Submitted by Cássia Santos (cassia.bcufg@gmail.com) on 2016-05-31T09:35:51Z No. of bitstreams: 2 Tese - Frederico Augusto Martins Valtuille Faleiro - 2016.pdf: 7096330 bytes, checksum: 04cfce04ef128c5bd6e99ce18bb7f650 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-05-31T10:52:51Z (GMT) No. of bitstreams: 2 Tese - Frederico Augusto Martins Valtuille Faleiro - 2016.pdf: 7096330 bytes, checksum: 04cfce04ef128c5bd6e99ce18bb7f650 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2016-05-31T10:52:51Z (GMT). No. of bitstreams: 2 Tese - Frederico Augusto Martins Valtuille Faleiro - 2016.pdf: 7096330 bytes, checksum: 04cfce04ef128c5bd6e99ce18bb7f650 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2016-03-07 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The climate changes are one of the major threats to the biodiversity and it is expected to increase its impact along the 21st century. The climate change affect all levels of the biodiversity from individuals to biomes, reducing the ecosystem services. Despite of this, the prediction of climate change impacts on biodiversity is still a challenge. Overcoming these issues depends on improvements in different aspects of science that support predictions of climate change impact on biodiversity. The common practice to predict the climate change impact consists in formulate ecological niche models based in the current climate and project the changes based in the future climate predicted by the climate models. However, there are some recognized limitations both in the formulation of the ecological niche model and in the use of predictions from the climate models that need to be analyzed. Here, in the first chapter we review the science behind the climate models in order to reduce the knowledge gap between the scientific community that formulate the climate models and the community that use the predictions of these models. We showed that there is not consensus about evaluate the climate models, obtain regional models with higher spatial resolution and define consensual models. However, we gave some guidelines for use the predictions of the climate models. In the second chapter, we tested if the predictions of correlative ecological niche models fitted with presence-absence match the predictions of models fitted with abundance data on the metrics of climate change impact on orchid bees in the Atlantic Forest. We found that the presence-absence models were a partial proxy of change in abundance when the output of the models was continuous, but the same was not true when the predictions were converted to binary. The orchid bees in general will decrease the abundance in the future, but will retain a good amount of suitable sites in the future and the distance to gained climatic suitable areas can be very close, despite of great variation. The change in the species richness and turnover will be mainly in the western and some regions of southern of the Atlantic Forest. In the third chapter, we discussed the drawbacks in using the estimations of realized niche instead the fundamental niche, such as overpredicting the effect of climate change on species’ extinction risk. We proposed a framework based on phylogenetic comparative and missing data methods to predict the dimensions of the fundamental niche of species with missing data. Moreover, we explore sources of uncertainty in predictions of fundamental niche and highlight future directions to overcome current limitations of phylogenetic comparative and missing data methods to improve predictions. We conclude that it is possible to make better use of the current knowledge about species’ fundamental niche with phylogenetic information and auxiliary traits to predict the fundamental niche of poorly-studied species. In the fourth chapter, we used the framework of the chapter three to test the performance of two recent phylogenetic modeling methods to predict the thermal niche of mammals. We showed that PhyloPars had better performance than Phylogenetic Eigenvector Maps in predict the thermal niche. Moreover, the error and bias had similar phylogenetic pattern for both margins of the thermal niche while they had differences in the geographic pattern. The variance in the performance was explained by taxonomic differences and not by methodological aspects. Finally, our models better predicted the upper margin than the lower margin of the thermal niche. This is a good news for predicting the effect of climate change on species without physiological data. We hope our finds can be used to improve the predictions of climate change effect on the biodiversity in future studies and support the political decisions on minimizing the effects of climate change on biodiversity. / As mudanças climáticas são uma das principais ameaças à biodiversidade e é esperado que aumente seu impacto ao longo do século XXI. As mudanças climáticas afetam todos os níveis de biodiversidade, de indivíduos à biomas, reduzindo os serviços ecossistêmicos. Apesar disso, as predições dos impactos das mudanças climáticas na biodiversidade é ainda um desafio. A superação dessas questões depende de melhorias em diferentes aspectos da ciência que dá suporte para predizer o impacto das mudanças climáticas na biodiversidade. A prática comum para predizer o impacto das mudanças climáticas consiste em formular modelos de nicho ecológico baseado no clima atual e projetar as mudanças baseadas no clima futuro predito pelos modelos climáticos. No entanto, existem algumas