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生态系统服务模型集填补了全球确定性和能力差距。

Model ensembles of ecosystem services fill global certainty and capacity gaps.

机构信息

Net Zero and Resilient Farming, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK.

School of Natural Sciences, Bangor University, Bangor, Gwenydd LL57 2DG, UK.

出版信息

Sci Adv. 2023 Apr 7;9(14):eadf5492. doi: 10.1126/sciadv.adf5492.

Abstract

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.

摘要

维持对人类福祉至关重要的生态系统服务(ES)受到许多缺乏 ES 模型(“能力差距”)或对现有模型准确性的了解(“确定性差距”)的从业者的阻碍,尤其是在世界上较贫困的地区。我们以前所未有的全球规模为五个具有高度政策相关性的 ES 开发了多个模型的集合。集合比单个模型准确 2%至 14%。集合的准确性与研究能力的替代指标无关,这表明准确性在全球范围内分布均匀,并且 ES 研究能力较弱的国家不会受到准确性的惩罚。通过免费提供这些 ES 集合和相关的准确性估计,我们提供了具有全球一致性的 ES 信息,这些信息可以支持在数据可用性低或实施复杂 ES 模型能力低的地区的政策和决策制定。因此,我们希望减少阻碍从地方到全球范围朝着 ES 可持续性方向发展的能力和确定性差距。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea6e/10081842/3d746fc7d098/sciadv.adf5492-f1.jpg

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