A modelagem de distribuição de espécies tem como objetivo induzir um modelo para predizer a distribuição potencial de uma dada espécie. O modelo é projetado em um mapa de distribuição potencial que representa a probabilidade da presença da espécie em cada ponto. Esse processo de indução está relacionado com a estimativa do nicho fundamental da espécie, através da busca por relações entre dados georreferenciados de ocorrência da espécie e variáveis ambientais. Vários algoritmos de modelagem podem ser utilizados nessa tarefa. Oferecer diversos algoritmos pode tornar as ferramentas de modelagem mais completas. Porém, surge uma questão importante: qual algoritmo de modelagem escolher? Essa questão está relacionada com o desempenho preditivo das técnicas implementadas pelos algoritmos. Nesse contexto, o objetivo principal do trabalho foi organizar e especificar um método de análise de desempenho preditivo dos algoritmos de modelagem de distribuição de espécies. Através do método proposto é possível ter uma visão completa, estruturada e sistemática das etapas previstas em projetos de análise de desempenho preditivo dos algoritmos. O método pode ser utilizado como referência em estudos de validação de novos algoritmos, de comparação entre técnicas e na seleção de um ou mais algoritmos de modelagem. Como estudo de caso, o método proposto foi adotado nos testes de validação de um algoritmo baseado em Redes Neurais, desenvolvido e integrado ao framework openModeller, através da comparação com outros algoritmos já utilizados na modelagem. Além da própria validação, os testes tiveram como objetivo demonstrar a aplicabilidade do método. Os resultados mostraram que o algoritmo de Redes Neurais apresentou desempenho semelhante ao desempenho dos demais algoritmos, tendo sido, portanto, validado como adequado à tarefa de modelagem. Ainda no contexto da pesquisa, um algoritmo baseado na técnica de amostragem denominada Jackknife foi integrado ao openModeller, para aplicação na etapa de pré-análise. Testes relacionados com o tempo de execução foram realizados e uma versão paralela desse algoritmo foi desenvolvida. / The species distribution modeling aim is to induce a model to predict the potential distribution of a given species. The model is projected onto a potential distribution map that represents the presence probability of the species at each point. This induction process is related to the fundamental niche estimation of the species, through the search for relationships between georeferenced data of species occurrence and environmental variables. Several modeling algorithms can be used for this task. Providing different algorithms can make the modeling tools more complete. However, an important question arises: what modeling algorithm to choose? This issue is related to the predictive performance of the techniques implemented by the algorithms. In this context, the aim of this research was to organize and to specify a predictive performance analysis method of the species distribution modeling algorithms. Through the proposed method, it is possible to have a complete and structured vision of the steps in the planning of predictive performance analysis of the algorithms. The method may be used as a reference in validation studies of new algorithms, in comparison among techniques and in choosing one or more modeling algorithms. As a case study, the proposed method was adopted in the validation tests of an algorithm based on Neural Networks, developed and integrated into the openModeller framework, which was compared with other algorithms already used in modeling. Besides the validation itself, the tests intended to demonstrate the applicability of the method. The results showed that the Neural Networks algorithm presented similar performance to those of other algorithms and was validated as adequate to the modeling task. Still in the research context, an algorithm based on a sampling technique called the Jackknife was integrated to the openModeller, to be applied in the pre-analysis step. Tests related to the running time were carried out and a parallel version of this algorithm was developed.

