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

Tyler WagnerErin M SchliepJoshua S NorthHolly KundelChristopher A CusterJenna K RuzichGretchen J A Hansen
Published in: Proceedings of the National Academy of Sciences of the United States of America (2023)
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.
Keyphrases
  • climate change
  • genetic diversity
  • electronic health record
  • antibiotic resistance genes
  • human health
  • machine learning
  • risk assessment
  • deep learning
  • artificial intelligence
  • social media
  • big data
  • current status