­ other turtles ­were penned in large corrals, ensuring a ready supply of meat for the colonists, who often ate turtle twice a day. Naturalist phi­los­o­pher Alexandre Ferreira observed that the turtles, christened “everyday beef” by locals, w ­ ere particularly appreciated during the wet season, when fish and game w ­ ere more difficult to find in the flooded forest (the “season of dearth,” Bates called it).22 Missionaries ­were equally appreciative; Jesuits categorized turtle meat as fish, allowing Catholics to continue enjoying “river c­ attle” during Lent. Turtle shells ­were repurposed as bowls for cooking, eating, and washing, as well as cut to make utensils or combs. The neck skin could be dried and stitched for bags or pouches, or stretched to make drums and tambourines.23 Priests reportedly used shells as baptismal fonts. Extra shells ­were even used as stepping stones over the slippery, muddy streets during rainy season. Brazilian colonists w ­ ere never far from turtles and turtle-­derived objects, in much the same way con­temporary socie­ties are replete with plastic. With the arrival of Eu­ro­pe­ans, large-­scale commercial exploitation replaced local subsistence.24 Although Indigenous ­peoples had long relied on turtles as a source of protein, using wooden pens to supply fresh meat for the large settlements along the river, they also maintained a

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system of rules and taboos that prevented overharvesting.25 Indigenous communities carefully l­imited their harvest of tartarugas; the Paumari swam underwater to catch the turtles by hand. But colonization sparked an orgy of turtle destruction.26 The most abundant haul-­out sites ­were commandeered by the Portugese Crown, which taxed turtle butter production throughout the expanding colony.27 Systematic counts of turtle deaths ­were not recorded, but reliable estimates suggest that two hundred million eggs ­were harvested between 1700 and 1900, and tens of millions of turtles ­were killed. The true number may be much higher.28 As the colonists flooded into the forest, naturalists gave l­ ittle thought to the rapidly disappearing turtles, focusing their attention instead on the wide variety of birds and insects. Bates, for instance, spent eleven years pursuing Amazonian butterflies. He was intrigued by the repeating pattern of spots on dif­fer­ent species of butterflies, which he realized was a form of mimicry. Batesian mimicry is the term still used to describe a form of mimicry used by one species (usually harmless) to imitate the warning signals of another, harmful species pursued by the same predators. Ironically, while Bates discovered one impor­tant form of signaling in animals, he entirely overlooked another: turtle sounds.

Turtle Tales By the twentieth ­century, Podocnemis expansa turtles ­were thinly spread across the Amazon. Their disappearance was noteworthy, given that turtles emerged on Earth at roughly the same time as dinosaurs. One of the planet’s oldest species, they feature in mythologies and origin stories as symbols of patience, wisdom, fertility, and good fortune.29 For the Igbo of West Africa, turtles are clever manipulators: able to figure a way out from even the most dangerous, tricky situations. For the ancient Greeks, the turtle was a symbol of fertility; Aphrodite rests her foot on a turtle in many carvings. In ancient Egypt, turtles ­were symbols that warded off evil. Some of the earliest examples of Chinese writing are preserved on turtle shells, whose shapes recall the flat earth and domed sky of ancient Chinese cosmology. For the Anishinaabe, the origins of Earth—­supported on the back of a turtle—­represent the social and

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spiritual relationship between the Anishinaabe p­ eople and the land. In the Amazon, the Tapajó made clay lamps in the image of turtles. The Tumpasa spoke of a huge female spirit-­turtle that protected its species, watching over the ­mothers while they ­were giving birth to ensure no one would disturb their ­labor.30 Eu­ro­pean colonizers, by contrast, viewed turtles as mere sources of meat; colonial naturalists assumed turtles ­were mute and deaf. With colonization, the relationship between ­humans and turtles was reduced to a brutally s­ imple equation: encroaching predator and endangered prey. Caught between the more dangerous terrain deeper in the forest and fast-­growing ­human settlements, tartaruga numbers dwindled rapidly. In the Rio Trombetas Biological Reserve, which once harbored one of the largest Podocnemis populations in Brazil, fewer than six hundred females now remain. Across all species of freshwater turtles, perhaps thirty thousand remain on the Amazon’s protected beaches; tartarugas are no longer found in the Upper Amazon.31 Podocnemis had lived with ­humans for millennia, the historian and geographer Nigel Smith observed, “but 300 years of the intense pressure of [colonial] civilization had driven it to the brink of extinction.”32 As their populations dwindled, turtles began seeking out beaches deeper in the forest. But ­these locations ­were more precarious. Nesting females gather in large groups (arribadas) for safety in numbers from land predators, like jaguars. Wide river sandbanks (tabuleiros) offer more protection; the narrower beaches deeper in the forest are less secure. Before laying their eggs, females first need to speed up their metabolism, so they sun themselves daily to warm up, allowing nutrients—­ like calcium and magnesium—to flow through the oviducts to the developing egg shells. When it is time to lay their eggs, they ascend the beach at night to avoid the heat and daytime predators. Working feverishly for several hours, each m ­ other digs a deep hole into which it ­settles and lays around one hundred eggs (twice that for the largest ones). Spending so much time out of the ­water makes the ­mother turtles an easy target. If the females are lucky, they go undetected; if not, a nocturnal jaguar finds them.

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­ ese circumstances presented a challenge to Ferrara’s proj­ect: findTh ing enough turtles to study. First, however, her supervisors challenged her to prove that turtles made sounds at all. She recorded turtles in captivity for four months before capturing her first sound. But that was enough. Hoping that turtles in the wild w ­ ere more talkative, she headed up river. At least the recordings in captivity had helped her establish a frequency range that could assist with field recordings. ­After months of searching for turtle nesting beaches, she found a suitable site for her study; at a discreet distance, she popped a microphone underwater and began to listen. Then the real work began. As discussed above, turtle vocalizations tend to be quiet, low (at the edge of or below ­human hearing range), and infrequent. Ferrara could spend six hours recording without hearing a single sound. It would take months, and then years, to gather enough data. Eventually, she recorded a sufficient number of sounds to publish her first study. Recording turtles in nature and captivity, she documented 2,122 unique turtle sound samples, which demonstrated unequivocally that turtles produce sound in and out of the ­water. Ferrara painstakingly coded ­these sounds into eleven dif­fer­ ent categories, manually rechecking each waveform in the spectrograms (the visual repre­sen­ta­tion of the sound wave). She also found evidence that turtles use sounds to coordinate their be­hav­iors, like hauling out of the w ­ ater to bask in the sun. She had solved her mystery.33 In response to initial disbelief from the scientific community, Ferrara and Giles noted: One must realize that ­these sounds are very low frequency, near the lower range of ­human perception. . . . ​Also, the sounds are usually very short, usually only fractions of a second, at low volume. Thus, if you are ­under ­water, the mere paddling of your feet or breathing through a snorkel is enough noise to obscure the sounds of turtles vocalizing.34 Bioacoustic research on turtle vocalizations had not been undertaken due to the ­earlier, widespread misconception that they are voiceless. But most researchers, Ferrara and Giles went on to point out, simply ­hadn’t been listening.

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Ferrara soon returned to her Amazon field sites for two more years of fieldwork, pursuing an answer to a new question: How young are turtles when they start to communicate? In her ­earlier research, a tantalizing pattern had emerged: female turtles w ­ ere recorded in the river approaching and responding to hatchling vocalizations, with similar sound patterns. This, Ferrara believed, suggested that the adult turtles communicated with hatchlings. Her hypothesis flatly contradicted received scientific wisdom, which held that ­mother turtles simply abandoned their eggs ­after laying them. What if, Ferrara wondered, this belief was untrue? In order to test her hypothesis, she set up hydrophones in the ­water, as well as microphones near the turtle nests and, to establish a baseline, in the nests themselves. The hatchlings, she told herself, ­were unlikely to make noise ­until their eggs cracked; listening to the nests was just a control. Still, she admits, “I felt a ­little silly.”35 In order not to disturb the eggs, she crept up alone on the beach with a set of very small, sensitive microphones. Slowly, over the course of several minutes, she inserted a microphone into each nest; once inside, the microphone could pick up individual grains of sand falling, or a mosquito whining. Ferrara was expecting to hear noises ­after the turtles hatched. But she discovered, to her amazement, that baby turtles make noise before they hatch. And the turtle embryos ­were relatively talkative: in a group of ten thousand hatchlings, Ferrara heard a sound, on average, ­every thirty seconds. This enabled her to build up an impressively large dataset compared with her ­earlier recordings. In one study of turtle embryos and hatchlings, her team detected 189 sounds that they classified into seven dif­f er­ent types.36 Some baby turtle sounds are made only in the nest. ­Others overlap with the same categories of sound used by adults. What could the yet-­to-­be-­born turtles possibly be talking about? Researchers have yet to provide a definitive answer, but the most likely answer is that the yet-­to-­be-­hatched turtles are sharing their readiness to break out of their eggs, in order to coordinate a time to hatch.37 By synchronizing hatching, the baby turtles also synchronize communal digging up through the sand, which allows the siblings to work together to make their way ­toward the surface and out of the nest.38 Acoustic

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coordination appears to be a survival mechanism: synchronizing their departure from the nest makes each individual hatchling less vulnerable to predators.39 Si­mul­ta­neously, Ferrara recorded the ­mother turtles—­waiting patiently in the w ­ aters just off the beach for their eggs to hatch. Even before they are born, Ferrara explains, hatchlings are calling to their ­mothers. In turn, the ­mother turtles make specific noises as the hatchlings are born. As the turtle hatchlings journey to the ­water, the adult females in the ­water continue to call to them. Ferrara eventually tracked the m ­ others and babies swimming together downstream, to areas of safety in the flooded Amazonian forests where they would spend the winter.40 Hatchlings and ­mothers outfitted with transmitters have been found to migrate closely together for more than 50 miles over the course of two weeks.41 Ferrara’s findings challenged the beliefs of most herpetologists, who ­were skeptical of turtles’ hearing abilities and astonished to learn that turtles engage in parental care. But subsequent research by other scientists revealed that hatchlings’ hearing was, in some cases, exquisitely attuned to their environment. For instance, recent research has shown that leatherback hatchlings’ most sensitive hearing range (50–400 Hz) corresponds to the sound of waves on the beach (50–1,000 Hz), which may help them find their way to the waves immediately ­after being born.42 Ferrara and Giles’s findings about turtle vocalization led them to make another controversial claim: the extensive repertoire displayed in turtles’ vocal communication is highly suggestive of complex social organ­ization.43 This hypothesis contradicts mainstream views of turtles. As researcher Julia Riley explains, turtle sociality was understudied in the past. Turtles do not exhibit be­hav­iors that ­humans (as mammals) find innately social—­such as grooming or feeding their young. Reptile social traits are dif­fer­ent: sharing a resource like a crevice, basking together in close proximity, associating with young ­after birth, swimming together to a mutually agreed upon destination. Th ­ ese social be­hav­iors likely do have mea­sur­able survival benefits (“fitness,” in evolutionary terms) for young turtles, particularly through the presence and guidance of their ­mothers. This research not only helps us understand

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turtles, it may also help us understand ourselves. Our deepening understanding of turtles’ vocalization and communication patterns may lead to new insights in the evolution of vocalization of our distant ancestors, the amniotes, who occupy the branch of the evolutionary tree shared by mammals, reptiles, and dinosaurs.

Digital Turtles, Digital Twins Studying the vocal communication of turtles requires enormous patience. Few researchers are able or willing to spend years recording turtles in the wild. And ­human presence likely interferes with turtle be­hav­ior. So turtle scientists are beginning to use digital bioacoustics as a method to link vocalizations to be­hav­ior in turtles. Although this research is still in its infancy, the results are intriguing. For instance, researchers have begun exploring how turtles use sounds to guide hunting. One study in Fiji found that higher numbers of green turtles ­were found at sites with the largest number of sounds made by fish and crustaceans—­not in sites with maximum seagrass or the largest number of fish—­suggesting that sea turtles, like other predators, follow sounds to find their prey and perhaps signal their intentions to other turtles.44 Whereas conventional methods used to assess biodiversity in freshwater ecosystems can be invasive (sometimes leading to injury or disturbance of vulnerable species), passive digital monitoring does not interfere with organisms or their habitat. Passive acoustic monitoring devices can enable remote monitoring to reveal how many turtles are at a given spot, and rec­ord the sounds they are making. Researchers are now developing machine learning algorithms for analyzing datasets generated by passive acoustic recordings of turtle and other reptile vocalizations. Merely on the basis of the sounds produced, t­ hese algorithms can identify specific species. In one recent study, two machine learning algorithms (K-­Nearest Neighbors and Support Vector Machine) ­were used to identify and differentiate twenty-­seven reptile species with an average classification accuracy of 98 ­percent.45 Other researchers are developing new ways to use digital acoustic monitoring to study the waterscapes that turtles inhabit. Machine learning

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algorithms based on ecoacoustics indices can monitor the health of ­water bodies and ecosystems.46 By listening to the entirety of noises in the soundscape of, say, a pond, we can discern sounds from multiple species and the landscape they inhabit. This sheds new light on ecosystem function and condition.47 Digital listening can rec­ord and analyze the multitude of sounds: insects and birds, fish and turtles. But it can also home in on specific sounds and frequencies that m ­ atter most to turtles. This means that we can listen not only to turtles but also like turtles. Digital bioacoustics can reveal subtleties that might escape ­human listeners, like the sound of piranhas feeding.48 Perhaps we would hear the soft sounds of tadpole shrimp like Triops cancriformis or Lepidurus lubbocki, which make rapid, quiet noises likely too subtle for ­humans to hear, but squarely within turtles’ hearing range.49 Or we could tune our ears to the slap of w ­ ater on the shore or pond’s edge, guiding us to where midges are likely to lay their eggs—­a tasty larval treat for turtles.50 Digital ecoacoustics can also reveal information that turtles are likely to glean about surrounding forests; t­ here is a close relationship, it turns out, between the sounds of the forest, its vegetation, and its structure.51 Our ­human ears cannot listen like this, but our computers can listen on our behalf—­and then tell us what they are hearing. ­These novel digital techniques are fascinating, yet it is impor­tant not to overstate the potential of digital listening. ­There is a long history of richer, deeper listening in the Amazon. In a dense forest, sound is more informative than sight. And for many Indigenous communities, sound communication is often understood to be shared between ­humans and nonhumans. Much ceremonial ­music in the Amazon is received from, ­shaped by, and directed to nonhuman persons: animals, plants, and “inanimate” beings, such as rocks or rivers. The nonhuman and h­ uman ­people who inhabit the forest take many forms but share common attributes: ­every species speaks a language, and participates in ceremonies through m ­ usic and dance. The Kĩsêdjê/Suyá of Brazil, for example, know that the forest animals and fish also speak to one another, sing songs during their own ceremonies, and communicate with other species in sounds akin to m ­ usic.52 Sound is also central to some shamanistic

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traditions; a period of isolation in the forest—to learn its language— is a necessary initiation rite.53 Brazilian scholar Rafael José de Menezes Bastos argues that this acoustic-­musical “world hearing”—­which combines the precision of Western science with practices of spiritual attunement—is predominant in some Amerindian cultures, such as the Xinguano and the Kamayurá. Listening to the sounds and songs of nonhumans is a form of sacred ecol­ogy.54 As Bastos writes, his Kamayurá friends and collaborators often patiently encouraged him to learn to listen.55 One eve­ning, while traveling by canoe across Ipavu Lake in northeast Brazil, his friend Ekwa s­ topped rowing and went s­ ilent. When Bastos asked why they had s­ topped, Ekwa responded: “­Can’t you hear the fish singing?” Bastos heard nothing. While Ekwa insisted he needed to train his hearing, Bastos ­later wrote: “Back in the village, I concluded that Ekwa had experienced some kind of hallucination, a fit of poetic inspiration or holy ecstasy, the ­whole event just a flight of imagination.” It was only some years l­ater, when Bastos went to a bioacoustics workshop or­ga­nized by scientists at the University of Santa Catarina, that he heard the sound of fish songs and realized that Ekwa’s claims to have heard the dourados singing ­were more than merely fanciful. Suddenly, Bastos realized, Ekwa “appeared more like a diligent ichthyologist than an inspired poet, a victim of hallucination or holy rapture.” Bastos’s ears had been closed, but Ekwa’s ears ­were open. Even Kamayurá ­children, Bastos writes, w ­ ere able to hear the sounds of planes and boats arriving well before he could. In the Kamayurá language, the word anup (to hear) also evokes “to comprehend,” in a manner superior to the word tsak (to see), which evokes “to understand” only in a narrow, analytic sense. Whereas seeing too powerfully is associated with antisocial be­hav­ior, hearing is associated with holistic, integrated forms of perception and knowledge. Good listeners, among the Kamayurá, are often t­ hose with virtuosity in m ­ usic and the verbal arts. A special accolade—­maraka´ùp (master of ­music)— is given to t­ hose who, like Ekwa, are able to sense, remember, reproduce, and relate the sounds of other forest beings. This ability (akin, the Kamayurá say, to the capacities of the best audio-­recording equipment) requires inborn talent, which is then cultivated through vigorous training throughout one’s lifetime.

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The Kamayurá’s interpretive acoustic abilities often surpass Western scientific knowledge: they are able to draw precise and accurate inferences regarding which species or objects have made a sound, linked to knowledge of where and why a sound might be made. But this interpretive meaning is also relational: as good listeners, h­ umans are in constant dialogue with nonhumans. When moving through the forest, the Kamayurá not only listen but converse with the animals, plants, and spirits, telling them that they mean no harm and asking in return to remain unharmed. Deep listening is a form of dialogue. In contrast, digital listening—as conventionally practiced by Western scientist—is an enhanced form of eavesdropping.

The Turtle and the Canoe The Western scientific discovery of turtle sound began with one turtle quietly roaring its distress in the bottom of Jacqueline Giles’s canoe. ­Earlier generations of Westerners also dealt with turtles in canoes, but less ­gently. When the turtle hunters of the expanding colonial Amazonian frontier loaded ­mother turtles into canoes, they reserved a few empty boats. While the m ­ others waited, pinned helplessly on their backs, the men dug up their eggs and then crushed them by the thousands. The empty canoes became cooking pots: the mixture of dead turtle hatchlings, yolk, and albumen was doused with ­water and left in the sun. As the yolk fat ­rose to the surface, it was skimmed off, boiled, and then stored in clay plots (camotins).56 While waiting for the fat to render, the men would roast alive any remaining hatchlings. The ­mothers would have listened, helplessly, to the sounds of their babies being killed. They might have cried out to their ­children. Perhaps their ­children called back. Perhaps the hunters ­didn’t hear them; or perhaps they heard but ­didn’t care. Perhaps we choose to deafen ourselves not only physically, but also psychologically and spiritually, in order enact the vio­lence of colonization and environmental destruction. Each generation has its silences, at which ­later generations can only won­der. Attitudes ­toward turtles have begun changing in the Amazon. Realization has grown that, from an environmental standpoint, turtles are impor­tant ecosystem actors. They maintain ­water quality, disperse

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vegetation, help cycle nutrients from ­water to land, and keep energy flowing within and between ecosystems. Conservation plans have been introduced, but with l­ imited success. Once widespread across the Orinoco and Amazon basins, in a range spanning eight countries, the Podocnemis expansa turtle is now in danger of extinction, pre­sent in less than 2 ­percent of its former territory. Even now, illegal turtle harvesting continues for meat, medicines, pets, and ornaments.57 In the Brazilian city of Tapauá, with fewer than twenty thousand inhabitants, an estimated 35 metric tons of turtles ­were consumed in one year alone.58 Coastal ecotourism parks that feature sea turtles have had some success in protecting marine turtles. But freshwater turtles, which range over hundreds of miles in the Amazon river system, have no such protection. The Brazilian government’s current plans to build several hundred additional dams in the Amazon basin w ­ ill flood much of the tartarugas’ remaining habitat.59 It is unclear w ­ hether they w ­ ill survive in the increasingly fragmented Amazon. In order to save the remaining Podocnemis expansa turtles, Ferrara argues that we should begin deploying bioacoustics to create aquatic quiet zones for turtles while they are nesting and swimming, particularly as they guide their newborn hatchlings to safety in the flooded forest. It seems likely, although as yet unproven, that noise pollution from boats could be disrupting turtle sociality, just as it has in w ­ hales. In the f­ uture, we may be able to use bioacoustics to protect turtles, predict when eggs are about to hatch, and listen at a respectful distance as the newborn hatchlings swim with their ­mothers into the depths of the Amazon. Having just begun to listen to our turtle cousins, we still have much to learn. For now, they have taught us an impor­tant lesson: animals that scientists once believed to be s­ ilent can hear, make noise, and exchange information. Bioacoustics provides a win­dow into their world of social be­hav­ior, which is much more complex than scientists had previously suspected. Who knew that baby hatchlings sang to one another to coordinate their birth? Who knew their m ­ others called to their babies from the w ­ ater and shepherded them to safety? In retrospect, we should ask ourselves why we w ­ ere surprised. Turtles have the physiology that

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makes them capable of both making and hearing sound, and it seems narrow-­minded to assume that parental care is unique to mammals. Scientific surprise at ­these findings reveals as much about the observer as about the observed; and bioacoustics has only begun to subvert established beliefs. Indeed, as I explore in the next chapter, even more astounding findings have recently emerged: evidence of hearing ability in species that do not even possess ears.

5 Reef Lullaby

Climate change is slowly poisoning the oceans. As the seas warm, their ­waters hold less oxygen, suffocating life. And as carbon dioxide concentrates in the atmosphere, it reacts with seawater to create carbonic acid. This, in turn, increases ocean acidity, which has risen by approximately 30 ­percent since the pre­industrial period.1 Think of climate change as a planetary SodaStream, forcing ever-­increasing amounts of carbon dioxide into the oceans. Some marine plants—­like kelp—­may grow faster in the presence of increased carbon, but other, more delicate forms of marine life—­like corals—do not thrive in such conditions. At best, acidification deforms coral reefs; acidic seawater holds less of the calcium carbonate that corals need to produce their skele­tons, and survivors often grow in distorted assemblages.2 But many corals simply succumb.3 The quietly insidious effects of ocean acidification on corals are less widely known than megafires and sea level rise. But the consequences are no less dire. The majority of the planet’s coral reefs are dwindling rapidly due to the combined effect of ocean warming and acidification. Scientists predict that, if we continue along the current global warming trajectory, coral reefs w ­ ill entirely dis­appear from the oceans within thirty years, threatening the livelihoods of the more than one billion ­people who depend on them for food, medicines, and coastal protection.4 The disappearance of corals is a death knell for many other species. Corals are like the rainforests of the marine world: although reefs occupy less than a tenth of a ­percent of the ocean floor, they support one-­third of all known ocean species.5 And the corals most likely to be affected by 80

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climate change—­the branched, treelike variety—­are the kind that offer fish space to swim, hunt, rear their young, and hide from predators. Tougher coral survivors tend to have low-­profile, domelike shapes, which are less hospitable to fish and other marine life.6 The devastation is difficult to imagine, except by way of analogy. If climate change w ­ ere impacting New York City to the same extent as that experienced by the ocean’s coral reefs, rec­ord heat waves would be killing millions of inhabitants and the streets would be eerily quiet. The city’s buildings would be collapsing and covered in toxic mold, and no new materials would be available to rebuild. The city’s complete destruction would be predicted to occur by 2050. This is the fate of coral reefs in a warming world: ­silent ghost towns of the sea.

Death by Climate Change Tim Gordon was still a gradu­ate student when he witnessed mass coral deaths firsthand. He had originally intended to study the effects of tourist motorboats on fish along Australia’s ­Great Barrier Reef. Covering over 130,000 square miles, so enormous that it is vis­i­ble from space, the ­Great Barrier Reef is home to fifteen hundred species of fish, two hundred species of birds, thirty species of ­whales and dolphins, and the world’s largest turtle breeding area. It is, by far, the largest coral structure in the world. Given that coral reproduce by cloning, the reef is also one of the oldest living organisms on the planet. Aboriginal communities of Australia’s north coast tell of a time more than three hundred generations ago, when sea levels ­were around 400 feet lower than they are ­today and their ancestors lived and walked on the land now covered by seawater and corals. The birth of the G ­ reat Barrier Reef is told in Indigenous songlines—­landscape histories sung as chants and wayfinding narratives, like maps in musical form. Complex and ancient, songlines are teachings: ancestors’ explanations, instructions for ceremonies, cultural memory. Songlines also contain socioenvironmental history from deep time: knowledge of landscapes, plants, and animals; instructions and laws formed by the ancestors and embodied in the Dreaming, the Indigenous cosmology that Palyku

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l­ egal scholar Ambelin Kwaymullina describes as the “ongoing creation of all that is . . . ​the ever-­moving web of relationships that . . . ​recognizes familial relationships with all forms of life.”7 Near the G ­ reat Barrier Reef, the coastal songlines tell of terrible flooding, followed by the formation of the reef several thousand years ago. Western archaeologists have corroborated the information contained in the songlines, tracing the reef back to the period of global warming, and consequent rise in sea levels, at the onset of the Holocene.8 On the northern side of the G ­ reat Barrier Reef, both songlines and tourist itineraries converge on Lizard Island, known as Dyiigurra to the Dingaal ­people. Once a piece of the continent, the island is sacred to the Dingaal, who recount its origins in the Dreaming: the island forms the body of a stingray; the reef forms its tail. To the Dingaal, Dyiigurra Island is a hallowed place for initiation rites and ceremony. To the tourists, Lizard Island is one of the world’s premier scuba diving spots, host to cruise ships and an ecotourism lodge that touts its enviable location on one of the most biodiverse coral reefs in the world.9 Gordon’s field site, off the coast of the island, was a perfect location to study the impacts of tourism on corals. Around Dyiigurra grows one of the most complex, abundant zones of the reef, with an abundant harvest of shellfish, turtles, dugongs, and fish. But just as he was about to begin his fieldwork in 2016, a devastating heat wave swept over northern Australia.10 When corals are stressed by changing ocean temperatures, they commit a form of mass suicide. Healthy corals—­tiny, translucent, soft-­bodied animals related to jellyfish and anemones—­depend on symbiotic algae, known as zooxanthellae, which live within their tissues. The microscopic, brightly colored algae provide corals with oxygen and other vital nutrients; in return, the corals provide the algae with a protected environment for photosynthesis. This symbiosis enables coral colonies to thrive and reproduce; the relationship between plants (algae) and animals (corals) is so intimate that some refer to reefs as “planimals.” When ocean temperatures are stable, coral colonies can live for hundreds or even thousands of years.11 But warmer w ­ ater disturbs the delicate symbiosis; the algae begin producing chemicals that threaten the corals,

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which expel their colorful plant symbionts. Without the algae, corals lose their color and begin to starve, a phenomenon referred to as bleaching. When the under­lying calcium carbonate skeleton is revealed, the color of coral death manifests as a dull yellowish-­white.12 As ocean surface temperatures ­rose to unpre­ce­dented levels in 2016, a mass bleaching event killed more than half the shallow-­water corals on the northern side of the ­Great Barrier Reef. The reef had already suffered from back-­to-­back cyclones, and the heat wave pushed the corals over the edge: pallid, brittle, d­ ying. Gordon arrived in Australia just in time to witness the death of one of the oldest, largest, living animals on Earth. Scuba diving near Dyiigurra was like “swimming in a graveyard.”13 Sitting in front of the devasted seascape, Gordon was tempted to simply walk away; t­ here w ­ ere healthy reefs in other parts of the world he could study. But he felt a strange sense of obligation to bear witness. Eventually, rather than change his field site, he de­cided to pivot his PhD: he would document the decline of the d­ ying ­Great Barrier Reef by recording its changing sounds, as the fish began ­dying along with the corals. In crafting his new research topic, Gordon drew on the long-­standing research tradition of fish bioacoustics, which first emerged in the mid-­ twentieth c­ entury through the efforts of trailblazers like William Tavolga. Tavolga began his ­career working at Marineland in Florida, where dolphin shows—­put on by the world’s first dolphin trainer, Adolf Frohn—­attracted half a million visitors per year. Despite the hype surrounding the dolphins, Tavolga and his wife, fish behaviorist Margaret Tavolga, developed a curiosity about something more mundane: the ­little goby fish (Bathygobius soporator) in the ponds that dotted Marineland’s landscaped grounds.14 One day in the early 1950s, Tavolga met with Dr. Ted Baylor from the Woods Hole Oceanographic Institution, who had come to the park to rec­ord dolphin sounds. They ­stopped to watch some goby fish, the males rapidly and dramatically changing colors as they courted the females. Idly, Baylor wondered w ­ hether the gobies made sounds. His question was an unorthodox one, as common scientific knowledge at the time held that fish did not make sounds.15 Tavolga had long harbored similar suspicions, and had heard stories

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about certain species (like croakerfish) making sounds when spawning; he had also heard about the catfish that was trained to respond to the whistles of Nobel Prize–­winning scientist Karl von Frisch.16 But since it was widely believed in the scientific community that fish could neither make noise nor hear, it was impossible to convince funders to pay for recording equipment.17 Tavolga eyed Baylor’s recording equipment: a microphone, a speaker, and a beat-up second­hand Williamson amplifier, none of which was waterproof. So Tavolga wrapped the microphone in a condom and placed it near a male goby’s home (an empty snail shell) in a tank without females.18 When the two men dropped a female goby into the tank, they heard ­little grunts: sound pulses, synchronized perfectly with the male’s head shakes. The goby’s mating dance was one of the first documented instances of fish noise recorded by Western scientists. Tavolga realized only l­ater how lucky his timing had been: male gobies make sound only during courtship.19 Inspired by Tavolga’s study, other fish biologists soon began ­doing similar studies. What they found astounded them: snapping shrimp, clicking dolphins, and singing fish who ground their teeth or vibrated their swim bladders to make sounds that could be heard miles away.20 Sea creatures, it turned out, w ­ ere noisy beasts, and they could both hear and make sound along a wide range of the sonic spectrum—­often well beyond h­ uman hearing. Scientific studies of marine sound and acoustic communication blossomed in subsequent de­cades, but remained a niche activity; only the most tenacious researchers ­were willing to lug the bulky analog equipment to locales quiet enough to make useful recordings. In the years immediately preceding Tim Gordon’s studies, however, a new generation of inexpensive autonomous recording devices had suddenly made it relatively cheap and easy to become a marine bioacoustician. At relatively l­ ittle cost, Gordon could use t­ hese devices to monitor far larger areas, less invasively, than traditional analog methods.21 Healthy coral reefs, Gordon knew, are lively with sound; the unfolding waves of sound are like an underwater orchestra or the endless improvisation of a jazz band.22 On the ­Great Barrier Reef, the humpback

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­ hales sing the soprano melody.23 Fish supply the chorus: whooping w clownfish, grunting cod, and crunching parrotfish.24 Sea urchins scrape, resonating like tubas. Percussion is the domain of chattering dolphins and clacking shrimp, who use their pincers to create b­ ubbles that explode with a loud bang. Lobsters rasp their antennae on their shells like washboards. Rainfall, wind, and waves provide the backbeat.25 To get the best seat, you would have to attend the concert in the m ­ iddle of the night at the full moon, when fish chorusing typically crests.26 But you ­wouldn’t necessarily need to have a front row seat: mass fish choruses can be heard up to 50 miles away, and ­whale sounds resonate for hundreds of miles.27 This is one reason that coral reefs are among the most studied types of underwater soundscapes: the sheer, joyous abundance of the marine cacophony contains a trove of information that can be decoded from the layers of sound.28 Acoustic monitoring of coral reef noise has other advantages for scientists. Leaving microphones to monitor sensitive habitats over long periods of time allows for more effective, and less invasive, monitoring than visual surveys. Traditional methods of collecting data about coral reefs involve ­human divers visiting sites. This pro­cess is logistically complicated, expensive, and time consuming, and ­human data collection on coral reefs can also introduce a sampling bias: in the presence of ­humans, fish may hide or flee. By contrast, a discreet digital recorder placed in the reef allows scientists to monitor without disturbing the environment. Within a few hours, a recording of a coral reef can provide enough data to assess key ecosystem functions: scientists can decode spectrograms to detect the presence and estimate the biomass of herbivores and planktivores, and even distinguish dif­fer­ent types of corals inhabiting the reef. Specific events—­like schools of fish suddenly fleeing a predator—­have distinctive sound signatures, allowing researchers to follow events on the reef acoustically.29 Since sound travels better and farther than light underwater, digital bioacoustics is a power­ful, reliable, noninvasive way to capture the complexities of the underwater world.30 Recordings of marine soundscapes can also serve as a proxy for assessing coral reef health. The soundscape of a degraded ecosystem is usually missing sounds found in a healthy ecosystem; its spectrogram

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looks like a puzzle missing vari­ous pieces—or, if invasive species are pre­sent, one with odd-­looking pieces from a dif­fer­ent puzzle. Much as a radiologist assesses your health by looking at an X-­ray, a trained scientist can assess the health of a coral reef, or any ecosystem, by looking at its spectrogram over time.31 The degradation of a reef may reveal itself in sound before its decline is vis­i­ble to the ­human eye.32 Scientists had been recording the soundscapes of the ­Great Barrier Reef for years in order to assess changes in biodiversity; a dataset of recordings of the reef ’s underwater soundscapes already existed.33 Postbleaching, Gordon reasoned, the reef would have a degraded soundscape. To test this hypothesis, he set up a passive acoustic monitoring array to rec­ord the sounds of the bleached reef. When Gordon systematically compared his recordings to ­those taken in the same spots before the coral bleaching, t­ here was indeed a massive reduction in the amount and diversity of sound.34 Compared to the e­ arlier, prebleaching recordings, the degraded reef was eerily quiet. Although scientists usually try to remain objective, few can visit a ­dying reef without coming away with a profound sense of loss. Gordon’s recordings progressively documented the sounds of the reef ’s demise.35 By the end, some of the recordings ­were bereft of sound altogether.

Fish Wayfinding Gordon knew that the ­Great Barrier Reef was unlikely to regenerate. ­Earlier research had revealed that fish avoid ­dying and dead reefs; even fish larvae prefer not to ­settle on them. Gordon’s supervisor, Steve Simpson, had demonstrated that bioacoustics plays a significant role in this decline, as fish and fish larvae avoid ­dying reefs out of an aversion to how they sound. This discovery had been prompted by Simpson’s curiosity about a scientific mystery.36 The larvae of nearly all reef fish develop out at sea for a period of days to weeks before coming back to the reefs, where they “­settle” as juvenile fish. ­Until the late 1990s, scientists tended to assume that marine fish larvae w ­ ere passive, simply floating wherever currents would take them. But this understanding was overturned by

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experiments that demonstrated that the tiny fish larvae choose to navigate in specific directions; in other words, they deliberately select the place that ­w ill become their home.37 In one field study, researchers marked and tracked ten million fish larvae, then followed their movements u­ ntil they settled. Rather than being helpless, the larvae demonstrated both precise navigational capacities and strong swimming abilities, battling strong currents back through the open ocean to specific reefs.38 To underscore how astounding ­these findings are, consider the following: when born, fish larvae might mea­sure less than one-­twentieth of an inch. By the time they s­ ettle, they are still less than an inch long. Larvae have no ears, no tails, no fins. Lab studies had demonstrated that marine fish larvae—­which grow quickly—­are fairly feisty and relatively good swimmers within a week or two of being born.39 But that still ­didn’t explain how they know which way to travel. Could the larvae be following acoustic cues, Simpson wondered, to find their way back to their birthplaces? Simpson knew that sounds travel well underwater in any direction, irrespective of current flow. He also knew that healthy coral reefs create a clamor that can be heard for many miles. Around the same time he began his research, a series of Navy reports on coral reef noise w ­ ere declassified—­revealing that noise from coral reefs could travel up to 55 miles in calm conditions.40 The intensity of reef sounds also varies with celestial patterns: peaking at dusk and during the night, and varying on a lunar cycle (peaking at the new moon, when baby fish have a better chance of avoiding predators ­under the cover of darkness) as well as a seasonal cycle (peaking in late spring and summer).41 Intriguingly, Simpson observed, t­ hese celestial patterns are mirrored by the timing of the settlement cycle of reef fishes. Early playback experiments in the lab had shown that fish larvae could hear reef sounds and detect the direction it was coming from. But was this capacity sufficient to explain the ability of fish to navigate back home from the open ocean, with its storms and currents, wind and waves? To answer this question, Simpson devised an ambitious, large-­scale acoustic playback experiment.42 He recorded a dusk chorus of reef sounds during the new moon phase at the height of summer (the most

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alluring soundscape, from the perspective of fish larvae looking to find a home). Then he placed his experimental equipment on moorings anchored in the sandy bottom of the open ocean, well away from coral reefs. Attached to half of the moorings w ­ ere light traps with loudspeakers that played his recorded reef m ­ usic on a loop. The other half of the moorings served as a control: they also had light traps and loudspeakers, but the speakers w ­ ere s­ ilent. Would larvae choose the traps with the sound of healthy coral reefs, or would they be randomly attracted to the lights (in which case they would appear in the traps in equal numbers)? Each night for three months, the researchers put out the traps—­ ultimately collecting over three hundred thousand invertebrates and forty-­five thousand settlement-­stage fish larvae—­and compared how many larvae appeared at the two types of traps. They found that significantly more fish larvae (67 ­percent) w ­ ere attracted to the reef chorus, which also attracted a much greater diversity of larvae. The team counted eighty-­one dif­fer­ent species of fish in the traps attached to the loudspeakers playing the reef sounds, indicating that the ability to detect and follow the sounds of healthy coral reefs was widespread across species. With this study, Simpson provided definitive evidence that marine fish larvae are attracted to coral reefs by sound, much like moths are attracted by light.43 In a follow-up study, Simpson studied slightly older juvenile fish. As with his ­earlier study, the fish followed the recorded coral reef sounds back to the source; but this time, Simpson also demonstrated that dif­ fer­ent species displayed dif­fer­ent preferences.44 Nemipteridae (whiptail bream) preferred lagoon reef sounds; Pomacentridae (damselfishes and clownfishes) preferred sounds from the outer, fringing reefs. Fish, Simpson’s experiment demonstrated, are able to determine a desired direction of travel from sound cues. The fish also interpret the reef sounds to learn relevant habitat information, which then informs the decisions they make about where they would like to live. (In scientific terms, this is referred to as microhabitat se­lection.) When Simpson’s papers w ­ ere first published, revealing the importance of community-­generated coral reef noise for both directional orientation and habitat se­lection by coral reef fish, scientific opinion was

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divided. For some, it seemed a step too far. Yet subsequent research both confirmed and expanded Simpson’s findings to other species. Healthy reefs with the highest number of acoustic emissions by fishes and crustaceans attract more fish, crustaceans (including crab and lobster larvae), and even sea turtles.45 Research has also shown that some larvae swim away from motorboat noise.46 Up close to the coral reef, fish larvae and other sea creatures might be responding to chemical cues. But out in the open ocean, sound cues appear to dominate. Rather than randomly drifting to a reef, many invertebrates and fish larvae use sound information to detect and ­settle on suitable reef habitat (or avoid unsuitable reefs); some holoplanktonic crustaceans, on the other hand, avoid predator-­rich reefs by swimming away from them.47 The scientific community was astonished by ­these revelations about fish larvae. As one se­nior marine biologist put it: “Nearly e­ very aspect of the be­hav­ior of fish larvae examined so far has produced surprising evidence of the sophistication and range of abilities, revolutionizing our view of what larval fishes can do.”48 But the perceived novelty of scientific revolutions often depends on the myopia of the beholder. Had scientists thought to ask local fishers, they might have perceived their own blind spots much e­ arlier. Musician and informatics scientist Alice Eldridge recounts a story shared by Indonesian fisherfolk in the archipelago north of the ­Great Barrier Reef; placing their ears on wooden oars lowered into the sea, they can hear the distinctive soundscape of a healthy coral reef (which peaks between 4 kHz and 6 kHz, at the lower end of ­human hearing range): the calls and grunts of fish and the snapping of shrimp combine in a “crackling” sound that fishers follow to find their prey.49 In the mid-1980s, UNESCO published an edited volume on Indigenous knowledge of coastal systems across the Pacific.50 It tells the story of an ­earlier generation of scientists that spent time studying traditional fishing practices. One of t­ hese scientists, Bob Johannes, spent de­cades in Palau, a Micronesian archipelago that lies north of the G ­ reat Barrier Reef. T ­ here, he collaborated with local Indigenous fishers, whose elaborate prohibitions on certain fishing practices provided a model of sustainable fisheries management during a time when an industrial

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onslaught was decimating fisheries across much of the world’s oceans. Coral reef fish larvae, Palauan fishers told him, did indeed spend several weeks to several months in the open ocean; much like Western scientists thought, t­ hese larvae lived as tiny plankton, far from their birth reefs. But once they had developed sufficiently, the fishers told him, the larvae of at least five species could detect reefs from more than a mile away and swim ­toward them. The Palauan fishers not only recognized that fish larvae could navigate across the oceans; they actively cultivated this knowledge to their advantage. They used sticks and ropes to make artificial fish nurseries by suspending specific species of algae and vegetation in the ­water. By ­doing so, fishers ­were able to encourage larvae to gather and ­settle in certain spots, which the fishers would ­later return to for the harvest. In other places (such as Vanuatu), fishers also enacted prohibitions during fish spawning periods, sometimes for months at a time, to guard against overfishing and maintain healthy fish stocks. By organ­izing their fishing practices symbiotically with larval be­hav­ior during and ­after fish spawning, Palauan fishers acted like shepherds of the sea. Other Pacific Ocean fishing communities, like the Marshall Islanders, incorporated their knowledge of the sounds of the sea into complex songs that layered knowledge of oceanic currents, weather, animal, and environmental sounds into chanted rhythms that w ­ ere si­mul­ta­neously cultural practices and precise navigational aids.51 Johannes published t­ hese findings in 1981, but they w ­ ere met with re­sis­tance and indifference from mainstream fish biologists. The Palauan fishers’ claims implied that coral reef fish larvae could choose where they settled—in other words, that they chose the reef that would become their home. This seemed unlikely (if not preposterous), so few scientists chose to investigate ­these claims more closely; despite receiving a Guggenheim award for his work, Johannes’s observations ­were not in­de­pen­dently confirmed ­until nearly twenty years l­ ater by Steve Simpson and his colleagues. ­Until that time, mainstream fisheries scientists routinely assumed that coral reef fish larvae w ­ ere hapless creatures that floated along with the current. This mistaken assumption was not only incorrect, it was also harmful; in some cases, it led to significant errors in modeling the abundance of reef fish, and hence mismanagement.52

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Coral Chorus ­ ntil 2010, Simpson’s research was focused primarily on fish; his pivot U to studying coral larvae was prompted by an email that arrived, out of the blue, from a Dutch research team based in Curaçao. The scientists, their email explained, ­were studying mass coral spawning. On the ­Great Barrier Reef, mass spawning occurs once a year during a full moon: synchronized by some unknown signal, corals across the entire reef release their eggs and sperm into the ­water at the same time. The result is an aquatic equivalent of fireworks: millions of egg bundles and clouds of sperm billowing through the ­water in red, orange, white, and yellow.53 This synchronized timing is crucial, b­ ecause the sperm and eggs only survive for a few hours; mass spawning makes it more likely that fertilization w ­ ill occur. The underwater display generates a feeding frenzy, attracting squid and fish, sharks, and even w ­ hales. The surviving fertilized coral larvae then drift out to sea for several weeks or months, growing larger while avoiding predators, before settling back on a reef where they remain for the rest of their lives. ­Until the Dutch research team reached out to Simpson, mainstream scientists assumed that coral larvae simply drifted helplessly a­ fter mass spawnings, pushed by waves and currents ­until they randomly arrived on a reef and settled. But what if, the Dutch researchers asked, coral larvae respond to sound just like fish larvae? What if they, too, could hear reef soundscapes and navigate ­toward a specific reef, choosing where to ­settle? Simpson was incredulous: “You guys have got to be crazy. I mean, ­these ­things look like a floating blob—­about a millimeter long, the shape of an egg, covered in t­ hese tiny hair cells that they use to swim around and to feed.”54 Coral larvae, Simpson knew, are s­ imple organisms: they have no ears or hearing apparatus, no brain, no central ner­vous system. ­There seemed to be no way that they could possibly detect sound, much less respond to it. But the Dutch scientists proposed an ingenious experimental design. They w ­ ere growing hundreds of thousands of coral larvae in special coral rearing tanks. The tanks could be set up as “choice chambers”: an aquatic equivalent of a maze, with a series of tubes arranged as spokes around the central holding zone for the coral. Speakers playing healthy reef sounds w ­ ere positioned

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in some tubes, speakers playing white noise in other tubes, and ­silent speakers in the remaining tubes. If the microscopic coral larvae preferred certain sounds, then their concentration should increase in specific tubes within the choice chamber, but not in ­others. Intrigued, Simpson agreed to collaborate, although he was dubious that an effect would be observed. But to his astonishment, the coral larvae swam ­toward and clustered around speakers playing their preferred reef sounds.55 Aware that their results ­were likely to be received with skepticism, the team repeated the experiments several times to confirm their findings, which they controlled for tides, phases of the moon, and ocean currents. It was one ­thing to assert that fish, which have a sense of hearing and smell, can choose where to s­ ettle, but the scientific community was astounded to learn that coral larvae do the same.56 How could coral larvae perform this feat? The larvae, Simpson ­later reasoned, must be hearing with their bodies. Coral larvae are covered in cilia: tiny hair cells that act like a ­simple receptor system. Like other marine invertebrates, coral larvae rely on cilia to sense ­water currents and movements, which are impor­tant sources of information about the environment.57 This is also how coral larvae are able to sense sound. In ­humans, our inner ears are covered in a thin layer of hair cells; ­these hairs vibrate in response to sound, which is amplified across the tympanic membrane. Coral larvae also have tiny cilia, but they are on the outside of their bodies rather than inside their ears. Imagine coral larvae as “­little inside-­out ears swimming around,” Simpson explains.58 Sound creates particle motion in w ­ ater, which can be sensed by larval hair cells; the hair cells detect sound gradients by gradually moving around, which allows coral larvae to hear and locate the direction of sounds. Coral also beat their cilia, like miniature oars, to produce tiny currents that propel them to their preferred locations. Simpson’s explanation is elegant in its simplicity: cilia serve as ears, eyes, arms, and legs for coral larvae. This enables them, despite their near-­microscopic size, to hear and then swim in a chosen direction: home.59 Simpson had proved that corals can respond to the sounds of reefs. But no one knew ­whether corals themselves made sound. In 2021, a team of researchers at South Florida State College found genes related

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to the reception and emission of sound in the coral species Cyphastrea. In a follow-up study, the researchers recorded ultrasonic frequencies emitted by living corals; the sounds, mostly emitted at night, seemed to correspond with the time frame during which both fish and coral larvae ­settle.60 Much remains unanswered about ­these findings. Although we know that corals communicate biochemically with their symbiotic algae, are they also using ultrasonic communication between one another? Is it just a coincidence that corals make more ultrasonic noise at night—­the time when larvae settling tends to occur? Could coral larvae drifting in the sea hear and recognize the specific ultrasonic sounds made by their kin on their home reef?

