Can estimates of genetic effective population size contribute to fisheries stock assessments?

Ovenden, J. R., Leigh, G. M., Blower, D. C., Jones, A. T., Moore, A., Bustamante, C., Buckworth, R. C., Bennett, M. B. and Dudgeon, C. L. (2016) Can estimates of genetic effective population size contribute to fisheries stock assessments?. Journal of Fish Biology, 89 6: 2505-2518. doi:10.1111/jfb.13129


 
Related Publications and Datasets
 
Author Ovenden, J. R.
Leigh, G. M.
Blower, D. C.
Jones, A. T.
Moore, A.
Bustamante, C.
Buckworth, R. C.
Bennett, M. B.
Dudgeon, C. L.
Title Can estimates of genetic effective population size contribute to fisheries stock assessments?
Journal name Journal of Fish Biology   Check publisher's open access policy
ISSN 1095-8649
0022-1112
Publication date 2016-10-11
Year available 2016
Sub-type Article (original research)
DOI 10.1111/jfb.13129
Open Access Status Not yet assessed
Volume 89
Issue 6
Start page 2505
End page 2518
Total pages 14
Place of publication Chichester, West Sussex, United Kingdom
Publisher Wiley-Blackwell Publishing
Language eng
Subject 1105 Ecology, Evolution, Behavior and Systematics
1104 Aquatic Science
Abstract Sustainable exploitation of fisheries populations is challenging to achieve when the size of the population prior to exploitation and the actual numbers removed over time and across fishing zones are not clearly known. Quantitative fisheries' modeling is able to address this problem, but accurate and reliable model outcomes depend on high quality input data. Much of this information is obtained through the operation of the fishery under consideration, but while this seems appropriate, biases may occur. For example, poorly quantified changes in fishing methods that increase catch rates can erroneously suggest that the overall population size is increasing. Hence, the incorporation of estimates of abundance derived from independent data sources is preferable. We review and evaluate a fisheries-independent method of indexing population size; inferring adult abundance from estimates of the genetic effective size of a population (N). Recent studies of elasmobranch species have shown correspondence between N and ecologically determined estimates of the population size (N). Simulation studies have flagged the possibility that the range of N/N ratios across species may be more restricted than previously thought, and also show that declines in N track declines in the abundance of model fisheries species. These key developments bring this new technology closer to implementation in fisheries science, particularly for data-poor fisheries or species of conservation interest.
Formatted abstract
Sustainable exploitation of fisheries populations is challenging to achieve when the size of the population prior to exploitation and the actual numbers removed over time and across fishing zones are not clearly known. Quantitative fisheries' modeling is able to address this problem, but accurate and reliable model outcomes depend on high quality input data. Much of this information is obtained through the operation of the fishery under consideration, but while this seems appropriate, biases may occur. For example, poorly quantified changes in fishing methods that increase catch rates can erroneously suggest that the overall population size is increasing. Hence, the incorporation of estimates of abundance derived from independent data sources is preferable. We review and evaluate a fisheries-independent method of indexing population size; inferring adult abundance from estimates of the genetic effective size of a population (Ne). Recent studies of elasmobranch species have shown correspondence between Ne and ecologically determined estimates of the population size (N). Simulation studies have flagged the possibility that the range of Ne/N ratios across species may be more restricted than previously thought, and also show that declines in Ne track declines in the abundance of model fisheries species. These key developments bring this new technology closer to implementation in fisheries science, particularly for data-poor fisheries or species of conservation interest.
Keyword Absolute abundance
Fishery-independent data
Linkage disequilibrium
Ne
Sustainable exploitation
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Published online 11 October 2016. Early view

 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 6 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 7 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Fri, 28 Oct 2016, 20:52:53 EST by Carlos Bustamante Diaz on behalf of School of Biomedical Sciences