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Late-successional forests provide a unique opportunity to explore adaptive management approaches that mitigate atmospheric carbon dioxide levels through carbon storage while also enhancing ecological resilience to novel climate and disturbances. Typical benchmarks for adaptive forest management include species diversity and structural complexity, which are widely considered to increase ecosystem stability and productivity. However, the role of functional trait diversity (e.g., variation in leaf and stem traits) in driving forest productivity and ecosystem resilience remains underexplored. We leveraged existing continuous forest inventory (CFI) data and collected local functional trait observations from CFI plots within late-successional forests in western Massachusetts to explore links between aboveground carbon storage and different types of forest diversity. We then fit a linear model within a Bayesian hierarchical framework applying functional diversity, species diversity, and structural complexity as predictors of live aboveground biomass (AGB) within CFI plots. Our framework integrates local functional trait information with database species mean trait values using a multivariate structure to account for inherent trait syndromes and estimate functional diversity in each plot. Across 626 plot-timepoints, we found that integrating individual functional trait information from co-located plots yielded the best predictions of live AGB. Contrary to expectations, functional diversity had a negative relationship with live AGB. Whereas plots with low functional diversity and higher AGB were dominated by mid-to-late successional hardwood species, plots with high functional diversity had more shade-intolerant species and lower AGB mediated by recent small-scale disturbances. Our results reveal an ontogenetic shift in the effects of functional diversity on AGB productivity over the course of succession in northeastern temperate forests. Corroborating with classical models of biomass development in late-successional northern hardwood forests, our findings support the need for adaptive forest carbon management to facilitate a mosaic of different forest successional stages across the landscape to maximize live aboveground carbon benefits in northeastern mixed hardwood forests.

The study of spatiotemporal dynamics of fish populations is important for both stock assessment and fishery management. I explored the impacts of environmental and anthropogenic factors on spatiotemporal patterns of fish populations, and contributed to stock assessment and management by incorporating the inherent spatial structure. Hierarchical models were developed to specify spatial and temporal variations, and Bayesian methods were adopted to fit the models. Yellow perch (Perca flavescens) is one of the most important commercial and recreational fisheries in Lake Erie, which is currently managed using four management units (MUs), with each assessed by a spatially-independent stock-specific assessment model. The current spatially-independent stock-specific assessment assumes that movement of yellow perch among MUs in Lake Erie is statistically negligible and biologically insignificant. I investigated whether the assumption is violated and the effect this assumption has on assessment. I first explored the spatiotemporal patterns of yellow perch abundance in Lake Erie based on data from a 27-year gillnet survey, and analyzed the impacts of environmental factors on spatiotemporal dynamics of the population. I found that yellow perch relative biomass index displayed clear temporal variation and spatial heterogeneity, however the two middle MUs displayed spatial similarities. I then developed a state-space model based on a 7-year tag-recovery data to explore movements of yellow perch among MUs, and performed a simulation analysis to evaluate the impacts of sample size on movement estimates. The results suggested substantial movement between the two stocks in the central basin, and the accuracy and precision of movement estimates increased with increasing sample size. These results demonstrate that the assumption on movements among MUs is violated, and it is necessary to incorporate regional connectivity into stock assessment. I thus developed a tag-integrated multi-region model to incorporate movements into a spatial stock assessment by integrating the tag-recovery data with 45-years of fisheries data. I then compared population projections such as recruitment and abundance derived from the tag-integrated multi-region model and the current spatial-independent stock-specific assessment model to detect the influence of hypotheses on with/without movements among MUs. Differences between the population projections from the two models suggested that the integration of regional stock dynamics has significant influence on stock estimates. American Shad (Alosa sapidissima), Hickory Shad (A. mediocris) and river herrings, including Alewife (A. pseudoharengus) and Blueback Herring (A. aestivalis), are anadromous pelagic fishes that spend most of the annual cycle at sea and enter coastal rivers in spring to spawn. Alosa fisheries were once one of the most valuable along the Atlantic coast, but have declined in recent decades due to pollution, overfishing and dam construction. Management actions have been implemented to restore the populations, and stocks in different river systems have displayed different