Global hotspots of shark interactions with industrial longline fisheries

Burns, Echelle S.; Bradley, Darcy; Thomas, Lennon R.

doi:10.3389/fmars.2022.1062447

Cited by 5 publications

(4 citation statements)

References 55 publications

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“…Compared to other solutions, our workflow does not use gear/logbook data and spatially explicit catch information from Regional Fisheries Management Organizations (RFMO) (Palmer and Wigley, 2009;Lee et al, 2010;Gerritsen and Lordan, 2011;Olesen et al, 2012;Muench et al, 2018;Roberson et al, 2019;Burns et al, 2023). This information would likely increase our workflow accuracy in identifying unreported fishing activity hotspots.…”

Section: Discussionmentioning

confidence: 99%

“…However, it would also increase the workflow dependency on the RFMO regions for which data are available, consequently lowering the current cross-region applicability. Moreover, our workflow does not consider the environmental effects on stock presence, which can enhance vessel activity prediction accuracy in multi-source integration models (Chang and Yuan, 2014;Coro et al, 2022a;Burns et al, 2023). The principal reason is that our workflow assesses the potentially involved stocks after identifying unreported fishing activity hotspots.…”

Section: Discussionmentioning

confidence: 99%

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Estimating hidden fishing activity hotspots from vessel transmitted data

Coro,

Sana,

Ferrà

et al. 2023

Front. Sustain. Food Syst.

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Monitoring fishery activity is essential for resource planning and guaranteeing fisheries sustainability. Large fishing vessels constantly and continuously communicate their positions via Automatic Identification System (AIS) or Vessel Monitoring Systems (VMSs). These systems can use radio or Global Positioning System (GPS) devices to transmit data. Processing and integrating these big data with other fisheries data allows for exploring the relations between socio-economic and ecosystem assets in marine areas, which is fundamental in fishery monitoring. In this context, estimating actual fishing activity from time series of AIS and VMS data would enhance the correct identification of fishing activity patterns and help assess regulations' effectiveness. However, these data might contain gaps because of technical issues such as limited coverage of the terrestrial receivers or saturated transmission bands. Other sources of data gaps are adverse meteorological conditions and voluntary switch-offs. Gaps may also include hidden (unreported) fishing activity whose quantification would improve actual fishing activity estimation. This paper presents a workflow for AIS/VMS big-data analysis that estimates potential unreported fishing activity hotspots in a marine area. The workflow uses a statistical spatial analysis over vessel speeds and coordinates and a multi-source data integration approach that can work on multiple areas and multiple analysis scales. Specifically, it (i) estimates fishing activity locations and rebuilds data gaps, (ii) estimates the potential unreported fishing hour distribution and the unreported-over-total ratio of fishing hours at a 0.01° spatial resolution, (iii) identifies potential unreported fishing activity hotspots, (iv) extracts the stocks involved in these hotspots (using global-scale repositories of stock and species observation data) and raises an alert about their possible endangered, threatened, and protected (ETP) status. The workflow is also a free-to-use Web Service running on an open science-compliant cloud computing platform with a Web Processing Service (WPS) standard interface, allowing efficient big data processing. As a study case, we focussed on the Adriatic Sea. We reconstructed the monthly reported and potential unreported trawling activity in 2019, using terrestrial AIS data with a 5-min sampling period, containing ~50 million records transmitted by ~1,600 vessels. The results highlight that the unreported fishing activity hotspots especially impacted Italian coasts and some forbidden and protected areas. The potential unreported activity involved 33 stocks, four of which were ETP species in the basin. The extracted information agreed with expert studies, and the estimated trawling patterns agreed with those produced by the Global Fishing Watch.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Estimating hidden fishing activity hotspots from vessel transmitted data

Coro,

Sana,

Ferrà

et al. 2023

Front. Sustain. Food Syst.

