Les modifications environnementales qui affectent aujourd'hui les milieux marins recouvrent des problématiques scientifiques et sociétales majeures, d'autant que ces changements devraient s'accélérer au cours du 21ème siècle. Comprendre et anticiper la réponse de la biodiversité marine à ces changements représente un enjeu scientifique d'actualité. Les approches biogéographiques et macroécologiques constituent un cadre scientifique dans lequel il est possible d'étudier, de décrire, et de comprendre les motifs de distribution des espèces à large échelle et d'estimer leur évolution possible face aux changements environnementaux. C'est notamment le cas dans l'océan Austral où les effets du changement climatique se font déjà sentir et où les modifications environnementales associées pourraient avoir des effets profonds sur la structure et le fonctionnement des écosystèmes. Malgré de récents efforts d'échantillonnage, nos connaissances sur la distribution des espèces dans l’océan Austral comptent encore de nombreuses lacunes attribuables au caractère récent des découvertes, à l'isolement et à l'éloignement de cet océan d'accès difficiles. Dans ce contexte, les objectifs de cette thèse consistaient à mieux comprendre les motifs de distribution d'espèces à l’échelle de l’océan Austral, à mettre en évidence les facteurs qui en sont à l’origine et enfin, à évaluer l’impact du changement climatique sur leur distribution. Pour cela, différents types de modèles de niche écologique (MNE) ont été employés. Les échinides (oursins), organismes communs des communautés benthiques de l’océan Austral ont servi de modèle d'étude pour ce travail. / Current environmental changes, which impact marine environments, cover major scientific and societal issues, especially as these environmental changes are expected to accelerate along the 21st century. Understanding and forecasting the response of marine biodiversity to these changes is a pregnant scientific issue. Biogeographic and macroecological approaches provide a scientific framework for that purpose. They allow describing and understanding species distribution patterns at large spatial scale as well as estimating their potential shift with regards to environmental change. This is particularly true in the Southern Ocean, where the effects of climate change are already occurring and where environmental changes could have a deep and manifold impact on the structure and functioning of marine ecosystems. Despite recent sampling efforts, our knowledge of the Southern Ocean species distributions still faces many shortcomings due to the rather recent discovery of this ocean, its isolation and remoteness along with difficult access conditions. In this context, the aims of this thesis are to better understand the factors that drive species distribution patterns at the Southern Ocean scale, and to assess the impact of climate change on their distribution. For this purpose, different types of Species Distribution Models (SDM) have been used. Echinoids (sea urchins), which are common organisms of benthic communities in the Southern Ocean, have been used as a biological model for this work.

What explains the geographic distribution of pathogens? Better understanding and characterising disease patterns will help scientists to identify areas likely to host future epidemics and epizootics and to prioritise surveillance and intervention. However, the use of disease surveillance data to assess the risk of transmission and generate risk maps raises conceptual and methodological issues. Indeed, pathogens and more particularly viruses aren't ”species” like others that live in the open environment and must be studied with methods and concepts of their own. Avian influenza (AI), a disease caused by a virus infecting bird populations, has been selected to study these issues. AI has a major economic impact on the poultry industry in many countries, raises concerns of livelihood in low and middle-income countries, and represents a major concern for human health. The aim of this PhD thesis was to improve the knowledge on the spatial epidemiology of AI in different settings and conditions (i). For this, recent epizootics caused by the subtypes A (H5N1) and A (H7N9) were selected as case studies. First, highly pathogenic subtypes of the A (H5N1) virus have been studied in poultry farms (ducks and chickens) at different spatial scales: at the continental scale and the regional scale in the Mekong (Cambodia, Laos, Vietnam, Thailand) and the Nile Delta in Egypt. All these cases occurred between 2003, the date on which the virus starts to spread outside China, and 2015; the HPAI A (H5N1) subtypes are still reported today in many countries. Human infections caused by the A (H7N9) virus in China from March 2013 to 2017 were also studied. Studied different AI subtypes at different spatial scales within different host species also allowed to develop a conceptual model of AI transmission and to discuss the issue of the transferability of results in epidemiology (ii). Lastly, this PhD thesis leads to a discussion about the transfer of methods and concepts from ecology to spatial epidemiology, with a particular emphasis on their possible limitations (iii). / Doctorat en Sciences agronomiques et ingénierie biologique / info:eu-repo/semantics/nonPublished

