Evolutionary Behavioral Ecology and Psychopathy 303132885X, 9783031328855

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Janko Međedović

Evolutionary Behavioral Ecology and Psychopathy

Evolutionary Behavioral Ecology and Psychopathy

Janko Međedović

Evolutionary Behavioral Ecology and Psychopathy

Janko Međedović Inst. Criminological & Sociological Rsch Belgrade, Serbia

ISBN 978-3-031-32885-5    ISBN 978-3-031-32886-2 (eBook) https://doi.org/10.1007/978-3-031-32886-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Acknowledgments

I would like to express my gratitude to Todd Shackelford and Satoshi Kanazawa for their support in my publication efforts, it is deeply appreciated. I would like to thank Rebecca Sear and Monique Borgerhoff Mulder for being the constant source of inspiration due to their tremendous work in the field, and for constructive discussion on my previous work. Biljana Stojković and Jelena Čvorović also have my gratitude for support and consultations regarding the publication of this book. But my innermost gratitude is for Branislava Gemović for standing beside me all these years, being my grounding, put up with me, and being the only person with which I had constant discussions about behavioral evolution. For these and many other things, she has not only my gratitude but my deepest love.

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Contents

Prologue: A Personal Reflection on Human Behavioral Ecology��������������     1  Basic Concepts of Evolutionary Biology������������������������������������������������������     7 Evolution����������������������������������������������������������������������������������������������������������     7 Fitness��������������������������������������������������������������������������������������������������������������     8 Evolutionary Tradeoffs������������������������������������������������������������������������������������    10 Evolutionary Processes: Mutations, Genetic Drift, Gene Flow, and Natural Selection��������������������������������������������������������������������������������������    10 The Types of Natural Selection������������������������������������������������������������������������    12 Sexual Selection����������������������������������������������������������������������������������������������    15 Adaptations������������������������������������������������������������������������������������������������������    16 Evolutionary Behavioral Sciences����������������������������������������������������������������    19 The Foundation: Animal Behavior in the Light of Evolutionary Processes��������������������������������������������������������������������������������    19 Human Behavioral Ecology: A Brief History��������������������������������������������������    21 The Tsimane Health and Life History Project: A Representative Example of Ethnographic Research in HBE����������������������������������������������������    22 Do We Still Evolve? Natural Selection in Modern Human Populations����������    24 HBE: Critiques, Controversies, and Unresolved Questions����������������������������    26 Evolutionary Psychology: The Basic Tenets����������������������������������������������������    29 Criticisms of EP ����������������������������������������������������������������������������������������������    31 EP and HBE: Opposed or Complementary Disciplines? Could Both Be Possible? ��������������������������������������������������������������������������������    38  Evolutionary Ecology of Family ������������������������������������������������������������������    41 Parental Investment: Sexual Selection Strikes Again��������������������������������������    41 Parental Care in Humans ��������������������������������������������������������������������������������    43 Grandparental Investment��������������������������������������������������������������������������������    45 Parental–Offspring Interactions ����������������������������������������������������������������������    46 Reproductive Motivations and Intentions��������������������������������������������������������    49 Understanding Demographic Transition����������������������������������������������������������    51 vii

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Evolutionary Tradeoffs in Humans��������������������������������������������������������������    55 Fertility-Longevity Tradeoff����������������������������������������������������������������������������    55 The Tradeoff Involved with the Age of First Reproduction (AFR) ����������������    57 Quantity-Quality Tradeoff�������������������������������������������������������������������������������    58 Mating-Parenting Tradeoff������������������������������������������������������������������������������    60 Life History Theory ��������������������������������������������������������������������������������������    63 The Basic Tenets of Life History Theory (LHT) ��������������������������������������������    63 LHT in Evolutionary Psychology and Its Critiques����������������������������������������    65 Covariation Between Life History Traits ��������������������������������������������������������    66 Environmental Context and the Life History Traits����������������������������������������    68 Network Approach to Life History������������������������������������������������������������������    72 LHT Criticisms, Debates, and Open Questions ����������������������������������������������    76  Behavioral Ecology of Personality����������������������������������������������������������������    81 Evolution of Animal Personalities ������������������������������������������������������������������    81 Human Personality Ecology: The Associations Between Personality Traits and Fitness��������������������������������������������������������������������������    85 Explaining Three Evolutionary Puzzles of Personality in Humans����������������    86 Human Personality Ecology: The Extensions��������������������������������������������������    89  Psychopathy and Its Current Evolution������������������������������������������������������    93 Psychopathy: Definition and Measurement ����������������������������������������������������    93 Nomological Network of Psychopathy in Brief����������������������������������������������    95 Genetic, Neurobiological, and Environmental Precursors of Psychopathy������������������������������������������������������������������������������������������������    99 Psychopathy in an Evolutionary Context��������������������������������������������������������   102 The Links Between Psychopathy and Fertility������������������������������������������������   105 The Answers on the First Evolutionary Puzzle of Psychopathy����������������������   107 The Empirical Study: Psychopathy, Fertility, Longevity, Interacting Phenotypes, and Parental Effects ��������������������������������������������   111 Goals of the Present Study������������������������������������������������������������������������������   111 Sample��������������������������������������������������������������������������������������������������������������   113 Measures����������������������������������������������������������������������������������������������������������   113 The Plan of Data Analysis��������������������������������������������������������������������������������   114 Results: Analyzing the Links Between Psychopathy and Fitness��������������������   115 Results: Analyzing Interacting Phenotypes ����������������������������������������������������   119 Results: Analyzing Parental Effects����������������������������������������������������������������   123 Discussion��������������������������������������������������������������������������������������������������������   128 Epilogue: Another Personal Reflection on Human Behavioral Ecology������������������������������������������������������������������������������������������������������������   139 References ������������������������������������������������������������������������������������������������������   143 Index����������������������������������������������������������������������������������������������������������������   183

Prologue: A Personal Reflection on Human Behavioral Ecology

Ten years ago, I read the manuscript entitled “Evolutionary Genetics of Personality” authored by Lars Penke, Jaap Denissen, and Geoffrey Miller. I was a PhD student back than and I did not have any formal education in evolutionary biology, except for a basic knowledge on evolution we all acquired in secondary school. Hence, I did not understand almost half of the manuscript. However, I had an impression that the manuscript covers a very important topic regarding the personality science, and I also had a feeling that I encountered something very exciting and thrilling. So, despite the frustration generated by my lack of understanding, I decided to pursue this topic. I tried to overcome my lack of formal education on evolution by taking two courses on the Faculty of Biology in Belgrade, as a part of my PhD education, with limited success. So, basically, I had to read and search for relevant publications by myself. As I researched the field, I found manuscripts which emerged from a field of evolutionary psychology. However, I was not satisfied by the evolutionary psychology research I stumbled upon. I felt that there is a lack of crucial biological criteria in that research, especially the ones related to (evolutionary) fitness. Hence, it seemed to me that these manuscripts, although sometimes very interesting in topics and ideas, lacked explanatory power to interpret their results in evolutionary context. Despite the elaborate explanations from evolutionary psychologists defending the way of interpreting empirical results and connecting them with evolution, I am still not convinced in evolutionary psychological explanations (although the field is very heterogeneous and there are indeed quite convincing evolutionary psychological research; I will discuss this in more detail further in this book). It needed several more years in order for me to understand that the scientific field I wanted to enter seemed to be called human behavioral ecology (HBE) or human evolutionary ecology. There was additional obstacle for the slow pace in my understanding of the field. HBE does not have clear, relatively simple and homogenous theoretical perspective; furthermore, it is also very dispersed in an

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Međedović, Evolutionary Behavioral Ecology and Psychopathy, https://doi.org/10.1007/978-3-031-32886-2_1

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empirical sense—integrating many social and natural sciences. But this initial obstacle regarding the nature of the field proved to be one of the most interesting facts about it because this interdisciplinary feature was partly why it is so fascinating. Human behavioral ecology is a “bastard” child, unloved by both of her parents— evolutionary biology and sociocultural anthropology. Anthropologists dislike HBE because for them it still carries the threat of eugenics—a vulgar misinterpretation of Darwin’s conception of natural selection and biological inheritance of behavioral traits. It is not a scientific theory, but more an ideology which promoted a form of racism and it fully blossomed in historical Nazism and Fascism. However, I must state that the contempt of anthropologists and some other social scientists toward HBE (and evolutionary psychology as well) is often a prejudice. We need to scientifically explore the evolution of human behavior—there cannot be a veto on this specific topic because this would be a form of dogma itself; on the other hand, most of the contemporary researchers in the field of evolutionary social sciences are very well aware of the eugenics’ threat. The long shadow of eugenics is indeed still above us and it forces us to not to misinterpret, vulgarize, and oversimplify the results of our research. If this still happens (and unfortunately, it does), this is not a fault of some scientific discipline but specific scientists and researchers. Hence, we should not advocate against the scientific disciplines but for adequate standards in doing science, especially in this sensitive field. Evolutionary biologists have other complaints against HBE.  These concerns seem more justifiable because they are empirical in nature. Evolutionary biologists claim that there are at least two major obstacles why research in humans can do little in advancing our understanding of evolutionary processes. Firstly, fortunately for us but apparently not for evolutionary sciences, humans are a long-living species. Evolution is a process that works across generations, and it often takes a long time to observe the effects of evolution. Therefore, it is quite hard to explore evolutionary processes in humans. Furthermore, the reproduction of modern humans is very different not only comparing to other species but to ancestral human populations as well. Technologies which enable the control of reproduction (e.g., contraception) made human reproduction intentional and planned to a higher extent. This is one of the factors that contributed to demographic transition—a shift to high longevity and low fertility in many human populations. Evolutionary biologists pose a question if a species with such a peculiar way of reproducing can provide us with knowledge about the evolutionary processes in general. There are several counterarguments that can be offered to this criticism. Firstly, one of the approaches of studying evolution does not assume intergenerational comparisons in a genetic and phenotypic structure of a population—for example, animal behavioral ecologists frequently study relations (both genetic and phenotypic) between a suit of traits and fitness in a certain population in a specific time-point. This is a study of micro-evolution processes or more precisely, the “snapshots” of evolution as animal behavioral ecologists sometimes label it. The limits of this approach are apparent: we cannot generate predictions based on these measurements; the predictions would be flawed by a lack of information on intergenerational processes, both biological and environmental. However, the need for this research is

Prologue: A Personal Reflection on Human Behavioral Ecology

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quite evident as well: there is no way to empirically examine evolutionary processes in current humans if we do not measure fitness and examine its determinates. We may refrain from predictions, but it is still extremely valuable to gain insights of what human behavioral traits are currently adaptive in specific ecological conditions. Simply put, it gives us an indispensable understanding of human behavior in a context of current biological adaptation and therefore its potential future population dynamics. I agree that the full picture of behavioral evolution will be gathered by some future researchers, but if you think a bit about it, this stands for other scientific disciplines as well. I do not have any problems in acknowledging that I will not witness the findings that will provide full picture about behavioral evolution in contemporary humans; but nevertheless, I am willing to engage in this adventure. Next, while the longevity of humans represents an obstacle to evolutionary research, there are facilitating factors for evolutionary studies on humans as well: there is a vast quantity of information regarding the human species. Natural and social sciences provided high amount of cross-sectional and longitudinal research on humans; cohort studies where individuals were assessed several times during their lifetimes provided new insights on potential causal forces which shape phenotypic traits, including behavior. The data on several generations of humans are becoming increasingly accessible, including the contemporary human populations, but “historical” societies as well, e.g., the information collected by churches in certain countries that enable information about birth, property, marriages, reproduction, and death—variables that are among crucial traits examined in human behavioral ecology. Large-scale internationals surveys can provide information about different human societies around the globe: these “big data” are often easily accessible to all researchers via open-data policies. Finally, it is true that modern humans have peculiar reproductive ecology that highly differs from other species, but we should empirically study it nevertheless, for several reasons. Firstly, natural selection still operates on contemporary humans—there is both phenotypic and genetic evidence which confirm this. Hence, despite the unique characteristics of contemporary humans, biological evolution continues to exist and it should be empirically examined. It is unlikely that humans would simply “forget” about reproduction control—this aspect of reproductive ecology is here to stay, at least as far as we are aware. Therefore, by exploring the evolution in contemporary humans, we are building knowledge for future. But more importantly, reproductive control is an ecological feature—an environmental condition that changed the reproduction patterns of modern humans. HBE does exactly that—studies how ecological characteristics influence the evolution of behavioral traits. Therefore, we should not examine the behavioral evolution of modern humans despite the fact that their reproduction is more controlled and intentional compared to other species, but exactly because of it. Especially since there is a between-population variation in access to contraceptive techniques and usage of it, this variation provides the means to empirically explore behavioral evolution in societies which heavily rely on reproductive control and the ones with reproductive schedules that are more similar to our ancestors.

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Prologue: A Personal Reflection on Human Behavioral Ecology

To be honest, I was not very eager regarding writing this book in the beginning. Writing a book is hard; it demands much time, effort, and patience. On the other hand, the results hardly justify the investment—it is hard to argue that books make larger impact in some scientific field compared to papers and articles. However, in the process of conceiving the content of this publication, I changed my mind, for several reasons. While I like quite a lot studying human behavioral ecology, the discipline is virtually nonexistent in Serbia and even in the whole region of Western Balkans; what’s more, it has rarely heard of, even in scholars who belong to disciplines related to HBE. Even if we take a look at the international community of HBE scholars and texts, a handbook of HBE still does not exists: HBE is scattered across empirical scientific papers, review articles, or thematic publications which do not reveal its full scope. Hence, I believe that the first part of this book, where I intend to provide an overall review of HBE may be useful to various scholars interested in human behavioral evolution. I cannot promise that the review would be completely comprehensive: it is probable that the topics I am interested in or the ones I empirically study may be given more space. However, even such an overview may contribute to disseminating knowledge about HBE and participate in future academic courses in the field of evolutionary social sciences. This book is not only about HBE in general—it has a more focused topic: studying the evolution of psychopathy. My examination of the behavioral ecology of psychopathy started almost accidentally. Psychopathy was and still is one of the major topics of my research programs. When I started studying behavioral evolution, I collected the data about psychopathy in the population of inmates in Serbia. The research protocols we administered contained several questions which could be used to measure evolutionary fitness. It was due to a sheer curiosity that I analyzed the relations between psychopathy and fitness in this database; hence, it was not intentional, nor I really believed that the results would support the assumption that psychopathy may be related to fitness. Well, there was quite a surprise that expected me. Not only these results, but many other that followed them showed congruent and convincing results regarding the associations between psychopathy traits and different fitness components. Indeed, psychopathy turned out to be very fruitful behavioral trait for an analysis in the evolutionary-ecological context, and thus this specific topic become more and more important in my research practice in the past years. Therefore, in this publication I will show the analyses obtained on previously unpublished dataset that comprises psychopathy; I think that these analyses provide new insights of how psychopathy may be adaptive in contemporary humans and hence which selection forces may affect it. A book, in fact, can provide some more flexible outlet for analyses, especially the ones that are exploratory in their nature, than scientific papers. The latter ones are often constricted by the page numbers and word count and therefore they usually contain only a restricted number of hypotheses and results. While it is certainly very important to be precise, clear, and focused in scientific publications, every researcher dream about a bit more liberal and flexible outlet where we can present not only more data but more of our own views and observations. But do not fear, I do not intend to be subjective or colloquial nor I want to bore the readers with long and unnecessary paragraphs. Quite the opposite:

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both behavioral ecology and psychopathy represent intriguing and exciting topics that stimulate imagination. In this book, they are combined into one subject; I expect that this amalgam will prove to be quite an interesting story for both social and biological scientists, and thus, I hope that this book will find its way to them. In Zemun, 15 January 2022

Basic Concepts of Evolutionary Biology

Human behavioral ecology (HBE) is a multidisciplinary research field. However, its core concepts, together with the basic methodological approach, are derived from evolutionary biology and animal behavioral ecology. Thus, first I will provide brief definitions of elementary concepts used in evolutionary biology.

Evolution Evolution can be defined in several ways, but the definition I still find the most relevant is the one used in population genetics: Evolution represents a change in the frequency of gene alleles in a certain population during time (Fisher, 1930).1 Alleles are variants of the same gene: most genes have two or more alternative variants—if an organism has the same allele on both chromosomes, it is homozygous for that gene; if the alleles are different, the organism is heterozygous. The frequencies of genotypes and alleles when there are no evolutionary forces acting on the locus are provided by the mathematical model derived by Godfrey Hardy and Wilhelm Weinberg: if the alleles’ frequencies are not changed across generations (Hardy,  There are scholars who think that this definition of evolution is too narrow, especially in regard of neglecting epigenetic inheritance, cultural and other environmental processes as evolutionary forces, the role of developmental processes and other potential factors that can impact evolution. This broadened view of evolution is labeled as the Extended Evolutionary Synthesis; I will not pay further attention to this conceptual framework, but the readers can find some information about it in the following texts: Danchin et al. (2011), Laland et al. (2014, 2015), Mesoudi et al. (2013), Odling-Smee et  al. (2013), and Pigliucci (2009). The definition of evolution based on the frequency of gene alleles may be too conservative but it is still very useful in understanding the major effects of evolutionary processes on a population level. Furthermore, although very appealing, the conceptions of the Extended Evolutionary Synthesis are still not widely applied in human behavioral ecology and this is why they are not discussed further in the text. 1

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Međedović, Evolutionary Behavioral Ecology and Psychopathy, https://doi.org/10.1007/978-3-031-32886-2_2

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Basic Concepts of Evolutionary Biology

1908), we say that the alleles are in the Hardy-Weinberg equilibrium. This equilibrium is very important for the empirical research of evolution because it represents a null hypothesis—an assumption that evolution did not act on a locus. We can compare the empirically measured alleles’ frequencies with the ones expected under the Hardy-Weinberg equilibrium: if the frequencies statistically differ, we can state that some evolutionary process acted in a population. Evolutionary biologists and especially social scientists are often not interested in the change of the alleles’ frequency per se: genetic variants are related to the phenotypic expression of the traits. The change of frequency of alleles may produce a change in mean phenotypic values of certain traits or a change in their variation on a population level. Hence, we are often interested in a phenotypic evolution of the traits, especially behavioral traits in the field of behavioral ecology. Other aspect of the given definition implies that the evolutionary changes take time: usually, it is implied that evolution demands large time periods; while this is very frequently the case, it is not necessarily true as we will see later. Finally, evolution is a process that takes place in populations, and its effects are visible in populations. Individuals cannot evolve, only populations can.2

Fitness Fitness is one of the central terms in evolutionary biology and the same could be said for behavioral ecology. The most comprehensive description of fitness is that it represents a trait that enables individuals to transfer their genes to the next generation (Hunt & Hodgson, 2010). This definition of fitness is clearly abstract: there is a reason for it because fitness in fact is not a singular characteristic of organisms but a set of characteristics. However, some of these characteristics are more relevant for fitness than others. The core trait that contributes to fitness is a reproductive success (i.e., the number of offspring) which can clearly be deduced from the definition of fitness itself. Reproductive success in humans can be measured by the number of children or grandchildren, and it is widely acknowledged that the best measure is completed fertility or lifetime reproductive success: a number of living children in individuals who finished their reproductive stage. Here, it is also apparent that fitness is not one trait: in order to reproduce, living beings must find partners and copulate; since the number of living offspring is the best measure of fitness, individuals frequently need to further invest in their offspring. Researchers who are familiar with the concept of factor analysis can imagine fitness as a latent factor: reproductive success has the higher loading on this factor while other traits have  In my teaching experience, this aspect of evolution is especially hard for psychologists to acquire. Psychology is a science about individuals, and psychologists are trained to think about individuals in their understanding and application of psychological knowledge. A shift in thinking is necessary in order to comprehend evolutionary processes—from individuals toward populations or at least subpopulations (or groups). Therefore, in applying evolutionary thinking psychologists should converge to other social sciences with group-oriented thinking like anthropology or sociology. 2

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lower contributions to this latent dimension. In fact, some researchers suggested that measuring fitness as a latent factor is probably the best way of assessing it because the statistical procedure of factor analysis can help researchers to reduce measurement error (Helle, 2018). There is another feature of organisms that is highly important in order to reproduce: if you want to have children, you need to be alive. In evolutionary biology, it is often considered that survival/longevity is the crucial feature of fitness—in fact, this is a fitness component that emerges as a consequence of adapting to the conditions of the environment: individuals who adapt to their ecological pressures manage to stay alive. Survival/longevity implicates many other features that are closely related to fitness in a great number of taxa such are physical health, body mass, energy reserves, foraging success, ability to construct shelter, and others. Beside animal species, this criterion of fitness is highly important to human preindustrial populations, small-scale societies, agro-pastoralists, and subsistence societies. However, it is questionable to what degree this fitness component matters in industrial and postindustrial societies with an easy access to medical care, especially in WEIRD countries (western, educated, industrialized, rich, and democratic). In the twentieth century, there has been a large development of medicine, and it became available to the majority of individuals in many countries; this in turn largely decreased the mortality of newborns and children. In other words, a vast majority of individuals in these societies enter the reproductive stage and have the opportunity to have their own children. It seems that survival/longevity does not have such importance as a fitness component in these human populations. This may make our task of measuring fitness more simple; however, we should bare in mind that there still are human societies where longevity matters (unfortunately, many human societies still have low access to medical care, highly nutrient food, or other types of resources needed for survival) and, furthermore, longevity may still be important in industrial and postindustrial populations because it provides an opportunity for parental and grandparental care. From the previous definition, it seems that the fitness is a feature of individuals. Although this is in fact the closest approximation of fitness, it is not entirely true. Simply put, genes travel from one to the next generation, not individuals. If we think about the genes as carriers of fitness, then we must think about not one but individuals in plural: this is a direct consequence of the fact that biological relatives share the same genes among them. This concept of fitness is called an inclusive fitness, and it has interesting implications because it allows the possibility of evolution of the traits that do not enhance individual fitness but the fitness of one’s biological relatives. Inclusive fitness can explain the evolution of the traits like kin altruism (Hamilton, 1964; Maynard Smith, 1964), or even more extreme traits like “helpers in the nest” where individuals do not bare their biological offspring but help their parents in taking care of other siblings: the data showed that this behavior may increase the chances of siblings’ survival to the reproductive stage, i.e., elevate inclusive fitness (these data were collected in a traditional Berber society located in south Morocco: Crognier et al., 2001).

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Basic Concepts of Evolutionary Biology

Evolutionary Tradeoffs As I stated previously, although crucially driven by reproductive success, fitness has different components: growth rate, physical health, success in obtaining resources, mating success, fertility, parental investment (in some cases), longevity, and so forth. If all fitness components are positively related to each other, the process of fitness maximization would be relatively straightforward because increasing one component would lead to an increase in other components as well. However, the evidence shows quite the opposite—fitness components constrain each other. Investing energy in one component prevents individuals to invest it in others—this fact gives rise to evolutionary tradeoffs. There are several tradeoffs that are especially important for fitness both in humans and other animals. Probably the fundamental tradeoff is the one between survival/longevity and reproduction (Williams, 1966a): there are empirical evidence that higher reproductive success is related to lower longevity (Gagnon et  al., 2009). Second, providing parental investment prevents individuals both to search for new mating partners and to produce new offspring. Hence, parental investment enables mating–parenting tradeoff (Trivers, 1972) and quantity–quality tradeoff (Lack, 1947). The latter one is reflected in a fact that sometimes a large number of offspring is followed by lower survival chance at least for some offspring (due to a lack of parental care), while fewer offspring might have elevated survival rate. Finally, there is a tradeoff linked to the timing of first reproduction: early reproduction might have adaptive benefits due to increased fertility, but delayed reproduction sometimes can be beneficial as well, because parents can invest more resources in their offspring (Borgerhoff Mulder & Schacht, 2012). Tradeoffs force individuals and populations to take specific pathways of fitness optimization; for example, as a consequence of resource scarcity in a certain environment, individuals may maximize their reproductive output and decrease parental investment or produce smaller number of offspring with elevated investment in rich and favorable ecological conditions. These trajectories of fitness optimization forged by evolutionary tradeoffs and dependent on ecological and individual conditions are labeled as life history pathways and hence, tradeoffs are sometimes called life history tradeoffs. Although ubiquitous in natural fertility populations, tradeoffs do not necessarily emerge, but they are dependent on certain individual and population conditions: this may be especially true for modern humans. However, they do imply that maximizing fitness is a difficult job for many organisms—a fact that largely complicates the job of evolution researchers but makes it much more interesting in the same time.

 volutionary Processes: Mutations, Genetic Drift, Gene Flow, E and Natural Selection Why does the frequency of gene alleles in some population change? What are the mechanisms of evolution that cause the change in a genetic (and consequently phenotypic) structure of populations? First, the genetic structure is changed by

Evolutionary Processes: Mutations, Genetic Drift, Gene Flow, and Natural Selection

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mutations—changes in DNA caused by nonperfect replications of genes from parental to offspring generation or some environmental causes that impact DNA structure. Mutations are important because they increase genetic variation in a population; on the other hand, larger genetic variation increases the potential of population to evolve, i.e., enhances its evolvability. The frequency of gene alleles can change as a consequence of genetic drift—the impact of stochastic, accidental processes on a population. If an earthquake hits a habitat of a certain population and a significant part of population members randomly die during this occasion (i.e., if these individuals accidently were at the spot where earthquake hit), the genetic structure of the population will be changed as a result of this accidental event. Gene flow occurs when members of a certain population migrate in a different population where they reproduce—in this process the genes from one population enter the gene pool of another population. As a consequence, the genetic variation in the latter population is increased once again. The process that perhaps has a central tenet in modern evolutionary theories is natural selection. In its basic sense, it was conceived by Charles Darwin and Alfred Russel Wallace, and it is further presented and developed in the Darwin’s work (Darwin, 1859). Basic concept of natural selection as a population process is in fact first conceived by Thomas Robert Malthus (1798) and applied to demographic processes.3 Natural selection is based on a fact that the expansion of populations is much faster compared to the availability of resources in some habitats. Hence, individuals must compete for resources; however, not all of them will be able to acquire resources—only the ones which succeed in this task will survive and reproduce. Crucially for evolution, there are individual differences in a population (genetic differences which are related to phenotypic differences), and some of these individual differences are positively associated with survival and reproduction, or simply put—fitness. These can be various physiological, morphological, or behavioral traits. Gene alleles of the traits that enhance fitness will be transferred to the next generation to a higher extent; since fitness itself means higher number of offspring, the frequency of these alleles in a generation of offspring will be higher than in a parental generation. In other words, evolution by natural selection will occur. For example, let us say that high aggressiveness enhances fitness in a certain population, i.e., more aggressive individuals have higher survival rates and an elevated number of offspring. Since aggressiveness is a heritable trait (Araya-Ajoy & Dingemanse, 2017; Ariyomo et al., 2013; Dochtermann et al., 2015; Løvendahl et al., 2005), gene alleles associated with the phenotypic expression of aggressiveness will be transferred to next generation and their frequency will be increased compared to previous population. The increasement of these alleles’ frequency driven by natural selection will continue as long as high aggressiveness is associated with  Hence, demography is in the hart of the theory of evolution by natural selection. Interestingly, despite of this, demography was largely disconnected from evolutionary biology, probably because evolutionary biologists rarely applied their knowledge to humans. This started to change only in recent years in evolutionary demography (Sear et al., 2016) which is, in fact, closely related to human behavioral ecology. 3

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higher fitness. Consequently, we expect congruent changes in the mean phenotypic levels of aggressiveness in this population: we expect them to increase as well. However, note that this may not be the case. The reasons for this are various. One of them is inheritance structure: complex inheritance may buffer the phenotypic changes on a trait initiated by selection. Another reason may be environment: phenotypic expressions of all traits are dependent on environmental conditions; however, environmental characteristics may not be congruent with the selection, in fact, sometimes they can be opposite to the direction of natural selection. Both mathematically, and perhaps intuitively, it is relatively easy to understand that the common consequence of natural selection on a specific gene is a decrease in the allelic variation. If we take into account the loci with two variants, selection acting against allele A (for example, if this allele is related to an illness which buffers survival and reproduction) will result in decreasing its frequency; at the same time, the frequency of the other allele (a) will increase. This process will continue until the time point where all individuals in population will carry only allele a, while the other variant will be nonexistent. This assumption holds for the selection on homozygotes and negative selection on heterozygotes4 with the different speed of selection to deplete the variation (examples for these processes that can be easily understood, even for those who are not affiliated with population genetics, can be found in Relethford, 2012). The phenotypic consequence of this process should be expressed in an absence of phenotypic variation as well. We can see the example of this in various traits where there are no individual differences in a population or even species—the phenotypic traits which were adaptive during the evolutionary course and they spread across populations until they removed all other variants.

The Types of Natural Selection However, the situation is not so simple: the fact that both makes studying evolution so complicated but fascinating and exciting at the same time. Natural selection comes in many ways. There are three basic modes of natural selection. When I described the evolution of aggressiveness in the previous subsection, I was in fact depicting directional selection: the selection that acts if the extreme values of a phenotype have the highest fitness. Note that this selection regime applies both to the cases when a high value of a trait has highest fitness (positive directional  Negative selection on heterozygotes is particularly interesting because the direction of selection depends on the frequency of alleles before selection. The allele with higher frequency would be affected in a manner that its frequency would continue to rise, the contrary stands for the allele with lower frequency. If the alleles have the exact same frequency before selection (i.e., 0.5), their frequency will not be changed under selection. However, although this situation is possible, it is not very probable: other evolutionary processes (mutation, drift, gene flow) would eventually change this sensitive balance and increase the frequency of a certain allele. Even the small changes in frequency would enable selection to act and eventually deplete the variation on the loci in question. 4

The Types of Natural Selection

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selection, like in the example of aggressiveness) or a low value (negative directional selection, e.g., diseases or other maladaptive traits). If positive, the result of directional selection is to elevate the phenotypic levels of a trait in subsequent generations, but not necessarily to deplete its variance; although, if selection moves the mean values to its upper limits, the variation would inevitably be depleted—the ceiling effect. However, the directional selection cannot go indefinitely: many traits have their optimum population values—if they continue to evolve increasing their values above the optimum level, they will start to decrease fitness. Apparent examples of body mass or height easily come to mind. When the phenotypic value of a trait reaches its adaptive optimum, the stabilizing selection starts to act. This form of selection typically influences the traits where intermediate values are associated with the highest fitness (like body mass at birth—very high and very low body mass are associated with lower probability of survival: Karn & Penrose, 1951). Stabilizing selection acts against the extreme values of a phenotypic trait; this means that it will not change the mean level of a trait, but it will decrease it variation. There is also a form of selection that actively increases the variation on a trait: disruptive selection. This form of selection appears when both extremes (both positive and negative) of a trait have the highest fitness (and, hence, intermediate levels have the lowest fitness). If this selection continually acts on large time scales, eventually it will produce distinct morphs, even on a quantitative trait. An example can be found in the gamete sizes (Randerson & Hurst, 2001). In many species, including humans, female gametes are large while the male gametes are small. This may be the product of disruptive selection: the gametes of intermediate size had the lowest fitness and thus they were eliminated by selection. We saw that the previously described mechanisms of selection do not necessarily deplete the genetic variation on a trait, only stabilizing selection reduces the genetic variation. There are selection regimes that actively maintain genetic variation: these are generally labeled as variable selection. One of the broadest and most frequent types of variable selection is balancing selection. This selection mechanism occurs when selection actively maintains the frequency of gene alleles on a certain optimum. The common example is positive selection on heterozygotes: if heterozygotes have the highest fitness (this example still refers to the single locus with only two alleles), then selection cannot remove either allele from the population. Instead of that, selection acts against the homozygotes, until an equilibrium is reached when a frequency of both alleles is established as a result of initial decrease in fitness in each homozygote. Hence, both alleles are preserved in a population and the genetic variation on a trait is maintained. Balancing selection can act if the frequency of a certain trait (and therefore of alleles themselves) is related to the fitness of a phenotype. For example, predators can hunt only the prey that has a phenotype which is less frequent in a population, e.g., the insects with certain color. In this case, more frequent phenotypes would have an adaptive advantage and a positive frequency-dependent selection would occur. In human behavioral ecology, the opposite case, negative frequency-­ dependent selection is perhaps more interesting for the researchers because it is more common. Let us think again about our initial example for natural selection: a

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situation where selection may positively act on the aggressiveness trait. Aggressive behavior may be adaptive under certain ecological conditions, but if positive directional selection on aggressiveness would not be constricted by some factors, it would result in a population where all individuals are highly aggressive: this will deplete the mean fitness of a population due to high mortality as a consequence of conspecific killings. One of these restricting factors can be the frequency of aggressive phenotype: aggressiveness may be adaptive only if the number of aggressive phenotypes in a population is small while the majority of individuals are nonaggressive (i.e., cooperative). If the frequency of aggressive phenotypes reaches a certain threshold, its fitness value may begin to drop, and selection may act against aggressive individuals. This hypothesis is in line with the observable levels of aggressiveness in human societies: although aggressiveness exists, and its consequences may be devastating, the modal behavioral pattern in humans is reflected in cooperative behavior, while aggressiveness is relatively rare. Frequency-dependent selection is an example of social evolution: the situation where the fitness value of a certain phenotype is dependent on some characteristics of the conspecifics (in this case, the frequency of other phenotypes in a population). If aspects of environment maintain the genetic variation on a trait, balancing selection dependent on environmental heterogeneity would act on the trait. Simply put, certain behavior may be adaptive (i.e., fitness enhancing) in certain ecological conditions while the same behavior may decrease fitness in other ecological niches. This type of selection is theoretically applicable to aggressive behavior as well. If a population exists in environmental conditions where the resources are abundant and the predators are rare, then aggressive behavior may elevate fitness; however, if the predators are common in a certain habitat, then cautious and cooperative behavior may be the most beneficial for fitness. Some authors assumed that environmental heterogeneity is the main ecological factor that maintains the variation in human personality traits (Penke et al., 2007). What happens when deleterious mutation enters the genotype? The most intuitive answer points us to negative directional selection that would eventually eliminate the mutation from the population. However, although intuitive, this answer is incorrect. Selection against the mutation does not eliminate the mutated allele from the population—the equilibrium state on the frequency of a mutated and nonmutated allele is reached once again. If the mutated allele is recessive, its frequency at the equilibrium will be higher than the mutated dominant allele (although the frequency of the deleterious mutated allele will certainly be lower than the nonmutated allele). Another obstacle to negative directional selection on deleterious mutated alleles is the rate of de novo mutations that enter the genotypes in every new generation. The equilibrium state between mutations and the selection against them is called mutation-selection balance, and its outcome is the preservation of genetic variation as well. The application for human phenotypes is apparent in the case of various illnesses including the mental illnesses as well: mutation-selection balance can explain why natural selection cannot eliminate harmful alleles that are associated with mental illnesses, i.e., why there is still a genetic component to mental illnesses despite the fact that mental disorders decrease fitness in humans (Keller & Miller, 2006).

