Ensembles of ecosystem service models can improve accuracy and indicate uncertainty

dc.contributor.authorWillcock, Simon
dc.contributor.authorHooftman, Danny A.P.
dc.contributor.authorBlanchard, Ryan
dc.contributor.authorDawson, Terence P.
dc.contributor.authorHickler, Thomas
dc.contributor.authorLindeskog, Mats
dc.contributor.authorMartinez-Lopez, Javier
dc.contributor.authorReyers, Belinda
dc.contributor.authorWatts, Sophie M.
dc.contributor.authorEigenbrod, Felix
dc.contributor.authorBullock, James M.
dc.date.accessioned2021-04-13T15:35:16Z
dc.date.available2021-04-13T15:35:16Z
dc.date.issued2020-12
dc.description.abstractMany 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 themeswhich 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 regionswhere 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 individualmodels.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.en_ZA
dc.description.departmentZoology and Entomologyen_ZA
dc.description.librarianam2021en_ZA
dc.description.sponsorshipThe UK Ecosystem Services for Poverty Alleviation program (ESPA; www.espa.ac.uk) and ‘EnsemblES - Using ensemble techniques to capture the accuracy and sensitivity of ecosystem service models and the Spanish Government through María de Maeztu excellence accreditation 2018-2021.en_ZA
dc.description.urihttp://www.elsevier.com/locate/scitotenven_ZA
dc.identifier.citationWillcock, S., Hooftman, D.A.P., Blanchard, R. et al. 2020, 'Ensembles of ecosystem service models can improve accuracy and indicate uncertainty', Science of the Total Environment, vol. 747, art. 141006, pp. 1-11.en_ZA
dc.identifier.issn0048-9697 (print)
dc.identifier.issn1879-1026 (online)
dc.identifier.other10.1016/j.scitotenv.2020.141006
dc.identifier.urihttp://hdl.handle.net/2263/79422
dc.language.isoenen_ZA
dc.publisherElsevieren_ZA
dc.rights© 2020 The Author(s). This is an open access article under the CC BY license.en_ZA
dc.subjectAfricaen_ZA
dc.subjectCarbonen_ZA
dc.subjectCharcoalen_ZA
dc.subjectFirewooden_ZA
dc.subjectGrazingen_ZA
dc.subjectModel validationen_ZA
dc.subjectNatural capitalen_ZA
dc.subjectPoverty alleviationen_ZA
dc.subjectSustainable developmenten_ZA
dc.subjectWateren_ZA
dc.titleEnsembles of ecosystem service models can improve accuracy and indicate uncertaintyen_ZA
dc.typeArticleen_ZA

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