Submitted by PPG Ecologia e Evolu??o da Biodiversidade (eebpg.ciencias@pucrs.br) on 2018-10-16T17:56:11Z No. of bitstreams: 1 Tese - Pereira - Joana.pdf: 10961538 bytes, checksum: b96f5fee6d02e741124bc5a31abdb086 (MD5) / Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2018-10-18T12:48:41Z (GMT) No. of bitstreams: 1 Tese - Pereira - Joana.pdf: 10961538 bytes, checksum: b96f5fee6d02e741124bc5a31abdb086 (MD5) / Made available in DSpace on 2018-10-18T13:37:00Z (GMT). No. of bitstreams: 1 Tese - Pereira - Joana.pdf: 10961538 bytes, checksum: b96f5fee6d02e741124bc5a31abdb086 (MD5) Previous issue date: 2018-09-24 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES / Distribution models are considered important tools in biogeography and ecology studies as they allow spatial and temporal extrapolation of species distribution from a set of occurrence data points as function of environmental predictors. In this study, we aimed to propose logistic distribution models for 50 fish species along the Sinos River Basin (Rio Grande do Sul, Brazil), by using altitude and basin area as geomorphological descriptors. Upstream-downstream richness model for entire basin by using multiple and logistic regression was also presented. The dataset derived from several sampling programs performed from 1998 to 2014, comprising 86 sampled locations with fish collections with gillnets, electric fishing or both. Two logistic models were constructed using environmental data of altitude and upstream basin area, testing linear (Model 1) and nonlinear (Model 2) factor responses for each species. Performance of the two models was evaluated using sensitivity, specificity, AUC (Area Under Curve) and TSS (True Skill Statistical). The adjusted models presented sensitivity values ranging from 47,1 to 99,8, specificity from 49,94 to 98,10, AUC from 0,75 to 0,99 and TSS from 0,26 to 0,98. The linear response model, although simpler, proved to be effective in predicting species distribution, as observed in previous studies. The basin area had a positive effect on the distribution of most species according to the linear model, although this was not verified for nonlinear models due to the interaction between variables. In general, the nonlinear model presented higher performance values for the evaluated metrics for the majority of species, but suffered from overfitting and a patchy distribution estimation. The species richness increased along a longitudinal gradient, presenting its maximum value downstream of the basin. Of the four estimated richness models, models with six variables were the ones with the lowest residual variation. / Modelos de distribui??o s?o considerados como ferramentas importantes em estudos de biogeografia e ecologia, pois permitem a extrapola??o espacial e temporal da distribui??o de esp?cies a partir de um conjunto de dados de ocorr?ncia em fun??o de preditores ambientais. Neste estudo, objetivamos propor modelos de distribui??o log?stica para 50 esp?cies de peixes ao longo da bacia do rio dos Sinos (Rio Grande do Sul, Brasil), utilizando altitude e ?rea da bacia como descritores geomorfol?gicos. Modelos de riqueza montante-jusante para toda a bacia usando regress?o m?ltipla e log?stica tamb?m foram propostos. O conjunto de dados derivou de v?rios programas de amostragem realizados de 1998 a 2014, compreendendo 86 locais amostrados com coleta de peixes com redes de emalhar, pesca el?trica ou ambos. Dois modelos log?sticos foram constru?dos utilizando dados ambientais de altitude e ?rea de bacia a montante, testando respostas lineares (Modelo 1) e n?o lineares (Modelo 2) para cada esp?cie. O desempenho dos modelos foi avaliado usando sensibilidade, especificidade, AUC (Area Under Curve) e TSS (True Skill Statistical). Os modelos ajustados apresentaram valores de sensibilidade variando de 47,1 a 99,8, especificidade de 49,94 a 98,10, AUC de 0,75 a 0,99 e TSS de 0,26 a 0,98. O modelo de resposta linear, embora mais simples, mostrou-se eficaz na predi??o da distribui??o de esp?cies, como observado em estudos anteriores. A ?rea da bacia teve um efeito positivo na distribui??o da maioria das esp?cies de acordo com o modelo linear, embora isso n?o tenha sido verificado para os modelos n?o lineares devido ? intera??o entre as vari?veis. Em geral, o modelo n?o linear apresentou valores de desempenho mais altos para as m?tricas avaliadas para a maioria das esp?cies, mas sofreu de hiperajuste e uma estimativa de distribui??o fragmentada. A riqueza de esp?cies aumentou ao longo de um gradiente longitudinal, apresentando seu valor m?ximo a jusante da bacia. Dos quatro modelos estimados de riqueza, os modelos com seis vari?veis foram os que apresentaram a menor amplitude de varia??o dos res?duos.