recovery trends. I developed a Bayesian hierarchical spatiotemporal model to identify the population trends of these species among rivers in the Chesapeake Bay basin and to identify environmental and anthropogenic factors influencing their distribution and abundance. The results demonstrated river-specific heterogeneity of the spatiotemporal dynamics of these species and indicated the river-specific impacts of multiple factors including water temperature, river flow, chlorophyll a concentration and total phosphorus concentration on their population dynamics. Given the importance of these two case studies, analyses to diagnose the factors influencing population dynamics and to develop models to consider spatial complexity are highly valuable to practical fisheries management. Models incorporating spatiotemporal variation describe population dynamics more accurately, improve the accuracy of stock assessments, and would provide better recommendations for management purposes. / Doctor of Philosophy / Many fish populations exhibit complex spatial structure, but the spatial patterns have been incorporated into stock assessment only in few cases. A full understanding of spatial structure of fish populations is needed to better manage the populations. Stock assessment and management strategies should depend on the inherent spatial structure of the target fish population. There have been many approaches developed to analyze spatial structure of fish populations. In this dissertation, I developed quantitative models to analyze fish demographic data and tagging data to explore spatial structure of fish populations. Yellow perch (Perca flavescens) in Lake Erie and Alosa group including American Shad (Alosa sapidissima), Hickory Shad (A. mediocris) and river herrings (Alewife A. pseudoharengus and Blueback Herring A. aestivalis) in selected tributaries of the Chesapeake Bay were taken as examples. Fishery-independent data for yellow perch displayed spatial similarities in the central basin of Lake Erie. Distinct temporal trends were observed in relative abundance data for Alosa sp. in different tributaries of the Chesapeake Bay. Substantial yellow perch movement among the central basin of the Lake was observed in tagging data. Ignoring the inherent spatial structure may cause fish to be overfished in some regions and underfished in others. To maximize the effectiveness of management in all regions for fish populations, I highly recommend incorporating spatial structure into stock assessment and management such as the ones developed in this dissertation.

In this dissertation, we focus on two research topics related to mixture models. The first topic is Adaptive Rejection Metropolis Simulated Annealing for Detecting Global Maximum Regions, and the second topic is Bayesian Model Selection for Nonlinear Mixed Effects Model. In the first topic, we consider a finite mixture model, which is used to fit the data from heterogeneous populations for many applications. An Expectation Maximization (EM) algorithm and Markov Chain Monte Carlo (MCMC) are two popular methods to estimate parameters in a finite mixture model. However, both of the methods may converge to local maximum regions rather than the global maximum when multiple local maxima exist. In this dissertation, we propose a new approach, Adaptive Rejection Metropolis Simulated Annealing (ARMS annealing), to improve the EM algorithm and MCMC methods. Combining simulated annealing (SA) and adaptive rejection metropolis sampling (ARMS), ARMS annealing generate a set of proper starting points which help to reach all possible modes. ARMS uses a piecewise linear envelope function for a proposal distribution. Under the SA framework, we start with a set of proposal distributions, which are constructed by ARMS, and this method finds a set of proper starting points, which help to detect separate modes. We refer to this approach as ARMS annealing. By combining together ARMS annealing with the EM algorithm and with the Bayesian approach, respectively, we have proposed two approaches: an EM ARMS annealing algorithm and a Bayesian ARMS annealing approach. EM ARMS annealing implement the EM algorithm by using a set of starting points proposed by ARMS annealing. ARMS annealing also helps MCMC approaches determine starting points. Both approaches capture the global maximum region and estimate the parameters accurately. An illustrative example uses a survey data on the number of charitable donations. The second topic is related to the nonlinear mixed effects model (NLME). Typically a parametric NLME model requires strong assumptions which make the model less flexible and often are not satisfied in real applications. To allow the NLME model to have more flexible assumptions, we present three semiparametric Bayesian NLME models, constructed with Dirichlet process (DP) priors. Dirichlet process models often refer to an infinite mixture model. We propose a unified approach, the penalized posterior Bayes factor, for the purpose of model comparison. Using simulation studies, we compare the performance of two of the three semiparametric hierarchical Bayesian approaches with that of the parametric Bayesian approach. Simulation results suggest that our penalized posterior Bayes factor is a robust method for comparing hierarchical parametric and semiparametric models. An application to gastric emptying studies is used to demonstrate the advantage of our estimation and evaluation approaches. / Ph. D.

