Predicting fish responses to climate change using a joint species, spatially dependent physiologically guided abundance model

Predicting the effects of warming temperatures on the abundance and distribution of organisms under future climate scenarios often requires extrapolating species-environment correlations to thermal conditions not currently experienced by a species or to temperatures that exceed the range of observed data. For poikilotherms, incorporating species’ thermal physiology to inform extrapolations under novel thermal conditions can result in more realistic predictions. Here, we present a joint species, spatially dependent physiologically guided abundance (jsPGA) model for predicting multispecies responses to climate warming. The jsPGA uses a basis function approach to capture both species and spatial dependencies. We first show that the jsPGA model accurately estimates parameters through a simulation study. We then apply the jsPGA to predict the response of eight fish species observed across thousands of lakes in Minnesota, USA to projected climate warming. The jsPGA provides a new tool for predicting changes in abundance, distribution, and extinction probability of poikilotherms under novel thermal conditions.