Unpacking ecosystem service bundles: Towards predictive mapping of synergies and trade-offs between ecosystem services. (November 2017) - Spake, Rebecca; Lasseur, Rémy; Crouzat, Emilie; Bullock, James M.; Lavorel, Sandra; Parks, Katherine E.; Schaafsma, Marije; Bennett, Elena M.; Maes, Joachim; Mulligan, Mark; Mouchet, Maud; Peterson, Garry D.; et al.
Highlights: A bottom-up understanding of the determinants of ES bundles is a critical research gap in sustainability science. We evaluate the state of the art of methods in ES bundle science and synthesize these into four steps. We apply these four steps to a case study (the French Alps) and use the results to critique the current approaches. We conclude that inconsistency of spatial scale remains the primary barrier for understanding and predicting ES bundles. We outline a roadmap for hypothesis-driven research aimed at understanding and predicting ES bundles. Abstract: Multiple ecosystem services (ES) can respond similarly to social and ecological factors to form bundles. Identifying key social-ecological variables and understanding how they co-vary to produce these consistent sets of ES may ultimately allow the prediction and modelling of ES bundles, and thus, help us understand critical synergies and trade-offs across landscapes. Such an understanding is essential for informing better management of multi-functional landscapes and minimising costly trade-offs. However, the relative importance of different social and biophysical drivers of ES bundles in different types of social-ecological systems remains unclear. As such, a bottom-up understanding of the determinants of ES bundles is a critical research gap in ES and sustainability science. Here, we evaluate the current methods used in ES bundle science and synthesize these into four steps that capture the plurality of methods used to examine predictors of ES bundles. We then apply these four 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. We use the results of this case study to assess the strengths and limitations of current approaches for understanding distributions of ES bundles. We conclude that inconsistency of spatial scale remains the primary barrier for understanding and predicting ES bundles. We suggest a hypothesis-driven approach is required to predict relationships between ES, and we outline the research required for such an understanding to emerge.