Estimating uncertainty in density surface models

• Published in: PeerJ
• Main Authors: Miller, David L., Becker, Elizabeth A., Forney, Karin A., Roberts, Jason J., Cañadas, Ana, Schick, Robert S.
• Other Authors: University of St Andrews. School of Mathematics and Statistics, University of St Andrews. Applied Mathematics, University of St Andrews. Centre for Research into Ecological & Environmental Modelling
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2022
• Subjects: Density surface models; Distance sampling; Uncertainty quantification; Spatial modelling; Species distribution modelling; Model uncertainty; Environmental uncertainty; QH301 Biology; GA Mathematical geography. Cartography; DAS; MCC; QH301; GA; Balaenoptera physalus
• Online Access: http://hdl.handle.net/10023/25879; https://doi.org/10.7717/peerj.13950

This work was funded by OPNAV N45 and the SURTASS LFA Settlement Agreement, and being managed by the U.S. Navy’s Living Marine Resources program under Contract No. N39430-17-C-1982. 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. Publisher PDF Peer reviewed