Ensembles of ecosystem service models can improve accuracy and indicate uncertainty

Simon Willcock,Danny A.P Hooftman,Ryan Blanchard,Terence P Dawson,Thomas Hickler,Mats Lindeskog,Javier Martinez-Lopez,Belinda Reyers,Sophie M Watts,Felix Eigenbrod,James M Bullock

doi:10.1016/j.scitotenv.2020.141006

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 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.

Highlights

Planning and implementing sustainable development approaches requires knowledge on the ecosystem services (ES; nature's contributions to people (Pascual et al, 2017)) provided in a region and how they might respond to management choices or other drivers of change (Guerry et al, 2015)
We demonstrate that decision-making based on single ES models is not robust for large regions within sub-Saharan Africa as high variation between model estimates means that using a different model or incorporating an additional model into the decision-making process is highly likely to result in a different decision
In addition to increased robustness, we show that ensembles of ES models can provide improved accuracy over individual models, as well as an indication of uncertainty

Summary

Introduction

Planning and implementing sustainable development approaches requires knowledge on the ecosystem services (ES; nature's contributions to people (Pascual et al, 2017)) provided in a region and how they might respond to management choices or other drivers of change (Guerry et al, 2015). Models can provide credible information where empirical data on ES are sparse, which is especially the case in many developing countries (IPBES, 2016; Suich et al, 2015). Claims of superiority are sometimes made for specific models, independent evaluations of models have often been unable to demonstrate the preeminence of any individual model in terms of accuracy or other aspects of their utility (Box 1; Table SI-1-1) (Araújo and New, 2007; Willcock et al, 2019). Decisions based on a single ES modelling framework are unlikely to be robust (Box 1) (Refsgaard et al, 2007; Walker et al, 2003)

Methods

Results

Discussion

Conclusion