Sexual Selection

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Sexual Selection Sexual selection is a subtype of natural selection, but it has some specific features that justify its detailed description. Early naturalists, including Darwin himself, noticed that organisms have some characteristics that do not necessarily facilitate survival and longevity; in fact, some morphological characteristics even can buffer survival by restricting physical and motoric performance in an individual. Peacocks’ tale is certainly the most common example. Darwin thought that these traits may be positively affected by selection despite the fact that they are not enabling survival; however, selection may favor them because they are attractive for the opposite sex (Darwin, 1871). In this case, these traits may facilitate mating and, consequently, reproductive success. Why are certain features attractive for the members of the opposite sex? The main hypothesis assumes that the existence of sexually evolved traits represents a signal of some underlying qualities of an individual that are adaptive in some sense, for example, signaling the genotypic potential for health and longevity. Darwin recognized two basic modes in which sexual selection operates: these are intrasexual and intersexual selection. Intrasexual selection emerges when the members of the same sex compete for the access to the members of the opposite sex; this sex is usually, but not exclusively, males. Intersexual selection results in an evolution of choosiness: usually one sex is choosier in picking the partners to reproduce with—these are often, but not exclusively, females (I highlight that this is not inevitably the case because there are species where females are more competitive and males are choosier: Forsgren et  al., 2004; Jones et  al., 2005a). From this example, it is clear that sexual selection results in sexual dimorphism: the higher magnitude of sexual selection in the population, the sexes will be more different— this applies to physiological, morphological, and behavioral traits. Why would females be choosier than males? It is often assumed that this is a consequence of the higher reproductive investment of females. I already mentioned the difference in gametes between males and females: the production of female gametes demands more somatic energy compared to male gametes. Females also pay a higher cost regarding carrying the pregnancy and initial somatic investment in the offspring (e.g., feeding the offspring in mammals); in fact, females exhibit elevated parental investment generally, compared to males (Kokko & Jennions, 2008; Trivers, 1972). Hence, females may benefit in choosing the males that possess some qualities that would be expressed in elevated fitness in offspring, or in the tendencies and capabilities of males to participate in parental investment. More comprehensive and precise view of sexual selection has been proposed relatively recently—this was a result of theoretical and empirical attempts to define all significant parameters that define the sexual selection. A crucial parameter is anisogamy: the fact that male and female gametes largely differ in their size and number. Anisogamy is probably the most important biological condition that further drives the influence of sexual selection in a population (Jennions & Kokko, 2010). However, if anisogamy would be the only driver of sexual selection than the adult

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sex ratio would be even in a population; an equal sex ratio (both on birth and in adulthood) emerges from the Fisher’s condition—a fact that in sexually reproducing species every individual has to have exactly one biological mother and one biological father. The first parameter that violates this assumption is the sex difference in mortality rates: if adult sex ratio is even, then there can be no differences in mortality rates between the sexes, and this is often not the case. Therefore, various factors may act to change the frequencies of adult males and females in a population, i.e., to change the adult sex ratio. Another indicator of sexual selection is the operational sex ratio—I already mentioned that females usually have larger levels of parental investment, compared to males. This covers the forms of postcopulatory investment like carrying the pregnancy in females. Higher parental investment causes the absence of inseminated females from a mating pool: there is a time out (Clutton-­Brock & Parker, 1992) when females do not participate in further mating until they are ready to go back to the mating pool. The absence of females decreases their frequency in mating and hence changes the operational sex ratio. Adult sex ratio and operational sex ratio are the population parameters and even if they are highly increased they do necessarily predict the strength or even the direction of sexual selection. Bateman gradients are very useful measures for the  empirical examination of sexual selection because they assess individual differences, not population characteristics, and more direct indicators of selection itself. The gradients are empirically established in the experiments that Angus Bateman conducted on Drosophila melanogaster (Bateman, 1948). If sexual selection acts in a population, a more limited sex (usually males) would have increased variation in matting success, increased variation in reproductive success, and higher association between mating and reproduction. The higher difference between males and females in these three coefficients would indicate a higher rate of sexual selection. Recent research in humans showed high potential in using the Bateman gradients in human behavioral ecology and evolutionary demography (Borgerhoff Mulder, 2020). The data showed that in many human populations males indeed have higher variation in mating and reproductive success, but also that there is a considerable variation between the populations, thus suggesting ecological and cultural processes that may affect these Bateman’s gradients (Brown et al., 2009). Congruently, the findings confirmed the third gradient in humans as well: males have a higher association between mating and reproductive success than females; this stand both for a number of mating partners (Courtiol et al., 2012; Jokela et al., 2010) and the duration of romantic relationships (Borgerhoff Mulder & Ross, 2019; Međedović, 2021d) as two distinct aspects of mating success.

Adaptations The traits that enhance fitness are called adaptations. Hence, all characteristics that increase survival and reproduction are adaptations—these may be morphological, physiological, or behavioral traits. It is important to note that the definition of adaptation has a historical component—adaptations are the traits that were targeted

Adaptations

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by natural selection; in fact, selection was the primary force that spread adaptations through populations. Furthermore, adaptations are often clearly associated with some adaptive problem or a task—they evolved because they solved some adaptive tasks and consequently enabled higher fitness of their carriers. Behavioral ecologists have devoted great attention to empirical measurement of the links between traits and fitness. Hence, they do not study adaptations in a historical sense—they examine current adaptiveness of a trait and link it with specific ecological conditions or other characteristics of an organism. These traits cannot be straightforwardly labeled as adaptations—they may be currently adaptive in a certain population and in certain conditions; but only if selection would increase their frequency or a phenotypic mean in the case of continuous traits, in future generations, we may call them adaptations. Note that the label of adaptations is not so clear in human populations that undergo demographic transition, WEIRD countries, or post-industrial populations. As I said, we study the current adaptiveness of a trait; we operationalize adaptiveness as a positive association with reproductive success or some other fitness component. In the terms of natural selection and evolution this is all we need—if a trait is related to a number of offspring and it has a genetic component, then it can evolve under natural selection. This is the crucial aspect of the process that we are interested in (but note, this is only a beginning of a behavioral ecological analysis of a trait or a set of traits). However, the “adaptive” aspect of a trait may be more elusive in this context. Why a trait is associated with fertility? This may be a consequence of some demographic processes that operate in human populations. There are some indications that intelligence is negatively associated with reproductive success (Reeve et al., 2018). This may be explained by the fact that more intelligent individuals pursue higher levels of education; this, in turn, leads to delayed first reproduction and, consequently, lower completed fertility (Međedović, 2017a; Međedović & Petrović, 2020; Rodgers et  al., 2008). In this case, we cannot make a clear link to an “adaptive” context of intelligence, at least in its basic evolutionary biological sense. A more interesting case involves the traits that elevate reproductive motivation. In societies where technology for reproductive control makes reproduction more intentional, reproductive motivation becomes an important force that influences fitness. Hence, other traits that are associated with reproductive motivation (e.g., religiousness: Međedović, 2020a, 2021a) can be affected by natural selection. An interesting theoretical question emerges: reproductive motivation is clearly adaptive but does it represent an adaptation in a classical sense? A potential positive answer to this question must involve some selective pressure or an ecological condition that would represent an evolutionary context in which this trait is adaptive. Perhaps, this environmental context may be a demographic transition in general: when reproduction can be more easily controlled, the motivation to have children represents one of the crucial aspects of fitness maximization. Finally, there are traits that can be more easily linked to some adaptive role. Not surprisingly, these traits can be associated with reproductive success itself or some aspects of mating, not to survival/longevity in modern humans. Individuals with higher levels of some traits may find sexual partners and reproduce more easily than individuals who express lower levels of some traits. Psychopathy, in fact, may be one of these traits (Jonason et al., 2009).

Evolutionary Behavioral Sciences

 he Foundation: Animal Behavior in the Light T of Evolutionary Processes The first scientific discipline that was dedicated to the examination of animal behavior was ethology, founded and led by Niko Timbergen and Konrad Lorenz. Ethologists explored animal behavior by detailed observations of animals in their natural habitats or conducting field experiments; their theoretical framework was not an evolutionary one and they rarely provided explanations guided by the evolutionary principles. However, ethology had important contributions to the field even before behavioral ecology was founded as a distinctive scientific discipline. For example, Timbergen himself posed four fundamental scientific questions about animal behavior (Tinbergen, 1963) that would have a large impact on behavioral ecology and evolutionary social sciences. These are (1) What are the physiological processes that cause behavior? (2) What are developmental determinants of behavior? (3) What is the adaptive outcome of a specific behavior? (4) How did the behavior evolve? The former two are related to the proximate causes of behavior (biological processes, environment, and development), while the latter ones are aimed at the ultimate causes, i.e., evolutionary processes. There were other works as well, both theoretical and empirical that would later play a great role in behavioral ecology. Tinbergen, once again, conducted one of the first field experiments that will became common in behavioral ecology: he and his colleagues examined the adaptive benefits of eggshell removal from the nest by the parental black-headed gulls (Tinbergen et  al., 1962). A bit latter, mathematical solutions were offered which corroborated that under some conditions (genetic resemblance), altruistic behavior may be still adaptive, i.e., leading to a higher transmission of genes to the next generation (Hamilton, 1964). This process was also labeled as kin selection (Maynard Smith, 1964) and led to the concept of inclusive fitness. Formal models were developed that described what choices © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Međedović, Evolutionary Behavioral Ecology and Psychopathy, https://doi.org/10.1007/978-3-031-32886-2_3

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animals should make when moving between food patches in order to make the most optimal decisions, i.e., the behavioral outcomes with highest utility (MacArthur & Pianka, 1966). Empirical data showed that the adaptive outcomes of many behaviors depend of how other individuals in a group behave, i.e., they are frequency dependent (Maynard Smith & Price, 1973); the concept of evolutionary stable strategies emerged from these findings. The work of many scientists was quite fruitful for behavioral ecologists including some books that are still highly cited but contested as well, like the publications of Robert Trivers (1972) and Richard Dawkins (1976). Finally, behavioral ecology was born (Krebs & Davies, 1978). It can broadly be defined as a study of the fitness consequences of behavior (Birkhead & Monaghan, 2010). Conceptually, the main task of a new discipline was to investigate the third Tinbergen’s question: what is the function of some behavioral traits in an adaptive sense, or how animals maximize fitness by expressing specific behaviors in certain ecological conditions. Hence, behavioral ecology applies a functionalistic evolutionary approach in studying behavior, but physiological, genetic, and environmental factors that influence behavior are included in the research as well. Therefore, although the potential adaptive benefits of some behavior are in the focus of the discipline, the other Tinbergen’s questions are represented in the field as well: behavioral ecology tries to connect proximate and ultimate processes (Monaghan, 2014). I believe it is important to emphasize that behavioral ecology is interested in explaining variation in behavior. This refers to behavioral variation between species, populations, and individuals in the same population; in fact, behavioral ecology examines even intra-individual variation of behavior or its repeatability. Individual differences as one of the key topics in behavioral ecology is especially interesting here: many evolutionary biologists saw individual differences only as a first step in evolution, an initial state of a population under natural selection; selection itself acts to eliminate interindividual differences. However, we saw that this is not necessarily the case and individual differences may be preserved in a population by various forces including the selection itself. Alternative behavioral patterns may lead to enhanced fitness for different individuals. This said, behavioral ecology pays attention to the normative processes as well and to many other features besides behaviors. For example, a long-standing topic in behavioral ecology is optimality models: what type of behavior (e.g., foraging) is optimal, i.e., has the highest fitness benefits under certain conditions (e.g., ecological conditions or the state of an individual). Furthermore, behavioral ecologists often examine other traits than behavior, like morphological or physiological characteristics. To sum up, behavioral ecology highly diverged in its research topics and methods in just a few decades after it was constituted. It had many successes in answering various research questions but in the process of growing and maturing it always asked new ones (Owens, 2006). Interested readers who would like to find out more about this intriguing discipline can consult many books that reviewed and described the topics and concepts in behavioral ecology (e.g., Davies et  al., 2012; Fox et  al., 2001; Székely et al., 2010; Westneat & Fox, 2010).

Human Behavioral Ecology: A Brief History

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Human Behavioral Ecology: A Brief History HBE is an anomaly within sociocultural anthropology due to its hypothetico-deductive research strategy and its neo-Darwinian theoretical sources. Winterhalder and Smith (2000)

HBE started to develop in mid-seventies of the twentieth century. Similarly to the basic field of behavioral ecology, optimality analysis was the primary focus of early HBE researchers. The majority of the scholars dedicated to the new field were anthropologists who applied evolutionary reasoning to their ethnographic or archeological work. The research topics were various, but they can be broadly divided into two major groups: production and reproduction (Winterhalder & Smith, 2000). Production research was aimed to explain resource selection and allocation, foraging behavior, including children’s foraging, food processing, and others. An example of optimality modeling in the field of production can be a model for field processing in foraging used by archeologists to predict if the obtained resource (e.g., a large animal killed in a hunt) would be processed by hunter-gatherers before taking it back to their camp (Metcalfe & Barlow, 1992). Reproduction research was aimed at various topics like mating behavior (including mating systems like polygyny, monogamy, and polyandry), reproductive timing and tempo (optimizing interbirth intervals due to ecological constrains), parental, grandparental investment, and alloparenting in general. An example of optimality analysis in the field of reproduction is a polygyny threshold model (Borgerhoff Mulder, 1990). This model predicts female choice of a suitor which is unmarried vs. a married suitor depending on the resources they have; hence, the model suggests that polygyny does not necessarily decrease female fitness, but the fitness outcome depends on ecological conditions (interestingly, the model assumes that polygyny would became more frequent with larger wealth inequality, but recent revisions of the model show that even in populations with high inequality monogamy can became modal marriage system: Ross et al., 2018). These concepts were successfully investigated on various production systems like hunter-gatherers, pastoralists, agriculturists, and horticulturalists. Certainly, these two topics (production and reproduction) are not mutually exclusive; indeed, the most interesting research questions emerge at the intersection of different study topics. The sex division of labor represents such a research question in early HBE. In many foragers’ societies there is a division of labor expressed in a higher amount of fat and protein resources acquired by men, compared to women; furthermore, men aim at obtaining resources with high variation in the returning outcome (e.g., a large prey that involves risk of failure to capture, and a risk of injury or death for the hunter). This difference in labor may reflect actual complementarity between males and females in their provisioning abilities (and their tradeoffs with child care) and energetic needs (Hill, 1988). However, an alternative explanation involves the possibility that the characteristics of male labor are a part of male intrasexual competition and mate acquisition (Hawkes, 1991). If there are individual differences in male abilities to successfully hunt more valuable pray, individual differences in mating success would inevitably arise for males—as we previously discussed, this is the first Bateman’s coefficient, and it represents a reliable indicator that sexual selection operates in a population.

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HBE rapidly developed in twenty-first century; this development affected the topics behavioral ecologists study, the conceptual frameworks, populations under study, and research methodologies. This overall change in the first decade of this century is captured by the review article by Nettle et al. (2013). First, their analysis shows that anthropolgists (including archaeologists) still dominate the field, but HBE is reinforced by the engagement of psychologists, biologists, and other scientists to a lesser extent (demographers, epidemiologists, sociologists, and political scientists). This change clearly reflects the fact that HBE is essentially complex and multidiciplinary field and that it demands cooperation from diverse scientific disciplines; what it makes particularly interesting is the fact that HBE bridges natural and social sciences. Second, the studied populations have changed as well; contrary to the early research in the field, modern trends in HBE involve more industrialized populations, agricultural, horticultural, and pastoral populations, while the research conducted on foragers declined in their frequency. Finally, the main topics of the research changed too—HBE turned mostly to the topics related to reproduction while production and distribution of resources were investigated to a lesser extent; this change is expected having in mind the increasing focus on industrialized societies. Reproduction-related outcomes represent the key topic of this book as well.

 he Tsimane Health and Life History Project: T A Representative Example of Ethnographic Research in HBE Ethnographic research has a high value in HBE; I have chosen The Tsimane Health and Life History Project (THLHP) as an example of such research (Gurven et al., 2017). The main reason for this decision is a major strength of THLHP: longitudinal assessment of various behavioral, morphological, physiological, and neurobiological data using multiple methodologies (including echocardiograms, ultrasounds, CT scans, tissue doppler and others). Tsimane are Bolivian foragers-horticulturalists whose subsistence system incorporates horticulture, fishing, hunting, and gathering. Tsimane kept their traditional lifestyle in the first half of twentieth century; however, the development of roads facilitated the participation of Tsimane in the market economy in late twentieth century. Notably, there is a variation in this participation due to the exact location of villages (more than 90 villages) and the accessibility of the roads. THLHP formally started in 2002 (although the initial contacts of the research team members with Tsimane begin several years before), and it was conducted by an interdisciplinary team lead by anthropologists but closely cooperating with cardiologists, psychologists, biologists, gerontologists, demographers, epidemiologists, and other scientists. THLHP produced many fruitful findings about various aspects of Tsimane life; I will briefly present several key results related to the major fitness components— longevity and reproduction. Tsimane have a high prevalence of pathogens and

The Tsimane Health and Life History Project: A Representative Example…

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parasites including the ones that directly affect fitness: hookworms decrease fertility while, on the other hand, roundworm can shorten interbirth intervals—the mechanism of the latter process may be the increment of maternal immunological tolerance of a fetus (Blackwell et  al., 2015). Due to this high transmissibility of pathogens, the Tsimane children develop immune defenses earlier in their lifetime compared with the populations with lower pathogen presence (Blackwell et  al., 2011). Somatic investment in the immune system triggered a tradeoff with somatic growth—Tsimane children have slower body growth in general which is particularly expressed in their early peak of immunoglobulin-G production. Elevated levels of immune defenses protected Tsimane from autoimmune diseases; however, the levels of CRP and IL-6 (inflammatory biomarkers) are higher in Tsimane due to their exposure to pathogens (Blackwell et al., 2016). Tsimane have high fertility rates (nine births per woman), and there are ecological conditions that influence reproductive-related outcomes: menarche is earlier and interbirth intervals are shorter among individuals who live closer to towns (McAllister et al., 2012). However, there are no evidence that these high fertility rates decrease longevity in a woman (i.e., fertility-longevity tradeoff), and the only health consequences may be reflected in osteopenia and cystocele (Stieglitz et al., 2015). Similarly to the general trends in the societies that gained access to the market economy, younger members of Tsimane started to delay first reproduction (Kaplan et al., 2015). The dominant mating system in Tsimane is monogamy, with only small percentage of polygynous marriages. Furthermore, long-term mating is prevalent since the marriages are stable; in fact, vast majority of men do not further reproduce when their wives reach menopause (Kaplan et  al., 2010). Extra-pair mating is rare, but it exists in younger couples—this can be a source of quarrel between the mates, and it may result in physical violence against women. Specifically, the conflict emerges because extra-pair mating represents a signal of potential decreased paternal investment which is diverted to a new sexual mate. Males acquire higher social status via various traits like dominance, success in physical confrontations, expertise, or valued character traits, oratory skills, and hunting success. High status males tend to have increased fitness: this can be seen both in lower levels of certain illnesses (von Rueden et al., 2014) which may elevate longevity and in higher intramarital fertility and children survival (von Rueden et al., 2008), together with the fact that the wives of prestigious men have lower age of first reproduction. Women compete for status as well: physical attractiveness is one of the key characteristics of status but other traits like childcare, communication abilities, and working ethics are important too (Rucas et al., 2006). Interestingly, there are no data so far regarding the impact of social status on females’ fitness. Finally, THLHP showed that personality traits are probably under natural selection in Tsimane as well (Gurven et al., 2014). This is particularly interesting because THLHP investigated one of the major models of personality which is explored around the globe: the Big Five personality traits (Goldberg, 1990). The data on relations between personality traits and fitness in Tsimane are especially valuable since they are rare in preindustrial populations; furthermore, they can provide the base for the comparison between traditional and low fertility populations

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regarding the selection forces and evolution of personality. THLHP results showed differential selection gradients for personality in Tsimane women who lived in various distances from town, thus confirming the presence of fluctuating selection dependent on environmental heterogeneity. In this way, the Tsimane data can help us to understand the ultimate mechanisms that maintain genetic variation in personality traits.

 o We Still Evolve? Natural Selection in Modern D Human Populations The research in preindustrial human populations has great value because these societies live in the conditions that are more similar to ancestral populations. By analyzing evolution in these societies, we can capture a glimpse of evolutionary processes that may existed in the past. However, the majority of modern humans do not live in these conditions. A vast number of humans live in societies where health services are more or less available together with the reproductive technology that can enable relative control over reproduction. Furthermore, these societies are marked by low fertility: more than half human populations have a below replacement fertility (i.e., less than two children per couple), and this trend may further continue. The fall of fertility rates together with mortality rates which started at the late 19th and throughout twentieth century is labeled as a demographic transition. Low fertility represents an evolutionary puzzle because it reflects a deviation from fitness maximization—majority of modern humans do not achieve their potential number of offspring; and indeed, HBE researchers did offer some explanations for this phenomenon and empirical tests for their hypotheses (e.g. Mace, 2014; Kaplan et al., 2002; Shenk, 2009; Winterhalder & Smith, 2000). Demographic transition has additional implications for human evolution. Due to low mortality, selection on survival/longevity is weak; furthermore, selection on provisioning and obtaining resources is probably weak as well in WEIRD populations (western, educated, industrialized, rich, and democratic) at least for the majority of individuals. A widespread debate emerged from this, both in science and in general public with the powerful question in its core: are humans still evolving (Balter, 2005; Zampieri, 2009)? In my own opinion, the answer is quite simple: despite low fertility, the variation in reproductive success still exists even in WEIRD societies. Some people do not have children at all, some individuals have one, others have two children, and there are couples with three or more biological children, however rare they are. The variation in reproductive fitness is reduced, compared to ancestral population, which may decrease the magnitude of natural selection, but it still exists and this is the most important condition for evolution. This is why human behavioral ecologists think that measuring fertility in contemporary humans is crucial in our exploration of evolutionary processes, a starting point and a grounding level for every more complex analysis of the selection regimes in modern humans

Do We Still Evolve? Natural Selection in Modern Human Populations

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(Stulp et al., 2016a). Although this may look simple, this indeed represents a major question in the research of evolution of contemporary humans, so I will reinforce this issue with empirical findings. The research on contemporary evolution in humans highlights the importance of the age of first reproduction (AFR) on fitness. Several datasets collected in contemporary human populations revealed natural selection on AFR, both on a phenotypic and genetic level (Bolund et al., 2015; Byars et al., 2010; Kirk et al., 2001; Milot et al., 2011; Pelletier et al., 2017; Tropf et al., 2015). Hence, there is a relatively strong selection on AFR: individuals who have their first child earlier in life have higher fitness. If selection alone would operate on AFR, we could expect a populational decrease in AFR; however, the phenotypic trends are exactly opposite: both males and females still prolong their age of first reproduction. Therefore, there are environmental factors, both economical and cultural, which act against selection and are quantitatively stronger than natural selection so far (Tropf et al., 2015). The curious case of AFR reminds us that we have to be very cautious when we predict phenotypic trends in future populations by relying on natural selection alone. AFR is under selection, but this is certainly not the only trait influenced by natural selection: age at last reproduction (Bolund et  al., 2015) is under positive directional selection, while the age of menarche and menopause may be affected by natural selection as well (Byars et al., 2010; Kirk et al., 2001). Other physiological traits (lower cholesterol levels and systolic blood pressure) and morphological traits (height and weight) are probably under natural selection as well (Byars et al., 2010). Finally, behavioral traits like lower education levels and elevated religiousness are related to fitness in modern humans too (Kirk et al., 2001). Particularly interesting are the results provided by Bolund et  al. (2015) because they compared the heritabilities (heritability—the proportion of a phenotypic variation of a trait that is explained by genetic variation) of the key life history traits (number of children, age of first and last reproduction, and lifespan) in historical Finnish population before and after demographic transition started. They found similar G-matrices (matrices of genetic variance and covariance between the analyzed traits; since the traits are correlated, G-matrices in fact represent a more accurate way to study evolution) of these traits before and after the transition which suggest that transition itself did not affect evolution of these traits. Quite the contrary, authors find elevated additive genetic variation of these traits after the demographic transition started—since additive genetic variance represents necessary condition for a trait to respond to natural selection, it seems that the potential of these traits to evolve is even higher in demographic transition. There are various empirical data that natural selection (Briga et al., 2017) and sexual selection (Wilson et al., 2017) operate in modern humans. Studying evolution in humans do present specific obstacles: weak selection pressures, high longevity, novel environments, no possibility of conducting experimental studies, high number of potential covariates that should be controlled in research, and so forth. However, there are some advantages in examining human evolution: there is high variation in environmental contexts (both physical and social) where modern humans live; furthermore, there is abundance of data on humans including genetic, health-related,

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physiological, behavioral, and other information that is collected using different methods (historical data, longitudinal and cohort studies, quantitative and molecular genetics information, and comprehensive cross-sectional data from crosscultural research). This is why some scholars think that human data have high potential to contribute to evolutionary biology in general, with some achievements already made, especially in the fields of demography, genetics (the topics of missing heritability and gene-culture coevolution), predictive adaptive response (the influence of environmental characteristics on fertility), and post-reproductive lifespan (Briga et al., 2017).

HBE: Critiques, Controversies, and Unresolved Questions There are various critiques of HBE, most of them coming from the discipline itself (which may indicate the reflexiveness of HBE researchers and their ability for auto-­ critique) but from other scientists as well. Interestingly, some of these criticisms are not scientific but political ones, and they have roots in an older debate generated from the E. O. Wilson’s book “Sociobiology: The new synthesis” (1975). Namely, this book was the apparent predecessor of animal behavioral ecology because its focus was the evolution of social behavior in animals in view that such a behavior represent an adaptation. However, in its last chapter, Wilson introduced an idea of the evolutionary analysis of human behavior; this represented a provocation for many biologists and social scientists because it reminded them to eugenics and other forms of vulgar Darwinism that were used to justify racism, fascism, and imperialism (e.g., Gould & Lewontin, 1975, 1978). Despite the fact that many contemporary researchers believe that these critiques were unfounded and exaggerated (Alcock, 2001; Segerstrale, 2000), they had devastating consequences on Wilson’s work and in fact delayed the birth of behavioral ecology for several years. However, it could be anticipated that they would be awaken again in the case of HBE, which indeed happened. HBE researchers were accused for promoting a political agenda based on social conservatism, neo-liberal capitalistic economy, xenophobia, and ethnic, sex, and other group prejudices (McKinnon, 2005; Pavelka, 2002; Turner, 2005), even for eugenics and Nazi purity laws (Ehrlich & Feldman, 2003). HBE researchers responded to these critiques by claiming not only that they do not have any political agenda but that the students of evolutionary anthropology tend to have more liberal political views (Lyle & Smith, 2012). Indeed, such criticisms seem to become rather rare in recent years. However, are there other aspects of HBE, and especially early concepts in this discipline that may motivate criticisms of conservative views on human nature that HBE allegedly had? HBE applies reductionism in explaining human behavior: optimality models were often based on the simplest assumptions regarding behavior. Generally, I believe that this approach is not erroneous, quite the contrary, it acknowledges one of the most important logical rules in science—parsimoniousness or the Occam’s razor. Phenomena should be explained by the simpliest models, and

HBE: Critiques, Controversies, and Unresolved Questions

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only if these models fail, more complex explanations should be formulated. Indeed, HBE often showed that it possess an ability to admit that some of the assumptions it held failed to explain empirical data. For example, the early models of optimal foraging assumed that the foragers move randomly across the landscape and that they are passive in regard to their environment (Winterhalder & Kennett, 2006). On the contrary, it has been shown that the movements of foragers are not random but knowledge-based and that they can actively change their environment in a way that can enhance the quantity of resources in their habitats (Nathan et al., 2008; Smith, 2007). HBE not only acknowledged that these early models were overly simplistic (Smith, 2009), but also it further used this knowledge to build more complex and accurate explanations of human behavior (Smith, 2013). Sometimes the critiques assert that HBE and evolutionary psychology have a rigid view on human nature: since they are examining genetic adaptations it may seem that according to their view the explored behavior is strictly under genetic control. This is basically a genetic determinism, and it can be said without a doubt that neither HBE nor evolutionary psychology hold this view on human behavior: both disciplines acknowledge interactive position on human behavior (and other phenotypic traits) where genes and environment together influence phenotypic traits. Furthermore, HBE thinks that phenotypic plasticity which leads to behavioral flexibility is especially important in the phenotype of modern humans and that it helps us to cope even with the evolutionary novel environments (Nettle et al., 2013; Winterhalder & Smith, 2000). There are criticisms that HBE should be more connected to the other fields in order to appropriately analyze complex evolution of behavioral traits. Most of the critiques appeal that HBE should involve cultural processes in the analysis of behavioral evolution to a greater extent (e.g., Borgerhoff Mulder, 2013; Brown, 2013; Mace, 2014; West & Burton-Chellew, 2013). Culture, in this sense, is described very broadly—as information learned via social interaction; in a species that has widespread mechanisms of social learning, culture must highly influence behavioral evolution. It is often assumed that some cultural characteristics of a population have developed as a means to elevate population fitness, i.e., that they may be congruent with natural selection processes. For example, in Serbian Roma, there is a custom of child marriages (Đorđević et al., 2017; for the broader perspective of the child marriages in the HBE framework see Schaffnit & Lawson, 2021), despite the negative health consequences (which may be expected under the assumption of fertility—longevity tradeoff) and other detrimental outcomes, this custom may increase reproductive fitness. On the other hand, it can also be clearly demonstrated that cultural processes may lead to the outcomes marked by decreased fitness—for example by copying successful individuals who have practices which buffer longevity or reproduction (Mace, 2014). Hence, a closer cooperation between HBE researchers and sociocultural anthropologists could certainly be beneficial for the advancements in the study of human evolution. On the other hand, it has been noticed that HBE should be closer to its parent discipline—behavioral ecology (BE) (Nettle et al., 2013). An empirical proof that the two disciplines are not closely related is a fact that HBE researchers rarely

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publish their work in the key BE journals like Behavioral ecology and Behavioral ecology and sociobiology. But the publishing is certainly not the key problem in this missing link—evolutionary biologists that examine the evolution of behavior in animals have developed a vast number of conceptual models which they use in the examination of behavioral evolution (for examples see the texts in Westneat & Fox, 2010). If HBE researchers do not pay attention to the conceptual advancements in BE, they will miss the opportunity to apply these conceptual models in the investigation of human behavioral evolution. In fact, I believe that this is already happening: much of my own approach in HBE is based on the behavioral ecology of animal personality; unfortunately, the theoretical concepts from this field are rarely utilized by human personality psychologists (Međedović, 2018a). Evolutionary psychologists sometimes criticize HBE on a basis of fertility estimates’ validity. This criticism is related especially to measuring reproductive success in males: EP scholars claim that the nonpaternity rates are quite high (about 10%) in human societies—therefore, measuring fertility by asking males how many biological children they have carries a large portion of measurement error (Anderson, 2006). However, it has been noticed that these claims are often unsubstantiated and based on allegorical evidence and a priori assumption that the extra-pair mating and sperm competition rates are high in humans (Sear, 2016). In fact, empirical evidence suggests the opposite: nonpaternity rates are quite low in contemporary humans, ranging from 1% to 3% (Anderson, 2006; Voracek et  al., 2008), while some estimates in western countries are even lower: 0.94% (Wolf et al., 2012). Hence, males have valid knowledge whether they have conceived their biological children or not. This is indeed a relief, not only regarding fathers’ peace of mind but in methodological context as well—our main method in measuring reproductive success is to ask individuals how many children they have; others methods indeed exist but they involve medical or biological assessments and therefore much more resources. Furthermore, if EP scholars would be right in this matter, this would seriously affect demographic models and the estimates of demographic trends. However, there is an issue of relying solely on phenotypic measures, which is highly prevalent in HBE—majority of research collects only phenotypic assessments of behavioral traits, fitness, end life history measures. These phenotypic estimates are afterward used to draw conclusions about evolutionary processes; however, evolutionary changes ensue on a genetic level—can we directly translate phenotypic data onto genetic changes? An assumption labeled as phenotypic gambit (Grafen, 1984; van Oers & Sinn, 2011) claims that we can: phenotypic associations (i.e., relations between behavioral traits and fitness) are thought to adequately reflect genetic associations and therefore can be used as a source for inference regarding evolution. Indeed, while this may be the case in many situations, I recently provided examples of phenotypic gambit failure, including systemic factors that may elevate the chance that phenotypic gambit may not sustain (Međedović, 2023). Furthermore, I proposed a solution to this problem: integrating HBE with behavioral genetics would provide an advanced research platform where we can obtain more conclusive evidence whether there is a genetic covariation between traits and fitness in different ecological conditions.