Coral Reef DJs Simpson’s research provided a ray of hope for Tim Gordon, whose doctoral research demonstrated that climate change had created a vicious cycle of decline in the ­Great Barrier Reef. Due to dwindling marine biodiversity, bleached reefs produce soundscapes that are less rich and complex;61 this degraded soundscape is less attractive to juvenile fish and corals alike, who avoid the reef, thereby accelerating the reef ’s decline.62 When ­human noise—­like drilling, seismic exploration, or motorboats—­masks natu­ral ocean sounds, the ability of fish and coral larvae to find their way home is further compromised. Yet even if overfishing and marine noise pollution could be controlled, it seemed that ­there was no way to reverse the effects of warming ocean temperatures and increasing ocean acidification. Gordon was haunted by the thought of the ­Great Barrier Reef disappearing in his lifetime. Could anything practical come out of his research that might help save the reefs? What if, Gordon wondered, he tried using recordings of healthy reefs to restore the health of degraded reefs? Acoustic enrichment experiments, he knew, had been shown to be effective with other species (including h­ umans and zoo animals), but it seemed like a rather large leap to apply this technique to fish.63 Indeed, the marine scientists that he consulted ­were dubious. But Gordon de­ cided, nonetheless, to focus the next phase of his research on acoustic

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regeneration: rather than recording the sounds of the ­dying reef, he would devise digital soundscapes that might help regenerate the reef. Given the skepticism he faced, Gordon’s initial study design was modest. In late November 2017, the start of the season in which fish ­settle on the reefs, he found a series of barren spots off the coast of Lizard Island. H ­ ere, Gordon built thirty-­three artificial reefs on open sand, each precisely 25 meters from the nearest natu­ral reef. Each artificial reef was built of 70 pounds of dead coral rubble arranged the same way: branching, tabular, and s­ pherical rubble arranged in precise patterns, like a coral cairn. On one-­third of his artificial reefs, Gordon placed nothing; on another third, he placed loudspeakers that would remain ­silent throughout the experiment; on the final third, he placed loudspeakers that played healthy reef sounds recorded in November 2015, before the mass bleaching event had occurred. As fish settlement is primarily nocturnal, he de­cided to turn his loudspeakers on only at night. ­Every eve­ning for forty days, the loudspeakers turned on at dusk and off at dawn. Gordon was confident that his loudspeakers would attract fish in the short term, but the real results of the acoustic enrichment would only be known at the end of the study: Would the fish stay and form a stable community, or would they realize they had been duped and swim off? To Gordon’s astonishment, the acoustic enrichment experiment was a resounding success. On the ­silent artificial reefs, few fish appeared. But on the sites playing healthy reef sounds, fish community development increased significantly, with a doubling in overall abundance and 50 ­percent more species in the ecosystem. The fish not only came to visit; they stayed. This also confirmed the e­ arlier findings: despite spending weeks drifting in the open ocean, fish larvae can not only hear and respond to the sounds of the reef, they also swim ­toward healthy reef sounds.64 Like a self-­fulfilling prophecy, acoustic enrichment can help dead reefs regenerate. The most recent research confirms that the success of coral reef restoration is detectable in the soundscape; recordings of the reviving reefs in Indonesia even include sounds entirely new to science—­whoops, grunts, and growls that perplex researchers.65 Other researchers have taken up t­ hese insights and called for a methodical

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approach to marine restoration, by installing loudspeakers throughout the oceans, then using bioacoustics playbacks to create “highways of sound” that convey navigable information for fish and other marine organisms.66 Acoustic enrichment would thus become a widely used tool for guiding marine species to food, shelter, and safety. As Gordon cautiously points out, acoustic enrichment is not a blanket solution, as individual species respond very differently. The sound of a healthy reef attracts some fish species but acts as a deterrent to some crustaceans, which swim away from predator-­rich reefs. Some reef fishes may be attracted by sounds; but pelagic (open ocean–­swimming) fishes may ignore them. Playbacks may only work over relatively short distances and are highly dependent on oceanographic conditions.67 And marine noise pollution could theoretically attract fish to inhospitable spots or simply mask healthy sounds altogether, leaving larvae adrift in the ocean without their usual guideposts; even boat noise from ecotourists could lead coral larvae astray.68 Nonetheless, with due care for t­ hese ­factors, bioacoustics may be a tool not only for passive monitoring of reefs but also for active ecosystem regeneration and management.69 Of course, underwater coral symphonies might not be enough to turn the tide in the fight to save the world’s coral reefs in the face of climate change.70 But when combined with other techniques, like coral transplanting and coral gardening, they may enhance the repopulation rate of suitable habitat. And if boat noise ­were reduced, the effectiveness of sound regeneration would be enhanced. Gordon and Simpson hope that bioacoustics might be used to support the health of at least a few coral reefs around the world. Scientists are now engaging in global triage campaigns, concentrating efforts on the most robust reefs that might survive. The list of innovative technologies being deployed in an attempt to save the ­Great Barrier Reef sounds like a science fiction mashup: coral IVF, robotic coral delivery systems (dubbed LarvalBots), inflatable coral nurseries, and cryopreservation.71 Globally, the 50 Reefs initiative has identified fifty coral reefs that have the potential to survive the impacts of climate change, like marine seed banks for a ­f uture ocean.72 Marine biologist Sylvia Earle calls t­ hese “hope spots”: islands of biodiversity that might resist

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the ­human onslaught.73 Acoustic enrichment could be mobilized as an additional tactic to help t­ hese refuge reefs survive. To accelerate acoustic enrichment proj­ects, Gordon and other researchers are also using a range of machine learning techniques to enable real-­time bioacoustics monitoring to quickly detect species and ecosystem change, and thereby accelerate the design of customized acoustic enrichment soundtracks (personalized coral play­lists) for reefs around the world.74 Simpson and Gordon are now testing t­ hese techniques on the largest coral reef restoration proj­ect in the world: a 5-­hectare restored reef off the coast of Indonesia, in a zone devastated by dynamite fishing.75 The proj­ect entails installing interconnecting, star-­shaped steel frames, called reef stars, in degraded coral reef zones. The reef stars provide a substrate for coral transplants (like plant cuttings), which are planted on the sand-­ coated frames in the hope that they w ­ ill regrow. Indeed, some zones have seen dramatic increases in coral growth.76 But reef recovery, particularly in the early phases of growth, depends on young fishes; t­ hese herbivores graze away fast-­growing algae on the steel frames that would other­w ise smother the coral. In the absence of fish, workers have to spend months scrubbing the frames with brushes to limit algal growth. In early experiments, Gordon’s “soundscape enrichment” experiment has shown results similar to t­ hose in the G ­ reat Barrier Reef: playing recordings of healthy soundscapes increases fish abundance and grazing rates.77 Gordon plays the role of a sonic marketer: his job is to advertise the coral habitat to fish, testing dif­fer­ent reef soundtracks to determine which brings algae-­eating fish in faster. Attracted by the healthy reef sounds that Gordon broadcasts over underwater loudspeakers, the algae-­eating fish help the artificial reef grow faster and healthier. A c­ areer as a digital coral reef DJ was not what he envisioned when he started his scientific c­ areer, Gordon admits; but if it can save the corals, it’s worth trying.

Returning Home One of the most curious capabilities of coral larvae is that, a­ fter weeks afloat in the open ocean, they are able to find their way back to their home reef.78 Fish larvae, too, find their way back to their home reefs—​ significantly more than would be expected if fish settlement ­were

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random.79 Both fish and coral larvae seem to recognize and prefer the sounds of where they ­were born. The h­ umble coral larvae now joins the pantheon of species that perform impressive feats of homeward migration: salmon leave their natal streams as fingerlings, traverse thousands of miles of ocean, and swim thousands of feet in elevation to spawn where they w ­ ere born in the Rocky Mountains; homing pigeons return to their home lofts, even when released from more than a thousand miles away; Arctic terns are born in the Arctic, then fly 25,000 miles to winter on the Antarctic coast before returning home to become parents themselves. The mechanisms by which animals find their way home are somewhat understood by scientists: an ability to sense the Earth’s magnetic field or electric fields; the capacity to perceive polarized light from the Sun; the smell of even one diluted drop of one’s birth stream. But unlike birds and fish, we do not yet understand the sensory mechanisms by which coral larvae—­ one of the tiniest, simplest organisms on the planet—­might navigate. Simpson speculates that larvae of both corals and fish may sonically imprint, at birth, on the sounds of their home reef. The sounds of reefs are often loudest at the full moon, when coral reefs usually spawn. During the few hours in which mass spawning occurs, the newborn coral larvae somehow learn the unique soundscape of their natal coral colony. Larvae must then remember this soundscape as they grow and be able to distinguish it, weeks or even months ­later, from other very similar soundscapes. At the moment of giving birth, each reef sings its own lullaby to its young. Each night, the reef sings into the sea, guiding its young back home. Perhaps the revelation of coral reefs singing to their young and coral larvae following songs across the sea should not surprise us. Aboriginal songlines—­one of the oldest recorded forms of h­ uman oral history—­ travel across both the land and the “sea country” where the G ­ reat Barrier Reef lies. Perhaps each species has its own songlines it is singing right now, if only we could hear them. Whereas h­ umans once assumed that the oceans ­were ­silent, we have now learned that they are full of sound, to which even the smallest of marine creatures is exquisitely attuned. The world of sound is infinitely more vast and complex than we had ­imagined. Yet corals, although very

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s­ imple organisms, are still members of the animal kingdom. A leap of faith might be necessary to recognize that they respond to sound, but their animal nature might—at least unconsciously—­reassure us. As I explore in the next chapter, however, the unfolding mystery of nonhuman sound extends well beyond the animal kingdom. Even plants, it turns out, are sonically attuned creatures.

6 Plant Polyphonies

In March 2020, an unusual message was posted on Microsoft’s Twitter account: “What if we could talk to plants? That’s exactly the question Proj­ect Florence explores.” The message was accompanied by a video of Asta Roseway, a Microsoft designer and self-­described fusionist.1 Smiling, dressed in jauntily mismatched clothing, holding a small potted plant, Roseway asks the viewer: “What if we could talk to our plants? How might that change our relationship with the natu­ral world? What would they say? How would they respond to us?”2 As the video unfolds, the viewer learns that the potted plant, named Florence, is one of Roseway’s proteges. Talking to Florence is ­simple: she can receive and send texts. Your conversation might start with an innocuous, “Good morning, how are you?” A natu­ral language pro­ cessing algorithm then infers the meaning of your words and translates them to a light shining on the plant; a positive sentiment is translated into red light, a negative sentiment into blue light. Both generate an electrochemical response in Florence, which is detected by sensors that mea­sure her relative state of hydration, as well as her temperature. The data is combined and analyzed by the algorithm, which then texts a response back to you. Thus digitally enhanced, Florence can communicate in a rudimentary fashion. When asked, “Are you thirsty?” she might respond, “I would like a drink, please.” As Roseway explains, “That’s not magic. That’s science.” The video concludes with Roseway musing 99

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about the ­f uture: a brave new world of happier, healthier crops and ­house­plants.3 The Twitterverse ­didn’t respond as positively to Roseway’s video as Microsoft might have hoped. @MidnightWildspirit commented facetiously: “Hopefully we ­won’t have a lawn that talks. Imagine how terrifying it would be to hear the lawn scream as you mow it.”4 ­Others recalled the Google Tulip video: a spoof (launched on April Fool’s Day 2019) that featured imperious, uncooperative tulips pestering subservient, harried ­humans with ever-­accelerating demands for more ­water, space, sunlight, and compost.5 In Google’s mock version of the f­ uture of plant-­ human communication, thoughtful plants pose existential questions (“What is the meaning of my existence?”) that are brushed aside by their overwhelmed caregivers. If we ever manage to create a human-­ plant communication interface, Google’s parody implied, we might not like what plants have to say. Or we might not take the time to listen. Proj­ect Florence is one of many examples of digitally enhanced plants that have been in­ven­ted in recent years. PhytlSigns converts voltage differences between the plant and its soil into a digital tone that rises as the soil dries out—as the plant becomes thirstier, a rising squeal is emitted through an app on your phone.6 Botanicalls (which was covered in Wired, praised by Silicon Valley pundit Tim O’Reilly, and eventually added to the permanent collection of the Museum of Modern Art) equips your h­ ouse­plants with soil moisture sensors and access to social media. When thirsty, the plant posts a status update on Twitter (“­Water me, please”), and a­ fter being watered, the plant sends a thankful tweet.7 PlantWave, a self-­proclaimed “bio-­sonification device,” translates plants’ electrical signals into ambient chill electronic dance ­music. Disney’s version is tactile: an orchid wired to a computer that plays musical sounds in response to ­human touch; stroking the leaves produces a melody, tapping the stem delivers a drumbeat. ­These devices are a new twist on a long tradition of associating m ­ usic with plants. Charles Darwin argued that m ­ usic was a precursor of language, the emergence of which he attributed to its adaptive value in sexual se­lection.8 In Rustic Sounds, Darwin’s son Francis (himself a renowned biologist) mused about the origins of ­music in tubular stems:

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“­There is romance in the fact that plants made tubular stems to their own private profit for unnumbered ages before the coming of man: the hollow reeds waiting all t­ hese aeons till Pan should come and make them musical.”9 But the claims that ­these devices give a voice to plants are deceptive. Rather than allowing plants to “speak” to h­ umans, ­these devices rely on a technological sleight of hand. In the case of Botanicalls, for example, a sensor is placed in the soil: as the soil dries out, the sensor mea­sures the change in electrical conductivity. The device might warn us about dehydration, but the plant is not speaking to us; rather, we are hearing from the computers that translate soil moisture data to speech or text using a s­ imple algorithm. Similarly, although Disney’s musical greenery might sound high tech, you can create your own singing plant using inexpensive mail-­order parts that are now commonly used in makerspaces or college science labs; inserting them into a plant ­will make it touch sensitive, just like a smartphone screen.10 In fact, the under­lying science that enables Florence to “speak” to ­humans has been understood for well over a ­century. Following correspondence with Charles Darwin, En­glish physiologist John Burdon-­ Sanderson discovered electrical signals in Venus flytrap plants in 1873, for which he was awarded the Royal Medal of the Royal Society in 1882.11 Building on this work, Indian scientist and polymath Jagadish Chandra Bose demonstrated, over a hundred years ago, that plants generate electrical impulses in response to environmental stimuli, similar to patterns in nerves in animals.12 We now know that electrical signals (along with biochemical signals) regulate a wide variety of physiological pro­cesses in plants. Devices like PhytlSigns and Singing Plants take advantage of this property. Similarly, other so-­called plant communication devices sonify data derived from physical variables associated with plant or soil physiology. However, ­these should not be mistaken for sounds made by plants; what we are hearing is a ­human construct, the sound of our own sensors and transducers. In “listening” to Florence, we are merely hearing our own voices wearing a thinly veiled plant disguise; like a fun h­ ouse mirror at an amusement park, we are interacting with distorted versions of ourselves. And, as explored below, their inventors have overlooked a key

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fact: plants themselves both make and hear sound, and thus possess their own unique plant voices, to which scientists are just beginning to listen.

Phytoacoustics The study of plant bioacoustics, known as phytoacoustics—­how plants themselves make and respond to sounds—is a relatively recent and still somewhat controversial endeavor. Intuitively, we understand that plants respond to light and touch. Other methods of plant communication and signal transmission—­such as airborne volatile organic compounds and nutrient exchange through symbiotic, subsoil, root-­fungal associations—­are also well documented.13 More recently, however, researchers have demonstrated that plants respond to sound (phonotropism).14 Agricultural plants exposed to specific frequencies, for example, experience increased growth, drought tolerance, and higher flavonoid content;15 they also exhibit changes in their physiology, biochemistry, and gene expression.16 Researchers have demonstrated that ultrasound can enhance pest re­sis­tance in some plants, and have begun experimenting with using aerial ultrasound as an alternative to pesticides.17 Crop scientists also use phytoacoustics to mea­sure mechanical properties of plant structures, optimize harvesting, and assess crop plant physiology and health.18 The idea that plants respond to sound is well established in the scientific lit­er­a­ture. The idea that plants are making sound, however, is viewed with more skepticism. This is perhaps b­ ecause plants are not anatomically equipped like animals: no vocal cords, no ears. Our re­sis­tance may also be rooted in a long-­standing conceptual divide between plants and animals in Western thought, which dates back centuries. Aristotle, arguably the originator of the Western tradition of the scientific study of life, classified ­humans, animals, and plants into three dif­fer­ent realms. Although all beings have souls, Aristotle declared, only h­ umans have rational souls. Animals, which possess varying degrees of knowledge, have merely “sensitive” souls. And plants, at the bottom of the hierarchy, possess merely “vegetative” or “nutritive” souls. Aristotle’s detailed study

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of animals remained a model of scientific inquiry ­until the ­Middle Ages, leading many to consider him the founding ­father of Western zoology; but he left the study of plants to one of his students, Theophrastus, often considered to be the ­father of Western botany. The divide between zoologists and botanists has persisted to this day, but it is gradually being challenged by a small but growing group of scientists who are conducting experiments in plant acoustic signaling and be­hav­ior. Over the past de­cade, a series of publications by phi­los­o­ phers, botanists, and science educators has explored a quickly evolving frontier of research on plant sensing, from phi­los­o­pher Michael Marder’s Plant-­Thinking, to forest ecologist Suzanne Simard’s In Search of the ­Mother Tree, to evolutionary ecologist Monica Gagliano’s Thus Spoke the Plant.19 As pop­u­lar­ized by journalist Michael Pollan in a 2013 New Yorker article titled “The Intelligent Plant,” ­these researchers have conducted experiments that demonstrate that plants possess memory, anticipate events, and even communicate with other plants and with animals.20 Plants, for example, have been shown to remember the precise timing of the last frost; orient themselves to the expected direction of a ­future sunrise, even if uprooted and displaced in an intentionally confusing manner; and display a kind of “swarm intelligence” via their roots.21 Plants express ­these capacities as they actively sense and respond to their surroundings. Botanist-­turned-­anthropologist Natasha Myers refers to the complex set of sensing mechanisms that underlie ­these be­hav­iors as a “vegetal sensorium,” the full scope of which researchers are only beginning to uncover.22 This research does not seek to assess ­whether plants sense ­things in a manner similar to ­humans; rather, as Myers puts it, researchers of plant sensing are developing a vegetal epistemology—­a novel, plant-­centered framework for analyzing how plants sense and signal to the world. Phytoacoustics is one relatively understudied aspect of this emergent research field. By using sensitive microphones, researchers have detected some plants making ultrasonic sounds, beyond the upper limit of most ­humans’ hearing. Drying leaves of both deciduous and evergreen trees produce ultrasound, perhaps related to drought stress. Th ­ ese ultrasonic sounds can be heard by some insects and mammals but are

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inaudible to h­ umans. The precise mechanisms by which plants produce and perceive sound are still unclear.23 Some scientists believe that sounds might result from mechanical changes related to hydration (a wilting plant’s mass, stiffness, and structure changes as it dries out); ­others believe that sounds might come from b­ ubbles or changes in pressure arising from respiration and metabolic growth activity; still o­ thers have hypothesized that sounds could be caused by movements of organelles.24 ­These latter sounds are a by-­product of plant physiology, somewhat akin to our stomachs grumbling when hungry; as Potawatomi plant ecologist Robin Wall Kimmerer explains: “­these are the sounds of being, but they are not the voice.”25 Scientists have also mea­sured tiny vibrations emitted by plants. Using extremely precise instruments, like l­ aser doppler vibrometers, they have found that plants emit almost imperceptible vibrational frequencies. Young corn plants, for example, produce click-­like sounds that vary according to their level of dehydration.26 In one study of tomato plants, a ­laser vibrometer was used to mea­sure the relationship between the vibrational frequency of a leaf (­after a force was applied) and the amount of ­water in a leaf; water-­stressed leaves had lower vibrational frequencies.27 In another experiment, researchers detected the distinct sounds that tomato and tobacco plants make when ­water stressed or when their stems are cut—­tobacco, it turns out, makes louder sounds when deprived of ­water and quieter sounds when cut.28 In this experiment, researchers successfully developed a machine learning algorithm that could identify the condition of the plants (dry, cut, or intact) based solely on the sounds they emitted. Put simply, we have now designed computer programs that can detect the relative health of plants just by listening to them. If our computers can listen, surely other organisms are listening as well. This gives rise to the following hypothesis: plants not only detect and respond to sound but also make sound that conveys information to other organisms. Scientists have begun to test this hypothesis; but in ­doing so, they are transgressing a scientific taboo. In the 1970s, the publication of a book titled The Secret Life of Plants raised a firestorm of disdain within the mainstream scientific community. The authors—­Peter

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Tompkins, a journalist and former spy, and Christopher Bird, a Harvard gradu­ate and Vietnam vet who worked at Rand Corporation—­published several books that became icons of a New Age fringe intrigued by extraterrestrial communications and covert military operations. The book and follow-up documentary in 1979 (which featured a soundtrack by Stevie Won­der) told of plants hooked up to lie detectors, and was suffused, as the New York Times put it, with a “popular-­science pastiche of New Occult hopes glibly tailored for middle-­class respectability.”29 The books ­were best sellers, but for professional scientists they ­were enraging emblems of New Age pseudoscience; hence the taboo on research on the topic of plant sounds. One of the first scientists to break this taboo was Monica Gagliano, who directs the Biological Intelligence Lab at Southern Cross University in Australia. Her initial question was deceptively ­simple: What if we applied experimental protocols usually reserved for animals, like playback experiments, to plants? This might seem like an innocuous query, but as Gagliano says, “Researchers are often castigated for asking ‘what if?’ questions at the frontier of scientific exploration.”30 Gagliano’s follow-up questions indeed generated controversy. She next asked, what if the experimental results demonstrated that the plants could respond to sound? And what if we began studying plants with an open mind, asking ­whether they could make, sense, and respond to sound? Gagliano de­cided to conduct an experiment using an acoustic playback design—­a type of experiment that is frequently used with animals but never with plants. Designing an acoustic playback experiment for plants is more complicated than it seems. In a typical animal be­hav­ior playback experiment, specific frequencies are used to test ­whether animals respond to specific sounds with detectable be­hav­iors: for example, by carefully emitting a series of sounds ranging from 0 to 1,000 Hz in 100 Hz intervals, researchers can watch when a bird flies away, thereby determining the specific range of sounds to which the par­tic­u­lar bird species might be sensitive. By repeating the experiment, the range of frequencies can be determined with a relatively high degree of precision and accuracy.

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Adapting this animal playback method for use with plants posed two challenges for Gagliano. First, plants ­don’t have obvious mobile be­hav­ ior—­they just stand still. In animal playback experiments, the in­de­pen­ dent variable is sound; the dependent variable is the animals’ movement. If plants ­don’t move, which dependent variable should be used? Second, demonstrating that any par­tic­u­lar be­hav­ior was a response to sound would be tricky, as most plants ­don’t respond immediately to stimuli; the longer time frame needed for a response could introduce confounding variables. Eventually, Gagliano settled on an easily observed phenomenon that could be monitored in a tightly controlled environment: the bending of roots, which is a well-­known and widely studied response in plants. Her research question thus came into focus: Would roots bend in response to certain sonic frequencies? She de­cided to test this hypothesis with baby corn plants. Newly germinated plants ­were arranged in identical pots in the lab and exposed to a range of sound frequencies. ­After multiple exposures, Gagliano determined that the plants’ roots bend when exposed to acoustic tones in the range of 200 to 400 Hz, but not below or above that.31 Gagliano then went one step further. ­Humans, she reasoned, emit sounds we can hear; we both vocalize and listen in roughly the same range of frequencies. By analogy, if plants are responding to sound in specific frequencies, it seems reasonable to ask ­whether they are also emitting sounds at ­these frequencies. Listening with sensitive microphones, she was able to detect sounds made by the baby corn plants, and, as she suspected, ­these sounds ­were within the same frequency range at which the corn responded to sound. Her paper describing the results was the first peer-­reviewed journal article with experimental proof that plants have the capacity to detect sound, make sound, and exhibit a behavioral response to sound.32 Upon publication, Gagliano’s experiment provoked a firestorm of controversy. Her methods ­were clearly described, the experiment was easily replicable, and the results w ­ ere approved for publication by in­de­ pen­dent reviewers. But many scientists voiced concerns about the vocabulary she used to describe her results. Some opponents referred to her choice of terminology—­Gagliano used terms like plant learning and

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plant intelligence—as “inappropriate” and “bullshit.”33 Although plants might display be­hav­iors that can be labeled with terms like learning and memory, Gagliano’s opponents cautioned that ­these do not necessarily imply intelligence; some argued this term should be reserved for organisms with a brain and neurons. Gagliano and her allies argued, in response, that the term intelligence should be defined, more broadly, as an ability to perceive and respond effectively to changes and challenges in one’s environment.34 Defining intelligence as a be­hav­ior confined to organisms with neurons, Gagliano argued, displayed a bias that privileged animals. She and other researchers have argued that our concept of intelligence needs to be redefined and expanded to include plants.35 In pushing back against her critics, Gagliano also argued that plants have evolved analogues of other h­ uman senses: touch (a root, for example, reacts when it encounters a solid object); sight (plant leaves react differently to light versus shadow, as well as to dif­fer­ent wavelengths of light); smell and taste (plants emit, sense, and respond to biochemicals in the air or on their bodies). Why would plants not possess an analogue to the ­human sense of hearing? In a follow-up experiment, she demonstrated that the roots of pea plants detect the sound of ­running ­water and grow in that direction. The plants exhibit this be­hav­ ior even when the ­water is isolated within a watertight tube, and no difference in soil humidity can be detected.36 Again, Gagliano designed a classic protocol used in animal experiments: she put her young pea plants in a maze. On one side, ­there was the sound of ­running of ­water; on the other side, white noise, a ­silent recording, or nothing at all. Her experimental setup was designed to test three questions: Do plants know how to find w ­ ater? Can plants find w ­ ater solely by the sound of ­water in a localized area? And can plants find w ­ ater in the context of complex soundscapes (as opposed to simply growing ­toward any sound at similar frequencies)? In each case, her experiments returned an affirmative answer. When offered a choice between sound and silence, the pea roots grew ­toward sound. And notably, when offered a choice between white noise and the recorded sound of ­water—­both played at identical frequencies—­the pea plants’ roots grew ­toward the sound of ­water. In the absence of a humidity gradient, Gagliano’s pea plants w ­ ere

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able to detect the sound of r­ unning w ­ ater, and distinguish this from a similar sound without ecological significance.37 Other researchers have found similar be­hav­iors in other plants. Ecologist Heidi Appel at the University of Toledo found that Arabidopsis thaliana plants (a common weed and a widely studied model organism in plant science) produced defensive chemicals when a recording of a caterpillar chewing a leaf was played nearby, even though the plants ­hadn’t been touched by the insects.38 The plants ­were also able to distinguish between vibrations caused by predators chewing on leaves and vibrations caused by wind or insect song; the latter sounds did not induce the same defensive response.39 In follow-up studies, Appel also found evidence of plant learning and memory. She exposed one set of A. thaliana plants to caterpillar vibrations but left a control group alone; a­ fter a period of time, she exposed both sets of plants to a new round of caterpillar chewing sounds. The plants that had previously been exposed to the sound displayed higher levels of defensive secretions than the control group. A. thaliana, in other words, both remembers and anticipates the effects associated with the sounds of specific predators chewing on leaves. Appel’s plants even distinguished between the sounds of dif­fer­ent insects, responding defensively to sounds associated with insect predators but ignoring the sounds of insects that posed no threat.40 Gagliano’s and Appel’s research provides robust evidence of three capacities in plants: an ability to detect sound; an ability to respond to sound; and an ability to distinguish ecologically relevant sounds from a mixture of irrelevant sound frequencies. Darwin, père et fils, had been on the right track, although plants are more attuned to ecological vibrations than h­ uman m ­ usic. Why w ­ ouldn’t plants themselves, along with their animal denizens, have developed a sensitivity to nature’s sounds? As plant biologist Daniel Chamowitz notes: “[­Human] m ­ usic is not ecologically relevant for plants, but ­there are sounds that could be advantageous for them to hear.”41 The question is no longer ­whether plants can perceive sound but how and why they do so. This raises a perplexing conundrum: Without ears or nerves, how do plants “know” what they are listening to? For

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example, how do they recognize that a sound is made by ­running ­water versus white noise? And how do they recognize the signals arising from feeding caterpillars: vibrations (sonic or mechanical), the removal of tissue (mechanical), oral secretions (biochemical), or some combination of all three? Scientists still do not have a comprehensive understanding of plant signaling mechanisms, although they do know that perception of a sound vibration can cause changes in plant hormones, gene expression, and emissions of volatile organic compounds—­which are used frequently by plants as defensive signals against predators.42 As scientists continue to unravel the mysteries of signal transduction in plants, evidence of the importance of acoustic signals continues to accumulate. Vibration sensing is an ancient system in evolutionary terms, arising before the emergence of vascular plants on Earth; microalgae, for example, have mechanosensory proteins that respond to vibration. Gagliano speculates that sound is an impor­tant signal b­ ecause vibrational signals have faster transmission speeds than other signals (e.g., chemical) sent via plant tissues. As she puts it: “Chemistry works up to a certain point, but sound is so much faster. If you have an aggressive predator, you want to [detect and] tell other plants quite quickly.”43 In contrast to a complex biochemical signal, like a pheromone, sound is a high-­speed signal that is easy to detect at l­ ittle cost. And sound also travels farther, and through more diverse substrates—­air and ­water, soil and stone. Plants’ ability to provide rapid systemic responses to stress via acoustic signals may mean that the ability to emit and sense sound conveys an evolutionary advantage, since it increases their odds of survival.44 If so, the ability to sense sound is likely to be both ancient and universal in plants.45 This insight seems less startling when we consider that sound is a fundamental form of transmitting energy. It has been omnipresent as organisms have evolved over time, so plants (and other organisms) should have evolved the capacity to make use of it.46 ­Those organisms that hear better adapt and survive better in their environments. Scientists call this the auditory scene hypothesis.47 Just as organisms evolved an ability to sense energy in the form of heat (energy flowing as a result of temperature differences), they evolved an ability to sense sound.

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From this perspective, plants’ hearing abilities are, in fact, unsurprising: given that the environment contains many sounds that convey useful information, an ability to detect and respond to sound should have adaptive value for plants, just as it does for animals. But does this mean that plants can “hear,” in the ­human sense? Some scientists remain skeptical. The precise physical mechanism by which plants detect sound is still unclear, although some speculate that anything with hairlike cilia cells—­including crustacean antennae, coral cilia, or plant roots—­can respond to sound.48 Scientists are also exploring the possibility that mechanosensors in cell walls or plasma membranes can be triggered by certain sounds, causing fluxes of specific biochemicals, plant hormones, and even the rapid expression of genes.49 In plants, the senses of touch (mechanoreception) and sound may be intimately interrelated. For example, Appel’s Arabidopsis thaliana plants detect sound through tiny hairs (called trichomes) on the surface of their leaves; the hairs, which selectively vibrate in the frequency range of the plant’s primary insect predator, function like mechanical acoustic antennae, exquisitely tuned to environmental threats.50 If plants listen with their bodies, from the tips of their roots to their leaves, their sense of hearing would be profoundly dif­fer­ent from, and ­orders of magnitude more sensitive than, our own.

Acoustic Tuning Many animals have evolved mechanisms for picking out signals from the noise. Imagine being in a crowded restaurant, with every­one clustered in the ­middle of the room talking in loud voices at once. No one can hear anything. Now imagine, instead, ­people organ­izing themselves into groups by language (En­glish, Cree, French, Hindi, Spanish, Swahili) and moving to dif­fer­ent parts of the room. Standing in the center of the room, your mind w ­ ill tune into the group that speaks languages that you personally understand, and filter out the rest. In finding a niche, each group gains relative tranquility rather than cacophony; tuning into recognizable sounds and tuning out unintelligible ones, the listener expends less energy. In nature, species adopt a similar tactic. As species

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evolve their ability to hear, they also evolve to f­ avor specific frequencies. If one species sings in high tones, and another sings in low tones, it is easier for members of each species to pick out the signal from the noise. In any ecosystem, dif­fer­ent species partition themselves (in time, acoustic frequency, or space) to reduce competition for dif­fer­ent parts of the soundscape spectrum. This partitioning of sonic space is known in ecol­ogy as the acoustic niche hypothesis: in any given ecosystem, dif­fer­ent species evolve to occupy unique acoustic niches, much like dif­fer­ent radio stations occupy dif­fer­ent frequencies on the radio dial. The hypothesis was first proposed by bioacoustician Bernie Krause. In the 1970s, Krause abandoned a successful ­career as a musician and Hollywood composer in order to venture into forests and jungles, lugging hundreds of pounds of equipment and reel-­to-­reel tape in a quest to preserve the sounds of the natu­ral world before it was destroyed by industrialization, agricultural expansion, and urbanization.51 Listening to soundscapes as if they ­were soundtracks, Krause hit upon the acoustic niche hypothesis. His idea has since been demonstrated in a wide variety of environments and organisms, from Antarctic killer ­whales and leopard seals to Puerto Rican rainfrogs.52 Krause’s recordings demonstrate the complex complementarity that evolution has generated between surprisingly diverse set of sounds generated in any one place, which he calls the “­great animal orchestra”: an interwoven chorus, much of which occurs beyond ­human hearing range, segmented by evolution into distinct frequencies for dif­fer­ent species.53 Much of Krause’s work took place in tropical rainforests, rich with animal sounds. But as biologist David Haskell writes in The Forest Unseen, forests are also rich with sounds of plants.54 Listening to the rainfall on trees in Ec­ua­dor, the melodies leap out: “a spatter of metallic sparks,” a “low, clean, woody thump,” or a “speed-­typists’ clatter.” When teaching his students, Haskell challenges them to learn to tell the difference between an oak and a maple merely by listening. “Our unaided ears can hear how a maple tree changes its voice,” Haskell explains, as the soft, spring leaves turn brittle with the approach of winter. The world has its own interweaving compositions, akin to ­music, even if most h­ umans

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have tin ears. Krause originally coined the term the “­great animal orchestra,” but Haskell reminds us that plants participate, too, in Earth’s symphony of living beings. Krause’s “acoustic niche” insight led to an additional, intriguing hypothesis: that the mechanisms for hearing sound coevolved with mechanisms for making sound.55 Scientists refer to this idea as the matched filter hypothesis.56 If this hypothesis is true, the auditory sensitivity of organisms receiving the sound should match the energy distribution of the senders’ signals; we should literally be tuned to members of our own species. Gagliano’s experiment with baby corn is an example of the matched filter hypothesis: the corn plants both hear and make sounds within a specific, relatively narrow frequency range. The matched filter hypothesis also leads scientists to expect a mutual tuning of signals between predator and prey. And, indeed, this has been observed in certain animals; for example, tiger moth wings have evolved in a specific shape that jams bat sonar (and, it turns out, antibat ultrasound production in moths is widespread).57 Acoustic tuning also occurs between animals and plants. A particularly striking example can be seen in the relationship between bats and plants. Using their biosonar, bats can scan entire landscapes. As they do so, they receive images of plant shapes, in the form of biosonar echoes scattering back from plants. Yossi Yovel, a neuroecologist at the University of Tel Aviv, has demonstrated that plants have characteristic sounds related to their shapes that attract bats ­because of how they “look” in echolocation. In a pro­cess of coevolution, bats have developed the ability to classify plants by using their biosonar; reciprocally, plants that are pollinated by bats have developed flowers and leaves that act as echo reflectors, serving as beacons, identification signals, or guideposts to attract bats.58 This helps bats deal with echolocation “clutter,” which can make it harder to spot a food source, particularly if it is embedded in other vegetation, as is the case for nectar-­feeding bats. Consider, for example, one par­tic­u­lar species of bats (Pallas’s long-­ tongued bat), which depends on nectar as a food source. When studying ­these bats in the lab, scientists noted that this par­tic­u­lar species excelled at finding oval or hemispherical shapes hidden among artificial leaves. When one of the team members saw a picture of a Cuban flowering

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vine (Marcgravia evenia) with a dish-­shaped leaf perched above the flower, a lightbulb came on: “Wow, that must be a signal for bats.”59 At the time, the researcher ­didn’t even know that the vine was bat pollinated. In a subsequent experiment, bats found a hidden feeder 50 ­percent faster when the specialized vine leaf was attached. The leaf, they realized, was a highly effective beacon: it would send back a strong, invariant sound signature to the bats’ echolocation clicks. The leaf ’s specific shape also made the reflected sound wave “conspicuously constant at any ­angle,” another cue for the bat: the vine had evolved a leaf that made it acoustically obvious to bats. Other vines, researchers subsequently found, act like an acoustic equivalent of a cat’s-­eye mirror: the shape attracts echolocating pollinators to their flowers by reflecting most of the energy of the bats’ echolocation calls back to them, enhancing the intensity of the signal.60 A similar acoustic tuning relationship exists between flowers and bees. In some flowering plants, anthers open through small pores or slits; bees rest their bodies on the flower and literally shake it, vibrating at the right frequency ­until pollen is released.61 The neat match between bees’ buzzing be­hav­ior and floral size and shape is one example among many of coevolution between plants and bees.62 Yovel has also demonstrated that, in some cases, the sound of a buzzing bee is sufficient to produce the behavioral response. When plants are exposed to the sounds of buzzing pollinator bees (and synthetic sounds at similar frequencies), they respond by producing sweeter nectar within minutes.63 While scientists d­ on’t yet understand the precise mechanism, Yovel speculates that flowers are designed to vibrate mechanically in response to specific honeybee frequencies, suggesting an exquisite alignment between form and function in two species.64 As one of the scientists noted, “We have found amazing ­things. We are expecting to find many more. I think the acoustic world out ­there is just waiting for us.”65

Making Meaning ­ ere is something wondrous, even enchanting, to discover that floral Th acoustics act as a guide and a lure for bats, and that plants and bees share intimate conversations. But is this merely an example of nature’s

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exquisite interconnections, or something more? Some scientists argue—­controversially—­that plants’ capacities to make and hear sound are paradigm shifting: they fundamentally change our understanding of plant communication. Gagliano’s research generated controversy, for example, b­ ecause of her claim that plants are able to actively communicate through sound, not just passively respond. But when most botanists write about the complex sounds made by plants and their landscapes, they tend to use the passive voice. In Songs of Trees, for example, Haskell treats forest sounds in musical terms—by turns soothing, delightful, or terrifying to h­ umans, but not as the result of communication between trees themselves. Indeed, scientists fiercely disagree on the question of w ­ hether sounds made by plants, or heard by plants, should be defined as communication. In part, this is a dispute over definitions. Some scientists define communication in physical terms: the stimulation of a receiving individual’s sensory system, such as the cilia in your ear waving in response to sound.66 But other scientists disagree: they define communication in cognitive terms, as the transmission of meaningful information between sender and receiver. According to this latter definition, communication occurs if the sound carries information that reduces uncertainty for the receiver—­beyond merely hearing the sound, you must interpret it for meaningful information. This definition is similar to ­those used in information theory and computer science, and it opens the door to new approaches to studying plant communication.67 To answer the question of ­whether plants actively communicate by emitting acoustic information, scientists need to demonstrate, first, that plants produce as well as respond to sound; and, second, that ­doing so provides an adaptive benefit.68 For example, could plants be making sounds that attract beneficial animals and deter harmful ones?69 Speculatively, this seems plausible. Acoustic attraction might have several advantages for plants: enabling them to compete for the attention of pollinators, or to attract pollinators without expending the resources required to produce large flowers. Plants might even be able to communicate acoustically through the soil itself. In the past few years, acoustic ecologists have begun recording

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the sounds of life beneath the Earth’s surface. Ecologists have long known that the ground u­ nder our feet harbors an im­mense array of organisms. But only recently have researchers developed microphones that can capture the sounds of soil, like small piezoelectric devices similar to the contact microphones that clip onto guitars. Many of ­these under­ground sounds are too high, low, or quiet for ­humans to ear. But, suitably amplified, they reveal a sonic world begging for a new vocabulary; as insects burrow and roots grow, the Earth is filled with sounds of slithering and scraping, rustling and rubbing.70 What can researchers learn from listening to the sounds of soil? Carolyn-­Monika Görres, an applied ecologist at Geisenheim University in Germany, studies Scarabaeidae larvae grubs. She describes the sounds of t­ hese root-­feeding insects as a combination of sandpaper scraping, a grasshopper singing, and trees rasping their branches. The patterns, which Görres refers to as “under­ground Twitter,” are species specific: a trained ear can distinguish between the larvae of dif­fer­ent species. And although scientists are unsure w ­ hether larvae use ­these sounds to communicate, Görres has already observed an intriguing pattern: her larvae make many more sounds when placed in close proximity but fall ­silent if isolated in a container.71 Animals, of course, have long been attuned to the sounds beneath our feet. When birds hop across rainy lawns with their heads cocked, they are listening for earthworms; when they pounce, it is not random. When desert moles swim through the sand with their heads below the surface, they too are listening for prey.72 But could plants hear ­these sounds as well? Botanists and ecoacousticians have begun developing hypotheses: acoustic signals from a growing plant root might attract earthworms whose burrowing, in turn, might enrich the soil. If correct, this would explain as yet unanswered mysteries about observed associations between plant growth and concentrations of under­ground organisms. Whereas scientists have long paid attention to visual and chemical signals, it may be that acoustic waves or vibrations transmitted through the soil itself are a new frontier for phytoacoustic research—­a subterranean world of sound, of which we are only beginning to become aware.73

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Plant Songs Whereas w ­ hale song evokes won­der, the notion of plant communication often evokes ridicule. Are ­these sounds communicative signals or merely incidental sounds?74 In answering this question, some scientists invoke the concept of “plant neurobiology.” But many of their peers object fiercely, on the grounds that plants possess neither neurons nor brains. Other scientists use dif­fer­ent terms, like plant signaling and be­hav­ ior. ­These may be more precise but also seem somewhat euphemistic. The debate about plant communication has become caught up in controversy over plant cognition, and even plant consciousness.75 In exploring ­these debates, we must be wary of anthropocentrism or even a mammalian bias. Why do we speak of w ­ hale song with a degree of comfort, but feel uncomfortable suggesting that plants and other living creatures are participating in a vast symphony of sound, via which they transmit information and communicate? Perhaps ­because it implies that animals and plants are conveying meaning, or expressing emotion, ideas with which most scientists remain uncomfortable. Or perhaps we are fearful of projecting our own emotions onto nonhumans. As Carl Safina argues, we need to walk a fine line: while it is impor­tant to avoid projecting human-­derived concepts onto other species, it is equally impor­tant not to deny the possibility of nonhuman communication. The former is an error of commission; the latter is an error of omission.76 Perhaps scientists’ unease in attributing the power of sonic communication to nonhumans also arises from the close relationship between sound and feeling in ­humans. Sonic communication is si­mul­ta­neously psychological and physiological, intellectual and emotional, linked to our most intimate, visceral experiences. Sound moves us even at a distance; the voice of a loved one, the cry of a baby. M ­ usic immerses and soaks us in sound; we feel it in our bones.77 But as Natasha Myers points out, research on plant sounds is not necessarily concerned with plant feelings. The latter topic implies an anthropomorphic perspective, which many scientists adamantly reject. ­There is also an implicit moral ­hazard: asking ­whether plants have feelings may denigrate plants; plants, mea­ sured by a ­human yardstick, ­will always be a “lesser us.” Myers suggests

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we should instead adopt a phytomorphic (plant-­centered) perspective, which acknowledges that plants can do marvelous t­ hings (like performing photosynthesis or synthesizing complex compounds, like caffeine). In this way, we would be seeking to understand plant capacities on their own terms. Studying plant sounds acknowledges how complex plants are; but acknowledging this complexity need not fall prey to anthropomorphism nor vitalism. One promising frontier of research, which may answer ­these questions, lies at the intersection of botany and anthropology. Whereas ethnographic and ethnobotanical research once focused solely on Indigenous knowledge of plants, scientists are now seeking to understand the sensory properties of plants (from the bioacoustic to the phytochemical) as si­mul­ta­neously biophysical and cultural phenomena. ­These “phytoethnographies” treat plants as subjects rather than passive bystanders; in reframing plants as persons (much as Indigenous communities do), ­these researchers are challenging the anthropocentrism of anthropology and also reconsidering the divide between animals and plants that Western science inherited from Aristotle and Theophrastus. In taking this approach, anthropologists are also reconsidering the previously held biases regarding Indigenous theories of plants’ sensory capacities, perceptions, and communication. What if Indigenous theories of plant sounds, rather than operating at the level of mere meta­phor, are empirically accurate—­facts rather than myths?78 As Potawatomi plant ecologist Robin Wall Kimmerer points out, traditional ecological knowledge of plants shares much in common with scientific knowledge. As she notes: “With its worldview of re­spect, responsibility, and reciprocity with nature, [traditional ecological knowledge] does not compete with science or detract from its power but extends the scope of science into ­human interactions with the natu­ral world.”79 Some anthropologists have even begun incorporating plants into “multispecies ethnographies.” As Gagliano has claimed, her own experimental designs and novel hypotheses w ­ ere developed through dialogue with both Indigenous communities and plants themselves.80 Bioacoustics may enable Western scientists to rediscover what Indigenous knowledge holders have long known.

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If the idea of plant communication still seems dubious, consider the fact that many scientific ideas that are commonplace ­today seemed outlandish not so long ago. As the following chapters reveal, two other pathbreaking research agendas—on bat and honeybee acoustics—­ followed a similar trajectory: from widespread disbelief to ac­cep­tance. The first discoveries of bat echolocation ­were met with skepticism and denial. But the new frontier of controversy, as we explore in the following chapter, is not ­whether bats can echolocate, but ­whether they can use echolocation to engage in symbolic communication—­a capacity once thought to be uniquely ­human.

