Situational Prevention of Poaching: 15 (Crime Science Series) [1 ed.] 0415634342, 9780415634342

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Situational Prevention of Poaching

For centuries, criminologists have looked for scientific ways to study, understand and ultimately prevent crime. In this volume, a unique offense, poaching, is explored in various contexts to determine what opportunity structures favour this crime and how situational crime prevention may reduce its prevalence. The data sources used range from publically available secondary data about animal populations and interviews with hunters, to actual law enforcement data collected inside protected areas. Various methods are utilized to look for patterns in poaching behaviour regarding where poachers strike, which species they target and their modus operandi. Collectively, the volume shows that principles of criminal opportunity theory and situational crime prevention are useful for studying and preventing poaching in a variety of contexts. The methods employed by each chapter are easily replicated and meant to stimulate empirical poaching research where data are available. While the theoretical grounding of this volume is drawn from criminology, it is written for a broad audience of academics, practitioners and those interested in wildlife conservation. AM Lemieux is a Researcher at the Netherlands Institute for the Study of Crime and Law Enforcement. His main areas of interest are the spatial and temporal distributions of crime, the use of technology to improve law enforcement operations and anti-poaching operations in Africa.

Crime science series Edited by Richard Wortley, UCL

Crime science is a new way of thinking about and responding to the problem of crime in society. The distinctive nature of crime science is captured in the name. First, crime science is about crime. Instead of the usual focus in criminology on the characteristics of the criminal offender, crime science is concerned with the characteristics of the criminal event. The analysis shifts from the distant causes of criminality – biological makeup, upbringing, social disadvantage and the like – to the near causes of crime. Crime scientists are interested in why, where, when and how particular crimes occur. They examine trends and patterns in crime in order to devise immediate and practical strategies to disrupt these patterns. Second, crime science is about science. Many traditional responses to crime control are unsystematic, reactive and populist, too often based on untested assumptions about what works. In contrast, crime science advocates an evidencebased, problem-solving approach to crime control. Adopting the scientific method, crime scientists collect data on crime, generate hypotheses about observed crime trends, devise interventions to respond to crime problems, and test the adequacy of those interventions. Crime science is utilitarian in its orientation and multidisciplinary in its foundations. Crime scientists actively engage with front-line criminal justice practitioners to reduce crime by making it more difficult for individuals to offend, and making it more likely that they will be detected if they do offend. To achieve these objectives, crime science draws on disciplines from both the social and physical sciences, including criminology, sociology, psychology, geography, economics, architecture, industrial design, epidemiology, computer science, mathematics, engineering and biology. 1 Superhighway Robbery Graeme R. Newman and Ronald V. Clarke 2 Crime Reduction and Problem-oriented Policing Edited by Karen Bullock and Nick Tilley

3 Crime Science New approaches to preventing and detecting crime Edited by Melissa J. Smith and Nick Tilley 4 Problem-oriented Policing and Partnerships Implementing an evidence-based approach to crime reduction Karen Bullock, Rosie Erol and Nick Tilley 5 Preventing Child Sexual Abuse Stephen Smallbone, William L. Marshall and Richard Wortley 6 Environmental Criminology and Crime Analysis Edited by Richard Wortley and Lorraine Mazerolle 7 Raising the Bar Preventing aggression in and around bars, pubs and clubs Kathryn Graham and Ross Homel 8 Situational Prevention of Organised Crimes Edited by Karen Bullock, Ronald V. Clarke and Nick Tilley 9 Psychological Criminology An integrative approach Richard Wortley 10 The Reasoning Criminologist Essays in honour of Ronald V. Clarke Edited by Nick Tilley and Graham Farrell 11 Patterns, Prevention and Geometry of Crime Edited by Martin A. Andresen and J. Bryan Kinney 12 Evolution and Crime Jason Roach and Ken Pease 13 Cognition and Crime Offender decision-making and script analyses Edited by Benoit LeClerc and Richard Wortley 14 Affect and Cognition in Criminal Decision Making Between rational choices and lapses of self-control Edited by Jean-Louis van Gelder, Henk Elffers, Daniel Nagin and Danielle Reynald 15 Situational Prevention of Poaching Edited by AM Lemieux

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Situational Prevention of Poaching

Edited by AM Lemieux

First published 2014 by Routledge 2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN and by Routledge 711 Third Avenue, New York, NY 10017 Routledge is an imprint of the Taylor and Francis Group, an informa business © 2014 Andrew Lemieux, selection and editorial material; individual chapters, the contributors. The right of Andrew Lemieux to be identified as editor of this work has been asserted by him in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data Situational prevention of poaching : an international perspective / [edited by] A.M. Lemieux. pages cm. — (Crime science series ; 15) Includes bibliographical references. 1. Poaching—Prevention. 2. Game protection. 3. Wildlife conservation. 4. Tiger—Effect of poaching on. 5. Rhinoceroses—Effect of poaching on. 6. Parrots—Effect of poaching on. I. Lemieux, A.M. (Andrew Michael), 1983– editor of compilation. SK36.S58 2014 364.16′2859—dc23 2013035979 ISBN 13: 978-0-415-63434-2 (hbk) ISBN 13: 978-0-203-09452-5 (ebk) Typeset in Times New Roman by Apex CoVantage, LLC

To those who have inspired, supported and criticized, thank you for everything. The learning experiences, opportunities and dreams you brought into my life undoubtedly made this possible.

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Contents

List of abbreviations List of figures List of tables Preface Author information 1

Introduction

xi xiii xv xvii xix 1

AM LEMIEUX

2

Rhino poaching in Kruger National Park, South Africa: aligning analysis, technology and prevention

18

C O R N É E L O F F AND AM L E MI E UX

3

Does opportunity make the poacher?: an analysis of neo-tropical illicit parrot markets

44

S T E P H E N F. P I R E S AND ROB T. GUE RE T T E

4

Can the Problem Analysis Module (PAM) help us imagine new preventative solutions to a specific tiger poaching issue?

62

J E N N I F E R M A IL L E Y

5

Law enforcement monitoring in Uganda: the utility of official data and time/distance-based ranger efficiency measures WI L L I A M D . MORE TO, AM L E MI E UX, A. RWE T S I B A , N . G U M A , M . DRI CI RU AND H. KUL U KI RYA

82

x

Contents

6

Tracking poachers in Uganda: spatial models of patrol intensity and patrol efficiency

102

A M L E M I E U X , W. BE RNAS CO, A. RWE T S I BA, N . G U M A , M. DRI CI RU AND H. KUL U KI RYA

7

Potential uses of computer agent-based simulation modelling in the evaluation of wildlife poaching

120

J O A N N A F. HI L L , S HANE D. JOHNS ON A N D H E RV É BORRI ON

8

Poaching and tiger populations in Indian reserves: useful outcomes of a failed risky facilities analysis

154

J E O N G H Y U N KI M, RONAL D V. CL ARKE A N D J O E L MI L L E R

9

Eyes on the forest: CCTV and ecotourism in Indian tiger reserves

177

R O N A L D V. CL ARKE , KE VI N CHE T T Y A N D M A N G A I NATARAJAN

Index

201

List of abbreviations

3G 4G ABM ANPR CCTV CITES CPF CPUE GIS GPS IP IR IRR IUCN KNP LED LEM LTE MAUP MEE MIST MRA MVT NTCA PA PAM QENP QEPA QGIS RELA SAPS SCP

Third generation mobile telecommunication Fourth generation mobile telecommunication Agent-based modelling Automatic number plate recognition Closed-circuit television Convention on International Trade of Endangered Fauna and Flora Central-Place Foraging Catch-per-unit effort Geographic information system Global positioning system International protection Infrared Incidence rate ratio International Union for Conservation of Nature Kruger National Park Light emitting diode Law enforcement monitoring Long-term evolution Modifiable areal unit problem Management effectiveness evaluation Management information system Market reduction approach Marginal value theorem National Tiger Conservation Authority Protected area Problem Analysis Module Queen Elizabeth National Park Queen Elizabeth Protected Area Quantum geographic information system Ikatan Relawan Rakyat Malaysia (Malaysian Volunteer Corps) South African Police Service Situational crime prevention

xii

List of abbreviations

SWIFT TCM TR TSEA TTF UAS UAV UPDF uRNG USD UTM UWA WiMAX WLAN WPSI WWF

Special wildlife tourism protection force Traditional Chinese medicine Tiger reserve TRAFFIC Southeast Asia Tiger Task Force Unmanned aerial system Unmanned aerial vehicle Uganda People’s Defence Force Uniform random number generator United States dollar Universal transmercator Uganda Wildlife Authority Worldwide interoperability for microwave access Wireless local area network Wildlife Protection Society of India World Wide Fund for Nature

List of figures

1.1a 1.1b 1.2a 1.2b 1.3 1.4 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 3.1 3.2 5.1 5.2

Basic crime triangle Basic crime triangle for poaching Extended crime triangle Extended crime triangle for poaching Hypothetical spatial distribution of animal, poacher and ranger foraging Hypothetical overlap of animal, poacher and ranger foraging Number of rhinos poached in South Africa, 2000–2012 Number of rhinos poached in Kruger National Park by month, January–May 2011 Number of rhino poaching incidents in Kruger National Park by moon phase, January–May 2011 Spatial distribution of rhino poaching in Kruger National Park, January–May 2011 Distance between roads and poaching incidents in Kruger National Park, January–May 2011 Distance between water pans and poaching incidents in Kruger National Park, January–May 2011 Distance between park borders and poaching incidents in Kruger National Park, January–May 2011 Location of rhino poaching incidents in Kruger National Park by distance from park border, January–May 2011 Weapons confiscated from rhino poachers in Kruger National Park, January–May 2011 Hypothetical application of eCognition software for identifying and mapping sand trails in protected areas Map of cities and annual projected parrot counts Density of parrot species in north-west South America Example of data hierarchy recorded by Uganda Wildlife Authority ranger foot patrols Queen Elizabeth Protected Area south-west Uganda

3 4 4 5 7 8 24 27 28 29 30 30 31 32 33 36 49 53 87 89

xiv 5.3 5.4 5.5 5.6 7.1 7.2 7.3 7.4 7.5 7.6 7.7 8.1 8.2 9.1 9.2 9.3

List of figures Time-based rates of poaching detection by ranger foot patrols in Queen Elizabeth Protected Area (2000–2010) Distance-based rates of poaching detection by ranger foot patrols in Queen Elizabeth Protected Area (2000–2010) Time/distance-based rates of poaching detection by ranger foot patrols in Queen Elizabeth Protected Area (2000–2010) Time/distance-based rates of poaching detection by ranger foot patrols in Queen Elizabeth Protected Area (2004–2010) Overview of the structure of the poaching simulation arranged by time scale. Visualization of the Queen Elizabeth National Park wildlife poaching model showing environmental features and mobile agents Animal agent behavioural processes Poacher agent behavioural processes Ranger agent behavioural processes Illustration of the spatial and temporal patterns of simulated poaching activity in Queen Elizabeth National Park Illustration of the simulated spatial patterns of all illegal activities detected by rangers Photograph of tiger pugmark The 28 tiger reserves ranked by tiger densities Photographs of Indian tiger reserve terrain Photographs of camera traps used in Indian tiger reserves Photo of tiger near tourist Jeep in an Indian tiger reserve

94 94 96 96 127 128 135 138 142 144 145 161 168 181 182 188

List of tables

1.1 1.2 3.1 3.2 3.3 3.4 3.5 4.1 4.2 6.1 6.2 6.3 6.4 7.1 7.2 7.3 7.4 8.1 8.2 8.3 8.4

Definitions of poaching found in the academic literature The 25 techniques of situational crime prevention with example applications for preventing urban crimes and poaching Independent variables The spatial relationship between market species and their ranges Relationship between known parrot range and market availability Explaining types of illicit markets with proxy measures of supply & demand Characteristics of illicit market types Self-reported occupation, RELA gun access and general hunting frequency, 2008 Possible preventive interventions for the specific tiger poaching problem analysed in this chapter Variables and equations in spatial autocovariate Poisson models 1 and 2 Spatial autocovariate Poisson model of patrol intensity Spatial autocovariate Poisson model of patrol efficiency for all poaching activity Spatial autocovariate Poisson models of patrol efficiency for separate poaching activities Global and patch variables and parameters Snare and animal agent variables and parameters Poacher agent variables and parameters Ranger agent variables and parameters Tigers illegally killed (1998–2011): comparison of WPSI and NTCA records Variable definitions and sources Comparison of 14 reserves with high tiger densities and 14 with low tiger densities Correlation matrix of tiger variables (Spearman’s Rho)

2 12 50 51 51 54 55 65 75 111 112 113 114 129 133 137 141 158 159 162 169

xvi

List of tables

8.5

Comparison of 14 reserves with high tiger densities and 14 with low tiger densities: group means and significance test results (Mann-Whitney Test) International Protection (IP) X/Y ratings

9.1

171 197

Preface

The idea for this volume originated at the Rutgers School of Criminal Justice. I came to the school in the fall of 2006 after completing my Biochemistry and Molecular Biophysics degrees at the University of Arizona. With only a minor in Criminal Justice but an immense curiosity for the subject, I was eager to work on as many topics as possible. Marcus Felson and Ronald Clarke took me under their wings and together helped me navigate a new discipline and conduct what I found to be interesting research. One day, Professor Clarke asked if I would like to help him evaluate the impact of an ivory trade ban on elephant poaching in Africa. The idea was fascinating. I knew little about elephants, poaching or Africa at the time but was keen to learn. Ron suggested I read a book Richard Leakey had written about his time with the Kenya Wildlife Service as an introduction to the topic. Leakey’s stories of brutal elephant killings and the decision to burn Kenya’s ivory stockpile in support of the 1989 trade ban were remarkable. Digging into the academic literature, I soon realized wildlife crime, not just elephant poaching, was something I found intrinsically interesting. The victims of criminal attack and settings for crime were a far cry from examples used in criminology textbooks. After finishing the project with Ron, I decided to visit Africa to learn more about the continent and wildlife I had read so much about. My first visit to Uganda in 2009 was unforgettable. I went as a volunteer teacher and had no idea what to expect. In the end, my experiences were life changing and fueled a passion to prevent wildlife crime. It was during this trip that I first met with Uganda Wildlife Authority’s law enforcement division. Over several meetings, we discussed my work on elephant poaching and possible opportunities for training while I was in country. They arranged for me to visit Queen Elizabeth and Murchison Falls National Parks to discuss poaching prevention strategies with rangers. When I returned to Kampala, I knew that would not be the end of my working relationship with Uganda’s rangers. The short time I spent with them in the field had opened my eyes to a new world of thinking about crime prevention and law enforcement strategy. I promised the rangers I would be back, but I didn’t know when. After finishing graduate school, I became a postdoctoral research fellow at the Netherlands Institute for the Study of Crime and Law Enforcement in

xviii Preface Amsterdam. In the summer of 2011, I returned to Uganda to field test equipment and software capable of mapping and analysing illegal activity in protected areas. I had kept in touch with my ranger contacts via email, but it was now finally time to go back into the field. I spent six weeks between two parks, validating the low-cost crime analysis program I envisioned for ranger teams in Uganda. At the end of my fieldwork, I flew to South Africa to present my preliminary findings at the Environmental Crime and Crime Analysis (ECCA) conference in Durban. At some point during that conference, Ron approached me about compiling an edited volume on poaching. We both knew criminologists studying poaching in a number of contexts and thought the collective papers would make an interesting book. In the coming months, Ron helped me formulate a proposal and suggested contributors. His encouragement and help were vital and appreciated. The volume that follows is the final product of many months of hard work by the contributors and myself to produce new and rigorous academic research on poaching. Thanks to the comments and suggestions of many anonymous reviewers, I believe we have compiled an interesting volume that will inspire and guide future studies of poaching. While we admittedly only begin to scratch the surface on the broad crime that is poaching, the volume provides a stepping stone for criminology’s growing interest in wildlife crime. In the future, I hope more volumes such as this are produced, especially if they contain empirical evaluations of antipoaching interventions. In closing, I would like to thank Patrick Mogridge for helping design the cover of this volume. His guidance through the artistic process was more than helpful as he really helped me create something from nothing. The cover pays homage to men and women on the front line of anti-poaching efforts. These rangers often risk their lives to protect the world’s wildlife and are truly fundamental to successful conservation. AM Lemieux Amsterdam, Netherlands January 2014

Author information

Wim Bernasco Wim Bernasco is a Senior Researcher at the Netherlands Institute for the Study of Crime and Law Enforcement (NSCR) in Amsterdam, and holds a chair “Spatial Analysis of Crime” at the department of Spatial Economics at VU University Amsterdam. His research interests include situational and spatial aspects of criminal behavior, including offender travel and target selection.

Hervé Borrion Hervé Borrion is the Deputy Director of UCL’s Security Research Training Centre. He pursued his education at the Ecole Nationale Supérieure d’Aéronautique et de l’Espace (Masters) and at University College London (PhD) where he specialised in radar signal processing. He developed a strong interest in modelling techniques for security applications at the Commissariat à l’Énergie Atomique, Los Alamos National Laboratory, University of Cape Town and Tsinghua University. He is the founder of the UK National Environmental Crime Conference, and he contributed to a number of EU projects including RIBS, BASYLIS and PRIME. Dr Borrion is a member of the European Reference Network for Critical Infrastructure Protection at the EU Joint Research Centre.

Kevin Chetty Dr Kevin Chetty is a Lecturer in the Department of Security and Crime Science at University College London. His research focuses on new passive RF sensor systems that exploit wireless communication networks for uncooperative detection of personnel, vehicle and marine targets. Other research interests include target detection and classification using acoustic micro-Doppler signatures, ultra-wideband and through-the-wall radar, and software defined sensor systems. Kevin is a member of the IEEE and IET, a reviewer for the IET Radar, Sonar and Navigation journal, and author to over 20 conference and journal papers.

xx Author information

Ronald V. Clarke Ronald V. Clarke has spent a lifetime studying ways to prevent the crimes that plague Western society. More recently, he has turned his attention to wildlife crimes, especially poaching of endangered species. Most of these crimes occur in economically deprived countries in Africa, East Asia and South America. He is finding the lessons learned from his earlier years of research apply with little modification to his new field of interest.

M. Driciru Margret Driciru is the warden of Monitoring and Research for Queen Elizabeth Conservation Area in southwestern Uganda.

Corné Eloff Corné Eloff started his career in the South African Police Service (SAPS) in 1993 and served in the Forensic Science Laboratory as well as within the Crime Intelligence division until 2000. Experience in tactical profiling and geo-spatial intelligence during his career in the SAPS contributed to his knowledge and experience to analyse crime. In 2000, he joined the Council for Scientific and Industrial Research (CSIR) at its Satellite Applications Centre situated on the farm Hartebeesthoek. Experience and knowledge in systems engineering and remote sensing for more than 10 years provided solid understanding of this satellite based technology and to apply it at relevant application level. He furthered his career in the CSIR by joining the Built Environment unit to understand the principles associated with land management and the urban and rural dynamics of the built environment science. The challenges faced by the human race from a socio-economic point of view as well as the depletion of natural resources within Africa were soon realised. In May 2013, he joined the European Aeronautics Defence Space (EADS) South Africa team with a line reporting structure to Astrium situated in Toulouse, France (the EADS group consist of Airbus, Eurocopter, Astrium and Cassidian). He is now the sales manager for southern Africa, representing the Astrium portfolio of geo-information products and services (more information available at www.astriumservices.com and www.astrium-geo.com). Corné holds the formal qualifications of MCSE, BA(Pol), BA(Hons), LLB, MBA, PhD in Criminology and is a registered Professional GISc Practitioner in South Africa.

Rob T. Guerette Rob T. Guerette is Associate Professor of Criminal Justice at the School of International and Public Affairs, Florida International University, Miami, Florida.

Author information

xxi

N. Guma Nelson Guma is the Conservation Area Manager of Queen Elizabeth Conservation Area in southwestern Uganda.

Joanna F. Hill Joanna F. Hill is a doctoral student at the UCL Security Science Doctoral Research Training Centre (UCL SECReT), University College London, England. She has a BSc in Ecology and Conservation from Anglia Ruskin University, Cambridge, and an MRes in Biosystematics from Imperial College London. She manages the UK National Environmental Crime Conferences, is a cofounder of UCL’s AgentBased Modelling Working Group (www.ucl-abm.org.uk) and has volunteered for several UK and international conservation organisations. Her research interests focus on applying novel technological solutions and agent-based simulation models to understand and prevent a broad range of environmental crime problems.

Shane D. Johnson Shane D. Johnson is a Professor at the UCL Department of Security and Crime Science, University College London, England. He has a PhD and an MA in psychology and a BSc in computer science. He has published over 80 original articles in journals including Criminology and the Journal of Research in Crime and Delinquency on topics including the spatial and temporal distribution of crime, complexity science and evaluation methods. He has directed research projects funded by agencies including the British Academy and the Engineering Physical Sciences Research Council. He is currently associate editor of the journal Legal and Criminological Psychology.

Jeong Hyun Kim Jeong Hyun Kim is a Doctoral Candidate at the School of Criminal Justice, Rutgers University, Newark, NJ, USA. His research interests include environmental criminology, wildlife criminology, international crime, cybercrime and urban policing.

H. Kulu Kirya Haruna Kulu Kirya is the Warden of Law Enforcement for Queen Elizabeth Conservation Area in southwestern Uganda.

Jennifer Mailley Dr Jennifer Mailley has spent much of the past 4 years living and working in Southeast Asia. As Senior Lecturer in Criminology at HELP University

xxii Author information Kuala Lumpur, she devised and delivered the first MSc module focused solely on environmental criminology in Malaysia. As project manager for TRACE Wildlife Forensics Network, she coordinated capacity building of over 100 enforcement officers and 15 laboratory staff spanning nine countries, to increase the utility of forensic science in investigations into the illegal wildlife trade. As a consultant for TRAFFIC Southeast Asia, she had input in to UNODC’s Transnational Organised Crime Threat Assessment (TOCTA) and researched the issue of rhino poaching driven largely by demand from Vietnam. She is about to embark on a period working for Anglia Ruskin University in the United Kingdom, as senior lecturer, where she will continue to focus research efforts on wildlife crime.

Joel Miller Joel Miller is an assistant professor at Rutgers University’s School of Criminal Justice. His research has examined a range of applied policy research topics, including police tactics and accountability, juvenile justice, risk assessment and crime prevention. It focuses in particular on strategies for improving the effectiveness of and public confidence in criminal justice agencies and policies, and it has been published in a variety of journal articles and government reports. His work is international in scope, reflecting a career that has seen employment at the British Home Office, the University of Malaga in Spain and the Vera Institute of Justice, New York.

William D. Moreto William D. Moreto is an assistant professor in the Department of Criminal Justice at the University of Central Florida. He completed his doctoral studies at the Rutgers School of Criminal Justice. His research interests include environmental criminology, situational crime prevention, spatiotemporal analysis, wildlife crime and policing.

Mangai Natarajan Mangai Natarajan, PhD, is a professor in the Department of Criminal Justice at John Jay College of Criminal Justice. She is an active policy-oriented researcher who has published widely in four areas: organized crime (drug trafficking), women police, violence against women and international crime and justice. Her wider academic interests revolve around crime theories that promote crime reduction policy thinking. Being of Indian origin, she has developed an interest in preventing the poaching of tigers, the national animal of India. Dr Natarajan is currently developing a study of elephant rampages in villages, which have become a serious problem of human–animal conflict.

Author information

xxiii

Stephen F. Pires Stephen F. Pires completed his graduate work at the School of Criminal Justice at Rutgers University in Newark, New Jersey. While pursuing his studies there, he worked closely with Dr Ronald Clarke on several projects related to the illegal parrot trade. Stephen Pires’ current research involves applying crime mapping to illuminate how the illegal parrot trade operates. In addition to this, he is also currently researching the topic of kidnappings for ransom in Colombia through a geographic lens.

A. Rwetsiba Aggrey Rwetsiba is the Monitoring and Research Coordinator for the Uganda Wildlife Authority headquarters in Kampala, Uganda.

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1

Introduction AM Lemieux

For centuries, criminologists have looked for scientific ways to study, understand and ultimately prevent crime. Their work has produced a wealth of information about who commits crime, why they do so, and the opportunity structures that make crime possible. In this volume, a unique offense, poaching, is explored in various contexts to determine what opportunity structures favour this crime and how situational crime prevention may reduce its prevalence. The current chapter is meant to orient the reader, criminologist or not, with the basic elements of criminal opportunity theory, the concept of triple foraging, and the 25 techniques of situational crime prevention. It transitions from a description of these items to an overview of how each chapter elucidates information about them. In general, the objective is to paint a picture of poaching that not only shows how it differs from traditional urban crimes like burglary and robbery, but how like all crimes, it can be understood through the scope of simple criminological principles.

Basic definitions: poaching, wildlife and protected areas To better understand the chapters that follow, it is important in this introduction to present definitions for commonly used terms. Most important is the word ‘poaching’ – the crime which this volume seeks to better understand through empirical research. Surprisingly, there are a number of different, yet similar, definitions of poaching found in the academic literature (see Table 1.1). As the table shows, most definitions of poaching make reference to the illegal ‘taking’, ‘extraction’, ‘stealing’ or ‘hunting’ of ‘game’, ‘wildlife’, ‘renewable resources’ and/or ‘fish’. Where some definitions, like Muth and Bowe’s (1998), clearly define the different types of wildlife, others, like Eliason’s (2004), simply use the word wildlife as an umbrella term. The variety of poaching definitions available speaks to the breadth of this crime type and the natural resources it affects. Although this volume only includes studies of birds and land mammals, it is still possible to use broad definitions of poaching and wildlife. The theoretical approaches and research methodologies presented here do not exclude themselves from the study of

2 AM Lemieux Table 1.1 Definitions of poaching found in the academic literature Source

Definition of poaching

Muth and Bowe (1998)

‘any act that intentionally contravenes the laws and regulations established to protect wild, renewable resources, such as plants, mammals, birds, insects, reptiles, amphibians, fish, and shellfish’ (p. 11). ‘the illegal “taking” of wildlife. Taking can include such activities as hunting, fishing, trapping, seining, netting and other methods of capturing and/or killing wildlife’ (p. 979). ‘The illegal taking of wildlife resources’ (p. 27) ‘the illegal taking of wildlife’ (p. 121) ‘any extractive use of wildlife that is considered illegal by the state . . . poaching can also be defined in terms of breaking hunting laws or norms in private sector wildlife conservation initiatives and rural community based conservation’ (pp. 97–98). ‘taking a wild resource out of season or through an illegal means . . . it can also be the killing or trapping of endangered, rare, or protected species’ (p. 557). ‘simply to hunt or fish illegally’ (p. 1)

Musgrave et al. (1993)

Eliason (1999) Eliason (2004) Duffy (1999)

Blevins and Edwards (2009)

Wilkinson (1990)

poaching no matter the modus operandi or species targeted. Thus wildlife and poaching are defined as: •

Wildlife includes all forms of non-domesticated plants and animals living in the wild.

•

Poaching is the illegal taking of wildlife.

It should be noted here that most poachers are ‘taking’ wildlife because they remove the entire specimen, or portions of it, from its natural environment. In many cases, this requires killing the wildlife; in others, live specimens are wanted. Examples of items taken include meat, timber, ivory, medicinal plants and animal skins. In the sections that follow, poaching is described in the context of this book using examples relative to land mammals. This is in no way prioritizing these species above other types of wildlife threatened by poaching, but rather an attempt to make the volume more cohesive. Another commonly used term throughout the book is ‘protected area’. Common examples of protected areas include national parks, game reserves and national forests. It is important to remember that protected areas are diverse with regard to their size, shape, wildlife and governing laws. The most widely recognized definition of protected area, and the one used here, comes from the International Union for

Introduction 3 the Conservation of Nature (IUCN). According to this definition, protected areas are as follows: a clearly defined geographical space, recognised, dedicated and managed, through legal or other effective means, to achieve the long term conservation of nature with associated ecosystem services and cultural values. (Dudley, 2008, p. 8) With the terms poaching, wildlife and protected area clearly defined, it is now possible to lay out the theoretical framework of this volume. The section that follows is a brief overview of how criminal opportunity theory can be used to describe the situations that lead to poaching as well as situations where poaching is prevented or deterred.

The criminal opportunity structure of poaching The focus of this volume is how criminal opportunity structures for poaching develop and are exploited by poachers. Poaching, like all other crime, is the result of motivated offenders seizing criminal opportunities they encounter. Although some offenders, who are highly motivated, may seek out criminal opportunities, others may come across them inadvertently and seize them anyway. In either case, the existence of criminal opportunities is a requirement for criminal success. For example, an ivory poacher, even a highly motivated one, will never be successful if he cannot find an elephant or gain access to a protected area. This section is a basic overview of how criminal opportunity theory can be applied to the study of poaching. Drawing from the routine activity approach (Cohen and Felson, 1979), the creation of criminal opportunities can be thought of as an interaction between three important groups: offenders, victims/targets and guardians. To be clear, guardians are individuals capable of protecting victims/targets from criminal attack. According to this approach, opportunities for crime are highest when victims and offenders meet in the absence of capable guardians. Figure 1.1a

Figure 1.1a Basic crime triangle

4 AM Lemieux

Figure 1.1b Basic crime triangle for poaching

shows this graphically using a basic crime triangle; Figure 1.1b shows a crime triangle for elephant poaching. For both triangles, guardians are not present, indicating offenders are likely to be successful in these situations. To better visualize the interaction between victims, guardians and offenders, an extended crime triangle, also referred to as the Problem Analysis Triangle (Clarke and Eck, 2005), is presented in Figure 1.2a. In this graphic, the inner triangle (solid line) is the same as the basic crime triangle in Figure 1.1a. However, an outer triangle (dashed line) has been added to show how the presence of other individuals might reduce criminal opportunities, and most importantly, per the routine activity approach, if guardians are available

Figure 1.2a Extended crime triangle Source: Adapted from Clarke and Eck (2005)

Introduction 5

Figure 1.2b Extended crime triangle for poaching

to protect potential targets/victims. The other individuals included in this outer triangle, handlers (Felson, 1986) and place managers (Eck, 1994), were not included in Cohen and Felson’s (1979) original work but have since been added to the extended crime triangle to better describe the groups responsible for preventing crime. Using elephant poaching as an example, Figure 1.2b shows how each piece of the extended crime triangle is related to the presence or absence of criminal opportunities. With the inner triangle already described and presented in Figure 1.1b, the focus here is the outer triangle. In this example, rangers are guardians capable of protecting elephants – a reality for most protected areas where these teams are the first line of defence against poaching. The poacher’s handler is someone capable of influencing his behaviour through informal social control (Felson, 1987). The example uses a community elder, but a handler could be any individual such as a parent, friend, spouse or sibling who is respected by the offender. Finally, a place manager is the individual responsible for controlling the space where the crime occurs (Eck and Weisburd, 1995). In this example, the protected area manager may use fencing to keep poachers from accessing the reserve and away from elephants. In short, the extended crime triangle gives a more complete view of how the actions of many individuals, not just victims and offenders, are an important element of poaching opportunity structures. When considering options for dismantling opportunity structures for poaching, guardians stand out as the most direct way to protect victims. Unlike handlers and place managers, the role of guardians is to deter offenders with their

6 AM Lemieux presence or intervene during an attack. On the other hand, place managers try to secure a location, while handlers try to reduce offender motivation. Although these roles are important, guardianship is the last resort when handlers and place managers are not effective. Inside protected areas, guardianship is almost exclusively provided by law enforcement rangers on patrol. Theoretically, this means where and when rangers patrol will have a significant impact on opportunities for poaching. In the next section, the spatial and temporal distribution – or ‘triple foraging’ – of wildlife, poachers and rangers is described to show how the presence of guardians not only affects opportunity structures, but official records of poaching activity as well.

Triple foraging: who’s hunting whom in protected areas? Delving deeper into criminal opportunity structures requires knowing more about the spatial and temporal movements of those involved – namely, victims, offenders and guardians. To determine where crime is likely to occur, it is important to think about when and where these three groups move during the course of a day. According to the routine activity approach (Cohen and Felson, 1979), their movements dictate how criminal opportunities will be distributed across time and space. For example, opportunities for burglary arise when homes are left unattended and thus many burglars strike during the day while victims are at work. Applied to poaching, one would consider the routine movements of animals, poachers and rangers to determine when and where the risk of poaching is highest. The movements of animals, poachers and rangers can be described as a ‘triple foraging’ process that dictates where poaching opportunities exist. In essence, each of these groups is searching for a different form of ‘currency’ on any given day (Schoener, 1971); animals forage for food, poachers forage for animals, and rangers forage for poachers. According to optimal foraging theory, each group will search ‘patches’ inside a protected area for the currency they desire. Patches which produce the highest amount of currency intake relative to energy spent foraging will be preferred (for a good review of optimal foraging theory, see Pyke et al., 1977). This means the spatial and temporal distribution of these groups will depend on the availability of different currencies across patches that make up a protected area. Figure 1.3 helps visualize the triple foraging process by depicting the hypothetical spatial distribution of foraging by animals, poachers and rangers. Combining the individual layers of Figure 1.3, the graphic shown in Figure 1.4 is the hypothetical overlap of foraging by animals, poachers and rangers. Assuming each grid cell is a foraging patch within a protected area, the figure shows there are numerous combinations of victim, offender and guardian interactions possible. Included in Figure 1.4 is a brief description of each interaction as well as information regarding the poaching threat, likelihood of arrest, and poaching detection probability for each type of interaction. It should be noted here that the figure is unable to capture the temporal element of triple foraging. For simplicity’s sake,

Figure 1.3 Hypothetical spatial distribution of animal, poacher and ranger foraging

8 AM Lemieux

Figure 1.4 Hypothetical overlap of animal, poacher and ranger foraging

it is assumed that if foraging overlaps spatially in the diagram, it is also overlapping temporally. Looking at Figure 1.4, it is immediately clear that in some grid cells, each group will forage alone. Where this happens, the chances of both poaching and arrest are low because poachers do not encounter animals, and rangers do not find poachers. However, this situation is troublesome for rangers because poachers, while not hurting animals, are still moving around the protected area undetected. In grid cells where animals and poachers forage together, this is the most dangerous. Without rangers to act as guardians, poaching is very likely to happen

Introduction 9 and go undetected. Conversely, if animals forage with rangers only, opportunities for poaching are very low. When all three groups forage simultaneously, this produces a low risk of poaching and high probability of arrest because guardians are present. Interestingly, the optimal scenario occurs when rangers and poachers forage together in the absence of animals because the risk of arrest is high and the threat to animals extremely low. To fully characterize and operationalize the triple foraging process, one would need to create models for specific poaching problems. For example, the movement of animals between foraging patches is likely to be species dependent; lions, elephants and antelope all search for different types food. Moreover, poacher foraging patterns will depend on their modus operandi and the species being targeted. Active hunters, who hunt and kill animals with guns, may prefer different patches than passive hunters who trap or snare animals. Additionally, access to vehicles would also alter how attractive patches are to either type of hunter because it reduces the energy required to travel to/from the patch. Finally, the location of outposts, access to vehicles, and real-time intelligence concerning poacher movements may all influence when and where rangers will patrol. While not included here, specific models of the triple foraging have great potential for describing and predicting where poaching is likely to occur. The triple foraging process is not only important for understanding opportunity structures of poaching: it also has a very large impact on the reliability of data collected by law enforcement agencies inside protected areas. Take for example the grid cells in Figure 1.4 where poachers foraged without meeting rangers. In some cases, poachers were unsuccessful because they did not meet any animals; in others they were successful because they met animals without guardians. No matter the outcome, all of this will not appear in official data sources because rangers were not there to detect the poaching activity. This is very different than traditional police data which are typically a combination of law enforcement observations and victim reports. In the case of poaching, non-human victims have few, if any, ways of (a) altering guardians during the commission of a crime or (b) reporting victimization after a crime has occurred. These ‘silent victims’ are completely reliant on guardians being in the right place at the right time to both protect them and record offenses against them. This reality calls into question how much poaching goes unnoticed and places constraints on how to interpret official sources of data when patrol coverage of a protected area is not 100 per cent complete. Because some of the studies in this volume use official data collected by rangers on patrol, it was necessary to highlight the selectivity of such data sources in this section on triple foraging. In summary, the triple foraging process is useful for understanding the dynamic nature of opportunities for poaching. Most importantly, the autonomous foraging of animals, poachers and rangers results in a distinct spatial and temporal distribution of interactions between these groups. While some interactions favour successful poaching, others favour poacher arrest. Moreover, the triple foraging process shows official data on poaching are unable to capture poaching activity

10 AM Lemieux in areas not covered by ranger patrol and thus questions how large the ‘dark figure’ (Biderman and Reiss, 1967) of poaching really is. The next section builds on what has been written about the criminal opportunity structures of poaching and how they might be minimized or dismantled using situational crime prevention.

Situational crime prevention (SCP) and poaching After introducing above the basics of how criminal opportunities for poaching develop, in this section, an overview of situational crime prevention (SCP) (Clarke, 1995, 1997) and its applicability to poaching is presented. Preventing poaching is a major concern for conservationists as this crime is ultimately what satisfies the world’s demand for wildlife products and threatens the sustainability of wildlife populations. By combining criminal opportunity theory with a rational choice model of offending, situational crime prevention offers a well-structured approach for limiting or completely removing criminal opportunities. In the paragraphs that follow, the theoretical background and potential applications of SCP to crime and, more specifically, poaching are described. According to Clarke (1997), ‘situational prevention comprises opportunityreducing measures that: (1) are directed at highly specific forms of crime, (2) involve the management, design or manipulation of the immediate environment in as systematic and permanent way as possible, (3) make crime more difficult and risky, or less rewarding and excusable as judged by a wide range of offenders’ (p. 4). According to Clarke, the definition is intentionally broad to convey the notion that situational crime prevention can be applied to nearly any type of crime, setting or offender. The crime specific approach suggested by SCP is derived from numerous criminological theories which collectively imply opportunities for crime are highly specific. For example, environmental criminology (Brantingham and Brantingham, 1981) contends the design and purpose of physical environments will influence criminal opportunity structures. Situational crime prevention embraces this idea and suggests prevention measures should consider how places that host crime can be altered to remove criminal opportunities. Crime prevention through environmental design (CPTED) (Jeffery, 1971) and ‘defensible space’ (Newman, 1972) are seen as practical applications of this approach and indeed were fundamental to the development of SCP. To describe how those involved with crime converge in specific places, SCP draws from the routine activity approach (Cohen and Felson, 1979). As noted earlier, this line of thinking helps explain where crime will occur based on the movements of victims, offenders and others who may prevent crime. Generally, prevention measures prescribed by SCP attempt to decrease offender/victim interactions or increase guardianship in places where the interactions are unavoidable. Finally, SCP uses the rational choice perspective (Clarke and Cornish, 1985; Cornish and Clarke, 1986) to characterize offenders as actors who calculate the potential risks and rewards of crime and generally attempt to minimize

Introduction 11 risks while maximizing rewards. Because these calculations are highly dependent on the situational context of opportunities and offender needs, SCP recommends targeting specific types of offenders rather than offenders in general. In short, SCP has used a wealth of criminological theorizing to create a holistic prevention approach that considers not only the actors involved but the physical environments hosting crime. Moving beyond the theoretical grounding and onto practical applications of SCP is made easy using the 25 techniques of situational crime prevention (Cornish and Clarke, 2003). These are organized into five broad categories that highlight the general goal of individual techniques. The five categories are (1) increase the effort, (2) increase the risks, (3) reduce the rewards, (4) reduce provocations and (5) remove excuses. Each category has five different techniques that in some way aim to alter the physical environment of crime, offender rationality or victim/ offender/guardian interactions. The 25 techniques, and examples of how they can be applied to crime, are shown in Table 1.2. To better describe the SCP techniques and their applicability to poaching, two examples of opportunity reduction measures are shown alongside each technique in Table 1.2. The first examples, marked by a dot (•), show how each technique could be used to prevent violent or non-violent crimes in urban settings. The other examples, marked by a star (Ì), show how each technique could be used to prevent poaching. Because most applications of SCP have occurred in urban environments, this comparison should help readers draw a link between how SCP is typically used and how it could be applied to a vastly different crime problem. The SCP applications found in Table 1.2 clearly indicate that being opportunity specific is possible for a variety of settings and offenses. Without going into the details of each technique, it is still worth noting in the text a few examples of how SCP can be applied to urban crime and poaching. The first column shows various ways to increase the efforts of crime. Screening exits is one way to do this; stores can use electronic tags to deter shoplifting just as airports can use sniffer dogs to deter wildlife smuggling. The second column shows how interventions can increase the risks associated with crime. Strengthening formal surveillance increases the risk of apprehension and makes places with more security guards or ranger foot patrols less attractive. The third column shows different ways to reduce the rewards of criminal opportunities. Removing targets is meant to deter offenders as they gain nothing from committing the crime. The advent of removable car radios and practice of rhino dehorning are good examples of how this technique has been applied to urban crime and poaching respectively. The final two columns in Table 1.2 show ways to reduce provocations for offending and remove excuses for crime. Reducing temptation and arousal is an interesting technique that attempts to keep offenders away from enticing situations. Banning paedophiles from jobs that involve working with children reduces the temptation this type of offender encounters on a routine basis. Because many poachers hunt for income or food, reducing temptations may require offering stable employment or reliable sources of protein. Finally, alerting conscience is

7. Assist natural surveillance • Improve street lighting Reward community informants

2. Control access to facilities • Electronic card access Fence national park 3. Screen exits • Electronic merchandise tags Sniffer dogs at airports 4. Deflect offenders • Street closures Checkpoints along protected area roads 15. Deny benefits • Disable stolen cell phones Add dye to rhino horn

13. Identify property • Cattle branding Require RFID chips for legal wildlife exports 14. Disrupt markets • Monitor pawn shops Ban international trade

11. Conceal targets • Off-street parking Translocate animals away from villages 12. Remove targets • Removable car radio Rhino dehorning

Reduce the rewards

20. Discourage imitation • Rapid repair of vandalism Forbid profit sharing with communities producing poachers

18. Reduce temptation/arousal • Ban paedophiles from jobs with children Provide alternative sources of income/protein 19. Neutralize peer pressure • Disperse troublemakers at school Conservation education

25. Control drugs/alcohol • Alcohol-free events Substance abuse programs for communities

23. Alert conscience • Roadside speed display boards Clearly mark game reserve borders 24. Assist compliance • Litter receptacles Allow regulated hunting

21. Set rules • Rental agreements Memorandums of understanding for wildlife use 22. Post instructions • ‘No Parking’ signs ‘No Trespassing’ signs

16. Reduce frustrations/stress • Avoid overcrowding bar Strong community outreach 17. Avoid disputes • Separate seating for rival soccer fans Elephant trenches

Remove excuses

Reduce provocations

• Examples of how each technique could be used to prevent traditional forms of violent and non-violent crime in urban settings Examples of how each technique could be used to prevent poaching Adapted from Cornish and Clarke (2003)

5. Control tools/weapons • Restrict spray paint sales to juveniles Limit public sale of spears/traps/nets

6. Extend guardianship • Go out in a group at night Gunshot detectors

1. Target harden • Steering wheel locks GPS collars on vulnerable animals

8. Reduce anonymity • ‘How’s my driving?’ decals Automatic number plate readers on park roads 9. Use place managers • Two clerks for convenience stores Encourage lodge owners to report suspicious activity 10. Strengthen formal surveillance • Security guards More ranger foot patrols

Increase the risks

Increase the effort

Table 1.2 The 25 techniques of situational crime prevention with example applications for preventing urban crimes and poaching

Introduction 13 listed as one way to remove excuses for offending. For speeders, roadside display boards showing their current speed and the posted speed limit are a clever way to do this. With poachers, well-marked game reserve borders are a practical way to remove excuses for hunting on protected land. In summary, the 25 techniques of situational crime prevention are clearly useful for developing crime reduction strategies against urban crime and poaching. The effectiveness of situational crime prevention was first published as 23 successful case studies found in Clarke’s original volume (1997). Since then, the ongoing series Crime Prevention Studies has published many more examples of success. A continual criticism of SCP contends this approach will only result in displacement of crime to other areas. Guerette and Bowers (2009) investigated the claim and found displacement occurred with about 1/4 of the interventions. Importantly, they also showed diffusion of benefits, crime reductions beyond the intervention, occurred in a 1/4 of the interventions as well. Thus, it appears that the literature supporting the use of SCP is strong and that the displacement criticism may be overstated. The application of SCP to poaching is relatively new, but a limited number of studies do exist. One study has used situational crime prevention as an analytical framework (Lemieux and Clarke, 2009), while others suggest SCP measures based on empirical findings about a specific poaching problem (Pires and Clarke, 2011, 2012; Pires and Moreto, 2011). Unfortunately, there are no rigorous studies of poaching levels before and after an SCP intervention. Because SCP has proven effective at reducing various crimes in a multitude of settings, its utility for poaching prevention warrants further investigation. The final section of this chapter presents an overview of the eight studies found in the volume and how they relate to the criminological theory and practice presented thus far.

The current volume This volume is a collection of studies that examine how criminal opportunities for poaching develop in a variety of contexts. The purpose was to study poaching involving different species in different places and suggest situational crime prevention measures based on empirical evidence. The data sources used in the chapters range from publically available secondary data about animal populations, to interviews with hunters, to actual law enforcement data collected inside protected areas. Various analyses are used to look for patterns in poaching behaviour regarding where poachers strike, which species they target and their modus operandi. In this section, a brief overview of the chapters is presented to show how each draws from the theoretical framework presented above. The book begins with a study of rhino poaching in Kruger National Park, South Africa, by Eloff and Lemieux (Chapter 2). The surge in African rhino killings that began in 2008 and has yet to reverse course makes this piece timely and particularly relevant to the international outcry over rhino poaching. The chapter provides detailed information about the demand for rhino horn products

14 AM Lemieux and how supply is created in South Africa. Using rhino poaching data from the South African Police Service, the authors use basic spatial and temporal analyses to find avenues for prevention. In the discussion, they make numerous arguments for the use of SCP interventions that employ technology to deter poaching and help commanders collect better information about their poaching problem. The chapter that follows, written by Pires and Guerette, asks a simple question, ‘Does opportunity make the poacher?’ (Chapter 3). Drawing from basic opportunity theory, the authors investigate if the presence of parrots in South American bird markets is related to their distribution in the wild. In essence, if parrot poaching was highly opportunistic, the birds seen on local markets would all come from the nearby area. If parrot poaching was a bit more structured, birds would be available from a variety of regions. By comparing market availability with parrot range data, Pires and Guerette make an interesting argument that markets are diverse and can be characterized as local, feeder or regional, each of which demands its own tailored SCP interventions. Moving away from South America and into Southeast Asia, Mailley’s work explores the modus operandi of tiger poachers in Malaysia (Chapter 4). Using interviews with local hunters as a data source, Mailley employs the Problem Analysis Module to carefully sift through what is known about those who kill tigers illegally. The module is designed to help law enforcement practitioners think about a crime problem in very specific terms and thereby derive useful SCP measures. Mailley’s work not only shows the utility of the Problem Analysis Module for poaching prevention, but also the importance of offender interviews to fully understand a local poaching problem. The next three chapters in the book concern poaching in Uganda. Two of the pieces use official data from Queen Elizabeth Protected Area (QEPA) in southwestern Uganda to calculate ranger efficiency measures. The data used is comprised of GPS waypoints taken by rangers during routine patrols. In essence, when rangers patrol in QEPA, handheld GPS units are used to note the time and location of observations such as animal sightings, poaching and ranger positions. The work by Moreto et al. (Chapter 5) uses these data to calculate time- and distance-based measures of efficiency. These rates show how much poaching is observed per kilometre and/or hour of patrol between 2000 and 2010. The methodology used by Moreto and colleagues is different, and more precise, than most efficiency measures found in the ranger patrol literature. The chapter presents various arguments for the continued and improved use of ranger patrol data for understanding poaching in specific protected areas. Using the same dataset from Queen Elizabeth Protected Area, Lemieux et al. (Chapter 6) move a step further by building spatial models of patrol intensity and patrol efficiency. These models are used to examine the link between ranger efficiency and features of the physical environment associated with poaching. The underlying idea is that certain features create good opportunities for poaching and thus are likely to be exploited by poachers. Access points such as borders and roads, animal attractors such as water bodies and rivers, and villages near the protected areas which may be home to poachers are all considered by the

Introduction 15 models. In line with the triple foraging idea presented earlier in this Introduction, the spatial models of efficiency do not include unpatrolled areas as they may or may not have poaching, and thus, including these unobserved areas would bias the models. The results produce interesting information about where rangers are most likely to detect poaching activity with respect to their distance from roads, borders, rivers, water bodies and villages. Regarding SCP, the findings are useful for putting rangers in the right place and reducing search areas. The final study of poaching in Uganda by Hill et al. (Chapter 7) uses simulation modelling to create hypothetical foraging patterns of animals, poachers and rangers. This study is a prime example of how the triple foraging process can be operationalized for a specific poaching problem in a well-defined protected area. Hill and colleagues use Queen Elizabeth National Park as a backdrop for their agent-based model. They simulate the triple foraging process to determine hypothetical spatial distributions of snare placement by poachers, snare detection by rangers, and animals trapped by snares. Using a simple model, their results indicate simulations could be very useful for understanding when and where poaching is most likely to occur. As the authors note, much more information about the actual foraging of animals, poachers and rangers is needed to create and test agent-based models. However, if refined models could be designed and tested, agent-based modelling has great potential for limiting search areas, increasing poacher detection rates and identifying locations to target with SCP interventions. The closing chapters of the book discuss tiger poaching in India. The work by Kim et al. (Chapter 8) discusses how a failed ‘risky facilities’ analysis led the authors to a policy relevant approach aimed at national legislation concerning tiger conservation in India. Although the authors’ original intention was to determine how and why poaching varies between Indian tiger reserves, they were unable to find reliable poaching data. This led them instead to look at the variation in tiger densities across 28 of the country’s reserves. Kim et al. investigate the relationship between tiger densities and reserve features such as size, the extent of tourism, size of indigenous populations living inside the reserve, and the presence or absence of political unrest; these are a small sample of the variables used. Their findings give rise to numerous suggestions about tiger conservation practice in India at both the national and reserve level. Finally, the last chapter in this volume differs from the rest in that it does not investigate a poaching problem per se. Instead, the work by Clarke and colleagues (Chapter 9) suggests a unique way to promote tiger tourism in India that would potentially reduce poaching incentives. Because tigers are typically active around sunrise and sunset, viewing these animals in the wild can be very difficult, and many tourists visiting a reserve will never have a sighting. This makes attracting tourists difficult and limits the revenue available for anti-poaching programs in and around reserves. This chapter details how closed-circuit television (CCTV) cameras could be used to stream images of tigers in real time to lodges where tourists stay or on the Internet to attract tourists and donor money. Moreover, the authors suggest CCTV systems have the potential to help monitor tiger

16 AM Lemieux populations as well as direct tourist Jeeps to areas where tigers are likely to be seen. In short, the chapter presents an innovative approach for decreasing poaching incentives and increasing conservation revenue. In closing, even though the works of this volume examine poaching in a variety of contexts and against a number of species, there is still much more work to be done. Because poaching problems are very specific to the setting where they occur, it would take many more volumes like this one to determine which prevention avenues are likely to be successful. Future studies should not only consider plant and animal species not included here, but more importantly, should evaluate the effectiveness of poaching prevention measures.

References Bidermann, A. D. and Reiss, A. J. (1967). ‘On Exploring the “Dark Figure” of Crime’. The ANNALS of the American Academy of Political and Social Science. 374: 1–15. Blevins, K. R. and Edwards, T. D. (2009). ‘Wildlife Crime’. In J. Miller (Ed.), 21st Century Criminology: A Reference Handbook (pp. 557–564). Thousand Oaks, CA: Sage. Brantingham, P. J. and Brantingham, P. L. (1981). Environmental Criminology. Beverly Hills, CA: Sage. Clarke, R. V. (1995). ‘Situational Crime Prevention’. Crime and Justice: A Review of Research. 19: 91–150. Clarke, R. V. (1997). ‘Introduction’. In R. V. Clarke (Ed.), Situational Crime Prevention (pp. 1–45). Monsey, NY: Criminal Justice Press. Clarke, R. V. and Cornish, D. B. (1985). ‘Modeling Offender’s Decisions: A Framework for Policy and Research’. In M. Tonry and N. Morris (Eds.), Crime and Justice: An Annual Review of Research (Vol. 6, pp. 147–185). Chicago, IL: University of Chicago Press. Clarke, R. V. and Eck, J. (2005). Crime Analysis for Problem Solvers: In 60 Small Steps. Washington, DC: Office of Community Oriented Policing. Cohen, L. E. and Felson, M. (1979). ‘Social Change and Crime Rate Trends: A Routine Activity Approach’. American Sociological Review. 44 (August): 588–608. Cornish, D. B. and Clarke, R. V. (1986). ‘Introduction’. In D. B. Cornish and R. V. Clarke (Eds.), The Reasoning Criminal: Rational Choice Perspectives on Offending (pp. 1–16). New York, NY: Springer-Verlag. Cornish, D. and Clarke, R. V. (2003). ‘Opportunities, Precipitators and Criminal Decisions: A Reply to Wortley’s Critique of Situational Crime Prevention’. In M. Smith and D. Cornish (Eds.), Theory for Practice in Situational Crime Prevention, Crime Prevention Studies (Vol. 16, pp. 41–96). Monsey, NY: Criminal Justice Press. Dudley, N. (Ed.). (2008). Guidelines for Applying Protected Area Management Categories. Gland, Switzerland: IUCN. Duffy, R. (1999). ‘The Role and Limitations of State Coercion: Anti-Poaching Policies in Zimbabwe’. Journal of Contemporary African Studies. 17(1): 97–121. Eck, J. E. (1994). ‘Drug Markets and Drug Places: A Case-Control Study of the Spatial Structure of Illicit Drug Dealing’. Doctoral dissertation, University of Maryland, College Park. Eck, J. E. and Weisburd, D. L. (1995). ‘Crime Places in Crime Theory’. In J. E. Eck and D. L. Weisburd (Eds.), Crime and Place (pp. 1–33). Monsey, NY: Criminal Justice Press.

Introduction 17 Eliason, S. L. (1999). ‘The Illegal Taking of Wildlife: Toward a Theoretical Understanding of Poaching’. Human Dimensions of Wildlife: An International Journal. 4(2): 27–39. Eliason, S. L. (2004). ‘Accounts of Wildlife Law Violators: Motivations and Rationalizations’. Human Dimensions of Wildlife: An International Journal. 9(2): 119–131. Felson, M. (1986). ‘Routine Activities, Social Controls, Rational Decisions, and Criminal Outcomes’. In D. Cornish and R.V. Clarke (Eds.), The Reasoning Criminal (pp. 119–128). New York, NY: Springer-Verlag. Felson, M. (1987). ‘Routine Activities and Crime Prevention in the Developing Metropolis’. Criminology. 25(4): 911–932. Guerette, R. T. and Bowers, K. J. (2009). ‘Assessing the Extent of Crime Displacement and Diffusion of Benefits: A Review of Situational Crime Prevention Evaluations’. Criminology. 47(4): 1331–1368. Jeffery, C. R. (1971). Crime Prevention through Environmental Design. Beverly Hills, CA: Sage. Lemieux, A M and Clarke, R. V. (2009). ‘The International Ban on Ivory Sales and Its Effects on Elephant Poaching in Africa’. British Journal of Criminology. 49(4): 451–471. Musgrave, R. S., Parker, S. and Wolak, M. (1993). ‘The Status of Poaching in the United States – Are We Protecting Our Wildlife?’ Natural Resources Journal. 33: 977–1013. Muth, R. M. and Bowe Jr., J. F. (1998). ‘Illegal Harvest of Renewable Natural Resources in North America: Toward a Typology of the Motivations of Poaching’. Society and Natural Resources: An International Journal. 11(1): 9–24. Newman, O. (1972). Defensible Space: Crime Prevention Through Urban Design. New York, NY: Macmillan. Pires, S. F. and Clarke, R. V. (2011). ‘Sequential Foraging, Itinerant Fences and Parrot Poaching in Bolivia’. British Journal of Criminology. 51(2): 314–335. Pires, S. and Clarke, R. V. (2012). ‘Are Parrots CRAVED? An Analysis of Parrot Poaching in Mexico’. Journal of Research in Crime and Delinquency. 49(1): 122–146. Pires, S. and Moreto, W. D. (2011). ‘Preventing Wildlife Crimes: Solutions that Can Overcome the “Tragedy of the Commons”’. European Journal of Crime Policy and Research. 17: 101–123. Pyke, G. H., Pulliam, H. R. and Charnov, E. L. (1977). ‘Optimal Foraging: A Selective Review of Theory and Tests’. The Quarterly Review of Biology. 52(2): 137–154. Schoener, T. W. (1971). ‘Theory of Feeding Strategies’. Annual Review of Ecology and Systematics. 2: 369–404. Wilkinson, T. (1990). ‘Poachers: Driving Wild Things to Extinction’. Endangered Species Update. 7(5): 1–8.

