Frequentemente eventos intermediários fornecem informações mais detalhadas sobre o processo da doença ou recuperação, por exemplo, e permitem uma maior precisão na previsão do prognóstico de pacientes. Tais eventos não fatais durante o curso da doença podem ser vistos como transições de um estado para outro. A ideia básica dos modelos multiestado é que o indivíduo se move através de uma serie de estados em tempo contínuo, sendo possível estimar as probabilidades e intensidades de transição entre eles e o efeito das covariáveis associadas a cada transição. Muitos estudos incluem o agrupamento dos tempos de sobrevivência como, por exemplo, em estudos multicêntricos, e também é de interesse estudar a evolução dos pacientes ao longo do tempo, caracterizando assim dados multiestado agrupados. Devido ao fato de os dados virem de diferentes centros/grupos, os tempos de falha desses indivíduos estarem agrupados e a fatores de risco comuns não observados, é interessante considerar o uso de fragilidades para que possamos capturar a heterogeneidade entre os grupos no risco para os diferentes tipos de transição, além de considerar a estrutura de dependência entre transições dos indivíduos de um mesmo grupo. Neste trabalho apresentamos a metodologia dos modelos multiestado, dos modelos de fragilidade e, em seguida, a integração dos modelos multiestado com modelos de fragilidade, tratando do seu processo de estimação paramétrica e semiparamétrica. O estudo de simulação realizado mostrou a importância de considerarmos fragilidade sem modelos multiestado agrupados, pois sem considerá-las, as estimativas tornam-se viesadas. Além disso, verificamos as propriedades frequentistas dos estimadores do modelo multiestado com fragilidades aninhadas. Por fim, como um exemplo de aplicação a um conjunto de dados reais, utilizamos o processo de recuperação de transplante de medula óssea de pacientes tratados em quatro hospitais. Fizemos uma comparação de modelos por meio das medidas de qualidade do ajuste AIC e BIC, chegando à conclusão de que o modelo que considera dois efeitos aleatórios (uma para o hospital e outro para a interação transição-hospital) ajusta-se melhor aos dados. Além de considerar a heterogeneidade entre os hospitais, tal modelo também considera a heterogeneidade entre os hospitais em cada transição. Sendo assim, os valores das fragilidades estimadas da interação transição-hospital revelam o quão frágeis os pacientes de cada hospital são para experimentarem determinado tipo de evento/transição. / Often intermediate events provide more detailed information about the disease process or recovery, for example, and allow greater accuracy in predicting the prognosis of patients. Such non-fatal events during the course of the disease can be seen as transitions from one state to another. The basic idea of a multistate models is that the person moves through a series of states in continuous time, it is possible to estimate the transition probabilities and intensities between them and the effect of covariates associated with each transition. Many studies include the grouping of survival times, for example, in multi-center studies, and is also of interest to study the evolution of patients over time,characterizing grouped multistate data. Because the data coming from different centers/groups, the failure times these individuals are grouped and the common risk factors not observed, it is interesting to consider the use of frailty so that we can capture the heterogeneity between the groups at risk for different types of transition, in addition to considering the dependence structure between transitions of individuals of the same group. In this work we present the methodology of multistate models, frailty models and then the integration of models with multi-state fragility models, dealing with the process of parametric and semi-parametric estimation. The conducted simulation study showed the importance of considering frailty in grouped multistate models, because without conside- ring them, the estimates become biased. Furthermore, we find the frequentist properties of estimators of multistate model with nested frailty. Finally, as an application example to a set of real data, we use the process of bone marrow transplantation recovery of patients in four hospitals. We did a comparison of models through quality measures setting AIC and BIC, coming to the conclusion that the model considers two random effects (one for the hospital and another for interaction transition-hospital) fits the data better. In addition to considering the heterogeneity between hospitals, such a model also considers the heterogeneity between hospitals in each transition. Thus,the values of the frailty estimated interaction transition-hospital reveal how fragile patients from each hospital are to experience certain type of event/transition.

