Sabiia Seb
        Busca avançada

Botão Atualizar

Botão Atualizar

Registro completo
Provedor de dados:  ArchiMer
País:  France
Título:  Outstanding Challenges in the Transferability of Ecological Models
Autores:  Yates, Katherine L.
Bouchet, Phil J.
Caley, M. Julian
Mengersen, Kerrie
Randin, Christophe F.
Parnell, Stephen
Fielding, Alan H.
Bamford, Andrew J.
Ban, Stephen
Marcia Barbosa, A.
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.
Heinanen, 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
Crespo, Guillermo Ortuno
Peterson, A. Townsend
Rapacciuolo, Giovanni
Roberts, Jason J.
Ross, Rebecca E.
Scales, Kylie L.
Schoeman, David
Snelgrove, Paul
Sundblad, Goran
Thuiller, Wilfried
Torres, Leigh G.
Verbruggen, Heroen
Wang, Lifei
Wenger, Seth
Whittingham, Mark J.
Zharikov, Yuri
Zurell, Damaris
Sequeira, Ana M. M.
Data:  2018-10
Ano:  2018
Resumo:  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.
Tipo:  Text
Idioma:  Inglês

Editor:  Elsevier Science London
Formato:  application/pdf
Fonte:  Trends In Ecology & Evolution (0169-5347) (Elsevier Science London), 2018-10 , Vol. 33 , N. 10 , P. 790-802
Direitos:  info:eu-repo/semantics/openAccess

restricted use

Empresa Brasileira de Pesquisa Agropecuária - Embrapa
Todos os direitos reservados, conforme Lei n° 9.610
Política de Privacidade
Área restrita

Parque Estação Biológica - PqEB s/n°
Brasília, DF - Brasil - CEP 70770-901
Fone: (61) 3448-4433 - Fax: (61) 3448-4890 / 3448-4891 SAC:

Valid HTML 4.01 Transitional