Local knowledge enhances the sustainability of interconnected fisheries

Carine Emer, Miguel Lurgi, Sérgio Timóteo, João Vitor Campos-Silva, Shai Pilosof 

1. Local knowledge (LK) refers to the ancestral understanding that Indigenous Peoples and local communities have developed over centuries through trial-and-error and hands-on management of natural resources. LK may provide valuable insights for biodiversity conservation and human well-being. However, its effectiveness remains under-explored at large scales, especially where multiple communities manage ecosystems. One example is fisheries, which form complex, interconnected networks where fish move across spatial boundaries between managed areas. Fisheries are critical for food security and income, yet face threats from overharvesting. Fisheries Co-Management (FCM)---a partnership between local communities and governments---leverages LK. However, the value of LK in designing protection strategies remains unclear.


2. Using a process-based dynamical model parameterized with empirical data, we evaluated FCM strategies for pirarucu (\textit{Arapaima gigas}) fisheries, which form a metapopulation network of protected and unprotected lakes in the Brazilian Amazon. We combined that with LK, fish biology and network theory to assess how lake protection and fishing quotas, including illegal fishing, impact pirarucu population abundance at the riverscape scale.


3. Our analysis of 13 FCM-protected lakes and 18 unprotected lakes compares six hypothesis-driven management strategies with the current one, which is based on LK. In all strategies, protected lakes support higher pirarucu populations and buffer against increased fishing pressure, while unprotected lakes face population collapse due to the lack of fishing regulations. While a strategy that provides preferential protection to lakes with high pirarucu carrying capacity provided the highest population persistence, the currently applied one closely matched its efficacy.


4. Our findings reveal that current FCM strategy, grounded in LK, is highly efficient but can still be optimised using ecological knowledge. Our modelling approach can be applied to strengthen strategies for scaling up areas of community-led protection in the Amazon, reinforcing the critical role of LK in contributing to key global conservation targets such as the Kunming-Montreal Global Biodiversity Framework under the Convention on Biological Diversity.