Temperature fluctuations influence predictions of landscape-scale spruce budworm defoliation in a physiologically-informed species distribution model

Abstract

AimWhile many studies on ectotherm thermal tolerance consider temperature exposure, the frequency of temperature exposures is emerging as an important and generally overlooked driver of survival and fitness which may influence species ranges. We use a physiologically-informed species distribution model to evaluate the influence of temperature fluctuations on the historical distribution and intensity of defoliation of a Lepidopteran forest pest, and on predicted future defoliation. LocationEastern Canada. Time period2006-2016, projections to 2041-2070. Major taxa studiedChoristoneura fumiferana (Lepidoptera: Tortricidae, spruce budworm). MethodsWe combined publicly-available maps of spruce budworm-induced defoliation between 2006-2016 in the Canadian province of Quebec with climate, forest composition, and de novo temperature fluctuation predictors to train a species distribution model. Our model evaluated how predictor categories influence spruce budworm defoliation and compared these results to a model trained without temperature fluctuations. Additionally, we predicted future spruce budworm defoliation under 2041-2070 climate change conditions using the models trained with and without temperature fluctuation predictors to determine the impact of temperature fluctuations on future defoliation predictions. ResultsWe found that the inclusion of temperature fluctuation predictors improved model performance, and these predictors ranked highly in importance relative to predictors in other categories. The model trained with temperature fluctuation predictors also predicted vastly different defoliation distribution and severity across Quebec, Ontario, and Labrador than the model trained without them under future climate. Main conclusionsOur study reveals the previously overlooked importance of temperature fluctuations on landscape-scale spruce budworm defoliation and demonstrates the importance of including physiologically-informed predictors in species distribution models. It also provides a novel framework for including thermal variation in correlative species distribution models of ectotherms.