SPATIAL AND TEMPORAL ANALYSIS OF THE MOSQUITO VECTORS OF SYLVATIC DENGUE AND CHIKUNGUNYA VIRUSES IN SENEGAL REBECCA RICHMAN, B.A., M.A.

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1 SPATIAL AND TEMPORAL ANALYSIS OF THE MOSQUITO VECTORS OF SYLVATIC DENGUE AND CHIKUNGUNYA VIRUSES IN SENEGAL BY REBECCA RICHMAN, B.A., M.A. A thesis submitted to the Graduate School in partial fulfillment of the requirements for the degree Interdisciplinary Master s Degree Major Subjects: Geography and Biology New Mexico State University Las Cruces, New Mexico December 2013

2 Spatial and Temporal Analysis of the Mosquito Vectors of Sylvatic Dengue and Chikungunya Viruses in Senegal, a thesis prepared by Rebecca Richman in partial fulfillment of the requirements for the degree, Interdisciplinary Master s Degree in Applied Geography and Biology, has been approved and accepted by the following: Loui Reyes Dean ad interim of the Graduate School Michaela Buenemann, Ph.D. Co-Chair of the Examining Committee Kathryn A. Hanley, Ph.D. Co-Chair of the Examining Committee Date Committee in Charge: Dr. Michaela Buenemann, Co-Chair Dr. Kathryn A. Hanley, Co-Chair Dr. Michael N. DeMers Dr. Karen E. Mabry ii

3 DEDICATION To my family, who have supported me in everything that I do. iii

4 ACKNOWLEDGMENTS First and foremost I would like to thank my two advisors, Dr. Michaela Buenemann and Dr. Kathy Hanley for supporting me and challenging me. I would like to thank the faculty of both the Geography and Biology Departments, who have been so supportive of me in this work. I would also like to thank members of my family and friends for the countless hours they spent editing my writing. I would also like to acknowledge the many collaborators on this project including, Diawo Diallo, Mawlouth Diallo, Amadou Sall, Ousmane Diop, Bakary Sadio, Abdourahmne Sow, Abdourahmne Faye, Oumar Faye and Scott Weaver as well as the mosquito collectors in Senegal, without whom this work would not have been possible. Funding for this research came from NIH grants R01AI and 2P20RR NM-INBRE. iv

5 VITA July 6, 1982 Born at Gainesville, Florida 2000 Graduated from Las Cruces High School, Las Cruces, New Mexico 2004 Graduated from University of North Carolina, Chapel Hill with a B.A. in Anthropology and Biology 2006 Graduated from University of California, Santa Barbara with an M.A. in Anthropology Graduate Research Assistant, Department of Geography, New Mexico State University Teaching Assistant, Department of Geography, New Mexico State University Professional and Honorary Societies Association of American Geographers American Society for Photogrammetry and Remote Sensing Publications and Presentations Hanley, K., T. Monath, S. Weaver, S. Rossi, R. Richman, and N. Vasilakis Fever versus fever: The role of host and vector susceptibility and interspecific competition in shaping the current and future distribution of the sylvatic v

6 cycles of dengue virus and yellow fever virus. Infection, Genetics and Evolution 19: DeMers, M., A. Klimaszewki-Patterson, R.Richman, S. Ahearn, B. Plewe, and A. Skupin Toward an Immersive 3D Virtual BoK Exploratorium: A Proof of Concept. Transaction in GIS 17(3): Richman, R., M. Buenemann, and K Hanley Analysis of the spatial-temporal distribution of the mosquito vectors of sylvatic dengue and chikungunya viruses. Presented at the 2013 Annual Meeting of the Association for American Geographers in Los Angeles. Walker, P., R. Bathurst, R. Richman, T. Gjerdrum, and V. Andrushko The causes of porotic hyperostosis and cribra orbitalia: a reappraisal of the irondeficiency-anemia hypothesis. American Journal of Physical Anthropology 139(2): Buzon. M. and R. Richman Traumatic injuries and imperialism: The effects of Egyptian colonial strategies at Tombos in Upper Nubia. American Journal of Physical Anthropology 133(2): Hutchinson, D. and R. Richman Regional, social, and evolutionary perspectives on treponemal infection in the Southeastern United States. American Journal of Physical Anthropology 129(4): Field of Study Major Fields: Geography and Biology vi

7 ABSTRACT SPATIAL AND TEMPORAL ANALYSIS OF THE MOSQUITO VECTORS OF SYLVATIC DENGUE AND CHIKUNGUNYA VIRUSES IN SENEGAL BY REBECCA RICHMAN, B.A., M.A. Interdisciplinary Master Geography and Biology New Mexico State University Las Cruces, New Mexico, 2013 Dr. Michaela Buenemann, Co-Chair Dr. Kathryn Hanley, Co-Chair Both dengue and chikungunya viruses originated in sylvatic cycles between nonhuman primates and arboreal mosquitoes. Both have achieved sustained transmission in endemic cycles between humans and anthropophilic mosquitoes and infect millions of people every year. However, sylvatic cycles persist in West Africa and Malaysia. This creates a possibility that these sylvatic strains may spill over into human hosts and generate new human cycle strains. To evaluate the risk of spill over, the distribution of arboreal mosquito vectors of the sylvatic cycle in Department of Kédougou, Senegal was modeled using vii

8 ecological niche modeling. The resulting models were compared to hotspot analyses performed on vector abundance data collected over two years in the study area. A distinct hotspot of high vector abundance was identified in the study area. This hotspot coincides with elevated amount of chikungunya in the collected mosquitoes. These results support the use of ecological niche modeling of disease vectors for predicting areas of elevated risk of vectorborne disease to humans. viii

9 TABLE OF CONTENTS LIST OF TABLES... XII LIST OF FIGURES... XIII DATA ON COMPACT DISK... XV LIST OF ABBREVIATIONS... XVII 1. INTRODUCTION LITERATURE REVIEW Emerging vector-borne diseases Vector-borne zoonotic diseases DENV and CHIKV as emerging diseases Landscape epidemiology Components of the nidus Predicting nidi in Senegal Dengue and chikungunya Arboviruses Clinical symptoms Evolution and history Sylvatic DENV and CHIKV Mosquitoes Lifecycle Mechanism of disease transmission...23 ix

10 2.4.3 Mosquito vectors in Senegal Ecological niche modeling General ecological niche modeling Maxent Spatial scale GIS and remote sensing in modeling disease vectors distribution METHODS Study area Conceptual Model Data Mosquito species presence data Environmental data Analysis Vector mosquito abundance analysis Species distribution modeling Positive CHIKV pool analysis RESULTS AND DISCUSSION Mosquito abundance analysis Analysis of mosquito abundance by month Analysis of abundance by land cover class Spatio-temporal analysis of abundance Mosquito distribution models...74 x

11 4.3 CHIKV distribution analysis Correlation between Maxent models, abundance, and CHIKV CONCLUSIONS...85 LITERATURE CITED...89 xi

12 LIST OF TABLES Table 1. Vectors of sylvatic dengue virus and chikungunya virus in the study area Table 2. Summary of Maxent models for Aedes spp Table 3. Comparison of common satellite remote sensing systems used for vector-borne disease research Table 4. Factors from the conceptual model and the variables/proxy variables used in the species distribution models Table 5. Environmental layers used in models Table 6. Results of Blocked ANOVA of mosquito abundance by month, year, landcover class and study area block Table 7 Moran s I results for abundance by month Table 8. Results of Tukey-Kramer post-hoc test on land cover by year Table 10. Descriptive statistics for different land cover classes...67 Table 11. Summary of Maxent models Table 12.Results of correlation analysis between models and abundance xii

13 LIST OF FIGURES Figure 1. The sylvatic and human cycles of dengue virus and chikunguna virus Figure 2. The case distribution of a). human dengue virus and b). human chikungunya virus. Based on WHO data from 2010 and 2008 respectively....3 Figure 3. The distribution of sylvatic dengue virus and chikungunya virus Figure 4. Realized vs. potential distribution Figure 5. a) The location of Senegal within Africa, b) the Department of Kédougou within Senegal and c) the study area with the Department of Kédougou Figure 6. Conceptual model of the spatial distribution of sylvatic Aedes mosquitoes in study area Figure 7. Location of sampling blocks and sites. From Diallo et al. (2012)...49 Figure 8. Total monthly rainfall and mosquito abundance for the two collection seasons Figure 9. Moran s I for the two collection season by month Figure 10. Abundance by land cover class Figure 11. Average abundance for the 2009 to 2010 collection season. The Ngari/Tenkoto area is circled Figure 12. Average abundance for the 2010 to 2009 collection season. The Ngari/Tenkoto area is circled Figure 13 Getis-Ord Gi* analysis of average abundance by year...71 Figure 14 Average abundance in villages, for each year and the average abundance for both years Figure 15. Abundance Getis-Ord Gi* analysis for the 2009 collection season Figure 16. Abundance Getis-Ord Gi* analysis for the 2010 collection season Figure 17. Maxent probabilities distributions for A. Four species model, B. Ae. aegypti model, C. Ae. africanus model, D. Ae. taylori model xiii

14 Figure 18. Response curves for A. Four species model, B. Ae. aegypti model, C. Ae. taylori model, D. Ae. africanus model Figure 19 Distribution and Getis-Ord Gi* analysis of CHIKV positive pools Figure 20. Bivariate Fit for A. Four species Maxent probability and CHIKV positive pools and B xiv

15 DATA ON COMPACT DISK Spreadsheets 1. Mosquito collection data from the collection season 2. Mosquito collection data from the collection season ArcMap Project Files Abundance Abundance Hotspot Analysis Hotspot Analysis 5. CHIKV Presence Shapefiles 1. Abundance 2. Hotspot Analysis by Month 3. Sampling Blocks 4. Roads 5. Villages 6. Land Cover 7. CHIKV Presence xv

16 ASCII files 1. Environmental data layers used in Maxent analysis xvi

17 LIST OF ABBREVIATIONS CDC: Center of Disease Control CHIKV: Chikungunya virus DENV: Dengue virus DHF: Dengue hemorrhagic fever IPCC: Intergovernmental Panel on Climate Change WHO: World Health Organization xvii

18 1. INTRODUCTION Over the past ten years dengue fever and chikungunya, two highly debilitating and sometimes fatal arthropod-borne viral diseases, have increased in incidence and severity (Guzman et al. 2010; Tsetsarkin et al. 2011). Typical symptoms of infection by both dengue virus 1 (DENV) and chikungunya virus (CHIKV) include fever, rash, headache, and joint pain. There is no approved vaccine or treatment for either disease. The number of dengue cases has more than doubled over the past ten years: fewer than one million dengue cases were reported per year to the WHO between 2000 and 2007, while over 2.2 million cases were reported in 2010 (WHO 2012). The number of reported cases is much lower than the number of actual cases, which has been estimated to be as high as 390 million every year (Bhatt et al. 2013). This increase is associated with rising numbers of severe cases and fatalities (Guzman et al. 2010). Likewise, prior to 2005, small non-fatal outbreaks of chikungunya were reported from Africa and Asia; in 2005 and 2006 more than 272,000 cases and 225 deaths were reported in the Indian Ocean islands, and more than 1.5 million cases were reported from India (WHO 2008; Staples, Breiman, and Powers 2009). In 2007, chikungunya cases were reported in Europe for the first time when an outbreak in coastal Italy resulted in 197 cases and one death (WHO 2013). Both DENV and CHIKV are maintained in two distinct cycles (Figure 1): a human cycle and a sylvatic cycle. In the human cycle, humans serve as reservoir hosts 1 In this thesis, I will refer to the diseases by their full names (e.g. dengue) and to the viruses that cause them by their abbreviation (e.g. DENV)

19 and anthropophilic mosquitoes, Aedes aegypti (the yellow fever mosquito) and Ae. albopictus (the Asian tiger mosquito), transmit the virus from infected to uninfected humans. Human DENV is present in both the Old World and New World tropics and temperate zones, while CHIKV limited to the Old World tropics and Europe (Figure 2). In the sylvatic DENV cycle (Figure 3), non-human primates such as African green monkeys or baboons act as reservoir hosts and a wide variety of arboreal Aedes mosquitoes act as vectors. The sylvatic CHIKV cycle (Figure 3) shares many of the same vectors as the sylvatic DENV cycle, but CHIKV or antibodies to CHIKV have been found not only in monkeys, but also rodents, squirrels, bats, and birds and it is not clear which of these are the main reservoir hosts (Chevillon et al. 2008). The human cycles of both viruses are descended from their respective sylvatic cycles (Hanley and Weaver 2008). Figure 1. The sylvatic and human cycles of dengue virus and chikunguna virus. Based on Vasilakis et al

