Estimating uncertainty in density surface models

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 b...

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Bibliographic Details
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
Description
Summary: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

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