1. O manejo da vida silvestre em matrizes antropizadas, fora de reservas naturais, é importante para melhorar a eficácia de esforços conservacionistas que visam proteger e restaurar a biodiversidade. Desta forma, é fundamental entender as características da matriz que aumentam a adequabilidade do habitat para as espécies remanescentes e também avaliar as respostas das espécies à práticas de manejo alternativas. 2. Neste estudo utilizamos uma abordagem de modelagem hierárquica, que leva em conta o efeito de falhas de detectabilidade das espécies, para estimar o efeito de práticas de manejo adotadas dentro de talhões de plantios de Eucalyptus sobre a ocupação de aves. Nossos modelos incorporam características sítio-específicas, como tipo de prática de manejo e o contexto da paisagem em que se inserem cada unidade amostral. Também incorpora características espécie-específicas, em particular à sensibilidade das espécies aos distúrbios e estrato de forrageio. 3. O principal fator associado a diferenças na ocupação de aves dentro de nossa área de estudo é o tipo de pratica de manejo. A presença de árvores nativas dispersas e de sub-bosque em estágio inicial de sucessão dentro dos talhões está associada com um aumento na proporção de espécies do pool regional capaz de ocupar a matriz silvicultural. O contexto da paisagem teve um efeito relativamente menor sobre a ocupação de aves na área de estudo. 4. Síntese e Aplicação. A ocupação de aves está associada positivamente ao aumento da complexidade estrutural dentro dos talhões de eucalipto, as espécies respondem a retenção de árvores nativas dispersas e a presença de sub-bosque. Gerenciadores interessados em aumentar o valor conservacionista de plantios de Eucalyptus para as aves deveriam preservar as arvores maduras e adotar práticas que estimulem a regeneração do sub-bosque durante o preparo do local e rotação do plantio. / 1. Managing for wildlife in human-dominated matrices outside natural reserves has great importance to improve the efficacy of conservation efforts aiming to protect and restore biodiversity. As such, it is critical to understand which features of the matrix that enhance habitat suitability to the remaining species and also to evaluate species responses to alternative management practices. 2. We used hierarchical modeling to estimate the effect of stand level management practices adopted in Eucalyptus plantations on bird occupancy while accounting for species detection failure. Our models incorporate site- specific traits, such as management practice type and the landscape context of each sampling unit. It also incorporates species specific-traits, particularly species sensitivity to disturbance and species foraging stratum. 3. The major factor associated with differences in bird occupancy within our study site is the management practice type. Scattered native trees and early successional stage understory within mature stands was associated with an increase in the proportion of bird species from the regional pool that was able to occupy the silvicultural matrix. Landscape context had a relatively minor effect on bird occupancy in our study area. 4. Synthesis and applications. Bird occupancy is positively associated with increasing spatial heterogeneity within eucalyptus stands with species responding to the retention of native scattered trees and understory presence. Managers interested in improving the conservation value of Eucalyptus plantations to birds should preserve some existing trees and adopt practices that trigger understory regeneration during site preparation and stand rotation.

We assess the relationship between model size and complexity in the time-varying parameter VAR framework via thorough predictive exercises for the Euro Area, the United Kingdom and the United States. It turns out that sophisticated dynamics through drifting coefficients are important in small data sets while simpler models tend to perform better in sizeable data sets. To combine best of both worlds, novel shrinkage priors help to mitigate the curse of dimensionality, resulting in competitive forecasts for all scenarios considered. Furthermore, we discuss dynamic model selection to improve upon the best performing individual model for each point in time. / Series: Department of Economics Working Paper Series

In this paper, we discuss methods for developing a stochastic model which incorporates behavior differences in the predation movements of Anelosimus studiosus (a subsocial spider). Stochastic models for animal movement and, in particular, spider predation movement have been developed previously; however, this paper focuses on the development and implementation of the necessary mathematical and statistical methods required to expand such a model in order to capture a variety of distinct behaviors. A least squares optimization algorithm is used for parameter estimation to fit a single stochastic model to an individual spider during predation resulting in unique parameter values for each spider. Similarities and variations between parameter values across the spiders are analyzed and used to estimate probability distributions for the variable parameter values. An aggregate stochastic model is then created which incorporates the individual dynamics. The comparison between the optimal individual models to the aggregate model indicate the methodology and algorithm developed in this paper are appropriate for simulating a range of individualistic behaviors.