O tamanduá-bandeira (Myrmecophaga tridactyla) se distribui amplamente ao longo da região Neotropical, porém é provável que esteja extinto da maior parte de sua extensão original, notadamente na América Central e nos limites austrais de sua distribuição. O táxon está ameaçado de extinção globalmente (IUCN) e também em âmbito nacional. Embora historicamente a espécie ocorra em todos os biomas brasileiros, hoje é considerada extinta nos Pampas, quase extinta na Mata Atlântica, sendo que na Caatinga sua presença necessita de confirmação e no Cerrado suas populações vem sofrendo drásticas reduções. Atualmente não há estudos de revisão da distribuição da espécie nos biomas brasileiros, tão pouco foi avaliado se as áreas mais adequadas à espécie estão sendo protegidas e o conhecimento existente é insuficiente para adotar estratégias de conservação adequadas. Dessa maneira, o presente estudo teve como principal objetivo modelar a distribuição potencial e atual do tamanduá-bandeira no Brasil e nos biomas brasileiros, a fim de identificar quais variáveis preditoras melhor explicam a ocorrência da espécie em diferentes escalas. Além disso, a partir dos modelos de distribuição atual, os biomas foram avaliados quanto à adequabilidade ambiental (i.e. probabilidade de presença) e foram realizadas uma análise de lacunas e a identificação de áreas prioritárias para a conservação. A distribuição potencial do tamanduá-bandeira foi melhor explicada em escala continental, por variáveis bioclimáticas (sazonalidade de temperatura e precipitação) e topográficas (altitude), enquanto que a distribuição atual foi bem explicada nas duas escalas, por variáveis de uso e cobertura da terra (porcentagens de cobertura arbórea, de silvicultura e de cana-de-açúcar). O Cerrado foi o bioma de maior adequabilidade ambiental à espécie, seguido da Amazônia, Pantanal, Mata Atlântica e Caatinga, sendo que não foram obtidos registros recentes para os Pampas. Menos de 10% da distribuição atual do tamanduá-bandeira no Cerrado e Pantanal encontra-se protegida por Unidades de Conservação, existindo uma lacuna parcial de conservação. Áreas prioritárias para a espécie incluem um corredor central no Cerrado, grande parte do Pantanal e áreas de transição (ecótonos) com outros biomas. Os resultados obtidos neste estudo permitiram preencher lacunas de conhecimento acerca da distribuição do tamanduá-bandeira, bem como dar suporte para o planejamento de sua conservação. / The giant anteater (Myrmecophaga tridactyla) is widely distributed throughout the Neotropical region, but is probably extinct from most of its range, notably in Central America and the southern limits of its distribution. The species is listed as Vulnerable on the IUCN and national Red Lists. Although historically present in all Brazilian biomes, there are no studies reviewing its distribution, nor has it been evaluated if the Brazilian federal conservation units are protecting the areas most suitable to the species. Thus, the aim of this study was to model the potential and current distribution of the giant anteater in Brazil and Brazilian biomes, to identify which predictor variables best explain the occurrence of the species at different scales. Current distribution models were used to evaluate the biomes environmental suitability (i.e. probability of presence) and a gap analyses were performed. Also, priority areas for conservation were identified. The potential distribution of the anteater was better explained on a continental scale by bioclimatic (seasonality of temperature and precipitation) and topographic (altitude) variables, while the current distribution was well predicted in both scales, by land cover variables (percentages of tree cover, silviculture, and sugarcane). The Cerrado was the biome of greater environmental suitability to the species, followed by the Amazon, the Pantanal, the Atlantic Forest and the Caatinga. No recent records were obtained for the Pampas. Conservation units protect less than 10% of the current distribution of the giant anteater in the Cerrado and Pantanal. Priority areas for the species include a central corridor in the Cerrado, much of the Pantanal and ecotones. The results obtained in this study helped to fill knowledge gaps on the distribution of the giant anteater in Brazil, supporting actions for its conservation.