limitações reconhecidas na formulação do modelo de nicho ecológico e no uso das predições dos modelos climáticos que precisam ser analisadas. Aqui, no primeiro capítulo nós revisamos a ciência por detrás dos modelos climáticos com o intuito de reduzir a lacuna de conhecimentos entre a comunidade científica que formula os modelos climáticos e a comunidade que usa as predições dos modelos. Nós mostramos que não existe consenso sobre avaliar os modelos climáticos, obter modelos regionais com maior resolução espacial e definir modelos consensuais. No entanto, nós damos algumas orientações para usar as predições dos modelos climáticos. No segundo capítulo, nós testamos se as predições dos modelos correlativos de nicho ecológicos ajustados com presença-ausência são congruentes com aqueles ajustados com dados de abundância nas medidas de impacto das mudanças climáticas em abelhas de orquídeas da Mata Atlântica. Nós encontramos que os modelos com presença-ausência foram substitutos parciais das mudanças na abundância quando o resultado dos modelos foi contínuo (adequabilidade), mas o mesmo não ocorreu quando as predições foram convertidas para binárias. As espécies de abelhas, de modo geral, irão diminuir em abundância no futuro, mas reterão uma boa quantidade de locais adequados no futuro e a distância para áreas climáticas adequadas ganhadas podem estar bem próximo, apesar da grande variação. A mudança na riqueza e na substituição de espécies ocorrerá principalmente no Oeste e algumas regiões no sul da Mata Atlântica. No terceiro capítulo, nós discutimos as desvantagens no uso de estimativas do nicho realizado ao invés do nicho fundamental, como superestimar o efeito das mudanças climáticas no risco de extinção das espécies. Nós propomos um esquema geral baseado em métodos filogenéticos comparativos e métodos de dados faltantes para predizer as dimensões do nicho fundamental das espécies com dados faltantes. Além disso, nós exploramos as fontes de incerteza nas predições do nicho fundamental e destacamos direções futuras para superar as limitações atuais dos métodos comparativos filogenéticas e métodos de dados faltantes para melhorar as predições. Nós concluímos que é possível fazer melhor uso do conhecimento atual sobre o nicho fundamental das espécies com informação filogenética e caracteres auxiliares para predizer o nicho fundamental de espécies pouco estudadas. No quarto capítulo, nós usamos o esquema geral do capítulo três para testar a performance de dois novos métodos de modelagem filogenética para predizer o nicho térmico dos mamíferos. Nós mostramos que o “PhyloPars” teve uma melhor performance que o “Phylogenetic Eigenvector Maps” em predizer o nicho térmico. Além disso, o erro e o viés tiveram um padrão filogenético similar para ambas as margens do nicho térmico, enquanto eles apresentaram diferentes padrões espaciais. A variância na performance foi explicada pelas diferenças taxonômicas e não pelas diferenças em aspectos metodológicos. Finalmente, nossos modelos melhor predizem a margem superior do que a margem inferior do nicho térmico. Essa é uma boa notícia para predizer o efeito das mudanças climáticas em espécies sem dados fisiológicos. Nós esperamos que nossos resultados possam ser usados para melhorar as predições do efeito das mudanças climáticas na biodiversidade em estudos futuros e dar suporte para decisões políticas para minimização dos efeitos das mudanças climáticas na biodiversidade.

Submitted by Franciele Moreira (francielemoreyra@gmail.com) on 2017-08-17T18:33:27Z No. of bitstreams: 2 Dissertaçao - Lara de Macedo Monteiro - 2017.pdf: 68300760 bytes, checksum: ba7fbce35b9ab3e46180337ae3129580 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2017-08-18T12:02:30Z (GMT) No. of bitstreams: 2 Dissertaçao - Lara de Macedo Monteiro - 2017.pdf: 68300760 bytes, checksum: ba7fbce35b9ab3e46180337ae3129580 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-08-18T12:02:30Z (GMT). No. of bitstreams: 2 Dissertaçao - Lara de Macedo Monteiro - 2017.pdf: 68300760 bytes, checksum: ba7fbce35b9ab3e46180337ae3129580 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-03-15 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Earth is facing the highest species' extinction rates of its history, and humans are the major stressar. Adding up to this biodiversity crisis, species-rich areas, which also coincide with areas highly transformed by humans (e.g. biodiversity hotspots), are poorly covered by protected areas. ln Brazil this reality is not different. Responsible for harbouring a third of all plant species already classified under a threat category (n= 645), the Brazilian Cerrado has only 8.3% of its area legally protected. ln this biorne, the campos rupestres, a mountaintop grassland ecosystem, stands out for its high number of threatened species currently underrepresented in conservation strategies. ln chapter 1, we aimed at indicating priority areas to secure protection of the threatened plant species from the southern Espinhaço mountains, a region that encampasses large areas of campos rupestres. We found that it is possible to protect, on average, more than 25% of the threatened species' ranges, avoiding sites with extensive use for farming and mining and favouring areas with intensive fire frequency by constraining the management to a relatively small area of only 17% of the region. Conservation plans such as these proposed for campos rupestres represent important opportunities to fulfil the gap