LAND USE AND FOREST COMPOSITION EFFECTS
ON THE ECOLOGY OF VECTOR-BORNE AND ZOONOTIC DISEASE
by
Micah Brianne O’Brien Hahn
A dissertation submitted in partial fulfillment of
the requirements for the degree of
Doctor of Philosophy
(Epidemiology / Environment & Resources)
at the
UNIVERSITY OF WISCONSIN-MADISON
2013
Date of final oral examination: 2/13/13
The dissertation is approved by the following members of the Final Oral Committee: Jonathan A. Patz, Professor, Nelson Institute for Environmental Studies & Population Health Sciences Ronald E. Gagnon, Professor, Population Health Sciences Tony L. Goldberg, Professor, Nelson Institute for Environmental Studies & Pathobiological Sciences Mutlu Ozdogan, Assistant Professor, Nelson Institute for Environmental Studies & Forest Ecology Monica G. Turner, Professor, Zoology Jonathan H. Epstein, Associate Vice President, EcoHealth Alliance
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© 2013
Micah B. O’Brien Hahn All Rights Reserved
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ABSTRACT
LAND USE AND FOREST COMPOSITION EFFECTS
ON THE ECOLOGY OF VECTOR-BORNE AND ZOONOTIC DISEASE
Micah Brianne O’Brien Hahn
Under the supervision of Professor Jonathan A. Patz
At the University of Wisconsin-Madison
Emerging infectious diseases (EIDs) caused by newly recognized, resurging, and evolving pathogens are a significant threat to global public health and biodiversity. Interactions between social, genetic and biological, physical environmental, and ecological conditions facilitate disease emergence. Understanding these complex relationships is one of the major challenges facing humankind today. The majority of EIDs are zoonotic (originating from a non-human source), and the incidence of these and vector-borne diseases is rising.
Consequently, the most effective strategies for the control and prevention of EIDs will likely arise from interdisciplinary research that explicitly addresses the relationships between human, animal, and ecosystem health. This dissertation aims to understand the impact of land use and forest composition on the ecology of two diseases: Nipah virus encephalitis in Bangladesh and malaria in the Brazilian Amazon.
Chapter 1 provides an introduction to the disciplinary methods and assumptions that were used in this dissertation as well as background on the epidemiology of Nipah virus
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ii encephalitis and malaria. The following three chapters each present an independent body of work on the ecology of Nipah virus encephalitis and malaria, and finally, Chapter 5 summarizes the major findings, policy implications, and areas of future research.
Chapter 2 presents a landscape case-control study to identify the role of landscape composition and configuration on Nipah spillover risk and the roosting ecology of the natural reservoir of Nipah virus, Pteropus giganteus (the Indian flying fox). All Nipah outbreaks in
Bangladesh have been clustered both seasonally (December-April) and spatially
(central/northwest Bangladesh, now known as the “Nipah Belt”). The results of this analysis show that there is less forest cover in Nipah Belt villages compared to communities in the rest of the country, and the remaining forest in this area is highly fragmented. Additionally, the number of reported bat roosts (active and occupied in the last 5 years) is higher in the Nipah
Belt. Villages with at least one roost with Polyalthia longifolia or Bombax ceiba trees were more likely to be case villages, and the risk of being a case village increases as canopy cover in
P. giganteus roosts decreases. This study demonstrates that the degree of forest fragmentation, forest composition, and Pteropus roosting structure may play a role in Nipah virus spillover risk in Bangladesh.
Chapter 3 focuses on understanding the roosting behavior and habitat selection of the fruit bat reservoir of Nipah virus. This study described the characteristics of P. giganteus roosts and identified suitable roosting habitat across Bangladesh using ecological niche modeling. Results show that P. giganteus prefer particular tree species and taller, wider, canopy trees for roost sites. They form larger colonies when roosting in areas with dense forest canopy cover. Roosts were located in areas with low annual precipitation and high human
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P. giganteus.
Chapter 4 turns to the Brazilian Amazon and assessing the impact of deforestation, roads, selective logging, and fire on the risk of malaria. Resource extraction in the Amazon alters the forest ecosystem and brings non-immune workers into contact with Anopheles darlingi, the primary malaria vector in Brazil. Previous research has shown the impact of deforestation on increasing mosquito breeding sites, but the effect of other forest disturbances has not been assessed. The results of this study show that roads, forest fires, and selective logging are previously unrecognized risk factors for malaria incidence in the Brazilian
Amazon. Malaria control efforts in Brazil would benefit from continued monitoring and regulation of deforestation and initiation of these activities for sub-canopy forest disturbances.
The concluding chapter addresses the policy implications of this research and disease ecology studies more generally. Here I argue that research that addresses upstream risk factors such as the role of landscape on the ecology of disease reservoirs and vectors presents the greatest potential for effective population health interventions and present several examples. I also address recommendations for future research and priorities for the prevention and control of EIDs, in particular, Nipah virus encephalitis and malaria.
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ACKNOWLEDGEMENTS
The work presented in this dissertation would not have been possible without the support and guidance of many people and organizations.
I want to thank the faculty at Brandeis University whose passion for their subjects, engaging teaching style, and holistic view of global public health and environmental sustainability inspired me to seek a career where I could learn more about the intersection of the two. In particular, I want to thank Dan Perlman, Sarita Bhalotra, Peter Conrad, and Kosta
Tsipis. I would also like to thank Jonathan Epstein for taking time out of his day to enthrall a group of college seniors with stories about his work on SARS. His narrative set my pursuit of a career in conservation medicine into motion.
I could not have asked for a better introduction to the world of public health than I received at Rollins School of Public Heath at Emory University. My sincerest gratitude to all of the faculty members who made Rollins such a steadfast place to learn. Special thanks go to
Paige Tolbert, Christine Moe, Deb McFarland, and Anne Reiderer who were outstanding teachers, supporters, and exceptional role models. Thanks also goes to Anne for her critical eye reviewing my thesis. It is not possible to express in words the respect and gratitude that I have for Stan Foster. His never-ending enthusiasm for teaching and learning has inspired countless students to dedicate their lives to improving global public health. His confidence in me has given me the courage to pursue many challenges I may not have otherwise. I count myself lucky to have known him.
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I would like to thank the staff and faculty at the Nelson Institute for Environmental
Studies at the University of Wisconsin-Madison. This is truly a center of excellence, and I cannot express appreciation enough for its support of my joint degree. I could not have navigated the university paperwork and protocols without the help and patience of Jim Miller,
Tara Mohan, Martina Gross, Elizabeth Appelt, and Quinn Fullenkamp. Thanks also goes to the faculty in the department of Population Health Sciences, specifically Leonelo Bautista, Paul
Peppard, and Mari Palta. I learned much from their challenging course work and their demand for precision.
My doctoral career was enriched and made infinitely more enjoyable by my affiliation with the Center for Sustainability and the Global Environment (SAGE). Standing on the outside of the Enzyme Institute, one would never guess how much cutting-edge, creative, and interdisciplinary research was emerging from within its offices. SAGE provided a wonderful working environment and a rich community of smart, friendly, and interesting scholars who were always willing to learn more about my research, provide feedback, or collaborate. Mary
Sternitzky is the computer genius who held the fate of my dissertation in her hands on several occasions. Thank you. George Allez deserves warm thanks for applying his mastery of the
English language to my writing throughout my time at UW. Thank you also to Carol Barford,
Tracey Holloway, Annemarie Schneider, and Holly Gibbs for being such supportive female role models.
My time in Bangladesh was an experience I will always remember. I would like to thank the unbelievably hospitable staff at the International Centre for Diarrheal Disease
Research, Bangladesh (icddr,b) for their scientific support and good humor. In particular, I
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vi want to thank Dr. Hossain MS (Shovon) Sazzad whose willingness to help at any time of day and his infectious good nature made my work so much easier. My gratitude extends also to
Ausraful (Rajib) Islam for taking me on many adventures, introducing me to the delights of
Bangladeshi street food, and for more cups of cha than I can count. Thanks also to the rest of the veterinary team – Najmul Haider, Ireen Shanta, Minhaj Hannan, Touhid Khan, and Mamun
Bhuyan for always inviting me to lunch at NIPSOM and letting me join the field team in
Hakaluki. Thank you to Meghan Scott for being a wonderful office mate who was always willing to listen to my latest tribulations or kitten-sit on a moment’s notice. I appreciate greatly
Dorothy Southern and Andrea Mikolon for always taking a moment to say hello and offer me their tried-and-true cat advice. Thank you to Nizam Uddin Chowdhury, Zaheer Iqbal, and
Shahidul Islam for sharing data and inviting me into their respective organizations.
Many people made the study and the field work in Bangladesh possible including Md.
Ariful Islam, Golam Dostogir Harun, A.K.M. Dawlat Khan, Salah Uddin Khan, and Sonia
Hegde. No bigger thanks can be given to anyone but our dedicated field team who worked through an amazingly cold winter, dodged bat urine, counted thousands of date palm trees, and subsisted on bananas and biscuits for many meals.
Thank you to Steve Luby for having faith in my ability as a researcher and for allowing me to join his research staff for a short time. It was such a privilege to work at icddr,b alongside the staff who have such obvious respect for him. I sincerely hope we cross paths again in the future. I am grateful to Emily Gurley for the time she spent reviewing my writing, thinking about my research, and providing detailed feedback and suggestions for improvement.
I have taken her critiques to heart and am a better scientific writer as a result.
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I am indebted to Ariful Islam, his wife Sonia, their beautiful son Alvi, and their extended family for taking me on as family member during my time in Bangladesh. They were always understanding of my need to bury myself in my computer but made sure that I tried every Bangladeshi dish at least once.
Thank you to my co-authors, Christovam Barcellos and Gregory Asner for making their malaria and land cover data available for my use and for their valuable feedback.
Funding from the National Science Foundation CHANGE-IGERT grant made my research possible. Additionally, I think my participation in the CHANGE program made me a more thoughtful scientist, and I thank Rob Beattie and Carmela Diosana for their mentorship.
Also thank you to my CHANGE colleagues who challenged me think outside the bounds of my quantitative comfort zone.
I thank the Patz lab family who came before me for welcoming me into the fold.
Thanks to Sarah Olson, Jill Baumgartner, Chris Uejio, Megan Christenson, and Clara Arias.
My current lab mates – Maggie Grabow, Vijay Limaye, Jason Vargo, Melissa Hatch, Valerie
Stull, Suzanne Gaulocher, and Selamawit Zewdie – are the small group of people who were always willing to listen to a practice talk, review a manuscript, or just listen to me vent. I am so lucky to have them as colleagues, and I look forward to working with them for the rest of my career.
Sincere thanks to Jonathan Patz, my advisor, for his unconditional support, life coaching, and encouragement to strike a balance between work and my personal life. His ability to put things in perspective helped me through many difficult situations. My committee
– Mutlu Ozdogan, Tony Goldberg, Monica Turner, Ron Gangnon, and Jonathan Epstein – were
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Immense thanks go to my extended Madison family who made my time in this beautiful, bikeable Midwestern city such a memorable time in my life. In addition to those I have mentioned above, I would also like to extend my love to Anna Zeide and Justin Horn,
Ariana and Andrew Stuhl and Baby Everett, Lisa Sudmeier and Jesse McDaniel, Jack
Buchanan, Menachem Tabanpour, and our wonderful neighbors and friends on East Mifflin
Street.
Nathan Ottinger has heard more about research, my aspirations, and my experiences completing this work than anyone else. I am grateful for his company and friendship over many years. Thank you to my sisters, Avital and Mara O’Brien Hahn, for always taking my side, no matter the situation. Finally, my love and gratitude to my parents, Jan Hahn and
Heather O’Brien, for checking in on me every week, reading and editing drafts of my work, sending me care packages, and assuring me that there is no challenge beyond my capacity to surmount.
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TABLE OF CONTENTS
ABSTRACT…………………………………………………………………………………...………………....….i ACKNOWLEDGEMENTS………………………………………………………………………..…………...…iv TABLE AND FIGURE DIRECTORY…………………...……………………………………………………..xiii
CHAPTER 1: INTRODUCTION ...... 1!
1.1! Motivation ...... 1!
1.2! Objectives ...... 3!
1.3! The Disciplinary Landscape ...... 4!
1.4! The Epidemiology of Nipah Virus Encephalitis ...... 10!
1.4.1.! Foodborne transmission ...... 11! 1.4.2.! Domestic animal transmission ...... 12! 1.4.3.! Person-to-person transmission ...... 13! 1.4.4.! Direct transmission ...... 14! 1.4.5.! Directed acyclic graph of risk factors for Nipah transmission ...... 14! 1.4.6.! Questions that remain ...... 16!
1.5! Resource Selection: Drivers and Methods for Evaluation ...... 18!
1.5.1.! Pteropus habitat selection at multiple scales ...... 18! 1.5.2.! Methods for evaluating habitat selection ...... 19! 1.5.3.! Assumptions of habitat selection models ...... 21!
1.6! Epidemiology of Malaria ...... 23!
1.6.1.! Malaria eradication and control efforts ...... 25!
1.7! Malaria in Brazil ...... 27!
1.7.1.! Land use change in Brazil ...... 29! 1.7.2.! Impact of land use change on malaria ...... 31!
1.8! Summary ...... 33!
References ...... 35! Tables ...... 47! Figures ...... 51!
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CHAPTER 2: THE ROLE OF LANDSCAPE COMPOSITION AND CONFIGURATION ON PTEROPUS GIGANTEUS ROOSTING ECOLOGY AND NIPAH VIRUS SPILLOVER RISK IN BANGALDESH ...... 60! 2.1! Introduction ...... 61!
2.2! Methods...... 62!
2.2.1.! Locating study villages and conducting roost ecological assessment ...... 63! 2.2.2.! Satellite data derivation ...... 63!
2.3! Data Analysis ...... 64!
2.3.1.! Assessing tree species composition of roost sites ...... 64! 2.3.2.! Comparing characteristics of roosts sites and villages in the field-validated sample ...... 65! 2.3.3.! Comparing human population and village landscape characteristics in the remotely-sensed sample ...... 66!
2.4! Ethical Considerations ...... 66!
2.5! Results ...... 66!
2.5.1.! Comparing characteristics of roosts sites and villages in the field-validated sample ...... 67! 2.5.2.! Identifying environmental risk factors for Nipah virus spillover ...... 68! 2.5.3.! Assessing relationships between P. giganteus roosting ecology, forest structure, and human population density ...... 69! 2.5.4.! Comparing human population and village landscape characteristics in the remotely-sensed sample ...... 69!
2.6! Discussion ...... 70!
Acknowledgements ...... 75! References ...... 76! Tables ...... 79 Figures ...... 83!
CHAPTER 3: ROOSTING BEHAVIOR AND HABITAT SELECTION OF PTEROPUS GIGANTEUS IN BANGLADESH REVEALS POTENTIAL LINKS TO NIPAH VIRUS EPIDEMIOLOGY ...... 85! 3.1! Introduction ...... 86!
3.2! Study area ...... 88!
3.3! Materials and Methods ...... 89!
3.3.1.! Sample selection and locating roosts ...... 89! 3.3.2.! Measuring roosting behavior and structural characteristics of roost sites ...... 90! 3.3.3.! Derivation of remotely-sensed roost site characteristics ...... 90!
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3.3.4.! Statistical analysis ...... 92! 3.3.4.1! Roost tree selection ...... 92! 3.3.4.2! Assessing roosting behavior in relation to environmental characteristics of roosts ...... 93! 3.3.4.3! Roost site selection: maximum entropy modeling ...... 94!
3.4! Results ...... 95!
3.4.1.! Roost tree selection ...... 95! 3.4.2.! Roost plot characteristics ...... 96! 3.4.3.! Roosting behavior ...... 97! 3.4.4.! Roost site selection: maximum entropy modeling ...... 98!
3.5! Discussion ...... 101!
Acknowledgements ...... 107! References ...... 108! Tables ...... 114! Figures ...... 120!
CHAPTER 4: INFLUENCE OF DEFORESTATION, LOGGING, AND FIRE ON MALARIA IN THE BRAZILIAN AMAZON ...... 124! 4.1! Introduction ...... 125!
4.2! Study Area ...... 127!
4.3! Methods...... 128!
4.3.1.! Human population data and pre-processing ...... 128! 4.3.2.! Land cover data and pre-processing ...... 129! 4.3.3.! Negative binomial regression modeling ...... 130!
4.4! Results ...... 131!
4.4.1.! Human population and land cover characteristics of municipalities ...... 131! 4.4.2.! Impact of forest disturbance on malaria in the Legal Amazon ...... 132! 4.4.3.! Impact of forest disturbance on malaria within the timber production states ...... 133!
4.5! Discussion ...... 134!
Acknowledgements ...... 139! References ...... 140! Tables ...... 143! Figures ...... 146!
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CHAPTER 5: CONCLUSIONS ...... 148!
5.1! Summary ...... 149!
5.2! Policy Implications of Disease Ecology Research ...... 151!
5.3! Recommendations for Future Research ...... 155
References ...... 158!
Appendix A: Ecological Risk Factors for Nipah Virus Spillover Field Training Manual ...... 160! Appendix B: Roost Site Questionnaire ...... 166! Appendix C: Roost Site Environmental Assessment Protocol ...... 173!
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TABLE AND FIGURE DIRECTORY
LIST OF TABLES
1.1. Summary of bat resource selection literature………………………………………………………………….47
1.2. Movement and habitat selection processes in relation to spatial scales and structures……………………….50
2.1. Characteristics of laboratory-confirmed Nipah spillover case villages and control villages…………………79
2.2. Multivariate logistic regression models showing the odds of being a Nipah spillover village……………….80
2.3. Correlation matrix of characteristics of villages from field-validated study sample………………………….81
2.4. Characteristics of Nipah spillover case villages and 20,000 remotely-sensed control sites…………………..82
3.1. Derived variables, units, year of raw data acquisition, original resolution and satellite and source of remotely-sensed data………………………………………………………………………………114
3.2. Characteristics of Pteropus giganteus roost trees versus non-roost trees and roost sites versus random comparison sites……………………………………………………………………………………………...115
3.3. Pteropus giganteus roost tree selection based on use of a species as a roost tree versus prevalence of the species in surveyed roosts…………………..………………………………………………………………..116
3.4. Diagnostic tree species identified in indicator species analysis……………………………………………...117
3.5. Duration of Pteropus giganteus roost occupation, roost seasonality, and size of roost colony by classification of P. giganteus roost site tree species composition…..…………...…………………………..118
3.6. Mean, range, and percent contribution of land cover, climate, and human disturbance variables to the habitat suitability model for Pteropus giganteus in Bangladesh………………………………………...119
4.1 Human population and land cover characteristics of municipalities………………………………………...143
4.2 Effect of forest disturbance on malaria incidence in the Legal Brazilian Amazon………………………….144
4.3 Effect of forest disturbance on malaria incidence in the timber production states…………………………..145
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LIST OF FIGURES
1.1. The disciplinary landscape…………………………………………………………………………………….51
1.2. Directed acyclic graph of Nipah virus transmission in Bangladesh…………………………………………..52
1.3. Location of human Nipah virus spillover cases in Bangladesh, 2001-2011…………………………………..53
1.4. Spatial distribution of Plasmodium falciparum malaria endemicity worldwide in 2010……………………..54
1.5. Spatial distribution of Plasmodium vivax malaria endemicity worldwide in 2010…………………………...55
1.6. Global distribution of malaria, ~1900-2002…………………………………………………………………..56
1.7. Number of slide-confirmed malaria episodes recorded yearly in Brazil, 1960-2007…………………………57
1.8. Areas of malaria transmission in Brazil in 2000 and 2008……………………………………………………58
1.9. Annual and cumulative area deforested in the Brazilian Amazon, 1970-2012……………………………….59
2.1. Location of field-validated study villages…………………………………………………………………….83
2.2. Comparison of human and bat population indicators, P. giganteus roost characteristics, and village metrics in Nipah case and control villages……………………………………………………………………84
3.1 Location of all study villages, roosts, and available sites……………………………………………………120
3.2 Predicted probability of suitable conditions for Pteropus giganteus in Bangladesh with location of human Nipah spillover cases from 2001-2011……………………………………………………………121
3.3 Amount of habitat suitable for Pteropus giganteus at varying probability thresholds………….…………...122
3.4 Predicted probability of suitable conditions for Pteropus giganteus roosts in Bangladesh using variable buffer sizes around study villages…..………………………………………………………………123
4.1 Map of the Legal Brazilian Amazon and the five timber production states………………………………....146
4.2 Maps of forest disturbance and malaria incidence in the Brazilian Amazon………………………………..147
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1CHAPTER 1 ! Introduction
1.1 Motivation
Emerging infectious diseases (EIDs), including newly recognized pathogens and infections that are increasing their incidence or geographic range (Morens et al. 2004), are a global burden that have shaped the course of human history (Morens et al. 2004) and caused significant morbidity, mortality, and economic losses (Aguirre et al. 2012; Daszak et al. 2000;
Morens et al. 2004; Smolinski et al. 2003). For example, in 2003, a previously unrecognized virus, later identified as the coronavirus that causes severe acute respiratory syndrome (SARS), infected around 10,000 people, and killed approximately 1,000 (Smith 2006). The fear associated with this outbreak led to an economic impact estimated at US$ 30-100 billion (Smith
2006). Further investigation of the natural reservoir of the coronavirus implicated bats (Li et al.
2005), likely brought into contact with people and other animals at Chinese wet markets
(Perlman and Netland 2009).
West Nile Virus (WNV) was first discovered in Uganda in 1937, but it gained international recognition when it was detected in New York City in 1999 (Petersen and Hayes
2004). Since then, WNV has spread westward across the United States (Kilpatrick et al. 2006b;
Petersen and Hayes 2004). The 2012 season was the most severe since 2003 with over 5,300 reported cases to date from 48 states (Centers for Disease Control and Prevention 2012a). The timing of previous WNV outbreaks has been linked with the migration of American robins
(Turdus migratorius), the most important host for the virus in the United States (Kilpatrick et
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2 al. 2006a, 2006b). When robins migrate, the mosquito vectors shift their feeding preferences to humans, causing a spike in WNV infections (Kilpatrick et al. 2006a).
In 1993, the Sin Nombre virus caused a small but highly lethal outbreak of hantavirus pulmonary syndrome (HPS) in the southwestern United States (Yates et al. 2002). Trends in
HPS outbreaks have been linked to a time-lagged cycle of precipitation, followed by increased vegetative breeding habitat for the deer mouse reservoir of the virus, and eventually a boom in the deer mouse population. This in turn leads to an increased likelihood of transmission to humans (Yates et al. 2002). More recently, researchers found that the prevalence of Sin
Nombre virus in deer mice was highest in forests that were significantly disturbed due to sudden aspen decline (Lehmer et al. 2012). Another highly publicized outbreak in Yosemite
National Park in 2012 brought HPS into public notice once again (Centers for Disease Control and Prevention 2012b).
These examples are illustrative of the four concepts that motivated my work in this dissertation: 1) EIDs will present a perpetual challenge to humanity as we continue to alter the global ecosystem (Morens et al. 2004), 2) social, genetic and biological elements, the physical environment, and ecological factors converge to modify the human-microbe interface leading to disease emergence (Smolinski et al. 2003), 3) the percentage of EIDs caused by zoonotic pathogens and vector-borne diseases is on the rise (Jones et al. 2008), and 4) the health of ecosystems, animals, and humans are inextricably interconnected (Aguirre et al. 2012). These ideas both underscore the significance of the threat posed by EIDs and suggest that interdisciplinary research and public health practice that explicitly address the ecology of human, animal, and environmental health could be the most effective strategy to control EIDs.
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1.2 Objectives
I applied these four principles to the study of two diseases: Nipah virus encephalitis in
Bangladesh and malaria in the Brazilian Amazon. By integrating methods from epidemiology, landscape ecology, forestry, remote sensing, and wildlife biology, I sought to improve our understanding of the disease ecology and the role of landscape and its changes on the epidemiology of these zoonotic and vector-borne diseases. Following this introductory chapter there are three core papers, each of which represents an independent body of work. A concluding chapter summarizes the major findings, policy implications, and areas of future research.
I assessed the ecology of Nipah virus encephalitis from two perspectives. First, in
Chapter 2, I demonstrate the utility of a landscape case-control study of the virus in
Bangladesh. Using a combination of field-collected and remotely-sensed data, I described the roosting ecology of the viral reservoir, Pteropus giganteus (a species of fruit bat). I also compared the landscape composition and configuration around villages where Nipah spillovers have occurred to the village environments where no Nipah cases have been reported. Finally, I identified unique ecological characteristics of the region in Bangladesh where all Nipah cases have occurred to date.
Next, in Chapter 3, I examine the habitat selection preferences of P. giganteus. I evaluated P. giganteus roosting preferences for particular tree species. I assessed seasonal and long-term roosting behavior in relation to environmental variables at the roost site. I synthesized land cover and climate variables from eight secondary data sources and derived variables from seven to create a database of environmental characteristics of the P. giganteus
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In Chapter 4, I present an analysis of the impact of forest disturbance on malaria incidence in the Brazilian Amazon. This assessment utilized a high-resolution map of selective logging as well as maps of paved and unpaved roads, forest fires, and deforestation to quantify the impact of forest damage on malaria incidence. Government malaria surveillance data, available throughout the Amazon basin, were used to measure malaria incidence at the municipality (muncipio) level.
In the final chapter, I summarize the results and conclusions of this research and discuss its policy implications. In addition, I review the applications of disease ecology research to public health policy. The chapter concludes with suggestions for future research.
This dissertation was accomplished in collaboration with colleagues from EcoHealth
Alliance (New York, NY) and the International Center for Diarrheal Disease Research,
Bangladesh (icddr,b, Dhaka, Bangladesh). Data were provided by colleagues at the Center for
Environmental and Geographic Information Services (CEGIS, Dhaka, Bangladesh), Fundação
Oswaldo Cruz (FIOCRUZ, Rio de Janeiro, Brazil), and the Carnegie Institution for Science
(Stanford University, CA).
1.3 The Disciplinary Landscape
This research utilized ideas, methods, and assumptions from several disciplines. Here I define the fields that were most influential and discuss how they contributed to my work.
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Epidemiology is “the study of the distribution and determinants of health-related states or events in specified populations and the application of this study to control health problems”
(Szklo and Nieto 2007).
Spatial Epidemiology is “a branch of study concerned with describing, quantifying, and explaining geographical variations in disease distribution” (Elliot et al. 2000)
Landscape Ecology/Epidemiology is the study of the “dynamics of spatial heterogeneity, interactions among heterogeneous landscapes, effects of spatial heterogeneity on abiotic and biotic processes, and the management of spatial heterogeneity” (Risser et al. 1984; Turner
1989) with landscape epidemiology working to understand these processes in relation to the distribution of disease in the human population, generally by analyzing landscape structure as a risk factor for health outcomes (Kitron 1998).
Conservation Medicine “overtly recognizes the health component of conserving biodiversity” by examining “the interactions between pathogens and disease and their linkages with the synergies that occur between species and ecosystems” (Aguirre et al. 2012).
These definitions demonstrate distinctions, although not mutually exclusive ones, among traditional epidemiology, spatial epidemiology, landscape ecology/epidemiology, and conservation medicine. Figure 1.1 shows one possible interpretation of how these disciplines and sub-disciplines converge. The fields shown in light gray circles (i.e. biology, geography,
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6 ecology, geology, and anthropology) are foundational science disciplines (although ecology is traditionally thought of as a sub-discipline of biology). The fields shown without circles (i.e. spatial epidemiology, spatial ecology, biogeography, and cultural ecology) are integrative sub- disciplines that are linked to their parent disciplines with dashed lines (e.g. spatial epidemiology is a sub-discipline of epidemiology and geography). Veterinary medicine and epidemiology (shown in dark gray circles) are highly applied scientific fields based in biology.
The fields shown in black circles (i.e. conservation medicine, landscape epidemiology, and landscape ecology) are more recent, highly interdisciplinary fields that were created by combing methods and concepts from the disciplines to which they are linked with solid black lines (e.g. conservation medicine pulls together veterinary medicine and epidemiology).
Epidemiologic research is designed to test associations between exposure and outcome with the goal of discovering a causal relationship. The Bradford-Hill criteria are a list of guidelines that, when met, suggest increased likelihood of a cause-and-effect relationship (e.g. consistency means that consistent findings are observed by different people in different places with different samples) (Szklo and Nieto 2007). Another principle used in epidemiology is that of the counterfactual condition. When designing epidemiology studies, the gold standard experimental design would be to expose individual A to exposure E at time T and assess the outcome compared to the outcome of individual A not exposed to exposure E at time T.
