Towards acoustic discrimination of tropical tuna associated with Fish Aggregating Devices

Abstract

Tropical tuna support some of the largest and most valuable artisanal and industrial fisheries worldwide, conducted to a large degree with Fish Aggregating Devices (FADs). Yellowfin, bigeye and skipjack are the main tuna species found in mixed aggregations around FADs and they are simultaneously encircled by the purse seining operation. One of the key challenges that purse seine fleets fishing with drifting FADs face in all oceans is to be able to target species in healthy condition such as skipjack, while reducing impacts on bigeye and yellowfin in areas where there is a need to reduce fishing pressure on these species. The present paper explores a technical solution for selective fishing at FADs by means of acoustic equipment used by purse seiners. Acoustic frequency response of skipjack and bigeye tuna were determined at 38, 120 and 200 kHz. Skipjack showed stronger response at higher frequencies. On the contrary, bigeye showed stronger responses at lower frequencies. The robust pattern shown in frequency responses of the two species demonstrates the potential to predict abundance and species proportions based on purely acoustic measures. The paper also addresses the conditions that need to be met to successfully apply this technology for selective fishing as well as other uses of direct acoustic observations to support tuna conservation.

Machine learning for characterizing tropical tuna aggregations under Drifting Fish Aggregating Devices (DFADs) from commercial echosounder buoys data

“…Despite this trend, very few studies have been conducted on the implementation of automated classification methods for analysing the extensive datasets collected by commercial vessels (Uranga et al, 2017). This paper presents a new methodology, based on machine learning, for processing the echosounder data collected from one of the main models of echosounder buoy used to equip DFADs worldwide (Moreno et al, 2019).…”

How do tuna schools associate to dFADs? A study using echo-sounder buoys to identify global patterns

“…Diallo et al (2019) conclude that the higher sensitivity of the newer model could be driving the differences in aCRT and aCAT, so it stands to reason that buoys from different manufacturers would also register biomass differently. For example, the use of different frequency echo-sounders likely impacts the biomass estimates provided by different buoy brands (Lopez et al, 2014; Moreno et al, 2019). Indeed, fishing masters perceive differences in the biomass readings of different manufacturers (Lopez et al, 2014), so these differences should be handled with care.…”

Tuna-AI: tuna biomass estimation with Machine Learning models trained on oceanography and echosounder FAD data

“…Most traditional echo-sounder buoys do not currently differentiate between species when giving biomass estimates, though recent buoy models, such as the ISD+ buoys included in the study, provide a daily estimate of species composition together with biomass estimates. Although previous studies have highlighted the importance of considering species composition when estimating biomass (Moreno et al, 2019; Santiago et al, 2016), the information from these buoys has not yet been applied, and should be considered in future studies.…”

“…The potential applications of accurately quantifying tuna presence or absence around dFADs, as well as school aggregation size, are numerous. As highlighted by previous authors, data could be used for fishery-independent abundance indices, improving knowledge on species distribution or better understanding the factors driving aggregation and disaggregation processes of tuna at dFADs (Santiago et al, 2016; Lopez et al, 2016; Moreno et al, 2019). The current study represents an important step in this direction, having successfully evaluated the performance of numerous models on achieving tasks of varying levels of complexity with high degrees of accuracy.…”