Estimating uncertainty in density surface models

PeerJ

article

David L. Miller¹  Elizabeth A. Becker²  Karin A. Forney³  Jason J. Roberts⁵  Ana Cañadas⁵  Robert S. Schick⁵ 

[1] Centre for Research into Ecological & Environmental Modelling and School of Mathematics & Statistics, University of St Andrews;Ocean Associates, Inc. under contract to Marine Mammal and Turtle Division, Southwest Fisheries Science Center National Marine Fisheries Service, National Oceanic and Atmospheric Administration;Marine Mammal and Turtle Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration;Moss Landing Marine Laboratories, San Jose State University;Marine Geospatial Ecology Laboratory, Nicholas School of the Environment, Duke University

关键词: Density surface models;    Distance sampling;    Uncertainty quantification;    Spatial modelling;    Species distribution modelling;    Model uncertainty;    Environmental uncertainty;   

DOI  :  10.7717/peerj.13950

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY