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The biogeography of thermal risk for terrestrial ectotherms: scaling of thermal tolerance with body size and latitude

Citation

Rubalcaba, Juan; Olalla-Tárraga, Miguel Ángel (2020), The biogeography of thermal risk for terrestrial ectotherms: scaling of thermal tolerance with body size and latitude, Dryad, Dataset, https://doi.org/10.5061/dryad.ffbg79cqv

Abstract

1. Many organisms are shrinking in size in response to global warming. However, we still lack a comprehensive understanding of the mechanisms linking body size and temperature of organisms across their geographical ranges. Here we investigate the biophysical mechanisms determining the scaling of body temperature with size across latitudes in terrestrial ectotherms. 2. Using biophysical models, we simulated operative temperatures experienced by lizard-like ectotherms as a function of microclimatic variables, body mass and latitude and use them to generate null predictions for the effect of size on temperature across geographical gradients. We then compared model predictions against empirical data on lizards’ field body temperature (Tb), and thermal tolerance limits (CTmax and CTmin). 3. Our biophysical models predicted that the allometric scaling of operative temperatures with body size varies with latitude, with a positive relationship at low latitudes that vanishes with increasing latitude. The analyses of thermal traits of lizards show a significant interaction of body size and latitude on Tb and CTmax and no effect of body mass on CTmin, consistent with model’s predictions. The estimated scaling coefficients are within the ranges predicted by the biophysical model. The effect of body mass, however, becomes non-significant after controlling for the phylogenetic relatedness between species. 4. We propose that large-bodied terrestrial ectotherms exhibit higher risk of overheating at low latitudes, while size differences in thermal sensitivity vanish towards higher latitudes. 5. Our work highlights the potential of combining mechanistic models with empirical data to investigate the mechanisms underpinning broad-scale patterns and ultimately provide a null model to develop baseline expectations for further empirical research. 08-Jan-2020

Funding

Universidad Rey Juan Carlos