AUTHORS: Cade Roach, University of Missouri; Matthew R. Acre, US Geological Survey; Allison A. Pease, University of Missouri
ABSTRACT: Efficient methods for estimating species abundance are critical for assessing the status of Silver Carp (Hypophthalmichthys molitrix) in the Mississippi River basin, given the ecological and economic threat this invasive species poses to water resources. Measuring Silver Carp abundance helps to characterize range expansion dynamics and evaluate management strategies. Recreational-grade side-scan sonar (SSS) offers a novel approach to monitoring fish abundance, yielding estimates comparable to traditional mark-recapture methods but at a much lower cost. Regardless of the approach used to estimate abundance, imperfect detection resulting from factors such as survey method, observer subjectivity, environmental conditions, and species behavior can lead to inaccurate estimates. N-mixture models, a suite of hierarchical regression techniques, simultaneously estimate abundance and detection probability from spatially and temporally replicated count data, addressing the issue of imperfect detection. Our study aimed to estimate the abundance of Silver Carp before, between, and after mechanical removal efforts to determine the efficacy of successive removals and characterize the ensuing population dynamics. We conducted SSS surveys in the Lamine River, a tributary of the Missouri River, before and after each of two removal efforts carried out by the Missouri Department of Conservation and the U.S. Fish and Wildlife Service in September 2024. We georectified the SSS images, used semiautomated image processing to extract counts of Silver Carp, and fit N-mixture models in frequentist and Bayesian frameworks to estimate abundance and detection probability. Here, we compare abundance estimates between different stages of the removal efforts to quantify removal success and identify trends in population dynamics. We also compare the estimates of abundance and detection, computational intensity, and measures of fit between the frequentist and Bayesian approaches. Finally, we discuss potential methods to improve the differentiation of Silver Carp from other species with similar morphology in SSS imagery.