2

Rhino poaching in Kruger National Park, South Africa Aligning analysis, technology and prevention Corné Eloff and AM Lemieux

Introduction For many years, the health and sustainability of South Africa’s rhinoceros population was clear. Thanks to successful breeding programs, regulated sport hunting and increased protection, the number of rhinos had continued to rise for more than a century. Both white and black rhinoceros populations, once near extinction, were viable and growing. From 20 white rhinos in 1895, to 550 in 1948, 1,800 in 1968, almost 8,000 by 1997 and 18,800 in 2010, the country’s conservation strategies were clearly working (Emslie and Brooks, 1999; Milliken and Shaw, 2012). The black rhino population also saw a marked increase from 110 in 1933 to nearly 2,000 by 2011 (Milliken and Shaw, 2012). These gains were applauded by conservationists and enjoyed by tourists from around the world. But in 2008, something happened that questioned the rhinos’ future: the number of rhinos poached every year started to increase rapidly and without warning. This chapter gives a detailed view of South Africa’s rhino poaching problem and suggests various avenues for prevention. We begin by describing where demand for rhino products comes from and why South Africa is targeted by poachers. We then use data from Kruger National Park to explore how crime analysis, mapping and other techniques are useful for studying poaching in a single protected area. By examining the who, what, where, when and how of rhino poaching in Kruger, we are able to derive numerous prevention techniques. The strategies presented would increase the risk poachers face during a hunt, increase the effort required to kill a rhino and reduce the rewards of poaching. We integrate the use of technology in our discussion of prevention to show how patrolling large areas of land can be made more efficient using advanced equipment and techniques. The question guiding this piece is simple: How can we decrease the number of rhinos poached in South Africa every year?

Rhino horn: who wants it and why? Like most poaching victims, rhinos are hunted because products derived from their body are valuable. However, unlike many animals, the diversity of rhino products consumed by wildlife markets around the world is very limited. For

Rhino poaching in Kruger National Park 19 example, whilst nearly every part of a tiger can be sold individually as a sort of medicine, charm or trophy (see Mailey in this volume), consumers of rhino products are only interested in one thing, rhino horn. And although the horn has a variety of uses, the fact remains that rhinos are poached for their horns alone, not their meat, skin, teeth, etc. Thus poachers targeting the animal are only interested in monetary rewards and do not see it as a source of food or other practical materials such as skins1. This profit-driven poaching is much different that hunger-driven poaching whereby poor rural residents hunt protected animals to stay alive, not to make thousands of dollars by selling their catch to someone else. The paragraphs that follow describe how rhino horn is used and where global demand is concentrated. Without question, the traditional Chinese medicine (TCM) markets of Asia, specifically those in China and Vietnam, create more demand for rhino horn than any other collective group in the world (Beech and Perry, 2011; TRAFFIC, 1997; Wilson-Wilde, 2010). For centuries, rhino horn has been used to treat various ailments including severe headaches, delirium, high fever, measles, epilepsy and strokes; a review of major Vietnamese pharmacopeia books between 2002 and 2007 found five still included sections on rhino horn (Milliken and Shaw, 2012). Most recently, claims that rhino horn cures cancer have created even more demand for this ‘healing’ product (Moore, 2011; Wilson-Wilde, 2010)2. In general, users buy a small piece of rhino horn at a local market or pharmacy that can be ground into a power using a specially designed bowl and mortar. The typical dose is one gram of rhino horn powder mixed with liquid which is consumed orally (Mander, 2012). The duration of treatment is unclear and will likely depend on changes in the user’s health, advice from medical practitioners and the financial ability of an individual to continue treatment. When buying rhino horn for medicinal purposes, an important consideration is the horn’s potency. High potency, or ‘fire’ horn, comes from the Asian rhinoceros, while ‘water’ horn is taken off an African rhino (Leader-Williams, 1992). Fire horn is thought by most to be more powerful and effective making it much more expensive (Emslie and Brooks, 1999). No matter the species, all rhino horn is composed of α-keratin, a structural protein found in the hair, nails horns and hooves of mammals. The empirical evidence that rhino horn is a viable cure for many ailments is limited and unconvincing (for a detailed and recent review, see Nowell, 2012). For example, one study, which showed the horn was able to lower fever in rats, also showed similar reductions when the horns of Saiga antelope and water buffalo were used. Moreover, the rats were given doses much higher than typically used by humans, in milligrams per kilogram of body weight, and the horn powder was injected, not swallowed (described by Moore, 2011). Despite the lack of convincing medical research proving the value of rhino horn’s curing properties, there remains an entrenched belief in this traditional source of medicine. Besides medicine, in Asia, rhino horn is also seen as a valuable luxury item that can be used to flaunt one’s wealth or be given as a decadent gift. Some individuals

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simply put the horn on display in their home with no intention of using it for medicinal purposes (Mander, 2012). Others attend parties where elites drink wine with rhino horn powder, thinking it will prevent hangovers; others mix it with water to cure hangovers (Milliken and Shaw, 2012). This extravagant sort of demand for rhino horn is limited compared to TCM, but worrisome no less. A subculture that not only uses rhino horn, but uses it wastefully and flamboyantly, has created nontraditional consumers willing to pay top dollar for horn and made poaching more profitable. While today’s demand for rhino horn primarily emanates from Asia, the Middle East at one time was also a major source of demand. In Yemen, daggers, known locally as jambiyas, are worn by men older than 14. The handle of these jambiyas can be made of many materials but rhino horn is the most exquisite. For many years, demand for horn from Yemen was a considerable threat to rhinos (TRAFFIC, 1997) though this appears to have dried up in recent years (Mouton and de Villiers, 2012). The reason for this is unclear, but rapidly escalating prices may be a factor.

Rhino horn: what’s it worth? The global trade in African rhino horn has been documented for more than a century and is now regulated by the Convention on International Trade of Endangered Species of Fauna and Flora (CITES). In 1977, the African rhino, black and white, was placed on Appendix I of the CITES register, thereby prohibiting international trade in rhinos and rhino parts with rare exception such as for scientific purposes. However, in 1994, South Africa’s white rhinos were relisted on Appendix II thanks to massive population growth and successful conservation strategies. This enabled the country to sell live specimens to other countries for breeding programs and to sell sport hunting permits to garner revenue for conservation. The hunting permits allowed an individual to kill a rhino and export any trophies, such as horns and skins, out of South Africa. Besides these limited exceptions, the international trade ban was still in effect for all rhino products coming out of South Africa. This section is an overview of how rhino horn prices have changed over time as a result of these bans and how valuable the horn is to different actors in the supply line. In the late 1800s, rhino horn coming from Africa was selling for approximately 1 USD or less per kilogram, and by 1930, the price had risen to approximately 20 USD (Leader-Williams, 1992). In the 1950s, markets in Asia were selling a kilogram of horn for approximately 20–30 USD, which rose to 30–40 USD in the 1960s and nearly 100 USD by the end of the 1970s (Leader-Williams, 1992). Prices went up dramatically in the 1980s when a kilogram of horn was selling for approximately 300–500 USD (Leader-Williams, 1992). This dramatic rise in price coincides with the 1977 CITES trade ban and was seen not only in Asia, but also in Yemen, where rhino horn was selling for more than 1,000 USD per kilogram in the late 1980s (Leader-Williams, 1992). By the 1990s, wholesalers in Taiwan were paying approximately 800 USD per kilogram and final consumers nearly

Rhino poaching in Kruger National Park 21 1,600–3,000 USD (Milliken et al., 1993). Sadly, these increasing and inflated prices of the late twentieth century are not even 10 percent of today’s market value. Recent surveys of market prices in Asia indicated the horn typically sells for 35,000–60,000 USD per kilogram (Eustace, 2011; Mander, 2012; Moore, 2011); this is roughly the same value as a kilogram of gold at today’s prices. Because rhino horn is so valuable, the economic incentives to become involved with the trade are undeniable. If one considers the fact that a single rhino horn can weigh 6–7 kilograms, this means one horn could be worth nearly 200,000 USD on the black market. However, there is good evidence that those selling the horn as TCM are making far more profit than the person responsible for procuring the horn in Africa. Milliken et al. (1993) reported a poaching gang would typically receive 100–400 USD per horn, importers in Taiwan would pay 800 USD per horn, and final consumers would pay at least 1,600 USD per kilogram. Hypothetically, this means if poachers sold a 6-kilogram horn to their middleman, they would be paid less than 5 percent of the horn’s potential value. More recent data on rhino horn’s value throughout the trade continuum is not as specific; however, it appears approximately 50 percent of the horn’s value goes to the final seller, 45 percent to the middlemen in Africa and Vietnam who connect poachers to sellers, and just 5 percent will go to the poacher (Milliken and Shaw, 2012). Even though the profit sharing is highly skewed against poachers, at today’s prices, this may still equate to nearly 10,000 USD for a 6-kilogram horn; almost a full year’s income for an average South African and nearly 10 years’ worth of income for a hunter coming from neighbouring Mozambique (The World Factbook, 2013). Thus the extreme value associated with rhino horn on today’s global market makes becoming involved with the trade, as a poacher, seller or middleman, very attractive to those with access to rhinos, trade routes and final consumers.

Horn supply: pseudo-hunting, pseudo-conservation and theft To meet the global demand for rhino horn, those involved with the trade have found numerous ways to supply markets. In South Africa, there are at least four ways rhino horn is obtained for the illegal trade: pseudo-conservation, pseudohunting, theft and poaching. While the focus of this chapter is poaching, understanding the other methods is also important as they speak to the breadth of crimes against rhinos and the countless criminal opportunities available to become involved with the trade. This section describes sources of rhino horn supply that do not involve poaching in a protected area. While many of the crimes described below occur inside protected areas, they are typically not recorded as poaching because they involve collusion with enforcement agencies. Thus the very people responsible for ‘protecting’ rhinos are sometimes exploiting their professional networks and unrestricted access to the animals to earn large sums of money. The supply sources described here clearly show how extremely high market values can entice a variety of actors to obtain rhino horn illegally.

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Perhaps the most disturbing way rhino horn is obtained without poaching the animals is through ‘pseudo-conservation networks’ (Ayling, 2012). These networks consist of conservation professionals who work cooperatively to skirt laws and secretly procure horn destined for Asian markets. The Groenewald Gang is a perfect example of pseudo-conservation. As owner of his own reserve, Groenewald conspired with veterinarians, safari operators, professional hunters and others to buy rhinos and dehorn them covertly (Ayling, 2012; Larson, 2010b). After purchasing rhinos from national parks in South Africa and removing their horns, Groenewald decided to kill and bury the animals to reduce upkeep costs and increase his profit margins (Dean, 2011). The gang was ultimately discovered and charged for their crimes, but at the time of this writing, no verdict or sentence has been handed down. A different sort of abuse committed by reserve owners, both public and private, is the mismanagement of rhino horn stockpiles. Over the years, most reserves with rhinos will begin to accumulate horn stockpiles because of natural causes, management actions, trophy hunting, pre-CITES ban collection and confiscations (Milledge, 2005). The size, weight and origin of these horns are supposed to be documented carefully, and trade in these horns is strictly forbidden. Thus a reserve owner or manager should do nothing with the horns other than store or display them. As the price per kilogram of horn continually rises, so too do incentives to smuggle it out of the country under false pretences. In some cases, these ‘loose’ horns are sold to a variety of middlemen intent on getting it to Asia (Milliken and Shaw, 2012). While this sort of pseudo-conservation is less egregious than the Groenewald Gang’s actions, it no less shows how those charged with protecting the rhino can perpetuate the demand for horn. Another group of individuals capable of obtaining rhino horn are those involved with the practice of pseudo-hunting (Milliken and Shaw, 2012). Remember, the South African white rhino is listed on Appendix II of the CITES register and therefore can be hunted and trophies exported if the proper permits are obtained. This has led to a relatively new phenomenon known as pseudo-hunting, whereby Asians with no hunting experience will apply for the right to hunt a rhino and export its horns to their home country. In fact, between July 2009 and May 2012, 48 percent of foreign hunters with permits in South Africa were Vietnamese who spent more than 22 million USD to hunt rhinos (Milliken and Shaw, 2012). Many of the hunters were so inexperienced that they had to be taught how to shoot a gun or had their guide shoot the animal for them, which is illegal (Beech and Perry, 2011). Even though the permit to hunt a white rhino cost nearly 60,000 USD in 2010, the astronomical market value of horn made pseudo-hunting a wise investment, whereby hunters would easily recoup all costs of the trip. Moreover, there is evidence to suggest import permits used to bring the hunted horn into Vietnam were recycled until their expiration date, which enabled additional undocumented horns to enter the country illegally (Milliken et al., 2009). In April of 2012, South Africa refused to issue any more hunting permits to Vietnamese applicants believing the extent of pseudo-hunting by this group was too large.

Rhino poaching in Kruger National Park 23 The final method of rhino horn procurement discussed in this section is much less complex than pseudo-hunting and pseudo-conservation: it’s good old fashioned theft. Indeed, the continued rise in demand for horn coupled with increased market value has triggered a wave of robberies and thefts in South Africa and other countries around the world. The targets are usually museums, game reserves, safari lodges and taxidermies that have horns on display or in stockpiles. Between November 2002 and October 2010, at least 65 horns were reported stolen from these facilities in South Africa alone; the largest hauls came from a game reserve, where thieves took 16 horns, and a taxidermist, where 18 were taken during a daylight robbery (Milliken and Shaw, 2012). While this approach requires much less sophistication and planning than the organised operations discussed above, it again shows how the tremendous value of rhino horn entices criminals, even those without access to live rhinos, to become involved with the lucrative trade. The section that follows brings us to the central problem this chapter addresses, poaching of rhinos in South Africa.

Horn supply: poaching At this point in the chapter, it is time to move away from background information concerning the rhino horn trade and begin to describe an evolving and increasingly dangerous threat to South Africa’s rhino population: poaching. Poaching is considered separately from other sources of rhino horn supply because the criminal opportunity structure for this crime is vastly different than pseudoconservation or pseudo-hunting. Both of these ‘pseudo’ supply lines favour criminals with extensive and influential professional networks and unrestricted access to rhinos. Successful poaching on the other hand favours criminals with knowledge of animal and ranger foraging patterns, access to weapons, and hunting experience. Moreover, the risk poachers face while obtaining horn is much different than the other groups who conceal their actions from enforcement agencies through collusion and legal loopholes. In essence, poachers can be thought of as common thieves, while pseudo-conservationists and pseudo-hunters are more like white collar criminals. This section explores the modus operandi of poachers and describes recent trends in poaching. A decade ago, rhino poaching in South Africa was not a major concern. Between 2000 and 2007, the country was losing an average of 15 rhinos per year to poaching (see Figure 2.1), which represented a very small percentage of a growing population with more than 10,000 animals3. In fact, the number of white rhinos hunted legally every year was much larger than the number poached. For example in 2007, 124 white rhinos were shot legally compared to 13 poached animals (Milliken and Shaw, 2012). Interviews with the South African Police Service’s Endangered Species Protection Unit at the time indicated that ‘although elephant and rhinoceros are poached in South Africa and Namibia, the present threat to these domestic species is not very serious’ (Warchol et al., 2003, p. 12). However, in 2008, this sense of security was snatched away when the country lost more rhinos in one year than it had in the previous five combined.

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Figure 2.1 Number of rhinos poached in South Africa, 2000–2012 Source: www.stoprhinopoaching.com

The wave of rhino poaching that began in 2008 only worsened after the initial onslaught. Official statistics from South Africa show 83 animals were poached in 2008, 122 in 2009, 333 in 2010, 448 in 2011 and 668 in 2012 (see Figure 2.1). The pace at which the problem grew, and continues to grow, has forced conservationists to seriously reconsider the severity of South Africa’s poaching threat. One team of researchers has even argued that current levels of poaching in Kruger National Park have the potential to cause population declines by 2016 (Ferreira and Okita-Ouma, 2012). As for South Africa as a whole, the current increase in the number of rhinos poached every year, approximately 17 percent, indicates the country wide population will begin to see declines in 2020 (Knight, 2012). In short, the market demand for rhino horn has startled conservationists with the speed at which it has reversed the seemingly stable trajectory of South Africa’s rhino population. The modus operandi of poachers in South Africa is varied and evolving. Different methods and organizational structures used by poaching operations to target rhinos can be grouped in four categories: subsistence, commercial, skilled and chemical (Jackson, 2012; www.stoprhinopoaching.com). Subsistence poaching is uncommon and typically involves poor rural residents living near a protected area. These hunters use wire snares to trap rhinos and subsequently kill them with a spear or panga (aka machete). Before 2008, when the number of rhinos killed each year was low and stable, subsistence poaching was thought to account for these loses. Using more sophisticated methods, commercial, skilled and chemical poachers have driven the massive increase in rhino killings since 2008. Commercial poachers are thought to be responsible for most of the rhino poaching in South Africa. This type of poacher typically works in groups of

Rhino poaching in Kruger National Park 25 four to six individuals, some with a military background, to track rhinos and kill them using firearms. They glean information from communities living near protected areas about rhino movements and monitor law enforcement activities in the area. When attacking, they prefer the late afternoon and night, especially around a full moon. Multiple shots to the head and chest are used to bring down and/or kill a rhino before the horn is removed using an axe or panga. In some cases, poachers will hamstrung a dying rhino, to immobilize the animal without shooting another bullet, which would attract more attention from rangers; this involves severing the rhino’s hamstrings so the legs will not move. After removing the horn, commercial poachers typically rendezvous at a planned extraction point and flee the area. These poachers have well-established networks for selling the horn and will have no difficulty selling it quickly (Jackson, 2012; www. stoprhinopoaching.com). Skilled and chemical poachers are the evolving face of rhino poaching in South Africa as these require much higher levels of sophistication and organization than the other two poaching groups. Rhino carcasses found with single, well-placed bullet wounds from high-calibre rifles, indicate a trained marksmen has shot the animal. These skilled poachers use vehicles to move around the protected area and some are highly proficient with horn removal. These poachers minimize their risk of detection by only firing one shot unlike commercial poachers who may fire dozens of shots over an extended period. Even more sophisticated than skilled poachers are chemical poachers who use tranquilizer darts to immobilize a rhino and remove its horn4. In most cases, the poachers will shoot the rhino with a dart from a helicopter, land next to the animal once it falls down, and remove the horn quickly. Chemical poachers minimize their risk of detection by spending very little time inside the protected area, escaping by air, and using a nearly silent dart gun. While this type of poaching has been documented, it is very rare compared to commercial poaching (Jackson, 2012; www.stoprhinopoaching.com). In summary, this section has described how rhino poaching in South Africa has changed over the years from low levels of hunger-driven poaching by rural residents to high levels of profit-driven poaching by various types of criminals. While pre-2008 levels of poaching seemed manageable, the number of rhinos poached per year since 2008 is beginning to question the sustainability of rhino population growth in South Africa. In the sections that follow, data from Kruger National Park in South Africa are analysed to determine if there are patterns in when and where rhino poachers strike, who they are, and what weapons they prefer.

The current study Mitigating threats to South Africa’s rhino population is difficult because it is targeted by a variety of criminal actors with different modus operandi. To prevent the illegal procurement of horn, prevention strategies should be tailored to the criminal opportunity structure enabling the crime, as well as offender motivation. This study concerns one group of criminal actors, poachers, operating within a

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single protected area, Kruger National Park (KNP). Using a well-defined area and crime type enables a more detailed discussion of the who, what, when, where and why of rhino poaching and provides essential information for the design of prevention strategies. The objectives of the study are threefold: (1) identify spatial and temporal patterns in KNP rhino kills, (2) describe the demographics and modus operandi of rhino poachers in KNP and (3) propose innovative ways to prevent rhino poaching in KNP based on the results of this analysis. The study is an example of how data analysis can be used to study a specific poaching problem and guide prevention strategies. As noted earlier, the question guiding this chapter is simple: How can we decrease the number of rhinos poached in South Africa every year? Although the focus of this study is KNP, the methodology and prevention strategies discussed should be considered as options for all protected areas with rhinos.

Data and methodology Kruger National Park (KNP) is one of the most well-known wildlife reserves in Africa. Established in 1898, the park has flourished over the years as a tourist destination and wildlife haven. Its massive size, nearly 20,000 km2, encloses numerous ecosystems harbouring hundreds of different plants and animals. The white rhino population in KNP is impressive and accounts for approximately 75 percent of all white rhinos in South Africa (Milliken and Shaw, 2012). Unfortunately, this high concentration of rhinos in one protected area also means KNP is an attractive target for poachers. Between 2000 and 2012, 58 percent of all rhinos poached in South Africa were killed inside KNP (www.stoprhinopoaching.com). In this study, rhino poaching in KNP is examined using South African Police Service (SAPS) data from January–May 2011. During the study period, 78 incident reports were filed concerning rhino poaching activity inside the park. Each report indicates the time, date, location and moon phase of the incident and, where appropriate, the method of kill (some missing/unknown). The 78 incidents reported during this time period resulted in 72 rhino deaths; some poachers were arrested before they could kill an animal. When poachers were arrested, demographic information was collected concerning their age, sex and nationality. According to the data, poachers were able to kill a rhino and exit the protected area without arrest in 79.5 percent of the incidents. In most cases, poachers only killed one rhino during their hunt (92 percent). The 78 incidents can be broken down as follows: • • • • •

no rhinos poached one rhino poached two rhinos poached three rhinos poached four rhinos poached

(n (n (n (n (n

= = = = =

16) 57) 1) 3) 1)

Using the data described above, a spatial and temporal analysis was used to look for patterns in when and where rhino poaching activity in KNP occurs.

Rhino poaching in Kruger National Park 27 Location quotients are used to compare the poaching activity of different areas of the park. The formula for this calculation is LQ = [(# of killings per area / Total # of killings within KNP) / (km2 of area / km2 of KNP)] Location quotients were calculated to show the overall distribution of rhino poaching from January through May 2011. Another portion of the spatial analysis explores the distance between rhino poaching incidents and KNP’s borders, roads and water pans. These features of the environment are important elements of the criminal opportunity structure for rhino poaching inside KNP. They act as access points (borders/roads) and target attractors (water) both of which should help poachers enter the park, find prey and escape more quickly. The temporal analysis used was basic and describes how poaching incidents were distributed across the months and in relation to phases of the moon. The results of this work are presented below and inform the crime prevention strategies discussed later in the chapter.

Results Temporal distributions We begin our discussion with a description of the temporal distribution of rhino poaching in Kruger National Park (KNP) between January and May 2011. During this time period, a total of 78 rhino poaching incidents were recorded by the South African Police Service (SAPS) resulting in the death of 72 rhinos. No clear pattern was observed in the number of rhinos poached per month with the exception of a small spike in April (Figure 2.2); on average, 14 animals were

Figure 2.2 Number of rhinos poached in Kruger National Park by month, January–May 2011 Source: South African Police Service (SAPS)

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Figure 2.3 Number of rhino poaching incidents in Kruger National Park by moon phase, January–May 2011 Source: South African Police Service (SAPS)

lost per month (SD = 5.5, CV = 0.4). When the phase of the moon is considered, it appears poachers prefer to hunt during a gibbous moon, which is likely to provide them with more light (Figure 2.3). The limited amount of data available for this study, five months, makes it difficult to ascertain how these patterns relate to long term temporal distributions of rhino poaching in KNP. The need for analyses using longer cross-sectional datasets is discussed later in the chapter. Location quotients Location quotients were calculated to explore the spatial distribution of rhino killings across KNP including the Sabie Sabie private reserve (see ‘Data and methodology’). These were grouped to show which areas had higher levels of poaching than others. The analysis examines the 72 rhino killings recorded from January to May 2011. The results of these calculations are mapped in Figure 2.4 which categorizes individual areas as having no, low, moderate or high levels of poaching. The maps clearly show rhino killings are predominately occurring in the southern and central sectors of KNP.

Rhino poaching in Kruger National Park 29

Figure 2.4 Spatial distribution of rhino poaching in Kruger National Park, January– May 2011

Near distance analysis Delving deeper into the spatial components of rhino poaching, this portion of the analysis considers the distance between poaching and environmental features of KNP that create opportunities for a successful hunt. The features considered are roads, water sources and the park border; the border and roads act as access points to rhinos, while sources of water are thought to attract and concentrate the animals. To be clear, all water features within KNP – which includes artificial drinking areas, boreholes, etc. – were not considered in this analysis which only examined the link between rhino poaching and natural water pans. The distance from all

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poaching incidents (n = 78) to these features was calculated using a straight line measure between the two. The results are presented graphically in Figures 2.5–2.7 which show the percentage of incidents occurring within a specific distance from a road (Figure 2.5), water pan (Figure 2.6) or park border (Figure 2.7).

Figure 2.5 Distance between roads and poaching incidents in Kruger National Park, January–May 2011

Figure 2.6 Distance between water pans and poaching incidents in Kruger National Park, January–May 2011

Rhino poaching in Kruger National Park 31

Figure 2.7 Distance between park borders and poaching incidents in Kruger National Park, January–May 2011

Looking to Figure 2.5, it appears that rhino poachers in KNP are typically operating near roads within the park. The analysis shows 58 percent of the incidents recorded were within 1 km of a road and 90 percent were noted within 2.5 km of a road. The same was not true for water pans where just 7 percent of the poaching incidents were found within 2.5 km of these features (Figure 2.6). When the park’s border is considered, an interesting pattern is observed where areas 2–3 km inside the park accounted for nearly twice as much poaching as the 1 km intervals closer and farther from the border (Figure 2.7). In fact, the proportion of incidents found within 2–3 km of the border (16 percent) is equivalent to the proportion of incidents found within 2 km of the border (16 percent) or 3–5 km (16 percent), indicating poaching is overrepresented in the middle interval. When a maximum distance of 5 km from the border is used, almost 50 percent of the incidents fall within this category indicating the other 50 percent happens deeper inside the park. Incidents occurring within 5 km of the park border are concentrated along the eastern border of KNP, which also acts as South Africa’s international border with Mozambique (Figure 2.8). Poacher demographics and weapon choice During the study period, a total of 55 offenders were arrested by the South African Police Service (SAPS) inside Kruger National Park (KNP). All persons arrested for rhino poaching were male, and 96 percent were black. Those arrested were citizens from both South Africa (60 percent) and Mozambique (40 percent). Age is the only other demographic variable available in the incident reports and was only recorded for 22 of the suspects. In many cases, this was the result of arresting poachers, especially those from Mozambique, who had no identification documents or an unknown date of birth. Of the 22 suspects where age was recorded, 41 percent were

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Figure 2.8 Location of rhino poaching incidents in Kruger National Park by distance from park border, January–May 2011

20 to 29, 41 percent were 30 to 39 and the remaining 18 percent were older than 40. Thus it appears age is not a defining characteristic of poachers whereby only young or only old individuals engage in the behaviour. In fact, it is likely that older poachers may have more experience and knowledge than younger poachers making them more successful or better leaders of a poaching gang.

Rhino poaching in Kruger National Park 33

Figure 2.9 Weapons confiscated from rhino poachers in Kruger National Park, January– May 2011 Source: South African Police Service (SAPS)

Besides demographic information, many of the rhino poaching incident reports also included information about weapon confiscations. During the study period, a total of 51 weapons were recovered by SAPS in KNP. Figure 2.9 shows the number of weapons confiscated by type or calibre. The most common weapon used by poachers was a 0.375 calibre rifle, which accounted for 39 percent of all confiscations; 0.458 calibre rifles were the second most common accounting for 18 percent of seizures. These large calibre weapons are suited for penetrating the thick skin and bones of rhino and are regularly used by big game hunters in Africa. Surprisingly, the number of AK-47s confiscated was rather low considering the low cost and wide availability of these weapons in the region. This suggests hunters may prefer large calibre weapons because they kill the animal more efficiently, requiring fewer shots than an AK-47 and thus attracting less attention from those who would report the crime.

Summary of results and study limitations This chapter was an analysis of poaching activity recorded in Kruger National Park (KNP) from January through May of 2011. The results indicate poachers in this protected area are concentrating their operations in the southern and central regions especially along KNP’s eastern border with Mozambique. The near-distance analysis showed poaching incidents were found closer to roads and borders than natural water pans, indicating access points (border/roads) may be more important to patrol than rhino attractors (water pans). The temporal analysis showed poachers were more active during certain phases of the moon than other; the most incidents were recorded during the gibbous moon phases which precede and follow a full moon. The limited information available on offenders indicates they are exclusively male and almost exclusively black. The nationalities of those involved were split between South Africa (60 percent) and

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Mozambique (40 percent). Records of weapon confiscations showed two large calibre rifles, the 0.458 and 0.375, accounted for 57 percent of all seizures; the 0.375 accounted for nearly 40 percent by itself. In the sections that follow, these findings are used to rationalize different poaching prevention strategies for reducing the number of rhinos killed by poachers in South Africa every year. Before suggesting prevention techniques, it is important to qualify our results by noting the limitations of our data and analysis. First, five months of crosssectional data from KNP is simply not enough to make strong conclusions about spatial concentrations, temporal patterns, long-term trends and non-spurious relationships. Indeed poaching data from KNP that covered a longer period of time, especially the pre- and post-surge in rhino poaching which began in 2008, would provide much more power to the approaches used here and even enable more complex, inferential statistics to be used. Second, because this study uses official law enforcement data, it is subject to the selectivity problem described by Lemieux in the introductory chapter of this volume (see section on triple foraging). In short, observations of poaching activity or poached carcasses are dependent upon ranger movements, and therefore, it is possible rangers missed rhino carcasses because patrol coverage was incomplete. That said, because rhinos are large land mammals, and poachers leave the carcass behind, this form of poaching is much easier to observe, both on the ground and from the air. Thus we believe the selectivity issue for this type of poaching is less worrisome than for other forms, such as instances where the whole animal is removed. If the data used here were combined with patrol coverage information, testing more refined, spatially explicit models of rhino poaching would be possible (see Lemieux et al. in this volume). Finally, it is worth noting that any temporal analysis of poaching performed with official data must recognize the time lag between a poaching event and when it is observed by law enforcement. For example, rangers responding to a report of shots fired may find a carcass within hours, but it could also take days. Thus one must interpret temporal patterns very carefully to avoid making false conclusions. For example, in this study, very few of the poaching incidents were recorded during a full moon where it is assumed poachers would benefit from the natural light. Instead, many reports were dated in the moon phases before and after the full moon. One explanation may be that poachers hunt during the full moon and carcasses are found a day or two later during a different moon phase. Ideally, official poaching data should include when the carcass was found and an estimated time of death to better temporal analyses. Without such data, we are only able to note this limitation of the dataset used here. The descriptive analyses and techniques in this chapter are helpful for understanding the basics of rhino poaching in KNP, which helps guide suggestions for prevention strategies. The discussion of prevention found below was framed using the 25 techniques of situational crime prevention (Cornish and Clarke, 2003). The techniques are divided into five categories which describe the general goal of a crime prevention strategy: increase risk, increase effort, reduce rewards, reduce provocations and/or remove excuses. Because the results of our analysis say little about the provocations that incite rhino poaching or excuses criminals give for

Rhino poaching in Kruger National Park 35 poaching, these categories of prevention are neglected here. In future studies with better data about offender motivation or rationalizations, it may be possible to have an informed discussion about the utility of these techniques for poaching prevention. As noted earlier, the prevention strategies outlined below are tailored to KNP but may be useful for other protected areas with rhinos and similar poaching problems.

Poaching prevention: increase the risks poachers face Assuming rhino poachers are rational actors who weigh the costs and benefits of their actions, one avenue for prevention is to increase the risks poachers face during a hunt. While it is impossible to alter some of the risks inevitable with any hunt, such as animal attacks or hunting accidents, a major risk, the risk of apprehension, can be changed. Increasing the likelihood that rangers will meet poachers in time and space is a crucial element of increasing the risk of apprehension. As Lemieux notes (Introduction of this volume), it is only when the foraging of poachers and rangers overlap that poaching activity is detected. In the paragraphs that follow, examples of analysis techniques and technology capable of putting rangers in the right place at the right time are discussed5. At the most basic level, the temporal analysis presented in this chapter would be of more use to law enforcement commanders if it were repeated using a much larger dataset. For example, because of data limitations, it was not possible to determine here if monthly levels of rhino poaching in Kruger National Park (KNP) are stable or changing throughout the year. However, national data from South Africa indicate the number of rhinos poached per day is highest during the final quarter of the year (Emslie and Knight, 2012; Knight, 2011), which is outside the study period of data used here. This suggests ranger deployments and patrols should be increased during those times when poachers are more active. Additionally, the relationship between phases of the moon and rhino poaching should also be explored in greater detail as this further narrows the timeframes when ranger deployments should be intensified. The current analysis indicated poachers prefer operating during a gibbous moon that gives them more light than any other phase except the full moon. While few incidents in our dataset occurred during a full moon, South African data from 2011 showed a spike in killings associated with the last full moon in December of that year (Knight, 2012). In short, it does appear there are some commonalities in the temporal aspects of rhino poaching that should be considered during discussions of ranger deployment and further investigated using longitudinal datasets. Regarding the spatial aspects of rhino poaching in KNP, it does appear that analyses such as the ones presented here would help commanders limit their search area for poachers. Even with only five months of data, clear patterns in the spatial distribution of rhino poaching were evident. If a larger dataset were used, it would be easier to determine if these concentrations are static or changing and thus easier to determine where rangers should be sent. The data at hand suggest the park’s south-eastern border with Mozambique is a major concern that needs to be monitored intensely. Indeed, 40 percent of the poachers arrested inside

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KNP during the study period were from Mozambique indicating many poachers are crossing the border to hunt in South Africa. There is speculation cross border poaching is due to an expanding Chinese workforce in that region of Mozambique which has created new demand for rhino and elephant products (Larson, 2010a)6. Because KNP’s border with Mozambique is very large, approximately 180 km, knowing where poachers enter the park would greatly enhance the ability of rangers to detect trespassers, whether they are rhino poachers or not. Using foot patrols to find these crossings would require a great deal of manpower because of the vast area. However, there is technology available that commanders could use to identify illegal crossings and other hunting trails without a single patrol; it is commonly referred to as remote sensing. Put simply, remote sensing is the ability to collect information about an object without coming into physical contact with it. For this discussion, remote sensing is seen as an innovative way to collect information about the presence, emergence and disuse of hunting trails and border crossings in KNP. Using high resolution satellite imagery and a computer program such as eCognition, it is possible to create rules for the software which enable it to detect specific features in the imagery. For example, if commanders are interested in identifying sand trails in the park created by poachers or animals, they would train the software to identify areas that are sand coloured and ignore all other areas such as green vegetation. After scanning the imagery, the software would create a spatially referenced file that showed the location of sand trails in the park. While the authors were able to do this for the south-eastern section of KNP, the results are not displayed in this chapter because they require a high resolution image, preferably in colour, to truly show the utility of this tool. Instead, a simple diagram is used in Figure 2.10 to show how this process would play out with hypothetical data. When actual images

Figure 2.10 Hypothetical application of eCognition software for identifying and mapping sand trails in protected areas

Rhino poaching in Kruger National Park 37 of KNP’s border with Mozambique taken between 2006 and 2010 were analysed with the software, it was very easy to note the emergence of at least one illegal border crossing that could be targeted by ranger patrols. Although remote sensing requires access to certain types of imagery and software, it has great potential to change the amount of information commanders have access to concerning dynamic, and unmapped, features of the environment like sand trails. While directing patrols into areas preferred by poachers is one way to increase their apprehension, decreasing response times to reports of poaching is another. For example, if rangers knew the exact location of gunshots within KNP, this would greatly reduce their response time. A series of gunshot detectors, placed in well-known poaching areas, is an example of a system with the potential to give commanders such information. Typically used in urban areas or during military combat, the systems use a network of microphones to triangulate the location of gunshots. Their potential for conservation applications is great especially when one considers the lack of background noise in protected areas, which can limit the detector’s range. Ultimately, the cost of deploying such systems and the manpower needed to operate and repair them may require collaboration and funding from multiple agencies to ensure sustainability. In any case, gunshot detectors offer an innovative approach for reducing response times and increasing the chance of poacher apprehension in KNP. Finally, the use of unmanned aerial systems (UASs) for conservation purposes has been growing rapidly in recent years. The term UAS includes all systems associated with real-time remote sensing such as ground stations, sensors and data links, not only the drones or unmanned aerial vehicles (UAVs) transporting the equipment. A tethered balloon using a hard line to transmit information from sensors to a ground station where it can be analysed is one example of a UAS. An unmanned aircraft that transmits sensor feed over a wireless network to a ground station is another. Both systems could be invaluable to law enforcement operations for reconnaissance, real time monitoring of large areas, and during the deployment of rapid response teams. Along borders, balloons at a significant height would enable extended monitoring using thermal imaging devices, video cameras and other equipment for monitoring the movement of rhinos and poachers. Rapid response teams might use UAVs to scout an area before ambushing poachers. As these systems are integrated into the protection of wildlife, empirical research should monitor their effectiveness and sustainability to avoid wasteful spending. If used properly, UASs, like the other technologies discussed here, have the potential to greatly increase the risk of apprehension poachers face while hunting in KNP.

Prevention strategies: increase effort needed to kill a rhino It is possible to reduce criminal opportunities for rhino poaching by increasing the effort required to access and kill a rhino. In this section, three different prevention strategies are discussed, which can be broadly described as controlling access to facilities and screening exits. Unlike prevention strategies discussed in the previous section, which focused on offender apprehension, this section suggests

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ways to discourage offenders from targeting Kruger National Park (KNP) and the rhinos within because it requires more effort than they are willing to expend. While the modus operandi of rhino poachers in South Africa varies from traditional snaring methods to darting rhinos from helicopters, accessing protected areas is a necessary element of any successful hunt. To control or limit access to KNP, it will be necessary to target three types of trespassers: those on foot, those in vehicles and those in the air. Because KNP was fenced in the 1970s to control wildlife diseases, prevent human-animal conflict and protect animals from the civil unrest in Mozambique (Venter, 2007) this barrier is an effective blockade for those on foot and in vehicles. However, patrols must check the fence regularly for damage and maintenance crews should be dispatched quickly to repair any faults (Dean, 2010). To dissuade poachers using vehicles from targeting KNP, records of number plates and visitor names should be maintained electronically and analysed for suspicious activity. Since there is evidence poachers will pose as tourists to scout reserves and even poach rhinos, identifying those involved would enable undercover cars to shadow the vehicles (Bewick, 2012). For example, on days that rhinos were poached, are there certain individuals or cars always present in KNP? Moreover, the use of sniffer dogs and vehicle searches at park exits also increase the effort of getting rhino horn out of the park. Finally, because aircraft are rarely used for poaching, the cost of controlling or monitoring their access to KNP using radar systems may be better spent on controlling access of vehicles and individuals on foot as these poachers seem to account for the majority of poaching.

Prevention strategies: reduce the rewards poachers receive To wrap up the discussion of prevention strategies Kruger National Park (KNP) might use to slow or stop rhino poaching, this section focuses on ways to reduce rewards paid to poachers. Again drawing from the 25 techniques of situational crime prevention, the strategies discussed are aimed at removing the targets, denying benefits and identifying property7. The logic is simple, if poachers do not have a criminal target to attack, or will be paid little for any horn they procure, this alters the risk-reward calculation they make when deciding to poach or not. The strategies outlined in this section are all aimed at making poaching less desirable not because the risks or efforts are high, but because the rewards are low. Rhinos are valuable to poachers because of their horns and nothing else. Knowing this, it seems one of the best ways to dissuade poaching is by removing the target of criminal attack. There are two ways of doing this, translocation of rhinos or dehorning. Dehorning is perhaps one of the most hotly debated issues in rhino conservation circles at the moment. This process involves tranquilizing an animal and carefully removing its horn before it wakes up. Proponents of dehorning argue this approach is necessary to make rhinos less valuable to poachers. Unfortunately, there is evidence that poachers will still target dehorned rhinos by mistake, out of revenge or to remove the small bit of horn left after the procedure (Bewick, 2012). Moreover, the procedure, like

Rhino poaching in Kruger National Park 39 any medical operation, carries inherent risk to the animal and can result in death (Milner-Gulland et al., 1992) or leave the rhino with an infection, maggot infestation and/or regrowth deformities if not performed properly (Trendler, 2011). It should be noted that rhino horn will regrow and thus the animal would need to be dehorned regularly thereby continually exposing it to these risks. Du Toit (2011) also makes an interesting argument that dehorning is only effective if the risk of apprehension is high; low rewards are acceptable with low risk. Because of the evidence suggesting dehorning is not effective, and a wealth of other prevention strategies exist such as those discussed in this chapter, a dehorning campaign in KNP is not advised as a solution to the poaching crisis. Next to dehorning, translocating rhinos out of intense poaching areas is another option for removing targets. For example, excluding rhinos from a buffer inside KNP along its south-eastern border with Mozambique may prevent poachers from hunting in this area because there are no targets to poach. While translocation operations have been successful in protecting rhinos in Zimbabwe (Milliken et al., 2009) the feasibility of using this strategy to protect KNP’s rhinos warrants further investigation. Other possibilities for reducing the rewards poachers are paid concern the horns’ value to final consumers. Because rhino horn is widely used for traditional Chinese medicine (TCM), adding poison or dye to the horn would essentially make it worthless to a final consumer and in turn worthless to the middlemen who buy them from poachers. One potential intervention suggests adding poison/ dye to rhino horns and posting signs around the reserve to warn poachers of the protective measure (Anonymous, 2011). Like dehorning however, this method requires tranquilizing the animal, which has its risks, and poachers may still kill the rhino because the horn is there, by accident or as revenge to discourage such prevention measures. Because this method is relatively new, untested and will likely suffer from similar problems as dehorning campaigns, it is not suggested that KNP implement such a strategy at this time. A major drawback of the reward reduction strategies presented here is the large number of rhinos inside KNP. To dehorn, translocate or alter the horn of this protected area’s rhinos would be an absurdly costly operation both from a financial and animal welfare perspective. While intriguing, it may be more effective to spend money on programs aimed at increasing the risk of apprehension and effort needed to access rhinos.

Conclusion The severity of South Africa’s rhino poaching problem has continued to worsen over the last five years with little indication it will reverse course any time soon. The breadth of crimes committed to procure rhino horn is expansive including pseudo-conservation, pseudo-hunting, theft and poaching. So long as demand for rhino horn from Asia persists, especially from China and Vietnam, South Africa’s rhinos are likely to face an uphill battle to maintain the population increases seen over the last 50 years. To win this battle, protected area managers will need to develop innovative poaching prevention strategies tailored to their

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specific problem. In this chapter, data from Kruger National Park (KNP) was used to show how analysing poaching data for spatial and temporal patterns can elucidate information about a poaching problem. The descriptive analyses presented shed some light on spatial concentrations of poaching inside KNP however the limited time frame of data used, January through May 2011, does not lend itself to concrete conclusions. Instead, the chapter has shown simple ways official law enforcement records of rhino poaching can be analysed and used to guide prevention strategies. Moreover, the concluding sections on prevention highlighted the utility of technology for reducing rhino poaching. When technology and data analysis are combined, there is great potential to reduce search areas, focus anti-poaching patrols and reduce response times. In the end, the rhino’s best hope is an evolved policing strategy that prevents poaching by putting rangers in the right place at the right time.

Notes 1 Although there is evidence that rhinos are sometimes hunted for their meat, the current problem in South Africa is not related to bush meat consumption or markets (www. stoprhinopoaching.com). 2 Despite popular belief, the horn is not used, or ever was used, as an aphrodisiac (Beech and Perry, 2011). 3 White rhino population statistics are used throughout the discussion on poaching as 95 percent of the rhinos killed between 1990 and 2012 were white rhinos (Milliken and Shaw, 2012). 4 For a first-hand account of the tragic aftermath of chemical rhino poaching, see Fowlds (2012) who tells the story of Geza, a young male rhino who awoke after the attack and suffered immensely until being put down by a veterinarian. 5 Shoot-to-kill policies, which give rangers the authority to kill poachers on site whether it is self-defense or not, are another option for increasing the risk poachers face. However, South Africa does not have such a policy, so the issue is not explored here. Moreover, in some protected areas like privately owned reserves, guards do not have the authority to carry weapons, so the policy is not possible (Adams, 2012). For a review of how shoot on sight policies, or lack thereof, influenced elephant populations in six African nations, including South Africa, see Messner (2010). 6 In January 2009, three Chinese businessmen were arrested and taken to court for their involvement in the smuggling of 50 rhino horns. At the time, it was the largest rhino poaching case ever taken to court in South African history. The hunting ring that procured the horns included poachers from South Africa and Mozambique, and most of the horns were taken from KNP (TRAFFIC, 2009). 7 Disrupting markets is another way to reduce rewards. Ideally, lower demand from final consumers in countries such as China and Vietnam would result in lower market prices for horn and fewer monetary incentives to poach. Considering KNP’s jurisdiction, it is not possible for law enforcement to disrupt markets that exist in faraway lands; therefore, this prevention strategy is not discussed here.

References Adams, L. (2012). ‘Shoot to Kill?’ The Horn. Spring: 24–25. Anonymous. (2011). ‘A New Idea to Stop Rhino Poaching’. Wildlife Extra News. 20 April 2011.

Rhino poaching in Kruger National Park 41 Ayling, J. (2012). ‘What Sustains Wildlife Crime? Rhino Horn Trading and the Resilience of Criminal Networks’. Working Paper for the Transnational Environmental Crime Project. Australian National University, Canberra, Australia. Beech, H. and Perry, A. (2011). ‘Killing Fields: How Asia’s Growing Appetite for Traditional Medicine Is Threatening Africa’s Rhinos’. TIME. June: 1–7. Bewick, K. D. (2012). Security Manual for Game Reserves and Provincial Nature Reserves Carrying Rhino Populations (pp. 1–33). Johannesburg, South Africa: Anti-Poaching Intelligence Group Southern Africa. Cornish, D. B. and Clarke, R. V. (2003). ‘Opportunities, Precipitators and Criminal Decisions’. In M. J. Smith and D. B. Cornish (Eds.), Crime Prevention Studies (Vol. 16, pp. 41–96). Monsey, NY: Criminal Justice Press. Dean, C. (2011). ‘Back to the Trenches: The Bloody Battle Against Rhino and Elephant Poaching.’ Proceedings of the 2011 International Elephant and Rhino Conservation and Research Symposium. Rotterdam, 10–14 October 2011. Pp. 65–125. Du Toit, R. (2011). ‘Zimbabwe Lowveld: Dehorning Experience’. In B. G. Daly, A. Greyling, Y. Friedmann, S. Downie, R. du Toit, R. Emslie, M. Eustace, J. Malan, K. Nghidinwa, C. O’Criodain and K. Trendler (Eds.), Perspectives on Dehorning and Legalised Trade in Rhino Horn as Tools to Combat Rhino Poaching. Proceedings of a workshop assessing legal trade in rhino horn as a tool in combating poaching as well as a detailed assessment of the efficacy of dehorning as a deterrent to poaching (p. 13). Johannesburg, South Africa: Endangered Wildlife Trust. Emslie, R. and Brooks, M. (1999). African Rhino. Status Survey and Conservation Action Plan. IUCN/SSC African Rhino Specialist Group. Gland, Switzerland and Cambridge, UK: IUCN. Emslie R. H. and Knight, M. H. (2012). Update on African Rhino Status and Trends from IUCN SSC African Rhino Specialist Group (AfRSG). Gland, Switzerland and Cambridge, UK: IUCN. Eustace, M. (2011). ‘Current Supply and Demand in the Rhino Horn Market – A Model for Regulating the Rhino Horn Trade (Rewards to Trade)’. In B. G. Daly, A. Greyling, Y. Friedmann, S. Downie, R. du Toit, R. Emslie, M. Eustace, J. Malan, K. Nghidinwa, C. O’Criodain and K. Trendler (Eds.), Perspectives on Dehorning and Legalised Trade in Rhino Horn as Tools to Combat Rhino Poaching. Proceedings of a workshop assessing legal trade in rhino horn as a tool in combating poaching as well as a detailed assessment of the efficacy of dehorning as a deterrent to poaching (p. 10). Johannesburg, South Africa: Endangered Wildlife Trust. Ferreira, S. M. and Okita-Ouma, B. (2012). ‘A Proposed Framework for Short-, Medium-, and Long-Term Responses by Range and Consumer States to Curb Poaching for African Rhino Horn’. Pachyderm. 51(January–June): 52–59. Fowlds, W. (2012). Poached! The Tragic Story of Geza the Rhino: Told by the Veterinarian Who Attended Him. Nikela. . Jackson, T. (2012). ‘The Crisis’. Africa Geographic. 20(3): 30–36. Knight, M. (2011). ‘African Rhino Specialist Group Report’. Pachyderm. 50(July–December): 7–14. Knight, M. (2012). ‘African Rhino Specialist Group Report’. Pachyderm. 51(January–June): 10–21. Larson, R. (2010a). ‘Rhino Killings on the Rise: A Troubling Correlation between Rhino Killings and the Spreading Chinese Footprint in Southern Africa.’ San Francisco, CA: Saving Rhinos LLC. . Larson, R. (2010b). ‘Shocking Discovery: Game Reserve in Limpopo Found Emptied of All Rhinos.’ San Francisco, CA: Saving Rhinos LLC. .

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Leader-Williams, N. (1992). The World Trade in Rhino Horn: A Review. Cambridge, UK: TRAFFIC International. Mander, D. (2012). ‘Damned if You Do and Damned if You Don’t – Legalizing the Rhino Horn Trade: My Journey to Vietnam’. Victoria, Australia: International Anti-Poaching Foundation. Messer, K. D. (2010). ‘Protecting Endangered Species: When Are Shoot-On-Sight Policies the Only Viable Option to Stop Poaching?’ Ecological Economics. 69: 2334–2340. Milledge, S. (2005). Rhino Horn Stockpile Management: Minimum Standards and Best Practices from East and Southern Africa. TRAFFIC East/Southern Africa. Milliken, T., Emslie, R. H. and Talukdar, B. (2009). African and Asian Rhinoceroses – Status, Conservation and Trade: A Report from the IUCN Species Survival Commission (IUCN/SSC) African and Asian Rhino Specialist Groups and TRAFFIC to the CITES Secretariat Pursuant to Resolution Conf. 9.14 (Rev. CoP14) and Decision 14.89. CITES CoP15 Doc. 45.1 Annex. Geneva, Switzerland: CITES Secretariat. Milliken, T., Nowell, K. and Thomson, J. B. (1993). The Decline of the Black Rhino in Zimbabwe: Implications for Future Rhino Conservation. Cambridge, UK: TRAFFIC International. Milliken, T. and Shaw, J. (2012). The South Africa – Viet Nam Rhino Horn Nexus: A Deadly Combination of Institutional Lapses, Corrupt Wildlife Industry Professionals and Asian Crime Syndicates. Johannesburg, South Africa: TRAFFIC. Milner-Gulland, E. J., Beddington, J. R. and Leader-Williams, N. (1992). ‘Dehorning Africa’s Rhinos: A Model of Optimal Frequency and Profitability’. Proceedings: Biological Sciences. 249(1324): 83–87. Moore, A. (2011). ‘Drivers of the Trade in Rhino Horn’. In B. G. Daly, A. Greyling, Y. Friedmann, S. Downie, R. du Toit, R. Emslie, M. Eustace, J. Malan, K. Nghidinwa, C. O’Criodain and K. Trendler (Eds.), Perspectives on Dehorning and Legalised Trade in Rhino Horn as Tools to Combat Rhino Poaching. Proceedings of a workshop assessing legal trade in rhino horn as a tool in combating poaching as well as a detailed assessment of the efficacy of dehorning as a deterrent to poaching (p. 8). Johannesburg, South Africa: Endangered Wildlife Trust. Mouton, H. and de Villiers, J. P. (2012). ‘A Prologue to Estimating the Intent of a Potential Rhino Poacher’. In B. van Niekerk, L. Leenen, T. Ramluckan and M. Maharaj (Eds.), Proceeding of the 4th Workshop on ICT Uses in Warfare and the Safeguarding of Peace 2012 (IWSP 2012). South Africa: Council for Scientific and Industrial Research. Nowell, K. (2012). An Assessment of Rhino Horn as Medicine. TRAFFIC report prepared for the CITES Secretariate, 62nd meeting of the CITES Standing Committee, Geneva, 23–27 July. SC62 Doc. 47.2 Annex. The World Factbook 2013–2014. (2013). Washington, DC: Central Intelligence Agency. Available at www.cia.gov/library/publications/the-world-factbook/index.html TRAFFIC. (1997). Rhino Progress? The Response to CITES Resolution Conference 9.14: A TRAFFIC Network Report. June: 1–5. TRAFFIC. (2009). TRAFFIC Bulletin. 22(2) June: 73–78. Trendler, K. (2011) ‘Dehorning Rhino: Welfare, Ethics and Behavioural Issues’. In B. G. Daly, A. Greyling, Y. Friedmann, S. Downie, R du Toit, R. Emslie, M. Eustace, J. Malan, K. Nghidinwa, C. O’Criodain and K. Trendler (Eds.), Perspectives on Dehorning and Legalised Trade in Rhino Horn as Tools to Combat Rhino Poaching. Proceedings of a workshop assessing legal trade in rhino horn as a tool in combating poaching as well as a detailed assessment of the efficacy of dehorning as a deterrent to poaching (p. 7). Johannesburg, South Africa: Endangered Wildlife Trust.

Rhino poaching in Kruger National Park 43 Venter, F. J. (2007). ‘Balancing Conservation Management and Tourism Development with Wilderness Stewardship in the Kruger National Park, South Africa’. USDA Forest Service Proceedings RMRS-P-49. Warchol, G. L., Zupan, L. L. and Clack, W. (2003). ‘Transnational Criminality: An Analysis of the Illegal Wildlife Market in Southern Africa’. International Criminal Justice Review. 13: 1–27. Wilson-Wilde, L. (2010). ‘Wildlife Crime: A Global Problem’. Forensic Science Medicine and Pathology. 6: 221–222. www.stoprhinopoaching.com. (2013). Accessed 3 July 2013.

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Does opportunity make the poacher? An analysis of neo-tropical illicit parrot markets Stephen F. Pires and Rob T. Guerette

Introduction In their seminal work, Opportunity Makes the Thief, Felson and Clarke (1998) made the argument that crime was largely a product of opportunity structures. In this, several axioms were outlined which were intended to help make sense of property crime patterns and to inform activities devoted to preventing them. The propositions were not unfounded but rather were derivatives of the routine activity and rational choice theories of crime. The idea that opportunity has a causal role in crime was novel since most criminological theories view crime from a dispositional point of view. That is, certain distinct tendencies exist among those who engage in crime compared to those who do not. Generally, these deviant tendencies are explained to be the product of differential socialization processes which result in individual ‘propensities’ to commit crime. From the dispositional perspective then, crime on the aggregate is ostensibly explained by these collective and concentrated dispositions rather than by the environmental and situational structures that exist across place and time. Recognizing the limitation of that view, the opportunity perspective extended by Felson and Clarke (1998) instead recognizes the broader and more important role that the situational landscape plays in the production of crime. In this way it contends that with even the presence of the most motivated offender, crime is unlikely without the presence of suitable opportunity structures. At the same time, the presence of crime opportunities can serve to entice even low motivated offenders to engage in crime. With these ideas in hand, crime scientists have begun to understand a variety of crime pattern formations and have found consistent concentrations of crime across time and place (Brantingham and Brantingham, 1981; Sherman et al., 1989), among victims (Pease, 1998) and among facility types (Eck et al., 2007), which are best explained by opportunity formations found in the situational landscape. Though the majority of crime science research originally focused on conventional crimes, there has been a diversity of non-traditional crime types examined from the opportunity structure perspective. This includes identity theft (White and Fisher, 2008), terrorism (Clarke and Newman, 2006), cell phone fraud (Clarke et al., 2001), improvised explosive device attacks (Gibbs, 2010; Johnson and

Does opportunity make the poacher? 45 Braithwaite, 2009) and migrant smuggling (Guerette, 2007), among others. Only recently crime scientists have begun to undertake the study of wildlife crimes and illicit wildlife markets (Lemieux and Clarke, 2009; Petrossian, 2012; Pires, Under Review; Pires and Clarke, 2011, 2012; Pires and Moreto, 2011), and though this research has produced useful insights, much remains to be known about the applicability of crime science to the topic of crimes involving the natural world. Here we examine the nature of illicit parrot markets in two South American countries, Bolivia and Peru. We ask whether the presence of opportunities to sell exotic birds in city markets serves to facilitate poaching from nearby habitats.