7 Bat Banter

By the time he made his way to Harvard as an undergraduate in 1939, Donald Griffin had developed an interest in birds and bats, and an abiding curiosity about one of the best-­known mysteries in the field of natu­ ral history. When blindfolded, bats navigate nimbly, even in pitch-­dark caves. But when their ears are blocked, they crash into obstacles, even in broad daylight.1 The phenomenon had been known since at least the eigh­teenth c­ entury, when Italian scientist Lazzaro Spallanzani meticulously covered, and then removed, bats’ eyes; to his astonishment, their flying ability was not hampered in the slightest.2 Two centuries ­later, the mystery remained equally puzzling: How, Griffin wondered, could bats dodge obstacles in total darkness? Could they be using an invisible means of detection?3 Bats seemed to know something that h­ umans ­didn’t, and had mastered something that ­humans—­for all their mechanical ingenuity—­couldn’t fathom. Few professors w ­ ere interested in undergraduate musings about bats. Luckily, Griffin crossed paths with Professor George Washington Pierce, an early pioneer in communications engineering. A former ­cattle wrangler, son of a rancher, and once penniless gradu­ate of a Texas one-­ room school­house, Pierce had gone on to a Harvard professorship and was a busy entrepreneur on the side; he held dozens of patents and a burgeoning portfolio of investments.4 Pierce was best known for his practical research on telephony systems, but in one of his experiments he stumbled on a method for converting “supersonic” sound—­that is, sound above ­human frequency range—­into audible sound. His original 119

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apparatus included vacuum tubes, a telephone, an oscillating piezoelectric crystal, a parabolic horn, a loudspeaker, and a piece of cardboard; this elaborate setup in a Harvard basement was the first device that could detect and analyze sounds above the upper limit of ­human hearing.5 Professor Pierce’s fascinating apparatus served no obvious purpose and had no established method for calibration. Searching about for a source of ultrasonic noise on which to test his devices, Pierce settled on abundant, easily available crickets and grasshoppers; t­ hese insects, he discovered to his delight, made abundant ultrasonic noise. Unexpectedly, the insects’ vocalization patterns attracted his attention, and he devoted much of the rest of his c­ areer to decoding their sounds, thereby becoming one of the world’s first bioacousticians. Pierce went on to develop techniques to assess insects’ sound-­making physiology, and ­later proved that the pulsating pattern of each insect’s chirps was a kind of code, sufficiently distinctive to allow accurate identification of the species. Much to the surprise of ecologists, Pierce even determined that insect vocalizations w ­ ere like a natu­ral thermometer.6 The striped ground cricket, for example, emits twenty chirps per second at 31.4°C, but only sixteen chirps per second at 27°C.7 The implications of Pierce’s research would not be widely understood ­until many years ­later. Yet at the end of his life Pierce displayed a keen sense of the ­future relevance of his work. His final book, The Songs of Insects, focused on his work with crickets and grasshoppers rather than summing up his influential c­ areer in wireless communications. “The casual reader may be interested,” Pierce wrote in the introduction, “to inquire how the efforts of a specialist in physics and communications engineering become directed to the study of insects. The answer might be that anyone who is ignorant has the obligation to seek enlightenment.”8 Pierce was only a year away from retirement when Donald Griffin sought him out. Griffin had developed a suspicion that the bats ­were using ultrasonic sounds, inaudible to h­ umans, to navigate; Pierce had built the only machine in the world that could detect w ­ hether this hypothesis was correct. A middling student challenged by calculus and haunted by a recent C+ grade in his mandatory undergraduate physics course, Griffin hesitated. “Once I worked up the courage to knock on

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his door,” Griffin ­later wrote, “I found Pierce a jolly fellow whose apparatus clicked and rattled delightfully whenever my bats ­were at all active.”9 ­After discussing vari­ous methods, the pair de­cided to start with something s­ imple. Griffin brought his caged bats to the lab; one by one, the student and the eminent physicist held the squirming creatures in front of the apparatus and simply listened. The results ­were immediate and startling: not only did bats emit infrasound, they intensified their vocalizations when struggling to escape their captors. But when the bats ­were allowed to fly about the room, the scientists had difficulty detecting any sound at all; without this evidence, Griffin c­ ouldn’t prove that the bats used ultrasound for orientation and navigation. When Pierce and Griffin published their results, they referred to the “suprasonic” sounds made by bats using mea­sured tones that Griffin ­later described as “absurdly cautious.”10 Despite this breakthrough, Griffin’s mystery remained unsolved: he had proved that bats make ultrasonic sounds when immobile, but could not discern ­whether they did so when flying. ­Were the bats ­silent, or was his experimental design flawed? Griffin began gradu­ate studies, but World War II intervened, and he joined the military to work on acoustic communication.11 ­After the war, he returned to Harvard to pursue a PhD on the more mainstream topic of migrating birds, and even acquired a pi­lot’s license so he could follow gulls in light airplanes. But the unsolved mystery of bat echolocation continued to haunt him; the experiments with Pierce had been tantalizingly inconclusive. The turning point came when Griffin combined forces with physiologist Bob Galambos, a fellow student. The pair soon realized that Griffin’s initial difficulty had arisen ­because bats’ emission of sounds is highly directional. Bats emit sounds in narrow beams, like an acoustic flashlight; in order to detect them, the apparatus had to be aimed straight at an oncoming bat. Pierce’s apparatus had not been tightly focused enough to pick up on ­these narrow beams. Once they had stumbled onto this finding, the experimental design crystallized. Working late at night to avoid disturbing the “legitimate” physics experiments during the day, Griffin and Galambos covered the ears of some bats and the eyes of o­ thers with a soluble glue, then

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listened as the bats flew in a room strung with ultrafine wires. Bats with their eyes covered flew just like the untouched group of control bats. But covering a bat’s ears resulted in frequent bumping of walls, entanglement with wires, and a noticeable reluctance to fly; bats with only one ear covered did no better. ­These results proved that bats ­were indeed using ultrasonic sound for avoiding obstacles.12 But for good mea­sure, Griffin verified the source of the ultrasonic sound by ­gently tying some of the bats’ snouts shut with thread, then applying a layer of collodion (a syrupy solution that dried into a thick film); the gagged bats “flew in the same clumsy, hesitant, and bewildered manner as deaf bats.”13 A few bats managed to scratch a hole in the collodion; even a slight crack would enable the bat to resume its obstacle avoidance techniques. This further proved that the bats listened, just like military sonar devices, to the echoes of their own ultrasonic vocalization signals—­creating an auditory map of their environment that rivaled the most sophisticated electronic techniques of the day. Evolution had honed what humankind had only recently discovered. The two students ­were keen to share their results with the world. The world, however, was less keen to receive them. As Griffin l­ ater recalled, “One distinguished physiologist was so shocked by our pre­sen­ta­tion at a scientific meeting that he seized Bob by the shoulders and shook him while expostulating, ‘You c­ an’t r­ eally mean that!’ ”14 Sonar and radar w ­ ere still highly classified military secrets. The suggestion that bats might do something even remotely analogous (let alone superior) to the latest triumphs of military technology was unwelcome. Bat symbolism in Western society was also at play; bats conjured up images of vampires and ill omens—­not sophisticated communicators. In the ensuing de­cades, skepticism about bat echolocation persisted; the technology required to test some of Griffin’s hypotheses did not yet exist. Eventually, the improvement of cathode ray oscillographs allowed him to display and accurately mea­sure bat sounds, which he and Pierce had only been able to characterize in a general fashion.15 In a series of experiments conducted over several de­cades, Griffin demonstrated that bat biosonar was exquisitely attuned and unbelievably precise. His methods w ­ ere laborious: using an ancient 35 mm movie camera, he

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painstakingly filmed oscilloscope graphs that traced the bats’ sounds as lines across a screen. When lab studies failed to produce results, Griffin began d­ oing fieldwork. He would bundle his movie camera, an oscilloscope, a battery-­operated portable radio, a microphone, a parabolic sound-­collecting reflector, and a gasoline-­powered generator into the back of a battered pickup and drive to local ponds, where—­after spending several hours setting up his apparatus—he would wait patiently for the brief fifteen-­to twenty-­minute period during which brown Eptesicus fuscus bats would hunt insects at dusk. The results ­were spectacular. The echolocation rates Griffin observed during the wild bats’ insect hunt ­were astonishingly high. This led to another breakthrough discovery. He had previously understood echolocation to be an equivalent to sonar: a means of detecting stationary obstacles, avoiding collisions, and orienting the bats in darkness. Along with other scientists, he had assumed that small, fast-­moving insects could not be detected by biosonar, and that the bat’s rapid, agile hunting maneuvers must be guided by vision. But Griffin’s pond experiments showed this assumption to be ill founded: in fact, bats ­were using biosonar to echolocate flying moths, flies, and even mosquitoes, tracking them in minute detail in order to catch them on the wing.16 “Echolocation of stationery objects had seemed remarkable enough,” Griffin l­ ater wrote, “but our scientific imaginations had simply failed to consider, even speculatively, this other possibility with such far-­reaching ramifications.”17 Bats’ biosonar outperformed ­humans’ most sophisticated sonar device by ­orders of magnitude. Griffin then began to won­der w ­ hether dif­fer­ent species of bats might vocalize differently, in a manner adapted to dif­fer­ent environments and types of prey. His fellow scientists w ­ ere discouraging; Georg von Békésy, Nobel laureate and the leading investigator of hearing of the day, told Griffin this would be a waste of time. “A bat is a bat; ­those sounds are simply noise bursts, and nothing more is likely to emerge from further studies,” Griffin was told.18 But he persisted, sparking controversy by speculating that bat sounds w ­ ere not simply a navigational tool; they ­were, he claimed, analogous to birdsongs and perhaps even ­human language. ­Were bats, Griffin asked, capable of vocal learning and complex

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communication? Most scientists rejected his suggestion, which ran contrary to accepted beliefs regarding the uniqueness of ­human language. But in recent years a new generation of digital tools has enabled bat researchers to demonstrate the validity of some of Griffin’s ideas: bats are capable of vocal learning, and use complex communication to guide social be­hav­ior in a manner similar to ­humans.

Listening to Bat Songs How, exactly, do bats learn through vocalizations? To unravel this mystery, Mirjam Knörnschild, a bat researcher at the ­Free University of Berlin, travels each year to the Central Amer­i­ca to study the greater sac-­w inged bat (Saccopteryx bilineata), one of the most commonly found bat families in tropical rainforests. ­Because of her interest in social communication, Knörnschild needs to be able to visually observe the bats she studies, so she has chosen a species of bats with some unusual characteristics. Greater sac-­winged bats are easier to study than many other bat species for three reasons. First, they are geo­graph­i­cally stable; ­because they ­don’t migrate (as many temperate-­zone bats do), they are easier to study year-­round. Male greater sac-­winged bats defend specific roosting territories to which they attract females through competitive displays; having lured their mates, the males then remain to help with rearing pups. Researchers can return year ­after year to the same site and study the same families of bats from one generation to the next. Second, greater sac-­winged bats, compared to other species, are also exceptionally tolerant of h­ umans. Knörnschild recounts, for instance, that “sometimes juvenile bats, clumsily learning to fly and careening through the air, w ­ ill lose control and land right on me—­maybe ­because I resemble something like a tree trunk and look safe. Sometimes the ­mother comes right to me, lands on me, and picks up her pup to bring them back to the roost. The bats accept me, or at least d­ on’t seem to care that I’m ­there.”19 Third, unlike many other bat species, greater sac-­winged bats often roost in trees (rather than caves) and are active during daylight hours. This combination of ­factors—­geographically

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stable, tolerant of h­ umans, and diurnal—­makes greater sac-­winged bats almost uniquely easy to study year-­round. What has Knörnschild learned? First, greater sac-­winged bats display sophisticated vocal learning throughout their lives. Similar to ­humans, baby bats learn by vocal imitation of adults. Newborn bat pups emit frequency calls that match ­those of their ­mothers, and learn specific calls from their ­mothers that help them recognize one another when the ­mother returns to the nest.20 ­Mother bats speak “motherese” to their pups; just like ­humans, m ­ other bats use a special form of infant-­directed speech that facilitates language learning by increasing attention and arousal in the baby (like h­ uman m ­ others, bat m ­ others change the tempo and pitch of their speech when speaking to their babies, but they speak at a lower rather than higher pitch).21 As the pups start to fly, they learn their group signature call, a key vocalization that facilitates f­ uture decisions about mating partners.22 Juvenile males learn territorial songs from their ­fathers, and even undergo a “babbling” practice phase, much like h­ uman and songbird infants. The pups begin pronouncing individual syllables of their group songs at around two to three weeks of age; by ten weeks, t­ hese syllables have crystallized into songs.23 For a while, their songs are more variable than ­those of adults, as if they are still practicing.24 By the time they are adults, the bats have defined songs unique to their territory, signature calls unique to their immediate f­ amily, and a large vocabulary of specific sounds, including individual-­specific calls. As in other bat species, ­these vocalizations are learned rather than innate.25 Bat songs, like ­whale songs, are also culturally transmitted and evolve over time. The greater sac-­winged bats have two types of songs: territorial songs, which fend off rival males; and courtship songs, which lure females to roost in a male’s territory. Th ­ ese songs are vitally impor­tant, ­because subadult female Saccopteryx bilineata bats migrate away from the social group into which they ­were born, and rely on male territorial songs when choosing new bat colonies.26 Knörnschild has found evidence that male bats alter their territorial songs to reflect a greater degree of aggression (the lower the pitch, the more serious the dispute),

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and increase their daily courtship song rate in response to increasing numbers of females to attract and rivals to repel. As male bat pups learn ­these songs from adults via cultural transmission, subtle copying errors and modifications in song structure and pitch arise over time. This leads to the emergence of distinct regional song dialects. ­These dialects, just like h­ uman languages or killer ­whale dialects, mark out dif­fer­ent bat groups and persist over generations, even as they continue to evolve.27 Knörnschild and other researchers have documented songs in thirteen species of bats, and it is likely t­ here are many more. Many bat species (which make up about one-­quarter of all mammalian species on Earth) are highly vocal.28 And just like songbirds, most bat songsters tend to be males who sing to defend their territories and court females, particularly in polygynous socie­ties where males compete and mate with more than one female.29 While relatively ­little is known about bat song, much of which occurs only in the ultrasonic range, researchers have found evidence that the syllable structure and phonological syntax of bat song can be as rich and complex as birdsong. Moreover, the selective evolutionary pressures that ­favor singing in birds are also prevalent in bats.30 Nearly one hundred years a­ fter Griffin’s first experiment in a Harvard basement, his hypothesis has been proved. Bat vocalizations are much more than an impressive set of navigational tools; through social learning of song dialects, bats transmit cultural teachings through communities and generations. ­These astonishing discoveries have been made pos­si­ble by new, portable digital recording technology. Although behavioral observations of singing bats ­were reported in the 1960s, ­these studies only recorded the ­limited number of bat songs audible to h­ umans.31 The first spectrogram of ultrasonic bat song (rather than bat echolocation) was published merely twenty-­five years ago, in 1997; the authors used the output from a bat detector to rec­ord the flight songs of microbats (pipistrelles) onto a Sony Walkman.32 Field recording of bat song became easy only in the past decade, with the emergence of a new generation of inexpensive, lightweight, and portable digital recorders. Researchers can now rec­ord large, long-­duration datasets of bat sounds across a wide range of habitats, from the tops of trees to the depths of dark caves, both day and

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night.33 The creation of tiny bat tags, less than 1 gram in size, has also given scientists the ability to track bats more easily, combining location data with acoustic data that allows better understanding of how specific sounds relate to specific be­hav­iors. “When I finished my PhD a de­cade ago,” recalls Knörnschild, “the equipment I used was referred to as ‘portable.’ But all that meant was that I could carry it into the forest; once I set it up I was stuck ­there. If the bats switched to a dif­fer­ent tree, it took me half an hour or more to move my setup. By then they might have moved again. But now I can get recordings from a device as small as my cell phone. And I can move through the forest recording the bats without even tagging them.”34 The latest generation of digital bioacoustics technologies enables Knörnschild to be almost as mobile as the bats themselves. Other digital devices—­thermal cameras, playback speakers, drones, even lifelike 3D, furry bat robots equipped with speakers—­are now cheap enough that individual scientists can afford them, making interactive playback experiments a possibility. Only a few years ago, Knörnschild’s playback experiments ­were l­imited, as she says, to “­r unning around a dark forest at 8 metres per second with a speaker perched on top of a pole.”35 The bats w ­ ere not fooled. Knörnschild’s next interactive playback experiment w ­ ill use computerized drones carry­ing both speakers and microphones, so that the drones can interact with bats in real time, playing specific sounds in response to the bats’ vocalizations. This real-­time interactive playback technology ­will, she predicts, enable researchers to find larger and richer communication networks than we can presently imagine. ­Human knowledge of vocal learning in bats, in other words, is only pos­si­ble b­ ecause bat bioacoustics mobilizes both digital technologies and data science. But, Knörnschild cautions, although digital recorders can capture unpre­ce­dented amounts of sound, and machine learning algorithms can find patterns, it is still challenging to make sense of t­ hese patterns. Just like ge­ne­tic sequencing needs to be associated with biologically meaningful information, passive acoustic monitoring needs to be associated with ecologically and behaviorally meaningful information. This is, at least for the moment, something computers ­can’t tell us.

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“In the end,” Knörnschild says, “you need to ask the animals: ‘Is this perceivable to you? Is this meaningful to you?’ You need to get away from the big data and be in the field, just watching and listening.”36 Digital bioacoustics technology is a tool to help refine the questions scientists ask and gather more data to analyze, but it does not by itself provide the answers. Digital technologies, then, are perhaps best understood as a set of observational prosthetics, allowing scientists to ask new types of questions, to which they can get much faster answers than ever before.

Other Minds Digital bioacoustics has allowed bat researchers to uncover another, even more astounding set of findings: the complex social relationships and cognitive abilities of bats. Take, for example, the Machiavellian intelligence (or “social brain”) hypothesis.37 According to this hypothesis, the need to acquire adroit social skills was a driving force in the evolution of the ­human intellect; this enabled our cleverer ancestors to both cooperate and manipulate members of their social group, creating a positive evolutionary feedback loop. Greater social complexity drives greater vocal complexity, and vice versa; more complex social interactions require more complex communication, which should be associated, in turn, with a higher degree of complexity of vocal communication signals. This positive feedback loop has been investigated in mammals (such as primates and rodents) and songbirds, but only recently have scientists studied it in bats—­which are also highly vocal and gregarious, and live in communities with high degrees of social complexity.38 The species that Knörnschild studies, Saccopteryx bilineata, bears out the Machiavellian intelligence hypothesis. Compared to the relatively ­simple vocalizations of other, socially monogamous species within the sac-­w inged bat ­family, the Saccopteryx bilineata displays some of the most elaborate male songs, as well as one of the most complex social organ­izations.39 Dominant, related males defend adjacent roosting territories, competing for access to females, while subordinate males queue for territory access while defending their colony against unrelated

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immigrant males. Males mate with multiple females, who evaluate the quality of competing males year-­round and base their choice of mates—­ and their territories—on male courtship displays, which include elaborate songs. This intense sexual se­lection pressure, Knörnschild argues, has led to complex male vocalizations. Over time, t­ hese studies of the evolution of bat vocal repertoires are making a larger contribution to biolinguistics, as we deepen our understanding of how long-­lived bats learn from their kin. Knörnschild believes that we w ­ ill likely uncover similar findings in other bat species. Most bats have highly complex social lives; and social complexity drives greater vocal complexity. “The greater sac-­winged bat is in no way exceptional. It ­doesn’t have a large brain, it’s not a special bat. It’s just exceptionally easy to study by ­humans using our current technology. If you look long enough and with cool equipment, you find cool stuff.”40 The next frontier, she speculates, is interspecies communication between bats and other species: calls made by one species that might lead to behavioral changes in predation, cooperation, or competition in individuals from another species. Many of t­ hese discoveries, she predicts, ­will be made by using novel digital technologies to probe bat be­hav­ior, particularly their abilities to prob­lem solve, play, make social trade-­offs, and engage in complex decision making (such as navigational detouring). Digital technologies are necessary to both rec­ord and analyze bat vocalizations, she explains, b­ ecause of the sheer volume and rapidity of bat calls—­most of which last only milliseconds and many of which happen at the same time. Inside a crowded fruit bat cave, the noise is deafening. Parsing this cacophony requires computational techniques that can pro­cess and parse what the ­human ear cannot. For example, to study just one species, Yossi Yovel, a neuroecologist at the University of Tel Aviv, monitored twenty-­two captive Egyptian fruit bats (Rousettus aegyptiacus) continuously for two and a half months, recording over fifteen thousand types of vocalizations.41 His team then adapted a voice recognition program to analyze the sounds; the algorithm correlated specific sounds with dif­fer­ent social interactions captured by video, such as when two bats fought over food. Using the algorithm, the researchers ­were able to classify the majority of the

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bats’ sounds into four categories: arguing over food (the loudest calls), jockeying for preferred positions in their sleeping cluster, protesting mating attempts, and arguing when perched in close proximity. In the majority of cases, the algorithm was also able to identify the individual bat making the sound. And, b­ ecause the animals made slightly dif­fer­ent sounds when communicating with dif­fer­ent individuals (particularly members of the opposite sex), the algorithm could also detect which bat was being addressed about half the time. Using similar techniques, Yovel’s team then demonstrated that bats weigh complex variables—­ such as kinship and social association—­when foraging. Bats, they have shown, even trade food for sex.42 Gerry Car­ter, a behavioral ecologist at Ohio State University, has documented similarly complex reciprocal social structures in bats: they help one another, remember who does them ­favors, and perhaps even hold grudges if not treated fairly.43 In playback experiments, Car­ter has demonstrated that vampire bats recognize other individuals by their signature calls and prefer ­those with whom they have a history of food sharing.44 Car­ter’s team has even documented that immune-­challenged vampire bats produce fewer calls; when ill, bats socially distance just like ­humans, interacting less with friends but keeping their associations with close ­family.45 Car­ter’s detailed insights on bat interactions derives from the digital data he collects from tiny, portable biologgers, affixed by gluing the tags to bats’ dorsal fur. The tags continuously relay data to a wireless network-­ based monitoring system that enables Car­ter to continuously track individual bats, much like a social network.46 Over the past de­cade, the data loggers have dropped in price by a f­ actor of ten: from $1000 to $100. And whereas biologists used to face an enormous challenge in retrieving the loggers from bats (most devices got lost), the new generation of biologgers download other bats’ data; retrieve even one device, and you have information about the entire network.47 Car­ter says the resolution is even better than GPS: with proximity loggers, he can see a complete, continuous description of how bats are spending time with each other. As Car­ter says, “It’s like having the first DNA sequencer. It’s a huge step forward.”48 Now the decoding work can ­really begin.

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The Language of Bats ­ ese new understandings of bat vocalizations are likely, in turn, to shed Th light on the origins of h­ uman language.49 Like songbirds, bats share a common set of language-­related genes with ­humans.50 But ­because bats are even closer to ­humans on the evolutionary tree, a deeper understanding of bat vocalizations is likely to provide new insights into the evolutionary interplay between social be­hav­ior and complex vocal communication.51 Distinct from song or echolocation sounds, bats make a large repertoire of calls that have a communicative function, linked to social be­ hav­ior. Egyptian fruit bats, for example, have a highly nuanced repertoire of trills, screeches, and other sounds that reflect dif­fer­ent social contexts. Researchers have identified hundreds of types of calls. Aggressive calls, for example, include quarreling over food, protesting mating attempts, aggressively jockeying for position when perched in close proximity, and squabbling in a sleeping cluster. And t­ hese aggression calls are only one category of bats’ social calls, which include, to name just a few, isolation calls (from lost pups), babbling be­hav­ior (by baby bats), “motherese” (directed by ­mother bats to their babies), territorial songs, courtship whistles, physical distress calls, alarm calls, foraging coordination calls, and instructional calls that guide ­others to roosts or food.52 Some bats even encode information about the identity of the addressee (­whether male or female) in their vocalizations—­analogous to a ­human speaker using varying forms of address when speaking to male or female listeners.53 Bat calls may even encode individual, kin, and species identity.54 Michael Yartsev, a bat researcher at Berkeley, calls this repertoire the bats’ “vocabulary.”55 Aided by artificial intelligence algorithms that parse this vocabulary, scientists have demonstrated that ­these calls, as well as bat songs, are socially learned.56 Bats have thereby been admitted into a prestigious pantheon of animals that possess what scientists consider to be one of the most highly evolved cognitive traits on the planet: complex social be­hav­iors associated with vocal learning.57 Griffin, once again, was proved right.

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Mirjam Knörnschild believes that we are only at the beginning of our discoveries about the linguistic abilities of bats. As she explains, recent research has proved that bats have the necessary prerequisites for communication using symbols: vocal learning, trainability, associative learning, imitation, and social knowledge.58 Scientists are now beginning to test bats’ capacity for symbolic communication. Knörnschild has designed an experiment that trains bats to select arbitrary symbols on a touchscreen; once trained, scientists could enroll the bats in cognition research. Visually oriented bat species could be trained to physically touch visual symbols on a touchscreen, while echoacoustically oriented bats could be trained to activate a touchscreen with their biosonar beam. A similar method developed for dolphins allows them to use their sonar beam to choose reflective symbols that appear as three-­ dimensional symbols to them as they echolocate.59 Knörnschild has proposed using similar acoustic touchscreens to study w ­ hether bats have numerical competence or categorical perception. As far-­reaching as this sounds, Knörnschild sounds a cautionary note about such speculative studies. “Interspecies communication may be intriguing to some ­humans,” she says, “but perhaps not that in­ter­est­ing to other species. First, we have to establish ­whether bats even recognize us as entities to communicate with. Even if they do, we also have to ask, do they want to communicate with us?”60 Bats, Knörnschild notes, may not even be able to perceive ­humans as being capable of communication; much like ­humans are not innately enabled to sense biochemical signals in forests, bats may not be innately enabled to sense vocal signals in ­humans. Although we could devise elaborate digital technologies to act as translation devices, she won­ders ­whether it is more in­ter­est­ing to study what bats are saying to one another or to other species. This, she argues, w ­ ill shed more insight into how bats perceive the world for themselves. Knörnschild is taking this idea forward in one of her latest experiments, which uses artificial flowers equipped with RFID chips (similar to ­those in your credit card) to attract flower-­visiting bats and rec­ord their be­hav­iors and vocalizations.61 What, she won­ders, does a bat have to say to a flower? Perhaps this is more in­ter­est­ing than asking what bats would want to say to us.

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This research agenda sits in stark contrast to the view, common in Western socie­ties, of bats as pests, carriers of disease, and incarnations of evil spirits. In China, bats symbolize good fortune.62 Given their role as pollinators of plants and sacred trees, bats embody agricultural fertility in Indonesia. The Ainua of Japan worship a wise and crafty bat god.63 In Central Amer­i­ca, bats appear on Mayan t­ emples as winged deities bringing messages and nectar from the gods; and as wahy beings, associated with caves and sorcery, blood and sacrifice—­power­f ul beings for scribes, rulers, and healers.64 For the Moche of northern Peru, bats symbolize death as cosmological renewal for the body, agriculture, and ­human society; as nocturnal creatures, associated with the moon, bats dwell in the realm of dreamscapes, ensuring the continuity of cycles between the living and the dead.65 ­These mythological incarnations of bats often recognize the key ecological role played by bats: pollinating plants, eating insects, and dispersing seeds. They also celebrate bats as creatures that move between worlds: a nectar eater that is not a bee; a winged animal that is not a bird; a creature of the night that bears messages for the day. Th ­ ese are not merely historical anecdotes. ­Today, a significant proportion of the world’s bat species live in Indigenous territories, and scientists have documented the roles that Indigenous stewardship practices play in supporting bat habitat and protecting endangered bat species.66 Through digital listening, Western scientists are rediscovering what some communities have long known, through preserving the art of deep listening.

The Robot That Thought Like a Bat Digital bioacoustics allows researchers to explore worlds of nonhuman sound very dif­fer­ent from our own. Researchers continue to find deeper similarities between ­humans and bats than expected. How far do ­these similarities extend? Do bats speak to one another just like h­ umans do? Is it meaningful to make such a comparison? Scientists who ask ­these questions are interested in the comparative study of animal be­hav­ior, also known as ethology. T ­ oward the end of his c­ areer, Donald Griffin focused his attention on ethology, challenging prevailing scientific

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norms by asserting that animals should be studied in their natu­ral habitat. Lab results, he and other ethologists argued, w ­ ere likely to be distorted by the artificial conditions in which animals had been placed. What was needed was an engagement with the umwelt (literally, the “world around”) of other animals, the subjective worldview of an organism, understood from a nonhuman perspective.67 Stanford biologist Robert Saplonsky ­later summed up the debate: “Ethology is the study of animal be­hav­ior, where you are interviewing the animal in its own language, out in nature, in its own habitat. If you do ethology, you better be open-­minded regarding what counts as the animal’s communication, what counts as the animal’s language. . . . ​[Ethologists believe that] studying animal language in captivity is like studying a dolphin in a bathtub.”68 Lab researchers disagreed: without carefully controlled conditions, they argued, ­human bias might creep in. In the ­middle of this already heated debate, Griffin put forward an even more controversial claim. As he became interested in the more general question of modes of animal perception, of which he believed echolocation was merely one example, he suggested that scientists should study animal minds and animal consciousness. Despite his decades-­long work in animal be­hav­ior and his solid reputation, this generated the most significant controversy of his c­ areer.69 Griffin coined the term “cognitive ethology” to describe his proposed research program. Like ethology, it was based in naturalistic observations of animal be­hav­ior and the attempt to understand animal minds in the context of evolution, but with the added assumption that animal be­hav­ior may be influenced by intention and conscious awareness. Griffin went so far as to suggest that animals might have the capacity to think, reason, and feel emotions, and that scientists should study ­these m ­ ental pro­cesses. Not only did he postulate that nonhumans possess consciousness, he also conjectured that consciousness could be useful for compensating for ­limited neural machinery. Griffin even speculated that consciousness could be more impor­tant to animals with smaller brains than ­humans.70 Could bats not only be conscious, but more conscious than us? Few scientists ­were willing to entertain, much less study, Griffin’s claims; most dismissed him outright. Both classically trained ethologists

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and psychologists fiercely criticized Griffin’s definition of consciousness: “the subjective state of feeling or thinking about objects and events.”71 To many scientists, Griffin’s position was seen as anthropomorphist; in response, he argued that his opponents ­were guilty of anthropocentrism—­ assuming that ­humans ­were both unique and innately superior to other animals and ­were the yardstick by which other species should be mea­ sured. Still other scientists—­including many field bat biologists—­simply refused to engage with the question of consciousness, which could not be formally defined in terms of observable variables nor seemingly operationalized experimentally.72 But Griffin maintained that we should not preclude the possibility so quickly. As he once noted, “It’s a curious point that in the face of very weak evidence we scientists tend to make very strong, negative statements: no animal does this, animals c­ an’t that and so on, when we ­really ­don’t know. I think we should maintain an open mind.”73 Even if bats possessed language and consciousness, some argue, we could not discern this capacity; the communicative and cognitive gap between h­ umans and other species is, they argue, simply too g­ reat.74 In making this argument, many refer to an influential paper published in 1974 by phi­los­op­ her Thomas Nagel, titled “What Is It Like to Be a Bat?”75 Nagel argued that analyzing animal consciousness would remain a scientifically intractable prob­lem, even if we assume ­there is such a ­thing as animal consciousness (in other words, even if a bat has an awareness of what it is like to be a bat). This challenge hinges partly on the constraints of ­human language: the concepts that a bat might use are inexpressible by our species. According to Nagel, it is impossible to know which animals besides ­humans are conscious, ­because animals cannot describe their ­mental states to us in language we can understand. Moreover, Nagel claimed, we could never understand bat consciousness (even if it exists), ­because bats are so profoundly unlike h­ umans. In order to understand what it would be like to be a bat, we would have to live like bats: visualizing the world through echolocation, feeding on the wing, and sleeping upside down. But Nagel argues that this understanding is impossible ­because bats are, for ­humans, “a fundamentally alien form of life.”76 As phi­los­o­pher Ludwig Wittgenstein wrote in his Philosophical Investigations, “If a lion could speak, we could not understand him.”77

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But what if our computers and artificial intelligence algorithms could act as translators for us? We may not be able to speak directly to bats, nor they to us, but that does not preclude the possibility that our digital devices might decode their vocalizations. Computers offer a power­ful means of translating between the vocal communication patterns of dif­ fer­ent species. H ­ uman physiology constrains our ability to communicate with our relatives across the Tree of Life, but our computers, robots, and algorithms are not similarly constrained. Although ­humans cannot click or chirp like a bat, our digital devices can be programmed to do just that. Perhaps one day we might be able to refute Nagel’s argument by way of a digital analogy: although ­humans may never be able to think like a bat, our artificial intelligence algorithms may be able to do so. An AI system, embedded in a bat-­like “soft robot,” living among bats since infancy, may develop a better understanding of what it means to be a bat than ­humans. Perhaps the robot would be designed to roost upside down. Perhaps it could fly alongside its living counter­parts, calling in response to their vocalizations. And b­ ecause it would be equally able to understand the lifeworld of ­humans, this AI-­powered robot could act as our translator. Nagel might still disagree; as he ­later wrote in Mind and Cosmos, “the world is an astonishing place, and the idea that we have in our possession the basic tools needed to understand it is no more credible now than it was in Aristotle’s day.”78 Nagel may turn out to be correct. But the only way to find out is to conduct the experiments that Knörnschild and fellow researchers are now undertaking: attempting, via digital intermediaries, to decode and translate the language of bats. Yet even Knörnschild is cautious about the limits of h­ uman interpretation of digital data. Bats have very fast hearing, so bat social vocalizations sound more melodic to them than to us. When seeking to translate t­ hese sounds, we can only approximate. “When we listen to bat sounds,” Knörnschild muses, “by how much should we slow them down? U ­ ntil they sound like a bird? Or like a ­whale? We w ­ ill never know how a bat actually hears the sound.”79 Digital technologies, Knörnschild argues, ­will continue to push the bound­ aries of our knowledge of how bats learn, socialize, communicate, and

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perceive the world. But we w ­ ill never truly know what bat sounds are like for bats. Digital data, even when rendered quantifiable and interpretable, is always a ­human simulacrum of nonhuman sounds. Knörnschild also sounds a cautionary note about using our newfound knowledge to attempt to communicate with bats. Perhaps, she suggests, we should not try to communicate with them at all, or at least not without guardrails that would prevent exploitation of our newfound knowledge. But her caution is not equally shared by all scientists. As the next chapter explores, honeybee researchers have attempted for years to use digital technologies for interspecies communication. And they have recently succeeded.

8 How to Speak Honeybee

In his 1994 best seller, Out of Control, the founding editor of Wired magazine, Kevin Kelly, argued in ­favor of a self-­organizing computational culture: highly intelligent yet f­ ree from central control. His primary meta­phor was the swarm, a dense cloud of agitated bees that forms when the insects search for a new hive. In the opening chapter, Kelly relays a story about a beekeeper friend reacting to a sudden swarm: Mark d­ idn’t waver. He slipped into the swarm, his bare head now in the eye of a bee hurricane. He trotted in sync across the yard as the swarm eased away. Wearing a bee halo, Mark hopped over one fence, then another. He was now ­running to keep up with the thundering animal in whose belly he floated.1 The swarm eventually outflew their keeper and chose their new home without any guidance. Their be­hav­ior was an admirable analogy for distributed governance: a new form of decentralized social organ­ization that would be enabled by personal, yet collective, computing. For Kelly, this was simply an engaging meta­phor. But what if the hive mind was more than merely meta­phorical? What if bees ­were capable of communication as nuanced as our own? And if so, could we learn their language?

Bee Master The waggle dance of the honeybee (Apis mellifera) has been observed since antiquity. Imagine the insect equivalent of line dancing: a single worker bee waggles her abdomen from side to side, while walking in a 138

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figure eight pattern, over and over again. Other bees follow her lead, learning to imitate the pattern while g­ ently touching the lead dancer’s abdomen with their antennae.2 ­Until the mid-­twentieth ­century, scientists ­didn’t know why bees danced. Unlocking the secret to this mystery—­ and demonstrating that waggle dancing is a form of bee language—­won an Austrian researcher, Karl von Frisch, the Nobel Prize.3 Young Karl ­wasn’t obviously destined for professional success. Growing up in Austria, he loved skipping school to spend time with his menagerie, which comprised over one hundred animals, only nine of which ­were mammals.4 His most beloved companion was a small Brazilian parakeet named Tschocki, who was constantly by Karl’s side, sitting on his lap or on his shoulder, and even sleeping next to his bed. Together with Tschocki, Karl spent hours out in nature, simply watching. As he l­ater reflected: “I discovered that miraculous worlds may reveal themselves to a patient observer where the casual passer-by sees nothing at all.”5 ­After dropping out of medical school and joining the new (and relatively marginal) field of experimental zoology, Frisch began studying bees in 1912. For much of the nineteenth c­ entury, zoologists had focused on studying the morphology of dead animals. Frisch, however, took the unusual step of eschewing the lab for a more natu­ral setting, installing hives at his country home several miles outside Munich, where he had become a professor at the local university. For the next several de­cades, he watched his bees e­ very single day. He took only one holiday a year, on his wife’s birthday (but only ­after she protested). Frisch’s first major scientific discovery—­that bees are attracted to flowers by their colors and can be trained to prefer certain colors—­ astounded the scientific community. At the time, scientists held that bees ­were attracted to flowers solely by their odors, a theory that had prevailed for hundreds of years. In 1914, as Eu­rope hovered on the brink of war, Frisch began traveling the continent, training bees in each location to associate nectar with certain colored pieces of paper and performing live experiments for the public. Frisch’s training cards mischievously replicated a par­tic­u­lar shade of blue that was much favored by fash­ion­ able Eu­ro­pean ladies that year, ensuring that the honeybees not only swarmed to the correct pieces of paper but also descended upon

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members of the gentler sex. When audience members clad in blue became agitated as bees began to crawl on their clothing, they underscored Frisch’s scientific findings with decidedly unscientific shrieks.6 His bees had revealed themselves to be sentient, and slightly cheeky, beings.

Deciphering the Dance of the Honeybees Frisch ­didn’t stop ­there. While watching his bees in 1917, he noticed a pattern. During his experiments, individual bees would occasionally visit the empty feeding dishes, as if to monitor their contents. Soon ­after he replenished a dish with sugar w ­ ater, a large number of bees would appear within minutes. The bees, he speculated, must be informing their hive mates about the new source of food—­but how? Unraveling the answer to this question took nearly thirty years. In his search to understand the bees’ mysterious ability to communicate, Frisch began with a hunch that ran c­ ounter to prevailing wisdom: the bees’ waggle dance was a form of language. In pursuing this possibility, he was contesting the idea that only ­humans had complex forms of language, a core assumption of Western science and philosophy. Most scientists believed that bees ­were incapable of complex communication given their tiny brains, but Frisch decisively demonstrated the contrary. In hindsight, it’s easy to see why scientists ­were unable to grasp the complexity of bee communication. H ­ uman verbal language is largely based on the noises we make with our vocal cords and mouths, the expressions we make with our f­ aces, and the way we hold and move our bodies. Most of our communication uses sound: oscillations of air particles. In contrast, bee language is not verbal, but spatial and vibrational. Its syntax is based on something very dif­fer­ent from h­ uman language: the type, frequency, ­angle, and amplitude of vibrations made by the bees’ bodies (particularly their abdomens and wings) as they move through space. Imagine sign language as a dance designed by the Christian Shaker sect: vibrating and quivering, leaning and turning. Once a scout bee has found a good food source, she returns to the hive to inform her ­sisters. During the waggle dance, the bee moves in a figure eight pattern: a straight line while wing beating, and then a circular return

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without wing beating. We know now that the resulting pattern, which can be observed visually, encodes the direction to the food source relative to the sun’s position in the sky; the length of the dance is related to the distance the bees must travel. Frisch’s intuition that the waggle dance conveyed this information was a mere hunch; much like the war­ time cryptographers, he would need to crack bees’ code to prove that the dance was a mode of communication. Frisch de­cided on an ambitious experimental design: tracking thousands of individual bees in order to analyze the correlation between their dances and specific food sources. At the time, this seemed impossible, given that hive populations average somewhere between ten thousand and forty thousand bees. But Frisch, through painstaking attention to detail and near endless amounts of patience, was able to prove his hypothesis: as a lead bee dancer waggles, she orients her body relative to gravity and the position of the sun. By making subtle variations in the length, speed, and intensity of her dance, she is able to give precise instructions about the direction, distance, and quality of the nectar source.7 In so ­doing, she teaches other bees in the hive, who use the information they have learned from the waggle dance to fly to a nectar source they have never before visited. Frisch’s research progressively proved the astonishing accuracy of the bees’ communication system. In one of his most famous experiments, he trained his bees to navigate to a hidden food source several miles away, across a lake and around a mountain. This was an astounding feat, given that he had shown the site, once, to only a single bee. In another of his experiments, he demonstrated that dif­fer­ent hives have slightly dif­fer­ent dancing patterns. Bees appeared to learn ­these patterns from their hive mates. In essence, honeybee dance language has dialects, just like h­ uman communities.8

Cowbells and Color-­By-­Number Frisch himself was so amazed by his findings that he initially kept them secret. Contradicting prevailing scientific views, Frisch’s findings demonstrated that honeybees possessed learning, memory, and the ability to share information through sophisticated symbolic communication.9

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As he wrote to a confidante in 1946: “If you now think I’m crazy, you’d be wrong. But I could certainly understand it.”10 Frisch was right to worry. When he fi­nally went public, many scientists dismissed his research and argued that insects with such tiny brains w ­ ere incapable of complex communication.11 American biologist Andrew Wenner launched a challenge to Frisch’s theory, arguing that bees locate foods solely by odors (a theory that was subsequently proved wrong, although odors are impor­tant signals for bees).12 Eventually, Prince­ton University biologist James Gould settled the debate by devising a clever experiment that masked odors and exposed bees to specific light sources designed to mislead them; despite the lack of odor and the distracting light sources, the bees successfully found the experimental food source.13 ­After losing much of his funding and battling to retain his academic position, Frisch’s results ­were definitively and in­de­pen­dently validated. The Rocke­fel­ler Foundation began supporting Frisch, and he toured the United States as a scientific celebrity. Three de­cades ­after g­ oing public with his findings, he was awarded the Nobel Prize in Physiology or Medicine in 1973. In recognizing that honeybees ­were capable of complex communication, the Nobel Prize Committee avoided direct reference to the controversies that had plagued Frisch, but concluded their nomination statement by referring to the “shameless vanity” of Homo sapiens that refused to recognize bees’ extraordinary capacities.14 Frisch’s work was difficult to refute ­because of his meticulous, copious observations, which he made in tandem with a small army of volunteers: his wife and ­children, students, siblings, neighbors, and ­house­guests. During his experiments, each observer was positioned at a specific spot in the forest or field surrounding the observation hive. While Frisch counted the number of turns each individual bee dancer made at the hive, volunteers counted the number and times each bee alighted to feed. Observation sessions would last for hours. Strict instructions ­were issued: no one could leave a feeding station for any reason. The volunteers communicated by ringing cowbells. Frisch’s ­brother remembers agonizing hours spent during one particularly drawn-­out experiment: desperately craving a smoke (he had forgotten his pipe), he ­couldn’t slip away for even a few minutes.

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Every­thing hinged on a coding system that Frisch had in­ven­ted. Volunteers with the steadiest hands painted tiny dots of dif­fer­ent colors on the bees’ abdomens and chests. The numbers w ­ ere coded: the colors of the dots represented their numeric value, while their placement on the bees’ bodies indicated their decimal position. This ­simple system enabled Frisch and his volunteers to individually identify and track thousands of bees as they fed and danced with their ­sisters. Individual bee trips between hives and feeding stations (which Frisch placed at precise intervals in fields and forests surrounding the hives) could now be tracked. Sitting in front of his observation beehive with a stopwatch, Frisch watched the bees intently for hours, focusing on one “dancing bee” at a time. L ­ ater, the volunteers would use the coded numbers to match each individual bee observed in the hive to the same bees observed at individual feeding stations. It was a bit like trying to do air traffic control at Heathrow Airport with an abacus, a pencil, and a piece of paper. The devotion of Frisch’s volunteers was even more admirable given the circumstances. Their key discoveries w ­ ere made at the end of the Second World War, ­after Frisch had nearly lost his job ­because of his Jewish heritage. He and his ­family had retreated to their country home ­after his lab had been reduced to rubble by bombing, and had welcomed extended ­family as refugees all while surviving on starvation rations. Frisch’s most impor­tant experiment—in which he and his volunteers made 3,885 observations of individual waggle dances—­took place in 1945 as Rus­sians and Americans w ­ ere fighting their way across Germany. Amid the chaos, Frisch doggedly maintained his daily practice of sitting with his hives.15 Late in life, Frisch would look back at his research and remark that a seemingly small innovation made pos­si­ble all the work that followed. In the past, researchers h­ adn’t focused on the be­hav­ior of individual honeybees. Painting coded numbers on honeybees by hand and monitoring them with stopwatches and cowbells might seem antiquated, but Frisch’s insights ­were pos­si­ble only ­because of his systematic use of the best available technology at the time. His monitoring methods, he l­ ater reflected, w ­ ere the foundation for all of his impor­tant discoveries. The methods he used to study the vibrational sounds of bees could provide

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only a glimpse into the life of the hive, but they ­were still power­ful enough to generate deep insights into the bees’ unexpectedly rich social lives. Frisch referred to honeybee dances as a “magic well”: the more he studied them, the more complex they turned out to be.16 ­Every species, Frisch argued, has its own magic well. ­Humans have verbal language. Whales have echolocation, which endows them with the ability to visualize their entire environment via sound. Social insects have spatial, embodied language: we now recognize some of the subtle differences in their body movements and vibrations, which include waggling, knocking, stridulating, stroking, jerking, grasping, piping, trembling, and antennation, to name just a few.17 Yet the bees’ dance remains the only known nonhuman language that uses bodily movement to represent complex symbolic meanings, and is still considered by many scientists to be the most complex symbolic system that ­humans have decoded to date in the animal world. Although many scientists initially asserted that the waggle dance should be referred to merely as communication, Frisch insisted on using the term language: through a system of signs, bees exchange information, coordinate complex be­hav­ior, and form social groupings.18 Honeybee researchers following in Frisch’s footsteps have probed the magic well even more deeply. Bees make many other types of signals through nuanced movements, communicating through sounds and vibrations largely e­ ither inaudible to, or indecipherable by, h­ umans.19 However, by using computer software that automates the decoding of bee vibrations and sounds—­v ibroacoustics, as the field is known—­ researchers can now use algorithms to analyze bee signals.20 What have they found? Although it has been known for centuries that queens have their own vocabulary (including tooting and quacking sounds), researchers have found new worker bee signals, such as a hush (or stop) signal that can be tuned to specific types of threats, and a whooping danger signal that can be elicited by a gentle knocking of the hive.21 Worker bees also make piping, begging, and shaking signals that direct collective and individual be­hav­ior.22 ­These findings add to the growing body of research that demonstrates bees’ impressive capabilities.23 Bees have excellent eyesight and are

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capable (­after minimal training) of distinguishing between Monet and Picasso paintings.24 They can differentiate not only between flowers and landscapes but even ­human ­faces, demonstrating a remarkable capacity for pro­cessing complex visual information.25 In two breakthrough experiments in 2016 and 2017, researchers demonstrated that bees are capable of social learning and cultural transmission (a first in Western science for invertebrates): when trained to pull a string to receive a sugar reward (a novel task), bees taught the new skill to their hive mates, demonstrating that bees can learn from observing other bees, and that ­these learned skills can be shared and become part of the culture of the colony.26 A dark side of bee social life has also been uncovered: while bees are generally collaborative, accurate, and efficient, they are also capable of error, robbery, cheating, and social parasitism.27 They might even have emotions, exhibiting both pessimism and dopamine-­induced mood swings that are analogous to h­ uman highs and lows.28 As one researcher cautiously noted in a landmark study of a newly identified bee signal: “Communication in honeybees turns out to be vastly more sophisticated than originally i­ magined . . . ​revealing a collective intelligence that makes one pause to ask ­whether ­these creatures may be more than just ­simple, reflexive, unthinking automata.”29 Perhaps the most remarkable research is that of Cornell bee scientist Thomas Seeley, who has demonstrated that honeybee language extends beyond foraging be­hav­ior. For several de­cades, Seeley focused his research on bee swarming—­the be­hav­ior that had so fascinated Kevin Kelly. Swarming is the way in which honeybee colonies naturally reproduce; a single colony splits into two or more distinct colonies, and one group flies off to find a new home. How, Seeley wondered, did the colony decide on their preferred site? When Seeley first de­cided to focus on swarming, scientists knew very l­ ittle about the phenomenon. When a swarm is on the move, the fastest bees fly over 20 miles per hour, usually moving in a straight beeline ­toward their target, regardless of the fields, ­water bodies, hills, or forests in their way. ­There is no way a ­human can keep up with the swarm, much less keep track of several thousand individual bees to figure out which ones, if any, are guiding the rest. Seeley was interested in how the bees de­cided which home to select—­a

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high-­stakes decision, given that splitting the hive could cause the queen to be lost and choosing an inappropriate site could lead to the death of the hive. At first, Seeley used methods similar to ­those used by Frisch. But by the early 2000s, Seeley adapted digital technology to extend his experiments in a new direction: he convinced a computer engineer (who was intrigued by the similarities between bee swarms and driverless cars) to install a high-­powered video camera at Seeley’s research site on Appledore Island, off the coast of Maine. Their goal: to create an algorithm that could automatically identify and track ten thousand speeding bees at once. ­After two painstaking years, the algorithm fi­nally worked: powered by high-­speed digital cameras and novel techniques in computer vision, it could identify each individual bee from the video footage and analyze its unique frenzied flight pattern.30 The algorithm revealed patterns undetectable to the ­human eye; decoding the diversity, density, and interactions in t­ hese patterns led Seeley to label the swarm as a “cognitive entity.” Perhaps Seeley’s most startling finding was that, in choosing a new home, honeybees exhibit sophisticated forms of demo­ cratic decision making, including collective fact finding, vigorous debate, consensus building, quorum, and a complex stop signal enabling cross-­inhibition, which prevents an impasse being reached. The bee swarm, in other words, is a remarkably effective demo­cratic decision-­ making body in motion, which bears resemblance to some pro­cesses in the ­human brain and ­human society. Seeley went so far as to claim that the collective interactions of individual bees ­were strikingly similar to the interactions between our individual neurons when collectively arriving at a decision.31 Seeley’s findings, published in Science and widely pop­u­lar­ized in the media, bolstered the arguments of ­those who argued in f­ avor of referring to honeybee communication as language. And by demonstrating that the “hive mind” was more than mere meta­phor, Seeley also stimulated advances in swarm intelligence in robotics and engineering.32 Seeley’s research, predicated on digital technology (computer vision and machine learning) eventually came full circle: his findings inspired two computer scientists at Georgia Tech to create the Honey Bee algorithm,

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which is now an integral part of the multibillion dollar cloud computer industry. The algorithm, widely used in internet hosting centers (analogous to hives), optimizes the allocation of servers (analogous to foraging bees) among jobs (analogous to nectar sources), thereby helping to deal with sudden spikes in demand and preventing long queues. In 2016, the American Association for the Advancement of Science awarded Seeley and his computer science collaborators its Golden Goose Award, for apparently esoteric research that ­later proved to be extremely valuable.33 Now that we have decoded honeybee language, the next question is, could we speak to them in a way they would understand? Given our vast differences in physiology, how could communication with bees even be pos­si­ble? The first half of the answer lies in tuning in to the bees’ own capacity for language, rather than assuming that h­ uman verbalization is the only way to communicate. The other half of the answer lies in digital technologies, specifically bee-­imitating robots.