Clearly, it is not possible for individual A to simultaneously be exposed and not be exposed to exposure E, so we design our studies to get as close as possible to this gold standard experimental design by selecting non-exposed study subjects who are comparable to the exposed group in every way except their exposure status (Szklo and Nieto 2007).
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Broad-scale landscape ecology research, by contrast, has had difficulty establishing causal relationships between landscape patterns and ecological processes (Kent 2007) because of issues such as landscape legacies (Brown and Boutin 2009; Plue et al. 2008), cross-scale interactions (Allen 2007), spatial dynamics (Turner 2005), and non-linear processes (Turner
2005). Experimental design and replication is difficult at the scale at which landscape ecology research usually takes place (Turner 2005); however, landscape ecologists have utilized natural experiments (Mladenoff et al. 1993), neutral models, and experimental models where small landscapes were replicated to test the impact of landscape structure on the response of beetles
(Wiens et al. 1997) as a first step toward addressing causality.
Landscape epidemiology, an amalgamation of concepts and methods, utilizes the causal framework of traditional epidemiology. However, it borrows many novel principles, themes, and terms from the field of landscape ecology. For example, landscape metrics have provided an innovative exposure measurement for diseases with a natural reservoir or vector. A hantavirus study assessed the association between the degree of habitat fragmentation around the trapping site and the incidence of hantavirus seropositivity in the rodent reservoirs
(Langlois et al. 2001). Lyme disease research has focused on the impact of patch size (Allan et al. 2003) and isolation (Brownstein et al. 2005) on the density of infected nymphal black- legged ticks.
Although spatial epidemiology and mapping disease distributions has been a foundation of epidemiology since John Snow’s famous cholera outbreak investigation (Waller and Gotway
2004), the use of remote sensing data, heavily used in landscape ecology research, is a relatively recent development in epidemiology (Beck et al. 2000; Kitron 1998). For example,
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Kitron et al. (1996) used a GIS to determine suitable habitat for tsetse flies (trypanosomiasis vector) in Kenya by overlaying trapping sites on spectral data from Landsat TM imagery. They showed that tsetse fly density was correlated with spectral signals associated with moisture content of the soil and vegetation. In the southwestern United States, Glass et al. (2000) mapped vegetation using Landsat imagery in a case control study of hantavirus locations to identify environmental risk factors for the disease.
A major new consideration in landscape epidemiology is the concept of scale. The traditional epidemiologic approach generally deals with individual (human) level risk factors for disease. In ecologic studies, community or provincial level risk factors are the units of analysis. Landscape ecology research has explored the impact of scale on the measurement of spatial pattern as well as the conclusions drawn regarding pattern-process relationships (Turner
1989). Kitron et al. (1996) offer an example of scale effects within Lyme disease research: he explains that “tick habitat preference may operate on the microscale, mice and other intermediate host[s] may operate on the mesoscale, and establishment of new disease foci on the macroscale.”
An interesting new overlap between landscape ecology and traditional epidemiology is the assessment of neighborhood “contextual” effects on the health of its residents. Most investigators conducting contextual studies to date use traditional epidemiology language to describe their research and are struggling to develop theory and specific hypothesis to guide contextual research, select the appropriate geographic area for analysis, and incorporate both individual level and neighborhood level characteristics (Diez Roux 2001). The application of landscape ecology principles and lessons learned through the struggles of the early landscape
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9 ecologists may provide insight and solutions to these issues.
In a similar vein, conservation medicine, a field that brings together researchers and medical practitioners concerned with the health of ecosystems, wild and domestic animals, and humans, has also expanded the epidemiology discipline (Aguirre et al. 2012). One of its significant contributions is that it “challenged scientists and practitioners…to find new, collaborative, transdisciplinary ways to address ecological health concerns in a world affected by complex, large-scale environmental threats” (Aguirre et al. 2012). Conservation medicine does not offer specific tools and methods per se, but the enormous paradigm shift created by this interdisciplinary movement has spurred the development of new approaches (Aguirre et al.
2012; Daszak et al. 2004), the establishment of new governmental initiatives (e.g. the One
Health Office at the Centers for Disease Control1), increased funding for interdisciplinary EID surveillance (USAID’s PREDICT Project2 and the National Science Foundation Ecology and
Evolution of Infectious Disease awards3), and the establishment of professional organizations dedicated to advancing the field (e.g. the International Association for Ecology and Health4).
My dissertation has benefited immensely from the contributions of scientists and practitioners whose work led to and supported the development of landscape ecology and conservation medicine. The research presented here integrates principles and methods from both epidemiology and landscape ecology. All of my research questions were developed based on the tenets of conservation medicine, namely that the health and well-being of every species is intricately linked with that of all other species as well as the ecosystems in which they live !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 1 http://www.cdc.gov/onehealth/ 2 http://www.vetmed.ucdavis.edu/ohi/predict/index.cfm 3 http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=5269 4 http://www.ecohealth.net/).!
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10 and interact (Aguirre et al. 2012).
1.4 The Epidemiology of Nipah Virus Encephalitis
Nipah virus (family Paramyxoviridae, genus Henipavirus), is an RNA virus that causes respiratory illness and neurologic symptoms in pigs (Chua 2003) and serious illness or death in humans (Hossain et al. 2008). It is an emerging zoonotic disease characterized by inflammation of the brain (encephalitis) or respiratory disease in humans (Chua et al. 2000).
Fever, altered mental state, headache, cough, respiratory difficulty, vomiting, and convulsions are the most common symptoms (Hossain et al. 2008). Severe cases progress to coma and death (Hossain et al. 2008).
The virus was first recognized following a major outbreak in pigs and humans in peninsular Malaysia and Singapore that occurred between September 1998 and June 1999
(Chua et al. 1999; Luby et al. 2009b). Direct contact with infected pigs was the primary source of disease in humans (Chua et al. 1999), and the movement of pigs between farms facilitated the spread of the virus throughout Malaysia (Chua 2003). The outbreak resulted in 265 human cases (105 deaths) and ended with the culling of over 1 million pigs (Chua et al. 2000).
Subsequent investigations for a wildlife reservoir of the virus found serologic evidence of
Nipah virus in Pteropus fruit bats (“flying foxes”) in Malaysia (Johara et al. 2001) as well as
Cambodia (Olson et al. 2002; Reynes et al. 2005), Thailand (Wacharapluesadee et al. 2005),
India (Epstein et al. 2008), Bangladesh (Hsu et al. 2004), Madagascar (Iehlé et al. 2007), and most recently in Africa (Drexler et al. 2009). Nipah virus has also been isolated from Pteropus spp. urine and saliva and from swabs of partially eaten fruit (Chua et al. 2002; Rahman et al.
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2012). No other wildlife reservoirs have been identified in serologic testing of other bat species, birds, rodents, dogs, and pigs (Hsu et al. 2004; Johara et al. 2001; Mills et al. 2009).
Although another epidemic has not occurred in Malaysia (Luby et al. 2006), 14 Nipah virus outbreaks have occurred in Bangladesh since 2001 (Luby et al. 2009b; personal communication Hossain M S Sazzad). A total of 170 human cases has been confirmed and there have been 133 deaths (fatality rate = 78 percent) (Luby et al. 2009b; personal communication Hossain M S Sazzad). Blood samples collected in India and Bangladesh from
Pteropus giganteus, the only flying fox species found in Bangladesh, have tested positive for
Nipah virus antibodies (Epstein et al. 2008; Hsu et al. 2004). Genetic characterization of human and pig viral isolates from the Malaysian outbreak suggests that all infections may have been caused by one or two introductions of Nipah virus from its bat reservoir into the pig population (AbuBakar et al. 2004; Harcourt et al. 2005; Pulliam et al. 2012). In contrast, viral samples isolated from Bangladeshi patients showed more genetic diversity than the Malaysian strains, which suggests repeated spillover of virus from bats to humans (Harcourt et al. 2005).
1.4.1. Foodborne transmission
Several case control studies have been conducted following Nipah outbreaks in
Bangladesh to identify the transmission route from P. giganteus to humans and risk factors for the disease (Gurley et al. 2007a; Luby et al. 2006; Montgomery et al. 2008). Unlike the
Malaysian outbreak, the major route of Nipah virus transmission from bats to humans in
Bangladesh is likely via consumption of raw date palm sap (Luby et al. 2006). Collection of date palm sap is a common practice in the country. Sap collectors climb to the top of a tree
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(Phoenix sylvestris), shave the bark off one side, place a small bamboo tap at the base of the shaved area, and hang a clay pot to collect the clear, dripping sap overnight (Luby et al. 2006).
Pteropus bats have been documented in infrared photos drinking from pots, licking the shaved area on the tree trunk, and urinating while hanging near the collection pot (Khan et al. 2010).
In the morning, the collector gathers his pots and pours the sap from several trees into a common vessel for vending (Luby et al. 2006). One of the most common uses for the sap is to drink it fresh, early in the morning, before the process of fermentation begins to alter the taste
(and alcohol content) (Banerji 2012; Kamaluddin et al. 1998; Luby et al. 2006). A case control study following a 2005 Nipah outbreak found that the risk of contracting the virus was 7.9 times higher (p=0.01) for those who drank raw date palm sap compared those who did not consume sap (Luby et al. 2006).
Nipah virus has also been isolated from partially eaten fruit dropped by Pteropus bats during feeding (Chua et al. 2002). Because the virus can survive on fruit pulp for days
(Fogarty et al. 2008), consumption of these contaminated fruits has been suggested as a possible transmission route of the virus (Luby et al. 2009a).
1.4.2. Domestic animal transmission
In Bangladesh, as happened in Malaysia, Nipah virus may also be transmitted from bats to human via domestic animals (Luby et al. 2009a). Owners sometimes feed their livestock partially eaten fruit dropped by bats or date palm sap that had been too contaminated with animal feces for human consumption (Luby et al. 2009a). Case control studies of Bangladeshi outbreaks in 2001 (Hsu et al. 2004) and 2003 (icddrb 2003) found that contact with a sick cow
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(OR=7.9, p=0.001) or with an itinerant pig herd (OR=6.1, p=0.007) were strongly associated with risk of human Nipah infection (Luby et al. 2009a).
1.4.3. Person-to-person transmission
Nipah virus can also spread from person to person through respiratory secretions (Luby et al. 2009a). In the first seven years of documented outbreaks in Bangladesh (2001-2007), 51 percent of cases (62 of 122 cases) developed illness after close contact with a Nipah patient
(Luby et al. 2009b). All of these person-to-person infections were traced back to nine of the first 122 patients (7 percent) (Luby et al. 2009a). Human transmission of Nipah in Bangladesh is closely tied to caregiving (Luby et al. 2009a). Nipah patients generally stay in their homes and are cared for by family members (Blum et al. 2009). Close contact is the norm, with family members often sharing meals, utensils, and sleeping space with their sick relative (Blum et al. 2009). One of the largest outbreaks of Nipah in Bangladesh to date (2004) revolved around an infected religious leader (Gurley et al. 2007a). Contact tracing post-outbreak revealed that 22 of 34 cases (65 percent) in the outbreak contracted the virus after paying respects to the ill religious leader (Gurley et al. 2007a; Luby et al. 2009a). A seroprevalence survey of 105 healthcare workers and 11 cleaners and patient attendants from a 2004 Nipah outbreak in Bangladesh showed that the risk of nosocomial transmission is low (Gurley et al.
2007b).
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1.4.4. Direct transmission
Direct transmission of Nipah virus from bats to humans via contact with infected saliva, urine, or feces has been posited as a biologically plausible route of infection (Montgomery et al. 2008), although no such cases have been recognized (Luby et al. 2009a). The virus has been isolated from Pteropus urine in Malaysia, Cambodia, and Thailand (Chua et al. 2002; Rahman et al. 2010; Reynes et al. 2005; Wacharapluesadee et al. 2005). Although P. giganteus roosts are often located near homesteads in rural Bangladesh, there has been no documented association between living near a roost and infection (Luby et al. 2009a). In contrast, tree- climbing was a significant risk factor for infection in the 2004 outbreak in Goalando,
Bangladesh (Montgomery et al. 2008).
1.4.5. Directed acyclic graph of risk factors for Nipah transmission
Directed acyclic graphs (DAGs) are diagrams used to illustrate narratives of cause and effect relationships and to summarize assumptions about causality (Greenland et al. 1999).
They can also be used to identify which variables must be measured and controlled in order to obtain unconfounded effect estimates from a statistical model (Greenland et al. 1999). The risk factors for Nipah transmission and the causal assumptions used for this study are summarized in the DAG shown in Figure 1.2.
The four known transmission routes of Nipah, discussed above, are shown the red box in the center of the diagram: 1) direct person-to-person contact and indirect transmission via 2) eating contaminated fruit, 3) contact with infected livestock, and 4) drinking contaminated date palm sap. The risk factors that appear above the red area are those related to the human system,
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15 including behavior, socio-economic factors, and geographic location. The diagram shows that socio-economic status affects all four transmission routes indirectly through lack of medical/veterinary services and knowledge about Nipah transmission, tradition and culture, living in a rural area, and occupation. Moving closer in the chain of causation, tradition and culture influence three transmission routes via caring for sick family members, raising livestock in a homestead, or drinking date palm sap. Lack of medical services and social networks directly affect person-to-person transmission.
The risk factors associated with the P. giganteus fruit bat reservoir are depicted below the red line. This part of the diagram illustrates our understanding that ultimately, bats shedding Nipah virus must be present in order to pass on the virus to other animals or to contaminate fruit and date palm sap. As represented in the diagram, studies of P. lylei in
Thailand found seasonal variation in peak viral shedding, perhaps associated with physical separation of pups from their mother (Wacharapluesadee et al. 2010). The presence of P. giganteus in a village depends on food and habitat availability; specific factors associated with
P. giganteus habitat selection are discussed below and in Table 1.1. In addition to seasonal variation in food availability, the composition and configuration of the forests in rural villages influence resources available to P. giganteus (shown in the blue box). For this study, we assumed that these landscape features also affected the frequency of contact between shedding
P. giganteus and human food resources such as fruit and date palm sap, as well as the intra- and inter- P. giganteus colony contact rates (and subsequent viral transmission). The degree of fragmentation of the forest and the composition of its tree species are both influenced by human population density. Forest fragmentation and tree species composition are highlighted
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16 in the blue box because these ecological characteristics were the focus of the study presented in
Chapter 2.
1.4.6. Questions that remain
Nipah virus research to date has focused on assessing individual-level human risk factors for infection (Gurley et al. 2007a; Hsu et al. 2004; Luby et al. 2009b, 2006;
Montgomery et al. 2008), serologic testing in Pteropus spp. and other possible natural reservoirs (Breed et al. 2006; Luby et al. 2009b), or virological testing via culture and PCR
(Chua et al. 2002; Rahman et al. 2012; Wacharapluesadee et al. 2010). These studies have identified the animal reservoir and the most likely transmission routes of the virus from bats to humans, but questions remain about the epidemiology of the disease (Luby et al. 2009b). In particular, all documented human Nipah cases in Bangladesh have occurred in the central and
Northwestern part of the country, known as the “Nipah Belt” (Luby et al. 2009b) (Figure 1.3).
The question of what drives spatial patterning of outbreaks remains unanswered (Luby et al.
2009b)
To address this question, the International Center for Diarrheal Disease Research,
Bangladesh (icddr,b, Dhaka, Bangladesh) and the EcoHealth Alliance (New York City) designed a “community level risk factor” study of Nipah virus infection. The objective was to better understand characteristics that put some villages at risk for outbreaks. This collaborative study utilized the expertise of epidemiologists, veterinarians, physicians, sociologists, wildlife ecologists, and statisticians to carry out a number of research components including surveys of human behavior, infrared camera observation of bat feeding behavior, and wildlife data
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17 collection. At the start of the study, there had been no investigation of the impact of landscape, bat resource availability, and anthropogenic settlement patterns affecting P. giganteus behavior and subsequent spillover of Nipah into human populations. Consequently, I initiated a complementary study on P. giganteus roosting habitat and landscape-scale predictors of Nipah virus spillover. My objective was to assess ecological characteristics, including human population density, abundance of P. giganteus roosting colonies, and P. giganteus roosting habitat and forest structure, of villages in the Nipah Belt. I then compared these villages to communities in the rest of country. Within the Nipah Belt, I compared villages where there had been cases of spillover to villages where no cases had been reported in order to strengthen our understanding of Nipah virus encephalitis ecology in Bangladesh. This study is presented in
Chapter 2.
In a recent review and discussion of a framework for studying spillover of bat pathogens, Wood et al. (2012) delineate six key research themes. Their first addressed our understanding of fruit bats and their interactions with ecological structure and function. In particular, they emphasized that the distribution and ecology of fruit bats in countries where there are zoonotic spillovers are not well characterized despite the economic and public health importance of these bats (Mildenstein et al. 2005; Wood et al. 2012). An understanding of the habitat requirements of Pteropus fruit bats can provide information to support the design of forest management strategies that preserve both the roosting and the foraging landscape of these ecologically important species (Crampton and Barclay 1998; Mildenstein et al. 2005).
Such understanding will also help explain when, where, and why a virus emerges in a human population (Halpin et al. 2007), and thereby prevent spillover of pathogens from wildlife to
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18 humans (Wood et al. 2012). To address this information gap, I conducted a multi-scale habitat assessment of P. giganteus in Bangladesh. The objectives of this study were: (1) to understand
P. giganteus roost habitat preferences at the tree-level and in relation to human settlements and the broader landscape, (2) to assess P. giganteus roosting behavior across environmental gradients, and (3) to evaluate the use of ecological modeling to identify suitable roosting habitat in unstudied areas throughout Bangladesh. This study is presented in Chapter 3.
1.5 Resource Selection: Drivers and Methods for Evaluation
1.5.1. Pteropus habitat selection at multiple scales
Habitat selection has been described as a “hierarchical process” because organisms may make a series of tiered decisions about their selection based on the availability of resources
(Johnson 1980). A review of the bat resource selection literature, summarized in Table 1.1, shows that the choice of their home range, roosting sites, and foraging locations is influenced by a variety of biotic and abiotic factors. Table 1.1 is organized using the hierarchical movement and selection processes delineated by Johnson (1980). Within each hierarchical level, variables are grouped into broad categories. To the right of each variable are notes from the literature regarding how it was measured or the suggested explanations for the observed associations. Where possible, the direction of the association between the variable and selection of a particular resource (e.g. food, roost site, or home range) is indicated. Question marks indicate that the authors either had no explanation for the observed association or when there were conflicting results. Variables marked with an asterisk are those that have been referenced in at least one Pteropus study. Referenced articles in each row either explicitly
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19 studied the variable or referred to the variable as a possible explanation for their results (e.g.
Luskin 2010 found that Pteropus tonganus prefer to roost in native forest fragments but forage in farmlands. He suggested that aggressive feeding interactions in the forest that would be expected, given the large diurnal population density, were not observed because of this mass nighttime migration. Thus, his paper is referenced under “intraspecific competition” as a factor that influences feeding behavior.)
1.5.2. Methods for evaluating habitat selection
I reviewed two methods for evaluating habitat selection: resource selection functions
(RSFs) and maximum entropy modeling. RSFs are a method used to assess the influences of scale and landscape heterogeneity on habitat selection by organisms (Boyce et al. 2003) and can be defined as a function that is proportional to the probability of use by an organism
(Manly et al. 1993). Data used to estimate RSFs typically come from (1) a random sample of locations drawn and examined for the presence/absence of an organism, (2) a comparison of occupied locations with a random sample of unoccupied locations, or (3) a comparison of occupied locations with a random sample of available locations, which are selected from a sampling frame of all sites in the study, including the occupied sites (Boyce et al. 2002;
Chetkiewicz et al. 2006; Johnson et al. 2006; Jones 2001; Keating et al. 2004). Environmental characteristics of the sites compared are then measured at varying grains and extents to account for (or examine) the effects of scale on the associations between landscape heterogeneity and habitat selection. This analysis is usually performed using logistic regression, although some have expressed uncertainty regarding the validity of these statistical methods (Keating et al.
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2004) (see (Johnson et al. 2006) for a rebuttal). RSF modeling has a long history in ecology
(Boyce et al. 2002; Manly et al. 1993) and has many benefits, which give researchers the ability to incorporate variables measured at different extents and grain sizes and to explore the impact of measuring habitat attributes at varying buffer sizes (Boyce et al. 2003). However, biological limitations often inhibit the use of RSF results to produce a general habitat selection map for a species (Boyce et al. 2002).
Maximum entropy modeling is a relatively new machine learning method used for mapping habitat suitability (Phillips et al. 2006). This approach requires occurrence-only data
(Phillips et al. 2006). It is not very sensitive to small sample sizes (Wisz et al. 2008), and has been shown to consistently out-perform more traditional approaches in predictive power and ability to handle noisy data (Elith et al. 2006). In addition, one of the primary outputs of maximum entropy modeling is a map of the predicted potential geographic distribution of the species (Phillips et al. 2006).
Both methods offer an easier and less expensive method for producing species occurrence maps than by collecting abundance data (Gu and Swihart 2004). RSFs give researchers the ability to integrate data across several scales of analysis (Boyce et al. 2003), while ecological niche models carried out within the MaxEnt framework require all environmental inputs to be measured at the same scale (Phillips 2006). RSFs require more information than maximum entropy modeling in order select available comparison sites within a realistic geographic range (Anderson et al. 2005; Boyce et al. 2003; Jones 2001).
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1.5.3. Assumptions of habitat selection models
As with any model, RSFs and ecological niche models have inherent assumptions that must be considered. Both methods analyze habitat use (“the way in which an individual or species uses habitat to meet its life history needs” (Jones 2001)) to draw conclusion about habitat selection. This implies an understanding of the complex behavioral processes that drive an organism to use one location over another (Jones 2001). Although this criticism is semantic in nature, it serves as a reminder that implicit in habitat selection modeling is that pattern is driving process, and the selection of our environmental parameters and scales of analysis should reflect our hypotheses regarding these linkages. Similarly, many non-environmental processes affect habitat selection (e.g. competition, predation, interspecific interaction) (Jones
2001), and it is important to discuss how we think these impact our habitat selection models if we cannot include them explicitly.
Another assumption made in habitat selection modeling is that there is no misclassification of “available” sites (when using RSFs) or “occupied” sites (using either method). Available sites are generally a random sample of all locations within the study area.
In order to ensure that these sites are actually “available” for use by the organism, it is important to delineate boundaries that are defined by the life history or home range of the organism (Anderson et al. 2005; Boyce et al. 2003; Jones 2001). Occupied sites can be misclassified as a result of detection bias when rare or cryptic species are concerned, for example (Gu and Swihart 2004). Gu and Swihart (2004) used simulated data to assess the impact of non-detection on the coefficients from a wildlife-habitat logistic regression model.
They found that random non-detection that is unrelated to the underlying landscape pattern
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(non-differential misclassification in the language of epidemiology) resulted in a bias toward the null in the coefficients, while non-detection that is associated with the landscape, e.g. if dense forest makes it more difficult to detect species (differential misclassification), creates a bias away from the null.
Finally, selection of the spatial and temporal scales of analysis can have a significant impact on the results of a habitat selection model (Anderson et al. 2005; Boyce et al. 2003;
Chetkiewicz et al. 2006; Johnson 1980; Jones 2001; Manly et al. 1993). In a fundamental paper on RSFs, Johnson (1980) suggested a natural ordering of selection processes that has since been summarized in a tabular format by Chetkiewicz (2006) (Table 1.2). The delineation of this hierarchical structure of habitat selection points out that although we may be interested in a micro-scale relationship, we must assume or acknowledge that selection has already taken place on a broader scale. Johnson (1980) provides a succinct example using food resource selection: although it may be tempting to say that an organism “avoids” a particular food because it provides only 50 percent of the animal’s diet while it represents 90 percent of the available food resources in the area, this conclusion would overlook the fact that the animal likely chose this habitat because of the ubiquity of this particular food resource. Drawing this conclusion regarding a fourth order selection process would ignore the second and third order selection processes that already occurred. Often, the scale of analysis reflects hypotheses about the hierarchical behavioral levels of the organism (Boyce et al. 2003; Johnson 1980; Mayor et al. 2007). For example, Mayor (2007) analyzed the habitat selection of woodland caribou by sampling at four behavioral levels: craters, defined as contiguous areas of frequent caribou feeding, feeding areas (aggregations of craters), travel routes (paths between feeding areas),
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23 and the population's full winter range. By designing studies in this way, researchers can interpret significant covariates in their hierarchical (or multiple) RSF models in the context of ecological significance, as opposed to limiting interpretations to statistical significance
(Anderson et al. 2005). A key theoretical principle that supports multi-scalar investigation comes from MacArthur Award Winner, Simon A. Levin, who holds that there is no “correct” scale to describe ecological processes (Levin 1992). The challenge, he asserts, is to acknowledge that change takes place on many scales simultaneously and to try to understand the nature of these interactions.
1.6 Epidemiology of Malaria
In contrast to Nipah virus encephalitis, a disease that was recognized only within the last two decades, malaria is an ancient disease that has likely plagued humans for over 4700 years (Cox 2010). The malaria parasite was not discovered until 1880, and the mosquito vectors were implicated in the late 1890s (Cox 2010). However, the observation that malaria was tightly linked to the environment was made significantly earlier when Greeks such as
Homer (850 BC), Empedocles (550BC), and Hippocrates (400BC) noted that malarial fevers often affected people living in marshy places (Cox 2010) and learned to control the disease by draining swamps (Sawyer 1992). Despite this early recognition of a connection to the environment, the future of malaria in the changing global environment remains uncertain due to factors such as human population growth, drug resistance, rapid land use change, and climate change (Patz and Olson 2006).
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The clinical presentation of infection with malaria parasites can range from very mild symptoms to severe disease and death if left untreated (Centers for Disease Control and
Prevention 2010). An “uncomplicated” malaria attack generally lasts for 6-10 hours and consists of a cold stage (i.e. shivering), a hot stage (i.e. fever, headaches, vomiting, seizures), and finally a sweating stage (i.e. perspiring, a return to normal temperature, fatigue) (Centers for Disease Control and Prevention 2010). Severe malaria can present as serious organ failure, severe anemia, respiratory distress, acute kidney failure, or abnormalities in blood coagulation and low blood pressure, and the cerebral form can lead to seizure, coma, abnormal behavior and death (Centers for Disease Control and Prevention 2010).
Although a growing number of countries have reported decreases in malaria cases since
2000 (World Health Organization 2011), approximately 48 percent of the worldwide population is still at risk (Hay et al. 2004). In 2011, there were approximately 216 million cases of malaria and about 655,000 deaths (World Health Organization 2012). There are four protozoan parasites that cause malaria in humans: Plasmodium falciparum, Plasmodium vivax,
Plasmodium malariae, and Plasmodium ovale (World Health Organization 2012). Plasmodium knowlesi causes malaria in monkeys but can also cause severe disease in humans (World Health
Organization 2012). P. falciparum and P. vivax are the most common, and P. falciparum causes the most fatalities (World Health Organization 2012). The global distribution of these two protozoa is shown in Figures 1.4 and 1.5. These maps show the age standardized P. falciparum and P. vivax Parasite Rates (PfPR2-10 or PfPR0-99), respectively, which describes the estimated proportion of 2-10 year olds (P. falciparum) and 0-99 year olds (P. vivax) in the
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25 general population who are infected with these parasites at any one time, averaged over 12 months (Gething et al. 2011, 2012).
Malaria is transmitted exclusively through the bite of mosquitoes in the genus
Anopheles (World Health Organization 2012). There are 478 formally recognized anopheline species (Harbach 2011), and approximately 70 of these have been recognized as competent vectors of human malaria (Service and Townson 2002). Globally, there are 41 dominant vector species that transmit the majority of human malaria parasites. This is primarily due to their abundance, their propensity for feeding on humans, and their mean adult lifespan (Hay et al.
2010).
1.6.1. Malaria eradication and control efforts
Malaria control programs in the first half of the 20th century focused on mosquito reduction via environmental control of breeding sites (Konradsen et al. 2004) and contributed to a significant improvement in the global distribution of malaria (Figure 1.6). Indoor spraying with pyrethrum extracts started in the 1930s, but weekly applications were needed to maintain the necessary insecticidal strength (Nájera et al. 2011). In 1939, the discovery of the insecticidal and residual properties of dichloro-diphenyl-trichloroethane (DDT) meant that spraying could be limited to 1-2 times per year (Nájera et al. 2011) and led to the first widespread regional malaria elimination campaigns (Nájera et al. 2011; Scholtens et al. 1972).
