Combining acoustic telemetry and side-scan sonar to estimate abundance of endangered shortnose sturgeon in the Hudson River, New York

Canadian Journal of Fisheries and Aquatic Sciences
By: , and 

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Abstract

For endangered shortnose sturgeon (Acipenser brevirostrum), the ability to estimate and monitor population size is critical for tracking species’ recovery. Yet, contemporary abundance estimates have not been completed for many shortnose sturgeon populations, largely owing to the difficulty in using traditional abundance estimators for sturgeons. Here, we estimate the adult shortnose sturgeon population size of the Hudson River, NY by integrating data from two largely passive sampling methods – acoustic telemetry and side-scan sonar – into a Bayesian hierarchical model of abundance. We estimated the adult abundance to be 69,798 individuals (95% CI = 9,207-185,666), making the Hudson River the largest extant shortnose sturgeon population. Despite this, the population remains vulnerable to localized disturbances, as over 40% of the population congregated in a small overwintering habitat that coincides with an area of high anthropogenic activity. Accordingly, recurrent demographic surveys may be beneficial for gaining insight into the relative effects of anthropogenic and naturally stochastic processes shaping shortnose sturgeon demography. Our modeling framework provides a relatively low-cost alternative for future demographic monitoring of species of conservation concern.

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Publication type Article
Publication Subtype Journal Article
Title Combining acoustic telemetry and side-scan sonar to estimate abundance of endangered shortnose sturgeon in the Hudson River, New York
Series title Canadian Journal of Fisheries and Aquatic Sciences
DOI 10.1139/cjfas-2024-0395
Volume 82
Publication Date May 26, 2025
Year Published 2025
Language English
Publisher Canadian Science Publishing
Contributing office(s) Eastern Ecological Science Center
Description 12 p.
First page 1
Last page 12
Country United States
State New York
Other Geospatial Hudson River
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