Dancing Honeybee Robots Thanks to Frisch and his successors, researchers have long known that bees react differently to distinct vibration patterns that act like signals. In the past few years, the combination of computer vision with miniaturized accelerometers (ultrasensitive versions of the motion-­detecting sensors in your cell phone) has enabled scientists to decode the specific, subtle vibration signals made by living organisms—­vibrations that are vital to their communication but largely undetected by h­ umans. Indeed, ­these technological advances have made it pos­si­ble to analyze bees’ communication and activity over their entire life span.34 The next breakthrough—­bridging what engineers call the “real­ity gap” between robots and living bees—is the creation of robots that accurately mimic ­these vibration patterns. Tim Landgraf, a professor of mathe­matics and computer science in Berlin, has devoted himself to this task for the past de­cade. Much of his research has focused on automating identification of individual bees and tracking their movements using computer vision and machine learning. One experiment analyzed three million images taken over three days and tracked the

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trajectories of ­every single member of a honeybee hive—­w ith only a 2 ­percent error rate.35 Landgraf ’s most innovative work involves creating robotic devices to communicate with honeybees in their own language. Working together with colleagues in the ­Free University of Berlin’s Center for Machine Learning and Robotics, Landgraf built a s­ imple robot, which they christened RoboBee. The first prototypes, built in 2007, “sucked,” as Landgraf puts it. When the early RoboBees entered the hive, the bees would attack: biting, stinging, and dragging the robot out of the hive. The early robots ­were positioned at the end of a stick that moved with two motors in a seesaw arc, which likely seemed unnatural to the bees. But Landgraf kept iterating for the next five years. ­Later prototypes used a more sophisticated grid system, so the robot could move in the relevant plane. Landgraf also tried heating the RoboBee prototypes, wondering if the cold temperature of the metal and plastic was off-­putting (as the thoracic temperatures of waggle dancing bees are quite warm); but the bees rejected ­these robots even more decisively—­put off, perhaps, by the chemical signature of heated plastic. Opening the hive to insert the robot also caused a disturbance; as the temperature dropped and a current of air moved through the hive, the bees displayed defensive be­hav­iors—­ clinging to one another to form impenetrable “bee carpets” designed to keep the bees warm and defend the hive from intruders.36 So Landgraf created a plastic barrier that moved with the robot, in order to keep the hive temperature constant and minimize air flow. Successive designs also made the robot quieter; the bees ­were calm, Landgraf reasoned, so the robot needed to be calm and as “bee-­like as pos­si­ble.” While he initially placed food samples on the RoboBee, this ­didn’t seem to increase ac­cep­tance by the hive, so he focused on wing vibrations. Mimicking bee vibrations is a complex task. The bees’ abdomen—­ which vibrates during the waggle dance—­has six degrees of freedom, allowing nuances of movement and agile changes of direction, best (although imperfectly) mimicked by the sophisticated Stewart platforms used in flight simulators.37 Scaling this down to a tiny robot seemed impossible, but Landgraf d­ idn’t give up. E ­ very morning, for months, he would preprogram the RoboBee with a chosen destination to

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communicate to the other bees and then insert the robot into the hive. When he developed his sixth prototype, the bees no longer rejected the RoboBee. But neither would they necessarily follow it; mostly, they just ignored it. The RoboBee was coded with a preprogrammed series of vectors representing the path to a specific location chosen by Landgraf (with a sugar syrup as a reward), but if the bees w ­ ouldn’t follow the robot, ­there was no way to tell ­whether Landgraf ’s directions ­were being correctly communicated. The seventh prototype was the breakthrough. Occasionally, the bees would follow the RoboBee’s dance, mimicking its movements in the “dance follower” pattern that bees use to learn about food sources. When they did, Landgraf counted the number of bees leaving the hive and used harmonic radar to document the pathways of the tagged bees arriving at the food. A statistically significant number of the bees flew to the specific location that Landgraf had coded into his honeybee robots. The code was developed using a data-­driven dance model, taking hours of videos of dancing bees and formulating a model containing the relevant variables. Landgraf was not the first to think of this; in the 1950s, British scientist John Haldane had produced an elegant statistical analy­sis correlating honeybee waggle dances with the mean direction taken by bees t­ oward the food source.38 And in the 1970s, another group of researchers built a mechanical bee that could dance with sufficient accuracy to lead a small number of bees to a source of nectar.39 But Landgraf was the first to code instructions into an automated algorithm that directed the movements of a computerized robot and successfully communicated waggle dance information back to the hive. Landgraf created, in essence, a biodigital equivalent to Google Translate for bees.40 Landgraf is still not entirely sure why his robots’ commands are sometimes successful with the bees and sometimes not. His current hypothesis is that a separate, prior signal needs to be issued first: like a handshake before beginning a conversation. His robotic bees may be emitting this signal merely by chance, and in ­those cases the bees in the hive ­will listen. Or perhaps a separate vibrational signal from a separate device is also needed; one such tool, recently in­ven­ted by Cornell bee

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researcher Phoebe Koenig, accurately mimics the “shaking” signal that bees use to activate be­hav­ior.41 Landgraf may uncover this mysterious “handshake” in his next proj­ect, HIVEOPOLIS, which involves building robotic bees directly into brand-­new artificial beehives before the hives are newly colonized. His hope is that, as the bees arrive in their new home, the robots ­will seem familiar—­part of the furniture, so to speak. Innovating with the biomaterials to improve the robot’s shape and texture is also on his agenda, as biomimetic robots made with biomaterials are more likely to be accepted by the bees. His next goal is to incorporate machine learning into the robots’ training regimen, so that they can learn more nuanced signals before entering the hive. One day, he hopes, the RoboBees ­w ill be viewed as “native” by the honeybees themselves, able to issue commands and recruit bees to fly to specific locations by waggle dancing. ­Future robots might even learn local bee dialects (which vary with habitat).42 And this is only the tip of the iceberg; HIVEOPOLIS would enable the possibility of understanding how the colony itself pro­cesses and integrates dif­fer­ent kinds of information, somewhat like a living distributed computer with thousands of tiny, interconnected brains. HIVEOPOLIS is one of a series of proposals for “smart hives” that bring digital transformation to the world of beekeeping. In 2015, Irish engineer Fiona Murphy proposed a comprehensive platform for honey bee monitoring that would include sensors, infrared and thermal cameras, and an Internet of Th ­ ings–­enabled feedback system.43 Such a system could be useful in precision apiculture, as it would allow beekeepers to detect vibrations and sounds that indicate the presence of queens, predict the likelihood of swarming, and uncover early signs of infection.44 But Landgraf is proposing a step beyond mere monitoring. Smart hives, in his vision, are a two-­way communication device: vibrational, acoustic, and pheromone signals could be released to warn bee colonies about threats (such as nearby fields treated with pesticides, or approaching storms) or to guide bees to find the best food sources available. Smart hives are thus similar to smart cities, with one impor­tant difference: this is an interspecies network, where ­humans, robots, and bees interact, communicate, and cooperate with one another.45

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Honey Hunters As groundbreaking as ­these innovations might sound, Landgraf is not the first to have discovered how to speak to bees using vibroacoustics. Communication with bees is, in fact, an ancient ­human skill. How would our ancestors have been able to harness a swarm of bees? The answer lies in sound. The earliest known vibroacoustic device, the bullroarer, is well known among anthropologists as humanity’s oldest musical instrument: used in ceremonies by Indigenous ­peoples on all continents and in the Dionysian Mysteries by the ancient Greeks, it has a lesser-­known function as a bee-­hunting device.46 A bullroarer (turndun or bribbun to Australian Indigenous communities, kalimatoto padōk to the Pomo tribe) is deceptively s­ imple: a long string or sinew is attached to a thin, rectangular piece of wood, stone, or bone that is rounded at the ends. The cord is given a slight initial twist, and then the bullroarer is swung around in a circle. The resulting noise, caused by air vibrating between 90 and 150 Hz, is a surprisingly loud hum that sounds a bit like the sound of a propeller. The effect is startling and palpable: a resonating hum in your bones, like standing within a ­giant swarm of bees. Africa’s /Xam (San) still use bullroarers to cause bees to swarm and to direct them to new hives at locations that are easy for h­ umans to access.47 The /Xam word for bullroarer is !goin !goin, which literally means “to beat”—­like beating a drum. The bullroarer is spun in tandem with a dance that puts the /Xam into a trancelike state through which elders call upon and guide the bees. (Modern beekeeper practice employs a ­simple version of this method, called tanging, to calm bees and direct them to a hive.) Long before Western science discovered vibroacoustics, the /Xam had developed a nuanced understanding of bee communication. Anthropologists speak of a “copresence” that the /Xam developed with bees, based on mimetic sound capacities.48 The /Xam are not unique in their ability to communicate with bees. In many parts of Africa, p­ eople searching for honey are led to beehives by a bird: the greater honeyguide (its Latin name, Indicator indicator, is a bit of a giveaway).49 Honey hunting is an ancient art; some of the earliest recorded rock paintings in the world show ­humans hunting wild

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bees.50 And the animal kingdom’s preeminent honey hunters are honeyguides. Why do honeyguides and ­humans cooperate? Honeyguides are one of the only birds (and few vertebrates) on the planet that eat beeswax. Rich in nutrients and energy-­g iving lipids, beeswax is a sought-­after treat for the birds. But most honeybee nests in Africa are well hidden in tree cavities, guarded by fierce bees that can kill the birds if they come too close. Honeyguides—­likely guided by their outsize sense of smell—­know where the bees are but ­can’t get at the wax. So they partnered up with an animal that i­ sn’t nearly as good at finding bees but knows how to get the wax: ­humans. In hunting together, the honeyguides and honey hunters have evolved a subtle form of cooperative communication. Scientists have confirmed the claims of the Boran p­ eople in northern K ­ enya that they can infer the distance, direction, and time to the nest from the bird’s calls, perching height, and flight patterns.51 But can we ­really be sure that the honeyguides and h­ umans are actually talking? Researchers led by Claire Spottiswoode at Cambridge University took on this question, and their study of honey hunters in the Niassa National Reserve in Mozambique confirmed reciprocal signaling: when honey hunters made their special sound—­used to alert honeyguides that the ­humans ­were ready to hunt—­the probability of being guided by a honeyguide increased from 33 to 66 ­percent, and the overall probability of finding a bees’ nest from 17 to 54 ­percent.52 What does the cooperation between honey hunters and birds look like? First, the hunters make their special call, signaling that they are ready to hunt honey. In the case of the Yao hunters in the Niassa, Spottiswoode describes this sound as something like a brrr-­hmmm: a loud trill followed by a grunt. In return, the honeyguides approach and sing back to the hunters with a special chattering call. The birds then fly in the direction of the bees’ nest, followed by the hunters. When the birds’ chatter dwindles and they stop flying, the hunters know that they are close. They scan the tree branches and hit nearby tree trunks with their axes to provoke bees into revealing the location of the nest. The hunters then make a bundle of leaves and wood and set it alight just u­ nder the

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nest, smoking the bees into lethargy before felling the trees with their axes and chopping open the nest. As they fill buckets to take back home, flinging away dry combs containing no honey, they expose food for the birds. The honeyguides wait patiently, flying down to feed only ­after the ­humans are gone. Before the Yao hunters depart, they gather up the wax and pre­sent it on a ­little bed of fresh green leaves, honoring the contribution of the birds to their hunt.53 How do wild birds like honeyguides learn to interpret ­human sounds? We expect this kind of be­hav­ior from domesticated animals, like falcons and dogs, but not wild birds (although other cooperative hunting relationships between h­ umans and dolphins, killer w ­ hales, and ravens have been documented).54 While we ­don’t know exactly how they do it, we know that honeyguides d­ on’t learn cooperative hunting from their parents. Honeyguides are brood parasitic: their parents (whom they never meet) lay their eggs in other birds’ nests, puncturing the host eggs they find to enhance the honeyguide hatchlings’ survival rate. The honeyguide parents then leave their eggs in the unsuspecting adoptive parents’ nest; when the honeyguide birds hatch, they are equipped with sharp, hooked beaks, which they often use to kill any unfortunate host chicks that have managed to survive.55 We also know that the sounds exchanged between hunters and honeyguides are not innate: hunters use dif­fer­ent sounds in dif­fer­ent parts of Africa. Th ­ ese sounds are learned from elders, passed down from one generation to the next.56 How could honeyguides learn ­these sounds? Spottiswoode and her colleagues are combining digital technologies with traditional knowledge to find the answer to this question. They have developed a customized app that enables honey hunters to collect data on their activities. Deep in the forests of the Niassa National Reserve, an area the size of Denmark with few roads and no internet connectivity, Yao honey hunters are presently roaming the forest armed with handheld Android devices, earning income from Cambridge University as digital conservation research assistants, singing to their honeyguide companions as they search for bees.57

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Governing the Swarm We have come a long way since Frisch’s experiments with stopwatches and cowbells. Computer vision and machine learning now enable each individual member of an entire hive to be monitored, giving us unpre­ ce­dented insight into the life of the hive.58 Frisch’s largest dataset, which held 3,885 observations, required several months of time from dozens of volunteers. The first machine learning trajectory dataset used on honeybees analyzed three million images, gathered in just three days.59 A wave of new technologies for monitoring honeybees has accompanied this digital revolution. Automated hive monitoring systems, like BroodMinder, BuzzBox Mini, and IoBee, use sensors installed in thousands of beehives worldwide to enable beekeepers to track hive conditions, and function as early warning systems for previously undetectable threats.60 Bee lovers can upload a photo to the Bumble Bee Watch app or upload data to the BeeSpotter website, which citizen scientists worldwide are using to track bees in nature. Much of this data is stored in public databases for beehive research. Researchers have even used Intel chips to develop “backpacks” for bumblebees that use RFID tags, combined with data loggers scattered throughout the environment, which allow them to construct a 3-­D model of bumblebee flight anywhere in the world.61 The next step is to mobilize t­ hese technologies to enhance environmental protection. Smart hives could use sensors and cameras to monitor bees and provide them with information to guide crop pollination and avoid polluted sites. The same technologies might be used to harness bees to map zones too dangerous for h­ umans to reach, or power swarm robots to support environmental conservation, or even help out with search-­and-­rescue missions.62 As data accumulates, a twinning effect emerges. Just as some h­ umans have digital twins (online versions of their physical selves), some beehives now have “virtual hive” twins, in which the digital bee world mirrors the physical world. This may help turn the tide in our race to save not only honeybees but many other species as well. When gathering nectar, bees continuously sample from the environment, so who better to act as a sentinel for environmental risk? In the past few years, bees

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(and other insects) have been successfully trained to detect a range of chemicals and pollutants.63 Decoding a large number of dances from a specific area could help evaluate landscapes for sustainability and conservation. It could also make pollination more efficient and provide insights into how to ward off the widespread, alarming phenomenon of colony collapse disorder. Bees could also be recruited as live bioindicators: surveying, monitoring, and reporting the landscape in a fine-­grained, inexpensive way that would be impossible for ­humans to achieve.64 If t­ hese technologies fulfill their promise, bees could provide near-­real-­time data about the environment, giving us a better chance of staving off environmental threats before they spiral out of control. While some labs, like one at Harvard, are developing robotic bees—­autonomous flying microbots that can pollinate crops and conduct fine-­grained environmental monitoring—­some environmental advocates argue that digitally enhanced bees are more effective than artificial pollinators. Rather than replace bees with robots, we should use digital technologies to protect them. Critics have also warned of the potential for weaponization of digitized bees.65 Bees have a long history with the military: they ­were central to the war machine in the First World War, when most ammunition was coated with beeswax.66 But now they have become instrumental in broader military objectives. In the United States, the military has been actively testing bee biodetectors in antinarcotics, homeland security, and demining operations.67 The mobilization of what military scientists call “six-­legged soldiers” requires ge­ne­tic and mechanical manipulation of the bees’ ner­vous systems, migration patterns, and social relationships.68 The US military’s Stealthy Insect Sensor Proj­ect, for example, trains bees to extend their tongues when they detect dangerous chemicals; once trained, individual bees are inserted into cartridges in monitors carried by soldiers. When bees react to, say, military-­grade explosives, the microchip in the monitor translates this signal into an alarm. The trained bees live for no more than a few weeks, d­ ying within the cartridge. A replacement cartridge is shipped to the soldier, and, according to the scientist responsible for the proj­ect, “you simply slip out one

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bee cartridge and replace it with another.”69 Mobilizing bees to detect dangerous explosives might be beneficial for military personnel, but the manipulation and casual disposal of honeybees at scale should give us pause. Is digital tech merely a tool for the militarization of bees? The /Xam and the Yao offer us another way of thinking about our relationships with bees. For traditional cultures, communicating with bees is embedded in sacred ceremony. Honey is both a practical and a spiritual ­matter, both food and sacrament. This view is not ­limited to hunter-­gatherers in Africa; the earliest Neolithic repre­sen­ta­tions of bee goddesses from Eu­rope are over eight thousand years old. And many of humanity’s oldest written texts celebrate bees’ divinity. Over two thousand years ago, the scribes of the Salt Magical Papyrus wrote an origin story of the Egyptian world: the sun god Ra, with the sun as his right eye and the moon as his left, wept tears ­after creating the oceans and the earth. The falling tears became bees, who visited the flowers and trees, bringing honey and wax into being.70 Around five hundred years before that, the scribes of the Brihadaranyaka Upanishad (the teaching of the ­great wilderness) recorded the Honey Doctrine—­a theory of the organic, interrelated nature of life, wherein honey personifies cosmic nourishment for the luminous ground of being, in which “this earth is honey for all creatures, and all creatures are honey for this earth.”71 In many spiritual traditions, the divine nature of bees is intimately associated with ­human birth, death, and rites of passage. Honey is the world’s oldest natu­ral source of highly concentrated sugar, and bees create propolis, an impor­tant medicinal resin. The search to communicate with bees might have been equally motivated by mead, ­humans’ first alcoholic beverage. For the ancient Greeks, nectar (or ambrosia) was the “food of the gods,” drunk in the Dionysian rites in which the oracle, in the throes of divination, was fed honey and referred to as the personification of a bee.72 The Mayans and Romans, too, offered honey to their gods and goddesses. In many cultures, from India to Egypt, honey was the first food offered to a newborn infant, and was closely associated with the birth and death of the soul. Rivers of honey and mead flowed in paradise for Hebrews, Muslims, Celts, Norse (who mixed the honey with milk), and the /Xam (who preferred their honey mixed with

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locusts). In the vast majority of cultures, bees and beehives, si­mul­ta­neously sacred and mundane, ­were protected by ritual and ceremony.73 How are we to navigate between t­ hese very dif­fer­ent visions? To witness biohybrid bees engaging in reciprocal (if rudimentary) interspecies communication gives rise to a numinous sense of awe. To witness bees being converted into disposable, militarized sensing devices gives rise to a sense of dread. ­These two choices are emblematic of humanity’s relationship with nature: ­Will we choose dominion or kinship? If we choose the latter, ­there is likely to be a ­great deal more for bees to say to us, and for us to say to them. And they w ­ ill not be the only species that h­ umans engage with in dialogue. As the next chapter explores, a co­ali­tion of scientists is now attempting to mobilize artificial intelligence to crack the code of interspecies communication with a broad range of animals—­from primates to parrots and dolphins to ­whales.

9 The Internet of Earthlings

Vint Cerf—­vice president and Chief Internet Evangelist for Google, and self-­described ­father of the internet—­rarely speaks in public. So when he took the stage in February 2013 at the annual TED conference, one of the tech sector’s most high-­profile events, the audience took note.1 Cerf was joined onstage by an eclectic group: dolphin researcher Diana Reiss, musician Peter Gabriel, and physicist Neil Gershenfeld (director of MIT’s Center for Bits and Atoms). The audience was ­eager to hear from the star attraction, but Cerf remained s­ ilent as Reiss r­ ose to speak. As a backdrop to Reiss’s opening remarks, a video began playing of a young dolphin named Bayley, twirling in the w ­ ater. Bayley’s per­for­ mance charmed the audience. But, Reiss explained, Bayley was not performing for the camera. Rather, he was watching himself spinning in a two-­way mirror, as part of a scientific experiment. Bayley’s ability to recognize his reflection in a mirror, Reiss explained, was evidence that dolphins possess a trait known as mirror self-­recognition, a proxy for self-­awareness.2 Once thought to be a uniquely ­human quality, we now know that mirror self-­recognition is a trait that we share with g­ reat apes, elephants, and even magpies. This experiment and o­ thers, Reiss explained, proved that dolphins are far more intelligent than we have given them credit for: conscious, emotional, aware, capable of self-­organized learning. Gabriel took up the narrative. He was responsible for bringing the assembled group together. Years ago, he had begun a journey into world ­music, connecting with musicians across the world, finding common 158

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ground through m ­ usic despite not sharing a common spoken language. Fascinated by stories of animal communication, he dreamed of finding new interfaces—­video, audio, haptic—­that would enable him to communicate with other species. So he began reaching out to animal scientists with an unusual proposition. Could he visit their captive animals, bringing his piano and guitar and other instruments, and play m ­ usic with them? Raising murmurs of astonishment, Gabriel played a video clip of one of his jam sessions: a bonobo named Panbanisha being introduced to a piano keyboard for the very first time. With a delicate movement of a single fin­ger, Panbanisha tapped out a haunting melody, finding octaves and harmonies with Gabriel’s chords. One day, Gabriel had reached out to Gershenfeld and shown him the video of the bonobo learning to play the piano. “I lost it when I saw that clip . . . ​and realized that we h­ umans had missed something—­the rest of the planet.”3 The history of the internet, Gershenfeld added, is the history of “mostly middle-­aged white men.” The Internet of ­Things, he asserted, could be expanded to include animals and, indeed, the rest of the biomass on the planet. Striding over to a computer sitting on stage, Gershenfeld tapped a key, connecting the audience to livestreams with dolphins at the National Aquar­ium in Baltimore, orangutans in Texas, and elephants in Thailand. The audience was witnessing a historic moment: the birth of the Interspecies Internet. How would the architects of the internet feel about this interspecies agenda? As Cerf explained, the early designers thought they w ­ ere building a system to connect computers. But they soon realized that the internet is, rather, a system for connecting p­ eople. The next phase of the internet’s development, he predicted, would be an internet of species, of sentient beings. Through the internet, Cerf predicted, we would learn to communicate with ­others that are not ­human, from animals to aliens.

The Interspecies Internet The Interspecies Internet proj­ect—­now a global collective of over 4,500 animal researchers, computer scientists, linguists, and engineers—­ begins from a s­ imple premise: the digital tools we use to translate

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between ­human languages could be adapted to communicate with nonhumans.4 Specifically, artificial intelligence could be deployed to transduce signals from one species to another. This presupposes, of course, that other species have forms of complex communication, akin to language. Supposing this is true, could computational techniques be adapted to translate between ­human and nonhuman languages? Recent breakthroughs have brought this once unimaginable possibility seemingly within reach. The Cetacean Translation Initiative (CETI) is one example. Launched in the spring of 2021 by Harvard and Berkeley researchers working in marine biology, bioacoustics, AI, and linguistics, the proj­ect aims to use machine learning and noninvasive robots to decode the language of sperm w ­ hales (Physeter macrocephalus).5 The largest toothed predator on the planet, sperm w ­ hales can grow to 60 feet long and have the largest brains of any animal on Earth. Their brain size is one indicator, scientists believe, of their capacity for complex communication. Another suggestive indicator of the likelihood of complex language in sperm w ­ hales stems from the social complexity hypothesis, which holds that complex social structure is a driver of complexity and diversity in animal communication systems. The hypothesis, originally developed as an explanation for the development of ­human language, has more recently been applied to social animals, such as bats and elephants. Sperm ­whales are not only extremely vocal, they are highly social, living in hierarchical, tight-­knit, matrilineal ­family groups that stay together for their entire lives. And their vocalization patterns, like ­those of other ­whale species, have identifiable dialects; dif­fer­ent families (called vocal clans) have their own par­tic­u­lar patterns of sound. Taken together, t­ hese three clues—­large brains, social complexity, and dialects—­were sufficient to justify CETI’s focus on sperm ­whales. Sperm ­whales communicate using what sounds (to us) like buzzes, clicks, creaks, and squeals; when heard through a ship’s hull, the clicks can sound like tapping or hammering. Biologists believe that ­these sounds function somewhat like an old-­fashioned telegraph machine: by emitting pulses of sound at specific frequencies, of specific durations, and in specific patterns, the w ­ hales combine sounds in an intricate code. If this is correct, sperm w ­ hale communication would be akin to Morse

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code—­albeit much more complex. (This may have applications in the field of cryptography; researchers have proposed adapting ­whale vocalization patterns for use in a “bionic Morse code,” for the purposes of encrypted, covert communication.6) The sperm w ­ hale communication system could potentially be decoded using well-­known tools from information theory and linguistics. Studies of h­ uman languages have demonstrated the universality of several laws of linguistics. For example, the Zipf-­Mandelbrot law holds that ­human languages display a common pattern in which a few words are used very frequently, while the majority of words are used relatively rarely; Zipf ’s law of abbreviation holds that the more frequently a word is employed, the shorter it tends to be; and the Menzerath-­Altmann law notes that the longer a linguistic construct, such as a sentence, the shorter the size of its constituents, such as syllables.7 ­There is substantial evidence that the vocalizations of many animal species also follow t­ hese empirical laws of linguistic encoding.8 For example, aspects of h­ uman acoustic communication are directly comparable with ­those of other land vertebrates, including both the vocal apparatus and primary vocal modulatory cues.9 This does not necessarily imply that an animal possesses linguistic abilities; nevertheless, we can use ­these analogies and insights to guide our efforts in the search for complex animal languages. For example, researchers have used the concept of Shannon entropy—­w hich denotes the average level of uncertainty in communication—to assess the information potential of animal vocalizations.10 Assessing the applicability of laws of ­human linguistics to other species requires large datasets; ­until recently, studies of animal linguistics ­were constrained by small datasets. But with the rise of digital bioacoustics and artificial intelligence, scientists are now able to automate the analy­sis of large datasets of animal vocalizations. Algorithms can identify individual vocal ele­ments (words, calls, syllables), as well as higher-­level syntax and hierarchical organ­ization of communication.11 Scientists have recently discovered, for example, that animals share linguistic features once thought to be uniquely h­ uman, including previously unsuspected syntax in w ­ hale song and birdsong, and even combinatorial processing in primate and insect vocalizations.12

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Scientists have wondered for years w ­ hether w ­ hale sounds constitute an a­ ctual language. But up ­until now, they have had l­imited success in decoding ­whale sounds, in part ­because the ­whales spend much of their lives hundreds of feet below the surface, where individual sounds are hard to rec­ord and individual be­hav­iors are difficult to monitor. Due to advances in digital bioacoustics, scientists now have vast datasets, which they are using to develop the equivalent of w ­ hale dictionaries. Researchers are developing AI algorithms that identify the deep structure of ­whales’ communicative patterns; advanced statistical linguistics is used to correlate t­ hese patterns with ­whale be­hav­iors, while elucidating rules and structures (syntax, semantics, phonology, morphology).13 And by eavesdropping on m ­ other ­whales communicating with their babies, the researchers also hope to illuminate the pro­cess of language acquisition; their algorithms w ­ ill learn how to speak Sperm Whalish alongside the ­whale calves. If ­whale sounds have meanings that can be decoded, perhaps they have a form of language that can be translated; perhaps, more speculatively, their songs express oral histories that we could eventually learn from. The CETI proj­ect aims to transform not only our understanding of ­whale language, but also environmental conservation more broadly. As the proj­ect’s official website makes clear, by kickstarting “the path ­towards meaningful dialogue with another species” and “illustrating their incredible intelligence,” it hopes to accelerate conservation efforts.14 Speaking with other beings is, the researchers hope, a way to cultivate the ­human desire to protect them. But is interspecies communication even pos­si­ble?

Speaking in Animal Tongues The CETI proj­ect stands in sharp contrast to e­ arlier attempts to bridge the interspecies communication divide. In the mid-­twentieth c­ entury, Western scientists focused their efforts on attempts to teach animals how to speak h­ uman language. Some of the most well-­known proj­ects, in the 1960s and 1970s, focused on primates who lived in captivity. Most famously, a gorilla named Koko and a chimpanzee named Washoe lived

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in close contact with ­humans and ­were taught sign language by their American caregivers.15 Washoe learned over 250 signs; Koko learned over 1,000 and could understand over 2,000 En­glish words. In turn, Washoe taught sign language to another chimpanzee, Loulis—­the first observed example of a nonhuman teaching ­human language to another nonhuman. Kanzi, a bonobo, learned to communicate using keyboard symbols; in part, he learned through watching his m ­ other receive training.16 While the full extent of the apes’ achievements has been intensely debated (and is still not accepted by all scientists), it is broadly established that human-­trained primates can understand ­simple spoken language by h­ umans and learn to use more than a hundred symbols for communication. Koko, Kanzi, and Washoe not only understood and made transactional requests (“give me food”), they also used language to convey emotions. Th ­ ese results, according to proponents, proved that although the primates could not learn to speak ­human language, they could acquire sign language and understand complex ­human commands.17 Studies of nonprimate organisms have also found evidence of an ability to imitate ­human speech. Parrots are the best-­known example of birds that learn h­ uman words.18 Speech imitation and vocal learning have also been documented in other species. Hoover, a harbor seal raised by a Maine fisherman, could utter s­ imple phrases in En­glish;19 a beluga named Logosi could repeat his name.20 Koshik, an elephant born in captivity and raised in the Everland Zoo in South K ­ orea, reportedly matched Korean sounds with such precision and accuracy that Korean native speakers could readily understand and transcribe his words.21 While ­these animals’ abilities are fascinating, their h­ uman handlers ­were critiqued on ethical grounds. Many of ­these animals ­were deprived of contact with other members of their species; the research proj­ects ­were critiqued for researcher bias, at best, and cruelty and abuse at worst.22 Scientists have also objected that studying captive animals raised by h­ umans does not ­really help us understand how vocal learning works in the wild. But perhaps the most fundamental critique has been directed at the implicit anthropocentrism in ­these methods. Why

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should the ability to speak a h­ uman language be a mea­sure of communicative capacities of another species? This is as inappropriate as assessing ­human intelligence on the basis of our ability to speak dolphin. ­Today, scientists are taking a very dif­fer­ent approach to interspecies communication. The goal is not to teach other species to speak h­ uman languages, but rather to design devices that can communicate with nonhumans using their own modalities of communication. One example is the work of Denise Herzing, founder of the Wild Dolphin Proj­ect (which has studied dolphins in the Atlantic for the past thirty years), who has reengineered mobile devices—­smartphones and tablets—­for use by dolphins.23 Her attempts at interspecies communication, using purpose-­built underwater wearable computers and keyboards, have shown early success.24 Her team has also developed a machine learning algorithm, dubbed CHAT (cetacean Hearing and Telemetry), that has been able to detect meaningful dolphin sounds; for example, CHAT identified a specific sound that researchers had ­earlier trained the dolphins to associate with Sargassum seaweed (a floating plant dolphins sometimes play with). The dolphins, Herzing surmises, not only learned the new signal but began teaching it to one another. A ­human ear might not have been able to detect this finding, but her algorithm could.25 Diana Reiss, too, has sought to adapt digital technologies and AI algorithms to elicit information about dolphin communication. From the 1980s onward, she has been documenting vocal learning in dolphins and decoding their signature whistles. Reiss also made headlines when she demonstrated that dolphins could recognize themselves in a mirror (a sign, many researchers believe, of self-­awareness).26 In one breakthrough study, she developed an underwater keyboard designed for dolphins; the animals quickly figured out, even in the absence of specific instructions of explanation, how to use the keyboard to request a ball or a body rub.27 In subsequent iterations, Reiss used touchpads and interactive devices more appropriately adapted to dolphin physiology. In the mid-2010s, Reiss began collaborating with Rocke­fel­ler University biophysicist Marcelo Magnasco to develop underwater touchscreens outfitted with customized dolphin-­focused interactive apps; the goal of their Marine Mammal Communication and Cognition (m2c2)

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proj­ect is to use ­these devices to decipher dolphin communication and elucidate dolphin cognitive pro­cesses.28 Reiss and Magnasco are particularly intrigued by dolphins’ ability to synchronize their be­hav­iors and coordinate their movements precisely in tandem, even when they are given a command to perform an entirely novel task and without necessarily making sounds audible to ­humans.29 Could dolphins be coordinating their be­hav­ior through ultrasonic sounds that the h­ uman ear is unable to detect? In order to assess dolphins’ abilities, the algorithms need to be designed not only to track sounds but also to precisely identify the location and dolphin the sounds are emanating from. To date, the passive acoustic monitoring systems used in underwater bioacoustics research have not been able to attribute whistles to specific members of freely interacting and highly mobile social groups. But recently in­ven­ted AI algorithms can detect both the specific whistle and the identity of the source in real time, which ­will allow Reiss and Magnasco to determine w ­ hether dolphins’ apparently telepathic ability to convey information occurs through vocalizations. If not, this implies that they are using some other—as yet undiscovered—­means of communication. Reiss’s and Herzing’s research demonstrates that, over the past several de­cades, research on nonhuman communication has slowly begun to shed its ­human biases. Rather than seeking evidence that animals can understand or speak our languages, scientists are focused on understanding nonhuman communication on its own terms. But our ability to do so is hampered by our physiology: it is challenging for us to hear dolphin noise, much less speak it. Now, artificial intelligence may bridge this gap.

Google Translate Goes to the Zoo What if translation algorithms developed for ­human languages could be used to decipher nonhuman communication? In order to translate between two dif­fer­ent h­ uman languages, ser­vices such as Google Translate use artificial intelligence algorithms to analyze large datasets of text (such as documents translated into multiple languages by the United Nations or Eu­ro­pean Union). If the training datasets are sufficiently

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large and comprehensive, the algorithms are able to build a digital lexicon, which enables them to translate individual words (e.g., river in En­glish  = la rivière in French = sîpîy in Cree) and to derive general princi­ples of language, like grammar and usage. The speed of translation algorithms, as well as their ability to accurately translate entire sentences rather than individual words, has vastly improved in the past de­cade, as has their range: in 2016, Google Translate crossed the threshold of a hundred languages. This was also the year that Google began using a novel form of machine learning—­artificial neural networks—in its Google Translate. Although ­these translation algorithms are not yet able to match the nuance of h­ uman translators (and still make m ­ istakes that are easily detectable by h­ umans), they are very good at narrow, well-­ defined translation tasks. Applying t­ hese same techniques to analyze recordings of nonhuman sounds was challenging u­ ntil recently, b­ ecause of the absence of large datasets of nonhuman sounds. Manually labeling large datasets, in order to enable the algorithms to learn the under­lying patterns, is tremendously time consuming. For example, the world’s then largest database of ­whale songs (Whale FM) was made publicly available in 2011. Researchers placed the dataset of over four thousand w ­ hale calls on Zooniverse (the largest citizen science platform in the world) and called for volunteers to label the dataset.30 Over ten thousand volunteers supplied nearly two hundred thousand labels. This level of effort is rarely achievable, even if the recordings are available. A second challenge is the paucity of datasets: only a few species have attracted enough attention for researchers to have developed large databases with enough variability in call type. And for endangered species, it is challenging to gather enough recordings simply ­because so few individuals may be left. Two recent advances in AI research are enabling researchers to overcome ­these challenges. First, new methods have been developed for languages that lack large datasets. In the past, translation algorithms required large training datasets, which contained texts previously manually translated by ­humans into at least two languages (researchers use frequently translated texts like the Bible and the Koran, Wikipedia entries, Shakespeare, or Eu­ro­pean Union regulations). But this is not a

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v­ iable approach for languages that lack written texts. In ­these cases (for example, Indigenous languages of the Amer­i­cas), researchers must manually compile labeled datasets—­a painstaking and time-­consuming task.31 In the past five years, new types of AI algorithms have been developed that can translate between two languages, even with small training datasets.32 In other words, the latest generation of AI algorithms can learn what scientists refer to as low-­resource languages.33 A further breakthrough occurred with the invention, in 2013, of a novel approach to translating languages without bilingual dictionaries and even in the absence of prior examples of translation (so-­called zero-­ resource languages). In this approach, the algorithm analyzes a written dataset by generating a shape (called a latent space) that represents an entire language; this multidimensional geometric structure enables the algorithm to decode a previously unknown language.34 Over the past several years, ­these algorithms have become even more power­ful, successfully translating between distant languages (e.g., English-­Chinese) and even outperforming older algorithms based on dictionaries.35 The algorithms, versions of which are now widely used by major tech companies, can also analyze linguistic nuances, such as contextual meaning, polysemy (multiple meanings), and analogies.36 ­These algorithms are flexible enough to recognize patterns not pre­sent in the training dataset.37 In 2018, MIT computer scientist James Glass proposed extending ­these techniques from text to speech, using audio data. In a startling research result, his team was able to design an algorithm that translates from German audio to French text using acoustic recordings of only a few hundred hours.38 ­These developments would, Glass predicted, lay the foundation for automated speech recognition and speech-­to-­text translation systems for low-­and zero-­resource languages around the world—­the majority of ­human languages. This is, of course, the situation in which we find ourselves with re­ spect to nonhuman communication: we do not yet possess a dictionary of Sperm Whalish, but we now have the raw ingredients to create one. Bioacoustics provides the raw data for the training sets; AI algorithms detect patterns in ­these datasets, which (in theory) correspond to

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sounds that carry meaningful information.39 In practice, the work performed by the algorithms is likely to require some h­ uman interpretation. Once machine learning algorithms identify in­ter­est­ing signals in the acoustic data—­say, a cluster of sounds in a soundscape—­they often need to be interpreted by h­ umans in order to associate them with behaviorally relevant information. And for highly endangered species (for which t­ here are typically only a handful of acoustic datasets available), translation algorithms may be challenged by sparse training data; manual annotation of key aspects of a database (rather than manual labeling of the entire training dataset) may still be required. Despite ­these challenges, the technical breakthrough is significant. Collectively, ­these algorithms are capable of creating a series of acoustic Rosetta Stones: tools for decoding languages without written text or dictionaries, simply on the basis of audio recordings. The second technical breakthrough in AI algorithms is the advancement in pro­cessing physical gestures and movements, in addition to vocalizations. For example, in 2019, Google’s AI labs released MediaPipe, an open-­source algorithm for tracking hand and fin­ger positions; by pairing this with video data of government speeches with official translators, computer scientists ­were able to develop real-­time sign language translation engines. Gestures can be translated into other gestures, written words, or speech, which are then transmitted to viewers, readers, or listeners. Previously overlooked aspects of communication—­ gaze, posture, and facial expressions, as well as gestures—­could conceivably be incorporated into t­ hese algorithms. Building on t­ hese innovations, tech companies have an ambitious goal: for AI systems to be able to speak, see, hear, and understand ­every ­human language on Earth—­including sign language. AI algorithms in the ­future may be able to learn nonhuman communication systems akin to how babies learn: by listening to sounds and watching gestures, and thus discerning patterns in vocal activity and be­hav­ior rather than consulting a hardwired dictionary. Could ­these gesture-­and movement-­based AI translation systems be applied to nonhumans? This now seems more likely, given recent breakthroughs in a parallel research agenda: using AI to detect emotions,

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expressed through movements, in animals. A growing body of scientific evidence confirms the existence of emotional states in a wide variety of species, and new methods of analy­sis, made pos­si­ble by computer vision and machine learning, have enabled scientists to decode ­these emotions.40 In one experiment, for example, researchers used an unmonitored machine learning algorithm to classify a wide range of mouse emotions, such as disgust, active fear, passive fear, plea­sure, and curiosity. The algorithm categorized subtle mouse facial expressions—an ear drawn back, a nose twitch, the tilt of whiskers—at millisecond time scales, enabling researchers to analyze the intensity and per­sis­tence of emotional states in individual mice.41 Drawing on this research, one could imagine an AI capable of correlating mouse gestures and sounds with its emotions and be­hav­iors, providing a more robust basis for translation between mice and ­humans. This, proponents argue, would enable AI to address a common objection to the prospect of interspecies communication: the inability to link vocalizations to be­hav­iors. Researchers have also built AI algorithms that not only analyze the infrasonic squeaks made by a mouse, but also identify individual animals and link the sounds to be­hav­ior.42 When combined with an algorithm that can monitor the mouse’s face for signs of emotions, we would have a power­ ful translation device. In the past two years, prototypes of such integrated AI algorithms have been developed for mice, bearing names like BootSnap, DeepSqueak, and VocalMat. Similar algorithms have been developed for other species. In 2021, a team of researchers from France’s Natu­ral History Museum released a generalized, open-­source bat sound algorithm that comprises over one million bat sounds, achieves up to 98 ­percent accuracy, and can be used by anyone to study bats in any region of the world.43 And over the past de­cade, bioacoustics-­based deep learning algorithms have achieved remarkable results in bird species identification; the latest generation of algorithms has up to 97 ­percent accuracy, and one algorithm (BirdNET) can identify nearly one thousand species.44 It is impor­tant, however, not to overstate the efficacy of t­ hese AI algorithms. Machine learning methods are fast improving but still have

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shortcomings: algorithms may miss faint, transient, or partially masked calls, or have trou­ble coping with background noise.45 Using algorithms for automated identification of species thus involves a trade-­off of time against accuracy. Manual pro­cessing of data is often subjective and slow but, with the right oversight, can be highly accurate; fully automated pro­cessing is much faster but more error prone. ­Today, bioacoustics monitoring is still semiautomated, requiring experts to manually cross-­ check, resolve ambiguous interpretations, or apply rules of thumb. Nonetheless, the newest generation of AI algorithms (called convolutional neural networks) has vastly improved per­for­mance and shows promise in addressing ­these challenges.46 ­These AI algorithms fulfill a long-­held dream of bioacousticians. Researchers have long wondered ­whether they could build a “Shazam for nonhumans.” The analogy is enticing: Shazam is a smartphone app that can identify a song based on a short m ­ usic sample; a Shazam for nonhumans would be an app that could identify an animal based on a short vocalization—­whether a grunt, chirp, or squeak. Apps such as Bird Genie and BirdNET have fulfilled this goal, and surpassed it: based on an acoustic sample, the species can be automatically identified. Similar algorithms have been developed for other animals, some of which can identify unique individuals. Although the lexicons ­will change, the techniques are generalizable. The rapid pace of innovation is demonstrated by the fact that algorithms now easily outperform the tens of thousands of volunteers assembled to work on Whale FM. ­After the heroic manual labeling effort, the researchers used an algorithm (Wndchrm) to analyze the calls, replicating the work of the h­ uman volunteers.47 Wndchrm outperformed the ­humans: distinguishing between killer ­whales and pi­lot ­whales, and easily classifying the calls into specific populations (Icelandic versus Norwegian killer ­whales; Bahamian versus Norwegian pi­lot ­whales). The latest generation of machine learning algorithms (with names like Orca-­SLANG and BAT Detective) can identify the sounds of individual ­whales or bats as easily as facial recognition technologies identify ­humans.48 In the same era that game-­playing AI algorithms surpassed ­humans at playing Go and chess, they have surpassed h­ umans in both speed and accuracy at identifying ­whale songs and bat calls.

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A universal voice recognition system for living organisms (at least ­those that vocalize) is now an achievable goal. The hopes of the found­ ers of the Interspecies Internet have thus partially been achieved.49 A zoological version of Google Translate is likely to be available within a de­cade, or perhaps two. More speculatively, language prediction algorithms (such as GPT-3) might be adapted to vocally active species like bats or dolphins; this would enable researchers to use computers in interactive, live playback experiments with wild animals. This would be an impressive achievement. However, scientists caution that just ­because we can identify vocal signals ­doesn’t necessarily mean we can decode their meaning. Given the tremendous differences in our bodies and experiences of the world, it may be extremely challenging to find shared concepts between ­human and nonhuman languages. We may not share enough of an understanding of the experience of being a nonhuman animal, b­ ecause we do not share that animal’s umwelt (a term first coined by biophi­los­o­pher Jakob von Uexküll to refer to an organism’s worldview).50 With primates or domesticated pets, we might hope for shared insights; but would that be pos­si­ble with a w ­ hale? Moreover, the technical difficulties in translation should not be underestimated. Recordings of nature’s sounds often include sounds of many dif­fer­ent species; to distinguish between them, bioacoustics researchers usually manually label datasets, enabling an algorithm to learn to distinguish between an elephant and a tiger. But an unmonitored algorithm, trying to teach itself Sperm Whalish by listening to recordings of the ocean, ­will have to distinguish between subtle sounds coming from many dif­fer­ent sources—­and indeed species—at once. Although pos­si­ble, this requires calibrating algorithms to a wide range of species and ecosystems—no small task.

