The use of metabarcoding for meiofauna ecological patterns assessment

Area of Science:

• Marine Ecology
• Molecular Ecology
• Biodiversity Assessment

Background:

• Marine meiofauna, encompassing numerous phyla, traditionally requires expert morphological identification.
• Molecular tools offer a faster alternative for meiofaunal identification and diversity assessment.
• Understanding meiofaunal responses to environmental variables is crucial for marine ecosystem health.

Purpose of the Study:

• To compare metabarcoding (18S rDNA) and morphological methods for assessing meiofaunal diversity patterns.
• To investigate the effectiveness of these methods in detecting small-scale environmental interactions.
• To evaluate the utility of predictive models for interpreting metabarcoding data.

Main Methods:

• Application of a model selection approach to analyze diversity patterns.
• Utilizing 18S rDNA metabarcoding and traditional morphological identification.
• Employing rarefaction curves to determine adequate sampling effort (10 samples for OTU estimation).

Main Results:

• Both metabarcoding and morphological methods identified mean sand percentage, sediment sorting, and bacteria concentration as significant predictors of meiofaunal richness.
• Nematode genera composition showed significant differences between the two approaches due to taxonomic database mismatches.
• Model selection demonstrated consistency in predicting diversity descriptors, nematode richness, and meiofauna composition.

Conclusions:

• Metabarcoding, when integrated with predictive modeling, is effective for detecting small-scale ecological interactions of marine meiofauna.
• Despite taxonomic discrepancies, molecular approaches show promise for rapid and reliable meiofaunal diversity assessment.
• Environmental factors like sediment composition and bacterial abundance play key roles in structuring meiofaunal communities.