The home range encompasses an animal’s movements as it goes about its normal activity, and several home range estimators have been developed. I evaluated the performance of the Minimum Convex Polygon, Bivariate Normal, and several kernel home range estimators in a geographical information system environment using simulations and a large database of O. pumilio mark-recapture locations. A fixed 90% kernel estimator using Least-Square Cross-Validation (to select the bandwidth) outperformed other methods of estimating home range size and was effective with relatively few capture points. Home range size, core area size, intrasexual overlap, and movement rates among coordinates were higher in female frogs than in male frogs. These measures likely reflect behavioral differences related to territoriality (males only) and parental care (both sexes). The simple Biological Index of Vagility (BIV) generated movement values that scaled well with home range size while revealing more information than home range estimates alone.

There are two main parts in this thesis. The first one concerns valvometry, which is here the study of the distance between both parts of the shell of an oyster, over time. The health status of oysters can be characterized using valvometry in order to obtain insights about the quality of their environment. We consider that a renewal process with four states underlies the behaviour of the studied oysters. Such a hidden process can be retrieved from a valvometric signal by assuming that some probability density function linked with this signal, is bimodal. We then compare several estimators which take this assumption into account, including kernel density estimators.In another chapter, we compare several regression approaches, aiming at analysing transcriptomic data. To understand which explanatory variables have an effect on gene expressions, we apply a multiple testing procedure on these data, through the linear model FAMT. The SIR method may find nonlinear relations in such a context. It is however more commonly used when the response variable is univariate. A multivariate version of SIR was then developed. Procedures to measure gene expressions can be expensive. The sample size n of the corresponding datasets is then often small. That is why we also studied SIR when n is less than the number of explanatory variables p.

Deux parties principales composent cette thèse. La première d'entre elles est consacrée à la valvométrie, c'est-à-dire ici l'étude de la distance entre les deux parties de la coquille d'une huître au cours du temps. La valvométrie est utilisée afin de déterminer si de tels animaux sont en bonne santé, pour éventuellement tirer des conclusions sur la qualité de leur environnement. Nous considérons qu'un processus de renouvellement à quatre états sous-tend le comportement des huîtres étudiées. Afin de retrouver ce processus caché dans le signal valvométrique, nous supposons qu'une densité de probabilité reliée à ce signal est bimodale. Nous comparons donc plusieurs estimateurs qui prennent en compte ce type d'hypothèse, dont des estimateurs à noyau.Dans un second temps, nous comparons plusieurs méthodes de régression, dans le but d'analyser des données transcriptomiques. Pour comprendre quelles variables explicatives influent sur l'expression de gènes, nous avons réalisé des tests multiples grâce au modèle linéaire FAMT. La méthode SIR peut être envisagée pour trouver des relations non-linéaires. Toutefois, elle est principalement employée lorsque la variable à expliquer est univariée. Une version multivariée de cette approche a donc été développée. Le coût d'acquisition des données transcriptomiques pouvant être élevé, la taille n des échantillons correspondants est souvent faible. C'est pourquoi, nous avons également étudié la méthode SIR lorsque n est inférieur au nombre de variables explicatives p. / There are two main parts in this thesis. The first one concerns valvometry, which is here the study of the distance between both parts of the shell of an oyster, over time. The health status of oysters can be characterized using valvometry in order to obtain insights about the quality of their environment. We consider that a renewal process with four states underlies the behaviour of the studied oysters. Such a hidden process can be retrieved from a valvometric signal by assuming that some probability density function linked with this signal, is bimodal. We then compare several estimators which take this assumption into account, including kernel density estimators.In another chapter, we compare several regression approaches, aiming at analysing transcriptomic data. To understand which explanatory variables have an effect on gene expressions, we apply a multiple testing procedure on these data, through the linear model FAMT. The SIR method may find nonlinear relations in such a context. It is however more commonly used when the response variable is univariate. A multivariate version of SIR was then developed. Procedures to measure gene expressions can be expensive. The sample size n of the corresponding datasets is then often small. That is why we also studied SIR when n is less than the number of explanatory variables p.