The World Health Organization (WHO) first declared the goal of global eradication in 1955
(Hay et al. 2004) and continued to pursue this campaign though 1969 using a four-stage eradication plan (Scholtens et al. 1972) that included DDT spraying in the “attack” phase
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26 followed by the distribution of chemoprophylaxis in the “consolidation phase” (Hay et al.
2004). Despite little progress in sub-Saharan Africa, the global eradication program succeeded in eliminating malaria from Europe, North America, the Caribbean, and parts of Asia and
South-Central America (Tanner and De Savigny 2008). This reduction in the global distribution of malaria can be seen in the shift between 1946 and 1965 in Figure 1.6.
In 1970, WHO accepted a revised global malaria plan that recognized control as an acceptable step toward eradication and emphasized the use of diverse control measures rather than relying solely on DDT and choloroquine (Scholtens et al. 1972), as these “silver bullets” were beginning to cause resistance in the mosquito vectors and malaria parasites, respectively
(Carter and Mendis 2002). There was little improvement in the global malaria situation between 1969 and 1991, although research and development efforts led to advances in drug and vaccine development, vector control, and insecticide-treated nets (ITNs) (Tanner and De
Savigny 2008). The WHO launched the Roll Back Malaria initiative in 1998, whose goal was to reduce the global malaria burden by half by 2010 via distribution of ITNs and timely treatment as well as on-going surveillance and rapid detection of epidemics (Hay et al. 2004).
Today, the global community has set a goal to: (1) reduce global malaria deaths to near zero by the end of 2015; (2) reduce global malaria cases by 75 percent from 2000 levels by the end of
2015; and (3) by the end of 2015, eliminate malaria from 10 new countries beginning with a baseline in 2008 (World Health Organization 2011). US$ 2 billion is invested in malaria control efforts annually, but this amount is still more than US $3 billion short of what experts estimate is necessary to reach current malaria control targets (World Health Organization
2011).
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1.7 Malaria in Brazil
A species of mosquito, heretofore unknown in Brazil, and carrying a malaria parasite, arrived in the Northeast region of the country in the 1930s, likely by catching a ride on a
French warship from Senegal (Oliveira-Ferreira et al. 2010). After the arrival of Anopheles gambiae, Brazil experienced a severe malaria outbreak with 13 percent fatality (Oliveira-
Ferreira et al. 2010). Brazil managed to eliminate this mosquito vector through an organized eradication campaign in the early 1940s, but other mosquito species have made their way to the country and malaria remains a significant public health problem in Brazil today (Oliveira-
Ferreira et al. 2010).
In the 1940s, approximately 20 percent of Brazil’s population (six million people) was infected with malaria each year (Oliveira-Ferreira et al. 2010). However, a shift toward eradication in the late 1950s, following WHO’s global eradication announcement, brought malaria down to its lowest level in 1960, when less than 40,000 cases were reported (Oliveira-
Ferreira et al. 2010).
During five decades of control efforts since the 1960s, Brazil has seen both enormous reductions in malaria cases and disappointing surges in the disease (Ferreira and Da Silva-
Nunes 2010) (Figure 1.7). Massive migration into the Amazon region started in 1964 with the advent of government-sponsored colonization programs for agriculture and gold mining (De
Castro et al. 2006). Nearly four million non-immune immigrants relocated between 1970-80, which led to a significant rise in malaria transmission (Marques 1987). The malaria epidemic that resulted from incursion into the Amazon has since been characterized as “frontier malaria”
(Sawyer 1992). It is theorized that frontier malaria follows an epidemiologic pattern in new
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28 settlements that begins with a peak in incidence for the first 6-8 years in an area with weak institutions, high in- and out-migration, and poor housing infrastructure (De Castro et al. 2006).
As migration and ecological disturbance become more stable, the incidence of malaria subsides to a lower, endemic level (De Castro et al. 2006). Understanding this paradigm that links frontier expansion to malaria resurgence has been important for designing public health inventions to prevent the pattern from repeating in new settlements (De Castro et al. 2006).
In 1989, the Program for Malaria Control in the Amazon (PCMAM) was launched with
US$ 73 million from the World Bank to address the issue of increasing malaria transmission in the Amazon Basin (Barat 2006). After administrative setbacks that delayed the start of the project, by 1993 Brazil initiated the new program, which focused on targeting high-risk municipalities, strengthening surveillance and monitoring, rapid case management, and more selective use of residual spraying and environmental management (Barat 2006). The program had a definitive, short-term impact. It reduced malaria morbidity by 60 percent from the 1987 levels in just three years (Barat 2006). Between 1998 and 2002, reorganization of the malaria control program after the end of PCMAM resulted in a sharp increase in incidence (Ferreira and Da Silva-Nunes 2010). Subsequent intensification of early diagnosis and treatment throughout the country led to another temporary decrease (Ferreira and Da Silva-Nunes 2010).
Despite substantial improvements in surveillance infrastructure in rural areas, malaria rates increased again between 2003 and 2005 due to a number of drivers, including climate changes, disorganized settlements and migration patterns, land use change, and poor environmental management of mosquito breeding sites (Oliveira-Ferreira et al. 2010). Since
2006, the number of annual malaria cases has been dropping (Oliveira-Ferreira et al. 2010), but
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29 in 2010, over 330,000 cases were still reported in Brazil (Tauil 2011). Although Anopheles darlingi, the main mosquito vector in Brazil, is present in about 80 percent of the country
(Oliveira-Ferreira et al. 2010), 99.7 percent of contemporary malaria cases originate in the
Amazon region (Tauil 2011) (Figure 1.8). Within the region, 57 of the 807 municipalities (7.1 percent) account for 80 percent of the cases (Tauil 2011). In terms of the demographic burden, the proportion of cases reported in women and in children under the age of 10 has been increasing in recent years, although the reasons for this trend remain speculative (Tauil 2011).
1.7.1. Land use change in Brazil
The Amazon Basin is home to the largest remaining tropical rainforest (Foley et al.
2007), perhaps one-quarter of the world’s terrestrial species (Dirzo and Raven 2003; Malhi et al. 2008), and enormous carbon reserves that when released, have a substantial influence on regional and global climate (Malhi et al. 2008). In addition to these and many other ecosystem services, the Amazon also provides a multitude of ecosystem goods such as timber, food, and pharmaceutical components (Foley et al. 2007). Evidence from multiple disease systems shows that changes in ecosystems can influence the emergence and reemergence of infectious diseases
(Hassan et al. 2005), and environmental perturbation in the Amazon has been shown to be associated with illnesses such as Chagas disease (Coura et al. 2002), leishmaniasis (Silva et al.
2001), dengue and several arboviruses (Vasconcelos et al. 2001), and malaria (Olson et al.
2010; Vittor et al. 2006).
Amazonian forests are under immense pressure both locally (for food, fuel, and livelihoods) and globally, due to demands for soybeans, beef, and biofuel (Defries et al. 2004;
29
30
Foley et al. 2007). The annual trend of deforestation peaked in 2004, at a three-year average of approximately 25,000 km2 per year (Instituto Nacional de Pesquisas Espaciais 2012) (Figure
1.9). Since that year, annual deforestation rates have dropped as a result of transitions in the beef and soy industry to exclude Amazon deforesters, pressure from the international community, and strategies put in place by the Brazilian government, including expansion of protected areas, enforcement of deforestation bans, and publicity campaigns (Malhi et al. 2008;
Nepstad et al. 2009). Today, over 730,000 km2 of the Amazon has been deforested, or about 18 percent of the pre-1970 forests in the region (Instituto Nacional de Pesquisas Espaciais 2012).
Climate simulation models have shown that clearing 30-40 percent of the forest would likely shift Brazil into a permanently drier climate characterized by expanding savanna and desert
(Malhi et al. 2008; Oyama and Nobre 2003). In 2008, the Brazilian government committed to an 80 percent reduction in deforestation from the historical rate (1996-2005) by 2020 (Nepstad et al. 2009). Scenario analysis suggests that if Brazil could end deforestation by 2020, a challenge that would require between US$ 6.5-18.1 billion, more than 80 percent of the original forest would be preserved (Nepstad et al. 2009).
Although deforestation is the most prominent influence on the Amazonian forests, other land use changes such as selective logging, road construction, and forest fires have been more recently recognized as severe disturbances to the forest landscape (Asner et al. 2005; Foley et al. 2007; Nepstad et al. 2001). Estimates from the late 1990’s showed that 10,000-15,000 km2 of Amazonian forest were logged per year – an area equivalent to 50-95 percent of national deforestation estimates at the time (Nepstad et al. 1999). Roads increase access to Amazonian forests and decrease transportation costs for the agricultural and logging industries (Nepstad et
30
31 al. 2001). As a result, roads are an important predictor of future forest disturbance (Laurance et al. 2002; Nepstad et al. 2001). Selective logging severely diminishes the forest canopy and loads the forest floor with combustible material, making logged areas highly susceptible to forest fires (Holdsworth and Uhl 1997). Burning can be initiated by droughts (Nepstad et al.
2001) or even by five or six days without rain during the dry season (Uhl and Kauffman 1990).
These positive feedback loops between roads, agriculture, logging, and forest fires have the potential to rapidly transform the Amazon into fire-prone scrub vegetation and cattle pastures.
This has significant implications for carbon storage and downstream human health impacts from poor air quality during recurrent forest fires (Nepstad et al. 2001).
1.7.2. Impact of land use change on malaria
Changes to the landscape can affect mosquito breeding sites and microclimate. This has implications for the rate of larval development, biting frequency, and survival of the mosquito vector (Kovats et al. 2001; Mills et al. 2010; Patz and Olson 2006; Reiter 2001). Studies in western Kenya found that average ambient temperatures were 0.5°C warmer in deforested areas compared to forested regions (Afrane et al. 2005). As a result, the reproductive cycles of
Anopheles mosquitoes living in the deforested areas were accelerated by 2.6 days (56 percent) and 2.9 days (21 percent) in the dry and wet seasons, respectively (Afrane et al. 2005). A shorter larva-to-adult developmental time means increased biting frequency and risk of malaria transmission (Afrane et al. 2005). In Kibale, Uganda, Lindblade et al. (2000) found that both minimum and maximum daily average temperatures were higher near swamps that had been drained for cultivation compared to natural swamps. This microclimatic change contributed to
31
32 a seven-fold increase in the risk of being bitten by a parasite-bearing mosquito (Lindblade et al.
2000). Canopy cover can also affect the survival of larvae in their aquatic breeding sites. Tuno et al. (2005) found that A. gambiae larval survival was 55 times higher in open canopy habitats where pools of water were exposed to the sunlight compared to habitats with full or even partial canopy cover.
In the Peruvian Amazon, the degree of deforestation has been shown to be associated with the biting rate of Anopheles darlingi (Vittor et al. 2006), and complementary larval studies point to an increase in preferred breeding habitat as the likely ecological mechanism (Vittor et al. 2009). Field studies of larval breeding sites along the Iquitos-Nauto road in the Peruvian
Amazon found that A. darlingi larvae were present most often in sites with algae growth, a likely food source for the larvae, low forest cover, and high secondary vegetation (Vittor et al.
2009). Furthermore, studies in the same region showed that the rate at which humans were bitten by A. darlingi was 278 times higher in sites with less than 20 percent forest cover compared to sites with more intact forests (Vittor et al. 2006). Olson et al. (2010) assessed the relationship, by health district, between slide-confirmed malaria incidence and deforestation estimates in Mancio Lima, the westernmost county in the Brazilian Amazon. The deforestation estimates were provided by the Brazil space agency (INPE) satellite monitoring program. They found that a 4.3 percent increase in deforestation (1997-2000) increased malaria incidence 48 percent.
Despite strong evidence that connects deforestation to malaria risk, relatively little research has been conducted to understand the impact of other forest disturbances on the disease in the Brazilian Amazon. To explore this relationship, I examined deforestation, road
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33 density, fire, selective logging, and sociodemographic factors across the Brazilian Amazon from 1997-2003 and their association with malaria incidence in 2003 at the municipality level.
This study was one of the first basin-wide assessments of the relationship between forest disturbance and malaria. In addition, it contributed new information to our understanding of malaria dynamics in an area of rapid land use change. This study is presented in Chapter 4.
1.8 Summary
EIDs are expected to increases in frequency over the next several decades as a result of increased global travel and trade, expanding human populations, and continued encroachment on natural habitats that lead to novel interactions with wildlife (Binder et al. 1999; Karesh et al.
2012). To anticipate, detect, prevent, and manage these increasing risks will require the continued study of the social and environmental drivers that lead to outbreaks. In addition, effective research must combine the expertise of ecologists, veterinarians, clinicians, epidemiologists, and social scientists and recognize that the health of humans, animals, and the environment we share are inextricable.
Nipah virus encephalitis is an emerging infectious disease in Bangladesh, and there is currently no vaccine or treatment for infection. The virus naturally occurs in the Pteropus fruit bat that is endemic in the country, and the virus is transmitted to humans when they drink raw date palm sap that has been contaminated by infected fruit bats while they are feeding. All human cases of Nipah virus have occurred in the northwestern and central regions of the country, but the reasons for this spatial distribution remain unclear. I assessed the fruit bat roosting habitat in case and control villages to assess the impact of landscape on Nipah
33
34 dynamics in Bangladesh and to evaluate environmental characteristics of villages that could lead to increased probability of Nipah spillover from bats to humans. I also created an ecological niche model to predict P. giganteus habitat across Bangladesh.
Malaria is an ancient disease that continues to cause serious illness and death worldwide as a result of incomplete coverage of treatment, increasing resistance to available drugs, and changing environmental conditions that alter the distribution and survival of the mosquito vectors. In Brazil, the battle with malaria that began the early 1900s continues to this day, and rapid land use change in the Amazon region has increased transmission despite intensive surveillance and control programs. The effect of continued development in the region on malaria risk remains a question. I assessed the impact of deforestation, selective logging, road construction, and forest fires across the Brazilian Amazon on malaria incidence to understand the potential impact of future forest disturbance on human health.
The next chapters present the details of each of these studies, including more information on background, methods and results, and the interpretations and implications of my findings. In addition to the impact this research may have on Nipah inventions in Bangladesh or malaria control in Brazil and elsewhere, this work can serve as an example of the growing fields of One Health and conservation medicine and the value of utilizing interdisciplinary approaches to understand the connections between human and ecosystem health.
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47 Table 1.1. Summary of bat resource selection literature (*denotes that the variable was found in at least one Pteropus study)
RESOURCE PATCH LEVEL ! Resource use within general roosting area (e.g. foraging)
Direction of Association with Variable Category Specific Variable References Explanation of Variable/More Information Resource Selection Measured by use:availability in environment; could be affected by any covariates below Fruit preference* Banak1998 + (e.g. nutritional value, fruit quality, temperature, etc.) Fruit abundance* Banak1998, Dinerstein1986 Measured by biomass, number of ripe fruit available + Nutritional value* Banak1998, Dinerstein1986 Perhaps related to pregnancy ?
Distribution of food Bats observed flying from fruit to fruit, sniffing, taking a bit, and moving on; could be Fruit quality* Banak1998 ? resources affected by temperature/humidity
Bats observed flying long distances – maybe because food resources were distributed in Configuration of food resources* Walton1983 ? patches on landscape
Bats observed returning to same food patch – maybe bats knew this patch well so it is Knowledge of food resources* Walton1983 + easier to return than to look for new food sources
Intraspecific dynamics Intraspecific competition* Luskin2010, Walton1983 Suggests that bats choose foraging sites to avoid intraspecific competition -
Pregnancy/lactation* Dinerstein1986, Palmer1999 Feeding patterns changed during pregnancy/lactation ?
Reproductive behavior Females observed to fly farther distances between roosts and foraging sites during and strategy Sex* Palmer1999 pregnancy perhaps because they do not have the flexibility to shift roosts during this period ? and so have to fly further to find the necessary food resources
Foraging distances decreased during the build-up to and the wet season perhaps reflecting Abiotic factors Season* Palmer1999 ? a lower abundance of food or availability of nutritionally poor food
HABITAT PATCH LEVEL Resource use within home range (e.g. roost selection)
Direction of Association with Variable Category Specific Variable References Explanation of Variable/More Information Resource Selection Where roosts were clumped, female group sizes were larger and males showed higher Available roost density Campbell2006, Vohof1996 fidelity to single roosts; roosting in an area with lots of available roosts reduces the time and + energy needed to switch roosts
Small-scale Vohof1996, Sedgeley2004, Various studies have shown that different bat species prefer to roost in particular tree Tree species* ? environmental factors Crampton1998, Gumal2004 species – perhaps sturdier? Sedgeley2004, Most bats have communal roosts and those that roost inside tree cavities likely prefer to Tree diameter* Crampton1998, Gumal2004, + have more space Jung1999 Tables
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Table 1.1 (continued)
Direction of Association with Variable Category Specific Variable References Explanation of Variable/More Information Resource Selection Vohof1996, Sedgeley2004, Higher roosts provide protection from predators, are closer to food resources, provide more Tree height* Crampton1998, Gumal2004, room for branch-roosting species, provide more room for “free-fall take-off” space for large + Jung1999 Pteropus bats Number of tree cavities Sedgeley2004 Refers to bats that roost in tree cavities + Increased wall thickness, cavity size, height of cavity from ground – provides protection Cavity characteristics Sedgeley2004 from predators and comfort in roost – warmth is important for thermoregulation of pregnant ? females Microclimate Sedgeley2004 Temperature inside cavity is important for pregnant females ?
Prefer dying or newly dead trees because they have firm wood that is a better insulator and Small-scale Decay class of tree Vohof1996, Crampton1998 ? environmental factors less likely to fall down; may be associated with cavity-formation (continued) Low leaf cover areas are less cluttered and provide easier access for bats; may receive Vohof1996, Crampton1998, Percent leaf cover/canopy cover* more sunlight to keep pregnant bats warm; higher canopy closer may decrease predation ? Gumal2004, Jung1999 from above
Distance to edge of patch Crampton1998 Provides more foraging opportunities (for insectivorous bats) -
Older stands may have lower tree density, thus more edge habitat and more places to Age of trees/stands Crampton1998 + forage (for insectivorous bats)
Understory closure Jung1999 May make it easier to fly if understory is open -
South-facing* Jenkins2007 Increases sunlight and warmth on roost +
Social structure Harem-based social structure Campbell2006 Females group together and male fidelity is related to harem size ? Roosts locations change by season, which often coincide with pregnancy/lactating – Palmer1999, Gumal2004, Reproductive strategy Pregnancy* possible explanation is that juvenile, hairless bats are more susceptible to ecoparasites and ? Jenkins2007 frequent moving decreases prevalence Frequent switching of roosts could be related to reducing parasitism more generally and not Reduce parasitism* Crampton1998, Gumal2004 ? just during pregnancy
Avoid hunting pressure* Gumal2004, Jenkins2007 Possible explanation for frequent roost switching ? Response to outside stimuli Response to human disturbance* Gumal2004 Possible explanation for frequent roost switching ? Possible explanation for frequent roost switching – deters predators that are attracted to the Avoid predation* Gumal2004 smell of frequently used roosts or that learn the sites of roosts and sit and wait for bats to ? return Unexplained observed Notice that bats tend to have fidelity to a general roosting area rather than a particular roost Roosting area fidelity* Vohof1996, Gumal2004 ? behavior site Frequency of roost switching was related to number of days of consecutive rainfall (wet/cool Abiotic factors Rainfall Vohof1996 ? periods decreased roost switching)
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Table 1.1 (continued)
PATCH MOSAIC LEVEL Selection of home range
Direction of Association with Variable Category Specific Variable References Explanation of Variable/More Information Resource Selection Luskin2010, Landscape Pteropus prefer natural forest types (undisturbed lowland, beach, mangrove) compared to Land cover* Mildenstein2005, ? composition human-disturbed vegetation; prefer native forest fragments compared to grasslands Greaves2006, Jaberg2001 Palmer1999, Possibility used for navigation and feeding opportunities (fruit trees grow along rivers); Proximity to riparian areas* + Navigational strategies Mildenstein2005 navigational aids may be particularly important for juveniles learning to fly
Proximity to ecotones* Jenkins2007 May help with navigation + Patch size Gorresen2004 Varied by species ? Patch density Gorresen2004 Varied by species ? Edge density Gorresen2004 Varied by species ? Landscape Shape complexity Gorresen2004 Varied by species ? configuration Patch proximity Gorresen2004 Varied by species ? Degree of fragmentation Gorresen2004 Found highest diversity of bats in landscapes comprised of moderately fragmented forest ? Topography Greaves2006, Jaberg2001 May be because slope affects the tree species in the area - Human disturbance* Mildenstein2005 Pteropus prefer less disturbance - Response to outside May be influencing home range selection as well as micro-scale decisions about roosting stimuli Avoid hunting pressure* Luskin2010 ? sites Pteropus preferred bamboo and mangrove during dry season and rainforest in wet season Season* Palmer1999, Jaberg2001 – likely has to do with proximity to seasonal food resources; perhaps rainforest provides ? more protection during monsoon rains A more stable climate may be related to a more sedentary bat population because fruiting Abiotic factors Climate* Gould1978 and flower (food resources) are more stable; an area with intense wet/dry seasons may ? force bats to forage seasonally
Mean winter temperature Greaves2006 May be because temperature affects the tree species in the area -
Mean annual solar radiation Greaves2006 May be because solar radiation affects the tree species in the area +
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Table 1.2. Movement and habitat selection processes in relation to spatial scales and structures (adapted from Ims 1995, Johnson 1980). Reproduced from Chetkiewicz et al. (2006)
Movement type (after Ims Spatial scale Habitat selection (after Johnson 1980) 1995) Spatial Structure
Food item distribution, Food patch shape Resource Patch Food items within the patch (fourth order) Food items search (foraging) and size, Small-scale obstructions
Patch searching, traplining, Food patch configuration, Shelter, Abiotic Habitat Patch Patches within the home range (third order) territory patrolling factors and topography
Patch Mosaic Selection of home range (second order) Dispersal Patch distribution, Landscape features
Region Geographical range (first order) Migration Large-scale topographic barriers
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Figures
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(! (!(!(! (! (! (! ! (! (! ( (! (!(! (! Nipah Spillover Cases, 2001-2011 Nipah Belt Rivers and Inland Water (! (!
(! (!(!
(! (!(! (!(! (! (! (! (!(! (! (!(!(!(! (! (!(!(! (! (! (! (!(! (! (!(!(!(!(!(! (! (!
µ
0 25 50 100 Kilometers
Figure 1.3. Location of human Nipah virus “spillover” cases from Pteropus giganteus bats to humans in Bangladesh, 2001-2011.
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2CHAPTER 2 ! The role of landscape composition and configuration on Pteropus giganteus roosting ecology and Nipah virus spillover risk in Bangladesh
Micah B. Hahn1,2, Emily S. Gurley3, Jonathan H. Epstein4, Mohammad S. Islam5, Jonathan A. Patz1,2, Peter Daszak4, Stephen P. Luby3,6
1Nelson Institute for Environmental Studies, SAGE (Center for Sustainability and the Global Environment), University of Wisconsin-Madison, Madison, WI, United States, 2Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin- Madison, Madison, WI, United States, 3International Center for Diarrheal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh, 4EcoHealth Alliance, New York City, NY, United States, 5Center for Environmental and Geographic Information Services, Dhaka, Bangladesh, 6Centers for Disease Control and Prevention, Atlanta, GA, (current: Stanford University), Stanford, CA, United States
Abstract
Nipah virus has caused recurring outbreaks in central and northwest Bangladesh (the “Nipah
Belt”). Little is known about roosting behavior of the fruit bat reservoir, Pteropus giganteus, or factors driving spillover. We compared human population density and ecological characteristics of case villages and control villages (no reported outbreaks) to understand their role in P. giganteus roosting ecology and Nipah virus spillover risk. Nipah Belt villages have higher human population density (p<0.0001), and forests that are more fragmented than elsewhere in Bangladesh (0.50 vs 0.32 patches/km2, p<0.0001). The number of roosts in a village correlates with forest fragmentation (r=0.22, p=0.03). Villages with a roost containing
Polyalthia longifolia or Bombax ceiba trees were more likely case villages (OR=10.8, 95% CI
1.3-90.6). Villages were 2.0 times as likely to be cases for every 10% decrease in average canopy cover of roosts (95% CI 1.1, 3.6). This study suggests that, in addition to human
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2.1 Introduction
Nipah virus was recognized in Bangladesh in 2001 and has caused recurring outbreaks clustered seasonally, during December to April, and spatially, within central and northwest
Bangladesh, which we here term, the “Nipah Belt” (Luby et al. 2009b). The question of what drives spatial patterning of outbreaks remains unanswered. Principal routes of transmission to humans are consumption of raw date palm sap infected by the natural bat reservoir in
Bangladesh, Pteropus giganteus, and person-to-person contact (Luby et al. 2006). Because no other reservoir has been identified in Bangladesh (Hsu et al. 2004), index cases likely result from spillover transmission from the bat reservoir (Luby et al. 2009a). Outbreaks separated by many months in different locations in Bangladesh and genetic characterization of the virus in
Bangladeshi patients suggests repeated spillovers from bats to humans (Harcourt et al. 2005; Lo et al. 2012; Luby et al. 2009a). Little is known about the roosting ecology of Pteropus spp.
(Mildenstein et al. 2005) or why there are Nipah virus spillovers in only a subset of villages where P. giganteus roost. We compared human population density and ecological characteristics of case and control villages to understand the role of landscape on P. giganteus roosting ecology and likelihood of Nipah virus spillover to humans.
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2.2 Methods
This study is a component of a larger village-level investigation of Nipah virus spillover conducted by the International Centre for Diarrheal Disease Research, Bangladesh (icddr,b).
We compared characteristics of P. giganteus roosts, landscape structure, and human population density of villages within and outside the Nipah Belt and in spillover case villages to control villages within the Nipah Belt. A case village was defined as any village where a Nipah virus case-patient was identified between 2001 and 2011, and where the infection was apparently introduced from a spillover event from the bat reservoir. We defined the Nipah Belt using a 50 km buffer around case villages. To generate our control sample of villages with no reported
Nipah virus cases, we drew 5 km buffers around case villages, and then selected a geographically random sample of points inside and outside the Nipah Belt, avoiding areas within the buffers.
We conducted two case-control comparisons. First, we used random sampling to select approximately half of our case and control villages for field visits (referred to as the “field- validated sample”) to assess detailed information on P. giganteus roosting ecology. We also created a second study sample using the full spillover case list and 20,000 geographically random control points (10,000 inside and 10,000 outside the Nipah Belt) (Wisz and Guisan
2009). We used this group (referred to as the “remotely-sensed sample”) to conduct a second case-control analysis that utilized satellite and human population modeling data. The large sample size of the second study sample gave us the power to detect smaller effects of human population density and village forest structure on risk of Nipah virus spillover than would have been possible using only the field-validated sample.
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2.2.1. Locating study villages and conducting roost ecological assessment
We limited field data collection to December through early February to measure the environment in villages during the season when most human cases have been identified (Luby et al. 2009a). Field teams used GPS devices and GoogleEarth to identify and then enroll a control village near each set of random coordinates. They located P. giganteus roosts that were inhabited in the past 5 years inside and within 5 km of the village boundaries through interviews with community leaders. They collected GPS coordinates at all roosts and counted the number of bats in active roosts (i.e. at least one bat present when the roost was located). A subset of roosts was selected for an intensive ecological assessment (Appendix C) due to the restricted study duration. This assessment was conducted on the two largest, active roosts nearest the village center. Field teams used transects to delineate a 20x20 m plot around the central roost tree. Within the plot, they measured tree species, height (using a Suunto clinometer), and diameter at breast height (DBH) (using diameter tape) for all trees with a DBH
> 4 cm.
2.2.2. Satellite data derivation
We used satellite and human population distribution data to measure forest structure and human population density within a 20x20 km square-shaped buffer around the center of each study village (distance from village center to center of the buffer’s side was 10 km; referred to as a 10 km buffer, henceforth).
The land cover map used to calculate forest metrics was derived from GoogleMap and several satellites including MODIS (April 2012, 250m), IRS Pan (2000-2005, 5.8m), ASTER
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(2003-2008, 15m), IRS P6 LISS (2005, 23.5m). Classification results were resampled at 250 m for the final map. We measured forest fragmentation by calculating forest patch density
(number of patches / 10 km village buffer), edge density (total length of forest pixels bordering non-forest pixels / 10 km village buffer), and the largest patch index (percent of the 10 km village buffer occupied by the largest contiguous forest patch) using FRAGSTATS (McGarigal et al. 2012).
