Soil data quality and resolution matter when predicting woody plant species in temperate forests

Soil properties influence plant physiology and growth, playing a fundamental role in shaping species niches in forest ecosystems. Here, we investigated the impact of soil data quality on the performance of climate-topography species distribution models (SDMs) of temperate forest woody plants. We compared models based on measured soil properties with those based on digitally mapped soil properties at different spatial resolutions (25m and 250m). We first calibrated SDMs with measured soil data and plant species presences and absences from plots in mature temperate forest stands. Then, we developed models using the same soil predictors, but extracted from digital soil maps at the nearest neighbouring plots of the Swiss National Forestry Inventory. Our approach enabled a comprehensive assessment of the significance of soil data quality for 41 Swiss forest woody plant species. The predictive power of SDMs without soil information compared to those with soil information, as well as those with measured vs digitally mapped soil information at different spatial resolutions was evaluated with metrics of model performance and variable contribution. On average, performance of models with measured and digitally mapped soil properties was significantly improved over those without soil information. SDMs based on measured and high-resolution soil maps showed a higher performance, especially for species with an ‘extreme’ niche position (e.g. preference for high or low pH), compared to those using coarse-resolution (250m) soil information. Nevertheless, globally available soil maps can provide important predictors if no high-resolution soil maps are available. Moreover, among the tested soil predictors,  pH and clay content of the topsoil layers improved the predictive power of SDMs for forest woody plants the most. Such improved model performance informs biodiversity modelling about the relevance of soil data quality in SDMs for species of temperate forest ecosystems. In conclusion, the incorporation of accurate soil information into SDMs becomes indispensable for making well-informed forecasts for guiding decisions in forest management, also when addressing the potential distribution shifts of woody plant species due to climate change.