Illicit markets, crime and parrot poaching The relationship between illicit markets and crime remains stronger in theory than in empirical evidence. There are many theoretical reasons why the relationship between illicit markets and crime is to be expected. Under the rational choice theoretical approach, selecting crime targets near places where stolen products can be sold reduces the risk and effort for offenders, while making the rewards for crime more salient. Having secondary markets nearby to dispose of products means less distance to travel, thereby requiring less effort. This also translates into lower risk for offenders since they will have the stolen product(s) in their possession for less time making them less susceptible to detection and arrest by police. By some estimates, the average property offender maintains possession of stolen property for no more than 30 minutes (Sutton 2003, 2008). Though limited, empirical research does suggest that the existence of an illicit market can serve to increase theft of particular products around illicit markets. In studies examining the placement of undercover buy operations (referred to as ‘stings’) results have shown that they either increase property crime levels or that they achieve no discernible reduction in crime (Langworthy, 1989; Langworthy and Lebeau, 1992). Research carried out on the role of sting operations and crime conducted prior to this time offer somewhat conflicting evidence, yet still several studies associate the introduction of an illicit market and crime increases in the surrounding area (for review see, Langworthy, 1989; Pennell, 1979; Raub, 1984; Weiner et al., 1983). While most of the research in this area has been carried out within the study of conventional crime types, some recent extensions of these ideas have been applied to wildlife crimes. In a study of elephant poaching, Lemieux and Clarke (2009) found that following the 1989 international ban on the ivory trade, most African nations experienced increases in elephant populations. However, a few countries continued to exhibit decreases in their elephant populations, which were explained to be partly due to unregulated, illicit ivory markets within those countries and their neighboring countries. It was reasoned that because of the presence of these illicit markets, individuals were able to continue poaching elephants since they could easily dispose of the illegal ivory.

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Recognizing the causal role that illicit markets play in the occurrence of crime has led some to argue that the best way to reduce these sorts of market driven crimes is to target the demand for the illicit or illicitly obtained products (Sutton, 1998; Sutton et al., 2001). Referred to as the Market Reduction Approach (MRA) to crime prevention, the model calls for the use of data analysis to understand (1) what property is being stolen, (2) how and from where offenders acquire the property, (3) how the products are transferred or sold into the community and (4) whom within the community are the consumers. Once these things are determined, interventions targeting susceptible components of the illicit market can be formulated and implemented. The MRA model is consistent with the situational crime prevention approach, which also identifies the disruption of markets as a possible means of preventing crime (Clarke, 1997). Like SCP, the MRA approach was originally designed to respond to conventional crime types, though recent efforts have extended the model to study the illicit trade of flora and fauna (Schneider, 2008, 2012). The illegal parrot trade Parrots are prized in neo-tropical cultures because of their beautiful plumage, mimicry ability and intelligence. In the neo-tropics, parrots are common household pets much like dogs and cats in the United States (Cantu et al., 2007; Drews, 2002; Gonzàlez, 2003). For instance, a survey conducted in Costa Rica found about a quarter of all households had at least one parrot in possession (Drews, 2002). Historically, parrots have been taken from the wild without detrimental consequences to their populations. By the 1980s though, the parrot trade became a globalized business and over-trapping of parrots quickly put many species on the endangered list (Cantu et al., 2007). Parrots are now one of the most threatened bird species in the world (Wright et al., 2001; Juniper and Parr, 1998; Howell and Webb, 1995) with nearly a third of all species threatened to some degree (Pain et al., 2006). Many parrot species have disappeared from their native ranges and some have even gone extinct in the wild (Wright et al., 2001). In response to the population declines, nearly all neo-tropical countries created anti-poaching laws to curb the problem. Yet, these measures have had no evidence of working to reduce the illegal trade (Cantu et al., 2007; Gastanaga et al., 2010; Herrera and Hennessey, 2007). The illegal parrot trade is generally made up of three different actors: poachers, itinerant fences (i.e. middlemen) and market sellers. Poachers appear to be mostly peasants who take advantage of species breeding in the local area. Poaching parrots does not require much effort since much of it involves taking parrot nestlings (i.e. baby parrots) from their tree cavity nest. It is a very opportunistic crime since nestlings are unable to fly away. If it was up to the poachers alone to supply the trade with birds, the illegal parrot trade would be much smaller in scale. Since many of the poachers are peasants, they cannot afford to transport

Does opportunity make the poacher? 47 the birds to illicit markets in nearby cities. Itinerant fences make the lives of poachers much easier by filling this void (Pires and Clarke, 2011). Whether fences contact poachers to pick up parrots or just show up in their village at certain times of the year looking to purchase birds is unknown. It also unknown if there are higher-up middlemen who travel farther distances to dispose of parrots (Pires, Under Review). Once middlemen are ready to fence their parrot supply, they will sell them to market sellers who generally operate openly in cities. Typically, these are in market centers where one will see textiles, produce and other goods being sold alongside wildlife products, such as parrots. While many parrot species will be found on illicit pet markets, there is great variation in poaching counts amongst species. Of the 330 species in existence (Gastanaga et al., 2010; Pain et al., 2006), very few make up the large brunt of parrots found on illicit markets (Pires and Clarke, 2011, 2012). This is largely the same pattern found on traditional illicit markets made up of stolen goods – a few products make up the large proportion of illicit goods, otherwise known as the 80–20 rule (Clarke and Eck, 2005). Therefore, parrots can be considered ‘hot products’ (Clarke, 1999) much like other inanimate goods because they demonstrate dramatic variation in poaching, in addition to being illegally taken from the wild and sold on illicit markets. Variety of illicit parrot markets While the conservation literature anecdotally suggests that illicit parrot markets are local in nature, a recent study (Pires, Under Review) of multiple parrot markets in seven neo-tropical cities finds that markets are not exclusively local. The study found there are at least three types of markets in existence – local, regional and feeder markets. Local markets capture much of its parrots from a close area (i.e. 50–150 miles), while regional markets obtain a large proportion of parrots from very far distances (e.g. 900 miles away). Rather than relying on local poachers and fences, regional markets are able to acquire parrots from far off distances because they attract middlemen who traffic species from city to city. The cities where a large proportion of these ‘distant species’ are suspected of emanating from are termed feeder markets. Feeder markets are local markets that distribute poached parrots to other cities, as well as supply parrots for local demand. However, it remains unclear exactly why illicit markets operate in these ways. Could market types be explained by supply and demand forces, in which supply is the availability of parrot species around a city and demand is the number of people that live within a city? Are there other contextual factors that could play a role? This study examines these questions using illicit parrot market data from seven cities within Peru and Bolivia. Following an overview of the methodological approach, including data collection procedures, the chapter reports on the findings and concludes with a discussion of SCP techniques which could be deployed to reduce the poaching of parrots.

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The current study To further understand the nature of illicit markets, the present inquiry extends previous research on the illicit parrot trade and is guided by the following research questions: 1 2 3

Is there a relationship between parrot ranges and market availability? Are there contextual factors that can explain the types of illicit markets that take hold in cities? Can these contextual factors explain why some cities do not have active parrot markets?

Methodology Market data on the number of poached parrots in each city is based on secondary data emanating from two separate conservation research projects. As seen in Figure 3.1, these market data were collected by a team of researchers that visited six geographically dispersed Peruvian cities (Gastanaga et al., 2010) and one large market in Santa Cruz, Bolivia (Herrera and Hennessey, 2008). In the former study, Gastanaga et al. (2010) visited multiple markets in each of the six Peruvian cities on a quarterly basis for a one-year period. The quarterly method was utilized to control for temporal variations in parrots poached as well as for economic efficiency. Aggregate totals from markets in each of the cities could easily then be projected for yearly sums (Figure 3.1). In the latter study, Herrera and Hennessey (2008) hand-counted species coming through the Los Pozos market in Santa Cruz, Bolivia for two and half years on a daily basis (see Appendix for the complete sample of species). To obtain a yearly average for the Santa Cruz, Bolivia, market, the total number of parrots is divided by 2.5 in order to be comparable to the Peruvian market data. The first analysis re-examines whether there is a relationship between parrot ranges and market availability for the seven markets in Peru and Bolivia. The intention here is to improve understanding of how the presence of illicit markets may directly affect poaching. A descriptive analysis will be conducted for each city to measure (1) the mean distance between parrots found in city markets and their known range; (2) the percentage of local species found on city markets and (3) the percentage of all local species found on city markets. The second research question examines whether there are any situational characteristics related to particular markets operating in Peru and Bolivia (see Table 3.1 for summary). Total Parrots is a measure of the annual parrot supply that is seen within each city. Purely local markets are hypothesized to have the lowest parrot supply due to less customer demand, whereas feeder markets should have the highest parrot supply due to local and regional demand. To measure supply within the surrounding area of a city, Available Species quantifies the number of species within 150 miles of each city1. Cities with a larger supply of local species should have larger markets that rely on local

Does opportunity make the poacher? 49

Figure 3.1 Map of cities and annual projected parrot counts Source: Peru data, Gastanaga et al. (2010); Bolivia data, Herrera and Hennessey (2008)

poaching. The Human Population measure on the other hand, attempts to quantify aggregate demand for parrots by city. More populated cities, such as Lima (with nearly 8 million people), should attract more poached parrots due to having more people. Highway Accessibility is a measure of the ability to traffic species from city to city. Cities that have no access to highways should be

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Table 3.1 Independent variables Variable*

Definition

Source

Total parrots

Number of parrots on market annually

Available species

Number of parrot species within 150 miles serves as a proxy measure of supply Proxy measure for demand Do highways exist around the city so that species can be trafficked? Number of threatened species on city markets’ (according to IUCN Red List) Number of threatened species available within 150 miles

Peru data obtained from Gastanaga et al. (2010) and Bolivian data from Herrera and Hennessey (2008) GIS

Human population Highway accessibility

Threatened species**

Threatened species available

City population (INEI, 2012) Google Maps

Peru data obtained from Gastanaga et al. (2010) and Bolivian data from Herrera and Hennessey (2008) GIS and IUCN Red List

* The following variables – Number of Eco-Tourism Businesses and Elevation of Cities – were eliminated from the analysis because they had no relationship to the types of markets operating in cities. ** The IUCN Red List can be retrieved at www.iucn.org/about/work/programmes/species/our_work/ the_iucn_red_list/

(1) unlikely to have parrot species that are from far off distances and (2) unlikely to be feeder markets. Finally, the variables Threatened Species and Availability of Threatened Species examine the demand for rare species while taking into account the presence of rare species in the local area. Cities with larger human populations should have a corresponding relationship with having more rare species on their markets, even if they lack rare species in the local area. The third and final analysis investigates whether any of the aforementioned contextual factors explain why two Peruvian cities no longer have active parrot markets. According to the Gastanaga et al. (2010) study, researchers originally set out to study markets in eight Peruvian cities, but Cusco and Puerto Maldonado no longer had existing markets. The contextual factors of these two cities will be compared to the other seven cities to see if any patterns emerge as to why illicit markets do not exist in these two cities.

Findings Illicit markets’ direct and indirect effects on poaching can be seen in Table 3.2. For local markets, such as Iquitos, Santa Cruz, Chiclayo and Pucallpa, the distance traveled for many of the parrot species appears to be quite close to the

Does opportunity make the poacher? 51 Table 3.2 The spatial relationship between market species and their ranges Market

Type of Market

Mean Distance of Parrots (miles)*

Standard Deviation

Iquitos, Peru Puno, Peru Santa Cruz, Bolivia Chiclayo, Peru Pucallpa, Peru Arequipa, Peru Lima, Peru

Local Local Local-Feeder Local-Feeder Local-Feeder Regional Regional

0.0 216.4 28.5 79.8 51.6 367.6 225.6

0.0 182.5 49.6 105.0 136.9 273.4 95.2

* This measures the closest distance a species range is to a city. Therefore, this is a conservative estimate of distance traveled to city markets since species will not always be poached at the closest distance from a market.

Table 3.3 Relationship between known parrot range and market availability Market

# of species for sale

% of species for sale found within 150 miles*

% of species found within 150 miles appearing in the market**

Iquitos, Peru Puno, Peru Santa Cruz, Bolivia Chiclayo, Peru Pucallpa, Peru Arequipa, Peru Lima, Peru

6 12 35 21 20 20 12

100% 67% 74% 52% 90% 19% 33%

25% 24% 74% 50% 50% 100% 24%

Notes * Column was calculated using the following equation: (Species for sale found within 150 miles) / (Total number of species for sale) * 100 ** Column was calculated using the following equation: (Species for sale found within 150 miles) / (Total number of species found within 150 miles) * 100

cities. Conversely, regional markets attract species from the farthest distances, which substantiates their classification as non-local markets. Many of the species seen on markets in Arequipa and Lima also appear on other markets, particularly in feeder markets. This is not a coincidence because many of the species that appear on regional markets originally emanate from feeder markets (Pires, Under Review). For instance, the red-masked parakeet only appears in three city markets within this sample – markets in Chiclayo, Arequipa and Lima (see Appendix). Yet, its closest distance to the latter two cities is 777 and 368 miles, respectively. The red-masked parakeet is being poached and sold in Chiclayo (7 miles at its closest range) and then being trafficked to other city markets. Table 3.3 examines the proportion of market species that are found locally as well as the proportion of all local species found on city markets. According to

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Table 3.3, markets mostly obtain their parrots from the local area. Five of the seven cities received at least 50 per cent of their parrot supply from a 150-mile buffer. Table 3.3 also shows that some species are not being poached in the local area (with the exception of Arequipa). For instance, only a quarter of all local species are poached for markets in Lima, Iquitos and Puno. Many species are absent from markets for reasons that do not involve just proximity (see Pires and Clarke, 2011). Types of illicit parrot markets To better understand why different types of markets exist across cities, this section will explore some contextual factors that may shed light on the matter. Figure 3.2 reveals that the highest density of parrot species lies in the eastern side of Peru and in the northern section of Bolivia. More specifically, the cities of Iquitos, Pucallpa and Santa Cruz have the highest density of species within 50 miles (respectively 24, 26 and 24 species). The Peruvian coastal cities have the lowest density of species, yet the highest density of humans. For instance, Arequipa only has two parrot species within a 50-mile buffer of the city and is one of the largest human populated cities in Peru. Despite having very little variety of parrot species in the area, illicit parrot markets exist in these coastal cities due to customer demand. In examining Table 3.4, patterns begin to emerge as to why cities have particular markets. Local markets have a large variety of species in the area, ranging from 20 to 36 species (mean is 30). Exclusively local markets, such as Iquitos and Puno, have the lowest quantity of parrots appearing on markets (mean is 5,050), and have very low human populations. This makes sense because these markets only need to meet local demand (human population), which is very small. Additionally, the lack of a highway connecting Iquitos to any other city explains why Iquitos is the only city that does not receive ‘distant species’ (i.e. 100 per cent of the species on that market are found within the actual city)2. On the other hand, regional markets have very few local species (mean is 10) and large human populations. The only two regional markets in this sample also have the largest human populations within Peru (mean is over 4.2 million people). With this type of demand for parrots, one would expect regional markets to have higher parrot counts than exclusively local markets3. Additionally, this demand translates to the procurement of rare species on regional markets, even when rare species are locally unavailable (see Arequipa). In addition to being the source market of many distant species in the trade, feeder markets can be distinguished in a number of other ways. First, the Chiclayo, Pucallpa and Santa Cruz markets have amongst the highest parrot counts of all seven markets. The average number of parrots on feeder markets is 21,607 parrots per year, which is nearly triple the average size of the other four markets (i.e. 8,501). Despite having relatively smaller human populations in both Chiclayo and Pucallpa, feeder markets must supply local demand, as well as regional markets. Thus, poachers have to over-poach. Second, these types of markets should have a high density of species in the local area in order to distribute to

Does opportunity make the poacher? 53

Figure 3.2 Density of parrot species in north-west South America

multiple markets. Feeder markets have a large diversity of species with an average of 30 species within a 150-mile radius. Third, proximity to other markets can be a key feature of which markets become feeders. Pucallpa is centrally located in Peru, within a distance of 278 miles from Lima (the largest human populated city), and 327 miles from another large city, Chiclayo. Santa Cruz is

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Table 3.4 Explaining types of illicit markets with proxy measures of supply & demand City

Market Type

Human Highway Threat. Threat. Available Total Population Connectivity Species in Species Species* Parrots Markets Available

Iquitos Puno Santa Cruz† Chiclayo Pucallpa Arequipa Lima Cusco Puerto M.

Local 370,000 No** Local 100,000 Yes Feeder 1,756,000 Yes Feeder 738,000 Yes Feeder 204,000 Yes Regional 904,000 Yes Regional 7,605,000 Yes NA 359,000 Yes NA 92,000 Yes

0 0 9 4 1 3 2 0 0

0 2 3 6 4 0 3 4 4

24 33 35 20 36 4 16 32 36

3,400 6,700 11,013 18,233 35,574 13,335 10,567 0 0

* 150-Mile Buffer ** Iquitos has no highways connecting to other cities, but it does have an international airport as well as rivers that do connect. Yet, no birds in Iquitos’ markets have a range beyond 0 km from the city. This may be an indication that highways are the primary method of trafficking species. † Herrera and Hennessey (email comm.,11/29/2010) suggest parrots from Santa Cruz do get transported to other markets in Bolivia such as Chochabamba (Pires, Under Review b).

situated centrally in Bolivia and has highway access to all other major cities in Bolivia, including La Paz (337 miles) and Chochabamba (197 miles). Chiclayo is also in a key location because it can disseminate species from northern Peru (and even Ecuador) down along the coastal road to Lima and Arequipa. Finally, feeder markets are hubs for rare species because they need to meet local demand, as well as supply regional markets with highly craved species. Absence of parrot markets in Cusco and Puerto Maldonado Puerto Maldonado and Cusco do not have existing parrot markets despite having contextual factors that would allow for markets to exist in both cities (see Table 3.2). First, both cities have a high density of species in the local area – 36 and 32 – which allows local demand to be met easily without any assistance from other city markets (i.e. supply). Second, the size of the cities is comparable to Iquitos and Puno, which have existing parrot markets (i.e. demand). Finally, the cities are also similar to others in terms of the presence of highways as well as having a large availability of rare species. Based on this descriptive analysis, it cannot be explained why these cities do not have active illicit markets.

Discussion and conclusion Given the limited sample size of nine cities, this study is only exploratory in nature. That said, there are important preliminary findings that suggest that illicit

Does opportunity make the poacher? 55 markets stimulate theft of parrots. This study finds that city markets largely depend on the supply of local parrot species. Just two of the seven city markets in the study had a limited supply of parrots that could be poached locally, more common was a wide selection of parrots that came from areas nearby the market. Nevertheless, local species still made up a small proportion of birds on these two regional markets. This study also suggests contextual factors can explain, at least in part, why particular types of markets take hold in cities. Exclusively local markets tend to be located in cities in which the human population is small and there is a high abundance of species in the local vicinity. Regional markets tend to be in more populated cities where there is a shortage of species’ diversity in the surroundings. Finally, feeder markets resemble local markets in many ways with the exception of having a larger supply of parrots on the market. Because feeder markets must supply local and regional demand for parrots, the largest markets will be feeders. A summarization of these findings are compiled in Table 3.5. Exactly why cities do not have illicit parrot markets despite their close existence in surrounding parrot habitats remains open to further inquiry. In the case of Puerto Maldonado and Cusco, Peru, it seems most plausible that local species are being poached at much lower proportions due to the absence of active pet markets. Without an illicit pet market nearby, the incentive to poach parrots is largely absent given the difficulties of being able to dispose of them quickly. Although it is possible that poaching for personal pet ownership still takes place around these two cities, the harm incurred by this type of poaching is much less than that produced by large scale illicit markets. Although it is difficult to predict exactly which species would become poached if these two cities had operational markets, it is possible to list the species that would be at an elevated risk of being poached. For instance, all species that have a range up to 150 miles outside the two cities should be at risk. In this case, it means that 39 different species would be at risk for both markets. In Puerto Maldonado, two out of the 36 species in a 150-mile radius do not appear within any Peruvian markets4. In Cusco, there are five additional species within the 150-mile radius that are not poached for any Peruvian market. All seven of these species are in fact poached for the Santa Cruz, Bolivia market, which includes the second and third highest poached

Table 3.5 Characteristics of illicit market types Market type

Human population

Available species

Rare species

Market parrots (found locally)

Source of distribution

Total parrots

Local

low

high

low

high

low

low

Regional

high

low

high

low

low

medium

Feeder

varies†

high

high

high

high

high

† This can be any size. Pucallpa and Chiclayo both have relatively small human populations within Peru, but Santa Cruz is a very large city with 1.6 million people – the largest in Bolivia.

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species on that market5. Given this pattern, it seems very plausible that the existence of an illicit market near the ranges of these birds would put them at greater risk of becoming poaching victims. The importance of discovering why cities have particular types of illicit markets can help forecast the types of pet markets that exist in other neo-tropical cities. With the knowledge of just a few contextual factors, researchers and law enforcement can determine if markets are locally or regionally based. The importance in knowing this difference is essential to how the illegal parrot trade can be targeted effectively. Regional markets are predominantly stimulating theft indirectly because most of their parrot supply is emanating from other city’s parrot operations. Local markets, on the other hand, are directly stimulating theft in a close vicinity. Shutting local markets down can have an advantageous impact on local parrot populations. Shutting down local markets is one general strategy to reducing poaching, but there are potentially many other ways poaching can be reduced and animal populations protected. This includes the more formal MRA approach as well as an assortment of situational crime prevention techniques previously suggested by Pires (2012). For instance, because some birds are more regularly poached than others, resources could be focused on reducing the victimization rate of those species since they make up the largest proportion of the illegal parrot trade. Focusing resources on those at greatest risk will achieve the greatest reductions rather than focusing on those species which are less commonly poached. Identifying, understanding and targeting poaching hot-spots (that is, places where these species of birds are frequently poached) also stands to achieve credible reductions. These hot-spots will most likely be located within radial catchment areas (i.e. where most species are poached for markets) of illicit markets and will be accessible by poachers either due to the relative ease of approach and egress or because opportunistic poachers will regularly be in areas where the species are found during the course of their daily activities. Finally, poaching could be made more difficult and more risky, thereby making the practice less desirable in a number of ways. These include making certain areas inaccessible; increasing or establishing both informal and formal guardianship in strategic locations; systematically and regularly removing make-shift ladders from trees that are used to facilitate nest poaching of parrots from tree tops; establishing and enforcing a ban on the sale and possession of mist-nets which are used to trap some parrot species; and the use of law enforcement checkpoints along roads commonly used by poachers and traffickers (Pires, 2012). Though exploratory there is much here to suggest that opportunity does make the poacher. This means there is some convergence with what is known about domestic conventional illicit crime markets in the United States. It also suggests that illicit wildlife markets more generally (i.e. aside from parrots) may be subject to some of the same operational tenets. If this is true, then the ideas developed within the crime sciences have much to offer in the understanding and prevention of crimes affecting the natural world.

Appendix

Puno

Santa Cruz

Chiclayo

Pucallpa

Arequipa

Amazonian Ptlet Andean PK Barred PK Black-capped PK Black-headed PT Black-hooded PK Black-winged PT Blaze-winged PK Blue-headed MC Blue-and-yellow MC Blue-crowned PK Blue-fronted Ptlet Blue-fronted PT Blue-headed PT Blue-throated MC Blue-winged MC Blue-winged Ptlet Bronze-winged PT Brown-throated PK Canary-winged PK Chestnut-fronted MC Cobalt-winged PK Crimson-bellied PK Dusky-billed Ptlet Dusky-headed PK Festive PT Golden-collared MC Golden-plumed PK Gray-cheeked PK Gray-hooded PK Green-cheeked PK Hyacinth MC Kawalls PT Lears MC Maroon-tailed PK Mealy PT Military macaw Mitred PK Monk PK Mountain PK

NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0 NA 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

NA NA NA NA NA NA NA NA NA 120 NA NA NA 102 NA NA NA NA NA 485 NA 106 NA 126 NA NA NA NA NA NA NA NA NA NA NA NA NA 60 NA NA

NA NA NA NA 817 269 NA NA NA 0 0 NA 0 0 202 NA 103 1335 NA NA 56 0 NA NA 33 NA 0 NA 1336 NA 0 262 NA 1527 NA 15 46 26 45 NA

NA NA NA NA NA NA NA NA NA 145 NA NA NA 128 NA NA NA NA NA 260 NA 108 NA NA 188 244 NA NA 159 NA NA NA NA NA NA 178 62 292 NA NA

NA NA NA NA 0 NA NA NA NA 0 NA NA NA 0 NA NA NA NA NA 0 0 0 NA NA 0 0 NA NA 426 NA NA NA NA NA NA 0 NA 134 NA NA

NA 112 NA NA 451 NA NA NA NA 177 NA NA NA 174 NA NA NA NA NA 478 170 NA NA 187 NA 527 NA NA 952 NA NA NA NA NA NA 183 220 125 220 0

Lima

Name*

Iquitos

Table 1 Distance in miles between parrot range and local markets where species are sold

Mean

SD

NA NA NA NA NA NA NA NA NA NA NA NA NA 159 NA NA NA NA NA 209 NA NA NA NA 170 265 NA NA 546 NA NA NA NA NA NA NA NA 67 NA 0

– 112.0 – – 422.7 269.0 – – – 88.4 0.0 – 0.0 92.2 202.0 – 103.0 1335.0 – 272.9 75.3 42.8 – 156.5 97.8 259.0 0.0 – 683.8 – 0.0 262.0 – 1527.0 – 94.0 109.3 117.3 132.5 0.0

– – – – 409.2 – – – – 83.2 – – – 76.8 – – – – – 192.1 86.6 58.6 – 43.1 95.1 215.4 – – 463.2 – – – – – – 100.1 96.2 94.9 123.7 0.0

(Continued)

Name*

Iquitos

Puno

Santa Cruz

Chiclayo

Pucallpa

Arequipa

Lima

Table 1 (Continued)

Orange-cheeked PT Orange-winged PT Pacific Ptlet Painted PK Peach-fronted PK Red-bellied MC Red-lored PT Red-and-green MC Red-billed PT Red-faced PT Red-fan PT Red-fronted MC Red-masked PK Red-shouldered MC Rose-faced PT Saphire-rumped Ptlet Scaly-headed PT Scaly-naped PT Scarlet MC Scarlet-fronted PK Scarlet-shouldered Ptlet Short-tailed PT Speckle-faced PT Spot-winged Ptlet Tucuman PT Tui PK White-bellied PT White-eyed PK White-necked PK Yellow-chevroned PK Yellow-crowned PT Yellow-faced PT Yellow-faced Ptlet

NA 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0 NA NA NA 0 NA 0 NA NA NA NA NA

NA NA NA 82 NA NA NA NA NA NA NA NA NA NA NA NA NA 95 NA NA NA 537 NA NA NA 164 NA 102 NA NA 207 NA NA

NA 0 NA NA 0 NA NA 9 NA NA NA 52 NA 93 NA NA 0 NA 0 NA NA NA NA NA 73 NA 0 0 NA 0 58 226 NA

NA 121 0 NA NA NA NA 189 NA NA NA NA 7 NA NA NA NA 71 196 57 NA NA NA NA NA NA 366 105 NA NA 240 NA 105

NA 0 NA 0 NA 0 NA 0 215 NA NA NA NA NA NA NA NA NA 0 NA NA NA NA NA NA 0 NA 0 NA NA 0 NA NA

NA NA NA NA NA NA NA NA NA NA NA NA 777 NA NA NA NA NA 211 0 NA NA NA NA NA 210 203 191 NA NA 234 NA NA

NA NA 274 150 NA NA NA NA NA NA NA NA 368 NA NA NA NA NA NA 19 NA NA NA NA NA NA NA 155 NA NA NA NA NA

Mean SD

0.0 0.0

216.4 182.5

28.5 49.6

79.8 105.0

51.6 136.9

367.6 273.4

225.6 95.2

* PT = Parrot; Ptlet = Parrotlet; PK = Parakeet; MC = Macaw ** NA = Not applicable because the species does not appear in the city market.

Mean – 30.3 137.0 77.3 0.0 0.0 – 66.0 215.0 – – 52.0 384.0 93.0 – – 0.0 83.0 101.8 25.3 – 268.5 – – 73.0 93.5 189.7 79.0 – 0.0 147.8 226.0 105.0

SD – 60.5 193.7 75.1 – – – 106.6 – – – – 385.2 – – – – 17.0 117.7 29.0 – 379.7 – – – 109.6 183.4 79.8 – – 111.1 – –

Does opportunity make the poacher? 59

Notes 1 This is the suspected limit in which a market can have a direct impact on local poaching according to Pires (Under Review). 2 It should be noted that anecdotal evidence does suggest poachers sell parrots via rivers that connect Iquitos to other villages and possibly cities (Gonzàlez, 2003). 3 Interestingly, Lima’s parrot markets are smaller in parrot quantity compared to Arequipa – another regional market – despite having more than eight times their human population. 4 These species are the golden-collared macaw and the red-shouldered macaw. 5 The second and third highest poached parrots on the Bolivia market are the yellowchevroned parakeet and the blue-fronted parrot.

References Brantingham, P. L. and Brantingham, P. J. (1981). ‘Notes on the Geometry of Crime’. In P. J. Brantingham and P. L. Brantingham (Eds.), Environmental Criminology. Beverly Hills, CA: Sage. Cantu, J.C.G., Saldana, M.E.S., Grosselet, M. and Gamez, J. S. (2007). The Illegal Parrot Trade in Mexico: A Comprehensive Assessment. Mexico and Washington, DC: Defenders of Wildlife. Clarke, R. V. (1997). Situational Crime Prevention. Albany, NY: Criminal Justice Press. Clarke, R. V. (1999). ‘Hot Products: Understanding, Anticipating and Reducing Demand for Stolen Goods’. Police Research Series, Paper 112. Policing and Reducing Crime Unit, Research Development and Statistics Directorate. London, UK: Home Office. Clarke, R. V. and Eck, J. E. (2005). Crime Analysis for Problem Solvers: In 60 Small Steps. Washington, DC: Office of Community Oriented Policing Services, U.S. Department of Justice. Clarke, R. V. and Newman, G. R. (2006). Outsmarting the Terrorists. Praeger Security International Academic Cloth. Clarke, R. V., Kemper, R. and Wyckoff, L. (2001). ‘Controlling Cell Phone Fraud in the US: Lessons for the UK “Foresight” Prevention Initiative’. Security Journal. 14(1): 7–22. Drews, C. (2002). ‘Attitudes, Knowledge and Wild Animals as Pets in Costa Rica’. Anthrozoos: A Multidisciplinary Journal of the Interactions of People and Animals. 15(2): 119–138. Eck, J. E., Clarke, R. V. and Guerette, R. T. (2007). ‘Risky Facilities: Crime Concentration in Homogeneous Sets of Establishments and Facilities’. Crime Prevention Studies. 21: 225–64. Felson, M. and Clarke, R. V. (1998). ‘Opportunity Makes the Thief: Practical Theory for Crime Prevention’. Police Research Series Paper. 98. London, UK: Home Office. Gastanaga, M., Macleod, R., Hennessey, B., Nunez, J. U., Puse, E., Arrascue, A., Hoyos, J., Chambi, W. M., Vasquez, J. and Engblom, G. (2010). ‘A Study of the Parrot Trade in Peru and the Potential Importance of Internal Trade for Threatened Species’. Bird Conservational International. 1–10. Gibbs, S. (2010). Applying the Theory and Techniques of Situational Criminology to Counterinsurgency Operations: Reducing Insurgency through Situational Prevention. Naval Postgraduate School Monterey, California. Gonzàlez, J. A. (2003). ‘Harvesting, Local Trade, and Conservation of Parrots on the Northeastern Peruvian Amazon’. Biological Conservation. 114: 437–446.

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Guerette, R. T. (2007). Migrant Death: Border Safety and Situational Crime Prevention on the U.S.-Mexico Divide. New York, NY: LFB Scholarly. Herrera, M. and Hennessey, B. (2007). ‘Quantifying the Illegal Parrot Trade in Santa Cruz de la Sierra, Bolivia, with Emphasis on Threatened Species’. Bird Conservation International. 17, 295–300. Herrera, M. and Hennessey, B. (2008). ‘Monitoring Results of the Illegal Parrot Trade in the Los Pozos Market, Santa Cruz De La Sierra, Bolivia’. Proceedings of the Fourth International Partners in Flight Conference: Tundra to Tropics. 232–234. Howell, S.N.G. and Webb, S. (1995). A Guide to the Birds of Mexico and Northern Central America. New York, NY: Oxford University Press. INEI (National Institute of Statistics and Information). (2012). Retrieved from www.inei. gob.pe/ Johnson, S. D. and Braithwaite, A. (2009). ‘Spatio-Temporal Modelling of Insurgency in Iraq’ (From Reducing Terrorism Through Situational Crime Prevention, pp. 9–32, Joshua D. Freilich and Graeme D. Newman (Eds.), see-NCJ-229596). Juniper, T. and Parr, M. (1998). Parrots: A Guide to Parrots of the World. New Haven, CT: Yale University Press. Langworthy, R. (1989). ‘Do Stings Control Crime? An Evaluation of a Police Fencing Operation’. Justice Quarterly. 6(1): 27–45. Langworthy, R. and Lebeau, I. (1992). ‘The Spatial Evolution of Sting Clientele’. Journal of Criminal Justice. 20(2): 135–145. Lemieux, AM and Clarke, R. V. (2009). ‘The International Ban on Ivory Sales and Its Effects on Elephant Poaching in Africa’. British Journal of Criminology. 49: 451–471. Pain, D. J., Martins, T.L.F., Boussekey, M., Diaz, S. H., Downs, C. T. Ekstrom, J.M.M. . . . Widmann, I. D. (2006). ‘Impact of Protection on Nest Take and Nesting Success of Parrots in Africa, Asia, and Australia’. Animal Conservation. 9: 322–330. Pease, K. (1998). ‘Repeat Victimization: Taking Stock’. Crime Detection and Prevention Paper Series Paper #90. London, UK: Home Office. Pennell, S. (1979). ‘Fencing Activity and Police Strategy’. The Police Chief. (September): 71–75. Petrossian, G. (2012). ‘The Decision to Engage in Illegal Fishing: An Examination of Situational Factors in 54 Countries’ (Doctoral Dissertation). Pires, S. F. (Under Review). The Heterogeneity of Illicit Parrot Markets: An Analysis of 7 Neo-Tropical Markets. Pires, S. F. (2012). ‘The Illegal Parrot Trade: A Literature Review’. Global Crime. 13(3): 176–190. Pires, S. F. and Clarke, R. V. (2011). ‘Sequential Foraging, Itinerant Fences and Parrot Poaching in Bolivia’. British Journal of Criminology. 51: 314–335. Pires, S. F. and Clarke, R. V. (2012). ‘Are Parrots CRAVED? An Analysis of Parrot Poaching in Mexico’. Journal of Research in Crime and Delinquency. 49: 122–146. Pires, S. F. and Moreto, W. (2011). ‘Preventing Wildlife Crimes: Solutions that Can Overcome the “Tragedy of the Commons”’. European Journal on Criminal Policy and Research. 17: 101–123. Raub, R. (1984). ‘Effect of Antifencing Operations on Encouraging Crime’. Criminal Justice Review. 9(2): 78–83. Schneider, J. L. (2008). ‘Reducing the Illicit Trade in Endangered Wildlife the Market Reduction Approach’. Journal of Contemporary Criminal Justice. 24(3): 274–295. Schneider, J. L. (2012). Sold Into Extinction: The Global Trade in Endangered Species. Santa Barbara, CA: Praeger.

Does opportunity make the poacher? 61 Sherman, L., Gartin, P. R. and Buerger, M. E. (1989). ‘Hotspots of Predatory Crime: Routine Activities in the Criminology of Place’. Criminology. 27: 27–56. Sutton, M. (1998). ‘Handling Stolen Goods and Theft: A Market Reduction Approach’. Home Office Research Study. 178. London, UK: Research, Development and Statistics Directorate. Sutton, M. (2003). ‘How Burglars and Shoplifters Sell Stolen Goods in Derby: Describing and Understanding the Local Illicit Markets: A Dynamics-of-Offending Report for Derby Community Safety Partnership’. Internet Journal of Criminology. Sutton, M. (2008). ‘How Prolific Thieves Sell Stolen Goods: Describing, Understanding, and Tackling the Local Markets in Mansfield and Nottingham: A Market Reduction Approach Study’. Internet Journal of Criminology. Sutton, M., Schneider, J. and Hetherington, S. (2001). ‘Tackling Theft with the Market Reduction Approach’. Crime Reduction Research Series Paper. 8. London, UK: Research and Statistics Directorate, Policing and Reducing Crime Unit. Weiner, K., Stephens, C. and Besachuk, D. (1983). ‘Making Inroads into Property Crime: An Analysis of the Detroit Anti-Fencing Program’. Journal of Police Science and Administration. 11(3): 311–27. White, M. D. and Fisher, C. (2008). ‘Assessing Our Knowledge of Identity Theft: The Challenges to Effective Prevention and Control Efforts’. Criminal Justice Policy Review. 19(1): 3–24. Wright, T., Toft, C. A., Enkerlin-Hoeflich, E., Gonzalez-Elizondo, J., Albornoz, M., RodriguezFerraro, A. . . . Wiley, J. W. (2001). ‘Nest Poaching in Neotropical Parrots’. Conservation Biology. 15: 710–720.

4

Can the Problem Analysis Module (PAM) help us imagine new preventative solutions to a specific tiger poaching issue? Jennifer Mailley

Introduction While working within the conservation community of Malaysia in 2010, the author noticed that criminologists were not involved in conservation programmes designed to decrease tiger poaching incidents. Furthermore, conservation resources were consistently focused on increased enforcement efforts rather than the prevention of poaching. This raised the question of whether the application of the problemsolving methodology of situational crime prevention (Clarke, 2008) might lead to the development of new ways to prevent tiger poaching. This chapter describes what happened when data about tiger poaching collected by the non-governmental organisation TRAFFIC South East Asia were analysed using the Problem Analysis Module (PAM), an online interactive tool, commonly used by police analysts to divide a specific crime problem into its constituent parts and therefore aid in devising possible solutions. The data available were (a) interviews conducted in 2008 with local Malaysian hunters living in and around the forest reserve of interest and (b) intelligence-style informant information gathered in 2009, describing the modus operandi of one specific group of local tiger poachers. This chapter describes how the PAM, a tool typically used by urban police forces, is useful for the study and prevention of tiger poaching in remote, rural areas. The process is described in detail to walk readers, especially those new to crime prevention theory and practice, through each step of the module. The aims of the analysis were to (a) organise information already known about the specific tiger poaching problem under scrutiny; (b) to identify new preventive solutions to the problem; (c) to clarify specific knowledge gaps which needed to be filled in order for further progress to be made; and (d) to communicate other recommendations and observations regarding future efforts to replicate the process described here. Despite the work’s limitations, both the usefulness of the problem-solving approach, and the need for organised and properly funded collaboration, are evidenced.

Context: tiger populations and legal status in Malaysia Malaysia became party to the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) in 1977. All tiger subspecies are listed in Appendix I of CITES, which prohibits all commercial international trade of

Can the Problem Analysis Module help us? 63 tigers, their parts and derivatives (CITES, 2010). Each party to CITES is required to pass national legislation which enacts CITES limits on transnational trade. In Malaysia this legislation is titled the International Trade in Endangered Species Act 2008: Act 686. The law which protects tigers while still on Malaysian soil was, until August 2010, the Protection of Wildlife Act 1972. In August 2010, a revised version of this legislation was passed by parliament, titled the Wildlife Conservation Act 2010: Act 716. Tigers are listed as a totally protected species on both the old and new legislation, and hunting within the country is illegal and has been since 1972 (TRAFFIC SEA, 2010). Hence the term poaching is frequently used when discussing tiger killing, throughout this chapter, the exception being when tigers are killed due to (claimed) threats to human life. Over the past few decades, Malaysian tiger populations have decreased significantly due to poaching that supplies tiger parts to the illegal wildlife trade (Lynam et al., 2007). Poaching has been shown to be the most significant threat to tiger populations, exceeding even that of habitat loss, prey depletion and forest defragmentation. The prevention of poaching, especially of resident breeding females, has been argued to be the most essential short-term conservation effort to be made (Chapron et al., 2008). The only species of tiger found on Peninsular Malaysia is Panthera tigris jacksoni. This species is classified as endangered by the International Union for Conservation of Nature (IUCN) Red List (IUCN, 2009). The size of Malaysia’s remaining wild tiger population is not known for certain but thought to range from a few thousand (Clements et al., 2010) to perhaps as low as 500 (MYCAT, 2010a). The main motivation for tiger poaching can be broadly described as monetary gain, with meat sold in ‘wild meat’ restaurants; entire skins sold and prized as trophies and signs of prosperity and power (Shepherd and Magnus, 2004), while teeth, claws, penises, whiskers and bone are used in a variety of traditional Asian medicines and/or ‘magical’ concoctions (MYCAT, 2010b). However, the exact use of tiger parts varies according to local traditions and therefore geography (Mills and Jackson, 1994; Shepherd and Magnus, 2004). It is arguable that any attempts to disrupt poaching at the local level should be based on a detailed understanding of what the tiger carcasses from that local area are to be used for.

The data TRAFFIC South East Asia (TSEA) in Kuala Lumpur is a non-government organisation partially funded by the World Wildlife Fund (WWF). TSEA is one office of a global network of TRAFFIC offices, which together form a wildlife trade monitoring network. TSEA employs a variety of researchers, some of whom have contact with local Malaysian communities where wildlife poachers live. The vast experience and knowledge of TSEA staff spans many years and projects, and talking to them assisted the author, relatively new to the details of poaching problems, to understand the tiger poaching problem in general terms.

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The very process of asking questions lead to common answer on numerous topics, ‘Well, it depends, sometimes they do x but sometimes y’; this hinted a crime-specific approach might be beneficial. In addition to a wealth of wideranging information, TSEA were able to provide two related empirical data sets. These data were 1) 2008 hunting activity survey: The aggregate results of semi-structured interviews conducted in 2008 among communities living in or on the edge of one of Malaysia’s forest reserves. The survey explored general hunting practices and included some information on local tiger poaching. 2) 2009 tiger poacher data: Intelligence-style information compiled during 2009 from informants within the same communities as interviewed in the 2008 hunting survey. This information gave details about the sociodemographics and modus operandi of ten local tiger poachers. Because of the nature of this information, it is not ethical to detail in this chapter the precise location of the forest reserve where the poaching problem is concentrated. The translated and aggregate results available for this analysis were restricted to accounts where information was verified by at least one other person in contact with the original TSEA researcher. The majority of the reserve is covered in primary and secondary forest which has a few towns dispersed at its periphery, some indigenous peoples’ villages within it, and which has a main connecting road running through it. The peripheral towns are inhabited by a mixture of Malaysian ethnicities (Malay, Chinese Malaysian and Indian Malaysian), while the villages inside the reserve are inhabited by Orang Asli (meaning ‘original people’), who occupy a place in society similar to that of the aboriginal peoples of Australia and New Zealand. The Orang Asli typically live in small communities, often in the forested areas of Peninsular Malaysia; have restricted regular income sources; and prior to being settled into static villages, were traditionally semi-nomadic subsistence farmers and hunters (Abdullah, 2011). 2008 hunting activity survey In 2008 a semi-structured hunting survey was conducted among individuals who self-reported hunting of any sort, and who resided in the towns on the edge of the forest reserve or the Orang Asli villages within it. Developed by TSEA, surveys were conducted in local languages by a researcher who had strong personal links with the local communities, using a snowballing methodology to identify active hunters. The questions asked were about hunting of many species of conservation interest, including but not restricted to tigers. A total of 76 people who self-reported involvement in hunting were interviewed. The results paint a picture of general hunting behaviour among the populations accessible to the TSEA researcher and set the context for a more detailed analysis of tiger poaching activity.

Can the Problem Analysis Module help us? 65 Table 4.1 Self-reported occupation, RELA gun access and general hunting frequency, 2008 Ethnicity Occupation Farmer General labourer Businessman Government Total RELA gun access Hunting frequency Daily Weekly Monthly Occasional Total

Orang Asli n = 49 (%)

Chinese n = 8 (%)

Malay n = 16 (%)

Other n = 3 (%)

All hunters n = 76 (%)

25 (51) 21 (43)

2 (25) 1 (13)

8 (50) 4 (25)

1 (33) 2 (67)

36 (47) 28 (37)

– 3 (6) 49 (100) 2 (4)

5 (63) – 8 (100) –

3 (19) 1 (6) 16 (100) 4 (25)

– – 3 (100) 1 (33)

8 (11) 4 (5) 76 (100) 7 (9)

4 (8) 19 (39) 9 (18) 17 (35) 49 (100)

– 1 (13) – 7 (88) 8 (100)

– 2 (13) 1 (6) 13 (81) 16 (100)

– 1 (33) – 2 (67) 3 (100)

4 (5) 23 (30) 10 (13) 39 (51) 76 (100)

Table 4.1 above displays the aggregate results of self-reported hunting frequency amongst interviewees. Interviewees are divided by ethnic group. In Malaysia, ethnicity dictates to a large extent the likely socio-economic conditions and opportunities faced by Malaysians. Orang Asli make up the majority of the hunting group, and it is clear that although for some ethnic groups the sample size is small, Orang Asli commit the majority of general hunting and are typically employed in lower paying jobs than other ethnicities. Moreover, their access to RELA guns, licensed by the Malaysian Volunteer Corps or Ikatan Relawan Rakyat Malaysia, commonly referred to as RELA, is much lower than other ethnicities. The guns are only to be used for the protection of human life and property but often become involved in poaching. Only 4 per cent of Orang Asli reported having access to RELA guns, whereas 25 per cent of Malays interviewed did so. Orang Asli also reported hunting more frequently than did other ethnicities. The hunters displayed variation in hunting location according to ethnicity: the Orang Asli generally hunted in forested areas, while the majority of the eight Chinese hunters in the data set preferred to hunt in rubber and palm oil plantations. Only three people admitted they had ever been involved in tiger poaching. All three were Orang Asli who used guns for tiger kills, and all three claimed that the kills were in response to threats posed by the tigers. However, the perceived motivation for the tiger hunts according to those who had heard about the kills but did not admit involvement, was to sell the dead animal on. A whole tiger was reported being worth 30,000 Malaysian Ringgit – about 9,000 USD – to the hunters. This may be exaggerated, but is equivalent to 5 to 10 years’ worth of average Orang Asli household income, based on a recent income estimate (Rosliza and Muhamad, 2011).

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2009 tiger poacher data The information gained by the 2008 interviews is complemented by intelligencestyle information gathered by TSEA’s researchers during research projects and via personal ties with the communities in and around the forest reserve. The data described here are restricted to 2009 and relate to the same geographic area as the 2008 interviews. Ten significantly active poachers named in tiger poaching incidents were described. All were Malaysian and lived in the vicinity of the forest reserve. Their names, personal details, modus operandi, and the dates and times of incidents as described here all passed cross-referencing checks carried out by the original researcher. Pieces of data eliminated from the final data set included names of individuals only named once, and information based only on hearsay (as opposed to direct observation) and not corroborated by a separate source. The data also excluded cases where other individuals travelled from outside the local community or from other countries in order to hunt tigers. It is believed these ‘commuter’ poachers comprised organised groups who used local Orang Asli in tracking prey, but the numbers of tigers they killed was not known. None of the ten individuals named specialised in only tiger poaching. All ten also poached pangolin (scaly ant eaters), nine also poached barking deer and samba deer (which are both favourite tiger prey species and also protected by national law), while eight also hunted wild ox. The species recorded were restricted to those of particular conservation interest, and it is probable that all the hunters hunted or poached other species of animals not listed in the data. Further interviews with hunters would give more insight in to the full nature and extent of their activity. The ten poachers were together believed to be responsible for the deaths of 14 tigers during 2009. The exact make-up of the groups involved in each poaching incident was not recorded, since records are of the individuals rather than of incidents. Further details of their modus operandi are described in the Results section, which follows a brief description of the online tool used to analyse the tiger poaching problem.

The Problem Analysis Module (PAM) PAM is an interactive online tool which walks the user through the basic elements of a crime problem. Drawing from the routine activity approach (Cohen and Felson, 1979), it collects information about offenders, targets, guardians and other elements such as weapons. It has been described as ‘a controlled way of brainstorming’ (Center for Problem-Oriented Policing, n.d.a.). By asking specific questions about each element, and requiring typed input of the answers, it makes the user consider in detail and in isolation what is known about each element of the problem. The detailed questions are devised to guide police analysts who are seeking solutions to very well defined crime types (Clarke, 2008; Scott et al., 2008). PAM is available at www.popcenter.org/learning/pam/.

Can the Problem Analysis Module help us? 67 If enough answers are available, the software behind PAM compares the problem under analysis with a database of previous case studies in order to suggest which preventive responses might be useful and which are unlikely to be effective. PAM was designed for and is routinely used by police crime analysts involved in Problem-Oriented Policing (Clarke and Eck, 2003). The results section below presents some of the questions asked by the PAM, the answers given during the analysis of tiger poaching in the Malaysian Forest Reserve under consideration and the subsequent preventive solutions or further research questions which each question or section stimulated. A small selection of PAM’s questions are presented concerning definition of the specific problem; repeat offending; offender motives; offenders’ risks; human facilitators; offender information; offender tools; and a selection of questions about targets. These questions asked by PAM are underlined, with the non-underlined text representing the answers given during analysis. Where applicable, text in italics communicates the inner monologue stimulated by the PAM questions, often resulting in the identification of further research questions, possible preventive solutions or broader observations about the problem. The results are presented like this to show the utility of PAM in action, rather than assume that the reader can devise from the results, where in the process different ideas emerged. In the future, input from experts such as conservationists will be needed to hone the initial analysis presented here, and transparently presenting the thought process will make it easier to add to, or correct, those processes at the relevant point in the process.

Results PAM section 1: defining the problem Describe the problem Poaching incidents resulting in the deaths of an estimated 14 tigers, carried out during 2009 by 10 local residents of a Malaysian forest reserve. Shooting is the most common cause of death; snares sometimes initially trap the tiger. Anecdotal evidence suggests that a high proportion of known tiger deaths occur near to a main road running through the reserve and near water sources (forest streams). Which of the following best characterize the problem? (chosen from a dropdown list) Predatory behaviour by an offender against a specific victim. Have you thought of any possible responses? Yes: some actions are being implemented as per the Malaysian National Tiger Action Plan (Department of Wildlife and National Parks [DWNP], 2008). One purpose of conducting this analysis is to highlight further possible responses.

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PAM section 2: offenders Who are the offenders? Ten Malaysian males who live locally and have mixed occupations. The majority are in low-income jobs such as farming. Two act as traders as well as poachers. Additional thought: The majority of hunters according to the 2008 hunting survey were Orang Asli, but the majority of people described in this data set are not Orang Asli. This means that for this particular set of poachers, educational or outreach programmes directed at the Orang Asli must be to make them more capable guardians of the forest, rather than educating them of the illegality of tiger hunting. What proportion of the offenders are repeatedly involved [in the problem]? An estimated 70 per cent of offenders are involved in killing more than one tiger. What proportion of the problem events involves these repeat offenders? It is not possible to determine which events involved which people: data are organised at the species level rather than at the incident level. Additional thought: Altered data capture practice is needed where records are ordered around each incident (tiger death) and the full circumstances of that incident. Organising the data available into the sections suggested by the Problem Analysis Triangle (Clarke and Eck, 2003) would aid this process. Data organised at the incident level would allow easy identification of who are the repeat and worst offenders, and who should therefore be subject to closest scrutiny and investigation; whether there are patterns in the times and locations they use for poaching, and what does this suggest for preventive interventions; and which offenders regularly cooperate, and what does this suggest about how to decrease the success of any group. What are the offenders’ motives for engaging in these behaviours? The apparent motive for tiger poaching by the ten Malaysian poachers is monetary gain. However, Orang Asli poachers in the 2008 interviews used the excuse of killing in self-defence. It is not clear from the records available what proportion of tiger deaths locally, and presumably therefore nationally, is due to either motivation. Additional thought: Poaching for monetary gain and killing in genuine selfdefence are two very different problems, requiring different responses. For

Can the Problem Analysis Module help us? 69 example, a change in the market value of tiger carcasses or products will not affect the rate of deaths due to human/tiger conflicts but would be predicted to affect the rate of deliberate poaching acts. Conversely, an area with a high proportion of deaths due to genuine conflict situations would necessitate the input of experts in the resolution of human-animal conflict. Research questions remaining: (1) What proportion of tiger deaths are genuinely due to conflict situations? (2) Would the ten poachers decrease their tiger poaching rates if the monetary reward was decreased relative to other income sources? (3) How large need that decrease in reward be, to affect poaching rates? Those who value tigers intrinsically but act when opportunities present themselves, might be deterred more easily than those who do not see an intrinsic value in the tiger and would poach for even relatively small financial sums. Since the monetary reward for a tiger carcass is high (see earlier), it seems likely that only a significant increase in income from alternative means would decrease poaching rates. Do offenders rely on friends, acquaintances or other supporters to engage in problem behaviours? Yes. Five of the ten offenders borrow guns from acquaintances; all rely on acquaintances not speaking to local enforcement, or informed enforcement not taking effective action; some are rumoured to pay off local enforcement; groups sometimes hunt together and also rely on local Orang Asli to not intervene; and more than one person will typically be needed to take a whole tiger carcass from the ground to a vehicle for transportation. Can these human facilitators be kept from the offenders? No, most ‘collaborators’ are simply part of the local community. Additional thought: However, some collaborations might be able to be reduced: for example, by encouraging whistle blowing in the local community; decreasing law enforcement corruption; empowering Orang Asli and other forest users to guard the forest appropriately; and increase the effectiveness of the (few) existing enforcement patrols. What risks do the offenders face? Offenders’ perceptions of risk are not known for this specific data set but might include the risk of injury from tigers or other wild animals; the risk of injury or disease from hunts involving trekking and camping, though some of these risks will also be present in everyday life; and the risk of recrimination from other poachers or traders or from law enforcers. These are similar to those self-reported in other geographies (Knapp, 2012.) The perceived risk posed by enforcement is presumably low, since both the certainty of detection and the severity of punishment are low (DWNP, 2008).

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Additional thought: Further research is needed via in-depth interviews with poachers to describe their perceptions of risks in order to highlight how these risks can be manipulated, or new ones created. Can new risks be created? Yes: anti-poaching patrols as proposed in Malaysia’s National Tiger Action Plan (DWNP, 2008) should increase the (perceived) risks of being caught acting illegally. Additional thought: However, the probability of a patrol randomly coming across illegal activities would be low, just as police patrols will rarely be present during the commission of a crime (Kelling et al., 1974). Intelligence-led patrols would have a better chance of detecting activity in real time, and patrols using specialist software such as MIST (Management Information SysTem) are able to utilise GIS features and assess outcomes while controlling for variation in patrol activity levels (Hotte et al., 2009). Furthermore, patrols are not the only way to increase risks: an increase in whistle blowing from the local community; empowering Orang Asli and other forest users to intervene where safe to do so; decreasing any corruption of local law enforcement personnel, and increased publicity about any of these measures, would also increase the risks of apprehension and effective punishment. What information is needed by the offenders to be effective (e.g. about targets, places, tools, etc.)? In deliberate poaching events, the location or likely location of the tiger is the key piece of information needed. In addition, successful poachers need to know how to track tigers in the forest or where their territories overlap with locations providing the best chances of a successful and safe kill; how to minimise the risks of tiger attack or other injury while poaching; who to sell the carcass or parts to and how to arrange this; and perhaps how to process the carcass if their buyer requires this. How do offenders acquire this information? Knowledge of tiger locations is gained from either local rumour, especially if the tiger is close to accommodation, and supplemented by some poachers by a paid network of informants who seek out and report back on tiger sightings further afield from poachers’ homes. Local poachers have intimate knowledge of the surrounding forest. It is not clear from the data how the local poachers come to be aware of who can act as a trader; and how to process carcasses, if this is necessary. Can the usefulness or effectiveness of the information be reduced? Potentially: one example would be if disinformation was circulated stating that tigers were present where they were not, causing poachers to deploy on fruitless expeditions. The ‘hit rate’ of successful expeditions should fall. Some poachers

Can the Problem Analysis Module help us? 71 might not subsequently respond to information about an actual tiger, believing that effort outweighed the likelihood of reward. However, such a campaign would need to be executed carefully: in a small community those supplying incorrect information risk retaliation if their motives are suspected. Additional thought: Overall, detailed understanding of the decision-making processes of poachers, and those who collaborate with them, is required before it is possible to manipulate the factors which they take in to consideration. Researchers integrated into the local community are best placed to judge whether interventions such as disinformation might work. PAM section 3: offender tools What tools do offenders use? Offenders use a variety of tools to locate, track, kill and remove a tiger from the forest. Locating a tiger requires tracking skills; killing often requires a gun. Getting to and from the poaching site requires transport as far as possible in to the forest, and perhaps camping equipment and supplies if the poachers are to stay overnight in the forest. Guns: the tigers in the 2009 data set were all killed by bullets, but the type of gun used in each incident is not known. Nine of the known poachers had access to guns. Five reported borrowing guns, of unspecified types. Three reported using homemade guns. Homemade guns are reportedly made by some specialist locals using locally sourced timber for shotgun style butts, and car parts to make effective barrels and trigger mechanisms. (These handmade guns have not been seen by the author and the practicalities of making them remain unclear.) Regardless of their exact nature, homemade guns are entirely illegal and reportedly hidden in the forest between uses, in order to avoid detection by the authorities. Two poachers reported using RELA guns, and these were the same two individuals reported to be traders in tiger carcasses and parts as well as being poachers. Vehicles: six of the poachers owned their own vehicle (car or 4 by 4) and another owned a motorbike. These vehicles provided transport (as far as forest density would allow) to and from hunting areas. Forest access: although not in the strictest sense a tool, forest access enables poaching activity. At least 38 entry and exit points along the main intersecting road of the reserve were known to the TSEA experts. Some points were where a low road-side fence had been broken; others were at gaps in the fence which were due to the discontinuous nature of its design. Can offenders’ acquisition of these tools be prevented? The acquisition of guns appears to require a more detailed exploration but can be split in to at least two problem types: homemade guns and RELA guns. Both problems need to be fully mapped out to reveal any potential intervention pinch points, and would require collaboration between firearms experts; local non-government organization researchers; and enforcement personnel as well as criminologists.