Human population density was derived using the Landscan dataset, a global population surface created using an algorithm that integrates census data and ancillary datasets including land use, topography, and transportation (Oak Ridge National Laboratory 2011)
2.3 Data Analysis
2.3.1. Assessing tree species composition of roost sites
We used non-parametric ordination methods and PC-ORD software (McCune and
Mefford 2010) to derive a measure of tree species composition at each roost site. First, we calculated the average basal area of each tree species within a plot using our diameter at breast height (DBH) measurements. Next, we calculated Bray Curtis dissimilarity (Bray and Curtis
1957) on the log-transformed matrix and ran a hierarchical cluster analysis to group roosts into five significant clusters based on similarity of the tree species biomass in each roost site. We used Multi-Response Permutation Procedures (McCune et al. 2002) to ensure clusters were mutually exclusive. Finally, we conducted Indicator Species Analysis (McCune et al. 2002) to identify diagnostic tree species for each roost cluster.
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2.3.2. Comparing characteristics of roosts sites and villages in the field-validated sample
Using the field-validated sample, we compared human population density, number of households, village area, bat population density and number of bats, roost characteristics, and forest fragmentation metrics of villages inside and outside the Nipah belt region to identify unique ecological characteristics of the Nipah Belt. We also compared characteristics of case villages to control villages inside the Nipah Belt to identify possible risk factors for Nipah virus spillover. To test for significant differences, we used t-tests for continuous data, and χ2 or
Fisher exact tests for categorical data. Standard deviations around mean estimates were calculated to assess the magnitude of these differences. We considered p≤0.05 significant.
We constructed a correlation matrix to assess relationships between human population density, number of roosts (active and inactive), and landscape characteristics of villages. Only uncorrelated variables (r<0.7) were analyzed in the same regression model to prevent multicollinearity (Dormann et al. 2012). We built Firth logistic regression models (to correct for quasi-separation) using significant variables from the univariate analyses, and we calculated odds ratios (ORs) with 95% confidence intervals (CIs) to assess the increased odds associated with each environmental risk factor when controlling for human population density. We constructed models that compared cases to each of the control groups (inside and outside the
Nipah Belt) and a model comparing the control groups.
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2.3.3. Comparing human population and village landscape characteristics in the remotely- sensed sample
We compared human population density and forest landscape characteristics of villages inside and outside the Nipah Belt and of case villages and Nipah Belt controls using the remotely-sensed sample. We used logistic regression to compare forest metrics while controlling for human population density. We estimated a Satterthwaite-adjusted χ2 in the regression comparing case villages and Nipah Belt controls to account for the large difference in sample size.
2.4 Ethical Considerations
Each interviewee gave verbal informed consent for participation. We asked permission of the landowner at roost sites before taking field measurements. The study protocol was approved by the icddr,b and University of Wisconsin-Madison Institutional Review Boards.
2.5 Results
Our field-validated sample contained 100 villages: 21 case villages, 38 control villages inside the Nipah Belt, and 41 control villages outside the belt region (Figure 2.1). Our field teams located 222 active and non-active roosts, and 87 of the 100 study villages had at least one roost inside the village or within 5 km of the village boundary. GPS coordinates and bat counts were taken at 221 of the roosts (99.5%), and an ecological assessment in addition to the coordinates and bat counts was conducted at 143 (65%) of these roosts.
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2.5.1. Comparing characteristics of roosts sites and villages in the field-validated sample
Nipah Belt villages had a higher human population density within a 10 km buffer of the village center (1311 people/km2 ± 489) than villages outside the Nipah Belt (895 people/km2 ±
440, p<0.0001) (Table 2.1 and Figure 2.2). Within the Nipah Belt, there was no significant difference in human population density between case and control villages. There were no significant differences in the number of households or village area among the study groups.
There were no significant differences in the total number of bats, bat population density, or proximity of roosts to human settlements among the study groups. However, inside the
Nipah Belt, a greater proportion of villages had an active or inactive P. giganteus roost (93 vs
78%, p=0.03). Respondents from the Nipah Belt villages reported more roosts (inactive and active) per village (2.6 ± 1.8) than respondents from villages outside the Nipah Belt (1.6 ± 1.5, p=0.004). Additionally, field teams identified more active roosts within 5 km of the village boundary in Nipah Belt villages (1.7 ± 1.3) compared to villages outside the belt (1.2 ± 1.0, p=0.05). The percent canopy cover in the roost sites was denser in roosts located in Nipah Belt control compared to case villages (64% ± 13 vs 53% ± 12, p=0.002). Case villages were more likely to have at least one roost that was categorized by the presence of silk cotton (Bombax spp.) or Indian mast trees (Polyalthia longifolia) compared to controls inside the Nipah Belt (33 vs 3%, p=0.01).
Bat colony size differed by type of roost cluster (F=2.54, df=4, p=0.04). Roosts characterized by raintree (Albzia spp.) or mahagony (Swietenia mahagoni) supported the largest bat colonies (623 ± 708), while cotton silk or Indian mast tree roosts supported the smallest P. giganteus colonies (170 ± 283).
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Nipah Belt villages had less forest cover (26% ± 10), higher forest patch density and edge density (0.50 patches/km2 ± 0.18; 21 m/km2 ± 5.3) and a lower largest patch index (7.1%
± 11) compared to villages outside the Nipah Belt (33% ± 22, p=0.04; 0.32 patches/km2 ± 0.21, p<0.0001; 18 m/km2 ± 6.4, p=0.01; 19% ± 26, p=0.01). The forest around Nipah virus case villages was the most fragmented of the study groups, but the only significantly different forest metric between case villages and Nipah Belt controls was the largest patch index (3.6% ± 1.5 vs 9.0% ± 13, p=0.02).
2.5.2. Identifying environmental risk factors for Nipah virus spillover
In the model comparing cases to Nipah Belt control villages, the odds of being a case was 2.0 times higher (95% CI 1.1-3.6, p=0.02) for every 10% decrease in the average canopy cover of roosts sites in the village after controlling for human population density and other significant covariates from the univariate analysis (Table 2.2). Additionally, villages with at least one silk cotton/Indian mast roost were 10.8 times more likely (95% CI 1.3-90.6, p=0.03) to be a case village. In the comparison of cases to controls outside the Nipah Belt, decreased mean tree height in roosts and increased forest patch density in the village were both significant risk factors for Nipah virus spillover. In the model comparing the control groups, only an increase in mean tree height was significant.
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2.5.3. Assessing relationships between P. giganteus roosting ecology, forest structure, and human population density
Human population density was positively correlated with forest fragmentation using a variety of indicators (Table 2.3). Both the number of active and inactive P. giganteus roosts that were reported by community members as well as the number of active roosts identified by our field teams increased as the human population density and the degree of forest fragmentation increased. As the amount of contiguous forest in a village increased, the mean tree height and percent canopy cover in roosts sites increased, but the tree species diversity decreased.
2.5.4. Comparing human population and village landscape characteristics in the remotely- sensed sample
Univariate comparisons within the remotely-sensed sample yielded similar results to the field-validated sample, showing that human population density is higher and the forest is more fragmented inside the Nipah Belt compared to the rest of the country (Table 2.4). However, the larger sample size increased our statistical power to detect differences in the forest structure of case villages compared to Nipah Belt control points. Cases had less forest cover (22% ± 4.1), higher forest patch density (0.55 patches/km2 ± 0.14), higher forest edge density (21 meters/km2 ± 3.6), and a smaller contiguous forest area (3.5% ± 1.9) than remotely-sensed
Nipah Belt controls (23% ± 9.8, p=0.04; 0.49 patches/km2 ± 0.20, p=0.001; 20 meters/km2 ±
5.3, p=0.004; 6.3% ± 10, p=0.000) after controlling for human population density.
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2.6 Discussion
Our study demonstrates that villages inside the Nipah Belt of Bangladesh have significantly higher human population density and forests that are more fragmented than in the rest of the country. The finding that human population density correlates with spillover risk is not unexpected, but has not been shown previously. However, our findings that landscape factors also correlate with Nipah virus spillover risk are novel and suggests that these factors could be used to understand the distribution of Nipah cases in other regions.
Our study also shows that the roosting ecology of P. giganteus is associated with forest fragmentation, and that canopy density and tree species composition in roosts and degree of forest fragmentation in a village are associated with Nipah virus spillover. The number of roosts was higher in villages with more fragmented forests, although the number of bats in a village was not. This suggests that roost colony size is limited by tree availability and that P. giganteus can occupy fragmented landscapes. Studies have found that other bats with generalist diets thrive in fragmented forests with numerous small patches of remnant forest due to their diverse diet and ability to travel long foraging distances that allow them to utilize patchy landscapes that other forest-obligate species cannot (Gorresen and Willig 2004). Likely the same applies to P. giganteus.
We also found that biodiversity of tree species in roosts increased as the degree of forest fragmentation increased. Because villages with high human population density had the most fragmented forests in our study, the biodiversity in these Nipah Belt roosts is likely due to species-rich homegardens (Kabir and Webb 2008). A survey of homegardens found that on average, a single Bangladeshi household is growing 34 tree species (Kabir and Webb 2008). A
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71 benefit for Pteropus living in fragmented forests is that these mixed landscapes may provide a more consistent food source than habitats comprised solely of pristine forest species (Gorresen and Willig 2004). Some have suggested that agroforestry may support larger Pteropus populations than would be viable without human cultivated crops (Stier and Mildenstein 2005), suggesting that the P. giganteus population in the Nipah Belt may expand alongside the human population (Streatfield and Karar 2008).
Within the Nipah Belt, villages where there have been Nipah virus spillovers had more fragmented forests compared to neighboring communities. In these fragmented forests, P. giganteus tended to settle in several, small roosts scattered throughout the villages rather than in one large roosting colony. This roosting behavior, perhaps in response to the forest structure, could have implications for Nipah virus spillover. In these fragmented forests, the combination of more people and sporadic P. giganteus colonies could increase the likelihood that P. giganteus will feed on human food resources, both fruits from homegardens and date palm sap collection containers. Future assessments should assess differences in P. giganteus feeding behavior across gradients of forest fragmentation. Secondly, forest fragmentation could affect Nipah transmission within the P. giganteus population if roost colony size affects intra-population viral transmission dynamics. Future research should assess viral prevalence in roosting colonies with different sized populations and whether these roosting colonies represent distinct metapopulations or artificial divisions within a single colony to understand these relationships.
Two tree species have potential importance for Nipah virus spillover. The presence of a roost distinguished by silk cotton (Bengali: shimul) or Indian mast trees (Bengali: debdaru) was
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72 associated with an increased risk of Nipah virus spillover. Both trees are known Pteropus food resources (Elmqvist et al. 1992; Singaravelan and Marimuthu 2004; Vendan and Kaleeswaran
2011), have short flowering durations in the winter, and silk cottons (“morphologically a perfect bat plant” (Pierson and Rainey 1992)) have chiropterophilous (Marshall 1983) traits that facilitate pollination by Pteropus (Elmqvist et al. 1992; Singaravelan and Marimuthu 2004)
(e.g. brightly colored, open and increase nectar production at night when bats are feeding
(Elmqvist et al. 1992)). A possible link between these tree species and Nipah dynamics is that because these trees flower during the season of limited fruit resources in Bangladesh, they are visited by high concentrations of P. giganteus during their short flowering periods (Elmqvist et al. 1992). As a result of this “watering hole effect” where bats congregate around limited resources, saliva or other bodily fluids containing Nipah virus could be more likely exchanged when drinking from the same flower or during defensive behavior, behaviors that have been observed in Pteropus populations feeding on silk cottons in the Pacific Islands (Elmqvist et al.
1992). The result could be an increase in the infection prevalence of bats living near these food resources and increased likelihood of spillover to humans when bats drink from date palm sap containers. However, roosts with these indicator species only occurred in one-third of case villages, suggesting that although these species may be indicators of spillover risk, they are not a prerequisite. These trees could be indicators of forest fragmentation. Indian mast trees are often planted as ornamental border trees (Nagendra and Gopal 2010) so their presence could be associated with human impact on the landscape. Future studies are needed to assess the infection prevalence of bat colonies in relation to their roosting habitat.
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Several village and roost characteristics were not associated with Nipah virus spillover in this study including geographic size of a village and distance of roosts to the village boundary or nearest household. This finding supports the idea that although presence of P. giganteus near human activity is a likely precursor of viral spillover, there are other processes that determine the probability that spillover occurs. Our study suggests that the composition and structure of the landscape shared by P. giganteus and humans may be two drivers.
We relied on community members to identify and locate roosts. It is possible that we missed isolated roosts that respondents were not aware of, which would underestimate the number of roosts in less populated areas, like those in regions outside the Nipah Belt, perhaps making the number of roosts inside and outside the belt more comparable. However, this bias is unlikely because most roosts are highly conspicuous due to the bats’ size and noisy demeanor. Roosts without bats present during our study (inactive roosts) present another possible bias. Our results were the same when we assessed total roosts or restricted our analysis to only active roosts, so this bias is also unlikely to affect our conclusions.
The resolution of our satellite-derived forest cover map was 250 m, which made it difficult to identify small forest patches. If small patches were misclassified as non-forest, particularly in areas with highly fragmented forests, then our fragmentation measures would be underestimated and the association between fragmentation, spillover status, and bat roost abundance would have been conservative.
While it is important to note that human population density appears to be a key driver of
Nipah virus risk in rural Bangladesh, this study supports the hypothesis that the geographic distribution of Nipah virus spillover is also influenced by the configuration of suitable P.
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74 giganteus habitat and the roosting ecology of these bats. Although bats are present in similar numbers throughout Bangladesh, the abundance of roosts is higher in the region where all
Nipah outbreaks have occurred, suggesting that the distribution of these bats across the village landscape may increase risk. Although the precise mechanism is unknown, perhaps the fragmented forest landscape increases overlap between human and bat food resources, specifically in a region of high human population density. Additionally, tree species composition in roosts is associated with risk of Nipah virus spillover and may influence P. giganteus interactions and viral transmission within bat communities.
As the Bangladeshi population continues to grow to over 200 million by 2050
(Streatfield and Karar 2008), more undisturbed forest areas will become sites for homesteads and expanding villages. Ecosystem and land-use changes have played a significant role in infectious disease emergence and re-emergence in humans, including malaria, yellow fever, hantavirus, leishmaniasis, and hemorrhagic fevers (Campbell-Lendrum et al. 2001; Lindblade et al. 2000; Poletto et al. 2005; Suzán et al. 2008; Wolfe et al. 2005). Understanding how these changes alter the forest structure, and in turn, the habitats and the ecology of disease reservoirs may provide insights to reduce the spillover of zoonotic disease agents (Halpin et al. 2007;
Wood et al. 2012).
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Acknowledgements
We would like to thank Hossain M.S. Sazzad, Golam Dostogir Harun, A.K.M. Dawlat
Khan, and Sonia T. Hegde for their invaluable administrative and logistical support during the field component of this study. Our sincerest gratitude to the icddr,b field staff for their dedication to collecting accurate and complete data. Thank you to M.A. Yushuf Sharker and
Ron Gangnon for their statistical support. Finally, many thanks to the study respondents for their time and for welcoming us into their homes and communities.
This research investigation was funded by the NSF/NIH Ecology and Evolution of
Infectious Diseases grant number 2R01-TW005869 (Daszak, PI) from the Fogarty International
Center and the NSF IGERT grant number 0549407 (Patz, PI): the CHANGE-IGERT in the
Nelson Institute at the University of Wisconsin-Madison.
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Table 2.1. Characteristics of laboratory-confirmed Nipah spillover case villages and control villages inside and outside the Nipah Belt, Bangladesh ("field-validated sample," n=100)
All Nipah Belt Nipah Belt villages (case Case villages villages vs Nipah belt villages and Nipah Outside belt vs Nipah Belt outside belt Case villages controls Belt controls) controls controls controls Characteristics* Units (n = 21) (n = 38) (n = 59) (n = 41) p-value† p-value†
HUMAN POPULATION CHARACTERISTICS Human population density (10 km buffer) people / km 2 1333 ± 566 1299 ± 448 1311 ± 489 895 ± 440 0.80 <0.0001 Number of households 232 ± 157 173 ± 150 194 ± 154 252 ± 308 0.16 0.27 Village area km2 4.0 ± 13 1.2 ± 2.1 2.2 ± 8.1 1.1 ± 1.0 0.36 0.31 PTEROPUS POPULATION CHARACTERISTICS Total bats roosting within 5 km of village 618 ± 806 659 ± 683 646 ± 716 603 ± 994 0.85 0.82 Bat population density bats / km 2 86 ± 270 147 ± 503 128 ± 441 11 ± 28 0.56 0.06 ROOST CHARACTERISTICS Villages with at least one identified roost 20/21 (95%) 35/38 (92%) 55/59 (93%) 32/41 (78%) 1.00‡ 0.03 Number of roosts (active and non-active) 3.0 ± 1.9 2.4 ± 1.7 2.6 ± 1.8 1.6 ± 1.5 0.18 0.004 identified within 5 km of village boundary Number of active roosts identified within 5 km 1.6 ± 1.3 1.8 ± 1.3 1.7 ± 1.3 1.2 ± 1.0 0.53 0.05 of village boundary Mean tree height m 13 ± 3.7 14 ± 2.6 13 ± 3.0 17 ± 4.4 0.24 <0.0001 Mean tree diameter at breast height (DBH) cm 32 ± 20 40 ± 28 38 ± 25 38 ± 22 0.29 0.96 Mean percent canopy cover 53 ± 12 64 ± 13 60 ± 14 61 ± 22 0.002 0.91 Mean tree species richness 7.4 ± 2.7 6.4 ± 3.5 6.8 ± 3.2 6.0 ± 2.9 0.30 0.26 Mean distance to village boundary km 1.5 ± 1.0 1.6 ± 1.1 1.6 ± 1.1 1.7 ± 1.5 0.91 0.62 Mean distance to nearest household m 300 ± 744 127 ± 332 183 ± 504 74 ± 208 0.37 0.17 TREE SPECIES COMPOSITION OF ROOSTS ‡ Silk cotton and Indian mast tree Roosts 6/18 (33%) 1/34 (3%) 7/52 (14%) 1/32 (3%) 0.01 0.15 Bamboo Roosts 10/18 (56%) 26/34 (77%) 36/52 (69%) 17/32 (53%) 0.12 0.14 Banyan Roosts 4/18 (22%) 4/34 (12%) 8/52 (15%) 2/32 (6%) 0.42 0.31 Raintree and Mahagony Roosts 9/18 (50%) 15/34 (44%) 24/52 (46%) 18/32 (56%) 0.69 0.37 Teak Roosts 0/18 (0%) 1/34 (3%) 1/52 (2%) 1/32 (3%) 1.00 1.00 VILLAGE LAND COVER CHARACTERISTICS Percent forest cover (10 km buffer) 24 ± 4.3 27 ± 12 26 ± 10 33 ± 22 0.20 0.04 Forest patch density (10 km buffer) No. patches / km2 0.52 ± 0.14 0.48 ± 0.20 0.50 ± 0.18 0.32 ± 0.21 0.40 <0.0001 Forest edge density (10 km buffer) edge length (m) / km2 22 ± 3.3 21 ± 6.2 21 ± 5.3 18 ± 6.4 0.75 0.01 Largest forest patch index (10 km buffer) % of village 3.6 ± 1.5 9.0 ± 13 7.1 ± 11 19 ± 26 0.02 0.01 *Data presented as means ± 1 SD unless otherwise noted †Based on two-tailed, independent groups t-tests unless otherwise noted; Satterwaite method was used when p<0.05 for the F-test for equality of variances ‡Shows number of villages where a roost in that species cluster was identified / total villages in the case or control group where a roost was found and an ecological assessment was conducted; p-value results are based on χ 2 test or two-tailed Fisher exact test when tabular cell counts were <5 79
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Table 2.2. Multivariate logistic regression models showing the odds of being a Nipah spillover village compared to control villages inside and outside the Nipah Belt, and odds of being a Nipah Belt control village compared to outside belt control villages, Bangladesh (field-validated sample, n=100)*
Nipah Belt Case villages Case villages controls vs vs Nipah Belt vs outside outside belt controls belt controls controls Village characteristics Unit OR (95% CI)† p-value OR (95% CI)† p-value OR (95% CI)† p-value Human population density Increase of 1.1 (0.9, 1.2) 0.30 1.3 (1.0, 1.6) 0.06 1.1 (1.0, 1.3) 0.10 100 people per km 2 Number of active roosts Increase of 1 0.7 (0.4, 1.4) 0.37 1.4 (0.4, 4.7) 0.56 1.4 (0.8, 2.6) 0.24 identified within 5 km of village roost boundary Mean tree height of roosts Decrease of 1.1 (0.8, 1.3) 0.62 1.7 (1.1, 2.5) 0.01 1.3 (1.0, 1.6) 0.03 located within 5 km of village 1 m boundary
Mean percent canopy cover of Decrease of 2.0 (1.1, 3.6) 0.02 2.1 (0.9, 5.3) 0.10 0.8 (0.6, 1.2) 0.26 roosts located within 5 km of 10% village boundary At least one roost in a village Presence of 10.8 (1.3, 90.6) 0.03 5.8 (0.4, 75.4) 0.18 0.3 (0.0, 9.0) 0.46 classified as Silk cotton and cluster Indian mast tree roost Forest patch density (10 km Increase of 1 1.1 (0.8, 1.7) 0.53 2.8 (1.1, 7.1) 0.03 1.1 (0.8, 1.5) 0.44 buffer) patch per km2
*Results are from Firth conditional logistic regression, used to correct for quasi-separation in data †Adjusted for all predictors in the table
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Table 2.3. Correlation matrix of characteristics of villages from field-validated sample (n=100)
Number Mean of active percent and Number Mean tree canopy Species Human inactive of active height in cover in richness in Percent Forest Forest population roosts roosts roosts roosts roost sites forest patch edge Village characteristics* density ‡ reported† identified† identified† identified† identified†§ cover ‡ density‡ density ‡
Number of active and 0.30 inactive roosts reported † (0.003) Number of active roosts 0.30 0.78 identified† (0.002) (<0.0001) Mean tree height in roosts -0.21 -0.07 0.02 identified† (0.06) (0.50) (0.86) Mean percent canopy cover 0.002 -0.03 -0.06 0.18 in roosts identified† (0.98) (0.77) (0.62) (0.11)
Species richness in roost 0.09 0.02 -0.19 -0.35 -0.09 sites identified †§ (0.40) (0.86) (0.09) (0.001) (0.41) Percent forest cover‡ -0.20 -0.18 -0.13 0.28 0.37 -0.30 (0.05) (0.07) (0.19) (0.01) (0.001) (0.01)
Forest patch density ‡ 0.32 0.24 0.22 -0.22 -0.01 0.05 -0.54 (0.001) (0.02) (0.03) (0.04) (0.96) (0.63) (<0.0001)
Forest edge density‡ 0.40 0.21 0.17 -0.06 0.33 -0.09 0.38 0.15 (<0.0001) (0.04) (0.09) (0.58) (0.002) (0.43) (<0.0001) (0.13)
Largest forest patch index ঠ-0.40 -0.27 -0.20 0.38 0.26 -0.36 0.94 -0.65 0.12 (<0.0001) (0.01) (0.05) (0.000) (0.02) (0.001) (<0.0001) (<0.0001) (0.25)
*Data presented as Pearson's correlation coefficient (p-value); Bolded values are significant †Within 5 km of village boundary ‡In a 10 km buffer from village center §Mean number of tree species found in roost sites ¶ Percent of village area occupied by the largest forest patch
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Table 2.4. Characteristics of laboratory-confirmed Nipah spillover case villages (identified from 2001 to 2011) and 20,000 remotely- sensed control sites inside and outside the Nipah Belt, Bangladesh ("remotely-sensed sample," n=20,057).
All Nipah Belt Outside Case Nipah Belt villages (case belt villages vs villages vs Nipah Belt villages and remotely- Nipah Belt outside belt remotely- remotely- sensed remotely- remotely- sensed sensed Nipah controls sensed sensed Village Case villages controls Belt controls) (n = controls controls characteristics* Units (n = 57) (n = 10,000) (n = 10,057) 10,000) p-value† p-value†
Human population people / 1572 ± 2524 1381 ± 1355 1382 ± 1364 873 ± 760 0.57 <0.0001 density ‡ km2 Percent forest cover‡ % 22 ± 4.1 23 ± 9.8 23 ± 9.8 38 ± 27 0.04¶ <0.0001
Forest patch density ‡ # patches / 0.55 ± 0.14 0.49 ± 0.20 0.49 ± 0.20 0.30 ± 0.21 0.001¶ <0.0001 km2 Forest edge density‡ edge length 21 ± 3.6 20 ± 5.3 20 ± 5.3 16 ± 8.3 0.004¶ <0.0001 (m) / km 2 Largest forest patch % of village 3.5 ± 1.9 6.3 ± 10 6.3 ± 10 24 ± 31 0.000¶ <0.0001 index‡§ *Data presented as means ± 1 SD †Human population density comparison is based on a two-tailed, independent groups t-test; Forest metric comparisons are based on χ 2 results from logistic regression controlling for human population density ‡In a 10 km buffer from village center §Percent of village area occupied by the largest forest patch ¶The comparison of case villages to Nipah Belt controls is a Satterwaite-adjusted χ 2 to account for the large difference in sample size
! ! ! ! ! ! ! !
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(! Nipah Case Villages (! Nipah Belt Control Villages (! (! (! Outside Belt Control Villages (! (! (! (!(! Nipah Belt (!(! Rivers and Inland Water (!(!(! (!(!(! (! (! (! (! (! (! (! (! (! (! (!(! (! (! (! (! !(! (! (! (! ( (! (! (! (!! (! (! (! (! (! ( (! (! ! (! (! ( (! (! (!(! (!(!(!(! (! (! (!(! (!(! (! (!(!(! (! (! (! (! (! (! (! (! (! (! (! (! (! (!(! (! (!(!(! (! (! (! (! (! (! (! (! (! µ (! (! 0 50 100 Kilometers
Figure 2.1. Location of field-validated villages (n=100).
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! !
1600 20 0.6
*" 18 2 *" 1400 0.5
2 16 1200 *" 14 0.4 1000 12 800 10 0.3 meters peoplekm / 600 8 0.2 400 6 4 200 0.1
2 numberpatcheskm of / 0 0 0 Human population density Mean tree height Forest patch density (within 10 km village buffer) (within 10 km village buffer)
4 2 70 25 3.5 *" 60 3 *" *" 20 50 2.5 15 2 40 1.5 30 10
percent 1 20 5
numberroosts of 0.5 10
0 0 totallength edgekm of / (m) 0 Number of active and inactive roosts Mean percent canopy cover Forest edge density reported within 5 km of village (within 10 km village buffer) 2.5 35 **" 25 30 2 *" 20 25 1.5 20 15 1 15
percent 10 *" 10 0.5 numberroosts of 5 *" 5 0 0 percentvillageof landscape
occuppiedbylargest patchforest 0 1 Number of active roosts reported Percent of roosts in a village classifed Largest patch index within 5 km of village boundary as Silk cotton / Indian mast tree cluster (within 10 km village buffer)
Case Villages ecological risk factors for Nipah virus spillover Nipah Belt Controls unique characteristics of the Nipah Belt Nipah Belt Villages compared to the rest of the country Outside Belt Controls
Figure 2.2. Comparing human and bat population indicators, P. giganteus roost characteristics, and village forest metrics in Nipah virus case and control villages, Bangladesh, field-validated sample (n=100). Data are presented as means with error bars showing ± SE. An (*) or (**) over the Case Villages bar indicates significant t-test or Fisher's exact test results, respectively, at the α=0.05 level compared with Nipah Belt Controls, an indicator of an ecological risk factor for Nipah virus spillover ( ).
