Predicting climate change impacts on poikilotherms using physiologically guided species abundance models

Tyler Wagner, Erin M. Schliep, Joshua S. North, Holly Kundel, Christopher A. Custer, Jenna K. Ruzich, Gretchen J.A. Hansen

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Poikilothermic animals comprise most species on Earth and are especially sensitive to changes in environmental temperatures. Species conservation in a changing climate relies upon predictions of species responses to future conditions, yet predicting species responses to climate change when temperatures exceed the bounds of observed data is fraught with challenges. We present a physiologically guided abundance (PGA) model that combines observations of species abundance and environmental conditions with laboratory-derived data on the physiological response of poikilotherms to temperature to predict species geographical distributions and abundance in response to climate change. The model incorporates uncertainty in laboratory-derived thermal response curves and provides estimates of thermal habitat suitability and extinction probability based on site-specific conditions. We show that temperature-driven changes in distributions, local extinction, and abundance of cold, cool, and warm-adapted species vary substantially when physiological information is incorporated. Notably, cold-adapted species were predicted by the PGA model to be extirpated in 61% of locations that they currently inhabit, while extirpation was never predicted by a correlative niche model. Failure to account for species-specific physiological constraints could lead to unrealistic predictions under a warming climate, including underestimates of local extirpation for cold-adapted species near the edges of their climate niche space and overoptimistic predictions of warm-adapted species.

Original languageEnglish (US)
Article numbere2214199120
JournalProceedings of the National Academy of Sciences of the United States of America
Volume120
Issue number15
DOIs
StatePublished - Apr 11 2023

Bibliographical note

Funding Information:
ACKNOWLEDGMENTS. This research was supported by the US Geological Survey Midwest Climate Adaptation Science Center Grant No. G20AC00096 and the NSF (EF-1638679, EF-1638554, EF-1638539, and EF-1638550). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government. We thank two anonymous reviewers for comments that greatly improved this manuscript.

Publisher Copyright:
Copyright © 2023 the Author(s).

Keywords

  • cold-blooded
  • data fusion
  • extrapolation
  • freshwater fishes

PubMed: MeSH publication types

  • Journal Article
  • Research Support, U.S. Gov't, Non-P.H.S.

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