From fisheries to scientific data: A protocol to process information from fishers’ echo-sounder buoys

Orúe, Blanca; López, Jon; Moreno, Gala; Santiago, Josu; Boyra, Guillermo; Uranga, Jon; Murua, Hilário

doi:10.1016/j.fishres.2019.03.004

Cited by 15 publications

(14 citation statements)

References 30 publications

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“…The approach used in the current study differs from previous work in that several models were tested for each task, and our results show that Gradient Boosting was the best model across all methods. This contrasts with the algorithms and methods applied by previous studies using echo-sounder data from dFAD buoys to predict the size of tuna aggregations (Baidai et al, 2020; Orue et al, 2019b; Lopez et al, 2016). However, the assumptions and data-processing methods applied in other work may not be directly comparable to the process described here.…”

Section: Discussionmentioning

confidence: 66%

“…However, the assumptions and data-processing methods applied in other work may not be directly comparable to the process described here. For example, Orue et al (2019b) or Lopez et al (2016) assume that tunas only occupy layers deeper than 25 m, thus omitting biomass estimates from shallower layers in their analysis. In our case, all layers were considered, as skipjack tuna are known to prefer warmer surface waters in areas where the thermocline is shallow (Andrade, 2003).…”

Section: Discussionmentioning

confidence: 99%

“…This avoids including measurements that are potentially influenced by the sea-bed. • Using the last known position of the buoys, we computed buoy speed for each position, and dropped events and measurements where buoy speed was higher than 3 knots, since the surface currents in the tropical oceans rarely exceed this speed (Orue et al, 2019b). This avoids including measurements taken on-board a vessel and not representative of a dFAD.…”

Section: Data Cleaningmentioning

confidence: 99%

“…However, several authors have reported substantial differences between the biomass estimates provided by echo-sounder buoys and observed biomass (Lopez et al, 2016; Escalle et al, 2019; Orue et al, 2019b), evidencing the variable nature of fish aggregations under dFADs, often made up of pelagic species other than tuna (Castro et al, 2002). Likewise, the influence of oceanic conditions on fish distribution and behavior likely drives aggregation patterns of tuna around dFADs (Lopez, 2017; Druon et al, 2017; Schaefer et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

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

Precioso

Navarro-García

Gavira-O’Neill³

et al. 2021

Preprint

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Echo-sounder data registered by buoys attached to drifting FADs provide a very valuable source of information on populations of tuna and their behaviour. This value increases when these data are supplemented with oceanographic data coming from CMEMS. We use these sources to develop Tuna-AI, a Machine Learning model aimed at predicting tuna biomass under a given buoy, which uses a 3-day window of echo-sounder data to capture the daily spatio-temporal patterns characteristic of tuna schools. As the supervised signal for training, we employ more than 5000 set events with their corresponding tuna catch reported by the AGAC tuna purse seine fleet.

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Section: Discussionmentioning

confidence: 66%

Section: Discussionmentioning

confidence: 99%

Section: Data Cleaningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Precioso

Navarro-García

Gavira-O’Neill³

et al. 2021

Preprint

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“…Furthermore, there are abundance of previous studies that have proven ICT role in developing the socio-economic aspects of the fishermen is undeniable (Nagaraj et. al, 2018;Orue et. al., 2019;Omar et.…”

Section: Fishery Ict Usage Among Coastal Fishermen Communitymentioning

confidence: 97%

The Challenges of Technology Usage Among Coastal Fishermen in Malaysia: Review

Mazuki¹,

Samah²,

Bolong³

et al. 2020

IJARBSS

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Fisheries industry is importance as the main source of national food security. The fisheries industry has long played a significant role in uplifting the socioeconomic status of Malaysia's rural communities. Coastal fishermen are currently undergoing a number of changes. Global development policies towards coastal fisheries shifted to focus on combating and eradicating poverty and climate change. This paper aims to address the main challenges among coastal fishermen in Malaysiapoverty and climate change and makes recommendations for the future development. Information Communication Technology (ICT) play the following roles in information exchange, knowledge sharing, promotion of education, monitoring illegal fishing and price information provisioning to the coastal fishermen. There are many potential benefits offered by the advance of the fishing ICT such as GPS, mobile phone and echo sounder. It is essential to expose the advance and modern technology to the rural community especially the fisherman community. Thus, this paper will reveal several potential ICT tools for coastal fishermen to encounter the challenges.

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A GRASP-based multi-objective approach for the tuna purse seine fishing fleet routing problem

Granado,

Silva,

Carravilla

et al. 2025

Computers & Operations Research

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From fisheries to scientific data: A protocol to process information from fishers’ echo-sounder buoys

Cited by 15 publications

References 30 publications

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

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

The Challenges of Technology Usage Among Coastal Fishermen in Malaysia: Review

A GRASP-based multi-objective approach for the tuna purse seine fishing fleet routing problem

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