An (*) over the Nipah Belt Villages bar indicates a significant t-test result at the α=0.05 level compared to 84
the the Outside Belt controls, an indicator of a unique ecological characteristic of the Nipah Belt compared to the rest of the country ( ). The colors of the columns correspond to the key in Figure 2.1. 85
3CHAPTER 3
Roosting behavior and habitat selection of Pteropus giganteus in Bangladesh reveals potential links to Nipah virus epidemiology
Micah B. Hahn1, 2, Jonathan H. Epstein3, Emily S. Gurley4, Mohammad S. Islam5, Stephen P. Luby4,6, Peter Daszak3, Jonathan A. Patz1,2
1Nelson Institute for Environmental Studies, SAGE (Center for Sustainability and the Global Environment), University of Wisconsin-Madison, Madison, WI, United States 2Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin- Madison, Madison, WI, United States, 3EcoHealth Alliance, New York City, NY, United States, 4International Center for Diarrheal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh, 5Center for Environmental and Geographic Information Services, Dhaka, Bangladesh, 6Centers for Disease Control and Prevention, Atlanta, GA, (current: Stanford University), Stanford, CA, United States
Abstract
In Bangladesh, Pteropus giganteus (Indian flying fox) is the reservoir for Nipah virus, which causes illness and death in people. Identifying P. giganteus habitat preferences could assist in understanding the risk of zoonotic transmission. We analyzed characteristics of P. giganteus roosts and constructed an ecological niche model to identify suitable habitat in Bangladesh.
Compared to non-roost trees, P. giganteus roost trees are taller (19.9 v 12.2m, p<0.001), have larger diameters (53.1 v 22.3cm, p<0.001), and are more frequently canopy trees (88.6 v
20.8%, p<0.001). Colony size was larger in densely forested regions and smaller in flood- affected areas. Roosts were predominately located in areas with lower annual precipitation and higher human population density than non-roost sites. We predicted that 2-17% of
Bangladesh’s land area is suitable roosting habitat. Nipah spillover villages were 2.6 times
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86 more likely (95% CI 1.2, 5.8) to be located in areas predicted as highly suitable
(probability>0.6) compared to non-outbreak villages.
3.1 Introduction
Many species of flying fox (genus Pteropus) are declining worldwide (Mildenstein et al. 2005; Stier and Mildenstein 2005), due to growing human populations and consequent demands for food and housing that cause destruction of bat habitat (Fujita 1991; Mickleburgh et al. 2002). Nearly 300 plant species rely on flying foxes for seed dispersal, and in turn, these plants produce almost 500 different products such as food, medicine, and timber (Fujita 1991).
Additionally, flying foxes play a key role in forest regeneration because of their ability to retain viable seeds in their gut for several hours (Shilton et al. 1999), their long-distance foraging movements (Epstein et al. 2009; Tidemann and Nelson 2004), and their flight paths over forest clearings that are generally avoided by other forest animals (Fujita 1991; Thomas 1982). Bats are also increasingly recognized as important reservoir hosts for viruses that can cause serious human and animal disease (Calisher et al. 2006; Halpin et al. 2007). In Bangladesh, Pteropus giganteus fruit bats have been implicated as the primary reservoir of Nipah virus (Luby et al.
2009a), a disease that was recognized in the country in 2001 and has caused human outbreaks almost every year since (Luby et al. 2009b).
Despite the ecological, economic, and public health significance of flying foxes, little is known about their habitat requirements, particularly in Southeast Asia (Mildenstein et al.
2005). Understanding their habitat selection can provide information for the design of forest management strategies that preserve roosting and foraging landscape (Crampton and Barclay
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1998; Mildenstein et al. 2005). Furthermore, preventing viral spillover from bats to humans requires understanding the ecological narrative linking bat habitat with human and livestock activity and explains when, where, and why a virus emerges (Halpin et al. 2007).
Roosting ecology of bats is driven by factors such as roost abundance and availability, distribution and abundance of food resources, and predation risks (Kunz 1982). Pteropus are dietary generalists that forage on a variety of fruit, nectar, and pollen (Fujita 1991; Palmer et al.
2000), and as a result these bats often have seasonal roosts that follow the fruiting and flowering of tree species (Palmer and Woinarski 1999). There is also evidence that Pteropus choose sites near riparian zones because they utilize rivers as landmarks for navigation
(Mildenstein et al. 2005). Pteropus tend to roost in large aggregations on exposed canopy branches, and there is evidence of long-term roost fidelity when habitat is undisturbed by humans (Pierson and Rainey 1992).
Previous studies of Pteropus habitat selection have demonstrated its hierarchical nature
(Johnson 1980). At the broadest level, studies have shown that Pteropus home range selection is driven in part by climate (Gould 1978) and land cover (Jenkins et al. 2007). Studies of roost site selection within the home range have found preferences for particular tree species and degree of canopy cover (Gumal 2004). At the finest scale, studies have assessed food resource use and observed selection based on fruit preference, availability, and nutritional value (Banack
1998) as well as in relation to configuration of food resources within the landscape (Walton and
Trowbridge 1983). The idea that landscape heterogeneity affects ecological processes over a variety of scales is well established (Levin 1992; Turner 2005, 1989; Wiens 1989), and other
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In this study, we describe the characteristics and landscape context of P. giganteus roost sites across Bangladesh. Our study objectives were: (1) to understand P. giganteus roost habitat preferences at the tree-level and in relation to human settlements and the broader landscape, (2) to assess P. giganteus roosting behavior across environmental gradients, and (3) to evaluate the use of maximum entropy modeling to identify suitable roosting habitat in unstudied areas throughout Bangladesh.
3.2 Study area
This was a countrywide study conducted in Bangladesh from December 2011 to
February 2012. Bangladesh is located south of the Himalayas within the world’s largest delta
(the Ganges), which drains into the Bay of Bengal. This varied geography provides some of the most fertile agricultural land in the world, but the low-lying plains that make up 80% of the country’s landmass are subject to frequent flooding, particularly during monsoon season.
Within Bangladesh, remnant tracts of native forest are rapidly being replaced by cropland to meet the needs of one of the densest populations in the world (FAO 2000; Lepers et al. 2005).
Forest cover has declined from 14% of Bangladesh’s land area in 1989 (Giri and Shrestha
1996) to just over 7% in 2006 (Bangladesh Space Research and Remote Sensing Organization
2007).
Bangladesh has a tropical to subtropical monsoon climate and typically experiences four distinct seasons: a hot, humid summer (March to May); a muggy, wet monsoon season
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(June to September); a cooler, less humid post-monsoon season (October to November); and a cool, dry winter (December to February). Temperatures are coolest in January, averaging around 26°C and warmest in April at around 35°C (Karmalkar et al. 2012). In some regions, temperatures drop to 10°C during the winter and reach more than 40°C during the summer
(Kabir and Webb 2008). Most regions receive over 2,000 mm of rainfall per year with 80% falling during monsoon season (Karmalkar et al. 2012).
3.3 Materials and Methods
3.3.1. Sample selection and locating roosts
Study sites were randomly selected among villages that have experienced a Nipah virus spillover event (where the virus was apparently introduced from a non-human source), known as “spillover villages” and among those that had not (control sites). Control sites were selected by creating a geographically random sample of points throughout Bangladesh (excluding areas within 5 km of spillover villages) that were linked to the nearest village by the field teams in situ using Garmin eTrex GPS devices and GoogleEarth (Figure 3.1).
Field teams identified the location of P. giganteus roosts (arboreal sites where P. giganteus sleep, mate, or otherwise remain during the day) in a village and within 5 km of the village boundary though interviews with community members. Data were collected for roost sites occupied for any amount of time within the past five years. The field teams took GPS coordinates at each roost and noted whether the roost was active (currently inhabited by bats) or inactive as well as the number of bats present.
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3.3.2. Measuring roosting behavior and structural characteristics of roost sites
Interviews were conducted with residents in the household nearest each roost about the duration and seasonality of bat activity at the roost (Appendix B). An environmental assessment was conducted at the two largest, active roosts closest to the village center
(Appendix C). Field teams used transect lines to delineate a 20x20 m plot around the central roost tree, which was visually selected as the tree with the largest number of bats. Within each plot, they recorded tree species, diameter at breast height (DBH), tree height (using a clinometer), and canopy versus sub-canopy designation for each tree with DBH > 4 cm. Trees with bats present were marked as “roost” trees and empty trees as “non-roosts.” Canopy cover was measured using densitometer readings every 1 m along the transect lines. Field teams recorded information about human activities within 50 m of the roost plot boundary.
3.3.3. Derivation of remotely-sensed roost site characteristics
Three random locations for each observed roost site were selected from within the
Bangladesh country boundary but excluding the 20x20 m area around an identified roost using
Geospatial Modeling Environment (Beyer 2012) to characterize the habitat available to P. giganteus within the study area (henceforth called “available sites,” Figure 3.1).
We used ArcGIS 9.3, FRAGSTATS (McGarigal et al. 2012), and Geospatial Modeling
Environment (Beyer 2012) to calculate the distance to the nearest river / perennial water body and to derive measures of land cover and forest fragmentation, climate patterns, and human disturbance within 1 km of each roost and available site (Table 3.1).
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To capture land cover, we obtained a land cover map derived from several satellites including MODIS (2 April 2012, 250 m), IRS Pan (2000-2005, 5. 8m), ASTER (2003-2008, 15 m), IRS P6 LISS (2005, 23.5 m), and GoogleMap. Classification results were resampled at a resolution of 250 m. Forest fragmentation was measured at each site by calculating the edge density (sum of the length of all forest pixels bordering non-forest pixels / 1 km village buffer), patch density (number of forest patches / 1 km village buffer), and largest patch index (percent of the 1 km village buffer occupied by the largest contiguous forest patch) using an eight- neighbor rule (McGarigal et al. 2012). We also derived a continuous measure of forest proximity by calculating the distance of each pixel to the nearest forest pixel.
We derived percent of the area around each roost/available site that experienced flooding using a maximum flood extent map creating using RADARSAT images acquired in monsoon seasons between 2000 and 2004. We also calculated the mean Vegetation Condition
Index (VCI) and the percent of the roost site area that has experienced drought conditions (VCI
<35%) from a VCI map used for drought monitoring. The VCI maps were derived using NDVI
MODIS 16 day composite images acquired between 2000 and 2009. All land cover data were selected as measures of food and shelter availability, the most fundamental requirements for habitat selection (Findley 1993).
We tested eight bioclimatic variables in our models from the WorldClim database
(Hijmans et al. 2005). These data were constructed via interpolation from a 50-year time series of weather station data (Hijmans et al. 2005). We obtained elevation above sea level from the
Shuttle Radar Topography Mission (SRTM) model (Jarvis et al. 2008). We included latitude as a proxy measure for climate (Kuemmerle et al. 2011).
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To characterize human disturbance, we calculated human population density and road density around roost/available sites from the Landscan dataset (Oak Ridge National Laboratory
2011) and a road network map (World Food Programme 2010), respectively. We also calculated the distance of each pixel to the nearest road for our ecological model. The
WorldClim, SRTM, and Landscan data have been used frequently in habitat selection studies
(De Knegt et al. 2011; Kuemmerle et al. 2011).
3.3.4. Statistical analysis
3.3.4.1 Roost tree selection
We compared the attributes of roost trees with non-roost trees within the roost plots where we conducted environmental assessments to evaluate P. giganteus roost tree selection.
We used independent sample t-tests to compare DBH and tree height, and a χ2 a test to compare the percentage of roost and non-roost trees that were identified as canopy trees. We used a binomial exact test to compare the abundance of tree species that comprise roost trees and non- roost trees to identify “selected” and “avoided” species (Sedgeley and Donnell 2004). If the abundance within the roost trees was significantly greater than expected based on its abundance within the plots, we considered it a preferred, or “selected” species. Alternatively, if use of the tree species as a roost tree was less than expected based on its general abundance, then we labeled it as an “avoided” species. If there was no significant difference between use as a roost and availability in the plots, then the species was considered “used at random.”
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3.3.4.2 Assessing roosting behavior in relation to environmental characteristics of roosts
We used independent sample t-tests to test for significant differences in environmental characteristics of roosts grouped by roosting duration (less than 10 years versus longer-term roosts) and by seasonal occupancy (seasonally-occupied versus year-round), as reported by key informants. We used Spearman rank correlation coefficients to test for associations between the number of bats in an active roost and roost characteristics. We considered p ≤ 0.05 significant.
We used non-parametric ordination methods and PC-ORD software (McCune and
Mefford 2010) to group roosts into clusters based on the basal area of the tree species present, calculated using our DBH measurements. We removed species from the database that occurred in fewer than 5% of roosts because rare species provide little reliability for identifying ecological clusters (McCune et al. 2002, 2000). The final data matrix contained 142 roosts and
30 tree species. We log transformed these species data to reduce the effect of very abundant species (Field et al. 1982; McCune et al. 2002). We used the Bray Curtis distance similarity measure (Bray and Curtis 1957) and group average linking for hierarchical cluster analysis
(Field et al. 1982). We used Multi-response Permutation Procedures (MRPP) to test for differences between identified clusters and indicator species analysis with 1000 permutations
(McCune et al. 2002) to identify the diagnostic species that drove cluster formation based on their abundance and fidelity in the roost plots. We used χ2 and Fisher’s Exact tests to examine associations between the cluster to which a roost was assigned and seasonality and years of roost activity, and analysis of variance (ANOVA) to test for associations with size of roost colony.
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3.3.4.3 Roost site selection: maximum entropy modeling
We used independent sample t-tests to test for significant differences in environmental characteristics of roosts versus available sites. Then we used maximum entropy modeling to identify areas of suitable P. giganteus habitat across Bangladesh based on locations of roost sites from our field study.
We used Maxent software, version 3.3.3 (Phillips et al. 2006) to build our ecological niche model. Maximum entropy modeling is a machine learning method (Phillips et al. 2006) that only requires occurrence data (Phillips et al. 2006), is not very sensitive to small sample sizes (Wisz et al. 2008), and has been shown to consistently out-perform more traditional approaches in terms of predictive power and ability to handle noisy data (Elith et al. 2006). To prevent collinearity in our model, which can affect the interpretation of variable influence from the MaxEnt output (Phillips et al. 2006), we selected 10,000 random points across Bangladesh and calculated the pairwise Pearson’s correlation coefficient for all potential predictors. We only included variables where r<0.75 in the same model (Dormann et al. 2012).
Our first model incorporated all land cover, climate, and human disturbance variables.
Next, we excluded mean diurnal range and temperature annual range because they were highly correlated with other variables and contributed the least amount of information to model.
Finally, we excluded annual mean temperature because it contributed <1% of the information to the model.
To validate our model, we withheld 25% of our roost locations for cross-validation. We also calculated the area under the curve (AUC), which indicates whether the model predicts the species distribution better than a random model and can be used to compare several working
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We used jackknife analysis of the AUC values of the training data and the percent contribution output from Maxent to determine which variables were most predictive of suitable P. giganteus habitat and response curves to assess how P. giganteus roost selection was affected by each environmental variable.
In order to assess possible bias in the model results related to sampling effort (i.e. different sample selection bias in the occurrence records compared to the background sample used by Maxent) (Elith et al. 2011), we ran the Maxent model three more times, restricting the study area first to 10 km around study villages, then to 25 and 50 km. We compared these results to identify areas where the model output was not consistent and to improve our predictions of suitable P. giganteus roosting habitat throughout Bangladesh.
3.4 Results
We located 215 roosts (Figure 3.1) and completed key informant interviews at each of these sites. Of these, 68% (n=147) were active roosts where at least one bat was present at the time of the assessment. We conducted an environmental assessment at 143 of identified roosts
(see above for selection methodology), and of these, 81% (n=115) were located outside the village boundary but within the 5 km search buffer.
3.4.1. Roost tree selection
Within the roost sites, 3782 trees were surveyed representing 78 tree species and 34 families. Roost trees were taller (19.9 m ± 7.4 v 12.2 m ± 6.5), had larger diameters (53.1 cm ±
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56.6 v 22.3 cm ± 38.3), and were more frequently canopy trees than non-roost trees (88.6 v
20.8%) (Table 3.2). Roost tree selection was non-random with respect to tree species (χ2=
672.12, df=34, p<0.0001, Table 3.3). For example, bamboo, Albzia spp., eucalyptus, and
Shorea robusta accounted for only 23% of all trees surveyed but 53% of roost trees. Similarly, a higher proportion of roosts were found in Ficus bengalensis, Neolamarckia cadamba, Ceiba pentandra, and Gmelina arborea compared to their availability. Conversely, Swietenia mahagoni, Mangifera indica, Artocarpus heterophyllus, Diospyros peregrina, Aphanamixis polystachya, Areca catechu, and Azadiracta indica, were selected as roost trees to a lesser extent than would be expected based on their relative abundance across roost sites.
3.4.2. Roost plot characteristics
Of the 143 roosts where we completed an environmental assessment, 87% of roost sites were located within 50 m of homestead activities which include the primary residence or buildings associated with the home (kitchen, bathroom, and animal house were generally separate structures) or the household’s water pump. 59% of roosts were located near a standing water source such as a large pond, and 55% were within 50 m of agricultural land. Although the majority of roosts were located outside the village boundary, there were no roosts without human activities within 50 m including the homestead activities listed above, other buildings such as schools or mosques, agriculture, a man-made water source, or a road.
The mean stand basal area of the roost plots was 140.2 m2/ha ± 186.7 (Table 3.2). The average canopy cover was 61% ± 18.6, with sites ranging from 21 to 100% canopy cover.
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Roost plots contained as few as one tree species and as many as 17, and the mean species richness was 6.4 ± 3.5.
3.4.3. Roosting behavior
Key informants reported that 65% of the roosts had been occupied intermittently for 10 or more years. The mean DBH of trees in roosts that had been occupied for less than 10 years was smaller than that of trees in long-standing roosts (30.8 cm ± 15.2 vs 43.1cm ± 35.4, p=0.01). No other land cover, climate, or human disturbance variables were significantly different between these comparison groups.
Respondents described over 87% of the roosts as “year-round,” meaning that bats were present throughout the year rather than seasonally. Of the 28 roosts that were identified as
“seasonal” by respondents, 18 roosts (64%) were occupied in only one of the four seasons
(rainy, post-monsoon, winter, and summer). The mean diurnal temperature range was lower in seasonal roosts compared to year-round roosts (9.3°C ± 0.7 vs 9.7°C ± 0.6, p=0.002). Also, the elevation above sea level in seasonal roosts was higher than year-round roosts (21.6 m ± 13.6 vs 14.1 m ± 12.8, p=0.03). No other land cover, climate, or human disturbance variables differed significantly by roost seasonality.
The mean number of bats in the active roosts was 387 ± 543, and the largest roost had over 2,700 bats. The number of bats in a roost increased with the percent canopy cover (
=0.24, p=0.01), the percent of the 1 km buffer area around a roost covered by forest ( =0.31, p=0.00), the amount of contiguous forest around the roost ( =0.37, p<0.0001), and distance to
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98 the nearest river ( =0.19, p=0.03). The size of the roosting colony decreased with the amount of flood-affected area around a roost ( =-0.23, p=0.01) and forest patch density ( =-0.24, p=0.01). No other land cover, climate, or human disturbance variables were significantly correlated with the number of bats in a colony.
The ordination analysis grouped the roosts into 5 significant clusters, containing between 2 and 74 roosts (Table 3.4). Our MRPP analysis yielded an effect size of A=0.26
(p=0.00) indicating good separation between the clusters. Indicator species analysis identified diagnostic tree species for each cluster using “Indicator Values” (IV) that range from 0 (no indication) to 100 (perfect indicator, meaning that the species points to that cluster with no error). Significance results from the Monte Carlo analysis indicate whether the IV is no larger than would be expected by chance (Table 3.4). The significance cut-off was relaxed (max =
0.067) so that the cumulative IV for each cluster was >85%.
There was no significant association between roost tree species cluster and duration or seasonality of roost activity. The mean number of bats in a roost colony was related to tree species composition (ANOVA = 2.54, p=0.04, Table 3.5). Raintree / mahagony roosts were more likely to support larger roosting colonies than Bamboo roosts (623 bats ± 708 vs 312 bats
± 440).
3.4.4. Roost site selection: maximum entropy modeling
P. giganteus tended to roost in areas of the country with less annual precipitation
(2085.5 mm ± 432.1 vs 2268.4 mm ± 581.1, Table 3.2) and that experience a greater range in
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P. giganteus also tended to roost in areas with less flooding (37.8% ± 23.2 vs 53.0% ± 30.8) and drought (14.5% ± 20.7 vs 18.5% ± 25.1). Human population and road density estimates were higher in roost sites compared to available sites (1746 people/km2 ± 3334 vs 959 people/km2 ± 992; 9.0 km of road/km2 ± 3.6 vs 6.5 km of road/km2 ± 4.3). Roosts were found in more fragmented forests than were the available sites (patch density = 0.85 patches/km2 ±
0.42 vs 0.73 patches/km2 ± 0.49).
A total of 135 unique roost sites were used to construct the ecological niche model and
45 were withheld for testing. Our initial Maxent model that utilized all land cover, climate, and human disturbance variables had a high predictive value (AUC=0.884). After dropping the correlated variables, the AUC was 0.882. Models with and without annual mean temperature were similar, so our final model which included 10 predictor variables (Table 3.6) had an
AUC=0.882.
The model output produced an estimated probability of occurrence of a P. giganteus roosting site for each pixel across Bangladesh. Our final model predicted the most suitable P. giganteus roosting habitat (probability >0.6) in a north-sound band running from central
Rangpur Division to the Faridpur region, south of where the Padma and Jamuna rivers join and extending southwest towards the Sundarbans (Figure 3.2). There was also a small patch of suitable habitat northeast of the city of Sylhet. Relatively suitable habitat (probability >0.4-0.6) was predicted primarily in the central part of the country with small suitable areas on the western coast of Chittagong Division and distributed throughout Sylhet Division. Areas shown
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Our final model predicted approximately 21,500 km2 of suitable P. giganteus roosting habitat, about 17% of Bangladesh’s land area (probability>0.5, Figure 3.3). The amount of suitable habitat was cut by more than half (10,000 km2, ~8% of Bangladesh) when the probability threshold was increased to 0.6 and dropped to just over 3,000 km2, ~2% of
Bangladesh at a threshold of 0.7.
Our results from modeling habitat suitability within restricted geographic areas around study villages demonstrate overall consistency with the model produced using the Bangladesh country boundary (Figure 3.4). Compared to the full country model, the model within 10 km of study villages shows higher P. giganteus suitability in villages in the lower third of the country near the eastern and western coasts. The models within 25 and 50 km of study villages show the areas of suitable habitat in a more focused area in the western part of the country compared to the full country model that shows suitable areas dispersed throughout eastern Bangladesh.
These restricted models also extend the area of suitable habitat along the northwest and western boundaries.
The variables that contributed the most information to the models were human population density, distance to roads, annual precipitation, and elevation, together accounting for 65-81% of the information in the geographically restricted and full country models (Table
3.6). The jackknife analysis of the AUC values from the training data showed that population density and annual precipitation were the two variables with the highest individual gains when used in univariate models. The largest loss in training gain occurred if annual precipitation was
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The response curves of probability of P. giganteus presence across the range of each environmental variable corresponded to our univariate comparisons presented above (Table
3.2). These curves showed that P. giganteus were more likely to select roost sites in areas with lower annual precipitation, low elevation, and high human population density that are closer to forests, roads, and water. Villages where there were Nipah virus spillovers between 2001-2011 were more likely (OR=2.6, 95% 1.2-5.8) to be located in areas identified as most suitable for P. giganteus compared to villages where there have been no reported outbreaks (Figure 3.2).
3.5 Discussion
Pteropus roost selection is likely strongly influenced by food availability and food proximity (Palmer and Woinarski 1999). One explanation for P. giganteus roosting preference in forests near areas of high human density is that homestead gardens provide a diversity of food resources that may not be present in natural forests. More than 20 million households in
Bangladesh maintain a home garden, and a survey of over 400 homesteads in southwestern
Bangladesh found an average of 34 plant species per garden (Kabir and Webb 2008). We also found that roosts were located in highly fragmented forests. Gorresen and Willig (2004) observed that the abundance of generalist frugivorous bats was positively associated with fragmentation of the landscape and proposed that their ability to feed on a variety of plant species allowed them to utilize heterogeneous landscapes. In a review of the genus Pteropus,
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Pierson and Rainey (1992), found that P. giganteus was one of the species that has been documented in forest remnants in populated areas as opposed to only in undisturbed natural forests. We also found that roosts were often near water, which may be used as a drinking source and are common in Bangladeshi villages.
Pteropodid bats are unusual within the Megachiroptera in their propensity to roost within trees in large aggregations that range from tens to thousands (Marshall 1983). Our finding that taller, larger, canopy trees were preferred as roosting sites may be because they provide more space for these large colonies (Gumal 2004). We found that the size of the bat colony was associated with the tree species composition in the roost site, likely to due to the architectural differences between raintrees and bamboo that allow a larger number of bats to congregate in the numerous branches of the former. Others have suggested that these large bats roost in tall trees because they need room to free-fall during take off (Pierson and Rainey
1992). We found that P. giganteus tended to roost in bamboo, Albzia spp., Eucalyptus spp., and Shorea robusta, among others. Other studies have also found that Pteropus tend to roost in only a subset of the available tree species (Gumal 2004; Pierson and Rainey 1992; Vardon et al.
2001). The reason for this is unknown, but it has been suggested that Pteropus prefer to roost in thick foliage for sun or rain protection (Vardon et al. 2001). Several of the tree species that were found within 20 m of the primary roost trees but were not frequently used for roosting have been documented elsewhere as food resources for Pteropus (Chakravarthy and Girish
2003; Markus and Hall 2004; Stier and Mildenstein 2005). One possibility is that in addition to selecting roost sites based on the roost tree, P. giganteus also choose sites near food resources
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103 but keep their roosting and feeding sites separate, a behavior that has been noted for other
Pteropus bats (Pierson and Rainey 1992).
The majority of roosts identified in our study had been occupied for more than 10 years.
This finding is consistent with observations that colonial megabats tend to have high roost fidelity (Marshall 1983; Pierson and Rainey 1992), although some have observed Pteropus fidelity to a home range rather than a single roost (Gumal 2004). The only environmental characteristic of roost sites that was associated with duration of roost occupancy was the mean diameter at breast height (DBH) of trees in the roost plot. The trees in more recently occupied roosts were smaller, which could be because they are simply younger trees that only became viable roost trees within the previous 10 years. We also found that bats inhabited most roosts year-round according to key informants. The elevation was higher and there was less variation in the day to night temperatures (diurnal range) in the small percentage of seasonal roosts we identified. Lower-lying areas tend to be more humid and experience less daily variation in temperatures, which may affect the consistency of food resources for P. giganteus. Most seasonal movements of Pteropus tend to be related to birthing season (Pierson and Rainey
1992) or changes in food abundance (Nelson 1965).
In addition to garnering information for conservation of this ecologically important species, a dual purpose of these habitat-modeling efforts is to improve our understanding of
Nipah virus ecology in Bangladesh. The location of suitable habitat for the viral reservoir will likely influence the geographic distribution of risk of viral spillover from bats to humans
(Halpin et al. 2007). An overlay of Nipah spillover locations on our habitat suitability modeling results shows that the majority of spillover events have occurred in regions predicted
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104 as highly suitable for P. giganteus roosting sites. The overlap is more pronounced in the models within restricted geographic areas around the study villages (discussed below).
Moreover, although we searched for roosts up to 5 km away from village boundaries, not one identified roost site was located more than 50 m from areas of human activity. This finding underscores the overlap in P. giganteus habitat and human settlements in the densely populated country. Refining and improving our knowledge of P. giganteus roosting and foraging habitat may provide further insight into the conditions that lead to spillover of Nipah virus into humans in Bangladesh.
Our field work was focused in and around villages in Bangladesh while our available comparison sites (and background sites used by Maxent) were random locations across the country. Consequently, it is possible that the identified roost sites were more likely to be located near human populations than the available sites. Similarly, there are potential biases in occurrence-only data including spatial autocorrelation and correlation with roads or other means of access that make occurrence locations easier to find (Elith et al. 2011; Phillips et al.
2006). We looked for roosts up to 5 km from the village boundary, which ensured that there was opportunity to identify roosts away from human populations. And although human population and road density were significant predictors in the ecological niche model, it is clear that these variables were not independently driving the habitat suitability map because there are densely populated areas that were predicted as low suitability such as near the large cities of
Dhaka and Chittagong. Sample selection bias can also manifest if the background locations used by Maxent are sampled from a different area than the presence locations (Elith et al.
2011). Our results from the Maxent models run in restricted geographic areas around study
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105 villages demonstrate the effect of using presence records from in and around study villages to predict suitable habitat for a much larger area in Bangladesh. Although the predicted habitat suitability results are fairly consistent across these models, there are some areas of inconsistency, particularly near the edges of our field work extent. Based on these results, if we continued our search for P. giganteus roosts in northwestern Bangladesh in Rangpur and
Rajshahi divisions and in the Sundarban mangrove forests, the areas of high suitability predicted by our model would likely extend into these regions that are currently unpredictable based on our model and our estimates of available P. giganteus habitat would be larger. It is, however, reassuring that all the maximum entropy models based on occurrence only data and the univariate comparisons of used and available roosts yield similar results. Future studies that extend the search for P. giganteus roosts into these unexplored regions are important for refining the habitat suitability model. In addition, this study was limited to the winter months.
