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Predicting Forest Tree Leaf Phenology Under Climate Change Using Satellite Monitoring and Population-Based Genomic

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Global climate change impacts forest phenology. This study links European beech leaf-out and shedding dates to environmental factors and specific genes, aiding future climate change adaptation forecasts.

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Area of Science:

  • Ecology
  • Genetics
  • Climate Science

Background:

  • Leaf phenology is vital for plant fitness and ecosystem function, but is shifting due to climate change.
  • The genetic and environmental drivers of these phenological shifts, especially in forests, are not fully understood.
  • Understanding phenological adaptation is key for predicting forest responses to climate change.

Purpose of the Study:

  • To investigate the environmental and genetic factors influencing leaf-out day (LOD) and leaf shedding day (LSD) in European beech (Fagus sylvatica).
  • To identify candidate genes controlling phenological timing.
  • To develop genomic prediction models for forecasting forest adaptation to climate change.

Main Methods:

  • Integrated multi-year satellite-derived phenology data from 46 Fagus sylvatica populations across Germany.
  • Conducted a population-based genome-wide association study (GWAS).
  • Developed genomic prediction models.

Main Results:

  • Environmental factors like temperature and water availability primarily drive LOD variation.
  • LSD is influenced by a more complex set of climatic cues.
  • Identified candidate genes related to circadian rhythms (LOD) and dormancy (LSD).
  • Genomic prediction models accurately reconstructed past phenological dynamics.

Conclusions:

  • Environmental factors are key drivers of European beech phenology, with genetic components identified.
  • Candidate genes provide insights into the genetic basis of phenological timing.
  • Genomic prediction offers a robust framework for forecasting forest vulnerability and adaptation under future climate change scenarios.