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Activity niches outperform thermal physiological limits in predicting global ant distributions

Citation

Guo, Fengyi et al. (2020), Activity niches outperform thermal physiological limits in predicting global ant distributions, Dryad, Dataset, https://doi.org/10.5061/dryad.v41ns1rrx

Abstract

Aim: Thermal physiology is commonly used in mechanistic models to predict species distributions and project distribution change. Such thermal constraints for ants are often measured under laboratory conditions as critical thermal limits (CTmax and CTmin), but have also been observed in the field as foraging thermal limits (FTmin and FTmax). Here we compared distribution projections based on ant physiological and behavioural thermal limits with their realized distributions to assess the validity of using ecophysiological models to predict species ranges over large scales.
Location: Global
Taxon: Ants
Methods: From published literature, we compiled a database of 148 lab-measured critical thermal limits and 137 field-observed foraging thermal limits for 20 ant genera distributed around the world. We projected their potential ranges using active hour thresholds by matching their thermal limits or preferred thermal breadth (incorporating thermal optima) with hourly surface temperature data. We then compared the projections against a comprehensive global database of ant distributions to assess the validity of physiologically and behaviorally based models.
Results: We found that surface temperature plays a clear role in generic-level ant biogeography. Projections based on foraging thermal limits were more conservative in constraining ranges to observed distributions, while projections based on critical thermal limits often overestimated ranges. Furthermore, thermal limit models can be improved by incorporating behaviourally derived thermal optima.
Main conclusions: Our results suggest that temperature-dependent activity niches and thermal optima better reveal species realized thermal niches than critical thermal limits, and hence can improve predictions of species distributions and future distribution changes under climate change.

Usage Notes

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Funding

University of Hong Kong