Hierarchical spatio-temporal models for ecological processes

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file viewed on (April 26, 2007)Vita.Thesis (Ph.D.) University of Missouri-Columbia 2006.Ecosystems are composed of phenomena that propagate in time and space. Often, ecological processes underlying such phenomena are studied separably in various subdisciplines, while larger scale, interlinking mechanisms are overlooked or only speculated about. As grows the burden of global climate change and human disturbance of natural systems, so grows the need for rigorous statistical methods focused on characterizing and forecasting large scale spatiotemporal environmental and ecological processes in the presence of limited data and multiple sources of uncertainty. Hierarchical models offer a powerful means with which to study complex phenomena in space and time. This dissertation develops and illustrates the utility of spatiotemporal hierarchical models for studying ecological phenomena.Includes bibliographical reference

A spatio-temporal model for estimating the long-term effects of air pollution on respiratory hospital admissions in Greater London

It has long been known that air pollution is harmful to human health, as many epidemiological studies have been conducted into its effects. Collectively, these studies have investigated both the acute and chronic effects of pollution, with the latter typically based on individual level cohort designs that can be expensive to implement. As a result of the increasing availability of small-area statistics, ecological spatio-temporal study designs are also being used, with which a key statistical problem is allowing for residual spatio-temporal autocorrelation that remains after the covariate effects have been removed. We present a new model for estimating the effects of air pollution on human health, which allows for residual spatio-temporal autocorrelation, and a study into the long-term effects of air pollution on human health in Greater London, England. The individual and joint effects of different pollutants are explored, via the use of single pollutant models and multiple pollutant indices

Bayesian spatio-temporal CPUE standardization: Case study of European sardine (Sardina pilchardus) along the western coast of Portugal

Understanding the key factors influencing population dynamics of fish stocks requires knowledge of their spatial distribution and seasonal habitat selection, but these spatio-temporal dynamics are often not explicitly included in ecological studies and stock assessment models. This study standardized the data of sardine fishery-dependent catch-per- unit- effort (CPUE) from the west coast of Portugal using Bayesian hierarchical spatio-temporal models (BHSTM) with the integrated nested Laplace approximation (INLA). Sardine CPUE was best explained by length of the vessel, vessel ID, month, year, and location (latitude, longitude). In terms of spatio-temporal distribution, sardine biomass prediction maps showed a constant pattern that changed every quarter of the year. In addition, sardine CPUE index showed a cyclical trend along the year with minimum values in July and maximum peak in November. This approach provided insights on variables and corresponding modelling effects that may be relevant in spatio-temporal fishery-dependent data standardization, and that could be applied to other fish species and areas.En prens

Population growth and persistence in a heterogeneous environment: the role of diffusion and advection

The spatio-temporal dynamics of a population present one of the most fascinating aspects and challenges for ecological modelling. In this article we review some simple mathematical models, based on one dimensional reaction-diffusion-advection equations, for the growth of a population on a heterogeneous habitat. Considering a number of models of increasing complexity we investigate the often contrary roles of advection and diffusion for the persistence of the population. When it is possible we demonstrate basic mathematical techniques and give the critical conditions providing the survival of a population, in simple systems and in more complex resource-consumer models which describe the dynamics of phytoplankton in a water column.Comment: Introductory review of simple conceptual models. 45 pages, 15 figures v2: minor change

Tests d'hypothèses en dynamique des populations fragmentées : développement et applications de modèles d'occupation des sites

