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Small Island Developing States (SIDS) of the Caribbean are one of the world’s ‘hottest’ ‘biodiversity hotspots’. However, this biodiversity continues to be threatened by habitat loss, and now, by climate change. The research reported here investigated the potential of species distribution modelling (SDM) as a plant conservation tool within Caribbean SIDS, using Trinidad as a case study. Prior to the application of SDM, ancillary analyses including: (i) quantification and mapping of forest cover change (1969 to 2007) and deforestation rates, and (ii) assessment of the island’s vegetation community distribution and associated drivers were carried out. Community distribution and commercial importance and global/regional rarity were used to generate a list of species for assessing the potential of SDM within Trinidad. Species occurrence data were used to generate species distribution models for present climate conditions within the SDM algorithm, MaxEnt. These results were assessed through expert appraisal and concurrence with results of ecological analyses. These models were used to forecast suitable species climate space forty years into an SRES A2 future. Present and future models were then combined to produce a ‘collective change map’ which showed projected areas of species’ range expansion, contraction or stability for this group of species with respect to Trinidad’s Protected Areas (PAs) network. Despite the models being indicative rather than accurate, it was concluded that species’ climate space is likely to decrease or disappear across Trinidad. Extended beyond Trinidad into the remainder of the Caribbean region, SDM may be a crucial tool in identifying which PAs within the region (and not individual islands) will facilitate future survival of given target species. Consideration of species conservation from a regional, rather than an individual island perspective, is strongly recommended for aiding the Caribbean SIDS to adapt in response to climate change.

Submitted by PPG Zoologia (zoologia-pg@pucrs.br) on 2018-08-01T18:22:00Z No. of bitstreams: 1 Almeida MAB___TESE___VERS?O FINAL.pdf: 2953118 bytes, checksum: 99cbefa9c38c7969abce4bafc4b20d54 (MD5) / Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2018-08-02T17:55:11Z (GMT) No. of bitstreams: 1 Almeida MAB___TESE___VERS?O FINAL.pdf: 2953118 bytes, checksum: 99cbefa9c38c7969abce4bafc4b20d54 (MD5) / Made available in DSpace on 2018-08-02T18:39:41Z (GMT). No. of bitstreams: 1 Almeida MAB___TESE___VERS?O FINAL.pdf: 2953118 bytes, checksum: 99cbefa9c38c7969abce4bafc4b20d54 (MD5) Previous issue date: 2018-03-22 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES / Nonhuman primates (NHP) are susceptible to many arboviruses, including the yellow fever (YF) virus. Although native to Africa, this virus found susceptible NHP and competent mosquito vectors for maintaining its transmission in American forests. A high sensitivity of NHP to YF led health agencies to monitor these animals as a way of monitoring the disease in Brazil. The State of Rio Grande do Sul (RS) began this surveillance in 2002, which has detected the arboviruses Oropouche and Saint Louis (SLEV) and a YF epizootic that killed more than 2,000 NHP (Alouatta caraya and A. guariba clamitans) between 2008 and 2009. The objectives of this PhD thesis research were to generate models of niche suitability for YF based on that epizootic and prospect arboviruses in NHP in northwestern RS. The maximum entropy algorithm - Maxent was used to generate distribution models of Alouatta spp. and the mosquito vector Haemagogus leucocelaenus. Together with climatic, topographic and vegetative variables, these models served as predictor layers to model the occurrence of the disease based on the points of death of NHP of YF. The most influential variables in the YF models were the variation in air humidity, distribution of Alouatta spp. and maximum wind speed followed by mean annual rainfall and maximum temperature. Therefore, support for the influence of the rainfall regime and the ambient temperature on the cycle of jungle YF was found. Wind speed and direction can play an important role in the dispersal