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Dangosydd eitem ddigidol (DOI)

  • Simon Willcock
  • Danny Hooftman
    Centre for Ecology and Hydrology, Wallingford, UK
  • Ryan Blanchard
    Council for Scientific and Industrial Research
  • Terence P. Dawson
    King's College London
  • Thomas Hickler
    Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Germany
  • Mats Lindeskog
    Lund University
  • Javier Martinez-Lopez
    Basque Centre of Climate Change
  • Belinda Reyers
    University of Pretoria
  • Sophie M. Watts
    University of Southampton
  • Felix Eigenbrod
    University of Southampton
  • James Bullock
    Centre for Ecology and Hydrology, Wallingford, UK
Many ecosystem services (ES) models exist to support sustainable development decisions. However, most ES studies use only a single modelling framework and, because of a lack of validation data, rarely assess model accuracy for the study area. In line with other research themes which have high model uncertainty, such as climate change, ensembles of ES models may better serve decision-makers by providing more robust and accurate estimates, as well as provide indications of uncertainty when validation data are not available. To illustrate the benefits of an ensemble approach, we highlight the variation between alternative models, demonstrating that there are large geographic regions where decisions based on individual models are not robust. We test if ensembles are more accurate by comparing the ensemble accuracy of multiple models for six ES against validation data across sub-Saharan Africa with the accuracy of individual models. We find that ensembles are better predictors of ES, being 5.0-6.1% more accurate than individual models. We also find that the uncertainty (i.e. variation among constituent models) of the model ensemble is negatively correlated with accuracy and so can be used as a proxy for accuracy when validation is not possible (e.g. in data-deficient areas or when developing scenarios). Since ensembles are more robust, accurate and convey uncertainty, we recommend that ensemble modelling should be more widely implemented within ES science to better support policy choices and implementation.

Allweddeiriau

Iaith wreiddiolSaesneg
Rhif yr erthygl141006
Nifer y tudalennau36
CyfnodolynScience of the Total Environment
Cyfrol747
Dyddiad ar-lein cynnar25 Gorff 2020
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsE-gyhoeddi cyn argraffu - 25 Gorff 2020

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