Over the past decades technological developments have both changed and increased human in influence on the marine environment. We now have greater potential than ever before to introduce disturbance and deplete marine resources. Two of the issues currently under public scrutiny are the exploitation of fish stocks worldwide and levels of anthropogenic noise in the marine environment. The aim of this thesis is to investigate and develop novel analyses and simulations to provide additional insight into some of the challenges facing the marine ecosystem today. These methodologies will improve the management of these risks to marine ecosystems. This thesis first addresses the issue of competition between humans and grey seals (Halichoerus grypus) for marine resources, providing compelling evidence that a substantial proportion of the sandeels consumed by grey seals in the North Sea are in fact H. lanceolatus, which is not commercially exploited, rather than the commercially important A. marinus. In addition, we present quantitative results regarding sources of bias when estimating the total biomass of sandeels consumed by grey seals. Secondly, we investigate spatially adaptive 2-dimensional smoothing to improve the prediction of both the presence and density of marine species, information that is often key in the management of marine ecosystems. Particularly, we demonstrate the benefits of such methods in the prediction of sandeel occurrence. Lastly this thesis provides a quantitative assessment of the protocols for real-time monitoring of marine mammal presence, which require that acoustic operations cease when an animal is detected within a certain distance (i.e. the "monitoring zone") of the sound source. We assess monitoring zones of different sizes with regards to their effectiveness in reducing the risks of temporary and permanent damage to the animals' hearing, and demonstrate that a monitoring zone of 2 km is generally recommendable.

The main objective of this dissertation was to evaluate long-term fluctuations in the distribution and abundance of Engraulis anchoita eggs and larvae in the Southeastern Brazilian Bight (SBB). Engraulis anchoita is a fish species that is ecologically and economically important. We analyzed samples and abiotic data from eighteen oceanographic cruises conducted during austral late spring and early summer from 1974 to 2010. Two different stocks were detected in the SBB based on egg size, with the predominant stock in the area having smaller eggs than the stock in the region further south. Using indicative kriging, we identified occasional (e.g. Florianópolis - 27°S and off Santos Bay) and avoided (e.g. off São Sebastião Island and off Cananéia-Iguape Coastal System) spawning sites. Through zero-inflated models, spatial factors (different areas and the local depth) were related to the probability of sampling false zeros and temporal and oceanographic conditions (different years and temperature) with egg and larvae abundance. We also described faster and more accurate methodology to identify E. anchoita eggs, and compared the mesh-size efficiency to sample eggs and analyzed how egg size varied seasonally. Our results may support future studies and may assist a future fishery management of E. anchoita, a species not yet exploited in the SBB. / O principal objetivo dessa tese foi analisar as flutuações de longo-prazo na distribuição e abundância de ovos e larvas de Engraulias anchoita, uma espécie de peixe de importância econômica e ecológica, na Plataforma Continental Sudeste do Brasil (PCSE). Nós analisamos amostras e dados abióticos de dezoito cruzeiros oceanográficos realizados durante o fim da primavera e o começo do verão de 1974 a 2010. Dois estoques distintos foram identificados com base no tamanho dos ovos, um predominante e com menor tamanho e outro de maior tamanho ao sul da PCSE. Através de \"krigagem\" indicativa, foram identificadas áreas de desova ocasional (como ao norte de Florianópolis e a área ao largo da baía de Santos) e áreas em que a desova foi evitada (como em frente à Ilha de São Sebastião e ao Sistema Costeiro Cananéia-Iguape). Usando modelos inflacionados de zeros, os fatores espaciais (diferentes áreas e profundidades amostradas) foram relacionados com a probabilidade de se amostrar falso zero, enquanto os fatores temporais e oceanográficos (diferentes anos e temperatura) foram relacionados com a abundância de ovos e larvas. Apresentamos também uma metodologia mais rápida e mais eficiente para identificar os ovos de E. anchoita, comparamos as amostragens realizadas com duas malhagens diferentes e analisamos variações sazonais do tamanho dos ovos capturados. Assim, nossos resultados poderão auxiliar estudos futuros e também no manejo pesqueiro da espécie em questão, ainda não explorada comercialmente na área de estudo.

