A flexible ecosystem service mapping approach to support multiple levels governance of joint landscape services provision

In order to support multi-level governance of joint services provision, landscape service mapping should be flexible in addressing decision makers' needs and in accounting for different spatial contexts. The latter can be generally referred to the following categories: i) landscape units, i.e. more homogeneous physiographic and ecological areas within a given landscape for which specific functional functions in terms of services' supply can have local or regional relevance depending on the scale on investigation; ii) administrative units, e.g. municipalities, provinces or regions, hierarchically linked along a chain of steering decisions; iii) land cover or land use classes, as strongly affecting the potential supply of services within a given landscape; and iv) target areas of specific measures and/or directives, e.g. nature protection, which might result in an increased or decreased efficiency in landscape service supply. This work presents some applications, tailored to inform about trade-offs and synergies of landscape service provision and demand in different spatial contexts, of a data-based, fine scale, spatially explicit probabilistic mapping approach, developed and tested within the framework of the FP7 EU project CLAIM in the case study area of Märkische Schweiz in North-East Germany. The methodology consists of: (i) observations of landscape elements and services at random points within a regular reference grid, (ii) indicator coding and variogram analysis, (iii) kriging of single indicators via sequential simulations, and (iv) probabilistic mapping of single and joint landscape services. The proposed methodology explicitly account some of the drawbacks often observed in ecosystem service mapping (Maes et al, 2012), i.e. possibility to be applied to historical land cover and to projected hypothetical changes as in scenarios analysis, the production of refined maps and tabular outputs (Haines-Young et al, 2012). Furthermore, the probabilistic framework does not require any further standardization of results as all the services are assessed in terms of probability of occurrence, i.e. ranging from 0 to 1, which are in turn based on meeting the criteria defined for the indicators of their potential supply. Therefore the joint probability calculation represents a straightforward tool to provide a valuable integration to approaches based on proportional overlap (Wu et al., 2013), weighting scheme (Gimona and van der Horst, 2007), or relative capacity (Baral et al., 2012).