The Importance of Scale and the MAUP for Robust Ecosystem Service Evaluations and Landscape Decisions

Abstract

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.

Analysis of Ecosystem Service Contribution and Identification of Trade-Off/Synergy Relationship for Ecosystem Regulation in the Dabie Mountains of Western Anhui Province, China

“…Due to the complexity of ecological processes, relevant studies, such as those on the simulation and optimization of ecological processes, ground experiments, micro monitoring, and large-scale ecological network monitoring, should be further strengthened [49]. Research on trade-offs/synergies among ESs with micro-to large-scale includes fine experimental research under the combination of coupling multi-dimensional factors (such as slope, vegetation coverage, rainfall, soil type, land-use type, and so on), and this is also an important theoretical basis for ecological problem identification, ecological restoration, and ecological compensation in the future [61,62]. (b) Scale effect is the core difficulty of ecosystem research.…”

Subspecies‐level distribution maps for birds of the Amazon basin and adjacent areas

“…Because we used a subspecies‐level taxonomy, this data set could be used in conjunction with spatial grids at higher resolutions, such as 50 km or 25 km, instead of the spatial resolutions higher than 100 km that are used traditionally (Fluck et al, 2020; Jetz & Rahbek, 2002; Oliveira et al, 2017; Rahbek & Graves, 2001; Rangel et al, 2018). We also acknowledge that our data set may be subject to the Modifiable Areal Unit Problem (MAUP), which describes how different spatial scales and areal shapes may influence statistical analysis with geographical data sets (Comber & Harris, 2022; Jelinski & Wu, 1996). For example, any change in the way we determined the shape of each taxon's polygon could affect the geographical arrangement of the zoogeographical regions.…”