DOI: 10.5061/dryad.vt4b8gtzz
title: Data from: Problems with combining modelling and social science approaches to understand artisanal fisheries bycatch

description: 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.
description: # 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