20 Figure 2. The case distribution of A) human dengue virus and B) human chikungunya virus. Based on WHO data from 2010 and 2008 respectively (WHO 2012, 2013). 3

21 Figure 3. The distribution of sylvatic dengue virus and chikungunya virus. Based on: Tsetsarkin 2011; Vasilakis et al. 2011; CDC 2012; WHO Defining when and where humans are likely to be exposed to zoonotic pathogens like sylvatic DENV and CHIKV can help researchers understand, and potentially prevent, the emergence of novel human diseases. Zoonotic diseases that can make the leap into a human-only transmission cycle pose a particularly serious threat to human health as they can spread beyond the geographic limits of their original animal host. Arthropod-borne diseases such as West Nile virus and Eastern equine encephalitis virus are usually maintained in non-human hosts, and human disease results from spillover events. From the point of view of the virus, humans are dead end hosts, in which the virus cannot reproduce effectively enough to be transmitted to the vector. However, as DENV and CHIKV demonstrate, it is possible for viruses to make a complete jump from non-human hosts to exclusive maintenance 4

22 in humans. The first step in the jump occurs when humans are exposed to the sylvatic strains. To define when and where humans are exposed to sylvatic DENV and CHIKV we need to understand the distribution of the mosquito vectors. It is not possible to use the distribution of human or non-human primate infections to model risk because both humans and non-human primates are extremely vagile, making it impossible to identify the specific location at which an individual was infected. For example, Guinea baboons travel an average of 8 kilometers per day and have home ranges of up to 50 square kilometers, within which the risk of infection may vary greatly (Sharman 1982). On the other hand, dispersal studies of the peri-domestic mosquitoes Ae. notoscriptus have found that the maximum distance traveled over 8 days was 238 meters while Ae. albopictus has been recorded as moving a maximum of 1,000 meters in one week (Watson, Saul, and Kay 2000; Maciel-de-Freitas et al. 2006). If we are able to predict where potentially infected mosquitoes are, and when they are likely to be infected, we can predict the risk of human infection. The value of using mosquito distributions in predicting human disease risk was demonstrated in , when remotely sensed imagery was used to predict increased mosquito occurrence two to six weeks before an outbreak of the mosquitoborne Rift Valley fever (Anyamba et al. 2009). This prediction allowed public health officials to increase control methods, including restricting animal movement and distribution of mosquito nets. Most of the reported human cases of Rift Valley fever 5

23 during the outbreak occurred within the predicted risk area, supporting the use of mosquito distribution models as a measure of risk for mosquito-borne diseases. The purpose of this thesis was to analyze the spatial and temporal distribution of mosquito vectors in the Department of Kédougou, Senegal and create a predictive model of this distribution using two years of mosquito abundance data, environmental data, and ecological niche modeling. Sylvatic DENV-2 and CHIKV are well documented to be epizootic in the area and have been monitored by the Institut Pasteur Senegal for fifty years making the area idea for the purposes of this study (Vasilakis et al. 2011; Diallo et al. 2012). I use Kédougou as a case study to explore the factors that affect the vector mosquito distribution, and to create a predictive model of that distribution. This model will a) provide a way for researchers to target their efforts in understanding where humans are exposed to sylvatic DENV and CHIKV and b) provide a way for public health officials to target villages for vector surveillance and control. 6

24 2. LITERATURE REVIEW In this chapter I will present an overview of key topics relevant to this thesis, including emerging vector-borne disease (2.1) and landscape epidemiology (2.2), biology and ecology of viruses and mosquitoes in southeastern Senegal (2.3 and 2.4), and ecological niche modeling and its use for predicting disease risk (2.5). 2.1 Emerging vector-borne diseases The World Health Organization (WHO) defines emerging infectious diseases as diseases that have appeared in a population for the first time, or that may have existed previously but [are] rapidly increasing in incidence or geographic range (Chavers, Fawal, and Vermund 2002; Weaver and Reisen 2010; WHO 2012). While a number of advances have been made in controlling known infectious diseases, emerging infectious diseases pose a constant threat to public health. This threat has increased greatly over the past one hundred years as human activity has expanded into wildlife habitat and as international air travel allowed people (and therefore pathogens) to travel between continents in a day (Gubler 1997; Chavers, Fawal, and Vermund 2002; Hanley and Weaver 2008; Tabachnick 2010). Other factors that can affect the spread of emerging diseases include: population growth, migration, housing density, social and behavioral changes (such as changes in sexual mores, exposure to the outdoors, and alcohol and drug use), overuse of antibiotics, mass production of foodstuffs, modern agricultural practices, evolution of pathogens, war, bio-terrorism, 7

25 and natural disasters (Chavers, Fawal, and Vermund 2002; Jones et al. 2008; Randolph and Rogers 2010) Vector-borne zoonotic diseases An analysis of 335 human disease emergence events between 1940 and 2004 by Jones et al. (2008) showed that 60.3% of these diseases were zoonotic and 71.8% of the zoonotic diseases were from wildlife populations. Vector-borne diseases accounted for 22.8% of all emergence events. The percentages of emergence events of both zoonotic diseases and vector-borne diseases increased significantly over time. Control of zoonotic and/or vector-borne diseases requires some way to manage the diseases in the host or the vector. However, the vaccination or treatment of wild animals is logistically difficult (although oral vaccination of wildlife for rabies has been successful (Rupprecht and Gibbons 2004)) and the control of vectors can also be problematic. While efforts to control mosquito populations have been successful in the past, concerns over the use of the most effective pesticide (DDT) limit its current use (Kinkela 2011). Furthermore, Aedes mosquito populations have evolved resistance against DDT and other pesticides in many areas (Hemingway and Ranson 2000; Bonizzoni et al. 2013). As it currently stands, vector-borne zoonotic diseases such as sylvatic dengue and sylvatic chikungunya can be maintained in animal populations and spread with ease by their uncontrolled vectors throughout large parts of the world. Even if a vaccine was produced against these diseases and 100% of 8

26 vulnerable human populations were vaccinated, persistence of these viruses in animal reservoirs means that the diseases can re-emerge DENV and CHIKV as emerging diseases DENV and CHIKV can be considered emerging diseases on multiple different levels. Firstly, they have both increased in range and intensity of outbreaks over the past 100 years (WHO 2012; Gubler 2004; Brady et al. 2012). Secondly, they have both adapted to new vectors, with CHIKV adapting to Ae. albopictus within the past ten years (Hanley and Weaver 2008; Tsetsarkin et al. 2011). Lastly, and most importantly for this thesis, they have both emerged from zoonotic cycles to human cycles (Hanley and Weaver 2008; Tsetsarkin et al. 2011). Both viruses have increased in intensity and geographic range. DENV currently infects an estimated 390 million people every year in 128 countries, although many of the infections are not reported (Brady et al. 2012; Bhatt et al. 2013). The number of dengue cases has more than doubled over the past ten years: less than one million dengue cases were reported per year to the WHO between 2000 and 2007 while over 2.2 million cases were reported in 2010 (WHO 2012). Dengue has also expanded geographically, although the true range is hard to estimate because of differences in reporting between countries (Gubler 2004; Brady et al. 2012). However, dengue has been reported for the first time in France and Croatia in 2010 and in Portugal in 2012 showing its expansion into temperate areas (WHO 2013). Data on numbers of cases of CHIKV are harder to obtain, because it is less 9

27 intensively monitored than DENV, but the massive outbreaks between 2005 and 2007 which affected millions of people created a dramatic increase in the number of people considered at risk (WHO 2010). As is the case with DENV, the outbreak in Italy shows that CHIKV is also expanding into temperate areas, putting new populations at risk (WHO 2013). Furthermore, the human strains of both viruses have jumped vectors. DENV was originally transmitted to humans by Ae. albopictus but Ae. aegypti now serves as its main vector in many parts of the world (Hanley and Weaver 2008). CHIKV has made the opposite jump, from Ae. aegypti to Ae. albopictus very recently, and it is believed that this adaptation was one of the drivers for the outbreaks between 2005 and 2007 (Tsetsarkin et al. 2011). Ae. albopictus can survive cooler temperatures, allowing the spread of CHIKV into temperate area (Rezza et al. 2007; Kelvin 2011). Lastly, both DENV and CHIKV have demonstrated the ability to repeatedly transition from a sylvatic cycle to a human cycle. Each of the four DENV serotypes in the human cycle emerged from sylvatic ancestors in four independent events (Vasilakis et al. 2011). Three of the four serotypes (DENV-1, -2, and -4) have been isolated from monkeys in Malaysia while DENV-2 has been isolated from monkeys in Western Africa (Figure 3) (Fagbami, Monath, and Fabiyi 1977; Rudnick 1986). A fifth sylvatic serotype, which until recently has been hypothetical, has been found in Macaques in Borneo and has the potential to emerge into a human cycle (Normile 2013). Genetic analyses place these sylvatic strains at the basal position within each serotype clade, supporting the hypothesis that the human cycles arose from spillover 10

28 of sylvatic infections of humans (Hanley and Weaver 2008). It is suspected that most cases of chikungunya in Asia derive from the human cycle, while most or all cases in Africa are sylvatic strains (Figure 3) (Chevillon et al. 2008; Apandi et al. 2009; Volk et al. 2010). Genetic analysis of CHIKV reveals that the virus most likely originated in Africa and has evolved into three lineages: a West African lineage, an East/Central/South African lineage, and an Asian lineage (Volk et al. 2010). These lineages are not clearly classifiable as sylvatic or human cycle and each have sylvatic and human strains, indicating that there are no adaptive barriers preventing sylvatic CHIKV from becoming human CHIKV. The emergence of a vector-borne zoonotic disease such as DENV and CHIKV is a function of the changing ecology of the pathogen, the vectors, and the hosts. To understand the changing distribution of a vector-borne zoonotic disease, researchers first have to comprehend the vector-pathogen-host-environment relationship and how it may be affected by changing environments (Tabachnick 2010). To do this requires a detailed understanding of the landscape epidemiology of the diseases. 2.2 Landscape epidemiology Landscape epidemiology, a biogeographical and holistic approach to studying disease distribution, is an important framework for understanding vector-borne zoonotic diseases (Pavlovsky 1966; Reisen 2010). Each constellation of pathogens, vectors, and animal hosts has different environmental requirements; only where all of these requirements are met within a landscape can the pathogen survive. Pavlovsky 11

29 (1966) was the first to investigate the association between vector-borne pathogens and the specific locations within the landscape where they can survive. These locations, which he termed nidi (singular nidus ), are the places in the landscape that contain vectors, animal hosts, and pathogens. Human infection occurs when humans enter the nidus; the end goal of landscape epidemiology is to predict where nidi exist to prevent human disease Components of the nidus In his review of the landscape epidemiology of vector-borne diseases, Reisen (2010) specified five components of the nidus for vector-borne pathogens: climate, vegetation, hosts, vectors, and pathogens. Variation in temperature and precipitation has a large role in determining the suitability of a landscape for vegetation, hosts, vectors, and pathogens. For example, cold winters can limit disease transmission because the vegetation may be dormant during the winter, decreasing food availability for animal hosts; vectors may be killed by cold temperatures; and pathogens may not be able to reproduce effectively below a temperature threshold. Vegetation can influence disease distribution by providing cover, breeding sites, and food for both animal hosts and vectors. Mosquito-borne viruses, including DENV and CHIKV, are generally limited to climates that have enough precipitation to support both food plants for hosts and pools for mosquito larvae, as well as temperatures high enough to support mosquito and virus reproduction. 12