Evolutionary Psychology: The Basic Tenets

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Therefore, there are many challenges in HBE. These challenges are reflected in the complexity of both human behavioral evolution (cultural influences on evolutionary processes or the complex interactions between genotype and environment on behavior) and the research methodology (combining various phenotypic measures with molecular and quantitative genetic data or the problems in sampling). But these challenges are not only obstacles and problems—for dedicated researchers, they represent puzzles and incentives to solve the intriguing conundrum of human evolution. This is why it is very important to approach the research problem from different sides and viewpoints, perhaps this is the main reason why HBE scholars come from various disciplines—biology, epidemiology, anthropology, psychology, demography, and many others. This variety is a challenge itself—all these researchers coming from the fields that differ much in their topics, methods, and terminology need to understand each other in the first place. In order to overcome this challenge, we are provided with the unique opportunity to learn from each other and to build a synthetic research field with heterogeneous approaches, concepts, and methodology. This is a slow and often difficult process but it is gratifying as well. The study of evolution teaches us that the variation (both genetic and phenotypic) is beneficial for a population; we may extend this notion to the evolutionary social sciences as well—the variation in disciplines, approaches, concepts, and methodologies is likely to be one of our main strengths in the task of exploring human behavioral evolution.

Evolutionary Psychology: The Basic Tenets Evolutionary psychology (EP) emerged approximately at the same time as BE—at the mid-seventies of the twentieth century. Furthermore, the two disciplines have similar origins as well: both are based on Wilson’s sociobiology; both fields even claim that they were influenced by the same authors like Robert Trivers, Richard Hamilton, and Richard Dawkins. In difference to BE and HBE, EP partially develops as a critique of the existing state in psychology and social sciences generally. EP scholars think that psychology largely viewed human mind as a blank-slate, developed only with a few general learning processes; the role of environment was crucial in developing psychic structures and processes—the view of human psyche that is probably the best represented in behaviorism as a school of psychology (Buss, 2020; Tooby, 2020). Furthermore, they thought that that social sciences in general completely underestimated biology in explaining social phenomena—they labeled this framework as the Standard Model of Social Sciences (Tooby & Cosmides, 1992). Evolutionary psychologists suggest the opposite view on human nature—the one which is highly influenced by biological processes, and among them evolutionary biology should have the central place. Hence, they believe that evolutionary psychology should be the core theoretical framework not only for psychology but also for social sciences in general (Tooby, 2020).

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In difference to HBE, evolutionary psychology has some general theoretical postulates that make it distinct among the evolutionary social sciences—I will only briefly discuss them in the following text (readers can be acquainted with the concepts and applications of EP via these publications: Buss, 2008; Confer et al., 2010; Cosmides & Tooby, 2013; Duchaine et al., 2001; Lieberman & Gangestad, 2010; Siegert & Ward, 2002; Tooby & Cosmides, 2015). EP advocates that studying cognitive processes is the most important for analyzing the evolutionary-shaped cognitive architecture of the brain—the main reason for this is that brain evolved as a machine for information processing (a complex biological computer, in a nutshell). Cognitive processes are thought to have modular nature—similarly to organs and the systems of organs, natural selection shaped human cognition to be structured of relatively narrow, functionally specific cognitive processes aimed to solve specific adaptive problems that existed in ancestral environment. Hence, EP rejects any domain general processes (the attitude which is in sharp contrast with general learning processes like classical or instrumental conditioning or general intelligence); this view is sometimes labeled as the massive modularity. In the case of EP, cognitive modules are primarily functionally specific, i.e., evolved to be activated by the specific stimuluses and solve specific adaptive challenges (Barrett & Kurzban, 2006), not structurally detached from one another, like in some other conceptions of modularity suggested by the philosophers of human cognition (Fodor, 1983). EP assumes that the minds of modern humans are composed of adaptations forged thousands of years ago. The reason for this assumption is that the environment in the last 10 or 12,000 years undergo rapid changes that started with the sedentary way of life in the beginning of Holocene. The changes in environment were and still are quite fast; in contrast, natural selection acts slowly by building adaptations in a continuous fashion with small increments in the existing organisms. Hence, adaptations cannot follow the fast pace of ecological change. EP thinks that contemporary humans have psychological adaptations that were evolved roughly in the Pleistocene (180,000  years  BC to 12,000  years  BC)—they believe that this period was stable enough to provide continuous selective pressures that produced psychological adaptations. But more importantly, EP tries to functionally analyze these selective pressures—what were they and how they acted on hunter-gatherers in order to generate new psychological phenotypes. These selective pressures, or more precisely their statistical composites (averaged exposition to some pressures over large amounts of time), are labeled as the Environment of Evolutionary Adaptiveness (EEA). Therefore, we psychologically evolved in EEA, but in our relatively recent history we experience markedly different environments compared to the ones we needed to adapt to. Our biological adaptations cannot follow the rate of environmental change and this is why there is an adaptive lag—evolved adaptations may not perform their function in novel environments. Hence, evolutionary mismatch (Li et al., 2018) is the primary cause of possible maladaptive behavior of humans in their contemporary ecologies. This is the main reason why EP thinks that we should not explore the current adaptiveness of behavioral traits; instead of this EP advocates reverse engineering—a form of functional analysis of existing psychological processes in order to deduce what selective pressures acted in the EEA in order to produce them.

Criticisms of EP

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EP is primarily interested in human universals—the traits that are culturally invariant and therefore can be found in all human populations across the globe. Evolutionary psychologists did not aim their research focus on the traits that show large individual differences,1 although this started to change in the last decade, human characteristics with large interindividual variation (like personality traits) were included in the EP program as well (Buss, 2009). In fact, EP researchers indeed applied this scientific program on a vast number of topics, not only the phenomena that are frequently explored by evolutionary scientists in general (e.g., mating, parenting, kinship, life history, reputation, competition, and aggression) but to psychological processes and characteristics as well (e.g., conflict, violence, morality, religion, cognitive biases, mental health and psychopathology, political processes, consumer behavior, and organizational leadership). This variety of research topics, concepts, and applied methodologies perhaps can be best seen in two handbooks edited by David Buss (2015; the second edition is much more comprehensive than the first one) and Robin Dunbar & Louise Barret (2007).

Criticisms of EP Similarly to HBE, there are political criticisms of EP accusing EP for political conservatism, economic capitalism, racism, right-wing authoritarianism, and xenophobia, encouraging prejudices against women and others; in fact, most of these critiques were pointed to EP (e.g., Dagg, 2005; McKinnon, 2005; Rose & Rose, 2000). Similarly to the case of HBE, there is no evidence that EP, as a scientific discipline holds these political views (Tybur et al., 2007).2 However, although EP as a whole do not promote political agenda, there are some examples of researchers

 I must admit that I never fully understood this position of EP. For example, one of the prominent research topics in EP are sex differences in jealousy: EP believes that males show higher levels of sexual jealousy (if their partner has extra-pair sexual relationships) due to paternity uncertainty, while females have increased emotional jealousy (if their partner falls in love with another woman) due to motivation to ensure paternal investment in offspring. Majority of the EP data show that these differences indeed exist (Sagarin et  al., 2012) although the matter is still unresolved (Carpenter, 2012). However, even if the differences exist, there is a fact that there are high interindividual differences in jealousy and that these differences are larger than sex differences (similarly to other psychological traits); but EP simply ignored interindividual differences and did not even try to explain them. 2  The book edited by Hilary Rose and Steven Rose named “Alas, Poor Darwin: Arguments Against Evolutionary Psychology” was published in Serbia in 2009. I was surprised by this publication and I felt that it was unfair towards EP for two reasons. First, scientific community and general public knew nothing about evolutionary psychology in that time (perhaps it is hard to believe but the situation remains largely the same even today, 14 years latter—there are no courses of evolutionary psychology on any of major universities in Serbia). I thought that it was unfair that the readers should be first introduced to a critique of a scientific discipline and not the discipline itself. Second, majority of the critiques in this book are of political, not scientific nature. These criticisms did not appear as convincing to me, and more importantly, by that time (the first edition of the book was published in 2000), EP scholars already responded to political criticisms so the book seemed outdated. I could not help in observing a certain irony as well: criticisms accused EP to have 1

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whose work in EP was reflecting racism and prejudices and I believe it is important to mention it here. Namely, in her recent work, Rebecca Sear analyzed if eugenics attitudes still exist in evolutionary social sciences (Sear, 2021). She found two examples of eugenics narratives in mainstream science and both originated from psychologists. The first one refers to the widely known national IQ data that facilitated between-countries comparisons in general intelligence despite the fact that the data itself is problematic in various ways (Ebbesen, 2020; Volken, 2003; Wicherts et  al., 2010). In fact, Richard Lynn, who was the leading figure in assembling this dataset published several books and manuscripts with his coauthors where they claimed that genetic potential for lower intelligence directly causes decreased economic development (Lynn & Vanhanen, 2002), that race and skin color are related to lower intellectual abilities in Black populations (Lynn & Meisenberg, 2010) and explicitly stated that these findings should be used in eugenics agenda (Lynn & Becker, 2019).3 Despite all flaws of the national IQ dataset and the publications associated with it, many other researchers continued to publish scientific reports based on these data (Sear, 2021). The second example of ideological implications in evolutionary psychology is the Rushton’s differential-K theory of life history (Rushton, 1995). He thinks that Asian, White, and Black race had been evolved under different life history regimes and that evolution is therefore caused lower intelligence and higher aggressiveness and antisocial behavior in Blacks compared to two other races (Rushton, 1990). In recent years, Rushton has been recognized as a promoter of racism (together with Richard Lynn), but unfortunately this did not stop their work to be further used in science. Besides the political criticisms, virtually all central tenets of evolutionary psychology have been heavily criticized. It was argued that EP cannot be the unifying approach in psychology and social sciences in general (Derksen, 2005). The view of EP on a brain as a biological computer that evolved to process information regarding adaptive problems has been frequently considered as reductionistic and erroneous (Barrett et  al., 2014; Brinkmann, 2011). One of the assumptions of EP that was most frequently attacked is the hypothesis of modularity—the notion that mind is consisted of modular adaptations. Many scholars argued that the modularity hypothesis contradicts some widespread knowledge about the human brain and behavior, namely a data showing that domain general processes are biological and psychological reality together with the increased plasticity that characterizes both brain and behavior (Barrett et al., 2014; Buller, 2006; Buunk & Park, 2008; Fodor, 2000; Scher & Rauscher, 2003). Interestingly, the modular hypotheses of the EP are mostly focused to cognitive processes that are highly dependent on cortical activity; in contrast, cortical political agenda, but it seems that some kind of political agenda (negative attitudes towards EP) motivated the editors of the Serbian volume to publish it in such context. 3  Richard Lynn is famous regarding his racial statements beyond the field of intelligence. Take a look at this title for example: “Rushton’s r–K life history theory of race differences in penis length and circumference examined in 113 populations” (Lynn, 2013). I suppose it would be funny if it is not dangerous—this paper was published in a mainstream psychological journal.

Criticisms of EP

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structures have the highest level of plasticity—this is why some authors argued that EP should specify their assumptions to subcortical regions instead (Peters, 2013). Finally, it has been empirically demonstrated that the existing brain modularity is not genetically determined in any sense, but it represents an outcome of development, i.e., the interactions between the organisms and their environment (Karmiloff-­ Smith, 2000). This is just one of the reasons why it was proposed that EP must pay attention to developmental processes in order to adequately understand the emergence of psychological adaptations in ontogeny and their associations with environmental characteristics (Lickliter & Honeycutt, 2003), similarly to evo-devo or even eco-evo-devo (ecology-evolution-development) research in evolutionary biology. While I tend to agree with the previous criticisms as well, I believe that there are three especially important problems in the EP’s conceptual structure; these are somewhat related issues and they are (1) the problem of declaring a trait as an adaptation, (2) problem of inferring evolutionary processes on available data, and (3) problem of environment of evolutionary adaptiveness. The first issue relates to the fact that EP does not have an empirical criterion to claim that certain trait is an adaptation, in contrast to HBE which empirically measures fitness and recognizes adaptive behavior as the one which is positively associated with fitness. To solve this problem, EP usually leans on the criteria for recognizing adaptations proposed by Williams (1966b) with the notions that adaptations are complex, species-­ universal traits with functions to solve adaptive problems. Others (Andrews et al., 2002) added supplementary criteria: adaptations can be recognized as biased outcomes of developmental or learning processes (e.g., fear of snakes), and the traits that should solve adaptive problems in ancestral environments but may easily be maladaptive in modern environments (e.g., cravings for food rich in sugar). If these criteria seem to be vague, it is because they are; it is not surprising that evolutionary-psychological analysis of adaptive status of a trait usually ends with an affirmative answer. I would like to illustrate this reasoning process using the phenomena of conspiracy theories: van Prooijen and Van Vugt (2018) analyzed conspiracy theories from an adaptationist perspective and concluded that they are evolutionary adaptations. Conspiracy theories are heterogeneous beliefs with some common narrative—they usually involve a group of actors with a malevolent goal which they achieve by secret methods (Bale, 2007). van Prooijen and Van Vugt (2018) claim that conspiracy theories satisfy the criteria of adaptations because they are complex and universal traits, they are fast, and require minimal effort to be generated. The trickiest criterion is related to domain specificity: authors claim that conspiracy theories increased survival and reproductive success in ancestral humans. The problem with this criterion is imminent: how can we know if conspiracy theories elevated fitness in ancestral humans? In fact, there is no unambiguous empirical way to answer to this question, and authors do not even try to achieve this in their manuscript. There are other problems with this proposition. Did hunter-­ gatherers have conspiracy theories at all? If they had, how did they look like; can they be compared to the modern conspiracy theories? The implicit, underlying notion of psychic continuity is very questionable here. Conspiracy theories are

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complex cognitions that are not only psychological in their nature, they have their sociological and anthropological aspects as well. They thrive in societies with large populations and complex organizations; did they even exist in their narrow sense in groups that had several hundred members? Apparently, this question can be posed not only for conspiracy theories but for many other complex cognitive structures. As I already stated, there is an indirect way of testing this hypothesis—to examine the relations between the trait and fitness in a traditional, small-scale societies because they are characterized by environmental conditions and reproductive ecology that are more similar to ancestral human populations. But EP researchers do this very rarely (conversely, this is one of the main research traditions of the ethnographic approach in evolutionary anthropology). Even when they do, and the evidence shows no positive relations between a trait and fitness they are still very reluctant to state that examined trait is not an adaptation (e.g., Lebuda et al., 2021).4 The conceptual status of adaptations in EP resembles the one which instincts had in psychology: adaptations and instincts share the characteristics like heritability, they are shaped by selection, they are species-typical and functionally specific. But the instincts are largely abandoned in psychology because psychologists of early twentieth century started to use them as designated explanations for almost every observed behavior—again quite similar situation to the EP’s usage of adaptations (Hampton, 2006). As a potential solution for this problem some authors suggested that EP should use neurobiological-comparative approach (Panksepp et al., 2002; Panksepp & Panksepp, 2000; Peters, 2013). This way EP could apply comparative examination (across various mammal and other taxa) of brain biochemistry, physiological processes, and molecular genetics; this would provide fruitful information about behavioral adaptations by detecting homologies, adaptations shared by various species because they are inherited from a common ancestor. However, this potential is still mostly unused in EP. Hence, fitness is not directly measured in EP, neither on the genetic level (for example, by detecting selective sweeps on gene alleles) nor on the phenotypic level (by measuring reproductive success or longevity). This has another implication for EP—how can we make inferences of evolutionary processes if we did not measure  Lebuda et  al. (2021) conducted very interesting research: they analyzed the relations between creative thinking and various fitness-related indicators including number of children. The sample was consisted of Meru tribe members, a semi-nomadic, preindustrial society from Kenya. They tested the hypothesis that creative abilities may evolved as an adaptation—this should be reflected in positive association between creativity and reproductive success. Alternative hypothesis says that creativity is a by-product of general intelligence—in this case creativity should show no associations with fertility when general cognitive abilities are controlled in the analysis. They obtained negative relations between creative potentials and number of children—mediated by lower number of spouses. Interpretation should be relatively straightforward: their data suggest that the trait does not have adaptive outcomes in this population. However, authors state that “negative correlation between a trait and the number of spouses and offspring does not necessarily indicate that the trait is not an adaptation” (Lebuda et  al., 2021, p.  6) and offered alternative explanations, namely that creativity can have complex mechanism (although they did not specify the meaning of this) or that the obtained result is a product of a mismatch between ancestral and modern ways of life (despite the fact that they conducted their research in a traditional society). 4

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these processes in any way? Again, I would like to illustrate this issue using an existing EP research. In their manuscript, Sell et  al. (2017) wanted to explore evolutionary designed mental adaptations for war-related decision-making. Since warfare was frequent even in ancestral humans, they think that warfare represented a selective pressure that produced evolved mental adaptations in men that would lead to beneficial attitudes toward was. “In short, men should be designed for war,” they state (Sell et  al., 2017, p.  338). In order to test this assumption they set a hypothesis that physically strong men should be more supportive for war. Participants originated from four countries answered the questions that examined their attitudes about the country-level warfare, and anthropometric measures related to physical strength were collected as well. Indeed, positive associations between physical strength and warfare support were found for men (in three countries5), but not for women. Based on this, authors claim that their hypothesis is confirmed: “These findings support the hypothesis that modern warfare is influenced by a psychology designed for ancestral war” (Sell et al., 2017, p. 334). Perhaps I did not understand various aspects of this manuscript, but I fail to comprehend how these data point to any evolutionary mechanism related to the explored variables. These data can be explained on various ways that involve proximal psychological processes. Furthermore, I must admit that I even did not understand the theory in the first place: why should warfare as a potential selective pressure promote gene alleles related to warfare support? Besides the apparently missing link between theory and data, should we asked ourselves if the research questions may be wrong in the first place? I have chosen this manuscript because some of the authors are of the most notable proponents of EP, because it is published in one of the most prestigious journals dedicated to human behavioral evolution, and because in the time I wrote this chapter the war in Ukraine started. The last reason is important because of the conceptual implications of the manuscript: if “men are designed for war,6” then we might conclude that all our attempts to build and maintain peace are ultimately futile. War is our natural state, and peace is an evolutionary novel ecological condition that cannot last because our evolved psychological adaptations seek warfare. Fortunately, neither theory nor empirical evidence in fact support this grim conclusion. Certainly, this is but one manuscript, but unfortunately, there are a large number of manuscripts with similar problems in EP. Therefore, I am under the impression that EP violates one of the basic premises of making inferences in science—the rule of parsimony (sometimes know as Occam’s razor as well) that guide us to explain the investigated phenomena using

 I cannot help but to wonder whether it is not at least a bit ironic that the association was not find only in Israel, a society entangled in a long-term violent conflict, i.e., the correlation was absent in the only one of investigated countries which actual ecological conditions are congruent with the study topic. 6  Furthermore, why using the term “designed”? This is more appropriate for theological literature. Natural selection cannot design anything—it is a mechanicistic process of differential intergenerational spread of gene alleles associated with phenotypic traits that enable higher fitness. Adaptations cannot be designed because there is no designer. 5

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the simplest explanations and models. In this case, there are various more parsimonious ways to explain the findings than invoking selection and evolved psychology. The observed associations can be explained by aggressiveness; both aggressive tendencies and right-wing ideology can make individuals more prone to pro-warfare attitudes and physical exercise. Even in ancestral populations, it is hard to imagine that simply due to a fact that someone is physically strong he would approve war; obtaining resources, achieving high social status, and other social, cultural, and economic factors would be far better candidates—in any case, the processes that do not have anything to do with selection.7 It is not surprising that many authors criticized EP’s research methodology (Buller, 2005, 2006) and challenged the evolutionary interpretations of their findings by noting that interpretations are not supported by the actual data (Peters, 2013). Some authors even claim that the EP hypotheses are unfalsifiable (Gannon, 2002). I would not go that far, but it is apparently questionable what makes appropriate evidence for a certain hypothesis. You do not have to be a psychologist to know that some men have favorable views about the warfare—if this is adequate evidence that men have evolved psychological adaptations which make them warfare-prone, then the hypothesis is certainly unfalsifiable; however, it does not. If EP wants to make inferences about evolutionary processes, it needs to have some empirical data that are associated with these processes, e.g., fitness-related measures. Still, EP rejects measuring fitness in contemporary humans: according to them this is not beneficial for our understanding of evolution because adaptations that evolved to maximize fitness emerged in ancestral environments (EEA), and they may not function in modern ecologies. However, focusing on the EEA is not justifiable, neither in conceptual nor in empirical way (Bolhuis et al., 2011; Smith et al., 2001). It is clear that modern humans have behavioral adaptations that evolved long before Pleistocene: sexual desire (Eastwick, 2009), attachment for caregivers, or basic emotional processes like fear and rage (Panksepp et al., 2002; Panksepp & Panksepp, 2000). On the other hand, and probably more important to the arguments of EP, there are evidence for evolution after Pleistocene, when ancestral humans transferred to the semi-sedentary and sedentary way of life. The first apparent fact that contradicts the EP’s claim that novel environments decreased fitness (by making our evolved adaptations useless) is the rapid rise in population growth at the start of Holocene. It seems that the environmental change did not buffer fitness but quite the opposite, it elevated it. There is other evidence of adaptations in the past 12,000 years: the proliferation of alleles related to lactose tolerance, adaptations against sickle-­ cell anemia (Irons, 1998), or the ones associated to enhanced immune response to the smallpox in middle ages that also affected the evolution of the HIV-resistant allele (Galvani & Slatkin, 2003). There are even estimates that the recent selection was relatively strong because data shows that large parts of human genome were affected by recent selective sweeps (Williamson et al., 2007).

 But once again: do we know the attitudes of common men about war in hunter-gatherers? Do we have any empirical way of investigating it? If we do not, is this question of relevance to science? 7

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These data seem to be in contrast with the claims of EP—then, how can these recently adaptations and selection processes be explained? The assertion of adaptive lag is largely based on an assumption that selection is slow and gradual process. While selection certainly can have these attributes, it is not necessarily slow. Empirical studies showed that selection can be much faster if the population is faced with strong selective pressures (Irons, 1998). In fact, meta-analytic data obtained on studies of the selection’s pace in 62 species showed that on average selection changes a quantitative trait for one standard deviation in 25 generations (Kingsolver et  al., 2001). Hence, the strength of selection is higher than it was previously thought. There are additional processes suggesting that modern humans do not experience such large adaptive lag as suggested by EP. Mainly, biological change via evolution is not an only way organisms can adapt to their environment—they can change environment in a way that suites their biological potentials. Indeed, many species do actively change ecological conditions they live in, a process that it is called niche construction (Odling-Smee et al., 2003, 2013). Humans can be rightfully considered as the greatest niche constructors in the living world; niche construction, combined with the enormous flexibility and plasticity of human behavior decreases the possibility of maladaptive functioning in novel environments (Brown et al., 2011; Laland & Brown, 2006) despite the ecological mismatch between current and ancestral ecologies.8 There is one more argument for a possibility that the behavior which elevated fitness in preindustrial populations continue to do so in modern environments, despite large ecological changes. Simply put, an adaptive function of a trait could remain the same, only the means used to achieve this function could change in a novel environment. Let us use a personality trait labeled extraversion as an example. This trait turned to be positive predictor of reproductive success especially for males (Penke & Jokela, 2016), but more importantly, it positively predicts fitness both in traditional societies like rural Senegal (Alvergne et al., 2010a), in Ache of Paraguay (Bailey et al., 2013), and Tsimane of Bolivia (Gurven et al., 2014) and in WEIRD societies like UK (Berg et al., 2013), Norway (Skirbekk & Blekesaune, 2014), and USA (Berg et al., 2014). Hence, the association between the trait and fitness is the same in traditional and modern societies—demographic transition, contraception,  Mismatch between ancestral and modern environment is evident. However, EP does not help us in investigating potential consequences of a mismatch in their empirical research. Even their descriptions of a mismatch are sometimes simply trivial. For instance, here is an example of a mismatch provide by Li et al. (2020): ancestral humans spend all their time in natural spaces— woods, meadows, savannas, and they “worked” (i.e., hunted, gathered fruit and vegetables) in this environment as well. However, modern humans spend much of their working time in offices and spending time sitting in front of computers or other machines. Because of this mismatch between ancestral and modern environments, humans experience both somatic and mental illnesses, accompanied by the work-related stress. Again, proximate mechanisms like precarious work, stress related with high working demands, underpayment, exploitation, and other mechanisms that generate health problems are omitted and the cause of phenomena is transferred to evolution but in a very vague and anecdotal way. 8

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accessible healthcare, and other aspects of modern environment did not change it. So far, we can only debate on why extroverts have higher reproductive success: extraverted individuals are more gregarious, friendly, socially self-confidant and bold; in ancestral time, they engaged in social interactions face-to-face, today they may use social networks and other forms of online interaction, but adaptive nature of the trait apparently persists. This may be true for many other behavioral traits. I analyzed these three criticisms of EP to a higher detail because they do have something in common—they are all related to the rejection of directly measuring fertility or other fitness components in empirical research. Hence, they are directly related to HBE—if the practice of measuring fitness is unjustifiable than all HBE could be questioned as a scientific discipline. I hope that the previous analysis contributed to the view that measuring fertility is not only justifiable in a scientific sense, but it also warns us that if it is omitted we would be less able to infer about evolutionary processes that operate on behavioral traits.

 P and HBE: Opposed or Complementary Disciplines? Could E Both Be Possible? I provided brief description of both major tenets and criticisms of EP and HBE; it would be beneficial if we would directly compare them here. For this purpose, I will use the analysis provided by Winterhalder and Smith (2000): (1) on a ground of explanatory mechanisms, EP is interested in psychological processes while HBE seeks to explain behavior; (2) EP sees cognitive and genetic factors as a key constraint to evolutions, while HBE pays more attention to ecological conditions in a population; (3) EP is interested in a long-term temporal evolutionary process, while HBE examines current adaptive outcomes of behavior; and (4) EP believes that the behavior of modern humans is largely maladaptive (due to adaptive lag), while HBE thinks that the behavior is still largely adaptive (due to behavioral plasticity). This comparison9 suggests that two disciplines have key conceptual differences; furthermore, it seems that they are practically opposed to each other. Indeed, the rivalry between the two disciplines had some positive outcomes for both fields because scholars need to advance their conceptual arguments in order to respond to the critiques. However, are the fields really contrasted? First, my opinion is that HBE and EP pose somewhat different questions: while both disciplines claim

 The third substantial paradigm in evolutionary social sciences, beside HBE and EP, is cultural evolutionary theory (Creanza et  al., 2017); however, I do not analyze this research field in the present text due to several reasons: (1) the field is mostly dedicated in implementing evolutionary framework in understanding emergence and dynamics of cultural phenomena, not biological evolution itself; (2) it produced far less empirical research compared to HBE and EP; and (3) I agree with the authors that HBE should integrate cultural evolution models to a higher extent to its framework (e.g., Colleran, 2016)—in this case, HBE and cultural evolution should not be viewed as separate fields but two indistinguishable conceptual frameworks. 9

EP and HBE: Opposed or Complementary Disciplines? Could Both Be Possible?

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that they are dedicated to answering two Tinbergen’s questions related to evolution, it is clear that HBE is more focused on the third (adaptive outcomes of behavior), while EP accentuates the fourth question (evolution of a trait during phylogeny). This makes these disciplines complementary, not contrasted. It is clear that the vast number of topics they research is the same, especially in fields of mating and parenting, and they also share similar conceptual frameworks, for example, by emphasizing life history theory in order to make predictions. I would also like to make explicit my own position in these two disciplines of evolutionary social sciences. Having in mind the differences between the two disciplines, it seems that my scientific interests clearly fall in the domain of HBE. Problematic conceptual assertions of EP, the rejection of fitness measurement, sometimes questionable methodology and the inferring on research results do not make identifying with EP easier as well. On the other hand, HBE researchers rarely explore specific behavioral dispositions; the only exceptions are general or “basic” personality traits. In contrast, I am very much interested in analyzing adaptive potentials of specific behavioral characteristics, psychopathy is just one of them. Hence, I still think that these disciplines may be complementary and work together in analyzing behavioral evolution. I think that every analysis of psychological characteristics in an evolutionary framework is evolutionary psychology—research do not need to conform to modularity, EEA, reverse engineering, and other concepts to conduct research in evolutionary psychology: if this is the correct assumption, then my work falls within evolutionary psychology too. Furthermore, I believe that if EP would start to empirically measure fitness (as some authors already proposed: Bolhuis et al., 2011), there would be no crucial distinctions between the two fields. This complementarity potential motivated some authors to predict that the cooperation and merge between HBE and EP would be facilitated in the past decade (Brown et  al., 2011; Lieberman & Gangestad, 2010). However, due to reasons which are above the scope of this text, this did not happen. Perhaps this book should be viewed as a step in that direction.

Evolutionary Ecology of Family

It seems that everything revolves around reproductive success; however, things are not so straightforward. One of the factors that complicate the situation is family: humans live in families, variable systems of individuals related by kin. Family dynamics directly affect the inclusive fitness of individuals through interaction between parents, siblings, and intergenerational interactions, all of which can assume both cooperation and conflict. Furthermore, family is a system where cultural processes (e.g., patterns of marriage, inheritance, raising children, and so forth) have an apparent influence on fertility outcomes, thus actively contributing to the selection regimes on behavioral traits. Evolutionary ecology of family is a broad research field of HBE; it studies various family processes that influence behavioral evolution including (but not limited to) mating systems (in the case of HBE, these are usually marriage patterns like monogamy, polygyny, polyandry, and others: Borgerhoff Mulder, 2009; Henrich et al., 2012; Schacht & Kramer, 2019), parental and grandparental investment, cooperation and conflict between family members, alloparenting and cooperative breeding, cultural processes affecting evolutionary-­ relevant outcomes in family, and the emergence and dynamics of demographic transition (Mace, 2015; Sheppard & Snopkowski, 2021).

Parental Investment: Sexual Selection Strikes Again When analyzing sexual selection, we already stated that the operational sex ratio is among the most important facilitators of sexual selection. Biased operational sex ratio emerges from various reasons—one of them is the differences in parental investment: the sex that invests more in offspring limits mating because the members of that sex retreat from the mating pool. In many species, these are females, or more precisely, the sex that produces larger gametes is the one that usually exhibits greater

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Međedović, Evolutionary Behavioral Ecology and Psychopathy, https://doi.org/10.1007/978-3-031-32886-2_4

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parental care. Why is this so? Trivers (1972) offered an explanation that seems intuitively clear: by investing more in the beginning (increased somatic investment in producing larger gametes), females have much more to lose if their offspring do not survive; hence, they express higher postcopulatory investment as well, including higher care for offspring. However, this line of reasoning suffers from an error, and the one psychologists are very well acquainted with: Concord fallacy or the Sunk cost fallacy (Arkes & Ayton, 1999). Past investment cannot be used to explain future investing because further investment does not necessarily provide higher utility. In fact, if we think once again on Fisher’s condition (every individual has exactly two parents of both sexes), we can easily see that diminished survival of offspring equally decreases the fitness of both parents. Based on this, it is not surprising that anisogamy alone does not straightforwardly predict higher female investment; quite the contrary, it can lead to diminished maternal care (Kokko & Jennions, 2008). Another Trivers’s notion is sometimes used to explain sex differences in parental care, especially widely embraced by evolutionary psychologists: Potential reproductive rate. This is a maximal reproductive rate of an individual, assuming unlimited mate availability (Clutton-Brock & Parker, 1992). As a consequence of their lower precopulatory and postcopulatory somatic parental investment, males have a higher potential reproductive rate. Therefore, it is expected that males would benefit if they shifted from paternal investment to finding new mates and producing more offspring. In fact, this is the main assumption to explain sex differences in short-term mating that show higher levels of short-term mating in males (Lippa, 2009; Schmitt, 2005). However, Fisher’s condition proves as ubiquitous once again: in every sexually reproducing species, average reproductive rates of both sexes must be exactly the same. Hence, the potential reproductive rate is a theoretical concept that in fact cannot be met in any natural conditions. Furthermore, if anisogamy is the only difference between the sexes, egalitarian parental care is in fact, the more probable outcome. Male-biased operational sex ratio makes mating success harder for males: therefore, males’ fitness benefits would be higher if they care for their offspring than if participating in zero-sum games where the benefits are low (Kokko & Jennions, 2008). But then again, why do we frequently see biased parental investments in nature? Here, sexual selection enters the scene: if there is nonrandom variation in mating, sexual selection could operate and generate sex-specific traits for intra-sexual competition. Males who are successful in mating will continue to mate, so they will not provide parental investment. On the other hand, feedback-loop would generate even more competition, making successful mating even harder; hence some males would still shift from mating to parenting. Therefore, the population can reach equilibrium at different stages of this process, which would generate differences in parental investment, from egalitarian to female or male-biased, all of which in fact exist in the living world (Jennions & Kokko, 2010).

