An automatic purse-seine set type classification algorithm to inform tropical tuna management

Management of large-scale tuna purse-seine fisheries often involves consideration of set type because the different set types yield different target species and bycatch species compositions, and may therefore impact those populations differently. Using the ensemble method random forests for classification, annual set type classification algorithms were developed based on a suite of operational characteristics, and catch and bycatch information, collected by onboard observers for the tropical tuna purse-seine fishery in the eastern Pacific Ocean (EPO) during 2010–2019. Two types of algorithms were built for each year: 1) a 2-class algorithm for sets on unassociated tuna schools (NOA) and sets on floating-object-associated tuna (OBJ); and, 2) a 3-class algorithm for all three types of sets that occur in the EPO, which includes sets on marine mammal-associated tuna (DEL). Results indicated that such data can be used to reliably distinguish between purse-seine sets types, highlighting inherent operational and ecological differences among them in the EPO. For the 2-class algorithms, the average misclassification error rates were 3.3% for NOA sets and 4.5% for OBJ sets. For the 3-class algorithms, the average misclassification error rates were 2% for DEL sets, 10.6% for NOA sets and 4.6% for OBJ sets. Comparison of results for NOA sets from the 2-class and 3-class algorithms suggests that the higher NOA-set misclassification error rate for the 3-class algorithm is due to a difficulty in distinguishing NOA sets from DEL sets with the predictors used in this analysis. The most useful bycatch information for predicting set type included amounts of dorado, wahoo, small fish species, such as triggerfishes, and silky sharks, illustrating the importance of bycatch data collection by observers, even for species of no commercial value. In the case of the 2-class algorithms, the annual misclassification error rates for NOA and OBJ sets were not only small, but also fairly stable over the 10-year period, even in the presence of the strong 2015 – 2016 El Niño event. This suggests that the methodology is robust and could be used to validate set type determinations based on other criteria, such as distance to a floating object (or a fish aggregating device), and to verify data quality, more generally.