A construção e ampliação de rodovias gera impactos significativos ao meio ambiente. Os principais impactos ao meio biótico são a supressão de vegetação, redução da riqueza e abundância de espécies de fauna como decorrência da fragmentação de habitats e aumento dos riscos de atropelamento de animais silvestres e domésticos. O objetivo geral do trabalho foi identificar padrões espaciais nos atropelamentos de fauna silvestre por espécie (nome popular) utilizando ferramentas de análise espacial e machine learning. Especificamente, buscou-se compreender a relação entre atropelamentos de animais silvestres e variáveis que representam características de uso e cobertura do solo e caracterização da rodovia, tais como formação florestal, corpos d\'água, silvicultura, áreas edificadas, velocidade máxima permitida, volume de tráfego, entre outras. Os atropelamentos de fauna silvestre foram analisados por espécie atropelada, a fim de identificar os padrões espaciais dos atropelamentos específicos para cada espécie. As ferramentas de análise espacial empregadas foram a Função K - para determinar o padrão de distribuição dos registros de atropelamento de fauna, o Estimador de Densidade de Kernel - para gerar estimativas de densidade de pontos sobre a rodovia, a Análise de Hotspots - para identificar os trechos mais críticos de atropelamento de fauna e, por fim, o Self-Organizing Maps (SOM), um tipo de rede neural artificial, que reorganiza amostras de dados n-dimensionais de acordo com a similaridade entre elas. Os resultados das análises de padrões pontuais foram importantes para entender que os pontos de atropelamento possuem padrões de distribuição espacial que variam por espécie. Os eventos ocorrem espacialmente agrupados e não estão homogeneamente distribuídos ao longo da rodovia. De maneira geral, os animais apresentam trechos de maior intensidade de atropelamento em locais distintos. O SOM permitiu analisar as relações entre múltiplas variáveis, lineares e não-lineares, tais como são os dados ecológicos, e encontrar padrões espaciais distintos por espécie. A maior parte dos animais foi atropelada próxima de fragmentos florestais e de corpos d\'água, e distante de cultivo de cana-de-açúcar, silvicultura e área edificada. Porém, uma parte considerável das mortes de animais dos tipos com maior número de atropelamentos ocorreu em áreas com paisagem diversificada, incluindo alta densidade de drenagem, fragmentos florestais, silvicultura e áreas edificadas. / The construction and expansion of roads cause significant impacts on the environment. The main potential impacts to biotic environment are vegetation suppression, reduction of the abundance and richness of species due to forest fragmentation and increase of animal (domestic and wildlife) vehicle collisions. The general objective of this work was to identify spatial patterns in wildlife-vehicle collisions individually per species by using spatial analysis and machine learning. Specifically, the relationship between wildlife-vehicle collisions and variables that represent land use and road characterization features - such as forests, water bodies, silviculture, sugarcane fields, built environment, speed limit and traffic volume - was investigated. The wildlife-vehicle collisions were analyzed per species, in order to identify the spatial patterns for each species separately. The spatial analysis tools used in this study were K-Function - to determine the distribution pattern of roadkill, Kernel Density Estimator (KDE) - to identify the location and intensity of hotspots and hotzones. Self-Organizing Maps (SOM), an artificial neural network (ANN), was selected to reorganize the multi-dimensional data according to the similarity between them. The results of the spatial pattern analysis were important to perceive that the point data pattern varies between species. The events occur spatially clustered and are not uniformly distributed along the highway. In general, wildlife-vehicle collsions have their hotzones in different locations. SOM was able to analyze the relationship between multiple variables, linear and non-linear, such as ecological data, and established distinct spatial patterns per each species. Most of the wildlife was run over close to forest area and water bodies, and distant from sugarcane, silviculture and built environments. But a considerable part of the wildlife-vehicle collisions occurred in areas with diverse landscape, including high density of water bodies, silviculture and built environments.

