Browsing by Subject "Bayesian modeling"
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Item Open Access Assessing the effectiveness of the Neuse nitrogen TMDL program and its impacts on estuarine chlorophyll dynamics(2011) Alameddine, IbrahimCoastal eutrophication is a complex process that is caused largely by anthropogenic nutrient enrichment. Estuaries are particularly susceptible to nutrient impairment, owing to their intimate connection with the contributing watersheds. Estuaries experiencing accelerating eutrophication are subject to a loss of key ecological functions and services. This doctoral dissertation presents the development and implementation of an integrated approach toward assessing the water quality in the Neuse Estuary following the implementation of the total maximum daily load (TMDL) program in the Neuse River basin. In order to accomplish this task, I have developed a series of water quality models and modeling strategies that can be effectively used in assessing nutrient based eutrophication. Two watershed-level nutrient loading models that operate on a different temporal scale are developed and used to quantify nitrogen loading to the Neuse Estuary over time. The models are used to probabilistically assess the success of the adopted mitigation measures in achieving the 30 % load reduction goal stipulated by the TMDL. Additionally, a novel structure learning approach is adopted to develop a Bayesian Network (BN) model that describes chlorophyll dynamics in the Upper Neuse Estuary. The developed BN model is compared to pre-TMDL models to assess any changes in the role that nutrient loading and physical forcings play in modulating chlorophyll levels in that section of the estuary. Finally, a set of empirical models are developed to assess the water quality monitoring program in the estuary, while also exploring the possibility of incorporating remotely sensed satellite data in an effort to augment the existing in-situ monitoring programs.
Item Open Access Bayesian Models for Combining Information from Multiple Sources(2022) Tang, JiuruiThis dissertation develops Bayesian methods for combining information from multiple sources. I focus on developing Bayesian bipartite modeling for simultaneous regression and record linkage, as well as leveraging auxiliary information on marginal distributions for handling item and unit nonresponse and accounting for survey weights.
The first contribution is a Bayesian hierarchical model that allows analysts to perform simultaneous linear regression and probabilistic record linkage. This model allows analysts to leverage relationships among the variables to improve linkage quality. It also potentially offers more accurate estimates of regression parameters compared to approaches that use a two-step process, i.e., link the records first, then estimate the linear regression on the linked data. I propose and evaluate three Markov chain Monte Carlo algorithms for implementing the Bayesian model.
The second contribution is examining the performance of an approach for generating multiple imputation data sets for item nonresponse. The method allows analysts to use auxliary information. I examine the approach via simulation studies with Poisson sampling. I also give suggestions on parameter tuning.
The third contribution is a model-based imputation approach that can handle both item and unit nonresponse while accounting for auxiliary margins and survey weights. This approach includes an innovative combination of a pattern mixture model for unit nonresponse and a selection model for item nonresponse. Both unit and item nonresponse can be nonignorable. I demonstrate the model performance with simulation studies under the situations when the design weights for unit respondents are known and when they are not. I show that the model can generate multiple imputation data sets that both retain the relationship among survey variables and yield design-based estimates that agree with auxiliary margins. I use the model to analyze voter turnout overall and across subgroups in North Carolina, with data from the 2018 Current Population Survey.
Item Open Access Environmental Impacts on the Population Dynamics of a Tropical Seabird in the Context of Climate Change: Improving Inference through Hierarchical Modeling(2008-04-25) Colchero, FernandoUnder the increasing threat of climate change, it is imperative to understand the impact that environmental phenomena have on the demography and behavior of natural populations. In the last few decades an ever increasing body of research has documented dramatic changes in mortality rates and breeding phenology for a large number of species. A number of these have been attributed to the current trends in climate change, which have been particularly conspicuous in bird populations. However, datasets associated to these natural populations as well as to the environmental variables that affect their biology tend to be partial and incomplete. Thus, ecological research faces the urgent need to tackle these questions while at the same time develop inferential models that can handle the complex structure of these datasets and their associated uncertainty. Therefore, my dissertation research has focused on two main objectives: 1) to understand the relationship that demographic rates and breeding phenology of a colony of seabirds has with the environment in the context of climate change; and 2) to use and develop models that can encompass the complex structure of these natural systems, while also extending the process not only to inference but to building predictions. I divided this work in three research projects; for the first one I developed a hierarchical Bayesian model for age-specific survival for long lived species with capture-recapture data that allows the use of incomplete data (i.e. left-truncated and right-censored), and builds predictions of years of birth and death for all individuals while also drawing inference on the survivorship function. I compared this method to more traditional ones and address their limitations and advantages. My second research chapter makes use of this method to determine the age-specific survivorship of the Dry Tortugas sooty tern population, and explores the effect of changes in sea surface temperature on their cohort mortality rates. Finally, my third research chapter addresses the dramatic shift in breeding season experienced by the Dry Tortugas sooty tern colony, the most unprecedented shift reported for any bird species. I explore the role of climatic and weather variables as triggering mechanisms.