Pathway is a set of genes which are predefined and serve a particular celluar or physiological function. Ranking pathways relevant to a particular phenotype can help researchers focus on a few sets of genes in pathways. In this thesis, a Bayesian hierarchical latent model was proposed using generalized linear random effects model. The advantage of the approach was that it can easily incorporate prior knowledges when the sample size was small and the number of genes was large. For the covariance matrix of a set of random variables, two Gaussian random processes were considered to construct the dependencies among genes in a pathway. One was based on the polynomial kernel and the other was based on the Gaussian kernel. Then these two kernels were compared with constant covariance matrix of the random effect by using the ratio, which was based on the joint posterior distribution with respect to each model. For mixture models, log-likelihood values were computed at different values of the mixture proportion, compared among mixtures of selected kernels and point-mass density (or constant covariance matrix). The approach was applied to a data set (Mootha et al., 2003) containing the expression profiles of type II diabetes where the motivation was to identify pathways that can discriminate between normal patients and patients with type II diabetes. / Master of Science

La méthode que nous présentons pour modéliser des données dites de "comptage" ou données de Poisson est basée sur la procédure nommée Modélisation multi-niveau et interactive de la régression de Poisson (PRIMM) développée par Christiansen et Morris (1997). Dans la méthode PRIMM, la régression de Poisson ne comprend que des effets fixes tandis que notre modèle intègre en plus des effets aléatoires. De même que Christiansen et Morris (1997), le modèle étudié consiste à faire de l'inférence basée sur des approximations analytiques des distributions a posteriori des paramètres, évitant ainsi d'utiliser des méthodes computationnelles comme les méthodes de Monte Carlo par chaînes de Markov (MCMC). Les approximations sont basées sur la méthode de Laplace et la théorie asymptotique liée à l'approximation normale pour les lois a posteriori. L'estimation des paramètres de la régression de Poisson est faite par la maximisation de leur densité a posteriori via l'algorithme de Newton-Raphson. Cette étude détermine également les deux premiers moments a posteriori des paramètres de la loi de Poisson dont la distribution a posteriori de chacun d'eux est approximativement une loi gamma. Des applications sur deux exemples de données ont permis de vérifier que ce modèle peut être considéré dans une certaine mesure comme une généralisation de la méthode PRIMM. En effet, le modèle s'applique aussi bien aux données de Poisson non stratifiées qu'aux données stratifiées; et dans ce dernier cas, il comporte non seulement des effets fixes mais aussi des effets aléatoires liés aux strates. Enfin, le modèle est appliqué aux données relatives à plusieurs types d'effets indésirables observés chez les participants d'un essai clinique impliquant un vaccin quadrivalent contre la rougeole, les oreillons, la rub\'eole et la varicelle. La régression de Poisson comprend l'effet fixe correspondant à la variable traitement/contrôle, ainsi que des effets aléatoires liés aux systèmes biologiques du corps humain auxquels sont attribués les effets indésirables considérés. / We propose a method for analysing count or Poisson data based on the procedure called Poisson Regression Interactive Multilevel Modeling (PRIMM) introduced by Christiansen and Morris (1997). The Poisson regression in the PRIMM method has fixed effects only, whereas our model incorporates random effects. As well as Christiansen and Morris (1997), the model studied aims at doing inference based on adequate analytical approximations of posterior distributions of the parameters. This avoids the use of computationally expensive methods such as Markov chain Monte Carlo (MCMC) methods. The approximations are based on the Laplace's method and asymptotic theory. Estimates of Poisson mixed effects regression parameters are obtained through the maximization of their joint posterior density via the Newton-Raphson algorithm. This study also provides the first two posterior moments of the Poisson parameters involved. The posterior distributon of these parameters is approximated by a gamma distribution. Applications to two datasets show that our model can be somehow considered as a generalization of the PRIMM method since it also allows clustered count data. Finally, the model is applied to data involving many types of adverse events recorded by the participants of a drug clinical trial which involved a quadrivalent vaccine containing measles, mumps, rubella and varicella. The Poisson regression incorporates the fixed effect corresponding to the covariate treatment/control as well as a random effect associated with the biological system of the body affected by the adverse events.