A modelagem de distribuição de espécies tem como objetivo induzir um modelo para predizer a distribuição potencial de uma dada espécie. O modelo é projetado em um mapa de distribuição potencial que representa a probabilidade da presença da espécie em cada ponto. Esse processo de indução está relacionado com a estimativa do nicho fundamental da espécie, através da busca por relações entre dados georreferenciados de ocorrência da espécie e variáveis ambientais. Vários algoritmos de modelagem podem ser utilizados nessa tarefa. Oferecer diversos algoritmos pode tornar as ferramentas de modelagem mais completas. Porém, surge uma questão importante: qual algoritmo de modelagem escolher? Essa questão está relacionada com o desempenho preditivo das técnicas implementadas pelos algoritmos. Nesse contexto, o objetivo principal do trabalho foi organizar e especificar um método de análise de desempenho preditivo dos algoritmos de modelagem de distribuição de espécies. Através do método proposto é possível ter uma visão completa, estruturada e sistemática das etapas previstas em projetos de análise de desempenho preditivo dos algoritmos. O método pode ser utilizado como referência em estudos de validação de novos algoritmos, de comparação entre técnicas e na seleção de um ou mais algoritmos de modelagem. Como estudo de caso, o método proposto foi adotado nos testes de validação de um algoritmo baseado em Redes Neurais, desenvolvido e integrado ao framework openModeller, através da comparação com outros algoritmos já utilizados na modelagem. Além da própria validação, os testes tiveram como objetivo demonstrar a aplicabilidade do método. Os resultados mostraram que o algoritmo de Redes Neurais apresentou desempenho semelhante ao desempenho dos demais algoritmos, tendo sido, portanto, validado como adequado à tarefa de modelagem. Ainda no contexto da pesquisa, um algoritmo baseado na técnica de amostragem denominada Jackknife foi integrado ao openModeller, para aplicação na etapa de pré-análise. Testes relacionados com o tempo de execução foram realizados e uma versão paralela desse algoritmo foi desenvolvida. / The species distribution modeling aim is to induce a model to predict the potential distribution of a given species. The model is projected onto a potential distribution map that represents the presence probability of the species at each point. This induction process is related to the fundamental niche estimation of the species, through the search for relationships between georeferenced data of species occurrence and environmental variables. Several modeling algorithms can be used for this task. Providing different algorithms can make the modeling tools more complete. However, an important question arises: what modeling algorithm to choose? This issue is related to the predictive performance of the techniques implemented by the algorithms. In this context, the aim of this research was to organize and to specify a predictive performance analysis method of the species distribution modeling algorithms. Through the proposed method, it is possible to have a complete and structured vision of the steps in the planning of predictive performance analysis of the algorithms. The method may be used as a reference in validation studies of new algorithms, in comparison among techniques and in choosing one or more modeling algorithms. As a case study, the proposed method was adopted in the validation tests of an algorithm based on Neural Networks, developed and integrated into the openModeller framework, which was compared with other algorithms already used in modeling. Besides the validation itself, the tests intended to demonstrate the applicability of the method. The results showed that the Neural Networks algorithm presented similar performance to those of other algorithms and was validated as adequate to the modeling task. Still in the research context, an algorithm based on a sampling technique called the Jackknife was integrated to the openModeller, to be applied in the pre-analysis step. Tests related to the running time were carried out and a parallel version of this algorithm was developed.

Anthropogenic climate warming is predicted to accelerate over the next century, with potentially dramatic consequences for wildlife. It is important to understand as well as possible how different organisms will respond to this stress. This project seeks to gain a better mechanistic understanding of the thermal biology of the Glanville Fritillary butterfly (Melitaea cinxia) at the latitudinal and elevational extremes of its range. Investigation of the temperatures at which adult butterflies took spontaneous flight revealed a significant difference between populations from the elevational extremes, with insects from high elevation taking flight at lower thoracic temperatures than those from low elevation. Contrary to expectation, there was no systematic effect of latitude on takeoff temperature. If these measures represent adaptation to climate, then these effects are not simple and the influences of elevation and latitude are not the same. Investigation of thermal tolerance across all life cycle stages found no difference in larval performance between the populations tested. There was however an effect of treatment. This suggests that in M. cinxia, even populations from different extremes of the range may not differ in their thermal tolerance. The effect of treatment suggests that there is temperature-induced plasticity. The adaptive significance of this has been explored to some extent. Investigation of heat shock protein expression between the latitudinal extremes finds no difference in Hsp21.4 expression when exposed to heat stress, however both Hsp20.4 and Hsp90 were upregulated in response to heat stress. For Hsp20.4, there were significant differences in expression between the populations. Finally, a species distribution model using maximum entropy techniques was conducted for M. cinxia, predicting both the current and future (2100) distributions of the species. The model closely matches the known current distribution, and predicts a clear northward range shift in response to climate change. / text

No description available.