Item Open Access Longterm Approaches to Assessing Tree Community Responses to Resource Limitation and Climate Variation(2011) Bell, David McFarlandThe effects of climate change on forest dynamics will be determined by tree responses at different life-stages and different scales -- from establishment to maturity and from individuals to populations. Studies incorporating local factors, such as natural enemies, competition, or tree physiology, with sufficient variation in climate are lacking. The importance of global and regional climate variation vs. local conditions and responses is poorly understood and may only be addressed with large datasets capturing sufficient environmental variation. This dissertation uses several large datasets to examine tree demographic and ecophysiological responses to light, moisture, predation, and climate in eastern temperate forests of North Carolina.
First, I use a 19-yr seed rain record from 13 forest plots in the piedmont, transition zone, and mountains to examine how climate-mediated seed maturation and density-dependent seed predation processes increase population reproductive variation in nine temperate tree species (Chapter 1). I address several hypotheses explaining interannual reproductive variation, such as resource matching, predator satiation, and pulse resource dynamics. My results indicate that (1) interannual reproductive variation increased as a result of seed maturation and seed predation processes, (2) seed maturation rates increased under warm, wet conditions, and (3) seed predation rates exhibited negative and positive density-dependence, depending of tree species and type of seed predator (specialist insects vs. generalist vertebrates). Because positive density-dependent seed predation dampened and negative density-dependent seed predation amplified the effects of climate-mediated maturation on reproductive variation, this study showed evaluations of tree reproduction need to incorporate both climate and seed predation.
Next, I use an 11-yr record of annual tree seedling growth and survival in 20 tree species planted in the piedmont and mountains to quantify individual tree seedling growth and survival responses to spatial variation in resources and temporal variation in climate (Chapter 2). First, I tested whether height-mediated growth provides an advantage to large individuals in all environments by amplifying responses to light and moisture or only when those resources were plentiful. Second, I tested whether allometric and survival responses differed among species based on life-history strategies. Individual height amplified tree seedling growth. However, some species exhibited amplification at moderate to high resource levels as well as depression of growth in large individuals growing in low light and moisture environments. Shade intolerant species exhibited an increasing ratio of height to diameter growth and increasing survival probability with both increasing light and moisture resources. Conversely, shade tolerant species exhibited decreasing height to diameter ratio with increasing light, possibly because of biomass allocation toward acquisition of limiting light resources. Despite relative small effects of drought and winter temperature of tree seedling demography, the results of this study indicate that individual tree seedlings sensitive to light and moisture environments, such as large seedlings and seedlings of shade intolerant species, growing in shaded or xeric sites may be particularly vulnerable to climate induced mortality.
Finally, I examine interannual and interspecific variation in canopy conductance using four years of environmental (vapor pressure deficit, above canopy light, and soil moisture) and stem sap flux data from heat dissipation probes for six co-occurring tree species. I developed a state-space modeling framework for predicting canopy conductance and transpiration which incorporates uncertainty in canopy and observation uncertainty. This approach is used to evaluate the degree to which co-occur deciduous tree species exhibited drought tolerating and drought avoiding canopy responses and whether these patterns were maintained in the face of interannual variation in environmental drivers. Comparisons of canopy conductance responses to environmental forcing across species and years highlighted the importance of tree sensitivity to moisture limitation, both in terms of high vapor pressure deficit and low soil moisture, and tree hydraulic characteristics within diverse forest communities. The state-space model produced similar parameter estimates to the more traditional boundary line analysis, performed well in terms of in-sample and out-of-sample prediction of sap flux observations, and provided for coherent incorporation of parameter, process, and observation errors in predicting missing data (i.e., gap-filling), canopy conductance, and transpiration.
Much needs to be learned about forest community responses to climate change, however these responses depend on local growing conditions (light and moisture), the life-stage being examined (seedlings, juveniles, or mature trees), and the scale of inference (individuals, canopies, or populations). Because climate change will not occur in isolation from other factors, such as stand age or disturbance, studies must characterize tree responses across multidimensional gradients in growing conditions. This dissertation addresses these challenges using large demographic and ecophysiological datasets well-suited for global change research.