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

• Autor(a) principal: Cord, Isadora
• Data de Publicação: 2022
• Outros Autores: Nunes, Lucas T., Barroso, Cristiane X., Freire, Andrea S., Gadig, Otto B. F. [UNESP], Gomes, Paula B., Gurgel, Carlos F. D., Lindner, Alberto, Mantelatto, Fernando L., Targino, Alessandra K. G., Floeter, Sergio R.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1007/s00227-022-04045-8; http://hdl.handle.net/11449/240828
• topic: Ecological filters; Latitudinal gradients; Marine biogeography; Reef environments; Species distributions

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.