Parental Care in Humans

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Parental Care in Humans The parental investment represents any investment by a parent which elevates the survival chances of the offspring, while preventing parents from investing in further reproduction (Trivers, 1972). It is reflected in many processes including lactation, gestation, provisioning with food and shelter, protection, and various forms of social learning and enabling offspring to acquire different skills that may enhance their future fitness (I am more focused on the behavioral aspects of parental care in the present text; for physiological investment during the fetal life and subsequent lactation see Wells, 2018). Life history of the human species is characterized by markedly elevated parental care. The primary caregivers during the course of human evolution were mothers-there are some data suggesting that mothers were essential for the offspring’s survival (Pavard et  al., 2005, 2007b; Sear & Mace, 2008). However, humans are largely biparental species; hence, paternal care was (and still is) substantial in our species (Marlowe, 2000; however, note that in contrast to maternal care, which is practically universal, paternal care has higher variation between populations and societies: Sear, 2016). What were the causes of such high expressions of parental care? We can trace evolutionary changes that led to at least two large transitions which imposed the need for high parental investment. First, the evolution of elevated body size in Australopithecus and Homo ergaster increased the survival rates in late childhood (McHenry & Coffing, 2000). However, in order to achieve high body size, the offspring needed greater investment in breastfeeding and nurturing in general. Second, the evolution of bipedalism in Homo Habilis and Australopithecus generated a tradeoff in the reduction of pelvic channel size. This, in turn, constrained in-utero brain development because it would be detrimental to both infant’s and mother’s survival. Brain development shifted to the postnatal phase; since brain development is quite expensive (it demands large amounts of energy), human babies developed secondary altriciality, their maturation became slower compared to other primates, and therefore, they demanded increased care from adults (Pavard et  al., 2007a). In fact, this generated prolonged juvenile dependency in human children. This is why even biparental care was not sufficient during human evolution; various forms of alloparenting and cooperative breeding had an important role in keeping human children alive (Kramer, 2010). Note that this elevated need for parental care had various other evolutionary-relevant consequences, including the evolution of long-term partner relationships and emotional attachment between caregivers and children (these adaptations were not generated by these evolutionary events because they exist in other species as well, but they were certainly enhanced in these two evolutionary transitions which made them so pervasive in humans). Parental care is dependent on various conditions; one of the most important are environmental characteristics like mortality rates, environmental risks, and the amount of resources parents have. The general assumption is that parental care is diminished in conditions of low resources and/or high environmental risks; in these conditions, parents may enhance their fitness by focusing on higher fertility at the

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expense of investment (Quinlan, 2007; Winterhalder & Leslie, 2002). There are empirical data that confirm this hypothesis; for example, it has been shown (Quinlan, 2007) that the levels of maternal care are lower in the conditions of famine and warfare while the care of both parents is diminished if the pathogen stress is higher (although it should be mentioned that this study shown some nonlinear quadratic effects between environmental risk and parental investment, suggesting that the relations between ecological conditions and parental care may be more complex). Congruently to previous findings, the data also show that more beneficial environments are related to higher parental care, this includes modernization effects, and hence it has consequences for modern human populations: parental care in contemporary humans is probably even more pronounced compared to ancestral populations (Gibson & Lawson, 2011). The reasons for this are twofold: first, modern markets made child rearing more expensive; on the other hand, diminished mortality may have changed parental perceptions in a way that they have more reliable influence on the children’s developmental outcomes, including their fitness. There are also various characteristics of offspring that influence parental investment. It is not surprising that these characteristics interact with the ecological conditions families are set upon. For example, firstborn children tend to receive more parental investment than the latter born ones; this could be an outcome of biased parental care to older children (Jeon, 2008) and the effects of siblings’ competition that usually end favorably for older children (Lawson & Mace, 2009). Furthermore, these effects are more pronounced in the beneficial environmental conditions (Gibson & Lawson, 2011; Gibson & Sear, 2010; Marteleto, 2010). Some theoretical considerations suggested that parents with abundant resources should benefit more if they invest in male children, on the contrary, parents with scarce resources may increase fitness by elevated investment in daughters—this assumption is known as the Trivers-Willard hypothesis (Trivers & Willard, 1973). Various empirical data confirmed its’ plausibility (for the meta-analytic results see Cameron, 2004). In fact, this hypothesis helped in explaining another observation—that fathers invest more in sons and mothers invest more in daughters: in many human populations (including the contemporary WEIRD societies) men are controlling family resources to a higher degree, hence, sex differences can be in fact explained by differences in resource control (Godoy et al., 2006). There are other offspring characteristics that may bias parental investment. Parents can invest more in children with the early indications of potential higher fitness: for example, mothers tend to invest more in children with higher body mass on birth among the Serbian Roma (Čvorović, 2020). Fathers show biased investment not only toward sons but to children who physically resemble them: this has been obtained both on subjective (Apicella & Marlowe, 2007) and objective (Alvergne et al., 2010b) estimates of parent-child physical similarity. Hence, various factors in parents and offspring can influence and bias parental investment; these factors can explain the variation in parental investment that exists both between populations and individuals.

Grandparental Investment

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Grandparental Investment Alloparenting is generally rare in the living world; this is why the role of fathers in offspring care is intriguing. But investment in children goes much farther in humans: grandparental investment is ubiquitous in our species (for a comprehensive review, see Sheppard, 2021). Apparently, the care for grandchildren had higher fitness benefits in ancestral humans than a further continuation of reproduction due to the costs of reproduction which are especially evident in older individuals. Generally, the investment of grandmothers dominated the research on this topic because it is thought that grandmothers are the primary caregivers (Chapman et al., 2021; Sear & Mace, 2008) and methodological problems with distinguishing the effects of grandfathers. Furthermore, research findings generally suggest that maternal grandparents tend to provide more care. The main explanation for this effect is paternity certainty—grandparents may bias their care toward grandchildren for whom they are convinced to be their biological descendants. Empirical data show that this assumption may be correct even in contemporary European populations. Findings from 13 European countries showed a higher investment of maternal compared to paternal grandparents; furthermore, grandparents of both sexes provide more investment to daughter’s children if they have grandchildren via both son and daughter (Danielsbacka et  al., 2011). However, biological relatedness can only partially explain biased grandparental investment. The meta-analysis of 17 patrilineal (a system of inheritance where wealth is inherited via male descendants) and dominantly patrilocal populations (where women who marry disperse and live in their husbands’ households) showed a greater investment of maternal grandparents as well, but this effect was best explained by the competition for resources between the members of the same family (Strassmann & Garrard, 2011). Hence, biased investment toward daughters cannot be explained solely by paternity certainty; various ecological conditions including cultural practices in inheritance and household’s structure can influence the direction and strength of grandparental care. Elevated care from maternal grandparents is documented not only in traditional but in modern WEIRD societies as well, for example, in the United Kingdom (Pollet et al., 2009); however, there are studies that showed opposite results as well—more beneficial outcomes resulting from paternal grandparents (Tanskanen et al., 2014), suggesting variability in the influence of lineage on grandparental investment. Furthermore, there are data in contemporary European societies suggesting not only the existence of grandparental investment but their positive effect on children reproductive success, thus confirming evolutionary significance of grandparental care (Kaptijn et al., 2010). Interestingly, economical support from grandparents and their actual time spent with grandchildren were not the primary predictors of children’s fertility; in contrast, the emotional closeness between individuals and their parents was more important to their subsequent fertility (Waynforth, 2012). On the other hand, enhanced fertility of their children can have a negative effect on the magnitude of grandparental care. Grandparental investment is dependent on the fertility patterns of the grandparents: individual with earlier age of first reproduction

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tend to have more children and grandchildren; this leads to diminished grandparental investment as well (Coall et al., 2009). Hence, the relation between investment and number of offspring resembles the one predicted by quantity-quality tradeoff (interestingly, these associations were detected only in data provided by grandchildren, not on the data obtained on grandparents which suggests that research methodology plays a role in these effects). In fact, grandparental investment can partially explain one of the most peculiar features of humans: elevated postreproductive longevity. In most of species individuals die soon after the shutdown of their reproductive functions; this make sense in a light of evolution—if individuals cannot add to their fitness, their further longevity would not be supported by natural selection. However, grandparents apparently further contributed to fitness even after cessation of their reproductive activity—by increasing their children’s and grandchildren’s fitness. During the evolutionary course, natural selection favorized individuals who produced higher grandparental investment which in turn elevated longevity itself (Hawkes, 2004). We can thank our ancestral grandparents on their care for grandchildren because our current longevity is partially a consequence of their efforts to help in grandchildren rearing. This is sometimes called a grandmother effect because it is assumed that grandmothers were the primary caregivers to grandchildren in the ancestral time; even if males did not provide grandparental care, their longevity could evolve simply because grandmothers passed their genes both to male and female offspring (Sheppard, 2021). Convincing evidence for the effects of grandparenting on human longevity is obtained by simulation models that showed positive effects of even low levels of grandparental care to offspring fitness and, consequently, longer postreproductive lifespan; approximately 275,000 years was required for a transition from a great ape to adult human lifespans (Kim et  al., 2014). Prolonged postreproductive lifespan certainly cannot be completely attributed to grandparenting because the data showed that the benefits of grandparental care decrease with age (Chapman et al., 2019), but grandparenting had its fair share in elevating human longevity.

Parental–Offspring Interactions Children have different fitness “interests” than their parents; for example, children can increase their fitness if parental investment in them is maximized. However, parents may obtain more fitness benefits if they invest in further mating and reproduction, not the investment in their offspring. Hence, a conflict emerges between parents and their offspring regarding parental investment, i.e., the parent-­ offspring conflict (Parker et  al., 2002; Schlomer et  al., 2011; Trivers, 1974). The conflict may be easily observed in families with several siblings: in these cases, the intensity of siblings’ competition depends on their number, relatedness, and the level of parental resources. However, the conflict exists even when parents have only one biological descendent: in this case, the offspring extracts parental

Parental–Offspring Interactions

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investment, which in turn decreases resources for future parental reproduction. There are various developmental transitions that instigate specific conflict situations, starting at the prenatal level (e.g., involving fetal nutrition and growth), waning, or resource inheritance. Several factors influence conflict resolution including resource control (the level that resource control is under parental or offspring influence) or the effects in the changes of parental investment to the offspring’ demands (which can be nonexistent, positive, or most likely negative—increased parental investment leads to the decrease in offspring demand). Parent-offspring conflict can be empirically observed through opposite attitudes toward future reproduction in parents (positive) and their children (negative) in modern China; furthermore, the attitudes of children had an impact on mothers’ fertility intentions (Liu et al., 2017). This study also found that negative offspring’ attitudes toward future parental reproduction decrease with offspring’ age (which is predicted by theory because as offspring become older, their fitness goals converge to parental ones to a higher extent) and that firstborn sons living in high-resource families held especially negative attitudes toward future parental reproduction (i.e., a three-way interaction between resources, sibling birth order, and sex on the intensity of parent-offspring conflict). Parents can try to alleviate sibling competition over parental resources in various ways. For example, recent research in Tibetan populations showed that sending male children to become monks may serve as a strategy for relaxing sibling competition and, consequently, the parent-offspring conflict (Zhou et al., 2022). Particularly interesting aspect of parent-offspring conflict arises around offspring mating. It is noticed that parents and offspring can have different fitness-related goals regarding the offspring’ mate choice; therefore, parents try to control their children’s mate choice, which leads to conflict (Apostolou, 2021; Buunk et  al., 2008). For example, in the analysis of the hunter-gatherers’ mating patterns and reproductive outcomes, it has been shown that most of the reproduction happened in marriages while the marriages themselves were largely controlled by the parents and other close kin (Apostolou, 2007). The control was mostly exercised by fathers, and women were more controlled than men. A similar situation was found in agro-­ pastoralist subsistence systems as well (Apostolou, 2010).1 On the other hand, there

 Menelaos Apostolou, a researcher who dedicated a lot of effort in exploring the parental–offspring conflict in mate choice offered some interesting implications of such a high parental control on mating in ancestral humans. He believes that parental control generated an additional aspect of sexual selection in humans: sexual selection did not operate only on the choosiness of mates themselves but on parental choosiness as well, i.e., sexual selection would promote characteristics chosen by parents in a potential mate because parents could have high impact on the mating success of their children (Apostolou, 2007, 2010). This hypothesis is interesting because it involves some intriguing implications as well. One of the crucial assumptions of the hypothesis is that the criteria for mate choice differs in individuals and their parents (if it does not differ then we do not need to invoke an additional mechanism of selection). For example, individuals may not see their potential mate’s status as an important criterion for suitable mates, but their parents would. However, this incongruence between parents and their children in their criteria for suitable mates may vary between different socio-economical groups: for example, parents and their children from higher social classes may be more similar in their elevated valuing of potential mates’ status. Therefore, we can devise even more complex models of sexual selection which are multilevel in their nature and encompass social structure as well. 1

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are data showing that in some traditional societies that entered urbanization, children do have relatively high autonomy in choosing marriage partners; therefore, parental control over mating is dependent on specific cultural and ecological conditions (Schaffnit et al., 2019). Parental control over mating is certainly decreased in contemporary industrial and postindustrial societies, but it still exists; for example, the conflict is detected in modern China, where parents and their daughters have a disagreement on how important the physical look of a potential mate is (Bovet et al., 2018). Generally, the data suggest that parents in contemporary human populations tend to control their daughters’ mating behavior to a greater extent, mostly in order to protect their sexual reputation and mate value in general, but to prevent them from being sexually exploited as well (Perilloux et al., 2008). Modern parents use different “manipulation” techniques in order to implement their criteria for choosing a mate for their children including coercion, communicating desirable traits a mate should have, matchmaking, generating guilt in their children, or directly intervening on potential mates themselves (Apostolou, 2013). Phenotypic influence of parents and other kin on the offspring reproduction should not be viewed only from the conflict perspective. In fact, the major part of interaction between the parents and their children over hominization was based on cooperation (Kramer, 2011). This stems from the fact that young human juveniles are key helpers both in resource production and in care for younger siblings. Another source of within-family social interaction is the motivation of kin to enhance the fertility of their relatives because by doing so they enhance their inclusive fitness (Newson et al., 2007). This topic is one of the major research fields in demography because the kin influence on reproduction can be an important factor that contributes to the intergenerational transmission of fertility and family size (Ji et  al., 2015). These effects were largely viewed as a part of cultural evolution since parental and kin influence are thought to reflect cultural processes (Newson et al., 2007). While I acknowledge that culture may have an influence on parental behavior toward their children, it is my opinion that the mere fact that this behavior is expressed by the parents, and therefore that its function is to promote parental fitness as well, is also highly relevant in this context. Therefore, I think that this behavior can be more adequately understood as parental effects: any parental behavior that changes offspring’ phenotype beside direct genetic transmission (Badyaev & Uller, 2009; Reddon, 2012), with an emphasis on behaviors that increase offspring fitness. These effects are apparently similar to parental and grandparental investment, but I believe they are useful explanatory constructs themselves  because they are broader and more comprehensive. For example, if parents suggest to their offspring that they should get married and have children of their own (a type of parental influence that is probably experienced by a vast number of young people), this can hardly be viewed as the parental investment; however, it can be conceived as a parental attempt to change offspring’ behavioral phenotype (e.g., by enhancing reproductive motivation) in order to promote higher fertility. These effects may not emerge only from direct fertility-related suggestions from parents to offspring: values and beliefs regarding the preferred family size can be incorporated by offspring via socialization (i.e., copied from parents) and they can influence fertility as well (Kolk, 2014a;

Reproductive Motivations and Intentions

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2014b). Certainly, cultural characteristics may influence parental pro-natal influences on their offspring, but these behaviors can also be genetically based to a certain extent, and therefore their impact can be viewed as indirect genetic effects.

Reproductive Motivations and Intentions Parental effects and grandparental investment likely influence the reproductive motivation of offspring. Having supportive parents with intentions to help in raising grandchildren elevates the motivation of offspring to start a family or to have an additional child. In fact, reproductive motivation is probably one of the most important determinants of observed fertility. There are several reasons for this; first, social and ecological conditions have a lower impact on fertility among modern humans compared to ancestral ones—this enabled individual desires and plans to more significantly impact fertility. Second, contraceptives and other reproductive technology can help humans in regulating fertility by providing a means for reproductive motives to be more precisely translated into the observed number of children (although the percentage of unplanned pregnancies can still be high even in populations with easily available technology for reproductive control: Singh et al., 2010). Thus, it is not surprising that reproductive motivation reliably positively correlates with observed reproductive success (e.g., Liefbroer, 2009; Međedović et al., 2022; Miller et al., 2010b; Schoen et al., 1999). Furthermore, reproductive motivation shows a genetic variation (Miller, 2011; Pasta & Miller, 2000), and the link between reproductive motivation and the observed number of children also exists on a genetic level (Miller et  al., 2010a). The implication is not only that reproductive motivation is among behavioral dispositions that are probably under the strongest positive directional selection; it suggests that other heritable traits related to reproductive motivation can be under selection as well. In other words, reproductive motivation can mediate the links between behavioral traits (personality, cognition, attitudes) and reproductive fitness. Hence, explaining the predictors of reproductive motivation holds invaluable benefits in our understanding of fertility and, therefore, the selection regimes on behavioral traits (for a comprehensive review, see McAllister et al., 2016). Perhaps the most apparent environmental influences on fertility motivation are financial security and future financial expectations; for many individuals, these are among the basic conditions to be met before having a child (Guedes et al., 2015; Kariman et al., 2014). Certain personal achievement motives, like desires for career success, can buffer childbearing motivations (Schytt et  al., 2014). Besides the current environment, the data show associations between childhood environment and reproductive motivation. For example, childhood stress is related to the earlier desired age of first reproduction (Clutterbuck et al., 2014), although some research suggested that only the congruence between childhood and current environmental stress predicts motivation for earlier reproduction (Griskevicius et al., 2011). When childhood stress has been operationalized as a father’s absence, both positive

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(Maestripieri et al., 2004) and negative relations (Clutterbuck et al., 2014) of such measure with childbearing motivation have been obtained. There are suggestions that the measures of environmental stress and harshness, in general, predict higher reproductive motivation because ecological harshness indicates higher mortality, while elevated mortality rates reliably positively predict reproductive motivation (McAllister et al., 2016). I previously analyzed the influences of kin on reproductive motivation: grandparental investment and other kin helping behavior facilitate pro-natal reproductive decisions. Biological relatives are not the only individuals who are important for fertility motivation: peers’ fertility behavior is associated to fertility motivation (Adair, 2013). However, the most influential individuals regarding fertility intentions and motivations are certainly sexual and romantic partners. First, long-term mating (duration of romantic relationships), in contrast to short-term mating (number of sexual partners), positively predicts reproductive motivation (Međedović, 2021b). There are several indicators of interpersonal dynamics in romantic dyads that are associated with childbearing motives: relationship quality, frequency of sex and positive partner’s beliefs about having a baby (Carter et al., 2013), and higher expectations of emotional and financial support from partner (Wilson & Koo, 2006). Being in a long-term, stable romantic relationship is also supported as a cultural norm for having children (Newson et al., 2007). One of the crucial questions in the field of reproductive motivation is how to measure it in the first place. Some standard measures include desired age of first reproduction and desired number of children. These measures can be expanded by behaviors like contraception usage, discussing pregnancy with partners, or preparing for pregnancy in some way (Rocca et al., 2010). Some authors proposed that interest in infants may serve as a measure of reproductive motivation (Clutterbuck et al., 2014; Maestripieri et al., 2004), while others suggested that baby fever is a useful indicator of childbearing motives (Brase & Brase, 2012); although the data regarding associations between these measures and more direct markers of reproductive motivation and observed fertility are still lacking. I am under the impression that specific inventories aimed at measuring reproductive motivation are the most beneficial in this line of research. The rationale for this is the fact that they are the most precise and highly comprehensive at the same time in capturing various aspects of reproductive motivation, keeping in mind that it has a complex motivational structure. These inventories usually have two broad scales that capture positive and negative motivations for childbearing (e.g., Guedes et al., 2015; Langdridge et al., 2005; Miller, 1995). However, most of them have several subscales that more precisely operationalize these two general motives. For example, the scale developed by Guedes et  al. (2015) measures positive childbearing motivations by including their socio-economic aspects, impacts on personal fulfillment, continuity of family lineage and values, and strengthening couple relationship; negative childbearing motives are represented by childrearing burdens, social and ecological worries, negative impacts on parental relations, financial and economic problems, and somatic problems and physical discomforts. Hence, reproductive motivation is covered quite broadly, which provides researchers with valuable tools for exploring

Understanding Demographic Transition

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different aspects of childbearing motives. Interestingly, while motivation is traditionally one of the main psychological fields of research, most studies in this field have been conducted by demographers. The topic of reproductive motivation clearly represents a research direction where psychologists, demographers, and anthropologists can fruitfully cooperate by bringing insights and methods from their respective disciplines. Needless to say, I anticipate that the contribution of psychologists will be especially important in the future research on this topic.

Understanding Demographic Transition Demographic transition is a population process marked by a shift from high mortality and fertility to low mortality and fertility rates. Thus, it represents an evolutionary puzzle par excellence because it reflects considerably lower fertility than the potential one, including the below-replacement fertility, where couples have less than two children on average. Demographic transition has high importance in a practical sense as well because it represents a crucial parameter in shaping demographic and populations’ growth/decline, together with the links between populations’ sizes and structures with wealth and economic resources. As we will see, human behavioral ecology, and especially the evolutionary ecology of family, has a lot to offer in shedding light on this phenomenon by integrating ultimate and proximate explanations of demographic transition (Borgerhoff Mulder, 1998; Colleran, 2016; Sear & Coall, 2011; Shenk, 2009; Shenk et al., 2013). In light of a discussion between evolutionary psychology and human behavioral ecology, a first impression on demographic transition aligns with the view of evolutionary psychologists on the reproductive success of modern humans: it is maladaptive, probably due to a mismatch between ancestral and contemporary ecologies that caused below-optimum functioning of evolved adaptations. The ecological condition that probably was a primary cause of dysfunctions is contraception and other forms of technology for reproductive control. While being intuitive and simple in form, this assumption was criticized because demographic transition can start even when contraceptives are not widely introduced in a population; on the contrary, there are populations that can maintain higher fertility rates despite the presence of reproductive control techniques (Borgerhoff Mulder, 1998). Furthermore, this hypothesis does not offer a mechanism that links contraceptives and reproductive decline including behavioral processes that mediate this association (Kaplan et al., 1995). Classical demographic transition theory posits that the drop in mortality rates promoted children’s survival—this first led to population growth, but soon the parents recognized that children survive to adulthood, they changed their behavior in favor of lower fertility (e.g., Leslie & Winterhalder, 2002; Quinlan, 2007). On the other hand, the models based on cultural transmission emphasize the role of various social actors in the environment and the processes of social learning that cause fertility decline. For example, copying models with high status and prestige may

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buffer reproduction if these models also have low fertility (Richerson & Boyd, 2005); historically, individuals belonging to high socio-economic classes were the  first to implement contraceptives and delaying of reproduction. Furthermore, this hypothesis also highlights the fact that modern humans less frequently live and spend their time with their kin compared to our ancestors; since kin promote high fertility norms and behavior, this negatively impacts reproductive intentions (Newson et al., 2007). Finally, the third class of models encompasses some kind of investment, either the ones related to the working markets or investment in offspring, or both because these two are related. For example, it is argued that in order to be competitive in modern labor markets, individuals must acquire education, status, and other skills that facilitate success in a market competition (Galor, 2012; Low et  al., 2002; Shenk et  al., 2016). Delayed reproduction and, therefore, a smaller number of offspring is one of the consequences. Furthermore, parents need to assure that their children will be successful in competing for resources as well, which implies high parental investment in various aspects (Kaplan, 1996); investment in offspring is additionally elevated due to fact that the cost of raising children is increased in modern economies—again the result is fertility decrease (Lawson & Mace, 2010; Mace, 1998). Apparently, these three sets of models are not mutually exclusive; having in mind the complexity of the demographic transition, it is not surprising that all models can help in explaining transition, but empirical data still suggest that the investment models have the highest explanatory power (Shenk, 2009; Shenk et al., 2013). Our focus should be on investment models for another reason: this is the only model suggesting that low fertility may be evolutionary adaptive as a reaction to modernization and the change in labor markets. The adaptive logic of this assumption is based on the quantity-quality tradeoff and it assumes that modern fertility represents a shift toward high quality (i.e., markedly increased investment)—low quantity fertility. Hence, in contemporary ecologies, it may be adaptive to produce fewer children with elevated embodied capital than many offspring with denied resources (Lawson & Mace, 2011). The implication is that modern humans do not tend to maximize short-term fertility but long-term reproductive success by heavily investing in their offspring; this, in turn, should positively affect reproductive fitness in future generations. Having in mind that fitness is a relative measure (we can compare the reproductive success of individuals from the same population), this assumption is congruent with the models showing that even a small number of offspring can lead to above-average fitness in modern humans (Jones & Bird, 2014). Empirical data showing the reverse in fertility decline in countries with the highest levels of human developmental index (Myrskylä et al., 2009) is also in line with the tradeoff model. However, large amount of empirical data show exactly the opposite outcome as well: despite the fact that the levels of parental investment are highly elevated, investment does not increase reproductive success in offspring, and may even reduce it (Goodman et al., 2012). On the contrary, due to the fact that fertility is partially heritable (Bras et al., 2013; Briley et al., 2015; Rodgers et al., 2007) and there are phenotypic processes that contribute to a positive correlation in fertility between parents and offspring (Kolk, 2014a; 2014b; Murphy & Knudsen, 2002), it

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is expected that any increase in global population fertility would be instigated by the individuals with maximized present reproductive success, not the intermediate one (Burger & DeLong, 2016; Collins & Page, 2019; Kolk et al., 2014a; 2014b; Mueller, 2001). Simply put, these data speak against the tradeoff hypothesis, suggesting that we still need to search for a satisfactory explanation of demographic transition, probably by integrating existing hypotheses but actively generating new ones as well.

Evolutionary Tradeoffs in Humans

Tradeoffs between fitness components represent core points in evolutionary conceptional framework. Analyzing tradeoffs enables an insight into a dynamic of fitness optimization in a certain population and provides accurate estimations of the selection regimes on given traits. Tradeoffs emerge because fitness components are negatively correlated: a positive change in a certain trait is linked with a negative change in another. They are most frequently explained by an allocation theory: due to limited resources, organisms cannot simultaneously elevate all fitness components— they increase certain ones at the expense of others. Sometimes it is a priori assumed that the tradeoffs are ubiquitous, at least in natural fertility populations, but we will see that this is not the case: tradeoffs sometimes do not exist in a certain population (due to some biological or ecological conditions) or they are more complex than expected (e.g., they are based on nonlinear associations between the traits). Tradeoffs are often hard to empirically measure, especially in humans. Observational studies often suffer from a risk of unmeasured variables that may obscure tradeoffs, experimental studies are not applicable to humans, while the quantitative genetic data on tradeoffs are still rare, but fortunately their number is rising (for a detailed review on methodological aspects of tradeoffs exploration in humans see Bolund, 2020).

Fertility-Longevity Tradeoff The tradeoff between fertility and survival/longevity represents a major evolutionary tradeoff because reproductive success and survival are two major fitness components. Its basic source is the cost of reproduction: reproduction is costly and therefore there is a trade-off against latter reproduction. However, this link is not a direct one because earlier reproduction is positively related to a lifetime number of children; the cost is usually somatic one, reflected in diminished health and, consequently, lower longevity. Hence, a negative correlation between fertility and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Međedović, Evolutionary Behavioral Ecology and Psychopathy, https://doi.org/10.1007/978-3-031-32886-2_5

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longevity is expected under this tradeoff; however, the empirical data obtained in human populations are mixed. Some researchers did not find the relations between fertility and post-reproductive survival (Hurt et al., 2006) or maternal health (Jaeggi et al., 2022), while others found theoretically expected negative association (Gagnon et al., 2009). However, some even found positive correlations between reproductive success and longevity in rural Gambia (Sear, 2007), effectively demonstrating that the existence of not only heterogeneity in effect sizes but in their signs as well. There are studies detecting tradeoffs, but their effect was highly constrained: tradeoff was found in modern populations (but not in traditional) and in individuals with higher fertility (Le Bourg, 2007). In Serbian Roma which are currently passing thorough demographic tradition (fertility is higher than average, birth control is present but not frequently used, health services are not easily available, poverty rates are elevated) the tradeoff is detected: number of surviving children is negatively related to somatic health in women (Čvorović & Coe, 2017). It is important to note that some genomic studies succeeded in detecting the tradeoff in the contemporary population in US (Wang et al., 2013). Why there is such a difference in effects? First, the heterogeneity in the effects signs may suggest that the examined relation is not a linear one. The meta-analysis of 37 studies (Högnäs et al., 2017) found J-shaped curve as the best estimation of the association between fertility and mortality—the highest mortality risk was in individuals with no children or one child, it was lowest in families with three children and elevated again with a higher number of offspring. This is in line with the recent study that found U-shaped curve between these fitness components in modern Sweden (Barclay & Kolk, 2019). Elevated mortality in high-fertility individuals is expected, but what is the cause of increased mortality risk in individuals without offspring or with low reproductive success? The first crucial factor is health itself: individuals in better somatic condition can have higher fertility without paying somatic costs of reproduction; in contrast, low health can buffer both survival and reproduction success (Högnäs et al., 2017). Second, children may assist and help elderly parents and thus prolonging their lifespan—individuals without children do not have these benefits. There are other factors that can influence this tradeoff—offspring’ sex is one of them. Some studies (Galbarczyk et al., 2018) suggested that carrying boys is metabolically more expensive than female children and thus giving birth and providing lactation to boys can diminish mother’s health to a higher degree. Congruently with this, female children are more motivated to help their elderly parents and thus diminishing their cost of reproduction (Grigoryeva, 2017). Off course, one of the main factors that influence this tradeoff are sex differences—majority of studies show that women pay higher price of reproduction than men. This effect is not only due to elevated risk of childbirth mortality but also to reduced post-reproductive health in women. This could be a consequence of the pregnancy costs (calcium loss, nutritional deficits, oxidative stress, and reduced immunological response) and heightened risk for breast cancer, osteoporosis, diabetes, and cardiovascular disease (Jasienska et  al., 2017). In fact, genomic studies found that the same genes that favor reproduction earlier in life also contribute to

The Tradeoff Involved with the Age of First Reproduction (AFR)

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the higher chance for cardiovascular disease later in life (Byars et al., 2017) and risk for cancers (Dujon et al., 2022). On the other hand, males suffer other health costs like the enhanced probability of prostate cancer as a consequence of elevated exposure to testosterone (Jasienska et al., 2017). Since women pay the higher cost of reproduction, the advances in healthcare and its availability, together with lowered fertility in modern human populations, had an effect especially to the elevated longevity of females compared to males (Bolund et al., 2016). The importance of fertility-longevity tradeoff in human evolution can be clearly seen in the implications of these findings for our understanding of the evolution of senescence and human lifespan. First, the data on the same gene alleles that contribute to fitness early in lifetime but decrease fitness later in ontogeny are in line with the antagonistic pleiotropy theory of aging: senescence occurs not only because of limited resources for somatic maintenance (the disposable soma theory) and suboptimal physiology in late life (the hyperfunction theory) but also by differential effects that genes have on fitness in different stages of life (the same genes are under positive directional selection early in life and negative latter in life). Second, higher costs of reproduction for females could contribute to the evolution of menopause. Since the costs of reproduction are especially high later in life, natural selection favored physiological processes that would stop reproductive function and facilitate the mothers’ investment in their offspring, thus elevating the offspring’ fitness (the prudent mother hypothesis). Finally, higher longevity of females compared to males that is evident in many populations of contemporary humans may be a relatively novel phenomenon, emerged as a consequence of reduced reproduction cost for females that occurred in demographic transition.

 he Tradeoff Involved with the Age of First T Reproduction (AFR) Timing of first reproduction is one of the crucial reproductive events in many taxa. The tradeoff associated with this fitness outcome is similar to the fertility-longevity tradeoff—individuals with early first reproduction may have benefits from early reproductive fitness but they may suffer fitness cost expressed in diminished health or other resources; on the other hand, delaying reproduction can produce better somatic state, larger body mass, and gathering of resources that can be invested in offspring (Liu & Lummaa, 2011). There is another important aspect of delaying first reproduction—this can provide additional time for individuals to find more suitable mates to reproduce with, which can provide both direct and indirect (biparental investment) fitness benefits (Lawson & Borgerhoff Mulder, 2016). Off course, if delayed above some adaptive threshold, reproduction can again inflict costs both for mothers and offspring: fecundity drops after the age of 30, offspring may have low birth weight (which decreases survival chances) and having children beyond 35 years is associated with higher stillbirth risk (Raymond et al., 1994).

