Unravelling the impact of soil data quality on species distribution models of temperate forest woody plants
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View abstract on PubMed
Summary
This summary is machine-generated.High-quality soil data, especially regional maps, significantly improves species distribution models (SDMs) for forest plants. This enhances predictions for biodiversity conservation and climate adaptation planning.
Area Of Science
- Ecology
- Forestry
- Environmental Science
Background
- Soil properties are crucial for plant physiology, growth, and niche determination in temperate forests.
- Species distribution models (SDMs) are vital tools for understanding and predicting species ranges.
Purpose Of The Study
- To assess the impact of soil data quality on the performance of SDMs for 41 woody plant species in Swiss forests.
- To compare SDMs using measured soil data, regional digital maps (Swiss Forest Soil Maps), and global digital maps (SoilGrids).
Main Methods
- Calibrated topo-climatic SDMs using measured soil data and species occurrence records.
- Developed comparative models using soil data from Swiss Forest Soil Maps and SoilGrids.
- Evaluated model performance using metrics and assessed variable contributions.
Main Results
- SDMs incorporating soil properties (measured or digital) significantly outperformed models without soil data.
- Models using measured soil data and Swiss Forest Soil Maps showed higher predictive power than those using SoilGrids, especially for species with extreme niche requirements.
- Topsoil pH and clay content were key soil predictors for improving SDM accuracy.
Conclusions
- Regional soil maps provide valuable data for enhancing SDMs of woody species in temperate forests.
- Accurate SDMs informed by soil properties can support forest management strategies for biodiversity and climate adaptation.
- SoilGrids can be a viable alternative when regional soil data are unavailable.
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