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Individual-based eco-evolutionary models for understanding adaptation in changing seas.

Amanda XuerebQuentin RougemontPeter TiffinHuijie XueMegan Phifer-Rixey
Published in: Proceedings. Biological sciences (2021)
As climate change threatens species' persistence, predicting the potential for species to adapt to rapidly changing environments is imperative for the development of effective conservation strategies. Eco-evolutionary individual-based models (IBMs) can be useful tools for achieving this objective. We performed a literature review to identify studies that apply these tools in marine systems. Our survey suggested that this is an emerging area of research fuelled in part by developments in modelling frameworks that allow simulation of increasingly complex ecological, genetic and demographic processes. The studies we identified illustrate the promise of this approach and advance our understanding of the capacity for adaptation to outpace climate change. These studies also identify limitations of current models and opportunities for further development. We discuss three main topics that emerged across studies: (i) effects of genetic architecture and non-genetic responses on adaptive potential; (ii) capacity for gene flow to facilitate rapid adaptation; and (iii) impacts of multiple stressors on persistence. Finally, we demonstrate the approach using simple simulations and provide a framework for users to explore eco-evolutionary IBMs as tools for understanding adaptation in changing seas.
Keyphrases
  • climate change
  • genome wide
  • human health
  • case control
  • copy number
  • dna methylation
  • risk assessment
  • machine learning
  • deep learning
  • transcription factor
  • big data
  • artificial intelligence