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Functional spatial units are fundamental for modelling ecosystem services in mountain regions

机译:功能空间单位是山地地区建模生态系统服务的基础

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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.
机译:依赖初级生物物理数据的生态系统服务的应用被认为是最有希望的最有希望的决策制定的准确图。然而,为了有效地应用于ES映射的这些建模方法,需要具有不同的社会生态特征的代表性功能空间单元,这允许从绘图水平到横向量表的测量变量的上升。在本研究中,我们提出了一种基于非生物和管理变量的划定功能空间单元的理论框架。我们将此框架应用于一个高山草地网站,并根据非生物(海拔,坡,方面)和管理变量(受精或未受精)三次步骤(2015,1953和1861 )。我们通过判别分析测试这些草地轨迹是否反映了植物和土壤性状的变化。我们的结果表明,与土地使用/陆地(LULC)或地形相比,地形和管理变量的结合导致了明显更好的分类结果。当包括过去和目前的Lulc的信息时,可以获得最佳结果,即51%的草地轨迹被正确分类。我们终于使用这些草地轨迹根据基于特质的模型来绘制五es(饲料生产和饲料质量,碳储存,水质和土壤肥力),以举例说明草地轨迹对景观量表的高档情节数据的操作适用性。目前的ES条款对于不同的草地轨迹而异,揭示了非生物和生物司机的综合影响。

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