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Using Side-Scan Sonar and N-Mixture Modeling to Estimate Atlantic Sturgeon Spawning Migration Abundance

  • Joshua R. Vine
  • , Yoichiro Kanno
  • , S. Chad Holbrook
  • , William C. Post
  • , Brandon K. Peoples

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Understanding the relationship between number of spawners and recruitment is essential for managing fish populations. Atlantic Sturgeon Acipenser oxyrinchus (ATS) are endangered anadromous fish inhabiting the rivers, estuaries, and marine environments along the Atlantic coast of North America. Atlantic Sturgeon are periodic life history strategists that exhibit both spring and fall spawning migrations. Traditional capture–mark–recapture techniques can be used to estimate spawning run abundance but are resource intensive and potentially stressful on migrating individuals. Noninvasive methods, such as side-scan sonar, can be a less stressful alternative to estimating abundance. We sampled the uppermost portion of the Savannah River, USA, over 50 occasions from August to November 2017 using side-scan sonar. Bayesian N-mixture modeling was used to estimate spawner abundance and covariate effects based on spatially and temporally replicated count data obtained from sonar recordings. We detected at least one ATS on each sampling occasion and estimated a maximum daily spawner abundance between 35 and 55 individuals (95% credible interval) within the sampled area during the 2017 fall spawning season. Maximum discharge significantly affected ATS detection, and site average maximum depth significantly affected ATS abundance. Our results suggest that side-scan sonar can be used as an alternative to traditional mark–recapture techniques for spawner abundance estimation. Routine sampling by using our methods will efficiently produce spawning run estimates and provide insight regarding the effects of environmental covariates on spawner abundance seasonally.

Original languageEnglish
Pages (from-to)939-950
Number of pages12
JournalNorth American Journal of Fisheries Management
Volume39
Issue number5
DOIs
StatePublished - Oct 1 2019

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 14 - Life Below Water
    SDG 14 Life Below Water

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