Date of Award
Open Access Thesis
Predicting spatial and temporal patterns in the responses of organisms and ecosystems to climate change has emerged as a major focus of macrophysiological research, with much work centered on the impacts of temperature. A potential difficulty lies in the observation that measures of 'habitat' such as air, land and sea surface temperature often differ greatly from the body temperature actually experienced by organisms, as the latter drives reproduction and survival. As a result, it is unclear how often these simple measurements of habitat are 'good enough' for predicting physiological stress in the field, and when more complicated methods are needed. Using a dataset of body temperatures of rocky intertidal mussels, I compared the predictive capacity of four methods for mapping patterns of physiological stress at five sites along the west coast of the United States. Using a thermal physiology framework, I divided predicted and measured temperatures into lethal, suboptimal (both too hot and too cold) and optimal categories. Results suggest that while the various methods have similar accuracy (skill) when compared using common metrics such as Root Mean Square Error, they differ significantly in their ability to predict physiological responses, especially extremely high temperatures. My results emphasize that tests of model skill need to be matched to physiologically meaningful metrics when attempting to predict patterns of stress in the field.
Kish, N.(2013). Modeling Approaches, Physiological Responses, and Climate Change: How Good is "Good Enough?". (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/2342