A construção e ampliação de rodovias gera impactos significativos ao meio ambiente. Os principais impactos ao meio biótico são a supressão de vegetação, redução da riqueza e abundância de espécies de fauna como decorrência da fragmentação de habitats e aumento dos riscos de atropelamento de animais silvestres e domésticos. O objetivo geral do trabalho foi identificar padrões espaciais nos atropelamentos de fauna silvestre por espécie (nome popular) utilizando ferramentas de análise espacial e machine learning. Especificamente, buscou-se compreender a relação entre atropelamentos de animais silvestres e variáveis que representam características de uso e cobertura do solo e caracterização da rodovia, tais como formação florestal, corpos d\'água, silvicultura, áreas edificadas, velocidade máxima permitida, volume de tráfego, entre outras. Os atropelamentos de fauna silvestre foram analisados por espécie atropelada, a fim de identificar os padrões espaciais dos atropelamentos específicos para cada espécie. As ferramentas de análise espacial empregadas foram a Função K - para determinar o padrão de distribuição dos registros de atropelamento de fauna, o Estimador de Densidade de Kernel - para gerar estimativas de densidade de pontos sobre a rodovia, a Análise de Hotspots - para identificar os trechos mais críticos de atropelamento de fauna e, por fim, o Self-Organizing Maps (SOM), um tipo de rede neural artificial, que reorganiza amostras de dados n-dimensionais de acordo com a similaridade entre elas. Os resultados das análises de padrões pontuais foram importantes para entender que os pontos de atropelamento possuem padrões de distribuição espacial que variam por espécie. Os eventos ocorrem espacialmente agrupados e não estão homogeneamente distribuídos ao longo da rodovia. De maneira geral, os animais apresentam trechos de maior intensidade de atropelamento em locais distintos. O SOM permitiu analisar as relações entre múltiplas variáveis, lineares e não-lineares, tais como são os dados ecológicos, e encontrar padrões espaciais distintos por espécie. A maior parte dos animais foi atropelada próxima de fragmentos florestais e de corpos d\'água, e distante de cultivo de cana-de-açúcar, silvicultura e área edificada. Porém, uma parte considerável das mortes de animais dos tipos com maior número de atropelamentos ocorreu em áreas com paisagem diversificada, incluindo alta densidade de drenagem, fragmentos florestais, silvicultura e áreas edificadas. / The construction and expansion of roads cause significant impacts on the environment. The main potential impacts to biotic environment are vegetation suppression, reduction of the abundance and richness of species due to forest fragmentation and increase of animal (domestic and wildlife) vehicle collisions. The general objective of this work was to identify spatial patterns in wildlife-vehicle collisions individually per species by using spatial analysis and machine learning. Specifically, the relationship between wildlife-vehicle collisions and variables that represent land use and road characterization features - such as forests, water bodies, silviculture, sugarcane fields, built environment, speed limit and traffic volume - was investigated. The wildlife-vehicle collisions were analyzed per species, in order to identify the spatial patterns for each species separately. The spatial analysis tools used in this study were K-Function - to determine the distribution pattern of roadkill, Kernel Density Estimator (KDE) - to identify the location and intensity of hotspots and hotzones. Self-Organizing Maps (SOM), an artificial neural network (ANN), was selected to reorganize the multi-dimensional data according to the similarity between them. The results of the spatial pattern analysis were important to perceive that the point data pattern varies between species. The events occur spatially clustered and are not uniformly distributed along the highway. In general, wildlife-vehicle collsions have their hotzones in different locations. SOM was able to analyze the relationship between multiple variables, linear and non-linear, such as ecological data, and established distinct spatial patterns per each species. Most of the wildlife was run over close to forest area and water bodies, and distant from sugarcane, silviculture and built environments. But a considerable part of the wildlife-vehicle collisions occurred in areas with diverse landscape, including high density of water bodies, silviculture and built environments.

