TY - JOUR

T1 - Ensembles of ecosystem service models can improve accuracy and indicate uncertainty

AU - Willcock, Simon

AU - Hooftman, Danny

AU - Blanchard, Ryan

AU - Dawson, Terence P.

AU - Hickler, Thomas

AU - Lindeskog, Mats

AU - Martinez-Lopez, Javier

AU - Reyers, Belinda

AU - Watts, Sophie M.

AU - Eigenbrod, Felix

AU - Bullock, James

PY - 2020/12/10

Y1 - 2020/12/10

N2 - 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.

AB - 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.

KW - Africa

KW - Carbon

KW - Charcoal

KW - Firewood

KW - Grazing

KW - Model validation

KW - Natural capital

KW - Poverty alleviation

KW - Sustainable development

KW - Water

U2 - 10.1016/j.scitotenv.2020.141006

DO - 10.1016/j.scitotenv.2020.141006

M3 - Article

VL - 747

JO - Science of the Total Environment

JF - Science of the Total Environment

SN - 0048-9697

M1 - 141006

ER -