30 The biology and behavior of the animal hosts and vectors are a major determinant of the distribution and transmission cycles of the pathogen. Animal host populations in which the pathogen is enzootic (endemic within the animal population) act as reservoirs for the pathogen. Whether or not humans become infected with the pathogen is, in part, dependent on the animal hosts. Hosts that are more mobile or more densely aggregated are more likely to spread disease. Hosts also vary in their susceptibility (their ability to become infected) and their competency (their ability to produce the viral load needed to infect vectors). For example, studies on West Nile virus have found that passerine birds such as crows, sparrows and robins are highly competent hosts with the virus reaching high viremia, while non-passerine birds such as chickens, ducks, and doves are either incompetent or weakly competent as hosts (Komar et al. 2003). Concurrently, vector biology also shapes pathogen transmission dynamics. For example, female mosquitoes live for several weeks during which they feed repeatedly, resulting in intense but temporally limited transmission. On the other hand, ticks can live for years but only feed three times during their lives, resulting in low transmission rates and persistence of the pathogen in the environment. Very much like the hosts, the vectors of a pathogen can vary in their susceptibly to the pathogen and their competency in transmitting it (Reisen 2010). Pathogens, then, require susceptible and competent animal hosts, susceptible and competent vectors, and climates that are conducive to pathogen replication. In the case of sylvatic DENV and CHIKV in the Kédougou study area, arboreal Aedes 13

31 mosquitoes are the susceptible and competent vectors (Diallo et al. 1999; Diallo et al. 2003) and non-human primates such as African green monkeys are susceptible and competent hosts (Fagbami, Monath, and Fabiyi 1977), although there is some evidence that other species may also serve as reservoir hosts in this system (Hanley, personal communication, Diallo et al. 1999). Arboreal Aedes are termed tree hole mosquitoes because they require trees and enough rain to fill the tree holes with water during the rainy season to reproduce successfully. In addition, they require warm temperatures to survive (Ae. aegypti and Ae. albopictus larvae cannot mature in air temperatures of less than 10 C (Tsuda and Takagi 2001)). The monkeys that are hosts for DENV and CHIKV in the study area require large ranges (up to 50 km 2 ) with enough food sources such as fruit trees. Temperature can also have a large impact on pathogen transmission. For example, a strain of DENV-2 isolated in Bangkok could only be transmitted to monkeys by Ae. aegypti mosquitoes that had been maintained at 30 C or greater for 25 days (Watts et al.1986). How well each of these requirements are met varies across the study area; sylvatic DENV and CHIKV can be sustained and transmitted to humans only in the nidi where there are sufficient arboreal mosquitoes, sufficient non-human primates, and acceptable temperatures for viral replication. Nidi are not static features of landscapes and change as the landscape changes, altering disease dynamics. Landscapes can change naturally due to changing natural conditions such as ecological succession (Molles 2008). They can also change as a direct result of human behavior. Human development of landscapes, resulting in 14

32 habitat loss and fragmentation, can alter disease dynamics (Reisen 2010). It has also been shown that fragmentation of habitat can concentrate hosts and vectors into small areas, increasing disease transmission. For example, in a study of Lyme disease (Borelia burgdorferi) transmission (Allan et al. 2003) smaller fragments of forest had increased host (white-footed mice) density, increased vector (black legged ticks) density, and increased numbers of ticks infected with the Borrelia bacteria (the Lyme disease pathogen). Global climate change will also have significant effects on the distribution of nidi (Tabachnick 2010). Many studies have linked vector-borne zoonotic diseases to climate change (Githeko et al. 2000; Watson and McMichael 2001; Hales et al. 2002; Patz and Olson2008; Benitez 2009; Pascual and Bouma 2009) and the Intergovernmental Panel on Climate Change (IPCC 2001; IPCC 2007) lists the emergence of vector-borne disease as one the major consequences of global climate change. However, climate change will have different effects on the different components of nidi, so the ultimate effect on nidi is difficult to predict (Reiter 2001; Gubler 2002; Lafferty 2009; Tabachnick 2010). For example, warming climates may expand the range of a vector but desertification may decrease the habitat of the host; the ultimate effect on the pathogen will depend on how each component of the nidus responds Predicting nidi in Senegal To meet the end goal of landscape epidemiology, i.e. to predict the location of nidi, we need to understand the landscape ecology of pathogens, vectors, and hosts. In 15

33 this study, I seek to predict the locations of sylvatic DENV and CHIKV nidi using an understanding of the landscape ecology of sylvatic DENV and CHIKV, arboreal Aedes mosquitoes, and non-human primates. Data on three of the known non-human primate reservoir hosts of both DENV and CHIKV African green monkeys (Chlorocebus sabaeus), patas monkeys (Erythrocebus patas), and Guinea baboons (Papio papio) were also collected as part of this project and these hosts were found to have nearly 100% infection by both DENV and CHIKV by the age of four (Hanley, personal communication). However, it is impossible to know where individuals were exposed as these monkeys have large home ranges. Further research that incorporates more spatial data on these primates may shed light on where they are getting infected and how close they come to human habitation. These questions, however, are beyond the scope of this study and in the next two sections I focus on the biology and ecology of the virus and vectors. 2.3 Dengue and chikungunya Arboviruses Both DENV, a flavivirus (Family: Flaviviridae), and CHIKV, an alphavirus (Family: Togaviridae) are arthropod-borne viruses (arboviruses), an ecological grouping that includes all viruses that are transmitted to vertebrate hosts by arthropod vectors (Hanley and Weaver 2008). Except for DENV and CHIKV, all arboviruses are zoonotic and are maintained in wild animal populations, with human infections resulting from spillover events. For the most part, these spillover events are dead-ends 16

34 for the virus and do not affect virus evolution. Arboviruses can be transmitted by mosquitoes (e.g. DENV, CHIKV, West Nile virus, yellow fever virus), ticks (e.g. tick-borne encephalitis virus, Crimea-Congo hemorrhagic fever virus), biting flies (e.g. Toscana virus, Bluetongue virus), or lice (e.g. Southern elephant seal virus) Clinical symptoms DENV and CHIKV produce very similar symptoms including fever, severe headache, joint pain, muscle pain, weakness, and rash. The incubation period for DENV is seven to ten days, while the incubation for CHIKV is usually three to seven days (Guzmán and Kouri 2002; Staples, Breiman, and Powers 2009). The fever for both may last for a few days to a week, but some patients have profound fatigue lasting for weeks. Approximately 0.5% of cases of dengue disease result in dengue hemorrhagic fever (DHF), which can involve abdominal pain, hemorrhage, circulatory collapse, and in some cases death (Guzman et al. 2010; Vasilakis et al. 2011). Most common in very young children, DHF is defined by the presence of high continuous fever (40-41ºC) for two to seven days, platelet counts greater than 100,000/mm 3, hemorrhagic manifestations, and excessive vascular permeability (Holtzclaw 2000). The most severe form of DHF leads to dengue shock syndrome, which can quickly lead to death. Chikungunya fever rarely causes fatalities, and those fatalities are more likely in the elderly (Holtzclaw 2000; WHO 2008; WHO 2009). However, individuals infected with CHIKV may have joint pain that lasts for months 17

35 or years (WHO 2008; Schilte et al. 2013). There are no approved vaccines or therapies for DENV or CHIKV (WHO 2008; WHO 2009) Evolution and history DENV is comprised of a suite of four genetically-distinct serotypes: DENV-1, DENV-2, DENV-3, and DENV-4 (Hanley and Weaver 2008). Most researchers currently believe that DENV evolved in Asia due to the presence of three of the four serotypes in sylvatic cycles there (Hanley and Weaver 2008). Sylvatic strains of DENV-1, DENV-2, and DENV-4 have all been identified from Asian monkeys; DENV-2 has also been isolated in monkeys in western Africa. While a sylvatic DENV-3 has not yet been isolated, it is thought that all four serotypes independently evolved from sylvatic strains (Vasilakis and Weaver 2008). Although Ae. aegypti aegypti is the main vector of human DENV, Ae. albopictus may have served as the bridge vector for these emergence events. Ae. albopictus is found both in forested and urban areas and is very susceptible to DENV, while Ae. aegypti formosus, the sylvatic subspecies and ancestral progenitor of the domestic Ae. aegypti aegypti, is much less susceptible to DENV and is found only in Africa (Hanley and Weaver 2008). The earliest known description of a dengue-like disease comes from Chin Dynasty in China, dating to CE (Vasilakis and Weaver 2008), but dengue was not formally described as a unique disease until 1779 (Holtzclaw 2000). Based on the rate of nucleotide substitution, dengue probably emerged no more than a thousand years ago from the sylvatic cycles in Asia and then spread globally via 18

36 shipping trade and war transport. Expansion was particularly explosive during WWII (Vasilakis et al. 2011). Since its global expansion, epidemics have occurred with increasing frequency and the four serotypes have each increased in range (Holtzclaw 2000). The first case of DHF was reported in the 1950s in the Philippines (Holtzclaw 2000). By the 1980s dengue had become hyperendemic, with multiple serotypes in co-occurring in numerous regions (Vasilakis and Weaver 2008). In the 2000s the trend continued with a large increase in the annual incidence of dengue fever and DHF (Vasilakis and Weaver 2008). Because the four serotypes do not provide cross immunity, individuals can contract dengue fever more than once (Vasilakis and Weaver 2008; Vasilakis, Hanely, and Weaver 2010). Infection by a second serotype can be much more severe than the initial infection and is associated with increased risk of DHF (Vaughn et al. 2000; Simmons et al. 2012). Chikungunya was first identified in 1953 in Tanzania and, until recently, was considered a relatively mild tropical disease that did not cause mortality. However, in the past ten years, CHIKV has adapted to Ae. albopictus as a vector, which can survive in more temperate climates than Ae. aegypti, the primary vector (Tsetsarkin et al. 2011). In Ae. albopictus diapause, or a delay of development, of the eggs can be triggered by cold temperatures ensuring that the eggs survive cool winters (Hanson and Craig 1995). The adaption of CHIKV to this new host has resulted in severe outbreaks in the Indian Ocean region with hundreds of deaths, as well as an outbreak in Italy in 2007 that resulted in one death (Rezza et al. 2007; Kelvin 2011). On the 19

37 island of La Réunion, one of the epicenters of the outbreak, the high population density resulted in Ae. albopictus being unusually anthropophilic and allowed CHIKV to be transmitted in a human-only cycle. Out of a population of 770,000 people on the island, there were a total of 265,000 clinical cases and 237 deaths. Genetic studies show that the adaptation of CHIKV to Ae. albopictus, which is also associated with increased infectivity, can be caused by four different mutations (Tsetsarkin et al. 2011). Given the apparent ease by which CHIKV adapts to this new vector, it is likely that outbreaks like the occurrences will not be isolated incidents. Tsetsarkin et al. (2011) stated that without control measures in Africa and Asia, the emergence of CHIKV into a stable endemic disease may be inevitable (p. 315) Sylvatic DENV and CHIKV Given that human DENV has already emerged four different times and a single amino acid mutation allowed the emergence of human CHIKV, it is possible that additional emergence events from sylvatic strains will occur. For a sylvatic strain to be maintained in a human cycle it must be able to replicate in both humans and the peri-domestic vectors and it must come into contact with human populations. Experimental evidence shows that no adaptation is required for sylvatic strains of DENV to be transmitted by Ae. aegypti (Diallo et al. 2005; Vasilakis et al. 2007; Diallo et al. 2008; Hanley and Vasilakis, unpublished data). Rural areas near forests where the sylvatic cycle occurs are the most likely regions for emergence to occur (Smith 1956; Rudnick 1986). In West Africa, non-human primate populations test 20

