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Vertical Distribution of Fish Biomass in Lake Superior: Implications for Day Bottom Trawl Surveys
Author(s) -
Stockwell Jason D.,
Yule Daniel L.,
Hrabik Thomas R.,
Adams Jean V.,
Gorman Owen T.,
Holbrook Beth V.
Publication year - 2007
Publication title -
north american journal of fisheries management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 72
eISSN - 1548-8675
pISSN - 0275-5947
DOI - 10.1577/m06-116.1
Subject(s) - pelagic zone , demersal fish , biomass (ecology) , environmental science , demersal zone , fishery , fish <actinopterygii> , coregonus , sampling (signal processing) , oceanography , ecology , biology , geology , filter (signal processing) , computer science , computer vision
Evaluation of the biases in sampling methodology is essential for understanding the limitations of abundance and biomass estimates of fish populations. Estimates from surveys that rely solely on bottom trawls may be particularly vulnerable to bias if pelagic fish are numerous. We evaluated the variability in the vertical distribution of fish biomass during the U.S. Geological Survey's annual spring bottom trawl survey of Lake Superior using concurrent hydroacoustic observations to (1) test the assumption that fish are generally demersal during the day and (2) evaluate the potential for predictive models to improve bottom trawl–determined biomass estimates. Our results indicate that the assumption that fish exhibit demersal behavior during the annual spring bottom trawl survey in Lake Superior is unfounded. Bottom trawl biomass (B BT ) estimates (mean ± SE) for species known to exhibit pelagic behavior (cisco Coregonus artedi, bloater C. hoyi, kiyi C. kiyi, and rainbow smelt Osmerus mordax; 3.01 ± 0.73 kg/ha) were not significantly greater than mean acoustic pelagic zone biomass (B APZ ) estimates (6.39 ± 2.03 kg/ha). Mean B APZ estimates were 1.6‐ to 4.8‐fold greater than mean B BT estimates over 4 years of sampling. The relationship between concurrent B APZ and B BT estimates was marginally significant and highly variable. Predicted B APZ estimates using cross‐validation models were sensitive to adjustments for back‐transforming from the logarithmic to the linear scale and poorly corresponded to observed B APZ estimates. We conclude that statistical models to predict B APZ from day B BT cannot be developed. We propose that night sampling with multiple gears will be necessary to generate better biomass estimates for management needs.