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Estimating uncertainty in density surface models.
Miller, David L; Becker, Elizabeth A; Forney, Karin A; Roberts, Jason J; Cañadas, Ana; Schick, Robert S.
Afiliação
  • Miller DL; Centre for Research into Ecological & Environmental Modelling and School of Mathematics & Statistics, University of St Andrews, St Andrews, Fife, Scotland.
  • Becker EA; Ocean Associates, Inc. under contract to Marine Mammal and Turtle Division, Southwest Fisheries Science Center National Marine Fisheries Service, National Oceanic and Atmospheric Administration, La Jolla, CA, United States of America.
  • Forney KA; Marine Mammal and Turtle Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Moss Landing, CA, United States of America.
  • Roberts JJ; Moss Landing Marine Laboratories, San Jose State University, Moss Landing, CA, United States of America.
  • Cañadas A; Marine Geospatial Ecology Laboratory, Nicholas School of the Environment, Duke University, Durham, NC, United States of America.
  • Schick RS; Marine Geospatial Ecology Laboratory, Nicholas School of the Environment, Duke University, Durham, NC, United States of America.
PeerJ ; 10: e13950, 2022.
Article em En | MEDLINE | ID: mdl-36032955
ABSTRACT
Providing uncertainty estimates for predictions derived from species distribution models is essential for management but there is little guidance on potential sources of uncertainty in predictions and how best to combine these. Here we show where uncertainty can arise in density surface models (a multi-stage spatial modelling approach for distance sampling data), focussing on cetacean density modelling. We propose an extensible, modular, hybrid analytical-simulation approach to encapsulate these sources. We provide example analyses of fin whales Balaenoptera physalus in the California Current Ecosystem.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Ecossistema / Baleia Comum Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Revista: PeerJ Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Ecossistema / Baleia Comum Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Revista: PeerJ Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Reino Unido
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