of infected mosquitoes and, consequently, the virus. The models based on the occurrence of dead NHP in the first months of the epizootic identified suitable areas to where the disease spread a few months later. In addition, 19 arboviruses were prospected in 40 blood (viral isolation and PCR) and serum (hemagglutination inhibition and neutralization tests [NT]) samples collected from 26 black howler monkeys (A. caraya) belonging to three populations in four field campaigns in the municipality of Santo Ant?nio das Miss?es, RS, between 2014 and 2016. There was no detection of circulating virus, but antibodies to Flavivirus SLEV and Ilh?us and Phlebovirus Icoaraci was found by NT. Evidence of the contact with Ilh?us and Icoaraci are the southernmost records in Brazilian NHP. An increase in antibodies to SLEV detected between two consecutive captures of the same individual is compatible with a recent contact with the virus. An adult male captured in one of the areas presented concomitant infection by the Oropouche, SLEV and YF viruses by NT. Further studies are necessary to understand the role played by NHP and other vertebrates in the circulation of arboviruses in the region, to assess potential risks to NHP and public health, and to identify the driving forces responsible for the dispersal of the YF virus during epizootics in wildlife populations. / Os primatas n?o-humanos (PNH) s?o suscet?veis a diversos arbov?rus, incluindo o v?rus da febre amarela (FA). Embora origin?rio da ?frica, esse v?rus encontrou PNH suscet?veis e mosquitos vetores competentes para sua transmiss?o em matas nas Am?ricas. Uma alta sensibilidade dos PNH ? FA levou ?rg?os de sa?de a monitorar esses animais como forma de vigiar a doen?a no Brasil. O Estado do Rio Grande do Sul (RS) iniciou essa vigil?ncia em 2002, a qual detectou os arbov?rus Oropouche e Saint Louis (SLEV) e uma epizootia de FA que matou mais de 2000 PNH (Alouatta caraya e A. guariba clamitans) entre 2008 e 2009. A presente tese de doutorado teve como objetivos gerar modelos de adequabilidade ambiental para FA com base nessa epizootia e prospectar arbov?rus em PNH no noroeste do RS. Foi utilizado o algoritmo de m?xima entropia ? Maxent para gerar modelos de distribui??o de Alouatta spp. e do mosquito vetor Haemagogus leucocelaenus. Esses modelos serviram como camadas preditoras para, junto a vari?veis clim?ticas, topogr?ficas e vegetacionais, modelar a ocorr?ncia da doen?a baseada nos pontos de morte de PNH por FA. As vari?veis mais influentes nos modelos da FA foram a varia??o na umidade do ar, a distribui??o de Alouatta spp. e a velocidade m?xima dos ventos, seguidas pela precipita??o m?dia anual e a temperatura m?xima. Portanto, foi confirmado suporte para a influ?ncia do regime de chuvas e da temperatura ambiente no ciclo da FA silvestre. A velocidade e a dire??o do vento devem desempenhar um importante papel na dispers?o de mosquitos infectados e, consequentemente, do v?rus. Os modelos baseados na distribui??o espacial de PNH mortos nos primeiros meses da epizootia identificaram ?reas adequadas para onde a doen?a avan?ou poucos meses mais tarde. Tamb?m foram prospectados 19 arbov?rus em 40 amostras de sangue (isolamento viral e PCR) e soro (inibi??o da hemaglutina??o e testes de neutraliza??o [NT]) coletadas em quatro campanhas de campo entre 2014 e 2016 de 26 bugios-pretos (A. caraya) de tr?s popula??es no munic?pio de Santo Ant?nio das Miss?es, RS. N?o houve detec??o de v?rus circulante, mas sim de anticorpos para os Flavivirus SLEV e Ilh?us e o Phlebovirus Icoaraci por NT. As evid?ncias de contato com Ilh?us e Icoaraci s?o as primeiras em PNH no extremo sul do Brasil. Um aumento de anticorpos para SLEV detectado entre duas capturas consecutivas do mesmo indiv?duo ? compat?vel com um contato recente com o v?rus. Um macho adulto capturado em uma das ?reas apresentou infec??o concomitante pelos v?rus Oropouche, SLEV e FA por NT. Mais estudos s?o necess?rios para compreender o papel de PNH e outros vertebrados na circula??o de arbov?rus na regi?o, avaliar poss?veis riscos para PNH e a sa?de humana e identificar as for?as motrizes respons?veis pela dispers?o do v?rus da FA durante epizootias em popula??es selvagens.