Animal-­Computer Interaction As we explore interspecies translation, we should be mindful of a parallel research agenda: animal-­computer interaction (ACI). Clara Mancini, a computer scientist at the Open University, articulated the founding princi­ples of ACI in a manifesto published in 2011.51 As Mancini argues, many nonhuman species have demonstrated the ability to use interactive

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digital devices, and may even appropriate them in novel and unexpected ways. Developments in pervasive and ambient computing make digital technologies more accessible to ­humans, and also to other species, particularly when we can engineer devices that interact with nonhumans’ superior sensory faculties (such as their sense of touch or ability to echolocate). Mancini’s manifesto sparked a cascade of novel digital devices targeted at other species. For example, vibrotactile harnesses have been designed for hunting and ser­v ice dogs; ­these cyberharnesses detect gestures and mea­sure physiological indicators (such as heart rate) of both dogs and their ­owners, converting this information into two-­way signals that enhance commands and communication.52 Similar devices allow handlers to provide haptic feedback to elephants via their trunks.53 ACI designers even have wearables for livestock; some are connected to virtual and mixed real­ity systems.54 (Poultry: Welcome to the metaverse.) ACI researchers also espouse the view that interspecies communication is much more likely to be sustained if it is playful.55 To test this idea, Mancini and other researchers have created multispecies video games for orangutans, pigs, cats, and crickets (vibrotactile plates provide feedback to the crickets, who play Pac-­Man with ­human players).56 PigChase, for example, uses an interactive touchscreen to enable h­ umans and domesticated pigs to play a cooperative game together: through a tablet, the h­ uman player controls a circle of light on a g­ iant touchscreen in the pigs’ pen, which lights up with animations if the pigs follow and guide the light to a destination (a geometric shape). Mancini has also designed digital devices that allow captive elephants to choose between dif­fer­ent types of prerecorded sounds (e.g., ­whale ­music versus elephant sounds) as a means of providing enrichment while in captivity.57 Some of ­these inventions also have practical applications, such as touchscreen interfaces for working dogs, as well as devices that stimulate imaginative play—­and thereby, purportedly, the well-­being—of captive animals in zoos or factory farms.58 Other researchers are using similar techniques to teach symbols to other species, including apes, gray parrots, and pigs.59 Scientists have used a variety of technologies—­touchscreens,

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interactive keyboards, TV monitors, acoustic signals—to demonstrate that animals engage in symbolic communication, possess numerical competence, form concepts, and can self-­organize their learning.60 Many of t­ hese experiments have been conducted with domestic pets or captive animals in zoos. To take t­ hese ideas further, researchers are now embedding artificial intelligence algorithms in biomimetic robots intended to communicate with wild animals in their own habitats, using vocal as well as gestural and physical signals. Landgraf ’s bee robots, discussed in the previous chapter, are accepted as members of beehives, where they influence honeybee be­hav­ior through vibroacoustic communication.61 Scientists have developed similar devices that have been accepted into schools of fish and influence shoaling be­hav­ior by giving ­simple directives (“swim left”),62 as well as devices that issue basic instructions to plants (“grow your roots in this direction”).63 Biomimetic robots have successfully been used to communicate with, a wide variety of species, including cockroaches, ducks, rats, locusts, moths, baby chicks, zebra fish, and lampreys.64 Other groups of researchers are advancing similar proj­ects, like the Vocal Interactivity in-­and-­between ­Humans, Animals, and Robots collective (VIHAR). Founded in 2016, the VIHAR collective brings together engineers, roboticists, biologists, and linguists to explore questions surrounding the relationship between language and the dif­fer­ent signaling systems used by h­ umans, animals, and robots.65 Some roboticists envision a f­ uture where biomimetic robots circulate in ecosystems of ­every kind, acting as translators between h­ umans and animals. Neuroscientists, computer scientists, and roboticists are collaborating to build AI-­enabled robots, capable of autonomously learning animal signals. The long-­term goal is to create a system in which animals provide the data and feedback necessary for robots to learn to “speak animal” without being programmed by ­humans. While such “evolutionary robotics” systems are still in their infancy, a commercial economy has already begun to emerge based on ­these innovations. Voice recognition software now exists for animals, ranging from birds to bats and prairie dogs to marmosets, along with wearables for pets and livestock, which interpret the meaning of their sounds for their ­owners.66

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­ ese biorobotic translators are intermediaries via which, their creTh ators argue, ­humans can develop an appreciation for and understanding of the lifeworlds of nonhumans. In a sense, ­these robots offer a twenty-­ first-­century version of the long-­standing science of ethology (the study of animal be­hav­ior in the field), mediated by robots and computers rather than ­human observers. Our computers, unconfined to ­human bodies, may be more capable than ­humans of understanding another creature’s experience of the world, its umwelt. It may indeed be true that “­water” has no meaning for a fish (or, at least, a very dif­fer­ent meaning than for ­humans), but this is perhaps no more insurmountable a barrier to communication than the conceptual differences between dif­fer­ent ­human cultures. As we translate between dif­fer­ent concepts of the world, and dif­fer­ent sensory experiences, we are engaging in meaning-­ making. But this does not render translation impossible—­merely more complex and nuanced. From the perspective of a computational ethologist, such complexity should be celebrated as deeply enriching. Imagine the insights we could garner if we could understand the ocean from the perspective of a ­whale, even if this understanding was imperfect. This digital utopian view of biohybrid robots glosses over an ethical conundrum. W ­ ill we use t­ hese robots to advance interspecies understanding, as the found­ers of the Interspecies Internet proj­ect hope? Or ­w ill we use our newfound ability to further domesticate nonhumans and bend them to our w ­ ill? This is not a hy­po­thet­ic­ al question: many of the devices described above are destined for use in industrial meat production facilities.67 In ­these cases, bioacoustics could serve to tame species previously resistant to h­ uman domestication, deepening exploitation rather than conservation of nature. Could biomimetic robots, acting as translators between ­humans and other species, simply serve to command animals to serve ­human ends?

Kinship Acoustics Which ethical guideposts might help us navigate this new world of digitally enabled interspecies communication? Indigenous traditions offer insight and guidance on t­ hese m ­ atters. Notably, Indigenous scholars

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and communities articulate a very dif­fer­ent approach to environmental data than mainstream science. Indigenous ­peoples (numbering an estimated 370 to 500 million ­people worldwide) manage or have tenure rights to a quarter of the world’s land surface, including nearly 40 ­percent of all terrestrial protected areas and ecologically intact landscapes.68 Many Indigenous communities have expertise in digital technologies and environmental conservation, including geo­graph­i­cal information systems (GIS) mapping, digital tracking technologies, and global earth observations.69 Yet Indigenous perspectives are often excluded from both digital and conservation debates, and bioacoustics and ecoacoustics are no exception. Environmental conservation has, at times, physically excluded Indigenous ­peoples from their territories (e.g., conservation refugees created by forced displacement to create national parks) or legally excluded them from decision making about their territories.70 Bioacoustics and ecoacoustics, as scientific agendas and communities, risk repeating ­these same exclusionary tactics, a form of environmental colonialism. In response, Indigenous scholars and activists argue that the data rights and interests of Indigenous ­peoples, as set out in the United Nations Declaration on the Rights of Indigenous ­Peoples (UNDRIP), must be secured; this requires that researchers acknowledge Indigenous data sovereignty and collaborate accordingly when harvesting data on Indigenous territories.71 Indigenous data sovereignty challenges the widespread notion that data collected about nonhumans has no ­owners. Currently, most bioacoustics data is gathered without observing the usual l­ egal protections and protocols, such as data privacy, applied to h­ uman data.72 Companies and researchers can use ­these datasets with minimal safeguards; experimental algorithms ultimately destined for use on h­ umans can be first tested on nonhumans, with minimal oversight.73 In the ­f uture, following princi­ples of Indigenous sovereignty, bioacoustics researchers might have to recognize Indigenous owner­ship, and perhaps recognize nonhumans as subjects (­legal persons) and safeguard their data accordingly—or refrain from data harvesting altogether.74 Indigenous-­ centered research protocols, like OCAP (owner­ship, control, access, possession) and CARE (collective benefit, authority to control,

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responsibility, ethics) might be invoked, in addition to conventional FAIR (findable, accessible, interoperable, and reusable) princi­ples.75 ­These protocols would implement consistent safeguards over harvesting, storing, and sharing digital acoustic data.76 Another ethical teaching from Indigenous knowledge holders emphasizes the importance of being in, and belonging to, par­tic­u­lar places. Mohawk/Anishinaabe scholar Vanessa Watts refers to this idea as “place-­ thought”: our words, concepts and ideas emerge and are rooted in specific landscapes. The corollary follows: if we want to engage in au­then­tic communication with nonhuman beings, we also need to cultivate a relationship with the places and the ecological communities that they inhabit.77 Other species may only be intelligible to ­humans if we understand their umwelt: their lived, embodied experience in the places in which they dwell. Digital listening does not provide us with access to umwelt, but deep listening does. Even with high-­powered digital translation devices, we still need to spend time on the land, simply listening, in order to fully understand. While digital listening is profoundly power­ful, digital data is a poor substitute for the embodied experience of listening to ­others in place. Many Indigenous scholars also emphasize a notion of nonhuman sentience as a cornerstone of human-­nature relationships.78 Animals, plants, and even geological features, like mountains, are known as nonhuman persons—­part of an extended f­ amily that shares ancestry and relationships.79 This “Indian metaphysics,” argues Lakota/Dakota (Sioux) scholar Vine Deloria, asserts that ­matter is infused with spirit and consciousness—rocks and mountains, as well as ea­gles, deer, and bears.80 Adopting this “interspecies thinking” perspective, as Kim TallBear explains, implies the understanding that the world—­both its biotic and abiotic ele­ments—is in constant conversation.81 From this perspective, we should not be eavesdropping on nonhumans, but rather communicating with them, in a reciprocal conversation about mutually beneficial sharing of our collective home. Anishinaabe elders, writes Vanessa Watts, explain that ­these “generative and primordial” dialogues are rooted in par­tic­u­lar place-­based relationships.82 Adopting a philosophy of kinship might enable the architects of digital translation devices to translate nature’s sounds with care; with an ethic of stewardship,

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rather than exploitation. As Potawatomi plant ecologist Robin Wall Kimmerer writes: Potawatomi stories remember that all the plants and animals, including h­ umans, used to speak the same language. . . . ​But that gift is gone and we are the poorer for it. ­Because we ­can’t speak the same language, our work as scientists is to piece the story together as best we can. We ­can’t ask [nonhumans] directly what they need, so we ask them with experiments and listen carefully to their answers.83 Kimmerer explains that one of the challenges of translation is expressed conceptually in the grammatical structure of Indigenous languages, which use verbs instead of the nouns used in En­glish. Hills, for example, are always in the pro­cess of being and becoming, of “hilling.” Objects considered inanimate in English—­such as plants or rocks—­are referred to as animate beings in many Indigenous languages. As Kimmerer observes, “The arrogance of En­glish is that the only way to be animate, to be worthy of re­spect and moral concern, is to be a ­human.” In contrast, Indigenous languages refer to nonhumans as animate; nonhumans are subjects, rather than mere objects. This “grammar of animacy,” Kimmerer argues, encourages a re­spect for nonhuman lives.84 Would nonhuman languages express a similar sense of animacy? If so, in order to engage in interspecies communication, we would need to learn entirely new ways of thinking about the world: a new grammar and vocabulary of living t­ hings. Perhaps Sperm Whalish nouns would be constructed like verbs, as fluid and ever-­changing as their home. Perhaps the ­whales’ language would have an entirely dif­fer­ent set of sensory referents, related to sonic rather than visual analogies; meta­phors of ­water, time, and the deep. Or perhaps they would speak the language of multiple times and places; one language for the cold ­waters of the Arctic, another language for the warm ­waters of their birthing grounds. In ­these languages, sounds may be combined with pheromones, biochemicals, and gestures to make meaning; we may not be able to decode nonhuman communication by relying on acoustic data alone. Kimmerer’s grammar of animacy implies that the languages of other species are profoundly dif­fer­ent than our own.

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From a traditional knowledge perspective, Kimmerer explains, communication is woven into webs of relationships, characterized by mutual re­spect and reciprocity. Nonhumans are relatives—­cousins, aunts, ­uncles, and grand­mothers: sentient, conscious beings. Perhaps this, too, might inform the ethics of interspecies translation. When we seek to learn the language of other creatures, we might accept that they are not mere research subjects; they are our teachers.

Lost in Translation In 2012, the Cambridge Declaration on Consciousness was published by an international group of prominent scientists, including some of the researchers engaged in the Interspecies Internet proj­ect. The signing ceremony, witnessed by Stephen Hawking and recorded by 60 Minutes, brought together cognitive neuroscientists, neuropharmacologists, neurophysiologists, neuroanatomists, and computational neuroscientists; ­humans, they declared, are not unique in possessing consciousness. The declaration asserts the view that a wide range of nonhumans (all mammals and birds, and many other creatures, including octopuses) possess the neurological substrates of consciousness.85 Diana Reiss was one of the signatories. She and other animal researchers began publishing their views on w ­ hales: cetaceans, she claimed, not only possess complex brains and consciousness but also language, culture, and complex cognition.86 ­These controversial claims remain unresolved, and are fiercely refuted by other scientists.87 As biologist Angela Dassow points out, scientists studying bioacoustics tend to avoid references to consciousness. They also often avoid discussing language, as this term implies a transfer of conceptual knowledge.88 Instead, bioacousticians carefully focus on the narrower concept of communication: conveying information in order to produce behavioral responses in other organisms. Thus constrained, mainstream bioacousticians study communication in other species as a ­matter of signal-­and-­response, avoiding thorny questions of consciousness and language. But if communication is defined merely as acoustic inputs and behavioral outputs, are we refusing to fully examine

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the capacities of other creatures? When we avoid asking ­whether they are conscious, are we reinforcing a notion of h­ uman superiority? The found­ers of the Interspecies Internet proj­ect believe that bioacoustics research must confront ­these questions head-on. Debates over animal language and consciousness ­w ill continue to rage.89 While it is true that bioacoustics research reveals unsuspected complexities and subtleties of nonhuman communication, the majority of bioacoustics researchers refrain from discussion of philosophical issues; they study vocalizations rather than language. Their interest is empirical, and the questions they pose eminently practical: How do vocalizations relate to animal be­hav­iors? What are the impacts of environmental noise pollution on nonhuman species? And how might we use this knowledge to better monitor and protect species in the face of biodiversity loss? Indeed, some bioacoustics researchers argue that the desire for interspecies communication and the associated curiosity about nonhuman consciousness risk becoming a distraction. Their skepticism is twofold. Learning nonhuman languages and decoding the meanings in nature’s messages may not be pos­si­ble; even if our AI algorithms decode a Sperm Whalish language, we may be unable to understand what the words actually mean (and perhaps such a language might not have words, as we understand them). And, even if achieved, interspecies translation may not trigger the transformation in humanity’s relationship to nature so ardently desired by the found­ers of the Interspecies Internet. Skeptics also argue that we have more immediate concerns given current catastrophic rates of biodiversity loss: by the time we figure out ­whether we can communicate with nonhuman species, many of them may have vanished from the Earth. Instead, some researchers argue, we should deploy bioacoustics as a tool for environmental conservation. As the following chapter explores, bioacoustics-­based devices have already demonstrated success in protecting endangered species. Indeed, some hope that bioacoustics might be key to halting the wave of mass extinction now sweeping our planet.

10 Listening to the Tree of Life Let us bring ­people back into conversation with all that is green and growing; a universe that never ­stopped speaking to us, even when we forgot how to listen. —­r obi n wa l l k i m m er er , br a i di ng s w e etgr a s s

In 2010, fewer than four hundred North Atlantic right w ­ hales remained alive off the northeastern coast of the United States. Struggling to recover a­ fter the end of industrial whaling, the w ­ hales had become one of the most endangered species in the world. That summer, when their traditional territory in the Gulf of Maine was hit by an unpre­ce­dented heat wave, their home became the fastest-­warming area on the planet.1 Soon ­after, the ­whales dis­appeared from the gulf. No one knew where they had gone, but scientists suspected they had become climate change refugees, migrating in a desperate hunt for food.2 Right ­w hales, one of the largest mammals in the world, nourish themselves primarily on one of the ocean’s smallest creatures: copepods. Copepods—­zooplankton that form the largest biomass of animals on the planet, the base of many marine food chains—­thrive in upwellings of cold, nutrient-­rich w ­ ater. As the heat wave hit the Gulf of Maine, colder w ­ aters retreated north and the copepod population declined precipitously.3 Soon ­after, the ­whales also vanished.4 180

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A few months l­ ater, hundreds of miles to the north, the w ­ hales ­were spotted in the Gulf of St. Lawrence, one of the richest marine zones in the world, where the mighty St. Lawrence River drains the ­Great Lakes (containing more than a quarter of the Earth’s freshwater) into the Atlantic. The ­whales ­were not alone in moving north; that year, salmon ­were seen in Arctic rivers like the Mackenzie, and Atlantic tuna ­were observed off the coast of Greenland, thousands of miles from their known ranges, hunting for new habitat.5 The ­whales had chosen wisely: they had found their way to the Shediac Valley, a biodiversity hotspot, refuge, and nursery for marine life. ­There, with abundant food, they should have thrived. But the Gulf of St. Lawrence is also one of the busiest shipping zones in the world. The w ­ hales had been fortunate to find an abundant buffet, but accessing it required them to navigate the marine equivalent of a twelve-­lane highway. As the w ­ hales congregated in the gulf, ships began striking them more frequently. Bloated ­whale bodies washed up on shore, their skin gouged by propeller cuts and distorted by blunt-­force trauma.6 A rec­ord number of w ­ hales became entangled in fishing gear, which often proved fatal. In 2017, more than a dozen ­whale deaths on the Canadian side of the border w ­ ere attributed to fishing gear entanglement and ship strikes; an additional eight w ­ hales died over the following two years.7 Many more bodies likely sank to the ocean floor before being spotted, a potential death knell for a species with so few remaining individuals.8 Government officials ­weren’t sure what to do. It was hard to pinpoint the w ­ hales’ location, and data from aerial surveys w ­ ere often outdated, sometimes up to a year old.9 Conventional ­whale protection strategies—­ such as fisheries closures, designation of critical habitat areas, and modifications to shipping routes—­are based on the assumption that ­whales visit the same foraging grounds at the same time each year. But with rapidly shifting ocean conditions, no one knew where the w ­ hales would appear next. Scientists asked for blanket restrictions on shipping: speed limits and fisheries closures that would last u­ ntil the w ­ hales’ new migration patterns could be established. But fishers and shipping companies protested. The politicians sided with industry; in the face of uncertain

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science with insufficient data, fisheries and shipping companies carried on with business as usual.10 One year passed, then two; the ­whales kept ­dying. By 2019, one in ten w ­ hales had died from ship strikes or fishing line entanglement, over fifty in all; time was ­running out to save them.11 Two challenges stood in the way of preventing more ­whale deaths: figuring out where the w ­ hales actually w ­ ere, and alerting ships quickly enough so that they could avoid striking the w ­ hales. Bioacoustics emerged as a novel solution to both challenges. Fisheries officials had been relying on aerial surveys to monitor the ­whales, but this method was expensive, inefficient, and often hampered by bad weather conditions. Locally based biologists like Kimberley Davies, a professor at the University of New Brunswick, knew that passive bioacoustic monitoring could provide continuous surveys of w ­ hale locations with greater accuracy and lower cost.12 Over the previous de­cade, marine biologists like Davies had been developing and refining passive acoustic monitoring systems as a means of tracking w ­ hale movements; their data confirmed that many ­whales w ­ ere spending more time in northern latitudes, and pinpointed ­whale location with high accuracy.13 The key to Davies’s approach was an innovative bioacoustics device: an underwater, autonomous acoustic glider equipped with hydrophones—­somewhat like a marine version of an aerial drone. ­These gliders, Davies explains, “can stay out in all kinds of weather, per­ sis­tently monitoring twenty-­four hours a day, seven days a week.”14 When Davies started reporting the w ­ hale location data in 2019, she sounded the alarm. As her gliders moved back and forth across the ­water, the data showed that ­whales w ­ ere using a much larger area than previously understood. Davies warned officials: ­unless more extensive shipping and fishing restrictions ­were implemented immediately, over a large expanse of ocean, more ­whales would die. In the face of their objections, she presented her bioacoustics-­based solution. If a right whale is detected by a glider, the location is transmitted to government officials, fishers, and ships’ captains, and a large area around the position of the detection (approximately 1,000 square miles) is closed to specific types of fisheries, including lobster and crab, for fifteen days.15 In some areas, if a ­whale is detected a second time, the area ­will be closed for the

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entire fishing season. Moreover, within designated slowdown zones, all ships are required to abide by mandatory speed limits (10 knots over the ground).16 The slower a ship travels, the less likely a strike is to be fatal. The bound­aries of ­these zones are dynamic and depend on ­whale sightings and ocean conditions, such as w ­ ater temperature, which influences where ­whales gather. In zones where ­whales are at higher risk, ships that exceed the speed limit are subjected to fines up to $250,000.17 The data on ­whale locations and speed restrictions is placed on open-­source maps, which are broadcast to all ships in the area, so pleading ignorance is not an option. ­After extended negotiations, Canadian government officials a­ dopted the bioacoustics-­based system as part of their governance framework for the Gulf of St. Lawrence.18 Davies’s gliders ­were repurposed for use in the new mobile marine protected area. The program was an immediate success: within hours of their first launch, the gliders detected w ­ hales, signaling ships to slow down. In 2020 and 2021, t­ here w ­ ere no recorded right ­whale deaths in the Gulf of St. Lawrence due to ship strikes.19 The tale of the North Atlantic right ­whales is a parable about a digital ­future in which bioacoustics could be mobilized to protect endangered species worldwide. Enabled by a handful of aquatic drones and an artificial intelligence algorithm in a small university lab, a population of four hundred w ­ hales now controls the movements of tens of thousands of ships, in a watershed that is home to forty-­five million p­ eople. Digital bioacoustics, in other words, enables us not only to eavesdrop on w ­ hales but also to protect them—­simply by staying out of their way. Similar systems are now being built around the world, in both terrestrial and aquatic environments. The next step, once machine learning algorithms are sufficiently reliable, is to move t­ hese algorithms directly onto the sensors in the field. If algorithms within each sensor can analyze the data in real time, this opens up new possibilities for conservation. For example, in a national park, real-­time detection of gunshots by an AI-­enabled acoustic sensor could trigger an immediate warning to an antipoaching patrol. Mobile protected areas—­supported by real-­ time bioacoustics data—­could play an impor­tant role in the ­future of environmental conservation.

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To achieve this goal of bringing computation and data storage onto sensors in the field (which researchers sometimes refer to as “edge computing”), two major challenges need to be addressed: a reliable supply of power for sensors and reliable communication networks, even in remote areas without cell phone coverage. Experts feel that ­these challenges are likely to be resolved in the next ten years; for example, power issues may be resolved by new sensor designs that do not require as much power or use batteries, and new satellite-­based global internet systems may resolve the communications challenge. Some researchers predict that this “batteryless internet of sounds” w ­ ill be operational in less than a de­cade.20 If this prediction comes true, it would enable real-­time acoustics-­based environmental conservation to protect endangered species, from the busiest to the most remote areas of our planet.

The Whale That Steered the Ship To be successful, bioacoustics-­based conservation systems require ­humans to accept something very novel: changing their be­hav­ior in response to something we ­can’t see or hear. It’s one ­thing to slow down if you see a moose crossing the road; it’s another m ­ atter to divert a cargo ship from its course ­because your computer tells you it detected a ­whale nearby. Operationalizing bioacoustics-­based conservation schemes depends on fostering trust in t­ hese novel technologies, and belief that the outcomes—­saving endangered species—­outweigh the costs. One of the most ambitious bioacoustics schemes in the world, launched off the California coast, is attempting to change the mindset of the global shipping industry. Observers are watching closely; if the just-­in-­time shipping industry consents to bioacoustics-­based conservation, this ­will set an impor­tant global pre­ce­dent. The California case is emblematic of the ­whale conservation challenge globally: as shipping has grown exponentially with globalized trade and large ships have increased their average speeds, rates of w ­ hale strikes have increased in many high-­traffic areas.21 The Santa Barbara Channel, just north of Los

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Angeles, is one of the busiest shipping routes in the world, where it is not uncommon to see tankers the length of skyscrapers. It is also the traditional migration route and feeding ground of endangered fin, humpback, and blue ­whales, which, as the largest animals on Earth, are particularly vulnerable to ship strikes. In the channel, ships tower so high above the ocean’s surface that w ­ hales are difficult to see, let alone avoid. A de­cade ago, the federal government created voluntary slow speed zones—­which have been shown to dramatically reduce ­whale deaths from ship strikes22—­but less than half the ships follow the voluntary speed limit.23 In Southern California, 2018 and 2019 w ­ ere the worst years on rec­ord for fatal ­whale strikes by ships. Even ­these dire statistics likely underestimate the true toll, as most corpses sink before they wash ashore.24 In response, a team led by marine scientist Morgan Visalli at the University of California, Santa Cruz, created a novel bioacoustics w ­ hale protection system. Christened Whale Safe, it combines bioacoustics with three other digital technologies.25 First, an underwater monitoring system uses bioacoustics to automatically detect w ­ hale calls;26 an array of underwater microphones (hydrophones) detects and pro­cesses sounds using artificial intelligence algorithms that are able to not only identify ­whales but also specify ­whether they are blue, humpback, or fin ­whales. This data is then sent via satellite to ­whale scientists for review and confirmation. Second, marine scientists in Santa Barbara run models that forecast probable w ­ hale location, combining oceanographic data (ocean temperatures, seafloor topography, and currents) with past studies of ­whale location using satellite tags.27 As ocean temperatures and conditions shift daily, so do w ­ hales’ movements; the models give near-­real-­time, highly accurate predictions. Third, the forecasts are complemented by ­actual ­whale sightings, which citizen scientists, mari­ners, and whale-­watching boats rec­ord through mobile apps.28 Fourth, Whale Safe tracks ships’ locations,29 and the data is layered together to create aw ­ hale presence rating, similar to a school zone notice (green = no ­whales; yellow  = proceed with caution; red = ­whales pre­sent, go slow). The rating is then communicated to ship captains in real time via their

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smartphones or tablets.30 Captains are encouraged to slow down and post more lookouts; the ships are then tracked to see ­whether they comply with voluntary slow-­speed zones. Whale Safe also helps regulators decide ­whether and how to extend slow-­speed zones by letting them know if ­whales are spending more time in an area than expected. The Whale Safe team then monitors ships and publishes public report cards that show how well ships are complying with speed restrictions. Ships that ­don’t comply receive a “failure” rating. To further enhance compliance, scientists are developing infrared thermal imaging cameras to mount on the bows of ships—­the equivalent of a dashcam—­that w ­ ill detect w ­ hales in real time, as well as w ­ hale strikes. In the f­ uture, if ships ­don’t comply with the whale-­designated avoidance zones, t­ hey’ll be caught red-­handed.31 Whale Safe is an exponential improvement over previous methods, which w ­ ere imprecise and reliant on patchy data, and which required scientists to retrieve recording instruments from the ocean before analyzing the data, resulting in time lags ranging from weeks to months. Now, scientists can generate near-­real-­time w ­ hale presence forecasts, much like weather forecasts, that provide estimates of the probability of ­whales appearing in dif­fer­ent places.32 ­After a successful launch in mid-2020, the team is now planning to expand to San Francisco Bay. Similar schemes have sprung up in other parts of the world. In the South Taranaki Bight (which lies between New Zealand’s North and South Islands), for example, researchers have recently used bioacoustics to identify a unique resident population of blue ­whales. The lead researcher, Leigh Torres, was sharply criticized for advancing a resident ­whale hypothesis; shipping and mining industry advocates argued that the ­whales ­were part of a migratory population (as most ­whales are indeed transient). But Torres’s meticulous bioacoustics research, combined with ge­ne­tic testing, proved that the blue w ­ hale population was genet­ically distinct, and resident year-­round.33 When applications for seabed mining in the area ­were put forward, the newfound knowledge of this unusual population spurred a national movement to save the New Zealand blue ­whale, culminating in a Supreme Court ruling to revoke the seabed mining permits and pressure the government to ban

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seabed mining altogether.34 In the meantime, researchers have developed a predictive model for blue w ­ hale locations that w ­ ill enable dynamic, mobile protected areas to be created in the South Taranaki Bight.35 Visalli points out that when ships slow down, the wider community benefits; slower ships not only hit fewer w ­ hales but also create less noise pollution, release fewer environmental pollutants, and emit less carbon dioxide. Saving w ­ hales from ship strikes also benefits the global environment by helping mitigate climate change. Whales are highly efficient at carbon storage. When they die, each w ­ hale sequesters an average of 30 tons of carbon dioxide, taking that carbon out of the atmosphere for centuries. For comparison, the average tree absorbs only 48 pounds of CO2 a year.36 From a climate perspective, each w ­ hale is the marine equivalent of thousands of trees. Whales also help sequester carbon by fertilizing the ocean as they excrete nutrient-­rich waste, in turn increasing phytoplankton populations, which also sequester carbon—­leading some scientists to call them the “engineers of marine ecosystems.” In 2019, economists from the International Monetary Fund (IMF) estimated the value of the ecosystem ser­v ices provided by each ­whale at over $2 million USD. They called for a new global program of economic incentives to return ­whale populations to pre­industrial whaling levels as one example of a “nature-­based solution” to climate change.37 Calls are now being made for a global w ­ hale restoration program, to support both marine biodiversity and climate change mitigation. Researchers are currently developing the governance architecture that would extend bioacoustics monitoring, and protected areas, across the entirety of the world’s oceans. T ­ oday, bioacoustics ­whale protection systems exist in isolated areas. But in the ­future, a network of bioacoustics listening stations could create flexible “­w hale lanes” across the world’s oceans, controlled by the ­whales themselves.

Mobile Protected Areas The most recent report on the state of the oceans from the Intergovernmental Panel on Climate Change (IPCC) predicts that marine heat waves, rising seas, d­ ying corals, and vanis­hing sea ice w ­ ill devastate

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current levels of biodiversity.38 With rising global sea surface temperatures and changing ocean currents, as well as increasingly common extreme weather events, massive migrations of marine populations are already ­underway.39 As the world’s ocean creatures move in unpredictable ways, mobile protected area schemes in the world’s oceans might become a necessary, widespread conservation mea­sure. Listening for their presence using digital bioacoustics ­will become even more urgent; a “new normal” in marine governance. Some of the under­lying architecture for mobile marine protected areas already exists in the form of acoustic telemetry networks, such as the Integrated Marine Observing System (IMOS) in Australia, the NOAA Ocean Noise Reference Station Network in the United States, and the Acoustic Tracking Array Platform in South Africa.40 ­These listening networks can help determine the presence of endangered species and estimate how marine organisms are moving, in order to enable marine protected areas to respond to changing environmental conditions.41 As new areas of the melting Arctic open up to shipping, for example, new means of preventing ships from striking ­whales ­will be needed in zones like the Bering Strait—­a bottleneck for both ships and migrating ­whales.42 ­These mobile marine protected areas are a hopeful example of novel strategies that are emerging as scientists and conservationists apply digital tools to pressing environmental challenges. While ­humans have tracked the movements of animals for millennia—­for survival, as well as for managing and protecting wildlife populations—­the degree of surveillance afforded by digital tools is unpre­ce­dented. In the past de­ cade, the miniaturization and proliferation of new, inexpensive, internet-­ enabled tracking technologies has led to a new golden age of biologging, which enables accurate and precise monitoring even of small species, such as insects, as well as long-­distance migratory species, such as salmon and turtles.43 Some of this tracking is visual, but much of it is acoustic.44 Why is this impor­tant? As biodiversity loss accelerates, the planet’s sixth mass extinction is u­ nder way. Many animals are responding by changing their habits—­for instance, becoming nocturnal—or by

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moving to new habitats. As ­humans continue to modify terrestrial and marine habitats, as well as the global climate, this creates a new prob­lem for conservation: habitats of endangered species are disappearing, or shifting geo­graph­i­cal location, due to climate change. The designated zones created to protect them no longer contain the food or appropriate habitat they need to survive. For an increasing range of species, ­these areas need to be geo­graph­i­cally mobile. As we add an estimated two billion ­people to the planet over the next few de­cades, bioacoustics is one of our best options for balancing ­human activities with other species. Digital acoustic monitoring, combined with advanced forms of artificial intelligence, like machine learning, enables scientists to model animal biodiversity in real time; this can be used to track vocally active species, as well as nonsoniferous species that depend on or closely interact with sound-­producing species.45 In turn, this could help re­orient or constrain the movements of h­ umans in the most sensitive places, at the most sensitive times. Rather than a small number of parks, large numbers of evolving “safe zones” could be created that follow animals as they move throughout the world’s rapidly changing habitats. Of course, bioacoustics-­enabled conservation schemes w ­ on’t address all threats to biodiversity, such as chemical pollution. But bioacoustics-­powered conservation still offers one of the best means available to protect biodiversity. Bioacoustics technologies could also be deployed to prevent conservation crimes. For example, bioacoustics is now being used to monitor the spatial distribution and hotspots for blast fishing (also known as dynamite fishing). The practice, in which fishers use illegally sourced or homemade explosives made from kerosene and fertilizer, has been described as the marine equivalent of elephant poaching; the fishers target coral reefs with high fish densities, using explosives to kill and stun fish so they may be more easily harvested. Deep ocean fish (like tuna) are also increasingly targeted with explosive blasts and then collected by scuba divers. Survivors are likely to be maimed and have permanent hearing loss, affecting ­future survival rates. Blast fishing, widespread in the Coral Triangle in Southeast Asia, as well as in Tanzania, is difficult to monitor and enforce; typically, small-­scale fishers find it easy to evade

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infrequent patrols.46 Passive acoustic monitoring, combined with automated algorithms for detecting explosive blasts, can easily pinpoint illegal fishing at distances up to 30 or 40 miles away, helping law enforcement quickly identify perpetrators.47 In addition to helping ­humans locate or avoid endangered marine life, acoustic technologies can also help marine life avoid ­humans. As global concern has grown about massive rates of fisheries bycatch (particularly turtles and dolphins, but also w ­ hales), acoustic alarms have been developed to warn marine mammals and fish. ­Today, hundreds of thousands of digital acoustic deterrent devices attached to boats, nets, docks, and pens are used to alert marine life; deterrents can even be calibrated to specific species.48 Some worry, however, that alarms may cause more harm than they prevent. For example, acoustic deterrents that work for some species can negatively affect o­ thers; much like installing a bright light to deter burglars might create light pollution that irritates the neighbors.49 Moreover, cumulative noise from acoustic deterrents may also result in acoustic masking—­a sort of fuzziness in the acoustic space—­even at relatively low noise levels; this chronic background hum might not kill marine animals immediately, but it may reduce the quality and range of their communication space; animals might go quiet or be able to listen only over shorter distances. For a fish or a dolphin, this would be like ­going slowly deaf and blind.50 In response, some in the marine bioacoustics research community have begun calling for quieting technologies and alternatives to acoustic deterrents.51 Yet even if we decide to abandon acoustic deterrents, this alone ­will not address the more general and much larger threat faced by marine life: an exponentially increasing onslaught of environmental noise.

Silencing a Noisy Ocean On the morning of September 11, 2001, biologist Rosalind Rolland was getting ready to launch her boat in the Bay of Fundy, into the placid ­waters on a brilliant, sunny fall day. When the news of the attacks came across the radio, it felt surreal. A ­ fter a while, the crew de­cided to head out onto the ocean, in defiance of the fear they felt, b­ ecause the bay was

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“calming for the soul,” as Rolland put it.52 At sea, her team busied themselves with the task at hand: collecting ­whale fecal samples as part of a study on the health and reproduction of right ­whales. Back in the lab, they would analyze their samples for hormone levels linked to ­whale stress and health. Oceanographer Susan Parks was also out in her boat, collecting data for a study on the social be­hav­ior of right ­whale ­mothers and calves. Although most ships docked for the week following the attacks, Parks continued her recordings in the bay. Two of the only ­whale researchers to continue working out on the bay during this exceptionally quiet period, Parks and Rolland realized only months ­later that their data could be combined to answer a groundbreaking question: Could lower noise levels in the ocean be correlated with lower stress levels in the ­whales? This question is an urgent one, given that marine noise has doubled ­every de­cade since the 1950s in many ocean regions.53 This is largely the result of the increasing industrialization of the ocean.54 The growth and globalization of trade has led to a tenfold increase in the tonnage of commercial ships. In recent years, the race to colonize deep-­sea oil and gas resources has led to a surge in seismic exploration; this, combined with increased boat traffic, sonar, construction, and acoustic deterrent devices, has exponentially increased the industrial clamor in the oceans.55 Parks and Rolland’s experiment thus captured the effects of de­cades of stress on w ­ hale populations. The results of their combined analy­sis made headlines: in the temporary hush that followed 9/11, the ­whales’ stress levels ­were markedly lower.56 As marine noise dropped to one-­ quarter of previous levels, a similar decrease was observed in stress-­ related hormone metabolites in the w ­ hales. As ship traffic and noise ­rose again, so too did the ­whale’s stress hormone levels. Similar effects have been observed in ­humans; noise exposure is associated with higher blood pressure, higher levels of stress hormones, cardiovascular effects, and coronary heart disease in ­humans.57 But no one had previously demonstrated the effects of noise pollution on ­whales.58 Curious researchers began ­running experiments with other marine animals, finding similar results for a broad range of species—­even in

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invertebrates like squid.59 Twenty years l­ ater, the evidence is conclusive: marine noise pollution not only increases stress levels in marine animals but also has many detrimental health effects. Even low-­intensity sounds, such as t­ hose from distant cargo ships—or even distant cars and airplanes—­can cause octopuses to hold their breath and oysters to shut their shells.60 The range of negative effects caused by cumulative marine noise is staggering: it can delay development, hamper reproduction, stunt growth, disturb sleep, and even kill creatures outright.61 Underwater seismic exploration is one particularly destructive source of noise pollution: a single shot from a seismic survey air gun can deafen fish and kill zooplankton—­the basis of the marine food chain—up to a mile away from the detonation site, as well as cause hearing loss in larger marine mammals, like seals and ­whales.62 And b­ ecause sound travels so well underwater, the effects are felt not only by individuals but also across entire marine ecosystems.63 Similar negative health effects attributed to noise have been documented in terrestrial species.64 Anthropogenic noise disrupts reproduction, foraging and hunting, migration, and activity patterns; it also interferes with animals’ neuroendocrine systems (raising cortisol levels), physiology (raising respiration rates), and ability to communicate—­ making it harder for them to gather, mate, hunt, and socialize.65 When human-­generated noise increases, animals raise their voices, just like ­humans raise their voices to be heard against a background of loud noise; this may deplete animals’ reserves, leaving less energy available for other vital activities.66 If animals try to flee loud noises, other ecological processes—­like seed dispersal and pollination—­are affected.67 In one innovative “phantom road” experiment, researchers placed fifteen bullhorn speakers along a roadless section of forest in Idaho’s Lucky Peak State Park. The speakers played recordings from a highway and mea­sured birds’ responses. One-­third of birds avoided the phantom road altogether; young (particularly year-­old) birds ­were the most likely to vanish. The birds that remained showed declines in body condition and strug­gled to put on weight—­a troubling result, given that stopovers to refuel are necessary for the survival of many migratory species. By 2050, the

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researchers note, enough new roads w ­ ill be built to circle the Earth more than six hundred times; ­whether mitigation mea­sures can lessen the impact of the resulting anthropogenic noise is doubtful.68 Even in parks and protected areas, animals are already contending with a growing deluge of ­human noise.69 Perhaps most worrisome, it appears that noise pollution disrupts embryonic development across a wide range of species. Prenatal sounds shape animals’ chances for survival, as embryonic acoustic developmental programming affects animals’ physiology and cognition through changes in brain connectivity, endocrinology, and gene expression. In healthy ecosystems, this helps animals adapt to their environments; the young of many species recognize their parents’ calls when they hatch. Some species, like zebra finches, even modify their size in response to the types of calls their parents make before they are born. Disrupting soundscapes may be profoundly damaging to organisms in ways we have yet to fully understand.70 What we do know is that animals are extremely sensitive to even small changes in noise; in one study, the impact of motorboats on fish embryos was found to depend on engine type—­w hile any boat motor raised embryo heart rates, two-­stroke outboard-­powered boats had more than twice the effect of quieter four-­ stroke-­powered boats.71 An article in Science grimly summed up the results: ­human noise is scrambling the eggs of baby fish.72

Songs of Seagrass The devastation of noise pollution, particularly in the marine world, is underscored by a recent study of one of the most ancient plants on Earth: seagrass meadows, the ­Great Plains of the sea. With the exception of Antarctica, our planet’s marine coastal zones w ­ ere once abundant with seagrass. Rivaling coral reefs in their extent and importance, seagrass meadows provide food and shelter for the young of many sea creatures, protect coasts against erosion, enable nutrient cycling, stabilize the seafloor, and improve ­water quality. And just like terrestrial forests, seagrass also plays a major role as a carbon sink, helping stabilize our global climate.73 In the past several de­cades, catastrophic seagrass

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loss has occurred in many of the world’s coastal zones; seagrass meadows the size of the Amazon have vanished.74 Scientists blamed the devastation on a range of threats—­climate change, chemical pollutants, boat anchors and dredging, and hypersaline ­water from desalinization plants. But Marta Solé, a se­nior researcher in environmental engineering at the Universitat Politècnica de Catalunya, BarcelonaTech (UPC), wondered if noise pollution might also be to blame. Solé, working with her PhD supervisor, Michel André, had already earned a reputation for unconventional research, studying the effect of human-­made noise on marine creatures without ears: cephalopods (like octopuses), cnidarians (corals and jellyfish), crustaceans (like shrimp), and sea lice.75 Still, her proposed study of the noise sensitivity of marine plants was uncharted territory. Solé de­cided to focus on the oldest seagrass in the world: Posidonia. Named ­after the Greek god of the sea, the Posidonia fossil rec­ord dates back to the Cretaceous period. One par­tic­u­lar species, P. oceanica, is a slow-­growing, clonal seagrass endemic to the Mediterranean, which develops networks of roots and rhizomes that can stretch several meters deep. At one time, P. oceanica covered the entire coastline; its free-­floating fruit was known as the “olive of the sea.”76 The seagrass meadows are ancient: one colony discovered off the south coast of Ibiza is over a hundred thousand years old, quite possibly closer to two hundred thousand—­w hich would make it the oldest living plant in the world.77 Solé’s ­earlier research had shown that cephalopods hear sound through small sensory organs called statocysts.78 When exposed to noise frequencies similar to marine seismic testing and boat noise, damage to statocysts is stark: they swell, explode, and die—­much like a ­human ear­drum might be damaged by loud noise.79 Embryos ­were equally harmed, with extensive epidermal lesions and damaged cilia.80 Could marine plants be similarly affected by marine noise pollution, wondered Solé?81 Seagrass, like other marine plants, has an analogue to statocysts: organelles called amyloplasts that help the plant orient to gravity, direct its roots, and also detect sound through particle motion in ­water.82 The researchers knew that amyloplasts ­were found in high

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concentrations in certain cells in the root caps and rhizomes of P. oceanica. Would loud sounds harm seagrass the way they harmed the octopuses? Just as she had done with marine animals, Solé assembled a sample of seagrass plants in tanks in the lab.83 The control group was left untouched, but the test group was blasted with loud, low-­frequency noise similar to the sounds generated by industrial activity, such as shipping and underwater seismic testing. She then examined cells in the roots and rhizomes, as well as the fungal symbionts attached to the roots. In the control group, the amyloplasts w ­ ere undamaged. But in the group of plants subjected to loud noise, the amyloplasts ­were severely deformed, and their numbers decreased dramatically. U ­ nder the scanning electron microscope, the researchers observed eerie similarities with the octopus statocysts: lesions and blasted-­open cells leaking their contents through gaping holes. Just like the octopuses, the seagrass had severe, permanent damage to their sensory organs. This damage, the researchers surmised, could affect the ability of the plants to sense gravity and store energy—­two functions basic to their survival. Even more worrisome: the symbiotic fungi attached to the roots w ­ ere also damaged. Their degradation meant that the plants might find it harder to gather nutrients from the ocean. Solé’s research sent a shock wave through the scientific community.84 Seagrass researchers had never thought about noise as a threat. Nor had bioacoustics researchers i­magined the possibility that marine plants could be harmed by environmental noise. ­These findings have enormous implications for marine biodiversity conservation. As offshore operations—­from seabed mining to oil and gas and renewable energy construction—­are proliferating, little attention has been paid to acoustic effects on marine plant life. While exposure threshold levels have not yet been determined, it is clear that this emerging science ­will eventually revolutionize the permitting and operations of marine industrial activities. As Solé explains, if e­ very plant and animal in the ocean is sensitive to sound, then noise pollution is not a species-­specific issue but rather an ecosystem issue. Michel André says the challenge is now clear:

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“Rather than merely imposing thresholds to protect specific species, we have to develop solutions to limit marine environmental noise pollution altogether.” This is not welcome news to the global shipping and mining industries. But as André puts it, the scientific bioacoustics community now has regulators “in a boxing ring with their backs against the wall.”85 André proposes the development of an ecoacoustics index to assess both biological activity and environmental noise pollution impacts.86 His reasoning is as follows: an area rich in biological activity has a rich and diverse soundscape. A dynamic ecoacoustics index (which monitors the evolution in the soundscape over time) can calculate changing ecosystem health, assessed via changes in acoustic patterns, with greater precision and accuracy than visual methods and at a fraction of the cost.87 Many ecoacoustics indices already exist. ­These tend to be computationally intensive; but André argues that the hardware and software are both robust and inexpensive enough to make the incorporation of ecoacoustics indices into environmental monitoring feasible at a global scale. An ecoacoustics index also requires an enormous amount of data to be well calibrated; but André points out that over 150 ecoacoustics observatories around the world have been streaming data continuously, twenty-­four hours a day, for over a de­cade. If André is correct, a universal ecoacoustics index would be a new standard for environmental health in the twenty-­first ­century.88 Just as the International System of Units (such as the meter and kilogram) facilitated standardization in commerce and fueled globalization of trade, the invention of a global ecoacoustics index could serve as a precursor to a global system of ecological monitoring, which could be a power­ful tool for regulators to combat industrial noise pollution. Why would we want to invent a global ecoacoustics index, and what purpose would it serve? As André explains, ecosystem health reports could combine data from many dif­fer­ent observatories to monitor environmental health, much like weather reports combine data from thousands of rainfall and temperature monitoring stations. In the face of global climate change, we could develop a better understanding of how ecosystems are changing and animals are moving. By archiving each recording, we would also be creating a memory bank of the world’s

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species—­a trea­sure trove for ­future scientists. But the most impor­tant reason to create an ecoacoustics index, André argues, is that environmental noise pollution is not only one of the major threats facing the world, it is one of the few types of pollution we can easily mitigate. Noise is a point-­source pollutant, the effects of which decline swiftly once the source is shut down. And unlike increased levels of carbon dioxide or per­sis­tent chemicals (which may take de­cades or centuries to dis­appear), noise pollution is easy to reverse. The effects are thus immediate and potentially very impactful. Ecoacoustics indices could set thresholds for environmental noise, enabling us to keep noise pollution below hazardous levels. This would benefit ­humans, too, who suffer from the impacts of environmental noise pollution in the form of stress and increased risks of premature births, heart attacks, cognitive impairment, and dementia.89 In marine environments, where creatures are exquisitely sensitive to sound, ­there are several steps we can take to reduce noise pollution. Changes to shipping can dramatically reduce noise: ships can be routed away from sensitive areas, reduce their speeds, and be designed with quieter propellers and engines. Seismic marine guns could be banned; other types of exploration devices could be used in their place. ­Until recently, reducing ocean noise was a seemingly fanciful daydream. In 2011, a group of scientists made a quixotic suggestion: halt marine shipping for a year, in order to study the ocean in the absence of h­ uman noise.90 It would be, as oceanographer Peter Tyack poetically declared, “a never-­before glimpse of the ocean with ­little ­human interference . . . ​ like looking at the night sky if most of the world’s lights ­were turned off.”91 The idea inspired another group of scientists to publish a plan for how to conduct the International Quiet Ocean Experiment—­which would last only for a few hours—­should the opportunity ever arise.92 Even that idea seemed out of reach. Then, COVID hit. As global shipping abruptly halted, researchers documented a massive decline in noise pollution on land and across the world’s oceans.93 In some regions, like the coast of the Pacific Northwest in North Amer­i­ca, the seas had not been this quiet for de­cades.94 The pandemic slowdown was a Quiet Ocean Experiment come to life, and

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it showed just how quickly the Earth might benefit from a reduction in environmental noise.95 The pandemic lockdown was a poignant reminder of how much we have lost as we have drowned out the Earth’s soundscapes, and how much the planet has to gain if we choose to quiet ourselves and begin listening again.

