Taxonomy-free approach overcomes the gaps in ecological knowledge: The case of foraminiferal metabarcoding applied to environmental impact assessment

Environmental DNA (eDNA) metabarcoding has emerged as a cost- and time-efficient alternative to traditional morpho-taxonomic methods for benthic foraminiferal inventories. However, the prevalence of soft-walled monothalamous taxa of unknown ecology, which are largely underrepresented in the barcode reference database, limits the accuracy of eDNA studies. Taxonomy-free approaches, which bypass species-level assignment, may overcome these constraints. Here, we use the case study of the Armida gas platform to confirm the potential of the taxonomy-free framework for ecological quality assessment. As highlighted in a PCA, stations located near the platform (i.e., from 0 to 125 m) exhibited significantly higher concentrations of Zn and Ba in the sediment, while stations farther away appeared unpolluted. Molecular indices based on diversity, e.g., ecological quality ratio (EQR) calculated from exp(H'bc), and a taxonomy-free adaptation of Foram-AMBI (Foram-gAMBI) were applied, with barium serving as an independent indicator for ecological group assignment. The indices EQR (morphology), gEQR and Foram-gAMBI (eDNA) were significantly correlated with Zn and/or Ba, while Foram-AMBI (morphology) did not show any significant meaningful correlation. These results are confirmed by the inferred ecological quality statuses which revealed a clear improvement in benthic habitat quality with increasing distance from the platform when assessed with molecular indices (poor to good for gEQR and moderate to good for Foram-gAMBI), while morphological indices, particularly Foram-AMBI, failed to capture this trend. The weak agreement between morphological and molecular indices is likely due to the greater number of Molecular Operational Taxonomic Units (275) than morphospecies (31), including abundant soft-walled monothalamous foraminifera detected only by eDNA. By adopting a taxonomy-free approach to compute Foram-gAMBI, we can fully exploit the metabarcoding dataset without relying on prior taxonomic or ecological knowledge. Our findings demonstrate the robustness and accuracy of taxonomy-free metabarcoding approaches for monitoring benthic ecosystem health using benthic foraminifera in impacted marine environments.