existent between research and implementation. However, we do not rule out the need for increasing sophisticated tools that account for the consequences of complex processes threatening biodiversity in the near future ( e.g. clima te change and deforestation) and especially the need for predictive and realistic conservation strategies that anticipate and mitigate their negative effects. Unfortunately, until now we have been relying species protection to a residual system of PAs that provide minimal conservation impact. Thus, in chapter 2 we aimed to select spatial conservation priorites that minimize the risk of deforestation while retaining sites with high plant biodiversity value threatened from climate change in the Brazilian Cerrado. We simulated two ways of spacing out priorities for conservation actions ("time-step action" and "acting now''), and two methods of setting priorities: one that minimizes expected habitat conversion and prioritizes high valuable sites to plant biodiversity at risk from climate change (maximum conservation impact) and another that prioritizes sites based only on their value for plant biodiversity at risk from climate change, regardless their vulnerability to land conversion ("usual approach''). We found that although the scenarios that maximize conservation impact avoided higher amounts of vegetation loss, they prevented least species' range loss. Moreover, the acting now scenarios always performed better in terms of range loss avoided compared to the time-step scenarios under the sarne method of prioritization. Finally, we believe that planning for vegetation loss avoidance is a more conservative strategy because vegetation information is less subjective to any source of bias and is a better surrogate for general biodiversity. We also recommend that acting as soon as possible is always the best strategy to guarantee biodiversity conservation in the Cerrado. / A Terra vem enfrentando as maiores taxas de extinção de espécies de sua história, e os humanos são a maior causa disso. Além da crise de biodiversidade, áreas ricas em espécies, que, por sua vez, coincidem com locais sob alta influência de atividades humanas (ex: hotspots de biodiversidade), são pouco representadas por Unidades de Conservação. No Brasil, essa realidade não é diferente. Responsável por abrigar um terço de todas as espécies de plantas já classificadas sob uma das categorias de ameaça (n=645), o Cerrado brasileiro possui somente 8.3% de sua área legalmente protegida. Nesse biorna, o ecossistema de campos rupestres destaca-se pelo seu alto número de espécies ameaçadas atualmente subrepresentadas em estratégias de conservação. No capítulo 1, nosso objetivo foi indicar áreas prioritárias para assegurar a proteção de espécies ameaçadas de plantas da Serra do Espinhaço Meridional, uma região que abrange grandes trechos de campos rupestres. Nós encontramos que é possível proteger, em média, mais de 25% da distribuição das espécies ameaçadas restringindo o manejo a uma área relativamente pequena de apenas 17% da região e evitando locais de uso extensivo do solo para agropecuária e mineração e favorecendo locais com alta ocorrência de queimadas. Planos de conservação como esse proposto para campos rupestres representam importantes oportunidades para preencher a lacuna existente entre pesquisa e implementação. No entanto, nós não descartamos a necessidade de ferramentas mais sofisticadas que considerem as consequências dos complexos processos que ameaçam a biodiversidade em um futuro próximo ( ex: mudanças climáticas e desmatamento) e, especialmente, a necessidade de estratégias de conservação preditivas e realistas que antecipem e mitiguem seus efeitos negativos. Infelizmente, até agora a proteção das espécies tem se restringido a um sistema residual de unidades de conservação de baixo impacto para a conservação. Portanto, no capítulo 2 nosso objetivo foi selecionar espacialmente locais de alto valor para a biodiversidade de plantas ameaçadas em um cenário de mudanças climáticas e ao mesmo tempo minimizar o risco de conversão da vegetação desses locais. Nós simulamos duas formas de particionar as ações de conservação ("ação em intervalos de tempo" e "agir agora") e dois métodos de estabelecer prioridades: um que minimiza a conversão de hábitat esperada e prioriza locais altamente importantes para a biodiversidade de plantas ameaçadas em um cenário de mudanças climáticas ("máximo impacto da conservação") e outro que prioriza locais baseando-se somente no seu valor para a biodiversidade de plantas ameaçadas em um cenário de mudanças climáticas, independentemente de sua vulnerabilidade ao desmatamento ("abordagem habitual''). Nós encontramos que, embora os cenários que maximizem o impacto da conservação tenham evitado maiores perdas de vegetação, eles evitaram uma menor perda no tamanho médio da distribuição das espécies comparado às abordagens habituais. Além disso, constatamos que os cenários "agir agora" tiveram um melhor desempenho em termos de perda de distribuição evitada comparado aos cenários de implementação sequencial de ações considerando um mesmo método de priorização. Finalmente, nós acreditamos que planejar para evitar perda de vegetação é uma estratégia mais segura, porque a informação sobre vegetação é menos sujeita a qualquer viés e é um melhor indicador para biodiversidade em geral. Também recomendamos que agir o quanto antes é sempre a melhor estratégia para garantir a conservação da biodiversidade no Cerrado.