Knowing where species are and understanding why is paramount for developing relevant and sustainable conservation and resource management strategies. The need for this information is becoming urgent as fishing activity, resource extraction and the impacts of coastal developments continue to put marine resources under increasing pressure. As logistical and financial constraints can restrict our ability to collect data in the marine environment, the ability to predict distributions based on known associations with different environmental variables would enhance our capacity to manage these resources. Before attempting to predict the distribution of species and groups of species, the underlying species-environment relationships must be examined to determine whether associations between species and the environment can: (i) be identified, (ii) be used to develop models that can accurately predict distributions, and (iii) are general enough to allow accurate predictions beyond the sampled area. Most studies to date have compared the composition of fish assemblages between sites to determine how different environmental variables influence distribution. While widely applied, these methods do not consider how individual species respond to multiple environmental gradients and they lack the ability to predict distributions across different combinations of variables along those gradients. This lack of prediction also limits our capacity to assess what marine biodiversity is presently threatened by global, regional, and local human pressures on marine ecosystems. '...' Thus, summarising and modelling species data at higher levels would result in models with poorer predictive accuracy and a loss of ecological information. The generality of the species-environment relationships defined by the models were assessed by evaluating the transferability of models between different areas. Models developed from data collected over a wider geographic extent could more accurately predict the distribution of species across a smaller spatial extent than vice versa. This indicated that while general theories of the ecology of temperate demersal fish can be defined, the actual patterns of distribution may vary from site to site, suggesting caution when using predictions beyond the sampled area for management purposes. Overall, species distribution modelling identified how different species and groups of species responded to the combined influence of multiple environmental gradients and was able to accurately predict distributions based on the defined associations. Their application has led to a greater understanding of the species environment relationships and will help to identify those areas that may be important for conservation. Their predictive ability will allow general predictions of distribution of fish species across unsurveyed areas and provides the ability to assess the potential impact from implementing different policy and management strategies.

[Truncated abstract] The aim of this research was to define key species-environment relationships to better understand the spatial ecology of demersal fish. To help understand these relationships a combination of multivariate analyses, landscape analysis and species distribution models were employed. Of particular interest was to establish the scale at which these species respond to their environment. With recent high resolution surveying and mapping of the benthos in five of Victoria's Marine National Parks (MNPs), full coverage bathymetry, terrain data and accurate predicted benthic habitat maps were available for each of these parks. This information proved invaluable to this research, providing detailed (1:25,000) benthic environmental data, which facilitated the development and implementation of a very targeted and robust sampling strategy for the demersal fish at Cape Howe MNP. The sampling strategy was designed to provide good spatial coverage of the park and to represent the park's dominant substrate types and benthic communities, whilst also satisfying the assumptions of the statistical and spatial analyses applied. The fish assemblage data was collected using baited remote underwater stereo-video systems (stereo- BRUVS), with a total of 237 one-hour drops collected. Analysis of the video footage identified 77 species belonging to 40 families with a total of 14,449 individual fish recorded. ... This research revealed that the statistical modelling techniques employed provided an accurate means for predicting species distributions. These predicted distributions will allow for more effective management of these species by providing a robust and spatially explicit map of their current distribution enabling the identification and prediction of future changes in these species distributions. This research demonstrated the importance of the benthic environment on the spatial distribution of demersal fish. The results revealed that different species responded to different scales of investigation and that all scales must be ix considered to establish the factors fish are responding to and the strength and nature of this response. Having individual, continuous and spatially explicit environmental measures provided a significant advantage over traditional measures that group environmental and biological factors into 'habitat type'. It enabled better identification of individual factors, or correlates, driving the distribution of demersal fish. The environmental and biological measures were found to be of ecological relevance to the species and the scale of investigation and offered a more informative description of the distributions of the species examined. The use of species distribution modelling provided a robust means for the characterisation of the nature and strength of these relationships. In addition, it enabled species distributions to be predicted accurately across unsampled locations. Outcomes of the project include a greater understanding of how the benthic environment influences the distribution of demersal fish and demonstrates a suite of robust and useful marine species distribution tools that may be used by researcher and managers to understand, monitor, manage and predict marine species distributions.