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In many environmental and climate applications, extreme data are spatial by nature, and hence statistics of spatial extremes is currently an important and active area of research dedicated to developing innovative and flexible statistical models that determine the location, intensity, and magnitude of extreme events. In particular, the development of flexible sub-asymptotic models is in trend due to their flexibility in modeling spatial high threshold exceedances in larger spatial dimensions and with little or no effects on the choice of threshold, which is complicated with classical extreme value processes, such as Pareto processes. In this thesis, we develop new flexible sub-asymptotic extreme value models for modeling spatial and spatio-temporal extremes that are combined with carefully designed gradient-based Markov chain Monte Carlo (MCMC) sampling schemes and that can be exploited to address important scientific questions related to risk assessment in a wide range of environmental applications. The methodological developments are centered around two distinct themes, namely (i) sub-asymptotic Bayesian models for extremes; and (ii) flexible marked point process models with sub-asymptotic marks. In the first part, we develop several types of new flexible models for light-tailed and heavy-tailed data, which extend a hierarchical representation of the classical generalized Pareto (GP) limit for threshold exceedances. Spatial dependence is modeled through latent processes. We study the theoretical properties of our new methodology and demonstrate it by simulation and applications to precipitation extremes in both Germany and Spain. In the second part, we construct new marked point process models, where interest mostly lies in the extremes of the mark distribution. Our proposed joint models exploit intrinsic CAR priors to capture the spatial effects in landslide counts and sizes, while the mark distribution is assumed to take various parametric forms. We demonstrate that having a sub-asymptotic distribution for landslide sizes provides extra flexibility to accurately capture small to large and especially extreme, devastating landslides.

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Wildfire activity has increased in recent years in the United States, endangering both environment and society. Appropriate management of this phenomenon is only achievable with a thorough understanding of the critical factors influencing wildfire activity in each region. In three essays, I use statistical and mathematical models to examine wildfires and propose solutions to mitigate their impact on society. In the first essay, I focused on building a systematic framework for modeling wildfire as a coupled human-natural system. I employ system dynamics modeling, which was previously applied in various fields, including healthcare, sustainability, and disaster mitigation. I show how, in the absence of exogenous factors such as temperature or lightning, the human perception of fire danger may establish a feedback loop that can yield significant trends such as fluctuation or even fluctuation with rising amplitude when linked with the natural system. This conclusion is counter-intuitive, given that the human contribution to wildfire is typically described in the literature using constant or semi-constant variables. Additionally, I analyzed the impact of three important fire protection measures on reducing burning rates (prescribed burning, enhancing immediate suppression accomplishment, and regulating the rate of WUI growth). The research concludes that appropriately integrating several policies can result in a synergistic effect that is greater than the sum of the effects of the individual policies. The second essay calibrates the model built in the first essay and examines wildfire trends across the contiguous United States. The simulation results closely match the real data, and the model serves as a foundation for data-driven policy research. To be more precise, I fit the model to each state separately and then compare the model's goodness of fit. Following that, I examine the influence of various policies and scenarios on wildfire behavior. In the scenario, I examine the effect of maintaining constant temperatures and precipitation levels relative to the average values for these variables over the last century. For the policy analysis, I examine the influence of three policies on each state (prescribed burning, increasing immediate suppression achievement, and regulating the rate of WUI development). Here, I provide state-specific suggestions about the primary factors that contribute to wildfires and the most effective policies for each state. In the third essay, I have implemented the Oregon wildfire history dataset and integrated it with two other aerial datasets, including meteorological data gathered by weather stations located around the state and counties. Then, using hierarchical modeling on over 10,000 wildfire ignitions, I developed a classification system for determining if a given fire has the potential to grow major or not. However, utilizing a huge dataset and a variety of resources presents several obstacles, such as the presence of missing data. I imputed the missing numbers using a sophisticated mathematical approach called "Predictive Mean Matching". / Doctor of Philosophy / Wildfire activity has increased in recent decades in the United States, which put many people in danger. Climate change, the Settlement of people in the Wildland Urban Interface, and an increase in vegetation density each play a role in this increase. In this dissertation, we discuss the wildfire in the United States in three essays. In the first essay, we develop a mathematical model to show how humans and nature affect wildfire activity in any area. We then test different major wildfire management policies on the hypothetical situation to compare the outcome of these policies together. In the second essay, we use the model developed in the essay (with some minor changes) to model the wildfire activity in 11 states of the U.S. which has the most wildfire activity in recent years. First, we show that our model can replicate the wildfire activity in each state. Second, we test the effect of wildfire mitigation policies on each state. This essay proposes state-specific policy recommendations based on the main reasons for the increase in wildfire activity in each state. Finally, in the third essay, we develop a statistical model to predict the existence of large wildfires in the next month in Oregon counties. We use climate, land, and fire history data to develop a warning system. Policymakers can use this system to move the fire suppression resources to counties with a high probability of experiencing large wildfires over the next month. Finally, all essays aim to enhance our understanding of the reasons for the increase in wildfire activity in recent years and suggest finding the appropriate way to deal with this change to reduce the effect of wildfire on human life.