Investigation of roosting locations in other times of year would also add to the accuracy of our model.
In summary, we found that P. giganteus show roost habitat selection preferences at the sub-forest level and at scales of several kilometers. For example, these bats appear to show preferences in terms of tree species and tree characteristics, degree of forest fragmentation, rainfall and temperature gradients, and level of human disturbance. We predicted that only 2-
17% of Bangladesh’s land area is currently suitable roosting habitat for P. giganteus, although this is likely a conservative estimate. Within these areas, humans and bats share significant natural resources. In order to conserve this keystone species and prevent spillover or emergence of zoonotic viruses, it is imperative that we continue to improve our understanding
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106 of Pteropus resource requirements and characteristics of the bat-human interface. The results presented in this study can be utilized to aid large-scale predictions of Pteropus habitat under future climate change scenarios (Daszak et al. 2012) and guide targeted surveillance of disease spillover from Pteropus reservoirs to humans and animals.
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Acknowledgements
Hossain M.S. Sazzad, Golam Dostogir Harun, A.K.M. Dawlat Khan, and Sonia T.
Hegde provided logistical support for the field component of this research. We would like to extend our gratitude to the icddr,b field staff for their dedication to collecting accurate and complete data. We also thank Tony Goldberg, Monica Turner, and Ron Gangnon for their helpful comments that greatly improved the manuscript.
This research investigation was funded by the NSF/NIH Ecology and Evolution of
Infectious Diseases grant number 2R01-TW005869 from the Fogarty International Center and the NSF IGERT grant number 0549407: the CHANGE-IGERT in the Nelson Institute for
Environmental Studies at the University of Wisconsin-Madison.
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Table 3.1. Derived variables, units, year of raw data acquisition, original resolution and satellite, and source of remotely sensed data
Year raw data Original resolution Derived variable Units acquired and satellite Source
LAND COVER Distance to nearest river km from roost/available site 1995 (updated vector data International Steering 2000) Committee for Global Mapping (2000) Percent forest/rural settlement % forest pixels within buffer 2000-2012 250m (resampled after CEGIS (2012) cover classification); MODIS, IRS Pan, ASTER, IRS P6 LISS Forest patch density # forest patches / km2 2000-2012 same as above CEGIS (2012) Forest edge density forest edge length (m) / km2 2000-2012 same as above CEGIS (2012) Largest patch index % of buffer region occupied 2000-2012 same as above CEGIS (2012) by largest contiguous forest patch Percent flooded area % pixels within buffer that 2001-2004 50m; RADARSAT CEGIS (2012) have experienced flooding ScanSAR Average vegetation condition 0-100% (where 100% = 2000-2009 500m; MODIS CEGIS (2012) index optimal vegetation condition) Percent extreme drought area % pixels within buffer where 2000-2009 same as above CEGIS (2012) VCI <35%
CLIMATE Annual mean temperature mean within buffer in °C 1950-2000 30 arc s (~1km) Hijmans et al. (2005) Mean diurnal range mean within buffer in °C 1950-2000 30 arc s (~1km) Hijmans et al. (2005) Mean temperature of warmest mean within buffer in °C 1950-2000 30 arc s (~1km) Hijmans et al. (2005) quarter*
Mean temperature of coldest mean within buffer in °C 1950-2000 30 arc s (~1km) Hijmans et al. (2005) Temperaturequarter annual range mean within buffer in °C 1950-2000 30 arc s (~1km) Hijmans et al. (2005) Annual precipitation mean within buffer in mm 1950-2000 30 arc s (~1km) Hijmans et al. (2005) Precipitation of warmest quarter mean within buffer in mm 1950-2000 30 arc s (~1km) Hijmans et al. (2005) Precipitation of coldest quarter mean within buffer in mm 1950-2000 30 arc s (~1km) Hijmans et al. (2005) Elevation mean within buffer in m 2000 1km Jarvis et al. (2008) Latitude ° from UTM 46N projection 2012 ------for roost/available site
HUMAN DISTURBANCE Human population density people / km 2 2010 30 arc s (~1km) Oak Ridge National Laboratory (2011) Road density length of road (km) / km2 1995 (updated vector data World Food 2010) Programme (2010)
*A quarter is a three month period
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Table 3.2. Characteristics of Pteropus giganteus roost trees versus non-roost trees and roost sites versus random comparison sites
Variable* Roost features Comparison features p-value†
ROOST TREE CHARACTERISTICS Roost Trees Non-Roost Trees Diameter at breast height (cm) 53.1 ± 56.6 22.3 ± 38.3 <0.001 Height (m) 19.9 ± 7.4 12.2 ± 6.5 <0.001 Percent canopy trees 88.6 20.8 <0.001
ROOST PLOT CHARACTERISTICS‡ Roost Plots Species richness 6.4 ± 3.5 Percent canopy cover 61.0 ± 18.6 Percent roosts with <50% canopy cover 29.3 Mean stand basal area (m2/ha) 140.2 ± 186.7
Roost Sites Random Comparison ROOST SITE CHARACTERISTICS (1 km buffer) (n = 215) Sites (n = 645) p-value† Land Cover Distance to river (km) 1.9 ± 1.8 2.2 ± 2.4 0.06 Percent forest/rural settlement cover 30.5 ± 16.0 31.3 ± 29.2 0.60 Forest patch density (patches/km2) 0.85 ± 0.42 0.73 ± 0.49 0.001 Forest edge density edge length (m) / km2 21.5 ± 7.1 16.2 ± 10.0 <0.0001 Largest patch index (%) 22.4 ± 17.9 24.9 ± 28.6 0.15 Percent flooded area 37.8 ± 23.2 53.0 ± 30.8 <0.0001 Average vegetation condition index (%) 73.0 ± 22.2 67.5 ± 24.3 0.004 Percent drought area 14.5 ± 20.7 18.5 ± 25.1 0.02
Climate Annual mean temperature (°C) 25.5 ± 0.4 25.5 ± 0.5 0.62 Mean diurnal range (°C) 9.8 ± 0.6 9.6 ± 0.8 0.0002 Mean temperature of warmest quarter (°C) 28.7 ± 0.4 28.6 ± 0.6 0.001 Mean temperature of coldest quarter (°C) 19.6 ± 0.7 19.9 ± 0.8 <0.0001 Temperature annual range (°C) 22.5 ± 1.6 21.8 ± 2.1 <0.0001 Annual precipitation (mm) 2085.5 ± 432.1 2268.4 ± 581.1 <0.0001 Precipitation of warmest quarter (mm) 895.0 ± 277.2 936.5 ± 300.5 0.07 Precipitation of coldest quarter (mm) 31.9 ± 6.4 34.4 ± 7.2 <0.0001 Elevation (m) 17.4 ± 9.7 26.0 ± 42.4 <0.0001 Latitude 24.3 ± 1.1 23.8 ± 1.2 <0.0001
Human disturbance Human population density (people/km2) 1746 ± 3334 959 ± 992 0.001 Road density (km/km2) 9.0 ± 3.6 6.5 ± 4.3 <0.0001
*Data presented as means ± 1 SD unless otherwise noted †Based on two-tailed, independent groups t-test or χ 2 test results ‡Roost plots were defined as the 20x20 m area around the central roost tree; No comparison plots were evaluated in the field for this study Tables
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Table 3.3. Pteropus giganteus roost tree selection (preferred and avoided species) based on use of a species as a roost tree versus prevalence of the species in surveyed roosts
Proportion Proportion Common name Scientific name Bangla name Family all trees roost trees p-value* Preference †
Bamboo Bambusoideae‡ bash Poaceae 0.099 0.201 <0.0001 selected raintree/koroi/ Raintree/Koroi Albizia ‡ "acacia" Fabaceae 0.054 0.145 <0.0001 selected eucalyptus/ Eucalyptus Myrtaceae§ akashi Myrtaceae 0.038 0.101 <0.0001 selected Gajari Shorea robusta gajari/shal Dipterocarpaceae 0.038 0.087 <0.0001 selected Mehogani Swietenia mahagoni mehogani Meliaceae 0.134 0.071 <0.0001 avoided Indian Mast Tree Polyalthia longifolia ‡§ debdaru Annonaceae 0.055 0.049 0.52 random Teak Tectona grandis‡ shegun Lamiaceae 0.030 0.042 0.06 random Mango Mangifera indica‡§ amm Anacardiaceae 0.066 0.035 0.001 avoided Banyan Ficus bengalensis‡§ bot/pakore Moraceae 0.009 0.033 <0.0001 selected Kadam Neolamarckia cadamba kadam Rubiaceae 0.010 0.028 <0.0001 selected Jackfruit Artocarpus heterophyllus kathal/chambol Moraceae 0.040 0.023 0.02 avoided Cotton Silk Ceiba pentandra‡§ shimul Malvaceae 0.011 0.023 0.001 selected Mabolo/Ebony Diospyros peregrina § gaab Ebenaceae 0.041 0.020 0.01 avoided Beechwood Gmelina arborea gamari/pitagora Lamiaceae 0.006 0.015 0.005 selected Mulberry Morus jam Moraceae 0.014 0.010 0.42 random Pithraj Aphanamixis polystachya pit raj/royna Meliaceae 0.036 0.006 <0.0001 avoided Rough Bush Streblus asper sheora Moraceae 0.014 0.006 0.07 random Tamarind Tamarindus indica‡§ tatul Fabaceae 0.004 0.006 0.46 random Dumur Ficus carica§ dumur Moraceae 0.011 0.004 0.09 random Blackboard Tree Alstonia scholaris ‡ chatim Apocynaceae 0.004 0.004 0.96 random Pitali Trewia nudiflora ‡ pitali/pithkhuli Euphorbiaceae 0.004 0.004 0.78 random Indian Ash Tree Lannea coromandelica§ jigha Anacardiaceae 0.009 0.003 0.10 random Ipil Ipil Intsia bijuga ipil ipil Fabaceae 0.007 0.003 0.17 random Indian Rosewood Dalbergia sissoo‡ shishu Fabaceae 0.005 0.003 0.47 random Betel Nut Areca catechu § shupari Arecaceae 0.104 0.001 <0.0001 avoided Neem Azadirachta indica § neem Meliaceae 0.009 0.001 0.04 avoided Palmyra Palm Borassus flabellifer‡§ tal Arecaceae 0.006 0.001 0.12 random Bishop Wood Bischofia javanica uriam/puia Phyllanthaceae 0.004 0.001 0.26 random Monkey Jack Artocarpus lacucha dewa Moraceae 0.003 0.001 0.38 random Hijol Barringtonia Acutangula hijol Lecythidaceae 0.003 0.001 0.48 random Date Palm Phoenix sylvestris khejur Arecaceae 0.003 0.001 0.48 random Banana Musa paradisiaca§ kala Musaceae 0.021 0.000 - not used Coconut Palm Cocos nucifera ‡ narikel Arecaceae 0.012 0.000 - not used Sacred Fig Ficus religiosa ‡§ peepal Moraceae 0.005 0.000 - not used Custard Apple Annona reticulata § ata Annonaceae 0.004 0.000 - not used
*Based on binomial exact test †If the abundance of a tree species within the roost trees was significantly greater than expected based on its abundance within the plots, we considered it a preferred, or “selected” species. Alternatively, if use of the tree species as a roost tree was less than expected based on its general abundance, then we labeled it as an “avoided” species. If there was no significant difference between use as a roost and availability in the plots, then the species was considered “used at random.” ‡Tree species has been documented in the literature as a Pteropus giganteus roosting site §Tree species has been documented in the literature as a Pteropus giganteus food source
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Table 3.4. Diagnostic tree species identified in indicator species analysis
Indicator † Indicators species value (IV) p-value Roost Cluster A (n=46)* Albzia (Raintree) 44.0 0.049 Swietenia mahagoni (Mahagony) 42.4 0.062
Roost Cluster B (n=74) Bambusoideae (Bamboo) 96.8 0.001
Roost Cluster C (n=9) Ceiba pentandra / bombax (Silk cotton) 58.2 0.018 Polyalthia longifolia (Indian mast tree) 31.4 0.067
Roost Cluster D (n=2) Tectona grandis (Teak) 93.0 0.003 Roost Cluster E (n=11) Ficus bengalensis (Banyan) 95.6 0.001 *n = number of roosts assigned to cluster †Species were included based on p-value and IV contribution to a roost cluster
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Table 3.5. Duration of Pteropus giganteus roost occupation, roost seasonality, and size of roost colony by classification of P. giganteus roost site tree species composition
Roosts Roosts Roost tree species occupied occupied for Seasonal Year-round Mean size of composition cluster <10 years 10+ years p-value* roosts roosts p-value* roost colonies †
Raintree and Mahagony 38% (17/45) 62% (28/45) 0.69 13% (6/46) 87% (40/46) 0.93 623 ± 708 Roosts (n=46) ‡
Bamboo Roosts (n=73) 38% (28/73) 62% (45/73) 0.46 11% (8/73) 89% (65/73) 0.53 312 ± 440 Silk cotton and Indian 33% (3/9) 67% (6/9) 1.00 11% (1/9) 90% (8/9) 1.00 170 ± 283 mast tree Roosts (n=9) Teak Roosts (n=2) 50% (1/2) 50% (1/2) 1.00 0% (0/2) 100% (2/2) 1.00 500 ± 283 Banyan Roosts (n=11) 9% (1/11) 91% (10/11) 0.10 18% (2/11) 82% (9/11) 0.63 586 ± 809
ANOVA Test Statistic 2.54 (p=0.04) §
*Based on χ2 test or two-tailed Fisher exact test when tabular cell counts were <5 †Data presented as means ± 1 SD ‡n = number of roosts assigned to cluster §Raintree / Mahagony vs Bamboo roosts have significantly different roost colony sizes; No other comparisons were significantly different
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Table 3.6. Mean, range, and percent contribution of land cover, climate, and human disturbance variables to the habitat suitability model for Pteropus giganteus in Bangladesh
Percent contribution
10 km 25 km 50 km around around around Full study study study country villages villages villages Variable Units Mean Range model model model model Human population density people/km2 1111.5 (0, 151746) 18.4 39.8 22.8 21.2 Distance to road km 9.1 (0, 61.5) 13.6 20.2 17.2 14.9 Annual precipitation mm 2227.4 (1334, 4492) 23.1 13.5 16.0 18.8 Elevation m 29.0 (-9, 911) 18.6 7.7 9.7 11.4 Distance to forest km 1.1 (0, 66.6) 6.7 7.6 8.7 6.9 Mean temperature of coldest quarter °C 19.9 (16.9, 21.8) 7.3 4.4 9.7 8.6 Distance to river/water body km 1.9 (0, 27.9) 0.9 2.7 2.0 0.9 Precipitation of coldest quarter mm 34.4 (18, 70) 4.9 2.2 5.9 7.0 Precipitation of warmest quarter mm 915.4 (350, 2000) 5.2 1.4 6.2 8.4 Mean temperature of warmest quarter °C 28.6 (23.3, 30.3) 1.3 0.5 1.8 1.8
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! Nipah Spillover Villages
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0 75 150 Kilometers
Figure 3.2. Predicted probability of suitable conditions for Pteropus giganteus roosts in Bangladesh with location of human Nipah spillover cases from 2001-2011. The probability of suitable habitat in blue regions is unpredictable based on the results of this study. See Figure 3.4 for an indication of model certainty across the country and areas where future research is needed to refine the model. Bangladesh’s administrative divisions are labeled for geographic reference.
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Probability of suitable Probability of suitable habitat modeled within habitat modeled within 10 km of study villages 25 km of study villages
Probability of suitable Probability of suitable habitat modeled within habitat modeled within Bangladesh 50 km of study villages (no geographic restriction)
0 95 190 Kilometers
Probability of Suitable Conditions Figure 3.4. Predicted probability of suitable unpredictable conditions for Pteropus giganteus roosts in >0.10 - 0.25 N Bangladesh using variable buffer sizes around >0.25 - 0.40 study villages. Comparison of the maximum >0.40 - 0.60 W E entropy model output across these restricted >0.60 - 1.0 S geographies can help identify areas where sample Water selection bias likely affects the results and predict what P. giganteus suitability would look like if the search for roosts was extended into the outlying regions of Bangladesh where model predictions 3 12
are uncertain (shown in blue). 124
4CHAPTER 4 ! Influence of deforestation, logging, and fire on malaria in the Brazilian Amazon
Micah B. Hahn1,2, Ronald E. Gangnon2, Christovam Barcellos3, Gregory P. Asner4, Jonathan A. Patz1,2 1Nelson Institute for Environmental Studies, SAGE (Center for Sustainability and the Global Environment), University of Wisconsin-Madison, Madison, WI, United States, 2Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin- Madison, Madison, WI, United States 3Health Information Research Department, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil, 4Department of Global Ecology, Carnegie Institution for Science, Stanford University, Stanford, CA, United States
Abstract
Malaria is a significant public health threat in the Brazilian Amazon. Previous research has shown that deforestation creates breeding sites for the main malaria vector in Brazil, Anopheles darlingi, but the influence of selective logging, forest fires, and road construction on malaria risk has not been assessed. To understand these impacts, we constructed a negative binomial model of malaria counts at the municipality level controlling for human population and social and environmental risk factors. Both paved and unpaved roadways and fire zones in a municipality increased malaria risk. Within the timber production states where 90% of deforestation has occurred, compared with areas without selective logging, municipalities with moderate selective logging had the highest malaria risk (1.69, 95% CI 1.16-2.48), and areas with severe selective logging had the lowest risk (0.40, 95% CI 0.23-0.69). We show that roads, forest fires, and selective logging are previously unrecognized risk factors for malaria in the Brazilian Amazon and highlight the need for regulation and monitoring of sub-canopy forest disturbance.
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4.1 Introduction
Malaria remains a significant public health threat in Brazil despite decades of control efforts (Ferreira and Da Silva-Nunes 2010) and millions of dollars of financing (Barat 2006).
Agriculture, timber extraction, and gold mining in the Amazon (De Castro et al. 2006; Marques
1987) have transformed the forest fringes and created abundant larval breeding sites for
Anopheles darlingi (Marques 1987), the dominant malaria vector in Brazil (Barbieri et al. 2005;
Sawyer 1992). As a result, the transmission of “frontier malaria” has accompanied the migration of non-immune workers to the Amazon for these projects (De Castro et al. 2006;
Sawyer 1992). In 2007, 99.9% of Brazil’s nearly 460,000 cases of malaria originated in the
Amazon (Ferreira and Da Silva-Nunes 2010).
Driven by the expansion of cattle and soybean production (Kirby et al. 2006), annual deforestation rates in the Amazon in the last decade have ranged between 6,000-28,000 km2/year (Instituto Nacional de Pesquisas Espaciais 2012). By 2008, more than 17% of the pre-1970 forests had been cleared (Foley et al. 2007; Instituto Nacional de Pesquisas Espaciais
2012). Brazil’s National Institute for Space Research (INPE) has been monitoring deforestation in the Amazon via satellite and publishing annual rates of deforestation since
1988 (Instituto Nacional de Pesquisas Espaciais 2012). These data have been important for measuring the magnitude and spatial distribution of forest degradation (Foley et al. 2007;
Nepstad et al. 1999). However, it has been widely recognized that these coarse estimates do not account for forest alterations that significantly thin the canopy and kill understory biomass but do not eliminate it entirely, such as selective logging and fire (Asner et al. 2005; Foley et al.
2007; Kirby et al. 2006; Nepstad et al. 1999; Stone and Lefebvre 1998). These diffuse forest
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126 disturbances are difficult to distinguish in satellite images and are essentially invisible from space three years after logging occurs (Stone and Lefebvre 1998). Regional estimates show that selective logging in the five timber production states overlaps with the INPE deforestation estimates by only 6% (±5%) and that logging adds 60 to 123% more forest damage than has been reported using deforestation extent alone (Asner et al. 2005).
Research linking forest alteration and malaria in the Amazon has likewise focused on the effects of deforestation. In the Peruvian Amazon, the degree of deforestation was shown to be associated with the biting rate of A. darlingi (Vittor et al. 2006), and complementary larval studies point to an increase in preferred breeding habitat as the likely ecological mechanism
(Vittor et al. 2009). On a larger scale, Olson et al. (2010) found that a 4.3% increase in deforestation (from INPE’s satellite monitoring) increased malaria incidence 48% in health districts in Mancio Lima, Brazil. Studies cite an increase in larval breeding sites as the ecological link leading to an increase in A. darlingi (and subsequent malaria transmission), due to the species’ preference for a combination of shade and sunlight, deep, clear water with vegetation, and neutral to high pH (Charlwood 1996; Hiwat and Bretas 2011; Tadei et al.
1998), conditions generally found at forest margins rather than within an intact forest (Singer and De Castro 2001; Yasuoka and Levins 2007).
As Amazonian development continues to alter the forests (Kirby et al. 2006), an understanding of the impact on malaria epidemiology remains vital for targeting surveillance, treatment, and control strategies and setting land use policy that prevents surges in malaria transmission or reintroduction into areas where the disease was previously eradicated
(Aramburú Guarda et al. 1999). Furthermore, there is little information available on the effect
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127 of forest disturbances other than deforestation on malaria transmission and few studies that assess land use and malaria across the entire Brazilian Amazon basin. In this study, we examine deforestation, road density, fire, selective logging, and sociodemographic factors across the Brazilian Amazon from 1997-2003 and their association with malaria incidence in
2003 at the municipality (municipio) level. Our hypothesis is that our basin-wide analysis will show a similar positive association between deforestation rates and malaria incidence as seen in previous field and sub-municipality studies (Olson et al. 2010; Vittor et al. 2006, 2009).
Further, we hypothesize that forest disturbances from roads, fire, and selective logging will further increase malaria risk in a municipality.
4.2 Study Area
Our study was conducted within the Legal Amazon of Brazil (Figure 4.1). The Legal
Amazon region covers over half of Brazil’s land areas (Kirby et al. 2006), sustains 40% of the world’s remaining tropical forests (Kirby et al. 2006; Laurance et al. 2001), and hosts approximately a quarter of the world’s terrestrial species (Dirzo and Raven 2003; Malhi et al.
2008). The average annual temperature in the region is 22−26°C (72−79°F), and it is the wettest part of the country, with some areas receiving 2000-3000 mm of rainfall each year.
Although there is not much variation in the temperatures throughout the year, the Amazon region has a three to five month dry season annually. The five timber production states include
Roraima, Pará, Rondônia, Acre, and northern Mato Grosso (Figure 4.1). These states account for 90% of deforestation in the Brazilian Amazon (Asner et al. 2005; Instituto Nacional de
Pesquisas Espaciais 2012).
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4.3 Methods
4.3.1. Human population data and pre-processing
Malaria incidence data by municipality in 2003 were from the System of Epidemiologic
Surveillance of Malaria (SIVEP-Malaria) of the Brazilian Ministry of Health. Operating within the Brazilian National Program for Malaria Control, the SIVEP-Malaria monitoring system is highly developed and has extensive coverage across the country due its network of public health staff who record malaria case reports via the internet (Oliveira-Ferreira et al. 2010).
Additionally, the simplicity of the collection form and free malaria medication for all cases limits data collection error and ensures high sensitivity (Oliveira-Ferreira et al. 2010). Malaria was reported as the Annual Parasite Incidence (API), which is the number of positive blood slides per population x 1000 (cases/1000 inhabitants). Malaria counts were calculated using population estimates from the 2000 census.
We controlled for several sociodemographic risk factors for malaria in the Amazon.
Occupational risks were measured using a ratio of male-to-female inhabitants in the municipality and the percent of the population that worked primarily in outdoor occupations such as agriculture, forestry, or fishing and hunting. Socioeconomic status of households was measured using data from the 2000 census on the average number of people living in a household, the percent of the population who receive at or below minimum wage each month, and the percent rural and indigenous populations in each municipality. These data were also proxy measures for housing quality. Access to healthcare was measured by the average cost of transportation to the nearest state capital. GDP growth, transportation costs, and migration rates were calculated by the Brazilian Institute for Geography and Statistics (IBGE) and
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Brazilian Institute of Applied Economic Research (IPEA) and provided by the Brazilian
Climate and Health Observatory, Oswaldo Cruz Foundation (FIOCRUZ). All other data were downloaded directly from the Brazilian census website.
4.3.2. Land cover data and pre-processing
We obtained data on the land cover distribution in municipalities in 2003 (percent water, remaining forest, savanna (cerrado), and deforested land) detected via Landsat imagery by INPE’s deforestation monitoring program (Programa de Cálculo do Desflorestamento da
Amazônia, or PRODES). We also calculated historical deforestation rates from the PRODES data. The presence of roads has been implicated as a key driver of deforestation in the Amazon
(Kirby et al. 2006), and the land use change associated with road construction has been linked with malaria transmission (Yasuoka and Levins 2007). We calculated the paved and unpaved road density in municipalities using roadway maps that were digitized by IBGE and obtained from the Woods Hole Research Center, Datasets for Amazonia and the Cerrado. We calculated the percent of the municipality affected by old (1996-99) and recent (2000-02) forest fires with data from the World Resources Institute (Barreto et al. 2006). Selective logging data were available only within the five timber production states (Figure 4.1). These data were derived by Asner et al. (2005) using a computational analysis system (Carnegie Landsat Analysis
System, or CLAS) that integrates automated image analysis, atmospheric modeling, and pattern-recognition to detect logging disturbance from Landsat ETM+ imagery. Pre-processing was conducted using ENVI image analysis software (Research Systems Inc., Boulder, CO),
ArcGIS 9.3 (ESRI, Redlands, CA), and Geospatial Modeling Environment (Beyer 2012).
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4.3.3. Negative binomial regression modeling
All statistical analyses were performed using SAS, version 9.3 (SAS Institute Inc.,
Cary, NC). We described human population and land cover characteristics of the Amazon municipalities and then constructed several negative binomial log-linear regression models for counts of malaria cases in a municipality in 2003. For each municipality, the logarithm of the
2000 census population was included as an offset term. First, we modeled the effects of forest disturbance on malaria counts in the Legal Amazon, and then we limited our analysis to the five timber production states.
Sociodemographic risk factors (percent of population who migrated in the previous two years, male-to-female ratio, average number of people per household, percent rural population, percent of households living under minimum wage, average transportation costs to the nearest state capital, percent GDP growth from 2000 to 2006) and the land cover distribution in 2003
(percent of municipality that was water, remaining forest, and savanna) were forced into all models as covariates. The first model in the Legal Amazon explored the following deforestation variables: absolute deforestation in 2003, percent deforestation change from
1997-2000 (rate of deforestation between 1997-2000), percent change between 2000-2001,
2001-2002, and 2002-2003. The most significant time period was retained for subsequent models. The second and third models added unpaved and paved road density (2001) and percent of the municipality affected by old fires (1996-1999) and recent fires (2000-2002), respectively. This stepwise modeling approach was also used for modeling the municipalities in the five timber production states. An additional fourth model was constructed in the timber production states that included selective logging as a categorical variable – municipalities
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131 where 0-7% of the remaining forest was selectively logged between 1999-2002 were considered “moderately logged,” while municipalities with higher rates of selective logging were “severely logged.”
Coefficients are presented as rate ratios (RR). Residual standard deviation (SD) after controlling for all other covariates was used as the unit of change for all forest disturbance risk factors, and p=0.05 was considered statistically significant.
4.4 Results
4.4.1. Human population and land cover characteristics of municipalities
Malaria incidence was 22.0 (± 58.3) per 1000 inhabitants in the Legal Amazon and 37.7
(±76.2) per 1000 inhabitants within the timber production states (Table 4.1). Migration rates within the past two years were higher in the timber production states (7.0% of the population ±
5.4) compared to the full Amazon (4.1% of the population ± 5.5). The male-to-female ratio was just over one in all municipalities, and there were approximately 4.5 people per household throughout Amazonia. Approximately one-fifth of the population throughout the Brazilian
Amazon was living at or below minimum wage in 2000. Almost half of the population made their living through agriculture, forestry, or fishing and hunting. The average cost to travel to the nearest capital in 2000 was just under US$ 1,000 across the Amazon, although the upper end of the range for transportation costs was lower for residents in the timber production states.