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Hence, the first aspect of AFR tradeoff is health consequences of earlier AFR, similar to the fertility-longevity tradeoff. Indeed, there are many studies showing that early first birth has negative health consequences and it rises mortality risks; analyzing the effects in women, this effect is sometimes more evident than the link between fertility and survival hazards (e.g., Chisholm et al., 2005; Ganchimeg et al., 2014; Hayward et al., 2015; Pirkle et al., 2014). However, this effect turned out to be variable as well: there are several studies that did not find a negative association between AFR and health (e.g., Čvorović, 2019; Gurven et  al., 2016; Helle et  al., 2005). Although, compared to fertility-longevity tradeoff, there are no data of the opposite sign correlations—earlier first reproduction may not have a negative influence on subsequent health status and longevity, but it certainly cannot be linked with better health. The explanation for the absence of the link can involve general health status, similarly as in previous tradeoff—healthier individuals can have their first child earlier without suffering somatic costs of early reproduction (Čvorović, 2019). It seems that the association between AFR and health in later life is still unresolved, in fact, it is probably complex and thus dependent on various moderators. However, an empirical fact is that the AFR is largely prolonged in modern humans, and it is still rising; hence, we should ask if there are some other adaptive consequences of delayed AFR. One plausible answer is mentioned when I discussed evolutionary origins of demographic transition—latter AFR is related to accumulating resources; consequently, these resources can be transmitted to (the fewer number of) offspring which would enhance the offspring’ position in the societies based on market economies (Kaplan et al., 2002). Nevertheless, the key question still remains: is the delaying of  AFR related to the subsequent fitness advantages in offspring, expressed as higher longevity and especially higher reproductive success? Empirical data suggest (Liu & Lummaa, 2011) that the answer is negative: delaying AFR does not provide survival or reproductive benefits in offspring, in fact delaying AFR after 30 years elevates the chances of reproductive failure (the results are obtained on a preindustrial Finnish population; in modern societies, the threshold is probably higher, but after 35  years AFR is associated with various costs of reproduction). Hence, it seems that via various fitness-related outcomes, earlier AFR is adaptive; one potential cost of early AFR is that it is related to earlier age of last reproduction (ALR) as well, even on a genetic level (Bürkli & Postma, 2014). Early reproduction diminishes overall reproductive lifespan. However, having in mind the elevate health and survival risks of late fertility both for mother and infants, it seems that the fitness advantages of early AFR are largely surpassing the costs of early ALR.

Quantity-Quality Tradeoff Quantity–quality tradeoff rests on the fact that resources (both somatic and material) are restricted; hence, the more offspring individuals have, less investment can be provided to each of them. This tradeoff is therefore based on parental investment. Humans are the species that generally has relatively high parental investment rates;

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parental care is even heightened in modern low fertility populations. Albeit parental investment certainly has various proximate positive effects on offspring welfare (elevating motoric, cognitive, affective functioning, social skills, and effects for well-being in general), the question here is the same as for other tradeoffs: does parental investment increase fitness in offspring? If we examine offspring survival as the fitness component, the answer is affirmative. The studies examining these effects are mostly conducted in preindustrial populations; the review provided by Lawson and Mace (2011) provides an illustrative depiction of how family size (the number of siblings) is related to fitness outcomes (but see also the more recent review of Lawson & Borgerhoff Mulder, 2016). Higher number of siblings is negatively related to survival in contemporary hunter-gatherers like !Kung of Botswana (Draper & Hames, 2000; Pennington & Harpending, 1988), and Ache of Paraguay (Hill & Hurtado, 1996), contemporary agriculturist and pastoralist like Dogon of Mali (Strassmann & Gillespie, 2002) and Bimoba and Kusasi of Gana (Meij et al., 2009), and in historical agriculturists (eighteenth and nineteenth century) of North America (Penn & Smith, 2007), Germany (Voland & Dunbar, 1995), and Finland (but only in poor socio-economic conditions: Gillespie et  al., 2008). Some studies provided null results with only one positive association—in Kipsigis of Kenya (Borgerhoff Mulder, 1998). Other studies found similar signatures of quantity–quality tradeoff when offspring survival was the criterion measure: Agta foragers in Philippines (Ross et al., 2016), and in 27 sub-Saharan countries (Lawson et al., 2012). Furthermore, lower number of siblings was positively related to literacy and employment in skilled and high-­income professions in preindustrial England (Klemp & Weisdorf, 2019). Hence, higher parental investment, and competition of siblings for restricted parental resources predicts higher survival chance in children with fewer siblings. These data suggest that individuals should not tend to maximize their reproductive success but to achieve optimal number of offspring in a given population (the association between reproductive success and overall fitness is represented in inverted U curve). However, higher survival still does not necessarily enable higher reproductive success of offspring. What is the evidence of quantity–quality tradeoff when offspring’ fertility is analyzed as the criterion measure of fitness? The findings are much more mixed in this case. As predicted by the tradeoff, the negative associations between the number of siblings and fertility are found in Gabbra of Kenya (Mace, 1996), Arasi Oromo of Ethiopia (Gibson & Gurmu, 2011), and nineteenth-century Sweden (Low, 1991). However, there is research that found positive associations including the data obtained in !Kung (Draper & Hames, 2000; Pennington & Harpending, 1988), Ache (Hill & Hurtado, 1996), and in preindustrial Finland, but only in families with higher amount of resources (Gillespie et al., 2008). Besides parental resources another factor that can moderate the link between number of siblings and siblings’ fertility is the sex of siblings: Higher number of brothers decreased the reproductive success of males of Kipsigis, but higher number of sisters elevated fertility in males (Borgerhoff Mulder, 1998). This result suggests that there is competition between the male offspring in the same family, but probably there is cooperation with sisters and it may facilitate the reproduction of males.

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Hence, the data on quantity-quality tradeoff vary in preindustrial populations when fertility is analyzed as the fitness criterion measure. In fact, there are even positive associations between the number of siblings and their fertility with the indications that this association may be particularly present in families with large amounts of resources (Gillespie et  al., 2008). What is the situation in modern, industrial, and postindustrial populations, having in mind that families in these societies have more resources than in preindustrial populations? When we discussed demographic transition, I already mentioned positive associations between family size in parents with the one in their children (e.g., Burger & DeLong, 2016; Collins & Page, 2019; Kolk et al., 2014, 2014a, 2014b; Mueller, 2001; Murphy & Knudsen, 2002). Not only that there is positive association between number of children and grandchildren, the association exists on a genetic level as well, and some authors even found perfect genetic correlation between number of offspring and grand offspring (Zietsch et al., 2014). Other research typically find high genetic correlations but with stronger effect sizes in high-resources environments compared to scarce environmental conditions (Bolund & Lummaa, 2017). These data suggest that there is no quantity-quality tradeoff in modern human populations. This result is not surprising: with below-replacement fertility, parents typically do have resources to provide high investment in their offspring (for example, parents in many countries do not suffer resource cost when investing in two compared to only one children; again, for various populations, two children is already above-average fertility). Quantity– quality tradeoff is therefore more likely to be found in populations with high fertility and resource scarcity—the higher these two parameters are, the tradeoff should be stronger. The data of high positive genetic correlations between number of offspring and grand-offspring have another implication for the measuring of fitness: lifetime number of children is a  very good approximation of fitness in modern humans, even without measuring the number of grandchildren.

Mating-Parenting Tradeoff This tradeoff refers to the possibility that allocation of energy towards care for offspring prevents parents to find new mates and vice versa (Trivers, 1972). This tradeoff is conceptually quite important in evolutionary psychology, an indeed, when parental care and matting effort (e.g., motivation to find new mates) are empirically measured they do tend to show negative correlation (Beall & Schaller, 2014, 2019; Međedović, 2019a, 2021c). However, much less is known about the dynamic that mating vs. parenting allocations have for fitness outcomes. The magnitude of mating-parenting tradeoff is probably influenced by mate value and relationship status: males who perceive themselves as more attractive and the ones who are not in romantic relationship had lower parental care and elevated motivation to find new mates (Apicella & Marlowe, 2007). In an interesting study, Anderson (2011) found that men who pay child support for children from previous relationships have lower probability of a subsequent reproduction, which seems congruent with the tradeoff.

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However, lower chance for reproduction was in fact not related to future probability of these men marriages, in fact they were more likely to remarry. This was interpreted as a possibility that paying child support represents honest signal that is attractive to females: therefore, when mating is specifically analyzed, the results are in fact incongruent with the mating-parenting tradeoff (and more congruent with the quantity–quality tradeoff because current investment in offspring reduces the chance for future reproduction). In the majority of research on this tradeoff, mating effort was operationalized as the number of sexual partners or changing the partners in serial monogamy. However, this represents only a fraction of mating behavior in humans, i.e., short-­ term mating; humans tend to form long-lasting relationships characterized by emotional investment and intimacy which produce biparental investment in offspring. When long-term mating is explored the tradeoff is not present, quite the contrary—the data show that the duration of romantic relationships is positively related to parental care (Međedović, 2021b, c). This is probably a result of partner choosiness (usually attributed to females, but both sexes are choosy in humans) in long-term relationships: one of the characteristics that we positively evaluate in our long-term romantic partners is their potentials for parenthood and parental investment.

Life History Theory

The Basic Tenets of Life History Theory (LHT) LHT is a direct extension of the concepts of evolutionary tradeoffs. The existence of tradeoffs suggests that fitness cannot be maximized straightforwardly because elevating one fitness component may lead to a decrease in the other. Hence, depending on various aspects of organisms and their environments, the allocation of energy into the traits like body mass, growth, maturation timing, age at first reproduction, intervals between reproduction occurrences, number of offspring, parental investment, age at senescence, and longevity are different between-species and populations. Note that the listed traits are labeled as life history traits because they are the crucial indicators of the fitness optimization patterns that emerge in different species. Hence, it should be emphasized that LHT is not a theory in the narrowest sense; its predictions in evolutionary biology are formulated mostly via formal modeling (Stearns, 1992). LHT is first conceived by analyzing the differences between the species in life history traits. It has been assumed that population density has a pivotal role in shaping life history trajectories: organisms living in populations with low density are selected to maximize population growth (i.e., fertility) while the ones in high-density conditions are selected for elevated competition success and longevity (MacArthur & Wilson, 1967). Furthermore, a whole suit of traits should be related to life history allocations toward maximizing reproductive output, e.g., smaller body size, earlier age of maturation and first reproduction, and lower parental care (Pianka, 1970). These assumptions were the first ones to suggest that one major life history continuum that explains most of the variation in between-­ species life history traits may exist. This continuum is labeled as the fast-slow dimension, where fast corresponds to species and populations that maximize fertility at the expense of longevity and investment, while slow depicts the opposite pattern.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Međedović, Evolutionary Behavioral Ecology and Psychopathy, https://doi.org/10.1007/978-3-031-32886-2_6

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However, this idea was largely abandoned in evolutionary biology afterward (Stearns, 1992), although some researchers are still pursuing it. There are two reasons why this assumption was neglected by evolutionary biologists. First, there are disputes about how much it was empirically accurate. More precisely, there are ongoing debates about how much data the continuum can explain: some researchers claim that the explanatory power of this single life history dimension is relatively low (reviewed in Stearns & Rodrigues, 2020), while other claim that it is sufficient enough for the idea to be kept in LHT (Del Giudice, 2020). Conceptually, the model was viewed as too simplistic: various ecological factors (hence, not only population density) can be associated with growth, survival, and reproductive outcomes, and these life history trajectories may be diverse and dispersed, and therefore, they are not likely to be reduced to a single continuum (Roff, 2002; Stearns, 1992). The first extensions from these basic models of LHT were developed in animal behavioral ecology. There are two main advancements of the framework devised in behavioral ecology: (1) the patterns of associations between the life history traits are empirically examined not only between the species but within a population (i.e., inter-individual differences in life history are explored); (2) the framework encompasses not only life history traits but physiological and (most importantly for evolutionary social sciences) behavioral traits. Although it is firmly based on LHT, this framework is apparently broader, and it is labeled as the Pace of Life or POL (Réale et al., 2010; Ricklefs & Wikelski, 2002; Mathot & Frankenhuis, 2018). Pace of life term clearly suggests that in POL conceptual space an existence of a single continuum of covariation between life history, physiology, and behavior has an important place and it is labeled as the Pace of Life Syndrome (POLS). Populations and individuals can be labeled as fast or slow (to a certain extent) regarding their POLS similarly to the early works of Pianka (1970): faster POLS organisms have earlier development and age of first reproduction, a higher number of offspring, and lower survival chance; physiological traits like high metabolic rates and behavioral characteristics like aggressiveness are thought to be a part of faster POLS as well (Réale et al., 2010). LHT has been extensively used in evolutionary social sciences for at least 20 years as well (see Del Giudice et al., 2015, for the review of LHT in evolutionary psychology; see Lawson, 2011, for a review of LHT in explaining the variation in human reproductive behavior in preindustrial and modern human populations). In fact, LHT is probably one of the most important conceptual frameworks that are used to derive predictions in human evolutionary research. Interestingly the term POLS is probably more suitable to describe this research direction in humans due to frequent attempts of evolutionary social researchers to describe inter-individual differences, not the differences between the species, and to explicitly include behavior in the analyzed variables (Nettle & Frankenhuis, 2019), however, despite this argumentation and the fact that initial studies showed the suitability of POLS framework to humans (Lehmann et al., 2018), the term LHT is still preferred by the researchers of human behavioral evolution.

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LHT in Evolutionary Psychology and Its Critiques As it has been recently noted LHT framework in evolutionary psychology has some distinctive features which are criticized on several occasions and make this approach somewhat disconnected from evolutionary biology and behavioral ecology (Međedović, 2020b; Nettle & Frankenhuis, 2019, 2020; Stearns & Rodrigues, 2020), so I would like to discuss it here. LHT in evolutionary psychology did emerge from Pianka’s work (1970) and adopted his terminology of the K (slow life history) and r (fast life history) selected populations. Unfortunately, these concepts were introduced in psychology by the controversial and racist work of Rushton (Rushton, 1990, 1995), who claimed that different human populations (and races) have differently evolved life histories. Fortunately, the largest part of LHT work in psychology is aimed at explaining individual differences in life histories, but the research that are focused on the differences between populations still carry these problematic implications (e.g., Figueredo et al., 2021). LHT approach in psychology has been largely based on the phenotypic changes produced not by natural selection but by developmental plasticity during ontogeny. It has been argued that the early environmental characteristics (mainly harshness and unpredictability) can trigger the development of life histories; e.g., harsh environment facilitates the development of faster life history trajectory (Ellis et al., 2009). Hence, this research often assumes that the effects of selection in an evolutionary time are similar to the effect in individual time (ontogeny) via phenotypic plasticity. The concept of the fast-slow continuum is especially important in psychological LHT research, and it is often viewed as a fact, despite the research showing that it oversimplifies the covariation between life history traits (Zietsch & Sidari, 2020). Nevertheless, evolutionary psychologists often use the fast-slow continuum to predict the associations between the examined measures, thus using the fast-slow continuum as the theoretical concept instead as an empirical pattern of life history indicators’ covariation. Many authors criticized this and pointed out that evolutionary psychology could much more benefit from developing verbal theoretical concepts and formal models of life history in specific ecological conditions (Frankenhuis & Nettle, 2020; Nettle & Frankenhuis, 2020; Stearns & Rodrigues, 2020). There is another, quite problematic aspect of exploring life history in evolutionary psychology—the empirical measurement of life history. Despite the fact that there is a vast consensus in evolutionary sciences  on what life history traits are, there is a large number of psychologists that decided not to measure these traits but to rely on “psychometric” measures of life history in their research. Probably the most prominent set of these measures is labeled as Arizona Life History Battery (ALHB: Figueredo et al., 2004, 2005, 2007, 2014). ALHB consists of the following life history markers: (1) Insight, Planning, and Control; (2) (secure) Romantic Partner Attachment; (3) General altruism; (4) Religiosity; (5) Parental Relationship Quality; (6) Family Contact and Support, and (7) Friends Contact and Support. Specific theoretical rationale of why each of these indicators are included in the battery is missing: there is an empirical claim that they tend to form a single latent

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component, and authors interpreted this component by a fast-slow continuum with individuals with higher scores having slower life history. The apparent problem with this approach is the validity of these measures as indicators of life history: these measures are simply not the ones that are recognized as the life history traits in evolutionary biology and ecology. To use the terminology proposed by Del Giudice (2020), these measures could be related to life history measures, e.g., they could be their correlates, but they are not life history indicators themselves. Furthermore, these indicators are very loosely related to life history traits (Međedović, 2020b): the strengths of associations are too low that they could be considered as the replacement for biological life history markers. In fact, some measures like religiousness and altruism have positive relations with number of offspring and negative with the age of first reproduction—which clearly makes them behavioral phenotypes related to faster, not slower life history as the framework assumes (Međedović, 2020b). These arguments and empirical data raise serious questions regarding validity of ALHB and similar measures to operationalize life history pathways (Sear, 2020). This problem facilitated other issues in life history research: psychologists are quite quick to label behavioral phenotypes as fast or slow by simply establishing correlations between behavioral traits and general scores on ALHB. This led to a vast number of traits that are proclaimed as “fast” or “slow” only by their links with a scale that has high validity problems, and despite the evident other problems of such labeling. Furthermore, behavioral traits are not life history traits: life history traits are precisely defined in evolutionary biology— growth rate, the age of puberty, age of first reproduction, reproductive success, age of last reproduction, and longevity. Behavioral traits may be labeled as POLS traits, and this can be done after empirical examination of whether they are involved in the syndrome in a specific population; yet, many evolutionary psychologists still label behavioral characteristics as LH traits (in fact, I made similar mistakes in my earlier work). The consequence of this is that LHT in psychology lost its specificity, roots, and foundations from evolutionary biology and become “theory of everything” that can be used to explain associations between almost every imaginable set of behavioral traits. Epistemological fallacies of such theoretical concepts are apparent.

Covariation Between Life History Traits Associations between the major life history traits are already described in the section covering evolutionary tradeoffs (including robust negative links between the age of first reproduction and reproductive success). Here, I will try to illustrate the relations between broader sets of life history indicators. First, maturation age shows relations with various life history traits. Age of menarche is associated with reproductive motivation, i.e., earlier maturation is related to the earlier preferred age to have the first child (Batres & Perrett, 2016) and earlier first desire for children (Vigil et al., 2005). This is congruent with the data showing that the age of menarche is positively associated with the onset of sexual behavior and the age of first

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reproduction (Vigil & Geary, 2006; Vigil et  al., 2005). The association between menarche and reproductive timing is present not only in modern human populations: positive correlation was found in the data representing 22 natural fertility populations (Hochberg et al., 2011). However, the latter study failed to detect the association between menarche and reproductive success itself. The reason for this may lie in a fact that the association is not strictly linear but have elements of inverted U curve (Kirk et al., 2001). On the other hand, nonlinear associations can be explained by complex genetic covariations between age of menarche and reproductive success. Some studies (Day et al., 2016) found genetic polymorphisms that were positively associated with both age of menarche and reproductive success (Immunoglobulin Superfamily Member 1: IGSF1) and the  ones with negative associations with menarche but positive with reproductive success (Semaphorin 3F: SEMA3F). Hence, there are genetic variants with opposite influences on the associations between menarche and fertility, and they can generate null phenotypic associations. There are other indications that early menarche may be a marker of elevated reproductive output: earlier age of menarche is related to a longer reproductive lifespan (Menken et al., 1986) and shorter time to pregnancy (Guldbrandsen et al., 2014). If early menarche is an indicator of elevated fertility, does it negatively predict parental investment as the quantity-quality tradeoff would suggest? Indeed, there are data on negative associations between age of menarche and prenatal maternal investment, expressed as the shorter gestational age and infant’s birth weight (Wells et al., 2016). Another trait that is potentially important in understanding life history dynamics is body mass. It is most frequently analyzed using the body mass index (BMI) which is calculated as the mass divided by squared height in meters. The data shows that BMI is positively related to variables indicating heightened reproductive output. First, there are negative relations between BMI and pubertal timing both in traditional (Hochberg et al., 2011) and modern human societies (Sheppard et al., 2016); these findings are corroborated by meta-analytic results for males (Xu et al., 2018). Furthermore, body mass is inversely associated with the onset of sexual activity (Kogan et al., 2015), age of first reproduction, and health, which can suggest lower survival rates (Mell et  al., 2018). Congruently with this, there are positive associations between body mass and fertility on a phenotypic level (Ellis & Haman, 2004; Schooling et al., 2011); phenotypic associations are further corroborated by shared gene alleles related both to BMI and reproductive success (Day et al., 2016). Hence, when analyzed from the viewpoint of the fast-slow continuum, it seems the data suggest that earlier pubertal timing and elevated body mass are relatively consistent markers of faster life history. What about mating, one of the crucial behaviors related to reproductive success itself? Evolutionary psychologists often suggest that short-term mating (e.g., a higher number of sexual partners) is a marker of faster life history (Chua et al., 2016; Dunkel & Decker, 2010; Patch & Figueredo, 2017). However, these claims are based on the associations between short-term mating and “psychometric” assessments of life history. When we analyze the relations between short-term mating and fertility measures, we obtain a different picture, representing additional evidence for the lack of validity of “psychometric”

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measures of life history. Short-term mating is negatively related to the number of children and positively to the age of first reproduction (Gutiérrez et al., 2022; Mell et al., 2018; Međedović, 2021d), the data suggesting that short-term mating even has some features of slow life history if we should try to place it on the fast-slow continuum (it is very important to note that many evolutionary psychologists still use short-term mating as a marker of fast life history and that in some research this is the key life history criterion measure). Conversely, long-term mating (i.e., duration of the longest romantic relationship) has an opposite pattern of associations: longer relationships are associated with earlier first birth and a higher number of children (Međedović, 2021c; Međedović et al., 2022). However, it cannot be clearly pinpointed at the continuum as well because individuals with longer partner relationships have elevated levels of parental care (Međedović, 2021b). Apparently, the roles of mating patterns in fast-slow life history need to be empirically investigated more thoroughly; or perhaps these data suggest that something is wrong with the continuum. An interesting recent research analyzed the clustering of fertility, parental investment, and health indicators across different ethnic groups in United Kingdom (Brown & Sear, 2021). The authors found that the clustering of life history was only partially in line with the fast-slow continuum and only in the group of White mothers. On the contrary, in the group of Pakistani mothers, both higher levels of fertility and parental investment (breastfeeding) was found, which is in line with some previous studies on US mothers (Maralani & Stabler, 2018). The authors note that cultural practices of Pakistani mothers can promote both fertility and investment (Kulu & Hannemann, 2016) and hence, generate the patterns of associations between life history traits that are opposite to fast-slow continuum. These data warn us to be cautious regarding fast-slow life history continuum because its existence may be fragile and sensitive to many factors including the ecological conditions expressed as cultural practices in a certain population.

Environmental Context and the Life History Traits One of the most important implications of LHT is the association between environmental characteristics (usually unpredictability and various indicators of harsh vs. beneficial environment) and life history traits. These associations will be briefly analyzed here, with an emphasis on the traits related to reproductive output. First, it should be said that sometimes it is not easy to entangle unpredictability and harshness because unpredictability is defined as a spatial-temporal variability in harshness (Ellis et al., 2009). This sometimes leads to the conflated operationalizations of these two measures; for example, Maranges and Strickhouser (2021) included the indicators of parent absence and parental separation as the markers of unpredictability, although they can be viewed as the indicators of harshness as well. Even if the measures are not conflated, harshness, and unpredictability typically have relatively high correlations. This problem has been addressed recently with an emphasis on

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better conceptualization and measurement of unpredictability; for example, organisms may use their experience to estimate unpredictability via statistical learning processes (Young et al., 2020). When individuals explicitly estimate the certainty of future acquisition of resources and status, higher uncertainty predicts earlier reproduction and higher fertility in general, even in a prospective study setting (Davis & Werre, 2008). Other studies also confirmed that childhood unpredictability is related to earlier pubertal timing (Kogan et al., 2015), but there are also studies that did not find a relationship between unpredictability and age of maturity and reproductive motivation (Maranges & Strickhouser, 2021). Many studies found associations between environmental harshness and faster life histories. Lower community income predicts earlier onset of sexual behavior and age of first birth in US women (Vigil & Geary, 2006). The poorer housing grade scale is associated both with earlier reproduction and higher reproductive success, although the link between environment and fertility is completely mediated by the age of first reproduction in the postwar British cohort (Sheppard et  al., 2016). Deprived neighborhoods accelerate reproduction and decrease parental (and potentially grandparental) investment, which is reflected in lower birthweights and shorter breastfeeding (Nettle, 2010). Meta-analytic results show negative associations of family SES with the onset of sexual behavior, age of marriage, and first birth among males but no relations with pubertal timing (Xu et al., 2018). Childhood environmental harshness was the major ecological predictor of faster life history in human studies (reviewed in Coall et  al., 2016). For example, it is shown that early stress is related to earlier pubertal timing and reproduction, together with a lower expected lifespan (Chisholm et al., 2005), but some studies did not find associations with fertility (Mell et al., 2018). This may be due to the fact that these relationships are complex. For example, a prospective study found an association between childhood hardships and pregnancy likelihood, but only in unmarried women; on the other hand, the link between hardships and lower time to pregnancy was found in married women (Harville & Boynton-Jarrett, 2013). Faster life histories are predicted by specific harshness indicators, like early childhood sexual abuse in a woman (Vigil et al., 2005) and peer-related fighting and harassment in adolescence (Davis & Werre, 2007). Relied on the theoretical work of Belsky (2008), I examined the potential influence of a postconflict environment (living in a society with a history and ongoing presence of violent intergroup conflict) on life history: individuals from a postconflict environment had higher reproductive motivation reflected in earlier desired age of first reproduction and higher preferred number of children (Međedović, 2019b). The core factor that lies behind the link between harshness and life history is mortality rates: harshness indicates higher mortality  - earlier reproduction, and higher number of children with decreased investment should be adaptive in high mortality environments according to LHT theoreticians (because delaying reproduction could lead to reproductive failure). This assumption was frequently tested and confirmed using the measures of life expectancy: shorter life expectancy is often related to earlier first birth (Bulley & Pepper, 2017), earlier marriage (Krupp, 2012), and higher fertility (Anderson, 2010; Bulled & Sosis, 2010; Nettle, 2011).

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Furthermore, many studies pointed out the mediation role of decreased educational levels (Bulled & Sosis, 2010; Krupp, 2012) and elevated reproductive motivation (Anderson, 2010; Nettle, 2011) in this link. This hypothesis can even be tested in experimental research: the studies show that exposing the participants to mortality cues elevates their reproductive motivation (Griskevicius et al., 2011; Mathews & Sear, 2008; Wisman & Goldenberg, 2005). I tried to explore this link in a real-world context by examining the link between reproductive motivation and biotic ecological stressor that elevated mortality rates worldwide—coronavirus (Međedović, 2021e). The results I obtained were only partially in accordance with LHT: after the first wave of COVID-19 epidemics in Serbia, participants wanted to have their first child earlier, compared to measurements during the first wave and before the epidemics, a finding which is in line with LHT. However, individual differences in exposure to the information related to coronavirus-induced mortality were negatively related to reproductive motivation, which is opposed to LHT. Since the pandemic still lasted in the time of data collection (and is still, while I am writing this), I concluded that because of imminent danger, some individuals may still invest more in survival than reproduction (i.e., fertility-longevity tradeoff). On the other hand, another study found more conclusive positive links between pandemic experiences and reproductive motivation (Gordon, 2021). The population density was considered as one of the crucial environmental determinants of life histories in the early LHT research (MacArthur & Wilson, 1967; Pianka, 1970). Human research showed the benefits of further exploring this environmental condition: population density is associated with delaying reproduction in traditional societies (Hochberg et  al., 2011). Furthermore, density is inversely related to fertility in 174 countries, but interestingly, this link is weaker in the populations with higher harshness, religiousness, and strength of social norms (Rotella et al., 2021). The developmental approach in analyzing the relations between environment and life history was largely based on estimating family characteristics (mainly father absence) and various life history indicators (most frequently age of maturity). This research tradition was based on a conceptual framework of “psychosocial acceleration” (and other theoretical concepts that emerged subsequently as the further elaboration of this assumption) which asserted that father absence and other family markers of childhood harshness and uncertainty accelerated development toward earlier age of menarche and faster life history in general (Belsky, 2012; Belsky et al., 1991; Draper & Harpending, 1982). This topic acquired much attention from the researchers, and the results were quite consistent with the assumptions: meta-analytic data found negative associations between father absence and age of maturation in girls (Webster et al., 2014); other family characteristics which indicate elevated stress are related to pubertal timing in the same manner  (e.g., Ellis & Garber, 2000; Henrichs et al., 2014). These findings are not restricted only to girls— meta-analytic findings showed that parental absence is related to early onset of sexual behavior, marriage, and reproduction in males as well (Xu et al., 2018). A recent study found that the parental influence on the reproduction timing of their offspring is partially mediated via university attendance (parents support their

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children’ education); however, it was also found that the link between parental separations and AFR was weaker in a recent generation of participants (Moya et al., 2021). This opens the question of how robust is the link between parental absence and reproduction-related outcomes in different cultural and ecological contexts. Some theorists assumed that the link is stronger in societies that are dependent on family organization and paternal investment, and thus, they may be variable across populations (Sear et al., 2019). The link may vary in time as well, keeping in mind that family structure is going through various changes in modern humans and that for example, divorce rates are becoming higher. Hence, this topic is still quite fruitful for empirical research. Finally, there is an intriguing relationship between status ad fertility in modern humans. The intricacy lies in the possibility that the role of status or SES in general in fitness maximization changed in modern humans compared to ancestral human populations. For example, the research in traditional human societies typically shows positive associations between different aspects of wealth, resources, status, and reproductive success (e.g., Borgerhoff Mulder & Beheim, 2011; Kaplan et al., 2002; Nettle & Pollet, 2008). However, in contemporary human populations, the data suggest the opposite: the links between wealth and fertility are negative (Colleran et  al., 2015; Kaplan et  al., 2002), which especially stands for women (Hruschka et al., 2019). During the demographic transition, wealthier families more quickly transferred to the low-fertility reproductive regimes, while the fertility rates of low-status individuals were kept at higher levels for a longer time (Skirbekk, 2008). The negative link between wealth and fertility is not evident right after society switch to a market economy but later, with increased liberalization of the market (Alvergne & Lummaa, 2014). This is in lie with the suggestion that status and wealth decrease fitness, especially in conditions of high-status competition and inequality (Shenk et  al., 2016). Note that although evolutionary puzzling, the negative link between wealth and fertility is in line with LHT—harsher conditions (low amount of wealth, resources, and low social status) are related to higher reproductive success. However, the link between wealth and fertility in modern human populations may not be as clear as some findings suggest. First, as already stated, there are sex differences in this association, and it seems that the negative link is more characteristic for females; in contrast, the association is usually positive for males (Goodman & Koupil, 2009; Hopcroft, 2019, 2021). Furthermore, the problem is that status and wealth are complex constructs, and they may be composed of various variables with different impacts on fertility. For example, education and prestige occupations related to higher educational levels  are typically negatively related to fertility, but material wealth is positively associated with reproductive success (Hackman & Hruschka, 2020). Indeed, the measures of material wealth like net worth (assets minus debt) positively predict fertility even for women (Stulp et al., 2016b). Finally, the review of longitudinal studies on the effects of material wealth on fertility found that most of these studies also show positive influences of wealth on fertility (Stulp & Barrett, 2016). We see that the links between environmental conditions ad fertility are mostly in line with the LHT—higher harshness and unpredictability are related to earlier

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reproduction and higher reproductive success. However, there are also findings, including some systemic data on wealth and fertility, that bear the opposite results— beneficial environments are related to higher fertility. We should not be surprised by such a state regarding empirical results if we bear in mind that LHT is just one of many evolutionary hypotheses regarding the associations between ecological conditions and fitness. For example, the silver spoon hypothesis predicts that individuals in better conditions (including more beneficial environments) should have higher fitness because scarce, harsh, and dangerous environments have detrimental effects on organisms, causing health and reproductive impairments (Monaghan, 2008). Indeed, there are studies that found that harsher environmental conditions in childhood result in decreased fitness expressed both as lower longevity and fertility (Hayward & Lummaa, 2013; Hayward et  al., 2013). Negative links between resource scarcity and fertility were probably more frequent in historical populations as well (Volk, 2021). Second, the predictive adaptive response hypothesis states that switching to fast life history trajectories based on childhood environmental harshness may be adaptive only if the childhood environment matches the adult environment (Gluckman et al., 2005). In contrast, the organism may be exposed to ecological mismatch. In long-living species such as humans, the environments may not match, and gathering resources and delaying reproduction may still be more adaptive. Hence, heterogeneity in findings is expected because there are different hypotheses on the links between environment and fitness. These hypotheses need specifications reflected in adding parameters that could more precisely identify the moderators that influence the type of associations between environmental conditions and reproductive outcomes.