Wetlands of the Prairie Pothole Region of North America provide habitat for over 50% of the continent's breeding waterfowl, but most of the region's wetlands have been lost or degraded through intensive agricultural development. Despite widespread wetland losses in much of the Canadian prairies, there is little information about trends in degradation of remaining wetlands. Using habitat data collected for ~10,500 wetlands across the Canadian prairies during annual waterfowl surveys, 1985-2005, I employed multistate models in Program MARK to estimate rates of impact and recovery of wetlands resulting from agricultural activities. Then, I characterized the incidence of agricultural degradation to these wetlands. Rates of impact to wetland margins (natural vegetation around flooded basins) declined over time, likely due to a decreasing percentage of unaffected wetlands; recovery rates for margins were always lower than impact rates, suggesting increased cumulative degradation of wetlands over time. Unlike margins, impact and recovery rates for basins fluctuated with spring pond densities. Shallow ephemeral wetlands located in agricultural fields had the highest impact and lowest recovery rates. Multistate modeling could also be used to estimate rates associated with other landscape processes.<p><p> My second objective was to determine whether physical characteristics of prairie Canada wetlands could be used to predict breeding duck abundance. First, I sought to determine how pre-existing models developed in the Dakotas (USA) performed when predicting breeding duck abundances on Canadian prairie wetlands. I related duck pair abundance to pond area, and then compared observed to predicted duck abundance. The Dakota models performed reasonably well in predicting numbers of blue-winged teal (<i>Anas discors</i>), gadwall (<i>A. strepera</i>), and northern pintail (<i>A. acuta</i>), but predicted fewer mallards (<i>A. platyrhynchos</i>) and northern shovelers (<i>A. clypeata</i>) than were observed on wetlands. Pond area was an important predictor of duck abundance in all models, but results were less biased and more consistent in models developed specifically for Canadian wetlands. Spatiotemporal variation in the relationship of breeding duck abundance and wetland characteristics was also affected by regional duck and pond densities. Overall, the new applications and models developed and validated in this study will be useful for wetland and waterfowl management in the Canadian prairies.

Wetlands of the Prairie Pothole Region of North America provide habitat for over 50% of the continent's breeding waterfowl, but most of the region's wetlands have been lost or degraded through intensive agricultural development. Despite widespread wetland losses in much of the Canadian prairies, there is little information about trends in degradation of remaining wetlands. Using habitat data collected for ~10,500 wetlands across the Canadian prairies during annual waterfowl surveys, 1985-2005, I employed multistate models in Program MARK to estimate rates of impact and recovery of wetlands resulting from agricultural activities. Then, I characterized the incidence of agricultural degradation to these wetlands. Rates of impact to wetland margins (natural vegetation around flooded basins) declined over time, likely due to a decreasing percentage of unaffected wetlands; recovery rates for margins were always lower than impact rates, suggesting increased cumulative degradation of wetlands over time. Unlike margins, impact and recovery rates for basins fluctuated with spring pond densities. Shallow ephemeral wetlands located in agricultural fields had the highest impact and lowest recovery rates. Multistate modeling could also be used to estimate rates associated with other landscape processes.<p><p> My second objective was to determine whether physical characteristics of prairie Canada wetlands could be used to predict breeding duck abundance. First, I sought to determine how pre-existing models developed in the Dakotas (USA) performed when predicting breeding duck abundances on Canadian prairie wetlands. I related duck pair abundance to pond area, and then compared observed to predicted duck abundance. The Dakota models performed reasonably well in predicting numbers of blue-winged teal (<i>Anas discors</i>), gadwall (<i>A. strepera</i>), and northern pintail (<i>A. acuta</i>), but predicted fewer mallards (<i>A. platyrhynchos</i>) and northern shovelers (<i>A. clypeata</i>) than were observed on wetlands. Pond area was an important predictor of duck abundance in all models, but results were less biased and more consistent in models developed specifically for Canadian wetlands. Spatiotemporal variation in the relationship of breeding duck abundance and wetland characteristics was also affected by regional duck and pond densities. Overall, the new applications and models developed and validated in this study will be useful for wetland and waterfowl management in the Canadian prairies.