abstract: Climate and land use change are projected to threaten biodiversity over the coming century. However, the combined effects of these threats on biodiversity and the capacity of current conservation networks to protect species' habitat are not well understood. The goals of this study were to evaluate the effect of climate change and urban development on vegetation distribution in a Mediterranean-type ecosystem; to identify the primary source of uncertainty in suitable habitat predictions; and to evaluate how well conservation areas protect future habitat in the Southwest ecoregion of the California Floristic Province. I used a consensus-based modeling approach combining three different species distribution models to predict current and future suitable habitat for 19 plant species representing different plant functional types (PFT) defined by fire-response (obligate seeders, resprouting shrubs), and life forms (herbs, subshurbs). I also examined the response of species grouped by range sizes (large, small). I used two climate models, two emission scenarios, two thresholds, and high-resolution (90m resolution) environmental data to create a range of potential scenarios. I evaluated the effectiveness of an existing conservation network to protect suitable habitat for rare species in light of climate and land use change. The results indicate that the area of suitable habitat for each species varied depending on the climate model, emission scenario, and threshold combination. The suitable habitat for up to four species could disappear from the ecoregion, while suitable habitat for up to 15 other species could decrease under climate change conditions. The centroid of the species' suitable environmental conditions could shift up to 440 km. Large net gains in suitable habitat were predicted for a few species. The suitable habitat area for herbs has a small response to climate change, while obligate seeders could be the most affected PFT. The results indicate that the other two PFTs gain a considerable amount of suitable habitat area. Several rare species could lose suitable habitat area inside designated conservation areas while gaining suitable habitat area outside. Climate change is predicted to be more important than urban development as a driver of habitat loss for vegetation in this region in the coming century. These results indicate that regional analyses of this type are useful and necessary to understand the dynamics of drivers of change at the regional scale and to inform decision making at this scale. / Dissertation/Thesis / M.S. Biology 2012

O tamanduá-bandeira (Myrmecophaga tridactyla) se distribui amplamente ao longo da região Neotropical, porém é provável que esteja extinto da maior parte de sua extensão original, notadamente na América Central e nos limites austrais de sua distribuição. O táxon está ameaçado de extinção globalmente (IUCN) e também em âmbito nacional. Embora historicamente a espécie ocorra em todos os biomas brasileiros, hoje é considerada extinta nos Pampas, quase extinta na Mata Atlântica, sendo que na Caatinga sua presença necessita de confirmação e no Cerrado suas populações vem sofrendo drásticas reduções. Atualmente não há estudos de revisão da distribuição da espécie nos biomas brasileiros, tão pouco foi avaliado se as áreas mais adequadas à espécie estão sendo protegidas e o conhecimento existente é insuficiente para adotar estratégias de conservação adequadas. Dessa maneira, o presente estudo teve como principal objetivo modelar a distribuição potencial e atual do tamanduá-bandeira no Brasil e nos biomas brasileiros, a fim de identificar quais variáveis preditoras melhor explicam a ocorrência da espécie em diferentes escalas. Além disso, a partir dos modelos de distribuição atual, os biomas foram avaliados quanto à adequabilidade ambiental (i.e. probabilidade de presença) e foram realizadas uma análise de lacunas e a identificação de áreas prioritárias para a conservação. A distribuição potencial do tamanduá-bandeira foi melhor explicada em escala continental, por variáveis bioclimáticas (sazonalidade de temperatura e precipitação) e topográficas (altitude), enquanto que a distribuição atual foi bem explicada nas duas escalas, por variáveis de uso e cobertura da terra (porcentagens de cobertura arbórea, de silvicultura e de cana-de-açúcar). O