Detection of fish movement patterns across management unit boundaries using agestructured Bayesian hierarchical models with tag-recovery data

Simple item page
dc.contributor.authorBi, Rujiaen
dc.contributor.authorZhou, Canen
dc.contributor.authorJiao, Yanen
dc.contributor.departmentFish and Wildlife Conservationen
dc.date.accessioned2021-03-02T19:02:00Zen
dc.date.available2021-03-02T19:02:00Zen
dc.date.issued2020-12-07en
dc.description.abstractTagging studies have been widely conducted to investigate the movement pattern of wild fish populations. In this study, we present a set of length-based, age-structured Bayesian hierarchical models to explore variabilities and uncertainties in modeling tag-recovery data. These models fully incorporate uncertainties in age classifications of tagged fish based on length and uncertainties in estimated population structure. Results of a tagging experiment conducted by the Ontario Ministry of Natural Resources and Forestry (OMNRF) on yellow perch in Lake Erie was analyzed as a case study. A total of 13,694 yellow perch were tagged with PIT tags from 2009 to 2015; 322 of these were recaptured in the Ontario commercial gillnet fishery and recorded by OMNRF personnel. Different movement configurations modeling the tag-recovery data were compared, and all configurations revealed that yellow perch individuals in the western basin (MU1) exhibited relatively strong site fidelity, and individuals from the central basin (MU2 and MU3) moved within this basin, but their movements to the western basin (MU1) appeared small. Model with random effects of year and age on movement had the best performance, indicating variations in movement of yellow perch across the lake among years and age classes. This kind of model is applicable to other tagging studies to explore temporal and age-class variations while incorporating uncertainties in age classification.en
dc.description.sponsorshipThis work was supported by a grant #458000 awarded to YJ by Ontario Commercial Fisheries’ Association (https://www.ocfa.ca).en
dc.format.mimetypeapplication/pdfen
dc.identifier.citationBi R, Zhou C, Jiao Y (2020) Detection of fish movement patterns across management unit boundaries using age-structured Bayesian hierarchical models with tag-recovery data. PLoS ONE 15(12): e0243423. https://doi.org/10.1371/ journal.pone.0243423en
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0243423en
dc.identifier.issue12en
dc.identifier.urihttp://hdl.handle.net/10919/102515en
dc.identifier.volume15en
dc.language.isoenen
dc.publisherPLOSen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleDetection of fish movement patterns across management unit boundaries using agestructured Bayesian hierarchical models with tag-recovery dataen
dc.title.serialPLOS Oneen
dc.typeArticle - Refereeden

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
pone.0243423.pdf
Size:
2.24 MB
Format:
Adobe Portable Document Format
Description:
Download

Collections

Open Access Subvention Fund Articles
Scholarly Works, Fish and Wildlife Conservation