The main objective of this dissertation was to evaluate long-term fluctuations in the distribution and abundance of Engraulis anchoita eggs and larvae in the Southeastern Brazilian Bight (SBB). Engraulis anchoita is a fish species that is ecologically and economically important. We analyzed samples and abiotic data from eighteen oceanographic cruises conducted during austral late spring and early summer from 1974 to 2010. Two different stocks were detected in the SBB based on egg size, with the predominant stock in the area having smaller eggs than the stock in the region further south. Using indicative kriging, we identified occasional (e.g. Florianópolis - 27°S and off Santos Bay) and avoided (e.g. off São Sebastião Island and off Cananéia-Iguape Coastal System) spawning sites. Through zero-inflated models, spatial factors (different areas and the local depth) were related to the probability of sampling false zeros and temporal and oceanographic conditions (different years and temperature) with egg and larvae abundance. We also described faster and more accurate methodology to identify E. anchoita eggs, and compared the mesh-size efficiency to sample eggs and analyzed how egg size varied seasonally. Our results may support future studies and may assist a future fishery management of E. anchoita, a species not yet exploited in the SBB. / O principal objetivo dessa tese foi analisar as flutuações de longo-prazo na distribuição e abundância de ovos e larvas de Engraulias anchoita, uma espécie de peixe de importância econômica e ecológica, na Plataforma Continental Sudeste do Brasil (PCSE). Nós analisamos amostras e dados abióticos de dezoito cruzeiros oceanográficos realizados durante o fim da primavera e o começo do verão de 1974 a 2010. Dois estoques distintos foram identificados com base no tamanho dos ovos, um predominante e com menor tamanho e outro de maior tamanho ao sul da PCSE. Através de \"krigagem\" indicativa, foram identificadas áreas de desova ocasional (como ao norte de Florianópolis e a área ao largo da baía de Santos) e áreas em que a desova foi evitada (como em frente à Ilha de São Sebastião e ao Sistema Costeiro Cananéia-Iguape). Usando modelos inflacionados de zeros, os fatores espaciais (diferentes áreas e profundidades amostradas) foram relacionados com a probabilidade de se amostrar falso zero, enquanto os fatores temporais e oceanográficos (diferentes anos e temperatura) foram relacionados com a abundância de ovos e larvas. Apresentamos também uma metodologia mais rápida e mais eficiente para identificar os ovos de E. anchoita, comparamos as amostragens realizadas com duas malhagens diferentes e analisamos variações sazonais do tamanho dos ovos capturados. Assim, nossos resultados poderão auxiliar estudos futuros e também no manejo pesqueiro da espécie em questão, ainda não explorada comercialmente na área de estudo.

Les données comptées (count data) possèdent des distributions ayant des caractéristiques particulières comme la non-normalité, l’hétérogénéité des variances ainsi qu’un nombre important de zéros. Il est donc nécessaire d’utiliser les modèles appropriés afin d’obtenir des résultats non biaisés. Ce mémoire compare quatre modèles d’analyse pouvant être utilisés pour les données comptées : le modèle de Poisson, le modèle binomial négatif, le modèle de Poisson avec inflation du zéro et le modèle binomial négatif avec inflation du zéro. À des fins de comparaisons, la prédiction de la proportion du zéro, la confirmation ou l’infirmation des différentes hypothèses ainsi que la prédiction des moyennes furent utilisées afin de déterminer l’adéquation des différents modèles. Pour ce faire, le nombre d’arrestations des membres de gangs de rue sur le territoire de Montréal fut utilisé pour la période de 2005 à 2007. L’échantillon est composé de 470 hommes, âgés de 18 à 59 ans. Au terme des analyses, le modèle le plus adéquat est le modèle binomial négatif puisque celui-ci produit des résultats significatifs, s’adapte bien aux données observées et produit une proportion de zéro très similaire à celle observée. / Count data have distributions with specific characteristics such as non-normality, heterogeneity of variances and a large number of zeros. It is necessary to use appropriate models to obtain unbiased results. This memoir compares four models of analysis that can be used for count data: the Poisson model, the negative binomial model, the Poisson model with zero inflation and the negative binomial model with zero inflation. For purposes of comparison, the prediction of the proportion of zero, the confirmation or refutation of the various assumptions and the prediction of average number of arrrests were used to determine the adequacy of the different models. To do this, the number of arrests of members of street gangs in the Montreal area was used for the period 2005 to 2007. The sample consisted of 470 men, aged 18 to 59 years. After the analysis, the most suitable model is the negative binomial model since it produced significant results, adapts well to the observed data and produces a zero proportion very similar to that observed.

