The Importance of Scale and the MAUP for Robust Ecosystem Service Evaluations and Landscape Decisions
The Importance of Scale and the MAUP for Robust Ecosystem Service Evaluations and Landscape Decisions
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DOI：
10.3390/land11030399
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发表时间：
2022-03
期刊：
影响因子：
3.9
通讯作者：
A. Comber;P. Harris
中科院分区：
文献类型：
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作者：
A. Comber;P. Harris
空间数据被用于许多科学领域，包括生态系统服务（ES）和自然资本（NC）的分析，其结果用于规划和政策。然而，数据空间尺度（或支持）对分析输出以及过程理解和推理具有根本性影响。可修改面积单位问题（MAUP）描述了尺度对空间数据和产出分析的影响，但在许多环境研究中被忽视，包括关于ES和NC的土地利用评价。本文通过一个ES优化问题来说明MAUP。结果表明，MAUP效应是不可预测的和非线性的，具有特定于案例研究的空间特性的不连续性。四项主要建议如下：(1)在ES评估中应始终对MAUP进行测试。这通常在社会经济分析中执行。(2)空间聚集尺度应与过程粒度相匹配，通过识别在该聚集尺度上，过程在方差、协方差和其他矩方面被认为是稳定的（平稳的）。(3)综合尺度应与决策尺度（如农田、农场、集水区）相结合。(4)生态系统及相关学科的研究人员应提高空间分析和尺度相关核心范式的技能，以克服许多研究普遍存在的尺度盲目性。
Spatial data are used in many scientific domains including analyses of Ecosystem Services (ES) and Natural Capital (NC), with results used to inform planning and policy. However, the data spatial scale (or support) has a fundamental impact on analysis outputs and, thus, process understanding and inference. The Modifiable Areal Unit Problem (MAUP) describes the effects of scale on analyses of spatial data and outputs, but it has been ignored in much environmental research, including evaluations of land use with respect to ES and NC. This paper illustrates the MAUP through an ES optimisation problem. The results show that MAUP effects are unpredictable and nonlinear, with discontinuities specific to the spatial properties of the case study. Four key recommendations are as follows: (1) The MAUP should always be tested for in ES evaluations. This is commonly performed in socio-economic analyses. (2) Spatial aggregation scales should be matched to process granularity by identifying the aggregation scale at which processes are considered to be stable (stationary) with respect to variances, covariances, and other moments. (3) Aggregation scales should be evaluated along with the scale of decision making (e.g., agricultural field, farm holding, and catchment). (4) Researchers in ES and related disciplines should up-skill themselves in spatial analysis and core paradigms related to scale to overcome the scale blindness commonly found in much research.