Consideramos dois tópicos distintos relacionados a modelos clássicos da mecânica estatísticas de equilíbrio. O primeiro constitui-se na análise de equação parabólicas semi-lineares associadas à transformação de grupo de renormalização para o gás de Coulomb hierárquico bidimensional e o gás dipolos hierárquicos em dimensão d>1 após tomarmos um limite apropriado (limite L 1 do tamanho do bloco). O outro tópico estudado foi a construção de uma função majorante (, z) para a pressão termodinâmica de um gás formado por partículas interagentes com atividade z e temperatura -1, cuja interação entre dois corpos pode ser decomposta em escalas como um potencial estável. Somos capazes de demonstrar que o problema de valor inicial dado pela equação do gás de Coulomb está bem definido (existência, unicidade e dependência contínua das soluções) em um espaço funcional adequado e a solução converge assintoticamente para uma das infinitas contáveis soluções de equilíbrio. Quanto ao gás de dipolos, embora não tenhamos conseguido provar a existência e unicidade das soluções, garantimos que a única solução estacionária limitada inferiormente é a trivial nula, que é uma solução estável. Ao menos no caso dos modelos hierárquicos, os resultados obtidos permitem dar uma resposta definitiva à conjectura de Gallavotti e Nicolò sobre uma sequência infinita de transições de fase. A função majorante é construída como a solução de uma equação diferencial parcial quase-linear de primeira ordem. Através da do método das características relacionamos a solução (majorante) à função W de Lambert cuja expansão em série possui uma singularidade originada pelo corte que a função W possui no plano complexo. A descrição da função majorante como uma função W possui no plano complexo. A descrição da função majorante como uma função W permite uma melhora nas estimativas de raio de convergência para série de Mayer para pressão. / We have considered in this thesis two distinct topics related to classic models in equilibrium statistical mechanics. The first one is the analysis of semilinear parabolic partial differential equations given by a suitable limit (size of block L 1) in the renormalization group for the dipole gas in any dimension d>1. The other topic is the construction of a majorant function (, z) for the thermodynamic -1 whose potential admits a scale decomposition in terms of some stable potential. We are capable to demonstrate the well-posedness (existence, uniqueness and continuous dependence of solutions) for Coulomb gas equations and the global asymptotic convergence of the flow to one of its countably many equilibrium solutions. The dipole gas equations are technically more difficult and lack the results weve achieved in Coulomb gas but, despite its difficulties, we can establish the uniqueness of the trivial solution as a equilibrium ane and its stabilish. At least for hierarchical models, the established results give a definite answer to Gallovotti and Niclolòs conjecture of na infinite of phase transitions. The majorant function is constructed as the solution of a first order quase-linear partial differential equation. By means of the characteristics method we are able to relate its solution (the majorant) to Lamberts W-function whose series expansion possess a singularity given by W-function allows better estimates for Mayer series convergence.

