Abstract:
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 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.
