Multigenerational exposure to elevated temperatures leads to a reduction in standard metabolic rate in the wild

1. In light of global climate change, there is a pressing need to understand and predict the capacity of populations to respond to rising temperatures. Metabolic rate is a key trait that is likely to influence the ability to cope with climate change. Yet, empirical and theoretical work on metabolic rate responses to temperature changes has so far produced mixed results and conflicting predictions.

2. Our study addresses this issue using a novel approach of comparing fish populations in geothermally warmed lakes and adjacent ambient-temperature lakes in Iceland. This unique ‘natural experiment’ provides repeated and independent examples of populations experiencing contrasting thermal environments for many generations over a small geographic scale, thereby avoiding the confounding factors associated with latitudinal or elevational comparisons. Using Icelandic sticklebacks from three warm and three cold habitats, we measured individual metabolic rates across a range of acclimation temperatures to obtain reaction norms for each population.

3. We found a general pattern for a lower standard metabolic rate in sticklebacks from warm habitats when measured at a common temperature, as predicted by Krogh’s rule. Metabolic rate differences between warm- and cold-habitat sticklebacks were more pronounced at more extreme acclimation temperatures, suggesting the release of cryptic genetic variation upon exposure to novel conditions, which can reveal hidden evolutionary potential. We also found a stronger divergence in metabolic rate between thermal habitats in allopatry than sympatry, indicating that gene flow may constrain physiological adaptation when dispersal between warm and cold habitats is possible.

4. In sum, our study suggests that fish may diverge toward a lower standard metabolic rate in a warming world, but this might depend on connectivity and gene flow between different thermal habitats.

This dataset includes respirometry data that were processed through LabChart and analysed using R.

This work is licensed under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license.