Submitted by Luciana Sebin (lusebin@ufscar.br) on 2016-09-23T13:03:28Z No. of bitstreams: 1 DissRSC.pdf: 1649931 bytes, checksum: c3449a4367ea7de9e327fa7dc9110861 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-09-27T19:22:02Z (GMT) No. of bitstreams: 1 DissRSC.pdf: 1649931 bytes, checksum: c3449a4367ea7de9e327fa7dc9110861 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-09-27T19:22:09Z (GMT) No. of bitstreams: 1 DissRSC.pdf: 1649931 bytes, checksum: c3449a4367ea7de9e327fa7dc9110861 (MD5) / Made available in DSpace on 2016-09-27T19:22:16Z (GMT). No. of bitstreams: 1 DissRSC.pdf: 1649931 bytes, checksum: c3449a4367ea7de9e327fa7dc9110861 (MD5) Previous issue date: 2016-03-31 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Often intermediate events provide more detailed information about the disease process or recovery, for example, and allow greater accuracy in predicting the prognosis of patients. Such non-fatal events during the course of the disease can be seen as transitions from one state to another. The basic idea of a multistate models is that the person moves through a series of states in continuous time, it is possible to estimate the transition probabilities and intensities between them and the effect of covariates associated with each transition. Many studies include the grouping of survival times, for example, in multi-center studies, and is also of interest to study the evolution of patients over time, characterizing grouped multistate data. Because the data coming from different centers/groups, the failure times these individuals are grouped and the common risk factors not observed, it is interesting to consider the use of frailty so that we can capture the heterogeneity between the groups at risk for different types of transition, in addition to considering the dependence structure between transitions of individuals of the same group. In this work we present the methodology of multistate models, frailty models and then the integration of models with multi-state fragility models, dealing with the process of parametric and semi-parametric estimation. The conducted simulation study showed the importance of considering frailty in grouped multistate models, because without considering them, the estimates become biased. Furthermore, we find the frequentist properties of estimators of multistate model with nested frailty. Finally, as an application example to a set of real data, we use the process of bone marrow transplantation recovery of patients in four hospitals.We did a comparison of models through quality teasures setting AIC and BIC, coming to the conclusion that the model considers two random effects (one for the hospital and another for interaction transition-hospital) fits the data better. In addition to considering the heterogeneity between hospitals, such a model also considers the heterogeneity between hospitals in each transition. Thus, the values of the frailty estimated interaction transition-hospital reveal how fragile patients from each hospital are to experience certain type of event/transition. / Frequentemente eventos intermediários fornecem informações mais detalhadas sobre o processo da doença ou recuperação, por exemplo, e permitem uma maior precisão na previsão do prognóstico de pacientes. Tais eventos não fatais durante o curso da doença podem ser vistos como transições de um estado para outro. A ideia básica dos modelos multiestado é que o indivíduo se move através de uma série de estados em tempo contínuo, sendo possível estimar as probabilidades e intensidades de transição entre eles e o efeito das coivaráveis associadas a cada transição. Muitos estudos incluem o agrupamento dos tempos de sobrevivência como, por exemplo, em estudos multicêntricos, e também é de interesse estudar a evolução dos pacientes ao longo do tempo, caracterizando assim dados multiestado agrupados. Devido ao fato de os dados virem de diferentes centros/grupos, os tempos de falha desses indivíduos estarem agrupados e a fatores de risco comuns não observados, é interessante considerar o uso de fragilidades para que possamos capturar a heterogeneidade entre os grupos no risco para os diferentes tipos de transição, além de considerar a estrutura de dependência entre transições dos indivíduos de um mesmo grupo. Neste trabalho apresentamos a metodologia dos modelos multiestado, dos