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Diving in hot water: a meta-analytic review of how diving vertebrate ectotherms will fare in a warmer world.

Essie M RodgersCraig E FranklinDaniel W A Noble
Published in: The Journal of experimental biology (2021)
Diving ectothermic vertebrates are an important component of many aquatic ecosystems, but the threat of climate warming is particularly salient to this group. Dive durations typically decrease as water temperatures rise; yet, we lack an understanding of whether this trend is apparent in all diving ectotherms and how this group will fare under climate warming. We compiled data from 27 studies on 20 ectothermic vertebrate species to quantify the effect of temperature on dive durations. Using meta-analytic approaches, we show that, on average, dive durations decreased by 11% with every 1°C increase in water temperature. Larger increases in temperature (e.g. +3°C versus +8-9°C) exerted stronger effects on dive durations. Although species that respire bimodally are projected to be more resilient to the effects of temperature on dive durations than purely aerial breathers, we found no significant difference between these groups. Body mass had a weak impact on mean dive durations, with smaller divers being impacted by temperature more strongly. Few studies have examined thermal phenotypic plasticity (N=4) in diving ectotherms, and all report limited plasticity. Average water temperatures in marine and freshwater habitats are projected to increase between 1.5 and 4°C in the next century, and our data suggest that this magnitude of warming could translate to substantial decreases in dive durations, by approximately 16-44%. Together, these data shed light on an overlooked threat to diving ectothermic vertebrates and suggest that time available for underwater activities, such as predator avoidance and foraging, may be shortened under future warming.
Keyphrases
  • climate change
  • electronic health record
  • big data
  • risk assessment
  • magnetic resonance imaging
  • data analysis
  • machine learning
  • case control
  • deep learning