Cerrado foi o bioma de maior adequabilidade ambiental à espécie, seguido da Amazônia, Pantanal, Mata Atlântica e Caatinga, sendo que não foram obtidos registros recentes para os Pampas. Menos de 10% da distribuição atual do tamanduá-bandeira no Cerrado e Pantanal encontra-se protegida por Unidades de Conservação, existindo uma lacuna parcial de conservação. Áreas prioritárias para a espécie incluem um corredor central no Cerrado, grande parte do Pantanal e áreas de transição (ecótonos) com outros biomas. Os resultados obtidos neste estudo permitiram preencher lacunas de conhecimento acerca da distribuição do tamanduá-bandeira, bem como dar suporte para o planejamento de sua conservação. / The giant anteater (Myrmecophaga tridactyla) is widely distributed throughout the Neotropical region, but is probably extinct from most of its range, notably in Central America and the southern limits of its distribution. The species is listed as Vulnerable on the IUCN and national Red Lists. Although historically present in all Brazilian biomes, there are no studies reviewing its distribution, nor has it been evaluated if the Brazilian federal conservation units are protecting the areas most suitable to the species. Thus, the aim of this study was to model the potential and current distribution of the giant anteater in Brazil and Brazilian biomes, to identify which predictor variables best explain the occurrence of the species at different scales. Current distribution models were used to evaluate the biomes environmental suitability (i.e. probability of presence) and a gap analyses were performed. Also, priority areas for conservation were identified. The potential distribution of the anteater was better explained on a continental scale by bioclimatic (seasonality of temperature and precipitation) and topographic (altitude) variables, while the current distribution was well predicted in both scales, by land cover variables (percentages of tree cover, silviculture, and sugarcane). The Cerrado was the biome of greater environmental suitability to the species, followed by the Amazon, the Pantanal, the Atlantic Forest and the Caatinga. No recent records were obtained for the Pampas. Conservation units protect less than 10% of the current distribution of the giant anteater in the Cerrado and Pantanal. Priority areas for the species include a central corridor in the Cerrado, much of the Pantanal and ecotones. The results obtained in this study helped to fill knowledge gaps on the distribution of the giant anteater in Brazil, supporting actions for its conservation.

Piggvarens (Scophthalmus maximus) val av habitat i Östersjön har modellerats utifrån provfiskedata och miljövariabler. Vid totalt 435 stationer i Östersjön har data samlats in i form av provfiske, CTD-mätningar (konduktivitet, temperatur och djup) och videofilmer. Genom att analysera videofilmerna från havsbotten i Östersjön har den klassificerats efter fyra olika förklaringsvariabler: täckningsgrad mjukbotten, strukturbildande växter, övriga alger och täckningsgrad blåmusslor. Ytterligare sex förklaringsvariabler har samlats in från mätningar och befintliga kartor: bottensalinitet, bottentemperatur, djup, siktdjup, vågexponering och bottenlutning. Dessa tio förklaringsvariabler har använts i tio olika enskilda statistiska modelleringsmetoder med förekomst/icke-förekomst av piggvar som responsvariabel. Nio av tio modeller visade på bra resultat (AUC > 0,7) där CTA (Classification Tree Analysis) och GBM (Global Boosting Model) hade bäst resultat (AUC > 0,9). Genom att kombinera modeller med bra resultat på olika sätt skapades sex ensemblemodeller för att minska varje enskild modells svagheter. Ensemblemodellerna visade tydligt fördelarna med denna typ av modellering då de gav ett mycket bra resultat (AUC > 0,949). Den sämsta ensemblemodellen var markant bättre än den bästa enskilda modellen. Resultaten från modellerna visar att största sannolikheten för piggvarsförekomst i Östersjön är vid grunt (< 20 meter) och varmt (> 10 oC) vatten med hög vågexponering (> 30 000 m²/s). Dessa tre variabler var de med högst betydelse för modellerna. Täckningsgrad mjukbotten och de två växtlighetsvariablerna från videoanalyserna var de tre variabler som hade lägst påverkan på piggvarens val av habitat. Med en högre kvalitet på videofilmerna hade de variablerna kunnat