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RELA guns are authorised for use only to protect life and property, and licenses are issued only after a person has been active in the RELA community for five years. It is of interest that the two poachers from the 2009 data set who also acted as traders were the same two individuals who used RELA guns for tiger poaching. It may be that these individuals are generally better connected within the local community. Any individuals who abuse their RELA licenses to kill tigers should be closely scrutinised by local enforcement authorities. Furthermore, since it is known that homemade guns are hidden in the forest between uses in order to avoid detection by police, intelligence gathered by anti-poaching patrols should aim to identify these hiding places and pass the information on to the relevant authorities so these firearms can be confiscated. Metal detectors might help the search for these guns. Finally, technologies such as ANPR (automatic number plate recognition), or other means of monitoring vehicles activity, should be used to increase the intelligence gathered and passed to local enforcement authorities concerning the movements of vehicles of known suspects. This would assist local authorities when they were investigating past poaching incidents, and when they were planning any interventions to intercept ‘live’ incidents or prevent future ones. How do the offenders bring the tools to the problem location or target? Offenders carry tools and weapons on their person, and so both legal and illegal firearms will be present on persons and in vehicles which are travelling to or from tiger poaching events. This provides ideal reason to stop and search vehicles known to be regularly involved in poaching activities. PAM section 4: targets What are the targets, or who are the victims? Tigers Are there some victims/targets that are repeatedly involved in the problem? No, by definition the death of an individual animal means that it cannot again be victimised, but tigers are repeatedly targeted at a species level. Why are these targets/victims at the locations where the problem takes place? Tigers are present in the forest because they live there. The opinion of TRAFFIC’s researchers was that tiger killings in the data set displayed some geographical concentration:

Can the Problem Analysis Module help us? 73 a) Near or on a main road which intersects the forest reserve, or b) Near watering holes and streams which tigers frequent for drinking, swimming or hunting. Water sources are frequented by tigers during their routine activities (drinking, swimming, hunting), and this is well known to poachers. Some experienced poachers reportedly rely on local knowledge of water sources to exploit the situation during the dry season when both tigers and prey are naturally concentrated around fewer streams and smaller ponds and lakes. Anti-poaching patrols should use this information to inform their patrol routes. Can the target/victim be relocated somewhere else? No. Apart from the immense difficulties in relocating the elusive and territorial tiger, an outcome where tigers were relocated away from what is designated as a forest reserve and one of their last habitat strongholds in Malaysia (MYCAT, 2010a) is unacceptable. What barriers keep offenders from the target? Tigers are naturally offered protection by the cover of dense forest. In some areas, fences also prevent offenders from entering the reserve. How do these barriers (between offenders and targets) fail and under what circumstances? Natural barriers are breached when logging (both legal and illegal) decreases natural cover, and when humans clear areas of forest for other reasons such as to build roads or settlements, and for agriculture including large-scale oil-palm plantations, and small-scale clearance for subsistence crops. Can the strength of existing barriers between offenders and targets be increased? Yes: if temporary logging roads are left so that vegetation regrows, in so far as reforestation is possible, this increases the cover available for tigers and decreases forest permeability. Roads could be blocked off to vehicular access while the forest regenerates. The opportunities for forest penetration offered by the main intersecting road could perhaps be minimised by increasing the height of fences, but this has the complication of then blocking the routes of all wildlife which needs to cross the road. It is important to harness the knowledge of conservationists who have studied the effects of human encroachment in order to assess whether and how the barriers between man and forest can be increased.

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Can the attractiveness of the target/victim be removed? Yes possibly: a decrease in the price paid for tiger carcasses could be achieved through reducing the demand for tiger skins, meat and parts. Malaysia’s National Tiger Action Plan already has in place an extensive education and outreach programme which aims to achieve a decrease in demand from within Malaysia. Demand reduction is a complex and critical issue. A substantial report from TRAFFIC East Asia (Nowell and Ling, 2007) reports that there is some evidence that a total ban on the use of tiger bone or other tiger derivatives in China has decreased the volume of tiger bone available for sale in China but there remains a market for tiger skins in the Tibetan landscapes. Furthermore, there is persistent debate about how farmed tigers would affect market demand (Nowell and Ling, 2007). It is likely that there are many parallels with the market dynamics for protected wildlife and the illegal drugs trade (Schneider, 2008), and this is another area where criminological knowledge might be applicable. The trade in bone and in skins may need to be treated as separate problems.

Discussion Preventing the death of a tiger is clearly a more effective conservation goal than retrospectively investigating its demise. First, it is probably of little consequence to tiger populations whether a poacher is apprehended or not: that tiger and its unique genetic variation are already gone, and the poacher is unlikely to be so unique that they are not replaced by another. Second, even if the National Tiger Action Plan’s goal of increasing the number of incarcerated poachers (DWNP, 2008) is achieved, this may not have the desired effect on poaching rates. Evidence from criminology has shown that in the USA, increased numbers of prisoners and increased sentence length can only account for a small proportion of the decrease in crime which America experienced in the 1990s (Farrell et al., 2010). In contrast, the 25 techniques of situational crime prevention (Cornish and Clarke, 2003) have proved effective in decreasing an array of crime types ranging from burglary to car theft, child sexual abuse and cybercrime (Clarke, 2008). Finally, if law enforcement is seen as just one means among many for achieving crime or harm reduction (Scott et al., 2008), then reliance on law enforcement is lowered. This is particularly beneficial where enforcement resources are limited, as they typically are within conservation (Nijman and Shepherd, 2007), or where the suspicion of corruption casts doubt over law enforcement’s reliability. The previous section of this chapter described some stages of the problemsolving methodology of situational crime prevention, when analysing the problem of tiger poaching in one forest reserve in Malaysia using the online tool PAM. The concluding sections of the chapter describe the full range of preventive interventions which were conceived of as a result of the analysis, and the two sizeable knowledge gaps highlighted by the analytical process. Table 4.2 below lists the preventive actions which appear to offer hope of decreasing tiger poaching in the forest reserve of focus.

Table 4.2 Possible preventive interventions for the specific tiger poaching problem analysed in this chapter Increase effort • Utilize disinformation about tiger locations to decrease the hit rate of hunting expeditions • Disincentivise locals who report tiger sightings to poachers by monetary rewards • Increase the cost that informants charge poachers, where used • Metal detectors to detect and remove illegal home-made guns • Stricter controls and audits on RELA gun use Increase risk • Encourage whistle blowing by local community and forest visitors • Empower local communities to intervene powers of civil arrest for example • Increase effectiveness of enforcement patrols (police and any non-government organization activity) • Decrease corruption of local police • Intelligence-led clampdown on collaborators who assist or enable poaching activities and associated trade (along the trade route as well as in the poaching area) • Use technology such as CCTV or ANPR to monitor the activity of vehicles belonging to known poachers, especially along the 38 forest exit/entry points of the intersecting road • Increase certainty and severity of punishment (already achieved in part due to new legislation) • Increase perception of certainty and severity of punishment – maximize the publicity given to successfully prosecuted poaching investigations; garner support for making ‘examples’ of poachers from sympathetic judiciary; ensure all judiciary are fully aware of how few wild tigers are left Decrease reward • Decrease demand from destination markets • Increase income from alternative legal sources (decreases relative reward) Remove excuses • Erect signs at all forest entry and exit points along the intersecting road, stating that poaching is illegal (and perhaps using pictures to communicate key species) • Publicity campaigns to educate all sectors of society about legal status of various species including tigers • Effectively deal with genuine human–tiger conflict situations so that conflict is no longer a valid excuse Remove provocations • Effectively deal with genuine human–tiger conflict situations • Increase alternative income sources, where poverty is a driver of poaching

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Clearly a number of these measures are not novel ideas and are already implemented or partly implemented. For example, the charity WildAid has devised the strapline ‘When the buying stops, the killing can too’ and focuses heavily on demand reduction. Several organisations such as WWF and The Body Shop in Malaysia have been involved in many educational campaigns to increase public knowledge about the legal status of species such as tigers, and to highlight the consequences of continued poaching on future populations. The recently overhauled national wildlife legislation aims to increase the severity of punishment for poaching key species such as tigers. Malaysia’s National Tiger Action Plan is a nationwide response to growing recognition of the decreasing tiger population. The overarching goal of the Action Plan is to secure a population of 1000 wild tigers in Peninsular Malaysia by 2020. The Action Plan is coordinated and progress monitored by MYCAT. MYCAT is a joint programme of four charities which is further supported by the Department of Wildlife and National Parks Peninsular Malaysia or DWNP, who have responsibility for wildlife law enforcement in peninsular Malaysia (MYCAT, 2010b). The goal of an increased wild tiger population is to be achieved by a set of 80 actions spread across four topics. However, where the analysis presented here is of particular and specific use is in highlighting immediate and practical interventions which could arguably decrease poaching rates. The four priority suggestions are: 1

Decrease forest permeability. The permeability of the forest afforded by the intersecting road can be decreased by: a)

Increasing the effort required to enter and exit the forest reserve from the intersecting road if the fences along the road were redesigned and mended b) Removing the excuses of ignorance of the law by placing clear signs at every entry and exit point along the intersecting road, and ensuring the signs can be understood by all sectors of Malay society including the Orang Asli c) Using technology such as closed-circuit television (CCTV) or automatic number plate recognition (ANPR) to record vehicular activity at known entry and exit points along the intersecting road, especially where there is little reason to stop other than to poach 2

Monitor vehicle activity. It might be beneficial to record and pass on to local enforcement authorities the registration details of vehicles used or suspected of being used by local poachers. This together with suggestion 1c above, using CCTV or ANPR to record vehicular activity, should greatly increase the ability of local enforcement authorities to catch poachers in the act, and to gather useful intelligence. Publication of the results of this strategy, and of other strategies suggested here, would provide an incentive for enforcement authorities to act upon the information received while at the same time increasing the knowledge base available to those facing similar poaching

Can the Problem Analysis Module help us? 77

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4

problems. Any issues of local enforcement corruption need to be addressed for interventions such as this to be effective. The issue of local corruption in itself requires a separate analysis. Increase gun control. It appears that the availability of guns could be targeted by a discrete programme of measures if it was clearly understood how and when existing licensing rules are contravened. Furthermore, handmade guns which are rumoured to be hidden in the forest could be sought out and removed on the basis of their illegality. The use of guns meant only for protection of property and life (RELA guns) might flag offenders who are well enough connected to act as traders as well as poachers, at least in the particular forest reserve considered here. Consider using disinformation. The possibility of spreading disinformation about tiger presence may have the potential to occupy poachers on fruitless hunting missions and deter them from following information supplied in the longer term. However, such methodologies would have to ensure the safety of the person acting as the (dis)informer.

Remaining knowledge gaps One of the key benefits of using PAM to assess the tiger poaching problem was that it very quickly became apparent that further specificity was needed than was typically used when discussing tiger poaching. Within the very first section, ‘Defining the problem’, it became apparent that poaching by locals and by ‘commuter’ poachers required separate analyses, and that it was not possible to focus analysis on the ‘worst’ type of poaching, because no one could quantify the prevalence of poaching by locals, by commuters, motivated by money, or due to genuine human-animal conflict. Finally, it quickly became apparent that the details of offender perceptions of the choice-structuring properties of tiger poaching (that is the risks, rewards and other factors affecting their decisionmaking processes), were simply not known. Therefore the detail of the analysis possible using PAM was limited, but the need for more detailed knowledge was exemplified. A typology of tiger poaching is needed In general it is apparent that tiger poaching is a generic term used to describe a variety of specific problems. In addition to the monetarily motivated poaching by local hunters analysed here, where guns are used, there appear to be incidents where (a) tigers are killed due to ‘commuter’ poachers, who are more organised and travel from well outside the forest reserve to poach tigers, often employing local people to act as trackers and guides; (b) tigers are caught in snares set for other species, and then opportunistic individuals kill or leave the trapped tigers to die; and (c) tiger deaths due to genuine threat to human life, as reported in the 2008 general hunting survey.

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There are not to the author’s knowledge any systematic reviews of data at a local or national level which assess what proportion of tiger deaths in Malaysia are due to the various typologies of poaching: therefore the worst poaching problem posing the biggest threat to tiger populations cannot be empirically described. The research questions arising from this knowledge gap are 1 2 3

What proportion of tiger deaths are caused by local as opposed to commuter poachers? What proportion of tiger deaths occur following opportunistic capture by indiscriminate snares, and what proportion by gunshot only? What proportion of tiger deaths are motivated by monetary gain, as opposed to the killing of a tiger to end a conflict situation?

A rational choice model of tiger poachers is needed A significant amount of detail is absent from our understanding of the factors considered by the local poachers analysed here, when they plan and execute tiger hunts. Two pertinent questions raised by the analysis presented in this chapter are described below: others will undoubtedly exist for the different typologies of tiger poaching described above. Both could be answered if detailed interviews allowed rational choice involvement, event and desistance models (Clarke and Cornish, 1985) to be developed for tiger poachers in Malaysia. 1 2

Which factors do tiger poachers perceive as risks and as rewards? Therefore, how can these be manipulated to decrease poaching prevalence? What is the relationship between poachers’ financial needs, legitimate income opportunities, and their intrinsic valuation of tigers? Therefore, how large a decrease in demand for tigers, or increase in risk from poaching, or generation of alternative income is needed to achieve a significant reduction in poaching activity? Are these approaches realistically viable?

Conclusion This chapter has presented an exploratory analysis of tiger poaching by local hunters in one Forest Reserve of Malaysia. Analysis of semi-structured interview data provided by TRAFFIC South East Asia results in the identification of some of the choice-structuring properties of tiger poaching, and four conclusions which are (1) that different motivations exist for tiger poaching; (2) that separate analyses are warranted for each motivation for tiger poaching, since each may require different solutions; (3) that several empirical research questions need answering in order to fully understand the micro and macro dynamics of the tiger poaching problem; and (4) that a combination of criminological and conservation expertise is needed to move forward from the status quo and increase the effectiveness and variety of measures employed to prevent tiger poaching.

Can the Problem Analysis Module help us? 79 The aim of utilising PAM to analyse the data available has proved to be useful in several ways. First, existing knowledge about the specific poaching problem was organised in systematic manner, which was a significant bonus for the author who found herself on a steep learning curve while in Malaysia. Second, the absence of two types of knowledge, essential for problemprioritisation and in depth analysis, was quickly exemplified. Third, even given the limitations of the data available, a wide range of possible preventive solutions resulted from the analysis, many of which are believed to never have been implemented in the forest reserve of focus. Finally, it is hoped that this chapter has also demonstrated a key benefit of using PAM to encourage input from a variety of experts: regardless of whether the current author ever returns to Malaysia, the analysis and thought processes are transparently laid out, awaiting further input from conservationists who might have knowledge to challenge assumptions made, or to assist in implementing some of the solutions. PAM has assisted the communication of crime prevention’s crime-specific focus to conservationists, and assisted the communication of conservation’s species focused expertise to criminologists. Any tool which enables such collaborative and cross-disciplinary effort is a tool worth using.

Acknowledgements Much of the information gathered about tiger poaching was provided by staff of TRAFFIC South East Asia; Malaysia’s Department of Wildlife and National Parks; the ASEAN-Wildlife Enforcement Network; MYCAT and WWF-Malaysia. Immense thanks are due to all those who spent time discussing the many issues surrounding conservation and tiger poaching issues, especially given the absence of any dedicated resources to carry out the analysis presented here. While in Malaysia, the author was employed by TRACE Wildlife Forensics Network as Project Manager for the project ‘Developing wildlife forensic capacity for ASEAN biodiversity conservation’, funded by the UK’s Darwin Initiative (Project reference 17019).

References Abdullah, A. (2011). ‘Collectors and Traders: A Study of Orang Asli Involvement in Wildlife Trade in the Belum-Temengor Complex, Perak’. Centre for Malaysian Indigenous Studies (CMIS), University of Malaya, Kuala Lumpur. Chapron, G., Miquelle, D. G., Lambert, A., Goodrich, J. M., Legendre, S. and Clobert, J. (2008). ‘The Impact on Tigers of Poaching versus Prey Depletion’. Journal of Applied Ecology. 45: 1667–1674. CITES. (2010). Appendix I of CITES, valid from June 12, 2013. Accessed December 2013 at: www.cites.org/eng/app/appendices.php Clarke, R. V. (2008). ‘Situational Crime Prevention’. In R. Wortley and L. Mazorelle (Eds.), Environmental Criminology and Crime Analysis. Crime Science Series. Devon, UK: Willan Publishing.

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Clarke, R. V. and Cornish, D. B. (1985). ‘Modeling Offenders’ Decisions: A Framework for Policy and Research’. In M. Tonry and N. Morris (Eds.), Crime and Justice: An Annual Review of Research (Vol. 6). Chicago, IL: University of Chicago Press. Clarke, R. V. and Eck, J. (2003). Become a Problem Solving Crime Analyst: In 55 Small Steps. Jill Dando Institute of Crime Science, University College London, London. Clements, R., Rayan, D. M., Zafir, A.W.A., Venkataraman, A., Alfred, R., Payne, J, Ambu, L. and Sharma, D. (2010). ‘Trio Under Threat: Can We Secure the Future of Rhinos, Elephants and Tigers in Malaysia?’ Biodiversity Conservation. 19: 1115–1136. Cohen, L. E. and Felson, M. (1979). ‘Social Change and Crime Rate Trends: A Routine Activity Approach’. American Sociological Review. 44: 588–605. Cornish, D. B. and Clarke, R. V. (2003). ‘Opportunities, Precipitators and Criminal Decisions: A Reply to Wortley’s Critique of Situational Crime Prevention’. In M. J. Smith and D. B. Cornish (Eds.), Crime Prevention Studies (Vol. 16, pp. 41–96). Devon, UK: Willan Publishing. DWNP. (2008). National Tiger Action Plan for Malaysia. DWNP (Department of Wildlife and National Parks), Kuala Lumpur. Farrell, G., Tilley, N., Tseloni, M. and Mailley, J. (2010). ‘Explaining and Sustaining the Crime Drop: Clarifying the Roles of Opportunity-Related Theories’. Crime Prevention and Community Safety. 12(1): 24–41. Hotte, M., Bereznuk, S. L., Stokes, E. and Tang, J. (2009). ‘Monitoring the Effectiveness of Anti-poaching Patrols with MIST’. In The Amur Tiger in North East Asia: Planning for the 21st Century. IBSS, PIG, WWF and WCS conference material, accessed December 2013 at: www.wcsrussia.org/DesktopModules/Bring2mind/DMX/Download.aspx?EntryId =6726&PortalId=32&DownloadMethod=attachment IUCN. (2009). IUCN Red List of Threatened Species. Version 2009.1. Accessed December 2013 at: www.iucnredlist.org Kelling, G. L., Pate, T., Dieckman, D. and Brown, C. E. (1974). ‘The Kansas City Preventive Patrol Experiment. A Summary Report’. Washington DC: Police Foundation. Knapp, E. J. (2012). ‘Why Poaching Pays: A Summary of Risks and Benefits Illegal Hunters Face in Western Serengeti, Tazmania’. Tropical Conservation Science. 5(4): 434–445, 201 Lynam, A. J., Laidlaw, R., Wan Shaharuddin, W. N., Elagupillay, S. and Bennett, E. L. (2007). ‘Assessing the Conservation Status of the Tiger Panthera Tigris at Priority Sites in Peninsular Malaysia’. Oryx. 41: 454–462. Mills, J. and Jackson, P. (1994). Killed for a Cure: a Review of the Worldwide Trade in Tiger Bone. A TRAFFIC Network Report. Cambridge, UK: TRAFFIC International. MYCAT (2010a). Plight of the Tiger. Malaysian Conservation Alliance for Tigers. Accessed December 2013 at: www.malayantiger.net/v4/ MYCAT (2010b). MYCAT TRACKS, Volume 3, 2010. Accessed December 2013 at: www. malayantiger.net/web/Pdf%20files/MYCAT%20Tracks%202008-2009.pdf Nijman, V. and Shepherd, C. R. (2007). ‘Trade in Non-native, CITES-listed, Wildlife in Asia, as Exemplified by the Trade in Freshwater Turtles and Tortoises (Chelonidae) in Thailand’. Contributions to Zoology. 76(3): 207–212. Nowell, K. and Ling, X. (2007). Taming the Tiger Trade: China’s Markets for Wild and Captive Tiger Products Since the 1993 Domestic Trade Ban. Hong Kong, China: TRAFFIC East Asia. Rosliza, A. M. and Muhamad, H. J. (2011). ‘Knowledge, Attitude and Practice on Antenatal Care Among Orang Asli Women in Jempol, Negeri Sembilan’. Malaysian Journal of Public Health Medicine. 11(2): 13–21

Can the Problem Analysis Module help us? 81 Schneider, J. L. (2008). ‘Reducing the Illicit Trade in Wildlife: The Market Reduction Approach’. Journal of Contemporary Criminal Justice. 24: 274–295. Scott, M., Eck, J., Knutsson, J. and Goldstein, H. (2008). ‘Problem-oriented Policing and Environmental Criminology’. In R. Wortley and L. Mazorelle (Eds.), Environmental Criminology and Crime Analysis. Crime Science Series. Devon, UK: Willan Publishing. Shepherd, C. R. and Magnus, N. (2004). Nowhere to Hide: The Trade in Sumatran Tiger. Petaling Jays, Malaysia: TRAFFIC Southeast Asia. TRAFFIC SEA. (2010). Malaysia Gets Tough New Wildlife Law. Kuala Lumpur, Malaysia, 5th August 2010. Accessed December 2013 at: www.traffic.org/home/2010/8/6/ malaysia-gets-tough-new-wildlife-law.html

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Law enforcement monitoring in Uganda The utility of official data and time/distance-based ranger efficiency measures William D. Moreto, AM Lemieux, A. Rwetsiba, N. Guma, M. Driciru and H. Kulu Kirya

Introduction Categorically, few crimes are comparable in breadth and scope to wildlife crime. Ranging from local level subsistence poaching to an international black market for wildlife products, wildlife crime encompasses a myriad of actors, activities and settings (Broad et al., 2005; Reeve, 2002; Schneider, 2008; Wilson-Wilde, 2010). Although the problem is broad, many forms of wildlife crime can be traced to a primary event: the poaching of plants and animals. For without poaching, hunters have no meat to eat and ivory dealers have no tusks to sell. Indeed, it is when a wild plant or animal is removed from its natural setting that it becomes a product to be consumed or sold. While conservationists and biologists have long studied the topic, poaching is a relatively new item on the research agenda of criminologists. The study of wildlife crime is not possible with traditional, well-established sources of data used to monitor and measure crime. This crime category is absent from most police records and victimisation surveys which focus on crimes against humans, not animals. Indeed, while data can be obtained from self-report victimisation surveys regarding forms of wildlife crimes, including livestock theft (Sidebottom, 2012), such data are associated with domesticated species rather than those in the wild. That is not to say official data concerning wildlife crime do not exist: they merely come from non-traditional sources as far as criminologists are concerned. Official records of wildlife crime, especially poaching, are usually collected and managed by agencies concerned with conservation, not crime. In some cases, these are non-profit organizations focused on the protection of specific species such as lions, but more often, it is law enforcement agencies inside the world’s protected areas that monitor poaching activity. In this chapter we discuss the reliability and utility of the latter source. Using Queen Elizabeth Protected Area (QEPA) in Uganda as an example, the present

Law enforcement monitoring in Uganda 83 study is a modest attempt to contribute to the topic of law enforcement monitoring by reporting various time and distance-based ranger efficiency measures. The chapter is divided into three parts: (1) background information on law enforcement monitoring (LEM) inside the world’s protected areas including limitations of this approach, (2) an overview of LEM in Uganda and (3) descriptive analyses of Uganda Wildlife Authority (UWA) ranger efficiency in QEPA that show general trends and patterns of illegal activities within the protected area.

Law enforcement monitoring (LEM) inside protected areas Much like city management, law enforcement is a vital element of protected area (PA) management (Brockelman et al., 2002; Hilborn et al., 2006; Morse, 1973; Norgrove and Hulme, 2006). As noted in the Introduction of this book, protected areas are publically or privately owned tracts of land set aside to ensure the sustainability of ecosystems and the wildlife within them. At times used in conjunction with community-based conservation initiatives and programs (Nyirenda and Chomba, 2012), law enforcement fulfils the role of detecting and neutralizing threats to a PA’s security and biodiversity in order to ensure ecological and economic sustainability. While official law enforcement ranger duties will vary from one place to another, the types of activities they are responsible for typically include maintaining the integrity of a PA through • • • • •

Enforcing protected area rules and regulations Providing security to wildlife and tourists Preventing and investigating illegal activities within protected area borders Monitoring and documenting the behaviour and movement of wildlife Addressing problem species that come in conflict with nearby communities

Law enforcement monitoring (LEM), is an opportunistic approach that streamlines data collection whereby rangers collect information about illegal activities and animal movements simultaneously while on patrol. LEM often requires minimum training and equipment, making it a sustainable option where resources are limited (Gray and Kalpers, 2005; Stokes, 2010). In general, ranger-based LEM is beneficial because it collects standardized data that are readily available for use by park managers (Stokes, 2010). The information can be used for: (a) monitoring wildlife, (b) documenting illegal activities, (c) informing patrol deployment and (d) measuring management and enforcement performance (Schmitt and Sallee, 2002). As such, data collected during patrols are an excellent tool for law enforcement research and data are often utilized in LEM-based analyses and assessments. To be clear, LEM is often referred to as ranger-based data collection in some protected areas, including the study site of this analysis. While both terms are valid and have the same meaning, we will only use LEM to avoid confusion and because LEM seems to be more widely used in the academic literature.

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Given the remote and often rugged terrain of PAs, the primary method of LEM is ranger foot patrols (Gray and Kalpers, 2005; Jachmann, 1997, 2008; Jachmann and Billiouw, 1997; Hilborn et al., 2006; Leader-Williams et al., 1990; Nyirenda and Chomba, 2012). There are two main forms of LEM currently in practice: the first involves the use of standardized patrol forms to record the duration of patrol, the patrolled area and the number of rangers or scouts in the group. These forms also include information on observations or events encountered by rangers while on patrol, including the type, number and location of illegal activities and wildlife observed (Jachmann, 2008; Jachmann and Bell, 1984). This information is then assigned to specific grids on a printed map of the protected area for documentation and analyses. The second form of LEM involves the use of handheld global positioning system (GPS) devices and geographic information system (GIS) programs. Such data consist of time stamped, x-y coordinates of observed illegal activities, animal sightings and ranger movements. Used in conjunction with GPS-based LEM data, GIS software can be used to visualise and analyse information collected on patrol (Jachmann, 2008; Kakira, 2010). The LEM data analysed in this chapter were collected using handheld GPS units; therefore, we focus the remainder of the chapter on this method of LEM.

Limitations of law enforcement monitoring (LEM) with GPS units While LEM data, particularly information collected via GPS devices has significant advantages, there are several problems associated with this methodology. We categorize these problems as: (1) information overload, (2) the ‘dark figure’ of illegal activities, (3) limitations of the technology and (4) errors or falsifications in data collection and entry. The paragraphs that follow discuss each problem individually to highlight limitations of LEM data recorded with handheld GPS units. Despite the aforementioned limitations, such data are arguably the most useful in representing the problems found within PAs around the world. Information overload Most researchers and analysts do not complain about having ‘too much data’; however, recent technological advancements enable collection of micro-level data in protected areas which have the potential to create such a problem. When GPS devices are used, decision-makers may have an overabundance of raw data. For example, during the ‘data cleaning’ phase of the current chapter (see ‘Data and methodology’), the researchers spent a considerable amount of time identifying patrols and/or GPS waypoints that should and should not be included within the analysis and found several issues with the data, including missing observations. Thus, it takes time, patience and technical expertise to separate useful data from those that are less useful. Generally speaking, it can be assumed that decision-makers and analysts in protected areas may lack one or all of these

Law enforcement monitoring in Uganda 85 requirements and therefore may be overwhelmed with information or interpret it incorrectly. The ‘dark figure’ of illegal activities Leader-Williams and Albon (1988) argued that unless conservation schemes are sufficiently funded, which is not the case in many developing nations, resources should be appropriately targeted to smaller geographic spaces within PAs. Thus ranger patrols are not systematic surveys of a protected area and patrol coverage is by no means complete. Patrol deployment is often determined by information derived from prior patrols and other forms of intelligence (e.g. community information) (Kakira, 2010; Pantel, 2007; Schmitt and Sallee, 2002). This results in patrols repeatedly targeting ‘known’ problem areas, while other areas remain unpatrolled or patrolled infrequently. As such, it can be argued that observations found on patrol, including documented illegal activities, are simply a function of patrol. Hypothetically, if poaching were evenly distributed throughout a PA, heavily patrolled areas would appear to have more crime simply because the rangers have seen it. Conversely, unpatrolled areas would appear to be ‘crime free’ because the rangers have not made observations there. Essentially, documented illegal activities are simply that: documented. Such a reality emphasizes the difficulty in monitoring and researching PAs in that observations can only be attributed to areas that have been patrolled and cannot be inferred to places that have not. In the same vein, certain illegal activities, particularly poaching, often include taking the specimen; thereby, possibly removing any evidence or indication of the act. Thus, as with other crimes, there is the potential of a significant ‘dark figure’ (Biderman and Reiss, 1967) with regard to the number of illegal activities that occur within PAs. Victim reports are also non-existent as wildlife or wild plants cannot report forms of victimisation (see Lemieux et al. in this volume for a more detailed description of the ‘silent victim’ problem). In short, reports of poaching found in LEM data are likely to represent a small percentage of all poaching activity because: (a) this crime is difficult to detect and (b) patrol coverage will never be complete. Limitations of the technology As discussed, ranger patrols occur in a variety of settings and environments, some of which may impact the function of GPS devices. Some GPS devices may have problems receiving signals from satellites in forests with dense canopies or during severe weather, thereby limiting the documentation and accuracy of waypoints. In such scenarios, it has been suggested that rangers simply document nearby waypoints once they get a better reading or enter approximate waypoints (Pantel, 2007). However, as will be discussed, such documentation may be open to error. Therefore, the choice of GPS device used is a critical decision. As recommended by Ecological Software Solutions (2005),

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the ‘three P’s’ should be considered when choosing which GPS device to use: place (where it will be used), price (cost per unit) and purpose (the intended use). Regardless, decision-makers and researchers should recognize that any device may be limited in its ability, given the setting or environment. Errors and falsifications in data collection and entry When GPS devices are used to document ranger movements and observations of poaching, rangers must ensure that the patrol waypoints are in chronological order. If waypoints are changed at any point, a patrol route will be inaccurate. This can be especially problematic in situations where rangers or assistants enter GPS locations manually. In an assessment of GPS-based LEM in Cambodia, Pantel (2007) discussed how inaccuracies in manual documentation were attributed to entering the wrong Universal Transverse Mercator (UTM) information, the wrong UTM zone or when an accurate waypoint is uploaded erroneously into a database. Further, it may be possible that data have been falsified. Indeed, it is difficult to determine whether incorrectly documented observations and/or GPS waypoints are a result of deceit, limited knowledge of the area, issues associated with the technology or simply a mistake (Pantel, 2007). Falsification of data could potentially be even more problematic given the ease of simply marking where and when an area was patrolled and what was supposedly observed (or not observed). Lastly, as noted by Jachmann and Bell (1984), the primary focus of law enforcement in PAs is to deter poachers and not to collect data for research purposes; therefore, rangers may not necessarily be as prudent in their data collection methods as one would hope.

Law enforcement monitoring in Uganda’s protected areas In 1996, the Uganda Wildlife Act (cap. 200 of 2000) was established in an attempt to develop a coordinated body which was responsible for overseeing the monitoring and supervision of wildlife in Uganda. The statute outlined the laws and punishments associated with wildlife within Uganda’s borders, including species that were protected by the Minister or an international convention or treaty (e.g. the Convention on International Trade in Endangered Species of Wild Fauna and Flora). In order to establish the statute’s ground-level presence, the Uganda Wildlife Authority (UWA) was created by merging the Uganda National Parks and Game Departments. UWA is the governing body responsible for the monitoring and management of Uganda’s PAs, which include its national parks, wildlife reserves, wildlife sanctuaries and community wildlife areas. Further, UWA is responsible for the management and monitoring of wildlife species within and outside of PAs; encouraging and supporting the socio-economic benefits of wildlife management; addressing reported problem species; and enforcing international conventions, treaties or other arrangements to which Uganda is party to (Kameri-Mbote, 2005).

Law enforcement monitoring in Uganda 87 In an attempt to standardize GPS-based LEM inside Uganda’s protected areas, Management Information System (MIST) software, developed by Ecological Software Solutions LLC with support from the German Technical Cooperation, was developed to provide decision-makers with up-to-date information useful for planning and evaluation (Schmitt and Sallee, 2002). MIST is designed to be user-friendly, and it can be downloaded (www.ecostats.com/software/mist/mistdownload.htm) and installed at no cost to the end user, thereby highlighting its applicability for resource-strapped PAs in developing countries. In Uganda, MIST data collection and management is overseen by the departments of Research and Monitoring in the individual PAs and is also used as a central depository for LEM data collected from various PAs around the country (Pantel, 2007; Schmitt and Sallee, 2002). As a central depository, MIST data from individual PAs are typically transferred to headquarters in Kampala using USB sticks (Pantel, 2007; Schmitt and Sallee, 2002); MIST is currently used in nine PAs in Uganda (Makombo and Schmitt, 2003).

Strengths of MIST as a law enforcement monitoring platform One of the benefits of MIST is the way in which it standardizes data collection on patrol. Data collected on patrols are categorized by observation group, observation and observation type (see Figure 5.1). In most cases, GPS data are uploaded directly from the handheld device to the program in order to limit any potential mistakes associated with manually entering the information (Schmitt and Sallee, 2002). Importantly, as long as the GPS device is configured accurately (see ‘Limitations of law enforcement monitoring’, above), all GPS coordinates include temporal information including the date and time of day, therefore, providing micro-level spatiotemporal information on ground-level operations and documented illegal activities and other observations. MIST is not only useful as a database tool for storing and organizing data but it is also capable of conducting

Figure 5.1 Example of data hierarchy recorded by Uganda Wildlife Authority ranger foot patrols Adapted from Schmitt and Sallee (2002)

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basic analyses useful for informing decision-making. MIST is capable of producing patrol coverage maps, maps showing the spatial distribution of animals and illegal activity, and performance reports for individual rangers indicating how many days they patrol per month and how much distance they cover. These different forms of output help a PA’s management design patrol strategies, reward staff performance and identify areas, for example animal hot-spots, where tourism infrastructure could be developed. The data used in this chapter were extracted from Queen Elizabeth Protected Area’s MIST database and analysed using different software packages, as described in the data and methods section below. This enabled us to clean and organize the data more efficiently and calculate ranger foot patrol efficiency measures that have not appeared in the LEM literature before. While it would have been possible to perform some of these calculations in MIST, we performed our analysis without the software to create an analysis template that could be used with any type of LEM data even if MIST was not used to manage such data.

Study site: Queen Elizabeth Protected Area, Uganda Queen Elizabeth Protected Area (QEPA) is comprised of Kigezi Wildlife Reserve, Kyambura Wildlife Reserve and Queen Elizabeth National Park (QENP) (Olupot et al., 2010). Located in the south-west region of Uganda (see Figure 5.2), QEPA is approximately 2,400 km2, with Kyambura Wildlife Reserve covering an estimated 157 km2 and Kigezi Wildlife Reserve approximately 265 km2. Established in 1952 after the passage of the National Park Act, QENP has a total area of 1,978 km2 and is one of the four savannah parks in Uganda (Olupot et al., 2010). Known for its high biodiversity, QENP is considered to be an Important Bird Area by Birdlife International, is one of two biosphere reserves in Uganda (the other being Mount Elgon) and is home to Lake George, a wetland protected by the Ramsar Convention. Given the unique ecological make-up of QENP, it should not be surprising that it is one of the most visited parks in Uganda. In general, the three areas that make up QEPA have significant conservation and economic implications for Uganda (Muhweezi, 2003). QEPA, like other PAs in Uganda, is subject to various illegal activities, including poaching, plant harvesting and encroachment. Further, additional threats such as bush fires, political instability and the growth of fishing communities also threaten the biodiversity of QEPA (Plumptre et al., 2003). The poaching of wildlife is considered to be one of the most problematic issues in PAs in many developing countries (Bruner et al., 2001; Plumptre et al., 2003; Skonhoft and Solstad, 1996), and Uganda is no different (National Environment Management Authority [NEMA], 2008, 2009). While some poaching may occur for economic and trading purposes, the majority of poaching within QEPA seems to be conducted by locals for subsistence purposes; however, no matter the motivation, all hunting inside QEPA is illegal. In addition to the poaching of wildlife and illegal fishing, the illegal harvesting of plants also has a significant impact on Uganda’s biodiversity. Plants are often used for various purposes including fire wood, construction and medicine.

Figure 5.2 Queen Elizabeth Protected Area south-west Uganda

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Historically, QEPA has been home to both wildlife and humans. Within QENP, there are eleven fishing villages – five located on Lake Edward and six on the Kazinga Channel and Lake George (Risby et al., 2002). From 1952 to 1969, the villages were afforded limited grazing rights within the park (within 500 meters of village borders), had permit-based resource access and participated in revenue sharing. Due to perceived increasing pressure on resources, particularly within the park, QEPA management strategies shifted and resulted in further limiting rights of communities. This scenario is common in PAs across Africa where in many cases inhabitants of the land are often evicted, given limited access to resources which they need for subsistence and trade or removed from areas or resources that merit significant cultural value (LeaderWilliams and Albon, 1988; Nahonyo, 2005). The resource needs of communities within and adjacent to QEPA are a major cause of illegal activity inside the protected area. There are currently 216 UWA staff working within QEPA. Law enforcement personnel include 124 field patrol rangers, three prosecution staff members and one assistant warden. Additionally, there are 46 military operators from the Uganda People’s Defence Force (UPDF) who are part of the Special Wildlife Tourism Protection Force (SWIFT) joint coalition with UWA. Foot patrols within the PA are directed by the warden of law enforcement. There are three different types of ranger patrols in QEPA: routine, extended and response. Routine patrols are 1-day patrols conducted on a daily basis and typically involve patrolling in close proximity to a ranger base. Extended patrols are multi-day expeditions and end when the ranger group reaches an extraction point. Finally, response patrols are dispatched in situations requiring immediate attention (e.g. sighting of a poaching group, injured wildlife, etc). When available, handheld GPS units are carried during all three types of patrols, and the data collected are uploaded into MIST for storage and analysis.

Data and methodology Given the limited resources available for park management and the sheer size of the area needed to be monitored, measuring ranger effort is an important component of law enforcement within PAs. Considered to be the best all-purpose measure of patrolling effort, the catch-per-unit effort (CPUE) index was first introduced to quantify patrol efficiency by comparing documented illegal activities observed on patrol to standardized measures of patrol effort (Jachmann, 2008; Jachmann & Billiouw, 1997; Kakira, 2010; LeaderWilliams et al., 1990; McShane, 2010). In general, indices such as CPUE can be used as a means to monitor patrol group performance and examine changes in the identified amount and type of illegal activity within a particular area. Notably, the most useful measure of patrol effort, used as a denominator for CPUE measures, has been identified as the number of effective patrol days (or time spent in the field patrolling) and the distance patrolled (Jachmann & Bell, 1984).

Law enforcement monitoring in Uganda 91 The denominators used in previous CPUE calculations are limited by the operationalization of patrol effort as a function of time or distance. First, most distance-based CPUE studies have tended to simply aggregate patrol points to grid cells (e.g. 4 × 4 kilometre cell sizes) on maps rather than analyse the points themselves. In essence, this approach equates a single point within a grid cell to the entire cell being patrolled: an overestimation of the patrolled area. For example, Kakira (2010) found that the mean visibility profile for rangers was approximately 200 meters depending on the surrounding terrain. Second, timebased denominators have often represented a constant quantity of patrolling effort. For example, effective patrol days have been standardized to four to eight hours (Jachmann, 2008; Kakira, 2010) despite the actual variability in time spent in the field. While such standardized measures are useful for comparative purposes, we felt the need to utilize actual measures for our current objective because our data contain enough detail for this. In the current study, we quantify patrol effort in two ways: the number of hours spent patrolling and the distance covered. This enables us to calculate three different CPUE indices based on actual, not standardized, measures of patrol effort: (1) poaching observations per 100 hours of patrol, (2) poaching observations per 100 kilometres of patrol and (3) poaching observations per 100 hours and 100 kilometres of patrol. Our goal is to determine if CPUE measures based on distance, time and the combination of both produce different descriptions of ranger efficiency. We use raw data exported from the MIST database in QEPA for our analysis. While MIST could be used to calculate ‘encounter rates’ such as these, we chose to work with the raw data instead. Using different software for the analysis shows the utility of database management software such as MIST that captures in depth LEM data that are both user friendly and necessary for high-level analyses (see also Lemieux et al. in this volume). The data used for the current analysis include GPS waypoints recorded during ranger patrols between 2000 and 2010. The raw data included waypoints inside and outside of the protected area boundaries, but we confine our analysis to observations made inside the reserve to mirror UWA’s jurisdiction; this included 52,165 individual GPS observations during the study period. Within the initial data set, there was sufficient information to determine the number of days each patrol group patrolled for. A patrol group is referred to by an identification number that helps organize single and multi-day patrols. For example, a three-day overnight patrol will have the same patrol group number, but the patrol day will change (e.g. ID23-Day 1, ID23-Day 2, ID23-Day 3). Because patrols of 4 or more days were very rare, we excluded waypoints collected during these additional days (n ⫽ 869 waypoints) leaving us with 98 per cent of the observations; this helped streamline the data-cleaning process described below. Given the micro-level nature of the data and the potential problems described earlier in this chapter, it was vital to ‘clean’ the data prior to analysis. After separating the data by patrol day (e.g. 1, 2 and 3), the duration of each patrol, in hours, was determined using simple arithmetic, whereby the start time was

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subtracted from the end time in Excel. This resulted in the identification of 5,491 patrol days, each of which had a unique duration. We decided to focus our analysis on patrol days rather than patrol groups since aggregating at the group level might result in the removal of an entire patrol when some days were suitable for inclusion. To determine how many kilometres rangers travelled, we used Excel to calculate the distance between each waypoint taken during the patrol day and summed these distances. We subsequently removed patrol days lasting less than 30 minutes or 1 kilometre as these were likely to be response patrols rather than traditional foot patrols (n ⫽ 841 patrol days). We then removed patrol days with inaccurate information or obvious data entry errors (e.g. ‘start’ and ‘end’ point had different years) (n ⫽ 75 patrol days). For the remaining patrols, we quantified the speed in kilometres per hour and calculated the average (2.24 km/hr) and standard deviation (2.48 km/hr). Using 5 kilometres per hour as a cut-off, approximately one standard deviation above the mean, we deleted patrols moving faster than this to exclude data where it appears rangers had moved in a vehicle (n ⫽ 210 patrol days). After this process, we were left with 4,365 patrol days recorded inside QEPA between 2000 and 2010 in our sample: 79 per cent of the original patrol days. We then calculated patrol effort and patrol efficiency on a yearly basis using the measures described above. Patrol effort was measured in three ways: (1) the total time spent (in hours) on patrol each year, (2) the total distance covered (in kilometres) on patrol each year and (3) the total time spent by total distance covered. During this phase, rather than standardizing each patrol day to a specific duration, we decided to use actual figures for our analyses. As mentioned earlier, we believed that such an approach was necessary to reflect ground-level operations. For the purposes of our analysis, we measured patrol efficiency as the ratio of specific documented poaching activities by the three separate measures of patrol effort (time spent on patrol, distance covered on patrol and time spent by distance covered on patrol). Once the ratio for each measure of patrol efficiency was measured, we then decided to standardize each ratio per 100 hours, 100 kilometres and per 100 kilometres by 100 hours for comparative purposes. To our knowledge, these efficiency indices have not been published in the literature on LEM.

Results We begin our discussion of law enforcement monitoring (LEM) in QEPA by describing general trends in the patrol effort of ranger teams between 2000 and 2010. There is clear evidence that the number of patrols entered into the Management Information System (MIST) database has continued to increase over the years. In 2000, there were just 24 days of foot patrol recorded in the database; in 2010, there were 912, an increase of 3,800 per cent. The large increase in MIST recorded foot patrols is certainly due to availability of handheld GPS units and increased management effort in staff

Law enforcement monitoring in Uganda 93 training to improve the use of the software as a monitoring tool. Unfortunately, we do not have data concerning the number of patrols deployed during this time period that did not have GPS technology. This includes patrols that may have been the result of a quick, reactive-based deployment (e.g. gun shot heard and reported by a concerned citizen); patrols where a GPS device was not available; and/or ranger inability to use such devices even when available due to lack of training. Such patrols were therefore not included within the MIST database. Thus we must limit our discussion of LEM in QEPA to information recorded with GPS and uploaded to MIST. In general, single day patrols were much more common than multi-day patrols during the study period. Of the 4,365 patrol days in our sample, only 1,165 or 27 per cent were from the second or third day of a patrol. While the total number of patrol hours increased from 170 hours in 2000 to 4,056 hours in 2010, the data indicate the average time spent on patrol did not fluctuate greatly over the years. Patrols lasted an average of 4 to 5 hours except for the first year. To be clear, even during multi-day patrols where rangers sleep in the bush, they are actively patrolling for 4–5 hours a day. The remaining time is set aside for sleeping, resting, and food preparation which can take a very long time because meals are cooked over a campfire. The heavy reliance on single day patrols is due to limited access to transport in QEPA for picking up rangers who have moved very far from an outpost during a multi-day patrol, the added expense of food rations for such patrols and staffing limits. With respect to the total distance covered on patrol, there was a steady increase during the study period. In 2000, ranger teams covered 262 kilometres while on patrol; in 2010, they covered more than 7,000 kilometres. The average distance covered ranged between 7 and 10 kilometres during the study period except for patrols in 2000. The speed at which rangers moved was very consistent at approximately 2 kilometres per hour. This relatively slow pace attests to the rugged terrain rangers move through, time spent investigating a possible poaching site and time required for rest. Figures 5.3 and 5.4 show time-based and distance-based rates of poaching observed in QEPA from 2000–2010. The time-based rates in Figure 5.3 are reported as observations per 100 hours of patrol, and distance-based rates report observations per 100 kilometres of patrol (Figure 5.4). Because these rates give nearly identical trends, most likely due to the constant patrol speed, we discuss them concurrently but present them separately. Beginning with all poaching activity observed, the data indicate there was a sharp increase in 2002 and 2003 followed by a sudden decline in 2004. Since the decline in 2004, it appears the total amount of poaching detected has remained relatively stable between 8 and 10 observations per 100 hours of patrol and 4 to 6 observations per 100 kilometres of patrol. With the current data, it is nearly impossible to tell if this stability is indicative of (a) unchanging ranger efficiency or (b) relatively unchanged levels of poaching in QEPA. In either case, we can say that hourfor-hour rangers encountered similar levels of poaching between 2004 and 2010. In follow-up analyses, it may be interesting to disaggregate the data by patrol

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Figure 5.3 Time-based rates of poaching detection by ranger foot patrols in Queen Elizabeth Protected Area (2000–2010)

Figure 5.4 Distance-based rates of poaching detection by ranger foot patrols in Queen Elizabeth Protected Area (2000–2010)

sector to see if the current observed pattern holds true for smaller sections of the protected area. When poaching activity is disaggregated into separate categories, distinct patterns emerge for each crime type. Hunting shows a very similar pattern to total poaching activity whereby there was a spike in 2002 and 2003 followed

Law enforcement monitoring in Uganda 95 by a steep decline to a stable level that began in 2005. Between 2005 and 2010, hunting was detected by rangers at much lower levels than the other two crime types. This may be attributed to a change in recording procedures where the general category of hunting was overused to describe observations of items such as snares or other poaching activities; unfortunately, there is no record or research on the topic of recording procedures in QEPA to allow this hypothesis to be tested. Snares and other forms of poaching activity showed a different pattern whereby rangers have seen an increase in observations of these offenses since 2005. Where snare detection has levelled off around three to four observations per 100 hours of patrol and two observations per 100 kilometres of patrol, observations of all other types of poaching appear to be increasing with a marked peak in 2006. While this too may be the result of changes in recording practices, it is interesting to note that these two forms of criminality appear to account for the majority of all poaching activity observed inside QEPA. Up to this point, we have discussed ranger efficiency as a function of time or distance. Going further into the topic, we now explore the two simultaneously by calculating the number of poaching observations per 100 kilometres and 100 hours of patrol. Figure 5.5 shows how the index has changed from 2000–2010, while Figure 5.6 focuses on the years 2004–2010. Before discussing the trends presented in Figures 5.5 and 5.6, it is important to give a brief explanation as to how the time/distance-based index should be interpreted. Because the denominator of this new index is very large, the calculation produces a small number relative to the traditional efficiency measures. However, like the time-based and distance-based rates presented in Figures 5.3 and 5.4, a decrease in the time/distance index indicates rangers are observing less poaching when the duration and length of patrol are considered at the same time. Conversely, an increase means rangers are finding more poaching per 100 kilometres and 100 hours of patrol. We believe, and the results show, this index provides a different assessment of ranger efficiency by accounting for both how much land they patrol and how long they patrol it for. Figure 5.5 shows the total amount of poaching detected by rangers has decreased sharply between 2000 and 2010 when the new index is used. From 2000–2004 the number of observations per 100 kilometres and 100 hours of patrol decreased from 3.6 to 0.4 and continued to decrease from there. Hunting showed a similar trend, whereas snares and other forms of poaching were detected at much lower and consistent levels. Because detection levels were considerably lower after 2004, Figure 5.6 focuses on this time period to give a clearer picture of how the trends have varied during these seven years. Unlike the time-based and distance-based rates which showed poaching detection has levelled off and may be increasing, the new index presented in Figure 5.6 suggests observations of all poaching activity have continued to decrease between 2004 and 2010. When poaching is disaggregated, detection of snares and other types of poaching saw a slight increase in 2005 followed by a steady and continued decrease. Between 2004 and 2010, hunting was detected at much

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Figure 5.5 Time/distance-based rates of poaching detection by ranger foot patrols in Queen Elizabeth Protected Area (2000–2010)

Figure 5.6 Time/distance-based rates of poaching detection by ranger foot patrols in Queen Elizabeth Protected Area (2004–2010)

lower levels than snares and other types of poaching. Moreover, there was no peak in hunting around 2005 but rather a massive decrease followed by little, if any, change in the remaining years. As with the other indices presented in this section, it is hard to definitively say if these decreases are the result of lower

Law enforcement monitoring in Uganda 97 levels of poaching or lower ranger efficiency. However, given the large amount of data and simultaneous consideration of patrol duration and coverage, we believe rangers are indeed finding less poaching per hour and kilometre of patrol because there is less to find.

Discussion In most areas of law enforcement and policing, whether it is conducted in a large urban metropolitan city or in a dense rural forest, measures of efficiency are invaluable for resource allocation, personnel appraisal and assessing trends in illegalities. In particular, patrol efficiency measures are especially important within the realm of protecting the world’s PAs and natural resources given the difficulties associated with limited manpower and large jurisdictions. Indeed, Lind and Lipsky (1971) argue that police output measures are useful ways for police personnel to measure and maintain acceptable performance standards. Such measures also allow the possibility to better separate the potential impact that police practices have on crime factoring in other issues. Importantly, in situations where police practices and strategies are developed and evaluated, performance indicators enable policy makers and police personnel to evaluate whether a particular strategy was effective, how effective it was and how the strategy compares to alternatives. In essence, by assessing prior and current practices through patrol efficiency measures, conservation managers and law enforcement supervisors may be able to compare different forms of patrol strategies. For example, experimental patrol strategies could be compared to current standards to determine whether such strategies are more effective and/or efficient. The current study used a different measure of patrol efficiency than traditional measures based on catch-per-unit effort indices. As has been mentioned, while prior studies have tended to standardize figures and aggregate microlevel information, our analysis specifically focused on actual, micro-level data as a means to better understand the relationship between patrol effort and ‘catches’ of poaching activities. While we eventually did standardize our indices per 100 patrol hours, per 100 patrol kilometres and per 100 patrol kilometres by 100 patrol hours for comparative purposes, we believe that by using patrol hours and patrol distance rather than standardized patrol days and distance coverage, we have better operationalized the concept of patrol effort. Moreover, the current study further highlights the utility of incorporating time-based, distance-based, and time/distanced-based measures in analyzing patrol performance and illegal activities in PAs. While the results have produced knowledge about patrol effort and poaching detection in Queen Elizabeth Protected Area (QEPA), we must emphasize a limitation of the approach: because records of poaching are a direct function of patrol activities, the efficiency measures reported in this chapter are unlike those of urban police forces whereby crime levels are a function of victim/citizen reporting and patrol activities. Thus when ranger efficiency measures decrease it could be a sign that (a) patrol

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groups are not performing as well or (b) that levels of poaching are decreasing meaning rangers find less poaching per hour or kilometre of patrol. Undoubtedly, it may even be a combination of both. In the latter case, one would argue decreases in ranger efficiency are desired, not despised; in other words, finding less poaching would indicate a job well done. Without a clever way to collect information about the actual, not detected, amount of poaching occurring inside a PA, official data based on documented observations remain the best option for research. We suggest future studies may combine such data with aerial animal counts or poacher surveys recognizing even these data sets have their own limitations. While the current analysis sheds light on the annual trends related to patrol efficiency in QEPA, further studies are needed. For example, time-based measures can be used in conjunction with spatial analyses (see Lemieux et al. in this volume) to further contextualize ground-level realities of both illegal activities and law enforcement. Arguably, the time spent by law enforcement rangers in specific places will differ, thereby, possibly impacting the detection and documentation of specific illegal activities. Moreover, avenues for future research with this data set include investigations of how patrol efficiency varies between single and multi-day patrols, dry and wet seasons, patrol sectors and vegetation types. The researchers also plan to apply the current methodology to other protected areas in Uganda and around the world where data are available. As mentioned earlier, while official data on wildlife crime are limited, the use of available data combined with LEM data would be useful in further determining whether poaching behavior is changing. By using proxy measures (e.g. customs seizures) as an indirect means to gauge products available in the illegal wildlife market, the dark figure of wildlife crime may be somewhat illuminated. However, as mentioned earlier, as much of the poaching that occurs in QEPA is related to subsistence poaching, the value of such data may be limited as such products may not be present in the latter stages of the market. Future research incorporating local level market surveys and interviews with community members within and surrounding the QEPA may help alleviate this issue. Finally, it is important to note that although the measures presented here are not overly sophisticated or impervious to scrutiny, they provide a simple way for conservation managers and law enforcement supervisors to assess current practices. While advanced mathematical and statistical models may be of use to the academic community, such studies may be limited for ground-level decision-making as conservation managers and law enforcement personnel require real-time information that is simple and easy to understand. As Sheil (2001) cautions, for those responsible for protecting PAs ‘[t]he most immediate challenge is less one of science than of common sense and the effective allocation of resources’ (p. 1179). This is not to say that more advanced models do not have a place in effective management and resource allocation, but rather that simple models also have a place in assessing and advising proper management of conservation areas.

