An approach to assess data-less small-scale fisheries: examples from Congo rivers

Abstract

Small-scale fisheries (SSF) account for much of the global fish catch, but data to assess them often do not exist, impeding assessments of their historical dynamics and status. Here, we propose an approach to assess 'data-less' SSF using local knowledge to produce data, life history theory to describe their historical multispecies dynamics, and length-based reference points to evaluate stock status. We demonstrate use of this approach in three data-less SSFs of the Congo Basin. Fishers' recalls of past fishing events indicated fish catch declined by 65–80% over the last half-century. Declines in and depletion of many historically important species reduced the diversity of exploited species, making the species composition of the catch more homogenous in recent years. Length-at-catch of 11 of the 12 most important species were below their respective lengths-at-maturity and optimal lengths (obtained from Fishbase) in recent years, indicating overfishing. The most overfished species were large-bodied and found in the Congo mainstem. These results show the approach can suitably assess data-less SSF. Fishers' knowledge produced data at a fraction of the cost and effort of collecting fisheries landings data. Historical and current data on fish catch, length-at-catch, and species diversity can inform management and restoration efforts to curb shifting baselines of these fisheries. Classification of stock status allows prioritizing management efforts. The approach is easy to apply and generates intuitive results, having potential to complement the toolkits of researchers and managers working in SSF and engage stakeholders in decision-making processes.

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