Habitat loss and fragmentation occur at unprecedented rates, especially in tropical countries where human activities have deforested or degraded around 80% of tropical rainforests. Tropical forest fragmentation is considered the main cause of extinction of tropical forest avifauna, yet the mechanisms by which fragmentation affects bird populations are poorly understood. The present study investigates the pattern of bird species distribution in a fragmented landscape in tropical southeastern Mexico and the relation of bird community and species distribution patterns to landscape and fragment characteristics. Area and isolation of forest fragments were the main determinant of species richness and abundance in fragments, especially for forest-dependent species. The significant effect of isolation on species persistence in forest fragments suggests that limitation of dispersal is one potential mechanism by which fragmentation affects species distribution in the landscape. To understand how fragmentation can affect bird dispersal in a fragmented landscape, the effect of open areas and corridors on movement patterns of forest birds was investigated. The evidence presented in this study supports the idea that bird movement is restricted by open areas, especially for forest-restricted birds. Forested corridors had a positive effect on movement rates of forest birds, potentially acting to preserve movement and dispersal processes, and ultimately species persistence, in heavily fragmented landscapes.

<p>This dissertation focuses on solving some common problems associated with ecological field studies. In the core of the statistical methodology, lies spatial modeling that provides greater flexibility and improved predictive performance over existing algorithms. The applications involve prevalence datasets for hundreds of plants over a large area in the Cape Floristic Region (CFR) of South Africa.</p><p>In Chapter 2, we begin with modeling the categorical abundance data with a multi level spatial model using background information such as environmental and soil-type factors. The empirical pattern is formulated as a degraded version of the potential pattern, with the degradation effect accomplished in two stages. First, we adjust for land use transformation and then we adjust for measurement error, hence misclassification error, to yield the observed abundance classifications. With data on a regular grid over CFR, the analysis is done with a conditionally autoregressive prior on spatial random effects. With around ~ 37000 cells to work with, a novel paralleilization algorithm is developed for updating the spatial parameters to efficiently estimate potential and transformed abundance surfaces over the entire region.</p><p>In Chapter 3, we focus on a different but increasingly common type of prevalence data in the so called <italic>presence-only</italic> setting. We detail the limitations associated with a usual presence-absence analysis for this data and advocate modeling the data as a point pattern realization. The underlying intensity surface is modeled with a point-level spatial Gaussian process prior, after taking into account sampling bias and change in land-use pattern. The large size of the region enforces using an computational approximation with a bias-corrected predictive process. We compare our methodology against the the most commonly used maximum entropy method, to highlight the improvement in predictive performance.</p><p>In Chapter 4, we develop a novel hierarchical model for analyzing noisy point pattern datasets, that arise commonly in ecological surveys due to multiple sources of bias, as discussed in previous chapters. The effect of the noise leads to displacements of locations as well as potential loss of points inside a bounded domain. Depending on the assumption on existence of locations outside the boundary, a couple of different models -- <italic>island</italic> and <italic>subregion</italic>, are specified. The methodology assumes informative knowledge of the scale of measurement error, either pre-specified or learned from a training sample. Its performance is tested against different scales of measurement error related to the data collection techniques in CFR.</p><p>In Chapter 5, we suggest an alternative model for prevalence data, different from the one in Chapter 3, to avoid numerical approximation and subsequent computational complexities for a large region. A mixture model, similar to the one in Chapter 4 is used, with potential dependence among the weights and locations of components. The covariates as well as a spatial process are used to model the dependence. A novel birth-death algorithm for the number of components in the mixture is under construction.</p><p>Lastly, in Chapter 6, we proceed to joint modeling of multiple-species datasets. The challenge is to infer about inter-species competition with a large number of populations, possibly running into several hundreds. Our contribution involves applying hierarchical Dirichlet process to cluster the presence localities and subsequently developing measures of range overlap from posterior draws. This kind of simultaneous inference can potentially have implications for questions related to biodiversity and conservation studies. .</p> / Dissertation