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Acknowledgements This work would not have been possible without the vision and support of those who designed, developed, implemented and maintained MIST. These include Ecological Software Solutions who created and updated the MIST software and the World Conservation Society who helped fund its implementation and continued success in Uganda. And not to be forgotten are the hundreds of rangers who patrolled Queen Elizabeth Protected Area collecting data and preventing illegal activity: their effort is truly what made this study possible.

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Lind, R. C., & Lipsky, J. P. (1971). The Measurement of Police Output: Conceptual Issues and Alternative Approaches. Law and Contemporary Problems, 36(4), 566–588. Makombo, J., & Schmitt, K. (2003). Experiences in Quantitative Management Effectiveness Assessment Using the Management Information System MIST in Bwindi Impenetrable National Park, Uganda. Report presented during the 5th World Parks Congress, Durban, South Arica, September 2003. p. 15. McShane, T. O. (1990). Wildlands and Human Needs: Resource Use in an African Protected Area. Landscape and Urban Planning, 19, 145–158. Morse, W. B. (1973). Law Enforcement – One Third of the Triangle. Wildlife Society Bulletin, 1(1), 39–44. Muhweezi, A. (2003). Introduction. In A. Roberts (Ed.), Protected Areas in Uganda: Benefits Beyond Boundaries (pp. 13–40). Kampala, Uganda: Acha Graphics. National Environment Management Authority. (2008). Building a Foundation for Sustainable Wildlife Trade in Uganda: A Review of the National Wildlife Trade Policies in Support of the Convention on International Trade in Endangered Species in Fauna and Flora (CITES). National Environment Management Authority. Ministry of Water and Environment. Department of Wildlife Conservation, Ministry of Tourism, Trade and Industry. National Environment Management Authority. (2009). Fourth National Report to the Convention on Biological Diversity. National Environment Management Authority. Ministry of Water and Environment. Nahonyo, C. L. (2005). Assessment of Anti-Poaching Effort in Ruaha National Park, Tanzania. Tanzania Journal of Science, 31(2), 13–21. Norgrove, L., & Hulme, D. (2006). Confronting Conservation at Mount Elgon, Uganda. Development and Change, 37(5), 1093–1116. Nyirenda, V. R., & Chomba, C. (2012). Field Foot Patrol Effectiveness in Kafue National Park, Zambia. Journal of Ecology and the National Environment, 4(6), 163–172. Olupot, W., Parry, L., Gunness, M., & Plumptre, A. J. (2010). Conservation Research in Uganda’s Savannas: A Review of Park History, Applied Research, and Application of Research to Park Management. New York, NY: Nova Science Publishers. Pantel, S. (2007). MIST Specialist – Evaluation Report. Srepok Wilderness Area Project Technical Paper Series, 2, 35. Plumptre, A. J., Kujirakwinja, D., & Kobusingye, S. (2003). Transboundary Collaboration between Virunga Park, Democratic Republic of Congo and Queen Elizabeth, Rwenzori and Semuliki Parks, Uganda. Report of Transboundary Meeting (June 20–21, 2003). Mweya, Queen Elizabeth National Park. Reeve, R. (2002). Policing International Trade in Endangered Species: The CITES Treaty and Compliance. London, UK: Earthscan Publications. Risby, L. A., Blomley, T., Kendall, C., Kahwa, I., & Onen, M. (2002, August). Environmental Narratives in Protected Area Planning – The Case of Queen Elizabeth National Park, Uganda. Policy Matters, 10, 40–49. Schmitt, K., & Sallee, K. (2002). Information and Knowledge Management in Nature Conservation: Supporting Planning, Decision-Making, Monitoring and Evaluation in Wildlife Management in Uganda with Spatial Management Information System (MIST). Eschborn, Germany: Deutsche Gesellschaft fur Technische Zusammenarbeit. Schneider, J. L. (2008). Reducing the Illicit Trade in Endangered Wildlife: The Market Reduction Approach. Journal of Contemporary Criminal Justice, 24(3), 274–295. Sheil, D. (2001). Conservation and Biodiversity Monitoring in the Tropics: Realities, Priorities, and Distractions. Conservation Biology, 15(4), 1179–1182.

Law enforcement monitoring in Uganda 101 Sidebottom, A. L. (2012). On the Application of CRAVED to Livestock Theft in Malawi. International Journal of Comparative and Applied Criminal Justice, 37(3): 195–212. Skonhoft, A., & Solstad, J. T. (1996). Wildlife Management, Illegal Hunting and Conflicts. A Bioeconomic Analysis. Environment and Development Economics, 1, 165–181. Stokes, E. J. (2010). Improving Effectiveness of Protection Efforts in Tiger Source Sites: Developing a Framework for Law Enforcement Using MIST. Integrative Zoology, 5, 363–377. Wilson-Wilde, L. (2010). Wildlife Crime: A Global Problem. Forensic Science, Medicine, and Pathology, 6(3), 221–222.

6

Tracking poachers in Uganda Spatial models of patrol intensity and patrol efficiency AM Lemieux, W. Bernasco, A. Rwetsiba, N. Guma, M. Driciru and H. Kulu Kirya

Introduction If you were a poacher, where would you hunt? Would you set your traps near water sources? Would you choose sites close to roads or protected area borders that are easy to access from your village? What if you were a ranger looking for poachers? Would you consider the same things? Tracking poachers is a difficult task for law enforcement officers who patrol protected areas. The remote and often rugged terrain that hosts the world’s wildlife is a far cry from the urban or suburban jungle. Whereas police in cities and towns traverse their beats on streets, rangers in protected areas typically move on foot via paths created by animals, poachers or both. Moreover, the population of a city helps police do their job more effectively. ‘Eyes on the street’ (Jacobs, 1961) deter some criminals by their very presence but also help police respond to crime by reporting it. This is very different from protected areas where the populations at risk, plants and animals, are unable to call for help whether they be a victim or witness. These ‘silent victims’ rely on rangers to patrol their habitat, detect poaching, arrest offenders and ultimately deter and prevent illegal activity. But when it comes to deciding where to focus patrol activities, rangers mostly rely on their own judgment and experiences and are only occasionally informed by real-time intelligence. This chapter presents a spatial analysis of patrol intensity and patrol efficiency in Queen Elizabeth Protected Area (QEPA). It demonstrates how researchers can use ranger patrol data to help understand not only where rangers go to on patrol, but also where their presence is most efficient in terms of amount of detected poaching activity per unit of time spent. The explanatory models presented measure two variables, patrol intensity (patrol hours per square kilometer) and patrol efficiency (poaching activities detected per 100 patrol hours), and relate both concepts to a set of explanatory variables that represent (a) access points such as borders and roads, (b) animal attractors such as rivers and lakes and (c) anchor points of poachers such as neighboring settlements. Our goal is to produce methodological, empirical and practical knowledge that helps us understand the spatial decisions of rangers and their implications for patrol efficiency and that might be used to study and prevent poaching in future endeavors.

Tracking poachers in Uganda  103

Tracking poachers: linking motive to location Like all criminals, poachers in Africa are motivated to crime for a variety of reasons. In general, their actions are driven by a need for income, sustenance or both (Barnett, 2000; Herbig and Warchol, 2011). In rural Africa, rapidly expanding human populations are putting undue pressure on natural resources once shared by infinitely smaller communities and civilizations (Born Free, 2004). Moreover, the urban centers of African nations (Wilkie and Carpenter, 1999) and international markets abroad (Engler and Parry-Jones, 2007; Stiles, 2009) are creating a great deal of demand for poached products. Thus finding an equilibrium between biodiversity and the needs of human populations, whether they be legitimate or not, is becoming increasingly difficult. Hunting for income is a well-documented motivation for poaching across the African continent. Typically, the sale of bushmeat or trophies derived from poached animals is seen as an alternative way to earn money in economically depressed areas (Bennett and Robinson, 2000). In West and Central Africa, interviews with poachers have shown that between 33 percent (Pailler et al., 2009) and 66 percent (Kümpel et al., 2010) of bushmeat hunters engage in the activity because they have no other way to earn wages. The bushmeat trade has been attributed to ‘complex interactions between extractive industries (logging, mining, oil), transportation systems (roads and railroads), human population growth, absence of dietary alternatives, lack of governmental infrastructure, widespread poverty’ (Eves et al., 2008, p. 327). These situational and environmental factors also influence trophy and sustenance poaching as each relates to target access, availability and/or demand. Sustenance hunting is another well-documented rationale for poaching in Africa’s protected areas. For many communities, wild animals are a traditional and low-cost source of protein (Kaltenborn et al., 2005; Steinhart, 1989; Warchol and Johnson, 2009). However, surveys of poachers indicate sustenance hunters often sell ‘extra’ meat as a form income generation (Obua et al., 1998; Pailer et al., 2009; van Vliet et al., 2011). In QEPA, the study site of this analysis, ex-poachers reportedly ate 33 percent of the bushmeat they harvested, sold 26 percent to the local area and sold 39 percent to distant areas (Olupot et al., 2009). Thus demand for bushmeat poached in QEPA is derived from people living in and near the reserve as well as those living further away. The literature on African poacher motivation is useful for spatial studies of ranger patrol intensity and efficiency because it gives hints as to what locations poachers may seek out. Knowing that economic hardships and sustenance hunting are factors which drive poaching, this suggests human settlements near protected areas are a likely source of poachers. In the next section, we discuss how patrols will consider this as ranger teams ‘forage’ for poachers.

Why here and not there? How modus operandi affects location When considering the spatial behavior of rangers, it is useful to know how poachers pick hunting sites. In essence, ‘triple foraging’ occurs inside protected areas (see Introduction of this volume). Animals forage for food, poachers forage for

104  AM Lemieux et al. animals, and rangers forage for poachers. Indeed the movement of all actors in the triple foraging process has the potential to create feedback loops whereby the movements of one group will change another’s. For example, animals may try to avoid areas where poachers forage, and poachers may try to avoid areas where rangers forage. To fully describe and understand this process, one would need data that to date do not exist; see Hill et al. in this volume for an example of how such data can be simulated. However, we choose to link ranger movements, for which we have data, to the theoretical foraging behavior of poachers. Like poachers, ranger success will be related to overlap between their foraging space and that of their prey. The modus operandi of poachers in Africa can be thought of as a series of ordered events describing site selection for a successful hunt: 1 The poacher enters a protected area. 2 The poacher finds an animal or good location to set traps. 3 The poacher stalks and kills the animal with a weapon at that moment in time or returns to the site later to kill a trapped animal. 4 The poacher exits the protected area. 5 The poacher consumes, sells, or consumes and sells animal products derived from the kill. From a criminological standpoint, the opportunity structure of each event will determine how poachers operate and influence their selection of hunting sites. This section describes each of these five events separately and helps us derive the independent variables used in this analysis of patrol intensity and efficiency. In the final paragraph, the foraging behavior of rangers is also described as a series of ordered events to show the similarities between ranger and poacher foraging behavior. The criminal opportunity structure of entering a protected area illegally is by no means uniform across Africa. At a basic level, the way poachers trespass in these areas depends on natural or manmade barriers and access points. Natural barriers such as dense forests, cliffs, large bodies of water and harsh terrain impede the ability of poachers to gain access to protected areas. Moreover some areas have manmade barriers such as a fence to keep poachers out. Conversely, manmade access points such as roads enable poachers to move into and out of the protected area more quickly; they also help them overcome natural barriers such as thick jungle. Rivers and lakes often provide the same benefit making these features of the environment natural access points. The ability of poachers to reach these access points and overcome barriers to entry will depend on where they live and the type of transportation they use. In general, poachers moving on foot will have a limited range over which they can operate compared to those using vehicles or aircraft. Thus rural Africans, who are unlikely to have access to vehicles, living in or near a protected area have more opportunities to poach than those who are not in close proximity to these areas. Indeed, an analysis of elephant poaching in Kenya found a concentration of this activity near protected area borders and settlements; poachers arrested in these areas were either hunting for themselves or acting as guides

Tracking poachers in Uganda  105 for foreign hunters (Maingi et al., 2012). This finding highlights the importance of considering the awareness space (Brantingham and Brantingham, 1993) of potential poachers; people living near protected areas are likely to know more about access points than those living at a distance. Once inside a protected area, poachers will select a hunting site: a decision dependent upon target availability and considerations of personal safety. Like other crimes, poachers, or their traps, must meet their victims in time and space. Put simply, an ivory hunter will go where the elephants are, not where the buffalo roam. On the other hand, a bushmeat hunter using snares may seek out areas with a high density of multiple species because these traps are indiscriminate (Noss, 1998). In general, poachers will prefer areas that attract or concentrate animals such as water sources, breeding grounds and migration trails. Even if poachers know where to find their prey, they must also consider threats to their personal safety such as apprehension and animal attacks (Pailler et al., 2009). To do so, poachers avoid ranger patrols by moving at night (Tumusiime et al, 2010) or in under-patrolled areas. A study of snare hunting in Kenya found the concentration of these traps increased as observers moved away from the protected area’s borders; likely the result of poachers’ attempts to avoid detection in heavily patrolled border areas (Wato et al., 2006). In short, the locations where poachers hunt will depend upon the movements of animals and ranger teams and poachers will seek out areas with high concentrations of targets and low concentrations of rangers. Once poachers identify a suitable target, they must kill the animal using any number of weapons. In Queen Elizabeth Protected Area, poachers indicated they use snares, nets and various traps (i.e. pit traps, nail traps and box traps) to catch animals or slow them down. To kill animals, the same group of poachers reported using guns, poison, spears sticks and stones as weapons (Olupot et al., 2009). With respect to spatial aspects of poaching, the type of hunting tool used to catch and kill animals will influence the location of hunting sites. To be successful, snares and traps will need to be placed in areas with high levels of animal activity. These locations must also be easy to access relatively quickly as decomposition or scavenging by other carnivores in the protected area will reduce poachers’ profits (Noss, 1998). When hunting with a spear or gun, poachers have more spatial options because they can stalk and kill an animal rather than wait for it to fall into a trap. For both types of hunting, trapping or stalking, distances from animal attractors, access points and settlements are important considerations for a successful hunt. Once an animal has been killed, the poacher must exit the protected area with a carcass or trophies such as ivory. Much like entering the protected area and selecting a hunting site, poachers at this stage of the poaching event will try to avoid detection and threats to their personal safety. Thus minimizing the amount of time spent in the protected area after the kill is a forefront concern and suggests poaching near roads and borders would increase the efficiency of escape. When vehicles are used, roads also help poachers move large quantities of meat and trophies which can weigh hundreds of kilograms. Once poachers have exited the protected area undetected, they can enjoy their profits.

106  AM Lemieux et al. After a successful hunt, poachers will consume, sell, or consume and sell products derived from the animal. Unlike the first four stages of the poaching event, this stage does not occur within protected areas; except where legal settlements are found inside the area’s border. This means animal attractors and access points are not important, but demand for animal products is. For rangers, combating these illegal markets is not a part of their patrol duties as they are not within the protected area’s jurisdiction. That said, demand for animal products created by human settlements near a protected area may influence the location of poaching there. Where refrigeration and access to vehicles is limited, poachers may prefer to hunt nearer the markets where their products will be sold. Thus human settlements can influence the location of poaching because they are both a source of poachers and a source of consumers. For ranger teams, the modus operandi of poachers will ultimately dictate how and where patrols are deployed. Perhaps the greatest difference between the movements of rangers and poachers is the reality that rangers need not move covertly as they are not breaking the law. To track poachers, rangers must think and move like one. This means rangers are essentially tracking animals because they are potential targets/victims, but when poachers leave evidence of their own movements, rangers are better able to track these offenders. Indeed the modus operandi, or foraging behavior, of rangers is similar to poachers: 1 Enter the protected area or begin patrol from a ranger post within the protected area. 2 Move to potential hunting sites to look for traps or track poachers by following signs they leave behind (i.e. footprints, campfires, sounds). 3 Ambush and arrest poachers at their hunting site. 4 Exit the protected area with poachers and put them in police custody. 5 Prosecute poachers in a court of law. By examining poaching as an ordered series of events, this section has highlighted the importance of (a) animal attractors such as water sources, (b) access points such as borders and roads and (c) sources of poachers such as human settlements near the protected area. We build spatial models of ranger patrol intensity and efficiency with these same considerations in mind.

The silent victim problem If a ranger does not find a snare, does it really exist? Unlike most police agencies, law enforcement rangers in protected areas are protecting victims who cannot report crimes to the police; the target of criminal attack is a plant or animal. Thus the ‘dark figure’ (Biderman and Reiss, 1967) of crimes against wildlife is of considerable concern because crime reporting is completely dependent upon ranger patrol. This is similar to law enforcement data concerning ‘victimless’ problems such as drug use and prostitution. Like these crimes, arrests for poaching in protected areas are the result of deployment decisions more so than calls for service.

Tracking poachers in Uganda  107 In their discussion of species monitoring data, Yoccoz et al. (2001) identified two sources of error that threaten the reliability of biological diversity estimates: (1) detection error and (2) spatial variation and survey error. The first refers to the ability of observers to detect the target being monitored such as birds, lions or in our case, poachers. The second refers the ability of observers to monitor large areas such as forests reserves, national parks and public land. Both of these sources of error can be found in patrol data collected by ranger teams. Because poachers are autonomous and mobile, rangers must be in the right place at the right time to observe them; this is a source of detection error. Moreover, snares and traps are very small and well hidden, which also makes them hard to observe: a second source of detection error. Finally, because a ranger’s field of view will be limited by terrain, weather and basic human physiology, the actual size of the area observed on patrol is extremely small: a source of spatial variation and survey error. At the very least, this suggests poaching estimates based on ranger data may underestimate the scale of the problem when detection error and spatial variation and survey error exist. Devising a solution to the silent victim problem is well beyond the scope of this paper. We raise the issue as a rationalization of our choice to model ranger patrol intensity and patrol efficiency rather than poacher behavior. Data on poaching collected with random, systematic surveys of a protected area would be more appropriate for modeling the spatial choices poachers make. Lacking such data, we instead focus on how rangers move inside protected areas and study poaching as a function of ranger patrol.

Data and methods To model ranger movements and poaching locations, we use patrol data collected by the Uganda Wildlife Authority (UWA) in Queen Elizabeth Protected Area (QEPA) and stored in their Management Information System (MIST) database between 2000 and 2010. Because this data set and the methodology used to clean it are discussed at length in the preceding chapter of this volume (see Moreto et al.), the majority of this section will focus on how patrol data can be aggregated and analyzed to build inferential statistical models. However, to summarize the cleaning procedure, it is important to note that the observations used for this analysis only include those located within the borders of QEPA which (a) were made on patrols lasting more than 30 minutes, (b) were made on patrols where rangers moved more than 1 kilometer and (c) were made on patrols where the speed of rangers was less than 5 kilometers per hour (to exclude patrols where vehicles were used). While the spatial analysis described below could not be performed using MIST software, it is important to note here the utility of the software for storing and managing data so that they can be used for a variety of complex statistical analyses. During the study period, UWA rangers made 52,165 individual observations inside QEPA; after cleaning, we were left with 41,940 observations or 80 percent of the original data. Each observation, all of which include the latitude and longitude where they were recorded, was placed into one of six broad categories which

108  AM Lemieux et al. helped aggregate the specific categories used by the UWA. For example, elephant sightings, buffalo sightings and baboon sightings were aggregated to the general category of animal sightings. The six categories used were the following: (a) (b) (c) (d) (e) (f)

ranger positions (n = 20,529) animal sightings (n = 16,513) poaching activity (n = 1,937) encroachment (n = 881) plant harvesting (n = 671) other observations (n = 1,409)

Ranger positions indicate the location of a ranger team throughout the patrol. Animal sightings are where rangers observe animals such a buffalo, hippos and elephants. Poaching activity, encroachment (people settling within the protected area’s borders) and plant harvesting are all types of illegal activity rangers observe during their patrol. ‘Other observations’ include animal signs such as footprints or droppings, the location of natural animal deaths and observations without a specific code. Our study includes ranger positions and poaching activity in the analysis but excludes the other four observation types. While models considering plant harvesting, encroachment and animal sightings could be made with the same data set, we limit our analysis to poaching and explore the utility of ranger data for building spatial models of ranger success. The two types of ranger observations used in this analysis, poaching activity and ranger position, are critical for understanding poaching in QEPA. First, ranger position data give us information as to where rangers went and for how long. Using the position data, we can assess where a patrol starts, where it ends, as well as waypoints taken at 30-minute intervals between observations. To build an accurate model of where rangers are most likely to find poachers, we must control for patrol coverage using this information. Remember, the silent victims rangers protect do not report crimes and therefore unpatrolled areas are not necessarily free from poaching. Moreover, heavily patrolled areas may seem to have high concentrations of poaching activity because rangers spend more time there, thereby increasing the chances of coming across a poacher or trap. We link patrol coverage information with six categories of poaching activity observed by rangers (n = 1,937): (a) (b) (c) (d) (e) (f)

poached animal carcasses (n = 187) snares (n = 610) hunting (n = 377) suspect arrests (n = 76) fires (n = 70) other poaching observations (n = 464)

We do not include illegal fishing observations (n = 151) in the model as the chapter’s focus is hunters who target animals on land, and exclude illegal honey

Tracking poachers in Uganda  109 collection (n = 2) because it is a rare poaching activity. In the data, ‘other poaching observations’ indicate rangers observed some form of poacher activity but were not specific as to what they saw. Thus the location of these events help us explain general spatial aspects of poaching but not specifics such as where traps or active hunters (hunting) were observed as these may have different distributions within the protected area. In summary, combining ranger position and poaching activity data from QEPA gives a more complete picture of poaching by accounting for patrol coverage and intensity. After selecting the observations to be used in the current analysis, it was necessary to define spatial units of analysis within QEPA. To do so, square grids of varying sizes were overlaid on a shapefile of the protected area using the vector grid function in Quantum GIS (QGIS); the protected area’s shapefile was provided by the UWA. The grid sizes used were 1 km × 1 km, 500 m × 500 m and 100 m × 100 m. We used different grid sizes to determine if the modifiable areal unit problem (MAUP) (Openshaw, 1984) influenced the results of our models. Because each grid size produced very similar results, indicating that the MAUP can be neglected in these data, we only present and discuss the results of the analysis on the largest grid of 1 km × 1 km. Using the points in polygon count function in Quantum GIS, we determined the number of ranger positions and poaching observations inside each cell. The number of ranger positions measures the time rangers have spent patrolling in the grid cell. The number of poaching observations measures the poaching activities detected by rangers during patrol in the grid cell. Both counts were used as dependent variables in the analyses described below; ranger position counts were also used as an independent, exposure variable when poaching activity was the dependent variable. The independent variables, derived from the motivation and modus operandi of poachers, were determined by measuring the distance between the centroid of each grid cell and various natural and man-made objects inside and near the borders of QEPA. Using additional shapefiles obtained from UWA, the length between cells and the nearest road, river, border, village and water source was calculated using the distance matrix function of QGIS. These distances were used as explanatory variables in univariate and multivariate models of patrol intensity and patrol efficiency. We estimated bivariate and multivariate models, but only present the multivariate models as they correct for any correlations between the independent variables (e.g. road networks are more dense near villages), and also because the results of the bivariate models in terms of size and directionality of effects were similar to those in the multivariate models. We use spatial autocovariate Poisson regression to model both patrol intensity and patrol efficiency. The choice of this Poisson model was based on various considerations. One consideration is that the Poisson model proper is most appropriate given the nature of the available data on patrol intensity and patrol efficiency, which are both based on count variables (number of hours spent on patrol in the grid cell and number of poaching activities detected in the grid cell). The Poisson distribution represents the number of events that occur within a certain time period when there is a constant risk of occurrence. Poisson regression models attempt to explain the number of events of an observation as a

110  AM Lemieux et al. function of the characteristics of the observation. The Poisson distribution can be characterized as f ( y, µ ) =

e − µi ( µi ) yi yi! 

(1)

where yi is the number of events for observation i (e.g. number of detected poaching activities in grid cell i) and where the exponent of the intensity parameter μ (the risk of occurrence per unit of time) is a linear function of the covariates, i.e.

µ i = X i β + ε i

(2)

A second consideration is that the Poisson model can easily incorporate differences in risk exposure. In our analysis, this is important for two reasons. First, some grid cells are located partly outside the borders of QEPA. When modeling patrol intensity, we must adjust estimates in order to take into account that foot patrols are not recorded outside the QEPA borders. In other words, we must divide the number of recorded ranger positions by the proportion of grid cell surface that is inside the boundaries of QEPA. Second, our measure of ranger efficiency in a grid cell is the number of detected poaching activities. To model patrol efficiency, we must divide the number of detected poaching activities in a grid cell by the number of hours that rangers spent on patrol in that grid cell. To incorporate exposure, the linear equation (2) model is re-written as

µi = Xi β + ln(ti) + εi 

(3)

in which ti represents the amount of exposure of observation i. The logarithm of the exposure variable is thus added (with the coefficient fixed to 1) in the equation. Note that multiple exposure variables can be included by simply multiplying them. For example, in a model of patrol efficiency, we can define the exposure variable as a product of the number of patrol hours in the grid cell and proportion of grid cell surface that is on land and inside the boundaries of QEPA. The reason to use a spatial autocovariate Poisson model instead of the regular Poisson model is that the regular model assumes the model residuals are independently and identically distributed. Because most natural and man-made phenomena display positive spatial autocorrelation (Tobler’s First Law of Geography reads ‘everything is related to everything else, but near things are more related than distant things’, Tobler, 1970, p. 236), and because most grid cells in the data are surrounded by other grid cells, this assumption is likely to be violated. In other words, estimates of a regular Poisson model could be biased because of spatial autocorrelation in unmeasured but relevant differences between research units. For example, rangers may not patrol certain areas that are particularly rough, and if roughness is not included in the model, this may bias the estimates. Building spatial autocorrelation into models of count data is common in epidemiological and ecological studies (Kaboli et al., 2005; Ma et al., 2012; Mohebbi et al., 2011)

Tracking poachers in Uganda  111 but underutilized in poaching research. The autocovariate Poisson model is indicated for count data such as ours (Dormann et al., 2007).

µi = Xi β + ρ Z i + ln(ti ) + ε i

(4)

where Zi is a spatially weighted sum or weighted average of μi, and is calculated as Zi =

∑ wij µi ∑ wij

(5) 

where wij is the weight given to grid cell j’s influence over grid cell i. The weight matrix W is usually constructed to reflect spatial distances between the observations, with values of 0 on the diagonal by convention (see Bernasco and Elffers, 2010). In the analyses presented here, we used a simple binary adjacency criterion according to which the borders of grid cells i and j share at least a single point, and 0 otherwise (first order spatial lag, Queen’s criterion). Thus, all grid cells except those on the boundary of QEPA have eight neighbors. In supplementary analyses, we used the second order and third order spatial lags, according to which grid cells have 24 (i.e., 52 – 1) and 48 (i.e., 72 – 1) neighbors respectively. As the results of these analyses did not differ substantively from those with an autocovariate based only a single-order spatial lag, they are not presented here. Table 6.1 Table 6.1  Variables and equations in spatial autocovariate Poisson models 1 and 2 Symbols Dependent R P Independent B W H G S V Exposure A R Equations1

Description

Models2

Number of ranger observations in grid cell (#) Number of poaching observations in grid cell (#)

I E

Distance to protected area border (km) Distance to water (km) Distance to road (km) Distance to big river (km) Distance to seasonal river (km) Distance to village (km)

I,E I,E I,E I,E I,E I,E

Area of grid cell (km2) Number of ranger observations in grid cell (#)

I,E E

Ri = β1Bi + β2Wi + β3Hi + β4Gi + β5Si + β6Vi + ρZi + ln(Ai) + εi

I

Pi = β1Bi + β2Wi + β3Hi + β4Gi + β5Si + β6Vi + ρZi + ln(AiRi) + εi

E

1 The symbols β and ρ refer to different estimates in both equations, e.g. β1 in the first equation is not the necessarily the same as β1 in the second equation 2 I refers to the patrol intensity model, E refers to the patrol efficiency model

112  AM Lemieux et al. summarizes the variables and equations used in both model 1 (patrol intensity) and model 2 (patrol efficiency).

Results Table 6.2 summarizes the outcomes of the patrol intensity analysis (model 1). The Incidence Rate Ratio (IRR) equals and is interpreted as the effect of a 1-kilometer increase in the shortest distance to some object (protected area border, water, road, etc.) on the number of recorded ranger positions per square kilometer (i.e. patrol intensity). For example, the value 0.79 of the distance to the protected area border means that for every kilometer a grid cell is further away from the border, the patrol intensity decreases by 21 per cent. Thus, patrol intensity is relatively high near the border and decreases quickly moving away from the border into the protected area. All other distance effects are in the same direction, which implies that patrol intensity decreases when moving away from water, from roads, from big and from seasonal rivers and from villages. Patrol intensity decreases with distance to water bodies, big rivers and villages by approximately 1 to 3 per cent per kilometer. For distances to roads and seasonal rivers, the decrease is 9 and 7 per cent per kilometer respectively. The conclusion is thus that patrol intensity consistently decreases with distance from access points such as borders and roads, from animal attractors such as rivers and lakes, and from villages, and that distance from the protected area border is most decisive. These findings should not come as a huge surprise if we realize that the majority of ranger outposts are located near an access point, so that rangers must necessarily spend the first and the last hours of a patrol trip near a border or road. Table 6.2 Spatial autocovariate Poisson model of patrol intensity (Number of recorded ranger positions, exposure variable is land surface in grid cell in km2). N = 2,870 1 km2 grid cells Variable

1

Distance to protected area border (km) Distance to water (km) Distance to road (km) Distance to big river (km) Distance to seasonal river (km) Distance to village (km)

0.79** 0.99** 0.90** 0.98** 0.93** 0.97**

Autocovariate Residual autocorrelation Zero-order spatial autocorrelation

1.03** 0.19** 0.28**

  1  IRR is short for Incident Rate Ratio   *  p < .05 **  p < .01

IRR = e β

Tracking poachers in Uganda  113 The model outcomes demonstrate a significant autocovariate effect, indicating that patrol intensities in adjacent grid cells are correlated, for reasons unrelated to the distances covered in the set of independent variables. In a regular Poisson model without spatial autocovariate (estimates not shown), there is considerable autocorrelation (Moran’s I of 0.40). The estimation of a spatial autocovariate model has reduced the amount of residual autocorrelation to 0.19 but has not completely removed it. Again, this is a natural phenomenon given the fact that ranger foot patrols move around slowly relative to the size of grid cells. Knowing that a foot patrol has patrolled a certain grid cell elevates the likelihood they have also patrolled one of the adjacent grid cells. Table 6.3 summarizes the outcomes of the patrol efficiency analysis (Model E). This model includes the number of detected poaching activities as the dependent variable. For reasons explained above this variable cannot be interpreted as a reliable measure of poaching intensity because its measurement is completely dependent on the patrol routes of rangers. However, when ranger patrols are included as an exposure variable, the model parameters can be interpreted as patrol efficiency measures: they inform us about the number of detected poaching activities per unit of time spent on patrol1. Note that this model is estimated on the subset of 1,936 grid cells with at least a single recorded ranger position The effects of distances to represent access points such as borders and roads are strong and significant. Most of the independent variables in the model are insignificant. Interestingly, the distance to protected area borders and to roads increase patrol efficiency. As rangers move away from the border, the number of detected poaching activities per hour rises by 10 per cent per kilometer. Table 6.3  Spatial autocovariate Poisson model of patrol efficiency for all poaching activity (Number of detected poaching activities, exposure variables are patrol land surface in grid cell in km2 and number of recorded ranger positions). N = 1,936 1 km2 grid cells with at least 1 recorded ranger position Variable

1

Distance to protected area border (km) Distance to water (km) Distance to road (km) Distance to big river (km) Distance to seasonal river (km) Distance to village (km)

1.10** 1.00 1.08** 1.01 1.04* 1.00

Autocovariate Residual autocorrelation Zero-order spatial autocorrelation

1.37** 0.10** 0.28**

  1  IRR is short for Incident Rate Ratio   *  p < .05 **  p < .01

IRR = e β

114  AM Lemieux et al. Table 6.4  Spatial autocovariate Poisson models of patrol efficiency for separate poaching activities (Number of detected poaching activities, exposure variables are patrol land surface in grid cell in km2 and number of recorded ranger positions). N = 1,936 1 km2 grid cells Poached animals 1 IRR = e β Distance to protected area border Distance to water Distance to road Distance to big river Distance to seasonal river Distance to village Autocovariate Residual autocorrelation Zero-order autocorrelation

Snares IRR = e β

Hunting IRR = e β

Fires IRR = e β

Other IRR = e β

1.10*

1.15**

1.11**

1.10

1.09**

0.96 1.12* 1.04

0.94** 1.01 0.95**

1.04 1.10* 1.03

1.00 1.07 0.96

1.04* 1.16** 1.02

0.95

0.99

1.09

1.22*

1.09*

0.94*

1.00

0.96*

1.05

1.00

2.13** 0.06**

2.98** 0.02*

1.28 0.03*

1.78** 0.03*

0.28**

0.16**

0.00

0.11**

7.20* –0.01 0.15**

  1  IRR is short for Incident Rate Ratio   *  p < .05 **  p < .01

It increases by 8 per cent for every kilometer they move away from a road. Thus, whereas patrol intensity decreases as they move away from access points into the protected area, patrol efficiency increases when they do. In contrast to Table 6.3, where all types of poaching activity are aggregated, Table 6.4 presents the outcomes of separate models for different poaching activities. All estimates vary considerably across types of poaching activity, but again for all types of poaching, ranger efficiency seems to increase with distance from the border and with distance from roads. The other effects fluctuate considerably and many are not significant (whereas the number of observed grid cells equals 1,936 in all models, the distribution of the count of detected poaching activities is quite skewed for infrequent poaching activities (e.g. 70 fires detected), which makes us reluctant to draw conclusions beyond the ones made above.

Discussion In this study, we used ranger patrol data from Queen Elizabeth Protected Area to create models of patrol intensity and patrol efficiency. The models explored how access points, animal attractors and human settlements relate to where rangers patrol and what they find. A key finding is that although rangers patrol

Tracking poachers in Uganda  115 border regions heavily, they detect poaching more efficiently as they move away from the protected area’s borders. Although this finding may appear puzzling at first sight, we suggest that it is actually understandable and in line with a prediction from optimal foraging theory, a theory concerned with food search and selection (Stephens and Krebs, 1986). The prediction is that in searching for food items in space, there is a trade-off between energy gained from finding the food item and energy spent searching and traveling to find it. The trade-off results the time traveled to a food patch being proportional to the energy intake obtained in the patch. Rangers will be prepared to travel further into the area if this allows them to detect and prevent more poaching activity than patrols closer to the border. So ranger efficiency may increase as patrols move away from the border because poachers are more successful and profitable when they hunt deeper inside the protected area since they: (a) find larger concentrations of animals and/or (b) avoid heavily patrolled border areas. When interpreting the models presented in this analysis, we must emphasize that there may exist non-linearities in the distance effects that were not captured by the models. It should be understood that the interpretation ‘moving away from the border’ should actually read ‘moving further away from any border’ because moving away from one point on the border implies moving towards another point on the border. This means rangers walking from one side of the protected area to another may see an initial increase in efficiency followed by a decrease in efficiency as they come closer to the opposite border. Indeed the models we present give an overall estimate of how much efficiency increases or decreases per kilometer of movement neglecting the idea that patrol efficiency may increase by 5 per cent at 1 kilometer, 10 per cent at 2 kilometers and 15 per cent at 3 kilometers for an overall effect of 10 per cent per kilometer. Future research can address this issue and may ultimately provide commanders with even more precise estimates about where ranger teams will be most successful. Improvements in patrol data collection would also be beneficial for building spatial models of patrol intensity and patrol efficiency. Most importantly, GPS devices that automatically record ranger positions at intervals less than 30 minutes would help (a) reduce the likelihood that ranger forget to mark their position and (b) give a clearer picture of where rangers actually go. Referring to the second point, positions taken every minute or five minutes would result in better ‘exposure’ measures and ultimately produce a more exact track of ranger movements to determine how environmental features such as terrain and elevation affect the speed at which rangers move. New and improved GPS devices may also help ranger data overcome problems related to poor GPS signal reception in dense canopy; a problem that makes patrolled areas seemed unpatrolled because there is no GPS information. Finally, more detailed information about poaching activity would also improve models such as those presented in this analysis. For example, knowing if snares found by rangers are for small or large game would help build species specific models that may help rangers better protect endangered animals

116  AM Lemieux et al. such as elephants. Additionally, studies which utilize random grid searches to identify poaching activity (see Wato et al., 2006) overcome selectivity issues seen in ranger patrol data and would provide excellent data to compare spatial models ranger efficiency to spatial distributions of poaching activity. In short, minor improvements in data collection by rangers, and researchers working with them, may result in much more detailed and useful models of patrol intensity and efficiency. Another point to be made in this discussion refers to spatial autocorrelation in ranger patrol data. Despite our attempts to remove autocorrelation from our models using spatial autocovariate Poisson regression, we still find significant autocorrelation in the residuals of many models. However, overcoming this issue is an enormous, if not impossible, task because rangers collect data while walking a specific route almost forcing consecutive observations within a single patrol to be adjacent in space. Moreover because ranger patrols are not random grid searches, but rather deployments targeting known or suspected poaching areas, even aggregated patrol data is likely to cluster in space. Thus spatial autocorrelation in the data, and models built using this data, is not only unsurprising, but it should be expected. This problem is also found in species monitoring data collected by biologists who have suggested a number of statistical models to overcome the problem, one of which is spatial autocovariate Poisson regression. However, other models have also been used to address the problem of spatial autocorrelation including: (a) spatial eigenvector mapping, (b) generalized least squares models, (c) conditional autoregressive models, (d) simultaneous autoregressive models and (e) generalized estimating equations (for a full review of all models, see Dormann et al., 2007). In future research, it would be useful to explore these models as alternatives to the spatial autocovariate Poisson regression model. Another limitation of the current analysis of intensity and efficiency is that it does not analyze foot patrol tracks. Essentially, it uses data that are coordinates aggregated from patrol tracks to grid cells and thus ignores that these data are generated by following a track (i.e. an ordered set of date and time stamped coordinates). There are many other interesting questions to be answered about patrol tracks that were not covered here. In response to the development of new animal tracking technologies, a growing literature in ecology uses track data to model animal movement (e.g. Jonsen et al., 2003). Many of the phenomena analyzed in this literature may be translated and adapted to human movement, including law enforcement patrols in protected areas. For example, after rangers discover poaching evidence, do they patrol more intensely (stay longer) in the area where it was discovered? Can we predict the route of a patrol based on the origin and a limited number of environmental variables? Better information about ranger movements would obviously aid such analyses; it would also help generate better simulation models like those proposed by Hill et al. in this volume. More important probably than advances in modeling, improving the explanation of patrol intensities and efficiencies requires understanding the decision-making of

Tracking poachers in Uganda  117 rangers and their spatial consequences. For example, do rangers get geographic or logistic instructions from supervisors? What makes them decide to travel a certain route? Is the route completely planned? Do they return to areas where poaching was recently detected? For future endeavors, we emphasize the importance of learning more about how rangers make spatial decisions while on patrol as this may help explain model outcomes.

Conclusion In protected areas around the world, ranger teams patrol vast and remote areas looking for poachers who threaten the sustainability of wildlife populations. While on patrol, they often collect georeferenced data that detail where they went and where they found poaching. In this study, we used such data from Queen Elizabeth Protected Area (QEPA) in western Uganda to create spatial models of ranger patrol intensity and patrol efficiency. Drawing from the modus operandi of the poachers, we explored how access points such as borders and roads, animal attractors such as water sources and rivers, and human settlements influence where rangers patrol and where they are most efficient. We built our models using spatial autocovariate Poisson regression to account for high level of spatial autocorrelation in the patrol data. Our results indicate the border of QEPA has the largest effect on both patrol intensity and patrol efficiency. We found that patrol intensity is highest near the border and decreases substantially as rangers move deeper into the protected area. Conversely, we find patrol efficiency increases as ranger teams move deeper into the protected area; they detect more poaching per hour of patrol. The results also indicate patrol efficiency increases as rangers move away from roads within the protected area. While animal attractors and human settlements were significant explanatory variables in some models of patrol efficiency, the effect of these variables was much smaller than that of access points. Thus access points in protected areas appear to be the most important environmental features related to patrol efficiency amongst the variables considered. We suggest more spatial models of ranger patrol intensity and patrol efficiency should be built using the methodology presented here to expand the limited literature on this subject.

Acknowledgements This work would not have been possible without the vision and support of those who designed, developed, implemented and maintained Management Information System (MIST). These include Ecological Software Solutions who created and updated the MIST software and the World Conservation Society who helped fund its implementation and continued success in Uganda. And not to be forgotten are the hundreds of rangers who patrolled Queen Elizabeth Protected Area collecting data and preventing illegal activity: their effort is truly what made this study possible.

118  AM Lemieux et al.

Note 1 His interpretation of efficiency ignores the fact that patrols may deter poaching in the absence of any detected poaching activity.

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Tracking poachers in Uganda  119 Maingi, J. K., J. M. Mukeka, D. M. Kyale and R. M. Muasya. (2012) ‘Spatiotemporal Patterns of Elephant Poaching in South-Eastern Kenya’. Wildlife Research. 39(3): 234–249. Mohebbi, M., R. Wolfe and D. Jolley. (2011). ‘A Poisson Regression Approach for Modelling Spatial Autocorrelation Between Geographically Referenced Observations’. BMC Medical Research Methodology. 11: 133. Noss, A. J. (1998) ‘The Impacts of Cable Snare Hunting on Wildlife Populations in the Forests of the Central African Republic’. Conservation Biology. 12(2): 390–398. Obua, J., W. Gombya-Ssembajjwe and G. Mugabe. (1998) ‘Illegal Resource Use and Resettlement of People from Karuma Wildlife Reserve in Uganda’. East African Geography Review. 20(2): 72–79. Olupot, W., A. J. McNeilage and A. J. Plumptre. (2009) ‘An Analysis of Socioeconomics of Bushmeat Hunting at Major Hunting Sites in Uganda’. Uganda: Wildlife Conservation Society. Openshaw, S. (1984). The Modifiable Areal Unit Problem. Norwich: Geo Books. Pailler, S., J. E. Wagner, J. G. McPeak and D. W. Floyd. (2009) ‘Identifying Conservation Opportunities among Malinké Bushmeat Hunters of Guinea, West Africa’. Human Ecology. 37: 761–774. Steinhart, E. I. (1989) ‘Hunters, Poachers and Gamekeepers: Towards a Social History of Hunting in Colonial Kenya’. Journal of African History. 30: 247–264. Stephens, D. W. and J. R. Krebs (1986). Foraging Theory. Princeton, NJ: Princeton University Press. Stiles, D. (2009). An Assessment of the Illegal Ivory Trade in Viet Nam. Petaling Jaya, Selangor, Malaysia: TRAFFIC Southeast Asia. Tobler, W. R. (1970). ‘A Computer Movie Simulating Urban Growth in the Detroit Region’. Economic Geography. 46: 234–240. Tumusiime, D. M., G. Eilu, M. Tweheyo and F. Babweteera. (2010). ‘Wildlife Snaring in Budongo Forest Reserve, Uganda’. Human Dimensions of Wildlife. 15: 129–144. van Vliet, N., R. Nasi and A. Taber. (2011) ‘From the Forest to the Stomach: Bushmeat Consumption from Rural to Urban Settings in Central Africa’. In S. Shackleton (Ed.), Non-Timber Forest Products in the Global Context. Berlin, Germany: Springer. Warchol, G. and B. Johnson. (2009). ‘Wildlife Crime in the Game Reserves of South Africa: A Research Note’. International Journal of Comparative and Applied Criminal Justice. 33(1): 143–154. Wato, Y. A., G. M. Wahungu and M. M. Okello. (2006). ‘Correlates of Wildlife Snaring Patterns in Tsavo West National Park, Kenya’. Biological Conservation. 132: 500–509. Wilkie, D. S. and J. F. Carpenter. (1999). ‘Bushmeat Hunting in the Congo Basin: An Assessment of Impacts and Options for Mitigation’. Biodiversity and Conservation. 8: 927–955. Yoccoz, N. G., J. D. Nichols and T. Boulinier. (2001). ‘Monitoring of Biological Diversity in Space and Time’. TRENDS in Ecology and Evolution. 16(8): 446–453.

7

Potential uses of computer agent-based simulation modelling in the evaluation of wildlife poaching Joanna F. Hill, Shane D. Johnson and Hervé Borrion

Introduction Wildlife poaching is a serious and growing problem in many developing countries (Fa & Brown, 2009; Harrison, 2011). Different approaches to tackling the problem exist, but one common strategy is to identify those locations at which poaching appears to be most likely so that rangers can be deployed to them to either prevent or detect poaching activity. Much of the research (e.g. Haines et al., 2012; Maingi et al., 2012; Nyirenda & Chomba, 2012) on spatial patterns of poaching – and why they form – conducted hitherto has used so-called top-down approaches that are commonly used in the social and natural sciences. These include the statistical analysis of observation data and the mapping of spatial concentrations of poaching incidents. Agent-based modelling (ABM) is an alternative method currently gaining traction in the social sciences. This employs a bottom-up or generative approach, whereby the researcher specifies a theoretical model proposed to explain a phenomenon, and then tests this using a computer simulation to examine the extent to which the model generates the behaviours it seeks to simulate (Gilbert & Troitzsch, 2005). In this chapter, we explain why this type of approach has the potential to increase understanding of spatial patterns of poaching, consider the additional insights it might provide and discuss the early stages of a project that uses this approach. The chapter is divided into six sections. In the first, we briefly discuss what might be described as the ecology of poaching. In the second, we provide a very brief introduction to simulation models, focusing on ABM in particular, followed by a description of what we mean by ‘agents’ in section three. In section four, we discuss the development of a model of animal poaching in Uganda to illustrate the potential of the approach. In the fifth section, we illustrate the kinds of results that are generated by the model, and in the sixth, discuss the limitations of using ABM to simulate poaching and consider some of the future directions of the research.

The ecology of poaching Just like other direct contact predatory crimes, poaching occurs when a motivated offender encounters a suitable target in the absence of a capable guardian who might otherwise restrain or deter the offender (Eck, 1994; Felson &

Potential uses of simulation modelling  121 Cohen, 1980). Those familiar with the environmental criminology literature will recognize these ideas as being at the core of routine activity (Cohen & Felson, 1979) and crime pattern theory (Brantingham & Brantingham, 1993) and will not be surprised to learn that just like urban crimes, spatial patterns of poaching are non-random. Instead, incidents tend to form geographic clusters, with the risk of poaching varying across the landscapes animals inhabit (e.g. Wato et al., 2006). Considering the activity spaces of animals, these are influenced by a range of factors. Compared to most urban environments, these may be rather dynamic on both short (from day to day) and long (by season) time scales. Consequently, most animals have to routinely move through changing and sometimes unpredictable environments to avoid predators, to find food or to engage in other social interactions (Coughenour, 2008). Where foraging behaviour is concerned, food resources will typically be unequally distributed across what might be described as a patchy landscape. Moreover, this landscape will be subject to change as a result of environmental conditions, including the amount of rainfall and the probability of bush fire (Archibald, 2008), as well as a consequence of the actions of animals, such as grazing, predator-prey and population dynamics (Dunning et al., 1992). Optimal foraging models, such as the Marginal Value Theorem (MVT) consider such factors to try to explain animal foraging behaviour (Charnov, 1976). Considering the human actors involved, viewed from a utility perspective, poachers may be seen as rational actors who aim to maximize the rewards of their activity whilst minimizing the associated effort and risk of getting caught (Cornish & Clarke, 1986). All poachers will be subject to constraints, being able to travel limited distances in any given period of time, and being limited as to how far they can move animals successfully poached. Similarly, rangers will be subject to constraints to movement and the geographic areas that they can patrol at any one time. Both actors will also routinely have to return to a home or other location and will have to rest at certain times of the day. This is in fact, a core element of the Central-Place Foraging (CPF) theory (Schoener, 1979) of animal foraging. Initially used to study physical systems, tools from complexity science are gaining popularity in the study of social systems. The underlying principle of complexity science is that simple rules of behavior at the level of an entity (e.g. individual) can lead to unexpected outcomes at the macro level of the overall system as a consequence of the non-linear dynamic feedback processes which occur through the interactions of the individual actors (Kernick, 2004). For example, the complex patterns observed in bird flocks (or fish shoals) can be shown not to occur because of an invisible hand that guides the group, but because the individuals simply maintain a preferred distance from their nearest neighbour (Kernick, 2004; Wilensky, 1998). Where complexity arises from such simple rules, it is referred to as an ‘emergent phenomenon’. An emergent phenomenon requires new properties to describe it other than those that are used to define its underlying components (Gilbert & Troitzsch, 2005); that is,

122  Joanna F. Hill et al. the phenomenon should be difficult to predict based upon the properties and interactions of the individual components which produce it (Grimm & Railsback, 2005). In this way, patterns of poaching can be seen to emerge as the result of the interactions of at least three sets of actors (animals, poachers, rangers) and the environment they inhabit (see ‘Triple foraging’ in Introduction of this volume). Such complex systems can be captured inside computer simulation models, particularly agent-based ones, which we discuss in a little more detail in the next section. Whilst an exhaustive review of agent-based models is beyond the scope of this chapter, our aim is to outline key concepts and discuss how such an approach differs to normal standard statistical techniques.

An overview of computer simulation modelling A computer simulation is a simple representation of a real world system (Gilbert & Troitzsch, 2005) that can be used to model and understand the nonlinear dynamic mechanisms which occur between interacting components of a complex system (such as an ecosystem) that would be difficult to accommodate in statistical methods (Grimm & Railsback, 2005). In simulation modelling, a theory or set of theories believed to explain a phenomenon of interest is formalised using computer models. The need to formalise theories is a useful exercise as it requires the researcher to articulate their understanding of the problem in a way that is conducive to the identification of inaccurate and incomplete components. This typically involves identifying the relevant elements of the problem, establishing their properties and interactions, articulating the mechanisms underpinning these interactions and then encapsulating them in a structured programming language, which allows inspection and evaluation. This rigorous iterative process can significantly help clarify vague ideas and identify gaps in the theory that otherwise might go undiscovered. ABM is one particular method in the toolbox of simulation that is particularly appropriate here. Such models comprise two basic components: (1) agents or actors who engage in behaviors described by condition-action rules and (2) the environment they inhabit. ABM is generally used to address two types of questions: (1) those related to theory testing and falsification and (2) ‘what if’ questions for policy development. With the sorts of observational methods typically employed in the social sciences, data are usually collected and statistical explanations tested by establishing whether two or more variables are associated with each other (see Eck & Lui, 2008). The generative mechanisms are then usually inferred. In contrast to these ‘top down’ approaches, in the case of simulation methods the idea is to see if algorithms that formalise a theory of interest can ‘grow’ the phenomenon concerned. A basic question that may be asked of such bottomup approaches like ABM, is whether the theory or model implemented is

Potential uses of simulation modelling  123 sufficient (see Eck & Lui, 2008) to generate the patterns the researcher seeks to simulate, or whether other processes are necessary to produce expected outcomes. To examine the sufficiency of a model, a researcher may compare the model outcomes to very general (often theoretical) patterns, to stylized facts, or to empirical data. Stylized facts are those that are known to be quite typical across contexts and time periods and generally reflect what might be described as empirical facts. An example of a stylized fact would be the finding that the probability of an offender committing crime at a location is inversely proportional to how far that location is to where they live (e.g. Rengert et al., 1999). Facsimile models (Gilbert, 2007) are those that seek to replicate particular phenomena for a given situation as closely as possible. Such models allow for theory testing but can also be used to generate predictions – the accuracy of which will partly be a function of the validity of the assumptions on which the model is based. Both facsimile models and those that are developed to test what Gilbert (2007) describes as middle range theories (those that are compared to stylized facts) can also be used to examine ‘what if’ questions. In this case, the idea is that once a researcher has developed a model that is believed to have a satisfactory level of sufficiency, aspects of the model may be manipulated to allow the researcher to simulate the impacts of particular changes on model outcomes. By way of an urban crime example, Johnson (2009) shows how such an approach can be used to estimate the possible effects of varying the number of police officers available for foot patrols, or the way in which they patrol. In empirical research, random samples of the population will typically be selected and surveyed to (for example) enable estimates of a population parameter (e.g. the frequency of a particular crime) to be estimated. However, sampled data, such as police recorded crime data, do not reflect every incident of criminal activity that occurs and may particularly under-represent some types of crime, or crimes committed against particular sections of society (Coleman & Moynihan, 1996). As such, it may generate a distorted or incomplete picture of a phenomenon of interest. In the case of wildlife poaching, patterns of incidents will reflect those that are either reported to, or detected by, rangers. In reality, this is likely to provide only a partial picture of the poaching problem (particularly as animals cannot report crimes), and one that may systematically differ from the true state of the world. In contrast, for ABM limitless data can be collected and can include the position of each agent at every time step, what they perceived at that point in time, how this affected their decision-making, and so on. Eck and Lui (2008) refer to this as the ‘gods eye view’ and this perspective gives the researcher unparalleled access to detailed data on a population (albeit simulated) of decision-makers. In the following section, we introduce the concepts of agents and condition-action rules in more detail and illustrate how ABM has been utilised in the fields of ecology and how criminology can inform the design of a poaching model.

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Agent-based modelling: agents and their environments Agents can represent a variety of things including groups or individual people or animals (Railsback & Grimm, 2012). In a spatially explicit simulation, agents are placed on an artificial grid-based landscape upon which they interact with each other and the environment (Gilbert & Troitzsch, 2005). In such models, agents can perceive and react to their environment, make decisions and undertake goal-directed behaviour to meet objectives (Gilbert, 2007). For example, one could endow an animal agent with a particular foraging behaviour, and they may be given the ability to ‘sense’ n patches around them and to assess the characteristics of the locations to which they might next move. Agents may be heterogeneous, in that they can be programmed to have individual characteristics that can affect their choices, their interactions with other agents, or their impact on the environment (Crooks & Heppenstall, 2012). For example, one could equip poacher agents with different numbers of snares, or vary the amount of time they have available for poaching. Considering the condition-action rules used, these will often, albeit not always, reflect a rational choice framework, with agents considering the costs and benefits of a given decision. However, as in real life, such rationality may be imperfect or bounded in that individuals will usually lack perfect knowledge or the cognitive ability to make purely rational decisions when faced with multiple choices (Simon, 1997). Thus, just as in reality, decisions may be those that are ‘good enough’ (satisficing) rather than optimal (satisfying). One of the ways in which simulation models accommodate bounded rationality is through the use of stochastic processes. That is, rather than agents making purely deterministic choices by always selecting the most efficient alternative, different alternatives are usually assigned a probability of selection. In this way, even when an optimal choice is presented, agents may do something unexpected (Grimm & Railsback, 2005). Such stochasticity also serves to model factors that are not explicitly specified in a model, but that might nevertheless affect agent decision-making (see Gilbert & Troitzsch, 2005). As it happens, simulation modelling has frequently been used in the field of ecology and has been used to study the dynamic processes that occur between foraging animals and their environment (see Grimm & Railsback, 2005; Tang & Bennett, 2010). ABM is also starting to be used in the study of crime, particularly urban crimes. For example, Birks et al. (2012) used ABM for theory testing in the context of residential burglary. This model was able to replicate patterns observed in empirical studies regarding offender behaviour even though the (condition-action) rules that determined offender behaviour were quite simple. The Birks et al. study was implemented in an abstract environment, but Groff (2007) shows how ABM can be used for theory testing using real street networks, digitized using a geographical information system. Given that wildlife poaching can be considered a complex and dynamic system, it makes it particularly interesting to study using ABM. In fact, some researchers

Potential uses of simulation modelling  125 have already used ABM to examine poaching, with Imron et al. (2011) exploring how poaching affects wildlife populations and Keane et al. (2012) exploring the rule-breaking decisions of poachers. Other examples have investigated hunter decision-making and include research by Ling and Milner-Gulland (2008), who developed a realistic spatially explicit model to explore the interactions of hunter behaviour and ibex population dynamics, and van Vliet et al. (2010) bushmeat hunting model that suggested how differing habitat preferences of small antelopes and the spatial distribution of their hunters can affect animal population abundance and sustainable hunting rates. However, as far as we are aware, no published studies have investigated the combined spatial and temporal patterns of poaching by examining the interactions of animals, poachers and rangers using ABM. For the purposes of this chapter, poaching is broadly defined here as the illegal taking of wildlife (Eliason, 2004). Many aspects of poaching are essentially the same as hunting (e.g. both use snares and pursue animals) and the two largely differ only insofar as hunting is illegal in some jurisdictions1. For this reason, in what follows we use the terms hunting and poaching interchangeably. To introduce some of the key concepts underpinning agent-based simulation, we present a model for an area of Queen Elizabeth National Park, Uganda. The example is intended to illustrate how ABM could be used to inform understanding of spatial and temporal patterns of poaching, with a view to informing the prevention of this type of crime.