Face à l’érosion globale de la biodiversité des écosystèmes aquatiques continentaux, l’identification des mesures de gestion les plus urgentes à mettre en place est cruciale. En s’appuyant sur une approche innovante et multi-facettes de la diversité, les priorités de conservation pour les assemblages de poissons de rivière ont pu être déterminées à l’échelle de la France. La durabilité de ces priorités de conservation face aux principales composantes des changements globaux a ensuite été évaluée afin d’identifier les zones qui protégeront efficacement la biodiversité actuelle dans le futur. La méthodologie développée a finalement été appliquée au réseau hydrographique du département du Pas-de-Calais dans le but d’identifier précisément les priorités locales de conservation et de restauration. Ces outils pourront par la suite être utilisés comme support d’aide à la décision et adaptés selon les besoins des gestionnaires des milieux aquatiques. / The global loss of biodiversity affects freshwater ecosystems, making it crucial to identify the priority management actions in order to protect freshwater biodiversity in an effective and sustainable way. Based on an innovative multi-faceted framework of diversity, the spatial priorities for the conservation of stream fish assemblages have been identified at the scale of France. Their robustness to several drivers of global changes has then been assessed to identify the areas that are likely to efficiently protect the present-day biodiversity in the future. The methodological framework proposed herein has finally been applied to the river network of the Pas-de-Calais department located in northern France to accurately identify the local conservation and restoration priorities. These management tools can be used to support the establishment of management actions in accordance with the needs of the local environmental decision-makers.

Les changements climatiques et leurs impacts sur la biodiversité font aujourd'hui l'objet d'une attention croissante de la part de la communauté scientifique et des gestionnaires des écosystèmes naturels. En effet, le climat influence la biologie et l'écologie des espèces animales et végétales, depuis leur physiologie jusqu'à leur répartition. Les modifications climatiques pourraient donc avoir des répercussions importantes sur les espèces et les assemblages. Au sein des écosystèmes aquatiques continentaux, les poissons de rivière sont des organismes incapables de réguler leur température corporelle et soumis à une variabilité hydrologique importante ainsi qu'à de fortes pressions anthropiques. Leur réponse aux modifications du climat actuelles et à venir a pourtant été encore peu abordée. L'objectif de ce travail de thèse est donc d'évaluer les impacts potentiels du changement climatique sur les poissons des rivières françaises, et plus particulièrement sur la distribution des espèces et la structure des assemblages. Des données fournies par l'Office National de l'Eau et des Milieux Aquatiques ainsi qu'une approche de modélisation basée sur les niches écologiques des espèces (i.e., modèles de distribution) ont été utilisées. Différentes sources d'incertitude ont également été testées dans une approche d'ensembles afin de prendre en compte la variabilité entre les impacts projetés et fournir ainsi une évaluation robuste de ces impacts. La première partie de ce travail a consisté en l'identification des principaux déterminants environnementaux qui structurent la répartition spatiale des espèces de poisson au sein des réseaux hydrographiques. Globalement, il apparaît qu'une combinaison de facteurs climatiques et de variables décrivant l'habitat local et la position des habitats au sein des réseaux hydrographiques est importante pour expliquer la distribution actuelle des espèces. De plus, les espèces ont toutes des réponses différentes aux facteurs de l'environnement. Dans un second temps, nous avons mis en évidence que le choix de la méthode statistique de modélisation de la niche écologique est crucial, les patrons actuels et futurs de distribution prédits étant fortement contrastés selon la méthode de modélisation considérée. Cette dernière s'avère même être la principale source d'incertitude dans les projections futures, bien plus encore que les modèles climatiques de circulation générale et les scénarios d'émission de gaz à effet de serre. La variabilité entre les prédictions issues de plusieurs techniques de modélisation peut être prise en compte par une approche de consensus. Un modèle consensuel basé sur la valeur moyenne de l'ensemble de prédictions est capable de prédire correctement la distribution actuelle des espèces et la composition des assemblages. Nous avons donc choisi de retenir cette approche pour évaluer au mieux les impacts potentiels du changement climatique sur les poissons des rivières françaises à la fin du 21ème siècle. Nous avons montré que la majorité des espèces de poisson pourrait être affectée par les futures modifications du climat. Seules quelques espèces d'eau froide (e.g. truite fario, chabot) pourraient restreindre leur distribution aux parties les plus apicales