Brazilian marine biogeography: a multi-taxa approach for outlining sectorization

Species distribution patterns in the Brazilian Marine Province (BMP) are extensively debated, nevertheless no study used a multi-taxa approach to investigate possible biotic distinctions and the role of environmental factors in determining biogeographical patterns in this province. Here, we compiled the largest distributional multi-taxa dataset in the southern Atlantic (2412 reef species) to address the following: (1) similarities among areas accounting for species composition and environmental characteristics; (2) the absolute species richness of nine taxonomic groups among geographical bins; and (3) how species biogeographical patterns are explained by the environmental similarities. We hypothesized sub-provinces' limits will be strongly correlated to environmental delimitations, being sea surface temperature a central component influencing biotic subdivision on the BMP. We found eight different geographical bins considering the environmental factors, while five considering species distributions. We also observed a latitudinal gradient of species richness for most taxa, some presenting a mid-domain shape pattern. Beta diversity among sub-provinces was low, and the nestedness component more important, indicating high connectivity along the BMP. Using a db-RDA, we demonstrated that environmental variables explained 64% of species clustering patterns, with sea surface temperature, water turbidity and current velocity explaining the biotic clustering of the Brazilian northeastern coast. Sub-provinces North and Abrolhos Bank were the most distinct areas regarding environmental and biotic data. Our study highlights the importance of using a multi-taxa approach to understand the relationship between biogeographical patterns, as well as its response to environmental and historical factors.

Automated summary

The study investigates species distribution patterns in the Brazilian Marine Province (BMP) using a multi-taxa approach and environmental factors. The results show that environmental variables explain a significant portion of species clustering patterns, with sea surface temperature, water turbidity, and current velocity being important factors. The study also reveals a latitudinal gradient of species richness and identifies distinct areas in terms of environmental and biotic data.