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Energetic costs increase with faster heating in an aquatic ectotherm.

Lucy HardingAndrew L JacksonNicholas Payne
Published in: Conservation physiology (2023)
The thermal sensitivity of metabolism is widely studied due to its perceived importance for organismal fitness and resilience to future climate change. Almost all such studies estimate metabolism at a variety of constant temperatures, with very little work exploring how metabolism varies during temperature change. However, temperature in nature is rarely static, so our existing understanding from experiments may not reflect how temperature influences metabolism in natural systems. Using closed-chamber respirometry, we estimated the aerobic metabolic rate of an aquatic ectotherm, the Atlantic ditch shrimp Palaemonetes varians, under varying thermal conditions. We continuously measured oxygen consumption of shrimp during heating, cooling and constant temperatures, starting trials at a range of acclimation temperatures and exposing shrimp to a variety of rates of temperature change. In a broad sense, cumulative oxygen consumption estimated from static temperature exposures corresponded to estimates derived from ramping experiments. However, further analyses showed that oxygen consumption increases for both faster heating and faster cooling, with rapid heating driving higher metabolic rates than if shrimp were warmed slowly. These results suggest a systematic influence of heating rate on the thermal sensitivity of metabolism. With influential concepts such as the metabolic theory of ecology founded in data from constant temperature experiments, our results encourage further exploration of how variable temperature impacts organism energetics, and to test the generality of our findings across species. This is especially important given climate forecasts of heat waves that are characterised by both increased temperatures and faster rates of change.
Keyphrases
  • climate change
  • social support
  • mental health
  • depressive symptoms
  • air pollution
  • machine learning
  • human health
  • data analysis