The present thesis aims to introduce process-based model for species range dynamics that can be fitted to abundance data. For this purpose, the well-studied Proteaceae species of the South African Cape Floristic Region (CFR) offer a great data set to fit process-based models. These species are subject to wildflower harvesting and environmental threats like habitat loss and climate change. The general introduction of this thesis presents shortly the available models for species distribution modelling. Subsequently, it presents the feasibility of process-based modelling. Finally, it introduces the study system as well as the objectives and layout. In Chapter 1, I present the process-based model for range dynamics and a statistical framework to fit it to abundance distribution data. The model has a spatially-explicit demographic submodel (describing dispersal, reproduction, mortality and local extinction) and an observation submodel (describing imperfect detection of individuals). The demographic submodel links species-specific habitat models describing the suitable habitat and process-based demographic models that consider local dynamics and anemochoric seed dispersal between populations. After testing the fitting framework with simulated data, I applied it to eight Proteaceae species with different demographic properties. Moreover, I assess the role of two other demographic mechanisms: positive (Allee effects) and negative density-dependence. Results indicate that Allee effects and overcompensatory local dynamics (including chaotic behaviour) seem to be important for several species. Most parameter estimates quantitatively agreed with independent data. Hence, the presented approach seemed to suit the demand of investigating non-equilibrium scenarios involving wildflower harvesting (Chapter 2) and environmental change (Chapter 3). The Chapter 2 addresses the impacts of wildflower harvesting. The chapter includes a sensitivity analysis over multiple spatial scales and demographic properties (dispersal ability, strength of Allee effects, maximum reproductive rate, adult mortality, local extinction probability and carrying capacity). Subsequently, harvesting effects are investigated on real case study species. Plant response to harvesting showed abrupt threshold behavior. Species with short-distance seed dispersal, strong Allee effects, low maximum reproductive rate, high mortality and high local extinction are most affected by harvesting. Larger spatial scales benefit species response, but the thresholds become sharper. The three case study species supported very low to moderate harvesting rates. Summarizing, demographic knowledge about the study system and careful identification of the spatial scale of interest should guide harvesting assessments and conservation of exploited species. The sensitivity analysis’ results can be used to qualitatively assess harvesting impacts for poorly studied species. I investigated in Chapter 3 the consequences of past habitat loss, future climate change and their interaction on plant response. I use the species-specific estimates of the best model describing local dynamics obtained in Chapter 1. Both habitat loss and climate change had strong negative impacts on species dynamics. Climate change affected mainly range size and range filling due to habitat reductions and shifts combined with low colonization. Habitat loss affected mostly local abundances. The scenario with both habitat loss and climate change was the worst for most species. However, this impact was better than expected by simple summing of separate effects of habitat loss and climate change. This is explained by shifting ranges to areas less affected by humans. Range size response was well predicted by the strength of environmental change, whereas range filling and local abundance responses were better explained by demographic properties. Hence, risk assessments under global change should consider demographic properties. Most surviving populations were restricted to refugia, serving as key conservation focus.The findings obtained for the study system as well as the advantages, limitations and potentials of the model presented here are further discussed in the General Discussion. In summary, the results indicate that 1) process-based demographic models for range dynamics can be fitted to data; 2) demographic processes improve species distribution models; 3) different species are subject to different processes and respond differently to environmental change and exploitation; 4) density regulation type and Allee effects should be considered when investigating range dynamics of species; 5) the consequences of wildflower harvesting, habitat loss and climate change could be disastrous for some species, but impacts vary depending on demographic properties; 6) wildflower harvesting impacts varies over spatial scale; 7) The effects of habitat loss and climate change are not always additive. / Das Ziel