Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerland.
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerland.
Sci Total Environ. 2024 Sep 20;944:173719. doi: 10.1016/j.scitotenv.2024.173719. Epub 2024 Jun 3.
Soil properties influence plant physiology and growth, playing a fundamental role in shaping species niches in temperate forest ecosystems. Here, we investigated the impact of soil data quality on the performance of species distribution models (SDMs) of 41 woody plant species in Swiss forests. We compared models based on measured soil properties with those based on digitally mapped soil properties on regional (Swiss Forest Soil Maps) and global scales (SoilGrids). We first calibrated topo-climatic SDMs with measured soil data and plant species presences and absences from mature temperate forest stand plots. We developed further models using the same soil predictors, but with values extracted from digital soil maps at the nearest neighbouring plots of the Swiss National Forestry Inventory. The predictive power of SDMs without soil information compared to those with soil information, as well as measured soil information vs digitally mapped, was evaluated with metrics of model performance and variable contribution. On average, models with measured and digitally mapped soil properties performed significantly better than those without soil information. SDMs based on measured and Swiss Forest Soil Maps showed higher performance, especially for species with an 'extreme' niche position (e.g., preference for high or low pH), compared to those using SoilGrids. Nevertheless, if no regional soil maps are available, SoilGrids should be tested for their potential to improve SDMs. Moreover, among the tested soil predictors, pH, and clay content of the topsoil layers most improved the predictive power of SDMs for forest woody plants. In conclusion, we demonstrate the value of regional soil maps for predicting the distribution of woody species across strong environmental gradients in temperate forests. The improved accuracy of SDMs and insights into drivers of distribution may support forest managers in strategies supporting e.g. biodiversity conservation, or climate adaptation planning.
土壤特性影响植物生理学和生长，在塑造温带森林生态系统中物种生态位方面发挥着基础性作用。在这里，我们研究了土壤数据质量对 41 种瑞士森林木本植物物种分布模型（SDM）性能的影响。我们比较了基于实测土壤特性的模型与基于区域（瑞士森林土壤图）和全球尺度（SoilGrids）数字化土壤特性的模型。我们首先使用实测土壤数据和成熟温带森林标准样地的植物物种存在和缺失数据来校准地形气候 SDM。我们使用相同的土壤预测因子开发了进一步的模型，但使用瑞士国家林业调查的最近邻标准地的数字土壤图中的值。我们使用模型性能和变量贡献指标来评估无土壤信息的 SDM 与有土壤信息的 SDM 以及实测土壤信息与数字化土壤图的预测能力。平均而言，具有实测和数字化土壤特性的 SDM 比没有土壤信息的 SDM 表现要好。基于实测和瑞士森林土壤图的 SDM 表现更好，尤其是对于具有“极端”生态位（例如，对高或低 pH 的偏好）的物种，与使用 SoilGrids 的 SDM 相比。然而，如果没有区域土壤图，应测试 SoilGrids 以确定其是否有可能改进 SDM。此外，在所测试的土壤预测因子中，顶层土壤的 pH 和粘粒含量最能提高 SDM 对森林木本植物的预测能力。总之，我们证明了区域土壤图在预测温带森林中强环境梯度下木本物种分布方面的价值。SDM 准确性的提高和对分布驱动因素的了解可能有助于森林管理者支持例如生物多样性保护或气候适应规划的战略。