des réseaux hydrographiques. Au contraire, les espèces tolérant des températures plus élevées pourraient coloniser de nouveaux habitats et étendre ainsi leur répartition. Ces modifications de la distribution des espèces pourraient conduire à un réarrangement des assemblages au niveau taxonomique et fonctionnel. Une augmentation de la diversité locale et de la similarité régionale (i.e., homogénéisation) sont ainsi prédites simultanément. L'ensemble de ces résultats apporte donc des éléments sur la compréhension de la distribution des poissons d'eau douce et sur les conséquences du changement climatique qui peuvent être envisagées. Ce travail fournit ainsi une base aux acteurs de la gestion de la biodiversité afin d'initier des mesures de conservation concrètes. De plus, les considérations méthodologiques développées dans cette thèse sont une contribution importante à l'amélioration des projections issues de modèles statistiques de distribution et à la quantification de leur incertitude. / Climate change and its impact on biodiversity are receiving increasing attention from scientists and people managing natural ecosystems. Indeed, climate has a major influence on the biology and ecology of fauna and flora, from physiology to distribution. Climate change may thus have major consequences on species and assemblages. Among freshwater ecosystems, stream fish have no physiological ability to regulate their body temperature and they have to cope with streams' hydrological variability and strong anthropogenic pressures. Yet their response to current and future climate change has been poorly studied. The aim of this PhD thesis is to assess the potential impact of climate change on fish in French streams, mainly on species distribution and assemblages' structure. Data provided by the Office National de l'Eau et des Milieux Aquatiques combined with a modelling approach based on species' ecological niche (i.e., distribution models) have been used. Several sources of uncertainty have also been considered in an ensemble modeling framework in order to account for the variability between projected impacts and to provide reliable estimates of such impact. First, we have identified the main environmental factors that determine the spatial distribution of fish species within river networks. Overall, it appears that a combination of both climatic variables and variables describing the local habitat and its position within the river network is important to explain the current species distribution. Moreover, each fish species responded differently to the environmental factors. Second, we have highlighted that the choice of the statistical method used to model the fish ecological niche is crucial given that the current and future patterns of distribution predicted by different statistical methods vary significantly. The statistical method appears to be the main source of uncertainty, resulting in more variability in projections than the global circulation models and greenhouse gas emission scenarios. The variability between predictions from several statistical methods can be taken into account by a consensus approach. Consensual predictions based on the computation of the average of the whole predictions ensemble have achieved accurate predictions of the current species distribution and assemblages' composition. We have therefore selected this approach to assess the potential impacts of climate change on fish in French streams at the end of the 21st century with the highest degree of confidence. We have found that most fish species could be sensitive to the future climate modifications. Only a few cold-water species (i.e., brown trout, bullhead) could restrict their distribution to the most upstream parts of river networks. On the contrary, cool- and warm-water fish species could colonize many newly suitable habitats and expand strongly their distribution. These changes of species distribution could lead to a rearrangement of fish assemblages both at the taxonomic and functional levels. An increase in local diversity together with an increase in regional similarity (i.e., homogenization) are therefore expected. All these results bring new insights for the understanding of stream fish species distribution and expected consequences of climate change. This work thus provides biodiversity managers and conservationists with a basis to take efficient preservation measures. In addition, methodological developments considered in this PhD thesis are an important contribution to the improvements of projections by statistical models of species distribution and to the quantification of their uncertainty.

<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>

