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Neotropical ferns community phenology: climatic triggers in subtropical climate in Araucaria forest.

Andressa MüllerMarina Zimmer CorreaCamila Storck FührThábia Ottília Hofstetter PadoinDaniela Müller de QuevedoJairo Lizandro Schmitt
Published in: International journal of biometeorology (2019)
Climate regulates the fern phenology and climatic triggers influence plants from tropical and subtropical regions differently. Ferns depend on climate to regulate their life cycle, because they do not require animal interaction to reproduce. Through the pioneering study of the phenology of Araucaria forest understory in subtropical climate of Brazil, our main aims were (i) to verify which climatic variables influenced the phenological pattern of the community, (ii) to identify the differences in seasonality of ferns in distinct climatic zones of Brazil, and (iii) to compare the phenological pattern of ferns growing in other subtropical regions of the world. In an Araucaria forest fragment, we monitored the phenology of the fern community (leaf production, leaf senescence, and sporangium formation) over 2 years. At the same time, we collected photoperiod, temperature, and precipitation data. Ferns phenology was classified as continuous, discontinuous, regular, and irregular. Our results showed photoperiod and mean temperature as the best predictors for phenology. The reproductive event was seasonal, and the fern community presented themselves as continuous, irregular (activity index), and regular (intensity index) phenophases. Unlike ferns from tropical regions that generally regulate themselves by the rainfall, some ferns in a non-seasonal environment have seasonal behavior in their phenophases due to the greater amplitude of photoperiod and temperature. The community showed the same pattern of leaf production observed in populations of other subtropical regions in the world. This behavior represented the biological response of the vegetation dynamics in relation to the climatic variability of subtropical environment.
Keyphrases
  • climate change
  • mental health
  • healthcare
  • life cycle
  • electronic health record
  • dna damage
  • machine learning
  • deep learning