38 seropositive for DENV-2, and several cases of dengue fever and one case of DHF (Diallo et al. 2003; Cardosa et al. 2009; Franco et al. 2011) caused by sylvatic strains have been reported in humans. However, there is little direct evidence of secondary transmission within human populations or evidence that sylvatic strains have caused human outbreak, however, a sylvatic strain outbreak has been suggested in Nigeria in 1966 (Vasilakis et al. 2008; Vasilakis and Weaver 2008). Why sylvatic strains are not transmitted from human to human is not known, although lower pathogenicity, limited contact with Ae. aegypti and Ae. albopictus, and exclusion by cross-immunity with circulating human strains have been suggested as possible explanations (Vasilakis et al. 2008; Vasilakis and Weaver 2008; Vasilakis, Hanley, and Weaver 2010). This potential for emergence of sylvatic strains presents a problem for DENV/CHIKV control efforts. Firstly, if on-going efforts to create a vaccination against human cycle DENV-1-4 are successful, the current circulation of sylvatic strains could result in the emergence of a strain of DENV-1-4 that was not neutralized by existing vaccines. However, within the serotypes, antibodies against human strains had strong neutralizing effects (Vasilakis et al. 2008). Another more worrying possibility is that fifth serotype of DENV could emerge into the empty niche created by local or global DENV eradication. Until recently, the fifth serotype was hypothetical; however, a DENV strain that was distinctly different from the four known serotypes was isolated from macaques in Borneo (Normile 2013). Given that having immunity to one serotype increases the chance that an individual will have 21

39 DHF when infected with a second serotype, having a fifth serotype circulating in a human population with immune response against four serotypes could result in a massive increase of DHF and fatal cases. Furthermore, while mosquito control could be effective in controlling human cycle DENV and CHIKV, it would not be feasible to control sylvatic mosquitoes by the standard methods such as aerial spraying or larvacides because of the size and inaccessibility of their habitat. 2.4 Mosquitoes Lifecycle Mosquitoes (Family: Culicidae, Order: Diptera) exhibit a complete metamorphosis with four life stages: egg, larval, pupa, and adult. Female mosquitoes lay eggs either in the water or on sites that are likely to flood, depending on the species (Clements 2000). Eggs of most mosquitoes hatch within two days to a week, although the eggs of some mosquitoes can survive for months before hatching during drought or cold seasons (Clements 2000). The larvae of most species live in small or shallow bodies of still water including puddles, shallow pools, sheltered stream edges, water-filled tree-holes, or man-made containers (Clements 2000). Most mosquito larvae live on the particulate matter in the water pool such as bacteria, diatoms, algae, and particles of decayed plant material (Clements 2000). Larval maturation can take as little as four or five days in tropical mosquitoes, allowing them to exploit temporary bodies of water (Bates 1949). After adults emerge, it may take a day or two before they are ready to mate (Clements 2000). Both males and females 22

40 consume plant sugar as their main source of energy, but the females of the subfamilies Anophelinae (including the mosquitoes that transmit malaria) and Culicinae (including the genus Aedes) also require protein from blood meals to lay eggs (Clements 2000). In fact, multiple studies have found that Aedes aegypti females can live exclusively, or almost exclusively, on human blood making them particularly good vectors for disease (Edman et al. 1992; Scott et al. 1997; Harrington, Edman, and Scott 2001). Most species have preferred times (e.g., morning or evening) for mating, feeding, and laying eggs (Clements 2000). Life spans of mosquitoes range from a few days to months (Clements 2000) Mechanism of disease transmission After a mosquito has fed on a host infected with an arbovirus, the virus multiplies in the gut of the mosquito and then disseminates into other tissues including the salivary glands, allowing the virus to be transferred to the next host the mosquito bites (Clements 2000). This time period between being infected and being able to infect (the extrinsic incubation period) can vary greatly depending on the virus, the mosquito, and environmental factors such as temperature. For DENV the process can range from two to fifteen days at 30 C and for CHIKV from two to seven days at 28 C, but the time span for both varies on temperature and mosquito species (Dubrulle et al. 2009; Chan and Johansson 2012). 23

41 2.4.3 Mosquito vectors in Senegal There are many different mosquito species present in Senegal and the study area, and many of them serve as vectors for multiple arboviruses. Of these seven species are possible vectors of sylvatic CHIKV and/or DENV-2 to humans (Table 1). The ecology and biology of these species are not equally well known. However, given their general similarities, we can assume that they face similar challenges and have similar adaptations to their environment. Table 1. Vectors of sylvatic dengue virus and chikungunya virus in the study area. Whether the evidence of vector capacity is known from laboratory experiences or from mosquitoes caught in the field is noted. Species DENV Vector? (Evidence) CHIVK Vector? (Evidence) Other known transmitted viruses Ae. africanus X(field 4 ) Yellow fever 5, Zika 6 Ae. aegypti formosusǂ X(field 1, lab 2 ) X(field 4 ) Ae. dalzieli X(field 3,4 ) Rift Valley fever 7 Ae. furcifer X (field 1, lab 2 ) X(field 3,4 ) Yellow fever, Zika, Bouboui, Bunyamwera 8 Ae. luteocephalus X (field 1, lab 2 ) X(field 3,4 ) Yellow fever 9 Ae. taylori X (field 1 ) X(field 3,4 ) Yellow Fever, Zika, Bouboui, Bunyamwera 8 Ae. vittatus X (lab 2 ) X(field 3 ) 1 Diallo et al. 2003; 2 Diallo et al. 2005; 3 Diallo et al. 1999; 4 Diallo et al. 2012; 5 Haddow 1948; 6 Haddow 1964; 7 Fontenille et al. 1998; 8 Huang 1986; 9 Thonnon et al ǂ Ae. aegypti formosus has been shown to be capable of being infected by DENV, but experimental evidence suggests that it is much less competent of a host then the other species listed here (Diallo et al. 2003). These seven species have several traits in common that will affect their spatial distribution: similar egg-laying sites, need for access to water, larval dietary 24

42 requirements and therefore competition among co-occurring larvae, and predation by the larvae of other mosquitoes. All seven species are tree-hole mosquitoes. There is no evidence that any of these species have any preference for a particular species of trees as breeding sites (Bang, Bown, and Arata 1980). Larvae have also been found in fresh fruit husks, decaying fruit husks, puddles, bamboo holes, discarded containers, tires, rocks holes and storage containers (Diallo et al. 2013). As the larval forms are aquatic, this use of tree holes rather than permanent water means that the eggs can only hatch in the rainy seasons when the holes are filled with water. While these mosquitoes require rainfall to breed, too much rainfall may be a problem an sudden heavy downfalls can also wash out the larvae from their tree-holes (Sempala 1983). Larvae may face competition for resources from other larvae, both of their own species and of other species. At the same time, the larvae of the mosquito genus Toxorhynchites prey on the larvae of other mosquito species and may thus be a major factor in larval mortality (Sempala 1983). The seven mosquito species are well adapted to survive the dry season. Experiments have shown that Ae. furcifer eggs were still viable after 14 months out of water, while Ae. vittatus and Ae. aegypti have been found to still be viable after 4.5 months of 40 C temperatures and air relative humidities as low as 5% (Muspratt 1955; Irving-Bell, Inyang, and Tamu 1991). Thus neither severe dry seasons nor high temperatures represent limitations on the distribution of these species. Although all of the species focused on in this study can be infected by and transmit DENV, CHIKV, or both, they vary in their potential to act as spillover 25

43 vectors to humans because of differences in infection rates, abundance, and behaviors. Ae. furcifer and Ae. taylori have high rates of infection, are abundant for most of the rainy season, often take two blood meals per gonotropic cycle, and bite in villages. Ae. luteocephalus also has high infection rates, but drops in abundance before the end of the rainy season when infection is more likely. Ae. aegypti and Ae. vittatus can both be infected by DENV and Ae. vittatus can be infected by CHIKV but they do not reach high abundances (Diallo et al. 1999; Diallo et al. 2003; Diallo et al. 2005; Diallo et al. 2012). Ae. africanus and Ae. dalzieli are both capable of being infected with CHIKV, but are not very common in the study area (Diallo et al. 2012; Diallo et al. 2012). All of these species, particularly Ae. furcifer and Ae. taylori, have the potential to transmit sylvatic DENV and CHIKV to humans. However, to transmit these viruses to humans, they must first come into contact with humans. To know where that is most likely to happen, it may be necessary to predict the distribution of these vectors using ecological niche modeling. 2.5 Ecological niche modeling General ecological niche modeling Ecological niche models (ENMs), also known as habitat suitability models or species distribution models, have been used to model the distribution of a wide range of phenomena. ENMs predict the spatial probability that a phenomenon (usually a species) will occur based on environmental variables and presence, presence-absence, 26

44 or abundance data of the phenomenon (Guisan and Thuiller 2005). ENMs are most commonly used to predict the distribution of plants or animals of interest (Miller, Franklin, and Aspinall 2007; Elith et al. 2011). However, they also can be used to predict the probability surface of any response variable that is correlated with environmental variables (e.g., soil properties, tree height, species richness) (Miller, Franklin, and Aspinall 2007). Recently, researchers have begun to use ENMs to predict disease risk. This has been done both directly by using disease cases as presence points (e.g. Kleinschmidt et al. 2000; Machado-Machado 2012) and indirectly for vector-borne diseases by using the vector presence as a proxy for disease risk (Moffett, Shackelford, and Sarkar 2007; Larson et al. 2010; Waltari and Perkins2010). Soberón (2007) created a conceptual model of ENMs (Figure 4), in which he distinguished between the potential, or niche, distribution and the realized, or actual, distribution of a species (Phillips et al. 2004; Soberón and Nakamura 2009). The potential distribution is the overlap where both the biotic (e.g. sufficient food sources, limited competition, and limited predation) and the abiotic (e.g. temperature, precipitation, soil) requirements of a species are met. The realized distribution is the portion of the potential distribution that is accessible to the species given its level of mobility and initial distribution. In order to model the realized distribution, a researcher would have to know the abiotic and biotic requirements of a species as well as whether or not the species was present in all locations where these requirements were met. In reality, data on abiotic conditions such as temperature and 27

45 rainfall are fairly easy to obtain, and some biotic conditions such as net primary productivity are possible to obtain, but comprehensive data on species locations are hard, if not impossible, to acquire. Thus, ENMs are always approximations of the potential distribution, and not of the realized distribution. Figure 4. Realized vs. potential distribution. A is the area where the abiotic requirements of the species are met, B is the area where the biotic requirements are met, M is the area that is accessible to the species given its level of mobility, RD is the realized distribution, and PD is the potential distribution. After Soberón (2007). While ENMs have been used in a wide variety of studies, there is considerable debate on what they actually model and how useful they are (Jiménez-Valverde, Lobo, and Hortal 2008; Elith and Leathwick 2009; Godsoe 2010; Warren 2012). Both Elith and Leathwick (2009) and Jiménez-Valverde et al. (2008) have argued that ENMs predict species distributions or realized niches rather than ecological niches or 28

46 potential niches because there may not be any clear connection between a model and the biological aspects of the species being modeled. Thus ENMs are also known as species distribution models or habitat suitability models depending on the researcher. Another potential problem with ENMs is that the variables that are most easily accessible and therefore most commonly used (e.g. primary production and precipitation), a) may have an indirect relationship with species distribution and b) do not or cannot include hard-to-measure ecological variables such as competition, community processes, and niche interactions (Guisan and Thuiller 2005; Hirzel and Le Lay 2008; Godsoe 2010). For example, the white-footed mouse, the preferred host of Lyme disease, is in competition with other small mammals such as squirrels. When those small mammals are stressed and decline because of forest fragmentation, the white-footed mouse populations increase. As mouse populations increase so do rates of Lyme disease. This results in fragmentation having a positive association with the distribution of Lyme disease. Forest fragmentation does not, however, have any direct biological effect on the bacteria that causes Lyme disease, Borrelia. Thus, a model that used forest fragmentation to predict Borrelia niche would actually be modeling mouse distribution and miss locations where the bacteria infect different hosts (Allan, Keesing, and Ostfeld 2003). Spatial autocorrelation can also pose a problem for ENMs. Spatial autocorrelation is the co-variation of a phenomenon and geographic space that the phenomenon occupies; i.e. measurements of the phenomenon that are close together are more similar than they would be if the phenomenon was randomly distributed 29

