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Ensembles of ecosystem service models can improve accuracy and indicate uncertainty

Willcock, Simon ; Hooftman, Danny A.P. ; Blanchard, Ryan ; Dawson, Terence P. ; Hickler, Thomas LU ; Lindeskog, Mats LU ; Martinez-Lopez, Javier ; Reyers, Belinda ; Watts, Sophie M. and Eigenbrod, Felix , et al. (2020) In Science of the Total Environment 747.
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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Africa, Carbon, Charcoal, Firewood, Grazing, Model validation, Natural capital, Poverty alleviation, Sustainable development, Water
in
Science of the Total Environment
volume
747
article number
141006
publisher
Elsevier
external identifiers
  • scopus:85088925160
  • pmid:32768767
ISSN
0048-9697
DOI
10.1016/j.scitotenv.2020.141006
language
English
LU publication?
yes
id
dd663863-aac0-48bc-acae-ea48a5387444
date added to LUP
2020-08-11 11:22:09
date last changed
2021-01-12 01:17:19
@article{dd663863-aac0-48bc-acae-ea48a5387444,
  abstract     = {<p>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.</p>},
  author       = {Willcock, Simon and Hooftman, Danny A.P. and Blanchard, Ryan and Dawson, Terence P. and Hickler, Thomas and Lindeskog, Mats and Martinez-Lopez, Javier and Reyers, Belinda and Watts, Sophie M. and Eigenbrod, Felix and Bullock, James M.},
  issn         = {0048-9697},
  language     = {eng},
  publisher    = {Elsevier},
  series       = {Science of the Total Environment},
  title        = {Ensembles of ecosystem service models can improve accuracy and indicate uncertainty},
  url          = {http://dx.doi.org/10.1016/j.scitotenv.2020.141006},
  doi          = {10.1016/j.scitotenv.2020.141006},
  volume       = {747},
  year         = {2020},
}