Les données comptées (count data) possèdent des distributions ayant des caractéristiques particulières comme la non-normalité, l’hétérogénéité des variances ainsi qu’un nombre important de zéros. Il est donc nécessaire d’utiliser les modèles appropriés afin d’obtenir des résultats non biaisés. Ce mémoire compare quatre modèles d’analyse pouvant être utilisés pour les données comptées : le modèle de Poisson, le modèle binomial négatif, le modèle de Poisson avec inflation du zéro et le modèle binomial négatif avec inflation du zéro. À des fins de comparaisons, la prédiction de la proportion du zéro, la confirmation ou l’infirmation des différentes hypothèses ainsi que la prédiction des moyennes furent utilisées afin de déterminer l’adéquation des différents modèles. Pour ce faire, le nombre d’arrestations des membres de gangs de rue sur le territoire de Montréal fut utilisé pour la période de 2005 à 2007. L’échantillon est composé de 470 hommes, âgés de 18 à 59 ans. Au terme des analyses, le modèle le plus adéquat est le modèle binomial négatif puisque celui-ci produit des résultats significatifs, s’adapte bien aux données observées et produit une proportion de zéro très similaire à celle observée. / Count data have distributions with specific characteristics such as non-normality, heterogeneity of variances and a large number of zeros. It is necessary to use appropriate models to obtain unbiased results. This memoir compares four models of analysis that can be used for count data: the Poisson model, the negative binomial model, the Poisson model with zero inflation and the negative binomial model with zero inflation. For purposes of comparison, the prediction of the proportion of zero, the confirmation or refutation of the various assumptions and the prediction of average number of arrrests were used to determine the adequacy of the different models. To do this, the number of arrests of members of street gangs in the Montreal area was used for the period 2005 to 2007. The sample consisted of 470 men, aged 18 to 59 years. After the analysis, the most suitable model is the negative binomial model since it produced significant results, adapts well to the observed data and produces a zero proportion very similar to that observed.

The aim of this thesis is to provide a comprehensive overview of the main approaches to modeling data loaded with redundant zeros. There are three main subclasses of zero modified models (ZMM) described here - zero inflated models (the main focus lies on models of this subclass), zero truncated models and hurdle models. Models of each subclass are defined and then a construction of maximum likelihood estimates of regression coefficients is described. ZMM models are mostly based on Poisson or negative binomial type 2 distribution (NB2). In this work, author has extended the theory to ZIM models generally based on any discrete distributions of exponential type. There is described a construction of MLE of regression coefficients of theese models, too. Just few of present works are interested in ZIM models based on negative binomial type 1 distribution (NB1). This distribution is not of exponential type therefore a common method of MLE construction in ZIM models cannot be used here. In this work provides modification of this method using quasi-likelihood method. There are two simulation studies concluding the work. 1

