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Climatic variables influence the temporal dynamics of an anuran metacommunity in a nonstationary way.

Karoline CeronDiego José SantanaElaine M LucasJairo José ZoccheDiogo B Provete
Published in: Ecology and evolution (2020)
Understanding the temporal dynamics of communities is crucial to predict how communities respond to climate change. Several factors can promote variation in phenology among species, including tracking of seasonal resources, adaptive responses to other species, demographic stochasticity, and physiological constraints. The activities of ectothermic vertebrates are sensitive to climatic variations due to the effect of temperature and humidity on species physiology. However, most studies on temporal dynamics have analyzed multi-year data and do not have resolution to discriminate within-year patterns that can determine community assembly cycles. Here, we tested the temporal stability and synchrony of calling activity and also how climatic variables influence anuran species composition throughout the year in a metacommunity in the Atlantic Forest of southern Brazil. Using a multivariate method, we described how the relationship between species composition and climatic variables changes through time. The metacommunity showed a weak synchronous spatial pattern, meaning that species responded independently to environmental variation. Interestingly, species composition exhibited a nonstationary response to climate, suggesting that climate affects species composition differently depending on the season. The species-climate relationship was stronger during the spring, summer, and winter, mainly influenced by temperature, rainfall, and humidity. Thus, temporal community dynamics seem to be mediated by species life-history traits, in which independent fluctuations promote community stability in temporally varying environments.
Keyphrases
  • climate change
  • healthcare
  • genetic diversity
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  • machine learning
  • risk assessment
  • dna methylation
  • single molecule
  • heat stress
  • deep learning
  • artificial intelligence
  • advanced cancer