Outstanding Challenges in the Transferability of Ecological Models

Katherine L Yates , Phil J Bouchet , M. Julian Julian Caley , Kerrie L. Mengersen (1) , Christophe F Randin , Stephen Parnell , Alan H Fielding , Andrew J Bamford , Stephen Ban , A Márcia Barbosa , Carsten F Dormann , Jane Elith , Clare B Embling , Gary N Ervin , Rebecca Fisher , Susan Gould , Roland F Graf , Edward J Gregr , Patrick Halpin (2) , Risto K Heikkinen , Stefan Heinänen , Alice R Jones , Periyadan K Krishnakumar , Valentina Lauria , Hector Lozano-Montes , Laura Mannocci (3) , Camille Mellin (4) , Mohsen B Mesgaran , Elena Moreno-Amat , Sophie Mormede , Emilie Novaczek , Steffen Oppel (5) , Guillermo Ortuño Crespo , A. Townsend Peterson (6) , Giovanni Rapacciuolo , Jason Roberts (7) , Rebecca E Ross , Kylie L Scales , David Schoeman , Paul Snelgrove , Göran Sundblad , Wilfried Thuiller (8) , Leigh G Torres , Heroen Verbruggen , Lifei Wang , Seth Wenger , Mark J Whittingham , Yuri Zharikov , Damaris Zurell (9) , Ana F. Sequeira (10) , A. Márcia Barbosa , Guillermo Ortuño Crespo , Ana M.M. Sequeira

1 QUT - Queensland University of Technology [Brisbane]
2 Marine Geospatial Ecology Laboratory [USA]
3 UMR MARBEC - MARine Biodiversity Exploitation and Conservation
4 Coreus
5 RSPB - Royal Society for the Protection of Birds
6 KU - University of Kansas [Lawrence]
7 UQ [All campuses : Brisbane, Dutton Park Gatton, Herston, St Lucia and other locations] - The University of Queensland
8 LECA - Laboratoire d'Ecologie Alpine
9 Inst. Geoecol.
10 INSA - Instituto Nacional de Saùde Dr Ricardo Jorge [Portugal]

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 'trans-ferability') 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. Predicting the Unknown Predictions facilitate the formulation of quantitative, testable hypotheses that can be refined and validated empirically [1]. Predictive models have thus become ubiquitous in numerous scientific disciplines, including ecology [2], where they provide means for mapping species distributions, explaining population trends, or quantifying the risks of biological invasions and disease outbreaks (e.g., [3,4]). The practical value of predictive models in supporting policy and decision making has therefore grown rapidly (Box 1) [5]. With that has come an increasing desire to predict (see Glossary) the state of ecological features (e.g., species, habitats) and our likely impacts upon them [5], prompting a shift from explanatory models to anticipatory predictions [2]. However, in many situations, severe data deficiencies preclude the development of specific models, and the collection of new data can be prohibitively costly or simply impossible [6]. It is in this context that interest in transferable models (i.e., those that can be legitimately projected beyond the spatial and temporal bounds of their underlying data [7]) has grown. Transferred models must balance the tradeoff between estimation and prediction bias and variance (homogenization versus nontransferability, sensu [8]). Ultimately, models that can Highlights Models transferred to novel conditions could provide predictions in data-poor scenarios, contributing to more informed management decisions.

Domaines: Sciences de l'environnement; Biodiversité et Ecologie

Katherine L Yates, Phil J Bouchet, M. Julian Julian Caley, Kerrie L. Mengersen, Christophe F Randin, et al.. Outstanding Challenges in the Transferability of Ecological Models. Trends in Ecology & Evolution, 2018, 33 (10), pp.790-802. ⟨10.1016/j.tree.2018.08.001⟩. ⟨hal-02346319⟩