|DOI / URL||link|
|Title (Primary)||Spatial prioritisation for conserving ecosystem services: comparing hotspots with heuristic optimisation|
|Author||Schröter, M.; Remme, R.P.;|
|POF III (all)||T12;|
|Keywords||Hot spot; Mapping; Modelling; Overlap|
|UFZ wide themes||RU1|
The variation in spatial distribution between ecosystem services can be high. Hence, there is a need to spatially identify important sites for conservation planning. The term ‘ecosystem service hotspot’ has often been used for this purpose, but definitions of this term are ambiguous.
We review and classify methods to spatially delineate hotspots. We test how spatial configuration of hotspots for a set of ecosystem services differs depending on the applied method. We compare the outcomes to a heuristic site prioritisation approach (Marxan).
The four tested hotspot methods are top richest cells, spatial clustering, intensity, and richness. In a conservation scenario we set a target of conserving 10 % of the quantity of five regulating and cultural services for the forest area of Telemark county, Norway.
Spatial configuration of selected areas as retrieved by the four hotspots and Marxan differed considerably. Pairwise comparisons were at the lower end of the scale of the Kappa statistic (0.11–0.27). The outcomes also differed considerably in mean target achievement, cost-effectiveness in terms of land-area needed per unit target achievement and compactness in terms of edge-to-area ratio.
An ecosystem service hotspot can refer to either areas containing high values of one service or areas with multiple services. Differences in spatial configuration among hotspot methods can lead to uncertainties for decision-making. This also has consequences for analysing the spatial co-occurrence of hotspots of multiple services and of services and biodiversity.
|Persistent UFZ Identifier||https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=16489|
|Schröter, M., Remme, R.P. (2016):
Spatial prioritisation for conserving ecosystem services: comparing hotspots with heuristic optimisation
Landsc. Ecol. 31 (2), 431 - 450