Underwater visual censuses (UVCs) are one of the most widely used methods of studying species-rich coral reef fish assemblages. However, a considerable portion of reef fish diversity is missed or underrepresented by these traditional survey techniques. Environmental DNA (eDNA) sampling is an emerging technology that can detect traces of animal DNA from environmental samples, such as water and sediment, potentially including taxa that are missed by UVCs. Here, we assess the complementarity of eDNA to UVCs in surveying coral reef fish communities, particularly for cryptic and cryptobenthic taxa. We further investigate the effect of environmental sample source (water and sediment) and depth (10m and 30m). We conducted UVCs and eDNA sampling in three islands of the Farasan Banks, southern Saudi Arabia. A metabarcoding protocol was applied to environmental samples using a broad-spectrum fish assay targeting 16S mitochondrial DNA. Our eDNA surveys revealed 94 fish species, across 86 genera, 38 families, and 14 orders. Of the species detected by eDNA, 48.9% were also recorded on transects and 60.6% on roving diver surveys. eDNA also detected 6 cryptic, 10 cryptobenthic, and 13 pelagic species. Of these, only one (Eviota guttata) was recorded by UVCs. eDNA species composition was found to be significantly influenced by collection site (islands), and sample source (more species detected from water samples than sediment samples), but not by collection depth (10 versus 30 m depth). Our study provides further evidence that eDNA is an effective tool for the biomonitoring of tropical coral reef fish communities. However, we also stress that improvements are needed in methodology and reference sequence coverage for eDNA to realize its full potential of capturing cryptic and cryptobenthic diversity.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/669014 |
| Date | 03 1900 |
| Creators | Peinemann, Viktor N. Nunes |
| Contributors | Berumen, Michael Lee, Biological and Environmental Sciences and Engineering (BESE) Division, Coker, Darren, Benzoni, Francesca |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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