Assessing the impact of growth on estimates of fishing mortality — An illustration with Indian Ocean bigeye tuna

Abstract

Growth is a crucial biological feature and a key component in quantitative fish population stock assessments. Regional tuna fisheries management organizations have conducted extensive sampling programs to measure the growth of tuna species more accurately over the last decades. Although research improved growth estimates in most cases, competing or conflicting estimates of growth characteristics due to differences in sampling coverage, aging techniques, and modeling methodologies can introduce significant bias and uncertainty in the stock assessment. In the Indian Ocean, the recently determined growth curve for the bigeye tuna stock differs significantly from the length-at-age relationship now used in stock assessment. This study looked at how new growth affected estimation of fishing mortality for bigeye tuna caught with longline. Under steady-state assumption, the analytical method derives the fishing mortality and selectivity parameters from the longline length frequency dataset, conditioned on the growth and natural mortality factors. Simple simulations were used to validate the estimator’s performance. According to the estimates, the annual fishing mortality rates under the new growth curve will be 100%–200% higher than those under the current growth curve. The findings were also supported by an integrated Stock Synthesis model that was derived and simplified from the current bigeye tuna assessment model, as well as the length-based spawning potential ratio method, which used the same length frequency dataset as input. We emphasize that using a basic model to analyze the impact of alternative or competing growth scenarios on key management quantities can help decision makers easily understand the outcomes of more complex models, increasing the transparency of the stock assessment process.