The work describes the processing and analysis of remote sensing time series data for a comparative assessment of changes in different tropical rainforest areas in East Africa. In order to assess the effects of the derived changes in land cover and forest fragmentation, the study made use of spatially explicit modelling approaches within a geographical information system (GIS) to extrapolate sets of biological field findings in space and time. The analysis and modelling results were visualised aiming to consider the requirements of three different user groups. In order to evaluate measures of forest conservation and to derive recommendations for an effective forest management, quantitative landscape-scale assessments of land cover changes and their influence on forest biodiversity patterns are needed. However, few remote sensing studies have accounted for all of the following aspects at the same time: (i) a dense temporal sequence of land cover change/forest fragmentation information, (ii) the coverage of several decades, (iii) the distinction between multiple forest formations and (iv) direct comparisons of different case studies. In regards to linkages of remote sensing with biological field data, no attempts are known that use time series data for quantitative statements of long-term landscape-scale biodiversity changes. The work studies three officially protected forest areas in Eastern Africa: the Kakamega-Nandi forests in western Kenya (focus area) and Mabira Forest in south-eastern Uganda as well as Budongo Forest in western Uganda (for comparison purposes). Landsat imagery of in total eight or seven dates in regular intervals from 1972/73 to 2003 was used. Making use of supervised multispectral image classification procedures, in total, 12 land cover classes (six forest formations) were distinguished for the Kakamega-Nandi forests and for Budongo Forest while for Mabira Forest ten classes could be realised. An accuracy assessment via error matrices revealed overall classification accuracies between 81% and 85%. The Kakamega-Nandi forests show a continuous decrease between 1972/73 and 2001 of 31%, Mabira Forest experienced an abrupt loss of 24% in the late 1970s/early 1980s, while Budongo Forest shows a relatively stable forest cover extent. An assessment of the spatial patterns of forest losses revealed congruence with areas of high population density while a spatially explicit forest fragmentation index indicates a strong correlation of forest fragmentation with forest management regime and forest accessibility by roads. For the Kenyan focus area, three sets of biological field abundance data on keystone species/groups were used for a quantitative assessment of the influence of long-term changes in tropical forests on landscape-scale biodiversity patterns. For this purpose, the time series was extended with another three land cover data sets derived from aerial photography (1965/67, 1948/(52)) and old topographic maps (1912/13). To predict the spatio-temporal distribution of the army ant Dorylus wilverthi and of ant-following birds, GIS operators (i.e. focal and local functions) and statistical tests (i.e. OLS or SAR regression models) were combined into a spatial modelling procedure. Abundance data on three guilds of birds differing in forest dependency were directly extrapolated to five forest cover classes as distinguished in the time series. The results predict declines in species abundances of 56% for D. wilverthi, of 58% for ant-following birds and an overall loss of 47% for the bird habitat guilds, which in all three cases greatly exceed the rate of forest loss (31%). Additional extrapolations on scenarios of deforestation and reforestation confirmed the negative ecological consequences of splitting-up contiguous forest areas but also showed the potential of mixed indigenous forest plantings. The visualisation of the analysis and modelling results produced a mixture of different outcomes. Map series and a matrix of maps both showing species distributions aim to address scientists and decision makers. The results of the land cover change analysis were synthesised in a map of land cover development types for each study area, respectively. These maps are designed mainly for scientists. Additional maps of change, limited to a single class of forest cover and to three dates were generated to ensure an easy-to-grasp communication of the major forest changes to decision makers. Additionally, an easy-to-handle visualisation tool to be used by scientists, decision makers and local people was developed. For the future, an extension of this study towards a more complete assessment including more species/groups and also ecosystem functions and services would be desirable. Combining a framework for land cover simulation with a framework for running empirical extrapolation models in an automated manner could ideally result in a GIS-based, integrated forest ecosystem assessment tool to be used as regional spatial decision support system. / Die Arbeit beschreibt die Prozessierung und Analyse von Fernerkundungs-Zeitreihendaten für eine vergleichende Abschätzung von Veränderungen verschiedener tropischer Waldökosysteme Ostafrikas. Um Effekte der Veränderungen bzgl. Landbedeckung und Waldfragmentierung auf Biodiversitätsmuster abzuschätzen, wurden verschiedene räumlich explizite Modellierungssätze innerhalb eines geographischen Informationssystems (GIS) zur räumlichen und zeitlichen Extrapolation biologischer Felderhebungsdaten benutzt. Die Visualisierung der Analyse- und Modellierungsergebnisse erfolgte unter