COS 11-6 - Unpacking ecosystem service bundles: Towards predictive mapping of synergies and trade-offs between ecosystem services

Monday, August 7, 2017: 3:20 PM
B112, Oregon Convention Center
Rebecca Spake1, Rémy Lasseur2, Emilie Crouzat3, James M. Bullock4, Sandra Lavorel5, Katherine E. Parks6, Marije Schaafsma7, Elena M. Bennett8, Joachim Maes9, Mark Mulligan10, Maud A. Mouchet11, Garry D Peterson12, Catharina J.E. Schulp13, Wilfried Thuiller14, Monica G. Turner15, Peter Verburg16 and Felix Eigenbrod1, (1)Geography and Environment, University of Southampton, Southampton, United Kingdom, (2)Laboratoire d’Ecologie Alpine, CNRS, Université Grenoble Alpes, (3)Laboratoire d’Ecologie Alpine, CNRS - Universite Grenoble Alpes, Grenoble, France, (4)Natural Environment Research Council, Centre for Ecology and Hydrology, Oxford, United Kingdom, (5)Laboratoire d'Ecologie Alpine, CNRS - Universite Grenoble Alpes, Grenoble, France, (6)Centre for Environmental Science, University of Southampton, (7)Geography and the Environment, University of Southampton, (8)Department of Natural Resource Sciences and McGill School of Environment, McGill University, Ste. Anne de Bellevue, QC, Canada, (9)European Commission, (10)Department of Geography, King's College London, London, United Kingdom, (11)Muséum National d'Histoire Naturelle, Montpellier, France, (12)Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden, (13)Department of Earth Science, VU University Amsterdam, (14)Université Grenoble Alpes, France, (15)Department of Zoology, University of Wisconsin, Madison, Madison, WI, (16)Earth Sciences, VU Amsterdam, Netherlands
Background/Question/Methods

Understanding and predicting how multiple ecosystem services (ES) covary, particularly in relation to social and ecological drivers of change, is a research imperative for guiding sustainable environmental management for human well-being. The identification of ‘ecosystem service bundles’ has been operationalised to facilitate a search for general rules determining ES associations in relation to social-ecological drivers. An increasingly popular approach for identifying and mapping ES bundles, largely based on cluster analysis, has been applied across the world to a range of cultural landscapes to facilitate cross-study comparisons of ES associations and their drivers. We reviewed the use of this correlative approach in recent studies and synthesized its application into four steps that capture the plurality of methods used. To critically assess the strengths and limitations of current approaches, we then applied these steps to a cross-study comparison (North and South French Alps) of relationships between social-ecological variables and ES bundles, as it is widely advocated that cross-study comparisons are necessary for achieving a general understanding of predictors of ES associations.

Results/Conclusions

We found that even within the French Alps, there is enormous variation in the degree to which different social-ecological variables can explain the distributions of ES bundles. A key assumption with correlative approaches is that consistency in the spatial congruency between ES likely emerges from common social-ecological drivers. In actuality, ES may trade-off against each other simply because they compete for space (e.g. agricultural production and timber production), rather than being causally linked (e.g. agricultural production reducing water quality through diffuse pollution). The former trade-off can easily be represented through current correlative approaches but the latter ultimately requires a process-based description of the system to detect and quantify them. We therefore conclude that the approach reviewed is poorly suited to enabling sound understanding and prediction of ES bundles. A more hypothesis-driven approach than is currently taken is required to make real progress in predicting relationships between ES bundles, and we outline a roadmap of the types of research required to enable such an understanding to emerge.