Breaking the Earth’s Beat Even if we manage to reduce noise pollution, the Earth’s soundscapes are facing another serious threat: climate change. Although ­humans are still largely oblivious, climate change is directly altering the Earth’s natu­ ral soundscapes. Sound-­sensitive organisms, both marine and terrestrial, are experiencing destabilizing shifts in their acoustic habitats. Three of the world’s leading acoustic scientists—­Jérôme Sueur, Bernie Krause, and Almo Farina—­have described climate change as literally “breaking the Earth’s beat”: rupturing the sonic rhythms of life, both biophony (the sounds made by animals, plants, and insects) and geophony (sounds coming from rain, ­water, wind, and the Earth itself).96 How is this happening? As weather and ocean conditions change, the patterns of sound transmission in the environment also change, b­ ecause sound speed varies with temperature, humidity, wind, and even rain intensity. In a warming world, with more extreme weather events, the range of communication between individual organisms can dramatically change; sounds might transmit less far, limiting the ability of animals to communicate, socialize, mate, and even find one another. Or it might require more energy to communicate, hampering their ability to survive. Ambient temperature also directly influences the vocalizations and hearing pro­cesses of many species, from birds and insects to amphibians, fish, and crustaceans. For instance, the rate, pitch, and volume at which amphibians, fish, and arthropods vocalize are temperature dependent—­recall the discussion in chapter 7 of Pierce’s experiments at Harvard that revealed that crickets chirp at a rate proportional to ambient air temperature. Climate change also affects the patterns of cyclical and seasonal natu­ral phenomena, including acoustic phenomena, which

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play such an impor­tant role in both ecol­ogy and evolution. Climate change induces shifts in t­ hese seasonal acoustic patterns e­ ither by affecting organisms directly or by affecting the resources on which they depend (such as food). If copepods dis­appear from large swathes of the warming ocean, ­whales may no longer come ­there to sing. As temperatures change, the cicadas and the crickets, the frogs and the fish may change their songs or even cease singing. According to one group of scientists, the long-­term effects of changing oceans may result in “­silent winters and rock-­and-­roll summers,” as fish cease their choruses in response to winter storms of greater frequency and intensity.97 ­These acoustic changes are likely to have the most dramatic impacts on tropical species, which have a low tolerance for heat changes and a ­limited ability to acclimate.98 Even the furthest reaches of the planet are likely to be affected, including the Arctic and Antarctica. Using autonomous recording devices placed in Alaska’s remote Brooks Range, a team of researchers led by Ruth Oliver at Columbia University monitored the arrival times and vocalizations of migratory avian species at traditional breeding grounds.99 In contrast to bird-­tagging studies, which are laborious and cover only a small fraction of the birds, the recording devices generated data on region-­scale changes in the migratory timing of the birds over five consecutive years. Using machine learning methods borrowed from ­human speech recognition software, the researchers found that environmental conditions influenced not only arrival dates but also songbird vocal activity, particularly before the birds began laying their eggs. Just like birdsong patterns are changing, the habits of many other species are changing. As Sueur and his colleagues write, changing thermal and moisture conditions are “detuning” natu­ral sounds—­much like a musical instrument might become out of tune.100 As the planet’s atmosphere changes, the Earth’s weather and geological soundscapes are evolving as cyclones and tornadoes, floods and wildfires, heat waves and droughts intensify. As climate change warps soundscapes, nature’s sounds become more difficult to recognize and harder to hear, or even dis­appear altogether. The natu­ral sounds that cue animals’ be­hav­iors—­mating, migration,

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habitat choice—­are dif­fer­ent, disoriented, absent. Climate change–­ induced acoustic transformation thus poses a significant threat to species around the world. Mitigating climate change is already an urgent agenda; the realization that climate change is a source of sonic disturbance provides yet another reason to act. This is an urgent issue; as bio­semiotician Gregory Bateson once observed, any widespread ecosystem collapse is likely to be preceded by a collapse in nature’s communicative order and a dwindling of nature’s chorus.101 On a global scale, noise pollution may be as significant an ecological threat as chemical pollution. In 2017, UNESCO introduced a resolution on the importance of sound in t­ oday’s world, which proclaims “the sound environment is a key component in the equilibrium of all ­human beings in their relationship with ­others and with the world.”102 Few governments have acted, although the Eu­ro­pean Union’s Marine Strategy Framework Directive mandates that Eu­ro­pean Union member states monitor and mitigate noise pollution. Given the accumulating weight of scientific evidence, similar legislative changes are likely to follow.103 If so, bioacoustics technologies, and expanded environmental noise pollution standards, may one day become the norm in environmental regulation worldwide. But Michel André believes that t­ hese technologies are not merely a tool for regulators. As he puts it: “Thanks to digital technologies, we have developed a new sense—­like a sixth sense—of being able to listen to the environment. We can listen to the ocean just like a dolphin or a ­whale. But even better than a dolphin or a ­whale, as we have the capacity to listen everywhere, at the same time, all the time. Ultimately, this insight should help us reconnect with nature, to recover something that we had lost.” Th ­ ese are not new discoveries, he adds. “When we work in the Amazon, we hear many mysterious sounds through our microphones. We can rec­ord them, but we d­ on’t understand them. Local communities are able to explain t­ hese sounds to us; living in place, they have the wisdom and knowledge to identify the sounds, and understand their ecological context.” 104 While advocating for a universal ecoacoustics index to inform environmental assessment and regulation, André warns of the need for humility about the limits of science; although we

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should avoid recolonizing and appropriating Indigenous knowledge, we have much to learn and relearn from traditional knowledge. Without place-­based knowledge, ecoacoustics is merely an accounting exercise: counting sounds without understanding them. Only by combining digital listening with deep listening—to living communities of organisms in specific places—­will we achieve understanding of the meaning of the sounds around us. And only when we understand the meaning of the sounds w ­ ill we be motivated to protect the organisms that make them. This is why scientists are now implementing acoustics monitoring systems around the world, from the depths of the ocean to the deepest reaches of the world’s remaining frontier forests.105 Alice Eldridge, a musician and data scientist, imagines a ­future where such bioacoustics networks incorporate acoustic early warning signals; not merely documenting the Earth’s demise, but triggering action before it is too late.106 She also echoes calls of Indigenous leaders to preserve Earth’s natu­ral soundscapes, citing the words of a Kichwa elder: “The set of songs heard is like a symphony, which took millions of years to write. It is a unique and priceless creation, which we cannot let be destroyed or dis­appear.”107

A Sonic Microscope We are just beginning to understand the universal importance of sound for species across the full range of the Tree of Life. From the ­humble coral to the mighty ­whale, the nonhuman world is more sensitive to sound than we suspected. Many nonhuman creatures use sound to communicate with one another, in much more complex ways than scientists previously understood. By using digital bioacoustics tools, we can rec­ ord t­ hese complex forms of communication; by using artificial intelligence, we can decode them. Bioacoustics and artificial intelligence, combined, offer humanity a power­ful new win­dow into the world of nonhuman meaning-­making. You and I could never sing like a w ­ hale or buzz like a bee, but computers and biomimetic robots can. Our digital devices have brought us to the brink of a new era in digitally mediated interspecies communication.

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This could transform not only environmental conservation but also our understanding of nature, and what it means to be h­ uman. How might we choose to live on this planet when the voices of creation are (once again) both audible and meaningful to us? To appreciate just how far-­reaching t­ hese shifts in thinking might turn out to be, consider the impact of another revolutionary technology, several centuries ago: the microscope. As historian Catherine Wilson argues, the microscope was a foundational catalyst of the Scientific Revolution, transforming both scientific practice and humanity’s wider view of its own importance and relationship with the living world.108 Bioacoustics is poised to alter humanity’s relationship with our planet to a similar degree, but through expanding our sense of sound rather than our sense of sight. When first brought to scientific prominence by Anton van Leeuwenhoek, a Dutch fabric merchant with a grade school education, the implications of the microscope ­were not immediately apparent. Van Leeuwenhoek’s genius lay not only in building microscopes—he built over five hundred of them, many of which achieved unpre­c e­d ented resolution—­but also in his quirky habit: inspecting the mundane world. Whereas Galileo gazed at the heavens, van Leeuwenhoek gazed at well ­water, mold, lice, yeast, blood cells, ­human breast milk (his wife’s), and sperm (his own). When he put his eye to his homemade glass lenses, he saw something astounding: animalcules—­microscopic organisms endlessly varied in shape and size—­danced and wriggled across the view frame. The world was literally alive with tiny, wriggling, fantastical creatures whose existence humanity had not even suspected. Confronted with this strangeness, Van Leeuwenhoek initially kept his discoveries secret for fear of ridicule. Eventually, he penned a letter to the Royal Society in London—­the leading scientific society of the time.109 The Society’s fellows initially viewed his discoveries with skepticism, proving the maxim that h­ umans tend to believe that what­ever they cannot perceive does not exist. But van Leeuwenhoek insisted: magnification revealed a strange new world of beings, living in e­ very nook and cranny of our world, unseen by the unaided eye.110 Spectacles helped us focus on the written word; telescopes brought the starry heavens closer; but the microscope opened up entirely new, hitherto

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unimagined worlds. ­After sending out a del­e­ga­tion to inspect the microscopes, the Royal Society eventually accepted his findings.111 Van Leeuwenhoek’s research papers ­were published alongside ­those of Sir Isaac Newton in the leading scientific journal of the day.112 As microscopes proliferated, they created new possibilities for scientists and phi­los­o­phers alike, renewing interest in theories like atomism and mechanism. The exploration of the microscopic world—­and the growing realization of the role of animalcules in both generating life and spreading contagion and disease—­intrigued and influenced phi­los­o­ phers like Bacon, Descartes, and Locke. When the microscope revealed the existence of pathogens, commonly held ideas about disease (such as the theory that illness was caused by bad odors or sin) w ­ ere cast into doubt, and then cast aside. Van Leeuwenhoek’s use of microscopes as visual prosthetics—­artificial eyes that helped humanity see new t­ hings in new ways—­laid the foundation for countless f­ uture breakthrough discoveries, including the code of life itself (DNA). The microscope enabled ­humans to see anew, with both our eyes and our imaginations. Digital acoustics are an invention of similar significance. Like the microscope, they function like a scientific prosthetic: as they extend our sense of hearing, they expand our perceptual and conceptual horizons. As we encounter new soundscapes around the world and across the Tree of Life, we are learning about the power of sound to convey information and meaning, but also to harm and injure. In the meantime, we are learning how to use our newfound knowledge to better protect planet Earth. Just like van Leeuwenhoek peering through his newly built microscope, we do not yet understand every­thing brought to light by this new digital acoustics technology. ­Today, we are hearing t­ hings we never ­imagined we could hear. This is by no means novel (Indigenous traditions offer power­ful ways of nonhuman listening) or neutral (digital technologies can be misused and abused). But with caveats and safeguards, bioacoustics offers humanity a power­ful new win­dow into the nonhuman world. Through bioacoustics, we are learning about the universality of meaning-­making through sound, by all beings in creation. Aided by artificial intelligence, we may be on the verge of a breakthrough in interspecies communication. If we open our ears, a world of won­ders awaits.

A c k n o w l­e d g m e n t s a n d List of Interviewees

This proj­ect began with a year-­long sabbatical in 2015–16 at Stanford’s Center for Advanced Study in the Behavioral Sciences, supported by a Lenore Annenberg and Wallis Annenberg Fellowship in Communication. The same year, I held a Cox Visiting Professorship at Stanford’s School of Earth, Energy and Environmental Sciences. I’m grateful to my colleagues and hosts, including Dr. Rosemary Knight and Dr. Margaret Levi. In the course of writing this book, I conducted interviews with Dr. Michel André (Technical University of Catalonia), Dr. David Barclay (University of New Brunswick), Dr. Dyhia Belhabib (Ecotrust), Dr. Gerry Car­ter (Ohio State University), Dr. Kimberley Davies (University of New Brunswick), Dr. Christina Davy (Trent University), Dr. Richard Dewey (University of Victoria), Dr. Camila Ferrara (Wildlife Conservation Society Brazil), Dr. Jacqueline Giles-­Styants, Dr. Tim Gordon (Exeter University), Dr. David Hannay ( JASCO), Dr. Kim Juniper (Oceans Networks Canada), Dr. Mirjam Knörnschild (­Free University of Berlin), Dr. Tim Landgraf (­Free University Berlin), Dr. Lauren McWhinnie (Heriot-­Watt University), Dr. Katy Payne (Cornell University), Dr. Julia Riley (Macquarie University), Dr. Steve Simpson (Exeter University), Dr. Marta Solé (Technical University of Catalonia), Krista Trounce (Port Authority of Vancouver), and Dr. Morgan Visalli (Benioff Ocean Institute, University of California, Santa Barbara). During ­earlier research with my brilliant gradu­ate student–­ turned-­collaborator, Dr. Max Ritts (now at Cambridge), the following interviews ­were also conducted (although not directly cited in this book): Ian Agranat (Wildlife Acoustics), Dr. Jesse Barber (Boise State 205

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University), Dr. Erin Bayne (University of Alberta), Dr. Christopher Clark (Cornell University), Dr. Almo Farina (International Institute of Ecoacoustics), Dr. Kurt Fistrup (Colorado State University/National Park Ser­v ice), Dr. Susan Fuller (Queensland University of Technology), Dr. Gianni Pavan (University of Pavia), Dr. Katy Payne (Cornell University), Dr. Alex Rogers (University of Oxford), Dr. Holger Schulze (University of Copenhagen), Michael Stocker (Ocean Conservation Research), and Dr. Peter Tyack (St. Andrews University). Vari­ous iterations of this book w ­ ere discussed at pre­sen­ta­tions at Cambridge University, Oxford University, Stanford University, the University of Toronto, Ohio State University, Wageningen University, and the University of Waterloo, as well as in a keynote talk to the International Environmental Communication Association. Thank you, to all of ­those audiences, for your comments and curiosity, skepticism and support. A research-­intensive book often emerges from collective conversation and documentation. John Borrows, Jim Collins, Aimée Craft, Courtenay Crane, Dirk Brinkman, Jonathan Fink, Leila Harris, Nina Hewitt, Holger Klinck, Rosemary Knight, Kevin Leyton-­Brown, Alan Mackworth, Raymond Ng, Chris Reimer, Max Ritts, and Doug Robb provided feedback and suggestions. The members of the Decolonizing ­Water collective continue to provide inspiration. Research assistance was ably provided by Amanda Chambers, Alycia Felli, Oliver Gadoury, Sophie Galloway, Caroline Hanna, Charlotte Michaels, Gabrielle Plowens, Clare Price, Adèle Therias, Bentley Tse, and Sophia Wilson. Their support was, in turn, enabled by my fellowship funding from Stanford University’s School of Earth, Energy and Environmental Sciences and Stanford’s Center for Advanced Study in the Behavioral Sciences; and research grant funding from the Social Sciences and Humanities Research Council of Canada and the Pierre Elliott Trudeau Foundation. To my collaborators, helpers, and funders: thank you. To my ­daughters and husband, for their patience with my endless drafting and redrafting. To Robin Kimmerer, Aimée Craft, Monica Gagliano, Suzanne Simard, Katy Payne, Camila Ferrara: thank you for asking “what if?” questions and living with the tension: acknowledging the power of the scientific method, yet recognizing that t­ here is—­beyond

Ack now l e dgm e n ts 

207

science—­something more. To ­Uncle John, storyteller, adventurer, historian; jester, magician, and friend. To Sylvia Bowerbank, for teaching me about writing, and sleeping outside. To Louise Mandell, wise Otter ­Woman, for teaching me about land, law, and nonhumans as kin. To Aimée Craft, for teaching me about ­water, decolonization, and spirit. To Judy Schmidt, for teaching me about tobacco, and the light shining through; long may your garden grow. To Anne Gorsuch, for her lessons in honeybees and holding the space. To Courtenay Crane, for her gifted eye and kind presence. To Nina Hewitt, for her encouraging words, just when they w ­ ere needed most. To David Abram, for teaching me how to pay attention in the woods. To Caleb Behn and the crew at Moberly Lake in Dunne-­Zaa territory, for teaching me about bannock, belonging, and home. To the Saaghii Naachii/Peace River, for teaching me to listen, the most impor­tant lesson of all. The Saaghii Naachii is a tributary of the mighty Mackenzie River system—­Canada’s equivalent of the Amazon, a place of tremendous biodiversity and beauty. Spending time at bush camps and ­doing research in this region has taught me many of my most impor­tant lessons. As the rapid industrialization of this landscape—­for forestry, oil and gas, hydropower, coal, and now renewable energy—­has taken place, I have seen and heard how the cumulative effects of h­ uman presence have degraded landscapes and soundscapes alike. May this book be a small contribution to making collective amends. Last but not least, thanks are due to my editor Alison Kalett. Without your patience and gentle guidance, this book would not have been born. To you, the book reviewers, and the entire team at Prince­ton University Press: gratitude.

Appendix A how t o s ta r t l i s t e n i ng

Sound can both harm and heal, regenerate and reveal much about our fellow Earthlings. My hope in writing this book is to inspire awe at the tremendous vibrancy of the world of nonhuman sound; my invitation is to begin listening more deeply again to the world around us. H ­ ere are some resources to get you started; you can learn more at https://­ smartearthproject​.­com​/­.

Learning about Nature with Acoustic Apps • The Orcasound web app allows citizens to listen to the sounds made by killer ­whales on the northeast Pacific coast in real time. Citizens are invited to identify and label ­whale sounds they hear and create posts about their findings related to existing sound databases (https://­www​.­orcasound​.­net​/­portfolio​/­orcasound ​-­app​/­). • The Norfolk Bat Survey, led by the British Trust for Ornithology, uses acoustic data in bat monitoring centers to survey bat distribution and activity. Since 2013, citizens have been able to rec­ord 1.2 million bat recordings, creating a very extensive and high-­tech database of sounds (https://­www​.­batsurvey​.­org​/­). • BirdGenie allows anyone, from scientists to the general public, to identify birdsongs, like a “Shazam for birds” (https://­www​ .­birdgenie​.­com​/­).

209

210  A p p e n di x A

Becoming a Citizen Scientist • BirdNET is a citizen science program and AI-­powered app that enables anyone to identify mystery bird sounds (https://­birdnet​ .­cornell​.­edu​/­). • WildLabs is a global, open, online community of conservationists, technologists, engineers, and data scientists who share information and develop technology-­enabled solutions (including acoustic monitoring) to the world’s biggest conservation challenges (https://­www​.­wildlabs​.­net​/­). • Zooniverse is a crowdsourcing platform that enables anyone to contribute to scientific research, including manual tagging of acoustic recordings of wildlife (https://­www​.­zooniverse​.­org​/­). • Swift is a platform that allows scientists and citizens alike to gather acoustic data that enables wildlife conservation (https://­ www​.­birds​.­cornell​.­edu​/­ccb​/­swift​/­). • Whale Alert is an app that helps mitigate ship strikes by using bioacoustic technologies to track ­whales on the coasts of Canada and the United States. Whale sightings can be reported by citizens and marine boaters alike (https://­apps​.­apple​.­com​/­us​ /­app​/­whale​-­alert​/­id911035973).

Soundwalks and Sonic Walks • The NADA soundwalk focuses on how one can tune in and feel the sound of each ele­ment. For example, air is perceptible to ears and skin through sound and touch (https://­www​.­hildegard​ westerkamp​.­ca​/­sound​/­installations​/­Nada​/­soundwalk​/­). • WalkingLab is a research collective that studies how soundwalks can unearth the colonial issues of our time through sound (https://­walkinglab​.­org​/­). • “A Garden through Time” is a soundwalk available on the Echoes app that allows you to listen to an audio guide while walking on fifty specified routes in the UK (https://­www​.­theguardian

H o w t o S t a r t L i s t e n i n g   211

​.­com​/­travel​/­2020​/­nov​/­16​/­sound​-­walks​-­new​-­way​-­to​-­travel​-­in​ -­lockdown). • Acoustic ecologist Gordon Hempton’s Sound Tracker proj­ect shares recordings of nature from around the world, and his quest to save silence from extinction (https://­www​.­soundtracker​.­com​/­).

Appendix B F u r t h e r R e a di ng

The following list is only a small se­lection from the enormous lit­er­a­ture on bioacoustics, ecoacoustics, sound studies, and related topics, including acoustemology, biosemiotics, and zoomusicology. Bijsterveld, Karin. Sonic Skills: Listening for Knowledge in Science, Medicine and Engineering. Palgrave Macmillan, 2019. Farina, Almo. Soundscape Ecol­ogy: Princi­ples, Patterns, Methods and Applications. Springer Science & Business Media, 2013. Farina, Almo, and Stuart H. Gage, eds. Ecoacoustics: The Ecological Role of Sounds. John Wiley & Sons, 2017. Gagliano, Monica. “Green Symphonies: A Call for Studies on Acoustic Communication in Plants.” Behavioral Ecol­ogy 24, no. 4 (2013): 789–96. Hempton, Gordon, and John Grossmann. One Square Inch of Silence: One Man’s Search for Natu­ ral Silence in a Noisy World. Simon & Schuster, 2009. Hill, Peggy S. M., Reinhard Lakes-­Harlan, Valerio Mazzoni, Peter M. Narins, Meta Virant-­ Doberlet, and Andreas Wessel, eds. Biotremology: Studying Vibrational Be­hav­ior. Springer, 2019. Krause, Bernie. The G ­ reat Animal Orchestra: Finding the Origins of ­Music in the World’s Wild Places. Boston: ­Little, Brown, 2013. Payne, Katharine. ­Silent Thunder: In the Presence of Elephants. Simon & Schuster, 1998. Pettman, Dominic. Sonic Intimacy: Voice, Species, Technics (Or, How to Listen to the World). Stanford University Press, 2020. Pinch, Trevor, and Karin Bijsterveld, eds. The Oxford Handbook of Sound Studies. OUP USA, 2012. Robinson, Dylan. Hungry Listening: Resonant Theory for Indigenous Sound Studies. University of Minnesota Press, 2020. Schafer, R. Murray. The Soundscape: Our Sonic Environment and the Tuning of the World. Simon & Schuster, 1993. Stocker, Michael. Hear Where We Are: Sound, Ecol­ogy, and Sense of Place. Springer Science & Business Media, 2013. 213

214  A p p e n di x B Sterne, Jonathan, ed. The Sound Studies Reader. Routledge, 2012. Tosoni, Simone, and Trevor Pinch. Entanglements: Conversations on the ­Human Traces of Science, Technology and Sound. MIT Press, 2017. Truax, Barry. Acoustic Communication. Greenwood Publishing Group, 2001. Vallee, Mickey. Sounding Bodies Sounding Worlds: An Exploration of Embodiments in Sound. Palgrave Macmillan, 2020.

Sound artists are exploring environmental issues related to sound studies, including Gruenrekorder (https://­gruenrekorder​.­bandcamp​.­com​/­), Edzi’u (www​.­edziumusic​.­com​/­), Rebecca Belmore (www​.­rebeccabel​ more​.­com​/­), Lasse Marc Riek (www​.­lasse​-­marc​-­riek​.­de​/­), and Emeka Ogboh (en​.­w ikipedia​.­org​/­w iki​/­Emeka​_­Ogboh). See also Frederick Bianchi and V. J. Manzo, eds., Environmental Sound Artists: In Their Own Words (Oxford University Press, 2016); and Jonathan Gilmurray, “Ecological Sound Art: Steps ­towards a New Field” (Organised Sound 22, no. 1, 2017, 32–41). Scientific labs dedicated to bioacoustics and ecoacoustics, as well as sound studies, include Cornell University’s K. Lisa Yang Center for Conservation Bioacoustics (https://­bioacoustics​.­cornell​.­edu), the Harvard Hearing Modernity proj­ect (sound studies; hearingmodernity​ .­org), the International Institute for Ecoacoustics (www​.­insteco​.­org), and the Sonic Skills proj­ect (www​.­sonicskills​.­org​/­).

Appendix C br i e f ov e rv i e w of r e s e a r c h on bio -­ a n d e c oac ou s t ic s

Bioacousticians study the sounds produced by living organisms. Eco­ acousticians (sometimes referred to as acoustic ecologists) study soundscapes: the collection of sounds produced by a landscape. Both disciplines began prior to, but have been accelerated and expanded by, the application of digital recording technologies and artificial intelligence. Th ­ ese technologies generate extraordinary insights into the nonhuman worlds, through recording, decoding, and translating nonhuman sound. Intelligent bioacoustics devices distribute sensory perception and analytical pro­cessing of sound between h­ umans and computers. The age-­old practice of listening to nature is now computationally enhanced—­biodigital rather than merely biological.1 As historian Mickey Vallee observes, this new global “infrastructure of audibility” is spurring scientists to reconceive our relationship with Earth and its inhabitants in fundamentally new ways.2 Bioacoustics and ecoacoustics have been applied to thousands of species and landscapes, and used for a broad variety of purposes: taxonomic differentiation between species; population-­level monitoring; endangered species protection; and studies of vocal communication and learning, to name just a few.3 In recent years, the study of bio-­and ecoacoustics has accelerated due to the use of passive acoustic monitoring (PAM) devices. Th ­ ese devices are exceptionally power­ful, for four reasons. First, they are omnidirectional (sampling a three-­dimensional sphere); they capture more data from more ­angles, which is particularly useful in hard-­to-­reach places, dense forest, or rough terrain.4 Second, PAM devices sample a much larger area than most cameras (particularly underwater), operating 215

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both day and night (enabling more systematic study of nighttime patterns of be­hav­ior and movement).5 Third, PAM devices enable scientists to monitor vocally active species without disturbing them, particularly in pristine environments. PAM studies are thus more likely to reflect a­ ctual species be­hav­ior, detect rare or less vocally active species, and avoid the sampling biases of traditional ­human visual monitoring methods, which tend to cluster in easier-­to-­reach locations and in daytime.6 For some tasks—­such as assessing the presence and abundance of species—­these digital recorders are more accurate, precise, and comprehensive than visual monitoring methods. Fourth, PAM devices are also relatively cheap and easy to use, and thus are being deployed by many scientists who do not consider themselves acousticians. Historians of science Alexandra Supper and Karin Bijsterveld argue that bio-­and ecoacoustics combine synthetic, analytic, and interactive modes of listening.7 According to Bijsterveld, whereas bioacoustics is primarily an analytic form of listening, ecoacoustics is primarily an example of synthetic listening. The use of sound in playback studies or via biomimetic robots is an example of an interactive mode of listening. In recent years, bioacoustics and ecoacoustics have been applied to a rapidly expanding number and range of species and landscapes. In conducting background research for this book, I reviewed thousands of scientific articles, covering over a thousand species. This is only a snapshot of the rapidly growing scholarly lit­er­a­ture on bioacoustics and ecoacoustics (not to mention sound studies). The broad range of species listed below is not comprehensive, but is merely intended to be illustrative of the diversity of the research now being conducted. By design, ­these ­tables do not reiterate well-­known findings about birds and insects that have been widely studied. Rather, they highlight species previously thought to be mute or species that vocalize beyond ­human hearing range; by focusing on t­ hese cases, I hope to correct the common misconception that only species easily audible to ­humans make sounds. As a ­whole, ­these examples illustrate how biodigital acoustics expands our understanding of the power of sound across the Tree of Life. Why do scientists rec­ord the sounds of the world? Some bioacousticians devote themselves to recording the diversity of nonhuman sounds before they dis­appear ­under the onslaught of mass extinction. ­Others

R e s e a r c h o n B i o - a n d E c o a c ou s t ic s  217

seek to uncover new aspects of animal be­hav­ior or communication. ­Table 1 provides an overview of some common purposes of bio-­and ecoacoustics research devoted to studying other species. ­Table 2 provides some examples of bioacoustics and ecoacoustics used to communicate with other species, in four categories: acoustic deterrents, acoustic enrichment, biomimetic robots, and interspecies communication. Bio-­and ecoacoustics are distinct from, but related to, biotremology: the study of the production, dispersal, and reception of vibrations associated with substrate-­borne mechanical waves produced by living organisms, ­either incidentally or purposefully. Swedish entomologist Frej Ossiannilsen was one of the first scientists to suggest, in the 1940s, that substrate-­borne vibrations (rather than movement of air particles) are used to transfer information between organisms. Biotremology has become an accepted term in the past two de­cades, and the field has made significant contributions to understanding signal production and communication of information between organisms.8 Vibrations can travel by solid substrates, like the ground, a tree branch, a leaf, a plant stem, or a blade of grass, or even through soil.9 The ability to sense substrate-­borne vibrations occurs in many species, from crickets to elephants.10 Vibrational communication appears to be ubiquitous—an ancient system that remains the primary channel of communication among many organisms.11 Biologist Karen Warkentin’s work on treefrogs, for example, has used vibration playbacks to demonstrate that treefrog embryos can distinguish between vibrations caused by approaching snake attacks and rainfall—­the former accelerate their decision to hatch, while the latter c­ auses them to stay snug in their eggs.12 ­These minute vibrations are conveyed to the embryos through the trees themselves—­a minuscule quivering in a twig or leaf that most ­humans would likely not even notice. Scientists are now taking a fresh look at living organisms and discovering hitherto overlooked aspects of vibrational communication. For example, recent studies of the well-­known mating dance in Drosophila flies (a widely used model organism) have demonstrated that substrate-­borne vibrations—­not just sound traveling through air—­play a significant role in courtship.13 Biologist Peggy Hill asserts that ­these vibrational signals are, in fact, the most ancient form of communication—­older than sound waves.

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Notes Hill: “A change in worldview may be required for modern h­ umans to actually see what is not at all new to most of the animals on Earth.”14 Hill argues further that some modes of communication conventionally assumed to be sonic are, in fact, vibrational. Biotremology is beyond the scope of this book, but f­ uture developments in this area are worth following. As Hill writes, “Have a listen and find something wonderful!”15

­Table 1. Bio-­and ecoacoustics used to study other species (selected examples) Category

Activity

Example

Taxonomy

Differentiation of cryptic (sibling) species Updating taxonomic classifications

Differentiating sounds made by rainfrogs enables identification of new species.16 Taxonomic classifications of piranha species may be reclassified based on acoustic recordings.17 Ecoacoustics study detects a new-­to-­science population of blue ­whales.18 Acoustic recordings paired with bioacoustics indices may be a useful method of monitoring shifts in songbird communities due to climate change and other sources of anthropogenic disturbance.19 Ecoacoustics detects the health of a diverse coral reef community.20 Peacocks use infrasonic communication when mating.21 Sparrows change their calls in response to changes in urban noise levels.22 Treefrogs respond to seismic signals from rainfall and snakes.23 Automated bioacoustic recorders are significantly better than ­humans at detecting nightjar birds.24 Bioacoustics-­based monitoring schemes on the Atlantic and Pacific coasts of North Amer­i­ca are used to determine ­whale location; this data is then used to instruct ships to slow down and fishers to cease activity.25 In South Africa’s Kalahari, the decline of the white-­backed vulture, Gyps africanus, over seventeen years was estimated from the frequency of occurrence of meerkat alarm calls.26

Discovering new species Environmental monitoring

Monitoring individual species

Population monitoring using bioacoustics indices

Monitoring the health of an ecosystem Peacocks Sparrows Treefrogs

Endangered species protection Studying vocal communication and vocal learning

Replacing h­ uman (manual) with digital (automated) wildlife surveys Bioacoustics used to detect presence of endangered species and take protective action Bioacoustics used to monitor behavioral states warranting conservation actions (e.g., using prey species’ alarm calls as a proxy of predator abundance)

­Table 2. Bio-­and ecoacoustics used to communicate with other species (selected examples) Purpose

Species

Example

Acoustic deterrents: Blasting warning calls

Turtles

Low-­frequency acoustic cues reduce sea turtle bycatch in gillnets.27 Dolphin “pingers” attached to fishing nets reduce dolphin bycatch.28 “Howl box” playback of wolf howls deters wolves from approaching livestock.29 Acoustic beehive fence deters elephants in Africa from encroaching on farmers’ fields.30 Recordings of dogs barking deters raccoons from intertidal ecosystems.31 Ultrasonic frequencies warn bats away from wind farms.32 Playbacks of healthy coral reefs stimulate fish and crustacean populations to recolonize degraded reefs.33 Tactile devices that allow elephants to choose between dif­fer­ent types of prerecorded sounds (e.g., ­whale ­music versus elephant sounds) provides enrichment for captive elephants.34 Playing recordings of wild birds mating dramatically increases the success of reproduction in captive birds at the Bronx Zoo.35 White-­faced Saki monkeys design their own ­music play­list.36 Biomimetic squirrel robots use acoustic and infrared cues to warn wild squirrels of threats and ward off rattlesnake predators.37 Biomimetic frog robots use acoustic and visual (vocal sac pulsation) cues to stimulate territorial defensive be­hav­iors in live frogs.38 HIVEOPOLIS is a digital honeybee colony system where h­ umans, honeybees, and hives interact and socially cooperate via a network of sensors and robotic bees. Proj­ect Florence is a natu­ral language pro­cessing algorithm that infers the meaning of ­human words and translates them into a light shining on a plant. A positive sentiment is translated into red light, a negative sentiment into blue light. Both generate an electrochemical response, which is detected by sensors. The plant is also augmented with sensors that mea­sure moisture and temperature. The data is analyzed by an algorithm and coded with preprogrammed texts, which the plant uses to text responses to its ­human caregiver.39

Dolphins Wolves Elephants Raccoons Bats

Acoustic enrichment: Using sound to regenerate ecosystems or stimulate animals

Corals Elephants

Ibis Monkeys Interactive biomimetic robotics: Studying animal be­hav­ior

Squirrels

Interspecies communication

Bees

Dart-­poison frogs

Plants

Notes

Introduction 1. The Tree of Life is a term I deploy, much like Charles Darwin as well as con­temporary scientists, to refer to the idea that shared common descent among the vari­ous biological groups on Earth can be depicted—­although imperfectly—by an evolutionary tree, as a visual meta­phor of the relatedness of dif­fer­ent species. 2. Dakin et al. (2016); Freeman (2012); Freeman and Hare (2015). See also Yorzinski et al. (2013, 2015, 2017). 3. See, for example, Talandier et al. (2002, 2006). 4. Nishida et al. (2000); Suda et al. (1998); Webb (2007). 5. Some types of earthquakes generate atmospheric disruptions that alter patterns in the electrically charged particles in the outer part of the Earth’s atmosphere (the ionosphere); this phenomenon can be mea­sured by tracking anomalies in radio signals between ground receivers and satellites. This coupling between the lithosphere (the Earth’s mantle and crust) and the atmosphere is known to scientists as coseismic ionospheric disturbance (CID): Heki (2011); J. Liu et al. (2011); H. Liu et al. (2021). 6. Feder (2018); Narins et al. (2016); NOAA Infrasonics Program (2020). 7. French et al. (2009). See also Bedard and George (2000). Some of the microphones used to listen to Earth’s passive infrasound are part of the worldwide International Monitoring System (IMS) that was created to verify compliance with the Comprehensive Nuclear Test-­Ban Treaty. The primary purpose of the IMS is to detect nuclear testing, but it also detects data on a variety of climatological and geological phenomena. 8. Bakker et al. (2014); Gagliano (2013a, 2013b); Gagliano, Mancuso, et al. (2012); Gagliano, Renton, et al. (2012); Ibanez and Hawker (2021); Surlykke and Miller (1985). 9. Masterton et al. (1969); Sales (2012). 10. For recent studies on ultrasonic communication by primates, see Arch and Narins (2008); Gursky (2015, 2019); Geerah et al. (2019); Klenova et al. (2021); Ramsier et al. (2012); Sales (2010); and Zimmerman (2018). For some of the earliest studies on ultrasonic communication in bats, mice, moths, and porpoises, see Griffin (1958); Kellogg et al. (1953); Noirot (1966); Pierce (1948); and Roeder (1966). 11. Jones (2005). 12. Bats’ echolocation calls have been recorded at 130 decibels, the pain threshold for h­ uman hearing: Jones (2005). 13. D. Hill (2008). 221

222  N o t e s t o I n t r o du c t ion 14. For example, a continental-­scale bioacoustics observatory has been launched for the Australian continent, which provides a “direct and permanent rec­ord of terrestrial soundscapes through continuous ecoregions, including t­ hose periodically subject to fire and flood, when manual surveys are dangerous or impossible” (Roe et al. 2021). In the US, large bioacoustics monitoring frameworks have been set up, including a network of over two thousand listening stations in the Sierra Nevada (Reid et al. 2021; Wood, Gutiérrez, et al. 2019; Wood, Popescu, et al. 2019; Wood, Klinck, et al. 2021; Wood, Kryshak, et al. 2021). 15. Gibb et al. (2019); Hill et al. (2018); Whytock and Christie (2017). 16. Hoffmann et al. (2012); King and Janik (2013); King et al. (2013); Marconi et al. (2020); Melotti et al. (2021); Slobodchikoff et al. (1991); Slobodchikoff, Paseka, et al. (2009); Slobodchikoff, Perla, et al. (2009); Vergara and Mikus (2019). 17. A note on wording: This book uses the terms sound and vocalizations rather than “noise.” The term noise was often used to refer to nonhuman vocalizations in the past, but its etymology conveys an implicit meaning: disturbance, disharmony. The word’s Latin roots are also found in words like nausea (seasickness), nocere (to harm), and noxia (nuisance). I reserve the term noise to refer to environmental pollution. 18. Technical Committee on Bioacoustics. See https://­tcabasa​.­org​/­. 19. More precisely, bioacoustics focuses on the study of animal communication and associated be­hav­ior; auditory capacities and auditory mechanisms of animals; sound production anatomy and neurophysiology of animals; and biosonar. 20. Truax and Barrett (2011). 21. Cities have soundscapes too. The inventor of the term soundscape, Michael Southworth, first focused on urban noise. See Southworth (1967, 1969). 22. Farina (2018); Farina and Gage (2017); Ritts and Bakker (2021); Sueur and Farina (2015); Xie et al. (2020). 23. Carruthers-­Jones et al. (2019). 24. Benson (2010). 25. Farley et al. (2018). 26. Machine learning is a branch of artificial intelligence (and hence computer science) that seeks to emulate h­ uman intelligence by developing computational algorithms to analyze and draw inferences from patterns in datasets. In some cases, algorithms can learn without following explicit instructions, and can execute tasks (such as pattern recognition) much faster than ­humans. ­There are several dif­fer­ent types of machine learning, a detailed discussion of which is beyond the scope of this book. For a general introduction and critique of the limits of AI, see Marcus and Davis (2019). 27. See, for example, Bijsterveld (2019); Darras et al. (2019); and Mustafa et al. (2019). 28. Long-­duration acoustic recordings of the environment can rec­ord continuously for weeks or months and thereby generate terabytes of data, which are challenging to analyze using traditional methods. Acoustic monitoring has several benefits: better spatial and temporal coverage; objectivity (versus traditional in-­person observations, which are observer dependent); and per­sis­tence (recordings can be stored over time, enabling longitudinal comparisons). Machine learning analy­sis can be a power­ful method, but is typically time consuming to prepare. Supervised machine learning methods require high-­quality annotated data as training datasets

N o t e s t o C h a p t e r 1   223 for the algorithms. Preparing ­these labeled datasets is laborious, time consuming, and costly. Active learning is one method that can be used to reduce this burden; in cases where raw data is available but labeled data is scarce, patterns (­those most likely to contain information of interest) are selected at each iteration for manual annotation by a h­ uman expert, who can identify the specific animal making a sound and perhaps link the sound to a relevant be­hav­ior. See Kahl et al. (2021); Kholghi et al. (2018); Oliver et al. (2018); Shiu et al. (2020); and Zeppelzauer et al. (2015). 29. Kohlghi et al. (2018). Machine learning can relieve h­ umans of the tedious work of classification, and at least partially automate the laborious pro­cess of searching for patterns in vocalizations. Machine learning algorithms have enabled scientists to decode links between vocalizations and complex social be­hav­ior in well-­studied species (like birds), but also in species that are more challenging to study (like bats, which vocalize above h­ uman hearing range and can generate hundreds of calls per minute in a crowded colony). See Kershenbaum et al. (2016). 30. Kimmerer (2015, 158). 31. De Chardin (1964), cited in Fleissner and Hofkirchner (1998, 205); Kreisberg (1995); Steinhart (2008); Yeo et al. (2012); Yin and McCowan (2004). 32. McLuhan (1964). 33. Archibald (2008); Clutesi (1967); Parent (2018). 34. Zadeh and Akbari (2016). 35. Langdon (2018). 36. Gera (2003). 37. Crane (2013). The extensive debates about animal language in philosophy and animal studies are beyond the scope of this book; for further reference, see Derrida (2008). 38. Dillon (1997); Hughes (1983); Wertime (1983). 39. Borrows (2022); Watts (2013, 2020).

Chapter 1: Sounds of Life 1. New Bedford Whaling Museum (2020a, 2020b). 2. Heller (2020). 3. Shoemaker (2005, 2014, 2015). 4. Bockstoce (1986). 5. Webb (2011). 6. Demuth (2017, 2019a, 2019b, 2019c); Jones (2015). 7. Bockstoce (1986). 8. Aldrich (1889, 61). 9. Barr (2020); Barr et al. (2017). 10. Melville (2010). 11. Aksaarjuk (1987). 12. Wright (1895, 41). 13. See https://­www​.­whalingmuseum​.­org​/­collections​/­highlights​/­photography​/­finding​-­aids​ /­#aldrich​-­collection. 14. Aldrich (1889, 33).

224  N o t e s t o C h a p t e r 1 15. Aldrich (1889). 16. See https://­www​.­whalingmuseum​.­org​/­collections​/­highlights​/­photography​/­finding​ -­aids​/­#aldrich​-­collection. 17. Aldrich (1889, 33). 18. Aldrich (1889, 33). 19. Aldrich (1889, 34). 20. Aldrich (1889, 116). 21. Aldrich (1889, 49). 22. I believe that this is the first discussion of Aldrich’s story in the con­temporary period, at least with re­spect to ­whale bioacoustics. 23. Helmreich (2016). 24. Eber (1996). 25. Gogala (2014); Weber and Thorson (2019). 26. Godin (2017); Munk and Day (2008); Munk et al. (1995); Worzel (2000). 27. The SOFAR channel exists ­because of the relationship between sound speed, temperature, and pressure: the lower the depth, the colder the ­water and the slower sound travels. But pressure also increases with depth. As you descend into the depths of the ocean, t­ here is a point where w ­ ater temperature stabilizes, beyond which only pressure increases; at this depth, sound travels at its slowest speed through ­water (the “sound speed minimum”). The SOFAR channel is found at this depth, which may vary according to ocean conditions. Somewhat analogous to how a fiber optic cable guides light, this deep horizontal channel not only guides sound waves but also preserves them over much longer distances than at other depths; the sound waves are bent (or refracted) back ­toward the channel axis, where the lowest sound speed occurs. Thus channeled, low-­frequency sound can travel for long distances (higher-­frequency sound is absorbed more rapidly and is only detectable at shorter distances). 28. Evans (1994); Whitman (2005). Some of the Navy recordings are still classified. ­Later, another passive monitoring system was created: DIFAR, via which sonobuoys ­were dropped directly from planes or deployed from submarines. And in the 1970s, SURTASS, an active monitoring system that sends out power­ful pulses and listens for echoes, was created. DIFAR stands for directional LOFAR (lower-­frequency analy­sis and recording). See D’Spain et al. (1991). 29. Fish (1954); Fish et al. (1952); Tavolga (2012). 30. Tavolga (2012). 31. New York Times (1989). 32. Erskine (2013); Nishimura (1994). 33. Schevill (1962). 34. Lubofsky (2019). 35. Negri (2004). 36. Ibid. 37. Ketten (1997). 38. Bentley (2005). 39. Tyack and Clark (2000). See also Schevill and Lawrence (1949). 40. Deecke et al. (2000, 2010); Filatova et al. (2012, 2013); Foote et al. (2006); Janik (2014); Kremers et al. (2012); Weiß et al. (2011). 41. Brown (2019); Ivkovich et al. (2010).

N o t e s t o C h a p t e r 2   225 42. Holt et al. (2019). 43. Schiffman (2016). 44. Clark (1998); D’Spain et al. (1991); Ketten (1997); Mourlam and Orliac (2017). 45. Whitman (2005). 46. Watlington (1980); Yandell (2017). 47. Kwon (2019). 48. Payne (2021). 49. Kwon (2019). 50. Watlington (1982). 51. CBS Interactive Inc. (2014). 52. Allchin (2015); Rothenberg (2008). See also Johnston-­Barnes (2013). 53. McQuay and Joyce (2015). 54. Ibid. 55. Brody (1993). 56. Payne and McVay (1971); Negri (2004). 57. Payne and McVay (1971, 597); Van Cise et al. (2018). 58. Schevill and Lawrence (1949). See also Schevill (1962) and Gertz (2016). To listen to the recordings, refer to Watkins Marine Mammal Sound Database (2021). 59. Payne (2000). 60. Cummings and Philippi (1970); Guinee and Payne (1988); Payne and Payne (1985); Payne and McVay (1971); Payne and Webb (1971). 61. Garland et al. (2011, 2017). 62. Darling et al. (2014); Garland et al. (2011, 2013, 2017). 63. Ocean Alliance (2019). 64. Payne and Webb (1971). 65. Brody (1993). 66. Kwon (2019). 67. Schneider and Pearce (2004). 68. World Wildlife Fund (2013). 69. Payne and Webb (1971). 70. Marler (1974, 35). 71. Kwon (2019). 72. Kwon (2019); Mellinger and Clark (2003); Stafford et al. (1998). See also Brand (2005). 73. Schevill and Watkins (1972). 74. Clark and Clark (1980). 75. Rolfe (2012). 76. Nishimura (1994).

Chapter 2: The Singing Ocean 1. Albert (2001). 2. Royal Geo­graph­i­cal Society (2018). 3. Sakakibara (2009). 4. Demuth (2019c).

