TY - JOUR

T1 - Model Ensembles of Ecosystem Services Fill Global Certainty and Capacity Gaps

AU - Willcock, Simon

AU - Hooftman, Danny

AU - Neugarten, Rachel

AU - Chaplin-Kramer, Rebecca

AU - Barredo, Jose

AU - Hickler, Thomas

AU - Kindermann, Georg

AU - Lewis, Amy

AU - Lindeskog, Mats

AU - Martinez-Lopez, Javier

AU - Bullock, James

PY - 2023/4/7

Y1 - 2023/4/7

N2 - Sustaining ecosystem services (ES) critical to human well-being is hindered by many practitioners lacking access to ES models ("the capacity gap") or knowledge of the accuracy of available models ("the certainty gap"), especially in the world's poorer regions. We developed ensembles of multiple models at an unprecedented global scale for five ES of high policy relevance. Ensembles were 2 to 14% more accurate than individual models. Ensemble accuracy was not correlated with proxies for research capacity, indicating that accuracy is distributed equitably across the globe and that countries less able to research ES suffer no accuracy penalty. By making these ES ensembles and associated accuracy estimates freely available, we provide globally consistent ES information that can support policy and decision-making in regions with low data availability or low capacity for implementing complex ES models. Thus, we hope to reduce the capacity and certainty gaps impeding local- to global-scale movement toward ES sustainability.

AB - Sustaining ecosystem services (ES) critical to human well-being is hindered by many practitioners lacking access to ES models ("the capacity gap") or knowledge of the accuracy of available models ("the certainty gap"), especially in the world's poorer regions. We developed ensembles of multiple models at an unprecedented global scale for five ES of high policy relevance. Ensembles were 2 to 14% more accurate than individual models. Ensemble accuracy was not correlated with proxies for research capacity, indicating that accuracy is distributed equitably across the globe and that countries less able to research ES suffer no accuracy penalty. By making these ES ensembles and associated accuracy estimates freely available, we provide globally consistent ES information that can support policy and decision-making in regions with low data availability or low capacity for implementing complex ES models. Thus, we hope to reduce the capacity and certainty gaps impeding local- to global-scale movement toward ES sustainability.

KW - Accuracy

KW - Ensemble

KW - Implementation gap

KW - Modelling

KW - Nature's contributions to people

KW - Uncertainty

U2 - 10.1126/sciadv.adf5492

DO - 10.1126/sciadv.adf5492

M3 - Article

C2 - 37027474

VL - 9

SP - eadf5492

JO - Science Advances

JF - Science Advances

SN - 2375-2548

IS - 14

ER -