Designing agent-based models for wildlife poaching Two types of poaching methods are predominantly used by hunters in the real world that are amenable to modelling: (1) active methods that involve the pursuit of animals using (for example) dogs, spears and guns (Lindsey et al., 2013) and (2) the use of passive wire snares or pitfall traps near to animal trails or water bodies. The latter are a type of passive hunting technique widely used in Africa (Noss, 1998). The advantage of neck and foot snares is that they can be made quickly and cheaply (Noss, 1998). As a result, poachers can make many snares that can be laid over large areas, which in turn increases the probability of capturing an animal. Furthermore, unlike guns, snares do not emit noise (unless an animal is caught) and can be hidden from view, which makes their use less ‘risky’ in terms of the likelihood that poachers will be detected by rangers. Snare poaching is therefore a particularly challenging problem which enforcement depends on the effectiveness of the resource allocation strategies employed by rangers, and is the focus here. To extract the essence of poaching activities without capturing a wide range of interesting but less relevant details, we start by considering a relatively small set of events perceived to be critical to the development of the model. These include ‘snare placement’, corresponding to the illegal use of snares in the environment for the purpose of catching animals, and ‘snare detection’,

126  Joanna F. Hill et al. corresponding to the detection of a snare by a ranger. Both types of events can be characterized in terms of their timing and location. These two events are considered critical in the study of poaching because snare placement is a necessary precondition to catching animals, and snare detection is a necessary precondition to disabling or removing snares, one of the main prevention strategies used by rangers (Mugume, 2000). In short, the objective of this chapter is to simulate where and when snares will be placed by poachers, if animals are caught by these traps, and the likelihood that rangers will detect the illegal activity. The simulation model presented here should be seen as a stepping stone that will only benefit from refinement. While the spatial and temporal distributions of snaring activity reported below are interesting, they are unlikely to perfectly portray the interaction between rangers, poachers and animals. Better information about the actual foraging behaviour of these groups is necessary to mimic the real world more precisely. These refined models would have the capability to provide rangers with useful information for devising and implementing effective prevention measures. Conversely, models based on poor representations of animal, poacher and ranger movements may produce misleading data that have an adverse effect on rangers’ ability to protect animals. For this reason, modelling ought to be conducted in consideration to the potential impact that the results may have. Moreover, ethical application of the model would require unambiguous communication of the design choices made during model development. In terms of examining model sufficiency, an issue already raised is that patterns observed in the real world will be based on incomplete data as many events will go undiscovered, whereas those generated in the simulation will represent all events. To illustrate this, in section five, we consider differences in simulated patterns of poaching events that are and are not observed by ranger agents. In future work, our aim is to build a model that is able to simulate patterns that are similar to those observed by rangers in the real world, but that is also capable of simulating those events that the rangers do not see but that are likely to occur. The potential value of doing this is that it may provide insight as to where undiscovered poaching events occur in the real world and hence where rangers might focus their attention that they currently might not. For now, we limit the scope of this chapter to providing an overview of the model and of illustrating the point above.

Model overview In what follows, we describe the different elements of the simulation, and articulate the condition-action rules that govern agent behaviour. As noted above, the model discussed represents a first step, and only some of the parameters used to calibrate it are based on empirical data. At this stage, the model (see Figure 7.1) should provide a good idea of what ABM is and hopefully illustrate the potential of the approach and why it is an appropriate one. In what follows, Netlogo (5.0.2) software package was used to implement the model.

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Figure 7.1 Overview of the structure of the poaching simulation arranged by time scale. (Arrows illustrate some key relations between processes and model variables)

Environment The landscape represents a section of Queen Elizabeth National Park (QENP) in Uganda, South of the Kazinga channel that connects Lakes Edward and George (see Figure 7.2). In Netlogo, the environment is made up of a lattice of regularsized cells referred to as patches. In the model, each patch represents an area of 2,500 m2 (50 m by 50 m), and the environment is 601 patches by 481 patches (or 30 km by 24 km). Unlike a torus, the model has a hard boundary so that if an agent reaches the edge of the model, it does not appear on the other side.

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Figure 7.2 Visualization of the Queen Elizabeth National Park wildlife poaching model showing environmental features and mobile agents Note:  An example animal trail is shown (see circled animal agent), which has been created by the animal dropping footprints every time it moves forward. The six monitors in the top left corner present elapsed time.

Patches have given attributes (e.g. type of land, amount of grass, and so on) defined by the user, and these can be programmed to change over the course of a simulation as a result of the actions of agents, or to simulate other conditions, such as the weather. Geographic data can also be imported into Netlogo and in the current implementation, ‘shapefiles’ of landscapes (see Figure 7.2), road networks, water points, swamps and (five) villages were imported to represent the simulated environment. At the initialization of a simulation run, patches are assigned to one of these five types of land. Table 7.1 summarises all global and patch variables and parameters. Each land patch in the current model is assigned an amount of grass on which animal agents can graze. It is preferable to calibrate variables using real-world data, particularly if the models are intended to have real-world application. Here, the amount of grass per patch is based on data collected by Chansel (2011) in

Potential uses of simulation modelling  129 Zambia, which has a similar climate to QENP (Table 7.1). In addition to factors associated with the type of land that each patch represents (which is constant), patches are affected by agent behaviour, and consequently as the simulation runs, variables are used to record the states of the patches over time. For example, when animal agents graze, the amount of remaining vegetation on a patch is updated accordingly (see below). Table 7.1  Global and patch variables and parameters Variables

Description and parameter values

Global Time_counter

Affect all agents and patches in the simulation. Each time step represents 30 simulated seconds. This counter triggers all time-dependent patch and agent-related procedures. For simplicity, each simulated month comprises 30 simulated days. List of the number of simulated days of rain for each month. Monthly values are selected from the range of min-max values observed in QENP (Kapere et al., 2011) using a uRNG. List of the actual simulated days (up to 30 each month) on which it will rain, selected using a uRNG. Amount of grass regrown on land patches after it rains (10% of the maximum grass possible on a patch, 400 kg, see grass patch variable below). Using a uRNG, select up to 189,659 possible land patches to regrow grass after rain. Level of grass remaining on a patch before an animal agent decides to find another patch to graze on. Set to 10% of maximum possible (400 kg, see grass patch variable below). Number of animal signs decreases by two per patch after one simulated day (2,880 time steps). Probability of an animal becoming successfully caught by a snare agent if the distance between them is less than 0.2 patch (12.5 m) is 0.20, and 0 otherwise. Default probability (0.90) that agents will act given that the corresponding condition is true. Output: Total number of snares ever laid by poachers. Output: Total number of animals caught in snares. Output: Total number of poachers found by rangers. Output: Total number of snares with captured animals detected by rangers. Output: Total number of snares with no animals captured detected by rangers. Output: Average distance to the nearest water patch of all snare agents at the end of the simulation. Output: Average distance to the nearest village of all snares at the end of the simulation.

Monthly_rain Days_rain Grass_regrowth_rate Grass_regrowth Random n land patches Foraging_cut-off Sign_fade_rate Capture_rate General Snares_laid Animals_caught Poachers_ found Snares_found_(caught) Snares_found_(not caught) Av_distance_water Av_distance_village

(Continued)

130  Joanna F. Hill et al. Table 7.1 (Continued) Variables Patches Land Water Swamp Road Village_land Grass

Footprints Animal_signs

Description and parameter values Binary indicator (Y/N). Type of land patch on which agents can walk, and grass can grow. Binary indicator (Y/N). Type of patch containing water, and targeted by animals when foraging for water, but avoided when agents are navigating. Binary indicator (Y/N). Type of patch treated as water in the model. Binary indicator (Y/N). Type of patch on which all agents can walk, rangers begin patrols, and poachers do not lay snares. Binary indicator (Y/N). Distance between a patch and the nearest village less than 1 km (21 patches). Poachers do not lay snares within 1 km of village centres. Maximum capacity of grass (4,000 kg) per land patch. Every patch is initialized with a constant cover of 4,000 kg. Values selected based upon Chansel (2011), who estimated the biomass of vegetation in Zambia to be ~16,000 kg per hectare (10,000 m2). Therefore the 50 m by 50 m (2,500 km) model patch would contain up to 4,000 kg of grass. Amount of footprints on a given patch. Increases by one every time any mobile agent moves forward on a patch. Does not decrease in value. Amount of animal signs on a given patch. Increases by one every time an animal moves forward on a patch. Indicates recent animal activity which is used by poachers when deciding whether to lay a snare. Decreases by two signs after one simulated day (see above).

Note:  uNRG = uniform random number generator

The choice of spatial units used to represent the environment does, of course, affect the agent’s activity spaces and the geography they can traverse during a simulated interval. So too does the frequency with which agents can make decisions. Thus, it is important to match the time steps of a simulation to those of the environment so that agents cover a realistic amount of space per unit time. Here, each time step represents 30 simulated seconds. At the start of the simulation, a global timer is initialized to 8:00 hours, and at each time step, the number of seconds, minutes, days, months and years elapsed is updated (see Figure 7.2). As well as recording the passing of time, the counter is used to trigger timedependent routines such as weather procedures (a global factor), and patch (e.g. grass growth) and agent behaviour. Special emphasis is placed upon simulating the dynamics associated with animal foraging, rainfall, and grass regrowth. One reason for this is that animals create trails when foraging for resources and these trails are known to affect animal, poacher and ranger behavior in the real world. That is, animals (create)

Potential uses of simulation modelling  131 and use existing trails to locate sources of food (Ganskopp et al., 2000; Kanga et al., 2011), poachers use them when hunting animals (Hayashi, 2008; Yasuoka, 2006) and rangers use them when searching for poachers. Moreover, animals can only graze where grass exists, and grass cannot grow without water. Consequently, it was necessary to implement global procedures to simulate rainfall and grass growth, and to simulate signs of animal activity that fade over time. Empirical data (Kapere et al., 2011) regarding monthly rainfall in QENP are used to calibrate the simulation. At the start of the model, a list of 12 numbers is created to determine the number of rainy days for each simulated month. This list is reset for each simulated year, and the numbers reflect the range (minimum and maximum) in the number of days of rain per month as reported by Kapere et al. (2011). The precise days on which there will be simulated rain are then selected for each month using a uniform random number generator (uRNG), and for each simulated day of rain, a number of patches (selected at random) regrow a small percentage of grass. In the case of trails, these can be left by all mobile agents. Two types of trails can be laid in the current model. The first are ‘footprints’ which are left by all mobile agents every time they move forward. These never decrease in value. If an agent decides to follow trails (patches with footprints), they typically2 select the patch with the highest footprint count. With enough repetition, this will lead to the emergence of distinctive trails that the animal agents (and human agents, see below) preferentially follow to new grazing or other areas. To ensure that agents do not become ‘stuck’ following their own trails, agents store every patch they have visited in the last 1-hour simulated period and do not revisit patches within this time period. The second are ‘animal signs’ which are dropped only by animal agents. These represent recent animal activity. In this case, these animal signs fade over time at a rate of two signs per simulated day. Currently, the parameters used to set the animal sign fade rates are not based upon empirical data. This dynamic procedure is included because trails and recent animal activity are both important decision-making cues for poachers when laying snares (van Vliet & Nasi, 2008). Furthermore, a patch with many footprints (which do not fade) may not indicate constant animal activity, but simply a concentration of activity at some point in time. In the real world, there would be little point in a poacher laying a snare on a trail if it has not been visited by an animal for (say) more than 3 months and the same is true in the model. This process of trail formation and recent activity illustrates how the behaviour of one animal agent can indirectly affect those of other agents as well as the environment. Agents The model comprises two types of agents: mobile agents (animals, poachers, rangers), which can move between patches, and snares, which remain fixed to their current patch but can still react to the presence and actions of other agents.

132  Joanna F. Hill et al. All three categories of mobile agents share common behavioural characteristics. For example, in the model, they are all assumed to engage in a form of rational decision-making, albeit bounded (see Cornish & Clarke, 1986). Consequently, in the most basic terms, their decisions are motivated by the following two principle objectives: (a) they seek to increase their expected rewards and (b) to reduce their expected risks and effort. These objectives do, of course, affect the behaviour of the three classes of agents in different ways, but these generic rules motivate the overarching logic of the model. As should be evident, agent decision-making is based on an assessment of the information available to them. This can be obtained through direct sensing (i.e. seeing a current event) or through previous experience of either directly (i.e. having seen an event) or indirectly (i.e. having been told about an event). In the current model, all agents are limited to direct sensing, but implementing such abilities is challenging. For example, consider the ease with which an agent can detect an object in front of them. Research indicates that the association between the likelihood of an observer detecting an object and the distance that object is from them is non-linear, and that many factors are involved. These include the amount of light, size of object, angle of object, distance and the presence of obstacles (Buckland et al., 2004). In future work, we aim to collect empirical data to calibrate an accurate model of object detection, but for now use a simple logistic function (shown below) to approximate this.



  1 Pdet ( x) = 1 −  a−bx  1 + exp  

(1)

Where, Pdet(x) is the probability of detecting an object at range x a and b are constants x is the distance (m) the observer is from the object In what follows, we describe the properties of the different classes of agents and how the condition-action rules were implemented in greater detail. Due to there being a relatively large number of condition-action rules for the agents, only the core procedures will be discussed (full details are available upon request). Snares In the context of ABM, a snare can be defined as an individual immobile agent with two attributes: identity and location (see Table 7.2). As a result, both placement and detection events are specified in relation to a particular snare. The objective of snare agents is to ‘catch’ animal agents, but they do so ‘reactively’ after an animal moves within a certain distance of them. If a snare catches an animal, the snare deactivates so it can no longer catch further agents, and a

Table 7.2  Snare and animal agent variables and parameters Variables Snare agents Number Activated Checked Removed Animal_caught Location Animal agents Number Speed Day-time_foraging Night-time_foraging Forage_countdown Grass_consumption_rate

Decay_level

Resting Thirst Location

Description and parameter values One snare agent is hatched after a poacher lays a snare, after which a poacher’s available snares decreases by 1 unit (see Table 3). Binary variable (Y/N). Capability of a snare agent to trap an animal agent. It is set to ‘Y’ when first laid by a poacher and to ‘N’ when removed by a ranger or if an animal is caught. Binary variable (Y/N). Status of a snare once checked by its poacher on a different poaching trip. Resets after each poaching trip has completed. Binary variable (Y/N). Status of a snare agent that has been detected by a ranger. Detected snares can no longer trap animal agents. Binary variable (Y/N). Status of a snare with a caught animal. X and Y coordinate of agent at time t. Number of animal agents in the model (N = 100). Number of patches covered by moving animals in a single time step. Set at 0.6 ± 0.2 patch (30 m ± 10 m), the latter selected from a normal distribution. Time at which all animals start foraging for grass in the morning (7 am). Time at which animals start foraging for grass in the evening (7 pm). Duration of foraging in a particular foraging bout. Day-time foraging = 480 time steps (4 h) Night-time foraging = 600 time steps (5 h). Amount of grass consumed per time step by animals. Grimsdell and Field (1976) estimate that in a nine-hour period, buffalo can consume approximately 18kg of grass. Thus, per simulated time step (i.e. 30 seconds) buffalo may on average consume (18kg/9(hours)* 120 (there are 120 simulated times steps per hour)=) 17g. To model random variation, the amount consumed is sampled from a normal distribution centred around 17g. Countdown timer triggered as soon as a snare has caught an animal. Determines how ‘old’ the caught animal is before it has decayed, and indicates to poacher agents if it is salvageable – currently set at 28,800 time steps (10 days). Probability that an animal will remain on the same patch during a given time step (0.6). Probability that an animal will move towards a random water patch during a non-foraging bout (0.2) at a given time step. X and Y coordinate of agent at time t.

134  Joanna F. Hill et al. countdown timer is initiated that indicates the ‘freshness’ of the caught animal. After ten simulated days, the animal is assumed to have ‘decomposed’ and is ignored by poachers. In the current model, the location of a snare is constrained by the activity space of the poacher who carries it and their decision to set a snare at a particular location. There are three conditions for which snares are removed from the model: (1) if poachers have found a snare with a caught animal, (2) poachers perceive them to be no longer likely to catch animal agents or (3) a snare has been removed by a ranger. Snare agents detected by rangers are not permanently removed from the model but are ‘deactivated’ (a snare variable) so that they can no longer catch animals. The reason for this is so that poachers who are unaware that their snares have been detected by a ranger can still check them and discover they are missing. For a given snare detected by a ranger, the location attributes of where the snare was laid and where it was detected will generally be the same. In contrast, the times associated with these two events will most likely differ, reflecting the fact that most snares will be detected some time after they have been placed. The latency associated with snare detection may vary (systematically) by the location at which it is discovered. Consequently, we use variables to record the occurrence, time and location of all ‘snare placement’ and detection events. Animals At this stage, the animal agents used in the model represent a generic nomadic herbivore species frequently poached in QENP. Therefore, all behaviors and parameters are based upon empirical data collected from African buffalo (Syncerus caffer) biology and foraging behaviour (Grimsdell & Field, 1976). In later implementations of the model, individual species will be represented as different classes of agents. Animal agents have two foraging behaviours – to locate and consume food or water. At each time step, animal agents engage in one of these two behaviours, stay still, move randomly or are trapped by a snare. Figure 7.3 illustrates these dynamic processes. At the beginning of each time step, animal agents examine the patch they are on. If a snare is found on that patch, then with some probability (see Table 7.2) the animal agent is caught. When an animal is caught, a new animal is ‘born’ at a random patch on the landscape. This strategy is employed so that the number of animals remains constant over time. Of course, it is possible to model animal ‘births’ and ‘deaths’ in a more sophisticated way, but this was beyond the aim and scope of the current chapter. Based upon empirical data, animal agents forage twice each day; once at 7:00 and once at 19:00. For each animal, the duration of foraging activity is determined using a uRNG (see Table 7.2). Where the output of the uRNG is below a critical threshold, an animal agent will forage for food, where it is above the threshold, it will instead – with some probability – remain where it is currently located – or

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Figure 7.3  Animal agent behavioural processes Note: Boxes with asterisks indicate probability-determined choices, whereby a number between 0 and 1 is generated using the uRNG, and then one of two possible actions are carried out it if falls below (or above) the critical value.

move in a random direction. Note that, as with animal movement in the real world (Bovet & Benhamou, 1988), whenever an animal moves randomly, it follows a biased random walk so that it is more likely to continue travelling forward (rather than moving completely randomly). In the event that an animal decides to forage for food (grass), it will first assess the amount of grass available on its current patch. If there is a non-zero amount on that patch it will consume a small amount of grass (see Table 7.2). If there is no grass, or the amount is below a critical level, the ­animal moves to another patch to forage. This movement choice is det­ermined following an evaluation of the surrounding eight patches. If one or more of the adjacent patches contains grass, the animal agent moves to one of them. In line with the findings of observational research on a­nimal foraging behaviour (Lewison & Carter, 2004), the animal agents are programmed to maximise their energy intake by

136  Joanna F. Hill et al. selecting patches with the highest amount of suitable grass. However, the decision of which precise patch to select is determined using a uRNG. This adds stochasticity to the simulation and is intended to model factors that are not explicitly included in the model (see Gilbert, 2007). As discussed above, this also introduces an element of bounded rationality to the decision-making process. In the case that none of the adjacent patches contain grass, animal agents look for signs of an animal trail (footprints) in neighbouring patches. Where there is evidence of a trail, they move (in most cases) to one of the patches containing the highest value of footprints. Where no patches contain food or signs of animal trails, animal agents move randomly. When an animal foraging bout is complete, agents either remain stationary, move randomly, or with some probability, move towards a patch containing water (darker grey patches in Figure 7.2). The decision to move to water occurs only once during a ‘non-foraging’ session. When moving to water, animal agents preferentially move towards patches with trails in front of or diagonal to them. Once a water patch has been located, animal agents resume normal random movement procedures until the next foraging bout. Poachers The main objective of poacher agents is to capture animals by laying snares. Data regarding poachers in Uganda is unsurprisingly limited, and so we use stylized behaviours regarding human mobility and available information on poacher strategies from other parts of Africa (Noss, 1998; Coad, 2007). The number of poachers can be varied; here we use 25 poachers – five allocated to each of five villages. During initialization, each poacher is assigned as either a ‘day’ or ‘evening’ poacher. Day poachers begin hunting around dawn (4:00–6:00) whilst evening poachers start at dusk (16:00–18:00). Hunts last approximately three to six hours (for more details, see Table 7.3 and Figure 7.4). When moving, poachers preferentially move to patches in which there are signs of animal trails (footprints), and avoid roads. That is, they look for patches where the observable conditions for finding an animal, and avoiding detection, are likely to be highest. In the absence of trails in their locality, poachers move randomly. At the end of a poaching session, or if an animal has been caught, poacher agents return to their home location and wait a few days until the next hunt. At the beginning of each new hunting trip, poachers are assigned a new set of three to five snares and then decide (using a uRNG) whether to check snares that they have already laid (if any), or to lay new snares. Both animal footprints and signs are thought to particularly inform poacher spatial decision-making as their presence suggests that animals regularly use a location. Consequently, when deciding to lay new snares, poachers forage for patches that contain evidence of recent animal activity, as reflected by the presence of ‘animal signs’. Every time a poacher moves to a new patch with animal

Table 7.3  Poacher agent variables and parameters Variables

Description and parameter values

Number Speed

Number of poachers for each of the 5 villages (currently 5). Number of patches covered by poachers in a single time step is 0.5 ± 0.3 patch (25 ± 0.3 m.), the latter selected from a normal distribution. Note this is reduced by 50% if poachers are returning to their village with a caught animal. Probability (currently 0.5) that a poacher will hunt in the evening (‘night poacher’) or during the day (‘morning poacher’). Fixed after initialisation of the model. Number of snares assigned to a poacher at the start of every poaching trip (currently 3 to 5, set using a uRNG). Once a snare has been laid, a snare agent is hatched and the number of snares decreases by one unit. A list of individual snares laid by a poacher (varies over time). If their memory contains more than 15 snares, the two oldest snares are removed from the poacher agent’s memory each day. Time poachers leave their village to hunt (based upon whether they are assigned as night or morning poachers). Currently 5:00 ± 1 h (day-time poachers) and 17:00 ± 1 h (night poachers). The second part of the equation is determined by a uRNG. Amount of time allocated to poaching each day, 360 ± 360 time steps (3 ± 3 h). The latter determined using a uRNG. Number of days a poacher remains in their village before hunting again (0 to 4 selected using a uRNG). Number of time steps poachers remain on a patch after discovering and processing an ensnared animal = 60 ± 60 time steps (30 ± 30 min) the latter determined using a uRNG. Number of time steps poachers remain on a patch after laying a snare = 20 ± 20 time steps (10 ± 10 min). The latter determined using a uRNG. Probability (0.3) that poachers will remain on the same patch at a given time step. Probability (0.4) that poachers will not check any snares during this poaching session. Probability (0.2) that poachers will no longer check snares during a hunt (if snares are in their memory), and so decide to hunt for new locations. Probability (0.2) that a poacher will lay another snare on the same patch in which a snare has just been laid. X and Y coordinate of agent at time t.

Poacher_type Snares

Memory Snare_fade_rate Begin_hunt

Hunting_duration Next_hunt Animal_processing_counter

Snare_setting_counter Rest Check_snares_on_new_hunt Check_snare Relay_snare Location

138  Joanna F. Hill et al.

Figure 7.4  Poacher agent behavioural processes Note: Boxes with asterisks indicate probability-determined choices, whereby a number between 0 and 1 is generated using the uRNG, and then one of two possible actions are carried out it if falls below (or above) the critical value.

signs, they decide whether to lay a snare. As discussed, not all patches have the same likelihood of having a snare laid upon them, since animals preferentially move towards water and areas of grazing (and by selecting patches trodden on previously), which, naturally, will contain a higher concentration of animal signs and footprints. Here, the decision-making process uses a simplified

Potential uses of simulation modelling  139 marginal value theory criterion (Charnov, 1976) so that, if the number of animal signs on their current patch is more than the average number of animal signs which occur across the landscape, the poacher will lay a snare on their current patch. If a patch contains no animal signs, they continue to follow trails. If a poacher decides to lay a snare, to simulate the effort involved in so doing, they essentially remain at that location for a small period of time (Table 7.3) before the snare is activated. When a snare is laid, the number of snares a poacher possesses decreases by one, and a snare agent is activated at that location. Once a snare has been set, poacher agents are assigned a small probability of laying another snare (if available) at the same location to incorporate the finding that poachers may lay several snares together (Coad, 2007; Noss, 1998) and to compensate for the limitations associated with the large patch size used here (i.e. several trails could occur on each patch at this scale). Each snare is then stored in the poacher’s memory, so that they can check their snares on subsequent poaching trips. Research on snare hunting patterns by Coad (2007) suggests that hunters begin forgetting snare locations after one month, or so. Thus, it is important to simulate memory loss. Here, once poachers have more than 15 snares in their memory (which takes around three weeks), they ‘forget’ two of the oldest snares each day. As well as making the simulation more realistic, this serves to reduce the likelihood that poachers will exclusively engage in snare checking behaviour without hunting for new locations. If, at the beginning of each new hunt, a poacher agent decides to check the snares currently laid, they move towards the closest one. For simplicity, once a target has been selected, their objective is to move to this snare – all other snares or animals that may be caught are ignored1. Having reached a targeted snare, poachers check to see if an animal has been caught. If an animal is discovered after it has ‘decayed’ (which occurs after ten days), the catch is ignored. Otherwise, a global variable that records a successful hunt is updated. Since there is a time delay for processing an animal, poachers remain on a patch for around 30–60 minutes. This is reasonable for dealing with a large animal like a buffalo, whether by butchering and/or smoking the meat on sight. If the snare has not caught an animal and has not been removed by a ranger (see below) the poacher checks whether there are still signs of animal activity. If the number of animal signs falls below the average number for the landscape, the snare is removed and the poacher acquires a new snare that can be placed at a new location. Every time a snare is checked, there is a small probability that the agent will start a new hunt and refrain from checking further snares. Otherwise, poacher agents head towards the next nearest snare. Finally, for simplicity, poacher agents do not actively scan for rangers, or move away from rangers if they are on the same patch. Since rangers patrol in teams, it is reasonable to assume that rangers will be more likely to spot poachers first. However, future models will incorporate a ranger detection procedure. Poacher agent activity is constrained by several factors: (1) their origin point for any hunting trip is always their village (although in the model they avoid laying snares too close); (2) they can travel only a limited distance each day; (3) when they return with a caught animal, their speed is reduced to simulate the effort of carrying it; (4) checking snares limits the amount of new

140  Joanna F. Hill et al. opportunities they can seek out to lay additional snares, and (5) whilst laying multiple snares may increase the probability of catching animals, it reduces the time available for other activities. These constraints, along with the specific parameter settings used, will significantly impact upon the likelihood of poachers being detected by rangers, or catching animals. Rangers Ranger agent behaviour is guided by extensive fieldwork and interviews with rangers of QENP (Moreto, 2013). The use of this type of ground-truth information is quite common in the development of simulation models (Moss, 2008). The main objective of the ranger agents is to detect and record evidence of illegal activity in the park; in this case, the presence of snares or poacher agents. When ranger agents identify poaching activity, this is recorded so that the activity they observe can be compared to what actually occurs, regardless of whether ranger agents observe it or not. In the model, ranger agents are assigned to a road patch, selected at random with the condition that rangers are a certain distance away from each other. Each ranger begins patrolling between 6:00 and 9:00 for up to 12 hours. At the end of a patrol, or if a poacher has been successfully caught, they ‘camp’ and remain inactive for a number of days. They are then assigned to another patch of road to begin the next patrol. Table 7.4 and Figure 7.5 outline the main variables, parameters and processes of the model. When patrolling, ranger agents first check whether any snares or poacher agents are nearby. A number of logistic equations are used to estimate the probability of detecting a poaching event where they exist; the first is used to detect poachers, the second to detect snares with or without caught animals. The probabilities differ, reflecting the fact that it will be easier to detect a poacher (standing) than a small, inconspicuous flat snare (at the same distance). To illustrate, the current calibration (see Table 7.4) means that there will be a 0.35 probability of detecting a single snare at distance of 0.4 patch away (20 m) compared to 0.94 for detecting a poacher at the same distance. The probability function allows for the chance that even though illegal activity may be present on a patch, rangers may not see it. It also goes some way towards dealing with the fact that the patch size used in the model is large, and at this scale, each patch would contain different vegetation types and terrain that could obstruct ranger vision. Further experimental studies to test the probability of detection at smaller patch scales will be required for more realistic models. If a poacher is detected by a ranger, the poacher agent is returned to its village to simulate an ‘arrest’. To simulate the time needed to investigate the crime, rangers who make arrests cannot patrol for a minimum of 24 simulated hours after the arrest. If a snare is found (with or without a caught animal), the snare is ‘deactivated’ so that it can no longer catch animal agents. As mentioned previously, this allows for poachers to return to such snares later and discover they have been ‘removed’ at which point they are permanently removed from the model and the agent’s memory. In the current model, rangers do not preferentially recheck areas they have previously caught poachers or spotted snares, but this

Potential uses of simulation modelling  141 Table 7.4  Ranger agent variables and parameters Variables

Description and parameter values

Number Speed

Number of rangers in the model (N = 5). Distance covered by rangers in a single time step as they move forward = 0.5 ± 0.3 patch (25 ± 15 m), the latter selected from a normal distribution. This means that, for example, for a 12-hour period they could patrol approximately for 20–30 km, which is realistic based upon observations of ranger patrols in QENP. Start time for individual ranger’s patrol = 6:00 ± 3 h as determined by a uRNG (based on observed start times of rangers in QENP). Duration of a daily patrol. Number of time steps rangers patrol the park each day = 960 ± 480 time steps (8 ± 4 h) (uRNG). Number of rest days between two patrols = 0 to 3 (uRNG). The distance which rangers maintain between each other: 120 patches (6 km). Probability (0.2) that rangers stay on the same patch during a given time step when patrolling. Probability (p) of a ranger successfully detecting a poacher. p = (1 + exp(a – b.x))–1, with x = (–50.distance to poacher) A = –3, b = 0.0067 Note the distance is multiplied by 50 to account for the patch size in the model. The constants determine the shape of the logistic or ‘S’ curve. Probability (p) of a ranger successfully detecting a snare given that it has not caught an animal; where: p = (1 + exp(a – b.x))–1, with x = (–50.distance to poacher) A = –0.6, b = 0.006 Probability (p) of a ranger successfully detecting a snare given that it has caught an animal; where: p = (1 + exp(a – b.x))–1, with x = (–50.distance to poacher) A = –0.9, b = 0.00231 X and Y coordinate of agent at time t.

Time_to_patrol Patrol_duration Next_patrol Distance_from_ranger Rest Poacher_spot

Snare_(not caught)

Snare_(caught)

Location

type of ‘hot-spot policing’ strategy could be easily incorporated into future models to observe this policy change (e.g. see Johnson, 2009). If no snare or poacher agents are identified, ranger agents resume patrolling activity by following trails, or, if none are present, by moving randomly. It should be obvious at this point that many of the agent variables and processes are interrelated, and the behavioural choices of one agent can significantly impact upon the environment and other agents. For instance, as illustrated in Figure 7.1, the global time variable will affect the amount of rainfall which triggers grass to regrow. The amount of grass on a patch determines how long an animal will remain on that patch, which in turn, increases the amount of animal activity (animal signs) in that area. This increases the probability that a poacher will lay

142  Joanna F. Hill et al.

Figure 7.5  Ranger agent behavioural processes Note: Boxes with asterisks indicate probability-determined choices, whereby a number between 0 and 1 is generated using the uRNG, and then one of two possible actions are carried out it if falls below (or above) the critical value. Highlighted (speckled) boxes indicate the logistic distance calculations which rangers use to spot illegal activity.

a snare at this location (and, therefore) the likelihood that an animal will be caught. However, the longer a poacher spends within an area of animal activity, either to search for places to lay snares, processing a current snare or butchering a successfully caught animal, the more likely it will be detected by a ranger. To take another example, once an area has been depleted of grass through grazing, the number of animal signs will decrease (since animal agents will move to other areas). As a result, (and after some time) poachers may decide to move snares elsewhere as the likelihood of encountering a suitable target will be reduced. However, with elapsed time, grass may regrow following rainfall, and the area may be become inhabited again by animals, creating new snaring opportunities. Thus, patterns of poaching will vary not only in space but also in time. These dynamic processes will be explored in future work.

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Preliminary results: an illustration As should be clear, the model described above is far from complete. The aim of this chapter was not to implement a complete model, but to discuss our progress to date to illustrate how the approach adopted might inform understanding of poaching and possible responses to it. With that in mind, in this section, we provide an example of model outcomes for one run of the simulation. As we present results for illustrative purposes only, we did not consider whether observed patterns are statistically significant. Unless the model is calibrated with historic data regarding animal trails (and so on), the patterns of behaviour that occur shortly after initialization are often chaotic. Consequently, for this particular run of the model, we use a simulated 18-month interval that included a six-month ‘burn-in’ period. During this interval, only animals moved around the landscape to allow signs of their activity to form, and heterogeneity in the landscape to establish. To calibrate future models, we intend to collect data on the locations of actual trails. The results presented were generated using 25 active poachers, five rangers and 100 animals. When the simulation is running, we count the number of times poacher agents lay snares, the number of times ranger agents detect snares or poachers, and the number of times animal agents are successfully caught by agent poachers. The timing and location of all events are also recorded and, at the end of the simulation, we record the average distances of all snares to the closest water patch and village. For the model run considered here, there were a total of 11,145 incidents of snares being laid (approximately 37 snares were laid by each poacher every month). A total of 1,127 animals were caught in snares (~ 10 percent successful capture rate). However, only 190 of those caught were successfully located by poachers before they spoiled. Thus, overall, the success rate for poaching, in terms of successful hunts from all snares laid was only 1.7 percent. Of the snares laid, ranger agents detected 897 snares (~ 10 percent detection rate), 179 of which were for snares that had trapped animals. Thus, around 16 percent of caught animals were detected by rangers. Poachers were also detected on 107 occasions, meaning that about nine poachers were spotted each month. Finally, all traps present in the model at the end of the simulation, were on average 17.72 patches away from the nearest patch of water (886 m) and 76.8 patches away from the closest village (3.8 km). Figure 7.6 shows two maps representing the spatial patterns of snares laid by poachers after the burn-in period during the first six months of hunting (Figure 7.6A) and the following six months (Figure 7.6B). It appears that poaching events tend to cluster spatially, being located close to the home locations of the poacher agents, or around bodies of water. Snares laid around the villages on the west side of the environment and the single village to the north-east of the environment appear to be more densely clustered, whilst snares laid around the two villages in the south-east of the environment appear to be more dispersed. Also, in Figure 7.6B, it appears that snares laid during the second period of

Figure 7.6 Illustration of the spatial and temporal patterns of simulated poaching activity in Queen Elizabeth National Park Notes Figure 6A: Snares laid by poachers in Months 7–12 (white). Figure 6B: Snares laid in Months 13–18 (black).

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Figure 7.7 Illustration of the simulated spatial patterns of all illegal activities detected by rangers

hunting (coloured black) were laid much further from the village than those that were laid during the first six months of hunting (coloured white). Figure 7.7 shows only those poaching events, i.e. snares (coloured white) or sightings of poacher agents (coloured black) that were observed by the ranger agents after the burn-in period. In this case, a proportion of the four clusters that occurred around most of the villages are detected. However, in the case of the village to the south-east of the environment, relative to where simulated poaching events actually occurred, snares tended to be more likely to be detected further away from the village, but more likely to be detected closer to one of the small lakes, or near to rivers and roads around this area. Interestingly, snares that were laid towards the centre of the environment were not detected.

Discussion and next steps The aim of the current chapter was to discuss how ABM might be used to provide insight into the problem of animal poaching. In this final section, we discuss some of the issues associated with developing a model for this purpose and our next steps.

146  Joanna F. Hill et al. Key findings For reasons discussed above, as the model outcomes are based on only one run of the model, some caution is necessary when interpreting them. However, with this in mind, the high number of snares laid by poachers (11,145) may in fact be relatively realistic. For example, during a field visit to the Murchison Falls National Park (which is near to QENP) in early 2013, the first author observed a 3 m by 6 m wide pile of confiscated snares. According to a senior ranger, these two to three thousand snares had been collected over a three-month period. An interesting observation from Figure 7.6B is that in the second six-month period, poacher activity appears to be somewhat more dispersed, with poachers laying snares further into the park. This suggests that patterns of poaching are likely to be dynamic rather than time-stable. One possible explanation for observed patterns is that as the animals graze some patches will become over-foraged and they will consequently move to new locations, seeking out patches where grass is more abundant. In turn, the poacher agents may adapt accordingly. In future work, we will explore this in more detail and see if (which seems likely) there are any seasonal differences in the spatial patterns of poaching for the wet and dry seasons in QENP. One explanation as to why ranger agents did not detect illegal activity further into the park, may simply be because less illegal activity occurred there. That is, rangers patrolling within areas for which there has been a high density of poaching events will be much more likely to detect snares than those patrolling in areas where few snares have been laid. This is an interesting finding that requires further investigation. Data on the rate at which animals are caught in snares are limited, although Coad (2007) found that in a 12-month period in Gabon, 12 percent–28 percent of snares laid by hunters had successfully caught an animal. Therefore, the rate at which poacher agents located animals caught in snares (1.7 percent) in the model seems too low. This may simply be because there were too few animals in the model (set at 100). Alternatively, the poacher agents may not have been checking their snares as often as real poachers would have. Moreover, it could be because poacher agents were not programmed to detect caught animals further than one patch away from them, or from traps laid by other poachers. This is something that will be explored in future models. A number of other findings require further analysis. For example, in reality, we know that poachers typically place snares very close to water points, yet the average distance snares were laid from water patches was around 850 m in the model. It is possible that poachers should have been programmed to first move to water, and then begin their search. This could be modified in future models to see how the average distance to water changes. Empirical data collected in other areas of Africa suggest that poachers can travel between 2 and 20 km to lay snares (Coad, 2007). In the model, poachers travelled around 4 kilometres (on average), which is well within this range. However, whether this is how far QENP poachers actually travel is an empirical question and data are required to test it. Understanding why poachers travel different distances to lay snares (even in a model alone) in different contexts is

Potential uses of simulation modelling  147 an interesting issue that might inform understanding of poaching and enforcement strategies. There are a number of possible explanations for why the ranger agents travelled the distances they did in the model. For example, since there is more animal activity near water points, and many water patches are close to villages in QENP, poacher agents need not travel far to find optimal locations for placing snares. This may explain why the distribution of laid snares is more dispersed around those villages on the east side of the model (Figure 7.6), since poachers starting from these locations have to travel further to find more water patches. Second, it is possible that the model was simply not run for a sufficient amount of time and this resulted in few longer journeys. Third, poachers in QENP may hunt for longer than the three to six hours programmed in our model, and different patterns may emerge if they are allowed to do so. This is something that we will explore. Fourth, there may be other variables not included in the model that further influence poacher decision-making, such as (for example) vegetation type and elevation. For instance, poachers may avoid laying snares in certain types of habitat, such as dense thicket, or on hills. Nevertheless, this is why model building is a useful tool when undertaking this kind of research, because it gives the researcher an indication of the kinds of data which need to be collected in the field in order to build more realistic models and to understand phenomena of interest. Some limitations A core limitation of the current model is the patch size used of 50 m by 50 m. Since poaching occurs across large areas, the intention was, naturally, to try and model a large segment of the park. However, the trade-off is that one has to scale-up the model to a larger patch size, since a smaller scale would have meant even larger increases in computer processing as there would be more patches to analyse. But at a 50 m by 50 m scale, vital detail is lost such as habitat heterogeneity, i.e. at this scale patches can contain different types of habitat and several trails. The combinations of habitat and number of trails will have important influences on where poachers decide to lay snares. For example, if a patch contains only a single trail, but another adjacent to it contains two, a poacher may be more likely to lay a snare on the single trail patch, as the snare can be placed on the only trail in that patch – i.e. the path that the animals are most likely to use if they move through the patch. Furthermore, longer-established trails may be easier to detect than developing trails, which would have implications for poachers laying snares and for rangers finding evidence of illegal activity. We intend to collect information on such issues, including snare activity rates, through field interviews with poachers. Subsequent models could then be developed to accommodate what is learned. Another limitation is that the results presented (for the purposes of illustration) were the outcome of one model run (which took 36 hours to run). However, many repetitions are required for theory testing as different runs of the same model may generate different patterns, either because the model is sensitive to

148  Joanna F. Hill et al. the tuning of the model parameters, or because of stochastic effects. Moreover, the initial conditions of a simulation can affect simulated patterns quite substantially (e.g. Gilbert, 2007). Consequently, when the final model has been developed, our intention is to run this hundreds of times using a high performance computing cluster. General observations regarding ABM Considering ABM more generally, in cases where it is not be possible to collect the data required, the only approach is to use reasonable parameter values, the likely ground truth of which would ideally be discussed with experts. Even where this is possible, however, it is important to conduct analyses to see how sensitive the model is to variations in the parameter values. This can be achieved by running the model using different parameter values and then comparing model outcomes across runs. Where precise estimates are unavailable for multiple parameters, this can lead to an explosion in the number of possible combinations. Where this is the case, the approach adopted is to sample from the multidimensional parameter space rather than comparing all possible permutations (which may be intractable) to see how much model outcomes vary as a result. If they vary little, the model can be seen to be stable, but where parameters do have an effect on model outcomes, the researcher will often be limited to specifying what the effects are and the conditions under which they occur. On the one hand, where changes in the parameter values affect simulated outcomes, this may be seen as a weakness of these kinds of models. However, understanding how the parameter values may affect simulated outcomes can provide insight into the system of interest, which may be useful for theory testing or for the design of anti-poaching interventions. Moreover, it is important to consider the weaknesses of alternative approaches to research to see how simulation modelling might complement them. For example, when trying to understand a system, most people will have some model in mind as to how the system works, what affects it, and so on. This type of thought experiment will also be subject to problems associated with missing information, but it is also subject to other biases. For example, while agent-based models are explicitly designed to deal with the non-linear feedback loops that arise through the interactions of multiple agents and their reciprocal effects on the environment, people are (arguably) not particularly good at thinking in these ways. In fact, in all but the simplest model, it will not be possible to accurately predict outcomes with anything other than a computational model. As discussed, whilst statistical modelling approaches are clearly valuable, they typically test for ecological associations between variables that are consistent with one theory or another. As any scientist knows, correlation does not imply causation. So while showing that associations do (or do not) exist between variables is helpful in theory testing and falsification, this type of approach allows the researcher to test theories in a rather indirect manner that

Potential uses of simulation modelling  149 is subject to problems of (internal) validity (see Campbell & Stanley, 1963). Such models also are typically unable to take account of how the decisions of one actor affect those of another, as data regarding the actors involved are rarely incorporated into the analysis (for exceptions, see Bernasco & Nieuwbeerta, 2005). Even where they are, however, only a partial picture of all the actors involved will be included and any interactions modelled will tend to be linear and hence fail to allow for the modelling of dynamic processes. Nor will such models enable a researcher to reasonably ask ‘what if’ questions of the kind discussed above. Thus, statistical models offer a valuable but different insight than ABM. If one is willing to accept the assumptions used to build a model, ABM allows incredible flexibility in terms of what might be asked. For example, in the case of poaching, one might ask questions such as ‘what might the effect on poaching be if we increased the number of rangers by X percent?’ or ‘what might the effect be if we increased the time that rangers spend patrolling or if they tried different patrolling strategies?’. One might also want to ask how poaching may be affected by the introduction of non-enforcement strategies. For instance, how community-based measures – such as providing education or funding to local communities – might affect the rate of poaching. Simulated interventions such as this would require a little effort to implement but are more than possible (see Keane et al., 2012). Future work In terms of the current research, the next steps will clearly involve the identification and collection of missing data that could be used to calibrate the model; the incorporation of both active and passive hunting methods; the development of more sophisticated models of animal foraging; the incorporation of learning and feedback mechanisms; and the discussion of the model structure with experts in the field and, ideally, the rangers and poachers themselves. Other dynamics not covered in this chapter will also be explored. For example, in this model, animals caught by snares were assumed to remain on the patch they were caught. In reality, depending upon the body part entangled by a snare, an animal can remain alive for some time and could move some distance from where they were captured. This time delay between being caught in a snare and being found by a poacher may be important, since, the animal may have time to attract the attention of rangers or other witnesses (like tourists), as well as the poachers themselves. This would create a further opportunity for the convergence of poacher and rangers, creating a whole new dynamic to the model. Another area worthy of further research concerns way-finding; that is, the ways in which agents orient themselves in physical space and navigate from place to place, particularly in regards to obstacle avoidance (Klügl & Rindsfüser, 2007). In the current model, agents were allowed to ‘see’ one patch ahead of them to check for (say) water so that if there were any land

150  Joanna F. Hill et al. patches immediately around them, they would only have to scan for land within eight patches. They then moved one patch away from water so that if they needed to circumnavigate a water body, they could do so. Whilst this worked for the current model, future work is needed to test different strategies. This will become particularly vital when running multiple models with many repetitions. After addressing the issues discussed, we will be able to (for example) use the simulation to see how consistent outcomes would be for particular policies, or estimate how often desirable outcomes might be achieved. In many cases, policies are tested using field trials of one form or another. These can be expensive, both financially and in terms of how long they may take to implement (or for reliable results to be observed). In contrast, the development of an agent-based model will be relatively inexpensive and models can be run relatively quickly. Thus, using ABM to test a proposed policy may be a useful preliminary step that could help to assess the viability of a proposed intervention, help refine it, or to provide insight into the contexts within which it might be successful and those in which it might (for example) backfire, before experiments are conducted. Of course, such estimates will only be as good as the model used to generate them, and the use of ABM in policy simulation should be seen not as providing unambiguous answers but as a way of testing the plausibility of a particular policy or set of strategies (given certain sets of assumptions). Therefore ABM should be seen as a way of supporting the existing decision-making process, rather than replacing it. In conclusion, animal poaching takes place within a complex ecological system. Different approaches to studying poaching will offer different insights, and we suggest that ABM offers the potential not only to better understand the system but also to explore what might be done to positively affect it.

Acknowledgements We would particularly like to thank AM Lemieux for his continued and generous support. His knowledge and expertise informed the design and implementation of the model, and he provided access to some of the data used in this chapter without which the work would not have been possible. We would also like to thank the anonymous reviewers for their insightful comments and suggestions for future work.

Notes 1 For example, in Uganda, most hunting is illegal under the Uganda Wildlife Act (2000). See www.ugandawildlife.org/about-uganda-master/uganda-wildlife-act. 2 So that their choices are not entirely determined by their surroundings, agents select a patch at random with a probability of 0.10. 3 Future models may allow agents to steal other poacher’s snares or other trapped animals.

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152  Joanna F. Hill et al. Fa, J. E. & Brown, D. (2009). ‘Impacts of Hunting on Mammals in African Tropical Moist Forests: A Review and Synthesis’. Mammal Review. 39(4): 231–264. Felson, M. & Cohen, L. E. (1980). ‘Human Ecology and Crime: a Routine Activity Approach’. Human Ecology. 8: 389–406. Ganskopp, D., Cruz, R. & Johnson, D. E. (2000). ‘Least-Effort Pathways? A GIS Analysis of Livestock Trails on Rugged Terrain’. Applied Animal Behaviour Science. 68(3): 179–190. Gilbert, N. (2007). Agent-Based Models. Quantitative Applications in the Social Sciences. London, UK: SAGE. Gilbert, N. & Troitzsch, K. G. (2005). Simulation for the Social Scientist (2nd ed.). Berkshire, England: Open University Press. Grimm, V. & Railsback, S. F. (2005). Individual-Based Modeling and Ecology. Princeton, NJ: Princeton University Press. Grimsdell, J.J.R. & Field, C. R. (1976). ‘Grazing Patterns of Buffaloes in the Rwenzori National Park, Uganda’. African Journal of Ecology. 14(4): 339–344. Groff, E. R. (2007). ‘“Situating” Simulation to Model Human Spatio-Temporal Interactions: An Example Using Crime Events’. Transactions in GIS. 11(4): 507–530. Haines, A. M., Elledge, D., Wilsing, L. K., Grabe, M., Barske, D., Burke, N. & Webb, S. (2012). ‘Spatially-Explicit Analysis of Poaching Activity as a Conservation Management Tool’. Wildlife Society Bulletin. 35(4): 685–692. Harrison, R. D. (2011). ‘Emptying the Forest: Hunting and the Extirpation of Wildlife from Tropical Nature Reserves’. BioScience. 61: 919–924. Hayashi, K. (2008). ‘Hunting Activities in Forest Camps among the Baka HunterGathers of Southeastern Cameroon’. African Study Monographs. 29 (2): 73–92. Imron, M., Herzog, S. & Berger, U. (2011). ‘The Influence of Agroforestry and Other Land-Use Types on the Persistence of a Sumatran Tiger Population: An Individual-Based Model Approach’. Environmental Management. 48(2): 276–288. Johnson, S. D. (2009). ‘Potential Uses of Computational Methods in the Evaluation of Crime Reduction Activity’. In J. Knutsson & N. Tilley (Eds.), Evaluating Crime Reduction Initiatives. Crime Prevention Studies (Vol. 24). Cullompton, Devon: Willan Publishing. Kanga, E. M., Ogutu, J. O., Olff, H. & Piepho H.-P. (2011). ‘Hippopotamus and Livestock Grazing: Influences on Riparian Vegetation and Facilitation of Other Herbivores in the Mara Region of Kenya’. Landscape Ecological Engineering. 12. Kapere, R., Natwijuka Kayombo, S., Tumwesigye, A. & Namubiru, S. (2011). ‘Community and Park Management Strategies for Addressing Climate Change Impacts in Queen Elizabeth National Park, Uganda’. http://start.org/download/2012/biodiv/ugandaexternship-report.pdf, accessed 1 September 2012. Keane, A., Jones, J.P.G. & Milner-Gulland, E. J. (2012). ‘Modelling the Effect of Individual Strategic Behaviour on Community-Level Outcomes of Conservation Interventions’. Environmental Conservation. 1–11. Kernick, D. (2004). ‘An Introduction to Complexity Theory’. In D. Kernick (Ed.), Complexity and Health Care Organization (pp. 23–38). Oxford, UK: Radcliffe Medical Press. Klügl, K. & Rindsfüser, G. (2007). ‘Large-Scale Agent-Based Pedestrian Simulation. Multiagent System Technologies’. Lecture Notes in Computer Science. 4687: 145–156. Lewison, R. L. & Carter, J. (2004). ‘Exploring Behaviou of an Unusual Megaherbivore: A Spatially Explicit Foraging Model of the Hippopotamus’. Ecological Modelling. 171: 127–138.

Potential uses of simulation modelling  153 Lindsey, P. A., Balme, G., Becker, M., Begg, C., Bento, C., Bocchino, C. . . . ZisadzaGandiwa, P. (2013). ‘The Bushmeat Trade in African Savannas: Impacts Drivers, and Possible Solutions’. Biological Conservation. 160: 80–96. Ling, S. & Milner-Gulland, E. J. (2008). ‘Developing an Artificial Ecology for Use as a Strategic Management Tool: A Case Study of Ibex Hunting in the North Tien Shan’. Ecological Modelling. 210: 15–36. Maingi, J. K., Mukeka, J. M., Kyale, D. M. & Muasya, R. M. (2012). ‘Spatiotemporal Patterns of Elephant Poaching in Southeastern Kenya’. Wildlife Research. 39(3): 234–249. Moreto, W. D. (2013). ‘To Conserve and Protect: Examining Law Enforcement Ranger Culture and Operations in Queen Elizabeth National Park, Uganda’. Unpublished Rutgers University doctoral dissertation. Moss, S. (2008). ‘Alternative Approaches to the Ampirical Validation of Agent-Based Models’. Journal of Artificial Societies and Social Simulation. 11(1): 5. Mugume, S. (2000). ‘Snare Removal Program in Kibale National Park: A Preliminary Report’. Pan Africa News, December, 2. Noss, A. J. (1998). ‘The Impacts of Cable Snare Hunting on Wildlife Populations in the Forests of the Central African Republic’. Conservation Biology. 12: 390–398. Nyirenda, V. R. & Chomba, C. (2012). ‘Field Foot Patrol Effectiveness in Kafue National Park, Zambia’. Journal of Ecology and the Natural Environment. 4(6): 163–172. Railsback, S. F. & Grimm, V. (2012). Agent-Based and Individual-Based Modeling: A Practical Introduction. Princeton, NJ: Princeton University Press. Rengert, G. F., Piquero, A. R. & Jones, P. R. (1999). ‘Distance Decay Re-examined’. Criminology. 37(2): 427–446. Schoener, T. W. (1979). ‘Generality of the Size-Distance Relation in Models of Optimal Feeding’. American Naturalist. 114: 902–914. Simon, H. (1997). Models of Bounded Rationality. Volume III: Empirically Grounded Economic Reason. Cambridge, MA: MIT Press. Tang, W., & Bennett, D. A. (2010). ‘Agent-Based Modeling of Animal Movement: A Review’. Geography Compass. 4(7): 682–700. Van Vliet, N., Milner-Gulland, E. J., Bousquet, F., Saqalli, M. & Nasi, R. (2010). ‘Effect of Small-Scale Heterogeneity of Prey and Hunter Distributions on the Sustainability of Bushmeat Hunting’. Conservation Biology. 24(5): 1327–1337. Van Vliet, N. & Nasi, R. (2008). ‘Hunting for Livelihood in Northeast Gabon: Patterns, Evolution and Sustainability’. Ecology and Society. 13(2) Art. 33. Wato, Y. A. Wahungu, G. M. & Okello, M. M., (2006). ‘Correlates of Wildlife Snaring Patterns in Tsavo West National Park, Kenya’. Biological Conservation. 132: 500–509. Wilensky, U. (1998). ‘NetLogo Flocking Model’. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. Retrieved from http://ccl.northwestern.edu/netlogo/models/Flocking Yasuoka, H. (2006). ‘The Sustainability of Duiker (Cephalophus spp.) Hunting for the Baka Hunter-Gatherers in Southeastern Cameroon’. African Study Monographs. Supplementary Issue. 33: 95–120.

8

Poaching and tiger populations in Indian reserves Useful outcomes of a failed risky facilities analysis Jeong Hyun Kim, Ronald V. Clarke and Joel Miller

Introduction Tigers are beautiful, iconic animals. We flock to see them in zoos and they fill the screens of our TVs and the pages of our magazines. They are the top predators of their ecosystems and they serve as unrivaled indicators of the viability of the forests where they live. In the early 1900s, an estimated 100,000 tigers, belonging to nine subspecies, were to be found in the wilds of Asia and the far east of Russia and China. Despite the growing protection conferred on them by national governments and despite being listed as endangered by the International Union for Conservation of Nature (IUCN), and therefore forbidden to international trade, the numbers of tigers had declined to around 3,500–4,000 by 2011 and the subspecies had shrunk to six. Habitat loss was mainly to blame, resulting from the burgeoning human populations in the counties where the tigers live. Most of the remaining animals, around 2,500 (IUCN, 2012), are Bengal tigers and most of these (about 1,700) are found in the tiger reserves of India. There, unfortunately, they are not always safe from being poached or illegally killed. Their carcasses can yield enormous profits for poachers. Their skins are used for religious and ceremonial practices and their bones, claws, eyes, whiskers, penises, indeed every one of their parts, are used for traditional Chinese medicine, for which the demand has increased in line with the growing wealth of China and other Asian countries. As for tigers that are illegally killed, most have killed livestock in a nearby village. As tigers often return to the kill a day or two later, the villager may poison the kill or employ a hunter to shoot the tiger on its return. The carcass is then sold in the same way as if it had been poached. The original intention of the research reported here was to conduct a “risky facilities” analysis (described below) of poaching and illegal killings in Indian tiger reserves (TRs), crimes that pose a substantial threat to the viability of the reserves. For example, the recent disappearance of an estimated 53 tigers from two previously well-established reserves – Panna and Sariska – was almost certainly due to unchecked poaching and illegal killing. The risky facilities

Tiger poaching in Indian reserves 155 analysis was intended to identify measures to prevent poaching and killing of tigers, but, as explained below, it proved impossible to find a reliable measure of poaching and illegal killings. This led us to seek an alternative research design and we went on to examine the variation in tiger numbers in the reserves. Our reasoning was that, as poaching and illegal killings are important determinants of a reserve’s tiger population, identifying the variables associated with the variation in reserve populations might provide suggestions for preventing poaching and illegal killings. In fact, our analysis allowed us to comment on the value of current policies being pursued by the Indian authorities to safeguard the tigers in the reserves: improving reserve management, establishing buffer areas for all reserves, relocating indigenous villages from the core of the reserves and banning tourists from these same areas. The description of this more fruitful stage of our research will be assisted by a discussion of the attempted risky facilities analysis.

