School of Environment and Life Sciences, University of Salford, Manchester, UK; Centre for Excellence in Environmental Decisions, University of Queensland, Brisbane, QLD, Australia; Joint first authors.
School of Biological Sciences, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia; Joint first authors.
Trends Ecol Evol. 2018 Oct;33(10):790-802. doi: 10.1016/j.tree.2018.08.001. Epub 2018 Aug 27.
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.
预测模型是许多科学学科的核心，对于在瞬息万变的世界中提供管理决策至关重要。然而，对于模型在新条件下的准确性和精密度（即“可转移性”）的理解有限，这削弱了人们对其预测的信心。在这里，50 名专家确定了优先需要填补的知识空白，如果这些空白得到填补，将最能提高模型的可转移性。这些知识空白被总结为六个技术挑战和六个基本挑战，这些挑战是加强对生态可预测性决定因素（包括物种特征和数据质量）的研究以及制定模型转移最佳实践的基础，其中包括需要强化对生态可预测性决定因素（包括物种特征和数据质量）的研究，并制定模型转移的最佳实践。具有高度重要性的是确定一套广泛适用的可转移性指标，以及适当的工具来量化在新条件下预测不确定性的来源和影响。