In 2003, the average municipality in the Legal Amazon was 38% (±34) forested, 35%
(±33) deforested, and 25% (±34) savanna (Table 4.1 and Figure 4.2). Within the timber production states, the average municipality was 44% (±31) forested, 38% (±28) deforested, and
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10% (±19) savanna for the same period. The road density was 25 m/km2 (±27) in the Legal
Amazon and 31 m/km2 (±29) in the timber production states. The percent of the area in a municipality affected by fire between 1996-2002 was higher within the timber production states
(41% ± 31) compared to all of the Brazilian Amazon (19% ± 24). Approximately 1.7% (±3.8) of the timber production states was selectively logged between 1999-2002. This area was highly concentrated in northwest Mato Grosso and northeast Pará states (Figure 4.2).
4.4.2. Impact of forest disturbance on malaria in the Legal Amazon
We excluded municipalities where greater than 10% of the land area was unclassified due to cloud cover or where sociodemographic data were missing. Our models included 602
(77%) of the 782 municipalities in the Legal Amazon. None of the deforestation variables significantly predicted malaria rates in the first model at the broad municipality scale (Table
4.2).
The results of our second model showed that for each additional 19.0 m/km2 in unpaved roads in a municipality, the malaria risk increased by 51% (p<0.0001), after controlling for human population. Paved roads did not significantly affect malaria risk when paved and unpaved road density and deforestation were the only forest disturbances included in the model.
The final model in the Legal Amazon showed that deforestation rates were still not significant predictors of malaria incidence at the broad municipality scale, but the impact of unpaved roads increased slightly in this model, showing a 55% increase in malaria risk for each additional 19.0 m/km2 of unpaved roadways in a municipality (p<0.0001). Additionally, paved roads were a predictor of malaria risk, although with slightly less of an impact than unpaved
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133 roads – an additional 13.4 m/km2 in paved roads increased malaria risk by 22% (p=0.02).
Finally, a 18.8% increase in old fires zones or a 12.4% increase in recent fire zones increased malaria risk, 34% (p=0.001) and 37% (p=0.000), respectively.
4.4.3. Impact of forest disturbance on malaria within the timber production states
We included 246 of 289 (85%) municipalities in the timber production states with complete data in our models. The results from our first model in the timber production states showed that deforestation was not predictive of malaria risk at the municipality scale (Table
4.3). However, in our second model which included only recent deforestation and road density, for every 0.7% increase in deforested land in a municipality in 2003, malaria risk increased by
21% (p=0.04). Unpaved road density was a predictor of malaria risk – for each additional 18.5 m/km2 of unpaved roads in a municipality, malaria risk increased 42% (p=0.003). Paved road density did not significantly impact malaria rates in the model with deforestation and unpaved road density.
In the third model in the timber production states, the impact of current deforestation on malaria increased slightly (from 21% to 24% increase in malaria risk for each additional 0.7% of land that was deforested in 2003, p=0.02) and the impact of unpaved road density on malaria risk decreased slightly to a 40% increase in malaria risk for a 1 SD increase in unpaved road density (p=0.01). Paved road density was still not significant, and neither recent nor old fire zones affected malaria risk.
In the final model within the timber production states, deforestation in 2003 was no longer significant. Unpaved road density was still a predictor of malaria risk, although the
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134 impact was diminished when controlling for selective logging (27% increase in malaria risk for a 1 SD increase in unpaved road density (p=0.04)). Paved roads and forest fires were still not significant contributors to malaria risk. In comparison with areas with no selective logging, the risk for malaria was 1.69 times higher (95% CI 1.16-2.48) in areas that have experienced moderate selective logging pressure (0-7% of remaining forests were selectively logged between 1999-2002). In contrast, our results show the opposite association in severe selective logging areas (>7% of remaining forests were selectively logged) where malaria risk was 0.40 times lower than areas with no selective logging (95% CI 0.23-0.69). The most severe selective logging in our study was in Vera municipality in northern Mato Grosso where 43% of forests were affected so our results cannot be extrapolated past this level of logging impact.
4.5 Discussion
The relationship between malaria and the environment has been apparent since antiquity
(Sawyer 1992), but still today we seek to understand the connections between new land regimes, the mosquito vector, and human malaria incidence. We hypothesized that our basin- wide analysis would show a positive association between deforestation rates and malaria incidence as seen in previous field and sub-municipality studies (Olson et al. 2010; Vittor et al.
2006, 2009). Our study did not detect this relationship at the broad municipality level. Within the timber production states, current deforestation was a predictor of malaria when controlling for roads and fire, but was no longer significant when selective logging was considered. This inconsistent result suggests that the relationship between deforestation and malaria varies at a finer scale. Wiens (1989) provides several examples, from a variety of ecological systems, of
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135 the considerable effect that the scale of analysis can have on the relationships that are uncovered. Deforestation in the Amazon is a phenomenon occurring mostly in peripheral states, such as Mato Grosso, Rondonia, Pará, north of Tocantins and west of Maranhão (Figure
4.2b). It also tends to be spatially clustered within municipalities (Ferreira et al. 2007; Oliveira
Brandão Júnior et al. 2011), making it difficult to match levels of deforestation and malaria incidence across municipality boundaries. Utilizing a smaller level of analysis such as the health district (Olson et al. 2010) may be the most effective scale to assess variations in malaria incidence across a deforestation gradient.
Our results confirm our second hypothesis that forest disturbances from roads, fire, and selective logging increase malaria risk in a municipality. In contrast to past measures of deforestation, these forest disturbances are more diffuse (Figure 4.2c-e). Consequently, municipality-level measures of roads, fire, and selective logging are likely more representative of the actual level of forest disturbance and are more appropriate to compare with municipality- level malaria data. Although we assessed the impact of these forest disturbances on malaria independently, there is significant research demonstrating the overlap and cyclical nature of the relationships among these land use changes. In an assessment of biophysical, demographic, and infrastructural factors, Kirby et al. (2006) found that unpaved and paved roads were strong predictors of deforestation in the Amazon and furthermore, in areas with no road access, there was little deforestation. Transportation costs are the primary constraint on logging activity in
Brazil, and improved roadways would likely support expansion of this industry (Nepstad et al.
2001). Similarly, it has been shown that logging increases the fire vulnerability of otherwise naturally fire resistant Amazonian forests (Holdsworth and Uhl 1997; Malhi et al. 2008), and
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136 previously burned forests are more susceptible to future fire (Cochrane et al. 1999). Both fire and logging load the forest floor with combustible material and open the canopy, which speeds up the rate of fuel drying (Cochrane et al. 1999; Holdsworth and Uhl 1997; Nepstad et al.
2001). An implication of these linkages is that monitoring road construction might be an immediate and practical proxy approach to identifying high-risk malaria areas until malaria data are continuously available at a sub-municipality scale or deforestation can be detected at a fine enough scale across a large enough extent to monitor malaria risk associated with deforestation throughout the Amazon Basin (Laporte et al. 2000).
Over half (56%) of the municipalities in the timber production states were moderately selectively logged and as expected, also experienced more malaria cases compared to municipalities with no selective logging. Our finding that selective logging activity in over 7% of remaining forests in a municipality decreased malaria risk was unexpected. Holdsworth and
Uhl (1997) have described the significant differences in forest damage from low-impact and high-impact logging. Although these methods extract similar amounts of timber, high-impact logging creates significantly larger canopy gaps (Holdsworth and Uhl 1997). The selective logging documented by Asner et al. (2005) and used for this study was dominated by high- impact logging practices that left more than half of the forest with gap fractions between 10-
100% that were not fully recovered two years after harvest (Asner et al. 2006). In these areas, it is possible that the canopy was so significantly thinned that it prevented the accumulation of water bodies on the forest floor suitable for mosquito breeding. Other studies have shown that logging has substantial impacts on the understory light environment as well as many ecological processes such as nutrient cycling and hydrological function (Asner et al. 2009). Future
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137 research should assess rates of evapotranspiration, water accumulation, and larval breeding densities across a gradient of logging intensity. Additionally, although high-impact logging practices may initially decrease viable mosquito breeding habitat, the impact of forest regrowth on vector density is unknown. Ecosystem transformations during the recovery stages of high- impact selective logging may mirror the ecological transitions that support mosquito breeding and that are generally associated with frontier malaria, such as partial shade at forest fringes
(De Castro et al. 2006).
The unit of analysis for this study was the municipality. On one hand, this made a basin-wide analysis feasible, but it also prevented controlling for individual level risk factors and exploring finer scale relationships between forest disturbance and malaria. Additionally, although Brazil has a highly developed malaria surveillance system, we were not able to control for differences in surveillance intensity or quality across the Amazon. Our analysis was not spatially explicit. If high levels of deforestation occurred in a remote area of a municipality with few inhabitants while a large urban center nearby had low levels of malaria, our aggregated municipality level results might show that high deforestation rates are associated with low malaria risk. Finally, although the inclusion of selective logging in our models represents an advance in the analysis of forest disturbance and malaria risks in the Amazon, these data may suffer from the same limitations as the binary PRODES deforestation data because they are not qualified by type of logging practice. Future studies should assess the impact of logging severity and method on malaria risk. Our results also open the door for higher resolution and spatially explicit approaches to assess the impact of forest disturbances on malaria risk in the Amazon.
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In summary, we show that roads, fires, and selective logging are previously unrecognized risk factors for malaria in the Brazilian Amazon. Our approach utilized existing, high-reliability databases from government, university, and nonprofit research centers to understand the impact of forest disturbance on malaria risk in the most active land use frontier in the world (Food and Agriculture Organization 2006). As our capability to monitor forest changes via satellite continues to improve and malaria data become more readily available at finer spatial scales, these methods can provide a foundation for near real-time monitoring of disease spread to focus public health control efforts.
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Acknowledgements
This study was supported by the NSF IGERT grant number 0549407: CHANGE-IGERT in the
Nelson Institute for Environmental Studies at the University of Wisconsin-Madison.
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Table 4.1. Human population and land cover characteristics of municipalities
In the five timber In the Legal production Amazon states Characteristics (year)* N (n=782) Range N (n=289) † Range
HUMAN POPULATION CHARACTERISTICS Malaria incidence per 1000 population (2003) 740 22.0 ± 58.3 (0, 502) 282 37.7 ± 76.2 (0, 502) Percent of the population that has migrated in within the last two years (2000) 740 4.1 ± 5.5 (0, 41) 282 7.0 ± 5.4 (0, 41) Male:Female ratio (2000) 742 1.1 ± 0.1 (0.9, 1.5) 282 1.1 ± 0.1 (0.9, 1.5) Average number of people living in a household (2000) 742 4.5 ± 0.7 (3.8, 6.9) 282 4.5 ± 0.7 (3.3, 6.9) Percent rural population (2000) 742 47 ± 21 (0, 98) 282 52 ± 21 (0, 93) Percent indigenous population (2000) 740 2.1 ± 6.9 (0, 76) 282 2.4 ± 8.1 (0, 74) Percent of population who receive at or below minumum wage each month (2000) 742 23 ± 8 (7.0, 51) 282 19 ± 5 (7.0, 51) Percent of population that work in agriculture, forestry or fishing/hunting (2000) 742 47 ± 19 (1.3, 86) 282 45 ± 18 (1.3, 81) Transportation cost to the nearest state capitol in US$ (2000) 740 948 ± 805 (0, 5949) 282 957 ± 611 (14.0, 2928) Percent GDP growth (2000-2005) 740 18 ± 22 (-100, 91) 282 17 ± 22 (-100, 76)
LAND COVER CHARACTERISTICS Percent water (2003) 634 2.7 ± 6.1 (0, 44) 289 2.4 ± 5.5 (0, 34) Percent forest (2003) 634 38 ± 34 (0, 100) 289 44 ± 31 (0, 98) Percent deforested land (2003) 634 35 ± 33 (0, 100) 289 38 ± 28 (0, 94) Percent savanna (2003) 634 25 ± 34 (0, 100) 289 10 ± 19 (0, 97) Road density (m/km2) (2001) 782 25 ± 27 (0, 172) 289 31 ± 29 (0, 172) Percent affected by fire (1996-2002) 781 19 ± 24 (0, 93) 289 41 ± 31 (0, 112) Percent logged area (1999-2002) ------289 1.7 ± 3.8 (0, 31)
*Data presented as means ± 1 SD † Timber production states include Roraima, Pará, Rondônia, Acre, and northern Mato Grosso
Tables
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Table 4.2. Effect of forest disturbance (deforestation, roads, and fire) on malaria incidence at the municipality level in the Legal Brazilian Amazon
Model 1 § Model 2 Model 3
Variable* SD † RR‡ 95% CI p-value RR‡ 95% CI p-value RR‡ 95% CI p-value % deforestation 5.9 1.12 (0.87, 1.44) 0.37 ------(1997-2000) % deforestation 12.6 0.95 (0.80, 1.13) 0.54 ------(2000-2001) % deforestation 1.5 0.97 (0.85, 1.10) 0.63 ------(2001-2002) % deforestation 1.4 1.25 (0.97, 1.61) 0.08 1.25 (0.98, 1.60) 0.07 1.01 (0.84, 1.23) 0.88 (2002-2003) % deforested land 1.0 1.06 (0.87, 1.28) 0.58 1.08 (0.90, 1.28) 0.41 1.06 (0.89, 1.25) 0.52 (2003) Unpaved road 19.0 ------1.51 (1.27, 1.80) <0.0001 1.55 (1.31, 1.83) <0.0001 density (meters/km2) (2001) Paved road density 13.4 ------1.13 (0.95, 1.36) 0.17 1.22 (1.04, 1.45) 0.02 (meters/km2) (2001) % affected by old 18.8 ------1.34 (1.13, 1.57) 0.001 fire (1996-1999) % affected by recent 12.4 ------1.37 (1.17, 1.61) 0.000 fire (2000-2002)
*Variable (year of acquisition) †Residual standard deviation is the unit of change for all forest disturbance risk factors ‡Relative Risk §Models are adjusted for several sociodemographic and environmental risk factors at the municipality level including: percent of population who migrated in the previous 2 years, male to female ratio, average number of people per household, percent rural population, percent of households living under minimum wage, average transportation costs to the nearest capitol, percent GDP growth from 2000 to 2005, and land cover in 2003 including percent of municipality that was water, remaining forest, and savanna
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Table 4.3. Effect of forest disturbance (deforestation, roads, fire, and selective logging) on malaria incidence at the municipality level in the five timber production states (Roraima, Pará, Rondônia, Acre, and northern Mato Grosso)
Model 1 § Model 2 Model 3 Model 4 S S Variable* SD † RR‡ 95% CI p-value RR‡ 95% CI p-value E RR‡ 95% CI p-value E RR‡ 95% CI p-value
% deforestation (0.73, - - 4.2 0.95 0.71 ------(1997-2000) 1.24) - - % deforestation 1.9 1.01 (0.79, 0.96 ------(2000-2001) 1.27) - - % deforestation 1.2 0.91 (0.74, 0.36 ------(2001-2002) 1.12) - - % deforestation 1.3 1.05 (0.81, 0.72 1.07 (0.85, 0.57 1.15 (0.86, 0.33 1.12 (0.85, 0.43 (2002-2003) 1.35) 1.35) 1.54) 1.48) % deforested 0.7 1.15 (0.97, 0.12 1.21 (1.01, 0.04 1.24 (1.03, 0.02 1.22 (0.98, 0.07 land (2003) 1.37) 1.44) 1.48) 1.51) Unpaved road 18.5 ------1.42 (1.13, 0.003 1.40 (1.10, 0.01 1.27 (1.01, 0.04 density 1.79) 1.76) 1.60) (m/km2) (2001) Paved road 7.9 ------1.20 (0.97, 0.09 1.17 (0.94, 0.15 1.21 (0.96, 0.10 density 1.48) 1.46) 1.53) (m/km2) (2001) % affected by 17.9 ------0.86 (0.69, 0.18 0.94 (0.75, 0.59 old fire (1996- - 1.07) 1.18) 1999) % affected by 12.2 ------1.15 (0.91, 0.25 1.15 (0.91, 0.24 recent fire - 1.47) 1.45) (2000-2002) Moderate ------1.69 (1.16, 0.01 Logging¶ - - 2.48) Severe ------0.40 (0.23, 0.001 Logging# - - 0.69)
*Variable (year of acquisition) †Residual standard deviation is the unit of change for all forest disturbance risk factors ‡Relative Risk §Models are adjusted for several sociodemographic and environmental risk factors at the municipality level including: percent of population who migrated in the previous 2 years, male to female ratio, average number of people per household, percent rural population, percent of households living under minimum wage, average transportation costs to the nearest capitol, percent GDP growth from 2000 to 2005, and land cover in 2003 including percent of municipality that was water, remaining forest, and savanna ¶Between 0 and 7% of remaining forest was logged (1999-2002) #Between 7 and 43% of remaining forest was logged (1999-2002)
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Figure 4.1. Map of the Legal Brazilian Amazon municipalities (dark green with grey borders) and the five timber production states (bright green borders): Roraima, Pará, Rondônia, Acre, and northern Mato Grosso.
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Figures
Figure 4.2. Maps of forest disturbance covariates for malaria incidence in the Brazilian Amazon: a) land cover in the Brazilian Amazon from PRODES (2011), b) percent deforestation by municipality from PRODES (2003), c) paved and unpaved roads, d) old (1996-1999) and recent (2000-2002) fires, e) selective logging in the timber production states in 1999-2000, 2000-2001, and 2001-2002, f) malaria incidence by municipality in cases per 1,000 population (2003).
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5CHAPTER 5
Conclusions
In a recent New York Times article, American science writer, David Quammen (2012), writes:
Humans die in large numbers every day, every hour, from heart failure and automobile crashes and the dreary effects of poverty; but strange new infectious diseases, even when the death tolls are low, call up a more urgent sort of attention. Why?
His assertion, after shadowing experts in the field of EIDs for five years, is that we are anticipating the next pandemic, on a par with the 1918 influenza pandemic or AIDS. The factors that influence emerging and reemerging infections are complex and not subject to simple, quick interventions. Ecological, social, economic, and political elements all interact to shape disease dynamics (Wood et al. 2012). A key lesson is that although a detailed understanding of a disease system will emerge from the integration of multiple perspectives and disciplines, it is not realistic to expect a lone scientist to assess this web of drivers simultaneously. Therefore, investigators must have the ability to listen to and communicate with people outside of their area of expertise. Research focused on a single element in a disease system may be the missing piece that ties together the disparate strands of everyone else's research. But it will remain undiscovered unless researchers possess a framework for understanding their discipline’s relationships to one another and a common desire to integrate knowledge among them.
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5.1 Summary
My dissertation's goal is to improve our understanding of the disease ecology and epidemiology of Nipah virus encephalitis in Bangladesh and malaria in the Brazilian Amazon by focusing on the effects of landscape and environmental conditions on the disease reservoir and its vector. These investigations used concepts and methods from epidemiology and landscape ecology and integrated them under the collaborative paradigm of conservation medicine and One Health. Below I summarize the principle conclusions and the contributions of each chapter to the field of disease ecology.
• Chapter 2 identified relationships between landscape patterns and P. giganteus roosting
ecology. The number and size of fruit bat colonies in a village were associated with the
percent forest cover and the degree of fragmentation of the remaining forest in the
community. The forest in that part of Bangladesh where all Nipah cases have been reported
to date (the Nipah Belt) is significantly more fragmented than the forest in the rest of the
country. In addition, the diversity among tree species in P. giganteus roosts in the Nipah
Belt forests is higher than elsewhere in Bangladesh. This finding could be indicative of the
degree of overlap between fruit bat and human habitat because high biodiversity may be a
result of villagers planting multi-use home gardens. Finally, two tree species, Cotton silks
(Bombax spp.) and Indian mast trees (Polyalthia longifolia), were associated with increased
risk of Nipah spillover in a village. These trees, which flower during the season of limited
food resources for P. giganteus, could create a “watering hole effect” where the bats
congregate while feeding, leading to an increase in viral transmission within the bat
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community. In summary, my study elucidated relationships between the landscape, P.
giganteus roosting ecology, and Nipah dynamics in Bangladesh and identified several
ecological pathways for future research.
• Chapter 3 quantified and mapped the gradient of P. giganteus habitat suitability across
Bangladesh. The ecological niche model predicted that only 2-17% of the country's land
area is currently suitable roosting habitat for this species. Within these areas, humans and
bats share significant natural resources. My study revealed that P. giganteus shows roost
habitat selection preferences at the tree level and at scales of several kilometers. For
example, these bats appear to show preferences for tree species and tree characteristics as
well as the degree of forest fragmentation, rainfall and temperature gradients, and the level
of human disturbance. The areas of highest suitability for P. giganteus appear to overlap
significantly with the locations of the majority of Nipah spillover events in Bangladesh.
This study helped to refine our understanding of the distribution and habitat preferences of
the viral reservoir, information essential for unraveling the mechanisms underlying the
spillover of the Nipah pathogen from bats to humans.
• Chapter 4 detected previously undocumented risk factors for malaria incidence in the
Brazilian Amazon. Across Amazonia, the presence of both paved and unpaved roadways
and the occurrence of fires as much as seven years earlier increase malaria risk. Within the
five timber production states, the area most affected by deforestation in the Amazon,
unpaved roads have a larger impact on malaria than paved roadways. Also in the timber
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production states, the risk of malaria was the highest in areas where 0-7% of the remaining
forests were selectively logged compared to areas with no selective logging as well as areas
with more severe selective logging. This study provides the first basin-wide analysis of the
health impact of the major forest disturbances in the Brazilian Amazon. An understanding
of these impacts will be critical as new land use policies are established in Brazil to combat
deforestation.
5.2 Policy Implications of Disease Ecology Research
Knowing is not enough. We must apply. Willing is not enough. We must do.
Goethe
To understanding the connection between the environment, humans, animals, and pathogens is but one critical step to protect human and ecosystem health. Translating our scientific knowledge into effective preventative action is the next step. Disease ecology research that focuses on the role of landscape and the environment on infectious disease epidemiology poses a dilemma for public health practitioners who seek to improve the health of a population. There is a trade-off between causal certainty and opportunity for prevention that was recognized in Rose’s Strategy of Preventative Medicine and discussed further by Khaw and
Marmot (2008) in their introduction to his text. On one hand, traditional epidemiology places great emphasis on the certainty and universality of an exposure-disease relationship. We can be most certain about associations that are close together in the causal chain. However, the most effective population health interventions target upstream risk factors.
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Consider, for example, the goal of reducing teen pregnancy. High school administrators can be very certain that providing emergency contraceptives (“morning-after pills”) to their female students could prevent unwanted pregnancies at their school. An alternative prevention strategy might be to offer sexual education courses for all students, a holistic approach with less direct evidence of effectiveness on reducing teen pregnancy. Some might argue that there is no need to waste valuable school time teaching all students, even those not engaging in sexual practices, about healthy relationships and sexual health, particularly when there is less certainty about the impact of educational programs on teen pregnancy. However, another perspective is that by addressing the social and biological issues associated with teen pregnancy we can also impact related health issues. These might include sexually transmitted diseases, depression or other emotional issues related to young adult relationships, and individual preventative health practices such as the HPV vaccine, self-breast exams, and testicular and prostate health.
Vaccine research for Nipah and the related Hendra virus is underway (Bossart et al.
2012; von Messling and Cattaneo 2012). If this research is successful, it might one day be possible to vaccinate the entire Bangladeshi population against Nipah virus. This could be effective in the short term, but it is not difficult to imagine scenarios where this biologically proven intervention could fall short. Daszak et al. (2012) show that the distribution of
Pteropus fruit bats, and the pathogens they carry, will likely shift by midcentury in response to climate changes. In addition, over 65 viruses have been isolated from bats (Calisher et al.
2006), and experts think that these may represent only a fraction of the viral biodiversity hosted by these mammals (Calisher et al. 2006; Halpin et al. 2007). While vaccinating humans or bats
(Turmelle et al. 2010) against Nipah virus may indeed prevent serious morbidity and death,
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In order to prepare for emergence or re-emergence of vectorborne and zoonotic disease, we need to understand the environmental, social, and politico-economic drivers of pathogen spillover (Wood et al. 2012). Interdisciplinary disease ecology research can identify upstream risk factors of disease emergence that can facilitate creative interventions that benefit the health of the human population, domestic animals and wildlife, as well as the ecosystem we share.
For example, Milne et al. (2008) showed that efforts to eradicate swine flu could be improved by targeting adult male swine during the dry season. Using a simulation model, they showed that outbreaks could be initiated by roaming adult male pigs that connect otherwise isolated feral pig populations that are congregated around water sources. Disease ecology research can also identify landscape characteristics that affect wildlife dispersal. These natural barriers can be leveraged to enhance disease containment strategies (Blanchong et al. 2008), for example, by delivering oral vaccines to raccoons in corridors around rivers (Russell et al.
2006). More generally, understanding the ecological dynamics of a disease system can be useful in establishing parameters for mathematical models of disease emergence to aid in prediction of disease outbreaks (Daszak et al. 2012; Deter et al. 2010). In other cases, in-depth knowledge of the ecology of a disease can prevent unintended adverse consequences of a control strategy (Deter et al. 2010). For example, in the UK, strategies to control bovine tuberculosis targeted mass culling of badgers, the wildlife reservoir (McDonald et al. 2008).
The culling disturbed the social structure of the badgers and increased their rates of movement,
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Pongsiri et al. (2009) reviewed diseases where research has shown links between biodiversity loss and disease ecology. They emphasize the importance of understanding how people use the environment to help guide policies that focus on behavior change. Creating incentives to follow alternative livelihood strategies or the use of substitutes for natural products that prevent human encroachment into forests may be forms of intervention. We can also consider land management that maintains sufficient resources for wildlife inside intact habitats. A strategy that has been suggested to reduce damage to fruit crops is to plant trees that are the preferred food resources for Pteropus bats in core areas of forest fragments or away from human food crops (Law et al. 2002). Policies that prevent human-wildlife interactions could be an effective means to reduce the spillover of diseases like Nipah virus (Pongsiri et al.
2009).
In the Brazilian Amazon, government efforts to curb deforestation (Nepstad et al. 2009) and transitions within the beef and soy industries to exclude deforesters from their supply chains (Barrionuevo 2009; Nepstad et al. 2008) have decreased the rate of deforestation significantly over the past eight years. However, no policies are in place for other forest disturbances such as selective logging, fires, or road construction. As my dissertation shows, these are significant risk factors for malaria transmission. Efforts to control malaria in the
Amazon could include policies that Dennis et al. (2005) have suggested as mechanisms to control fire in Indonesia. Such policies provide incentives to use techniques other than fire for clearing land, and include tighter regulation and registration of fire burning, stricter review of
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5.3 Recommendations for Future Research
A changing climate, increasing global trade and travel, and continued movement of humans into wildlife habitat pose unprecedented and constantly shifting challenges in the prevention and control of vectorborne and zoonotic diseases. The necessity of truly integrative research is increasingly obvious to experts in the field (Wood et al. 2012). Building on the work in this dissertation, there are many opportunities to improve our understanding of Nipah virus, malaria emergence, and EIDs generally:
• With regard to Nipah virus, there is a great deal more work to be done to understand viral
dynamics in the Pteropus bat population despite ongoing research on this front. In
particular, future research should focus on quantifying the change in viral genetic diversity,
virulence, and prevalence in Pteropus populations over time and space. Evidence from
immunological studies of montane water voles, Arvicola scherman, shows that increases in
population induce high stress levels, which in turn triggers a decrease in immune function
and susceptibility to infectious agents (Charbonnel et al. 2008). Similarly, great ape
mortality from Ebola infection is higher during the dry season when food resources are
limited (Leroy et al. 2004). Do viral prevalence or strain diversity vary in Pteropus bats
over the course of a year?
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More work also needs to be done to understand gene flow and selection, and viral
transmission over geographic space. In experimental studies using a moth virus, Boots and
Mealor (2007) showed that increased connectivity among viral hosts selects for more
virulent viral strains. At a larger scale, longitudinal research on rabies outbreaks across the
United States showed that physical barriers (e.g. the Great Lakes) and natural breaks in
suitable raccoon habitat (e.g. high latitudes across mountain ranges) were responsible for
limiting the spread of rabies (Biek et al. 2007). How does habitat fragmentation affect
Pteropus connectivity and viral transmission and selection? What do global maps of
Pteropus habitat look like? How might these maps change as their habitat continues to
shrink due to human encroachment? Can we build on Daszak et al. (2012) to refine our
understanding of how Pteropus distribution will shift due to climate change at a more
detailed spatial scale?