47 across space (Koenig 1999). For example, weather is more likely to be similar in sites that are near each other than in sites that are far apart. Spatial autocorrelation in a species presence dataset can come from three sources: i) spatial autocorrelation in the species distribution; ii) spatial autocorrelation of the environment; and iii) observation bias (i.e. species are more likely to be observed where there are observers) (Fortin, Dale, and Hoef 2006; Yackulic et al. 2012). If spatially autocorrelated species presence data are used in an ENM the resulting model may not reflect reality because the model may be overly influenced by the cause of the spatial autocorrelation. However, while ENMs may not be perfect models of ecological niches, they are the best models that we have and can provide useful information (Warren 2012). In fact, ENMs can be considered to model ecological niches because they rely on the assumption that a species will be found in similar environmental conditions. Furthermore, although the relationship between the environmental variables and the species biology may be indirect, as long as we know what that relationship is, it is valid to use ENMs as a way to predict distribution (Warren 2012). In the example above, while forest fragmentation may have no direct effect on Borellia bacteria, the knowledge that fragmentation is associated with Lyme disease provides both a starting place for further research on that relationship and a way to model risk of Lyme disease, even if in an imperfect way. ENMs may be flawed and do not provide maps of species distribution or even all possible niches. However, when used correctly they do provide useful information about species that can help in understanding the ecology and therefore the niche of the species. 30

48 There are numerous ENMs including regression-based techniques, profile techniques, and machine learning techniques. Selection of the ENM technique must be based on the data characteristics, model use, and intended final product (Miller, Franklin, and Aspinall 2007). Regression-based techniques such as the Generalized Linear Model (GLM) and the Generalized Additive Model (GAM) are based on linear regression modeling, but allow for non-linearity and non-constant variance structures in the data. GLM and GAM both assume that the data are from multiple probability distributions (e.g. normal, binomial, Poisson), allowing better fits to structurally complex data (Guisan and Hastie 2002). However, GLM and GAM need either abundance data or presence-absence (or pseudo-absence) data, which are often unavailable. On the other hand, profile techniques, such as BIOCLIM and ecological factor analysis can use presence only data. Profile techniques are statistically simple algorithms that create a bioclimatic profile of a species and then calculate the environmental distance to this profile. Unfortunately, these techniques tend to overestimate the size of the niche because they have no information about where the species is not present (Engler, Guisan, and Rechsteiner 2004). Machine learning techniques, such as Maximum Entropy (Maxent) and Genetic Algorithm for Rule Set Production (GARP), are more statistically complex. In machine learning algorithms, the user inputs training data and environmental data and the algorithm learns how to predict the response variable. Maxent does this by maximizing the relative entropy between the presence data and environmental data (Elith et al. 2011). GARP uses sets of mathematically determined rules to determine 31

49 presence probabilities. The programs that use these algorithms can be considered black box programs because the user does not explicitly know how the output was created. They require the creation of pseudo-absence data when absence data are not available. However, these types of techniques can outperform other techniques, particularly for small datasets (Hernandez et al. 2006; Larson et al. 2010; Elith et al. 2011). As discussed further in the Methods section of this thesis, I chose Maxent, discussed more below, for the purpose of this study Maxent Maximum Entropy Species Distribution Modeling (Maxent) is one of the most commonly used algorithms for modeling niches. The principle of maximum entropy states that the probability distribution that has the largest entropy is the best representation of current knowledge as it makes the least assumptions about unknown data points (Guiasu and Shenitzer 1985). Maxent is a free downloadable software program that used this principle to create probability of species presence maps based on species point presence records and environmental raster data. Maxent is userfriendly, can create models from as few as 5 presence points, and generally has a similar or better accuracy compared to other methods (Hernandez et al. 2006; Larson et al. 2010; Elith et al. 2011). The machine-learning algorithm used by Maxent determines which basic functions (linear, product, quadratic, hinge, threshold, and categorical) best fit the presence data to the environmental data. The program then compares the species distribution model to a model based on random background 32

50 points to assess the accuracy. Because multiple basic functions can be used within the model, it can approximate the complex variable relationships commonly found in ecological data (Phillips and Dudík 2008). As with other ENMs, Maxent does not explicitly address spatial autocorrelation. There is disagreement in the literature about how big a problem this is, and multiple methods have been suggested to mitigate it (e.g. adding a autocovariate that accounts for spatial structure, separating the training and testing data geographically, or filtering out the spatial component) (Segurado, Araújo, and Kunin 2006; Carsten et al. 2007; Dormann et al 2007; Veloz 2009; Miller and Franklin 2010). Recently, researchers have begun to use Maxent to explore the spatial distribution of arthropod-borne diseases. These studies have addressed a wide number of questions about the distribution of these diseases at a number of different scales. For example, at a continental scale, Moffet et al. (2007) developed a Maxent-based model of the risk to humans of being bitten by any malaria vector in Africa, while Medley (2010) used Maxent to show that Aedes albopictus (the Asian tiger mosquito) experienced a niche shift as it moved from its native habitat to new areas. At a much finer scale, Khatchikian et al. (2011) and Arboleda et al. (2012) used Maxent to predict the distribution of dengue vectors for the purpose of mosquito abatement on the island of Bermuda and in the city of Bello, Columbia, respectively (Table 2). 33

51 Table 2. Summary of Maxent models for Aedes spp.. Reference Species Location Study Area Size (km 2 ) Most Important Variables Larson et al Ae. vexans Iowa, USA 145,743 Distance to water, urban areas Khatchikian et al Ae. albopictus, Ae. aegypti Medley 2010 Ae. albopictus SE Asia (China, Japan, Vietnam, South Korea, North Korea, Laos, Malaysia) Laporta et al Ae. serratus, Ae., scapularis, Bermuda < 54 Distance from shore, slope, elevation, distance to buildings Vale do Ribeira, Brazil 11,635 ~1,900,000 Annual mean temperature, mean minimum temperature of coldest month, annual precipitation, precipitation of wettest month, humidity during May, days with ground frost Slope, precipitation, temperature, vegetation cover Arboleda et al Rochlin et al Porretta et al Ae. aegypti Bello, Colombia 19.7 Landsat Bands 1, 2, 4, and 6, Ae. albopictus Ae. albopictus Northeastern US (Pennsylvania, New Jersey, New York, Connecticut, Rhode Island, Vermont, New Hampshire, Maine, and Massachusetts) East Asia (India, Bhutan, Bangledesh, Burma, China, Thailand, Laos, Vietnam, Cambodia, Indonesia, Malaysia, New Guinea, Japan, North Korea and South Korea) Mean temperature of coldest quarter, land use/cover, combined precipitation, January precipitation, precipitation of driest quarter, precipitation of wettest quarter ~14,774,985 Minimum temperature in coldest quarter, mean temperature of wettest quarter, precipitation of warmest quarter, precipitation of coldest quarter Numerous environmental variables have been used to predict the distribution of vector mosquitoes in the genus Aedes, the focus of this study, in different Maxent 34

52 models (Table 2). Which variables were the most useful for this purpose depended a lot on the study area. Some models included areas where the mosquitoes cannot survive because of cold temperatures or lack of rain (e.g. Medley 2010; Porretta et al. 2013; Rochlin et al. 2013). These models had a high emphasis on variables that reflected those limitations (e.g. minimum coldest temperature in the coldest quarter, precipitation of the driest quarter). In other study areas (e.g. Larson et al. 2010; Khatchikian et al. 2011; Laporta et al. 2012) those absolute limits are not reached; however, there is still heterogeneity in how well the landscape supports mosquito populations. The variables that are most important in these cases depend on local conditions. In general, distance to breeding sites, either as water bodies or in human containers associated with urban build-up, is a very strong indicator of mosquito population. The most common way to assess the relative performance of predictive distribution models such as Maxent is to use the Area Under the Receiver Operating Characteristic (ROC) Curve, known as the AUC. The AUC provides a single-number discrimination measure equivalent of the non-parametric Wilcoxon test which incorporates the sensitivity, specificity, commission errors, and omission errors of a model. The sensitivity is the portion of points that are correctly predicted as presence points; the specificity is the correctly predicted absence points; commission errors are the falsely predicted presence points; and omission errors are the falsely predicted absence points. However, as Maxent does not use absence points, the AUC for it compares presence points to background points (which may or may not be true 35

53 absence points) (Yackulic et al. 2013). The ROC plots sensitivity as a function of commission error which allows it to summarize overall model performance over all possible thresholds, rather than letting the user subjectively define the threshold (Phillips, Dudík, and Schapire 2004; Lobo, Jiménez-Valverde, and Real 2008). As useful as the AUC is, there are some problems with it. AUC is not a good measure of model performance because it ignores the goodness of fit of models, tests thresholds which are not realistic, weighs errors of commission and omission equally, and does not evaluate the spatial accuracy of the model (Lobo et al. 2007). Furthermore, a survey of 108 research articles found that most authors used the AUC as a measure of model accuracy, rather than a way to measure the relative model performance (Yackulic et al. 2013). Therefore it is recommended to report addition measures of model fitness such as estimated response curves so that the reader may evaluate the results for themselves (Lobo et al. 2007; Yackulin et al. 2013) Spatial scale As with any geographic analysis, it is important to use an appropriate scale for the ENM of a species (Guisan and Thuiller 2005). Spatial scale of an ENM can be expressed as spatial resolution of the dataset and the geographic extent of the study area and should be a function of both the question being asked and the mobility of the study organism. For example, a study on mosquito abundance across neighborhoods would require a small study area and fine spatial resolution, while a study exploring the effects on climate change on mosquito distribution would require a large study 36

54 area and coarse spatial resolution. However, the scale of ENMs is often determined by the spatial resolution of the available data and including finer scale data in models with coarser scale improved the accuracy of the models (Pearson et al. 2006). This is particularly true for species with low mobility GIS and remote sensing in modeling disease vectors distribution Modeling the distribution of disease vectors is a spatially explicit undertaking, requiring a large amount of spatial data. These data include the species presence point data and a wide variety of environmental surfaces. Species presence data may come from recorded sightings, natural history collections, or systematic sampling; however, they must have associated spatial data (Guisan and Thuiller 2005). Environmental data can come from many sources, including GIS datasets from local and state government or research institutions; interpolated data on weather and climate from sources such as WorldClim (Hijmans et al. 2005); remotely sensed images; and data derived from remotely sensed images such as land cover (Guisan and Thuiller 2005). The increasing availability of computing power, spatial modeling packages, GIS software, and remotely sensed data has driven great progress in the modeling of disease vectors (Reisen 2010). There are a number of papers that review the use of remote sensing in vectorborne disease research (e.g. Beck, Lobitz, and Wood 2000; Ostfeld, Glass, and Keesing 2005; Kalluri et al. 2007; Machault et al. 2011). These reviews show that a wide variety of remote sensing data can be used to model disease vectors (Table 3). 37

55 Table 3. Comparison of common satellite remote sensing systems used for vector-borne disease research. Satellite Spatial Resolution Temporal Resolution Spectral Resolution AVHRR ~1.1 km Twice daily 1 visible, 1 NIR 1, 2 SWIR 2, 1 MIR 3, 2 LWIR 4 Examples of Species Modeled Sand flies (Phlebotomus papatasi) References Cross et al MODIS 250 m, 500 m,1 km SPOT 1.5 to 20 m Tasked based collection Landsat TM 30 m Every 16 days IKONOS 1 to 4 m 3 to 5 days for off-nadir, 144 days for true nadir QuickBir d 1-2 days 1 visible, 1NIR (250 m) 5 visible, 1 NIR, 1 SWIR (500 m) 29 visible, 1 NIR, 1 SWIR, 1MIR, I TIR (1km) 2 visible, 1NIR, 1SWIR, 1 panchromatic 3 visible, 2 SWIR, 1 LWIR 3 visible, 1 NIR, 1 panchromatic 2.4 m 2.5 to 6 days 3 visible, 1 NIR, 1 panchromatic Mosquitoes (Anopheles atroparus) Mosquitoes (Anopheles spp.) Mosquitoes (Aedes spp., Culex spp.) Ticks (Ixodes spp.) Mosquitoes (Anopheles darlingi) Lourenço et al Achee et al. 2006, Arboleda et al. 2012, Dister et al. 1997, Zou et al Achee et al Rift Valley fever Soti et al Near infrared; 2 Short wave infrared; 3 Middle wave infrared; 4 Long wave infrared For example, vegetation index data derived from the Advanced Very High Resolution Radiometer (AVHRR) imagery were correlated with sandfly distribution in the Middle East in areas where no weather station data were available (Cross, Newcomb, and Tucker 1996); vegetation greenness and wetness indices derived from Landsat Thematic Mapper (TM) imagery were correlated with tick abundance in the northeastern United States (Dister et al. 1997); and the very fine spatial resolution of 38