Orientador: Edmur Azevedo Pugliesi / Resumo: A expansão urbana vem formando aglomerados populacionais desordenados, o que causa problemas para a administração municipal. A fim de reduzir este tipo de problema, uma das maneiras de reorganizar o território é o policentrismo, conceito que vem sendo aplicado em grande parte da área urbana de diversos países como Estados Unidos, China e países da Europa. O policentrismo pode ser entendido como uma área urbana com pluralidade de centros urbanos. Há duas abordagens para identificar possíveis centralidades: a morfológica e a funcional. Além disso, não foi encontrado quaisquer resultados de trabalhos científicos que tenham utilizado a variável inclinação do relevo nas análises de identificação de centralidades urbanas. Dessa maneira, a variável declividade pode ser um fator impactante na determinação de uma centralidade ou núcleo urbano para localidades brasileiras e que tenha característica similares da área de estudo. O objetivo desse trabalho propõe um estudo para a identificação de centralidades ou a possibilidade de identificar novos núcleos urbanos, por meio da avaliação da morfologia do terreno na formação de subcentros no município de Barra do Piraí no Estado do Rio de Janeiro, Brasil. Foram adquiridos arquivos vetoriais da base cartográfica do IBGE 2018, que posteriormente foram tratados e analisados. A fim de compreender a identificação de centralidades foram realizadas análises com a utilização da abordagem morfológica: Componentes Principais (CP), o Índice Global de ... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The urban expansion has been creating disorderly population agglomerations, which causes problems for the municipal administration. In order to reduce this type of problem, one of the ways to reorganize the territory is polycentrism, a concept that has been applied in a large part of the urban area of several countries such as the United States, China, and countries in Europe. Polycentrism can be understood as an urban area with a plurality of urban centers. There are two approaches to identify possible centralities: the morphological and the functional. In addition, no results were found from scientific studies that have used the variable slope of relief in the analysis of identification of urban centralities. Thus, the slope variable can be an impacting factor in determining a centrality or urban nucleus for Brazilian locations and which has similar characteristics of the study area. The aim of this work proposes a study for the identification of centralities or the possibility of identifying new urban centers, through the evaluation of the morphology of the land in the formation of sub-centers in the municipality of Barra do Piraí in the State of Rio de Janeiro, Brazil. Vector files were acquired from the IBGE 2018 cartographic base, which were later treated and analyzed. To understand the identification of centralities, analyzes were performed using the morphological approach: Principal Components (CP), the Moran Global Index, the Local Indicator of Spatial Association - ... (Complete abstract click electronic access below) / Mestre

Floods cause widespread damage to property and life in different parts of the world. Hence there is a paramount need to develop effective methods for design flood estimation to alleviate risk associated with these extreme hydrologic events. Methods that are conventionally considered for analysis of floods focus on estimation of continuous frequency relationship between peak flow observed at a location and its corresponding exceedance probability depicting the plausible conditions in the planning horizon. These methods are commonly known as at-site flood frequency analysis (FFA) procedures. The available FFA procedures can be classified as parametric and nonparametric. Parametric methods are based on the assumption that sample (at-site data) is drawn from a population with known probability density function (PDF). Those procedures have uncertainty associated with the choice of PDF and the method for estimation of its parameters. Moreover, parametric methods are ineffective in modeling flood data if multimodality is evident in their PDF. To overcome those artifacts, a few studies attempted using kernel based nonparametric (NP) methods as an alternative to parametric methods. The NP methods are data driven and they can characterize the uncertainty in data without prior assumptions as to the form of the PDF. Conventional kernel methods have shortcomings associated with boundary leakage problem and normal reference rule (considered for estimation of bandwidth), which have implications on flood quantile estimates. To alleviate this problem, focus of NP flood frequency analysis has been on development of new kernel density estimators (kdes). Another issue in FFA is that information on the whole hydrograph (e.g., time to the peak flow, volume of the flood flow and duration of the flood event) is needed, in addition to peak flow for certain applications. An option is to perform frequency analysis on each of the variables independently. However, these variables are not independent, and hence there is a need to perform multivariate analysis to construct multivariate PDFs and use the corresponding cumulative distribution functions (CDFs) to arrive at estimates of characteristics of design flood hydrograph. In this perspective, recent focus of flood frequency analysis studies has been on development of methods to derive joint distributions of flood