From Amazon Catfish to Mekong Money Fish: Size-based Assessment of Data-Limited Commercial Inland Fisheries

Abstract

Inland fisheries are often complex, spatially dispersed, and seasonal. A lack of monitoring can result in unreliable or incomplete catch data, suggesting a role for assessment methods based on population size structure. This paper evaluates and compares empirical size-based indicators and the length-based spawning potential ratio model as candidate tools for assessing data-limited commercial fisheries in inland systems. Case study applications are presented for a contrasting set of important fisheries in the Amazon Basin (Brazil, Bolivia, Colombia, and Peru), the Tonlé Sap River (Cambodia), Paraná River (Argentina), and Bayano Reservoir (Panama). These case studies were selected to explore the effects on assessment of factors including lack of life history information, spatial separation of life history stages, modality in population size structure of floodplain river fish, and fishing gear selectivity. An international workshop was organized to bring together experts from the study systems and elsewhere to discuss the results, and to highlight potential issues and caveats. It was concluded that length-based models may work well in cases where size-selective gears are used to target a few larger species with reliable life history parameter estimates. Empirical surveillance indicators are more flexible for integrating quantitative data with local expert knowledge in common data-poor situations. In general, size-based assessment can provide guidance for the sustainable management of target species in diverse inland fisheries.