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Ecological quality status of the Ilha Grande Bay (SE Brazil) through foraminiferal morphology and eDNA metabarcoding
Maria Virgínia Alves Martins1, Fabrizio Frontalini2, Inès Barrenechea Angeles3
1Universidade do Estado do Rio de Janeiro, UERJ, Av. São Francisco Xavier, 524, Maracanã. CEP, Rio de Janeiro, 20550-013, RJ, Brazil; Universidade de Aveiro, GeoBioTec, Departamento de Geociências, Campus de Santiago, Aveiro, 3810-193, Portugal.
Abstract:
The recent advances in molecular methodologies have further strengthened biomonitoring, which has become a fundamental approach for evaluating environmental quality in coastal ecosystems. In the present study, we evaluate the ecological quality status (EcoQS) within the Ilha Grande Bay (BIG, southeastern Brazil) by combining foraminiferal morphology-based and environmental DNA (eDNA) metabarcoding along with a comprehensive analysis of environmental parameters. The Ecological Quality Ratio (EQR), based on exp(H'bc) (the effective number of species) for eDNA and morphological communities, is used to assess the EcoQS in the BIG. Statistical results reveal that the EQR of expH'bc and the relative abundance of living foraminiferal species in both the morphological and eDNA datasets decrease in response to environmental stress (i.e., organic carbon and metal pollution). The EcoQS values derived from the EQR of exp(H'bc) from foraminiferal morphology and eDNA reveal a consistent identification of EcoQS classes. The lowest EcoQS (i.e., bad) are located in the Paraty region, in front of the São Roque River in the internal area west of the BIG where the highest values of PLI, PERI and TOC are found. From an environmental management perspective, the EcoQS categorization enables separating healthy areas from those reflecting ecological degradation and requiring restoration measures. A high level of agreement (72.2 %) is found between the morphology- and eDNA-based approaches in the classification of EcoQS. This work highlights the importance of combining molecular and morphological methods in environmental impact studies.
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