Fishes of the world’s oceans were once considered inexhaustible (Safina 1995;Pauly and Palomares 2005), and that human fishing could not deplete widely dispersed marine fish populations (Haddon 2001). Times have changed and there is now a crisis looming as humans witness the collapse of fisheries, destabilization of marine ecosystems, decreasing biodiversity, and increasing impoverishment of coastal communities (Pauly et al. 1998; Watson and Pauly 2001; Myers and Worm 2003; Hilborn 2001; Hutchings and Reynolds 2004). The large number of overfished populations (Jackson et al. 2001; Garcia and Grainger 2005; Froese 2004; Fulton et al. 2005), as well as indirect effects of fisheries on marine ecosystems (Myers and Worm 2005), indicate that fisheries management has failed to achieve its principal goal of sustainability (Botsford et al. 1997; Jackson et al. 2001).
Practicable methods for quantifying the status of fishery resources are critical to effective fisheries management (Gobert 1997; Aubone 2003). Stock assessment methods determine changes in the abundance of fishery stocks in response to fishing, and redict future trends of stock abundance. Stock assessment involves collecting and analyzing biological and statistical information based on resource surveys, knowledge of habitat, life-history, and behavior of the species, and catch data. Stock assessments are used as a basis to assess and specify the present and probable future condition of a fishery (NOAA 2005).
Mathematical models that underlie stock assessment have been developed to provide scientifically sound information on stock status and on predicted harvesting rates relative to sustainable harvest rates (Sissenwine 1981). Stock assessment models have been used to provide predictions of annual yields into the future under different harvest strategies, short-term yield forecasts, recommendations of allowable biological catch, and evaluation of the feasibility of stock rebuilding strategies (Rose and Cowan 2003). The results of these mathematical models form the basis of recommendations to fishery management agencies, which subsequently formulate management plans and strategies to ensure the sustainable and optimal use of the resource (Cadrin 1999; Caddy 2002; Hilborn 2001). Stock assessment models typically involve the use of life-history information on growth, mortality, and reproduction, coupled with indices of stock abundance derived from commercial fisheries data or scientific surveys (Gayanilo and Pauly 1997; Beddington and Kirkwood 2005).
While there has been much research in the area of stock assessment (Hilborn and Walters 1992), most effort has focused on temperate species and on situations where relatively long-term, age-based data are available. Commonly used methods include virtual population analysis (Gayanilo and Pauly 1997; Haddon 2001) and age-structured matrix projection models (Hilborn and Walters 1992; Quinn and Deriso 1999). These methods generally use catch or survey data, from which individuals have been sampled and their size (length or weight) measured and their age determined from hard-parts such as scales or otoliths (Gallucci et al.1996; Gayanilo and Pauly 1997; Quinn and Deriso 1999). While age-structured approaches provide a powerful framework for stock assessment, determination of ages is labor intensive, involves high costs and capital investment, and requires well-trained personnel (Craig 1999; Pilling 1999).
Practicable methods for quantifying the status of fishery resources are critical to effective fisheries management (Gobert 1997; Aubone 2003). Stock assessment methods determine changes in the abundance of fishery stocks in response to fishing, and redict future trends of stock abundance. Stock assessment involves collecting and analyzing biological and statistical information based on resource surveys, knowledge of habitat, life-history, and behavior of the species, and catch data. Stock assessments are used as a basis to assess and specify the present and probable future condition of a fishery (NOAA 2005).
Mathematical models that underlie stock assessment have been developed to provide scientifically sound information on stock status and on predicted harvesting rates relative to sustainable harvest rates (Sissenwine 1981). Stock assessment models have been used to provide predictions of annual yields into the future under different harvest strategies, short-term yield forecasts, recommendations of allowable biological catch, and evaluation of the feasibility of stock rebuilding strategies (Rose and Cowan 2003). The results of these mathematical models form the basis of recommendations to fishery management agencies, which subsequently formulate management plans and strategies to ensure the sustainable and optimal use of the resource (Cadrin 1999; Caddy 2002; Hilborn 2001). Stock assessment models typically involve the use of life-history information on growth, mortality, and reproduction, coupled with indices of stock abundance derived from commercial fisheries data or scientific surveys (Gayanilo and Pauly 1997; Beddington and Kirkwood 2005).
While there has been much research in the area of stock assessment (Hilborn and Walters 1992), most effort has focused on temperate species and on situations where relatively long-term, age-based data are available. Commonly used methods include virtual population analysis (Gayanilo and Pauly 1997; Haddon 2001) and age-structured matrix projection models (Hilborn and Walters 1992; Quinn and Deriso 1999). These methods generally use catch or survey data, from which individuals have been sampled and their size (length or weight) measured and their age determined from hard-parts such as scales or otoliths (Gallucci et al.1996; Gayanilo and Pauly 1997; Quinn and Deriso 1999). While age-structured approaches provide a powerful framework for stock assessment, determination of ages is labor intensive, involves high costs and capital investment, and requires well-trained personnel (Craig 1999; Pilling 1999).
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