Modeling Arthropod Traits in a Bayesian Framework

Abstract

As ectothermic organisms cannot fully regulate their own body temperature, they are dependent on external sources of heat. Because of this, temperature has a large influence on many of their physiological traits that affect fitness, such as development time or lifespan, which in turn, alters their ability to transmit disease-causing pathogens. The effect of temperature on traits is of interest as it can aid in developing effective control and mitigation strategies under changing climates. Researchers have been able to model this relationship using thermal performance curves (TPCs), or curves that quantify how an organism's performance changes as a function of temperature. However, most thermal analyses have focused on finding the ``best" TPC equation, often overlooking other key considerations. The purpose of this work is to use Bayesian methods to fit the most accurate TPCs with the least uncertainty based on data availability, as well as investigate certain overlooked pieces. Specifically, we conduct simulation experiments to explore the effect of the data-generating mechanism, or the distribution of the data underneath the curve. Additionally, we fit hierarchical models in two scenarios, within a genus and between genera, to explore the potential for information-sharing and generalization between species to improve curve estimation. Lastly, we extend these hierarchical models in a single-species context to incorporate relative humidity directly into the TPC through its parameters, providing an effective framework for modeling multiple environmental stressors simultaneously.