Researchers have published new findings on how local knowledge contributes to the sustainable management of interconnected fisheries, with a focus on the pirarucu (Arapaima gigas) in the Middle Juruá River Basin in the Western Brazilian Amazon. The study, conducted by an international team including Dr Miguel Lurgi from Swansea University’s School of Biosciences, Geography and Physics, appears in the Journal of Applied Ecology.
The research examined how experienced local fishers select oxbow lakes for protection based on generational ecological knowledge about river dynamics and fish behavior. These choices are combined with regulatory policies that set annual fishing quotas within a co-management framework. This approach aims to ensure sustainability and prevent illegal fishing.
To assess whether these community-led decisions were optimal, researchers developed six alternative scenarios for managing fisheries using data collected by local fishers between 2011 and 2022 from 13 protected and 19 unprotected lakes. They constructed a spatial network model that factored in each lake’s geographical position, protection status, pirarucu population size, growth potential, and carrying capacity. The scenarios varied which lakes would be protected based on different criteria such as connectivity, area, or carrying capacity.
The study also included simulations of various levels of illegal fishing in unprotected lakes. Results indicated that while strategies based strictly on maximizing carrying capacity could yield slightly better outcomes, the current co-management system guided by local expertise performed almost as well. Community-managed lakes sustained high pirarucu populations and served as important refuges against overfishing elsewhere.
Dr Lurgi stated: “Our study represents a pioneering step towards a better integration of traditional knowledge of local people into quantitative ecological approaches to the sustainable management of natural resources.
“By fully integrating social and ecological aspects of population management into quantitative predictive frameworks, we will have a better chance of providing policymakers and local communities with the tools needed to better address challenges at the interface between conservation and sustainable development."
The authors suggest their work offers guidance for combining local knowledge with ecological modeling to promote sustainability not only in Amazonia but also in other complex ecosystems globally.