Outstanding Challenges in the Transferability of Ecological Models. (October 2018)

• Record Type: Journal Article
• Authors: Yates, Katherine L.; Bouchet, Phil J.; Caley, M. Julian; Mengersen, Kerrie; Randin, Christophe F.; Parnell, Stephen; Fielding, Alan H.; Bamford, Andrew J.; Ban, Stephen; Barbosa, A. Márcia; Dormann, Carsten F.; Elith, Jane; Embling, Clare B.; Ervin, Gary N.; Fisher, Rebecca; Gould, Susan; Graf, Roland F.; Gregr, Edward J.; Halpin, Patrick N.; Heikkinen, Risto K.; Heinänen, Stefan; Jones, Alice R.; Krishnakumar, Periyadan K.; Lauria, Valentina; Lozano-Montes, Hector; Mannocci, Laura; Mellin, Camille; Mesgaran, Mohsen B.; Moreno-Amat, Elena; Mormede, Sophie; Novaczek, Emilie; Oppel, Steffen; Ortuño Crespo, Guillermo; Peterson, A. Townsend; Rapacciuolo, Giovanni; Roberts, Jason J.; Ross, Rebecca E.; Scales, Kylie L.; Schoeman, David; Snelgrove, Paul; Sundblad, Göran; Thuiller, Wilfried; Torres, Leigh G.; Verbruggen, Heroen; Wang, Lifei; Wenger, Seth; Whittingham, Mark J.; Zharikov, Yuri; Zurell, Damaris; Sequeira, Ana M.M.;
• Is Part Of: Trends in ecology & evolution. Volume 33:Number 10(2018)
• Page Start: 790
• Page End: 802
• Publication Date: 2018-10
• Subjects: Predictive modeling -- model transfers -- species distribution models -- habitat models -- extrapolation -- generality -- uncertainty; Ecology -- Periodicals; Evolution (Biology) -- Periodicals; 576.8
• DOI: 10.1016/j.tree.2018.08.001
• Languages: English
• ISSNs: 0169-5347

Abstract

Abstract : 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. Highlights: Models transferred to novel conditions could provide predictions in data-poor scenarios, contributing to more informed management decisions. The determinants of ecological predictability are, however, still insufficiently understood. Predictions from transferred ecological models are affected by species' traits, sampling biases, biotic interactions, nonstationarity, and the degree of environmental dissimilarity between reference and target systems. We synthesize six technical and six fundamental challenges that, if resolved, will catalyze practical and conceptual advances in model transfers. We propose that the most immediate obstacle to improving understanding lies in the absence of a widely applicable set of metrics for assessing transferability, and that encouraging the development of models grounded in well-established mechanisms offers the most immediate way of improving transferability.