Data-limited methods for Egyptian artisanal Red Sea fisheries

Rehab Farouk Abdelfattah Soliman, PhD student of Queen’s University Belfast presented a metareview of data limited methods (DLMs) to guide choices for evaluating the most common fish stocks in the artisanal fisheries of the Egyptian Red Sea (ERS). The review was further informed by a stakeholder survey covering the artisanal fishery of the ERS, for which the primary regulation is a four-month closed season. A disconnect exists between fishers and managers regarding the purpose and effectiveness of current regulations (paper under review). Previous stock assessments in the ERS revealed over-exploitation of multiple species, prompting a need for additional limitations on the fishing effort.

The PhD studies are exploring alternative management options, such as total allowable catches and alternative controls of fishing effort, based on available data. A review of data-limited methods was conducted, assessing the types of data required, and the reference points generated for management plans. Our survey among fishers found that some fishers are willing to participate in measuring their catch. There is time series data of catches for a few species, and life history traits obtained from surveying the literature.

Our review of appropriate DLMs used a scoring system with four criteria: data availability in the Egyptian Red Sea, track record of previous results, ease of use, and suitability for over-exploited stocks. Each DLM reviewed was assigned a score from 1 to 5 for each of these criteria, with DLMs ranked by their total weighted score. Weightings were based on subjective criteria, but sensitivity tested with Monte-Carlo bootstrapping statistics. The three consistently highest scoring DLMs were LBB, LBSPR, and CMSY. Incorporating fisher-derived data and training for stock assessment personnel were considered important aspects for improved stock management. The next steps involve designing an effective data collection strategy for fisher-derived length frequency data.