title: Fishing Fleet Selectivity in Lake Victoria's Nile Perch Fishery creator: Gómez-Cardona, Santiago creator: Kammerer, Johannes creator: Mrosso, Hillary subject: ddc-330 subject: 330 Economics description: Current regulations regarding the Nile Perch Fishery in Lake Victoria provide gear specifications for the two main gears employed, Gillnets and Longlines, by defining the minimum size available. Additionally, the legal range of catchable fish is regulated: between 50 and 84 cm. Gillnets are legal over 5 inches, and the Hooks, required for Longlining, are legal from number 10 and below (lower numbers refer to higher sizes). Using data from the Catch Assessment Survey, CAS, we identify gear selectivity for Gillnets and Longlines. We use a novel methodology, that is validated using previous results for Gillnets for Lake Victoria. This is particular relevant as there is no much data for selectivity using Longlines. The information on selectivity is used to derive the overall fishing selectivity of the fleet. We find evidence of shift towards higher fish sizes in the Gillnet fleet, but a stagnation on fish sizes for the Longline fleet, even though there has been a compositional change in the hook sizes employed for this activity. The results signal towards a regulatory success among Gillnet users, but challenges in the regulation of Longliners. date: 2022 type: Working paper type: info:eu-repo/semantics/workingPaper type: NonPeerReviewed format: application/pdf identifier: https://archiv.ub.uni-heidelberg.de/volltextserverhttps://archiv.ub.uni-heidelberg.de/volltextserver/31341/7/Gomez-Cardona_Kammerer_Mrosso_2022_dp712.pdf identifier: urn:nbn:de:bsz:16-heidok-313411 identifier: Gómez-Cardona, Santiago ; Kammerer, Johannes ; Mrosso, Hillary (2022) Fishing Fleet Selectivity in Lake Victoria's Nile Perch Fishery. [Working paper] relation: https://archiv.ub.uni-heidelberg.de/volltextserver/31341/ rights: info:eu-repo/semantics/openAccess rights: http://archiv.ub.uni-heidelberg.de/volltextserver/help/license_urhg.html language: eng