• With regard to malaria, we need more fine-scale assessments of the ecological mechanisms
connecting forest disturbance and malaria incidence. Understanding how disturbance
effects mosquito vectors and how humans come into contact with the vectors (e.g.
occupational exposures, residential exposures) can aid in identifying targets for intervention
in the face of continued land use change. In addition, land use policy in Brazil is in an era
of rapid change, and it is imperative that we understand the potential health impacts of
proposed policies. Future work should include the integration of health impact assessments
into analyses of new deforestation and land use policies in the Brazilian Amazon.
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• For all EIDs, research efforts should focus on understanding the underlying and proximate
drivers of land use dynamics. The most effective public health interventions and policies
address human behavior and offer solutions or alternatives that simultaneously protect
public health and satisfy the needs of the population they are trying to protect. Therefore, it
is essential that we illustrate our work with accounts and observations from people who
work and live in the communities affected by emerging pathogens.
Priorities for preventing and controlling EIDs should include the following: preserve remaining global ecosystems, expand targeted surveillance, and continue research on the ecology of identified diseases. It is also necessary that we train future researchers and establish organizational structures that recognize the need to quantify the ecological and biological aspects of a disease and uncover the disease narrative that links humans, vectors, wild and domestic animals, and ecosystems. The challenges that lie ahead in protecting public health from infectious diseases will be unprecedented. Our best defense is to expand our knowledge base regarding the issues we presently face and prepare our future leaders and our global organizations to work collaboratively so that they will be able to protect humanity from diseases, known and unknown.
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References
Barrionuevo A. 2009. Giants in cattle industry agree to help fight deforestation. New York Times, October 6.
Biek R, Henderson JC, Waller LA, Rupprecht CE, Real LA. 2007. A high-resolution genetic signature of demographic and spatial expansion in epizootic Rabies virus. PNAS 104: 7993–7998.
Blanchong J a, Samuel MD, Scribner KT, Weckworth B V, Langenberg J a, Filcek KB. 2008. Landscape genetics and the spatial distribution of chronic wasting disease. Biology Letters 4: 130–133.
Boots M, Mealor M. 2007. Local interactions select for lower pathogen infectivity. Science 315: 1284–1286.
Bossart KN, Rockx B, Feldmann F, Brining D, Scott D, LaCasse R, et al. 2012. A Hendra virus G glycoprotein subunit vaccine protects African green monkeys from Nipah virus challenge. Science Translational Medicine 4: 1–8.
Calisher CH, Childs JE, Field HE, Holmes K V, Schountz T. 2006. Bats: important reservoir hosts of emerging viruses. Clinical Microbiology Reviews 19: 531–545.
Charbonnel N, Chaval Y, Berthier K, Deter J, Morand S, Palme R, et al. 2008. Stress and demographic decline: A potential effect mediated by impairment of reproduction and immune function in cyclic vole populations. Physiological and Biochemical Zoology 8.
Daszak P, Zambrana-Torrelio C, Bogich TL, Fernandez M, Epstein JH, Murray K a, et al. 2012. Interdisciplinary approaches to understanding disease emergence: The past, present, and future drivers of Nipah virus emergence. PNAS 110: 1–8.
Dennis R a., Mayer J, Applegate G, Chokkalingam U, Colfer CJP, Kurniawan I, et al. 2005. Fire, People and Pixels: Linking social science and remote sensing to understand underlying causes and impacts of fires in Indonesia. Human Ecology 33: 465–504.
Deter J, Charbonnel N, Cosson J. 2010. Evolutionary landscape epidemiology. In The Biogeogrpaphy of Host-Parasite Interactions (S. Morand and B.R. Krasnoveds. ), pp. 173–188, Oxford University Press, New York.
Halpin K, Hyatt AD, Plowright RK, Epstein JH, Daszak P, Field HE, et al. 2007. Emerging viruses: coming in on a wrinkled wing and a prayer. Clinical Infectious Diseases 44: 711– 717.
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Khaw K, Marmot M. 2008. Rose’s Strategy of Preventative Medicine, Commentary. Oxford University Press, London, UK.
Law B, Eby P, Somerville D. 2002. Tree planting to conserve flying-foxes and reduce orchard damage. In Managing the grey-headed flying-fox as a threatened species, pp. 94–90, Royal Zooloigcal Society of New South Wales.
Leroy EM, Rouquet P, Formenty P, Kilbourne A, Froment J, Bermejo M, et al. 2004. Multiple Ebola virus transmission events and rapid decline of Central African wildlife. Science 303: 387–391.
McDonald R a, Delahay RJ, Carter SP, Smith GC, Cheeseman CL. 2008. Perturbing implications of wildlife ecology for disease control. Trends in Ecology & Evolutioncology & evolution 23: 53–56.
Messling V von, Cattaneo R. 2012. A Henipavirus vaccine in sight. Science 337: 651–652.
Milne G, Fermanis C, Johnston P. 2008. A mobility model for classical swine fever in feral pig populations. Veterinary Research 39.
Nepstad D, Soares-filho BS, Merry F, Lima A, Moutinho P, Carter J, et al. 2009. The end of deforestation in the Brazilian Amazon. Science 326: 1350–1351.
Nepstad DC, Stickler CM, Filho BS-, Merry F. 2008. Interactions among Amazon land use, forests and climate: prospects for a near-term forest tipping point. Philosophical transactions of the Royal Society of London. Series B 363: 1737–1746.
Pongsiri MJ, Roman J, Ezenwa VO, Goldberg TL, Koren HS, Newbold SC, et al. 2009. Biodiversity loss affects global disease ecology. BioScience 59: 945–954.
Quammen D. 2012. Anticipating the next pandemic. New York Times, September 22.
Russell C a, Real L a, Smith DL. 2006. Spatial control of rabies on heterogeneous landscapes. PloS one 1.
Turmelle AS, Allen LC, Schmidt-French BA, Jackson FR, Kunz TH, McCracken GF, et al. 2010. Response to vaccination with a commercial inactivated rabies vaccine in a captive colony of Brazilian free-tailed bats. Journal of Zoo and Wildlife Medicine 41: 140–143.
Wood JLN, Leach M, Waldman L, Macgregor H, Fooks AR, Jones KE, et al. 2012. A framework for the study of zoonotic disease emergence and its drivers: spillover of bat pathogens as a case study. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 367: 2881–2992.
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Appendix A
Version: 4 Dec 2011
Ecological Risk Factors for Nipah Virus Spillover Field Training Manual
Contact: Micah Hahn ([email protected]) University of Wisconsin-Madison Center for Sustainability and the Global Environment (SAGE) 1710 University Ave. Madison, WI 53726
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Overview
Ecological Risk Factors for Nipah Virus Spillover (a.k.a. Ecological Assessment) is a complimentary study to the ICDDR,B community-level case control study. This study focuses on characterizing the environmental characteristics of Pteropus roosting sites. With this study, we would like to answer two questions:
1. What characterizes preferred roosting sites of Pteropus bats? 2. Is there a difference between the Pteropus roost sites in case villages versus control villages (e.g. quantity, location, environmental characteristics?)
There are three parts of the Ecological Assessment:
• Cover Sheet • Roost Site Questionnaire • Roost Site / Available Site Environmental Assessment
These three sections are all contained in the Roost Site Packet, and they will be collected on paper not a PDA.
General Notes!
• Labeling every waypoint that is saved in the GPS is very important. Without these labels we will not know what feature each point represents. • For each roost site, you will have 3 coordinates named according to the following convention: These ID numbers always start with the village ID, followed by R (roost), H (household), HD (household domestic animals) – depending on the feature you are marking – and end with the ID of the roost assessment site. • For many of the forms, you will need to use the Tree Species Reference Sheet to find the Species Code of the tree you identify. ! Roost Environmental Assessment Notes!
• We will only assess roost sites that have been used within the last 5 years. • There are a few special instruments we will use for the environmental assessment. There are separate instructions sheets for these instruments. • Only include trees that are >4cm in diameter at breast height. • Bamboo bushes should be measured as one tree. Use the measuring tape (not the diameter tape) to measure all the way around the bush at breast height. Use the clinometer to measure the highest point on the bamboo bush. • If the tree is split below 1.3 m, measure each trunk of the tree separately. • If a tree is on the border of the plot, it is counted as “in” if more than half of its trunk is in the plot. If the center is right on the border, count the first case as “out” but the next one as “in.”
Protocol for Counting Bats
• If there are only a small number of bats, please count all the bats. • If there are many bats, identify a branch that has an average number of bats (i.e. don’t pick the one that has just a few or the one that has the most). Count the number of bats on this branch and multiply that number by the total number of branches that are occupied by bats. • We are looking for estimates that are very close to accurate. Take your time counting bats, but don’t worry if you can’t count all the bats individually. !
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GPS Training
• Demonstrate changing batteries • Go over key functions – Note that you get a different menu for each page when you hit Menu key
Set-up
• MENU > SETUP > Units
o Position format: hddd.ddddd° o Map Datum: WGS 84 o Distance/Speed: Metric o Elevation: Meters (m/min)
Compass
• Calibrate compass – PAGE > MENU > Calibrate Compass > Turn Slowly
o You will need to recalibrate compass if you change the batteries, travel more than 160 km, or the GPS experiences a large temperature change.
The numbers around the outside are Degrees. We PRACTICE! Marking a waypoint and will use these numbers changing the name. Now FIND a new to find the “available” sites waypoint at 23° and 30 m away. in the roost site ! environmental assessment. !
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How to Use a Diameter (DBH) Tape
A diameter tape is a tape measure that you wrap around the circumference of a tree to measure diameter. The tape has a special scale printed on it that converts circumference to diameter. We use the tape to measure Diameter at Breast Height (DBH). “Breast height” is 1.3 meters.
Steps to determine a tree’s diameter:
1. Cut a stick to 1.3 m in length and use this to set the height for all diameter measurements. 2. Find the uphill side of the tree. 3. Wrap the tape around the tree at 1.3 m up the tree. If the tree is leaning, make sure to wrap the tape so it is parallel to the ground. 4. Pull the tape taut and read the diameter from the tape. Note that there is a blank section at the start of the tape. Cross-over this section of the tape and read the diameter when it is lined up with the zero line on the tape. 5. Does the diameter make sense? For a reference, you should be able to reach around trees less than 50 cm in diameter. 6. There are some cases when you will not take the diameter measurement at exactly 1.3 m. You can see some examples on the back. If you move the diameter tape because of a misshapen tree, make a note of it on the data sheet.
Examples of trees where it might not be possible to take a diameter measurement at exactly 1.3 meters. If one of these cases occurs, make a note
on the data sheet.
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How to Use a Clinometer
Steps to determine a tree’s height: 1. Walk 15 m away from the tree you want to measure. Use the open-reel measuring tape to find this distance. You can have a partner hold the tape, or you can use a bent nail to hold the tape to the tree. Try to find the flat ground. 2. Face tree. Hold the clinometer near your eye with the red dot pointing away from you, and the glass face pointing to the left. 3. Look through the eyepiece. The clinometer contains a dial with two rows of measurements – a left-hand and a right-hand scale. We will be using the numbers on the right-hand side. 4. Keep both eyes open! Point the clinometer straight ahead, and look through the eyepiece until you see that the scale reads “0” on both sides.
15 meters
5. Point the clinometer at the top of the tree. Record the number from the right-hand scale that corresponds with your line of sight at the top of the tree under CL.TOP.
15 meters
6. Keeping both eye open, tilt the clinometer down to the base of the tree and read the height through the eyepiece. Record the number from the right-hand side under CL.BASE. If you are standing on flat ground, this number should be negative – be sure to record the negative sign!
15 meters
7. To calculate the height of the tree, you simply subtract the height at the base (CL.BASE) from the height of the tree (CL.TOP). Be sure to watch for negative signs in the CL.BASE data. (e.g. 100 m – (-16m) = 116 m tall). You do not have to make these calculations on the data sheet, but just keep this in mind so you can estimate whether your measurements are realistic.
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TREE SPECIES REFERENCE SHEET
Code CommonName ScientificName BanglaName 1 Mango Mangifera indica amm 2 Bohera Terminalia belerica bahera, boyra 3 Bamboo bash 4 Banyan Ficus bengalensis batgachh 5 Debdaru Polyalthia longifolia debdaru 6 Fig Ficus glomerata dumbar fig 7 Eucalyptus Myrtaceae eucalyptus/akashi 8 Nipa Palm Nypa fruticans gol pata (round leaves) 9 Horitaki Terminalia chebula horitaki 10 Mulberry jam 11 Neolamarckia cadamba kadam 12 Banana Musa paradisiaca kala 13 Jackfruit Artocarpus heterophyllus kathal 14 Keora Sonneratia apetala keora 15 Date Palm Phoenix sylvestris khejur 16 Acacia Albizia procera koroi 17 Krishnachura Delonix regia krishnachura 18 Lemon Citrus limon lebu 19 Litchi Litchi chinensis lichu 20 Mahua Madhuca longifolia mahua 21 Mignonette Tree Lawsonia inermis mehedi 22 Mehogani Swietenia mahagoni mehogani 23 Coconut Palm Cocos nucifera narikel 24 Neem Azadirachta indica neem 25 Guava Psidium payara 26 Papaya Carica papaya pepe 27 Rain Tree Albizia saman shirish 28 Betel Nut Areca catechu shupari 29 Palmyra Palm Borassus flabellifer tal 30 Other
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Appendix B
! ! ! COMPLETE THE ROOST SITE QUESTIONNAIRE FOR EVERY ROOST WITHIN 5 KM OF THE VILLAGE BOUNDARY THAT HAS BEEN OCCUPPIED WITHIN THE PAST 5 YEARS. ! ! ! A. ROOST QUESTIONNAIRE COVER PAGE ! ! ! VILLAGE INFORMATION ! ! A. Field Team Number |____| ! ! B. Village ID |____|____|____|____| ! C. Village Center Coordinates N __ __.______E __ __.______! ! D. Division______District______
! Upazila______Union ______! ! Village ______! ! ! ! ! ! INORMATION ABOUT ROOST SITE ! ! E. Roost ID |____|____|____|____| .R|____|(this is the ID you should use in the GPS device to label roost) ! Village ID begin numbering with 1, 2, 3…in each village ! ! F. Roost Coordinates N __ __.______E __ __.______! ! G. Roost Environmental Assessment Completed? Yes or No ! ! H. Consent Obtained (Field team can check box if they received a verbal consent). ! ! ! ! ! ! Interviewer Signature ! ! ! !
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CONSENT FORM FOR ROOST SITE QUESTIONNAIRE
Ecological Risk Factors for Nipah Virus Spillover in Bangladesh
* This consent form assumes that permission has already been obtainedfromthecommunityleadertoworkinthe village. This form should be used to explain the purpose to and receive consent from the person answering the roost site questionnaire.
University of Wisconsin, SAGE EcoHealth Alliance ICDDR,B (Cholera Hospital) Micah Hahn Jonathan Epstein Steve Luby or Emily Gurley 1710 University Ave 460 West 34th Street - 17th Floor GPO Box 128 Madison, WI 53726 USA New York, NY 10001 USA Dhaka 1000, Bangladesh [email protected] [email protected] [email protected] [email protected]
INTRODUCTION/PURPOSE:Hello,mynameis______.IworkforICDDR,B(CholeraHospital),and we are here in peace. We would like to learn how the trees, fruits, and wildlife in your community are affecting the health of your community and your animals. Would you have a few minutes to answer some questions about this bat roosting site? I have chosen to speak with you because you live the closest to the site. Several other community members here and in other villages will be completing the same survey.
RISKS AND VOLUNTARY PARTICIPATION: The questions are not sensitive, but if you find any question uncomfortable, you have a full right torefusetoanswer. There is no penalty for your refusal to answer a question or participate in the study. However, your assistance will be very helpful in focusing ICDDR,B’s work to be most helpful for you, your family, and your community. There is no risk to you, your family, or your community by participating in the survey.
CONTACT PERSONS: If you have any questions about the survey after I leave, you can contact the ICDDR,B office in Dhaka. I have the contact information if necessary. If you wish to obtain the results of this study upon its completion, please contact the ICDDR,B office.
Do you agree to participate in this survey?
CHECK BOX AND SIGN AS THE INTERVIEWER ON COVER SHEET IF THE PERSON AGREES TO PARTICIPATE.
Think carefully about each question, and if you do not understand a question please let me know and I will explain it further. You may choose not to answer any of the questions, and you can stop at any time. The questions I will ask refer to this bat roost site (POINT TO ROOSTING SITE).
ICDDR,B Office: GPO Box 128, Dhaka 1000 (+88 02) 8860523-32 Infectious Disease Unit Director, Steve Luby, [email protected]
Micah Hahn contact information: University of Wisconsin, Center for Sustainability and the Global Environment [email protected]
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Appendix C
C.!ROOST!ENVIRONMENTAL!ASSESSMENT!PROTOCOL! Measuring!environmental!characteristics!of!Pteropus!roost!sites! ! ! COMPLETE!THIS!ASSESSMENT!FOR!A!MAXIMUM!OF!2!ROOSTS!IN!A!VILLAGE.!!TO!SELECT!THE!ROOSTS!FOR! ASSESSMENT,!USE!THE!FOLLOWING!SELECTION!CRITERIA!IN!THIS!ORDER!OF!PRIORITY:!! 1.!Choose!active!roosts,!2.!Choose!roosts!within!the!village!boundary,!3.!Choose!roosts!with!largest!number!of!bats! ! IF!THE!SAME!ROOST!NEEDS!TO!BE!ASSESSED!FOR!TWO!VILLAGES,!FILL!OUT!THE!ROOST!PACKET!FOR!BOTH! VILLAGES:!! Take!GPS!coordinates!for!both!villages,!fill!out!the!questionnaire!for!each!village,!BUT!complete!the!environmental! assessment!only!one!time!–!You!can!copy!the!tree!data!from!the!first!roost!packet!into!the!second!roost!packet.! ! ! Equipment!needed!for!fieldwork:! ! • 2!28.28!m!white!transect!lines!with!knots!every!1!m!–!This!has!a!total!of!29!knots,!starting!with! knot!1!at!the!beginning!of!the!line!and!the!29th!knot!at!28!m.!!The!center!of!the!line!is!the!15th!knot.! • 1!80!m!neon!(pink,!blue,!purple,!or!green)!cord!with!loops!every!20!m! • 11!chaining!pins! • 1!densitometer! • 1!clinometer! • 1!diameter!(DBH)!tape! • 1!open!reel!measuring!tape! • 1!Garmin!GPS!receiver!+!2!batteries! • 1!roll!of!pink!flagging!tape! • 2!bent!nails! • 1!digital!camera!+!2!batteries!+!1!flash!card!+!1!case! • 1!outlet!adapter! • 1!battery!recharger!+!4!rechargeable!batteries! • copies!of!the!questionnaires/data!collection!sheets,!training!manual,!tree!species!code!sheet! • pens! • field!equipment!bag! ! ! Steps!to!Complete!Assessment:! 1. We!will!conduct!the!environmental!assessment!on!two!of!the!roost!sites!located!after!your!initial! key!informant!interviews.!!We!will!choose!these!sites!based!on!the!criteria!above!(active,!location,! size).! 2. Once!you!have!identified!a!roost!site!where!you!will!conduct!the!environmental!assessment,!flag! the!center!tree!with!pink!flagging!tape!on!one!of!the!branches!and!number!it!with!its!ID.!!This!tree! will!be!the!center!of!the!sampling!plot.!!If!there!are!multiple!roost!trees!in!a!cluster,!find!the! approximately!center!tree!and!use!this!as!the!plot!center.! 3. Fill!out!the!top!lines!of!the!data!sheet:!roost!ID,!GPS!coordinates,!and!elevation!(from!the!GPS).! !! a. Name!the!plot/waypoint!using!the!following!convention:! ! ! Village!ID.R#!of!plot! ! ! EXAMPLES:!1336.R1!=!Village!1336!Roost!#1!
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! b. Collect!a!GPS!point!at!the!center!roost!tree!using!the!Garmin!receiver.!!Note!the!coordinates! of!the!plot!center!on!the!top!of!the!data!sheet.!!Name!the!waypoint!in!the!GPS!with!the! Roost!ID!you!noted!on!the!top!of!the!field!sheet.! ! ! 4. Lay!out!the!28.28!m!ropes!in!an!X!so!that!they!cross!at!the!center!of!the!plot!at!the!15th!knot!on! each!rope.!!This!knot!has!been!marked!with!pink!tape.! 5. Secure!the!ropes!by!putting!a!chaining!pin!through!the!loop!on!the!15th!knot!of!each!rope!and! through!the!loops!at!each!end!of!the!ropes.! 6. Extend!the!perimeter!tape!around!the!20x20!m!plot!area!using!the!loops!to!find!the!corners!and! secure!on!pins.! 7. Make!sure!you!have!filled!out!the!information!on!the!top!of!the!field!sheet!before!beginning!data! collection.! 8. Start!with!the!tree!in!the!center!of!the!roost.!!Fill!in!SPECIES!and!SPECIES!CODE!using!the!code! sheet!on!your!clipboard.!!If!the!tree!is!not!on!the!code!sheet,!label!it!“27”!for!Other!and!write!the! name!of!the!species!in!the!blank.! 9. Dominance!(DOM):!Record!whether!the!species!is!a!canopy!(C)!or!subhcanopy!(S).! 10. Diameter!at!Breast!Height!(DBH):!Breast!height!is!1.3!m!above!the!ground!on!the!uphill!side!of! the!tree.!!Use!your!prehmeasured!stick!as!a!guide!for!finding!breast!height.!!Wrap!the!diameter!tape! around!the!trunk!of!the!tree!at!breast!height!and!record!the!value.!!If!the!tree!is!split!below!breast! height!record!each!trunk!as!a!separate!tree.!!Do!not!include!trees!<4!cm!in!DBH.!!See!“How!to!Use!a! Diameter!Tape”!for!training!on!this!instrument.! 11. Clinometer!(CL.TOP!and!CL.BASE):!The!clinometer!will!be!used!to!measure!tree!height.!!Use!the! measuring!tape!to!find!a!spot!15!m!away!from!the!tree.!!See!“How!to!Use!a!Clinometer”!for!training! on!this!instrument.!!Record!the!top!and!base!measurements!on!the!data!sheet.! 12. Mark!an!X!in!the!ROOST!column!if!you!are!measuring!a!roost!tree!in!the!plot.! 13. Densitometer:!Walk!along!each!of!the!white!crossing!tapes,!stopping!at!each!knot!(1!meter!apart)! to!look!through!the!densitometer.!!Count!the!number!of!stops!at!which!you!observe!canopy!and!the! number!at!which!you!observe!open!sky!or!branch.!!Canopy!is!defined!as!the!presence!of!foliage!(not! branch)!at!the!crosshhair!in!the!periscope.!!If!there!is!a!dense!understory,!please!move!it!aside! before!making!the!measurement.!!This!measurement!is!designed!for!the!subhcanopy!and!canopy! level!trees.!!You!will!start!at!~!0!m!and!end!at!~28!m!which!means!that!there!are!a!total!of!58! measurements!along!the!two!transect!lines!(29!on!each!line!–!you!will!measure!the!center! knot!twice).!!Write!down!the!total!number!of!positive!(green!vegetation!encountered)!and! negative!(sky!or!branch!encountered)!hits.!!This!should!total!to!58!measurements.!!! 14. Take!a!digital!photograph!of!the!field!sheet.!!We!will!use!the!data!sheet!photo!to!link!the! subsequent!photos!to!the!correct!plot!later.!!Then,!go!to!the!center!of!the!plot!and!use!the!compass! on!the!GPS!to!find!N.!!While!standing!in!the!center!of!the!plot,!take!the!first!photo!facing!N,!then!E,!S! and!W.!!Take!one!landscape!photo,!and!one!vertical!photo.!!After!taking!the!8!photos!of!the!plot!(2! in!each!direction),!feel!free!to!take!other!photos!but!be!sure!to!note!what!they!are!in!the!Notes! section!on!the!data!sheet!and!write!down!how!many!extra!photos!you!took.! 15. Review!the!data!sheet!to!make!sure!that!all!fields,!especially!densitometer!counts!and!GPS! coordinates!are!filled!in.! 16. Record!any!notes!about!the!site!or!issues!you!had!in!recording!data:!presence!of!gaps,!downed! trees,!thick!understory!made!it!difficult!to!get!to!a!spot!15!m!away,!etc.! 17. Put!checkboxes!next!to!each!of!the!features!that!are!presenting!within-50-m-of-the-roost-boundary! (20!x!20!meter!box).! 18. Break!down!the!plot.!!Before!you!leave!the!plot,!ensure!that!you!have!all!your!sampling!gear,! including!densitometer!and!especially!the!chaining!pins!(easy!to!leave!behind!).! !
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! ! CHECKLIST:! ! ! Do!you!have!a!GPS!coordinate!for!the!roost!center!(XXXX.RX)?! ! ! Did!you!mark!the!plot!centers!with!flagging!tape?!
! Did!you!write!the!species,!DBH,!DOM,!and!CL.!TOP/BOTTOM!for!each!tree!in!the!20!x!20!plot?!
! Did!you!do!the!densitometer!measurements!(58!TOTAL!–!count!the!center!twice)!and!write!them! on!the!data!sheet?! ! Did!you!put!a!check!next!to!all!the!features!within!50!km!of!the!plot!boundary?!
! Did!you!take!a!photo!of!the!data!sheet!(roost!ID)!+!photos!of!the!plots!in!every!direction!(9!photos! total)?! ! Did!you!write!down!any!important!notes!about!the!roost!site?!
! Did!you!collect!all!your!gear?!
! ! ! ! ! ! ! ! ! ! !
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TABLE!C.!!ROOST!SITE!ENVIRONMENTAL!ASSESSMENT!DATA!SHEET! ! ! ! ROOST!ID:!!!|____|____|____|____|!!!!.R!!!|____|!!!! GPS!ROOST!CENTER:!! ! ! ELEVATION:!!!!!|____|____|____|____|!!!meters!!!!!!!!!!!! N!___!!___!.!___!!___!!___!!___!!___!!!!! ! ! E!___!!___.!___!!___!!___!!___!!___!!!!!!XXXX.RX! ! ! TREE! SPECIES! SPP.! DOM! DBH! CL.TOP! CL.BASE! ROOST! NO.! CODE! (1.3m!from! (from! ground)! tree!ref.! sheet)! 1- - - C!!!or!!!S! - - +!/!h! Y!!!or!!!N- 2- ! ! C!!!or!!!S! ! ! +!/!h! Y!!!or!!!N! 3- ! ! C!!!or!!!S! ! ! +!/!h! Y!!!or!!!N! 4- ! ! C!!!or!!!S! ! ! +!/!h! Y!!!or!!!N! 5- ! ! C!!!or!!!S! ! ! +!/!h! Y!!!or!!!N! 6- ! ! C!!!or!!!S! ! ! +!/!h! Y!!!or!!!N! 7- ! ! C!!!or!!!S! ! ! +!/!h! Y!!!or!!!N! 8- ! ! C!!!or!!!S! ! ! +!/!h! Y!!!or!!!N! 9- ! ! C!!!or!!!S! ! ! +!/!h! Y!!!or!!!N! 10- ! ! C!!!or!!!S! ! ! +!/!h! Y!!!or!!!N! 11- ! ! C!!!or!!!S! ! ! +!/!h! Y!!!or!!!N! 12- ! ! C!!!or!!!S! ! ! +!/!h! Y!!!or!!!N! 13- ! ! C!!!or!!!S! ! ! +!/!h! Y!!!or!!!N! 14- ! ! C!!!or!!!S! ! ! +!/!h! Y!!!or!!!N! 15- ! ! C!!!or!!!S! ! ! +!/!h! Y!!!or!!!N! 16- ! ! C!!!or!!!S! ! ! +!/!h! Y!!!or!!!N! 17- ! ! C!!!or!!!S! ! ! +!/!h! Y!!!or!!!N! 18- ! ! C!!!or!!!S! ! ! +!/!h! Y!!!or!!!N! ! ! ! NOTES:! DENSITOMETER:!58!total! !
POSTIVE!(green):! ! !
NEGATIVE!(branch!or! ! ! sky):! ! ! CHECK!NEXT!TO!EACH!FEATURE!THAT!IS!PRESENT!WITHIN!50!M!OF!THE!ROOST!BOUNDARY!(20!X!20!BOX)! ! River!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ! Household!Activities! ! Road!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ! Jungle! (e.g.!house!or!water! pipe)! ! Other!Water!!(e.g.!pukur)!!!!!!! ! Other!Building!(e.g.! ! Agriculture!(e.g.!crops,!grassland)! school,!mosque)!!
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