Our project is dedicated to creating a sustainable fishery management system for communities in the Negro River Basin of the Amazon. By integrating advanced technologies with the communities' traditional knowledge, we aim to support the livelihoods of these communities, which rely heavily on fishing for both protein and income.
Traditionally, these communities practice "standby fishing," where groups wait in canoes at predetermined spots with large nets to catch fish as they migrate between two flooded forest areas. This traditional knowledge is invaluable, as it reflects generations of experience in understanding the natural rhythms of the environment. However, climate change has disrupted these patterns, leading to extreme droughts that prevent fish from reaching their usual spots. As a result, the communities are facing significant challenges, spending weeks waiting for fish that no longer come, which threatens their primary sources of food and income.
To address this issue, we propose deploying Hidromoth subaquatic recorders, which will monitor fish movements at key fishing spots within the Sustainable Development Reserve of Rio Negro. These devices will track fish school movements and sizes, providing crucial real-time data on the best fishing locations and times. In addition to monitoring, communities will photograph their catches, and this data is used to train machine-learning models that will identify the fish species and measure their sizes. This data will be shared with both traditional and scientific communities, enabling comprehensive analysis and comparison of species abundance, size, and seasonal variations. Such information is essential for developing effective fishery management plans in the Amazon and understanding the impacts of climate change on fishing practices

By combining this scientific and technological knowledge with the community's traditional practices, we can create a more resilient and effective fishery management system. This will help communities save time and resources while ensuring a more reliable catch.
In addition to monitoring, communities will photograph their catches, and our machine-learning models will identify the fish species and measure their sizes. This data will be shared with both traditional and scientific communities, enabling comprehensive analysis and comparison of species abundance, size, and seasonal variations. Such information is essential for developing effective fishery management plans in the Amazon and understanding the impacts of climate change on fishing practices.
Implementation of the project will begin in January 2025, aligning with the rising water levels in the Amazon. We will collaborate with the communities to assess fishing spots, update methodologies, and develop workshops to align priorities. This collaboration is key, as it allows for the co-construction of methodologies that honor traditional practices while incorporating scientific insights. By late February to early March, Hidromoth recorders will be deployed in ecotone areas, where flooded forests meet fishing spots. Weekly data collected by the Hidromoths will be uploaded to the Gainforest platform, where it can be accessed globally and attract investments to support the communities' stewardship of their natural resources.
This continuous data collection will allow us to refine our understanding of fish movements and optimize fishing strategies, ultimately benefiting 16 communities in the reserve. The integration of traditional knowledge with scientific data will ensure that fishery management plans are both culturally appropriate and scientifically sound, enhancing food security, supporting local economies, and informing conservation policies across 103,000 hectares, impacting around 5,000 people.
By leveraging decentralized science, our project not only empowers local communities but also contributes valuable data to the global scientific community, demonstrating the power of integrating traditional knowledge with modern science and technology to drive innovation in sustainable resource management.
