Tools for the occurrence of free-living and symbiotic organisms in space and time.

摘要

This doctoral dissertation broadly aims to improve methods for understanding the occurrence of organisms in space and time, including organisms that cause disease. The processes that drive occurrence are often represented mathematically as ecological theories, which can be applied to uncover actionable insights into disease management. This was a focus of my first chapter which characterized the gap between disease ecology theory and infectious disease management. My second chapter focused on filling in a gap theoretically at the intersection of multi-host pathogens and host community disassembly. Specifically, I construct a theoretical model to evaluate disease consequences when hosts are extirpated according to a variety of rules predicted from community ecology. The third chapter develops a method to link representations of latent processes that drive species occurrence to observable (with error) data, with the goal of understanding multiple causal pathways in an occupancy model. The fourth also extends occupancy models, but to allow for multi-host multi-symbiont (parasites included) systems. Chapter five is somewhat different topically, focusing on missing species trait interpolation, but methodologically is not much of a departure, drawing on Bayesian hierarchical modeling as in the previous two chapters. The final chapter develops a theoretical model to generate predictions about the effect of host diversity on the diversity and transmission of symbiotic organisms.