Introduction: The Aedes aegypti mosquito is responsible for the transmission of Yellow Fever, Dengue, Chikungunya and Zikavirus, making it a deadly vector and global public health threat. Zikavirus and Chikungunya, which were previously restricted to smaller geographic areas, have both appeared in the Western Hemisphere in the past three years and spread to areas where A. aegypti are present. This means that the pathogens have now entered areas in which the population has no previous immunity, which can lead to extensive outbreaks and epidemics. As the effects of global climate change become apparent, the areas of the globe that are suitable for inhabitance by A. aegypti may change. Additionally, this vector prefers human hosts for blood meals and requires standing water to breed, which is often created by water storage containers. This means that increasing urbanization and human population density are likely to put populations at higher risk of exposure to this vector. Methods: To create maps of the future risk of exposure to Aedes aegypti globally, species occurrence data for the vector and the Maxent modeling approach were used. Current and projected climate data were downloaded from WorldClim.org for the four representative concentration pathways (RCPs) used to model future climate change. Human population density, projected to 2050, the same timeframe as the future climate data, were used to model changes in human populations. To identify areas at high risk for future presence of A. aegypti populations, current and future models were compared across areas with at least a 50% probability of increased risk. These results where then used to create maps displaying high risk areas. Results: The AUC, an indicator of model fit, signaled that the models had high predictive power. However, high omission rates indicated that the trade-off of risk mapping may be a need to decrease probability thresholds below 50% to capture the full at-risk population. Future high-risk areas were most often those surrounding current cities, which supports the idea that the combination of urbanization and increasing human population density will work synergistically to increase the disease burden within and around urban centers. Additionally, expansion at the current geographic margins of this species shows that incursion into currently non-endemic areas is possible. Conclusions: Urban and peri-urban populations are likely to be at higher risk of exposure compared to rural areas due to global climate change and changes in population density. Attempts to model expansion of vector habitats should consider how these human population characteristics will change the risk to populations and how to best identify the areas at highest risk. Thresholds for the probability of a population being at risk of exposure to a vector may need to be different from those required to determine whether or not a habitat is suitable for a species. Appropriately determining which areas are high-risk results in maps and models can then be used to identify areas where climate change mitigation and vector control efforts are likely to have the highest impacts.

The Aedes aegypti mosquito is responsible for transmission of four vector-borne diseases that cause considerable global morbidity and mortality. Projections of the future effects of global climate change indicate that expansion of this species due to changing habitats is possible. Furthermore, since A. aegypti is highly dependent on human populations for feeding and egg-laying sites, changing human population characteristics are likely to alter the risk of exposure for humans based on geographic location. This study aims to create future potential risk maps for human exposure to A. aegypti using human population density as a predictor. Using current population density data and future growth trajectories, high-resolution human population density forecasts were created for 2050, then included as variables in ecological niche models developed using Maxent. Species occurrence data and high resolution climate data for current and future conditions (best and worst case scenarios) were included in the model, as well. Model fit indices and variable contributions indicated that the inclusion of human population density improves model accuracy for A. aegypti. Risk maps created by these models showed that areas currently adjacent to large cities within endemic regions, such as central Africa and western Brazil, are likely to see the greatest increase in risk to human populations. This corroborates current projections on increasing urbanization in the future and suggests that these models can be used to target interventions in high risk areas.

