Detection of a Diverse Marine Fish Fauna Using Environmental DNA from Seawater Samples

<div><p>Marine ecosystems worldwide are under threat with many fish species and populations suffering from human over-exploitation. This is greatly impacting global biodiversity, economy and human health. Intriguingly, marine fish are largely surveyed using selective and invasive methods, which are mostly limited to commercial species, and restricted to particular areas with favourable conditions. Furthermore, misidentification of species represents a major problem. Here, we investigate the potential of using metabarcoding of environmental DNA (eDNA) obtained directly from seawater samples to account for marine fish biodiversity. This eDNA approach has recently been used successfully in freshwater environments, but never in marine settings. We isolate eDNA from ½-litre seawater samples collected in a temperate marine ecosystem in Denmark. Using next-generation DNA sequencing of PCR amplicons, we obtain eDNA from 15 different fish species, including both important consumption species, as well as species rarely or never recorded by conventional monitoring. We also detect eDNA from a rare vagrant species in the area; European pilchard (<em>Sardina pilchardus</em>). Additionally, we detect four bird species. Records in national databases confirmed the occurrence of all detected species. To investigate the efficiency of the eDNA approach, we compared its performance with 9 methods conventionally used in marine fish surveys. Promisingly, eDNA covered the fish diversity better than or equal to any of the applied conventional methods. Our study demonstrates that even small samples of seawater contain eDNA from a wide range of local fish species. Finally, in order to examine the potential dispersal of eDNA in oceans, we performed an experiment addressing eDNA degradation in seawater, which shows that even small (100-bp) eDNA fragments degrades beyond detectability within days.</p> <p>Although further studies are needed to validate the eDNA approach in varying environmental conditions, our findings provide a strong proof-of-concept with great perspectives for future monitoring of marine biodiversity and resources.</p> </div

Primers and probe details showing sequences, target taxa and fragment sizes.

<p>Fragment sizes are given in base pairs including primers. All primers were designed for this study and amplify part of the Cytochrome b (<i>cyt-b</i>) gene. All regular PCRs were performed at 50°C annealing temperature and all qPCRs at 60°C annealing temperature. Probes are Minor Groove Binding (MGB) probes and have the modifications; 5′: 6-Fam (D-L-Probe), 3′: BHQ-1.</p

Number of fish species recorded by 9 different conventional survey methods and eDNA at The Sound of Elsinore, Denmark.

<p>Bars show mean number of fish species caught across surveys in 2009, 2010 and 2011 and error bars represent the standard deviation (see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041732#pone.0041732.s001" target="_blank">Table S1</a>). The eDNA bar represents the total amount of fish species recorded by this method in 2011. *) Depend heavily on competent experts in fish identification. **) Only possible where seabed conditions allow it.</p

Summary of results showing sampling site and panel of fish species recovered by eDNA.

<p>Sampling locality (The Sound, Elsinore, Denmark) for this study with the three sampling sites; 1) open beach, 2) outer pier, 3) inner pier. The 15 different fish species obtained by eDNA in this study are shown with colour codes explaining in which of the three sampling sites they were found. All fish drawings by Susanne Weitemeyer ©.</p

Summary of species-specific eDNA sequences recovered in this study.

<p>All sequences are generated by pyrosequencing using Roche GS FLX 454 platform, except the 5 sequences obtained with species-specific primers (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041732#pone-0041732-t002" target="_blank">Table 2</a>), which are generated by cloning and subsequent Sanger sequencing. All sequences are full-length 100% match to the particular species only, identified by BLAST to the Genbank nucleotide database. Sequences are given without primers.</p

Results from eDNA degradation experiment. eDNA concentration in seawater as a function of time for the two fish species; <i>Platichthys flesus</i> (circles) and <i>Gasterosteus aculeatus</i> (triangles), investigated in a 50 l aquarium.

<p>Time points with no detection of eDNA signals are shown in red. The lines show simple exponential decay models, <i>p</i><0.001 (<i>Platichthys flesus</i>) and <i>p</i><0.05 (<i>Gasterosteus aculeatus</i>). Dashed line shows the suggested detection threshold of 25 DNA molecules pr 400 ml seawater. Estimated time for eDNA to degrade beyond the detection threshold was estimated to be 0.9 days for <i>Gasterosteus aculeatus</i> and 6.7 days for <i>Platichthys flesus</i>. See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041732#s4" target="_blank">Materials and Methods</a> section.</p

Additional file 7: Figure S6. of Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies

A. False positive rate distributions for datasets A1s–A3s and A1m–A3m. Violin plot of distributions of false positive rate (FPR) in 150 iterations for datasets A1s–A3s and A1m–A3m (vertical panels), analyzed with all differential relative abundance methods (horizontal panels). FPR is defined as the fraction of OTUs with p < 0.05. P values were not corrected for multiple testing. Black dots represent medians in each distribution. B. Area under the curve distributions for multiplicative spike-ins in datasets A1s–A3s and A1m–A3m. Violin plot of distributions of area under the receiver operating characteristic curve (AUC) for spiked vs non-spiked p values from differential relative abundance (DA) tests. AUC distributions from 150 iterations for each multiplicative spike-in magnitude in datasets A1s–A3s and A1m–A3m (vertical panels), analyzed with all differential relative abundance methods (horizontal panels). Black dots represent medians in each distribution. C. Area under the curve distributions for additive spike-ins in datasets A1s–A3s and A1m–A3m. Violin plot of distributions of area under the receiver operating characteristic curve (AUC) for spiked vs non-spiked p values from differential relative abundance (DA) tests. AUC distributions from 150 iterations for each additive spike-in magnitude in datasets A1s–A3s and A1m–A3m (vertical panels), analyzed with all differential relative abundance methods (horizontal panels). Black dots represent medians in each distribution. D. Area under the curve distributions for mixed multiplicative spike-ins in datasets A1s–A3s and A1m–A3m. Violin plot of distributions of area under the receiver operating characteristic curve (AUC) for spiked vs non-spiked p values from differential relative abundance (DA) tests. AUC distributions from 150 iterations for mixed multiplicative spike-in magnitudes in datasets A1s–A3s and A1m–A3m (vertical panels), analyzed with all differential relative abundance methods (horizontal panels). Black dots represent medians in each distribution. (ZIP 4027 kb

Additional file 3: Figure S2. of Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies

A. False positive rate distributions for datasets A1–A3. Violin plot of distributions of false positive rate (FPR) in 150 iterations for each case proportion in datasets A1–A3 (vertical panels), analyzed with all differential relative abundance methods (horizontal panels). FPR is defined as the fraction of OTUs with p < 0.05. P values were not corrected for multiple testing. Black dots represent medians in each distribution. B. False positive rate distributions for dataset A4. Violin plot of distributions of false positive rate (FPR) in 150 iterations dataset A4, analyzed with all differential relative abundance methods (horizontal panels). FPR is defined as the fraction of OTUs with p < 0.05. P values were not corrected for multiple testing. Black dots represent medians in each distribution. (ZIP 649 kb

Additional file 13: of Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies

Supplementary methods. (DOCX 15 kb

Additional file 1: Table S1. of Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies

Overview of the datasets used in the study. Sampling and data characteristics of the seven datasets used in the study, A1–A4 for the false positive rate and spike-in retrieval tests and B1–B3 for the beta-diversity optimization tests. (XLSX 5 kb