Network Approach to Life History Evolutionary psychologists frequently use factor analysis to explore the relations between life history indicators. This procedure serves two functions: it analyzes the relations between the variables in a multivariate fashion, and it is in line with the conceptual view of life history as a latent dimension. However, we already discussed that the slow-fast continuum is probably oversimplification of the associations between life history indicators. Furthermore, factor analysis is a suboptimal method for analyzing life history data for a similar reason: it oversimplifies the relations between the variables by grouping them in latent factors—many important associations between the variables remain undetected by this procedure. I think that the network analysis is much more suitable for studying life histories (Međedović, 2021c). Network analysis represents the analyzed variables and the relations between them as dynamic networks: variables are represented as the networks’ nodes and the associations between them as the edges (you can see these manuscripts for more detailed applications of network analysis in social sciences: Costantini et al., 2015; Epskamp et al., 2018; Hevey, 2018). The associations between the variables can be computed via different statistics (usually they are partial correlations) and network models have additional penalization procedures aimed to remove

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spurious edges (and thus decrease the probability of type 1 error). Graphical representations of the networks are also important because the nodes with stronger links between them selves are placed closer to each other: this facilitates the interpretation of the network dynamics. Importantly, network analysis produces centrality indices as well: they provide information about the relative importance of every node for the network dynamics (Borgatti, 2005; Landherr et  al., 2010; Rodrigues, 2019). This property of network models can be very useful in the conceptual analysis of evolutionary tradeoffs and life history trajectories. Some of the most frequently used centrality indices are: (1) degree—the number of connections of the target node with other nodes in the network; (2) strength—the number of connections of the target node adjusted for the average weight of the target node (usually calculated as a product of these two parameters); (3) closeness—estimation of the position of the target node in the network based on direct and indirect connections with other nodes; (4) betweenness—the position of the target node in the shortest paths between other nodes in the network (the importance of a certain node to serve as a bridge between other nodes); (5) clustering—the centrality of the target node in a cluster of nodes (if a node has a high clustering coefficient its removal from the network decreases the ability to surround nodes to affect each other). Finally, I believe that the networks represent a conceptually plausible view on life history. Networks depict dynamic systems; life histories are also dynamic systems both on a population and temporal level, i.e., during the lifetimes of an organism (because life history traits are the events happening at different stages of the ontogeny). Change in one part of the system (e.g., ecological stressor that elevates environmental harshness) initiates subsequent changes in all other components of the system. It is my opinion that the abilities and potentials of the network model applied to life history would be best grasped using an empirical example. The network model I am showing here is based on unpublished data. It is a part of the data I collect annually: the participants are the parents of my students at the Faculty of media and communication in Belgrade, Serbia (see for example Međedović, 2019a, 2020a, 2021c). This sample (N = 497) was composed from one parent only per student to avoid assortative mating confoundings (53% females; Mage  =  51.56[6.35]; Meducation = 4.22[SD = 0.92]; Msalary = 859.33€[194.22]). If we take a look at the mean values of education (ranging from 1—elementary school to 5—finished faculty or another form of higher education) and monthly salary (average value in Serbia around 600€ at the time of data collection), we can see that the participants belong to the wealthier and more educated parts of Serbian citizens. The following life history measures are included in the analysis: childhood poverty, family dysfunctions (childhood family relations characterized by hostility, stress, and maltreatment), age of first sex, number of sexual partners, longest romantic partner relationship, a desired number of children, age of first reproduction, number of children, number of grandchildren (binarized), and parental investment (measured as socioemotional investment to children). The network is estimated under strict conditions: the variation of age, education, sex, and monthly salary is first partialled-out from the analyzed variables; the network is estimated using partial correlations afterward. An adaptive lasso was implemented as well in order to remove spurious edges, obtain the most parsimonious

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Fig. 1  The network model of life history dynamics Notes: CPOV childhood poverty, FDYS family dysfunctions, FSEX age of first sex, NSEX number of sexual partners, LREL longest romantic partner relationship, DNCHIL desired number of children, AFR age of first reproduction, NCHIL number of children, NGCHIL number of grandchildren, PIN2 parental investment

model, and facilitate interpretation. The network is estimated using the JASP program (https://jasp-­stats.org/). The network model is shown on Fig. 1 Blue edges are positive associations while red ones are negative; the thickness of the edges corresponds to the magnitude of associations. By examining Fig.  1, we can evaluate many of the associations we discussed previously. The key childhood environmental condition is family relations—economic poverty in childhood has links with the rest of the network only via family functioning. Dysfunctional family characteristics (harsher environment) have negative relations with the age of first sex and parental investment; furthermore, the number of sexual partners has a negative link to parental investment. These edges are congruent with the fast life history pathway; however, neither of these  nodes is directly  connected to reproductive success. In fact, family dysfunctions are negatively related to the duration of the longest partner relationship (long-term mating), while the latter seems to be associated with elevated fitness: there are positive links with parental investment and reproductive motivation, and a negative link with the age of first reproduction. Via these former nodes, long-term mating is connected with both the number of children and grandchildren (note the high magnitude of edges between AFR and both indicators of reproductive success). There is no quantityquality tradeoff in this data since parental investment and the number of offspring are unrelated (which is not surprising, keeping in mind that participants belong to higher social classes). Hence, we can make several conclusions from the network model:

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harsher environment produces maladaptive outcomes expressed in lower parental investment (probably produced by an intergenerational transmission of parenting) and lower long-term mating which indirectly decreases fitness. However, harsher childhood environment is also linked with earlier onset of sexual behavior and via this node with earlier first reproduction and higher fertility. Hence, these findings are in line both  with the silver spoon theory of fertility and the fast-slow life history continuum. Short-term mating is largely maladaptive (via lower parental care) except indirectly because it is positively related to long-term mating; long-term partner relationships are in fact adaptive both for the quantity of offspring (via heightened reproductive motivation and earlier AFR)  and potential higher offspring quality  (due to increased parental care). Reproductive motivation and age of first reproduction are the only direct predictors of fertility. We can see that the data are congruent with previous empirical studies on fertility and life history dynamics. Network analysis provides another fruitful tool for examining the relative importance of every node in the network. Since the JASP program does not allow the calculation of nodes’ strength, I used qgraph R package (Epskamp et al., 2012) to calculate strength. I do not show the results of this analysis graphically, but there are three nodes with the highest strength respectively: age of first reproduction, family dysfunctions, and the longest relationship duration. Other centrality indices (calculated in JASP) are shown in Fig. 2.

Fig. 2  Centrality indices of the network nodes Notes: CPOV childhood poverty, FDYS family dysfunctions, FSEX age of first sex, NSEX number of sexual partners, LREL longest romantic partner relationship, DNCHIL desired number of children, AFR age of first reproduction, NCHIL number of children, NGCHIL number of grandchildren, PIN2 parental investment

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We can see that other centrality indices are congruent with strength. The age of first reproduction and longest relationship duration is among the central nodes in the networks, according to several statistics. I would like to note that AFR was detected as the most central node in previous data as well (Međedović, 2021c) that are obtained on a similar sample (but the samples do not overlap), which suggests the stability of the networks. This finding contributes to a view that the tradeoff regarding first reproduction is of the highest importance in POLS and human life history. The centrality of longer partner relationships highlights the importance of long-term mating in life history dynamics, especially its adaptive properties, and suggests that further and more detailed examination of long-term mating should be quite fruitful for HBE. I showed only the basic applications of network analysis approach to life history here. Networks have many other properties like smallworldness and the ability to detect clusters of variables in the data. Networks can be used if we are focused on a specific variable and aim to explore its position in life history dynamics (e.g., Međedović, 2021a). It could be used to compare the networks based on some a priori assumption that the networks could have different dynamics: for example, previous data show differences in POL between the sexes (Tarka et al., 2018); we can compare life history dynamics in males and females both quantitatively and qualitatively using the network approach. I built the undirected network in this example: the direction of causal influence was not assumed because the network is estimated on cross-sectional data. Having in mind that life history outcomes occur in different ontogeny stages, we can use longitudinal data to build even more precise models which would truly reveal the dynamical nature of life history.

LHT Criticisms, Debates, and Open Questions There are several important issues in current LHT: a majority of them are discussed or at least mentioned in a recent reflection on LHT in social sciences (Frankenhuis & Nettle, 2020). First, the usage of LHT in explaining inter-individual differences is criticized on a conceptual basis (Stearns & Rodrigues, 2020; Zietsch & Sidari, 2020). Even if the continuum may be detected in inter-species observations under some conditions and with notable variability (Stearns & Rodrigues, 2020), the evolutionary processes that act between populations (and thus, generate speciation) are fundamentally different than the ones that act within a population (Zietsch & Sidari, 2020). Hence, not only the inter-individual life history continuum cannot be predicted using the inter-species data, but the traits can have a markedly different role in life history dynamics on these two levels of observation. I believe that the example of body mass nicely reflects this issue. Body mass is one of the crucial life history traits because it is involved in the growth-reproduction tradeoff. It is clearly seen in the between-species data that animals with larger body mass tend to have slower life history (Del Giudice, 2020). However, the vast amount of data in humans shows that larger body mass is consistently associated with faster life history at the

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inter-individual level: positive associations with harsher environments, earlier age of maturation and reproduction, number of children, and health problems that could decrease longevity (Day et al., 2016; Ellis & Haman, 2004; Hochberg et al., 2011; Kogan et al., 2015; Međedović, 2022; Međedović & Bulut, 2019; Mell et al., 2018; Sheppard et al., 2016; Schooling et al., 2011). Hence, the example of body mass reminds us to be cautious when building predictions of the within-population data using between-species observations. The next issue in LHT again involves the suitability of the evolutionary predictions from one context to another. LHT, on a between-species level, is built on the processes of natural and sexual selection. However, life history in evolutionary social sciences is usually examined within a single generation: therefore, the examined effects are not based on evolved adaptations but on developmental plasticity. By assuming a fast-slow continuum, human life history scholars implicitly assume that selection and plasticity act in the same manner in producing environmentally-driven adaptive phenotypes; however, there are no reasons to hypothesize this, and formal models do not predict equivalent processes (Stearns & Rodrigues, 2020; Zietsch & Sidari, 2020). Furthermore, developmental plasticity may not be adaptive in the first place; unfortunately, the adaptiveness of developmental plasticity in human life history research is rarely adequately tested (Nettle & Bateson, 2015). We saw that the silver spoon hypothesis is directly opposite (Monaghan, 2008) to fast-slow predictions, while the predictive adaptive response hypothesis (Gluckman et al., 2005) demands additional conditions in order for the fast-slow continuum to adequately describe the data (matching of childhood and adult environments). Both the tendency to extrapolate predictions from the cross-species data to explain inter-individual variation in life history traits and from selection processes to developmental plasticity demands the construction of more elaborate and precise theoretical framework (including the formal modeling which is very rare in human LHT) that can bridge the gaps between these different levels of evolutionary processes that are apparent. Next, it seems to me that the majority of life history data in humans suggest that the fast-slow continuum represents an oversimplification of the relations between life history traits. More precisely, if life history is measured by “psychometric” indicators like ALHB, the continuum is more likely to find; the major problem here is that there is both conceptual and empirical evidence that these indicators do not measure life history traits but some behavioral and environmental correlates of life history at best (Međedović, 2020b; Sear, 2020). Many empirical findings in humans show that life history traits (body mass, age of maturity, the onset of sexual behavior, mating, reproduction, parenting) exhibit only low magnitude correlations while the majority of associations are not significant at all (e.g., Kogan et al., 2015; Međedović, 2020b; Mell et al., 2018). These data contradict the concept of a single fast-slow continuum. If we take a step back, at the literature of evolutionary tradeoffs in humans, this empirical finding is expected. When we discussed the data on tradeoffs, we saw that tradeoffs are in fact not ubiquitous and if they are found, their magnitude is rather small. This especially stands for modern humans because low fertility depleted the effect sizes of tradeoffs—they can be more easily found in natural

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fertility populations, in populations in harsher environmental conditions, and perhaps in females more than males (Bolund, 2020). This made human life histories more plastic (e.g., reproductive success may not be dependent on the age of maturity) and the relations between them more context-dependent. Stearns (1992) advises researchers not to make general predictions about the life history but to formulate specific hypotheses about a single taxon, taking into account ecological context. We should do the same for humans: having in mind the ecological conditions of the population, researchers can make predictions about the evolutionary tradeoffs that can affect fitness optimization in the given population (Sear, 2020). While we tend to make the most parsimonious explanations of examined phenomena (like fast-­ slow continuum), we should also move on to more complex explanations when the former fail to accurately describe reality. If the fast-slow continuum itself is not a robust phenomenon, then we should be cautious about labeling behavioral traits as fast or slow; unfortunately, this has become a frequent practice in evolutionary psychological life history research. This is especially true for complex behavioral traits that can serve multiple functions and thus can have both fast and slow characteristics (Del Giudice, 2020). Our example of long-term mating can be one of these traits: previous findings (Međedović, 2021b) and my current example of life history network showed that long-term mating could elevate both reproductive success and parental investment; hence it shows both fast and slow characteristics. Another example is religious devotion: in the adolescent phase of ontogeny, religiousness is related to delaying the onset of sexual behavior (Jones et al., 2005b), but afterward, it is associated with an earlier age of first reproduction (Pearce & Davis, 2016) and higher reproductive success (Međedović, 2020a), showing both fast and slow features as well. Having in mind that religiousness is a highly culturally influenced trait, I agree with the notions of Brown and Sear (2021) that culture can contribute to the breaking of fast-slow life history patterns by promoting the adaptive value of behavior by enhancing both fast and slow components. These data and theoretical arguments influenced my own approach to LHT. I avoid the term “strategies” which is often associated with life histories, especially with the fast-slow continuum, and try to use more mechanistic terms like trajectories, pathways, or dynamics, which I think is the most appropriate to describe the nature of life histories on a population level. In my previous works, I used the term fast and slow to make the reference to the continuum; however, the existing state of theory and empirical evidence showed me that this is not an appropriate practice. While I still use these terms (and I will continue to do so in the remainder of this book), I am doing so in a much more precise manner. Many life history scholars assume that the age of first reproduction represents a key life history trait (Montiglio et al., 2018; Réale et al., 2010). When I label the trait as “fast” I mean that the trait is linked to earlier age of first birth, but consequently to a higher number of offspring as well; the term is used in this context specifically. Furthermore, I agree with evolutionary psychologists that when we measure life history, we should expand our list of indicators beyond the core LHT traits. But I believe that evolutionary psychologists expanded this list quite arbitrarily: if we add

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indicators to the list of life history measures, we should have a clear theoretical and empirical rationale for doing that. My arguments are the same as for the previous issue: the traits reliably connected to the age of first reproduction and offspring quantity make good candidates for measuring life history. In my belief, reproductive motivation is one of these traits. The benefits of measuring this trait are various: not only that it elevates our knowledge of life history dynamics (e.g., by serving as a mediator between other traits and fitness) but by providing proxy measures for fitness in young adults where we cannot measure fitness directly. In behavioral ecology this is sometimes called as residual reproductive value (RRV) or expected future fitness; while animal behavioral ecologists measure this by using other proxies, reproductive motivation may be especially convenient indicator of RRV in contemporary humans. Note that there are measures that do not clearly fulfill these conditions. For example, the usage of contraception or other means of reproductive control is a theoretically meaningful indicator of slow life history because they delay reproduction. However, I did not find robust-enough empirical data for this to claim that contraceptive use should be included in the set of life history indicators. An opposite case can be made for education: educational levels are related to delaying reproduction and, thus, a lower number of offspring (Snopkowski et  al., 2016). However, education lacks the theoretical rationale to be included in life history indicators as a measure of slow life history. The reason is that education may be viewed as an indicator of status that can enhance fitness under certain conditions. For example, I collected the  data (Međedović, 2017a) on lifetime reproductive success in Serbian elderly individuals who formed their families in the 60th years of twentieth century. In SFRY (Socialistic Federative Republic of Yugoslavia) this was the time when education was highly valued (e.g., by declaring secondary education as mandatory); in the same time the country was going through a phase of rapid industrialization and modernization. In this context, despite the positive links between education and AFR, the educational level also had positive associations with the lifetime number of children. Therefore, I do not think that we should include education in the list of life history measures, but we can think of education as a behavioral trait associated with slower  pace of life  in majority but not all ecological contexts. For me, this is an intellectual benefit of the LHT approach in humans: to think about some common traits in a different way by acknowledging their outcomes in an evolutionary context.

Behavioral Ecology of Personality

So far, we have been discussing the evolution of traits and behaviors that are closely related to fitness itself, i.e., life history traits. However, the behavioral repertoire of organisms is typically much richer, and it encompasses various functionally different behaviors. Personality traits are behaviors that have two main characteristics: (1) they show high inter-individual variation, i.e., there are large individual differences in behaviors, and (2) they exhibit diminished intra-individual variation, i.e., behaviors show some levels of stability within the individual. Aggressiveness can be considered as personality trait because individuals in a certain population differ in how much aggressive they are; at the same time, individuals high on aggressiveness tend to be more aggressive in different situations and through time (the same can be said for individuals low on aggressiveness). Other personality traits often explored by animal behavioral ecologists are boldness, risk-taking, sociability, fearfulness, and others. The discipline that explores the evolution of personality traits is called behavioral ecology of personality or evolutionary personality ecology. This subfield of behavioral ecology is a relatively young research program, but it increasingly attracts attention of the researchers in past two decades (for a useful reviews of evolutionary personality ecology, see Brommer & Class, 2017; David & Dall, 2016; Dingemanse & Wolf, 2010; Međedović, 2018a; Wolf & Weissing, 2010).

Evolution of Animal Personalities Personality traits show genetic variation; furthermore, they are related to fitness (Smith & Blumstein, 2008). Having this in mind, two core characteristics that we use to define personality in the first place became an intriguing question for behavioral ecology. If a certain trait is beneficial for fitness, e.g., high boldness, why do we still find genetic (and consequently phenotypic) between-individual variation in boldness in a certain population? Why did natural selection not erode the variation © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Međedović, Evolutionary Behavioral Ecology and Psychopathy, https://doi.org/10.1007/978-3-031-32886-2_7

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leaving only bold individuals in a population over time? Second, why there is diminished intra-individual variation in behavior? Diminished variation within individuals suggests that the plasticity and flexibility of behavior are somewhat limited and that behaviors are rigid to a certain extent. If we assume that the most adaptive behavior is flexible, we can ask why natural selection has preserved diminished flexibility in behavior. These two questions are usually accompanied by the third one—evolutionary account of behavioral syndromes. Functionally different personality traits are often found to be correlated between themselves; these correlations are labeled as behavioral syndromes. Their existence is intriguing for a similar reason as the previously discussed issue: associations between different behaviors again point to a lack of flexibility in behavior because they imply that an individual who is more prone to express certain personality trait (e.g., aggressiveness) will be more predisposed to exhibit another trait (e.g., boldness). Taken together, these three questions are sometimes called three evolutionary puzzles of personality (Bergmüller & Taborsky, 2010), and we will briefly discuss them next. Inter-individual variation in personality can be maintained by the selection itself (Brommer & Class, 2017; Dingemanse & Réale, 2013). As we already said, mutation-selection balance is a selection regime that cannot eliminate variation in a trait—selection acts in a certain way, for example, by elevating the mean population score on a trait if it is adaptive, but the effects of mutations have the opposite consequence in decreasing trait’s population mean until the equilibrium is reached. Disruptive selection does not diminish, but it actively elevates the variation in a trait: if, for example, very aggressive individuals and the ones with very low aggressiveness have the highest fitness in a population, disruptive selection will occur (although it should be mentioned that disruptive selection on behavior is rather rare in natural populations). Variable selection is especially good candidate to explain individual differences in behavior. If a trait is adaptive only when it is relatively rare in a population, its genetic variation will be maintained by negative frequency-dependent selection. Personality traits may not be equally adaptive in different environments: boldness may be beneficial for fitness in ecologies where predators are rare, but it may deplete fitness if the predation risk is high. In this case, balancing selection based on environmental heterogeneity actively preserves individual differences in a trait. Another form of balancing selection is dependent on organisms’ ontogeny phase: different levels of a trait may be adaptive when the organism is young and when it reaches maturity. All these mechanisms may lead to evolutionary stasis: the traits which are under selection may appear not to evolve in a micro-evolutionary context (Merilä et al., 2001). There are other, more specific models for explaining inter-individual variation in personality. Probably the most prominent ones are the state-dependent models (Dingemanse & Wolf, 2010; Međedović, 2018a; Sih et al., 2015; Wolf & Weissing, 2010). The state is every condition that influences the fitness outcomes of behavior. States can be internal (e.g., metabolic rate) or external (e.g., environmental harshness), labile (e.g., body mass), or stabile (e.g., sex). If a personality trait is linked to a state and the state moderates the association between a trait and fitness, then individual differences in personality will be preserved in the population. For

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example, if aggressiveness is more fitness-beneficial for individuals with high body mass, while cooperativeness provides fitness benefits for low body mass individuals, selection will maintain the variation in aggressiveness because individuals both high and low on this trait can achieve high fitness. Behavioral ecologists studied many states including energy reserves (Rands et al., 2003), body size (McElreath & Strimling, 2006), metabolic rate (Krams et al., 2017), residual reproductive value (Wolf et al., 2007), environmental harshness (Luttbeg & Sih, 2010), and exposure to parasites (Barber et al., 2017). Particularly interesting are the states that refer to the behavioral characteristics of other group members (Montiglio et al., 2013). The adaptive outcomes of behavioral traits may be dependent on the behavioral characteristics of one’s social partners: this approach in studying behavioral evolution is labeled as interacting phenotypes (McGlothlin et  al., 2010; Moore et al., 1997; Wolf & Moore, 2010). Interacting phenotypes represent the case of social evolution, and they are examined in two contexts: (1) the influence of social partners’ traits on the expression of behavioral characteristics of the focal individual; (2) the influence of social partners’ traits on the link between behavioral characteristics and fitness in the focal individual. The latter case is especially relevant for the present topic because behavioral traits in social partners may not only influence fitness in focal individuals but act as a state generated in the social environment and therefore explain the variation in personality traits (Dingemanse & Araya-Ajoy, 2015). The behavior of social partners represents ecological influences that are heritable themselves, and therefore they are often labeled as indirect genetic effects (Bailey et al., 2018). The simplest example of interacting phenotypes involves assortative or disassortative mating; e.g., the adaptive benefits of aggressiveness are highly likely to depend on the levels of aggressive behavior in other group members. Furthermore, while all other group members can be analyzed in this framework, there are individuals that may be particularly important for analyzing interacting phenotypes in personality like mating partners. Therefore, assortative mating can influence fitness—if assortatively mated individuals have higher fitness, the variation on a trait may be preserved because both high or low levels of a trait can enhance fitness, depending on the trait expression in a mating partner (David et al., 2015). For example, mating pairs of monogamous rodent (Mus spicilegus) with similar levels of anxiety had higher fitness expressed as earlier age of first reproduction (Rangassamy et al., 2015). However, disassortative mating may be elevating fitness as well, depending on the specific personality traits: excitable males of giant panda (Ailuropoda melanoleuca) had higher reproductive success if paired with low-excitable females (Martin-­Wintle et al., 2017). Evolutionary tradeoffs and, consequently, life histories can explain inter-­ individual variance in personality as well. For example, if a trait (e.g., aggressiveness) elevates certain fitness component (e.g., fertility) but decreases some other component (e.g., longevity), the trait is caught in the tradeoff, and its variance is maintained by the tradeoff: aggressive individuals have higher fitness via elevated offspring quantity while individuals with low aggressiveness achieve higher fitness via elevated longevity (Dunn et al., 2004). Similar tradeoffs like growth-longevity (individuals with higher growth rates have lower survival rates as well) can explain

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individual differences in various personality traits like aggressiveness, boldness, and activity (Stamps, 2007). Personality traits related to risk-taking are involved in tradeoffs because higher risk-taking is related to earlier first reproduction (Wolf et al., 2007). There is also research exploring the links between personality traits, reproductive outcomes (Monceau et al., 2017), and physiological traits (Jacques-­ Hamilton et al., 2017), in a broader pace of life framework which is promising for understanding the place of personality traits in life history dynamics. However, it should be noted that all these proposed mechanisms for explaining inter-individual variation in personality have mixed empirical support and nonsignificant effects or even effects with signs opposite from the expected ones found in the empirical data (Brommer & Class, 2017). A lack of intra-individual variation is sometimes labeled as the repeatability of personality—individuals tend to repeat the same behavioral responses through time and across contexts. Explaining why the behavior is repeatable, i.e., lacks flexibility, stems from the underlying assumption that only flexible behavior is adaptive. However, this may not necessarily be the case: flexible behavior may be the most adaptive one in the ecologies with reliable environmental cues; however, if the cues from environment are not reliable and consistent, then a more stable behavior could be adaptive (Watts et al., 2015). Consistent behavior is dependent on the stability of social relations as well. Stable and predictable behavior may be favored in populations where social partners pay attention to the behavior of conspecifics: if a  behavior is consistent, social partners can coordinate their actions more easily (Wolf et al., 2011). Hence, more stable social relations can produce a more consistent personality. Again, mating partners can provide an example of these processes: if biparental care is present in a species, consistent behavior related to investment facilitates the coordination of parental care between the partners; on the other hand, personality traits like aggressiveness and exploration are related to parental care (Chira, 2014). Behavioral consistency can be explained by state-dependent models of personality, more precisely by the state behavior feedback loops (Wolf & Weissing, 2010). If the state is stable, then the behavior will tend to be stable as well; furthermore, even labile states can produce consistent behavior—individuals with high body mass will tend to be more aggressive, and high body mass may help these individuals to win more fights which in turn can maintain both high body mass and elevated aggressiveness (Sih et al., 2015). Individuals with higher assets like residual reproductive value will tend to be less risky in order to protect their assets; in turn, this would keep both high assets and prudent behavior in individuals. Experience in executing certain behavior together with stable social roles can further facilitate behavioral consistency (Bergmüller & Taborsky, 2010; Montiglio et al., 2013). Finally, the explanations of behavioral syndromes have similar theoretical assumptions as the previously described ones. The most common adaptive explanation of behavioral syndromes invokes correlational selection (Sinervo & Svensson, 2002). For example, if high (or low) levels of two personality traits provide the highest fitness benefits under certain conditions, then selection will simultaneously act on gene alleles associated with these traits. Correlations between traits can also be

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linked to states—individuals with high body mass may achieve highest fitness if they are bold and aggressive. Finally, the suits of traits may be associated with evolutionary tradeoffs and life history dynamics, thus participating in the POLS.

 uman Personality Ecology: The Associations Between H Personality Traits and Fitness Personality traits are measured mostly via self-reports in humans due to the fact that the best sources of individual behaviors are individuals themselves. Some of the personality models in humans are more similar to the personality traits measured in other animals, like the models based on basic emotions (Davis et  al., 2003) or approach-avoidance systems (Carver & White, 1994). However, the most prominent personality taxonomies in humans are constructed using the lexical paradigm: extracting the personality markers from lexicons and dictionaries as the richest source of descriptions regarding human behavior (Saucier & Goldberg, 1995). There are two best-known models obtained by lexical paradigm: the Big Five/Five factor model (Costa & MacCrae, 1992; Goldberg, 1990) and the HEXACO taxonomy (Ashton & Lee, 2007). The former one will be further discussed in the text because the majority of the research regarding the links between personality and fitness used this model. It is composed of five broad and comprehensive personality traits: Extraversion (sociability, social dominance, gregariousness, activity), Neuroticism (tendency to experience negative emotions like fear, depressiveness, anxiety), Agreeableness (cooperativeness, patience, flexibility, care for others), Conscientiousness (orderliness, prudence, tendency to set long-term goals, elevated achievement motivation), and Openness to experience (imaginativeness, aesthetic appreciation, preference for novel stimuli, and inquisitiveness). Behavioral genetic research showed that these traits have moderate heritability—about 40% of phenotypic variation can be explained by additive genetic effects (Vukasović & Bratko, 2015). Similar to other animals, empirical data suggest that personality traits are under selection in humans as well. The data on Tsimane of Bolivia showed positive associations between Extraversion (E), Conscientiousness (C), and Openness (O), together with negative associations between Neuroticism (N) and reproductive success, but only in males (Gurven et  al., 2014). Consistent with this, E was positively related to male fertility in Ache of Paraguay (Bailey et al., 2013) and in rural Senegal (Alvergne et al., 2010a); the latter study found positive associations between N and female fertility as well. O negatively predicted reproductive success in rural Ghana, both in males and females (Mohammed et al., 2020). The data in modern human populations also showed significant links between personality and fertility. Findings show substantial heterogeneity, but the heterogeneity levels are different depending on the trait in question: E usually shows positive relations with fitness, especially for males, while N and O mostly show negative associations; the data of the remaining traits are less conclusive, but the majority of data show negative links between C and fertility while the opposite

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stands for Agreeableness  (A) (Allen & Robson, 2018; Berg, 2016; Dijkstra & Barelds, 2009; Jokela et al., 2011; Međedović et al., 2018; Skirbekk & Blekesaune, 2014). Personality traits were found to be related to fertility even on state levels in the United States: regions with higher levels of A and C and lower means of N and O showed higher fertility (Junkins et al., 2021). Higher E and lower C and O predict not only a higher number of children but grandchildren as well (Berg et al., 2014). Genetic studies on the personality-fertility link are still scarce, but the existing data show genetic associations between E and fertility in males (Berg et  al., 2016); another study found genetic correlations between higher A and lower C and reproductive success (Briley et  al., 2017). Certain level of inconsistency in data should be expected because many moderators probably influence the link between personality and reproductive success—pregnancy planning (Berg et al., 2013) and cohort effects (Jokela, 2012) are only some of them. Finally, some potential mediating processes in the personality-fitness link have been revealed as well, like reproductive motivation (Avison & Furnham, 2015), sexual satisfaction, and commitment to a partner (Jirjahn & Ottenbacher, 2022).

 xplaining Three Evolutionary Puzzles of Personality E in Humans The work on explaining inter-individual variation in human personality traits began within the framework of evolutionary genetics of personality. The main assumption was that balancing selection based on environmental heterogeneity was the main selection regime that maintains genetic variation in personality (Penke et al., 2007; Nettle, 2006). There are some data that directly supported this assumption: in Bolivian Tsimane different associations between personality traits and fitness have been found in individuals living in different environments (Gurven et  al., 2014). Other findings indirectly supported this hypothesis. Theoretically, congruent differences in personality were found between the individuals living in Italian islands compared to the individuals living in the nearest cities on the coast: the former had lower levels of O and E compared to the latter ones (Camperio Ciani et al., 2007). However, additional evidence showed that these differences were not the consequence of selection but the gene flow (Ciani & Capiluppi, 2011)—islands probably represent boring environments for individuals who are prone to sensation and stimulus seeking, so they migrate to more stimulating environments. Interestingly, congruent personality differences have been found in countries differing in the prevalence of infectious diseases—countries with a higher frequency of infectious diseases have lower mean levels of O and E (Schaller & Murray, 2008). It easily bears in mind that highly sociable, open to experience, and experience-­ seeking individuals may have lower fitness in the ecologies characterized by elevated levels of pathogens. However, genetic data suggested that other selection regimes may be better candidates for the explanation of inter-individual variation in personality. Verweij et  al. (2012) used genetic data to test several competing

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hypotheses of ultimate mechanisms that may preserve personality variation including selective neutrality, mutation-selection balance, balancing selection, negative frequency-dependent selection, and antagonistic pleiotropy: the data suggested that the mutation-selection balance model showed the best fit to the data. It is important to emphasize that different selection regimes are not mutually exclusive in explaining inter-individual personality variation, e.g., balancing selection and mutationselection balance can simultaneously act on personality in a given population. Other mechanisms are plausible as well; for example, it has been hypothesized that the sex ratios may participate in the maintenance of personality variation (Del Giudice, 2012). Personality is related to evolutionary tradeoffs1 as well. N was associated with elevated fertility but lower offspring quality in rural Senegal; hence, females with intermediate fertility had the highest fitness (Alvergne et al., 2010a, b). On the other hand, the same associations between personality traits and the number of children and grandchildren suggest that there is no quantity-quality tradeoff in personality traits (Berg et  al., 2014). As we previously discussed, C frequently negatively predicts reproductive success, but it is positively related to health and longevity in turn (Bogg & Roberts, 2013; Hill et al., 2011). E is negatively related to the age of first reproduction, while higher O is associated with delaying first birth (Jokela et al., 2011; Međedović et al., 2017; Tavares, 2010); these associations are found on the genetic level as well (Briley et al., 2017). Previous findings suggest that life history trajectories may participate in the maintenance of the variation in personality. However, we should be cautious about this assumption: the case of impulsivity is a good example of personality research in life history context. Impulsivity is often viewed as a trait that contributes to faster pace of life—this trait is related to the short-term maximization of resources; hence, it should be adaptive in harsh environmental conditions (Fenneman & Frankenhuis, 2020). In line with this hypothesis, some studies found that certain forms of impulsive behavior are more pronounced in countries with lower life expectancy

 There is a question of how to measure tradeoffs. Some researchers suggest that we should measure tradeoffs as latent variables. For example, Farkas et al. (2022) measured fertility-longevity tradeoff as a latent variable using number of children, age of first reproduction and health status as its manifest indicators; afterwards they set this latent variable as the criterion measure in a structural model. I believe that this is not adequate way of analyzing tradeoffs both from conceptual and methodological reasons. Firstly, it is hard to envisage tradeoffs as the latent variables - what latent variables may there be under the tradeoffs? The only answer that comes in mind are genetic polymorphisms that pleiotropically influence different fitness components if there are any, but certainly there is no reason to model some hypothetic genetic factors as latent variables in this manner. Secondly, the tradeoff may not exist in a certain population or the explored sample. For example, in Farkas et al. (2022) data both health and reproductive success had positive loadings on the “tradeoff” variable; hence, there was no tradeoff at all. Finally, there is a loss of information if variables are modelled in this manner—it would be much more informative if we analyze the associations between the predictors and all three variables that were used as the observable indicators of latent tradeoff variable. Note that even if there is no tradeoff on a whole sample, the associations of different signs between a trait and fitness components (e.g., if a trait has positive association with fertility and negative with longevity) still may maintain inter-individual variation on a given trait. 1

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(Lee et al., 2018); furthermore, individuals who grew up in harsher conditions tend to exhibit impulsive behavior when faced with resource scarcity (Griskevicius et al., 2013). On the other hand, impulsivity was found to be unassociated with certain life history indicators like pubertal timing (Copping et al., 2013). The direct test of the role of impulsivity in faster life history trajectory has been only recently conducted. The authors measured several forms of impulsivity, environmental harshness, and life history outcomes like the number of children and marriage probability (Kometani & Ohtsubo, 2022). The fast POLS hypothesis assumes that impulsive individuals should have higher reproductive success and marriage probability if they originate from harsher environmental conditions; however, this finding was not obtained. Interactions were not significant, and only one measure of impulsivity had negative associations with fitness; impulsivity was even unrelated to environmental harshness. The case of impulsivity warns us to be cautious when assuming the role of personality traits in the life history context, even for the traits that conceptually seem likely to participate in the pace of life syndrome. I was particularly interested in testing the state-dependent models of personality, focusing mostly, but not exclusively, on environmental characteristics. Interactions between environment and personality traits in predicting fitness-related outcomes are calculated in order to test these models. For example, I found that the proximity of intergroup conflict (as a marker of environmental harshness) moderated the links between personality and mating success (Međedović, 2018b): high E was associated with the mating success, but only in harsh environment. Some data suggested that the environmental moderation of the personality-fitness link can be detected even in small samples: low A and E decreased fitness in highly unstable environments; low O elevated fitness, especially in harsh environments (Međedović, 2020c). But there are intrinsic states that have been found to moderate the associations between personality and fitness: pregnancy planning moderated the relations between C and reproductive success—high C individuals have elevated number of planned children while low C individuals had more unplanned children (Međedović & Kovačević, 2020). The puzzle of personality’s stability practically was not directly tested in humans at all. In fact, this field of research represents the clearest example of the gap between animal behavioral ecology and human personality psychology. Animal behavioral ecologists frequently study personality plasticity and flexibility; on the other hand, this topic practically does not exist in human personality research. Data suggest that environmental influences on personality stability grow higher during the lifespan (Briley & Tucker-Drob, 2014); this is in apparent accordance with the behavioral ecological models suggesting that stable interpersonal relations produce consistency in personality (Bergmüller & Taborsky, 2010; Montiglio et al., 2013; Wolf et al., 2011). However, I think that there is more fundamental question that we need to seek answer to regarding the consistency in human personality. Flexibility is not always favored by natural selection; therefore, we need to explore if personality stability is adaptive at all. This could be done via longitudinal designs: first, we need to measure personality traits in two (preferably more, but at least two) time points. Then we can use the differences between the measurements as an indicator of plasticity/flexibility. Finally, these measures of flexibility for every personality trait

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can be used as the predictors of fitness (i.e., reproductive success), with potential moderators like sex or environmental conditions included in the analysis as well. It seems to me that this would be an appropriate starting point in the research of the behavioral ecology of human personality stability. Some recent research has implications for the explanation of the third evolutionary puzzle of personality in humans—the existence of behavioral syndromes. First, there is an interesting research program labeled as the ecological complexity hypothesis (Lukaszewski et  al., 2017; Smaldino et  al., 2019). This assumption posits inverse relation between the ecological complexity of human societies and the magnitude of associations between personality traits. The higher the number and specialization of different ecological niches the individuals occupy in a given population, the lower should be associations between personality traits. The rationale behind this hypothesis is that more specialized behavior (i.e., more flexible) should be more adaptive in complex environments. The hypothesis provided initial supporting empirical results (Durkee et al., 2022), and it will surely be a fruitful research program on this topic. With my emphasis on direct measurement of fitness, I tried to follow another way of exploring behavioral syndromes: a strategy that is based on a search for three-way interactions—between two personality traits forming a syndrome and a certain state that may provide conditions for a behavioral syndrome to be adaptive (Međedović, 2018a, 2020d). This prompted me to search for a correlational selection of personality traits around the world. I used the data from the World Value survey—wave 6, where personality traits were measured in 24 countries worldwide, to try to explain one of the most common behavioral syndromes in humans: positive correlation between A and C (Međedović, in review-a). I used four indicators of beneficial environmental conditions to analyze environmental moderation on the links between personality and fitness—family resources, neighborhood security, income, and social status. Systemic interactions were found: individuals with high scores on both personality traits had the highest reproductive success in all four conditions of beneficial environments. Furthermore, the same interactions were found for a probability to be in a long-term romantic relationship, hence, individuals with high A and C personality traits tend to have higher fitness in beneficial environments via elevated probability for long-term mating. These findings confirm the plausibility of a hypothesis that correlated selection can maintain behavioral syndromes in human personality.

