Better Model Transfers Require Knowledge of Mechanisms

Last modified: 
December 13, 2019 - 1:10pm
Type: Journal Article
Year of publication: 2019
Date published: 05/2019
Authors: Phil Bouchet, Townsend Peterson, Damaris Zurell, Carsten Dormann, David Schoeman, Rebecca Ross, Paul Snelgrove, Ana Sequeira, Mark Whittingham, Lifei Wang, Giovanni Rapacciuolo, Steffen Oppel, Camille Mellin, Valentina Lauria, Periyadan Krishnakumar, Alice Jones, Stefan Heinänen, Risto Heikkinen, Edward Gregr, Alan Fielding, Julian Caley, Márcia Barbosa, Andrew Bamford, Hector Lozano-Montes, Stephen Parnell, Seth Wenger, Katherine Yates
Journal title: Trends in Ecology & Evolution
ISSN: 01695347

Model transferability is an emerging and important branch of predictive science that has grown primarily from a need to produce ecological forecasts in the face of widespread data deficiency and escalating environmental novelty. In our recent article in Trends in Ecology and Evolution [1], we outlined some of the major roadblocks that currently undermine the practice of model transfers in ecology. The response of Radchuk et al. [2] to our work stresses the value of considering ‘first principles’ in projections of ecosystem change [3] and offers insights into outstanding challenges specific to mechanistic (synonym: process-based) models [4].

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