Species distribution models (SDMs) are often used to map biodiversity and forecast the effects of climate change on species occurrence. The reliability of the predictions is strongly dependent on the relevance and quality of the ecological predictors describing local climatic and edaphic conditions such as soil pH or texture. Although several studies emphasise the importance of soil properties for predicting plant species, most SDM studies are limited to climatic-topographic predictors, as spatially high-resolution soil information over larger areas is often lacking. In Switzerland, area-wide information on soil properties for the entire forest area has only recently become available.
This project investigates to what extent the predictive power of SDMs for different functional and taxonomic groups of forest plant and fungal species can be improved by incorporating soil information. For this, SDMs will be created using either high-resolution soil property maps and topo-climatic variables as predictors or solely topo-climatic variables.
In addition, we compare the performance of SDMs for some specific species calibrated with in-situ measured soil data to SDMs created with the corresponding soil information extracted from the soil maps. In this way, we can investigate how the loss of information from the spatially modelled versus measured soil data affects the performance of the SDMs.
Using high spatial resolution climate scenarios, we further investigate how the distribution of these species changes due to climate change and what influence the soil will have.
2022 - 2024