Abstract This study aims model the distribution of meiofauna indicators in relation to environmental variables from the Santos Basin continental margin, SE Brazil, using machine learning techniques, to provide baseline information and foster future monitoring programs. A total of 100 sampling stations were distributed in eight transects and 11 isobaths (25 to 2,400 m) perpendicular to the coast. In each station, three replicates were sampled for meiofauna and 38 environmental parameters. A total of 28 meiofauna taxa were found, with a mean richness varying from 3 to 15 taxa per station. Meiofauna mean density varied between 55 and 2,001 ind. 10 cm-2. Density of meiofauna and its most frequent taxa (Nematoda, Copepoda, Kinorhyncha, and Polychaeta), and taxa richness were used as descriptors for the models. Meiofauna and nematode density showed the highest training and testing accuracies, with R² values above 0.74. Based on the distribution of meiofauna descriptors and their responses to environmental conditions, we suggest a mosaic of six benthic zones. The La Plata Plume zone and the Cabo Frio Upwelling zone are two of the most diverse and productive zones in the continental shelf, wich are separated by the less productive Central Continental Shelf zone. A fourth zone, with very low meiofauna densities, corresponds to the carbonated sediments of the shelf-break. The Upper and Mid-Slope is a narrow zone along the entire basin, with intermediate densities and small amounts of high-quality organic carbon. The largest, impoverished zone, the Lower Slope and Plateau comprises the deepest areas and the São Paulo Plateau. The study showed that, although some zones can be recognized by most meiofauna descriptors, others are better characterized by specific ones, implying that meiofauna indicators should be monitored concomitantly. We recommend the optimization of sampling design based on our model to reduce costs and increase our understanding of the system. margin Brazil techniques programs 1 25 (2 2400 2 400 2,40 m coast station parameters found 5 2001 001 2,00 ind cm2. cm2 cm 2. cm-2 Nematoda, Nematoda (Nematoda Copepoda Kinorhyncha Polychaeta, Polychaeta , Polychaeta) models accuracies R 074 0 74 0.74 conditions shelf shelfbreak. shelfbreak break. break shelf-break MidSlope Mid basin highquality high quality carbon largest ones concomitantly system ( 240 40 2,4 200 00 2,0 cm- 07 7 0.7 24 4 2, 20 0.

Resumo Um grupo de trabalho de costões rochosos (GT) integrado à ReBentos (Rede de Monitoramento de Habitats Bentônicos Costeiros), e vinculado à Sub-Rede Zonas Costeiras da Rede Clima (MCT) e ao Instituto Nacional de Ciência e Tecnologia para Mudanças Climáticas (INCT-MC), foi criado para estudar a vulnerabilidade das comunidades bentônicas dos costões rochosos e os efeitos das alterações ambientais sobre a respectiva biota. A presente síntese foi um dos produtos desse GT e teve como objetivo principal levantar e revisar o conhecimento existente sobre as comunidades bentônicas de costões rochosos na costa brasileira, a biodiversidade associada, e verificar as potencialidades de estudos futuros para uma previsão/mensuração mais acurada dos efeitos das mudanças climáticas sobre os ambientes e sua biota.

Abstract A rocky shores working group (WG) integrated with ReBentos (Monitoring Network for Coastal Benthic Habitats; Rede de Monitoramento de Habitats Bentônicos Costeiros) was created and linked to the Coastal Zones Sub Network of the Climate Network (MCT; Sub-Rede Zonas Costeiras da Rede Clima) and to the National Institute of Science and Technology for Climate Change (INCT-MC; Instituto Nacional de Ciência e Tecnologia para Mudanças Climáticas), to study the vulnerability of benthic communities on rocky shores and the effects of environmental changes on biomes in such environments along the Brazilian coast. The synthesis presented here was one of the products of this GT, and aimed to collect and review existing knowledge on benthic communities present on rocky shores of the Brazilian coast, their associated biodiversity, and the potential of future studies to accurately predict/measure the effects of climate change on such environments and their biota.