Standardizing catch rates: is logbook information by itself enough?

Abstract

Bishop, J., Venables, W. N., Dichmont, C. M., and Sterling, D. J. 2008. Standardizing catch rates: is logbook information by itself enough? – ICES Journal of Marine Science, 65: 255–266. The goal of the work was to maximize the accuracy of standardized catch per unit effort as an index of relative abundance. Linear regression models were fitted to daily logbook data from a multispecies penaeid trawl fishery in which within-vessel changes in efficiency are common. Two model-fitting strategies were compared. The predictive strategy focused on maximizing the explained variance, and the estimation strategy on finding realistic coefficients for important components of changing catchability. Realistic values could not always be obtained, because the regression factors were not orthogonal, and data on the presence of technology were sometimes unreliable or systematically incomplete. It was not possible to separate fishing power from abundance by analysing logbook data alone; it was necessary to incorporate external information within the standardization model. Therefore, the resultant estimation models incorporated external information and expert knowledge by offsets. There was no single best estimation model. Instead, a series of models provided an envelope of possible changes in relative fishing power and prawn abundance since 1970. Compared with the prediction models, the estimation models revealed different trends in relative fishing power and relative abundance.

Choosing a fishery’s governance structure using data poor methods

“…Traditionally, moving to a new management system often involved evaluation using quantitative models such as Management Strategy Evaluation [12,10,21] or other quantitative approaches when the former is lacking. However, many of the world's fisheries are data limited to some degree [17].…”

“…All stakeholders should have as much information about the fishery as possible, and the impact of proposed changes should be analysed across a full range of objectives (i.e., ecological, economic, social and governance), with mechanisms in place to ensure that this process has an influence on the outcome. However, it is often seen as an impediment to management modifications when a system lacks the detailed information to produce sophisticated stock assessment models or is unable to quantitatively investigate management strategies through a management strategy evaluation process [10][11][12]. Despite this, and rather than maintain status quo while this information is developed, the precautionary approach [13] states that lack of information should not be an impediment to taking action.…”

Statistical models to standardize catch rates in the multi-species trawl fishery for Patagonian grenadier (Macruronus magellanicus) off Southern Chile

“…Although this method is commonly applied for variable selection in effort standardization analysis (Dunn and Livingston, 2004;Ortiz and Arocha, 2004;Ballara et al, 2006) there are other methods available such as standard hypothesis testing (i.e. F test) and information criteria; hypothesis testing methods can be meaningless in situations with a large sample size (Bishop et al, 2008); although Akaike information criterion (AIC) is the most used tool for model selection in ecology (Richards, 2008;Ward, 2008), as well as the Bayesian information criterion (BIC) which is frequently compared to the AIC, these criteria were not available for the quasi-Poisson model, because of the use of quasi-likelihood. Thus the use of a pre-specified 5% deviance reduction was considered a conservative and adequate criterion for variable selection.…”

“…This could be a source of significant confounding between changes in fishing power and changes in abundance, as the lack of such an interaction would not allow tracking changes in fishing power over time (Bishop, 2006). It is not clear if the estimation of a vessel effect was sufficient to eliminate the confounding of these changes with true changes in abundance (Bishop et al, 2008).…”

Calculating optimal effort and catch trajectories for multiple species modelled using a mix of size-structured, delay-difference and biomass dynamics models

“…Catch is a function of weekly stock size, the level of fishing effort expended each week, the relative fishing power of the fleet in that year (fishing power measures relative changes in the efficiency of the fleet over time; Bishop et al, 2008), the relative availability of each species in each week, the size selectivity of the fishing gear, and the catchability of the species. The fishing mortality on animals of species k in size class l during week w of year y, F k,y,w,l , is given by…”