Human Personality Ecology: The Extensions Unfortunately, behavioral ecology and personality psychology do not communicate well. This is quite strange when we see how much work behavioral ecologists invested in explaining the evolution of animal personality and the fact that the majority of this work is in fact implicative for humans as well. Therefore, there are many avenues where behavioral ecological approach can be implemented in human research, I will mention only the most apparent one here. I remind that the definition of “personality trait” in behavioral ecology assumes any behavior which is relatively

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stable and shows inter-individual differences. Here, the emphasis is on the word “any”: various behaviors that do not fall under personality definition in human psychology can be an object of behavioral ecological analysis, provided that the behavior shows genetic variation and that it can be theoretically linked with fitness. From a behavioral ecological point of view, the clearest example are cognitive traits: cognition has been explored in behavioral ecology for a long time and there is a subfield of behavioral ecology dedicated to the exploration of the evolution of cognitive traits named cognitive ecology. In fact, intelligence, as the most comprehensive trait depicting cognitive ability, has been studied in regard to reproductive success in humans as well (e.g., Chen et al., 2013; Kolk & Barclay, 2019, 2021; Meisenberg, 2010; Woodley of Menie et al., 2019), although it has been rarely done in an explicit behavioral ecological context (but see Međedović, 2017a; Međedović & Petrović, 2020). This research topic produced a controversial idea that humans lose genetic potential for intelligence due to a negative directional selection—it has been frequently found that intelligent individuals have fewer children (e.g., Chen et al., 2013; Meisenberg, 2010; Woodley of Menie et al., 2019; however, the research findings show positive associations as well: Kolk & Barclay, 2019, 2021; Međedović, 2017a). Therefore, the recent reversal of the Flynn effect (secular increment of the mean intelligence scores observed throughout the second half of twentieth century; some studies claim that the trend came to a halt and even reversal) is detected in some countries (Teasdale & Owen, 2008) is attributed to natural selection (Dutton et al., 2016). I believe that the explanations involving selection for the secular trends in intelligence are premature. First, the reversed Flynn effect is not reliably and robustly detected around the world; second, the most parsimonious explanation for the change in intelligence’s secular trends (both its rise and possible reversal) are environmental effects (Bratsberg & Rogeberg, 2018). In any case, the exploration of cognitive traits from the behavioral ecological conceptual framework represents a highly intriguing topic. The main question is probably what cognitive traits should be explored in this context—global intelligence measures (global IQ scores) may be too general and abstract measures, and this may omit our understanding of selection regimes on cognitive traits. This is why some authors propose that we search for more specific cognitive processes that may facilitate the adaptation of individuals living in challenging environments (Frankenhuis et al., 2020; Young et al., 2022). However, currently it is not known if these context-dependent cognitive processes do elevate fitness in harsh environments and this is important venue for future research in human cognitive ecology. Another extension of evolutionary personality ecology involves the traits related to psychopathology. Keller and Miller (2006) convincingly argued that the genetic and phenotypic variation in harmful mental illnesses (e.g., psychosis and schizophrenia) is maintained by mutation-selection balance. Since these mental dysfunctions reduce fitness, they are under negative directional selection that tries to remove associated gene alleles from the gene pool; however, the variation is generated by inherited and de novo mutations. However, an open question is if the same selection regime operates on subclinical levels of the psychosis-proneness behavioral dispositions, for example, schizotypy, a trait characterized by

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psychotic-­like experiences but with lower frequency and magnitude, and which are present in general population (Kwapil & Barrantes-Vidal, 2015; Nelson et  al., 2013). The model of mutation-selection balance could cover schizotypy as well; however, there are other hypotheses regarding the selection regimes on schizotypy. This behavioral trait has been linked to creativity (Acar & Sen, 2013; Batey & Furnham, 2008; Međedović & Đorđević, 2017); based on this, some authors proposed that schizotypy may in fact be adaptive due to elevated creative abilities (O’Reilly et al., 2001; Nettle & Clegg, 2006). Future research could empirically test these contrasted hypotheses. Finally, an integration of the behavioral ecological approach into the field of social attitudes, values, norms, and ideologies would be highly welcomed. Social attitudes represent beliefs about social phenomena, affiliated emotions, and behaviors; they are powerful motivators that can produce and predict behavior. Furthermore, social attitudes are heritable behavioral dispositions (Ludeke et  al., 2013). Considering that social attitudes and values frequently incorporate beliefs about family, having children, and reproduction in general, it is highly plausible that they are under natural selection. However, their evolution is very rarely empirically investigated. The only exception is religiousness: belief in God has positive associations with reproductive success (Blume, 2009; Fieder & Huber, 2016; Međedović, 2020a; Sanderson, 2008), and religious individuals tend to have earlier age of first birth (Pearce & Davis, 2016; Strayhorn & Strayhorn, 2009). Other social attitudes can be under positive directional selection as well; my opinion is that the most plausible candidate is conservatism: conservative individuals may have higher fertility, probably via elevated reproductive motivation (Međedović, 2021a). In line with this reasoning, I have found phenotypic signatures of positive directional selection on human values that contain conservative beliefs (Međedović, in review-b). The data originated from two waves of the World Value survey (wave 5—N  =  80,950, 57 countries; and wave 6—N  =  89,564, 60 countries) which additionally shows their robustness. Conservative values are positively associated with fitness because they elevate the probability of being in a long-term romantic relationship and via placing higher importance of having a family as a life goal. I think that the extensions of behavioral ecology to social attitudes and values are particularly important for two reasons. First, social attitudes are behavioral dispositions that are highly influenced by cultural processes; hence their empirical investigation can provide human behavioral ecology with a more detailed inclusion of cultural characteristics of a given population in the analysis of behavioral evolution. Second, social attitudes, values, and norms are more powerful predictors of fertility compared to various other behavioral characteristics, in my own opinion. Therefore, their analysis may significantly contribute to the demographic models of fertility prediction and perhaps add to our understanding of demographic transition and its future dynamics.

Psychopathy and Its Current Evolution

Psychopathy: Definition and Measurement Psychopathy has attracted the attention of researchers, forensic and clinical practitioners, and even writers and popular media for over half of century. Interestingly, after all this time, there is still no consensus on the exact definition of psychopathic characteristics, and the debate is still ongoing (Crego & Widiger, 2022). Major psychopathy models (Boduszek et  al., 2016; Hare, 2003; Levenson et  al., 1995; Lilienfeld & Andrews, 1996; Patrick et al., 2009) converge in viewing two characteristics as the core psychopathy traits: (1) manipulative, distrustful, deceitful behavior accompanied by the grandiose self-view and motivated by selfish personal goals; (2) affective characteristics depicted by lack of fear, guilt, emotional empathy, and even anxiety. The majority of models (Hare, 2003; Levenson et al., 1995; Lilienfeld & Andrews, 1996; Patrick et al., 2009) add the third trait: disinhibition, impulsiveness, lack of long-term goals, boredom proneness, sensation-­seeking and generally turbulent and erratic lifestyle. In fact, some theorists think that the core of psychopathy is these two etiologically distinct traits (sometimes called “deficits”) with different behavioral outcomes – affective callousness/shallowness and disinhibition/erratic lifestyle (Fowles & Dindo, 2006). Finally, some authors explicitly add antisocial and criminal behavior as the core psychopathy trait (Hare & Neumann, 2009, 2010). I agree with the scholars viewing antisocial behavior not as the core psychopathy trait but as one of its behavioral outcomes or correlates of psychopathy (Cooke et al., 2004, 2007; Međedović et al., 2015); the inclusion of this trait was a consequence of the fact that psychopathy is indeed very important  construct in criminological psychology and it was dominantly explored in this context for a long time. The relations between psychopathic traits are still under question. This is to be expected having in mind that there are many models that operationalize psychopathy: empirical correlations between psychopathy traits are the consequence of different © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Međedović, Evolutionary Behavioral Ecology and Psychopathy, https://doi.org/10.1007/978-3-031-32886-2_8

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operationalizations and theoretical assumptions underlying the models themselves. In some models, psychopathy traits are largely intercorrelated – significant positive correlations between the traits are found (Hare & Neumann, 2005, 2008). On the other hand, in other models, the traits depicting psychopathy are largely unassociated, i.e., independent (Marcus et al., 2013). If we agree that psychopathy traits are indeed positively associated with themselves, we can view psychopathy as a behavioral syndrome  – a set of functionally different personality traits that are mutually correlated. This view has another implication – it considers psychopathy traits as personality traits; it is important to emphasize this because many authors view psychopathy as a psychopathological construct, i.e., personality disorder (Cooke, 2018). Historically, the first instrument to measure psychopathy reliably and validly was Psychopathy Check List (PCL) and its revised version PCL-R (Hare, 2003). This is a rating instrument, the assessment is conducted by an educated professional, and administered individually: after the structured interview, a rater assesses the targeted individual on 20 indicators of psychopathy. The factor structure of these indicators was a subject of debate (Cooke et al., 2004, 2007; Međedović et al., 2015), but the authors mostly agree that it is optionally described by four traits labeled as interpersonal (manipulation), affective, (erratic) lifestyle, and antisocial characteristics (Hare & Neumann, 2009, 2010). There is a shortened version of this instrument (Hart et al., 1995) and one that is applicable to adolescent participants (Forth et al., 2003). There is also a self-report inventory for measuring this model (Paulhus et al., 2016); in fact, all of the following instruments are based on self-­ report methodology. One of them, labeled as LSRP was derived directly from PCL-R structure, and it operationalizes psychopathy via primary (manipulativeness and affective callousness) and secondary (disinhibited behavior and delinquency) psychopathy (Levenson et  al., 1995). Other models do not explicitly include antisocial behavior in psychopathy definition and measurement. Two most prominent are: (1) Psychopathic Personality Inventory and its revised version, which captures two broad factors labeled as Fearless Dominance and Self-Centered Impulsivity (Marcus et  al., 2013), and the third factor named Coldheartedness has also been found (Berg et al., 2015); (2) Triarchic psychopathy model (Patrick et al., 2009) that also covers three traits  – Boldness, Meanness, and Disinhibition (Somma et  al., 2019). Finally, the most recent model of psychopathy, Psychopathic Personality Traits Scale excludes impulsive/disinhibition traits from the taxonomy of psychopathy traits and assess four narrow characteristics: Affective responsiveness, Cognitive responsiveness (which describe the lack of emotional and cognitive empathy, respectively), Interpersonal manipulation, and Egocentricity (Boduszek et al., 2016). Despite their differences, we can see that the models do converge to some common traits: manipulativeness, emotional coldness, and (most often) lack of behavioral control. However, we will see that psychopathy is quite an unusual behavior syndrome – its narrower traits are very different and have differential and even opposite relations with different variables.

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Nomological Network of Psychopathy in Brief Psychopathy is notoriously (in)famous due to consistent relations between psychopathy traits and various amoral and antisocial behavioral outcomes (reviewed in Međedović, 2015). First, psychopathy is reliably associated with criminal behavior (Häkkänen-Nyholm & Nyholm, 2012) – because of this fact it has been explored mostly in criminal and forensic settings for several decades. Psychopathy is related to criminal behavior in general, earlier age of first criminal offence (Vaughn, et al., 2008), the number of offences, the total time an individual spent in correctional institutions (Žukauskienė et al., 2010), together with the maladaptive and violent behavior in correctional institutions (Campbell et al., 2009), violent offences and the offences motivated by a personal gain (Roberts & Coid, 2007). Psychopathic traits are related to homicide offences, and it has been empirically documented that murders committed by psychopathic individuals have some specific characteristics: they do not have emotional component, they are planned and intentional instead (Woodworth & Porter, 2002), and perpetrators often were unrelated to the victim (Häkkänen-Nyholm & Hare, 2009). Psychopathic traits are elevated in individuals who produce violent behavior toward their intimate partners and family members (Swogger et al., 2007). Finally, psychopathy is reliable predictor of criminal recidivism (Leistico et  al., 2008; Međedović et  al., 2012; Salekin, 2008; Walters & Duncan, 2005): this fact is of particular importance for criminological psychologists because the majority of criminal offences are committed by recidivists (e.g., Someda, 2009). Some scholars proposed that psychopathy represents the crucial construct of individual differences for our understanding of criminal behavior (DeLisi, 2009). Socially destructive correlates of psychopathy are not visible only in the context of criminal behavior and delinquency: they exist in a much wider world of immoral and socially aversive behavior. Psychopathy is related to aggressiveness. Impulsive and antisocial characteristics have positive correlations with physical and verbal direct aggression, while aspects of psychopathic personality (primarily manipulative tendencies and egocentrism) are associated with the exclusion and isolation of “victims” from social interaction and the use of malicious humor (Warren & Clarbour, 2009). Furthermore, psychopathic traits are related both to proactive (type of aggression based on planning, thoughtfulness instead impulsiveness, the role of emotions is minimal, and it represents instrumental behavior, i.e., its goal is not defense but the injury of another person) and reactive (spontaneous emission of aggressive behavior, driven by intense emotions and most often occurring as a response to a perceived threat) aggressiveness. Manipulation, self-centeredness, emotional insensitivity, and interpersonal coldness have the greatest association with proactive aggression (Wilson et al., 2011). On the other hand, the data show that impulsive and antisocial behavior is related to reactive aggression (Feilhauer et al., 2012), while the role of manipulative tendencies and affective coldness in this form of aggression are not clear (Reidy et al., 2011, 2007).

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Hence, psychopathic individuals tend to participate in amoral and antisocial behavior; therefore, psychopathy should be related with aberrant moral code, the one that allows hurting others. Indeed, psychopathic traits are negatively related to two main sources of human morality: emotional sources of moral behavior expressed in care and sympathy for others and cognitive aspects of moral behavior based on reciprocity, justice, and equality (Aharoni et  al., 2011; Glenn et  al., 2009a; Međedović & Petrović, 2016). Furthermore, some studies (Dolan & Fullam, 2010) showed that individuals with elevated psychopathy have a higher propensity for making both moral and social-conventional transgressions (social-conventional transgressions are not “moral” per se, but represent breaking of social norms like appropriate dressing for males and females and they show variation between cultures) while there are findings that highly psychopathic individuals distinguish these two type of transgressions to a lesser extent compared to individuals with low psychopathy (Blair, 1995). Psychopathic traits express themselves in various forms of social interaction including friendship, romantic, and sexual relations. Psychopathic individuals seem to have rather pragmatic criteria for choosing friends: they value individuals with different kind of resources with low emphasis on the characteristics that most of the people look for when choosing friends- trustworthiness, kindness, and similarity in personality characteristics (Jonason & Schmitt, 2012). There are various manipulation tactics related to psychopathy, which are expressed in different social interactions: coercion, use of violence, using charm, reciprocity (tit for tat), seduction, and self-humiliation as a means to achieve goals (Jonason & Webster, 2012). It seems that the friendships of psychopathic individuals are not based on emotional bonding and shared values but on more self-serving criteria; a similar could be said for their sexual and romantic partnerships. Psychopathic traits are related to the earlier age of first sexual intercourse, a higher number of sexual partners, and coercion to have sexual relations (Harris et al., 2007). Psychopathy is also positively associated with seeking new and exciting sexual experiences (Kastner & Sellbom, 2012), lower discriminativeness (choosiness) regarding sexual partners (Jonason et al., 2011), exploitation of partners, dominance in partner relationships, hostility and negative attitudes toward women (LeBreton et al., 2013), keeping the partner on a distance (Jonason & Kavanagh, 2010), poaching sexual partners, but being poached as well (Jonason et al., 2010b). Socially aversive characteristics imply that psychopathic individuals should be negatively perceived in social interactions; indeed, some findings confirm this assumption. Psychopathic individuals are seen as dominant, lacking nurturance, and somewhat impulsive (Rauthmann, 2012); congruently, their behavior is perceived to have a negative impact on others (Rauthmann & Kolar, 2012). However, there are the opposite findings as well. Individuals with psychopathic traits can be viewed as intelligent and physically attractive (Fowler et al., 2009). In fact, psychopathic traits can be seen as even more attractive compared to general personality traits that are often viewed as socially desirable (like Extraversion and Agreeableness: Meier et  al., 2010). These positive ratings of psychopathic traits are probably based on psychopathic charm: manipulative psychopathy characteristics include charm in

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this narrow psychopathy trait (Hare, 2003). Psychopathic charm has been described as superficial – longer time spent with such an individual reveals that this behavior may be only a façade presented in order to gain positive impressions; however, even if superficial, presenting themselves as charming may bring some positive ratings for psychopathic individuals. Previous discussion regarding the social perception of individuals with elevated psychopathy poses an interesting question: psychopathy is certainly destructive for the  people having contact with psychopathic individuals, but is it possible that psychopathy carries some benefits for psychopathic individuals themselves? Indeed, psychopathic traits show some adaptive potentials in a proximal sense, but here we find a crucial distinction between narrow psychopathy traits. The first example is the associations with psychopathological phenomena. Disinhibited lifestyle and antisocial behavior have positive relations with personality disorders, but the opposite stands for manipulative and affective traits (Benning et al., 2005). Only lifestyle and antisocial traits are related to generalized anxiety disorder (Swogger et al., 2010); in line with this, negative associations between emotional callousness and manipulativeness with internalizing symptomatology (which covers anxiety as well) have been found (Willemsen & Verhaeghe, 2012). The same relations are detected for schizotypy  – the disposition toward psychotic-like phenomena (Ragsdale & Bedwell, 2013; Ragsdale et al., 2013). These associations are crucial empirical evidence for my view that psychopathy cannot be conceived as psychopathological trait, at least not all psychopathy traits. In contrast, the data suggest that affective and interpersonal psychopathy traits can be the markers of mental health due to their consistent negative associations with various forms of psychopathology. Interestingly, similar associations are evident for the links between psychopathy end intelligence. First, if we analyze psychopathy as a singular score (mean score on all traits or all psychopathy items), there are no associations with intellectual abilities (Hare, 2003; O’Boyle et al., 2013); the two constructs seem to be orthogonal. Other researchers obtained negative associations between psychopathy and intelligence (e.g., Neumann & Hare, 2008). However, specific associations appear when narrow traits are included in the analysis; furthermore, the associations with opposite signs emerge once again. Generally, manipulative and deceitful tendencies seem to be positively related to intelligence, while emotional shallowness and disinhibited lifestyle are negatively associated with cognitive abilities (Salekin et al., 2004; Vitacco et al., 2005, 2008). Very similar data has been obtained when executive functions are explored: executive functions are the set of cognitive processes that enable planning and execution of behavior, forming goals, perform-­ goal directed behavior, but also the change in behavior when goals are switched (i.e., enable behavioral plasticity and flexibility), and facilitate functional manipulation of information in working memory (Jurado & Rosselli, 2007). Hence, these processes are largely correlated with intelligence, but they represent more basic cognitive processes, the ones that enable individuals to successfully solve cognitive tasks. First, data show the opposite relations between emotional/ manipulative and lifestyle/antisocial traits and general executive functioning (Ross

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et al., 2007), and similar findings are obtained for specific executive functions. The data suggest that antisocial tendencies are related to a deficit in suppressing predominant response, i.e., behavioral inhibition, but affective characteristics are related to the enhanced ability to inhibit behavior (Feilhauer et al., 2012). Opposite relations between affective/manipulative and lifestyle/antisocial traits are obtained with working memory as well – the former traits are related to the elevated ability for manipulating information in working memory in contrast to the former ones (Baskin-Sommers et al., 2010; Hansen et al., 2007). I would like to emphasize that the empirical data are in fact more heterogenous than it was illustrated in the previous examples, which is congruent with majority of psychological research in general. However, these findings do describe a certain trend in the data, a pattern that suggests crucial differences in psychopathy traits regarding their adaptive potential, with manipulative and affective traits showing functional and adaptive properties, which is in contrast with lifestyle and antisocial traits that produce mostly maladaptive outcomes. There are other potentially adaptive properties of psychopathy (again, in a proximal sense). While people are generally bad in both lying and successfully recognizing lying, there are data showing that psychopathy may be positively related to both (Lyons et al., 2013; Porter et al., 2012). Successful lying may be particularly related to affective psychopathic trait because this trait is reflected in lower facial expressions of emotions and aversive emotions that most people experience when lying. Affective psychopathic traits enable one more benefit: lower levels of social anxiety (Hofmann et al., 2009) that may facilitate functioning in social interactions. Finally, it seems that psychopathic traits can be correlated with high social status in certain social niches, especially vocations like higher management (Babiak et al., 2010; Boddy et  al., 2010) or even politics (Lilienfeld et  al., 2012). A lack of empathy, obtaining goals without regard for other people, and deceitful behavior certainly (and unfortunately) can be positively selected in some vocational roles; this may lead to a higher frequency of psychopathic personalities at important societal positions. These adaptive potentials of psychopathy are referred to as “successful psychopathy” (Lilienfeld et al., 2015). There are three models trying to explain how psychopathy can produce functional and adaptive behavior (Hall & Benning, 2006). The first is labeled as the subclinical model – this assumption simply states that the lower levels of psychopathy may lead to adaptive outcomes while the high expression of the traits produces maladaptive behavior. The model of moderating expression posits that other variables interact with psychopathy in producing adaptive or maladaptive outcomes. For example, intelligence or education may substantially change what kind of behavior will be associated with psychopathic traits. Finally, dual processes model assumes different adaptation-relevant consequences for the  two basic processes underlying psychopathy: affective callousness and disinhibition. This model proposes that individulas high in affective and manipulative psychopathic characteristics may manifest adaptive behavior if these traits are not accompanied by impulsiveness and antisocial tendencies. Note that previously

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discussed results are mostly in line with the dual processes perspective, but in fact all three models of successful psychopathy are relevant for the behavioral ecological analysis of psychopathy traits.

 enetic, Neurobiological, and Environmental Precursors G of Psychopathy Psychopathy is a heritable behavioral syndrome – psychopathic traits show significant genetic variation (Blonigen et  al., 2003; Taylor et  al., 2003; Tuvblad et  al., 2013); furthermore, there are shared genetic influences on several psychopathic traits that represent the evidence of genetic pleiotropy (Larsson et  al., 2006). Heritability estimates differ between the studies, but roughly about 40% of phenotypic variation in psychopathy is explained by additive genetic variation, similarly to general personality traits. Additive genetic variation can partially explain the stability of psychopathy traits (Forsman et al., 2008), while the influence of unique environment in the explanation of the developmental change in impulsive and antisocial traits (but not affective and manipulative characteristics) grows higher during the individual lifetime (Blonigen et al., 2006). Psychopathy is associated with some specific genetic polymorphisms as well, but once again, similarly to personality traits, the data on gene alleles related to psychopathy turned to be more elusive and harder to replicate. One of the genes associated with serotonin, mainly 5-HTTLPR (serotonin-transporter-linked promoter region) is frequently studied as a part of psychopathy’s genetic foundation. There are data showing that the long allele of this gene is associated with psychopathy, especially with the affective characteristics (Sadeh et  al., 2013b); furthermore, the link between the short 5-HTTLPR allele may be specific for the individuals with low SES (Sadeh et  al., 2010). Additionally, this allele may be related to risky decision-making (Roiser et  al., 2006), which is a marker of a psychopathic lifestyle as well. However, the findings are incongruent because there are data suggesting that the short allele of 5-HTTLPR is related to some psychopathy-­ related characteristics like aggressiveness and impulsiveness (Aluja et  al., 2009; Cadoret et  al., 2003), but 5-HTTLPR seems like a good candidate for future molecular-genetic research on psychopathy. Dopaminergic genes may be associated with psychopathy as well. This especially refers to the TaqIA polymorphism of the DRD2 gene: the association has been found with affective psychopathy characteristics once again (Hoenicka et al., 2007). However, the subsequent study found relations between TaqIA DRD2 polymorphism and both affective/manipulative and lifestyle/antisocial psychopathy traits; furthermore, epistatic interactions between this gene and ANKK1 (Ankyrin repeat and kinase domain containing 1) on psychopathy have been detected as well (Ponce et al., 2008). Finally, there are associations between psychopathy and polymorphisms in a gene related to the oxytocin receptor OXTR. Interestingly, the

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associations with callous and unemotional traits in children have been found once again (Beitchman et al., 2012; Dadds et al., 2014). Both studies show that polymorphisms that affect the reduced circulation of oxytocin in the blood are associated with emotional callousness. It seems that the molecular-genetic research obtained mostly the evidence for the genetic basis of affective psychopathy traits. However, we should mind that the associations between genetic polymorphisms and psychopathy are moderated by various conditions (other genes, i.e., epistasis, biological traits, environment), which both limit the successful findings of gene alleles associated with psychopathy and reminds us of the complexity of associations between genotype and phenotype.1 Neurobiological underpinnings of psychopathy are, on the other hand, relatively well-known and thoroughly empirically investigated. The hallmark of brain functioning in psychopathy is certainly the amygdala – a set of nuclei located in the dorsomedial part of the brain’s temporal lobe. Amygdala is highly relevant in the processing of affectively saturated stimuli, especially the emotion of fear (LeDoux, 2003). Individuals with highly expressed psychopathy show reduced activation of amygdala when processing fear (Blair, 2010a) and when they need to decide if a statement is morally acceptable (Marsh & Cardinale, 2014), especially in moral dilemmas that carry high emotional charge (Glenn et  al., 2009b). Furthermore, reduced amygdala activation is detected in psychopathic individuals in experiments that failed to establish a conditioned response to fearful stimuli (Birbaumer et al., 2005). Another brain area that seems to be under-activated in psychopathy is the ventromedial prefrontal cortex (Blair, 2010b). The prefrontal cortex, in general, is relevant for the integration of cognitive and emotional processes in decisionmaking and for executive functions – adaptive task-shifting, reaction inhibition, and working memory functioning. Hence, aberrations in the functioning of the ventromedial prefrontal cortex are related to difficulties in changing behavior in psychopathic individuals when already learned behavior is no longer adequate (Finger et al., 2008). Other regions that take part in this learning process are the orbitofrontal parts of the prefrontal cortex. Individuals with high scores on both affective/manipulative and impulsive/antisocial psychopathic traits show reduced activation in the orbitofrontal cortex when observing stimuli characterized by negative emotions (Sadeh et  al., 2013b). Hypoactivation of both orbitofrontal and dorsolateral prefrontal cortex is related to diminished executive functioning in psychopathy (Snowden et al., 2013).

 I did not include the gene alleles associated with antisocial behavior in this short review, e.g., DRD4 (Bakermans-Kranenburg & Van Ijzendoorn, 2006) or MAO-A (Taylor & Kim-Cohen, 2007), because I agree with the view that antisocial behavior is not a core psychopathy trait despite its robust positive relations with psychopathy. 1

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Hence, amygdala and prefrontal cortex are the key brain regions for explaining brain functioning in psychopathy;2 research shows that even the connectivity between these two regions is reduced in psychopathy, e.g., diminished connectivity is detected in performing implicit moral judgment in psychopathic individuals (Marsh et  al., 2011). Blair (2007) nicely summarized the role of amygdala and prefrontal cortex in psychopathic behavior. Amygdala is the key region in associating the consequences of behavior with reinforcement, both positive or negative. Seeing other people hurt or suffering because of our actions represent negative reinforcement for the majority of individuals – therefore, we avoid such behavior. However, with reduced amygdala activation, distress in others is failed to be recognized in psychopathy and hence, behavior is not suppressed. With aberrational functioning of prefrontal cortex and its lower connectivity with the amygdala, decision-making is dysfunctional as well (Blair et al., 2006), which leads to reduced morality based on care and empathy, difficulties in socialization process in general, and behavior that is harmful to others. Finally, besides the characteristics pertaining to an individual, environmental characteristics are related to psychopathy as well. Broadly speaking, it has been found that various aspects of harsh, depriving, hostile, and stressful environments facilitate the development of psychopathy traits. Physical and verbal violence, together with maltreatment during childhood are related to elevated psychopathy in adulthood (Borja & Ostrosky, 2013; Lang et al., 2002). Sexual abuse in childhood, as the most violent and traumatic form of abuse, is positively associated with manipulative, lifestyle, and antisocial psychopathic characteristics (Graham et al., 2012). There are data suggesting that early maltreatment and deprivation are related to affective psychopathy traits as well, mostly by emotional numbing regarding sorrow and anger in children (Kerig et al., 2012) which leads to decreased ability of emotion perception in adulthood (Young & Widom, 2014). Family dysfunctions do not have to take extreme levels in order to produce psychopathy: the findings suggest that certain parental styles are related to psychopathic traits. Inconsistent parenting and higher levels of parental punishment in raising a child are related to all narrow psychopathy traits in children (Deng et al., 2020). Similarly, authoritarian parenting, characterized by physical coercion, verbal hostility, and nonreasoning with a child shows positive associations with all psychopathy facets (Krupić et  al., 2020). Various characteristics of family members’ interaction may facilitate the emergence of psychopathic traits in children, like communication problems (Pardini & Loeber, 2008) and relations characterized by negative emotions (Tuvblad et  al., 2013). When addressing the question of the link between parental behavior and offspring psychopathy, it should be mentioned that this link is bidirectional: negative parental behavior is related to higher levels of psychopathy in children, but psychopathic

 Psychopathy is a complex set of traits and many brain regions are associated with it including nucleus accumbens (Pujara et al., 2014), anterior cingulate cortex (Bjork et al., 2012), lateralization of processes in brain hemispheres (Kosson et al., 2007), and decreased volume of gray mass (Ermer et al., 2012). However, amygdala and prefrontal cortex are probably the central nodes of this vast network associated with psychopathy. 2

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behavior in offspring (fearlessness, impulsiveness, behavioral problems) elevate negative parenting in return (Larsson et  al., 2008; Tuvblad et  al., 2013). Harsh environmental conditions outside of the primary family are also positively associated with psychopathy, e.g., various stressful events (Schraft et  al., 2013), childhood unpredictability (Jonason et al., 2016), lower SES, and presence of criminal behavior in a peer group (Lynam et al., 2008) are significantly related to psychopathic traits latter in the ontogeny. As I stated before, gene-environment interactions in psychopathy development are highly plausible; an example of such interactions is provided by a recent study: the data showed that the variation in a serotonin-related gene (HTR1B-rs13212041-T/T) interacted with the parental style (over-control and abuse) to predict adult psychopathy scores (Palumbo et  al., 2022). Similarly, Catechol-O-methyltransferase (COMT Val158Met polymorphism) interacts with childhood abuse, producing elevated levels of adult psychopathy (Zhang et al., 2022).

