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. 2020 Jun 10;15(6):e0234091.
doi: 10.1371/journal.pone.0234091. eCollection 2020.

"Too Big To Ignore": A feasibility analysis of detecting fishing events in Gabonese small-scale fisheries

Floriane Cardiec  1   2 , Sophie Bertrand  3 , Matthew J Witt  4 , Kristian Metcalfe  5 , Brendan J Godley  5 , Catherine McClellan  6 , Raul Vilela  2 , Richard J Parnell  2 , François le Loc'h  1
Affiliations

"Too Big To Ignore": A feasibility analysis of detecting fishing events in Gabonese small-scale fisheries

Floriane Cardiec et al. PLoS One. .

Abstract

In many developing countries, small-scale fisheries provide employment and important food security for local populations. To support resource management, the description of the spatiotemporal extent of fisheries is necessary, but often poorly understood due to the diffuse nature of effort, operated from numerous small wooden vessels. Here, in Gabon, Central Africa, we applied Hidden Markov Models to detect fishing patterns in seven different fisheries (with different gears) from GPS data. Models were compared to information collected by on-board observers (7 trips) and, at a larger scale, to a visual interpretation method (99 trips). Models utilizing different sampling resolutions of GPS acquisition were also tested. Model prediction accuracy was high with GPS data sampling rates up to three minutes apart. The minor loss of accuracy linked to model classification is largely compensated by the savings in time required for analysis, especially in a context of nations or organizations with limited resources. This method could be applied to larger datasets at a national or international scale to identify and more adequately manage fishing effort.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Map of the North of Gabon showing the size of the fleet and the number of fishing trips sampled by landing site.
Size of circles represents estimated number of boats by landing site and number inside is the count of fishing trips sampled in this location.
Fig 2
Fig 2. Boxplots of trip duration (A) and distance covered (B) for n trips by gear type.
SCG = Sardine Circling Gillnet (n = 10), SG = Surface Drifting Gillnet (n = 14), MCG = Mullet Circling Gillnet (n = 12), PS = Purse Seine (n = 32), HL = Handline (n = 12), LL = Longline (n = 8) and BG = Bottom Gillnet (n = 11).
Fig 3
Fig 3. Distribution of vessel speed for each gear type.
SCG = Sardine Circling Gillnet, SG = Surface Drifting Gillnet, MCG = Mullet Circling Gillnet, PS = Purse Seine, HL = Handline, LL = Longline and BG = Bottom Gillnet.
Fig 4
Fig 4. Mapping of 2 different methods used to identify a fishing event on a fishing trip (B: Visual interpretation, C: HMM) compared to observed data (A: On-board observer) for two different gear types (1: Surface drifting gillnet, 2: Purse seine).
Fig 5
Fig 5. Map of all tracks for surface drifting gillnet gear type and fishing areas identified by visual interpretation and HMM methods (In grey: Both methods predicted non-fishing, in blue: Both methods predicted fishing, in red: Only HMM predicted fishing, in green: Only visual interpretation predicted fishing).
Fig 6
Fig 6. Accuracy, sensitivity, specificity, fishing prediction and non-fishing prediction across different time steps for each gear type.
The dashed line shows the time step where values were highest. Values can be found in Table B in S1 File.
Fig 7
Fig 7. Stepwise overview of the processes of the two methods.
Their time investment and estimated accuracy (%) are compared to having an on-board observer.
See this image and copyright information in PMC

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References

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Grants and funding

This work was supported by US Fish and Wildlife Service, AFR-1427 / F14AP00555, https://www.fws.gov/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. BJG, KM, and MJW were supported by the Darwin Initiative (Projects 17-005/20-009/23-011/26-014) through funding from the Department for Environment, Food and Rural Affairs in the UK. SB was supported by the LMI TAPIOCA, program CAPES/COFECUB (88881.142689/2017-01) and EU H2020 TRIATLAS project under Grant Agreement 817578. FL was supported by Arc Emeraude Project (ANPN/AFD).