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A state-space approach to understand responses of organisms, populations and communities to multiple environmental drivers.

Luis GiménezAdreeja ChatterjeeGabriela Torres
Published in: Communications biology (2021)
Understanding the response of biotic systems to multiple environmental drivers is one of the major concerns in ecology. The most common approach in multiple driver research includes the classification of interactive responses into categories (antagonistic, synergistic). However, there are situations where the use of classification schemes limits our understanding or cannot be applied. Here, we introduce and explore an approach that allows us to better appreciate variability in responses to multiple drivers. We then apply it to a case, comparing effects of heatwaves on performance of a cold-adapted species and a warm-adapted competitor. The heatwaves had a negative effect on the native (but not on the exotic) species and the approach highlighted that the exotic species was less responsive to multivariate environmental variation than the native species. Overall, we show how the proposed approach can enhance our understanding of variation in responses due to different driver intensities, species, genotypes, ontogeny, life-phases or among spatial scales at any level of biological organization.
Keyphrases
  • genetic diversity
  • machine learning
  • deep learning
  • human health
  • cancer therapy
  • risk assessment
  • multidrug resistant