The American pika (Ochotona princeps) is considered a sentinel species for detecting ecological effects of climate change. Pikas are declining within a large portion of their range, but previous studies have focused only on local pika extinction as a metric of change. We designed a procedure which can provide an earlier warning signal, based on non-invasive sampling and analysis of physiological stress in living pikas. Pikas were sampled at several locations in the Rocky Mountains for the measurement of glucocorticoid metabolites (GCMs) in faeces. Using a time series of faecal pellets from 12 individuals, we detected a significant increase in faecal GCM level in response to capture, thus biologically validating the use of a corticosterone enzyme immunoassay. We also established baseline, peak, and post-peak GCM concentrations for pikas in the Rocky Mountains, which varied according to gender and individual. This is the first study to measure stress hormone metabolites in any species of pika. The methods developed and validated in this study can be used to add non-invasive measurements of physiological stress to pika monitoring programmes and other research designed to assess pika vulnerability to predicted changes in climate. Pika monitoring programmes currently in place use a protocol that relates current site use by pikas with data on local habitat characteristics, such as elevation, to infer potential effects of climate change. Data generated by these monitoring studies can be used to identify the trends in site use by pikas in relationship to habitat covariates. However, this approach does not take into account the role of behavioural thermoregulation and the pika's use of microhabitats to ameliorate variations in climate. Incorporating a stress metric, such as GCM concentration, will provide relatively direct evidence for or against the hypothesis that pikas can be stressed by climate regardless of behavioural adaptations.
- Climate change
- Field endocrinology
- Sentinel alpine species
ASJC Scopus subject areas
- Ecological Modeling
- Nature and Landscape Conservation
- Management, Monitoring, Policy and Law