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Benthic biogeographic patterns on the deep Brazilian margin.

Daniela Y GaurisasAngelo Fraga Bernardino
Published in: PeerJ (2023)
The Brazilian continental margin (BCM) extends from the Tropical to the Subtropical Atlantic Ocean, with much of its seafloor within deep waters, supporting rich geomorphological features and under wide productivity gradients. Deep-sea biogeographic boundaries on the BCM have been limited to studies that used water mass and salinity properties of deep-water masses, partly as a result of historical under sampling and a lack of consolidation of available biological and ecological datasets. The aim of this study was to consolidate benthic assemblage datasets and test current oceanographic biogeographical deep-sea boundaries (200-5,000 m) using available faunal distributions. We retrieved over 4,000 benthic data records from open-access databases and used cluster analysis to examine assemblage distributions against the deep-sea biogeographical classification scheme from Watling et al. (2013). Starting from the assumption that vertical and horizontal distribution patterns can vary regionally, we test other schemes incorporating latitudinal and water masses stratification within the Brazilian margin. As expected, the classification scheme based on benthic biodiversity is in overall agreement with the general boundaries proposed by Watling et al. (2013). However, our analysis allowed much refinement in the former boundaries, and here we propose the use of two biogeographic realms, two provinces and seven bathyal ecoregions (200-3,500 m), and three abyssal provinces (>3,500 m) along the BCM. The main driver for these units seems to be latitudinal gradients as well as water mass characteristics such as temperature. Our study provides a significant improvement of benthic biogeographic ranges along the Brazilian continental margin allowing a more detailed recognition of its biodiversity and ecological value, and also supports the needed spatial management for industrial activities occurring in its deep waters.
Keyphrases
  • climate change
  • machine learning
  • deep learning
  • big data
  • magnetic resonance
  • wastewater treatment
  • artificial intelligence