dieser Studie bestand daher darin, prozess-basierte Modelle zu entwickeln, die mit Daten zur Abundanz von Arten parametrisiert werden können. Die außergewöhnlich gut erforschten Proteaceen der südafrikanischen Kapregion (CFR), für die ein umfangreicher Datensatz zur Verfügung steht, stellen ein sehr geeignetes Untersuchungssystem zur Erstellung derartiger prozess-basierter Modelle dar. In Kapitel 1 beschreibe ich ein prozess-basiertes Modell für die Verbreitungsdynamik sowie die Methoden zur Parametrisierung des Modells mit Daten zu Abundanzverteilungen. Das Modell umfasst ein räumlich-explizites demographisches Modul und ein Beobachtungsmodul. Das demographische Modul verbindet artspezifische Habitatmodelle, die das geeignete Habitat beschreiben, und prozess-basierte demographische Modelle, die die lokale Dynamik und die Windausbreitung von Samen umfassen. Nach der Überprüfung der Parametrisierungs¬methoden mit simulierten Daten, wende ich die Modelle auf acht Proteaceenarten mit unterschiedlichen demographischen Eigenschaften an. Außerdem untersuche ich die Rolle von positiver (Allee-Effekte) und negativer Dichte-Abhängigkeit. Die Ergebnisse zeigen, dass Allee-Effekte und überkompensatorische Dynamik für viele Arten tatsächlich eine Rolle spielen. Der Großteil der geschätzten Parameter stimmt quantitativ mit unabhängigen Daten und beschreibt erfolgreich, wie die Abundanzverteilung aus der Bewegung und Interaktion der Individuen entsteht. Die vorgestellten Methoden scheinen daher zur Untersuchung von Ungleichgewichtsszenarien geeignet, die die Ernte von Infloreszenzen in Wildbeständen (Kapitel 2) und Umweltwandel (Kapitel 3) einschließen. In Kapitel 2 untersuche ich die Effekte der Ernte von Infloreszenzen in Wildbeständen. Das Kapitel beinhaltet eine Sensitivitätsanalyse über mehrere räumliche Skalen sowie demographische Eigenschaften. Darauf folgend wurden die Effekte der Ernte anhand von drei realen Arten untersucht. Die Reaktion der Pflanzen auf die Ernte zeigte ein Verhalten mit abrupten Schwellenwerten. Die durch die Ernte am stärksten gefährdeten Arten zeichneten sich durch kurze Samenausbreitungsdistanzen, starke Allee Effekte, geringe maximale Reproduktionsrate, hohe Mortalität und hohe lokale Aussterbewahrscheinlichkeit aus. Die Betrachtung größerer räumlicher Skalen wirkte sich trotz schärferer Grenzwerte positiv auf die Reaktion der Arten aus. Die drei untersuchten realen Arten konnten sehr geringe bis mittlere nachhaltige Ernteraten ertragen. Zusammenfassend lässt sich sagen, dass Kenntnisse über die Demographie des Untersuchungssystems und die umsichtige Identifizierung der zu betrachtenden räumlichen Skala zu einer besseren Einschätzung der Ernteintensität und der Naturschutzziele führen sollten. In Kapitel 3 wird die Reaktion der Arten auf vergangene Habitatverluste und zukünftigen Klimawandel sowie die Interaktion der beiden untersucht. Der Klimawandel wirkte sich dabei vornehmlich negativ auf die Größe des Verbreitungsgebiets und die Ausnutzung des potentiellen Habitats (‚Range Filling’) aus, wobei es zu einer Verschiebung des Habitats ohne erfolgreiche Kolonisierung kam. Der Habitatverlust reduzierte vor allem die lokalen Abundanzen. Die meisten Arten wurden vor allem durch das Szenario mit beiden Klimawandel und Habitatsverlust stark beeinträchtigt. Der negative Effekt war allerdings geringer als nach einer einfachen Aufsummierung der Einzeleffekte zu erwarten wäre. Dies erklärt sich aus einer Verschiebung des Verbreitungsgebiets der Arten in Regionen, in denen es in der Vergangenheit zu geringeren Habitatverlusten kam. Die Größe des Verbreitungsgebiets wurde am besten durch die Stärke des Umweltwandels vorhergesagt, wogegen das Range Filling und die lokalen Abundanzen hauptsächlich von den demographischen Eigenschaften abhingen. Aus diesen Ergebnissen lässt sich schließen, dass Abschätzungen des Aussterbensrisikos unter Umweltwandel demographische Eigenschaften einbeziehen sollten. Die meisten überlebenden Populationen waren auf Refugien reduziert, die im Fokus der Naturschutzmaßnahmen stehen sollten. Zusammenfassend zeigen die Ergebnisse, dass 1) prozess-basierte demographische Modelle für die Verbreitungsdynamik von Arten mit Daten parametrisierbar sind; 2) die Einbeziehung demographischer Prozesse die Modelle für die Verbreitung von Arten verbessert; 3) verschiedene Arten von unterschiedlichen Prozessen beeinflusst werden und unterschiedlich auf Umweltwandel und Beerntung reagieren; 4) Dichteregulierung und Allee-Effekte bei der Untersuchung der Verbreitungsdynamik von Arten berücksichtigt werden sollten; 5) die Ernte von Infloreszenzen in Wildbeständen, sowie Habitatverlust und Klimawandel für manche Arten katastrophale Folgen haben können, deren Effekte aber von den demographischen Eigenschaften abhängen; 6) der Einfluss der Beerntung in Abhängigkeit von der betrachteten räumlichen Skala variiert; 7) die Effekte von Habitatverlust und Klimawandel nicht additiv sind.