Na engenharia, existem diversas técnicas de análise estrutural que conduzem à obtenção de esforços e deslocamentos com considerável grau de eficácia e acurácia e, dentre estas, há a técnica de SUBESTRUTURAÇÃO. Esta técnica consiste na divisão e análise de um modelo estrutural completo, através da divisão de seu domínio em SUBESTRUTURAS, permitindo uma considerável eficiência nos cálculos e na avaliação de modelos estruturais combinados com a análise hierárquica de estruturas. Neste trabalho, seguindo-se a metodologia de pesquisa, que envolveu revisão bibliográfica e desenvolvimento de modelos computacionais, apresentaram-se as principais técnicas de subestruturas - seção 1, definições e formulação teórica dos principais métodos - desenvolvidos por Przemienieck (1963), Rosen e Rubinstein (1968), Rubinstein (1970) e El-Sayed e Hsiung (1990) - e a conceituação da análise hierárquica de estruturas com as respectivas aplicações combinadas com técnica de subestruturação para análise de fissuras em estruturas aeronáuticas, proposta de Starnes e Britt (1991) e aplicada por Potyondy (1993) - seção 2. Nesse contexto, na seção 3 se propôs a metodologia hierárquica para análise de estruturas típicas da construção civil, utilizando técnicas de subestruturação. Apresentaram-se instrumentos úteis à respectiva aplicação, dentre os quais se destaca a proposta de metodologia simplificada de análise hierárquica. Algumas precauções a serem adotadas na aplicação da metodologia também foram apresentadas. Na seção 4, desenvolveram-se cinco estudos de caso aplicando-se as metodologias, instrumentos e precauções desenvolvidos na seção 3, destacando-se a eficácia das metodologias. Conclui-se a pesquisa na seção 5 sendo que, na seção 6, indicam-se novas linhas de pesquisas a serem desenvolvidas sobre a metodologia proposta na pesquisa. / In engineering, there are several structural analysis techniques leading to obtain displacements and efforts with a considerable degree of efficacy and accuracy, and, among these, there is the SUBSTRUCTURING technique. This method consists in the division and analysis of a complete structural model, by subdividing his field in SUBSTRUCTURES, allowing a considerable efficiency in calculations and evaluation of structural models combined with hierarchical structures analysis. In this work, following the methodology research, which involved a literature review and computational models development, the key techniques of substructures were presented - section 1, as well as main methods definitions and theoretical formulation - developed by Przemienieck (1963), Rosen and Rubinstein (1968), Rubinstein (1970) and El-Sayed and Hsiung (1990) - and structural hierarchical analysis concept with their applications combined with aircraft structures cracks substructuring technical analysis, proposed by Starnes and Britt (1991), and implemented by Potyondy (1993) - Section 2. In this context, a civil construction typical structures analysis methodology was proposed in section 3. Besides, many useful tools for their implementation were presented, among which stands out the simplified hierarchy analysis methodology proposal. Some precautions in the methodology applying were also presented. In section 4, five case studies were developed, applying the methodology, tools and precautions explained in section 3, especially the methodologies effectiveness. The research is concluded in section 5, and new research lines, to be undertaken on the methodology proposed, were outpointed in section 6.

abstract: Although models for describing longitudinal data have become increasingly sophisticated, the criticism of even foundational growth curve models remains challenging. The challenge arises from the need to disentangle data-model misfit at multiple and interrelated levels of analysis. Using posterior predictive model checking (PPMC)—a popular Bayesian framework for model criticism—the performance of several discrepancy functions was investigated in a Monte Carlo simulation study. The discrepancy functions of interest included two types of conditional concordance correlation (CCC) functions, two types of R2 functions, two types of standardized generalized dimensionality discrepancy (SGDDM) functions, the likelihood ratio (LR), and the likelihood ratio difference test (LRT). Key outcomes included effect sizes of the design factors on the realized values of discrepancy functions, distributions of posterior predictive p-values (PPP-values), and the proportion of extreme PPP-values. In terms of the realized values, the behavior of the CCC and R2 functions were generally consistent with prior research. However, as diagnostics, these functions were extremely conservative even when some aspect of the data was unaccounted for. In contrast, the conditional SGDDM (SGDDMC), LR, and LRT were generally sensitive to the underspecifications investigated in this work on all outcomes considered. Although the proportions of extreme PPP-values for these functions tended to increase in null situations for non-normal data, this behavior may have reflected the true misfit that resulted from the specification of normal prior distributions. Importantly, the LR and the SGDDMC to a greater extent exhibited some potential for untangling the sources of data-model misfit. Owing to connections of growth curve models to the more fundamental frameworks of multilevel modeling, structural equation models with a mean structure, and Bayesian hierarchical models, the results of the current work may have broader implications that warrant further research. / Dissertation/Thesis / Doctoral Dissertation Educational Psychology 2015