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Watershed Planning within a Quantitative Scenario Analysis Framework
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Identifying potential provenances for climate-change adaptation using spatially variable coefficient models.

Marieke Wesselkamp1, David R Roberts2,3,4, Carsten F Dormann2

  • 1Department of Biometry and Environmental System Analysis, University of Freiburg, Tennenbacher Straße 4, Freiburg, 79106, Germany. marieke.wesselkamp@biom.uni-freiburg.de.

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|May 28, 2024
PubMed
Summary
This summary is machine-generated.

This study identifies climate-adapted ecotypes of Douglas-fir using advanced statistical analysis. The findings help pinpoint suitable varieties for changing climates, saving time and resources in tree breeding programs.

Keywords:
Pseudotsuga menziesiiClimate-change adaptationDouglas-firEcotypeGenetic variationIdentifying ecotypesProvenance testsSpatially variable coefficient models

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Area of Science:

  • Ecology
  • Genetics
  • Forestry

Background:

  • Traditional methods for selecting climate-change adapted tree ecotypes involve DNA screening and lengthy growth trials.
  • Developing faster methods to identify suitable tree varieties is crucial for forestry and conservation efforts.

Purpose of the Study:

  • To identify climate-adapted ecotypes of Douglas-fir (Pseudotsuga menziesii) in North America using a novel statistical approach.
  • To improve the efficiency of selecting tree varieties for future climate conditions.

Main Methods:

  • Utilized non-stationary statistical analysis with spatially varying coefficients on over 70,000 plot-level presence-absence data points.
  • Employed an unsupervised learning approach to cluster model terms and identify distinct ecotypes based on survival responses to climate.

Main Results:

  • The spatially-variable coefficient model significantly outperformed stationary analysis, as indicated by AIC.
  • Clustering identified six potential ecotypes with distinct climate niches, showing partial congruence with known genetic divergence regions.
  • Clear differences in climate niches were observed among the identified ecotypes.

Conclusions:

  • The developed statistical approach offers a valuable initial screening step for identifying climate-change adapted varieties, especially with increasing species distribution data.
  • While computationally intensive, this method can accelerate the search for resilient tree populations.
  • Further validation with high-resolution genotypic data is recommended to refine quantitative accuracy.