Ecological patterns and processes influence ecosystem function at scales from nanometres to global scales depending on the organisms involved. Predicting the presence and abundance of species, at scales appropriate to the organisms and the underlying processes, is central to ecology. Models of species’ distributions can provide important insights into pattern-process-scale relationships including the relative importance of various environmental factors and their interactions that influence habitat selection at the individual and population levels. Mapping current and potential distributions informs the conservation of threatened species by providing spatial information on where a species is likely to occur and the identification of habitat elements and their spatial configurations which influence occupancy and persistence. The aim of this thesis was to incorporate the principles of pattern, process and scale in the identification of habitat associations for threatened species within a species’ distribution modelling framework. Accurate modelling of species’ distributions depends on robust sampling designs, reliable data input and appropriate statistical methodologies that align with the ecological model. I applied a range of innovative statistical methods to various sources of data to identify important habitat associations for a threatened species at different scales and tested the discriminative ability of the resultant models. I integrated the results from extensive field sampling and expert elicitation to build connectivity networks using graph theory algorithms to identify important conservation priorities for threatened species. The threatened brush-tailed rock-wallaby (Petrogale penicillata) was chosen as a suitable study species for quantifying habitat relationships at multiple spatial scales using species’ distribution modelling. The distribution of brush-tailed rock-wallabies is restricted to a set of suitable habitat characteristics related to rocky terrain supporting cliffs and boulder piles that occur infrequently across a landscape. At the site scale, they require suitable resting and refuge sites provided by rocky habitats, while at a landscape scale their dispersal is dependent on the connectivity of suitable habitats. The species is listed as threatened throughout eastern Australia and endangered in some states. Information about its current distribution and occupancy status is essential to support habitat conservation and threat management. The first chapter provides a broad view of the literature on modelling of species’ distributions and the thesis aims and structure. In chapter 2, I assess the ecological scale relevant to habitat modelling for the brush-tailed rock-wallaby. In chapter 3 I test whether habitat models from one region can be extrapolated to neighbouring regions. I use a novel approach and elicitation tool in chapter 4 to collect expert knowledge and assess it with a comprehensive set of field data in a Bayesian framework. In chapter 5 I assess whether landscape connectivity is a determinant of site occupancy by using graph theory algorithms to identify important habitat patches and dispersal pathways for rock-wallaby movement in fragmented landscapes. The final chapter synthesises the individual chapters’ findings within the context of species’ distribution modelling. Management implications are discussed for the conservation of the brush-tailed rock-wallaby and its habitat network. Wider implications are also suggested for other rock-wallaby species and species living in similar environments. The results of the thesis showed the habitat of the brush-tailed rock-wallaby was affected by site-scale and landscape-scale factors, supporting the need for a multi-scale approach when investigating species-environment associations. I found that models performed well within a region at both scales. Extrapolating the models to neighbouring regions resulted in good predictive performance at the site scale but substantially poorer predictive performance at the landscape scale. When there is insufficient field data to build robust data models, management bodies would benefit from incorporating expert knowledge. The study demonstrates the potential errors in using experts with knowledge gained from outside the area of interest. Finally, I highlight the importance of accounting for the landscape connectivity between patches from the perspective of the individual animal. Least cost analysis, using graph theory algorithms, provides a cost-efficient and effective framework for identifying landscape connectivity patterns and key paths and patches to help inform suitable land management strategies for conservation of threatened species. There is much pressure from conservation and management agencies to produce models of species’ distributions that could be potentially be used in other regions or with similar species. The thesis combines ecological theory with rigorous statistical methodology to test different modelling techniques for species distribution modelling. It demonstrates how a combination of expert knowledge, extensive field data and landscape connectivity measures successfully predicts ecological relationships at a number of scales. Species’ distribution models can benefit from applying a robust sampling design and suitable modelling techniques to various data sources to generate ecologically-based information to improve our understanding of species-habitat associations and provide a reliable component to incorporate into conservation planning. This thesis therefore provides important advances to spatial ecology and ecological modelling of species distributions and management of threatened species.

Medical cartography can provide valuable insights into the epidemiology and ecology of infectious diseases, providing a quantitative representation of the distribution of these pathogens. Such methods therefore provide a key step in informing public health policy decisions ranging from prioritising sites for further investigation to identifying targets for interventions. By increasing the resolution at which risk is defined, policymakers are provided with an increasingly informed approach for considering next steps as well as evaluating past progress. In spite of their benefits however, global maps of infectious disease are lacking in both quality and comprehensiveness. This thesis sets out to investigate the next steps for medical cartography and details the use of species distribution models in evaluating global distributions of a variety of zoonotic diseases of public health importance. Chapter 2 defines a methodology by which global targets for infectious disease mapping can be quantitatively assessed by comparing the global burden of each disease with the demand from national policymakers, non-governmental organisations and academic communities for global assessments of disease distribution. Chapter 3 introduces the use of boosted regression trees for mapping the distribution of a group of vector-borne diseases identified as being a high priority target, the leishmaniases. Chapter 4 adapts these approaches to consider Ebola virus disease. This technique shows that the West African outbreak was ecologically consistent with past infections and suggests a much wider area of risk than previously considered. Chapter 5 investigates Marburg virus disease and considers the variety of different factors relating to all aspects of the transmission cycle that must be considered in these analyses. Chapters 6 and 7 complete the mapping of the suite of viral haemorrhagic fevers by assessing the distribution of Crimean-Congo haemorrhagic fever and Lassa fever. Finally, Chapter 8 considers the risk that these viral haemorrhagic fevers present to the wider African continent, quantifying potential risk of spillover infections, local outbreaks and more widespread infection. This thesis addresses important information gaps in global knowledge of a number of pathogens of public health importance. In doing so, this work provides a template for considering the global distribution of a number of other zoonotic diseases.