L'inférence de réseaux ou inférence de graphes a de plus en plus d'applications notamment en santé humaine et en environnement pour l'étude de données micro-biologiques et génomiques. Les réseaux constituent en effet un outil approprié pour représenter, voire étudier des relations entre des entités. De nombreuses techniques mathématiques d'estimation ont été développées notamment dans le cadre des modèles graphiques gaussiens mais aussi dans le cas de données binaires ou mixtes. Le traitement des données d'abondance (de micro-organismes comme les bactéries par exemple) est particulier pour deux raisons : d'une part elles ne reflètent pas directement la réalité car un processus de séquençage a lieu pour dupliquer les espèces et ce processus apporte de la variabilité, d'autre part une espèce peut être absente dans certains échantillons. On est alors dans le cadre de données inflatées en zéro. Beaucoup de méthodes d'inférence de réseaux existent pour les données gaussiennes, les données binaires et les données mixtes mais les modèles inflatés en zéro sont très peu étudiés alors qu'ils reflètent la structure de nombreux jeux de données de façon pertinente. L'objectif de cette thèse concerne l'inférence de réseaux pour les modèles inflatés en zéro. Dans cette thèse, on se limitera à des réseaux de dépendances conditionnelles. Le travail présenté dans cette thèse se décompose principalement en deux parties. La première concerne des méthodes d'inférence de réseaux basées sur l'estimation de voisinages par une procédure couplant des méthodes de régressions ordinales et de sélection de variables. La seconde se focalise sur l'inférence de réseaux dans un modèle où les variables sont des gaussiennes inflatées en zéro par double troncature (à droite et à gauche). / Network inference has more and more applications, particularly in human health and environment, for the study of micro-biological and genomic data. Networks are indeed an appropriate tool to represent, or even study, relationships between entities. Many mathematical estimation techniques have been developed, particularly in the context of Gaussian graphical models, but also in the case of binary or mixed data. The processing of abundance data (of microorganisms such as bacteria for example) is particular for two reasons: on the one hand they do not directly reflect reality because a sequencing process takes place to duplicate species and this process brings variability, on the other hand a species may be absent in some samples. We are then in the context of zero-inflated data. Many graph inference methods exist for Gaussian, binary and mixed data, but zero-inflated models are rarely studied, although they reflect the structure of many data sets in a relevant way. The objective of this thesis is to infer networks for zero-inflated models. In this thesis, we will restrict to conditional dependency graphs. The work presented in this thesis is divided into two main parts. The first one concerns graph inference methods based on the estimation of neighbourhoods by a procedure combining ordinal regression models and variable selection methods. The second one focuses on graph inference in a model where the variables are Gaussian zero-inflated by double truncation (right and left).

[pt] Demanda intermitente é definida por períodos de vendas nulas intercaladas com vendas positivas e de quantidade altamente variável. A maior parte das unidades de manutenção de estoque (stock keeping units, em inglês) ao nível loja pode ser caracterizada como contendo demanda desse tipo. Assim, modelos acurados para prever séries com demanda intermitente trazem grandes impactos em relação à gestão de estoque. Nesta dissertação nós propomos o uso do framework GAS com as distribuições adequadas para dados de contagem, além de suas versões com excesso de zeros, e aplicamos os modelos derivados a dados reais obtidos com uma grande rede varejista brasileira. Nós demonstramos que os modelos com excesso de zeros propostos são estimados de forma consistente por máxima verossimilhança e a distribuição dos estimadores é assintóticamente normal. A performance dos modelos propostos é comparada com benchmarks adequados das literaturas de séries temporais para dados de contagem e previsão de demanda intermitente. A avaliação das previsões é feita com base tanto na precisão da distribuição preditiva quanto na precisão das previsões pontuais. Nossos resultados mostram que os modelos propostos, em especial o modelo derivado sob distribuição hurdle Poisson, performam melhor do que os benchmarks analisados. / [en] Intermittent demand is defined by periods of zero sales interleaved with positive sales with highly variable quantities. Most stock keeping units at the store level can be characterized as containing such demand. Thus, accurate models for predicting series with intermittent demand have major impacts in relation to inventory management. In this dissertation we propose the use of the GAS framework with the appropriate distributions for count data, in addition to their versions with excess of zeroes, and apply the derived models to real data obtained from a large Brazilian retail chain. We demonstrate that the proposed models with excess of zeros are consistently estimated via maximum likelihood and the distribution of the estimator is asymptotically normal. The performance of the proposed models is compared to adequate benchmarks from the time series literature for count data and intermittent demand forecast. Forecasting is evaluated based on the accuracy of both the entire predictive distribution and point forecasts. Our results show that the proposed models, specially the one derived from hurdle Poisson distribution, perform better than the analyzed benchmarks.