Outstanding Challenges in the Transferability of Ecological Models

• Autor(a) principal: Yates, Katherine L.
• Data de Publicação: 2018
• Outros Autores: 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, et al.
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10174/23525

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 prac- tices 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.