Meadows are critical in arid and semi-arid Argentinean Patagonia because of their importance for regional biodiversity. Despite this, little information on the spatial distribution of meadows is available and no analysis of the potential effect of climate change on meadows has been performed, which hampers conservation planning. In this study, I modeled the spatial distribution of meadows and investigated how climate change may affect the current distribution of meadows in arid and semiarid Patagonia by 2050. In addition, I investigated conservation status and areas of desertification vulnerability of those areas predicted to contain meadows. I used high-resolution imagery available in Google Earth software to visually estimate presence and absence of meadows. To model current and future distribution of meadows I used these observations and different socio-environmental predictor variables. I implemented generalized linear, additive, boosting, and random forest models, as the basis for a mean ensemble technique. I predicted future distribution of meadows using four different general circulation models and the A2 SERES scenario. The final ensemble model was an accurate representation of the current distribution of meadows in Patagonia and indicates they are severely under-represented within protected areas. I determined that overall meadow abundance is going to decrease by 2050 given the changes in climate. However, there were two contrasting trends: severe reduction of meadows in northwest Patagonia and Tierra del Fuego Island, and an expansion of suitable areas for meadows in the south and a small section in the northwest. This first regional map of meadow distribution across Argentinean Patagonia and information on meadows vulnerability to climate change represent key information for planning actions to conserve this critical habitat.

Conservation of biodiversity and sustainable resource use are central aims within ecology. This thesis focuses on the current data and environmental frameworks used to support these aims across different states in Europe. In particular, it examines the impact of geo-political boundaries on data-use, funding and planning for temporal movement of species in response to climate change. It also examines the current environmental framework agreements in Europe and their capacity to deal with trans-boundary aspects of biodiversity change. Through examination of European biodiversity datasets, undertaking species distribution modelling of forest taxa, examining economic data, palaeo-ecological data, and assessing international environmental framework agreements, this thesis identifies a number of important knowledge gaps. Probably unsurprisingly, the distribution of biodiversity in Europe mostly does not match political entities, all of which have individual aims, financial resources, and biodiversity management regimes in place. All have a significant impact on biodiversity conservation planning because i) the use of geo-politically truncated data influences modelling predictions, ii) financial commitment to biodiversity conservation varies between countries influencing success outcomes, iii) biodiversity persistence in current and future climate change does not recognise geo-political boundaries, and iv) many of the key environmental frameworks are implemented within countries and do not considering trans-boundary issues. Overall these findings significantly improve the understanding of conservation and resource management in Europe and fill a number of important knowledge gaps. They highlight the importance of appropriate trans-boundary ecological datasets and the need for more consistency across Europe in financial resources for biodiversity conservation. They also highlight the need for appreciation of areas of high-persistent biodiversity regardless of geo-political boundaries and environmental framework agreements that support cross-border conservation measures.

Biodiversity is a complex concept incorporating genes, species, ecosystems, composition, structure and function. The global scientific and political community has recognized the importance of biodiversity for human well-being, and has set goals and targets for its conservation, sustainable use, and benefit sharing. Monitoring biodiversity will help meet conservation goals and targets, yet observations collected in-situ are limited in space and time, which may bias interpretations and hinder conservation. Remote sensing can provide complementary datasets for monitoring biodiversity that are spatially comprehensive and repeatable. However, further research is needed to demonstrate, for various spatial scales and regions, how remotely sensed datasets represent different aspects of biodiversity. The overall goal of this dissertation is to advance the mapping and monitoring of biodiversity indicators, globally and within Canada, through the use of remote sensing. This research produced maps that were rich with spatially explicit, spatially continuous data, filling gaps in the availability and spatial resolution or scalability of information regarding ecosystem function (primary productivity) at global scales, tree species composition at regional scales (Saskatchewan, Canada), and ecosystem structure at local scales (coastal British Columbia, Canada). Further, the remotely sensed indicator datasets proposed and tested in each of the research chapters are repeatable, ecologically meaningful, translate to specific biodiversity targets globally and within Canada, and are calculable at multiple spatial scales. Challenges and opportunities for fully implementing these or similar remotely sensed biodiversity indicators and indicator datasets at a national level in Canada are discussed, contributing to the advancement of biodiversity monitoring science. / Graduate

