Estimating uncertainty in density surface models

• Published in: PeerJ
• Main Authors: David L. Miller, Elizabeth A. Becker, Karin A. Forney, Jason J. Roberts, Ana Cañadas, Robert S. Schick
• Format: Article in Journal/Newspaper
• Language: English
• Published: PeerJ Inc. 2022
• Subjects: Density surface models; Distance sampling; Uncertainty quantification; Spatial modelling; Species distribution modelling; Model uncertainty; Medicine; R; Biology (General); QH301-705.5; Balaenoptera physalus
• Online Access: https://doi.org/10.7717/peerj.13950; https://doaj.org/article/b7af2697e3c346c9a671a56d49254c35

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.