期刊
TRENDS IN ECOLOGY & EVOLUTION
卷 33, 期 10, 页码 790-802出版社
ELSEVIER SCIENCE LONDON
DOI: 10.1016/j.tree.2018.08.001
关键词
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资金
- Australian Research Council [DE170100841, DE140100701]
- IOMRC (UWA/AIMS/CSIRO) collaborative Postdoctoral Fellowship
- Australian Government's National Environmental Science Programme (NESP)
- Swiss National Science Foundation [PZ00P3_168136/1]
- German Science Foundation [ZU 361/1-1, DO 786/10-1]
- Australian Research Council Centre of Excellence for Environmental Decisions [CE11001000104]
- USDA [17-8130-0570-CA]
- DEFRA
- US Navy Cooperative Agreement [N62470-15-2-8003]
- Spencer Gulf Ecosystem Development Initiative
- Goyder Institute for Water Research [CA-16-04]
- FEDER/COMPETE 2020 [IF/00266/2013/CP1168/CT0001]
- FCT
- Swiss National Science Foundation (SNF) [PZ00P3_168136] Funding Source: Swiss National Science Foundation (SNF)
- Australian Research Council [DE140100701, DE170100841] Funding Source: Australian Research Council
Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions.
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