Submitted by Cássia Santos (cassia.bcufg@gmail.com) on 2014-11-11T12:06:48Z No. of bitstreams: 2 Tese Geiziane Tessarolo - 2014.pdf: 5275889 bytes, checksum: fb092b496eb6eae85e89c28d423c44d9 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Jaqueline Silva (jtas29@gmail.com) on 2014-11-17T15:10:55Z (GMT) No. of bitstreams: 2 Tese Geiziane Tessarolo - 2014.pdf: 5275889 bytes, checksum: fb092b496eb6eae85e89c28d423c44d9 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-11-17T15:10:55Z (GMT). No. of bitstreams: 2 Tese Geiziane Tessarolo - 2014.pdf: 5275889 bytes, checksum: fb092b496eb6eae85e89c28d423c44d9 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2014-04-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Aim Species Distribution Models (SDM) can be used to predict the location of unknown populations from known species occurrences. It follows that how the data used to calibrate the models are collected can have a great impact on prediction success. We evaluated the influence of different survey designs and their interaction with the modelling technique on SDM performance. Location Iberian Peninsula Methods We examine how data recorded using seven alternative survey designs (random, systematic, environmentally stratified by class and environmentally stratified using p-median, biased due to accessibility, biased by human density aggregation and biased towards protected areas) could affect SDM predictions generated with nine modelling techniques (BIOCLIM, Gower distance, Mahalanobis distance, Euclidean distance, GLM, MaxEnt, ENFA and Random Forest). We also study how sample size, species’ characteristics and modelling technique affected SDM predictive ability, using six evaluation metrics. Results Survey design has a small effect on prediction success. Characteristics of species’ ranges rank highest among the factors affecting SDM results: the species with lower relative occurrence area (ROA) are predicted better. Model predictions are also improved when sample size is large. Main conclusions The species modelled – particularly the extent of its distribution – are the largest source of influence over SDM results. The environmental coverage of the surveys is more important than the spatial structure of the calibration data. Therefore, climatic biases in the data should be identified to avoid erroneous conclusions about the geographic patterns of species distributions. / Aim Species Distribution Models (SDM) can be used to predict the location of unknown populations from known species occurrences. It follows that how the data used to calibrate the models are collected can have a great impact on prediction success. We evaluated the influence of different survey designs and their interaction with the modelling technique on SDM performance. Location Iberian Peninsula Methods We examine how data recorded using seven alternative survey designs (random, systematic, environmentally stratified by class and environmentally stratified using p-median, biased due to accessibility, biased by human density aggregation and biased towards protected areas) could affect SDM predictions generated with nine modelling techniques (BIOCLIM, Gower distance, Mahalanobis distance, Euclidean distance, GLM, MaxEnt, ENFA and Random Forest). We also study how sample size, species’ characteristics and modelling technique affected SDM predictive ability, using six evaluation metrics. Results Survey design has a small effect on prediction success. Characteristics of species’ ranges rank highest among the factors affecting SDM results: the species with lower relative occurrence area (ROA) are predicted better. Model predictions are also improved when sample size is large. Main conclusions The species modelled – particularly the extent of its distribution – are the largest source of influence over SDM results. The environmental coverage of the surveys is more important than the spatial structure of the calibration data. Therefore, climatic biases in the data should be identified to avoid erroneous conclusions about the geographic patterns of species distributions.

