|What kind of fish stock predictions do we need and what kinds of information will help us to make better predictions?|
Brander, K.M. (2003). What kind of fish stock predictions do we need and what kinds of information will help us to make better predictions? Sci. Mar. (Barc.) 2003: 21-33
In: Scientia Marina (Barcelona). Consejo Superior de Investigaciones Científicas. Institut de Ciènces del Mar: Barcelona. ISSN 0214-8358, more
|Also published as |
- Brander, K.M. (2003). What kind of fish stock predictions do we need and what kinds of information will help us to make better predictions?, in: Ulltang, Ø. et al. Fish stock assessments and predictions: integrating relevant knowledge: SAP Symposium held in Bergen, Norway 4-6 December 2000. Scientia Marina (Barcelona), 67(Suppl. 1): pp. 21-33, more
Fishery management; Population dynamics; Prediction; Stock assessment; Stocks; AE, North Atlantic [Marine Regions]; Marine
Fish stock predictions are used to guide fisheries management, but stocks continue to be over-exploited. "Traditional" single-species age-structured stock assessment models, which became an operational component of fisheries management in the 1950s, ignore biological and environmental effects. As our knowledge of the marine environment improves and our concern about the state of the marine ecosystem and about global change increases, the scope of our models needs to be widened. We need different kinds of predictions as well as better predictions. Population characteristics (rates of mortality, growth, recruitment) of 61 stocks of 17 species of NE Atlantic fish are reviewed in order to consider the implications for the time-scale and quality of stock predictions. Short life expectancy limits the time horizon for predictability based on the current fishable stock and predictions are therefore more dependent on estimates or assumptions about future rates. Evidence is presented that rates of growth and recruitment are influenced by environmental factors and possibilities for including new information are explored in order to improve predictions.