Beyond the Mean: Biological Impacts of Cryptic Temperature Change.

Abstract

Studies have typically used shifts in mean temperatures to make predictions about the biotic impacts of climate change. Though shifts in mean temperatures correlate with changes in phenology and distributions, other hidden, or cryptic, changes in temperature, such as temperature variation and extreme temperatures, could pose greater risks to species and ecological communities. Yet, these cryptic temperature changes have received relatively little attention because mean temperatures are readily available and the organism-appropriate temperature response is often elusive. An alternative to using mean temperatures is to view organisms as physiological filters of hourly temperature data. We explored three classes of physiological filters: (1) nonlinear thermal responses using performance curves of insect fitness, (2) cumulative thermal effects using degree-day models for corn emergence, and (3) threshold temperature effects using critical thermal maxima and minima for diverse ectotherms. For all three physiological filters, we determined the change in biological impacts of hourly temperature data from a standard reference period (1961-90) to a current period (2005-10). We then examined how well mean temperature changes during the same time period predicted the biotic impacts we determined from hourly temperature data. In all cases, mean temperature alone provided poor predictions of the impacts of climate change. These results suggest that incorporating high frequency temperature data can provide better predictions for how species will respond to temperature change.