A observação de gradientes latitudinais em aspectos da história de vida de aves tem motivado o estudo da evolução e variabilidade das histórias de vida nestes organismos. Um exemplo bem documentado é a variação no tamanho da ninhada, onde aves de latitudes menores tendem a ter ninhadas menores do que os seus homólogos de latitudes altas. Uma hipótese que visa explicar esta variação propõe que a sobrevivência em latitudes tropicais é maior para compensar o tamanho da ninhada menor e evitar a extinção das populações. Esta explicação tem tido grande aceitação e apoio por parte de alguns estudos, mas tem sido questionada por outros que não encontraram taxas de sobrevivência mais elevadas em aves tropicais. De modo implícito, todos estes estudos basearam seus resultados na sobrevivência de indivíduos adultos. As populações com o tamanho da ninhada menor não poderiam crescer da mesma maneira que as populações com ninhadas maiores; portanto, se justifica acreditar que algo deve mudar com a latitude para manter o balanço em tamanho populacional. Na busca por explicações alternativas para a persistência das populações de aves tropicais com relativamente pequenos tamanhos de ninhada, surge outra hipótese que propõe que, se não houver diferenças na sobrevivência de indivíduos adultos entre latitudes, o aspecto fundamental que varia é a sobrevivência juvenil, com sobrevivência maior para os juvenis das zonas tropicais em comparação com os juvenis das zonas temperadas. No entanto, atualmente há pouca evidência que suporta esta conclusão. Os resultados contrastantes desses estudos sugerem a falta de um consenso geral sobre a hipótese de que as aves tropicais têm taxas de sobrevivência mais elevadas do que as aves de regiões temperadas, motivando a formulação de hipóteses alternativas e convidando novos testes de hipótese. Neste estudo, pretendemos a) avaliar o efeito da idade sobre a sobrevivência em aves tropicais, estimando as probabilidades anuais de sobrevivência aparentes idade-específicas para um conjunto de aves passeriformes de sub-bosque na Amazônia central brasileira; e b) contribuir para o debate sobre o gradiente latitudinal na sobrevivência de adultos, comparando nossas estimativas com estimativas de outras latitudes. Para estimar a sobrevivência idade-específica ajustamos aos nossos dados um modelo Cormack-Jolly-Seber (CJS) hierárquico para n espécies, que trata os parâmetros espécie-específicos como efeitos aleatórios, que são estimados e que descrevem todo o conjunto de espécies; para comparação de métodos, ajustamos uma versão de efeitos fixos do modelo. Para a determinação da idade das aves usamos o sistema WRP. Apresentamos uma nova variante do modelo CJS com um parâmetro de mistura para a sobrevivência de aves de idade incerta no momento da primeira captura. Encontramos efeito forte da idade na sobrevivência, com probabilidades de sobrevivência menor para os jovens do que para os adultos; evidência de efeito latitude sobre a sobrevivência, que suporta a hipótese amplamente aceita de variação na sobrevivência com a latitude; e discutimos diferenças metodológicas interessantes entre modelo de efeitos aleatórios e fixos relacionados com a precisão das estimativas e o âmbito de inferência, que nos levam a concluir que os modelos de efeitos aleatórios são os mais adequados para a nossa análise. Concluímos que não é necessário invocar uma hipótese alternativa de maior sobrevivência juvenil nos trópicos a fim de explicar o gradiente latitudinal no tamanho da ninhada. / The observation of latitudinal gradients in bird life history traits has motivated the study of avian life history evolution and variability. A well-documented example is the variation in clutch size, where lower latitude birds tend to have smaller clutches than their higher latitude counterparts. A hypothesis that explains this variation proposes that survival in tropical latitudes is higher to compensate for smaller clutch size and prevent population extinctions. This explanation has had a wide acceptance and support by some studies, but has been questioned by others who have not found such higher survival rates in tropical birds. In an implicit manner, all these studies have based their results on adult survival. Populations with smaller clutch size would not be able to grow as well as populations with larger clutches; therefore one is justified to believe that something else must change with latitude. In the search for alternative explanations to the persistence of tropical bird populations with relatively small clutch sizes it has also been proposed that, if there were no differences in adult survival among latitudes, the fundamental trait that varies is juvenile survival, with higher survival rates for tropical juveniles birds than for temperate ones. However, currently there is little evidence that supports this conclusion. The contrasting results of those studies suggest a lack of a general consensus about the hypothesis that tropical birds have higher survival rates than birds of temperate regions, motivating the formulation of alternative hypotheses, and inviting further tests of the hypothesis. In our study we aim to a) assess the effect of age on survival in a tropical bird community, estimating age-specific annual apparent survival probabilities for a set of passerine understory birds from the central Brazilian Amazon; and b) contribute to the debate about the latitudinal gradient in adult survival by comparing our adult survival estimates to estimates of temperate-zone adult survival probabilities. To estimate the age-specific survival we fit to our data a hierarchical multispecies Cormack-Jolly-Seber (CJS) model for n species, that treats species-specific parameters as random effects that are estimated and that describe the whole assemblage of species; for comparison of methods, we also fit a fixed-effects version of the model. To age birds we use the cycle-based WRP system. We introduce a novel variant of CJS model with a mixture component for the survival of birds of uncertain age at the time of banding. We found strong effect of age on survival, with juveniles surviving less than adults; evidence of latitude effect on survival, that supports the widely accepted hypothesis of variation on survival with latitude; and methodological differences between random and fixed effects model related to precision of estimates and scope of inference, that lead us to conclude that random-effects models are more appropriate for our analysis. We conclude that there is no reason for an alternative latitudinal trend in juvenile survival to account for the general trend in clutch size.

