Abstract
As Ecosystem Services (ES) are the products of complex socio–ecological systems, their mapping requires a deep understanding of the spatial relationships and pattern that underpin ES provision. Upscaling ES maps is often carried out to avoid mismatches between the scale of ES assessment and that of their level of management. However, so far only a few efforts have been made to quantify how information loss occurs as data are aggregated to coarser scales. In the present study this was analyzed for three distinct case studies in the eastern Alps by comparing ES maps of outdoor recreation at the municipality level and at finer scales, i.e. high-resolution grids. Specifically, we adopt an innovative and flexible methodology based on Exploratory Spatial Data Analysis (ESDA), to disentangle the problem of the scale from the perspective of different levels of jurisdiction, by assessing in an iterative process how ES patterns change when upscaling high-resolution maps. Furthermore, we assess the sensitivity to the modifiable areal unit problem (MAUP) by calculating global statistics over three grid displacements. Our results demonstrate that spatial clusters tend to disappear when their extent becomes smaller than the features to which values are upscaled, leading to substantial information loss. Moreover, cross-comparison among grids and the municipality level highlights local anomalies that global spatial autocorrelation indicators fail to detect, revealing hidden clusters and inconsistencies among multiple scales. We conclude that, whenever ES maps are aggregated to a coarser scale, our methodology represents a suitable and flexible approach to explore clustering trends, shape and position of upscaling units, through graphs and maps showing spatial autocorrelation statistics. This can be crucial to finding the best compromise among scale mismatches, information loss and statistical bias that can directly affect the targeted ES mapping.