PECES EN PELIGRO: REVELANDO LA DIVERSIDAD OCULTA DE UNA LAGUNA VULNERABLE EN LA REGIÓN AMAZÓNICA PERUANA

The study focuses on the Huachana lake, a vulnerable ecosystem in the Peruvian Amazon region that receives wastewater from the city of Iquitos. Despite its significance in ichthyology and its role as a type locality for various species, water bodies near Iquitos face threats. While a portion of the ichthyological diversity in the Loreto region is known, the Huachana lake had never been subject to sampling, making this study the first to examine fish diversity in this aquatic environment. A total of 52 fish species from 5 orders, 20 families, and 39 genera were identified. The Characiformes present the greatest species richness (30 species), followed by Cichliformes (12 species). Cichlidae and Characidae are the families richest in species. No species are endangered according to the IUCN; the majority are considered of least concern. The study also identified ornamental and commercial species, providing crucial information about ichthyofauna in nearby urban areas and its potential long-term environmental impact.  El estudio se enfoca en la laguna Huachana, un ecosistema vulnerable en la región amazónica peruana, que recibe aguas residuales de la ciudad de Iquitos. A pesar de su importancia en la ictiología y su papel como localidad tipo para diversas especies, los cuerpos de agua cerca de Iquitos enfrentan amenazas. Si bien se conoce parte de la diversidad ictiológica en la región de Loreto, la cocha Huachana nunca había sido objeto de muestreo, lo que hace que este estudio sea el primero en examinar la diversidad de peces en este ambiente acuático. Se identificaron 52 especies de peces de 5 órdenes, 20 familias y 39 géneros. Los Characiformes presenten la mayor riqueza (30 especies), seguido por Cichliformes (12 especies). Cichlidae y Characidae son las familias más ricas en especies. Ninguna especie está en peligro según la IUCN, la mayoría se considera de preocupación menor. El estudio también identificó especies ornamentales y comerciales, proporcionando información crucial sobre la ictiofauna en zonas urbanas cercanas y su posible impacto ambiental a largo plazo

Application of a deep learning image classifier for identification of Amazonian fishes

Abstract Given the sharp increase in agricultural and infrastructure development and the paucity of widespread data available to support conservation management decisions, a more rapid and accurate tool for identifying fish fauna in the world's largest freshwater ecosystem, the Amazon, is needed. Current strategies for identification of freshwater fishes require high levels of training and taxonomic expertise for morphological identification or genetic testing for species recognition at a molecular level. To overcome these challenges, we built an image masking model (U‐Net) and a convolutional neural net (CNN) to classify Amazonian fish in photographs. Fish used to generate training data were collected and photographed in tributaries in seasonally flooded forests of the upper Morona River valley in Loreto, Peru in 2018 and 2019. Species identifications in the training images (n = 3068) were verified by expert ichthyologists. These images were supplemented with photographs taken of additional Amazonian fish specimens housed in the ichthyological collection of the Smithsonian's National Museum of Natural History. We generated a CNN model that identified 33 genera of fishes with a mean accuracy of 97.9%. Wider availability of accurate freshwater fish image recognition tools, such as the one described here, will enable fishermen, local communities, and citizen scientists to more effectively participate in collecting and sharing data from their territories to inform policy and management decisions that impact them directly