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Fish endoparasite metacommunity in environments with different degrees of conservation in the western Brazilian Amazon.

Lucena Rocha VirgilioHenrique Paulo Silva de MeloFabricia da Silva LimaRicardo Massato TakemotoLuís Marcelo Aranha CamargoDionatas Ulises de Oliveira Meneguetti
Published in: Parasitology research (2023)
Parasite communities correspond to the definition of metacommunity, as species interact and disperse within hosts. The present study evaluated parasite metacommunities in a tropical floodplain. The study was conducted in the Western Amazon around the municipalities of Cruzeiro do Sul, state of Acre, and Guajará, state of Amazonas, Brazil. Six sampling sites were selected and grouped into conserved and degraded environments. Fish were caught between periods of drought and flood, using passive and active sampling methods; in the laboratory, they were measured weighed, and necropsied. Parasites found were fixed, evaluated, and identified. Physical and chemical variables and environmental conservation characteristics were measured in all sites. Diversity index, ANOVA, Tukey, local contribution to beta diversity (LCBD), species contribution to beta diversity by individual species (SCBD), and variance partitioning were summarized. The α species diversity increased in conserved environments and varied between seasonal periods, mainly in detritivorous and omnivorous hosts. Local contributions to beta diversity showed significantly higher values in conserved environments for the endoparasite fauna of piscivorous and omnivorous hosts, indicating that these environments presented unique parasite infracommunities and revealing the conservation status of these environments. Variations in infracommunities were explained mainly by niche-based processes, including environmental conditions, degree of conservation, and host characteristics. Thus, these data will serve as a tool to understand the way parasite communities are structured, which is important information for the management and conservation of aquatic environments.
Keyphrases
  • plasmodium falciparum
  • life cycle
  • toxoplasma gondii
  • climate change
  • risk assessment
  • mental health
  • healthcare
  • human health
  • machine learning
  • big data
  • deep learning
  • data analysis