Overfishing, uncertainty, and ocean governance: Lord Perry's question revisited

“Why does overfishing persist in the face of regulation?” The author argues that over fishing,a fundamental cause of the crisis facing our oceans, is the result of the failure of our fishing management agencies (ultimately our politicians and communities) to embrace a small suite of powerful tools (more correctly strategic approaches) which have been developed to account for uncertainty. Broad success in managing fisheries to achieve sustainability goals will only come if these tools are enthusiastically applied. This will not happen until organisational cultures within fishery management agencies undergo a major shift leading to an asset-based biodiversity conservation, rather than resource exploitation, to be placed at the centre of ocean governance.This thesis examines these issues in the context of case studies covering regional, national and provincial (State) fishery management agencies. With the exception of the case study of a regional fishery (the southern ocean krill fishery) all case studies are drawn from Australian experiences. The central recommendation of the thesis is that fishery management agencies, worldwide, should be replaced by biodiversity asset management agencies.

A meta-analytic approach to quantifying scientific uncertainty in stock assessments

Quantifying scientific uncertainty when setting total allowable catch limits for fish stocks is a major challenge, but it is a requirement in the United States since changes to national fisheries legislation. Multiple sources of error are readily identifiable, including estimation error, model specification error, forecast error, and errors associated with the definition and estimation of reference points. Our focus here, however, is to quantify the influence of estimation error and model specification error on assessment outcomes. These are fundamental sources of uncertainty in developing scientific advice concerning appropriate catch levels and although a study of these two factors may not be inclusive, it is feasible with available information. For data-rich stock assessments conducted on the U.S. west coast we report approximate coefficients of variation in terminal biomass estimates from assessments based on inversion of the assessment of the model’s Hessian matrix (i.e., the asymptotic standard error). To summarize variation “among” stock assessments, as a proxy for model specification error, we characterize variation among multiple historical assessments of the same stock. Results indicate that for 17 groundfish and coastal pelagic species, the mean coefficient of variation of terminal biomass is 18%. In contrast, the coefficient of variation ascribable to model specification error (i.e., pooled among-assessment variation) is 37%. We show that if a precautionary probability of overfishing equal to 0.40 is adopted by managers, and only model specification error is considered, a 9% reduction in the overfishing catch level is indicated.

Use of genetic data to assess the uncertainty in stock assessments due to the assumed stock structure: the case of albacore (Thunnus alalunga) from the Atlantic Ocean

Stock assessments can be problematic because of uncertainties associated with the data or because of simplified assumptions made when modeling biological processes (Rosenberg and Restrepo, 1995). For example, the common assumption in stock assessments that stocks are homogeneous and discrete (i.e., there is no migration between the stocks) is not necessarily true (Kell et al., 2004a, 2004b).

Beverton and Holt equations: spreadsheet functions and uncertainty

This contribution illustrates how modern spreadsheets aid the calculation and visualization of yield models and how the effects of uncertainties may be incorporated using Monte Carlo simulation. It is argued that analogous approaches can be implemented for other assessment models of simple to medium complexity justifying wider use of spreadsheets in fisheries analysis and training.