Methods of assessing extinction risk in marine fishes
Back to previous page
| Authors: | Dulvy, NK; Ellis, JR; Goodwin, NB; Grant, A; Reynolds, JD; Jennings, S |
| Year: | 2004 |
| Journal: | Fish and Fisheries 5: 255-276 |
| Title: | Methods of assessing extinction risk in marine fishes |
| Abstract: | The decline and disappearance of species from large parts of their former geographical ran-e has become an important issue in fisheries ecology. There is a need to identify which species are at risk of extinction. The available approaches have been subject to considerable debate - particularly when applied to commercially exploited species. Here we have compiled methods that have been used or may be used for assessing threat status of marine organisms. We organize the methods according to the availability of data on the natural history, ecology and population biology of species. There are three general approaches to inferring or assessing extinction risk: (i) correlative approaches based on knowledge of life histories and ecology: (ii) time-series approaches that examine changes in abundance: and (iii) demographic approaches based on age- or stage-based schedules of vital rates and fisheries reference points. Many methods are well suited to species that are highly catchable and/or have relatively low productivity, but theory is less well developed for assessing extinction risk in species exhibiting narrow geographical distributions or ecological specialization. There is considerable variation in both definitions of extinction risk and the precision and defensibility of the available risk assessment methods, so we suggest a two-tiered approach for defining and assessing extinction risk. First. simple methods requiring a few easily estimated parameters are used to triage or rapidly assess large numbers of populations and species to identify potentially vulnerable populations or species. Second. the populations and species identified as vulnerable by this process can then be subject to more detailed and rigorous population analysis explicitly considering sources of error and uncertainty. |
Please send suggestions for improving this publication database to
sass-support@sfu.ca.
Departmental members may update their publication list.