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

Rujia Bi,Yan Jiao,Can Zhou,Judi Hewitt

doi:10.1371/journal.pone.0243423

Abstract

Tagging 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.

Highlights

Individual movement can have profound consequences for populations by influencing their distribution and abundance, dynamics and persistence, and ecological community structure [1,2,3]
We developed a set of length-based, age-structured hierarchical models from tag-recovery data to quantify the movement patterns of yellow perch (Perca flavescens) in
Simulation of movement patterns of yellow perch across MUs based on tag-recovery data could be used to improve the current management regime

Summary

Introduction

Individual movement can have profound consequences for populations by influencing their distribution and abundance, dynamics and persistence, and ecological community structure [1,2,3]. Tags generally contain specific identification information and can be attached to individuals externally or internally [14] External tags, such as transbody, dart-style and internal-anchor tags, are inexpensive, visible and widle used, but are usually restricted to large fishes and with high tag loss [15]. We developed a set of length-based, age-structured hierarchical models from tag-recovery data to quantify the movement patterns of yellow perch (Perca flavescens) in. Very limited studies have been done to understand inherent spatial structure and movement patterns of yellow perch in Lake Erie, and movement rates between stocks are unknown and have not been incorporated into stock assessment models. Simulation of movement patterns of yellow perch across MUs based on tag-recovery data could be used to improve the current management regime. Bayesian approaches were used to construct these hierarchical models and provide straightforward estimates of parameters [34]

Methods

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Results

Discussion