The failed risky facilities analysis Risky facilities analyses, recently described by Eck et al. (2007), focus on a specific crime problem that occurs in a set of facilities of the same kind – for example, assaults in pubs and clubs, car thefts in parking lots and robberies of convenience stores. It is usually found that a small number of the facilities in question account for most of the crimes experienced by the group as a whole. This concentration of crime is often referred to as the 80/20 rule (Clarke and Eck, 2005), meaning that about 80 percent of the crimes experienced by the entire set of facilities are accounted for by a small group of 20 percent of the facilities. This 20 percent constitutes the “risky facilities” and the next step of the analysis seeks to identify variables that might account for the concentration of the crime risk. Some of these variables relate to the size of the facility (larger facilities will generally have more crime), their location (those in high crime areas might experience more crime) and to crime reporting practices (which might vary considerably among the facilities in the group). These three variables rarely lead to any important preventive suggestions, but they need to be controlled when examining the effect of other variables, mostly relating to the management of the facilities or their physical design and layout, that might be manipulated to reduce crime among the identified risky facilities. Most risky facilities analyses published to date have been undertaken on built facilities such as convenience stores, pubs and clubs and apartment complexes. These built facilities differ in many respects from nature reserves, which essentially consist of tracts of natural environment, which are designated by law as sanctuaries for endangered species, such as tigers. However, the kinds of variables that result in crime being concentrated in a minority of built facilities could also work to concentrate poaching and illegal killings in a minority of reserves. This will be true of size, reporting practices and geographic location. When the effect of these are controlled, it could be expected,

156

Jeong Hyun Kim et al.

just as in built facilities, that the highest concentration of problems (i.e. illegal killings and poaching) would be in the badly managed reserves. As for variations in the physical design and layout of reserves, these are relatively minor and might play little part in determining the distribution of crime in reserves; for reserves, the principal physical features that differentiate one from another relate to their geographic location and the associated ecology of the natural environment, such as the terrain (flat or hilly), forest cover and presence of prey. A further set of variables that could help to determine poaching and illegal killings in the reserves relate to human pressures on lands that have been designated as sanctuaries for the tiger. In a country as heavily populated as India, it might be expected that local people will question the legitimacy of the reserves and will resent the fact that the government has favored the tiger’s needs above their own. In fact, this kind of resentment was documented by the Tiger Task Force (TTF) convened by the Indian Government to investigate the extirpation of tigers from Sariska (Project Tiger, 2005). The TTF noted that in many reserves, local people hunt and illegally graze cattle or forage for firewood and other subsistence produce. In addition, substantial numbers of indigenous villagers (so-called Tribals) live in small villages scattered throughout most of the reserves. Some reserves also encompass sacred sites visited by millions of pilgrims each year, or massive hydro-electric and coal mining operations together with settlements for workers and their families. Finally, a large proportion of the reserves harbor cadres of Naxalites, communist insurgents, whom the government has not been able to dislodge. It is evident from the above that, for a variety of understandable reasons, the authorities have often failed to resist human pressure and maintain the kind of pristine habitat that is expected within a nature reserve. The result will not be habitat loss because the reserve boundaries are fixed by legal statute, but it could certainly be habitat degradation that might have equally serious consequences for the survival of the tiger. The intention of our risky facilities analysis, therefore, was to correlate measures of three groups of independent variables – relating to management, ecology and human pressure – with measures of poaching and illegal killing for the 28 TRs that had been in existence for the past 14 years. (Thirteen recently designated reserves were not included as their boundaries are not always clearly defined and information about them was even more scant than for the 28 in our sample.) As mentioned, however, it proved impossible to find reliable measures of poaching and illegal killings for each reserve despite an extensive reach of academic articles, of Indian government reports and those issued by relevant non-governmental organizations (such as WWF India and the Wildlife Protection Society of India) of Wikipedia entries and of the websites of individual reserves where these existed. Personal enquiries made to various tiger experts, who were unfailingly helpful, and a search of newspaper reports also drew a blank.

Tiger poaching in Indian reserves 157 Both the Wildlife Protection Society of India (WPSI) and the National Tiger Conservation Authority (NTCA) provide data on illegally killed tigers, but only the NTCA data are broken down for each TR. The reliability of these numbers is highly questionable given that they show only five tiger deaths in Panna and one in Sariska between 2002 and 2009, when the entire populations of these reserves were lost. Moreover, the NTCA overall totals of tigers illegally killed each year between 1998 and 2008 are invariably smaller, sometimes considerably so, than those for WPSI (see Table 8.1). From 2009 onwards, the numbers have somewhat converged, possibly because the NTCA has upgraded it statistical resources. It is not hard to understand why deaths due to poaching are so difficult to count. Poachers often work at night, they take great pains to cover their tracks, they remove the entire carcasses or they bury the parts that they do not want to take. Given the dense forest cover of many reserves and the large swathes of irregularly patrolled land, their chances of being caught in the act are very slim. All these reasons make the detection of poaching a difficult endeavor and, under these conditions, most reserves could lose several tigers a year to poachers without this being noticed, unless the losses continued year-after-year as apparently they did in Sariska and Panna. Prior to concluding that no reliable measures of poaching and illegal killings existed for the TRs, we had also been exploring the availability of possible independent variables, of the three kinds mentioned above, that could be used in our analysis. We searched the same sources mentioned above as well as geographic, local government and ecological sources that might yield data for local populations and for tourist and other resources.1 We found that for a variety of reasons, the data for most of these independent variables would not have supported a rigorous risky facilities analysis. In some cases, data were available for some reserves but not all; or data were available for all reserves, but not for the same years; or there were serious anomalies within the data for particular variables. However, we concluded that we could examine the relationship between tiger populations in each reserve and a number of variables relating to management and human pressure which in principle were open to government control. In the next section, we describe the policy-relevant analysis of tiger numbers that we undertook to meet this objective.

A policy-relevant analysis of tiger populations This was a less ambitious study than the risky facilities analysis originally planned. We believe it produced valuable information about current government policy designed to safeguard the tigers in the reserves. It should be noted at this point that the responsibility for the day-to-day management of the TRs rests with the state where they are located. Overall responsibility for ensuring that the reserves are properly resourced and managed rests with the Indian government

57

26

Difference between kill counts*

26

52 26

2000

45

72 27

2001

36

46 10

2002

32

38 6

2003

37

38 1

2004

46

46 0

2005

32

37 5

2006

17

27 10

2007

* The absolute difference is reported between the two agency counts (WPSI estimate – NTCA estimate). WPSI (Wildlife Protection Society of India): numbers of killed tigers (available at www.wpsi-india.org/statistics/index.php) NTCA (National Tiger Conservation Authority): numbers of killed tigers by poaching, poisoning, electrocution or seizures. 1998–2009: Personal communication from S. P. Yadav, NTCA (Jan 10, 2010). 2010–2011: Website of NTCA (available at www.tigernet.nic.in/Alluser/Map2.0.aspx).

81 24

39 13

WPSI NTCA

1999

1998

Reporting Agency

Table 8.1 Tigers illegally killed (1998–2011): comparison of WPSI and NTCA records

23

29 6

2008

1

32 33

2009

5

30 25

2010

1

13 14

2011

Tiger poaching in Indian reserves 159 under the legal framework provided by the Wildlife (Protection) Act, 1972 and 2006. These statutes confer wide powers on the NTCA including (1) to prohibit unsustainable land use such as mining and unrestrained tourism, (2) to ensure that the needs of local populations are adequately accounted for, (3) to coordinate research and information gathering designed to assist the good management of reserves and (4) to oversee training of reserve staff and to conduct routine inspections of reserve management. As will be seen below, particular aspects of these rights and duties are reinforced through rulings made by the Supreme Court of India.

Overview of the study design The design of this small study was straightforward. The dependent variable, “tiger densities”, was the number of tigers per reserve size. The 28 reserves were divided into 14 with “high” and 14 with “low” tiger densities, which were then compared on a range of independent variables that related to current policies pursued by the Indian government. The independent variables used covered the ecology of the reserves (three variables), human pressure (five variables) and management (one variable). These measures are summarized in Table 8.2 and explained more fully below. While they were the best that could be found, some had limitations that should be borne in mind when considering the results. This applies particularly to the measures of prey availability and tourism. It was not possible to obtain measures of all prey animals, and the measure we used provided only an estimate of the availability of sambar, just one of the tiger’s preferred prey animals (Hayward et al., 2012). The tourism measure was not based on numbers of tourist to each TR, but only on their access to the core area.

Table 8.2 Variable definitions and sources Variable

Definition

Sources

Tiger numbers

Estimate of tiger numbers in each TR

1) Conservation India (2011) 2) Gopal et al. (2011) 3) Indo Asian News Service (2012) 4) Jhala et al. (2011). 5) Periyar Tiger Reserve (2012) 6) Rang 7 Team (2011) 7) Sethi, N. (2008) 8) The Telegraph (2010) 9) Wildlife Protection Society of India (2011) (Continued)

Table 8.2 (Continued) Variable

Definition

Sources

Reserve size

Total size (km ) of TR including buffer and core areas

Stripes (NTCA, 2010a, 2011)

Core size

Size of TR core area (km2)

Stripes (NTCA, 2010a, 2011)

Core tiger density

Tiger numbers per 100 km2 of TR core area

By calculation

Forest cover

Percentage of land plots (> one hectare) with tree canopy density of more than 10%.

Forest cover in tiger reserves of India – Status and changes (Ministry of Environment and Forests, 2006)

Altitude variations

Altitude differences (m) between highest and lowest areas

Forest cover in tiger reserves of India – Status and changes (Ministry of Environment and Forests, 2006)

Prey availability

Overlap between TR boundary and State-wide distribution of Sambar: 0 = no overlap; 1 ≤ 50% overlap; 2 ≥ 51% overlap; 3 = complete overlap

Status of tigers, co-predators and prey in India (Jhala et al., 2008; Jhala et al., 2011)

Core villages rate

Numbers of villages per 100 km2 of TR core area

Stripes (NTCA, 2010)

Core families rate

Numbers of families per 100 km2 of TR core area

Stripes (NTCA, 2010)

Stability

Free from Naxalites, criminal groups or political unrest in TR(Yes = 1/No = 0)

1) Management effectiveness evaluation of tiger reserves in India: Process and outcomes (Mathur et al., 2011) 2) State of project tiger reserve (NTCA, 2009)

Core tourism

Permitted access to core area by tourists (and pilgrims); from 0 (lowest) to 4 (highest)

Information on tiger reserves (WPSI, 2011)

Management scores

Indian government TR management scores in 2011: 2.5 (lowest) to 10 (highest)

Management effectiveness evaluation of tiger reserves in India: Process and outcomes (Mathur et al., 2011)

2

Tiger poaching in Indian reserves 161 Tiger densities In order to calculate tiger densities, it was first necessary to obtain counts of tigers. These counts are made by analyzing tiger paw prints or “pugmarks” (see Figure 8.1) and are supposed to be published every four years for each reserve. Forestry guards are trained how to measure, trace and record the pugmarks and how to take plaster casts for verification purposes (Talwar and Usmani, 2005). This method can allow male and female tigers to be distinguished and also cubs to be distinguished from adults, but its use for identifying individual tigers, necessary for reliable counting, is subject to considerable error (Karanth, 2011).2 Pugmark counts are sometimes supplemented by other signs of tigers (such as claw marks on trees and posts) and experimental use is currently being made of more potentially reliable, but more costly methods, including DNA analysis of fecal matter and analysis of coat patterns obtained by camera traps (the possible use of closed-circuit television for counting tigers is discussed by Clarke et al. in this volume). In fact, no single up-to-date source of tiger numbers proved to be available and those we used for the 28 reserves were collated from nine sources (see Table 8.2). There was considerable variation among the 28 reserves in the estimated numbers of tigers, from 214 in Corbett and 4 each in Panna and Namdapha (See Table 8.3).

Figure 8.1 Photograph of tiger pugmark

15

43

31

Ranthambhore

Nameri

Satpura

Kanha

Buxa

60

Bandhavgarh

20

59

Bandipur

Bhadra

79

Dudhwa

11

112

Tadoba

70

69

Pench MP

Sundarbans

54

Corbett

Pench MA

214

9

n

Reserve

Tigers

2.8

3.2

3.8

4.1

4.1

4.3

4.5

6.5

8.2

9.1

10.2

11.0

13.1

26.1

Per 100 km2

Core Tiger Density

1,113

2,133

757

1,064

2,584

741

344

2,051

1,536

1,456

1,094

1,727

1,179

1,288

km2

Reserve Size

Tiger Data

1,113

1,339

390

492

1,699

257

200

917

716

872

1,094

625

411

821

Core Size

53.1

85.2

81.6

88.8

59.5

89.9

49.1

66.5

74.1

91.4

80.2

94.2

78.2

90.8

%

Forest

263

1,032

1,625

1,350

0.3

254

150

500

370

775

55

180

250

835

m

Alt. Var.

Ecology

2

3

1

3

1

3

0

3

3

3

3

3

3

3

0–3*

Prey Avail.

4.9

3.1

4.9

0.8

0.0

0.4

0.0

2.8

2.0

0.0

1.6

0.8

0.0

0.2

550.2

201.1

447.2

16.5

0.0

33.1

0.0

199.3

302.2

0.0

134.9

147.6

0.0

8.6

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1/0**

Stable

4

1

0

2

0

1

1

4

4

4

2

2

4

4

0–4*

Core Tour.

Human Pressure Core Fam.

Per 100 km2

Core Vill.

Table 8.3 Comparison of 14 reserves with high tiger densities and 14 with low tiger densities

0

1

1

1

1

1

1

1

1

1

0

1

1

1

1/0**

Buffer

7.5

10.0

7.5

10.0

10.0

7.5

5.0

10.0

10.0

10.0

7.5

7.5

10.0

7.5

2.5–10***

Mgt. Scores

Mgt

10

60

35

26

23

17

9

9

9

5

5

4

4

Palamau

Nagarjunasagar

Melghat

Indravati

Simlipal

Kalakad

Pakke

Manas

Valmiki

Dampa

Sariska

Panna

Namdapha

0.2

0.7

0.7

1.0

1.1

1.1

1.3

1.9

1.9

2.1

2.3

2.4

2.4

2.7

* Ordinal variable ** Dichotomous variable, 1 = yes, 0 = no *** Scale variable

24

Periyar

1,807

576

681

500

840

3,150

683

895

2,749

2,798

2,768

2,527

414

881

1,807

576

681

500

840

840

683

895

1,194

1,258

1,500

2,527

414

881

93.6

88.9

76.5

95.6

87.6

54.8

97.9

80.9

96.7

81.4

88.6

73.7

86.8

83.7

4,371

210

422

950

755

635

1,723

1,806

1,126

422

828

817

840

1,916

0

3

1

1

1

1

1

3

2

1

3

1

2

3

0.4

0.9

4.1

0.2

0.0

3.8

0.0

0.9

0.5

4.5

2.1

1.1

0.7

0.1

11.4

4.6

488

331

44.8

0.0

108.6

0.0

27.4

18.5

78.9

389

4

25.4

0

0

1

1

0

0

1

1

0

0

1

0

0

1

0

2

2

1

1

1

1

1

1

0

0

1

0

3

0

0

0

0

0

1

0

0

1

1

1

0

0

0

5.0

10.0

5.0

7.5

5.0

7.5

7.5

10.0

5.0

2.5

7.5

7.5

2.5

10.0

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Jeong Hyun Kim et al.

Calculating tiger densities requires tiger numbers for each reserve to be divided by reserve size, but, again, acquiring data about reserve size was not straightforward. This was because 16 of the TRs define a “core” area, the inner part of the reserve where most tigers are believed to breed and hunt, and a “buffer” area that surrounds the core to a depth of about 10km or more. At the time of the study, the remaining 12 TRs made no distinction between core and buffer areas. In selecting the denominator for the calculation of tiger densities, we therefore decided to use core area size when these were defined and, when they were not, we used the size of the whole reserve.3 Data about the size (km2) of core areas or reserves for each TR were obtained from Stripes, the journal of the NTCA (2010a, 2011). Tiger density scores were found to vary between 26.1 per 100km2 for Corbett and 0.2 for Namdahpa. Ecology Because variations in reserve environments might be expected to impact tiger numbers, we sought to include measures of relevant ecological variables. It is widely agreed that the most important of these is the availability of suitable prey – particularly chital (spotted deer), sambar deer and guar (Indian bison). Unfortunately, no reliable or consistent measure of prey populations in the reserves was available, though assessments of prey numbers have been issued for some reserves in the past. Accordingly, we had to estimate prey availability for each TR by using prey distribution maps for each state obtained from Status of Tigers, Co-predators and Prey in India (Jhala et al., 2008; Jhala et al. 2011). These maps are based on determinations of the presence or absence of a variety of prey species within carefully sampled square kilometer tracts within each state. The data published in 2008 for sambar were the most complete except that none were available for Sundarbans. However, data for both sambar and chital in other parts of the country were closely correlated and we therefore used the chittal distribution map for Sunderbans. Our measure of prey availability was a visual judgment about the extent of the geographic overlap between each TR and the statewide distribution of sambar: 0 ⫽ no overlap; 1 ≤ 50 percent overlap; 2 ≥ 51 percent overlap; 3 ⫽ complete overlap. Tigers are generally found in forests and we therefore included a measure of forest cover for each reserve, though it is believed that tigers breed easily in many kinds of environments. For example, a large population of tigers exists in the Sunderbans, an extensive, flat area of swampy marshland in coastal northeast India bordering Bangladesh. A preliminary analysis of the many other ecological variables explored for the risky facilities analysis – state or region of India,4 altitude, rainfall, temperature, monsoon intensity and duration, etc. – found only that altitude variations were related to reported numbers of tigers. The reasons for this relationship are unclear. It is possible that prey is not abundant in hilly reserves,5 but another possibility is that hilly reserves make it difficult to patrol and therefore difficult to achieve a full count of tigers. Whatever the reason, we included altitude

Tiger poaching in Indian reserves 165 variation in our analysis below. Altitude variations and forest rates were collected from Forest Cover in Tiger Reserves India-Status and Changes (Ministry of Environment and Forest of India, 2006). Human pressure Concerning “human pressure”, we used measures of five variables often believed to have a negative impact on tiger populations: large numbers of resident Tribal families and villages, the presence of Naxalites (Maoist insurgents) or large criminal groups, tourism to core areas and the absence of a buffer zone. Tribals (and Naxalites) The TTF (Project Tiger, 2005) estimated that there were about 1,500 indigenous Tribal villages within the boundaries of the 28 TRs – a figure little different from the number of tigers estimated to be in the reserves at that time. Though the villages are small, each accommodating about 45 families, the TTF estimated that in total some 350,000 Tribals were resident in the TRs. These alarming figures help to explain why the Indian government has sought for many years to relocate the villages outside the reserves. The TTF documented the extreme costs and difficulties of this policy, and it noted that only 10 percent of villages had been successfully relocated during the previous 30 years. This has meant that the Tribals have been living in effect as trespassers without any government services or support. Their consequent disillusion and discontent has been exploited by Naxalites, who have seized control of some reserves where it is now impossible for forestry guards to patrol in safety. Despite a four-decade campaign, the Indian government has been unable to suppress the Naxalites. At present, they are estimated to have 14,000 fighters who are said to have been responsible for some 998 deaths in 2009. In April 2010, they attacked a convoy of paramilitary police, killing 76 officers (The Economist, 2010). Other lawless groups have negatively affected some other reserves. For example, Manas was for many years under invasion by politically motivated elements of the Boodo tribe, and Namdapha has experienced large scale encroachment from the mid-1980s by Lisus, a tribe originating from China, who are reported to be skilled tiger hunters. Finally, the loss of tigers in Sariska was partly due to unrestrained criminal activity by dacoits, led by the notorious poacher Sansar Chand. In total, 8 of the 28 reserves were deemed “unstable” as result of insurgent control or the activities of criminal groups. The TTF discussed the serious nature of the Naxalite infiltration of the TRs, but unsurprisingly, made no specific recommendations to deal with this intractable problem. Concerning the Tribal villages, the TTF argued that it would be quite impossible to relocate all 1,500 villages outside the reserves and, indeed, this might not really be necessary. While it might seem that villages in the reserves’ core areas were those most needing to be relocated, the TTF recommended instead that a scientific assessment be made of all villages to identify those that should be relocated and a time-bound program for this to be done.

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The numbers of Tribal villages and families within the core area of each reserve (or in the whole reserve for those without formally defined core areas) were collected from Stripes, the journal of the NTCA (NTCA, 2010b). Tourism The Indian government regularly finds itself drawn into arguments between environmentalists and tour operators over the hotly contested issue of tiger tourism. This simmering dispute recently boiled over in July 2012 when India’s Supreme Court temporarily banned tourism in the reserves’ core areas. This action was prompted by a “Public Interest Litigation Petition” (or PIL) filed with the Court by a conservationist, Ajay Dubey, on grounds that tourist operators had been allowed to establish hotels and lodges in the core areas of certain TRs (Smith, 2012). The tour operators and environmentalists quickly took up their expected positions, for and against the ban, but some unexpected dissention was reported among conservationists. For example, “Wildlife enthusiast and photographer Shashanka Nanda, who has taken part in dozens of safaris in the past few years told CNN he believes that while the court’s heart is in the right place, its approach is flawed” (Gray, 2012). Nanda is reported as saying: “Responsible and regulated tourism forges a human connection to wildlife. Just seeing tigers in textbooks won’t affect people to change. . . . If you stop tourists and enthusiasts, you’re losing half the battle of wildlife conservation.” In addition, Belinda Wright, the widely respected head of the WPSI was quoted in an interview for Voice of America (Pasricha, 2012) as saying the following: Our huge concern was that if tiger reserves would be left virtually empty, poaching gangs would move in. . . . Tigers, and in fact many animals, as people must have seen in Africa as well, get very used to the presence of tourist vehicles and visitors. And, tigers are blasé. They will walk up to a car, sniff the wheel, stand there waiting for a car to cross the road and so on. They pretty much ignore the presence of tourists. (See Clarke et al. in this volume, Figure 9.3, for an example of this behavior.) In response to new rules announced by the NTCA, the Supreme Court lifted the ban on tourism in October 2012. Under the rules, no new tourism infrastructures would be permitted in core areas and existing ones would be phased out. State governments would have to present plans for bringing their TRs into compliance with national regulations. Tourism would be restricted to 20 percent of core areas for “regulated low impact” visitation. During the dispute over the ban, Julian Matthews, the chairman of Travel Operators for Tigers stated to a reporter for The Telegraph that “Seventeen tiger reserves have few or no tigers left in them. No tourism has ever been allowed in or near these reserves. Instead, loggers arrived and poachers have free range” (Smith, 2012). Accordingly, we therefore included data on tourism to the core

Tiger poaching in Indian reserves 167 area collected from the report Information on Tiger Reserves (WPSI, 2011). These data provide estimates of the extent to which tourists (and pilgrims) are permitted to enter the core areas of the reserves (or the reserve as whole when there is no official core area). The estimates are on a scale of 0 to 4: 0 = inaccessible due to difficult terrain or Naxalite control; 1 = minimal; 2 = moderate; 3 = high with many pilgrims; and 4 = highest). Buffer zones The Wildlife Protection Amendment Act, 2006, requires states to establish buffer areas to a distance of about 10km around each reserve. The purpose of the buffer zones is to offer scope for coexistence of human activity (such as grazing cattle and collecting firewood) and to provide supplementary habitat for dispersing tigers, primarily young male tigers which may leave a reserve in search of a new territory. Some states have not established buffer areas around their reserves (including 12 of the 28 TRs in this study) – a fact brought to the attention of the Supreme Court during the dispute over tiger tourism. The NTCA had said that buffer zones had corridor value and that their ecological sustainability was important to prevent the area around the reserves from becoming ecological sinks on account of overuse of resources and unwise land use. Some of these surrounding areas had witnessed construction of many hotels, mass tourism and night safaris – all disturbing the night roaming of tigers in search of corridors. In the analysis reported below, reserves without buffer zones were coded 0 and those with buffer zones were coded 1. As a further sign of the Indian government’s deepening resolve to protect its tigers, the Supreme Court issued another order in April 2012 that required all reserves to define a buffer area as expeditiously as possible. This order has been met with protests because of legal difficulties, concerns about violation of the rights of local people and, in some cases, the practical difficulties of establishing buffers (Dhar 2012; Kumar 2012).6 Management The Indian government has been making concerted efforts to improve the Forestry Services’ management of the reserves and teams of experts evaluated the quality of the management in each reserve in 2010–2011 using a standardized rating scale, the Framework for Assessing the Management Effectiveness of Protected Areas. Developed by the IUCN World Commission on Protected Areas (WCPA) (Hockings et al. 2006), the framework has been used to date in more than 140 countries worldwide. It aims to give overall guidance in the development of assessment systems and to encourage basic standards for assessment and reporting. It consists of six elements (Context, Planning, Inputs, Process, Outputs and Outcomes), each of which is evaluated. The framework was adapted by the Indian government to ensure that it would adequately assess the conservation capacity of each TR in the face of

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current threats (Mathur et al., 2011). The resulting Management Effectiveness Evaluation (MEE) required the six elements of the Framework to be assessed under 30 criteria. This instrument was used by five teams of experts, independent of the government, who visited the 28 TRs in 2010–2011 (Mathur et al., 2011). The ratings under the 30 criteria for each of the six elements were collapsed to provide overall scores for each reserve, which ranged from 2.5 to 10.

Analysis and results In Table 8.3, the 28 TRs are sorted by their tiger density scores, into two groups with high and with low scores7 (Figure 8.2 shows the location of each reserve). The remaining columns of the table provide scores for each reserve on the independent variables measuring aspects of ecology, human pressure and management. The small number of TRs precluded multivariate analysis of the data. Accordingly, two bivariate analyses were conducted, consisting of (1) Spearman’s Rho correlations for the variables included (see Table 8.4) and (2) tests of the

Figure 8.2 The 28 tiger reserves ranked by tiger densities

.43*

.40*

Buffer

Mgt.

+ p < 0.10 * p < 0.05 ** p < 0.01

.40*

–.06

Core fam. rate

.38*

.03

Core vill. rate

Core tour.

.53*

Prey avail.

Stability

–.29

Alti. vari.

.36+

Core size

–.18

.52**

Reserve size

Forest cover

.80**

Core tiger density

Tigers

.38*

.57**

.50**

.57**

–.12

–.15

.54*

.–41*

–.19

–.20

.11

1.00

Core tiger density

.11

.48**

–.09

–.22

.06

.30

.09

–.03

–.14

.74**

1.00

Reserve Size

–.03

–.11

–.18

–.25

.01

23

–.07

.15

–.12

1.00

Core size

–.12

–.08

–.11

–.09

–.15

–.37 +

.16

.45*

1.00

Forest cover

Table 8.4 Correlation matrix of tiger variables (Spearman’s Rho)

–.03

–.24

–.25

–.11

.65**

.25

.58**

.42*

.27

–.06

–.02 –.13

1.00

Prey avail.

–.08

1.00

Alti. vari.

–.08

.05

–.05

–.07

.86**

1.00

Core villages rate

.05

.01

.08

.10

1.00

Core family rate

.50**

.25

.41*

1.00

Stability

.50**

.05

1.00

Core tour.

.21

1.00

Buffer

1.00

Mgt.

170

Jeong Hyun Kim et al.

significance of differences in means for these same variables between the 14 reserves with the highest tiger density scores and the remaining 14 with the lowest scores (See Table 8.5). Group means were compared using the MannWhitney U Test which allows for comparisons using non-normal and ordinal variables.8 The results of the two analyses undertaken were consistent and can be summarized as follows: 1 2 3 4 5

Six variables in both analyses were found to be significantly related to tiger densities. For ecology, these were altitude variation (negatively related to tiger density) and prey availability (positively related to tiger density). For human pressure, these were stability (absence of Naxalites, other insurgents and criminal groups), core tourism, and the existence of a buffer (all positively related to tiger densities). Higher scores for management were related to higher tiger densities. Three variables showed no relationship with tiger densities: forest cover (ecology) and the number of core villages and core families (human pressure).

Discussion All the findings of this study must be regarded as provisional as result of the numerous data problems discussed above. Even so, several of the findings were expected and require little discussion. These include the relationship between tiger densities and prey availability, the existence of a buffer, the absence of Naxalites/criminal groups and effective reserve management. None of these findings would surprise anyone familiar with the reserves and they are consistent with the objectives of the management and the Indian government. Some other findings were less expected and do require comment. First, it was a little surprising that forest cover was not related to tiger densities, though this could be because, with two or three exceptions, forest cover was uniformly high among the reserves and in only one case did it fall below 50 percent (Nameri, 49.1 percent). Second, it is not known why tiger densities were negatively related to altitude variations in the reserves. It could be that in very hilly reserves, prey densities are low (though in our study there was no relationship between prey availability and altitude variations). Alternatively, it could be that very hilly reserves are difficult to patrol and manage, and consequently, tigers might be significantly undercounted in these reserves. Lack of appropriate data to test these explanations, means that they must be consigned to the category of future research needs. Two remaining findings of the analyses were not just unexpected, but they also have important implications for government policy. First, no relationship was found between tiger densities and the number of villages and families in the core of a reserve. This suggests that tigers and Tribals can coexist (possibly

Tiger poaching in Indian reserves 171 Table 8.5 Comparison of 14 reserves with high tiger densities and 14 with low tiger densities: group means and significance test results (Mann-Whitney Test) High density (mean)

Low density (mean)

Sig.

1

Tiger numbers

60.4

17.1

**

2

Core tiger density (tigers/100km2)

7.9

1.6

**

3

Reserve size (km2)

1,361.9

1,519.2

4

Core size (km )

781.9

1,042.6

5

Forest cover (%)

6

Altitude variations (m)

7

2

77.3

84.8

545.7

1,201.5

*

Prey availability (ordinal: 0–3)

2.4

1.6

*

8

Core villages (/100km2)

1.5

1.4

9

Core families (/100km2)

145.8

109.4

10

Stability (1 or 0)

1.0

0.4

**

11

Core tourism (ordinal: 0–4)

2.4

1.0

*

12

Buffer (1 or 0)

0.9

0.3

**

13

Management scores (scale: 2.5–10)

8.5

6.6

*

* p < 0.05 ** p < 0.01

because they have learned to avoid each other, (see below) and that the Indian government might relax the difficult and largely unsuccessful policy of relocating villages outside the reserve. This does not mean, of course, that more Tribals can be allowed to settle in the core of the reserves. The Tribal families already in the reserves might have learned over time how to accommodate their needs to those of the tiger as well as how to cooperate with reserve officials. Permitting more Tribals to settle in the reserves might well disturb this delicate balance. More important than removing the Tribals could be to remove Naxalites and criminal elements who, according to the TTF (Project Tiger, 2005), promote discontent among reserve villagers. Like the TTF before us, we have no advice about giving practical effect to this recommendation. The second unexpected finding with implications for government policy is that the relationship between tiger densities and core tourism was strong and positive, not negative as some Supreme Court petitioners have recently claimed. At one

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level, this relationship is not unexpected: tourists are much more likely to visit reserves with large numbers of tigers. However, it does not appear that more tourism, even “intrusive” tourism to the core of the TR, has a deleterious effect on tiger numbers. It could be, as claimed by some tour operators, that the presence of tourists deters poachers; or that tourist presence motivates reserve staff and managers to improve their performance; or that their entrance fees help to fund more and better-equipped anti-poaching patrols. Whatever the reason, it does not seem on the basis of our results that banning tourism from the core of the reserve would result in a substantial increase in tiger numbers. Since tourism can bring economic benefits to people living near the reserve, a better direction for policy would seem to be finding ways to manage tourism more effectively so that the core, the buffer and the wider surrounding areas do not become degraded. These arguments are pursued in more detail by Clarke et al. in this volume.

Conclusions The original purpose of the research described in this chapter was to conduct a “risky facilities” analysis of TRs in order to identify factors that might be related to tiger poaching and that might be subject to policy control. The failure to identify any reliable data about poaching, despite an extensive search, led to the focus of the research being switched from explaining poaching to explaining the considerable variation in tiger densities among the 28 reserves. In fact, this switch in the dependent variable was consistent with the original objective of the research – to assist policy makers in ensuring that the reserves meet their goal of providing a safe haven for the tiger, which apart from anything else is India’s national animal. Before concluding, we should comment further on the failure of the risky facilities analysis. As explained, this was due to the lack of consistent/reliable data on many of the reserve variables that we wanted to include, but particularly the lack of reliable data on poaching in the 28 reserves. In fact, the NTCA has recently embarked on an effort to maintain better records of poaching which might be useful for future comparisons among reserves. However, even more important than improved measurement and recording of poaching deaths is improved counts of tigers in the reserves. The limitations of pugmark data are now widely recognized and efforts are being made to develop more reliable counting methods such as the use of camera traps.9 The routine availability of more reliable counts of tigers could transform reserve management and greatly strengthen the NTCA’s supervisory role. That being said, we do not wish to seem critical of India’s efforts to collect data about its tigers. In fact, we have been impressed by the number and quality of reports issued by the NTCA, by various non-governmental organizations and by the many conservationists and other scientists who have wrestled with the problems faced by tiger conservation. We would only wish that in the future more attention would be paid to collecting data that permit rigorous comparisons to be made among reserves, which would permit more robust studies of the kind we have

Tiger poaching in Indian reserves 173 presented in this paper. In passing, we should also record our admiration for the level of state and government resources that have been devoted to tiger conservation, given the enormous competing demands of population growth and a rapidly expanding economy. We conclude with some broader implications of our findings. Perhaps the most important of these is that it is possible for tigers to thrive in reserves that are under considerable human pressure from tourism and resident Tribals. One reason could be, as suggested by a recently published study (Carter et al., 2012) that tigers have learned to adapt their activities to avoid humans. While both tigers and humans walk the same paths, the study found a pronounced shift among tigers towards night-time activity, which is when humans avoid the forest. If humans and tigers can successfully coexist in close proximity, this might relieve the authorities of the need to relocate Tribals outside the reserves. It might also allow them to authorize carefully regulated tourism without impacting tiger numbers. Conservationists might object that allowing Tribal settlements as well as tourism in core areas would defeat the object of establishing TRs – the maintenance of a few pristine, natural habitats for the tiger. But this could be the price that must be paid for ensuring the survival of wild tigers in India. Alternatively, the Indian government and the states might agree on a plan that would allow greater variety among TRs in their relationship with the surrounding environment. Some TRs might be deemed inviolate and be maintained in their unspoiled, natural state, thus satisfying the needs of conservation. Others might be managed in such a way as to allow Tribal villages to remain undisturbed, to allow some limited mining, to allow tourists to enjoy tigers and to allow the economic benefits of tourism to flow to local populations. Such a plan might be easier to implement at the present time now that an additional 13 TRs have recently been designated and 10 more have been approved in principle.

Acknowledgments We should like to thank Phyllis Schultze for her help in finding previous publications and S. P. Yadav (NTCA) and Belinda Wright (WPSI) for responding to our queries about tiger poaching data. In addition we received help from Ko-Hsin Hsu, Dr Mangai Natarajan and Dr Stephen Pires.

Notes 1 For management, we sought for each reserve numbers of tigers; numbers of rangers, foresters and guards and the number of unfilled posts; numbers of ranger offices and rest houses; and government inspectors’ ratings of the quality of the reserve’s management. For human pressure, we sought data for each reserve on numbers of Tribal villages and total population of Tribals; number of domestic livestock; distances from nearest town, airport and train station; number of nearby hotels/inns; and presence of Naxalites. For ecology, we sought for each reserve its size and shape; location within India; habitat, temperature, rainfall and altitude; prey species present in and estimated numbers of prey animals.

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2 Sharma and Wright (2005) have trialed a method in Ranthambore TR of analyzing pugmarks using digital photography. They conclude that the method is well suited to identifying individual tigers, but that “Total counts of tigers based on this method may only be feasible in very small reserves with a few tigers” (p. 8). 3 As described in footnote 5, we repeated the analyses simply using the total size of the reserves in calculating the dependent variable to check whether this produced very different results. 4 We used the following classification of six regions: (1) Shivaliks and the Gangetic Plain Landscape; (2) Central Indian Landscape; (3) Eastern Ghats Landscape; (4) Western Ghats Landscape; (5) The Northeast Hills and Brahmaputra Floods Plains Landscape; (6) Sundarbans (Jhala et al., 2008). 5 Our analysis reported below found no relationship between our measures of prey availability and altitude variations in reserves. 6 Despite the protests, the National Tiger Authority (2012) issued a set of “comprehensive guidelines” after the present study was concluded that indicated the area in square kilometers of cores and buffers for all reserves. The guidelines do not provide any details describing how these areas were established and how the difficulties of defining them were addressed. 7 We also undertook an analysis in which the “high” and “low” groups of reserves were determined, not by their core tiger density scores, but by their overall tiger density scores. This resulted in a change of group for some of the reserves near the high/low cut-off. Thus Periyar, Palamau and Nagarjunasagar, formerly in the low group, moved into the high group, whereas Pench, Bhadra and Buxa, formerly in the high group, moved into the low group. The overall pattern of the results was similar for both analyses, but for the one based on overall tiger density scores, the existence of a buffer was no longer significantly related to higher tiger densities; more important, there was less consistency for this analysis in the results of the two statistical tests (Spearman’s Rho and the Mann-Whitney U-Test). This fact coupled with the widespread belief that most tigers breed and hunt in the core of the reserve led us to present the results of this study using the analysis based on core tiger density scores. 8 The Mann-Whitney U-Test will tend to be less sensitive to associations between tiger density and other variables than the Spearman’s Rho because it collapses the interval level tiger density variable into a binary “high/low” measure. The Spearman’s Rho, by contrast, makes use of the full range of measured tiger density variation. 9 The use of camera traps is explained by Clarke et al. in this volume.

References Carter, N. H., Binoj, K., Shrestha, B. K, Karki, J. B., Man Babu Pradhan, N. and Liu, J. (2012). Coexistence between wildlife and humans at fine spatial scales. Proceedings of the National Academy of Sciences. 109(38): 15360–15365. Clarke, R. V. and Eck, J. (2005). Crime Analysis for Problem Solvers: In 60 Small Steps. Washington, DC: Office of Community Oriented Policing Services, U.S. Department of Justice. Conservation India. (2011, June 7). Tiger Population Doubles at the Kalakad-Mundanthurai Tiger Reserve (KMTR). Retrieved from www.conservationindia.org/news/tigerpopulation-doubles-at-the-kalakad-mundanthurai-tiger-reserve-kmtr Dhar, A. (2012, August 22). “Buffer Zones in Tiger Reserves Will Violate Human Right”. The Hindu. Retrieved from www.thehindu.com/news/national/article3804518.ece Eck, J., Clarke, R. V. and Guerette, R. (2007). “Risky Facilities: Crime Concentration in Homogeneous Sets of Facilities”. Crime Prevention Studies. 21. Monsey, NY: Criminal Justice Press.

Tiger poaching in Indian reserves 175 Economist, The. (2010, April 8). “India’s Naxalite Insurgents: Politics with Bloodshed”. Retrieved from www.economist.com/node/15869400 Gray, M. (2012, July 25). Indian Supreme Court Places Temporary Ban on Tiger Tourism. CNN. Retrieved from www.cnn.com/2012/07/25/world/asia/india-tiger-tourism/index.html Gopal, R., Sarmah, R., Karanth, K. U. and Sambakumar, N. (2011). India’s Next Frontier on Bio-Monitoring: A Case Study from Karnataka. New Delhi and Bangalore, India: National Tiger Conservation Authority and Center for Wildlife Studies. Retrieved from www.globaltigerinitiative.org/wp-content/uploads/2012/07/Bio_Monitoring.pdf Hayward M. W., Jedrzejewski, W. and Jedrzewska, B. (2012). “Prey Preferences of the Tiger Panthera Tigris”. Journal of Zoology. 286: 221–231. Hockings, M., Stolton, S., Leverington, F., Dudley, N. and Courrau, J. (2006). Evaluating Effectiveness: A Framework for Assessing Management of Protected Areas (2nd ed.). Gland, Switzerland: World Commission on Protected Areas, IUCN. Retrieved from https://portals.iucn.org/library/efiles/edocs/PAG-014.pdf Indo Asian News Service. (2012, April 25). Survey Confirms Tiger Presence in Mizoram’s Dampa Reserve. Retrieved from http://en-maktoob.news.yahoo.com/survey-confirmstiger-presence-mizorams-dampa-120406131.html International Union for Conservation of Nature. (2012). The IUCN Red List of Threatened Species: Panthera Tigris ssp. Tigris. Cambridge, UK: Author. Retrieved from www. iucnredlist.org/details/136899/0 Jhala, Y. V., Gopal, R. and Qureshi, Q. (Eds.). (2008). Status of the Tigers, Co-predators, and Prey in India. New Delhi, India: National Tiger Conservation Authority. Retrieved from www.projecttiger.nic.in/whtsnew/status_of_tigers_in_india_2008.pdf Jhala, Y. V., Qureshi, Q., Gopal, R. and Shinha, P. R. (2011). Status of Tigers, Co-predators, and Prey in India. New Delhi, India: National Tiger Conservation Authority. Retrieved from www.projecttiger.nic.in/whtsnew/Tiger_Status_oct_2010.pdf Karanth, K. U. (2011). “India’s Tiger Counts: The Long March to Reliable Science”. Economic & Political Weekly. 18: 22–25. Kumar, S. S. (2012). “Tigers Buffered”. Science and Environment Online: Down to Earth. Retrieved from www.downtoearth.org.in/content/tigers-buffered Mathur, V. B., Gopal, R., Yadav, S. P., and Sinha, P. R. (2011). Management Effectiveness Evaluation (MEE) of Tiger Reserves in India: Process and Outcomes. New Delhi, India: National Tiger Conservation Authority, Government of India. Retrieved from www.projecttiger.nic.in/whtsnew/mee_tiger_2011.pdf Ministry of Environment and Forests. (2006). Forest Cover in Tiger Reserve of India – Status and Changes. Dehradun, India: Author. Retrieved from www.projecttiger.nic.in/ whtsnew/tiger_reserves.pdf National Tiger Conservation Authority (NTCA). (2009). State of Project Tiger Reserves. New Delhi, India: Ministry of Environment and Forests. Retrieved from www.projecttiger.nic.in/whtsnew/State_PT.pdf National Tiger Conservation Authority (NTCA). (2010a). List of core and buffer areas of tiger reserves in India. Stripes. 1(3). New Delhi, India: Author. Retrieved from http:// projecttiger.nic.in/whtsnew/STRIPES_Issue3.pdf National Tiger Conservation Authority (NTCA). (2010b). Stripes. 1(4). New Delhi, India: Author. Retrieved from http://projecttiger.nic.in/whtsnew/STRIPES_MAY_JUN_ISSUE_4.pdf National Tiger Conservation Authority (NTCA). (2011). Stripes. 2(3). New Delhi, India: Author. Retrieved from http://envfor.nic.in/sites/default/files/STRIPES03-0411.pdf National Tiger Conservation Authority (NTCA). (2012). Comprehensive Guidelines for Tiger Conservation and Tourism as Provided Under Section 380 (1) (c) of The Wildlife Protection Act, 1972 (15th October) New Delhi, India: Author.

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Pasricha, A. (2012, October 17). Indian Supreme Court lifts ban on tiger tourism. Voice of America. Retrieved from www.voanews.com/content/indian-supreme-court-lifts-banon-tiger-tourism/1528019.html Periya Tiger Reserve. (2012). Census. Kerala, India: Author. Retrieved from www. periyartigerreserve.org/result.php Project Tiger. (2005). Joining the Dots: The Report of the Tiger Task Force. New Delhi, India: Ministry of Environment and Forest. Retrieved from www.projecttiger.nic.in/ TTF2005/pdf/full_report.pdf Rang 7 Team. (2011, November 4). “Tiger Census at Namdapha National Park in Arunachal Pradesh This Month”. Rang 7. Retrieved from www.rang7.com/news/national-park/ tiger-census namdapha-national-park-arunachal pradesh-this-month-966.htm Sethi, N. (2008, June 27). “Centre Puts Tiger Reserves on Alert”. The Times of India. Retrieved from http://articles.timesofindia.indiatimes.com/2008-06-27/flora-fauna/ 27776314_1_tiger-reserves-national-tiger-conservation-authority-sariska Sharma, S. and Wright, B. (2005). Monitoring Tigers in Ranthambhore Using the Digital Pugmark Technique. New Delhi, India: Wildlife Protection Society of India. Retrieved from www.wpsi-india.org/images/ranthambore_census_report.pdf Smith, O. (2012, October 4). “Tiger Tourism Decision Delayed”. The Telegraph. Retrieved from www.telegraph.co.uk/travel/travelnews/9586912/Tiger-tourism-decision-delayed. html Talwar, R. and Usmani, A. (2005). Reading Pugmarks: A Pocket Guide for Forest Guards. New Delhi, India: Tiger and Wildlife Programme, WWF India. Retrieved from http:// assets.wwfindia.org/downloads/reading_pugmarks.pdf Telegraph, The. (2010, September 29). “NGO Reports 15 Tigers in Buxa Reserve – DNAbased Technique Used”. Retrieved from www.telegraphindia.com/1100929/jsp/northeast/ story_12995111.jsp Wildlife Protection Society of India. (2011). Information on Tiger Reserves. New Delhi, India: Author. Retrieved from www.wpsi-india.org/images/Information_on_Tiger_%20 Reserves_WPSI_Oct_2011.pdf

9

Eyes on the forest CCTV and ecotourism in Indian tiger reserves Ronald V. Clarke, Kevin Chetty and Mangai Natarajan

Introduction It is widely agreed that nature reserves in developing countries can only prosper if they benefit the local population. In India, local people have often resisted the establishment of tiger reserves because they will be denied the opportunity to harvest forest products without any compensatory benefits. One way to bring benefits to local communities is through ecotourism. At present, only about one quarter of India’s 37 tiger reserves have substantial populations of ecotourists hoping to catch sight of the tigers. They are frequently disappointed because of the dense forest cover and because tigers tend to be active for short periods of the day immediately after sunrise and before sunset. Indeed, many, if not most, visitors to the reserves do not see a tiger. This is quite different from ecotourism in, say, Africa, where visitors are almost guaranteed sightings of lions, leopards, cheetahs, rhinos and elephants. This chapter focuses on the scope for promoting ecotourism by making the tigers more visible to tourists. We explore in depth the possibility of using closed-circuit television (CCTV) cameras, placed in strategic locations, to transmit images of tigers in real time. This would make it possible to stream live video displays to the reserves’ Visitor Centres and to nearby lodges. We believe this would substantially enhance the experience of visitors to the reserves. If images were also streamed on the Internet, it could promote tourism to the reserves and attract foreign donors. In addition, Jeeps containing tourist visitors could be more efficiently routed to where the tigers could be seen at any particular time with consequent savings in petrol, in harmful emissions and in environmental degradation. Finally, it would provide local people with an incentive to protect the tigers from poaching, which continues to be a significant problem.

The Sariska crisis In December 2004, conservationists were shocked to learn that tigers may have disappeared from Sariska, one of India’s best-known tiger reserves. The tiger is India’s national animal and the government’s efforts over the years to save this beautiful and charismatic species from extinction have attracted considerable

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publicity. The disappearance of Sariska’s tigers was confirmed some months later by the Wildlife Institute of India, which found not a single tiger remaining of the previously estimated 24/25 in the reserve. This was all the more dispiriting because it became evident that Project Tiger, launched with strong political support some 30 years before, was failing to achieve its goals of tiger preservation. The government of India responded quickly to the news from Sariska by appointing a Task Force to review the management of the tiger reserves and to suggest measures to strengthen tiger conservation in the country. The report of the Task Force was comprehensive, detailed, and produced with impressive speed. It concluded the following: It is the assessment of this Task Force that every tiger reserve in the country is not facing a Sariska-type crisis. But the Task Force also believes that the protection of tigers is happening in India against all odds. What we need to understand is that a Sariska-type crisis haunts every protected area in India – where islands of conservation are under attack from poachers, miners and every other exploitative activity. They are also under siege from their own inhabitants, the people, who live in these reserves and outside the islands of conservation, and who have not benefited from these protected areas but continue to lose livelihood options and face daily harassment. In these circumstances, if the defences are down, protection will fail. Like it did in Sariska. The challenge is to ensure that the siege can be lifted so that the tigers can survive. (Narain et al. 2005, p. 14). The Task Force was fully aware that turning around the situation would be fraught with difficulty and would require intervention on a wide variety of fronts. In this chapter, we focus on just one of the challenges identified by the Task Force – how to bring conservation benefits to local people, who as the Task Force found are often hostile to the reserves, the forestry officers and the tigers. This was a particularly disturbing finding because conservationists have increasingly come to believe that the viability of nature reserves, especially in less economically developed countries, depends crucially on the support of local people. If they are excluded from the economic benefits of the reserves as a result of being prohibited from hunting animals for food or trade and gathering firewood, honey and other forest products, it is natural for them to feel disenfranchised and hostile. In the case of tigers, local people have to bear the additional costs of attacks on their livestock and not infrequently on themselves.1 Under these circumstances, it may not be surprising if they do not share the goals of conservation, which they see as imposed by distant government officials, sometimes to satisfy demands from foreign governments and non-governmental organizations, and it is not surprising if they continue to poach endangered animals or gather protected plants.

CCTV and ecotourism in Indian tiger reserves 179 Several ways exist to bring economic benefits to local people, including managed off-take of forest products, their employment as rangers and trackers, and subsidized health care and education. In this chapter, we focus on benefiting local people by promoting ecotourism. The Indian Supreme Court has recently issued a hotly contested ban on tourism in the core zones of the tiger reserves (see Kim et al. in this volume), and we know that the benefits of tiger tourism are disputed (for a review see Curtin, 2011). However, we believe that if properly managed tiger tourism can bring many benefits to the local population. It can bring employment as guides and drivers, hotel and restaurant staff, and through making and selling of craft items. It can also promote the development of local infrastructure including roads, airports and hotels (Budowski, 1976; Higginbottom et al., 2001; Orams, 2002; Pennisi et al., 2004; Tisdell and Wilson, 2002; Turcq, 2010; Walpole and LeaderWilliams, 2002). Some of the tiger reserves have many visitors, but these are often poor pilgrims visiting forest temples and natural religious sites. Only about 10 of India’s 37 tiger reserves have substantial populations of ecotourists, particularly of affluent foreign visitors, hoping to catch sight of the tigers. In some cases, this is because the reserves are remote and inaccessible, but tigers are also difficult to see. They are often concealed by dense forest cover and they tend to be active for relatively short periods of the day immediately after sunrise and before sunset. Indeed, many if not most visitors to the reserves do not see a tiger. This is quite different from ecotourism in, say, one of the popular African destinations, where visitors are almost guaranteed sightings of the big five – lions, leopards, rhinos, elephants and Cape buffalo – as well as of other interesting animals such as cheetahs, hippos, zebras and giraffes.

The scope of our study We focus in this chapter on the scope for promoting ecotourism by making the tigers more visible to tourists who visit the reserves. We explore in depth the possibility of using CCTV cameras, placed in strategic locations, to transmit images of tigers in real time. This would make it possible to stream live video displays to the reserves’ Interpretation Centres and to screens in nearby lodges. We believe this would substantially enhance the experience of visitors to the reserves. If images were also streamed on the Internet, it could promote tourism to the reserves and attract foreign donors. In addition, Jeeps containing tourist visitors could be more efficiently routed to where the tigers could be seen at any particular time with consequent savings in petrol, in harmful emissions and in environmental degradation. There could also be a number of other benefits of the systematic deployment of CCTV cameras which we discuss below, including deterring poachers, producing more accurate counts of tigers, yielding savings in patrolling manpower and rewarding reserves for improvements in tiger populations.

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This chapter is focused on the feasibility of the scheme. We examine four main questions: 1 2 3

4

Would tigers be sufficiently visible to the cameras, given the terrain, the dense foliage of the jungle habitats and the fact that tigers are mostly active at the beginning and end of the day?2 How many cameras would be needed to provide adequate coverage of the reserve? Does the technology exist to permit the deployment of suitable CCTV systems in the rural environment of the reserves, where electricity supplies are absent, where there are extremes of climate, and where they might be subject to attacks from poachers? Would the CCTV scheme meet the goal of enhancing ecotourism?

Methods To answer the first question about the visibility of tigers to the cameras, two of us (Ronald Clarke and Mangai Natarajan) visited a sample of reserves to learn about conditions for ourselves. The six reserves we visited were Mudumalai and Kalakad (both in Tamil Nadu) Bandipur and Nagerhole (in Karnatika), Ranthambore (in Rajasthan) and Corbett (in Uttarakhand). Corbett is in the foothills of the Himalayas, while Kalakad is in the southern tip of the country so we covered a substantial part of India. We visited Ranthambore and Corbett primarily as tourists; our visits to the remaining reserves were facilitated by police and forestry officials, with whom we had many hours of conversation. Each visit lasted one to three days and they all took place in January 2011. For a map of reserve locations, see Figure 8.2 of Kim et al. in this volume. To answer the question about the number of cameras that would be needed, we examined existing camera trap data collected in Mudumalai by WWF-India. Questions about the adequacy of current technologies to provide a CCTV realtime tiger monitoring system were addressed by Dr Kevin Chetty, an engineer based in the Jill Dando Institute of Crime Science at University College London. This assessment involved examining a range of technical requirements for such a system. Included in the assessment were the capabilities of existing CCTV technologies to operate within the environmental conditions of tiger reserves, the types of data networks needed to transfer recorded video data back to the Interpretation Centre, the ability of a system to work in areas without electricity and the resistance of such a system to climatic variations and human assault.

Findings Our discussion of findings is organized around the four questions that we identify above. 1

Could the CCTV cameras see the tigers?

We were pleasantly surprised to find that the reserves (at least those we visited) were not entirely covered by dense jungle, though this could be found

CCTV and ecotourism in Indian tiger reserves 181 in most of the reserves. In several of the reserves we visited much of the habitat consisted of scrub and scattered trees, and in some of the reserves, open vistas of grassland afforded a wide view of the terrain (see Figure 9.1). Tigers would be visible to cameras in these areas. We also learned that tigers frequently use the dirt roads that crisscross the reserves for forestry vehicles, tourist Jeeps, pilgrims, electricity workers and “tribals” (small groups of indigenous residents of the reserves). Tigers find it is easier to walk on the roads than in the bush and they are also less likely to get thorns in the pads of their feet. Finally, at each of the reserves we visited, forestry officials and tourist guides knew the best places in the reserves to see tigers – at particular river banks, water holes and stretches of road. They were also familiar with the territories of particular tigers and their mates. Our visits convinced us that it would be feasible to install cameras at key locations that would provide regular views of tigers and other animals. 2

How many cameras would be needed to provide enough sightings to satisfy tourists?

This was a more difficult question to answer with much depending on the terrain of individual reserves and the tiger densities. In some reserves, camera traps (automated digital devices that take a flash photo whenever an animal triggers an infrared sensor; Hance, 2010) have been deployed on an experimental

Figure 9.1 Photographs of Indian tiger reserve terrain

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basis for counting tigers and other wildlife (see Figure 9.2). These data would need to be examined in detail to assist with answering the question. They could only provide a conservative estimate, however, because the need to satisfy scientific rigor in counting animals means that the camera traps are randomly deployed throughout the area of study, whereas if they were deployed primarily to entertain tourists, they would be placed in the locations where tigers are most likely to be found. Even so, we found instructive some preliminary data from a camera trap study conducted by the WWF-India that were kindly shown to us. The study was conducted in the Segur Plateau of Nilgiri North Forest, Western Ghats, which is next to the Mudulamai tiger reserve in Tamil Nadu. The total area covered by the camera survey consisted of nearly 200 km2, which was divided into forty-six 4 km2 grids. Forty-six pairs of cameras (to photograph both sides of the target) were deployed in the grids for 30 days in 2010. Within the grids, cameras were placed where animals were likely to pass, for example streams and animal paths, especially at junctions of these paths. In placing cameras, account was also taken for signs of: (1) carnivores – pugmarks (or footprints), scat (or droppings) and scraping of trees; and (2) prey – pellets, hoof prints and direct sightings. Care was taken to maintain a minimum distance between the camera placements. This survey yielded a total of 15,137 records of a variety of animals and some humans, of which number 13,671 could be identified. Tigers comprised 271 of the records, of which 77 were at night. The study therefore yielded an average of two to three daytime tiger sightings and six to seven per night.

