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Multiscale phenological niches of seed fall in diverse Amazonian plant communities.

Damie PakVarun SwamyPatricia Alvarez-LoayzaFernando Cornejo-ValverdeSimon A QueenboroughMargaret R MetzJohn TerborghRenato ValenciaS Joseph WrightNancy C GarwoodJesse R Lasky
Published in: Ecology (2023)
Phenology has long been hypothesized as an avenue for niche partitioning or interspecific facilitation, both promoting species coexistence. Tropical plant communities exhibit striking diversity in reproductive phenology, but many are also noted for large synchronous reproductive events. Here we study whether the phenology of seed fall in such communities is non-random, what are the temporal scales of phenological patterns, and ecological factors that drive reproductive phenology. We applied multivariate wavelet analyses to test for phenological synchrony versus compensatory dynamics (i.e. anti-synchronous patterns where one species' decline is compensated by the rise of another) among species and across temporal scales. We used data from long-term seed rain monitoring of hyperdiverse plant communities in the western Amazon. We found significant synchronous whole-community phenology at multiple time scales, consistent with shared environmental responses or positive interactions among species. We also observed both compensatory and synchronous phenology within groups of species (confamilials) likely to share traits and seed dispersal mechanisms. Wind-dispersed species exhibited significant synchrony at ~6 mo scales, suggesting these species might share phenological niches to match seasonality of wind. Our results suggest that community phenology is shaped by shared environmental responses but that the diversity of tropical plant phenology may partly result from temporal niche partitioning. The scale-specificity and time-localized nature of community phenology patterns highlights the importance of multiple and shifting drivers of phenology.
Keyphrases
  • healthcare
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  • climate change
  • genetic diversity
  • machine learning
  • artificial intelligence
  • life cycle
  • neural network