Berücksichtigung der Bedürfnisse von drei verschiedenen Nutzergruppen. Um Waldschutzmaßnahmen zu evaluieren und Empfehlungen für ein effektives Waldmanagement abzuleiten, sind quantitative Abschätzungen von Landbedeckungsveränderungen sowie von deren Einfluss auf tropische Waldbiodiversitätsmuster nötig. Wenige fernerkundungsbasierte Studien haben jedoch bislang alle der folgenden Faktoren berücksichtigt: (i) Informationen zu Veränderungen von Landbedeckung und Waldfragmentierung in dichter zeitlicher Sequenz, (ii) die Abdeckung mehrerer Jahrzehnte, (iii) die Unterscheidung zwischen mehreren Waldformationen, und (iv) direkte Vergleiche von unterschiedlichen Fallstudien. Hinsichtlich Verknüpfungen von Fernerkundung mit biologischen Felddaten sind bisher keine Studien bekannt, die Zeitreihendaten für quantitative Aussagen zu Langzeitveränderungen von Biodiversität auf Landschaftsebene verwenden. Die Arbeit untersucht drei offiziell geschützte Gebiete: die Kakamega-Nandi forests in Westkenia (Hauptuntersuchungsgebiet) sowie Mabira Forest in Südost-Uganda und Budongo Forest in West-Uganda (zu Vergleichszwecken). Es wurden Landsat-Daten für insgesamt acht bzw. sieben Zeitpunkte zwischen 1972/73 und 2003 in ungefähr gleichen Abständen erworben. Mit Hilfe von überwachten, multispektralen Klassifizierungsverfahren wurden für die Kakamega-Nandi forests und Budongo Forest jeweils 12 Landbedeckungsklassen (sechs Waldformationen) und für Mabira Forest zehn Klassen unterschieden. Eine Genauigkeitsprüfung mit Hilfe von Fehlermatrizen ergab Gesamtklassifizierungsgenauigkeiten zwischen 81% und 85%. Die Kakamega-Nandi forests sind durch eine kontinuierliche Waldabnahme von 31% zwischen 1972/73 und 2001 gekennzeichnet, Mabira Forest zeigt einen abrupten Waldverlust von 24% in den späten 1970ern/frühen 1980ern, während die Ergebnisse für Budongo Forest eine relativ stabile Waldbedeckung ausweisen. Während eine Abschätzung der räumlichen Muster von Waldverlusten eine hohe Deckungsgleichheit mit Gebieten hoher Bevölkerungsdichte ergab, deutet die Anwendung eines räumlich expliziten Waldfragmentierungsindexes auf eine starke Korrelation von Waldfragmentierung mit der Art von Waldmanagement sowie mit der Erreichbarkeit von Wald über Straßen hin. Um den Einfluss von Langzeit-Landbedeckungsveränderungen auf Biodiversitätsmuster auf Landschaftsebene für das kenianische Hauptuntersuchungsgebiet quantitativ abzuschätzen wurden drei Datensätze mit biologischen Felderhebungen zur Abundanz von Schlüsselarten/-gruppen verwendet. Zu diesem Zweck wurde die Zeitreihe zunächst um drei weitere Landbedeckungs-Datensätze ergänzt, die aus Luftbildern (1965/67, 1948/(52)) bzw. alten topographischen Karten (1912/13) gewonnen wurden. Zur Vorhersage der raum-zeitlichen Verteilung der Treiberameise Dorylus wilverthi wurden GIS-Operatoren und statistische Tests (OLS bzw. SAR Regressionsmodelle) in einem räumlichen Modellierungsablauf kombiniert. Abundanzdaten von drei sich hinsichtlich ihrer Abhängigkeit von Wald unterscheidenden Vogelgilden wurden direkt auf fünf Waldbedeckungsklassen hochgerechnet, die in der Zeitreihe unterschieden werden konnten. Die Ergebnisse prognostizieren Abundanzabnahmen von 56% für D. wilverthi, von 58% für Ameisen-folgende Vögel und einen Gesamtverlust von 47% für die Vogelgilden, was in allen drei Fällen eine deutliche Überschreitung der Waldverlustrate von 31% darstellt. Zusätzliche Extrapolationen basierend auf Szenarien bestätigten die negativen ökologischen Konsequenzen der Zerteilung zusammenhängender Waldflächen bzw. zeigten andererseits das Potential von Aufforstungen mit einheimischen Arten auf. Die Visualisierung der Analyse- bzw. Modellierungsergebnisse führte zu unterschiedlichen Darstellungen: mit einer Reihe von nebeneinander positionierten Einzelkarten sowie einer Matrix von Einzelkarten, die jeweils Artenverteilungen zeigen, sollen Wissenschaftler und Entscheidungsträger angesprochen werden. Aus den Ergebnissen der Landbedeckungsanalyse für die drei Untersuchungsgebiete wurden Landbedeckungsveränderungstypen generiert und jeweils in einer synthetischen Karte dargestellt, die hauptsächlich für Wissenschaftler gedacht sind. Um die wesentlichen Waldveränderungen auch auf einfache Weise zu den Entscheidungsträgern zu kommunizieren, wurden zusätzliche Karten erstellt, die nur eine aggregierte Klasse „Waldbedeckung“ zeigen und jeweils auf drei Zeitschritte der Zeitreihen begrenzt sind. Zusätzlich wurde ein leicht zu bedienendes Visualisierungstool entwickelt, das für Wissenschaftler, Entscheidungsträger und die lokale Bevölkerung gedacht ist. Für die Zukunft wäre eine umfassendere Abschätzung unter Berücksichtigung zusätzlicher Arten/-gruppen sowie auch Ökosystemfunktionen und –dienstleistungen wünschenswert. Die Verknüpfung einer Applikation zur Landbedeckungsmodellierung mit einer Applikation zur Ausführung von empirischen Extrapolationsmodellen (in stärkerem Maße automatisiert als in dieser Arbeit) könnte im Idealfall in ein GIS-basiertes Tool zur integrativen Bewertung von Waldökosystemen münden, das dann als räumliches Entscheidungsunterstützungssystem verwendet werden könnte.