Figure 9.2 Photographs of camera traps used in Indian tiger reserves

CCTV and ecotourism in Indian tiger reserves 183 It would be unwise to draw firm conclusions from just this one study undertaken in a relatively small area, only about half the size of the smallest tiger reserves. But on the basis of these results obtained by positioning one camera trap per 4km2, we would hazard a guess that 100 cameras, carefully positioned to yield the maximum number of tiger sightings in a tiger reserve of average size and tiger density, could provide one sighting per daylight hour and several times that number at night. On the assumption that each tourist spent an average of one hour in the reserve’s Interpretation Centre and several hours in the lounge or dining rooms of nearby lodges, they would have good chance of seeing a televised picture of a tiger in real time. 3

Does existing technology permit satisfactory deployment of CCTV cameras in the reserves?

Answering this question required us to consider several different requirements that the technology would need to meet. These requirements are briefly outlined below, while the feasibility of meeting them is discussed in the Appendix. The data network: We examined the type and possible setup configurations of a data network which would allow the transmission of recorded images from the CCTV cameras back to the Interpretation Centre and viewing terminals in real-time. We also assessed the data throughputs of various networking protocols such as 3G and WiFi (IEEE 802.11), which would be required to support small to large quantities of digital data moving around the network, respectively generated by highly functional to more basic CCTV setups. Additional systems functionalities which could be realized by employing twoway communication channels, including approximate costs of these solutions, were also investigated. The camera system: Aspects that one must consider in the deployment of a tiger monitoring CCTV system include the types of camera which can be employed (e.g. color or monochrome), the types of lenses mounted on the cameras (short or long focal lengths) and the frame rates and resolutions of the recorded images. Additional functionalities such as pan, tilt and zoom can also be incorporated. These variables dictate factors such as the cameras fieldof-view, the amount of digital data generated and sensitivity and specificity of system. Each of the options also has associated costs and power requirements which must be taken into account. We therefore examined the various environments (watering holes, dirt-tracks, open savannah, etc.) and scenarios (speed of tiger movements, light levels, etc.) in which the CCTV system would need to operate within. Due to the variability of these conditions, we advocate that areas with high levels of tiger activity be first identified and requirements assessed, before defining a specific set of systems specifications which are tailored to that particular scenario. Additional systems: We explore a number of other technologies that could be integrated into the main tiger monitoring system to either enhance its functionality, or improve its power efficiency. These include pattern recognitions algorithms

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for tiger identification or the use of other types of hardware sensors such as foliage penetrating radar and infrared cameras. Power: The CCTV cameras and other hardware technologies employed in the tiger monitoring system would need an electricity supply. We envisage that areas around, and including the control stations would likely be able to draw power from the main grid. However, systems employed in more remote locations around the reserve would need to utilize an alternative source of power. We therefore outline and discuss the possible use of battery and solar cells to fulfill power requirements. System protection: Many of the reserves are exposed to monsoon rains and to extremes of heat and cold which could prohibit the system from functioning effectively. Additionally the system is vulnerable to human attack from poachers or thieves and vandals. To address these issues, we examine areas where the system is susceptible, then define a minimum International Protection (IP) rating code of IP65 for the actual hardware which should provide a sufficient level of protection against the elements. We also put forward strategies to minimise the occurrence of human attack. 4

Would the CCTV scheme meet the goals of enhancing ecotourism?

These questions were in many ways the most difficult to answer. They include the costs of the scheme and whether these would be commensurate with the benefits, the logistics of setting up monitoring screens in the Interpretation Centre and nearby lodges and the bureaucratic difficulties that would be encountered in setting up the scheme, even on an experimental basis. We begin by exploring whether the cameras would add sufficient value to the experience of visitors to justify their deployment. A satisfying experience? We have made a rough estimate that 100 CCTV cameras strategically located within an average tiger reserve could provide one sighting per daylight hour and several times that number at night. Assuming that each tourist spent an average of one hour in the reserve’s Interpretation Centre and several hours in the lounge or dining rooms of nearby lodges, they would have good chance of seeing a televised picture of a tiger in real time. These estimates are subject to a wide a margin of error, but a larger problem is that seeing a televised image of a tiger would not be as thrilling as seeing the actual animal. Nevertheless, it would be a considerable improvement on the experience of most tourists today who would be lucky to see a single tiger while visiting a reserve. (In our own visits made under very favorable circumstances, we saw no tigers in four of the reserves we visited, two in Ranthambore and one for a split second in Corbett, at uncomfortably close quarters, which we surprised in our Jeep). And even if tourists did not see a tiger, the cameras would enhance their chances of seeing other interesting animals such as wild dogs, sloth bears and hyenas. Moreover, assuming that tourists spent several hours in a tourist

CCTV and ecotourism in Indian tiger reserves 185 vehicle, they might have a good chance (much better than without the cameras) of being directed to the actual location of a sighting while the tiger was still present. As an aside, we would comment that strict rules (as in many venues in Africa) would have to be enforced about racing to the location, about the number of tourist vehicles at any one location, and about the behavior of tourists in the presence of a tiger, who now frequently whistle and call to the animals. Monitoring the cameras We have assumed that: (1) the images produced by the cameras would be monitored by the rangers, (2) they would be displayed at the Interpretation Centre and at nearby lodges and (3) they might also be streamed on the Internet. Each one of these deployments would entail considerable difficulties. We confine ourselves to discussing deployment at the Interpretation Centres. Given our admittedly limited experience, we believe that much more could be done to make visits to these centres rewarding for tourists. Outfitting them with TV monitors to receive images from the forest cameras would be an important way to improve the service they provide to tourists. This would require full-time monitoring of the transmitted images (perhaps by staff in a separate facility) and selection of the images to be displayed at the Interpretation Centre where a knowledgeable ranger would have to be present to interpret what the monitors show. The opportunity might be taken to upgrade other services that could be offered at the Interpretation Centres including food service, restrooms and sales of postcards, craft items and souvenirs. Costs Clearly the financial costs of the system would be very considerable. These would include the costs of the cameras and monitors, the costs of installing the system changes to the Interpretation Centre and the costs of retraining of rangers. We make the assumption that the Indian government, which already spends considerable sums on the reserves (Walston et al., 2010), would not be able to fund the camera system and that the scheme could not be implemented without substantial contributions by overseas donors, whether private or governmental. This arrangement would be consistent with our belief that the costs of conservation should not fall wholly in the countries where endangered animals are found. These countries are often developing economies with burgeoning populations, like India, with enormous calls on their resources. But they are not the only beneficiaries of successful conservation – the world at large benefits. The disappearance from the wild of the tiger, a beautiful and charismatic species, would impoverish not just India and a few other Asian countries with tigers, but Western culture and that of other countries as well. In calculating the costs of conservation, it is important to balance costs against the value of the natural resource that is being protected. Some attempts have already been made to calculate the value of tigers (Mitra, 2006) and we believe that refining and further justifying the calculations would assist in making the case for the CCTV system we have advocated.3 This could be one objective of the pilot study of the scheme that we discuss below.

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Additional benefits of the cameras If cameras could be successfully deployed in reserves, other benefits, apart from enhancing tourism, might be realized. These include producing more accurate counts of tigers, deterring poachers, saving manpower and rewarding reserves for improvements in tiger populations. Counting tigers It is difficult to count the tigers in a reserve for the same reasons that they are rarely seen by tourists: they can readily conceal themselves in vegetation and they are active mostly around sunrise and nightfall. While their facial and body markings are unique, rangers/guards might only get brief glimpses of them, insufficient for purposes of identification. The principal method of counting tigers that was used until recently consisted of taking plaster casts of pugmarks, or pad prints. Unfortunately this method is highly subjective and prone to error and may be replaced by digital analysis of pugmarks (Sharma and Wright, 2005) or camera trap capture-recapture methods (Karanth et al., 2003, 2004, 2006). A carefully sited system of CCTV cameras could be superior to either of these alternatives because images would be transmitted for analysis rather than physically collected from the camera. The accuracy of counts would be further improved if analysts were provided with software, currently under development (see Technical Appendix), which permits the identification of tigers by analyzing facial and body markings. If the CCTV cameras were to be used to count tigers, they would have to be deployed more randomly in the reserve, or more cameras would have to be deployed than would be needed to promote ecotourism. On the other hand, this might not be necessary if the main objective of counting was to monitor changes in tiger numbers over time (see below), rather than to establish the exact number of tigers in the reserve. All that might be needed for the former purpose would be to leave the cameras in place in the same locations from year to year. Rewarding management and guards Few incentives exist for reserves to do a good job in protecting their tigers, apart from regular inspections by staff from the Forestry Department headquarters (Project Tiger Directorate, 2006). While inspections and the resultant grades might influence promotions for reserves managers, they probably have little consequence for rank-and-file guards and rangers. A reliable annual count of tigers could provide a basis for providing financial rewards for the managers and guards of reserves that showed increases in tiger populations. To ensure transparency and therefore acceptance of the reward scheme, it would have to be managed by some entity separate from the reserve itself, perhaps by a unit in the headquarters of the Forestry Department or by an independent nongovernmental organization.

CCTV and ecotourism in Indian tiger reserves 187 Improved manpower deployment Better information about the locations where tigers are most likely to be found might assist in deployment of the guards and rangers. In some cases, the cameras might substitute for patrols, which could release manpower for other work in the reserves – such as service in the Interpretation Centres and closer monitoring of tourists, pilgrims and guides. Protection from poachers and other intruders The cameras would not only keep watch for tigers, but also for poachers and other intruders, such as villagers who are illegally grazing cattle.4 At one of our meetings with forestry officials and police, two objections were raised to the use of cameras for anti-poaching purposes. First, it was suggested that poachers would simply avoid the places where the cameras were located – in other words that they would “displace” their activities elsewhere. But if poachers avoided the places where tigers are most numerous, they would be less to be successful in snaring or trapping tigers. In any case, the situational crime prevention literature has clearly established that the risk of displacement is much exaggerated and that it occurs much less often than predicted (Guerette and Bowers, 2009). An equally likely result of introducing situational prevention measures is “diffusion of benefits” (Clarke and Weisburd, 1994), which refers to the fact that crime drops not just in targeted locations, but also in other nearby places. This seems to be because offenders often overestimate the resultant increased risk of being caught or the increased difficulty of crime. The second objection raised at the meeting was that poachers would learn from the transmissions where tigers were most likely to be found and would accordingly begin to hunt in those places. This argument ignores two facts: first that poachers, who are usually local people, might already know which places are best for hunting and, second, that they would avoid placing themselves under greater risk of detection by hunting where cameras were located. Benefits of CCTV compared with camera traps and radio collars It might reasonably be asked whether the expected benefits of the CCTV system could be achieved (possibly at reduced cost) by other forms of electronic surveillance such as camera traps or radio telemetry (“radio collars”).5 As for camera traps, these would be unlikely to improve the chances of tourists seeing tigers, but they could meet some of the secondary benefits of a CCTV system. If properly sited, they can yield accurate counts of tigers, which in theory would permit reserves to be rewarded for increases in tiger populations and might assist in better deployment of manpower. As presently designed, however, the cameras would need to be visited regularly to collect photos6 and to change batteries, and they would provide little deterrent to poachers who would have long departed the scene by the time the photos would be collected.

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As for radio collars, while these are more “invasive” than CCTV surveillance, they have the great advantage over CCTV cameras of having the built-in capacity to transmit data wirelessly. The costs of maintaining them and monitoring the data they produce might be little different from the equivalent costs for a CCTV system and, in theory, they could deliver some of the same benefits. Fitting collars to the reserve’s tigers would require them all to be identified, and thus to be counted. This could provide the basis for rewarding officers and managers for any increases in tiger numbers. Information produced by the collars about tiger movements could assist the more efficient deployment of staff and, if they were of the “smart” variety, which can monitor details of activity in real time, they might make it possible to keep a watch on tigers that pose a threat to villagers or their livestock. These are attractive advantages, but would the collars serve the primary objective of the CCTV system – improving the tourist experience? In theory, collars could improve the chances of tiger sightings, as long as the tigers wandered into areas that were accessible to nearby tourist Jeeps. However, knowing the location of a tiger is not the same as seeing it, especially during the day when tigers might be laying up in dense scrub. Chasing after tigers that cannot be seen could therefore result in as much waste of fuel as at present and as much degradation of the landscape. On the occasions when a tiger was seen, its collar might suggest the animal was no longer wild, which could rob the moment of much of its excitement (see Figure 9.3). But even if the Jeep’s passengers enjoyed the sightings,

Figure 9.3 Photo of tiger near tourist Jeep in an Indian tiger reserve Source: Mangai Natarajan

CCTV and ecotourism in Indian tiger reserves 189 the sightings would not benefit the much larger number of tourists who leave the reserve without having seen a tiger, or for that matter without having enjoyed the live streamed images of tigers that could be produced by the CCTV system. It would seem, therefore, that radio collars would not encourage tourism whatever their other benefits. However, it is possible that some combination of radio collars and a more limited use of CCTV (say at waterholes) could yield the same tourism benefits, but at lower cost, than the full CCTV system described in this chapter. To explore that possibility would require a feasibility study of the same detail as that described in this chapter concerning CCTV alone.

Piloting the scheme A CCTV scheme such as we have described could not be introduced all at once in every reserve. Indeed there are some reserves – those in very remote or inaccessible places for example – where there might be too few potential tourists to justify the expense of the cameras. It would therefore be important to pilot the scheme in one reserve in such a way as to allow a careful evaluation of the costs and benefits. There are distinguished tiger specialists in India who could advise on the selection of the reserve, but the following criteria are ones that we would initially propose: 1 2 3 4 5 6 7 8 9

The reserve should already have experience of managing substantial numbers of tourists. Ranthambore, Kharna, Corbett, Bandahrgav and Nagerhole are some of the reserves that would meet this criterion. The reserve should be near large population centers with good transportation links and with some nearby lodges and hotels. The reserve should be located in a state that would be politically open to experimentation, where bureaucratic obstacles would be minimal and where there would be no objections to working with foreign donors. The reserve should be recognized as well managed and well staffed. The reserve should have “enough” tigers, say about 30, to allow them to be seen routinely by cameras. The reserve’s topography should favor CCTV surveillance, e.g. some grassland and scrub, open vistas and some variation in altitude. The reserve should have some of the necessary technology in place – for example existing electrical and cell phone service, observation towers and a well-developed Interpretation Centre. The reserve should not be one with many pilgrim sites or with established villages that graze their cattle in the reserve environs. The reserve should not be located in an area of the country under the control of Naxalites, left-wing extremists.

Summary and conclusions The main objective of our proposal to use CCTV cameras in tiger reserves is to promote ecotourism in order to benefit local people, who at present are often hostile to the reserves, the reserve staff and the tigers themselves

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(Narian et al., 2005). The cameras would promote tourism because, at present, tigers are too rarely seen by those who visit the reserves hoping to catch a glimpse of them. We acknowledge the many difficulties of our proposal including the costs of equipping the reserves with cameras and the fact that seeing a televised image of a tiger is not as rewarding as being in the presence of a tiger. But the cameras would also have many other potential benefits. If the images were streamed not just to the reserve’s Interpretation Centre and to nearby lodges, but also on the Internet, this could increase knowledge about the threatened status of tigers and contribute to efforts to protect them. The cameras could also be used to produce more reliable counts of tigers, which in turn could be used to reward reserves and their staff for achieving increases in the numbers of tigers. Pressure on the reserve’s environment could be eased if the cameras were used to direct tourist vehicles to places where a tiger was visible, rather than as at present driving around in hopes of seeing one. Finally, the cameras might assist in preventing poaching, or in arresting poachers who were not deterred by them, which again in turn, might permit rangers and guards to be deployed in more productive tasks. We believe these benefits could be obtained without harming the reserve habitats or threatening the tigers. Indeed there is evidence that the greatest breeding successes have been obtained in reserves with many tourists (Curtin, 2011). The largest barrier to the deployment of cameras, apart from the financial costs, would be skepticism about: (1) their effectiveness in meeting the purposes discussed above; (2) the ability of the forestry department to make the changes needed to implement the scheme; and (3) whether the benefits of increased tourism would reach local people. These are empirical questions and we think that fuller implementation of the scheme would have to be preceded by a pilot study in which the costs and benefits of the cameras would be carefully evaluated. This pilot study ought to be financed in part by overseas donors on grounds that conservation is a global, as much as a local responsibility, and its costs must be shared among rich and poor nations.

Acknowledgements We are deeply grateful to Dr R. Radhakrishnan IPS for making the arrangements for our site visits and to Dr AJT Johnsingh and Dr Harendra Bargali for their help at Ranthambore and Corbett. We also thank the senior police and forestry officers, as well as officials of WWF-India, who met with us to share their experience and insights. Finally we want to thank the police officers and forest guards and rangers who escorted us – for their careful driving and the meals and accommodation they arranged, for their kindness and companionship, and for patiently answering our questions. None of those we thank are responsible for the views expressed in this chapter, nor for any mistakes that we might have made.

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Appendix: Technical requirements for the deployment of a CCTV system in a tiger reserve Introduction A full tiger monitoring system must bring together a number of technologies to continually sense and identify the presence and locations of tigers in the wild over large game reserve areas. The system must then relay this information in near real-time to remotely located ranger and tourist stations for visualisation and interpretation. This task is also made more challenging because of the requirements to provide the system with sufficient power which may or may not be drawn from the local grid; deploy and run the system at relatively low cost; and be resistant to severe climatic conditions and human assault. There are however available a number of both simple (low cost) and complex (more expensive) technological solutions for meeting the above requirements, resulting in many potential permutations of system configurations for such a suite of technologies. This Appendix will therefore attempt to outline the key technical considerations for a CCTV tiger monitoring system and discuss the pros and cons of the various technological options that might be employed to meet these requirements. To do this, the following sections consider the numerous components which must make up the tiger monitoring system. Note that it is beyond the scope of this assessment to choose any particular system, or define any technical specifications, as this will have subsequent implications for many other aspects of the system. Instead, the work will aim to identify requirements, put forward opportunities for achieving specific goals using various technologies, and discuss the effects these will have on the overall functionality of a tiger monitoring system. The main components of the system which must be considered include (1) the data network, (2) the CCTV cameras, (3) additional functions of the system which may be realised using both hardware and software solutions, (4) technologies for providing power to the tiger monitoring system and (5) methods for minimising direct attacks on the system and making it resistant to weather and water damage.

Data network The data network will form the backbone of system and the type of network employed will have a significant influence on its functional capability. First, image data (video streams) originating from a variety of locations will need to be communicated back to a central control station where it can be initially analysed, and possibly re-routed to a number of other terminals, for example in lodges where tourists can view the images. The areas covered by tiger reserves typically have no wired infrastructure which can be utilised to communicate information. The creation of a wired network

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through the installation of cables either under or above ground is unattractive for three main reasons: (1) a network of cameras spatially distributed over a relatively wide area would result in very high costs for the installation of cables, especially if fibre-optic cables are utilised instead of Cat5 or coaxial copper cables for high date-rate throughputs; (2) the impact on the environment would be significant either on the surrounding vegetation where cables are placed underground, or visually if the cables were in view above ground; and (3) the environmental impact and cost would be further inflated by the large distances between the control station and camera locations. A wireless network seems to be the obvious solution for the data network as it will allow the video data to be transmitted back to the control station in-air. Certain system functionalities such as the ability of the CCTV cameras to pan, tilt and zoom (see below) would require the network to support two-way communication so as to allow the system operator at the control station to take charge of these functions. The wireless network protocol utilised would be dependent on the on the data throughput required. In short, a highly functional system that also produced good-quality high-resolution video data would require a network protocol that provided a high data-throughput capacity and could draw upon a substantial power reserve. A relatively low-tech system with minimal functionality and compromised image quality would require a low data-rate network which could operate at low powers. One option could be to make use of the country’s 3G communication network (third generation mobile telecommunication). 3G networks provide a minimum data rate of 200 kilobits per second but are typically much higher depending on the actual standard used. There are numerous standards used in 3G networks and that used in the tiger reserve must first be assessed to determine the specification. However, the data rates associated with 3G networks should be able to sustain the transference of relatively high-quality colour images. Three challenges include (1) the need to have adequate coverage of the surveillance area via the 3G network (i.e. the wireless video transceiver stations located in the reserve need to be able to receive the 3G signal) suggesting that there should be one or more 3G cellsite towers within 20–30 km (approx) of the transceiver stations; (2) the necessity to provide the transceiver stations with sufficient power to maintain the uplink to its closest 3G cellsite tower; and (3) cooperation from the cell phone companies to customise a portion of their network for the CCTV cameras, which probably has an associated cost. A major benefit with this type of network is that a significant proportion of the power required for relaying the video data back to the control station comes from the cellsite towers which can be drawn from the main power grid. Additionally, the positioning of the CCTV cameras depends of the coverage area of the cellsite towers which are typically quite large (20–30 km). Another option for the data network is to create a wireless local area network (WLAN) based on the IEEE 802.11 standard (WiFi) (Dobkin, 2005). The data rates are again adequate to maintain a CCTV network capturing high-resolution video images. However, the addition of more cameras to the system would at

CCTV and ecotourism in Indian tiger reserves 193 some point result in the network reaching its critical mass (depending on the resolution of the images being transmitted back) and thus an additional separate data network would need to be created, doubling the network power requirement. However, future versions of WiFi such 802.11ac will, in theory, be able to support throughputs of 500 megabits per seconds, allowing the single network to support a host of CCTV cameras. Finally, the emergence of fourth generation (4G) communication technologies (Holma and Toskala, 2010) in recent years could provide the network backbone for the system. Current realisations of these include the IEEE 802.16 standard known as Worldwide Interoperability for Microwave Access (WiMAX) and Long Term Evolution (LTE). These technologies have been developed to deliver high data rate (80 megabits per second) connectivity over large distances of up to 50 km, and though the standards are yet to be finalised, there is expected to be a large take-up of these technologies in developing countries, especially in the more rural areas where it’s less feasible to install wired infrastructures.

The CCTV system There are many camera types available and, when choosing which to use, tradeoffs must be made to optimise a number of factors which include the lighting conditions; the physical area and distances which the camera will cover; the need for functions such as pan, tilt and zoom; and recording frame rates, cost, and the resolution required to resolve certain details in the images. Many of these factors also influence the quantity of digital data generated, which must then be transported over the network for viewing and storage. Numerous areas around the game reserve will need to be monitored, including watering holes and vehicle dirt-tracks, large areas of open savannah and the edges of dense forest. Each of these areas will have their own set of requirements in order that they are monitored optimally. It is therefore envisaged that the tiger monitoring system will employ an assortment of camera types and functionalities tailored to these requirements. In some cases, this could mean that more than one type of camera is sited at a location. Camera and lens options It would be essential to use a digital CCTV system rather than an analogue system. Both systems can achieve high-resolution picture quality, but issues regarding the data storage (tape versus hard disk drive), achievable frame rates, ability to record continuously and transfer the data via a wireless network all favour digital systems (Kruegle, 2006). The main features of a digital CCTV system adequate for use in a tiger reserve are summarised below. The resolution of a CCTV system determines the detail which can be delineated in images captured by a camera. The resolution is limited by both the camera and recorder used. The observable range is affected by the resolution as well as other factors such light levels, weather, lens type, etc.

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The principal aim of the CCTV system is to allow observation of tigers in their habitat. There is no need to resolve very small features in the environment or on the tiger, or visualise the tigers running. It is therefore recommended that minimum resolution and frame rate of 352 x 240 pixels and 40 frames per second respectively be used. Though monochrome cameras can accomplish the goal of visualising tigers, colour cameras would be desirable from the ecotourism perspective. However the downsides of colour are the higher cost, and increased data flow through the network. Monochrome cameras however are more effective in low-light conditions and can integrate with infrared (IR) systems (see below) for operation in extremely low light. For the highest cost, the advantages of both monochrome and colour cameras can be brought together using dual night and day cameras which operate in a colour during normal light conditions, before switching to monochrome as light levels decrease. CCTV cameras may also be equipped with pan, tilt and zoom. These functions would be operated from the control station using a keypad, joystick, etc., and (as described above) must make use of a two-way communication channel in data network. Because, the conditions for observing tigers in the wild are rather variable, the various camera options and systems parameters must be chosen to maximise the utility of the system in a given scenario. For example to monitor tiger activity over long periods and in small areas such as watering holes, one might use a standard resolution camera with dual day and night capability (colour, monochrome and IR, see below) and a low focal length to enable full coverage of the area without the need for pan, tilt and zoom functions. Alternatively, surveillance over large areas of the savannah calls for a medium to high resolution long focal length camera for clearer identification of the tigers at large ranges; pan, tilt and zoom functionality to counteract the narrow field of view, and possible positioning of the camera at a high altitude, for example, on a tree to increase the coverage. Additional functionality such as the integration of motion detectors (see below) may also be useful for automatically slaving the cameras pointing direction to specific areas of interest and provide a means of power saving (see below).

Additional system functionality This section outlines a number of other technologies that could be integrated into the main tiger monitoring system to either enhance its functionality, or improve its power efficiency. Infrared cameras Infrared (IR) cameras (Kruegle, 2006) are appropriate for use in conditions where very low levels of light make the use of colour or monochrome CCTV ineffective. In tiger reserves, this applies to the vast majority of areas at night. IR cameras consist of an assembly unit made up of IR light emitting diodes (LEDs) which work by flooding the view area with radiation having wavelengths between 1

CCTV and ecotourism in Indian tiger reserves 195 and 300 micrometers, which is invisible to the naked eye. The field of view depends on the angle of the IR beam, and it normally works over short ranges of up to around 8 meters. Furthermore, they are four to five times more expensive that standard CCTV cameras. Foliage-penetrating radar Areas of dense vegetation in a tiger reserve could act as a hindrance to the monitoring system by obscuring the observation of tigers by rangers and/or tourists. A foliage-penetrating radar (Davies, 2011) coupled to a CCTV camera could detect movements of large animals concealed beneath the forest cover and could direct a CCTV camera towards these areas (assuming pan, tilt, zoom capability). Passive radar sensing If a WLAN is chosen for the data network, there will be an abundance of communication signals in the surveillance area which could then also be used for perimeter monitoring. By placing a set of low cost receivers in the signal environment (maybe next to the communication modules) reflected Doppler-shifted Wi-Fi signals generated by moving animals could allow relatively high-precision detection and positioning (Chetty et al., 2009) to again direct where the cameras are pointed. Pattern recognition for tiger identification Conservation Research (Hiby, 2011) has created a computer programme based on pattern recognition techniques called ExtractCompare that creates a 3-dimentional model of a tiger’s unique coat pattern from existing images. It then uses this information to identify that tiger when its image is again captured at a later date. The software, which can be downloaded at no cost, has a 95 percent success rate in identifying tigers, and this should be improved as the number of tiger images in the library database increases. Power saving using motion detection As described below, the supply of power to the tiger monitoring system is of vital importance and every effort to optimise power usage should be explored. For a CCTV system that records video data continuously for long periods of time, one would expect to find that only a small fraction of this data contains tiger images. This highlights the inefficiency of the system; much of the power used to run the system generates little useful data. The motion detector systems based on the radar sensors described above consume much less power than other camera systems, and therefore could be used as triggers to turn on a specific camera when motion is detected and direct its focus to the area of interest. This would lead to a dramatic decrease in the overall power consumption of the system. From the ecotourism

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perspective, one camera left to record continuously so as to provide a constant direct live feed of a surveillance area to the viewing terminals could be beneficial. Power An adequate supply of power to the tiger monitoring system is critical. Obviously, the overall power consumption of the tiger monitoring system will depend on many factors such as number and types of CCTV cameras and other sensors chosen, the type of data network employed, the number of viewing terminals, etc. Though a full CCTV system will have to be customised to the particular reserve for which it will be deployed, in many cases, it should be possible to draw a considerable amount of power from the main grid. However, this solution may not be feasible for powering all the systems’ hardware components such as repeater modules, transceiver stations and remotely located cameras. In these situations, use of batteries and/or photovoltaic (solar) cells should be considered. Batteries are rather costly (depending on the type and rating) and have to be replaced at regular intervals, while solar cells are climate- and season-dependent, requiring a minimal number of sunlight exposure hours to be effective. Providing solar power to certain hardware components within the tiger monitoring system would first need a thorough assessment of the intensity and hours of sunlight in that particular reserve, as well as consideration of seasonal variations and possible siting locations in order to determine whether or not this would be a feasible solution. When drawing up a blueprint for the system, the designer must concurrently factor-in the power consumption ratings of the various proposed technologies and options for providing power to these (which will differ from case to case) and then make an assessment in terms of the cost versus capability trade-off. System protection The components of the tiger monitoring system which are located in-field should be sufficiently rugged to withstand the weather and water, and employ solutions to reduce the probability of damage through human attack. CCTV cameras for outdoor use are typically housed in domes for protection, and the degree of protection is designated by International Protection (IP) rating code which is expressed in the form IP XY (where X and Y are numerical digits that represent the level of dust and water protection respectively). For various values of dust (X) and water (Y) protection, Table 9.1 describes the level to which the system is safeguarded. For the climate and general weather conditions of tiger reserves in India, a minimum camera rating of IP65 is recommended. For cameras which are positioned in constant direct sunlight, fans within the camera dome for additional cooling might be needed. Communication modules (such as wireless repeaters and transceiver stations) are generally small and therefore could be housed in

CCTV and ecotourism in Indian tiger reserves 197 Table 9.1 International Protection (IP) X/Y ratings IP rating

Dust protection (X)

Water protection (Y)

1 2

Non-protected Protected against a solid object greater than 50mm, such as a hand

Non-protected Protected against dripping water

3

Protected against a solid object greater than 12.5mm, such as a finger

Protected against dripping water when tilted up to 15º

4

Protected against a solid object greater than 2.5mm, such as wire or a tool

Protected against spraying water at an angle of up to 60º

5

Protected against a solid object greater than 1.0mm, such as wire or thin strips

Protected against splashing water from any direction

6

Dust-protected. Prevents ingress of dust sufficient to cause harm

Protected against jets of water from any direction

7

Dust tight. No ingress of dust

Protected against heavy seas or powerful jets of water. Prevents ingress sufficient to cause harm

8

n/a

Protected against the effects of temporary immersion in water

9

n/a

Protected against the effects of continuous immersion in water

small ruggedized boxes. These would though have to be plastic so as not to act as Faraday cages which disrupt the communication signals. To minimise the possibility of human attack, a number of methods could be utilised to conceal the equipment. This could include positioning both the cameras and communication modules above a person’s natural field-of-view to minimise the chances of it being spotted; camouflaging the camera domes or plastic encasing boxes so they naturally merge into the tree trunks/leaves/rocks, etc.; and disabling LED’s on any of the hardware. Assuming that the cameras would be monitored on a 24-hour basis, any type of human attack should become instantly known, which would also facilitate catching poachers.

Summary This evaluation has attempted to outline a number of technical considerations, potential issues and available technology options for the implementation of a full tiger monitoring system. It is apparent that when designing such a system, it must be bespoke to the characteristics and features of the particular environment. Additionally, number of decisions would have to be made to balance the cost vs. capability tradeoff. The next step for this work is therefore to identify a suitable reserve in which to fully map out a CCTV monitoring system. As well as identifying tiger trails, watering holes, and other hot-spot locations to site different types of cameras, this will also involve a full cost–benefit analysis of

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deploying the proposed technologies from their purchase and setup overheads, to costs associated with running, maintaining, protecting and providing power to them.

Notes 1 During our fact-finding visit to Corbett, which is mentioned in the text, one of the reserve’s tigers which had claimed its sixth human victim the previous day was shot and killed by Forestry officers mounted on elephants. 2 Recent research has shown that when tigers share the same paths as people, they tend to walk these paths when people are absent at dusk and night-time (Carter et al., 2012). 3 Matthews (2008), the director of Travel Operators for Tigers (TOFT), has estimated that one famous tigress in Ranthambore has generated US$130 million in direct tourist revenue in the course of her adult life or nearly US$800,000 per year. 4 In fact, according to a recent newspaper story (Pioneer, 2011) observation towers are to be installed at the borders of Corbett Tiger Reserve to watch for poachers. 5 Thanks to Julie Viollaz for raising this issue and for rehearsing some of the arguments. 6 Sanjay Ghandi National Park in Mumbai is reportedly experimenting with camera traps that can email pictures wirelessly.

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CCTV and ecotourism in Indian tiger reserves 199 Higginbottom, K., Northrope, C. and Green, R. (2001). Positive Effects of Wildlife Tourism on Wildlife. Griffith University, Australia: CRC for Sustainable Tourism. Holma, H. and Toskala, A. (2010). WCDMA for UMTS: HSPA Evolution and LTE (5th ed). West Sussex, UK: Wiley. Karanth, K. U., Nichols, J. D. Seidensticker, J., Dinerstein, E. David Smith, J. L., McDougal, C., Johnsingh, A.J.T., Chundawat, R. S. and Valmik Thapar, V. (2003). “Science Deficiency in Conservation Practice: The Monitoring of Tiger Populations in India”. Animal Conservation. 6: 141–146. Karanth, K. U., Chundawat, R., Nichol, J. D. and Kumar, N. S. (2004). “Estimation of Tiger Densities in the Tropical Dry Forests of Panna, Central India, Using Photographic CaptureRecapture Sampling”. Animal Conservation. 7: 285–290. Karanth, K. U., Nichols, J. D. Kumar, N. S. and Hines, J. E (2006). “Assessing Tiger Population Dynamics Using Photographic Capture-Recapture Sampling”. Ecology. 87(11): 2925–2937. Kruegle, H. (2006). CCTV Surveillance: Video Practices and Technology (2nd ed.). Oxford, UK: Butterworth-Heinemann. Matthews, J. (2008). “Can India Learn from African Wildlife Tourism?” Sanctuary Asia. October 2008: 44–45. Mitra, B. (2006, August 15). “Sell the Tiger to Save It”. New York Times. Narain, S., Singh, S., Panwar, H. S. and Gadgil, M. (2005). Joining the Dots: The Report of the Tiger Task Force. New Delhi, India: Union Ministry of Environment and Forrest. Orams, M. B. (2002). “Feeding Wildlife as a Tourism Attraction: A Review of Issues and Impacts”. Tourism Management. 23: 281–293. Pennisi, L. A., Holland, S. M. and Stein, T. V. (2004). “Achieving Bat Conservation through Tourism”. Journal of Ecotourism. 3(3): 195–207. Pioneer, The. (2011, February 4). “Watchtowers Soon at Corbett”. Dehradun, India. http://cmsenvis.cmsindia.org/newsletter/enews/NewsDetails.asp?id=33990, accessed December 2, 2013. Project Tiger Directorate. (2006). Evaluation Reports of Tiger Reserves in India. New Delhi, India: Union Ministry of Environment and Forests. Sharma, S. and Wright, B. (2005). Monitoring Tigers in Ranthambhore Using the Digital Pugmark Technique. New Delhi, India: Wildlife Protection Society of India. Tisdell, C. and Wilson, C. (2002). Ecotourism for the Survival of Sea Turtles and Other Wildlife. Biodiversity and Conservation. 11: 1521–1538. Turcq, D. (2010, August 10). “How Many Jobs Can a Tiger Create?” Boostzone Institute. www.boostzone.fr/how-many-jobs-can-a-tiger-create-2/ Accessed January 30, 2012. Walpole, M. J. and Leader-Williams, N. (2002). “Tourism and Flagship Species in Conservation”. Biodiversity and Conservation. 11: 543–547. Walston, J., Robinson, J. G., Bennett, E. L., Breitenmoser, U., Fonseca, G.A.B., Goodrich, J. . . . Wibisono, H. (2010). “Bringing the Tiger Back from the Brink – the Six Percent Solution”. PLoS Biol. 8(9): e1000485. doi: 10.1371/journal.pbio.1000485

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Index

access points 14, 102, 104, 106, 112, 114, 117 African buffalo 134, 139 agent-based modelling (ABM): description of model 126; design for wildlife poaching 125–6; future work 149–50; general observations 148–9; key findings 146–7; limitations of 147–8; overview of 120, 122–3; preliminary results 143–5; see also agent-based modelling (ABM) system components agent-based modelling (ABM) system components: agents 131–2; agents and their environments 124–5; animal agent behavioural processes 135; animals in 133–6; environment 127–31; global and patch variables and parameters 129–30; poacher agent behavioural processes 138; poacher agent variables and parameters 137; poachers 136–40; ranger agent behavioural processes 142; ranger agent variables and parameters 141; rangers 140–2; snare and animal agent variables and parameters 133; snares 132–4 agents: in ABM system 131–2; animals as 133–6; and their environments 124–5; poachers as 136–40; rangers as 140–2 animal attractors 14, 27, 102, 105, 106, 112, 114, 117, 146 animal signs: in ABM system 131; tiger pugmarks 161, 174n2, 182, 186 animals: in ABM system 133–5; African 179; African buffalo 134, 139; barking deer 66; behavioural processes of 135; foraging behaviour of 6–10, 15, 23, 121; pangolin 66; samba deer 66; variables and parameters of 133; as victims 4–6; wild ox 66; see also animal attractors;

animal signs; parrots; poaching; rhinoceros; tigers area managers 5 Asia: price of rhino horn in 20; tigers in 154; uses of rhino horn in 19–20 automatic number plate recognition (ANPR) 76 Bandipur Tiger Reserve 180 barking deer poaching 66 bird markets, in South America 14, 45; see also Bolivia; Lima; parrot markets black markets, international 82 Body Shop in Malaysia 76 Bolivia: parrot markets in 45, 52, 54; parrot poaching in 48 borders: as access points 14, 27; clear marking of 13; heavy patrolling of 105; poaching in relation to 15, 27, 31, 33, 102 buffalo, African 134, 139 bushmeat hunters 103, 105 camera traps 172, 174n9, 181–2, 187, 198n6 catch-per-unit effort (CPUE) 90–1 Central-Place Foraging (CPF) theory 121 Chand, Sansar 165 Chetty, Kevin 180 China: market for rhino horn in 39, 40n7; tigers in 154 closed-circuit television (CCTV) camera study 15–16; expected results 184–5; findings 180–4; methods 180 closed-circuit television (CCTV) camera system: additional system functionality 194–5; camera and lens options 193–4; costs of 185, 190; data network for 191–3; power supply for 184, 196;

202

Index

proposal for piloting 177, 189; protection for 184, 196–7; technical requirements for 191–8 closed circuit television (CCTV) cameras: benefits from using 179, 186–9; monitoring of 185; visibility of tigers to 180; see also closed-circuit television (CCTV) camera study; closed-circuit television (CCTV) camera system community elders 5 complexity science 121 computer simulation see simulation modelling computer software 36–7, 67, 70, 85–7, 93, 126, 195 conservation 3, 15, 16, 18, 20, 37, 178, 185, 190 conservation managers 97 Convention on International Trade of Endangered Species of Fauna and Flora (CITES) 20, 22, 62–3 Corbett Tiger Reserve 161, 164, 180, 184, 189, 198n1, 198n4 Costa Rica, parrot ownership in 46 crime pattern theory 44, 121 crime prevention: by alerting conscience 11, 13; by individuals 4–5; by reducing arousal 11; see also poaching prevention Crime Prevention Studies 13 crime prevention through environmental design (CPTED) 10–11 crime triangles, basic 3–5 criminal opportunity see opportunity perspective criminology, environmental 10, 121 Cusco 54, 55 darting, from helicopters 25, 38 data: limitations of 9–10, 34–5; sources of 13, 173n1; tiger pugmark 161, 172, 174n2; see also data analysis; data collection; data network data analysis: in Market Reduction Approach 46; from QEPA 91–2; of ranger patrols 107–12 data collection: distance-based 93–4, 95–6; errors and falsifications in 86; by MIST software 87–8, 91; on poaching 98; in QEPA 93; ranger-based 83; time-based 93, 95–6, 98 data network, for CCTV system 183, 191–3 dehorning, of rhinos 38–9

demand reduction 74 disinformation, as anti-poaching strategy 77 drones 37 Dubey, Ajay 166 dye, adding to rhino horns 39 eCognition software 36–7 Ecological Software Solutions 85–7 ecology: effect on tiger population 156, 157, 164–5; simulation modelling in 124 economic incentives, for poaching 21 ecotourism: in Africa 179; in India 177, 179, 184, 189; see also tourism, on tiger reserves educational strategies 76, 149 efficiency: of law enforcement 97; of ranger patrols 102, 113–17, 118n1 elephants: poaching of 3–5, 45, 104; products from 36 emergent phenomenon 121–2 environment: in agent-based modelling 127–31; manipulation of 10 environmental criminology 10, 121 exits, screening of 11 facsimile models 123; see also simulation modelling feeder markets 52, 55 fences, in illicit parrot trade 46–7 fishing, illegal 108 flora and fauna, illicit trade of 46 foliage-penetrating radar 195 foraging: by animals 121, 130–1, 134–6; by poachers 35, 126; by rangers 35, 126 forest permeability, decreasing 76 Framework for Assessing the Management Effectiveness of Protected Areas 167 GPS units 14, 84–6, 91, 92, 115; data from 93; waypoints for 91 grazing, in ABM systems 130–1, 142 Groenewald Gang 22 guardians, role of 4–6, 8 gun control 77 guns: homemade 71–2; large calibre 33, 34; from RELA 65, 71–2, 77; used in tiger poaching 67; see also weapons gunshot detectors 37 habitat degradation 156 handlers 4–6 helicopters, used for darting 25, 38

Index highway accessibility, and parrot poaching 49–50 honey collection 108–9 human population, and parrot poaching 49 human pressure, effect on tiger populations 156, 165–8, 173 human settlements 102, 103, 104, 106, 112, 114, 117, 170–1 hunters: active vs. passive 9; bushmeat 103, 105; foreign 22; in Malaysia 64–5; see also hunting; poachers hunting: darting from helicopters 25, 38; data analysis of 94–5; pseudo-hunting 21, 22, 23; site selection 105; sustenance 103; with traps 105; see also guns; hunters; snaring methods; weapons hunting permits 20, 22 Ikatan Relawan Rakyat Malaysia (RELA) 65 illegal activities, documentation of 85 illicit parrot markets: demand for parrots in 52; effects on poaching 50; regional 51; relationship with crime 45–6, 55; types of 52–4, 55 Incidence Rate Ratio (IRR) 112 income generation, poaching as source of 21, 103, 106 India 15; tiger reserves in 154, 157, 177–9, 180 infrared cameras 194–5 International Protection (IP) ratings 184, 196–7 International Trade in Endangered Species Act 2008: Act 686 (Malaysia) 63 International Union for the Conservation of Nature (IUCN) 2–3, 63, 154 IUCN World Commission on Protected Areas (WCPA) 167 ivory trade, ban on 45 jambiyas (rhinoceros horn daggers) 20 Kalakad Tiger Reserve 180 Kazinga Channel 90 Kenya: poaching in 104; snare hunting in 105 Kigezi Wildlife Reserve 88 Kruger National Park (KNP) 13, 18, 24, 25–6; discouraging poaching in 38; distance between park borders and poaching incidents in 31; distance between roads and poaching incidents

203

in 30; distance between water pans and poaching incidents in 30; location of rhino poaching incidents in 32; number of rhinos poached in 2011, by month 27; number of rhinos poached in 2011, by moon phase 28; rhino poaching in 33, 34, 40; spatial distribution of poaching in 29; temporal distributions of poaching in 27–8 Kyambura Wildlife Reserve 88 Lake Edward 90 Lake George 88, 90 law enforcement monitoring (LEM) 83–4, 92, 93; with GPS units 84–6; in Uganda 86–7 law enforcement rangers 6 Lima, parrot poaching in 59n3 local markets, and parrot supply 48, 55, 56, 57–8 local residents, benefits to 179 Malaysia: 2008 hunting activity survey in 64–5; and CITES 62–3; ethnicities in 64–5; tiger poaching in 62, 67, 78; in India 154 Malaysian Forest Reserve 67 Malaysian National Tiger Action Plan 67, 70, 74, 76 Malaysian Volunteer Corps 65 management: of Indian tiger reserves 167–8; as risky facilities variable 156 Management Effectiveness Evaluation (MEE) 168 Management Information SysTem (MIST) software 70, 87–8, 92, 93, 99, 107 Mann-Whitney U Test 170, 171, 174n7, 174n8 Marginal Value Theorem (MVT) 121, 139 market data, on poached parrots 48 Market Reduction Approach (MRA) 46, 56 market sellers, of parrots 46–7 markets: feeder 52, 55; ivory 45; local 48, 55, 56, 57–8; regional 52, 55, 56 Matthews, Julian 166 Middle East, demand for rhino horn in 20 middle range theories 123 mist-nets 56 modifiable areal unit problem (MAUP) 109 moon phase, effect on poaching 28, 34, 35 motion detection devices 195–6 Mozambique 21, 31, 33; civil unrest in 38; monitoring border with 35–7

204

Index

Mudumalai Tiger Reserve 180, 182 MYCAT 76 Nagerhole Tiger Reserve 180 Nanda, Shashanka 166 National Environmental Management Authority 88 National Tiger Conservation Authority (NTCA) 157, 167, 174n6; tiger kill count 158 nature reserves 177; see also tiger reserves Naxalites 156, 165, 170 near distance analysis 29–31, 33 NTCA 167 offenders, poachers as 4–6 opportunity perspective: of crime 3–6, 44, 56; of poaching 13, 104 opportunity reduction measures 10–11 Orang Asli, in Malaysia 64–5, 66, 68, 70, 76 pangolin poaching 66 Panna tiger reserve 154 parrot counts, projected 49 parrot markets: absence of 54, 55–6; in Bolivia 45, 52, 54; data on 48; illicit 45; in Peru 45, 52–5; in South America 14, 45; variety of 47 parrot poaching 14; independent variables 50; strategies for reducing 56; see also parrot trade parrot range, and market availability 48 parrot species, density of 53 parrot trade: illegal 46–7; study of 48; see also parrot poaching parrots: endangered status of 46; market demand for 52; relationship between market species and ranges 51; relationship between range and market availability 51; species and ranges of 57–8, 59n4, 59n5; threatened species of 50 passive radar sensing 195 patrol efficiency analysis 97, 102, 113–17, 118n1 patrol intensity analysis 102, 112, 115, 116 patrols: anti-poaching 70; data on 14, 83–4; deployment of 106; increasing 35, 37; of protected areas 84, 85; in QEPA 91–3; see also patrol efficiency analysis; patrol intensity analysis

pattern recognition software 183–4, 195 patterns, in poaching activity 13 Peru: parrot markets in 45, 52, 53–4, 55; parrot poaching in 48 place managers 4–6 plants, illegal harvesting of 46, 88, 178 poachers: behavioural processes of 138; chemical 25; commercial 24–5; demographics of 31–2, 33–4, 66; difficulties in tracking 102; foraging for animals by 6–10, 35; in ABM systems 136–40; location of 103–6; modus operandi of 9, 13–14, 23, 24, 25–6, 38, 62, 64, 66, 103–6, 109, 117; motives of 34, 68–9, 103; as offenders 4–6; of parrots 46; posing as tourists 38; profit to from rhino horns 21; rewards and risks of 68, 121; variables and parameters of 137; see also hunters; poaching; tiger poachers poaching: and ABM studies 125; active 125; categories of 108; chemical 25 ; commercial 24–5 ; ‘commuter’ 77; defined 1–2; ecology of 120–2; economic incentives for 21; of ivory 3–5; by locals 77; opportunities for 6–11; passive 125–6; patterns in 13; in QEPA, 93; rationalizations for 34; and situational crime prevention (SCP) 10–13, 14, 15; skilled 25; spatial patterns of 120–1; subsistence 24, 82; of tigers 63; in Uganda 88; see also poachers; rhinoceros poaching; tiger poaching poaching prevention: by decreasing forest permeability 76; by decreasing response time 37; by using disinformation 77; incentives for 177; by increasing effort needed 11, 12, 18, 34, 37–9, 75–6; by increasing gun control 77; by increasing risk factors 11–12, 18, 35–7, 70, 75; by monitoring vehicle activity 38, 76; non-enforcement strategies 149; by reducing demand 74; by reducing rewards 10–11, 18, 34, 38–9, 40n7, 75, 76; by removing excuses for 11–12, 13, 34, 75, 76; by removing provocations for 11–12, 34, 75, 76; by removing targets 11; by removing temptation 11; shoot-to-kill policies 40n5; by strengthening surveillance 11 poison, adding to rhinoceros horns 39

Index Poisson regression model 109–12 power supply, for CCTV system 184, 196 Problem Analysis Module (PAM) 14, 62, 66–7, 79; defining the problem 67; offender tools 71–2; offenders 68–71; targets 72–3 Problem Analysis Triangle 4–5, 68 Problem-Oriented Policing 67 Project Tiger 178 protected areas (PAs) 83, 84, 85, 88; defined 2–3; patrolling 97–8; Uganda 86–7; see also Queen Elizabeth Protected Area (QEPA) Protection of Wildlife Act 1972 (Malaysia) 63 pseudo-conservation 21–2 pseudo-hunting 21, 22, 23 Puerto Maldonado 54, 55 pugmarks 161, 174n2, 182, 186 Quantum GIS (QGIS) 109 Queen Elizabeth National Park (QENP), Uganda 14, 15, 82–3, 88, 125; in ABM system 127, 128; illustration of patterns of simulated poaching activity 144 Queen Elizabeth Protected Area (QEPA) 92, 93; analysis of data from 114, 117; bushmeat poached from 103; map 89; patrol analysis in 102; poaching in 97, 105; ranger patrols in 107–12; as study site 88–90, 98; wildlife management on 90 radar, foliage-penetrating 195 radio collars 187–9 rainfall, in ABM systems 131 random grid searches 116 ranger patrols see patrols rangers: in ABM systems 140–2; behavioural processes of 142; foraging for poachers by 6–10, 23, 35; as guardians 6, 8; increasing patrols of 35, 37; objectives of 140; observations of 108–9; in QEPA 90; spatial decisions of 116–17; tracking of poachers by 106; in Uganda 14; variables and parameters of 141 Ranthambore Tiger Reserve 180 rational choice perspective 10, 44, 124 regional markets 52, 55, 56 remote sensing 36–7 response time, decreasing 37

205

rhinoceros: dehorning of 38–9; population 18, 23; translocation of 39 rhinoceros horns: conspicuous consumption of 19–20; daggers made from 20; illegal sources for 21–2; markets for 18–20, 39, 40n7; medicinal uses of 19; products from 13–14, 36; stockpiles of 22, 23; value of 20–1 rhinoceros poaching 13–14, 21, 23–5; categories of 24; increase in 18, 23–4; limited data on 34; location quotients for 27, 28–9; modus operandi of 24–6, 38; near distance analysis 29–31, 33; spatial distribution of 29, 35;’ temporal analysis 27–8, 34; see also poaching risky facilities analysis 15, 154–5; failure of 155–7, 172 rivers, as animal attractors 14, 15, 102, 112, 117 routine activity approach 10, 44, 66, 121 Russia, tigers in 154 Sabie Sabie private reserve 28 samba deer poaching 66 Sanjay Ghandi National Park 198n6 Sariska tiger reserve, crisis in 177–9 satellite imagery 36–7 Segur Plateau (Nilgiri North Forest) 182 shoot-to-kill policies 40n5 silent victim problem 9, 85, 102, 106–7, 108 simulation modelling 15, 116, 122–4 situational crime prevention (SCP) 10–13, 34, 62,74; categories of 11–13 snares, in ABM systems 132–4 snaring methods 15, 24, 38, 67, 77, 95, 105, 115, 125–6, 139, 146–7, 149 social systems, study of 121 South Africa: increased poaching in 39–40; rhinoceros poaching in 18; rhinoceros population in 18, 23; see also rhinoceros poaching South African Police Service (SAPS) 27, 31; Endangered Species Protection Unit 23 South America, parrot market in 14, 45 Southeast Asia 14; see also Malaysia spatial autocorrelation 116 spatial autocovariate Poisson regression 109–13, 116, 117 Spearman’s Rho correlation 168–9, 174n7

206

Index

‘stings’ 45 surveillance, strengthening 11 sustenance hunting 103 Taiwan, price of rhino horn in 20–21 targets, removing 11 technology: limitations of 84–6; use of 14; see also closed-circuit television (CCTV) cameras; computer software; GPS units; unmanned aerial vehicles (UAVs) theft 21; see also poaching tiger parts: market for 63, 74; uses for 19, 63 tiger poachers: demographic information about 68; details about 78 tiger poaching 14, 15, 154–7; available data 64, 66, 77–8; in Malaysia 62; strategies for reducing 75–9; unreliable data on 157, 172; see also poaching tiger population study: analysis and results 168, 170; comparison between reserves 171; correlation matrix of tiger variables 169; ecological variables 164–5; human pressure in 165–8, 173; overview of design 159; tiger density calculation 161, 164; variable definitions and sources 159–60 tiger populations 154: analysis of 157–9; on tiger reserves 162–3, 168; in Malaysia 63; see also tiger population study tiger reserves (TR) 154; buffer zones around 167; improved deployment of manpower on 187; in India 154, 157, 177–9, 180; incentives for management and guards 186; management of 157, 159, 167–8, 170; population density comparison 162–3, 168; tourist operations on 15–16, 166–7, 171–2, 173, 177, 179, 184–5, 189–90 tiger skins 63, 154 Tiger Task Force (TTF) 156, 165 tigers: counting with CCTV cameras 186; illegal killing of 154–5, 156, 157, 158; protection from poachers 187; radio collars on 187–9 time/distance index, interpretation of 95 Tobler’s First Law of Geography 110 tourism, on tiger reserves 15–16, 166–7, 171–2, 173, 177, 179, 184–5, 189–90 trade bans, on rhinoceros products 20 traditional Chinese medicine (TCM) markets 19, 21, 39, 154

TRAFFIC South East Asia 62, 63, 64, 66, 74, 76 trails, in ABM systems 131 traps, hunting with 105; see also snaring methods Travel Operators for Tigers (TOFT) 166, 198n3 triangulation, of gunshot locations 37 Tribals 156, 165–6, 170–1, 173 triple foraging 6–10, 15, 103–4, 122 Uganda: analysis of poaching in 95–6; law enforcement monitoring in 83–4; poaching in 14–15, 88; research in, 98 Uganda Wildlife Act 86, 150n1 Uganda Wildlife Authority (UWA) 86, 107, 109 undercover buy operations 45 Universal Transverse Mercator (UTM) information 86 unmanned aerial systems (UASs) 37 unmanned aerial vehicles (UAVs) 37 urban crime 11, 13, 121, 124 urban police forces 62 uRNG 134–5, 136, 137–8 vehicle activity, monitoring 38, 76 victims: animals as 4–6; silent 9, 85, 102, 106–7, 108 Vietnam: market for rhino horn in 39, 40n7; rhino hunters from 22 villages: as possible poacher habitat, 14, 15; inside reserves 64, 90; in simulation modelling 143, 145, 147 water sources: in ABM simulation 128–31, 133, 134, 136, 138, 143, 149–50; as animal attractors 14, 15, 29, 73, 105, 106, 181; as patrol intensity variable 112–14; proximity of poaching to 27, 30, 31, 33, 67, 102, 109, 117, 125, 146, 147 weapons: large calibre 33, 34; used by poachers 33, 34, 63, 72–3, 105, 125 weather conditions, in ABM systems 128, 130–1 white rhinos 18, 20; see also rhinoceros wild meat restaurants 63 wild ox hunting 66 WildAid 76 wildlife, defined 1–2 Wildlife Conservation Act 2010: Act 716 (Malaysia) 63

Index wildlife crimes 45, 82, 98 wildlife markets, illicit 45 wildlife products 10, 82 Wildlife Protection Society of India (WPSI) 156–7, 166; tiger kill count 158 World Wildlife Fund (WWF) 63, 76

207

World Wildlife Fund (WWF)-India 156, 180, 182 Wright, Belinda 166 Yemen, rhino horn sales in 20 Zambia 128–9 Zimbabwe, rhino protection in 39