
BackgroundSmall-scale multi-gear and multi-species fisheries in the tropics are an importantsource of food and income for coastal populations. However, fishing is the mainanthropogenic threat to marine ecosystems such as coral reefs that coral reeffisheries depend. Indeed, worldwide coral reefs face an increasing degradation dueto pollution, sedimentation and climate change. The reef fishery of the Bay ofRanobe is the main exploitation area in southwestern Madagascar (143 km²), wherethese problems related to reef degradation have been reported for many years.Besides, this reef fishery suffers from poor management due to the lack of reliabledata and/or resources to allow an accurate diagnosis of the risk of theiroverexploitation and to ensure their sustainability.These situations highlight the greatneed to rebuild this ecosystem and the resources it provides. Artificial reefs areamong large spread solutions proposed to rebuild fisheries production. Known as anartificial reef, standard ARMS (Autonomous Reef Monitoring Structures) can collectrepresentative local communities and up to 85% of all known reef biodiversity.Through the ARMS Restore project, implemented in the Bay of Ranobe (2021-2024),new tool ARMS will be used to sample the diversity of coral around and seedartificial reefs. The project’s primary goals are to increase the number of fish andother marine species that can be harvested for food and income and better continueto protect shorelines.This study aims to provide complete and fine scale baselinedata to assess the effectiveness of artificial reefs seeded with ARMS inenhancement of fisheries production, in the one hand, and to evaluate exploitationlevel of this fishery based on fisheries indicators, in the other hand. MethodTo achieve these goals, a fishery survey and a fish biodiversity monitoring of 14months have started in 12 fishing villages along Bay of Ranobe (October 2021 -December 2022). To evaluate fishing effort and fishing power, a record of reeffishers and gear measurement (gillnet, beach seine and mosquito trawl net) wasdone before the start of the survey. Each 30 days, we monitor the activity of 103fishers targeting reef fish using gillnet, handline, fishing speargun, trawl net, beachseine and mosquito trawl net. Fisheries survey includes three components: boatmovement monitoring using GPS trackers, participatory survey of fishers catches,the study of fish biodiversity via catches landing survey (fish images collection) andDNA barcoding approach for a precise species identification. Also, indicators basedon size is recorded to determine length at first maturity (Lmat) of each speciescaught.ResultsA total of 1,460 reef fishers were recorded in the 12 villages of the bay, the majorityof fishers are using gillnet (40%) and localized in Andrevo village (16%). For thefishing gears, 248 gillnets, 83 mosquito trawl nets and 13 beach seines have beenmeasured within the bay. To date our results, concern the warm season (November– April). With 279 fishing landings monitored, we collected 850 fish images. It hasbeen found that 60 families of reef fish are exploited in Bay of Ranobe with thedominance of Labridae in terms of abundance. These fish will be identified at lowesttaxonomical rank after DNA barcoding analyses of the 1823 tissues collected duringthe survey. Analysis related to indicators based on size are yet in progress.ConclusionThis exhaustive study is the first within the bay that characterize the fishery withcomplete and accurate data thanks long period survey and with innovative methods.It actually involves: catch diversity at the species level and their spatial repartition inhabitats, effective fishing effort (in hour) and production distribution via GPS survey,seasonal variation and size structure of catches within the bay of Ranobe. Ourresults demonstrate the necessity of multidisciplinary approach to better manage themulti-gear fishing effort within a community and regulatory approach.
spatial dynamic, Fishery, Madagascar, temporal dynamic
spatial dynamic, Fishery, Madagascar, temporal dynamic
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