Estimating uncertainty in density surface models

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 mu...

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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.
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
Balaenoptera physalus
Online Access:https://risweb.st-andrews.ac.uk/portal/en/researchoutput/estimating-uncertainty-in-density-surface-models(aa605baa-db86-45ea-83c2-57136a782905).html
https://doi.org/10.7717/peerj.13950
https://research-repository.st-andrews.ac.uk/bitstream/10023/25879/1/Miller_2022_PeerJ_Estimating_CC.pdf
Description
Summary: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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