Made available in DSpace on 2017-05-12T14:47:01Z (GMT). No. of bitstreams: 1 Sergio_ Bazilio.pdf: 3134289 bytes, checksum: 8461a5a239e9c7674bb3db224824dc63 (MD5) Previous issue date: 2015-02-12 / Benthic macroinvertebrate community is very important to streams, since they make part of the energy flow and are also an important food source for adjacent and upper trophic levels. They form a very diversified fauna and their structure may be influenced by several environmental factors, which vary in time, space and in an analyzed scale. Samples were taken from benthic macroinvertebrates and environmental descriptors during winter, spring and summer of 2012 and autumn of 2013 in ten sampling points in Lontra river watershed (Paraná, Brazil). Thus, this study aimed at: a) characterizing the watershed morphometry in Lontra river with data from the Shuttle Radar Topography Mission (SRTM); b) analyzing the structure of benthic macroinvertebrate community according to their taxonomic family level and functional groups; c) investigating seasonal variation (during the four seasons) and spatial variation of this community structure due to spatial differences in morphological and physiographic characteristics of the sampled sections; d) identifying the community variability of benthic macroinvertebrates at three spatial scales (river, river segment and mesohabitat) emphasizing spatial scales, which best explain the community structure in this watershed; e) investigating which measured environmental descriptors influence on the community structure and f) which variability percentage in organisms richness can be explained by the measured local environmental descriptors. / Os macroinvertebrados bentônicos constituem uma importante comunidade em riachos, pois participam do fluxo de energia, logo são um importante recurso alimentar para níveis tróficos adjacentes e superiores. Formam uma fauna bastante diversificada e a estrutura dessa comunidade pode ser influenciada por diversos fatores ambientais, os quais variam no tempo, no espaço e na escala analisada. Realizaram-se amostragens de macroinvertebrados bentônicos e descritores ambientais nos períodos de inverno, primavera e verão de 2012 e outono de 2013 em dez pontos amostrais na bacia do Rio Lontra (PR, Brasil). O presente estudo objetivou: a) caracterizar morfometricamente a microbacia do Rio Lontra com dados do projeto da Missão do Radar Transportado Espacial (SRTM). b) analisar a estrutura da comunidade de macroinvertebrados bentônicos em nível taxonômico de família e de grupos funcionais; c) investigar a variação sazonal (nas quatro estações do ano) e a variação espacial da estrutura da comunidade em função de diferenças espaciais nas características morfofisiográficas dos trechos amostrados; d) identificar a variabilidade da comunidade de macroinvertebrados bentônicos em três escalas espaciais (rio, segmento de rio e mesohabitat) com ênfase nas escalas espaciais que melhor explicam a estrutura da comunidade nesta bacia; e) investigar quais descritores ambientais mensurados influenciam a estrutura da comunidade e f) qual porcentagem da variabilidade na riqueza de organismos pode ser explicada pelos descritores ambientais locais mensurados.

