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Estimating physiological mechanisms from monitoring data reveals challenges and opportunities for forecasting distribution shifts
  • +2
  • Julia Indivero,
  • Sean Anderson,
  • Lewis Barnett,
  • Timothy E. Essington,
  • Eric Ward
Julia Indivero
University of Washington

Corresponding Author:[email protected]

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Sean Anderson
Fisheries and Oceans Canada - Pacific Biological Station
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Lewis Barnett
AFSC
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Timothy E. Essington
University of Washington Seattle Campus
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Eric Ward
Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration
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Abstract

Species distribution modeling is increasingly used to describe and anticipate consequences of a warming ocean. These models often identify statistical associations between distribution and environmental conditions such as temperature and oxygen, but rarely consider the mechanisms by which these environmental variables affect metabolism. Oxygen and temperature jointly govern the rate of oxygen supply to oxygen demand, and theory predicts thresholds in these rates below which species population densities are diminished. However, parameterizing models with this joint dependence is challenging because of the paucity of experimental work for most species, and the limited applicability of experimental findings in situ. Here we ask whether the joint effects of temperature and oxygen can be reliably inferred from species distribution observations in the field, using the U.S. Pacific Coast as a model system. Through simulation testing, we found that our statistical model—which adapted the metabolic index to jointly consider oxygen and temperature by applying an Arrhenius equation and used a non-linear threshold function to link the index to fish distribution—could not precisely estimate the parameters due to inherent features of the distribution data. However, the model reliably estimated an overall metabolic index threshold effect, and provided a better fit to sablefish (Anoplopoma fimbria) spatial distribution than previously used models. This mechanistic approach may improve predictions of species distribution, even in novel environmental conditions. Further efforts to combine insights from mechanistic responses and realized species distributions will improve forecasts of species’ responses to future environmental changes.
23 Mar 2024Submitted to Ecography
03 Jul 2024Review(s) Completed, Editorial Evaluation Pending
11 Jul 2024Editorial Decision: Revise Major
27 Aug 20241st Revision Received
28 Aug 2024Submission Checks Completed
28 Aug 2024Assigned to Editor
28 Aug 2024Review(s) Completed, Editorial Evaluation Pending
03 Sep 2024Reviewer(s) Assigned
10 Oct 2024Editorial Decision: Revise Minor
17 Oct 20242nd Revision Received
18 Oct 2024Submission Checks Completed
18 Oct 2024Assigned to Editor
18 Oct 2024Review(s) Completed, Editorial Evaluation Pending