EFECTO DE TRES FRECUENCIAS DE ALIMENTACIÓN EN EL CRECIMIENTO, UTILIZACIÓN DE ALIMENTO Y SOBREVIVENCIA DE JUVENILES DE DONCELLA Pseudoplatystoma fasciatum (Linnaeus, 1766).

The tiger shovelnose catfish Pseudoplatystoma fasciatum (Linnaeus, 1766), a species widely distributed in South America, is a fish that due to its high-quality meat and fast growth, has been arising the interest of fish farmers, specially in countries like Brazil, Colombia, and Peru. The Peruvian governmental agency known as PROMPERU, has been looking for new international markets for this fish overseas, and at the same time linking efforts with other Peruvian organisms in order to develop strategies and studies that can lead to the establishment of a sustainable meat production of tiger shovelnose catfish, for international trade. One of these institutions is the Peruvian Amazon Research Institute–IIAP, which is developing a series of studies in order to generate culture technology for this catfish. The main goal of this study was to evaluate the effects of three feeding frequencies (FA2, FA4, and FA6) on the growth, feed utilization and survival of tiger shovelnose catfish (P. fasciatum) juveniles fed a pelleted diet (42% CP) during 45 days. At the end of the study, no significant differences (P>0.05) in fish growth, feed utilization and survival levels were recorded as result of the three feeding frequencies (2, 4 and 6 times/day) evaluated. To sum up, tiger shovelnose catfish juveniles of around 20 g of mean body weight would only need to be fed twice per day, which will allow the fish farmers to obtain an adequate fish yield performance with minimal work requirement.La doncella Pseudoplatystoma fasciatum (Linnaeus, 1766), especie que tiene una amplia distribución en Sudamérica, es un pez, que por la calidad de su carne y su rápido crecimiento viene despertando el interés del sector acuícola, principalmente en países como Brasil, Colombia y el Perú. La agencia gubernamental peruana PROMPERU, viene buscando mercados para la carne de este pez y al mismo tiempo articulando con otros actores, el desarrollo de planes e investigaciones para el establecimiento de una oferta exportable de su filete. Uno de estos actores, es el Instituto de Investigaciones de la Amazonía Peruana–IIAP, institución que viene desarrollando una serie de estudios que lleven a la generación de tecnologías para el cultivo de la doncella. El objetivo del presente trabajo fue evaluar los posibles efectos de tres frecuencias de alimentación (FA2, FA4 y FA6) sobre el crecimiento, utilización del alimento y sobrevivencia de juveniles de doncella (P. fasciatum) alimentados con una dieta peletizada (42% PB) durante 45 días. Al final del estudio, no se registraron diferencias significativas (P>0.05) en los índices de crecimiento, utilización de alimento, ni en los niveles de sobrevivencia de los peces como efecto de la aplicación de las tres frecuencias de alimentación (2, 4 y 6 veces/día). En conclusión, juveniles de doncella, de 20 g de peso promedio, sólo necesitarían ser alimentados dos veces al día, lo que permitirá al acuicultor obtener un adecuado rendimiento productivo de los peces, con un mínimo requerimiento de mano de obra

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