Using a metric of physiological stress to characterize favorable microclimates in an alpine indicator species

Background/Question/Methods

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, and ongoing research suggests pikas are disappearing from locations that are losing sub-surface ice.  However, no studies have demonstrated the pika’s physiological response to sub-surface ice features.  Here we present the first analysis of physiological stress in pikas living in and adjacent to habitats underlain by ice.

Fresh fecal samples were collected non-invasively from two adjacent sites in the Rocky Mountains: one with sub-surface ice (Green Lakes Valley Watershed) and one without (Niwot ridge).  Data related to local habitat characteristics (such as presence or absence of ice, elevation, etc.) were collected at each fecal sampling site, and data loggers were installed to record in situ temperature.  Samples were analyzed for glucocorticoid metabolites (GCMs), a common metric of stress, using previously validated methods.  Variation in GCM concentration among individuals was modeled within an information theoretic network (AIC_c), using the accumulated dataset on habitat characteristics, within generalized linear models.

Results/Conclusions

Validated results indicate lower GCM concentration in samples from the Green Lakes Valley watershed, suggesting that pikas are less stressed in favorable microclimates resulting from sub-surface ice features.  GCM response was well predicted by habitat characteristics commonly associated with sub-surface ice features, such as lower mean summer temperatures.

Sub-surface ice features are key to water production and storage, and should represent an increasingly important component of water resources as surface water storage features (glaciers and snowpacks) diminish in a warming climate.  By establishing a relationship between these features and a metric of physiological stress in pikas, our results suggest that pikas may serve as bio-indicators of hydrological change in alpine ecosystems.  Our novel approach compliments other efforts to model and monitor landscape change that will alter water resource availability.  Using an alpine mammal as a bio-indicator provides information on attributes of change that may not be detected by monitoring physical factors alone, and our method offers managers a sensitive early-warning system.