56 the commercially available SPOT and IKONOS imagery allowed to map tree debris that acts as larval habitat for malaria mosquitoes in Belize (Achee et al. 2006). Just from these few examples it is clear that remote sensing provides vital information that can greatly improve our ability to map and predict the distribution of disease vectors. Many of the papers mentioned above combine remotely sensed images with GIS data from a variety of sources. One commonly used GIS dataset is the WorldClim dataset of climate variables (Hijmans et al. 2005). The data were compiled from weather stations from around the globe and used to interpolate a geostatistical surface using thin plate smoothing splines (Hijmans et al. 2005). Many of the most important variables of the models described in Table 3 are WorldClim data. The WorldClim dataset includes both average current climate data and predicted future climate data, which makes it very useful when predicting changes in disease distribution. Other raster and vector GIS data that may be used for modeling mosquito borne diseases include data on distance-from-coast, distance-from-water surfaces, buildings, streets, trees, or host animal distributions. However, as with all models, the results are only as good as the data that are used. Lozier et al. (2009) make the point that spatial databases can vary greatly in accuracy by creating an ENM of Sasquatch based on sightings by the public. Even studies that have reliable observations may suffer from observation-based biases; e.g. a species of bird may be observed more frequently near roads because the observers are more likely to be near roads (Yackulic et al. 2012). Another potential problem is using data, however well documented, that are inappropriate for the question being 39

57 asked. For example, Landsat imagery has a spatial resolution of 30 meters but female Ae. aegypti may not travel more than 56 m over her lifetime (Muir and Kay 1998). As a result, Landsat data may be a great resource for studying broad distribution patterns of mosquitoes, but inadequate to understand variations in mosquito distributions at a block or street level. Furthermore, it is necessary to understand how to interpret remotely sensed data. As seen in Table 2, Arboleda et al. (2012), for example, used individual Landsat bands in their model of Ae. aegypti in Columbia and found that Landsat band 1 (the blue band) is useful in their model. However, it is unclear how blue reflectance is related to mosquito distribution. While they argue that the use of reflectance values instead of indices improves the qualities of their predictions, the inability to link the model back to the mosquito biology means that it has limited usefulness for prediction. The best models, therefore, use variables that are justifiable given mosquito biology and use the most appropriate and well-documented data to represent those variables. There is much potential in using GIS and remote sensing to model disease vectors, but it has yet to be fully explored. Increases in the availability of remotely sensed imagery with high spatial and spectral resolution are allowing researchers to develop increasingly precise models of species distribution. Improvements in image processing and GIS and statistical software are making complex modeling easier and faster. There are still many challenges to be met though. Standard and easy-to-use predictive models for countries with high disease rates and limited resources still need 40

58 to be developed. Global climate change is modifying the disease landscape in as yet unknown ways. While much research has been done on common diseases such as malaria, human cycle dengue, and Lyme disease, much less has been done on potentially emerging diseases such as sylvatic dengue and chikungunya. By developing an ENM to predict the spatial and temporal risk to humans of sylvatic DENV and CHIKV, this project takes a step towards understanding how and where vector-borne diseases are likely to emerge. 41

59 3. METHODS 3.1 Study area The study area encompassed 1,650 km 2 in the Kédougou Department in southeastern Senegal (center coordinates ~ N, W) (Figure 5). The area is characterized by a tropical savanna climate (Aw; Köppen 1936; Peel, Finlayson, and McMahon 2007) with two seasons: a dry season (generally December to May) and a wet season (generally June to November). The average annual rainfall in the Kédougou region is 1,293 mm. The driest quarter (January to March) receives an average of 0.5 mm of rain, while the wettest quarter (July to September) has an average rainfall of 891 mm (World Climate 2013). There is little variation in mean monthly temperature, which ranges from 25.4 C in December to 32.8 C in April (World Climate 2013).. In 2009, 74% of the land cover was savanna, 13% forest, 8% agricultural land, 5% barren land, 0.2% water, and 0.09% village (Diallo et al. 2012). Elevation in the study area ranges from 55 to 463 meters above sea level. The Gambia River bisects the eastern part of the study area; the western part is mountainous. The Kédougou region is sparely populated (a mean of 4 people/km 2 ) with its inhabitants dispersed in small villages averaging sixty individuals (Tappan et al. 2004). The study area is mostly rural with only one urban area, the town of Kédougou, with a population of 17,922 (GeoNames 2013). Slash-and-burn agriculture is widely practiced in the region. Crops include maize, sorghum, peanuts, and cotton (Tappan et al. 2004). Fields that are left fallow convert to savanna vegetation (Tappan et al. 2004). Barren lands tend to be the result of patches of 42

60 impermeable iron rich soils (Tappan et al. 2004). The agricultural economy in this area is supplemented with hunting, gathering, wood harvesting, and mining. Figure 5. a) The location of Senegal within Africa, b) the Department of Kédougou within Senegal and c) the study area with the Department of Kédougou. The hosts and vectors for the sylvatic cycles of DENV-2 and CHIKV are well documented in the study area. Known reservoir primate hosts of DENV and CHIKV in this region include African green monkeys (Chlorocebus sabaeus), patas monkeys (Erythrocebus patas), and Guinea baboons (Papio papio) (Vasilakis et al. 2011). Chimpanzees and Senegal bushbabies are known from the nearby Fongoli primate observation site (Pruetz, Socha and Kante 2010). As humans live in small villages dispersed throughout the landscape, they too are potential hosts. There is high mosquito diversity in the area. Mosquito collection in the area have found as many as 102 species of mosquitoes using a combination of human landing collections, light traps, and animal baited traps (Diallo et al. 1999). A total of fifty different mosquito species from six genera were collected as part of this project (Diallo et al. 2012). Of these seven are identified as important vectors of sylvatic DENV-2 and/or CHIKV (see Table 1). 43

61 Both DENV-2 and CHIKV have been well documented in the area. Virus isolations in mosquitoes, primates, and humans from 1965 to 2010 show that both viruses have distinct amplification cycles of about 8 years for DENV-2 and about 4 years for CHIKV(Althouse et al. 2012). Furthermore, nearly 100% of primates older than the age of four years tested in 2010 to 2012 were seropositive for CHIKV and DENV (Hanley, personal communication). 3.2 Conceptual Model Mosquito distribution is influenced by multiple factors including weather, predation, and interspecies competition. I developed a conceptual model of the possible factors influencing sylvatic Aedes mosquito distribution in the study area which was then used to determine the variables used in the quantitative model (Figure 6). In this model, distribution is affected by abiotic factors, biotic factors, and random chance. Abiotic factors include rainfall, temperature, wind, and landscape structure and are affected by random chance as well as larger regional climatic drivers. Biotic factors include the availability of nectar, larval competition, predation (both on larvae and adults), disease and/or parasitism, and host distribution and are affected by both abiotic factors and chance. However, GIS data layers of many of these factors may not be available for species distribution modeling. In some cases, other variables may serve as proxies or surrogate layers for the factor believed to affect species distribution (e.g. vegetation indices are often used as a proxy for rainfall) (Guisan and Zimmermann 2000; 44

62 Anyamba and Tucker 2005; Herrmann, Anyamba, and Tucker 2005). In other cases, the data are simply not available and must be excluded from the model (e.g. although it is known that larvae the prey on these mosquitoes are present in the study area, the spatial distribution is unknown (Diallo et al. 2012)) (Guisan and Thuiller 2005; Hirzel and Le Lay 2008; Godsoe 2010). Nonetheless, it is important to consider all factors that may be affecting distribution when interpreting the results of species distribution modeling. Figure 6. Conceptual model of the spatial distribution of sylvatic Aedes mosquitoes in study area. Variables that can be incorporated in an ENM are in italics. The abiotic factors that affect mosquito distribution rainfall, temperature, wind, and landscape can be represented in the distribution model to some degree 45

63 using available data (Table 4). As mosquitoes require standing water to reproduce, rainfall is a strong limiting factor in mosquito distribution (Bates 1949; Clements 2000). Although no rainfall layers are available for the time period of the study (there is only one weather station in the area), the thirty-year normal rainfall data are available. NDVI imagery, which is available across the study time period, can indicate relative levels of rain over that period. Temperature also plays a large part in mosquito distribution temperature (Clements 2000; Tun-Lin, Burkot, and Kay 2000). Like rainfall, temperature layers for the study time-period do not exist, but normal temperature data do. Wind, a major dispersal agent for mosquitoes (Takahashi et al. 2005), is in part determined by chance and larger weather patterns, but can also be influenced by elevation, slope and aspect (McCutchan and Fox 1986) so these can be used as indirect proxies for wind patterns. Landscape architecture (e.g., the size and location of patches of different land cover types) determines where mosquitoes are likely to find cover, oviposition sites, hosts and refugia where females and/or eggs can survive the dry season. Land cover data, and metrics derived from land cover data, can be used to represent landscape architecture. As the mosquitoes relevant to this study are arboreal, the locations and size of forests are of particular interest. Unfortunately, not enough is known about the ecology of these mosquitoes to be able to include most of the biotic factors in the distribution model (Table 4). The exception to this is nectar availability, in which case NDVI could indicate the presence of nectar sources across the landscape. However, as not all plants are equally good as nectar sources (Foster 1995) this is a rough proxy at best. Modeling 46

64 the effect of larval competition, predation, disease/parasitism, and host distribution requires having distribution models of the other organisms involved such as nonanthropophilic mosquitoes, predatory mosquito larvae, parasitic fungi, a wide range of vertebrate hosts, etc. No study to date has attempted to model these variables spatially, although the ecology and distribution of larval mosquitoes across different land covers is addressed by Diallo et al. (2012). Table 4. Factors from the conceptual model and the variables/proxy variables used in the species distribution models. Factors Model Variable/Proxy Variable Abiotic Biotic Rainfall Temperature Wind Land cover architecture Nectar availability Larval Competition Predation Disease/Parasitism Host distribution Random Chance BioClim precipitation variables (mean annual precipitation, precipitation of the wettest month, etc.) NDVI BioClim temperature variables (mean annual temperature, mean diurnal range etc.) Topography (Slope, Aspect, Elevation) Patch size, distance to edge, distance to forest, distance to medium and large forests, distance to large forests NDVI N/A N/A N/A N/A N/A As discussed in more detail in Section 2.5.1, abiotic factors can have predictive value in modeling distributions due to three underlying processes: i) the abiotic factors directly affect mosquito distribution and ii) the abiotic factors affect the biotic factors which in turn affect the mosquito distribution iii) the abiotic factors 47

65 both directly affect mosquito distribution, and also affect the biotic factors (Guisan and Thuiller 2005; Hirzel and Le Lay 2008; Godsoe 2010). As such, only limited conclusions can be made about the relationship between the distribution of mosquitoes and the variables used to predict it. Nonetheless, understanding which abiotic variables are predictive of species distribution can help focus efforts to understand the true relationships. 3.3 Data Species distribution models require both species presence data and environmental data. In the following two sections I outline how these data were assembled Mosquito species presence data Mosquitoes were collected at fifty sampling sites for the periods of June 2009 to February 2010 and May 2010 to February 2011 (Figure 7). The fifty sites were equally divided among the five major land cover classes (agricultural land, barren land, forest, savanna, and village) and selected to minimize spatial autocorrelation. This was done using block design and stratified random sampling methods, which are described in detail in Diallo et al. (2012). 48

66 49 Figure 7. Location of sampling blocks and sites. From Diallo et al. (2012).

