Predicting large-scale spatial patterns of marine meiofauna: implications for environmental monitoring
DOI:
https://doi.org/10.1590/Keywords:
Meiobenthos, Ecology, Random Forest, Santos Basin, Environmental MonitoringAbstract
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.
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