klassificeras i mer specifika grupper vilket eventuellt gett ett annat resultat. Generellt visade modellerna att denna typ av habitatmodellering med provfiske och miljödata både är möjlig att utföra. / The turbots’ (Scophthalmus maximus) selection of habitat in the Baltic Sea has been modeled on the basis of fish survey data and environmental variables. At a total of 435 stations in the Baltic Sea, data was collected in the form of fish survey data, CTD (Conductivity, Temperature and Depth) measurements and videos. By analyzing the videos from the seabed of the Baltic Sea, four different explanatory variables have been classified: coverage of soft bottom, structure-forming plants, other algae and coverage of mussels. Another six explanatory variables have been collected from measurements and existing rasters: salinity, temperature, depth, water transparency, wave exposure and the bottom slope. These ten explanatory variables have been used in ten different species distribution modeling methods with the presence/absence of turbot as a response variable. Nine out of ten models showed good results (AUC > 0.7) where the CTA (Classification Tree Analysis) and GBM (Global Boosting Model) performed the best (AUC > 0.9). By combining the models with good performance in six different ensemble models each individual models’ weaknesses were decreased. The ensemble models clearly showed strength as they gave a very good performance (AUC > 0.94). The worst ensemble model was significantly better than the best individual model. The results of the models show that the largest probability of occurrence of turbot in the Baltic Sea is in shallow (< 20 m) and warm (> 10 ° C) water with high wave exposure (> 30,000 m²/s). These three variables were those with the highest significance for the models. Coverage of soft bottom and the two vegetation variables, from the video analyzes, had the lowest impact on the turbots’ choice of habitat. A higher quality of the videos would have made it possible to classify these variables in more specific groups which might have given a different result. Generally, the models showed that this type of modeling of habitat is possible to perform with fish survey and environmental monitoring data and generates useful results.

No description available.

Predictions of species’ ranges from distribution modeling are often used to inform marine management and conservation efforts, but few studies justify the model selected or quantify the uncertainty of the model predictions in a spatial manner. This thesis employs a multi-model, multi-area SDM analysis to develop a higher certainty in the predictions where similarities exist across models and areas. Partial dependence plots and variable importance rankings were shown to be useful in producing further certainty in the results. The modeling indicated that glass sponges (Hexactinellida) are most likely to exist within the North Pacific Ocean where alkalinity is greater than 2.2 μmol l-1 and dissolved oxygen is lower than 2 ml l-1. Silicate was also found to be an important environmental predictor. All areas, except Hecate Strait, indicated that high glass sponge probability of presence coincided with silicate values of 150 μmol l-1 and over, although lower values in Hecate Strait confirmed that sponges can exist in areas with silicate values of as low as 40 μmol l-1. Three methods of showing spatial uncertainty of model predictions were presented: the standard error (SE) of a binomial GLM, the standard deviation of predictions made from 200 bootstrapped GLM models, and the standard deviation of eight commonly used SDM algorithms. Certain areas with few input data points or extreme ranges of predictor variables were highlighted by these methods as having high uncertainty. Such areas should be treated cautiously regardless of the overall accuracy of the model as indicated by accuracy metrics (AUC, TSS), and such areas could be targeted for future data collection. The uncertainty metrics produced by the multi-model SE varied from the GLM SE and the bootstrapped GLM. The uncertainty was lowest where models predicted low probability of presence and highest where the models predicted high probability of presence and these predictions differed slightly, indicating high confidence in where the models predicted the sponges would not exist.