226  N o t e s t o C h a p t e r 2 5. Sakakibara (2009, 292). 6. Brewster (2004); Sakakibara (2008, 2009, 2010); Turner (1993); Zumwalt (1988). 7. Cited in Sakakibara (2010, 1007). 8. Lantis (1938). 9. Adams (1979). 10. Blackman (1992); Bodenhorn (1990); Brower (1942); Hess (2003); Kruse et al. (1982); Sakakibara (2017). 11. Brewster (2004). 12. Brewster (1997). 13. Baker and Vincent (2019). 14. Blackman (1992); Brewster (2004); Huntington et al. (2001). 15. Albert (1992, 25). 16. Citta et al. (2015). 17. Ashjian et al. (2010); Grebmeier et al. (2006); Moore and Laidre (2006); Moore et al. (2010); Watanabe et al. (2012). 18. Wohlforth (2005). 19. Albert (2001). 20. Burns et al. (1993); George et al. (2004); Noongwook et al. (2007); Wohlforth (2005). 21. Wohlforth (2005). 22. Kelman (2010). 23. Huntington et al. (2017). 24. Joyce and McQuay (2015). 25. Joyce and McQuay (2015). See also Hess (2003) and Wohlforth (2005). 26. Clark and Johnson (1984). 27. Clark et al. (1986). 28. Ko et al. (1986). 29. Clark and Ellison (1989); Zeh et al. (1988). 30. The tracking algorithm, which was gradually refined over several years, became an integral component of the evaluation framework for the combined visual and acoustic data. See Clark (1998); Clark et al. (1996); Clark and Ellison (1989); Greene et al. (2004); and Sonntag et al. (1988). 31. Ko et al. (1986). 32. Ellison et al. (1987); George et al. (1989). 33. Brower (1942); Tyrrell (2007); George et al. (1989); Schell (2015). 34. Tyrrell (2007). 35. Erbe (2002); Greene (1987); Koski and Johnson (1987); LGL/Greeneridge Sciences (1995); Matthews et al. (2020); Patenaude et al. (2002); Richardson et al. (1985, 1986, 1990); Richardson and Greene (1993); Streever et al. (2008); Wartzok et al. (1989). 36. George et al. (1999); Wohlforth (2005). 37. Erbs et al. (2021); Johnson et al. (2011); Stafford et al. (2018); Würsig and Clark(1993). 38. George et al. (2004). 39. Albert (2001); Clark et al. (1996); Clark and Ellison (1989); George et al. (1989, 2004). 40. See “Description of the USA Aboriginal Subsistence Hunt: Alaska” at https://­iwc​.­int​ /­alaska. 41. Suydam and George (2021).

N o t e s t o C h a p t e r 2   227 42. IWC (1982, 44). See also Ikuta (2021) and “Description of the USA Aboriginal Subsistence Hunt: Alaska” at https://­iwc​.­int​/­alaska. 43. Duarte et al. (2021). 44. Clark et al. (2009) 45. Blackwell et al. (2013); Charif et al. (2013); Ljungblad et al. (1988); Richardson et al. (1999). 46. See also Weilgart (2007). 47. Weilgart (2007). 48. Eisner et al. (2013). 49. Comiso et al. (2008); Druckenmiller et al. (2018); Gearheard et al. (2006, 2010, 2013); Stroeve et al. (2008, 2011). 50. George et al. (2017); Hartsig et al. (2012); Hauser et al. (2018). 51. Berkman et al. (2016); Parks et al. (2019). 52. Matthews et al. (2020); Willoughby et al. (2020). See also NWMB et al. (2000) and Ferguson et al. (2012). 53. Herz (2019). 54. Clark et al. (2015); George et al. (2004); George and Thewissen (2020); Stafford and Clark (2021). 55. Nishimura (1994). 56. Clark (1995); Stafford et al. (2001); Watkins et al. (2000, 2004). 57. George et al. (2018). See also Fox et al. (2001); Wiggins (2003). 58. Shiu et al. (2020). 59. Vickers et al. (2021). 60. Johnson and Tyack (2003). 61. Green et al. (1994); Johnson and Tyack (2003); Miller et al. (2000); Parks, Clark, and Tyack (2007). 62. Thode et al. (2012). 63. Parks et al. (2007). 64. Parks et al. (2007). 65. Syracuse University (2019). 66. Johnson et al. (2015); Stafford et al. (2008, 2012); Tervo et al. (2011); Würsig and Clark (1993). 67. Clark and Johnson (1984); Cumming and Holliday (1987); Delarue, Laurinolli, et al. (2009); Delarue, Todd, et al. (2009); Ljungblad et al. (1980, 1982); Stafford and Clark (2021); Tervo et al. (2011); Würsig and Clark (1993). 68. Johnson et al. (2015); on the question of songbird diversity predicting population health, see Laiolo et al. (2008). 69. NOAA Fisheries (2020a, 2020b). 70. Gabrys (2016b, 90). See also Gabrys (2016a). 71. Brewster (2004); Huntington et al. (2021). See also “Description of the USA Aboriginal Subsistence Hunt: Alaska” at https://­iwc​.­int​/­alaska. 72. Brewster (2004); Wohlforth (2005). 73. Bodenhorn (1990). 74. Brewster (2004, 156). 75. Gillespie et al. (2020); Hastie et al. (2019).

228  N o t e s t o C h a p t e r 3

Chapter 3: Quiet Thunder 1. Parker and Graham (1989). 2. Estimates vary, but indicate that the African elephant population of over 1.3 million in 1979 was reduced to less than half that number by 1989: Douglas-­Hamilton (1987, 2009); Poole and Thomsen (1989); Roth and Douglas-­Hamilton (1991). 3. King (2019). 4. Douglas-­Hamilton and Burrill (1991). 5. Poole and Thomsen (1989). 6. Martin (1978). See also Larom et al. (1997). 7. Krishnan (1972). 8. Payne (1998, 20). 9. Payne (1998, 21). 10. Payne (1998, 21). 11. Payne et al. (1986). See also Webster (1986). 12. See Webster (1986). 13. Pye and Langbauer (1998). 14. Moss et al. (2011). 15. Joyce Poole now runs the NGO Elephant Voices (https://­elephantvoices​.­org​/­). 16. See, for example, McComb et al. (2001). 17. See, for example, Lee and Moss (1986). 18. Langbauer et al. (1991); Poole et al. (1988). 19. Moss (1983). 20. Langbauer et al. (1991). 21. McComb et al. (2000, 2003); Poole et al. (2005). 22. Byrne et al. (2008); Poole and Moss (2008). 23. Roca et al. (2001). 24. Hedwig et al. (2018). 25. Payne (2004). 26. Fox (2004); Payne (2004); Wrege et al. (2012). 27. Cornell Lab (2021). 28. Wrege et al. (2017). 29. Wrege et al. (2010, 2017). 30. Thompson et al. (2010); Turkalo et al. (2017, 2018). See also Maisels et al. (2013). 31. Maisels et al. (2013). 32. Chase et al. (2016). 33. Gobush et al. (2021). 34. Payne et al. (2003). 35. Simpson et al. (2015). 36. Bjorck et al. (2019). 37. Bjorck et al. (2019); Keen et al. (2017). See also Temple-­R aston (2019). 38. Sethi et al. (2020). 39. Nath et al. (2015). 40. Davies et al. (2011); Fernando et al. (2005); Hedges and Gunaryadi (2010); Guynup et al. (2020); Nyhus and Sumianto (2000).

N o t e s t o C h a p t e r 3   229 41. Vollrath and Douglas-­Hamilton (2002). 42. Barua et al. (2013); Jadhav and Barua (2012); Nath et al. (2009). 43. Calabrese et al. (2017); Thouless et al. (2016). 44. Shaffer et al. (2019). 45. Hoare (2015); Liu et al. (2017). 46. Thuppil and Coss (2016); Wijayagunawardane et al. (2016). 47. Vollrath and Douglas-­Hamilton (2002). 48. Ellis and Ellis (2009); França et al. (1994); Pereira et al. (2005). 49. Ibid. 50. King (2010). 51. King et al. (2007). 52. King et al. (2009). 53. King et al. (2011). 54. King et al. (2017). 55. King (2019). 56. King et al. (2017); King et al. (2011). 57. Branco et al. (2020); Dror et al. (2020); King et al. (2018); Ngama et al. (2016); Van de ­Water et al. (2020); Virtanen et al. (2020). 58. See https://­elephantsandbees​.­com​/­. 59. Herbst et al. (2012). 60. King (2019); McComb et al. (2003). 61. Arnason et al. (2002). 62. Leighty et al. (2008); Soltis (2010); Soltis et al. (2005). 63. King (2019); King et al. (2010). 64. Cheney and Seyfarth (1981); Seyfarth et al. (1980). Dif­fer­ent types of alarm calls have been reported for other species, including many species of birds and vervet monkeys, which make acoustically dif­f er­ent alarm calls for dif­fer­ent threats (leopards, ea­gles, or snakes). 65. Soltis et al. (2014). 66. Dutour et al. (2021). 67. McComb et al. (2014). 68. McComb et al. (2000, 2003); O’Connell-­Rodwell et al. (2007). 69. de Silva and Wittemyer (2012); de Silva et al. (2011); McComb et al. (2001); Stoeger and Baotic (2016). 70. Poole et al. (2005). 71. Poole et al. (2005). 72. Brainard and Fitch (2014). 73. Kamminga et al. (2018); Zeppelzauer et al. (2015). 74. Premarathna et al. (2020). 75. Chal­mers et al. (2019); Dhanaraj et al. (2017); Mangai et al. (2018); Ramesh et al. (2017); Premarathna et al. (2020). 76. Firdhous (2020); Hahn et al. (2017); Shaffer et al. (2019); Wright et al. (2018); Zeppelzauer and Stoeger (2015); Zeppelzauer et al. (2013). 77. Fernando et al. (2005); Lorimer (2010). 78. See https://­www​.­elephantvoices​.­org​/­about​-­elephantvoices​/­mission​.­html.

230  N o t e s t o C h a p t e r 4 79. Corbley (2017). See also https://­helloinelephant​.­com​/­. 80. French et al. (2020); Mumby and Plotnik (2018); Stoeger (2021).

Chapter 4: Voice of the Turtle 1. Giles, correspondence with author, April 2021. 2. While scientists now accept that turtles vocalize, how they do so is still a mystery. Ironically, given how many turtles ­were chopped up for food over the past few centuries, we still do not fully understand their anatomy. Previously, biologists had assumed that turtles could not make sounds underwater, b­ ecause they had assumed an anatomical basis of sound production that requires air to circulate. But turtles do not rely on rib-­based (costal) pumping for air flow and cycling. How, then, could they possibly make noise? Perhaps through a pro­cess known as gular pumping, which involves expanding and contracting the throat to produce air, in the same way that h­ umans use their diaphragms. At the pre­sent time, however, this is merely speculation. Although we have a better understanding of turtles’ specialized ears for hearing underwater, scientists remain stumped about precisely how they make noise when submerged underwater for long periods. See Russell and Bauer (2020). 3. Giles, correspondence with author, April 2021. 4. Vergne et al. (2009). See also Britton (2001) and Garrick and Lang (1977). 5. Pope (1955). See the useful review in Liu et al. (2013). Although a few scientists described turtle vocalizations, they ­were dismissed or forgotten. See also Campbell and Evans (1972) and Walter (1950). 6. Russell and Bauer (2020); Willis and Carr (2017). 7. Giles (2005); Giles et al. (2009). 8. ­These types of turtles are found only in Papua New Guinea, Australia, and South Amer­ic­ a: Giles, correspondence with author, April 2021. 9. Giles, correspondence with author, April 2021. 10. Ibid. 11. Giles et al. (2009). 12. Capshaw et al. (2021). See also Pika et al. (2018). 13. This discussion of Amazon river turtles draws on the following references: Alves (2007a, 2007b); Alves and Santana (2008); Bates (1864); Brunelli (2011); Cleary (2001); Coutinho (1868); dos Santos et al. (2020); Forero-­Medina et al. (2019); Gilmore (1986); Johns (1987); Klemens and Thorbjarnarson (1995); Mittermeier (1975); Papavero et al. (2010); Pezzuti et al. (2010); Smith (1974, 1979); Stanford et al. (2020); and Vogt (2008). 14. Coutinho (1868), cited in Smith (1974). 15. Bates (1864). 16. Darwin (1999, 326–27), cited in Egerton (2012). 17. Bates (1864, 322), cited in Egerton (2012). 18. Vogt (2008). 19. dos Santos et al. (2020). 20. Ferreira (1972a, 1972b), cited in dos Santos et al. (2020). 21. Landi (2002), cited in dos Santos et al. (2020). 22. Ferreira (1972a, 1972b), cited in dos Santos et al. (2020).

N o t e s t o C h a p t e r 4   231 23. Ferreira (1972a, 1972b), cited in Smith (1974). 24. Bates (1864); Smith (1974); Vogt (2008). 25. Pezzuti et al. (2010); Salera et al. (2006). 26. Smith (1974). 27. Bates (1864); Coutinho (1868). 28. Smith (1979) estimates two hundred million eggs ­were harvested; dos Santos et al. (2020) estimates much higher. Bates estimated that townspeople of one community, Ega, collected forty-­eight million eggs per year. Some villages reportedly produced as many as one hundred thousand pots of turtle oil per year. 29. See, for example, Allan (1991); Benton-­Banai (1988); Bevan (1988); Fischer (1966); Johnston (1990); McGregor (2009); Mohawk (1994); Peacock and Wisuri (2009); and Umeasiegbu (1982). 30. Smith (1974). 31. dos Santos (2020). 32. Smith (1974). 33. Ferrara et al. (2013, 2014a, 2014b, 2017). 34. Ferrara et al. (2014c, 266). 35. Ferrara, interview with author, November 2020. 36. Ferrara et al. (2019). For another study on turtle embryo sounds, see Monteiro et al. (2019). 37. Warkentin (2011). 38. Not all turtle species coordinate their hatching, and even when they do, they do not always coordinate primarily through acoustic communication (vibrations might stimulate cohatching instead): Doody et al. (2012); Field (2020); McKenna et al. (2019); Nishizawa et al. (2021); Riley et al. (2020). 39. Monteiro et al. (2019); Nuwer (2014); Rusli et al. (2016). 40. Crockford et al. (2017); Ferrara et al. (2013, 2014a, 2014b). 41. Ferrara et al. (2013). 42. Holtz et al. (2021); Nelms et al. (2016); Piniak (2012); Piniak et al. (2012, 2016). 43. Giles (2005); Giles et al. (2009). 44. Papale et al. (2020). 45. Noda et al. (2017, 2018). 46. Abrahams et al. (2021); Greenhalgh et al. (2020, 2021). 47. Abrahams et al. (2021). 48. Rountree and Juanes (2018). 49. Buscaino et al. (2021). 50. Chang et al. (2021) 51. Machine learning algorithms can reveal forest degradation from fire and logging; the physical devastation can be inferred from the impoverished soundscapes that result. The architecture of digital acoustics that is now being built w ­ ill enable the Amazon rainforest to be characterized and modeled as a landscape of digital acoustic information: the Amazon, in other words, may one day have an acoustical digital twin: Colonna et al. (2020); Do Nascimento et al. (2020); Rappaport et al. (2021); Rappaport and Morton (2017). 52. Seeger (2015).

232  N o t e s t o C h a p t e r 5 53. Brabec de Mori (2015); Brabec de Mori and Seeger (2013); Lima (1996, 2005); Pucci (2019); Thalji and Yakushko (2018); Viveiros de Castro (1996, 2012). 54. de Menezes Bastos (1999, 87). 55. de Menezes Bastos (2013, 287–88). 56. Ferreira (1972a, 27), cited in Smith (1974). 57. Cantarelli et al. (2014); Páez et al. (2015); Pantoja-­Lima et al. (2014); Rhodin et al. (2017). 58. Pantoja-­Lima et al. (2014). 59. Castello et al. (2013).

Chapter 5: Reef Lullaby 1. Doney et al. (2009); Gattuso and Hansson (2011); Hoegh-­Guldberg et al. (2007); Raven et al. (2005); Watson et al. (2017). 2. Doney et al. (2009); Gattuso and Hansson (2011); Hoegh-­Guldberg et al. (2007); Raven et al. (2005); Watson et al. (2017). 3. Doney et al. (2009); Gattuso and Hansson (2011); Guo et al. (2020); Hoegh-­Guldberg et al. (2007, 2017); Mongin et al. (2016); Raven et al. (2005); Wei et al. (2009). 4. Hoegh-­Guldberg et al. (2017). 5. Plaisance et al. (2011). 6. Guo et al. (2020); Hughes et al. (2018); Hoegh-­Guldberg et al. (2007); Mongin et al. (2016); Wei et al. (2009). 7. Kwaymullina (2018, 198–99). 8. Nunn and Reid (2016). See also Reid and Nunn (2015). 9. Fitzpatrick et al. (2018); Lambrides et al. (2020); Waterson et al. (2013). 10. Cheng et al. (2020); Cressey (2016); Hughes et al. (2018). 11. At the base of each coral polyp is a protective limestone skeleton called a calicle. Reefs emerge when a polyp attaches itself to a rock or the seafloor and then buds (self-­divides) into thousands of clones with interconnected calicles. Coral colonies aggregate together to build reefs. Using a ge­ne­tic approach to estimate the ages of corals, scientists have found some corals are up to five thousand years old. See NOAA (2021a). 12. Nielsen et al. (2018); Oakley and Davy (2018). 13. Preston (2021). 14. Burnett (2012); Gillaspy et al. (2014); Ruggieri (2012); St. Augustine Rec­ord (2014). See also Taylor (n.d.). 15. Tavolga (2002). 16. Coates (2005); Hawkins (1981). See also Hase (1923). 17. Tavolga (2012). 18. Tavolga (1981). 19. Tavolga (2012). 20. Tavolga (2012). 21. Aguzzi et al. (2019); Carriço et al. (2020); Dimoff et al. (2021); Lindseth and Lobel (2018); Lin et al. (2021); Lyon et al. (2019); Mooney et al. (2020); Popper et al. (2003); Roca and Van Opzeeland (2020); Tyack (1997). 22. See, for example, McCauley and Cato (2000).

N o t e s t o C h a p t e r 5   233 23. Barlow et al. (2019). 24. Erisman and Rowell (2017). 25. Glowacki (2015); Talandier et al. (2002, 2006). 26. Rice et al. (2017); Ruppé et al. (2015). 27. See https://­dosits​.­org​/­science​/­movement​/­sofar​-­channel​/­sound​-­travel​-­in​-­the​-­sofar​ -­channel​/­. 28. Radford et al. (2011); Simpson et al. (2004, 2005). 29. Elise et al. (2019). 30. Lin et al. (2019); Mooney et al. (2020). 31. Bohnenstiehl et al. (2018). 32. Bohnenstiehl et al. (2018); Linke et al. (2018). 33. Lin et al. (2021). 34. Gordon et al. (2018). 35. ­After the catastrophic mass bleaching in 2016, a second, similarly intense mass bleaching event occurred in 2017, giving the coral ­little chance to recover: Gordon et al. (2018). 36. Simpson et al. (2004); Simpson et al. (2005). 37. Leis (2006); Leis et al. (2011); Jones et al. (2009); Swearer et al. (1999). 38. Jones et al. (1999). 39. Neme (2010). 40. Lillis et al. (2013, 2016); Raick et al. (2021). See also Neme (2010). 41. Staaterman et al. (2014). 42. Simpson et al. (2004). 43. Simpson et al. (2004, 2005). 44. Radford et al. (2011). 45. Papale et al. (2020); Stanley et al. (2010). 46. Simpson et al. (2016). 47. Simpson et al. (2011). See also Lindseth and Lobel (2018). 48. Leis et al. (2011, 826). 49. Eldridge (2021). 50. Haggan et al. (2007). 51. Schwartz (2019). 52. Haggan et al. (2007); Hair et al. (2002); Johannes (1981); Johannes and Ogburn (1999); Leis et al. (1996); Leis and Carson-­Ewart (2000); Poepoe et al. (2007); Stobutzki and Bellwood (1998). 53. NOAA (2021b). 54. Neme (2010). 55. Vermeij et al. (2010). 56. Vermeij et al. (2010). See also Simpson (2013). 57. Budelmann (1989). 58. Neme (2010); Simpson, interview with author, March 2021. 59. See also Madl and Witzany (2014). 60. Using a well-­established ge­ne­tic analy­sis method (PCR), the researchers analyzed extracts of Cyphastrea coral DNA for FOLH1 and TRPV genes, which have previously been observed in sea anemones and freshwater polyps (organisms that are fairly similar to coral); TRPV

234  N o t e s t o C h a p t e r 6 is associated with hearing in other species, such as Drosophila: Ibanez and Hawker (2021); See also Peng et al. (2015). 61. Gordon et al. (2018). 62. Simpson et al. (2004); Gordon et al. (2018); Radford et al. (2011). 63. Karageorghis and Priest (2012); Koelsch (2009); Terry et al. (2020). 64. Gordon et al. (2019); Parmentier et al. (2015); Tolimieri et al. (2004). 65. Lamont et al. (2021). 66. Williams et al. (2021). 67. Suca et al. (2020). 68. Ferrier-­Pagès (2021); Simpson et al. (2011, 2016). 69. Gordon (2020); Mars et al. (2020). See also Ladd et al. (2019). 70. Lecchini et al. (2018). 71. ­Great Barrier Reef Foundation (2020). 72. See https://­www​.5­ 0reefs​.­org​/­. 73. Mission Blue (2020). 74. Gordon et al. (2018). 75. Mars (n.d.). See also Mars Coral Reef Restoration (2021). 76. Gordon et al. (2018). 77. Mars (n.d.). 78. Simpson et al. (2004, 2005). 79. Jones et al. (1999); Swearer et al. (1999).

Chapter 6: Plant Polyphonies 1. Microsoft (2020c). 2. Microsoft tweet, February 13, 2020: “What if we could talk to plants? That’s exactly the question Proj­ect Florence explores. Dig in: msft.it/6009TwcKT #MSInnovation.” https://­ twitter​.­com​/­microsoft​/­status​/­1228114232547381248. 3. Microsoft (2020a). 4. Microsoft (2020b). 5. Iribarren (2019). 6. Sarchet (2016). 7. O’Reilly (2008); Hammill and Hendricks (2013). 8. Gagliano et al. (2017); Kivy (1959); Ravignani (2018). 9. Darwin (1917, 107). 10. Arner (2017); Madshobye (n.d.); McIntyre (2018). 11. Burdon-­Sanderson (1873). 12. Bose (1926). 13. Bouwmeester et al. (2019); Selosse et al. (2006); Simard et al. (1997); Simard and Durall (2004); Twieg, Durall, and Simard et al. (2007). 14. See, for example, Rodrigo-­Moreno et al. (2017). 15. Choi et al. (2017); Fernandez-­Jaramillo et al. (2018); Hassanien et al. (2014); Jung et al. (2018, 2020); Khait, Obolski, et al. (2019); Kim et al. (2021); López-­Ribera and Vicient (2017a, 2017b); Mishra and Bae (2019); Prévost et al. (2020).

N o t e s t o C h a p t e r 6   235 16. Ghosh et al. (2019); Joshi et al. (2019); Sharifi and Ryu (2021). 17. See, for example, Kawakami et al. (2019). 18. Mankin et al. (2018). 19. See Chamovitz (2020); Gagliano (2018); Hall (2011); Holdrege (2013); Kohn (2013); Mancuso and Viola (2015); Marder (2013); and Simard (2021). 20. Myers (2015); Pollan (2013). 21. Baluška et al. (2010); Myers (2015); Sung and Amasino (2004). 22. Myers (2015). 23. On the related debate about plant consciousness, which is beyond the scope of this book, see Allen (2017); Allen and Bekoff (1999); Baluška and Levin (2016); Baluška and Mancuso (2018, 2020, 2021); Brenner et al. (2006); Calvo and Trewavas (2020a, 2020b); Calvo et al. (2020); Levin et al. (2021); Linson and Calvo (2020); Lyon et al. (2021); Maher (2017, 2020); Mallatt et al. (2021); Robinson et al. (2020); and Taiz et al. (2019, 2020). 24. Gagliano (2013a, 2013b); Kikuta et al. (1997); Kikuta and Richter (2003); Laschimke et al. (2006); Perks et al. (2004); Rosner et al. (2006); Zweifel and Zeugin (2008). 25. Kimmerer (2013, 128). 26. Gagliano, Mancuso, et al. (2012); Gagliano, Renton, et al. (2012). 27. Sano et al. (2013, 2015). 28. Frongia et al. (2020); Gagliano (2013a, 2013b); Gagliano, Mancuso, et al. (2012); Gagliano, Renton, et al. (2012); Khait, Lewin-­Epstein, et al. (2019); Khait, Obolski, et al. (2019); Khait, Sharon, et al. (2019); Szigeti and Parádi (2020). 29. Pace (1996). 30. Gagliano, Mancuso, et al. (2012); Gagliano, interview with author, March 2021. 31. Gagliano, Mancuso, et al. (2012). 32. Gagliano, interview with author, March 2021. 33. Pollan (2013). 34. See Brenner et al. (2006) and the subsequent exchange: Alpi et al. (2007) and Brenner et al. (2007). 35. This also raises the question, which is beyond the scope of this book, of w ­ hether plants are capable of cognition. Building on recent research that demonstrates plants’ capacities for sophisticated be­hav­iors once thought to be unique to the animal kingdom—­such as nutrient foraging and complex decision making—­a growing number of researchers argue that ­there is sufficient evidence to consider plants to be cognitive organisms: Segundo-­Ortin and Calvo (2021). 36. Gagliano et al. (2017). 37. Gagliano et al. (2017). Although subsequent research has in­de­pen­dently confirmed her claim that plants exhibit learning and memory, Gagliano’s research remains controversial among some scientists, particularly her account of her experimental design pro­cess, in which the plants themselves instructed her on the designs via dreams or while she was in a shamanistic trance: Gagliano (2018). For commentaries and critiques, see Cocroft and Appel (2013); Robinson et al. (2020); and Taiz et al. (2019). For a rebuttal, see Baluška and Mancuso (2020) and Maher (2017, 2020). See also Mancuso and Viola (2015). 38. Appel and Cocroft (2014). 39. Michael et al. (2019). 40. Kollasch et al. (2020).

236  N o t e s t o C h a p t e r 6 41. Quoted in Mishra et al. (2016, 4493). 42. Body et al. (2019); Ghosh et al. (2016). 43. Gagliano, interview with author, March 2021. 44. Kollist et al. (2019). 45. Paik et al. (2018); Sharifi and Ryu (2021). 46. Simpson (2013). 47. Rogers et al. (1988). 48. Gagliano, Mancuso, et al. (2012); Khait, Obolski, et al. (2019); Simpson (2013). 49. Monshausen and Gilroy (2009). 50. Liu et al. (2017); Yin et al. (2021). 51. Krause (2013). See also Farina et al. (2011). 52. Eldridge and Kiefer (2018); Farina et al. (2011); Mossbridge and Thomas (1999); Villanueva-­Rivera (2014). 53. Krause (1987, 1993, 2013). 54. Haskell (2013, 5). 55. On the related “acoustic habitat” hypothesis, see Mullet et al. (2017). 56. Capranica and Moffat (1983). 57. Barber et al. (2021); Corcoran et al. (2009); Neil et al. (2020). 58. Yovel et al. (2008, 2009). 59. Kaufman (2011). 60. von Helversen and von Helversen (1999). 61. De Luca and Vallejo-­Marin (2013); Vallejo-­Marin (2019). 62. Note that bees can also detect and learn floral electrical fields. See Clarke et al. (2013). 63. Veits et al. (2019). 64. For criticisms of this research, see Pyke et al. (2020) and Raguso et al. (2020). For a response, see Goldshtein et al. (2020). 65. Kaufman (2011). See also Simon et al. (2011). The authors have gone on to develop better sonar for UAVs using the plant-­based method: Simon et al. (2020). 66. Gagliano et al. (2014); Schaefer and Ruxton (2011). 67. Segundo-­Ortin and Calvo (2021). 68. Bailey et al. (2013). 69. Schöner et al. (2016). 70. Lacoste, Ruiz and Or (2018); Maeder et al. (2019); Quintanilla-­Tornel (2017); Rillig, Bonneval and Lehmann (2019). 71. Görres and Chesmore (2019). 72. Mason and Narins (2002). 73. Briones (2018); Hill and Wessel (2016). 74. Mishra et al. (2016); ten Cate (2013). 75. Segundo-­Ortin and Calvo (2021). 76. Safina (2015). 77. Supper (2014); Turino (2008). 78. Callicott (2013); Daly and Shepard (2019); Kirksey (2014); Russell (2018). 79. Kimmerer (2002, 436). 80. Gagliano (2017, 2018).

N o t e s t o C h a p t e r 7   237

Chapter 7: Bat Banter 1. Hahn (1908). 2. Dijkgraaf (1960). 3. Griffin (1958). 4. Saunders and Hunt (1959). 5. Pierce’s device could detect ultrasound in the range from 20 kHz to approximately 100 kHz. 6. Temperature also influences acoustic communication in fishes: Ladich (2018). 7. Pierce (1943). 8. Pierce (1948, 7). 9. Griffin, quoted in Squire (1998, 74). 10. Griffin (1980); Pierce and Griffin (1938). 11. Griffin (1980). 12. Griffin (1946); Griffin and Galambos (1941). 13. Griffin and Galambos (1941, 498). 14. Yoon (2003). 15. Griffin (1989). 16. Grinnell and Griffin (1958); Griffin et al. (1960). 17. Griffin (1989, 138). 18. Ibid. 19. Knörnschild, interview with author, June 2021. 20. Balcombe (1990); Jones and Ransome (1993); Wilkinson (2003). 21. Fernandez and Knörnschild (2020). 22. Knörnschild et al. (2012). 23. Knörnschild (2014). 24. Knörnschild and Helverson (2006). 25. Hörmann et al. (2020). 26. Knörnschild et al. (2017). 27. ­There is also evidence that bats can learn new dialects as adults. In studies of other species, scientists have demonstrated that bats that are moved from one colony to another can adjust the frequency of their calls to match t­ hose of their new community: Hiryu et al. (2006). 28. Smotherman et al. (2016). 29. Morell (2014). 30. Smotherman et al. (2016). 31. Goodwin and Greenhall (1961). 32. Barlow and Jones (1997). 33. Vernes and Wilkinsin (2020). 34. Knörnschild, interview with author, June 2021. 35. Ibid. 36. Ibid. 37. Byrne and Whiten (1994); Whiten and Byrne (1997). 38. Chaverri et al. (2018); Kerth (2008); Wilkinson et al. (2019). 39. Knörnschild (2017).

238  N o t e s t o C h a p t e r 7 40. Knörnschild, interview with author, June 2021. 41. Skibba (2016). 42. Harten et al. (2019); Moreno et al. (2021); Prat and Yovel (2020). 43. Car­ter and Wilkinson (2013, 2015). 44. Car­ter and Wilkinson (2016). 45. Ripperger et al. (2020). See also Stockmaier et al. (2020a, 2020b) and Waldstein (2020). 46. Dressler et al. (2016). 47. Dressler et al. (2016); Ripperger et al. (2016). 48. Visalli, interview with author, November 2021. 49. Why would bats be useful to study if scientists are interested in insights into the origins of ­human language? Conventionally, the animal model of choice for the study of vocal learning has been songbirds. Although birds and ­humans are separated by around three hundred million years of evolution, we share some ge­ne­tic and behavioral similarities. For example, the first gene discovered to cause a language disorder in ­humans, FOXP2, is expressed in similar patterns in songbird and ­human brains. In ­humans, disruptions to FOXP2 can result in impaired grammar and language expression; in songbirds, disruptions to the same gene can have pronounced effects on vocal learning, causing songbirds to drop syllables and perform abnormally variable, inaccurate songs. More than fifty genes have been identified with potential links to vocal learning; ­these genes have similar expressions in songbird and h­ uman brains (patterns not found in nonvocal learning species, such as doves or macaques). Th ­ ese similarities allow researchers to study the same gene in both h­ umans and songbirds, and perform experiments on birds that would not be ethical in h­ umans, such as knocking out or artificially boosting genes. But bird brain architecture is vastly dif­fer­ent from ours, lacking the layered mammalian ce­re­bral cortex and cortical-­basal ganglia cir­cuits, both of which are associated with higher functions, such as cognition and learning. This led to an e­ arlier belief—­since dispelled—­that the avian brain was not wired to learn; scientists no longer believe that birds act automatically, purely on instinct, like winged automata. Rather, songbirds learn their songs much as ­humans learn to sing—­ through imitation and repeated practice (Beecher et al. 2017). Only in recent de­cades have researchers demonstrated that songbirds have complex structures in their brains; but t­ hese are or­ga­nized in clumps (called nuclei) rather than layers, as in h­ umans. Avian brain cells may not have the same macrostructure as ­human brains, but they function at a similar level of complexity. Nonetheless, the differences are so significant that studies of birds do not necessarily shed light on analogous pro­cesses in h­ umans: (Dugas-­Ford (2012); Calabrese and Woolley (2015); Haesler et al. (2007); Heston and White (2015); Lai et al. (2001); Pfenning et al. (2014); Reiner et al. (2004)). 50. Rodenas-­Cuadrado et al. (2018). 51. Vernes and Wilkinson (2020). 52. Ripperger et  al. (2019); Wilkinson and Boughman (1998). See http://­m irjam​ -­knoernschild​.­org​/­vocal​-­repertoires​/­saccopteryx​-­bilineata/ for recordings and a discussion of ­these categories. 53. Prat et al. (2016); Skibba (2016). 54. Hörmann et al. (2020); Knörnschild et al. (2020). 55. Shen (2017).

N o t e s t o C h a p t e r 8   239 56. At the time of writing, evidence of vocal learning has been found in eight of the seventeen families of bats. Vocal learning has also been documented in other species, including ­whales, birds, and elephants: Lattenkamp et al. (2018); Petkov (2012); Vernes and Wilkinson (2020); Vernes (2017). 57. Knörnschild (2014). 58. Knörnschild and Fernandez (2020). 59. The method is known as ELVIS (echo location visualization and interface system): Amundin et al. (2008); Starkhammar et al. (2007). 60. Knörnschild, interview with author, June 2021. 61. Rose et al. (2020). 62. Zwain and Bahuaddin (2015). 63. Low et al. (2021). 64. Brady and Coltman (2016). 65. Alaica (2020). 66. Fernández-­Llamazares (2021). 67. Tønnessen et al. (2016). Related fields of study in the natu­ral sciences (biosemiotics, zoosemiotics) and social sciences (multispecies ethnography, posthuman animal studies) use the term umwelt in dif­fer­ent ways, as t­ here is no one universally accepted definition of the term—­which its inventor, Jakob von Uexküll, declined to precisely define. 68. Sapolsky (2011). 69. Trestman and Allen (2016). 70. Griffin (1976). 71. Griffin and Speck (2004, 6). 72. See, for example, Dennett (1995, 2001) and Searle and Willis (2002). 73. Yoon (2003). 74. Terrace and Metcalfe (2005). 75. Nagel (1974). 76. Nagel (1974, 436). 77. Wittgenstein (1953). 78. Nagel (2012, 7). 79. Knörnschild, interview with author, June 2021.

Chapter 8: How to Speak Honeybee 1. Kelly (1994, 7–8). 2. Hrncir et al. (2011). 3. Nobel Prize (1973a, 1973b). 4. Munz (2016). 5. Munz (2016, 19). 6. Frisch (1914). 7. Frisch (1967). See also Camazine et al. (2003); Gould (1974); Gould et al. (1970). 8. Dyer and Seeley (1991); Gould (1982). 9. De Marco and Menzel (2008); Menzel et al. (2006).

240  N o t e s t o C h a p t e r 8 10. Munz (2016, 1). 11. Gould (1976). 12. Munz (2005). 13. Gould (1974, 1975, 1976); Gould et al. (1970). 14. Munz (2016); Nobel Prize (1973a, 1973b). 15. Munz (2016). 16. Frisch (1950). See also Schürch et al. (2016). 17. For a review of this topic, see Hunt and Richard (2013). 18. Witzany (2014). 19. Dreller and Kirchner (1993); Kirchner (1993); Lindauer (1977). 20. Cecchi et al. (2018); Collison (2016); Nolasco and Benetos (2018); Nolasco et al. (2019). 21. Ramsey et al. (2017); Tan et al. (2016). 22. Boucher and Schneider (2009); Dong et al. (2019); Nieh (1998, 2010); Richardson (2017); Terenzi et al. (2020). 23. Witzany (2014). 24. Cheeseman et al. (2014); Wu et al. (2013). 25. Dyer et al. (2005); Wu et al. (2013). 26. Abramson et al. (2016); Alem et al. (2016). 27. Moritz and Crewe (2018). 28. Bateson et al. (2011); Perry et al. (2016). 29. Srinivasan (2010, R368). 30. Passino and Seeley (2006); Passino et al. (2008); Schultz et al. (2008); Seeley et al. (2006, 2012). See also Niven (2012). 31. Viveiros de Castro (2012); Seeley (2010); Seeley et al. (2012). 32. McNeil (2010); Seeley (2009, 2010); Seeley et al. (2012). 33. Nakrani and Tovey (2003, 2004); Seeley (2021). 34. Boenisch et al. (2018). 35. Boenisch et al. (2018). 36. Nouvian et al. (2016). 37. Liang et al. (2019). 38. Haldane and Spurway (1954). 39. Michelsen et al. (1993). 40. Singla (2020). 41. Koenig et al. (2020). 42. Dong et al. (2019). 43. Cejrowski et al. (2018); Murphy et al. (2015). 44. Kulyukin et al. (2018); Ramsey et al. (2020); Ramsey and Newton (2018); Zgank (2019). 45. For more information about HIVEOPOLIS, see https://­www​.­hiveopolis​.­eu. 46. Nunn and Reid (2016); Whitridge (2015). 47. Hollmann (2004); Rusch (2018a, 2018b); Swan (2017). 48. Sugawara (1990). 49. Gruber (2018); Isack and Reyer (1989); Marlowe et al. (2014); Spottiswoode et al. (2011). 50. Crane and Graham (1985). 51. Isack and Reyer (1989).

N o t e s t o C h a p t e r 9   241 52. Spottiswoode et al. (2016). 53. Spottiswoode (2017); Spottiswoode et al. (2016). See also FitzPatrick Institute of African Ornithology (2020). 54. Clode (2002); Dounias (2018); Hawkins and Cook (1908); Peterson et al. (2008). 55. Spottiswoode and Koorevaar (2012). 56. van der Wal et al. (2022). 57. Spottiswoode et al. (2011). See also FitzPatrick Institute of African Ornithology (2020). 58. Wario et al. (2015). See also Boenisch et al. (2018) and Wario et al. (2017). 59. Wario et al. (2015). 60. BroodMinder (2020); IoBee (2018); OSbeehives (n.d.). 61. McQuate (2018). 62. Wyss Institute (2020); MAV Lab (2020). 63. Hadagali and Suan (2017); Kosek (2010); Mehta et al. (2017). 64. Couvillon and Ratnieks (2015). 65. Kosek (2010); Moore and Kosut (2013). 66. Sinks (1944). 67. Kosek (2010); Schaeffer (2018). 68. Lockwood (2008). 69. Kosek (2010); Moore and Kosut (2013). 70. Ebert (2017); Leek (1975). 71. Rangarajan (2008). 72. Scheinberg (1979). 73. Cook (1894); Crane (1999); Crane and Graham (1985); Gimbutas (1974); Lawler (1954); Posey (1983); Ransome (2004); Sipos et al. (2004); Stillwell (2012).

Chapter 9: The Internet of Earthlings 1. Reiss et al. (2013). 2. The mirror self-­recognition test was developed in the 1970s by American psychologist Gordon Gallup, as a method for determining w ­ hether animals possess the capacity of visual self-­recognition: Gallup (1970). See also Bekoff (2002) and Bekoff and Sherman (2004). 3. Gershenfeld, quote from TED talk: https://­blog​.­ted​.­com​/­the​-­interspecies​-­internet​-d­ iana​ -­reiss​-­peter​-­gabriel​-­neil​-­gershenfeld​-­and​-­vint​-c­ erf​-­at​-­ted2013​/­. 4. See https://­www​.­interspecies​.­io​/­about. 5. Andreas et al. (2021). 6. Bilal et al. (2020). 7. Allen et al. (2017, 2018, 2019); Ferrer-­i-­Cancho and McCowan (2009); Gustison and Bergman (2017); Gustison et al. (2016); Heesen et al. (2019); Semple et al. (2010). 8. The Zipf-­Mandelbrot law holds true, with remarkable consistency, across all known h­ uman languages. The law establishes a quantifiable (inverse power law) relationship between individual signals and their frequency of use. As the amount of transmitted information increases, a communication channel increases in complexity, but this places higher motor and cognitive demands on an animal—­both for accurate signal interpretation and for meaningful signal production. This creates a trade-­off between information content and cognitive complexity; balancing this trade-­off

242  N o t e s t o C h a p t e r 9 may have played a role in the evolution of ­human languages and may be universal to communication in general. If so, a similar pattern may be used as an indicator of language-­like communication in nonhumans; conversely, communication systems that do not exhibit this pattern are unlikely to be complex languages. The greater the departure of the frequency distribution of animal sounds from the Zipf-­Mandelbrot curve, the lower the likelihood that vocalizations made by any par­tic­ u­lar species are complex languages: Fedurek et al. (2016); Ferrer-­i-­Cancho (2005); Ferrer-­i-­ Cancho and Solé (2003); McCowan et al. (2005); Seyfarth and Cheney (2010). 9. Matzinger and Fitch (2021). 10. Da Silva et al. (2000); Doyle et al. (2008); Freeberg et al. (2012); Freeberg and Lucas (2012); Kershenbaum et al. (2021); Shannon (1948); Suzuki et al. (2006). 11. See, for example, Mann et al. (2021) and Kershenbaum et al. (2021). 12. Allen et al. (2019); Engesser and Townsend (2019); Speck et al. (2020); Zuberbühler (2015, 2018). 13. Bermant et al. (2019). 14. See https://­audaciousproject​.­org​/­ideas​/­2020​/­project​-­ceti. 15. Gardner and Gardner (1969); Gardner et al. (1989). 16. Hurn (2020). 17. McKay (2020); Pedersen (2020); Perlman and Clark (2015); Reno (2012). 18. Pepperberg (2009). 19. Ralls et al. (1985). 20. Eaton (1979). 21. Stoeger et al. (2012). 22. Hurn (2020). 23. Herzing (2010). 24. Kohlsdorf et al. (2013); Ramey et al. (2018). 25. Herzing (2014, 2015, 2016); Herzing and Johnson (2015); Herzing et al. (2018); Kohlsdorf et al. (2014, 2016). 26. Hooper et al. (2006); Kaplan et al. (2018); Marino et al. (1993, 1994); McCowan and Reiss (1995, 1997); Morrison and Reiss (2018); Reiss and Marino (2001); Sarko and Reiss (2002). 27. Reiss and McCowan (1993). 28. Meyer et al. (2021); Woodward et al. (2020a, 2020b). 29. See http://­www​.­m2c2​.­net​/­. 30. Manual labeling worked as follows. To begin, volunteers w ­ ere presented with an enlarged image of a spectrogram and listened to the corresponding sound by clicking on the image. Then they listened to randomly paired calls from the proj­ect’s database. If they found a match, the volunteers clicked on the spectrogram and the results ­were stored as a match. By repeating the steps with large numbers of volunteers, the reliability of the matches increased. 31. Mager et al. (2021). 32. Novel techniques include the use of back translation and synthetic training data. However, ­these techniques are imperfect, and AI algorithms still fall prey to common flaws (overly literal interpretation, poor per­for­mance on colloquial language, conflating dif­fer­ent dialects). 33. Within the past ten years, a specific type of machine learning called deep learning (also referred to as artificial neural networks) has been deployed, with remarkable results, in natu­ral

N o t e s t o C h a p t e r 9   243 language pro­cessing (NLP) tasks, including machine translation and reading comprehension. ­These neural networks learn to encode words and sequences as vectors (directional sequences of real numbers). A key innovation of neural networks is that the vectors do not follow classical linguistic structures or rules; rather, mathematical operations applied to ­these vectors produce the outputs. In other words, the linguistic competence acquired by neural networks does not depend on prior knowledge of linguistic rules or structures. See Linzen and Baroni (2021). 34. Mikolov et al. (2013). 35. Artetxe et al. (2017); Conneau et al. (2017). 36. Ethayarajh (2019); Ethayarajh et al. (2018); Schuster et al. (2019). See also Dabre et al. (2020). 37. Acconcjaioco and Ntalampiras (2021); Huang et al. (2021); Wolters et al. (2021). 38. Chung et al. (2018). 39. Another building block required for a nonhuman dictionary is a standardized phonetic alphabet for nonhuman sounds. In 2021, computational linguist Robert Eklund proposed the creation of animIPA—­a nonhuman version of the International Phonetic Alphabet (known as IPA), which would incorporate sounds characteristic of nonhumans (such as the egressive and ingressive airstreams associated with sounds like purring and roaring) in a standardized chart of phonetic symbols and, eventually, unicodes. 40. Bekoff (2002); Bekoff et al. (2002); De Waal (2016); De Waal and Preston (2017); Dolensek et al. (2020); Panksepp (2004); Preston and De Waal (2002). 41. Dolensek et al. (2020); Girard and Bellone (2020). 42. Neff (2019). 43. Roemer et al. (2021). 44. On BirdNET, see Kahl et al. (2021). See also Gupta et al. (2021); Zhang et al. (2021); 45. This issue can be addressed by supplementing training data with background noise in order to simulate dif­fer­ent acoustic environments: Krause et al. (2016); Salamon and Bello (2017). 46. Fairbrass et al. (2019); Salamon and Bello (2017). 47. Wndchrm has been applied to analyze astronomy datasets (revealing new insights about the rotation of galaxies), pop songs (­they’ve gotten sadder and angrier since the 1950s), and even visual art; Wndchrm can distinguish between impressionism, expressionism, and surrealism (with an accuracy rate of over 90 ­percent): Kuminski et al. (2014); Napier and Shamir (2018); Shamir et al. (2008, 2010). 48. Bergler et al. (2021); Bermant et al. (2019); Kaplun et al. (2020); Lu et al. (2020); Mac Aodha et al. (2018); Shamir et al. (2014); Usman et al. (2020); Wang et al. (2018); Zhang et al. (2019). 49. Abbasi et al. (2021); Coffey et al. (2019); Fonseca et al. (2021); Hertz et al. (2020); Ivanenko et al. (2020); Marconi et al. (2020). 50. Barbieri (2007); von Uexküll (2001, 2010). See also Schroer (2021) and Tønnessen (2009). 51. Mancini (2011). See also Hirskyj-­Douglas et al. (2018); Mancini (2016). 52. Bozkurt et al. (2014); Byrne et al. (2017); Valentin et al. (2015). 53. French et al. (2020). 54. Neethirajan (2017).