Orientador: Célio F.B. Haddad / Resumo: Na última década houve um grande desenvolvimento nos Modelos de Distribuição de Espécies (MDE), com diversas aplicações na conservação da biodiversidade. No entanto, apesar dos avanços recentes, a seleção de variáveis preditoras tem sido relativamente negligenciada na construção dos MDE. Este procedimento deveria ser um dos passos cruciais do processo de modelagem, já que as variáveis preditoras estão relacionadas diretamente à capacidade dos modelos de capturar os requisitos ambientais das espécies. Neste contexto, os anfíbios são excelentes organismos modelo para avaliar a importância da seleção de variáveis preditoras ecologicamente significativas no MDE. Isto pode trazer avanços para a biogeografia e biologia da conservação, uma vez que os anfíbios são usados como bioindicadores da qualidade ambiental e da integridade de hábitat. A presente tese de doutorado teve como objetivo principal verificar o efeito da utilização de variáveis preditoras ecologicamente significativas no processo de modelagem dos anfíbios e posteriormente aplicar parte deste conhecimento na comunidade de anfíbios do Estado de São Paulo, visando verificar o potencial desta metodologia para identificar áreas de alto valor de riqueza de anfíbios e verificar também o potencial de invasão de Eleutherodactylus jonhstonei, uma espécie de anfíbio invasora registrada para o Estado de São Paulo. No primeiro capítulo avaliamos a importância da seleção de variáveis essenciais ao MDE usando os anfíbios como estudo... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: In the last decade there has been a great development in the Species Distribution Models (SDM), with several applications in conservation planning. However, despite recent advances, the selection of predictor variables has been relatively neglected in the construction of SDM. This methodological approach should be one of the critical steps of the modeling process, as the predictor variables are directly related to the ability of models to capture the environmental requirements of the species. In this context, amphibians are excellent model for assessing the importance of selecting ecologically meaningful variables in the SDM. This methodology may lead to advances in biogeography and conservation biology, since amphibians are used as bioindicators of environmental quality and habitat integrity. The aim of the work was to verify the effect of the use of ecologically meaningful variables in the amphibian modeling process and to apply part of this knowledge to the amphibian community of São Paulo state, checking the potential of this methodology to identify areas of high amphibian richness value and to verify the potential invasion of Eleutherodactylus jonhstonei, an invasive amphibian species registered in São Paulo state. In the first chapter we evaluated the importance of selecting essential variables in SDM using amphibians as a case study. The second chapter deals specifically with the amphibian modeling protocol of São Paulo state. The central focus of this chapter has been... (Complete abstract click electronic access below) / Doutor

<p dir="ltr">The lichen-feeding tiger moth tribe Lithosiini (Erebidae: Arctiinae) represent the largest radiation of invertebrate lichenivory. Caterpillars feed on lichen and as they feed, also sequester lichen polyphenolics, a behavior unique to these insects. The role of these compounds is believed to defend lithosiines against predators as larvae have been found to be protected against predators such as ants and moths to predators such as birds and bats. Experimental testing with controlled diets is necessary to fully make this connection, however little is known about host specifics for lithosiines. Furthermore, although lithosiines are monophyletic, the lack of a fully resolved phylogeny hampers investigation into many of the shallower level relationships, e.g. those among genera and species, within the group.</p><p dir="ltr">I addressed these knowledge gaps using the subtribe Cisthenina. Members of this group have been used to investigate predator-prey interactions and been included in morphological and molecular studies. Thus, while the group still needs attention, there is an ample amount of legacy loci data available for its members. I used these data to investigate the evolutionary relationships at the genus level, but to increase resolution in my analyses I additionally sampled taxa throughout the group with a recently developed anchored hybrid enrichment (AHE) probe set. I combined it with the legacy loci to both increase taxon sampling and resolution. I confirmed that trees made strictly from the legacy loci were unsuccessful and resulted in poorly supported relationships that made little sense. The addition of the AHE data greatly helped resolve relationships, however, there remained areas that were poorly supported and they appear to be genera with only a few loci. Thus, there is still room for improvement, but this offers a way for moving forward in lithosiine research, particularly to involve others who may have limited funding, equipment, and/or personnel and may only be able to afford legacy loci in diverse collaborations.</p><p dir="ltr">As the AHE probe set worked well with genus-level relationships I further attempted to use it in species delimitation of the notorious <i>Hypoprepia fucosa</i>-<i>miniata </i>species complex. Members of this group are varying shades of yellows, oranges and reds and have a convoluted taxonomic history. I gathered and organized over 4,000 specimens and using the AHE probe set found support for five distinct species. Interestingly, I used other morphological characters such as genitalia, but found no differences between species and a large amount of intraspecific variation. This suggests other courtship behaviors may be present and external morphology, i.e., color patterns, remain the best way to identify species. As part of this I am describing a new species and raising one from subspecies and as species are now readily distinguishable, they can be used for further investigations into lithosiines.