Filling a metabolism data gap in marine fish reveals dual pace-of-life and reproductive strategy axes

Authors

Beneat, M., Morell, A., Moullec, F., Barrier, N., Shin, Y.-J., Ernande, B.

Abstract

Resting metabolic rate, the basal energy required for an organism's maintenance, is central to predicting the eco-evolutionary consequences of environmental and anthropogenic pressures on marine ectotherms. Yet, direct measurements remain extremely scarce, covering less than 1% of marine fish species. Building on the Pace Of Life Syndrome (POLS) hypothesis, which posits a positive correlation between metabolism and life-history speed, we inferred temperature- and mass-specific resting metabolic rate for 18,214 fish species (16,998 Teleostei and 1,216 Elasmobranchii). Estimates were derived from relationships with 15 functional traits and phylogeny using phylogenetic structural equation modelling, a novel approach combining phylogenetic comparative methods with structural equation models. Cross-validations indicated high predictive performance, although some clades require cautious interpretation due to limited empirical data. In line with expectations from life-history theory, ecological niche and morphology, species with the highest metabolic rates were small, narrow, shallow-bodied, and long-jawed pelagic fish with high mortality, short lifespan and rapid growth - traits typical of small epipelagics. The metabolic rate variation aligned with the slow-fast life-history continuum, supporting the POLS hypothesis, but was equally explained by reproductive strategy: highly fecund, fast species exhibited the highest metabolic rate independent of mass and temperature, whereas low-fecundity slow species had the lowest. By filling a critical physiological data gap, our dataset provides a foundation to test long-standing ecological and evolutionary hypotheses such as POLS, and offers a powerful resource to improve models of species and community responses to climate change and exploitation, ultimately supporting physiology-informed ecosystem management.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 30 Sep 2026.

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