Standard Presentation (15 mins) Australian Marine Sciences Association 2022

Climate change impacts on populations and species: scalable habitat suitability projections provide realistic outlooks for species persistence under climate change (#22)

Sun Kim 1 , Jorge García Molinos 2 , Jennifer Sunday 3 , Gustav Paulay 4 , John Pandolfi 1
  1. The University of Queensland, St. Lucia, QLD, Australia
  2. Arctic Research Center, Hokkaido University, Sapporo, Japan
  3. Department of Biology, McGill University, Montreal, Canada
  4. Florida Museum of Natural History, University of Florida, Gainesville, USA

Climate-driven degradation of habitat suitability calls for global efforts to minimise ecological and economic impacts. A popular tool that generates data for such efforts is habitat suitability models (HSM). HSMs examine the rate and extent of climate change and identify future climate refugia. A major assumption of HSMs is that all individuals of a species tolerate identical ranges of environmental conditions regardless of individual- and population-specific exposures to unique sets of environmental conditions. However, empirical evidence suggests that a species likely comprises multiple groups of individuals with distinct environmental thresholds. Recent HSM approaches acknowledge this caveat and incorporate population-specific environmental thresholds. Here, we build on the recent theoretical developments in HSMs and show how the consideration of both population-and species-specific environmental thresholds can provide realistic insights into forecasting species persistence under climate change. We use genomic data to inform population structures of coral and fisheries species and associated climate data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to investigate population- and species-specific environmental threshold limits. We found substantial differences in model outcomes between population- and species-level taxonomic units. This study underscores that scalable habitat suitability projections can provide realistic and flexible data for adaptive resource management actions.