Assessing baseline stress physiology as an integrator of environmental quality in a wild avian population: Implications for use as a conservation biomarker

Abstract

Stress hormones (i.e., glucocorticoids such as corticosterone and cortisol) have been widely proposed as biomarkers of habitat quality and disturbance. However, there is growing evidence that baseline glucocorticoid (GC) levels are highly context-dependent, potentially confounding their utility for inferring population-level disturbance depending on the life history stage and the duration, severity, and type of environmental change being measured. Determining which aspects of an organism's environment are consistently reflected by baseline GC levels is therefore of paramount importance to establishing how they may be best suited to conservation monitoring goals. We investigated the relationship between baseline GC levels and three extrinsic (food availability, inter-specific nest competition, intra-specific competition) and two intrinsic (reproductive investment, body condition) environmental contexts in breeding female tree swallows (Tachycineta bicolor) at two reproductive stages. We combined this with a manipulation of energetic demand (i.e., a decrease in foraging profitability) to determine whether baseline GCs reflect the extrinsic or intrinsic environment when females are faced with an unexpected disturbance. Baseline GC levels were not reflective of any environmental component in control females, regardless of reproductive stage. However, levels increased and were reflective of a decrease in body mass when females were challenged during the offspring provisioning period. Our findings suggest that baseline GCs may not always be indicative of the environmental contexts we associate with variation in habitat quality, particularly when individuals are operating within their expected energetic demand. In a conservation sense, baseline GCs may be more valuable in reflecting unexpected perturbations, which could limit their applicability as sensitive, predictive biomarkers across a diversity of systems.