Login / Signup

Nematofauna of Bryconops cf. affinis (Characiformes, Iguanodectidae) and Saxatilia brasiliensis (Cichliformes, Cichlidae) from the Munim River basin, Northeastern Brazil.

Melissa Querido CárdenasMarciara Lopes SilvaDiego Carvalho VianaSimone Chinicz CohenFelipe Polivanov Ottoni
Published in: Revista brasileira de parasitologia veterinaria = Brazilian journal of veterinary parasitology : Orgao Oficial do Colegio Brasileiro de Parasitologia Veterinaria (2024)
Populations of freshwater species have been declining rapidly and species are becoming extinct. Thus, understanding freshwater species distribution, trends and patterns is required. The Munim River basin is situated in a region with a phytogeographic interface between the Amazon and Cerrado biomes. Although the Munim basin ichthyofauna is currently relatively well-known, data on its helminth fauna is scarce. The present study aimed to characterize the nematofauna of Bryconops cf. affinis (Günther) and Saxatilia brasiliensis (Bloch) from two different localities in the middle section of the Munim River, and thus to contribute to the knowledge of biodiversity in this region. Specimens of Bryconops cf. affinis were parasitized with the nematodes Procamallanus (Spirocamallanus) krameri (Petter, 1974) and "Porrocaecum-like" species (larvae) in both localities. Saxatilia brasiliensis presented the nematodes P. (S.) krameri, Pseudoproleptus sp. (larvae), Cucullanus sp. and Procamallanus sp. (larvae). Procamallanus (S.) krameri was found parasitizing S. brasiliensis only from the Feio stream. Morphometric data and parasitological parameters are given. The present study provides the first record of nematodes for B. cf. affinis and for S. brasiliensis contributing to the knowledge of the helminth fauna of freshwater fishes from locations that have not yet been studied, such as the Munim River basin.
Keyphrases
  • cystic fibrosis
  • healthcare
  • electronic health record
  • genetic diversity
  • big data
  • drosophila melanogaster
  • zika virus
  • deep learning
  • ultrasound guided
  • artificial intelligence