Les approches classiques de modèles spatiaux pour les processus binaires de distribution d'espèces (i.e. occupation des sites) présentent trois importantes carences. i) Elles ne prennent pas explicitement en compte l'incertitude dans le processus d'échantillonnage. ii) Il y a un manque de modèles spatio-temporels, notamment hiérarchique. iii) La plupart des modèles existants sont de type phénoménologique et ne considèrent pas explicitement les mécanismes écologiques sous-jacents. Cette thèse répond à ces limitations en présentant des modèles spatio-temporels d'occupation des sites pour des processus écologiques dynamiques. Ces modèles sont appliqués à des sujets essentiels en écologie, tels que la sélection de l'habitat, les espèces invasives et les changements climatiques. Comprendre la dynamique d'occupation des sites permet de prédire les changements d'occupation qui accompagneront des modifications de l'habitat et de prendre des décisions adaptées en gestion des populations.Classical approaches to the development of spatial models for binary processes of species distribution (i.e. occupancy processes) present three important deficiencies. i) They do not explicitly accommodate sampling uncertainty. ii) There is a lack of spatio-temporal occupancy models, especially in the framework of hierarchical modeling. iii) Most of existing models are phenomenological and do not explicitly consider underlying ecological mechanisms. This thesis develops spatio-temporal occupancy models for dynamical ecological processes in order to respond to these limitations while incorporating scientific knowledge in every modeling step. Those models are applied to critical ecological topics ranging from the spread of invasive species to habitat selection via climate changes. Understanding range and occupancy dynamics will permit prediction of occupancy changes that are likely to accompany future changes and hopefully will permit informed attempts to mediate changes in occupancy

Focus on the Role of D-serine and D-amino Acid Oxidase in Amyotrophic Lateral Sclerosis/Motor Neuron Disease (ALS)

Highly pathogenic avian influenza (HPAI) H5N1 virus has been circulating in Vietnam since 2003, while outbreaks of HPAI H5N6 virus are more recent, having only been reported since 2014. Although the spatial distribution of H5N1 outbreaks and risk factors for virus occurrence have been extensively studied, there have been no comparative studies for H5N6. Data collected through active surveillance of Vietnamese live-bird markets (LBMs) between 2011 and 2015 were used to explore and compare the spatio-temporal distributions of H5N1- and H5N6-positive LBMs. Conditional autoregressive models were developed to quantify spatio-temporal associations between agro-ecological factors and the two HPAI strains using the same set of predictor variables. Unlike H5N1, which exhibited a strong north-south divide, with repeated occurrence in the extreme south of a cluster of high-risk provinces, H5N6 was homogeneously distributed throughout Vietnam. Similarly, different agro-ecological factors were associated with each strain. Sample collection in the months of January and February and higher average maximum temperature were associated with higher likelihood of H5N1 positive market-day status. The likelihood of market-days being positive for H5N6 increased with decreased river density, and with successive Rounds of data collection. This study highlights marked differences in spatial patterns and risk factors for H5N1 and H5N6 in Vietnam, suggesting the need for tailored surveillance and control approaches

Melding Wildlife Surveys to Improve Conservation Inference

Integrated models are a popular tool for analyzing species of conservation concern. Species of conservation concern are often monitored by multiple entities that generate several datasets. Individually, these datasets may be insufficient for guiding management due to low spatio-temporal resolution, biased sampling, or large observational uncertainty. Integrated models provide an approach for assimilating multiple datasets in a coherent framework that can compensate for these deficiencies. While conventional integrated models have been used to assimilate count data with surveys of survival, fecundity, and harvest, they can also assimilate ecological surveys that have differing spatio-temporal regions and observational uncertainties. Motivated by independent aerial and ground surveys of lesser prairie-chicken abundance, we developed an integrated modeling approach that assimilates density estimates derived from surveys with distinct sources of observational error into a joint framework that provides shared inference on spatio-temporal trends. For implementation, we model these data using a Bayesian Markov melding approach and apply several data augmentation strategies for efficient sampling. Our integrated model decreased uncertainty in annual density estimates, facilitated prediction at unsampled regions, and quantified the inferential cost associated with reduced survey effort.Comment: 22 pages; 5 figures, 1 table, submitted to Biometric

A rigorous statistical framework for spatio-temporal pollution prediction and estimation of its long-term impact on health

In the United Kingdom, air pollution is linked to around 40000 premature deaths each year, but estimating its health effects is challenging in a spatio-temporal study. The challenges include spatial misalignment between the pollution and disease data; uncertainty in the estimated pollution surface; and complex residual spatio-temporal autocorrelation in the disease data. This article develops a two-stage model that addresses these issues. The first stage is a spatio-temporal fusion model linking modeled and measured pollution data, while the second stage links these predictions to the disease data. The methodology is motivated by a new five-year study investigating the effects of multiple pollutants on respiratory hospitalizations in England between 2007 and 2011, using pollution and disease data relating to local and unitary authorities on a monthly time scale