hydrograph related variables in a nonparametric setting. Further, in real world scenario, it is often necessary to estimate design flood quantiles at target locations that have limited or no data. Regional Flood Frequency analysis (RFFA) procedures have been developed for use in such situations. These procedures involve use of a regionalization procedure for identification of a homogeneous group of watersheds that are similar to watershed of the target site in terms of flood response. Subsequently regional frequency analysis (RFA) is performed, wherein the information pooled from the group (region) forms basis for frequency analysis to construct a CDF (growth curve) that is subsequently used to arrive at quantile estimates at the target site. Though there are various procedures for RFFA, they are largely confined to only univariate framework considering a parametric approach as the basis to arrive at required quantile estimates. Motivated by these findings, this thesis concerns development of a linear diffusion process based adaptive kernel density estimator (D-kde) based methodologies for at-site as well as regional FFA in univariate as well as bivariate settings. The D-kde alleviates boundary leakage problem and also avoids normal reference rule while estimating optimal bandwidth by using Botev-Grotowski-Kroese estimator (BGKE). Potential of the proposed methodologies in both univariate and bivariate settings is demonstrated by application to synthetic data sets of various sizes drawn from known unimodal and bimodal parametric populations, and to real world data sets from India, USA, United Kingdom and Canada. In the context of at-site univariate FFA (considering peak flows), the performance of D- kde was found to be better when compared to four parametric distribution based methods (Generalized extreme value, Generalized logistic, Generalized Pareto, Generalized Normal), thirty-two ‘kde and bandwidth estimator’ combinations that resulted from application of four commonly used kernels in conjunction with eight bandwidth estimators, and a local polynomial–based estimator. In the context of at-site bivariate FFA considering ‘peakflow-flood volume’ and ‘flood duration-flood volume’ bivariate combinations, the proposed D-kde based methodology was shown to be effective when compared to commonly used seven copulas (Gumbel-Hougaard, Frank, Clayton, Joe, Normal, Plackett, and student’s-T copulas) and Gaussian kernel in conjunction with conventional as well as BGKE bandwidth estimators. Sensitivity analysis indicated that selection of optimum number of bins is critical in implementing D-kde in bivariate setting. In the context of univariate regional flood frequency analysis (RFFA) considering peak flows, a methodology based on D-kde and Index-flood methods is proposed and its performance is shown to be better when compared to that of widely used L-moment and Index-flood based method (‘regional L-moment algorithm’) through Monte-Carlo simulation experiments on homogeneous as well as heterogeneous synthetic regions, and through leave-one-out cross validation experiment performed on data sets pertaining to 54 watersheds in Godavari river basin, India. In this context, four homogeneous groups of watersheds are delineated in Godavari river basin using kernel principal component analysis (KPCA) in conjunction with Fuzzy c-means cluster analysis in L-moment framework, as an improvement over heterogeneous regions in the area (river basin) that are currently being considered by Central Water Commission, India. In the context of bivariate RFFA two methods are proposed. They involve forming site-specific pooling groups (regions) based on either L-moment based bivariate homogeneity test (R-BHT) or bivariate Kolmogorov-Smirnov test (R-BKS), and RFA based on D-kde. Their performance is assessed by application to data sets pertaining to stations in the conterminous United States. Results indicate that the R-BKS method is better than R-BHT in predicting quantiles of bivariate flood characteristics at ungauged sites, although the size of pooling groups formed using R-BKS is, in general, smaller than size of those formed using R-BHT. In general, the performance of the methods is found to improve with increase in size of pooling groups. Overall the results indicate that the D-kde always yields bona fide PDF (and CDF) in the context of univariate as well as bivariate flood frequency analysis, as probability density is nonnegative for all data points and integrates to unity for the valid range of the data. The performance of D-kde based at-site as well as regional FFA methodologies is found to be effective in univariate as well as bivariate settings, irrespective of the nature of population and sample size. A primary assumption underlying conventional FFA procedures has been that the time series of peak flow is stationarity (temporally homogeneous). However, recent studies carried out in various parts of the World question the assumption of flood stationarity. In this perspective, Time Varying Gaussian Copula (TVGC) based methodology is proposed in the thesis for flood frequency analysis in bivariate setting, which allows relaxing the assumption of stationarity in flood related variables. It is shown to be effective than seven commonly used stationary copulas through Monte-Carlo simulation experiments and by application to data sets pertaining to stations in the conterminous United States for which null hypothesis that peak flow data were non-stationary cannot be rejected.