67 The mosquito dataset was created by collecting mosquitoes using human landing collections. Human landing collection, wherein individual collectors expose their legs and collect the mosquitoes that land on them during a prescribed time period, is the only effective method for collecting sylvatic Aedes species (Diallo et al. 2012). The collections took place monthly for one to four consecutive days for three hours each evening. All sites within a sampling block were sampled at the same time using teams of three or six collectors. Collection in Forest sites occurred both at ground level and on nine-meter high platforms with three collectors at both levels. In the Village sites, mosquitoes were collected both indoors and utdoors at sites at the ends and the halfway point of a transect from the center of the village to the periphery by a total of six collectors. At the remaining land covers (Agricultural Land, Barren Land, and Savanna) three collectors were present at each site. Once the mosquitoes were collected they were frozen, identified, and sorted into monospecific pools of up to forty individuals. Each pool was then tested for common African mosquito-borne arboviruses, including CHIKV and DENV. DENV was not detected during either year, but CHIKV was detected in a total of nineteen sites in There was also an amplification of yellow fever virus detected the following year (Diallo et al. 2013) Environmental Data Environmental layers were created using forty-five variables from three sources: a previously derived land cover map (Diallo et al. 2012), MODIS NDVI, and WorldClim (Table 5). 50

68 Table 5. Environmental layers used in models. Layer Explanation Source Resolution DistForest Distance from any forest patch Land Cover 30 m DistForestMed DistForestLarge Distance from forest patches larger than Land Cover 30 m 520, m 2 Distance from forest patches larger than Land Cover 30 m 2,139, m 2 DistEdge Distance from patch edge Land Cover 30 m PatchSize Patch size Land Cover 30 m 09-10MaxOct 09-10MeanOct 09-10MinOct 09-10RanOct 09-10StdOct 09MaxOct 09MeanOct 09MinOct 09RanOct 09StdOct 10MaxOct 10MeanOct 10MinOct 10RanOct 10StdOct Maximum NDVI values for October and November of 2009 and 2010 Mean NDVI values for October and November of 2009 and 2010 Minimum NDVI values for October and November of 2009 and 2010 Range of NDVI values for October and November of 2009 and 2010 Standard deviations NDVI values for October and November of 2009 and 2010 Maximum NDVI values for October and November of 2009 Mean NDVI values for October and November of 2009 Minimum NDVI values for October and November of 2009 Range of NDVI values for October and November of 2009 Standard deviations NDVI values for October and November of 2009 Maximum NDVI values for October and November of 2010 Mean NDVI values for October and November of 2010 Minimum NDVI values for October and November of 2010 Range of NDVI values for October and November of 2010 Standard deviations NDVI values for October and November of MODIS NDVI MODIS NDVI MODIS NDVI MODIS NDVI MODIS NDVI MODIS NDVI MODIS NDVI MODIS NDVI MODIS NDVI MODIS NDVI MODIS NDVI MODIS NDVI MODIS NDVI MODIS NDVI MODIS NDVI 250 m 250 m 250 m 250 m 250 m 250 m 250 m 250 m 250 m 250 m 250 m 250 m 250 m 250 m 250 m NDVIMean Mean of all NDVI images MODIS NDVI 250 m NDVIMax Maximum NDVI value of a NDVI images MODIS NDVI 250 m

69 NDVIRange Range of NDVI value of a NDVI images MODIS NDVI 250 m NDVIMin Minimum NDVI value of a NDVI images MODIS NDVI 250 m NDVIStd Standard deviations of all NDVI images MODIS NDVI 250 m Aspect Direction slope is facing WorldClim 30 arcseconds Elevation Elevation WorldClim 30 arcseconds Slope Slope WorldClim 30 arcseconds AMeanTemp Annual mean temperature WorldClim BioClim Aprecip Annual preciptiation WorldClim BioClim MaxTWarmM MeanDiurnal Maximum temperature of the warmest month Mean diurnal range (mean of monthly (max temp - min temp)) WorldClim BioClim WorldClim BioClim MeanTColdQ Mean temperature of coldest quarter WorldClim BioClim MeanTDryQ Mean temperature of driest quarter WorldClim BioClim MeanTWarmQ Mean temperature of warmest quarter WorldClim BioClim MeanTWetQ Mean temperature of wettest quarter WorldClim BioClim MinTColdM Minimum temperature of coldest month WorldClim BioClim PrecipColdQ Precipitation of coldest quarter WorldClim BioClim PrecipSeason Precipitation seasonality (coefficient of variation) WorldClim BioClim PrecipWarmQ Precipitation of wettest quarter WorldClim BioClim PrecipWetM Precipitation of wettest month WorldClim BioClim PrecipWetQ Precipitation of wettest quarter WorldClim BioClim TempRange TempSeason Temperature annual range (MaxTWarmM -MinTColdM) Temperature seasonality (standard deviation *100) WorldClim BioClim WorldClim BioClim 30 arcseconds 30 arcseconds 30 arcseconds 30 arcseconds 30 arcseconds 30 arcseconds 30 arcseconds 30 arcseconds 30 arcseconds 30 arcseconds 30 arcseconds 30 arcseconds 30 arcseconds 30 arcseconds 30 arcseconds 30 arcseconds 52

70 The land cover, MODIS NDVI, and WorldClim data have spatial resolutions of 30 meters, 250 meters, 30 arc-seconds (~1 km) respectively. However, for Maxent to work, all raster layers have to have matching pixel locations and sizes. To achieve this, the WorldClim and MODIS layers were resampled to match the size of the land cover layer pixels. This did not increase the actual spatial resolution of the data, but made it possible to include the data from these different sources in the model. All processing was done using ArcGIS 10 or 10.1 (ESRI 2013). Data on land cover configuration and composition were derived previously (Diallo et al. 2012) from Landsat 5 TM imagery. Data layers of patch size, distance to closest patch edge, distance to any forest patch, distance to medium or large forest patches, and distance to large forest patches were derived from the land cover map. To do this I converted the land cover raster layer to a polygon layer with each polygon representing a patch of a single land cover class. From this file I created a raster layer of the distance to the closest patch edge and a raster layer of patch size using the Euclidian Distance tool. I then extracted forest patch polygons into a new polygon layer, and classified the patches as small, medium or large using Jenks natural breaks classification method (small: to 520, m 2 ; medium: 520, to 2,139, m 2 ; large: 2,139, to 6,186,288 m 2 ). Based on this classification, I created rasters representing the distance from any forest patch, the distance from medium or large forest patches, and the distance from large forest patches. 53

71 The WorldClim data (WorldClim 2008) are a set of global climate layers interpolated from weather stations and elevation data. The BioClim variables (e.g. mean annual precipitation and temperature of the coldest and warmest months) from WorldClim are derived from the monthly temperature and precipitation values and summarization of all available weather data from From the available WorldClim data layers I used elevation and sixteen of the nineteen BioClim variables. I excluded isothermality, precipitation of the driest month, and precipitation of the driest quarter variables because there was no variation across the study area. I derived slope and aspect data from the DEM derived from the elevation data. MODIS NDVI (Normalized Difference Vegetation Index, a measure of vegetation productivity) imagery is derived from atmospherically corrected bidirectional surfaces reflection values that have been masked for water, clouds, heavy aerosols, and cloud shadows (NASA Land Processes Distributed Active Archive Center 2010). I acquired 16-day MODIS NDVI imagery (NASA Land Processes Distributed Active Archive Center 2010) from the USGS Global Visualization Viewer (glovis.usgs.gov) for all months in which collection occurred. The study area has frequent cloud cover during the wet season and dust and smoke in the dry season, making unprocessed imagery impractical to use. Even with the cloud masking, some images were still unusable due to heavy cloud presence, particularly during the rainy season. Unfortunately, this means that no images were available for the two months of heaviest rain (August and September) for either year. 54

72 As no individual NDVI distribution is likely to be a very good explanatory variable, the NDVI imagery was summarized to show the mean, minimum, maximum, range, and standard deviation of all NDVI data and of the last two months of the wet season (October and November of both years together and separately). Summarizing all NDVI images allowed patterns of variation (e.g. a large range in NDVI values indicates a large difference of available water between the rainy and dry season) to be included in the model. There is little to no rainfall in this area during the dry season, and mosquito eggs can survive long hot dry periods, so NDVI of the dry season will have little effect on the mosquito distribution. However, spatial variation of rain during the wet season will have a large effect on the distribution. Initially I summarized all of the rainy season (July to November), but found that this did not improve the model, possibly because of a lag between the beginning of the rainy season and the maximum greening of the vegetation. No data were available for the height of the rainy season, but October and November NDVI patterns should reflect the rainfall patterns of the previous months. Both years were summarized together and separately to represent the maximum rainfall patterns of a year with less rainfall (2009: ~1,087 mm of rain, some missing data) and a year with heavier rainfall (2010: 1,345 mm of rain). 3.4 Analysis The raw mosquito collection data of the seven vector species were used to create two separate datasets. One included the number of mosquitoes per collector per 55

73 night at the collection sites and was analyzed to find the spatial and temporal trends in vector abundance. The other only included data on whether or not each species was present at each site and was used in ecological niche modeling. This resulted in two different representations of mosquito distribution that could be compared. The results of the mosquito analysis were then compared to the number of mosquito pools that tested positive for CHIKV in 2009 to explore the relationship between vector distribution and virus distribution Vector mosquito abundance analysis Mosquito abundance, or the average number of female mosquitoes per collector per night, was calculated for the seven important vector species (Ae. aegypti, Ae. africanus, Ae. dalzieli, Ae. furcfier, Ae, luteocephalus, Ae. taylori, and Ae. vittatus) for year month, each year, and for the entire collection period. Abundance was then graphed and mapped by month, and analyzed for both global and local spatial autocorrelation using Moran s I and Getis-Ord Gi* analysis. Lastly abundance was compared to the species distribution models to assess the accuracy of the models. I calculated both the individual species abundance and the combined abundance for all seven vectors for each site and each month using the following formula: I graphed the combined abundance by month, year, and land cover class to show general trends of abundance. I then created graduated symbol maps for each 56

74 collection month s abundance values to show spatial temporal patterns in abundance. Moran s I and Getis-Ord Gi* statistics were calculated for each month to determine both global spatial autocorrelation and local clustering patterns. Blocked ANOVA was performed on abundance data for July to November for 2009 and 2010 in JMP 7.0 (Cary, NC: SAS Institute Inc.) to evaluate the effect of year, month, and landcover. May and June data were only available for 2010; data were available for December to February each year, but there were substantially fewer mosquitoes in these months. July to November monthly mosquito totals range from 1,776 to 14,769 mosquitoes; in contrast, 361 mosquitos were collected in December 2009 and 240 were collected in December The highest monthly total for January and February was 15 mosquitoes in January A Tukey-Kramer post hoc test was used to evaluate the relationships between land cover class, year, and abundance Species distribution modeling I used Maxent (Phillips, Dudík, and Schapire 2004) to model the distribution of mosquitoes for four reasons: (i) previous studies have found it to be equal to or better than most other species distribution modeling methods (Hernandez et al. 2006; Elith et al. 2011), (ii) it has been used successfully in similar studies (Moffett, Shackelford, and Sarkar 2007; Medley 2010; Larson et al. 2010; Khatchikian et al. 2011; Arboleda, Jaramillo-O., and Peterson 2012; Laporta et al. 2012; Porretta et al. 2013; Rochlin et al. 2013), (iii) it is user-friendly and easy to learn (Phillips and 57

75 Dudík 2008; Larson et al. 2010), and (iv) it is freely available (Phillips and Dudík 2008). Maxent requires presence data and environmental layers to create prediction models. For the presence data I created a single file with all species presence data from the abundance data for the entire collection period. Of the seven species of interest, four species (Ae. dalzieli, Ae. furcifer, Ae. luteocephalus, and Ae. vittatus) were collected from all fifty sites. Ae. aegypti was collected from forty-nine sites, Ae. taylori was collected from forty-five sites, and Ae. africanus was collected at twelve sites. Therefore, a single Maxent model was created for Ae. dalzieli, Ae. furcifer, Ae. luteocephalus, and Ae. vittatus, while individual models were created for the remaining three species. Models that have too many predictive variables run the risk of being overfitted (i.e. much better at predicting training data than testing data due to a lack of generalizing), so variable reduction is necessary. I selected the environmental variables used in the final models using the following stepwise process: - Step 1. All environmental variables were included in the model. Jack-knifing was not performed due to lack of computer memory. - Step 2. The variables that contributed one percent or more to Step 1 were used. - Step 3. The variables that increased the positive test gain of Step 2 were used. - Step 4. The variables that increased the test AUC of the model in Step 3 were used. 58

