Padronização do índice de abundância e avaliação do estoque de bonito listrado, (Katsuwonus pelamis Linnaeus, 1758), do Atlântico Ocidental.
Lima, José Heriberto Meneses de
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In this paper data on catch, fishing effort and landings from the Brazilian baitboat fishery, together with information on vessel characteristics were analyzed aiming to: (a) describe the fishery and characteristics of the fishing fleet, analyze catch composition and spatial and temporal distribution of catches, fishing effort and catch rates of skipjack (Katsuwonus pelamys); (b) develop standardized indices of abundance for skipjack using generalized linear models (GLM); and (c) apply a nonequilibrium surplus production model for stock assessment of west Atlantic skipjack through the ASPIC program version 5.0. Skipjack is the most important species caught from Brazilian tuna fisheries; its catches comprise more then 50% of the total tuna catches from this fishery. The fishing area is located in the south and southeast regions of Brazil, from 20oS to 35oS, but fishing operations are carried out mainly between 28oS and 34oS. The highest catch rates are recorded in the south region during the first and fourth quarters and the smallest ones in the third quarter. Skipjack landings are made in Rio de Janeiro, Santa Catarina and Rio Grande do Sul states, with Santa Catarina being the most important landing place. The highest landings are recorded during the summer months (February and March) and the smallest ones during the winter months (August and September). The baitboats shows different characteristics; length of vessels varies from 15 to 49.5 meters. Skipjack catch rates from each vessel varies according with its size, which means that fishing power is a function of vessel size. The frequency distributions of skipjack CPUE are highly skewed with a relatively large proportion of zero observations. Standardization of skipjack CPUE (catch per unit of effort) was performed through generalized linear models, using delta-GLM methods, which involves fitting of two sub-models to the data. A first sub-model was applied assuming the binomial error distribution for the proportion of positive catches and a second sub-model was used for the positive catches assuming a different error distribution. Two alternative distributions were assumed for the positive catches, the lognormal and the Gamma distribution. Deviance tables were performed to identify the best set of factors and interactions that most adequately explained the observed variability in proportion of positive catches and positive CPUE. Geographical distribution (fishing area) and sea surface temperature together with the interaction year*GRT were the most important explanatory effects for the occurrence of a non-zero catch. On the other hand GRT and season, together with year*area, year*season and year*GRT interactions explained the most variability on the observed CPUE of positive catches. The standardized indexes were estimated using Generalized Linear Mixed Models, in which year, area, season, sea surface temperature, vessel length and GRT were included as main explanatory fixed effect factors and all first order interactions with year as random components. Delta-lognormal and delta-Gamma models showed a good fit to the data but narrower confidence intervals and small coefficients of variation were shown for standardized CPUE estimated by the delta-Gamma model. Results from these analyses show the importance of the study of CPUE and factors that have effect on its variations to understand the dynamics of this fishery. However not all factors that have an effect on variations in skipjack CPUE were considered, such as, bait species and amount of life bait, because this sort of information was not present in the majority of data available for analysis. A great amount of the information collected through logbooks are incomplete and imprecise, implying that institutions responsible for the implementation of this data collection system are not aware or do not recognize its importance as an instrument that makes possible to get information of great value for the management and utilization of fishery resources. Results of the stock assessment analysis provided an estimate of 26,930 MT for skipjack maximum sustainable yield, which is about 14% higher than catches taken in 1998. This estimated yield may looks like realistic but other parameter estimates seems to be unrealistic, suggesting that the west Atlantic skipjack stock is in an overexploited state, which may not be true. Considering the uncertainties and limitations about the data some of the parameters estimates may be imprecise. Therefore, results from this analysis should be cautiously used to make decisions on management measures for this fishery. A good stock assessment depends not only on the adequacy of the model available for the analysis but also on the quality of the data that the model is fitted to. Therefore, in order to have effective stock assessment for skipjack in future, an efficient system for the collection of data from this fishery shall be implemented. This data collecting system should include mechanisms for data verification, such as observer programmes to monitor catch, effort and other details of the fishing operations.