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

https://doi.org/10.1101/2021.09.15.460261

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s00227-006-0511-1openalex-snapshot

Behavior of yellowfin (Thunnus albacares) and bigeye (T. obesus) tuna in a network of fish aggregating devices (FADs)

inferredcites →https://doi.org/10.1007/s00227-007-0689-xopenalex-snapshot

Movements, behavior, and habitat utilization of yellowfin tuna (Thunnus albacares) in the northeastern Pacific Ocean, ascertained through archival tag data

inferredcites →https://doi.org/10.1007/s00227-017-3075-3openalex-snapshot

Diel behaviour of tuna and non-tuna species at drifting fish aggregating devices (DFADs) in the Western Indian Ocean, determined by fishers’ echo-sounder buoys

inferredcites →https://doi.org/10.1007/s00227-020-03679-wopenalex-snapshot

Physiological and behavioural thermoregulation of juvenile yellowfin tuna Thunnus albacares in subtropical waters

inferredcites →https://doi.org/10.1016/j.biocon.2021.109004openalex-snapshot

Machine learning to detect bycatch risk: Novel application to echosounder buoys data in tuna purse seine fisheries

inferredcites →https://doi.org/10.1016/j.dsr2.2017.02.007openalex-snapshot

Environmental preferences of tuna and non-tuna species associated with drifting fish aggregating devices (DFADs) in the Atlantic Ocean, ascertained through fishers’ echo-sounder buoys

inferredcites →https://doi.org/10.1016/j.fishres.2014.02.033openalex-snapshot

Evolution and current state of the technology of echo-sounder buoys used by Spanish tropical tuna purse seiners in the Atlantic, Indian and Pacific Oceans

inferredcites →https://doi.org/10.1016/j.fishres.2019.03.004openalex-snapshot

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

inferredcites →https://doi.org/10.1016/j.fishres.2020.105613openalex-snapshot

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

inferredcites →https://doi.org/10.1016/j.marpol.2013.12.014openalex-snapshot

The past, present and future use of drifting fish aggregating devices (FADs) in the Indian Ocean

inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot

Random Forests

inferredcites →https://doi.org/10.1023/a:1020302414472openalex-snapshot

A general theory on fish aggregation to floating objects: An alternative to the meeting point hypothesis

inferredcites →https://doi.org/10.1046/j.1365-2419.2003.00220.xopenalex-snapshot

The relationship between the skipjack tuna (Katsuwonus pelamis) fishery and seasonal temperature variability in the south‐western Atlantic

inferredcites →https://doi.org/10.1093/icesjms/fsaa216openalex-snapshot

Quantifying the increase in fishing efficiency due to the use of drifting FADs equipped with echosounders in tropical tuna purse seine fisheries

inferredcites →https://doi.org/10.1093/icesjms/fsy041openalex-snapshot

Target strength of skipjack tuna (Katsuwanus pelamis) associated with fish aggregating devices (FADs)

inferredcites →https://doi.org/10.1093/icesjms/fsz131openalex-snapshot

In situ target strength of bigeye tuna (Thunnus obesus) associated with fish aggregating devices

inferredcites →https://doi.org/10.1111/fog.12417openalex-snapshot

Changes to vertical thermoregulatory movements of juvenile bigeye tuna (Thunnus obesus) in the northwestern Pacific Ocean with time of day, seasonal ocean vertical thermal structure, and body size

inferredcites →https://doi.org/10.1139/cjfas-2014-0458openalex-snapshot

Behaviour and vulnerability of target and non-target species at drifting fish aggregating devices (FADs) in the tropical tuna purse seine fishery determined by acoustic telemetry

inferredcites →https://doi.org/10.1145/2939672.2939785openalex-snapshot

XGBoost

inferredcites →https://doi.org/10.1214/aos/1013203451openalex-snapshot

Greedy function approximation: A gradient boosting machine.

inferredcites →https://doi.org/10.13140/rg.2.2.25562.75202openalex-snapshot

Mitigation of Silky Shark Bycatch in Tropical Tuna Purse Seine Fisheries

inferredcites →https://doi.org/10.1371/journal.pone.0128023openalex-snapshot

Large-Scale Examination of Spatio-Temporal Patterns of Drifting Fish Aggregating Devices (dFADs) from Tropical Tuna Fisheries of the Indian and Atlantic Oceans

inferredcites →https://doi.org/10.1371/journal.pone.0210435openalex-snapshot

Aggregation process of drifting fish aggregating devices (DFADs) in the Western Indian Ocean: Who arrives first, tuna or non-tuna species?

inferredcites →https://doi.org/10.1371/journal.pone.0216353openalex-snapshot

Towards acoustic discrimination of tropical tuna associated with Fish Aggregating Devices

inferredcites →https://doi.org/10.3389/fmars.2017.00315openalex-snapshot

Skipjack Tuna Availability for Purse Seine Fisheries Is Driven by Suitable Feeding Habitat Dynamics in the Atlantic and Indian Oceans

inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot

Scikit-learn: Machine Learning in Python

inferredcites →https://doi.org/10.7755/fb.114.2.4openalex-snapshot

A model based on data from echosounder buoys to estimate biomass of fish species associated with fish aggregating devices

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1016/j.ecolmodel.2022.110188openalex-snapshot

Modelling of tuna around fish aggregating devices: The importance of ocean flow and prey

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