Estimating uncertainty in density surface models

David L. Miller; Elizabeth A. Becker; Karin A. Forney; Jason J. Roberts; Ana Cañadas; Robert S. Schick

doi:10.7717/peerj.13950

Estimating uncertainty in density surface models

David L. Miller^*, Elizabeth A. Becker, Karin A. Forney, Jason J. Roberts, Ana Cañadas, Robert S. Schick

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Article number	e13950
Number of pages	19
Journal	PeerJ
Volume	10
DOIs	https://doi.org/10.7717/peerj.13950
Publication status	Published - 23 Aug 2022

Keywords

Density surface models
Distance sampling
Uncertainty quantification
Spatial modelling
Species distribution modelling
Model uncertainty
Environmental uncertainty

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10.7717/peerj.13950Licence: CC0

Miller_2022_PeerJ_Estimating_CC
This is an open access article, free of all copyright, made available under the Creative Commons Public Domain Dedication. This work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
Final published version, 4.64 MBLicence: CC0

ROMS grids for predictions
Miller, D. (Creator) & Becker, E. (Creator), Figshare, 2022
DOI: 10.6084/m9.figshare.20132291.v1
Dataset
Estimating uncertainty in density surface models (code)
Miller, D. L. (Creator), GitHub, 2022
https://github.com/densitymodelling/lemur_fin_analysis/
Dataset: Software

Cite this

@article{aa605baadb8645ea83c257136a782905,

title = "Estimating uncertainty in density surface models",

abstract = "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.",

keywords = "Density surface models, Distance sampling, Uncertainty quantification, Spatial modelling, Species distribution modelling, Model uncertainty, Environmental uncertainty",

author = "Miller, {David L.} and Becker, {Elizabeth A.} and Forney, {Karin A.} and Roberts, {Jason J.} and Ana Ca{\~n}adas and Schick, {Robert S.}",

note = "This work was funded by OPNAV N45 and the SURTASS LFA Settlement Agreement, and being managed by the U.S. Navy{\textquoteright}s Living Marine Resources program under Contract No. N39430-17-C-1982. ",

year = "2022",

month = aug,

day = "23",

doi = "10.7717/peerj.13950",

language = "English",

volume = "10",

journal = "PeerJ",

issn = "2167-8359",

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TY - JOUR

T1 - Estimating uncertainty in density surface models

AU - Miller, David L.

AU - Becker, Elizabeth A.

AU - Forney, Karin A.

AU - Roberts, Jason J.

AU - Cañadas, Ana

AU - Schick, Robert S.

N1 - 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.

PY - 2022/8/23

Y1 - 2022/8/23

N2 - 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.

AB - 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.

KW - Density surface models

KW - Distance sampling

KW - Uncertainty quantification

KW - Spatial modelling

KW - Species distribution modelling

KW - Model uncertainty

KW - Environmental uncertainty

U2 - 10.7717/peerj.13950

DO - 10.7717/peerj.13950

M3 - Article

SN - 2167-8359

VL - 10

JO - PeerJ

JF - PeerJ

M1 - e13950

ER -

Estimating uncertainty in density surface models

Abstract

Keywords

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Datasets

ROMS grids for predictions

Estimating uncertainty in density surface models (code)

Cite this