Abstract The application of ecosystem services (ES) models that rely on primary biophysical data is considered as most promising to generate accurate maps for decision making. However, to effectively apply these modelling approaches for ES mapping, representative functional spatial units with distinct socio-ecological characteristics are needed, which allow the upscaling of measured variables from the plot level to the landscape scale. In this study, we propose a theoretical framework for delineating functional spatial units based on abiotic and management variables. We apply this framework for an alpine grassland site and identify functional spatial units (here referred as to grassland trajectories) based on abiotic (elevation, slope, aspect) and management variables (fertilised or unfertilised) for three time steps (2015, 1953 and 1861). We test, via discriminant analyses, whether these grassland trajectories reflect variations in plant and soil traits. Our results indicate that the combination of topographical and management variables leads to significantly better classification results compared to land use/land cover (LULC) or topography alone. The best result could be obtained when information of past and present LULC was included, i.e. 51% of grassland trajectories were correctly classified. We finally use these grassland trajectories to map five ES (forage production and forage quality, carbon storage, water quality and soil fertility) based on trait-based models to exemplify the operational suitability of grassland trajectories to upscale plot-level data to the landscape scale. Current ES provision varies greatly for the different grassland trajectories, revealing the combined effects of abiotic and biotic drivers.

中文翻译：

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功能空间单元是山区生态系统服务建模的基础

摘要 依赖原始生物物理数据的生态系统服务 (ES) 模型的应用被认为最有希望生成准确的决策地图。然而，为了有效地将这些建模方法应用于 ES 制图，需要具有不同社会生态特征的代表性功能空间单元，这允许将测量变量从样地级别放大到景观尺度。在这项研究中，我们提出了一个基于非生物和管理变量来划分功能空间单元的理论框架。我们将此框架应用于高山草地场地，并根据三个时间步长（2015、1953 和 1861 年）的非生物（海拔、坡度、坡向）和管理变量（施肥或未施肥）确定功能空间单元（此处称为草地轨迹） ）。我们测试，通过判别分析，这些草地轨迹是否反映了植物和土壤特征的变化。我们的结果表明，与土地利用/土地覆盖 (LULC) 或单独的地形相比，地形和管理变量的组合导致明显更好的分类结果。当包括过去和现在的LULC信息时，可以获得最好的结果，即51%的草地轨迹被正确分类。我们最终使用这些草地轨迹基于基于特征的模型绘制了五个 ES（草料生产和草料质量、碳储存、水质和土壤肥力），以举例说明草地轨迹对将样地级数据升级到景观尺度的操作适用性. 不同的草原轨迹，当前的 ES 供应差异很大，