76 For each step, 75% of the presence points were used for model calibration and 25% of the presence points were used for model evaluation, i.e. as points to test the model s ability to predict their location. Unless otherwise stated, jack-knifing was performed on each model to assess the effect of each variable on the model. Autocorrelation was not addressed in the models, as the collection strategy was specifically developed to minimize spatial autocorrelation. This method was used at it was the simplest way to eliminate variables that did not influence the model and/or decreased the models ability to predict the testing data. Once the best environmental variables were determined, the model was run again using the cross-validation replicates in Maxent. In cross-validation, the occurrence data are randomly split into equal size groups, and then the model is run repeatedly with each group being left out in turn to be used as testing data. The results include the average probability distribution and AUC for the replications. Ten replications were used for each model. Species distribution models require accuracy assessment and the AUC measurement is the most commonly used and reported here, along with the estimated response curves. However, the AUC is not universally endorsed as a good measure of model accuracy (Yackulic et al. 2013). So, in addition to providing these standard measures of model performance, I also compared the probabilities produced by the Maxent models to the previously calculated abundances for each species. Additionally, the model derived from all fifty collection sites was also compared to the combined abundance of the four species present at all fifty sites, the combined 59

77 abundance of all seven vector species, and to the number of mosquito pools that were positive for CHIKV. Correlation statistics were computed in JMP 7.0 to assess the predictive value of each model Positive CHIKV pool analysis The mosquito pools that tested positive for CHIKV in 2009 were also analyzed so that vector distribution could be compared to virus distribution. The distribution of positive CHIKV pools was also mapped using graduated symbols. Moran s I and Getis-Ord Gi* statistics were calculated for each month to determine both global and local spatial autocorrelation, respectively. Lastly, correlations between number of positive mosquito pools, abundance, and presence probability were performed. 60

78 4. RESULTS AND DISCUSSION 4.1 Mosquito abundance analysis Analysis of mosquito abundance by month Abundance varied greatly across the course of each rainy season but not between the two years. Both the and collection years had similar patterns in vector abundance: a peak early in the rainy season starting in July, a trough during the height of the rainy season in September, a second lower peak in October as the rainy season tapered off, and then a drop to nearly zero as the rainy season ended in December (Figure 8). Unfortunately mosquitoes were not collected for May and June in 2009, although they were in 2010, so the 2010 data for these months had to be excluded from this analysis so that the two years could be directly compared. The variation in abundance by month is significant (p <0.0001) but the variation by year is not (p = ), as indicated by a blocked ANOVA of abundance data for July to December for 2009 and 2010 (Table 6). Table 6. Results of Blocked ANOVA of mosquito abundance by month, year, land cover class and study area block. Variable compared to abundance df F ratio Prob > F Month < Year Land Cover < Land Cover X Year Land Cover Land Cover < Block < Block X Year

79 Precipitation (mm) Average Abundnace (Females/Collector/Night) Precipitation (mm) Average Abundnace (Females/Collector/Night) M J J A S O N D J F M M J J A S O N D J F M Figure 8. Total monthly rainfall and mosquito abundance for the two collection seasons. Rainfall is shown in the grey bars, abundance is shown with the black line Global spatial autocorrelation of abundance varied by month and year. Abundance was moderately globally spatially autocorrelated when both years were grouped together (Moran s Index: 0.334, z-score: 2.591, p-value 0.001) and when the and period was analyzed individually ( : Moran s Index: 0.338, z-score: 2.625, p-value 0.009; :Moran s Index: 0.253, z- score: 1.991, p-value 0.046). Monthly abundance varies in levels of global spatial autocorrelation (Table 7) with five months showing significant but moderate global 62

80 clustering (Figure 9). This clustering was stronger and lasted three months (July to September) in 2009 while in 2010 slight clustering only occurred in July and August. Table 7 Moran s I results for abundance by month. indicates collection did not occur at four or five sites that month, indicates collection did not occur at fifteen collection sites that month. Month Moran s I z-score P Jul <0.001 Aug <0.001 Sep <0.001 Oct Nov Dec Jan Feb Jul Aug Sep Oct Nov Dec Jan Feb Overall, the spatial and temporal analyses of abundance are indicative of how important rain patterns are to mosquito abundance. Few, if any, mosquitoes were collected during the dry season months but abundance quickly increased once the rain started. However, although abundance was responsive to rainfall initially, abundance dropped during the height of the rainy season. This decrease in abundance during the heaviest rains is likely due to heavy rainfall washing out larval pools and limiting adult flight (Russell 1998). In other words, maximum abundance occurred when there 63

81 Moran's I was enough water in tree-holes and other containers to support larvae, but not so much rain that the containers were constantly being washed out. The temporal patterns of global spatial autocorrelation show that abundance values are strongly clustered in 2009, moderately clustered in 2010, and that this clustering occurs in the first half of the rainy season Month Figure 9. Moran s I for the two collection season by month Analysis of abundance by land cover class Although overall abundance was similar between the two years, there was a significant interaction between the effects of land cover and year (p = ). Savanna, Agricultural Land, and Barren Land for both years, as well as Forest for 2009 all had similar average abundances (12.3 to 14.8 females/collector/night) while Forest sites of 2010 had a higher average abundance (23.0 females/collector/night) than all other sites and Village sites of both years had a lower average abundance (4.2 64

82 females/collector/night) than all other sites (Table 8). There are significant differences between study area blocks (p<0.0001) but Tukey-Kramer post-hoc analysis shows overlapping groups with at least four blocks in each group with no block distinctly different from every other block (Tables 6 and 9). To ensure that there was not interaction by year on landscape, ANOVA was run on land cover for and separately. The results were identical to the blocked ANOVA. There was no significant variation by block between years (Table 6). Table 8. Results of Tukey-Kramer post-hoc test on land cover by year. Letters indicate statistical group Land Cover Group Average Abundance (females/collector/night) Group Average Abundance (females/collector/night) Forest B A Agricultural Land Barren Land B B B B Savanna B B Village C C Monthly abundance by land cover class (Figure 10) reflects overall abundance trends (Section 5.1.1), but also emphasizes the differences between the land cover classes and years. All land covers show the general abundance pattern with the highest peak in July, a drop in September, and a second lower peak in October. For both years, abundance in Village sites is low for all months. Abundance in forest sites is very similar to abundance in other land cover classes in 2009, but starts off higher in July 2010 and then gradually comes closer to the other land cover classes as the 65

83 Average Abundance (females/collector/night) Average Abundance (females/collector/night) rainy season tapers off. Forest sites also stand out from other sites in that they were the only ones in which mosquitoes were collected in January or February Jul Aug Sep Oct Nov Dec Jan Feb Jul 20 Aug Sep Oct Nov Dec Jan Feb Agricultural Land Barren Land Forest Savannah Village Agricultural Land Barren Land Forest Savannah Village 5 0 Jul Aug Sep Oct Nov Dec Jan Feb Figure 10. Abundance by land cover class. Land cover clearly has an effect on abundance, but these results suggest that this relationship is complex. While it would be expected that tree-hole mosquitoes would be most abundant in areas with the most trees (i.e. Forest) and least abundant in areas with the fewest trees (i.e. Barren Lands), this is not the case. While Forest has the highest abundance in , it groups with the other classes in This difference can perhaps be explained by the fact that 2010 was a wetter year than 66

84 2009 and rain was heavier earlier in the year (Figure 8). However, this raises the question of why only Forest abundance is increased with increasing rain. One possible explanation is that the forest provides more cover for the mosquitoes in heavy downpours and so mosquitoes in the forest benefit more from heavy rains compared to those in other land covers. The Forest canopy may also decrease evaporation, resulting in mosquitoes being able to survive in Forest sites into the dry season. The unexpectedly high abundance in Barren Land sites may be a result of the small size of Barren Land patches (average patch size = km 2 ) (Table 10). As Barren Land sites tend to be surrounded by Savanna or Agricultural Land, mosquitoes from these land covers may easily travel into Barren Land patches to find hosts, explaining the similiarity between these classes. However Village patches are smaller (average patch size = km 2 ) so patch size alone is not enough to explain this result. Table 9. Descriptive statistics for different land cover classes Land Cover Class Total Area (km 2 ) % of Study area Mean patch size (km 2 ) Maximum patch size (km 2 ) Barren Land Savanna Village Agricultural Land Forest Water

85 4.1.3 Spatio-temporal analysis of abundance Initial assessment of the monthly abundance maps (Figures 11 and 12) finds that the area around the villages of Ngari and Tenkoto has a continuous cluster of high abundance, but no other clear patterns are evident. While many of the collection sites have high abundance for some months, the Ngari/Tenkoto area is the only place where there is high abundance for ten out of the sixteen collection months. The Getis- Ord Gi* analysis shows a hotspot, or cluster of high abundance values, in this area for both collection seasons, separately and combined (Figure 13). Interestingly, the two villages in this area, Ngari and Tenkoto, have the highest average abundance of all the villages for 2009 and the second and third highest average abundance for 2010, and the highest total abundance (Figure 14). Also notable is the presence of a distinct cold spot, or area of low abundance values in the Kédougou area. When abundance is broken down by month, there is a greater difference between the two collection seasons than when abundance is averaged for the whole season (Figures 15 and 16). In the collection season there is a hotspot in the Ngari/Tenkoto area for every month analysis was possible (mosquitoes were only collected from two or fewer sites for January and February of each year). The Kédougou area has a cold spot in only some the months. Monthly local clustering is decreased and more variable by month in the season. Hotspots are found in the Ngari/Tenkoto area only in three months (July, August, and December) and the strength of the clustering is decreased for these month compared to the season. 68

86 Figure 11. Average abundance for the 2009 to 2010 collection season. The Ngari/Tenkoto area is circled. Blocks for which collection did not take place due to rain are indicated in grey. 69

87 Figure 12. Average abundance for the 2010 to 2009 collection season. The Ngari/Tenkoto area is circled. Blocks for which collection did not take place due to rain are indicated in grey. 70

88 Average Abundance (Females/collector/night Figure 13 Getis-Ord Gi* analysis of average abundance by year Abundance 2010 Abundance Average Abundance Figure 14 Average abundance in villages, for each year and the average abundance for both years. 71

89 Figure 15. Abundance Getis-Ord Gi* analysis for the 2009 collection season. Blocks for which collection did not take place due to rain are indicated in grey. January, February, and March are not included because mosquitoes were only present at one or two sites. 72

90 Figure 16. Abundance Getis-Ord Gi* analysis for the 2010 collection season. Blocks for which collection did not take place due to rain are indicated in grey. January, February, and March are not included because mosquitoes were only present at one or two sites. 73

91 These analyses show there is an area of high continuous abundance near the villages Ngari and Tenkoto, and a somewhat less persistent area of low abundance near Kédougou. Kédougou is the most developed area in the study area and therefore may be less suitable to the arboreal mosquitoes. It is also the place mostly likely to have some form of vector control. This would result in a lowered risk for humans in Kédougou of being bitten by these mosquitoes. At the same time, the villages of Ngari and Tenkoto appear to be at elevated risk for being bitten. It is not clear, however, why these villages, and not others in the study area, show high and continuous mosquito abundance. 4.2 Mosquito distribution models The stepwise process described in the methods section resulted in four final models (Figure 17) that used four or fewer environmental layers (Table 11). The four-species model and the Ae. aegypti model used the same variables (distance from large forest patches, distance from medium and large forest patches, distance from patch edge, and patch size), while the Ae. taylori model shared three of these variables (distance from large forest patches, distance from medium and large forest patches, and distance from patch edge) plus one different variable (elevation). The similarity between these models is not surprising because the difference between the presence datasets used for these three models was very small; the four species model included all 50 of the collection sites, the Ae. aegypti model included 49 sites, and the Ae. taylori included 45 sites. There is some redundancy between the variables in these 74

92 models, but Maxent adjusts for variable correlation, so the variables were kept in the models (Phillips 2005). None of these three finals models included any of the BioClim variables or vegetation variables. Figure 17. Maxent probabilities distributions for A. Four species model, B. Ae. aegypti model, C. Ae. africanus model, D. Ae. taylori model. In contrast, the presence probability surface for Ae. africanus has a strong south-north distribution and only one contribution variable: precipitation in the warmest quarter (Figure 17, Table 11). The limited number of presence sites (eleven) included in this model makes the validity of the model questionable, as does the use of only one explanatory variable. However, none of the presence points for Ae. africanus are in the northern portion of the study area, suggesting that this south-north 75