Modelos espaciais de captura-recaptura para populações abertas

• Ano de defesa: 2018
• Autor(a) principal: Pezzott, George Lucas Moraes
• Orientador(a): Salasar, Luis Ernesto Bueno
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs
• Palavras-chave em Português: Modelo Espacial de Captura-Recaptura; População aberta; Análise Bayesiana
• Palavras-chave em Inglês: Spatial Capture-recapture Model; Open population; Bayesian analysis
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA; CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::ESTATISTICA::INFERENCIA PARAMETRICA
• Link de acesso: https://repositorio.ufscar.br/handle/ufscar/10860

In this thesis we propose two spatial capture-recapture models for estimation of population abundance in the open population. The proposed statistical models conform to data obtained through individual tag capture-recapture sampling performed in different areas within the habitat, taking into account the rates of births and deaths during the study period and the geographical locations of the catches. In the first model, we propose a hierarchical modeling for local population sizes in order to obtain the predictive distribution of population abundance for regions not visited by sampling. In this step, a structure for zero-inflated data was adopted to accommodate situations when sampling is performed in areas without the presence of the species. The second proposed model takes into account the movement of the animals among the different sampling areas, generalizing the first model in which we consider the permanence of the animals in the same area. In this case, it became possible to estimate the size of the area of movement of the species and to predict areas with higher abundances of animals. In both models, we propose a Bayesian approach to the inferential process and derive algorithms from simple computational implementation, from the use of augmented data techniques. The frequentist properties of the Bayesian estimators were evaluated by simulation studies and, finally, these modeling proposals were applied to three real data sets of arachnids.