Yates KL, Bouchet PJ, Caley MJ, […], Rapacciuolo G, […], Whittingham MJ, Zharikov Y, Zurell D, Sequeira AMM
Model-based predictions of novel conditions and/or novel time periods (that is model transfers) could provide expectations in data-poor scenarios, contributing to more informed management decisions. However, the factors that constitute reliable predictions are still insufficiently understood. 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.