La diversité bêta est une mesure essentielle pour décrire l'organisation de la biodiversité dans l'espace. Le calcul des contributions locales à la diversité bêta (LCBD), en particulier, permet d'identifier des sites à forte unicité écologique montrant une diversité exceptionnelle au sein d'une région d'intérêt. Jusqu’à présent, l'utilisation des LCBD s'est restreinte à des échelles locales ou régionales avec un petit nombre de sites. Dans ce mémoire, j'ai examiné si les modèles de répartition d'espèces (SDM) permettent d'évaluer l'unicité écologique sur de plus grandes étendues spatiales. J'ai également étudié l’effet des changements d’échelle sur la quantification de la diversité bêta. Pour ce faire, j'ai utilisé la base de données eBird et des arbres de régression additifs bayésiens pour prédire la répartition des parulines en Amérique du Nord. J'ai ensuite calculé les LCBD sur ces prédictions, ce qui permet de couvrir de plus grandes étendues spatiales et un nombre de sites plus élevé. Mes résultats ont montré que les SDM fournissent des estimations d'unicité fortement corrélées avec les données observées et montrant une association spatiale statistiquement significative. Ils ont également montré que la relation entre la richesse et les LCBD varie selon la région et l'étendue spatiale et qu'elle est influencée par la proportion d'espèces rares dans les communautés. Ainsi, les sites identifiés comme uniques peuvent varier selon les caractéristiques de la région étudiée. Ces résultats montrent que les SDM peuvent être utilisés pour prédire l'unicité écologique, ce qui pourrait permettre d'identifier d'importantes cibles de conservation au sein de régions non échantillonnées. / Beta diversity is an essential measure to describe the organization of biodiversity through space. The calculation of local contributions to beta diversity (LCBD), specifically, allows the identification of sites with high ecological uniqueness and exceptional diversity within a region of interest. To this day, LCBD indices have primarily been used on regional and smaller scales, with relatively few sites. Furthermore, their use is typically restricted to strictly sampled sites with known species composition, leading to gaps in spatial coverage on broad extents. Here, I examined whether species distribution models (SDMs) can be used to assess ecological uniqueness over broader spatial extents and investigated the effect of scale changes on beta diversity quantification. To this aim, I used observations recorded in the eBird database and Bayesian additive regression trees to model warbler species composition in North America, then computed LCBD indices on the predictions, thus covering a broader spatial extent and a higher number of sites. My results showed that SDMs provide uniqueness estimates highly correlated with observed data with a statistically significant spatial association. They also showed that the relationship between richness and LCBD values varies according to the region and the spatial extent and that it is affected by the proportion of rare species in communities. Sites identified as unique may therefore vary according to regional characteristics. These results show that SDMs can be used to predict ecological uniqueness over broad spatial extents, which could help identify beta diversity hotspots and important targets for conservation purposes in unsampled locations.

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.

Les changements climatiques et les phénomènes météorologiques extrêmes sont devenus des moteurs importants de changements de la biodiversité, posant une menace pour la perte d’habitat et l’extinction d’espèces. Comprendre l’état actuel de la biodiversité et identifier les zones hautement adaptées (still strugling with this expression, high suitability for who or what?) sont essentiels afin de lutter contre la perte de biodiversité et guider les processus décisionnels en lien avec les études scientifiques (added scientifiques, as in scientific surveys), les mesures de protection et les efforts de restauration. Les modèles de distribution des espèces (MDE ou SDM en anglais) sont des outils statistiques permettant de prédire la distribution géographique potentielle d’une espèce en fonction de variables environnementales et des données recueillies à cet endroit. Cependant, les MDE conventionnels sont souvent confrontés à des limitations dues à la résolution spatiale et à la couverture restreinte des variables environnementales, lesquelles sont obtenues suite à des mesures au sol ou à l’aide de stations météorologiques. Pour mieux comprendre la distribution des espèces à des fins de conservation, le défi GeoLifeCLEF 2022 a été organisé. Cette compétiion comprend un vaste ensemble de données composé de 1,6 million géo-observations liées à la présence de 17 000 espèces végétales et animales. L’objectif principal de ce défi est d’explorer le potentiel des données de télédétection afin de prédire la présence d’espèces à des géolocalisations spécifiques. Dans ce mémoire, nous étudions diverses techniques d’apprentissage automatique et leur performance en lien avec le défi GeoLifeCLEF 2022. Nous explorons l’efficacité d’algorithmes bien connus en apprentissage par transfert, établissons un cadre d’apprentissage non supervisé et étudions les approches d’apprentissage auto-supervisé lors de la phase d’entraînement. Nos résultats démontrent qu’un ajustement fin des encodeurs pré-entraînés sur différents domaines présente les résultats les plus prometteurs lors de la phase de test. / Climate change and extreme weather events have emerged as significant drivers of biodiversity changes, posing a threat of habitat loss and species extinction. Understanding the current state of biodiversity and identifying areas with high suitability for different species are vital in combating biodiversity loss and guiding decision-making processes for protective measures and restoration efforts. Species distribution models (SDMs) are statistical tools for predicting a species' potential geographic distribution based on environmental variables and occurrence data. However, conventional SDMs often face limitations due to the restricted spatial resolution and coverage of environmental variables derived from ground-based measurements or weather station data. To better understand species distribution for conservation purposes, the GeoLifeCLEF 2022 challenge was introduced. This competition encompasses a large dataset of 1.6 million geo-observations linked to the presence of 17,000 plant and animal species. The primary objective of this challenge is to explore the potential of remote sensing data in forecasting species' presence at specific geolocations. In this thesis, we investigate various machine learning techniques and their performance on the GeoLifeCLEF 2022 challenge. We explore the effectiveness of standard transfer learning algorithms, establish an unsupervised learning framework, and investigate self-supervised learning approaches for training. Our findings demonstrate that fine-tuning pre-trained encoders on different domains yields the most promising test set performance results.

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