Multi-approach assessment of turfs
The increasing climate-driven replacement of kelp forests by turf algae highlights the need for efficient biodiversity monitoring. Traditionally, monitoring turf communities involves species identification based on morphology, which is challenging due to their highly variable morphology and reduced dimensions. Molecular methods promise to revolutionize this field, but their real-world effectiveness needs to be evaluated. Here, we evaluate the performance of DNA metabarcoding (COI and rbcL markers) and morphological identification (in situ and photo-quadrat identifications) to describe intertidal turf communities along the Portuguese coast. When comparing metabarcoding with in situ and photo-quadrat identification, it was observed that both COI and rbcL markers detected more taxa than the other two (277 and 140 vs 29 and 34 taxa, respectively). Metabarcoding also showed greater discrimination of turf communities between beaches and regions, improving the reconstruction of the geographical and climatic patterns. However, certain taxa that were identified by in situ and photo-quadrat approaches were not detected through metabarcoding, likely due to protocol bias (both extraction and amplification) but also lack of reference barcodes. Nevertheless, our multi-marker metabarcoding approach was more efficient than morphology-based methods in characterizing turf communities along the Portuguese coast, differentiating morphologically indistinguishable species and detecting unicellular organisms. Moreover, although not the main focus, COI also allowed the identification of metazoans that can be used in future ecological studies on species co-occurrence and algae-animal interactions. Metabarcoding is a valuable tool for monitoring these communities, especially when considering the requirements of long-term monitoring programs (in terms of accuracy, speed, and reproducibility).