Macroecology is the study of relationships between organisms and the environment at large spatial and temporal scales. This field of research examines patterns in species abundance, distribution and diversity. Understanding patterns in species distribution and richness can contribute significantly to our knowledge of community assembly and macroecological patterns, as well as to the effective conservation of threatened species and habitats. Although there have been a plethora of studies on birds in India over the years, there is a critical need to accurately delineate species distributions and understand patterns of richness. The focus of this study was to understand the factors (abiotic and biotic) that influence the distribution and composition of bird species in the Western Ghats, as well as to explore patterns in their geographic range sizes. The objectives of this study were addressed at the scale of the entire Western Ghats using a combination of field surveys, secondary data collection and species distribution modeling. The specific approaches to address these questions and the findings are outlined below. Chapter 2: Bird species in the Western Ghats – Patterns in composition and richness Fine-scale data on species presence and abundance are essential for exploring patterns in species distribution and richness. Despite the fact that birds have been extensively studied in the Western Ghats, systematic data collection and compilation of information over the entire mountain range has not been carried out, especially for the purpose of testing macroecological questions. This chapter describes patterns in bird species presence, abundance, composition and richness within the Western Ghats. The study area, site selection protocol and the sampling technique have also been described in detail. This dataset establishes a baseline of information about birds in the Western Ghats and subsets of this larger dataset will be used to address various questions in the following chapters. Chapter 3: Predicting bird species distribution in the Western Ghats Detailed knowledge of species’ ecological and geographical distributions is fundamental for conservation, as well as for understanding ecological and evolutionary determinants of spatial patterns of biodiversity. However, occurrence data for a vast majority of species are sparse, resulting in information about species distributions that is inadequate for many purposes. Species distribution models attempt to provide detailed predictions of distributions by relating presence or abundance of species to environmental predictors. In this chapter, we describe the usage of Maxent, a species distribution modelling technique based on presence-only data, to predict the distributions of bird species within the Western Ghats. For this purpose, we put together primary locations of bird species presence along with a published dataset. Using a number of important environmental layers, predicted species distribution maps were derived for 98 bird species, including 13 endemics, in the Western Ghats. Additionally, we calculated predicted range sizes for each of these species and obtained percentage contributions of important environmental predictors to each species’ distribution. This is the first study to develop species distribution models for bird species within the Western Ghats. Chapter 4: Patterns of range size among bird species Understanding large-scale patterns of variation in species geographic range size is fundamental to questions in macroecology and conservation biology. In general, range is believed to be influenced by a combination of environmental factors, evolutionary history and biotic interactions, mediated by species specific traits. These patterns need to be examined even for well-studied taxa like birds, especially within biodiversity hotspots faced by persistent degradation due to anthropogenic activities such as the Western Ghats. In this chapter, we use a dataset of 98 bird species within the Western Ghats to examine trends in range sizes, measured as latitudinal extent of occurrence and predicted range size from species distribution models. We show a significant relationship between latitude and range size for these bird species, supporting Rapoport’s rule. As far as we know, this relationship has never been tested at such low latitudes for birds. We also find that species traits such as body size, mean abundance and diet do not seem to show any discernable effect on patterns of range size. Additionally, we found that widely-used bird species range maps (in this case, from BirdLife International) are inaccurate representations of species ranges in comparison to the predicted species distribution maps that were derived in the previous chapter. We quantitatively demonstrated that these expert-drawn maps need to re-evaluated, especially since they are used to make conservation decisions. This is the first study to quantify species range sizes of birds within the Western Ghats and assess such range maps that are used to determine conservation status of species. Chapter 5: Environmental predictors of bird species distribution One of the major goals in ecology is to understand patterns and processes that determine species diversity. The drivers of global species richness gradients have been studied, especially in the case of birds, in terms of contemporary and historical factors. Such broad scale processes may not always reflect the processes affecting richness and distribution at smaller scales. Therefore, understanding the factors that influence individual species distributions is the first step towards this larger goal. In this chapter, we examined the environmental predictors that contributed to the predicted distribution of bird species observed in the Western Ghats, using the variable importance contribution values derived in Chapter 3. We found that a large proportion of the 98 bird species studied were influenced by normalized differential vegetation index, annual precipitation and elevation. The predictors did not differ among birds of different diet guilds and body size classes. Using Prinicipal components analysis, we observed that all 98 bird species are spread out across the environmental ordination space depicted by the PC axes 1 and 2. These axes are governed by measures of habitat heterogeneity and water-energy related variables, consistent with other tropical studies. The insectivorous guild seemed to occupy a variety of environmental niches across this space and other guilds seemed to be nested within the insectivorous guild. Similarly, larger sized birds were spread across the entire environmental ordination space, with species of smaller sizes nested within. This is the first step in trying to understand environmental predictors acting on birds in the Western Ghats. Further detailed studies need to be carried out to come to definite conclusions. Chapter 6: Relative roles of floristics and vegetation structure on bird species composition On the basis of the hierarchical model of habitat selection, it is known that birds select suitable habitats based on vegetation structure (physiognomy) at coarse biogeographic scales, and plant species composition (floristics) at more local scales. This chapter examines the relative influence of tree species composition and vegetation structure on bird species composition in the Western Ghats. These relationships were specifically assessed across the entire Western Ghats, within regions of the Western Ghats as well as within specific forest types. We found that floristics had a strong association with bird species composition across the Western Ghats and within evergreen and mixed deciduous habitat types. This association seems to be independent of the structural variation in the region. There was a decrease in association strength from the southern to the northern Western Ghats, in terms of both floristics and structure. We did not find an association between vegetation structure and insectivore composition, whereas phytophage composition did show a stronger association with floristics than structure. This is the first study at the scale of the entire Western Ghats to test the relative roles of floristics and vegetation structure. Taken as a whole, this dissertation examines large-scale macroecological questions regarding species distribution, range size and patterns of composition using primary data at the scale of the Western Ghats. The findings of this study have established a foundation that will help further our understanding of species distribution and richness in the Western Ghats, and aid in the decision making for conservation strategies in the future.

