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

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 d...

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Bibliographic Details
Published in:Canadian Journal of Fisheries and Aquatic Sciences
Main Authors: Grüss, Arnaud, Thorson, James T., Anderson, Owen F., O’Driscoll, Richard L., Heller-Shipley, Madison, Goodman, Scott
Format: Article in Journal/Newspaper
Language:English
Published: Canadian Science Publishing 2023
Subjects:
Aquatic Science
Ecology, Evolution, Behavior and Systematics
Bering Sea
New Zealand
Chionoecetes opilio
Snow crab
spiny dogfish
Squalus acanthias
Online Access:http://dx.doi.org/10.1139/cjfas-2023-0051
https://cdnsciencepub.com/doi/full-xml/10.1139/cjfas-2023-0051
https://cdnsciencepub.com/doi/pdf/10.1139/cjfas-2023-0051
Description
Summary: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.

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