In 1998, the National Research Council committee on Fish Stock Assessment documented that most fisheries management is based on fishery-dependent forecasting methods. Recent studies have graded the quality of management programs by the amount of fishery-independent data they have on the stock size. We use two applications in Alaska to illustrate contrasting fishery-independent approaches: (1) a direct and precise estimate of the biomass of adult herring, and (2) an indirect study of the conditions in a pink salmon nursery area that could influence survival of the fry to adults. The first, an 18-year study of the herring stock in Prince William Sound, shows that fishery-dependent models twice failed to detect population collapses, and had lag-times of up to five years to detect major population changes. The second, a similar long-term study of the pink salmon stock in Prince William Sound, shows that the adult returns were a function of at least four dynamic biological variables during the spring fry outmigration. In the case of the Pacific herring, we document that the assessment by traditional fishery-dependent models resulted in measures of abundance that were too late, too unrepresentative or too imprecise to make correct management decisions. The failure for timely detection led to overfishing that accelerated a population collapse. The pink salmon example illustrates the case for true ecosystem-based fisheries management. The biological forcing mechanisms that caused change in pink salmon returns were neither understood nor measured. Fisheries-independent data and targeted environmental monitoring offer the best solution to improving the conservation of exploited fish stocks. This approach is a prerequisite for quality fishery management, but must include timeliness, robustness and precision.
|Original language||English (US)|
|Title of host publication||Fishery Management|
|Publisher||Nova Science Publishers, Inc.|
|Number of pages||17|
|State||Published - Aug 1 2012|
ASJC Scopus subject areas
- Environmental Science(all)