The Connecticut River watershed is experiencing a rapid invasion of aggressive non-native plant species, which threaten watershed function and structure. Volunteer-based monitoring programs such as the University of Massachusetts’ OutSmart Invasives Species Project, Early Detection Distribution Mapping System (EDDMapS) and the Invasive Plant Atlas of New England (IPANE) have gathered valuable invasive plant data. These programs provide a unique opportunity for researchers to model invasive plant species utilizing citizen-sourced data. This study took advantage of these large data sources to model invasive plant distribution and to determine environmental and biophysical predictors that are most influential in dispersion, and to identify a suitable presence-only model for use by conservation biologists and land managers at varying spatial scales. This research focused on the invasive plant species of high interest - Japanese stiltgrass (Mircostegium vimineum). This was identified as a threat by U.S. Fish and Wildlife Service refuge biologists and refuge managers, but for which no mutli-scale practical and systematic approach for detection, has yet been developed. Environmental and biophysical variables include factors directly affecting species physiology and locality such as annual temperatures, growing degree days, soil pH, available water supply, elevation, closeness to hydrology and roads, and NDVI. Spatial scales selected for this study include New England (regional), the Connecticut River watershed (watershed), and the U.S. Fish and Wildlife, Silvio O. Conte National Fish and Wildlife Refuge, Salmon River Division (local). At each spatial scale, three software programs were implemented: maximum entropy habitat model by means of the MaxEnt software, ecological niche factor analysis (ENFA) using Openmodeller software, and a generalized linear model (GLM) employed in the statistical software R. Results suggest that each modeling algorithm performance varies among spatial scales. The best fit modeling software designated for each scale will be useful for refuge biologists and managers in determining where to allocate resources and what areas are prone to invasion. Utilizing the regional scale results, managers will understand what areas on a broad-scale are at risk of M. vimineum invasion under current climatic variables. The watershed-scale results will be practical for protecting areas designated as most critical for ensuring the persistence of rare and endangered species and their habitats. Furthermore, the local-scale, or fine-scale, analysis will be directly useful for on-the-ground conservation efforts. Managers and biologists can use results to direct resources to areas where M. vimineum is most likely to occur to effectively improve early detection rapid response (EDRR).