Psychopathy in an Evolutionary Context There is an aspect of psychopathy’s role in mating that can be of relevance to the evolutionary study of psychopathy: assortative mating. Assortment in mating describes the resemblance between the mating partners in a certain trait or a set of traits. As we discussed before, assortative mating has an effect on the frequency of genotypes associated with the traits with significant assortment between the partners, but it is plausible to assume that it may have effects on fitness as well (e.g., if the similarity between the partners provides more stable and satisfying relationship it may affect reproductive success as well). Hence, do psychopathic individuals tend to search for similar personalities or perhaps they seek cooperative, empathic, and prudent partners? The empirical data supports the former view: there is assortative mating in psychopathy expressed in significant positive correlations between psychopathy in romantic partners (Smith et al., 2014). Furthermore, the data suggest that the similarity is largely due to initial assortment: partners do not become more similar during their relationship; they actively seek similar partners in order to begin the relationship in the first place (Kardum et  al., 2017b). Finally, there are data suggesting that similarity in psychopathy may be positively related to relationship satisfaction as well (Kardum et  al., 2017a). Therefore it seems that similarity in psychopathy enables couples to function better in their relationship; interestingly, there are no research so far that explored if this can translate to elevated reproduction success or some other fitness component. Most of the research on psychopathy in an evolutionary context has been done in the framework of life history theory. Psychopathic traits indeed quite resemble the POL behavioral phenotypes related to faster life history (impulsiveness, aggressiveness, dominance), and therefore it is not surprising that many evolutionary psychological work on psychopathy is done in this context, and more precisely within the “fast-slow” continuum. However, the opinions on the psychopathy’s place on a fast-slow dimension are a bit different: while the majority of researchers

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view psychopathy as a  correlate of fast life history (e.g., Jonason et  al., 2010a), some claim that it is independent of the continuum (Gladden et al., 2009), while some think that manipulative/affective traits have slow while impulsive/antisocial traits have fast POL characteristics (McDonald et al., 2012). A major problem in all these studies is that they used “psychometric” measures of life history in order to measure life history, and we already discussed that they lack validity for estimating life history dynamics. The key behavioral correlate of fast life history was short-­ term mating measures (Jonason et al., 2009, 2010a, 2011) and we have shown that short-term mating is probably not an adequate measure of fast life history. Various other claims that psychopathy represents evolutionary adaptation have problematic assertions as well. For example, the evidence that psychopathic offenders have tendency not to harm their own kin was used as an empirical evidence that psychopathy is an evolved adaptation (Krupp et al., 2012). Indeed, not harming and even helping kin is certainly adaptive behavior because it leads to higher inclusive fitness. Consequently, a vast majority of human beings do not harm their own kin; therefore, I admit that I do not understand why psychopathy should be viewed as an adaptation based on this argument. Another problematic argument for psychopathy’s evolutionary adaptiveness is based on the rates on nonright-­ handedness associated with psychopathy (Pullman et  al., 2021). Nonright-­ handedness is related to the perturbations in neurodevelopment; therefore, the authors state that if psychopathic individuals have lower rates of nonright-­ handedness, this serves as evidence that psychopathy is an evolutionary adaptation. Indeed, they obtained this result for affective/manipulative traits with the opposite finding for impulsive/antisocial traits. I am under the impression that this evidence also does not meet the criteria to tell us anything about the evolution of psychopathy.3  I mentioned before the problematic work of Richard Lynn in evolutionary social sciences. I will address it here once again because Lynn explored psychopathy as well in his framework of racial differences that evolved along the slow-fast life history continuum. I provide it in a footnote because I do not want to address this work as legitimate scientific evidence. Namely, Lynn claimed that there are racial differences in psychopathy  – the highest scores have blacks and Native Americans, the second place is reserved for Hispanics, the third for whites and the East Asians have the lowest scores (Lynn, 2002). The emergence of these differences is explained by the hypothesis that psychopathy is a part of fast life history strategy, and, therefore, it is consistent with general life history variation between races (sic!). Several authors quickly replied claiming that the assertions are far overreached and that shown statistics and theoretical propositions are biased and cherry-picked, together with arguments that the differences between the groups are “more a function of social class, historical circumstance, and their position in Western society rather than racial genetics” (Zuckerman, 2003). Others highlighted that Lynn did not offer any evidence for evolutionary processes in his work and even more importantly, that the concept of race does not correspond to any meaningful differences between the genetic structures of populations (Skeem et al., 2003). Lynn replied simply by stating that the critics are wrong and that his and Rushton’s theory is correct using arguments like this: “American blacks are about the same as Native Americans (on average IQ scores) because they are a hybrid population with about 25 percent of Caucasoid genes” (Lynn, 2003). The meta-analysis that appeared shortly after this discussion showed that there are no race differences in core psychopathic traits – manipulativeness and callousness (Skeem et al., 2004), but even if there were significant differences, evolutionary biological framework for 3

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Nevertheless, there is some empirical evidence that psychopathy may be adaptive in the evolutionary sense. First, psychopathy is detected and reliably measured in chimpanzees as well (Pan troglodytes); the existence of psychopathy in other species may suggest its homological roots (Latzman et al., 2016; Lilienfeld et al., 1999). Second, there are data pointing that psychopathy may have faster life history characteristics because psychopathic traits are negatively related to the pubertal timing (Međedović, in review-c) and the onset of sexual behavior (Harris et  al., 2007; Međedović, 2018c). Furthermore, sexual behavior characterized partially by early onset is more present in psychopathic individuals who spent their childhood in harsh environmental conditions (Međedović, 2016). However, the relationship between psychopathy and the age of first reproduction as the major indicator of life history has not been detected so far. Finally, psychopathy may be related to elevated health; for example, it has been found that psychopathic offenders had lower obstetrical problems and fluctuating asymmetry (fluctuating asymmetry is related to poorer health status: Milne et al., 2003) than nonpsychopathic offenders (Lalumière et al., 2001). Note that the link between psychopathy and health is far from resolved because there are data showing that psychopathic individuals tend to have more health problems (Beaver et al., 2014; Hudek-Knežević et al., 2016; Jonason et al., 2015). Still, previous studies mostly analyzed psychopathy using a singular score on the trait; when narrow traits are analyzed, it has been shown that the negative link between psychopathy and health can be attributed to lifestyle/antisocial traits, while manipulative psychopathy characteristics even show indices of a better health status explaining these differences is clearly inadequate, due to apparentr fact that potential differences can be much better explained by social and economical factors. Interestingly, Lynn did not abandon his idea; instead he published a book in 2019 where he simply repeated the same claims from 16  years earlier (Lynn  & Dutton, 2019). I believe this should remind us of how motivation is important in ideological work (because this is certainly not science) and how historically, the elevated motivation of racists and other extreme right-wingers showed to be quite fruitful for them. Furthermore, we should all think about the fact that these views are still frequently confused with legitimate science in public opinion, which enables them legitimity. Another problem is that there are others in academic positions that will reimburse them. Let us see an example of the clearly racist text of Helmuth Nyborg (Professor Emeritus [retired], University of Aarhus, Denmark) where he praises Lynn’s book (this text is published on the Amazon’s webpage that advertises Lynn and Dutton’s book): “It takes courage to write books on race and intelligence in the present political climate. It takes even more courage to explore the origin, existence, and consequences of race differences in psychopathic personality. Richard Lynn has dared to do both. The relevance of this book cannot be overestimated in a time in which globalization increasingly forces different races to live and work together. May politicians responsible for migration programs – and everyone interested in peaceful coexistence – read Lynn’s books closely before making decisions that affect all our lives.” Needless to say, Nyborg himself published anti-immigrant scientific papers where he states that Denmark will experience a genetic loss in intelligence because of low-intelligence immigrants who have high fertility (Nyborg, 2012). These examples show us once again how controversial and politically relevant this field is, and especially the topic of psychopathy. They remind us to be vigilant and cautious and to warn and react about the misuse of scientific concepts in racists and right-wing agenda. This especially stands for journal editors but for universities as well – perhaps we also  should be a bit more concerned about who teaches the students. Moreover, specifically, maybe we should be concerned that, in contrast to the original Lynn’s manuscript from 2003, this more recent book did not provoke any commentaries, disputes, and rebuttals from scientific community.

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(Međedović & Kujačić, 2020). Similarly, manipulative traits negatively predict serious health problems in elderly individuals (Međedović, 2019c). Therefore, although the question of the psychopathy-health link is still unresolved, there are indications that at least some psychopathy traits may be related to elevated health status.

The Links Between Psychopathy and Fertility We saw that there is some indirect evidence that psychopathy traits may elevate fitness. But what about the core fitness component – reproductive success (for a recent review of the links between psychopathy and fitness, see Ene et al., 2022)? First, when psychopathy is measured as a single score, a negative association between psychopathy and the lifetime number of children is obtained (Carter et al., 2018). However, this is another example of the perils when psychopathy is analyzed as a single trait and not as a behavioral syndrome. When narrow traits are analyzed, different associations between psychopathy and reproductive success emerge. We obtained positive associations between manipulative traits and reproductive success, together with negative relations between lifestyle characteristics and the number of children in a sample of convicts (Međedović et al., 2017). The link between affective traits and reproductive success was moderated by participants’ childhood environment: the link was positive in families where there was substance abuse in parents and negative where substance abuse was absent. Another study was conducted on a sample of postreproductive individuals from the general population (Međedović, 2019c): manipulative traits once again positively predicted number of children, while neither psychopathy trait predicted number of grandchildren. Interestingly, lifestyle traits positively predicted serious physical illness in the participants’ offspring. The third study we conducted was based on a large sample from general population (Međedović & Petrović, 2019). Only affective psychopathy traits were positively associated with number of children in this study. Finally, manipulative/affective traits were significantly positively associated with reproductive success in the sample of parents as well (Pavlović & Međedović, 2022). It is important to note that we did not test for quadratic effects in the previous studies, i.e., testing the possibility of stabilizing selection, because the sample sizes were usually small – higher sample sizes are needed to detect nonlinear associations. Hence, the data consistently show that manipulative and affective traits may be beneficial for reproductive fitness; on the contrary impulsive lifestyle is either unrelated or negatively associated with reproductive success. Antisocial characteristics were measured in only one study (Međedović et al., 2017), and there were no associations between this psychopathy characteristic and reproductive success. Note that these differences between narrow psychopathy traits regarding the links with fitness mirror the differences when proximate adaptive outcomes are analyzed: manipulative/affective traits have adaptive potentials, while the opposite

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stands for lifestyle/antisocial traits. We can ask ourselves what are the mediators between manipulative/affective traits and fitness, and what adaptive function they serve. As we discussed before, many authors assume that the adaptive benefits of psychopathy stem from success in short-term mating (Jonason et al., 2009, 2010a, 2011). However, short-term mating is either unrelated or negatively related to reproductive success (Gutiérrez et al., 2022; Međedović, 2021b, d), and therefore, it is implausible mediator between any trait and fitness. I recently found that the psychopathy trait depicting fearlessness and high self-esteem is positively associated with reproductive motivation; hence, childbearing motivation may be a bridge between psychopathy and fitness (Međedović, in review-c), but the search for mediators has only begun. Note as well that positive links between manipulative and affective traits and fitness produce additional evidence that psychopathy does not represent mental disorder (Jurjako, 2019). In my view, this is a fine example of how evolutionary analysis can help psychologists to better understand some behavioral phenomena by capturing the adaptive side of it. The results regarding impulsive and antisocial traits may be incongruent with some previous findings. First, previous research did find positive associations between criminal behavior and reproductive success (Yao et al., 2014); in fact, there are positive genetic correlations between delinquency, reproductive success, and earlier age of first reproduction (Tielbeek et al., 2018). These findings suggest that criminal behavior may indeed be associated with the fast life history dynamics (Kwiek & Piotrowski, 2020). The differences between these findings and our own data (Međedović et  al., 2017) may lie in the fact that previous research mostly compared criminal and noncriminal individuals while we collected data only in the population of convicts. Furthermore, the relationship between antisocial behavior and faster life history may be dependent on the environment; for example, my earlier research (Međedović, 2016) showed indications of faster pace of life  in antisocial behavior, especially for individuals whose parents exhibited criminal behavior as well. The hypothesis of criminal behavior as a part of faster pace of life phenotype certainly seems plausible and demands further empirical research; inclusion of physiological traits, beside life history and behavior would be especially beneficial as is already postulated in POLS framework. Our existing research mostly shows nonexistent or negative relations between lifestyle psychopathy traits and reproductive fitness. Lifestyle and disinhibited psychopathic behavior may be understood as behavioral impulsivity; hence, this data may seem to be incongruent with the previously discussed data on the links between personality and fitness, where we saw that impulsiveness (as a negative pole of Conscientiousness personality trait) may be beneficial for fitness (via nonplanned pregnancies: Berg et  al., 2013; Međedović & Kovačević, 2020). Therefore, it is important to note that some psychologists highlighted that disinhibition should not be equated with impulsiveness – these two characteristics are not the same trait; disinhibition seems to carry more maladaptive potential because it shows more substantial relations with externalizing behavioral problems compared to impulsiveness (Joyner et  al., 2021). It seems that a psychopathic

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lifestyle is not mere impulsiveness but a trait that has more negative outcomes in various aspects of life. Besides conceptual differences between impulsiveness and disinhibition, this example may lead us to a more general issue that is pertinent to psychological measurement in general. Namely, psychological constructs are usually operationalized via many models; as we have seen at the beginning of this chapter, this stands for psychopathy as well. The instruments measuring these models have partly similar but partly different items; this heterogeneity of measurement is likely to generate heterogeneity in empirical findings. Considering that the links explored in behavioral ecology (e.g., associations between behavioral traits and fitness) are already complex because they are dependent on various moderators, heterogeneity in traits’ measurement may represent an additional source of confoundation. Certain consensus in the definition, conceptualization, and measurement of psychopathy would be highly beneficial. On the other hand, one may claim that the heterogeneity in measurement may be fruitful for science because it may represent additional tests for the robustness and replicability of findings. I do not agree with this view, but in its light, I would like to emphasize that the positive associations between manipulative/affective traits that we obtained in our empirical studies have passed this test: in four studies we have conducted, psychopathy was measured via four different inventories with large differences between study samples as well, and yet, the findings were highly congruent. Therefore, it seems that the assumption suggesting that manipulative/affective psychopathy traits may be under positive directional selection, at least on the phenotypic level, seems to be plausible.

The Answers on the First Evolutionary Puzzle of Psychopathy Exploring the relations between traits and fitness is the first step in a behavioral ecological analysis of a trait. Although this research is still in its infancy in the case of psychopathy, I will use the existing data to move on to the next step – addressing the first evolutionary puzzle of psychopathy: analyzing ultimate mechanisms that maintain inter-individual variation in psychopathy. This topic was already skillfully addressed (Glenn et al., 2011), but the previous review did not include the recent empirical data on the links between psychopathy and fitness. Psychopathy traits seem to be related to fitness; hence, the assumption of selective neutrality may be rejected: genetic drift is not the only evolutionary mechanism that maintains genetic variation in psychopathy. As we saw, manipulative/affective traits are beneficial for fitness; therefore, they may be under positive directional selection. However, even if there is positive directional selection on affective/ manipulative psychopathy traits, it is highly probable that the selection is constricted by various factors. One of them may be frequency-dependent links between psychopathy and fitness. For a long time (Mealey, 1995), researchers assumed that psychopathy might be adaptive only if the frequency of psychopathic phenotypes in the population is low. For example, if there is high number of manipulators in the

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population, individuals would become distrustful and cautious; this in turn would diminish any adaptive outcomes of manipulative behavior. Hence, negative frequency-dependent selection may preserve variation in psychopathy; this hypothesis is in line with the data showing that the frequency of individuals with highly elevated psychopathy is quite low (around 1%). Gene alleles associated with psychopathy may be under mutation-selection balance, similar to the basic personality traits (Verweij et al., 2012) and psychopathological characteristics (Keller & Miller, 2006). This selection mechanism may refer to the traits under positive directional selection but particularly refers to the characteristics under negative directional selection. We saw that many scholars think that psychopathy represents some form of mental disorder; in that case, it would be under negative directional selection (e.g., Leedom & Almas, 2012). Our empirical data showed that only a psychopathic lifestyle shows some phenotypic signals of negative directional selection, i.e., lower reproductive success and offspring quality. In this case, the selection would try to eliminate the alleles related to psychopathic lifestyle but inherited and de novo mutations may still maintain the variation. The robustness of the effects of negative directional selection on psychopathic lifestyle needs to be empirically documented in future research. But if they exist, they raise another possibility regarding the maintenance of genetic variation in psychopathy; the possibility is slim, but it is theoretically plausible. We saw earlier that the shared genetic influences on a general factor of psychopathy (i.e., shared variation between the phenotypic psychopathy traits) are detected (Larsson et al., 2006). This suggests that there are gene alleles that contribute to the phenotypic expression of all narrow psychopathy traits. If some psychopathy traits elevate fitness (e.g., manipulative/affective) while others decrease it (e.g., psychopathic lifestyle), these alleles may be under antagonistic pleiotropy  – their frequency remains the same because their net effect on fitness is equal to zero. Psychopathy researchers suggested that psychopathy may be an adaptation to harsh, hostile, depriving, and stressful environments (Glenn et  al., 2011). If psychopathy is adaptive in these ecological conditions and perhaps maladaptive in others, its genetic variation may be preserved by balancing selection based on environmental heterogeneity (or as a behavior dependent on environment as an external state in the state-dependent models of personality). This hypothesis focuses mostly on affective psychopathic traits because they are marked by higher stress tolerance and the absence of anxiety, which may be beneficial in these environmental conditions. We detected the effects congruent with this hypothesis – affective traits in a sample of convicts were positively related to reproductive success only in families where some form of harshness was present; in the absence of harshness, affective traits negatively predicted reproductive success. Previous research showed that the polymorphisms of 5-HTTLPR gene are associated with affective traits especially in low SES conditions, which may be viewed as a form of environmental harshness (Sadeh et  al., 2010). This raises the possibility that environmental heterogeneity may maintain this polymorphism via the opposite  links between psychopathic traits and fitness.

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Environmental harshness may not be the only ecological factor that contributes to the preservation of psychopathy’s variance. We saw that manipulative psychopathy traits show signatures of positive directional selection. However, the adaptive effects of manipulation may be dependent on the population size  – in a small-sized population manipulation may not be adaptive because information about the manipulators would be quickly disseminated through the population. In return, population members would become distrustful of the manipulators, and the latter ones could even be faced with social ostracism. This gives rise to another interesting possibility  – psychopathy may be more adaptive in large compared to small populations. The intricacy of this assumption lies in the implication that psychopathy may be more adaptive in contemporary than ancestral populations because contemporary settlements are far larger than in ancestral societies. What about psychopathic individuals that are born in small-size settlements? According to this assumption, they could elevate their fitness if they move to some larger populations. Hence, psychopathy may coevolve with the traits associated with positive gene– environment correlation, which occurs when organisms change their environment in order to find new ecological conditions that better suit their genetic potentials. Hence, this could be viewed as two states, one internal (tendency to migrate and change environment) and one external (population size), interacting to preserve the variation in psychopathy, according to state-dependent models of personality evolution. Finally, inter-individual variation in psychopathy may be preserved because psychopathic traits are involved in evolutionary tradeoffs (Ene et al., 2022). There are at least two tradeoffs for which we have empirical data so far. The offspring of psychopathic individuals have lower quality expressed in poorer physical and mental health (Međedović, 2019c), and this is associated with lower levels of parental investment produced by psychopathic parents (Međedović & Petrović, 2019); hence, we observed a quantity-quality tradeoff. Since there is lower parental care in psychopathic individuals, the existence of a mating-parenting tradeoff is not surprising as well – psychopathy negatively associates with parenting but positively with the search for new mates (Međedović, 2019a). Off course, the role of these tradeoffs in the maintenance of psychopathy’s variation is crucially dependent on the parental investment’s positive effects on offspring’s fitness in contemporary humans, and these effects are yet to be empirically confirmed. If they exist, high-­ psychopathy phenotypes can elevate their fitness via increased mating and fertility while low-psychopathy phenotypes elevate their fitness by producing offspring of higher quality. The other two major evolutionary tradeoffs are needed to be empirically examined in future research: the data on the fertility-longevity tradeoff is inconclusive due to heterogeneous findings on associations between psychopathy and health, while the data on associations between psychopathy and age of first reproduction do not exist yet. Hence, evolutionary ecology of psychopathy is the scientific story that only started to unravel, and I think we can expect interesting findings in forthcoming empirical research.

The Empirical Study: Psychopathy, Fertility, Longevity, Interacting Phenotypes, and Parental Effects

Goals of the Present Study The existing research showed indices that psychopathy is under natural selection. Furthermore, the effects of the environment are detected as well, together with the involvement of psychopathy in evolutionary tradeoffs. This makes psychopathy as an effective example of how behavioral ecological concepts can be implemented in the aspects of human personality. Selection regimes on psychopathy turned out to be complex because of profound differences between psychopathy traits, mainly affective/manipulative characteristics, and impulsive/antisocial traits. In the present research, I wanted to provide more insights into the adaptive role of psychopathy and, therefore, new evidence for the effects of natural selection on psychopathy. There are three main goals and associated contributions of the present research. First, I wanted to explore if there is a fertility-longevity tradeoff in psychopathy. As we saw before, there is mixed evidence for this tradeoff: some studies suggested negative links between psychopathy and health (Beaver et  al., 2014; Hudek-Knežević et al., 2016; Jonason et al., 2015), while others provided a possibility that manipulative and affective traits may not be related to physical health (Međedović & Kujačić, 2020) or even positively related to health in elderly individuals (Međedović, 2019c). Therefore, opposing hypotheses can be made for the associations between psychopathy traits and longevity—both positive and negative relations can be expected according to the existing data; however, note that fertility-longevity tradeoff would predict negative links between psychopathy and longevity. So far, data on the exact associations between psychopathy and longevity do not yet exist, as far as I am aware. Hence, I measured indicators of fertility (number of children, grandchildren, and children with different partners), parental investment, and longevity in the present research. In order to measure longevity, I had to collect the rating data on psychopathy—individuals rated their parents on

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Međedović, Evolutionary Behavioral Ecology and Psychopathy, https://doi.org/10.1007/978-3-031-32886-2_9

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psychopathy traits and provided information if their parents were still alive; if they were deceased, additional data on the year of their death were collected as well. Second, I wanted to exploit the fact that the participants in the present research are parental pairs and explore interacting phenotypes on psychopathy. As far as I am aware, these data do not exist so far. Having in mind the importance of interacting phenotypes for analyzing social evolution and providing more accurate findings regarding the links between behavioral traits and fitness (McGlothlin et al., 2010; Moore et al., 1997), this seems like a huge gap in the literature. I used the most basic model applied only on phenotypic data where the scores on the behavioral traits for both focal individual and their social partners (spouses in this case) are used to predict the fitness of the focal individual (Eq. 1 in Wolf & Moore, 2010). I measured reproductive success in each pair member separately; certainly, these variables have high correlations in parental pairs, but they are not the same due to extra-pair reproduction. Interacting phenotypes are expressed as the involvement of one pair member’s characteristics in the prediction of fitness in another pair member: e.g., we can examine the predictive role of mother’s psychopathy in the prediction of paternal reproductive success and vice versa. Interacting phenotypes can be captured by both additive and interaction effects of parental traits in the prediction of fitness. Interaction effects may be particularly interesting because they can indicate that the trait in one pair member is related to fitness only if other members have certain levels of some other trait. Previous research showed that there is assortative mating in psychopathy (Kardum et al., 2017a, b; Smith et al., 2014), and hence, I expected to find it in the present data as well. However, the fact that assortative mating exists in psychopathy does not necessarily means that it is adaptive. Contrasting hypotheses are plausible regarding this research topic as well: the highest fitness may be detected in pairs that have opposing combinations of psychopathy traits (if one member has high levels of psychopathy accompanied by low expressions of psychopathy in other pair members) or if pair members are assortatively mated (i.e., they have similar levels of a certain trait). The empirical data should show what combinations (assortative or disassortative) are beneficial for fitness, if any: the null hypothesis states that there are no interacting phenotypes in psychopathy—only male psychopathy may be predictive for male fitness, and the same may stand for females. Finally, I wanted to explore parental effects, their association with reproduction-­ related traits in offspring, and the associations between parental psychopathy and parental effects. As we discussed before, parental effects can be defined most broadly as any parental effect on offspring phenotype that affects offspring fitness. In the present research, I was interested in parental behavior that may be related to various aspects of reproduction in offspring. There are three parental effects that were measured in the present study: parental influence on the mate choice in their offspring, facilitating reproduction in offspring, and parental support in their offspring childbearing. Note that the last behavior is practically identical to grandparental investment in individuals who have their own children, but in childless individuals, they are manifested as parental expressions of their motivation to help in the upbringing of their future grandoffspring (see “Measures” section for more details). Furthermore, I wanted to see if these parental behaviors are indeed

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related to reproductive outcomes in offspring (e.g., reproductive success itself, valuing children upbringing, age of first reproduction, and the duration of longest partner relationships). Hypotheses on the relations between psychopathy and parental effects cannot be set for all parental effects with the same plausibility: it seems most probable that psychopathy is positively related to the manipulation of offspring’s mate choice and negatively to the support in childbearing (based on previous findings that show negative associations between psychopathy and parental investment). The relations between psychopathy and facilitating reproduction in offspring are hard to hypothesize due to lack of empirical data and unclear theoretical expectations. Finally, these analyses would reveal if parental psychopathy is related to reproductive outcomes in their offspring directly or via parental effects.

Sample The data was collected via an online study using the Google Forms platform. The survey was initially disseminated by the students of the Faculty of media and communications in Belgrade, and the initial participants were asked to find more participants, i.e., the snowball method was used for sampling. Participation in the research was voluntary, both for students and participants. Informed consent was placed on the first page on the survey; the research was approved by the Ethic committee of the Faculty of media and communication. The sampling procedure resulted in 447 participants (61% females; Mage = 25.35; SD = 9.07) with a double sample size in their parents. Participants’ father’s mean birth year was 1961 (SD = 9.73 years), while the mean birth year of mothers was 1964 (SD = 9.36). Participants’ parents were more educated than Serbian average, with the majority of participants who finished college (46.3% for fathers and 48.6% for mothers). The majority of participants’ parents were alive at the time of data collection (93%), and for those who passed away, the mean year of death was 60.78 (SD = 14.08).

Measures I used a new rating measure of psychopathy in order to assess psychopathy traits: Short Psychopathy Rating Scale (SPRS: Međedović & Petrović, 2018; Međedović et al., 2019). It is a 15-item scale that explores three psychopathic traits (five items for each): Deceitfulness (selfishness, high self-value, manipulation, lying, and tendency to use charm in order to obtain goals), Emotional coldness (lack of guilt and empathy and general emotional callousness), and Recklessness (lack of planning and self-control, irresponsibility, and risk-taking). The items of the SPRS can be seen in Međedović and Petrović (2019). The SPRS was shown to be valid operationalization of psychopathy traits, especially in the evolutionary-ecological context (Međedović & Petrović, 2019).

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The Empirical Study: Psychopathy, Fertility, Longevity, Interacting Phenotypes…

Reproductive success in parents was assessed via three measures: Number of children, and grandchildren, and Children with different parents. Parental investment was measured via six items of the Mother/Father relationship quality scale, which represents a subscale of the Arizona Life History Battery (Figueredo, 2007). The participants rated parental behavior toward them during their childhood on a 5-point Likert-type scale where 1 denoted “not at all” (absence of a given behavior), while 5 stood for “a lot” (behavior was highly pronounced). The items used for measuring parental investment are shown in Međedović and Petrović (2019) as well, they mostly assess socio-emotional parental investment in their children. We asked the participants if their parent were still alive in the time of data collection; this information was binary coded and the variable was labeled as Alive in the database. If a parent passed away the participants provided information how old they were when they died; this variable was labeled as the Time of death. Three parental effects were assessed. Mate manipulation in offspring was operationalized via the following question: “To what extent have your father and mother interfered in your choice of a romantic partner so far?” Facilitation of reproduction was measured using this question: “To what extent did your father and mother express their desire for you to have children and start your own family?” Finally, Support in childbearing was measured slightly differently in individuals who have children and those who do not. Individuals who already have children were asked: “To what extent did you have the support of your parents in raising your children?” Participants who did not have children during data collection were asked: “To what extent do you think you will have the support of your parents in raising your children?” All parental effects had the same 3-point response scale where 1 stands for “Not at all,” 2 stands for “To a small extent,” and 3 stands for “To a great extent.” Finally, I explored several reproduction-related outcomes in offspring as well. The duration of Longest relationship (expressed in months)  was assessed as the indicator of long-term mating. Number of children of the participants was registered as well. Participants indicated how much they value having children, as a measure of reproductive motivation (variable is labeled as Reproductive values) via the following item: “I believe that having and raising children is one of the most important things in life” (7-point response scale where 1 stands for “I completely disagree” while 7 stands for “I completely agree”). The age of first reproduction (AFR) was measured with the exact age of first reproduction in individuals who have children and the preferred age of first reproduction (expressed in years) in participants who do not have children.

The Plan of Data Analysis The data analysis followed three research goals that were previously described. First, I analyzed the relationship between parental psychopathy and fitness. Bivariate correlations were calculated, and the regression models were fitted with fitness outcomes (number of children, grandchildren, children with different partners,

Results: Analyzing the Links Between Psychopathy and Fitness

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whether is parent alive, and the year of death for those who are not) as criteria variables, and sex, age, education, and psychopathic traits as predictors. Since both theory and previous findings indicate that there may be sex differences in the links between psychopathy and fitness, the interactions between participants’ sex and psychopathy traits in the prediction of these outcomes are analyzed as well. The second set of analyses was aimed at interacting phenotypes in psychopathy. First, the correlations between parental psychopathy traits in mothers and fathers are analyzed in order to explore if there is assortative mating on psychopathy. Afterward, fitness variables are analyzed as the criteria variables separately for both parents. Parental psychopathy traits are also entered in the regression models for both parents separately. Their additive contributions to the prediction models, together with the interactions, are explored. Finally, I analyzed parental effects. The correlations between maternal and paternal effects are explored in order to analyze the level of parental congruence in their behavior toward offspring. Sex differences in parental effects regarding parents and their offspring are also explored. Associations between parental effects and offspring reproductive outcomes are analyzed in order to explore if parental behavior is in fact related to the reproduction-related variables in their offspring. Associations between parental psychopathy traits, parental effects, and reproductive outcomes in offspring are explored afterward. The final analysis was aimed to explore to what extent parental psychopathy is directly linked to offspring reproductive behavior and whether there are indirect links via parental effects; network model was estimated in order to obtain an insight into these associations.

 esults: Analyzing the Links Between Psychopathy R and Fitness First, I show bivariate associations between psychopathy traits and fitness indicators. The number of children, grandchildren, and the year of death (for deceased parents) have been normalized (using the Blom’s algorithm) before the analyses. Pearson’s correlation coefficients are calculated for all associations except the ones involving children with different parents and whether the parent is still alive—Point biserial correlation coefficients are calculated for these variables, together with the Contingency coefficient as a measure of association between these two variables themselves. Correlations between the examined variables are shown in Table 1. As we can see in Table  1, individuals with lower Recklessness have a higher chance of being alive, indicating higher longevity. On the other hand, for already deceased individuals, higher Deceitfulness and Emotional coldness are positively related with the age of death, indicating higher longevity as well (Emotional coldness has a marginally significant association with the year of death; I show marginal significance only for this criterion measure because the analyses are conducted on 77 participants and marginally significant coefficients can be informative on such a small sample). Neither psychopathy trait is associated with the number of children, while only Deceitfulness is positively related to the number

M(SD) 93.00% 60.78(14.08) 2.06(0.70) 0.42(0.90) 8.40% 3.412(0.75) 2.31(0.90) 2.27(0.87) 1.88(0.98)

Notes: †p