244  N o t e s t o C h a p t e r 9 55. Aspling (2015); Aspling and Juhlin (2017); Aspling et al. (2016, 2018); Barreiros et al. (2018); Grillaert and Camenzind (2016). 56. van Eck and Lamers (2006, 2017). 57. French et al. (2020). 58. Baskin and Zamansky (2015); Lee et al. (2020); Piitulainen and Hirskyj-­Douglas (2020); Pons and Jaen (2016); Webber et al. (2017a, 2017b, 2020); Westerlaken (2020); Westerlaken and Gualeni (2014); Zea­gler et al. (2014, 2016). 59. Cianelli and Fouts (1998); Fouts et al. (1984); Gardner and Gardner (1969); Gisiner and Schusterman (1992); Herman et al. (1984); Pepperberg (2009); Reiss and McCowan (1993); Schusterman and Krieger (1984, 1986); Sevcik and Savage-­Rumbaugh (1994). 60. Amundin et al. (2008); Boysen and Berntson (1989); Egelkamp and Ross (2019); Herman et al. (1984, 1990); Kilian et al. (2003); Knörnschild and Fernandez (2020); Pepperberg (1987, 2006, 2009); Reiss and McCowan (1993); Savage-­Rumbaugh and Fields (2000); Schusterman and Krieger (1984, 1986). 61. Landgraf et al. (2011, 2012, 2018). 62. Bonnet et al. (2018); Bonnet and Mondada (2019). 63. Hofstadler et al. (2017); Wahby et al. (2016). 64. Bonnet et al. (2018); Cazenille et al. (2018); Gribovskiy et al. (2015); Griparić et al. (2017); Halloy et al. (2007); Katzschmann et al. (2018); Landgraf et al. (2012); D. Romano et al. (2017a, 2017b, 2019); W. B. Romano et al. (2019); Shi et al. (2014); Stefanec et al. (2017); Swain et al. (2011); Vaughan et al. (2000); Wahby et al. (2018a, 2018b). 65. Moore et al. (2017). See also https://­vihar​.­lis​-­lab​.­fr​/­. 66. Mac Aodha et al. (2018); Bonnet et al. (2019); Brattain et al. (2016); Carpio et al. (2017); FitBark (2020); Haladjian, Ermis, et al. (2017); Haladjian, Hodaie, et al. (2017); Kreisberg (1995); Neethirajan (2017); Oikarinen et al. (2019); Siddharthan et al. (2012); Yonezawa et al. (2009). 67. Bonnet et al. (2019); Schaeffer (2017). 68. Garnett et al. (2018); Kimmerer (2013); Schuster et al. (2019). 69. Ansell and Koenig (2011); Kyem (2000); Louis et al. (2012); Pearce and Louis (2008); Pert et al. (2015); Rundstrom (1995). 70. Dowie (2009); Rundstrom (1991). 71. Carroll et al. (2019); Global Indigenous Data Alliance (2020); Kukutai and Taylor (2016); Kyem (2000); Rundstrom (1995). 72. Hagood (2018). 73. Ritts and Bakker (2021). 74. Carroll et al. (2019). 75. Lovett et al. (2019). 76. Kukutai and Taylor (2016). 77. Watts (2013). 78. Salmón (2000). 79. Cruikshank (2012, 2014); Hall (2011); Kimmerer (2013). 80. Deloria (1986, 1999). 81. TallBear (2011). 82. Watts (2013, 2020).

N o t e s t o C h a p t e r 10   245 83. Kimmerer (2017, 251). 84. Kimmerer (2017, 131). 85. Low et al. (2012). 86. Marino et al. (2007); Reiss (1988); Reiss et al. (1997); Whitehead et al. (2004); Whitehead and Rendell (2014). 87. See, for example, Andrews and Beck (2018). 88. For example, see the exchanges between Hauser, Chomsky, and Fitch versus Pinker and Jackendoff: Fitch (2005, 2010); Fitch et al. (2005); Hauser et al. (2002); Pinker and Jackendoff (2005). 89. Hurn (2020); Kulick (2017).

Chapter 10: Listening to the Tree of Life 1. Pershing et al. (2015). 2. Rec­ord et al. (2019). 3. Clark et al. (2010); Davis et al. (2017, 2020); Grieve et al. (2017); Meyer-­Gutbrod and Greene (2018); Meyer-­Gutbrod et al. (2018); Rec­ord et al. (2019); Scales et al. (2014); Simard et al. (2019); Woodson and Litvin (2015). 4. Almén et al. (2014); Grieve et al. (2017); Wishner et al. (2020). 5. MacKenzie et al. (2014). 6. Stokstad (2017). 7. Whale mortality statistics are gathered separately on the US and Canadian sides of the border; the total observed mortality, due to shipping and fishing, of North American right ­whales in 2017 was estimated at 4 ­percent of the population: Davies and Brillant (2019); Daoust et al. (2017); Johnson et al. (2021); Koubrak et al. (2021); Sharp et al. (2019). 8. Davies and Brillant (2019); Department of Fisheries and Oceans (2017). 9. Gavrilchuk et al. (2021). See also Williams (2019). 10. Davies and Brillant (2019). 11. Detailed statistics on North Atlantic right ­whale mortalities are kept by NOAA: https://­ www​.­fisheries​.­noaa​.­gov​/­national​/­marine​-­life​-­distress​/­2017​-­2021​-­north​-­atlantic​-­right​-­whale​ -­unusual​-m ­ ortality​-e­ vent. 12. Parks et al. (2011). 13. Davis et al. (2020). 14. CBC News (2020). See also Gervaise et al. (2021). 15. Government of Canada (2021a). 16. Government of Canada (2021b). 17. Subsection 38(1) of the Canada Shipping Act allows for fines of up to $1 million or a prison term not exceeding eigh­teen months (or both) for violations of a regulation that implements Canada’s international obligations: Koubrak et al. (2021). 18. All fishers and harvesters using ropes are also required to use only weak ropes in fixed-­ gear fishing, so that ropes can break to help ­whales self-­release if they become entangled. A major “ghost gear” initiative has been launched to fund the recovery of lost nets, ropes, and lines, which also pose a major threat to the ­whales. 19. Durette-­Morin et al. (2019). See also http://­whalemap​.­ocean​.­dal​.­ca​/­.

246  N o t e s t o C h a p t e r 10 20. Lostanlen et al. (2021). 21. Carnarius (2018); International Chamber of Shipping (2020). 22. Channel Islands National Marine Sanctuary (n.d.). 23. Morgan Visalli, interview with author, November 2020; see also Visalli et al. (2020). 24. Olson (2020). 25. Similar systems have already been used with some success for right ­whales in the North Atlantic: National Geographic (2020); NOAA Fisheries (2020a, 2020b); Nrwbuoys​.­org (2020). 26. Baumgartner et al. (2019). 27. Abrahms et al. (2019). 28. Whale Safe’s acoustic data is continuously monitored, and updates are sent e­ very two hours. Visual data is sourced via Whale Alert (a citizen science app, with more activity at peak ­whale watching, tourist, and recreational boater seasons) and Spotter Pro (an app used by professional naturalists and scientists). Modeling data on oceanographic conditions favoring ­whales is updated daily. Ship position data is updated daily, with a two-­to three-­day lag. 29. Fox (2020); Olson (2020); Simon (2020). 30. See http://­www​.­whalealert​.­org. 31. CBC News (2019); Jeffrey-­Wilensky (2019); Lubofsky (2019); Murray (2019). 32. Davies (2019); Durette-­Morin et al. (2019). 33. Barlow and Torres (2021); Barlow et al. (2018, 2020, 2021); Torres (2013); Torres et al. (2020). 34. New Zealand Supreme Court (2021). 35. Barlow and Torres (2021). 36. Lavery et al. (2010); Pershing et al. (2010); Roman et al. (2014). 37. Chami et al. (2019). 38. IPCC (2019). 39. Poloczanska (2018). 40. Abecasis et al. (2018); Cooke et al. (2011); Cowley et al. (2017); Currier et al. (2015); Haver et al. (2018); Steckenreuter et al. (2017). 41. Proulx et al. (2019). 42. Jones et al. (2020); McWhinnie et al. (2018); Siders et al. (2016). 43. Cooke et al. (2017); Hays et al. (2016); Wilmers et al. (2015). 44. Our ability to watch and listen to animals in places that we could not reach in the past has given rise to new bioacoustics methods in the field of movement ecology—­a scientific discipline dedicated to understanding the movements of organisms across space and time. See Nathan et al. (2008) and Fraser et al. (2018). 45. Chal­mers et al. (2021); Dodgin et al. (2020). 46. Burke et al. (2012). 47. Braulik et al. (2017); Showen et al. (2018); Woodman et al. (2003, 2004). See also Gibb et al. (2019). 48. Culik et al. (2017); Curé et al. (2013); Omeyer et al. (2020). 49. See, for example, Todd et al. (2019). 50. Clark et al. (2009). 51. Chou et al. (2021). 52. Lindsay (2012).

N o t e s t o C h a p t e r 10   247 53. Erbe et al. (2019). 54. Boyd et al. (2011). 55. Duarte et al. (2021). 56. Rolland et al. (2012). 57. Jariwala et al. (2017); Passchier-­Vermier and Passchier (2000). 58. Despite the findings, the federal government authorized permits for oil and gas exploration companies to use seismic noise cannons to map the ocean floor off the east coast, in preparation for pos­si­ble drilling. See Struck (2014). 59. Jones et al. (2020). 60. Charifi et al. (2017); Erbe et al. (2018); Kaifu et al. (2007). 61. de Soto et al. (2013); Hawkins et al. (2015); McCauley et al. (2003); Popper and Hastings (2009); Richardson et al. (1995). 62. Fewtrell and McCauley (2012); Kostyuchenko (1971); McCauley et al. (2017); Neo et al. (2015); Pearson et al. (1992). 63. Di Franco et al. (2020); Dwyer and Orgill (2020); Erbe et al. (2018); Kavanagh et al. (2019). 64. Francis and Barber (2013); Kight and Swaddle (2011); McGregor et al. (2013). 65. For a metareview, see Barber et al. (2010) and Duquette et al. (2021). 66. This is known as the Lombard effect. See, for example, Brown et al. (2021). 67. Gomes et al. (2021). 68. Cinto Mejia et al. (2019); McClure et al. (2013, 2017); Ware et al. (2015). Similar results have been found with “phantom gas fields” (recorders that play the sounds of compressors and other machinery used in natu­ral gas extraction)—­a concern given that six hundred thousand new gas wells have been drilled across North Amer­i­ca in the past twenty years. 69. Barber et al. (2011); Buxton et al. (2017). 70. Mariette et al. (2021). See also Nedelec et al. (2014) and Rivera et al. (2018). 71. Jain-­Schlaepfer et al. (2018). 72. Buehler (2019). See also Fakan and McCormick (2019). 73. Boudouresque et al. (2006, 2016); Hemminga and Duarte (2000); Lamb et al. (2017); UNEP (2020). 74. Boudouresque et al. (2009); Capó et al. (2020); Edwards (2021); Green et al. (2021); Jordà et al. (2012); Krause-­Jensen et al. (2021). 75. André et al. (2011); Solé et al. (2013a, 2013b, 2016, 2017, 2018, 2019, 2021a, 2021b). 76. den Hartog (1970). 77. Arnaud-­Haond et al. (2012). 78. Statocysts enable orientation, balance, sound detection, and gravity perception in marine organisms. They function in a manner similar to inner ear organs in fish, which detect particle motion and pressure in ­water. In cephalopods, which do not have ears, statocysts are located within the cephalic cartilage. Early stages of cephalopods pre­sent sensory hair cells grouped into lateral lines on their heads and arms. This explains how, even without ears, octopuses can locate prey or predators, particularly in low light conditions; with their statocysts and multiple arms lined with sensory hair cells, they sense even tiny sounds through vibrations in the ­water. 79. Two types of noise frequencies ­were used: scanning and transmission electron microscopy techniques. See Solé et al. (2013b).

248  N o t e s t o C h a p t e r 10 80. Solé et al. (2018). 81. Solé et al. (2021a). 82. Amyloplasts are starch-­filled plastids that orient the plant in the w ­ ater column, much like sound-­sensitive statocysts help marine invertebrates orient in space. Amyloplasts, somewhat like mitochondria in our cells, are separate organelles that are surrounded by a double-­lipid membrane, and that possess their own DNA. As they produce and store starch inside the internal membrane compartments, amyloplasts sediment within cells; as they do so, they trigger gravity signal transduction in the plant (sending a message to specific parts of the root, allowing it to direct itself downward). See Solé et al. (2021a). See also Hashiguchi et al. (2013); Kuo (1978); Pozueta-­Romero et al. (1991); and Yoder et al. (2001). 83. Solé et al. (2021a). 84. Solé et al. (2021a). 85. Michel André and Marta Solé, interview with author, October 2021. 86. An ecoacoustics index is a mathematical function that synthesizes key aspects of acoustic energy in an “auditory scene.” Indices can adopt a variety of methods, such as calculating the signal-­to-­noise ratio or the spectral distribution of energy, or segmenting the data into patterns associated with acoustic events: Barchiesi et al. (2015); Kholghi et al. (2018). 87. An ecoacoustics index is a dynamic mea­sure; b­ ecause the Earth is balanced dynamically, forever evolving in space and time, ecoacoustics indices also evolve dynamically. 88. See, for example, Bohnenstiehl et al. (2018). 89. Barzegar et al. (2015); Basner et al. (2017); Bates et al. (2020); Cantuaria et al. (2021); Dutheil et al. (2020); Thompson et al. (2020). 90. Boyd et al. (2011). 91. Tamman (2020). 92. For more on the International Quiet Ocean Experiment (or IQOE), see https://­www​ .­iqoe​.­org​/­. See also Tamman (2020). 93. Basan et al. (2021); Denolle and Nissen-­Meyer (2020); Derryberry et al. (2020); March et al. (2021); Nuessly et al. (2021). 94. Čurović et al. (2021); see also Coll (2020); Cooke et al. (2021); Ryan et al. (2021). 95. Asensio, Aumond, et al. (2020); Asensio, Pavón, et al. (2020); Lecocq et al. (2020); Silva-­Rodríguez et al. (2021); Vishnu Radhan (2020). 96. Sueur et al. (2019). 97. Siddagangaiah et al. (2021). 98. Burivalova et al. (2019); Chen et al. (2011); Francis et al. (2017); Gibbs and Bresich (2001); Larom et al. (1997); Narins and Meenderink (2014); Oliver et al. (2018); Parmesan and Yohe (2003); Sugai et al. (2019). 99. Oliver et al. (2018). 100. Sueur et al. (2019). See also Krause and Farina (2016). 101. Harries-­Jones (2009). 102. UNESCO (2017). 103. Chou et al. (2021); Duarte et al. (2021). 104. Michel André and Marta Solé, interview with author, October 2021. 105. See, for example, Williams et al. (2018); Zwart et al. (2014). 106. Eldridge (2021).

N o t e s t o A p p e n di x C   249 107. Eldridge (2021, 4). 108. Wilson (1997). 109. Coghlan (2015). 110. Poppick (2017). 111. Royal Society (n.d.). 112. Ford (2001).

Appendix C: Brief Overview of Research on Bio-­and Ecoacoustics 1. Bioacoustics is an assemblage of vari­ous technologies: recording devices that capture sound, artificial intelligence algorithms that analyze and classify the data, computers that store and pro­cess the data, the internet that shares the information, and apps that bring this data into our lives. 2. Vallee (2018). 3. Jacoby et al. (2016). 4. Gibb et al. (2019); Lucas et al. (2015); Wrege et al. (2017). 5. Frommolt and Tauchert (2014). 6. Isaac et al. (2014); Klingbeil and Willig (2015). 7. Supper and Bijsterveld (2015). 8. Cocroft et al. (2014); Hill (2008); Hill and Wessel (2016, 2021); Hill et al. (2019). 9. Cocroft and Rodríguez (2005); Cocroft et al. (2014); Gagliano, Mancuso, et al. (2012); Gagliano, Renton, et al. (2012); Hill and Wessel (2016); Maeder et al. (2019); Michelsen et al. (1982); Mortimer (2017). 10. Cocroft et al. (2014); Hill (2008); Hill and Wessel (2016); Narins et al. (2016). 11. Hill (2008). 12. Warkentin (2005, 2011). 13. Fabre et al. (2012); Hill and Wessel (2021); McKelvey et al. (2021). 14. Hill and Wessel (2021). 15. Hill and Wessel (2021). 16. Hutter and Guayasamin (2015). 17. Raick et al. (2020). 18. Cerchio et al. (2020). 19. Buxton et al. (2016). 20. Dimoff et al. (2021). 21. Freeman (2012); Freeman and Hare (2015). 22. Derryberry et al. (2020); Halfwerk (2020). 23. Warkentin (2005). 24. Zwart et al. (2014). 25. Durette-­Morin et al. (2019); Visalli et al. (2020). 26. Thorley and Clutton-­Brock (2017). 27. Piniak (2012); Piniak et al. (2018); Tyson et al. (2017). 28. Clay et al. (2019); Gazo et al. (2008). 29. Ausband et al. (2014). 30. King et al. (2017).

250  N o t e s t o A p p e n di x C 31. Suraci et al. (2016). 32. W. B. Romano et al. (2019). 33. Gordon et al. (2019). 34. French et al. (2020). 35. Clark et al. (2012). 36. Piitulainen and Hirskyj-­Douglas (2020). 37. Partan et al. (2009, 2010); Rundus et al. (2007). 38. Narins et al. (2005). 39. Steiner et al. (2017).

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Index

acacia trees, 56 acidification, 80, 93 acoustic masking, 190 acoustic niche hypothesis, 111–12 acoustic playback design, 87, 105 Acoustic Tracking Array Platform, 188 African honeybee (Apis mellifera scutellate), 56; color discrimination by, 139–40, 142; eyesight of, 144–45; honey hunting and, 151–53; military research into, 155–56; monitoring technology and, 154–55; in spiritual traditions, 156; swarming by, 145, 150; vibration signals of, 147–50; waggle dancing by, 138–39, 140–41, 143–44, 148, 149, 150 Ahgeak, Mae, 28 Aiken, Wesley, 33 Aksaarjuk, Ittuangat, 12 Alaska Eskimo Whaling Commission, 29–30, 37–38 Albert, Tom, 30 Aldrich, Herbert L., 11–14, 26–27 algae, 82–83, 90, 93, 96, 109 Amazon river turtle (Podocnemis expansa, tartarugas), 66–75, 68, 69, 70, 77–78 amniotes, 74 amyloplasts, 194–95 André, Michel, 194, 195–97, 200 anemones, 82 animal-­computer interaction (ACI), 171–72 Anishinaabe ­people, 9, 69–70, 176 Antarctica, 20, 25, 193, 199 anthropocentrism, 116, 117, 135, 163 anthropomorphism, 22, 116–17, 135

apiculture, 150 Apis mellifera scutellate. See African honeybees (Apis mellifera scutellate) Appel, Heidi, 108, 110 Arabidopsis thaliana, 108, 110 Arctic Ocean, 97, 177; acoustic monitoring in, 2, 38; climate change in 37, 188, 199; digital listening in, 2; ­w hales and whaling in, 10–12, 14, 26–31, 38, 40 Arctic terns, 31, 34–35, 97 Aristotle, 9, 102–3, 117 artificial intelligence (AI), 5, 40, 61, 131, 183, 185; for algorithms, 173; for background noise isolation, 52–54; for biodiversity modeling, 189; computer vision, 61, 146, 147, 154, 169; deep learning, 38, 242n33; for interspecies communication, 6, 136, 157, 160–61, 201, 203; for language translation, 165–69. See also artificial neural networks; machine learning artificial neural networks, 38, 52–54, 166, 170, 242n33, See also artificial intelligence AudioMoth, 3 auditory scene hypothesis, 109 Augustine of Hippo, Saint, 9 Australian snake-­necked river turtle (Chelodina colliei), 64–65 Ausube, Jesse, 197 autonomous recording units (ARUs), 49–50 Bacon, Roger, 203 baleen w ­ hale (Mysticeti), 13, 19, 25, 27 Bastos, Rafael José de Menezes, 76 Bates, Henry Walter, 67–69 345

346 i n de x Batesian mimicry, 69 Bateson, Gregory, 200 bats, 2, 3, 18; controversial claims about, 134–37; deterrence of, 219; digital recording of, 126–30; echolocation by, 119–23; language of, 131–33; social relationships of, 128–30, 131; sounds of, 121–23, 125, 126–27, 129–31, 137, 169; species of, 126; vocal learning by, 124–26, 132; Western vs. Indigenous views of, 133 Baylor, Ted, 83–84 bees, 4, 56–59, 113, 138, 219 beluga ­whale, 3, 22, 31, 163 Bering Strait, 13–14, 31, 37, 188 bestiaries, 9 Bijsterveld, Karin, 216 bioacoustics: for bat study, 126–30; for conservation, 95, 179–84, 189–90, 219; datasets enabled by, 161–62; defined, 3, 4; digitizing of, 38–39; ethical and philosophical issues surrounding, 174–79; ­human intervention needed for, 170; Indigenous knowledge and, 117; marine, 14; of plants, 102–10; research in, 215–19; for turtle study, 74–77; for ­whale study and protection, 29–32, 41, 162, 182–83, 185–87, 218. See also ecoacoustics biodiversity, 95, 195; assessments of, 54, 74, 86, 189; in Barrow Canyon, 31; in ­Great Barrier Reef, 82, 93; loss of, 93, 179, 187–88, 189; in Mackenzie River system, 207; in Shediac Valley, 181. See also conservation; endangered species biophony, 198 biosonar (echolocation), 2, 18; by bats, 19, 47, 112, 122–23, 132 biotremology, 217–18 Bird, Christopher, 105 birds: climate change and, 198, 218; echolocation by, 2; migratory, 31, 97, 121, 192, 199; naturalists’ interest in, 67, 69; noise pollution and, 192–93; songbirds, 40, 125, 126, 131, 161, 199, 218, 238n47; species of, 81, 169, 170. See also individual species

black-­legged kittiwakes, 31 blast fishing (dynamite fishing), 96, 189–90 bleaching, of coral reefs, 83, 86, 93, 94 blue ­whale, 24, 25, 45, 185, 186–87, 218 bonobos, 159, 163 Boran ­people, 152 Borrows, John, 9 Bose, Jagadish Chandra, 101 bowhead ­whale, 11, 13, 14, 19, 26–31, 33–37, 39–40, 42 Brihadaranyaka Upanishad, 156 Brower, Harry (Kupaaq), Sr., 30, 37–38, 42–43 Brunelli, Giovanni Angelo, 67 bullroarers, 151 Burdon-­Sanderson, John, 101 butterflies, 69 calicles, 232n11 Cambridge Declaration on Consciousness (2012), 178 Cameroon, 51 Canada, 22, 183 carbon dioxide, 80, 187 carbonic acid, 80 CARE (collective benefit, authority to control, responsibility, ethics) protocol, 175 Car­ter, Gerry, 130 cave swiftlets, 2 cephalopods, 194 Cerf, Vint, 158–59 cetaceans, 18, 19, 22, 25, 40–41, 178 Cetacean Translation Initiative (CETI), 160, 164 Chamowitz, Daniel, 108 CHAT (Cetacean Hearing and Telemetry), 164 chimpanzees, 162–63 China, 69, 133 cicadas, 199 Clark, Chris, 18, 19, 25–26, 30–31, 33, 37 climate change, 37, 93, 180, 194, 196; birds and, 198, 218; coral reefs and, 80–81, 95, 187–89; migration and, 198–200

i n de x   clownfish, 85, 88 cnidarians, 194 “cocktail party prob­lem,” 52 cod, 85 combinatorial pro­cessing, 161 computer vision, 61, 146, 147, 154, 169 conservation, 6, 44, 47, 179, 183–84, 202, 218; of elephants, 51–55, 60–62; of honeybees, 153–55; in Indigenous communities, 175; of marine life, 188–89, 195; noise pollution and, 4; of turtles, 78; of ­whales, 162. See also biodiversity; endangered species Conservation Metrics, 52–53 Convention on International Trade in Endangered Species (1973), 24, 54 convolutional neural networks, 170 copepods, 180, 199 coral larvae, 3, 91–97 coral reefs, 3–4, 218; bleaching of, 83, 86, 93, 94; climate change and, 80–83, 187–88; fish sounds in, 84–85, 87–88, 89; larval habitat se­lection and, 88, 90; regeneration of, 95–96, 219; sounds of, 92–93; spawning of, 91, 97 corn plants, 104, 106, 112 coseismic ionospheric disturbance (CID), 221n5 Cousteau, Jacques, 14 COVID pandemic, 197–98 crab, 89, 182 crickets, 120, 199, 217 croakerfish, 84 crustaceans, 31, 74, 89, 95, 110, 194, 198, 219 cryptography, 141, 161 Cyphastrea, 93 damselfish, 88 dart-­poison frogs, 219 Darwin, Charles, 67, 100, 101, 108 Darwin, Francis, 100 Dassow, Angela, 178 Davies, Kimberley, 182 Davy, Christina, 63–64 deep learning, 38, 242n33

347 deep listening, 7, 10, 77, 133, 169, 176, 201 deep sound channel (sound fixing and ranging [SOFAR] channel), 15, 24 De Flumine Amazonum (Brunelli), 67 deforestation, 9, 54 Deloria, Vine, 176 Descartes, René, 9, 203 digital technology, 2–4, 9–10, 85, 100, 200–203, 218, 231n47; artificial intelligence linked to, 6, 161, 164, 189; for bat study, 125, 126–28, 130, 133–37; for coral reef study, 94–99; early forecasts of, 7–8; for elephant study, 52–54, 60–62; for honeybee study, 146–50, 154–59, 219; for interspecies communication, 172–76; for marine study, 33–43, 162, 183–90; miniaturization of, 5; for translation, 161–71; for turtle study, 74–77 Dingaal ­people, 82 DNA, 130, 203 dolphins, 81, 157–59, 171; as bycatch, 190, 219; echolocation by, 132; ­human communication with, 153; sounds of, 3, 18, 25, 83–85, 164, 165 Douglas-­Hamilton, Iain, 44, 45, 54, 56 Drosophilae, 217, 233n60 drought, 48, 102, 103, 199 DTAGs (digital acoustic recording tags), 39–41 dwarf lemurs, 2 dynamite fishing (blast fishing), 96, 189–90 Dzanga-­Sangha National Park, 49 Earle, Sylvia, 95–96 earthquakes, 1, 16, 221n5 earthworms, 115 echolocation (biosonar), 2, 18, 25 by bats, 20, 47, 112 ecoacoustics, 75, 115, 175, 196–97, 201, 218–19; defined, 4–5. See also bioacoustics ecoacoustics index, 196–97, 200 Egypt, ancient, 69, 156 Egyptian fruit bat (Rousettus aegyptiacus), 129–30, 131 Eklund, Robert, 243n39

348 i n de x Ekwa (friend of Bastos), 76 Eldridge, Alice, 89, 201 Elephant Ethogram, 61 Elephant Listening Proj­ect, 49, 51, 53 elephants: alarm and distress calls of, 53, 58; bees and, 56–58; counts of, 50–51; deterrence of, 57, 219; forest-­dwelling, 49–50, 51; infrasound communication by, 45–49, 58, 62; learning by, 60, 163; mating by, 45, 48; poaching of, 44, 48, 51, 53–54, 55, 58; shrinking habitat of, 54–55; social networks of, 48–49; sounds of, 46–47, 52, 59; tracking of, 45; vocal recognition by, 59–60; warning systems for, 60–61 Elephants Without Borders, 50–51 ELVIS (echo location visualization and interface system), 239n57 endangered species, 70, 166, 168, 179, 180, 184–85, 188–90, 215, 218; bats, 133; designation of, 24, 51; international convention on, 54; river turtles, 63; ­whales, 183. See also biodiversity; conservation Eptesicus fuscus, 123 ethnobotany, 117 ethology, 133–35, 174 Eu­ro­pean Union, 165–66, 200 FAIR (findable, accessible, interoperable, reusable) princi­ples, 176 Farina, Almo, 198 Ferrara, Camila, 63–64, 66–67, 71–73, 78 Ferreira, Alexandre, 68 Fiji, 74 filtering, 38, 52 fin ­whale, 24, 25, 26, 45, 185 fish, 3–4, 74, 76, 83–85, 199, 219, 237n6, 247n78; ­dying coral reefs avoided by, 86–87; larvae of, 86–90, 94, 97; microhabitat se­lection by, 88, 90, 94; noise pollution and, 192, 193. See also individual species Fish, Marie Poland, 16 flies, 123, 217 flight simulators, 148 The Forest Unseen (Haskell), 111

Francis of Assisi, Saint, 8 Frick, Henry Clay, II, 20 Frisch, Karl von, 84, 139–44, 146–47, 154 frogs, 111, 199, 217–19, 218–19 Frohn, Adolf, 83 fungi, 67, 195 Gabriel, Peter, 158–59 Gabrys, Jennifer, 41 Gagliano, Monica, 103, 105–9, 112, 114, 117 Galambos, Bob, 121–22 Galileo Galilei, 202 Gallup, Gordon, 241n2 geophony, 198 George, John Craighead “Craig,” 30 Gershenfeld, Neil, 158–59 Giles, Jacqueline, 63–66, 71, 73, 77 Glass, James, 167 global positioning system (GPS), 32, 130 goby fish (Bathygobius soporator), 83 Gomes, Carla, 52–53 Google Translate, 1, 149, 165–66, 171 Gordon, Tim, 81–84, 86, 93–96 gorillas, 41, 162 Görres, Carolyn-­Monika, 115 Gould, James, 142 grasshoppers, 115, 120 ­Great Barrier Reef, 81–84, 86, 93, 95–97 greater sac-­w inged bat (Saccopteryx bilineata), 124–25, 128–29 Greek antiquity, 9, 69, 138, 151, 156 Greenpeace, 24 Griffin, Donald, 20, 119, 120–24, 131, 133–35 Gulf of St. Lawrence, 181, 183 Gutenberg, Johannes, 7–8 The Gutenberg Galaxy (McLuhan), 7 Gyps africanus (white-­backed vulture), 218 Haldane, John, 149 Haskell, David, 111–12, 114 Hawking, Stephen, 178 Helmreich, Stefan, 14 Herzing, Denise, 164 Hill, Peggy, 217–18

i n de x   HIVEOPOLIS proj­ect, 150, 219 homing pigeons, 97 Honey Bee algorithm, 146–47 honeybees. See African honeybee (Apis mellifera scutellate) Honey Doctrine, 156 honeyguides, 152–53 honey hunting, 151 Hopkins, Carl, 46 humpback ­whale, 13, 19, 20–21, 23, 40, 84, 185 hydration, 99, 102, 104 hydrophones, 15, 21, 32–34, 38–39, 65, 72, 182, 185 ibis, 219 Igbo ­people, 69 Indigenous knowledge, 7; of Amazon p­ eoples, 75–77; of Anishinaabe ­people, 9, 69–70, 176; bats and, 133; bees and, 151, 153; of Boran ­people, 152; of Dingaal ­people, 82; ­Great Barrier Reef and, 81, 89–90; of Igbo ­people, 69; interspecies communication and, 174–78; of Inuit ­people, 12, 34; of Iñupiat ­people, 10, 29–31, 35–36, 42; of Kamayurá ­people, 76–77; of Kamba ­people, 59; of Kĩsedje/Suya ­people, 75; of Maasai ­people, 56; of Marshall Islanders, 90; of Ma­ya ­people, 133, 156; of Moche ­people, 133; of Palauan ­people, 89–90; of Samburu ­people, 59; scientific knowledge linked to, 117; of Tapajó ­people, 70; of Tumpasa ­people, 70; turtles and, 69–70; of /Xam (San) ­people, 151, 156–57; of Xinguano ­people, 76; of Yao p­ eople, 152–53, 156 Indigenous ­peoples, 68, 151, 175; Amazon ­peoples, 75–77; Anishinaabe ­people, 9, 69–70, 176; Boran ­people, 152; Dingaal ­people, 82; Igbo ­people, 69; Inuit p­ eople, 12, 34; Iñupiat ­people, 10, 29–31, 35–36, 42; Kamayurá ­people, 76–77; Kamba ­people, 59; Kĩsedje/Suya ­people, 75; Maasai ­people, 56; Marshall Islanders, 90; Ma­ya ­people, 133, 156; Moche p­ eople,

349 133; Palauan ­people, 89–90; Samburu ­people, 59; Tapajó ­people, 70; Tumpasa ­people, 70; /Xam (San) ­people, 151, 156–57; Xinguano ­people, 76; Yao ­people, 152–53, 156 Indonesia, 89, 94, 96, 133 infrasound/infrasonic communication, 1, 17, 218; and bats, 121; and elephants, 45–49, 58–62; and mice, 169; and peacocks, 1, 218; and planet Earth, 1, 221n7; and ­whales, 19 In Search of the ­Mother Tree (Simard), 103 insects, 138, 142, 144, 155, 216–17; Darwin’s descriptions of, 67; hearing of, 103; as prey, 123; sounds of, 15, 75, 108, 115, 120, 161, 198 Integrated Marine Observing System (IMOS), 188 Intergovernmental Panel on Climate Change (IPCC), 187 International Monetary Fund (IMF), 187 International Monitoring System (IMS), 221n7 International Quiet Ocean Experiment, 197 International System of Units, 196 International Union for the Conservation of Nature, 51 International Whaling Commission (IWC), 24, 29, 34, 36 interspecies communication, 169–70, 172–74, 177, 201–2, 217, 219; with bats, 129, 132; with honeybees, 137, 150; internet and, 6, 8, 159–64, 171, 178–79 Inuit ­people, 12, 34 Iñupiat ­people, 10, 12, 14, 26–37, 41–43 ivory trade, 44, 48, 54–5 jaguars, 70 Japan, 17, 133 jellyfish, 82, 194 Johannes, Bob, 89, 90 Kamayurá ­people, 76–77 Kamba ­people, 59 Kelley, Edmund, 12–13, 14, 43 Kelly, Kevin, 138, 145

350 i n de x kelp, 80 ­Kenya, 44, 47–49, 57, 59, 60, 152 killer ­whale, 18, 19, 35, 37, 40, 111, 126, 153, 170, 209 Kimmerer, Robin Wall, 7, 9, 104, 117, 177–78, 180 King, Lucy, 54, 56–60 Kĩsedje/Suya ­people, 75 Knörnschild, Mirjam, 124–29, 132, 136–37 Koenig, Phoebe, 150 Krause, Bernie, 111–12, 198 krill, 24 Kwaymullina, Ambelin, 82 Landgraf, Tim, 147–51, 173 Langbauer, William, 46 Leeuwenhoek, Anton van, 202–3 Lepidurus lubbocki, 75 linguistic laws, 161 lions, 41 ­Little Bear, Leroy, 2 Lizard Island, 82, 94 lobsters, 85, 89, 182 Locke, John, 203 loons, 31 Lovejoy, Thomas, 47 low-­resource languages, 167 Maasai ­people, 56, 59 Machiavellian intelligence hypothesis, 128 machine learning, 38, 74–75, 166, 169, 183; defined, 222n26; for dolphin study, 164; for honeybee study, 146–47, 150, 154; limitations of, 127, 169–70; for plant study, 104; for rainforest study, 231n47; for real­time monitoring, 96, 189, 222–23n28; for songbird study, 199; strengths of, 223n29; for ­whale study, 160. See also artificial intelligence Magnasco, Marcelo, 164–65 magpies, 59, 158 Mancini Clara, 171–72 Marcravia evenia, 113 Marder, Michael, 103

marine bioacoustics, 14, 38, 190 Marine Mammal Communication and Cognition (m2c2), 164 Marine Mammal Protection Act (1972), 25 marine noise, 36, 93, 95, 191–94 Marine Strategy Framework Directive, 200 Marler, Peter, 25 Marshall Islanders, 90 Martin, Rowan, 45 matched filter hypothesis, 112 mating, 200; by bats, 125, 130, 131; by elephants, 50; by fish, 84; by flies, 217; by ibises, 219; by peacocks, 1, 218; by turtles, 64, 66; by ­whales, 19, 24 Ma­ya ­people, 133, 156 McComb, Karen, 59 McLuhan, Marshall, 7–8 McVay, Scott, 22 mechanoreception, 110 MediaPipe, 168 meerkats, 218 Menzerath-­Altmann law, 161 mice, 1, 3, 169 microhabitat se­lection, 88 microscopy, 202–3 migration: by bats, 125; by bees, 155; by birds, 31, 97, 121, 192, 199; climate change and, 199–200; by elephants, 48, 55; noise pollution and, 192; by turtles, 73, 188; by ­whales, 23, 30–31, 34, 48, 181, 185, 186, 188 military research, 15–16, 17, 26, 38, 105, 121–22, 155–56 Mind and Cosmos (Nagel), 136 minke ­whale, 25 mirror self-­recognition, 158, 241n2 Moby Dick (Melville), 12 Moche ­people, 133 moles, 115 monkeys, 219, 229n64 mosquitoes, 123 Moss, Cynthia, 45, 47–49 “motherese,” 3, 125, 131 moths, 2, 88, 112, 123, 173, 221n10 movable type, 7–8

i n de x   Murphy, Fiona, 150 Myers, Natasha, 103, 116 Mysticeti (baleen ­whale), 19, 25

351

OCAP (owner­ship, control, access, possession) protocol, 175 Ocean Alliance, 24 Ocean Noise Reference Station Network, 188 oceans: acidification of, 80, 93; industrialization of, 191; noise in, 36–37, 38, 78, 93, 95, 190–95; rising level of, 187–88 octopuses, 178, 192, 194–95 Odontoceti (toothed ­whales), 18, 25 oil birds, 2 Okalok, Rex, Sr., 28 Oliver, Ruth, 199 oral culture, 8 orangutans, 41 O’Reilly, Tim, 100 organelles, 104, 194, 248n82 Ossiannilsen, Frej, 217 Out of Control (Kelly), 138 oysters, 192

passive acoustic monitoring (PAM), 215–16 Payne, Katy, 19–25, 31, 44–49, 51, 53–54, 62 Payne, Roger, 19–25, 31 peacocks, 1, 218 pelagic fish, 95 Peru, 133 Philosophical Investigations (Wittgenstein), 135 phonotropism, 102 photosynthesis, 82, 117 phytoacoustics, 102–3, 115 phytoethnography, 117 phytomorphism, 117 phytoplankton, 31, 187 Pierce, George Washington, 119–22, 198 PigChase (interspecies video game), 172 pi­lot ­whales, 170 pipistrelles, 126 piranhas, 75, 218 plants, 4; acoustic tuning and, 110–13; anthropological study of, 117; bats attracted by, 112; communication and signaling by, 102–4, 109, 114–16; definitional questions surrounding, 107, 114; digitally enhanced, 99–102; learning and memory imputed to, 108; playback experiments with, 105–8; root bending by, 106. See also individual species Plant-­Thinking (Marder), 103 polar bears, 32 Pollan, Michael, 103 pollination, 57, 112–14, 133, 154–55, 192 polygyny, 126 Pomacentridae, 88 Poole, Joyce, 45, 47–49, 61–62 porpoises, 18, 47, 221n10 Posidonia oceanica, 194 prairie dogs, 3, 173 primates, 3, 128, 157, 161, 162–63, 171 Proj­ect Florence, 99–100, 219, 234n2

Palauans, 89–90 Palla’s long-­tongued bat, 112 Parks, Susan, 39–40, 191 parrotfish, 85 parrots, 157, 163, 172

raccoons, 219 radar, 15, 122, 149 rainfrogs, 111, 218 rats, 2, 173 ravens, 153

Nagel, Thomas, 135, 136 National Oceanic and Atmospheric Administration (NOAA), 188 Naturalist on the River Amazon (Bates), 67 Naval Arctic Research Laboratory, 30, 35 navigation, 2, 18; by bats, 119–21, 123, 126, 129; by coral larvae, 91, 97; at dif­fer­ent ocean depths, 19; by fish larvae, 87, 90, 95; beneath ice, 34; marine noise and, 37 Nemipteridae (whiptail bream), 88 Newton, Isaac, 203 noise pollution, 179, 196, 200; marine, 36–37, 38, 78, 93, 95, 190–95; reversibility of, 197–98 Noongwook, George, 37

352 i n de x Regen, Ivan, 15 Reiss, Diana, 158, 164–65, 178 right ­whale, 25–26, 39, 180–83 Riley, Julia, 73 robotics, 95, 146, 148–50, 155, 173, 219 Rolland, Rosalind, 190–91 root bending, 106 Roseway, Asta, 99–100 Rustic Sounds (Francis Darwin), 100 Safina, Carl, 116 Sakakibara, Chie, 28 salmon, 97, 181, 188 Samburu ­people, 59 Sargassum seaweed, 164 savannah elephants, 48–49, 50–51 Save the Elephants, 54 Scarabaeidae, 115 Schevill, Barbara, 17 Schevill, Bill, 17 Scientific Revolution, 202 sea anemones, 233n60 seagrass, 74, 193–95 sea level rise, 80, 187 sea lice, 194 seals, 12, 14, 31, 33, 111, 163, 192 sea turtles, 74, 78, 89, 219 sea urchins, 85, 164 seaweed, 164 The Secret Life of Plants (Tompkins and Bird), 104 Seeley, Thomas, 145–47 seismic exploration, 35, 37, 93, 191, 192, 194, 195, 197, 218, 247n58 Shahnameh (epic poem), 8 shamanism, 8, 75, 235n37 Shannon entropy, 161 sharks, 40, 91 Shediac Valley, 181 shrews, 2 shrimp, 75, 84–85, 89, 194 sign language, 140, 163, 168 The ­Silent World (Cousteau), 14

Simard, Suzanne, 103 Simpson, Steve, 86–93, 95–96, 97 Smith, Nigel, 70 snakes, 1, 218, 229n64 social complexity hypothesis, 128, 160 Solé, Marta, 194–95 Soltis, Joseph, 58–60 sonar, 15, 17–19, 29, 112, 122, 123, 132, 191 songbirds, 126, 131, 199; climate change and, 218; diversity of, 40; learning of, 125, 238n47; social complexity and vocal complexity in, 128; syntax of, 161 The Songs of Insects (Pierce), 120 Songs of the Humpback Whale (rec­ord ­album), 23 Songs of Trees (Haskell), 114 SOSUS (sound surveillance system), 15, 17, 19, 38 sound fixing and ranging (SOFAR) channel (deep sound channel), 15–16, 24, 224n27 soundscapes, 4, 41, 96, 107, 111, 193, 207, 222n14; assessments of, 53–54, 231n47; climate change and, 198–99; coral reef degradation and, 85–86, 93, 94, 97; defined, 5; Indigenous ­peoples and, 201; urban, 222n21. See also bioacoustics; ecoacoustics sound speed minimum, 224n27 southern right ­whale (Eubalaena australis), 25–26 Southworth, Michael, 222n21 Spallanzani, Lazzaro, 119 sparrows, 218 spectrograms, 33, 52, 71, 85–86, 242n30 sperm ­whale (Phyeter macrocephalus), 13, 18, 160–61 Spottiswoode, Claire, 152–53 squid, 91, 192 squirrels, 219 Sri Lanka, 60–61 statocysts, 194–95, 247n78, 248n82 Stewart platform, 148 striped ground cricket, 120 Sueur, Jérôme, 198–99 Supper, Alexandra, 216

i n de x   TallBear, Kim, 176 Tanzania, 44–45, 51, 189 Tapajó ­people, 70 tarsiers, 2 Tavolga, Margaret, 83–84 Tavolga, William, 83–84 Teilhard de Chardin, Pierre, 7 theodolites, 32 Theophrastus, 103, 117 Thomas, Elizabeth Marshall, 46 Thomas Aquinas, Saint, 9 Thus Spoke the Plant (Gagliano), 103 tiger moth, 112 tobacco, 104 tomato plants, 104 Tompkins, Peter, 104–5 Torres, Leigh, 186 traditional ecological knowledge, 69, 167; of Amazon ­peoples, 75–77; of Anishinaabe ­people, 9, 69–70, 176; bats and, 133; bees and, 151, 153; of Boran ­people, 152; of Dingaal ­people, 82; ­Great Barrier Reef and, 81, 89–90; of Igbo p­ eople, 69; of Inuit, 12, 34; of Iñupiat ­people, 10, 29–31, 35–36, 42; interspecies communication and, 174–78; of Kamayurá ­people, 76–77; of Kamba ­people, 59; of Kĩsedje/Suya ­people, 75; listening and, 7; of Maasai ­people, 56; of Marshall Islanders, 90; of Ma­ya ­people, 133, 156; of Moche p­ eople, 133; of Palauan ­people, 89–90; of Samburu ­people, 59; scientific knowledge linked to, 117; of Tapajó ­people, 70; of Tumpasa ­people, 70; of /Xam (San) p­ eople, 151, 156–57; of Xinguano p­ eople, 76; of Yao ­people, 152–53, 156 treefrogs, 217–18 trees, 2–3, 111, 114–15, 124, 127, 153, 156, 217; bats and, 133; as carbon sinks, 187; elephant be­hav­ior and, 56; ultrasound of, 103 trichomes, 110 Triops cancriformis, 75 Tumpasa ­people, 70

353 Turkalo, Andrea, 49 turtles, 3; acoustic coordination by, 72–73; as bycatch, 190, 218; commercial hunting of, 67–69, 77; hearing of, 73, 75, 79; in my­thol­ ogy, 69–70; poaching of, 78; sounds of, 63–67, 69, 71–72, 74–79 Tusker Alert (warning system), 60–61 Txeemsim (Raven), 8 Tyack, Peter, 197 Uexküll, Jakob von, 171 ultrasound/ultrasonic communication: and bats, 1, 112, 120–22, 126, 219, 237n5; and beetles, 1; and corn, 1; and corals, 1, 93–102; and dolphins, 165; and insects, 120; and lemurs, 2; and mice, 1; and moths, 1; and plants, 4, 103; and primates, 221n10; and tarsiers, 2; and ­whales, 17–18 UNESCO, 89, 200 United Nations Declaration on the Rights of Indigenous ­Peoples (UNDRIP), 175. See also Indigenous knowledge; traditional ecological knowledge Vallee, Mickey, 215 Vanuatu, 90 Venus flytrap, 101 vervet monkeys, 229n64 vibration sensing, 104, 108–9 vibroacoustics, 144, 151, 173 Visalli, Morgan, 185, 187 vitalism, 117 vocal clans, 160 Vocal Interactivity in-­and-­between ­Humans, Animals, and Robots (VIHAR), 173 voice recognition, 5–6, 39, 129, 171, 173 von Békésy, Georg, 123 walrus, 12, 14, 31, 33 Warkentin, Karen, 217 Watkins, William, 17 Watlington, Frank, 20–21, 23

354 i n de x Watts, Vanessa, 176 wavelength, of sounds, 47 Webb, Douglas, 24 Wenner, Andrew, 142 Whale FM, 166, 170 ­whales, 2, 3, 245n7, 245n11, 245n18, 246n25, 246n28; acoustic enrichment and, 219, 224n22; as bycatch, 190; commercial hunting of, 11–12, 14, 20, 23–24; counts of, 28, 29, 31–34, 35–36; hearing of, 17–18, 35, 144, 239n54; migration by, 23, 30–31, 34, 48, 180, 181, 185, 186, 188; in shipping zones, 181–82, 184–88, 191; sounds of, 12–13, 16–19, 21–23, 33–35, 38–40, 161–62, 166, 170; subsistence hunting of, 27–28, 29, 35, 36, 41; taxonomy of, 218; tracking of, 38–41 Whale Safe, 185–86, 246n28 The Whales, They Give Themselves (Brower), 42–43 “What Is It Like to Be a Bat?” (Nagel), 135 whiptail bream, 88 white-­backed vulture (Gyps africanus), 218 white sharks, 40

Wild Dolphin Proj­ect, 164 Wilson, Catherine, 202 Wittgenstein, Ludwig, 135 wolves, 9, 47, 219 Won­der, Stevie, 105 Woods Hole Oceanographic Institution, 17, 83 Wrege, Peter, 49–50, 52–53 /Xam (San) ­people, 151, 156–57 Xinguano ­people, 76 Yao ­people, 152–53, 156 Yartsey, Michael, 131 Yovel, Yossi, 112, 113, 129–30 zebra finches, 193 zero-­resource languages, 167 Zimbabwe, 45 Zipf-­Mandelbrot law, 161, 241n8 Zooniverse, 166 zooplankton, 31, 180, 192 zooxanthellae, 82