An evaluation of the effects of sample size on estimating length composition of catches from tuna longline fisheries using computer simulations

Abstract Length composition analysis can provide insights into the dynamics of a fish population. Accurate quantification of the size structure of a population is critical to understand the status of a fishery and how the population responds to environmental stressors. A scientific observer program is a reliable way to provide such accurate information. However, 100% observer coverage is usually impossible for most fisheries because of logistic and financial constraints. Thus, there is a need to evaluate observer program performance, identify suitable sample sizes, and optimize the allocation of observation efforts. The objective of this study is to evaluate the effects of sample size on the quality of length composition data and identify an optimal coverage rate and observation ratio to improve the observation efficiency using an onboard observer data set from China's tuna longline fishery in the western and central Pacific Ocean. We found that the required sample size varies with fish species, indices used to describe length composition, the acceptable accuracy of the estimates, and the allocation methods of sampling effort. Ignoring other information requirements, 1000 individuals would be sufficient for most species to reliably quantify length compositions, and a smaller sample size could generate reliable estimates of mean length. A coverage rate of 20% would be sufficient for most species, but a lower coverage rate (5% or 10%) could also be effective to meet with the accuracy and precision requirement in estimating length compositions. A non-random effort allocation among fishing baskets within a set could cause the length composition to be overestimated or underestimated for some species. The differences in effective sample sizes among species should be included in the consideration for a rational allocation of observation effort among species when there are different species management priorities.