Using commercial landings data to identify environmental correlates with distributions of fish stocks

Abstract We examined the efficacy of using commercial landings data to identify potential environmental correlates with fish distributions. Historical landings data for two commercially important species, Atlantic cod ( Gadus morhua ) and haddock ( Melanogrammus aeglefinus ), were used along with hi...

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Bibliographic Details
Published in:Fisheries Oceanography
Main Authors: SUNDERMEYER, MILES A., ROTHSCHILD, BRIAN J., ROBINSON, ALLAN R.
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2004
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Online Access:http://dx.doi.org/10.1111/j.1365-2419.2004.00302.x
https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fj.1365-2419.2004.00302.x
https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2419.2004.00302.x
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Summary:Abstract We examined the efficacy of using commercial landings data to identify potential environmental correlates with fish distributions. Historical landings data for two commercially important species, Atlantic cod ( Gadus morhua ) and haddock ( Melanogrammus aeglefinus ), were used along with historical conductivity, temperature, and depth (CTD) data to infer monthly mean spatial distributions of catch per unit effort (CPUE), temperature, salinity, density, and stratification over Georges Bank. Relationships between CPUE and these environmental variables plus bottom sediment type and bottom depth were examined on seasonal, annual, and interannual time scales. Empirical analysis suggests that both cod and haddock are found preferentially in water temperatures of approximately 5°C in winter/spring, and as high as 10–11°C during late fall. Both species are also found preferentially over coarse sand and gravel as opposed to fine sand, and in water depths between 60 and 70 m. These preferences appear to vary seasonally. The above results are consistent with findings of previous investigators using semi‐annual research trawl survey data, and suggest that commercial landings data, despite their known errors and biases, can be used effectively to infer associations between fish and their environment.