modelos de fragilidade e, em seguida, a integração dos modelos multiestado com modelos de fragilidade, tratando do seu processo de estimação paramétrica e semiparamétrica. O estudo de simulação realizado mostrou a importância de considerarmos fragilidades em modelos multiestado agrupados, pois sem consider´a-las, as estimativas tornam-se viesadas. Al´em disso, verificamos as propriedades frequentistas dos estimadores do modelo multiestado com fragilidades aninhadas. Por fim, como um exemplo de aplicação a um conjunto de dados reais, utilizamos o processo de recuperação de transplante de medula óssea de pacientes tratados em quatro hospitais. Fizemos uma comparação de modelos por meio das medidas de qualidade do ajuste AIC e BIC, chegando `a conclusão de que o modelo que considera dois efeitos aleatórios (uma para o hospital e outro para a interação transição-hospital) ajusta-se melhor aos dados. Além de considerar a heterogeneidade entre os hospitais, tal modelo também considera a heterogeneidade entre os hospitais em cada transição. Sendo assim, os valores das fragilidades estimadas da interação transição-hospital revelam o quão frágeis os pacientes de cada hospital são para experimentarem determinado tipo de evento/transição.

Range, areas of concentrated activity, and dispersal characteristics for juvenile Steller sea lions Eumetopias jubatus in the endangered western population (west of 144° W in the Gulf of Alaska) are poorly understood. This study quantified space use by analyzing post-release telemetric tracking data from satellite transmitters externally attached to n = 65 juvenile (12-25 months; 72.5 to 197.6 kg) Steller sea lions (SSLs) captured in Prince William Sound (60°38'N -147°8'W) or Resurrection Bay (60°2'N -149°22'W), Alaska, from 2003-2011. The analysis divided the sample population into 3 separate groups to quantify differences in distribution and movement. These groups included sex, the season when collected, and the release type (free ranging animals which were released immediately at the site of capture, and transient juveniles which were kept in captivity for up to 12 weeks as part of a larger ongoing research program). Range-use was first estimated by using the minimum convex polygon (MCP) approach, and then followed with a probabilistic kernel density estimation (KDE) to evaluate both individual and group utilization distributions (UDs). The LCV method was chosen as the smoothing algorithm for the KDE analysis as it provided biologically meaningful results pertaining to areas of concentrated activity (generally, haulout locations). The average distance traveled by study juveniles was 2,131 ± 424 km. The animals mass at release (F[subscript 1, 63] = 1.17, p = 0.28) and age (F[subscript 1, 63] = 0.033, p = 0.86) were not significant predictors of travel distance. Initial MCP results indicated the total area encompassed by all study SSLs was 92,017 km², excluding land mass. This area was heavily influenced by the only individual that crossed over the 144°W Meridian, the dividing line between the two distinct population segments. Without this individual, the remainder of the population (n = 64) fell into an area of 58,898 km². The MCP area was highly variable, with a geometric average of 1,623.6 km². Only the groups differentiated by season displayed any significant difference in area size, with the Spring/Summer (SS) groups MCP area (Mdn = 869.7 km²) being significantly less than that of the Fall/Winter (FW) group (Mdn = 3,202.2 km²), U = 330, p = 0.012, r = -0.31. This result was not related to the length of time the tag transmitted (H(2) = 49.65, p = 0.527), nor to the number of location fixes (H(2) = 62.77, p = 0.449). The KDE UD was less variable, with 50% of the population within a range of 324-1,387 km2 (mean=690.6 km²). There were no significant differences in area use associated with sex or release type (seasonally adjusted U = 124, p = 0.205, r = -0.16 and U = 87, p = 0.285, r = -0.13, respectively). However, there were significant differences in seasonal area use: U = 328, p = 0.011, r = -0.31. There was no relationship between the UD area and the amount of time the tag remained deployed (H(2) = 45.30, p = 0.698). The kernel home range (defined as 95% of space use) represented about 52.1% of the MCP range use, with areas designated as "core" (areas where