Speciation can be understood as a continuum occurring at different levels, from population to species. The recent molecular revolution in population genetics has opened a pathway towards understanding species evolution. At the same time, speciation patterns can be better explained by incorporating a geographic context, through the use of geographic information systems (GIS). Phaedranassa (Amaryllidaceae) is a genus restricted to one of the world’s most biodiverse hotspots, the Northern Andes. I studied seven Phaedranassa species from Ecuador. Six of these species are endemic to the country. The topographic complexity of the Andes, which creates local microhabitats ranging from moist slopes to dry valleys, might explain the patterns of Phaedranassa species differentiation. With a Bayesian individual assignment approach, I assessed the genetic structure of the genus throughout Ecuador using twelve microsatellite loci. I also used bioclimatic variables and species geographic coordinates under a Maximum Entropy algorithm to generate distribution models of the species. My results show that Phaedranassa species are genetically well-differentiated. Furthermore, with the exception of two species, all Phaedranassa showed non-overlapping distributions. Phaedranassa viridiflora and P. glauciflora were the only species in which the model predicted a broad species distribution, but genetic evidence indicates that these findings are likely an artifact of species delimitation issues. Both genetic differentiation and non-overlapping geographic distribution suggest that allopatric divergence could be the general model of genetic differentiation. Evidence of sympatric speciation was found in two geographically and genetically distinct groups of P. viridiflora. Additionally, I report the first register of natural hybridization for the genus. The findings of this research show that the genetic differentiation of species in an intricate landscape as the Andes does not necessarily show a unique trend. Although allopatric speciation is the most common form of speciation, I found evidence of sympatric speciation and hybridization. These results show that the processes of speciation in the Andes have followed several pathways. The mixture of these processes contributes to the high biodiversity of the region

Climate change is an important contributor to the modification of many bumblebee species’ range boundaries. It was linked to widespread decline at the southern edge of their distribution and to their inability to colonize new areas at the northern edge. Additionally, bumblebee decline is aggravated by other anthropogenic threats like land use change, agricultural practices and pathogen spillover. Predicted consequences are numerous, and could lead to severe economic and ecological impacts on human populations. A species-specific assessment of potential climate change impacts on North American bumblebees, based on the most recent global change scenarios as used in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), was done for the first time. Using a massive dataset of georeferenced bumblebee observations and general circulation models, a series of species distribution models explore the impact of different climate change scenarios on climatically suitable areas of 30 bumblebee species. Northward range shifts occur in most bumblebee species’ projected climatic niches, revealing potential hotspots – places projected to be climatically suitable to multiple species – under future climate scenarios. Areas where species are likely to be lost in the absence of intervention are substantial, particularly in eastern parts of the continent. Models showed significant contractions of current ranges even under the very optimistic scenario in which all species disperse at 10 km/year. Results indicate that managed relocation as well as habitat management should be considered as a conservation strategy for some species. This research serves as a foundation for broader discussion and research in a nascent research area. It may assist in establishing localities where first conservation efforts could be directed for vulnerable bumblebee species.

In 1999, a new species of minnow, Chrosomus sp. cf. saylori (Clinch dace), was discovered in the Tennessee drainage of Virginia. Chrosomus sp. cf. saylori are listed as a Federal Species of Concern and on Virginia\'s Wildlife Action Plan as Tier II- Very High Conservation Need because of potential threats from habitat degradation, high population fragmentation, and a largely unknown distribution. Consequently, a management plan for C. sp. cf. saylori is of utmost importance, but more information regarding its distribution and life history is required before such a plan can be implemented. In 2011 and 2012 I sampled 60 headwater streams in the upper Clinch River watershed, Virginia.  From this and historical data, I conclude that C. sp. cf. saylori are restricted to eight small tributaries to the Clinch River.  Multivariate analysis of habitat correlates indicated that C. sp. cf. saylori populations are found in small, high elevation streams with gravel substrate and forested watersheds.  Three species distribution models were unable to predict C. sp. cf. saylori distribution.  Morphological traits were significantly different between C. sp. cf. saylori and other Chrosomus, thereby providing an initial indication of speciation and differing niche roles. I observed a nest association with Campostoma anomalum.  Gonad weight was lower for C. sp. cf. saylori than closely-related congeners. Together, this information indicates that C. sp. cf. saylori are narrowly distributed and populations are small, fragmented, and of questionable viability.  In the future, long-term monitoring efforts and genetics analyses should be completed and additional protection measures pursued. / Master of Science