L'esca de la vigne fait partie des maladies de dépérissement incurables dont l'étiologie n'est pas complément élucidée. Elle représente un des problèmes majeurs en viticulture. L'objectif général de cette thèse est d'améliorer la compréhension des processus épidémiques et des facteurs de risque. Pour ce faire, nous avons mené une étude quantitative du développement spatio-temporel de l'esca à l'échelle de la parcelle. Dans un premier temps, pour détecter d'éventuelles corrélations spatiales entre les cas de maladie, des tests statistiques non paramétriques sont appliqués aux données spatio-temporelles d'expression foliaires de l'esca pour 15 parcelles du bordelais. Une diversité de profils spatiaux, allant d'une distribution aléatoire à fortement structurée est trouvée. Dans le cas de structures très agrégées, les tests n'ont pas montré d'augmentation significative de la taille des foyers, ni de propagation secondaire locale à partir de ceps symptomatiques, suggérant un effet de l'environnement dans l'explication de cette agrégation. Dans le but de modéliser l'occurrence des symptômes foliaires, nous avons développé des modèles logistiques hiérarchiques intégrant à la fois des covariables exogènes liées à l'environnement et des covariables de voisinage de ceps déjà malades mais aussi un processus latent pour l'auto-corrélation spatio-temporelle. Les inférences bayésiennes sont réalisées en utilisant la méthode INLA (Inverse Nested Laplace Approximation). Les résultats permettent de conforter l'hypothèse du rôle significatif des facteurs environnementaux dans l'augmentation du risque d'occurrence des symptômes. L'effet de propagation de l'esca à petite échelle à partir de ceps déjà atteints situés sur le rang ou hors rang n'est pas montré. Un modèle autologistique de régression, deux fois centré, qui prend en compte de façon plus explicite la structure spatio-temporelle de voisinage, est également développé. Enfin, une méthode géostatistique d'interpolation de données de nature anisotropique atypique est proposée. Elle permet d'interpoler la variable auxiliaire de résistivité électrique du sol pour estimer à l'échelle de chaque plante de la parcelle, la réserve en eau du sol disponible pour la vigne. Les méthodes géostatistique et spatio-temporelles développées dans cette thèse ouvrent des perspectives pour identifier les facteurs de risques et prédire le développement de l'esca de la vigne dans des contextes agronomiques variés. / Esca grapevine disease is one of the incurable dieback disease with the etiology not completely elucidated. It represents one of the major threats for viticulture around the world. To better understand the underlying process of esca spread and the risk factors of this disease, we carried out quantitative analyses of the spatio-temporal development of esca at vineyard scale. In order to detect the spatial correlation among the diseased vines, the non-parametric statistical tests were applied to the spatio-temporal data of esca foliar symptom expression for 15 vineyards in Bordeaux region. Among vineyards, a large range of spatial patterns, from random to strongly structured, were found. In the vineyards with strongly aggregated patterns, no significant increase in the size of cluster and local spread from symptomatic vines was shown, suggesting an effect of the environment in the explanation of this aggregation. To model the foliar symptom occurrence, we developed hierarchical logistic regression models by integrating exogenous covariates, covariates of neighboring symptomatic vines already diseased, and also a latent process with spatio-temporal auto-correlation. The Bayesian inferences of these models were performed by INLA (Inverse Nested Laplace Approximation) approach. The results confirmed the effect of environmental factors on the occurrence risk of esca symptom. The secondary locally spread of esca from symptomatic vines located on the same row or out of row was not shown. A two-step centered auto-logistic regression model, which explicitly integrated the spatio-temporal neighboring structure, was also developed. At last, a geostatistical method was proposed to interpolate data with a particular anisotropic structure. It allowed interpolating the ancillary variable, electrical resistivity of soil, which were used to estimate the available soil water content at vine-scale. These geostatistical methods and spatio-temporal statistical methods developed in this thesis offered outlook to identify risk factors, and thereafter to predict the development of esca grapevine disease in different agronomical contexts.