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Altered precipitation dynamics lead to a shift in herbivore dynamical regime.

Adam A PepiMarcel HolyoakRichard Karban
Published in: Ecology letters (2021)
The interaction between endogenous dynamics and exogenous environmental variation is central to population dynamics. Although investigations into the effects of changing mean climate are widespread, changing patterns of variation in environmental forcing also affect dynamics in complex ways. Using wavelet and time series analyses, we identify a regime shift in the dynamics of a moth species in California from shorter to longer period oscillations over a 34-year census, and contemporaneous changes in regional precipitation dynamics. Simulations support the hypothesis that shifting precipitation dynamics drove changes in moth dynamics, possibly due to stochastic resonance with delayed density-dependence. The observed shift in climate dynamics and the interaction with endogenous dynamics mean that predicting future population dynamics will require information on both climatic shifts and their interaction with endogenous density-dependence, a combination that is rarely available. Consequently, models based on historical data may be unable to predict future population dynamics.
Keyphrases
  • machine learning
  • climate change
  • deep learning
  • social media
  • artificial intelligence
  • working memory