</p><p dir="ltr">I used a member of this complex, <i>H</i>. <i>fucosa</i> to then evaluate the use of species distribution models (SDMs) to better understand their niche and how it relates to plausible lichen hosts. I evaluated 17 lichen species from two lichen genera, <i>Physcia </i>(13 species) and <i>Myelochro</i><i>a </i>(4 species)<i>. </i>These genera were selected based on previous feeding assays and the metabolites found in them have also been found in <i>H</i>. <i>fucosa </i>further suggesting caterpillars may feed on them. SDMs typically only use environmental factors to define and predict species niches. I compared the niches described by traditional SDMs to assess how similar they were, but I also investigated the use of lichens as biotic factors in the models. I assessed the influence each lichen had on the moth’s distribution found the niche of every lichen to be significantly different than that of the moth and their inclusion in SDMs of <i>H</i>. <i>fucosa </i>to improve model performance. This suggests <i>H</i>. <i>fucosa </i>caterpillars to be polyphagous, but to have some connection with these lichens. Further investigation with live specimens is needed, but these results support this as an effective way to describe lithosiine niches to better understand lichen feeding.</p>

Prédire les réponses de la biodiversité aux changements climatiques anthropiques est devenu un champ de recherche avec des enjeux scientifiques et sociétaux majeurs. Mon travail de thèse a consisté à évaluer les impacts du réchauffement climatique sur un parasitoïde de drosophiles, Leptopilina boulardi, à une petite échelle géographique, le sud-est de la France. L'objectif était non seulement d'examiner l'évolution de la distribution du parasitoïde en réponse à une hausse des températures qu'il fallait préciser à cette échelle géographique, mais aussi d'appréhender les adaptations mises en place dans la zone de progression de l'espèce. Dans un premier temps, l'analyse de données d'échantillonnages et de données météorologiques m'ont permis de mettre en évidence une rapide expansion de l'aire de répartition du parasitoïde vers le nord, à un taux moyen de 90km/décennie, simultanément à une augmentation moyenne de la température de 1,57°C ces 30 dernières années, dans l'aire d'étude. Après avoir identifié les principaux facteurs environnementaux, structurant la répartition spatiale de L. boulardi, j'ai modélisé sa distribution potentielle dans le sud-est de la France, sous conditions climatiques actuelles et pour 2050, pour deux scénarios d'émission de CO2. En 2050, la distribution géographique de L. boulardi devrait considérablement s'étendre vers le nord sous l'effet des changements climatiques. Ensuite, en mesurant plusieurs traits d'histoire de vie selon 4 régimes thermiques fluctuants, j'ai montré que les populations de L. boulardi situées en limite d'aire de répartition sont génétiquement différenciées de celles situées dans l'aire centrale de répartition. Le fait que les populations marginales aient une valeur sélective plus importante à faible température suggère une adaptation locale des parasitoïdes dans la zone de progression de l'aire de répartition. La dernière partie de ce travail de thèse a pour objectif de mieux comprendre le processus de colonisation de L. boulardi. Pour cela, j'ai entrepris le développement de marqueurs RAD-sequencing sur 15 populations de cette espèce, distribuées le long d'un cline de latitude dans le sud-est de la France. Les nombreuses données issues du séquençage Illumina me permettront de connaître la structuration génétique de ces populations. L'ensemble de ces résultats obtenus au cours de ma thèse révèlent la force avec laquelle les changements climatiques peuvent impacter les espèces, principalement celles de haut niveau trophique, en provoquant des changements très rapide de distribution et des modifications génotypiques et phénotypiques permettant une meilleure adaptation locale / Predicting biodiversity responses to anthropogenic climate change has become a field of research with major scientific and societal issues. The main goal of my thesis was to evaluate the impacts of global warming on a Drosophila parasitoid, Leptopilina boulardi, at a small geographical scale, the South-East of France. The aim was not only to examine the change in the distribution of the parasitoid in response to rising temperatures, but also to understand the adaptations associated with this change. First, the analysis of insect sampling and meteorological data allowed me to demonstrate a rapid expansion of the parasitoid range to the north with an average rate of 90km/decade as well as a simultaneous temperature increase of 1.57°C on average over the past 30 years in the studied area. Following the identification of the main environmental factors structuring the spatial distribution of L. boulardi, I fitted a model predicting its potential distribution in the south-east of France, under the current climate and in 2050, for two CO2 emission scenarios. In 2050, the geographical distribution of L. boulardi should significantly extend northward as a result of climate change. Then, by measuring several life history traits under four fluctuating temperature regimes, I have shown that populations of L. boulardi located on the border of the range are genetically differentiated from those in the central range. The fact that marginal populations have a greater fitness at low temperature suggests local adaptation of parasitoids in the area of progression of range. The last part of this thesis aimed to better understand the process of colonization of L. boulardi. For this, I undertook the development of RAD-sequencing markers to genotype 15 populations of this species distributed along a cline of latitude in the southeast of France. Numerous data from Illumina sequencing will allow me to characterize the genetic structure of the populations. All the results obtained in my thesis highlight the force with which climate change may impact species, in particular those of high trophic level, causing rapid changes in distribution along with genotypic and phenotypic changes underlying local adaptation