Data from: Problems with combining modelling and social science approaches to understand artisanal fisheries bycatch
Data files
Aug 19, 2024 version files 2.30 MB
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Bathymetry1kn.tif
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Bycatch_Interviews.csv
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Bycatch_Interviews.numbers
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CagesPresent1km.tif
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DistanceCages1km.tif
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DistanceNatPark1km.tif
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DistanceSettlement1km1.tif
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DistanceShore1km.tif
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Fisheries_Distribution_Prediction_Raster.tif
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Net_Density_500m.csv
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Net_Locations.xlsx
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README.md
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SurveyBiasFile.asc
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Totora1km.tif
Abstract
Aim: Artisanal fisheries account for 40% of the world’s fisheries catch, yet its environmental impacts remain poorly understood. This is especially the case in developing countries. In this study, we sought to integrate Local Fisher’s Knowledge with distribution modelling to estimate the annual bycatch of Titicaca Grebe (Rollandia microptera), an endangered endemic bird from Lake Titicaca whose main anthropogenic threat is bycatch.
Location: Lake Titicaca, Peru & Bolivia
Methods: We conducted transect and point counts of fishing nets in March – September, 2022, and conducted interviews with fishers across the Lake Titicaca region. Using bathymetry, distance from shore, distance from a settlement, distance from the protected area, presence/absence of aquaculture, distance from aquaculture, and wetland cover, we constructed a distribution model of fisheries using maximum entropy modelling. We conducted interviews with fishers asking about the frequency of grebe bycatch, and conducted short-term monitoring at various sites while conducting transect points for dead grebes.
Results: We estimate 3270 km2 of the surface area of Lake Titicaca is used for fishing, which amounts to 39.40% of the lake’s surface area. The area under the curve (AUC) of the distribution model was 0.89 and the True Skill Statistic was 0.67, which suggests maximum entropy modelling can model fisheries occurrence. The results of our interviews suggested a biologically implausible large number of grebes caught as bycatch annually. The cultural context of the interviews, being with fishers who often view the Titicaca Grebe as a nuisance species, might have caused over-reporting of bycatch, and hence lead to these implausible figures.
Main Conclusions: It is possible to map fisheries using distribution models as one might with species. However, obtaining accurate measures of fisheries bycatch through interviews is more difficult, due to cultural factors which affect the accuracy in fisher’s responses. While we hope that this method provides a low-cost alternative to monitoring, it is not a suitable replacement for it.
README: Data from: Problems with combining modelling and social science approaches to understand artisanal fisheries bycatch
The files contained in this Dryad are the raw data, R code, and results of the study this is linked to. To comply with Dryad policies regarding endangered species, the locations of the nets have been rounded, though non-rounded net values are available upon request for legitimate scientific purposes.
Data Used
Net Locations
Net_Locations.xlsx contains net locations, rounded
Environmental Rasters and Bias Raster
Bathymetry1kn.tif Bathymetry raster of Lake Titicaca
DistanceSettlement1km1.tif Distance from settlement raster of Lake Titicaca
CagesPresent1km.tif Pisciculture Presence/Absence raster of Lake Titicaca
DistanceCages1km.tif Distance from pisciculture raster of Lake Titicaca
DistanceNatPark1km.tif Distance from National Park raster of Lake Titicaca
DistanceShore1km.tif Distance from shore raster of Lake Titicaca
Totora1km.tif Totora wetland raster of Lake Titicaca
SurveyBiasFile.asc Survey Bias raster
Net Densities
Net_Density_500m.csv Average net density of each transect and point count where nets were observed
Interview Results
Bycatch_Interviews.csv Bycatch results from interviews with fishermen, Nets variable is the number of 100 metre net equivalents
R Code
D_D_R_Code_Dryad.R contains an annotated R code which was used in this study
Results
Fisheries_Distribution_Prediction_Raster.tif Result of MaxEnt of fisheries