Sparse modeling for climate variable selection across trophic levels.

Abstract

Understanding how populations respond to climate is fundamentally important to many questions in ecology, evolution, and conservation biology. Climate is complex and multifaceted, with aspects affecting populations in different and sometimes unexpected ways. Thus, when measuring the changing climate it is important to consider the complexity of the phenomenon and the number of ways it can be characterized through different metrics. We used a Bayesian sparse modeling approach to select among 80 metrics of climate and applied the approach to 19 datasets of bird, insect, and plant population responses to abiotic conditions as case studies of how the method can be applied for climate variable selection in a time series context. For phenological datasets, mean spring temperature was frequently selected as an important climate driver, while selected predictors were more diverse for population metrics such as abundance or reproductive success. The climate variable selection approach presented here can help to identify potential climate metrics when there is limited physiological or mechanistic information to make an a priori variable selection, and is broadly applicable across studies on population responses to climate.