<p>Greater prairie-chickens (<i>Tympanuchus cupido pinnatus</i>; GPC) have declined throughout large areas in the eastern portion of their range. I used species distribution modeling to predict most appropriate areas of translocation of GPC in and around Kankakee Sands, a tallgrass prairie in northwest Indiana, USA. I used MaxEnt for modelling the predictions based on relevant environmental predictors along with occurrence points of 54 known lek sites. I created four models inspired by Hovick et al. (2015): Universal, Environmental, Anthropogenic-Landcover, and Anthropogenic-MODIS. The Universal, Environmental, and Anthropogenic-MODIS models possessed passable AUC scores with low omission error rates. However, only the Universal model performed better than the null model according to binomial testing. I created maps of all models with passing AUC scores along with an overlay map displaying the highest predictions across all passing models. MaxEnt predicted high relative likelihoods of occurrence for the entirety of Kankakee Sands and many areas in the nearby landscape, including the surrounding agricultural matrix. With implementation of some management suggestions and potential cooperation with local farmers, GPC translocation to the area appears plausible.</p> <p>Franklin’s ground squirrels (<i>Poliocitellus franklinii</i>; FGS) have declined throughout a large portion of the eastern periphery of their range. Because of this, The Nature Conservancy is interested in establishing a new population of these animals via translocation. The area of interest is tallgrass prairie in northwest Indiana, USA: Kankakee Sands and the surrounding landscape. Species distribution modelling can help identify areas that are suitable for translocation. I used MaxEnt, relevant environmental variables, and 44 known occurrence points to model the potential for translocation of FGS to Kankakee Sands and the surrounding area. I created four models inspired by Hovick et al. (2015): Universal, Environmental, Anthropogenic-Landcover, and Anthropogenic-MODIS. I created maps of models with passing AUC scores. The final map was an overlay map displaying the highest relative likelihood of occurrence predictions for the area in all passing models. Only the Universal and Anthropogenic-MODIS models had passable AUC scores. Both had acceptable omission error rates. However, none of the models performed better than the null model (p < 0.05). MaxEnt predicted that a few areas in and outside of Kankakee Sands possess high relative likelihoods of occurrence of FGS in both the Universal and Anthropogenic-MODIS models. However, MaxEnt predicted high relative likelihoods in the surrounding agricultural matrix in the Universal Model. FGS prefer to cross through agricultural areas via unmowed roadside instead of open fields (Duggan et al. 2011). Because of this, high predictions in agricultural matrices in the Universal model are irrelevant. High relative likelihood predictions for linear sections that are obviously roads are disregardable in the context of my modeling efforts. Because of my low sample size, none of the models are really reliable in predicting relative likelihoods of occurrence for this area. Despite high relative likelihood predictions, the appropriateness of a translocation effort to the area is inconclusive.</p>

Lyme disease is an emerging tick-borne illness in Canada, with human case numbers increasing 15- to 20-fold since Lyme disease became nationally notifiable in 2009 until the present. In Ontario, Canada's largest province by population, average Lyme disease incidence across the province is similar to that of national estimates. However, in eastern Ontario, which is near tick endemic regions in the northeastern Unites States, Lyme disease incidence is disproportionately higher compared to the rest of the province. The objectives of this thesis are to identify environmental Lyme disease risk areas in Ontario, to explore spatiotemporal trends in Lyme disease emergence, and to identify neighbourhood-level socioecological risk factors for Lyme disease. In addition, this thesis also aims to assess the risk of other tick-borne illnesses that are transmitted by the blacklegged tick, Ixodes scapularis, which is also the main vector for Lyme disease in Canada. Using maximum entropy species distribution modelling to correlate blacklegged tick occurrence data with environmental variables, predictive risk models for I. scapularis and the Lyme disease pathogen, Borrelia burgdorferi, were developed. The model prediction was used to classify low and high environment risk areas and, using a case-control epidemiological study, we assessed that residence in risk areas was a strong predictor of Lyme disease. However, this relationship was modulated by socioecological factors linked to higher overall rurality of the locality of home residence. Spatial cluster analyses further revealed that human Lyme disease cases clustered in regions with the high numbers of reported B. burgdorferi-infected ticks in the environment. Many individuals residing in large metropolitan regions, like the City of Ottawa, reported tick exposures outside their public health unit of residence; however, local clusters of Lyme disease were also detected in suburban regions near conservation areas, trails, and urban woodlands. The prevalence of other tick-borne pathogens was low, although several pathogens of public health significance including Borrelia miyamotoi and Anaplasma phagocytophilum were detected at multiple sites surveyed for ticks between 2017-2021. Overall, this thesis identify patterns in Lyme disease emergence (and potentially other tick-borne illnesses), defines environmental risk areas for Lyme disease in Ontario, and highlights important socioecological risk factors for Lyme disease in eastern Ontario.