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Hierarchical approach to the assessment of fishing effects on non-target chondrichthyans: case study of Squalus megalops in southeastern Australia
A three-levelled hierarchical risk assessment approach was trialed using piked spurdog (Squalus megalops) to evaluate the suitability of the approach for chondrichthyan species. At level 1, a qualitative assessment indicated that the only fishing-related activity to have moderate or high impact on S...
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Published in: | Canadian journal of fisheries and aquatic sciences 2006-11, Vol.63 (11), p.2456-2466 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | A three-levelled hierarchical risk assessment approach was trialed using piked spurdog (Squalus megalops) to evaluate the suitability of the approach for chondrichthyan species. At level 1, a qualitative assessment indicated that the only fishing-related activity to have moderate or high impact on S. megalops was "capture fishing" by otter trawl, Danish seine, gillnet, and automatic longline methods. At level 2, a semi-quantitative assessment ranked S. megalops at risk because of its low biological productivity and, possibly, its catch susceptibility from cumulative effects across the separate fishing methods. Finally, at level 3, a quantitative assessment showed that population growth is slow even under the assumption of density-dependent compensation where the fishing mortality rate equals the natural mortality rate. Although published information indicates that relative abundance has been stable in several regions of southern Australia, it is concluded that given its low biological productivity, changed fishing practices leading to increased fishing mortality could quickly put S. megalops at high risk. The hierarchical approach appears particularly useful for assessment of chondrichthyan species in data-limited fisheries. This approach allows for a management response at any level, optimizing research and management efforts by identifying and excluding low-risk species from data intensive assessments. |
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ISSN: | 0706-652X 1205-7533 |
DOI: | 10.1139/f06-141 |