the sea lions spent fully 50% of their time) making up only about 6.27% of the entire MCP range and about 11.8% of the entire kernel home range. Area use was relatively limited – at the population level, there were a total of 6 core areas which comprised 479 km². Core areas spanned a distance of less than 200 km from the most western point at the Chiswell Islands (59°35'N -149°36'W) to the most eastern point at Glacier Island (60°54'N -147°6'W). The observed differences in area use between seasons suggest a disparity in how juvenile SSLs utilize space and distribute themselves over the course of the year. Due to their age, this variation is less likely due to reproductive considerations and may reflect localized depletion of prey near preferred haul-out sites and/or changes in predation risk. Currently, management of the endangered western and threatened eastern population segments of the Steller sea lion are largely based on population trends derived from aerial survey counts and terrestrial-based count data. The likelihood of individuals to be detected during aerial surveys, and resulting correction factors to calculate overall population size from counts of hauled-out animals remain unknown. A kernel density estimation (KDE) analysis was performed to delineate boundaries around surveyed haulout locations within Prince William Sound-Kenai Fjords (PWS-KF). To closely approximate the time in which population abundance counts are conducted, only sea lions tracked during the spring/summer (SS) months (May 10-August 10) were chosen (n = 35). A multiple state model was constructed treating the satellite location data, if it fell within a specified spatiotemporal context, as a re-encounter within a mark-recapture framework. Information to determine a dry state was obtained from the tags time-at-depth (TAD) histograms. To generate an overall terrestrial detection probability 1) The animal must have been within a KDE derived core-area that coincided with a surveyed haulout site 2) it must have been dry and 3) it must have provided at least one position during the summer months, from roughly 11:00 AM-5:00 PM AKDT. A total of 10 transition states were selected from the data. Nine states corresponded to specific surveyed land locations, with the 10th, an "at-sea" location (> 3 km from land) included as a proxy for foraging behavior. A MLogit constraint was used to aid interpretation of the multi-modal likelihood surface, and a systematic model selection process employed as outlined by Lebreton & Pradel (2002). At the individual level, the juveniles released in the spring/summer months (n = 35) had 85.3% of the surveyed haulouts within PWS-KF encompass KDE-derived core areas (defined as 50% of space use). There was no difference in the number of surveyed haulouts encompassed by core areas between sexes (F[subscript 1, 33] << 0.001, p = 0.98). For animals held captive for up to 12 weeks, 33.3% returned to the original capture site. The majority of encounter probabilities (p) fell between 0.42 and 0.78 for the selected haulouts within PWS, with the exceptions being Grotto Island and Aialik Cape, which were lower (between 0.00-0.17). The at-sea (foraging) encounter probability was 0.66 (± 1 S.E. range 0.55-0.77). Most dry state probabilities fell between 0.08-0.38, with Glacier Island higher at 0.52, ± 1 S.E. range 0.49-0.55. The combined detection probability for hauled-out animals (the product of at haul-out and dry state probabilities), fell mostly between 0.08-0.28, with a distinct group (which included Grotto Island, Aialik Cape, and Procession Rocks) having values that averaged 0.01, with a cumulative range of ≈ 0.00-0.02 (± 1 S.E.). Due to gaps present within the mark-recapture data, it was not possible to run a goodness-of-fit test to validate model fit. Therefore, actual errors probably slightly exceed the reported standard errors and provide an approximation of uncertainties. Overall, the combined detection probabilities represent an effort to combine satellite location and wet-dry state telemetry and a kernel density analysis to quantify the terrestrial detection probability of a marine mammal within a multistate modeling framework, with the ultimate goal of developing a correction factor to account for haulout behavior at each of the surveyed locations included in the study. / Graduation date: 2013