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Improving stock assessment and management advice for data-poor small-scale fisheries through participatory monitoring

Ramírez, John Gabriel, Lleonart, Jordi, Coll, Marta, Reyes, Francisco, Puentes, Gina Marcela
Fisheries research 2017 v.190 pp. 71-83
Haemulon, Lutjanus, employment, income, models, monitoring, mortality, overfishing, snapper
Undetected but underlying biases in model parameterization strongly reduce the reliability and value of assessments of data-poor fisheries. We explore the effects of missing and misunderstood data on single-species stock assessments used to provide management advice. From 2006 to 2014, the Colombian government monitored landings of small-scale fisheries. During the same period, communities implemented a participatory monitoring program in the Central Guajira region. We found that the two data sources gave different results for the population status of the highest-valued fish, lane snapper (Lutjanus synagris), and the largest-landed species, white grunt (Haemulon plumierii). Recordings of landing points by the government monitoring program led to year-to-year underestimations and therefore misconceptions regarding population status and fishery trends. Overexploited and underexploited population statuses were seen to arise from the same fishing pressure as a result of the interplay between natural mortality and erroneous estimates of fishing mortality. The tested von Bertalanffy growth parameters affected the exploitation level, but not the population status, of the species. When data from the participatory monitoring program were incorporated, higher landings and a more severe overfishing trend emerged for both species. The management scenarios simulated using the best verified data available provided reasonable advice for recovering the lane snapper and white grunt populations. Furthermore, simulation of management measures sustained the employment and incomes of fishers. Our findings indicate that participatory monitoring should be incorporated into the assessment and management of data-poor resources.