A Mathematical Modeling Approach to the Cort-Fitness Hypothesis

The Cort-Fitness Hypothesis has generated much interest from investigators integrating field endocrinology with evolutionary biology, ecology, and conservation. The hypothesis was developed to test the assumption that if glucocorticoid levels increase with environmental challenges and fitness decreases with environmental challenges, then there should be a negative relationship between baseline glucocorticoid levels and fitness. Indeed, studies across diverse taxa have found that the relationship between baseline glucocorticoid levels and fitness are not consistent: some studies show a positive relationship, others negative, and some show no correlation. Hence, a deeper understanding of the mechanisms underlying the relationship between baseline glucocorticoid levels, environmental challenges, and fitness is needed. We propose a mathematical model representing the links between baseline glucocorticoid levels, environmental challenges, and fitness. Our model describes how variation in the predictability and intensity of environmental challenges, reproductive strategies, and fitness metrics can all contribute to the variability observed in empirical tests of the Cort-Fitness Hypothesis. We provide qualitative results showing that much of the inconsistency in previous studies can be explained and we discuss how the model can be used to inform future Cort-Fitness studies.