Spatially varying catchability for integrating research survey data with other data sources: case studies involving observer samples, industry-cooperative surveys, and predators-as-samplers

Grüss, Arnaud; Thorson, James T; Anderson, Owen F.; O'Driscoll, Richard; Heller-Shipley, Madison; Goodman, Scott

doi:10.1139/cjfas-2023-0051

i

Spatially varying catchability for integrating research survey data with other data sources: case studies involving observer samples, industry-cooperative surveys, and predators-as-samplers

2023
By Grüss, Arnaud ; Thorson, James T ; Anderson, Owen F. ; ...

Details

Journal Title:

Canadian Journal of Fisheries and Aquatic Sciences
Personal Author:

Grüss, Arnaud ; Thorson, James T ; Anderson, Owen F. ; O'Driscoll, Richard ; Heller-Shipley, Madison ; Goodman, Scott
NOAA Program & Office:

NOAA General Documents
Description:

Spatio-temporal models are widely applied to standardise research survey data and are increasingly used to generate density maps and indices from other data sources. We developed a spatio-temporal modelling framework that integrates research survey data (treated as a “reference dataset”) and other data sources (“non-reference datasets”) while estimating spatially varying catchability for the non-reference datasets. We demonstrated it using two case studies. The first involved bottom trawl survey and observer data for spiny dogfish (Squalus acanthias) on the Chatham Rise, New Zealand. The second involved cod predators as samplers of juvenile snow crab (Chionoecetes opilio) abundance, integrated with industry-cooperative surveys and a bottom trawl research survey in the eastern Bering Sea. Our integrated models leveraged the strengths of individual data sources (the quality of the reference dataset and the quantity of non-reference data), while downweighting the influence of the non-reference datasets via the estimated spatially varying catchabilities. They allowed for the generation of annual density maps for a longer time-period, and for the provision of one single index rather than multiple indices each covering a shorter time-period.
Keywords:

Aquatic Science Ecology, Evolution, Behavior And Systematics Ecology, Evolution, Behavior And Systematics Ecology, Evolution, Behavior And Systematics
Source:

Canadian Journal of Fisheries and Aquatic Sciences (2023)
DOI:

https://doi.org/10.1139/cjfas-2023-0051
ISSN:

0706-652X ; 1205-7533
Format:

PDF
Publisher:

Canadian Science Publishing
Document Type:

Journal Article
Rights Information:

Other
Compliance:

Library
Main Document Checksum:

urn:sha256:a4bbd4256c259cabdcea068c8bb19523a02571fd753f8151512b9e3785d41c56
Download URL:

https://repository.library.noaa.gov/view/noaa/53299/noaa_53299_DS1.pdf
File Type:

[PDF - 2.98 MB ]

ON THIS PAGE

Details

The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.