Outstanding Challenges in the Transferability of Ecological Models.

Journal Article (Review;Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Yates, KL; Bouchet, PJ; Caley, MJ; Mengersen, K; Randin, CF; Parnell, S; Fielding, AH; Bamford, AJ; Ban, S; Barbosa, AM; Dormann, CF; Elith, J; Embling, CB; Ervin, GN; Fisher, R; Gould, S; Graf, RF; Gregr, EJ; Halpin, PN; Heikkinen, RK; Heinänen, S; Jones, AR; Krishnakumar, PK; Lauria, V; Lozano-Montes, H; Mannocci, L; Mellin, C; Mesgaran, MB; Moreno-Amat, E; Mormede, S; Novaczek, E; Oppel, S; Ortuño Crespo, G; Peterson, AT; Rapacciuolo, G; Roberts, JJ; Ross, RE; Scales, KL; Schoeman, D; Snelgrove, P; Sundblad, G; Thuiller, W; Torres, LG; Verbruggen, H; Wang, L; Wenger, S; Whittingham, MJ; Zharikov, Y; Zurell, D; Sequeira, AMM

Published Date

  • October 2018

Published In

Volume / Issue

  • 33 / 10

Start / End Page

  • 790 - 802

PubMed ID

  • 30166069

Electronic International Standard Serial Number (EISSN)

  • 1872-8383

International Standard Serial Number (ISSN)

  • 0169-5347

Digital Object Identifier (DOI)

  • 10.1016/j.tree.2018.08.001


  • eng