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“…All ML models fitted in the previous section included the georeferenced location of each set—so longitude and latitude were included as specific predictors. This approach is a common practice in ML‐based species distribution models to identify for instance apparent regional hotspots for interactions between marine species of concern and fishing gears (Burns et al, 2023). However, this approach does not explicitly account for potential local neighborhood effects or covariance structure between the georeferenced locations.…”

Section: Methodsmentioning

confidence: 99%

Evidence from interpretable machine learning to inform spatial management of Palau's tuna fisheries

Gilman,

Chaloupka

2024

Ecosphere

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Static and dynamic area‐based management tools hold substantial potential to balance socioeconomic benefits derived from fisheries and costs from bycatch mortality of at‐risk species. Palau longline fisheries have high bycatch of at‐risk species including the olive ridley marine turtle and silky and blue sharks. This study analyzed a two decades‐long time series of observer and electronic monitoring datasets from the Palau distant‐water and locally‐based pelagic longline fisheries. An interpretable or explainable machine learning‐based modeling approach was used to derive spatially resolved species‐specific catch rate predictions. These models were conditioned on a suite of potentially informative environmental, bathymetric, ocean‐climate metric, vessel, monitoring system, and set‐specific operational predictors. Overall, there would be limited ecological tradeoffs from focusing fishing effort within primary catch rate hotspots for target bigeye and yellowfin tunas. Mean field prediction surfaces also defined catch rate hotspots for at‐risk species of silky and blue sharks, olive ridley turtle, and pelagic stingray, which did not overlap the hotspots for target species. The predicted target species hotspots, however, overlap olive ridley and pelagic stingray warmspots. Results also identify opportunities for temporally dynamic spatial management to control catch rates of target and bycatch species. Management of fishery operational predictors of fishing depth and soak duration present additional opportunities to balance catch rates of at‐risk bycatch and target species. A transition to employing fleetwide or vessel‐based output controls that effectively constrain the fishery would alter the spatial management strategy to focus on zones with the lowest ratio of at‐risk bycatch to commercial catch. Our findings support evidence‐informed evaluation of spatial management strategies and complementary measures to meet objectives for balancing socioeconomic benefits derived from target species catch with costs to threatened species.

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“…Fishery- and country-level data were derived from detailed, spatially explicit catch reconstructions based on United Nations Food and Agriculture Organization (FAO)–reported catches combined with regional data and expert sources informing estimates of unreported catches and discards that are not included in the FAO statistics ( 11 ). Publicly available RFMO shark-catch data recorded by scientific observers or self-reported by fishers were collated, evaluated, and spatially allocated by using a recently developed Random Forest machine learning approach ( 12 ) (tables S7 to S10). All catch data were converted to fishing mortality estimates by using species-, gear-, and, where available, location-specific shark-catch fate and postrelease mortality information (figs.…”

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confidence: 99%

Global shark fishing mortality still rising despite widespread regulatory change

Worm,

Orofino,

Burns

et al. 2024

Science

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Over the past two decades, sharks have been increasingly recognized among the world’s most threatened wildlife and hence have received heightened scientific and regulatory scrutiny. Yet, the effect of protective regulations on shark fishing mortality has not been evaluated at a global scale. Here we estimate that total fishing mortality increased from at least 76 to 80 million sharks between 2012 and 2019, ~25 million of which were threatened species. Mortality increased by 4% in coastal waters but decreased by 7% in pelagic fisheries, especially across the Atlantic and Western Pacific. By linking fishing mortality data to the global regulatory landscape, we show that widespread legislation designed to prevent shark finning did not reduce mortality but that regional shark fishing or retention bans had some success. These analyses, combined with expert interviews, highlight evidence-based solutions to reverse the continued overexploitation of sharks.

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Global hotspots of shark interactions with industrial longline fisheries

Cited by 5 publications

References 55 publications

Estimating hidden fishing activity hotspots from vessel transmitted data

Estimating hidden fishing activity hotspots from vessel transmitted data

Evidence from interpretable machine learning to inform spatial management of Palau's tuna fisheries

Global shark fishing mortality still rising despite widespread regulatory change

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