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Journal article

Extending time-series of fish biomasses using a simple surplus production based approach

From

Section for Management Systems, National Institute of Aquatic Resources, Technical University of Denmark1

National Institute of Aquatic Resources, Technical University of Denmark2

Section for Ocean Ecology and Climate, National Institute of Aquatic Resources, Technical University of Denmark3

Long time series of fish biomasses are needed in order to understand the changes in marine ecosystems and to set appropriate targets for their management and conservation. Fish biomasses from stock assessments usually cover only the recent 3 to 4 decades, and extending these time series using conventional data-demanding methods will, for many stocks, likely not be possible due to limited historical data.

However, catch data are usually available for several decades longer than stock assessments. In the present study, we show that catch data, combined with information on average surplus production rate (SPR), of a stock could be used to derive proxies for pre-assessment biomasses. The sensitivity of biomass estimates to different SPR values was explored using data on 55 stocks from different taxa in the North Atlantic.

The time series of biomasses estimated from the assessments could, for most stocks, be reconstructed by applying average stock-specific SPR values for all years, i.e. neglecting inter-annual variability in SPR. The approach of applying the information on SPR from the assessment period to pre-assessment years performed reasonably well in extending the biomass time series by 2 to 6 decades for 5 out of 6 stocks used as test cases.

Density dependence and major climate variations were taken into account where such effects on SPR were expected. The main challenge for the approach appeared to be long-term changes in productivity regimes. The apparently consistent average SPR among stocks of a similar species could also facilitate use of the SPR-based approach to derive biomass estimates in contemporary data-poor situations.

Language: English
Publisher: Inter-Research
Year: 2011
Pages: 191-201
ISSN: 16161599 and 01718630
Types: Journal article
DOI: 10.3354/meps09346
ORCIDs: Eero, Margit and MacKenzie, Brian

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