Login | Request Account (DAF staff only)

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

View Altmetrics

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). pp. 2505-2518. ISSN 1095-8649

Full text not currently attached. Access may be available via the Publisher's website or OpenAccess link.

Article Link: http://dx.doi.org/10.1111/jfb.13129


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.

Item Type:Article
Business groups:Animal Science
Keywords:absolute abundance fishery-independent data linkage disequilibrium Ne sustainable exploitation
Subjects:Science > Statistics
Science > Statistics > Experimental design
Aquaculture and Fisheries > Fisheries > Fishery conservation
Aquaculture and Fisheries > Fisheries > Fishery research
Live Archive:23 Jan 2017 04:01
Last Modified:15 Jan 2023 23:49

Repository Staff Only: item control page