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The Dynamic Duo: Application of Complementary BRUVs and eDNA to Marine Fish Assemblages


Stat, M., John, J., DiBattista, J. D., Newman, S. J., Bunce, M., & Harvey, E. S. (2019). Combined use of eDNA metabarcoding and video surveillance for the assessment of fish biodiversity. Conservation Biology, 33(1), 196-205.


A very important part of conserving and managing marine fish and wildlife is knowing how these animals interact with their surroundings. Research involves the use of invasive and noninvasive methods to study how species survive. Invasive research methods would include acoustic or satellite tagging, netting, trawling, or other ways in which scientists physically interact or interrupt normal behavior. Noninvasive sampling methods do not involve scientists directly interacting with the fish or wildlife, but instead rely on indirect or observational studies such as video footage. Invasive and noninvasive methods each contribute to our understanding of wildlife ecology, but no one technique can answer all of the questions necessary for effective conservation and management. Instead, by combining a variety of sampling techniques scientists can more effectively study species ecology.

Figure 1. BRUVs footage of several different species of marine fish.

In the past decade video surveys of the marine environment have become accessible to many researchers due to decreasing equipment costs. For studies of fish, baited remote underwater video systems (BRUVs) represent a noninvasive way to sample fish abundance and diversity in a variety of habitats. Interestingly, this method allows researchers to study a wide range of additional topics as broad as habitat biomass (the total mass of fish or wildlife surrounding a singular BRUVs) to finer scale subjects like fish length. Despite our ability to use BRUVS to answer all manner of research questions dealing with fish and wildlife populations regardless of habitat and depth there are limitations. The main limitation of BRUVs is that shy (cryptic), small fish tend to be missed. 

Environmental DNA (eDNA) on the other hand has been successfully used before to detect shy fish like those missed by BRUVs. Environmental DNA is another noninvasive research method that involves collecting water samples from marine habitats and testing them for DNA. Fish release detectible levels of DNA into the water while undergoing daily activities like producing external mucus and feces. Scientists have been able to detect these trace amounts of DNA for up to 24 hours and identify what species they are from based on the DNA sequence detected. However, like BRUVs, eDNA is not perfect. Environmental DNA is a relatively new method of sampling the marine environment and as a result of this eDNA is subject to a lot of discussion about sampling practices, DNA sensitivity, eDNA movement in the water, and the rate the eDNA degrades.

Figure 2. The Jurien Bay Marine Park in Western Australia and sampling sites (Stat et al., 2019)

Study Goals

            The goal of this study was to compare the effectiveness of sampling using BRUVs and eDNA analysis as independent and combined methods. To test this, the Authors looked at 24 rocky reef sites and 24 sea grass sites in Jurien Bay Marine Park in Western Australia over two days. At each site 500 mL of sea water was collected to be filtered after field sampling was done. A BRUVs was deployed by rope to the seafloor with pilchards as bait for one hour. Several sampling sites were tested at the same time with a minimum of 250 m (825 ft.) between each location to prevent any overlap among samples. Once field sampling was complete the water samples were filtered for DNA analysis and metabarcoding (a method of identifying the DNA from different species) and image analysis was done with the BRUVs video footage.


Figure 3. The genera detected by BRUVs, eDNA analysis and both methods (Stat et al., 2019).

The most interesting take away from this study is that neither BRUVs or eDNA is superior to the other method, but they instead complement each other. When BRUVs and eDNA were used simultaneously the authors detected over 30% more fish species than using BRUVs or eDNA alone. There were instances with both sampling methods where a species was detected by one technique, but not the other.

In this study the authors were able to successfully detect small, shy, or nocturnal fish with eDNA that were not observed on BRUVs footage. By contrast, the BRUVs footage captured several species sharks that eDNA was not able to detect. Interestingly, the authors noted that part of the disagreement between what is detected by each method could be caused by fish behavior. Environmental DNA is dependent on the amount of trace DNA left in the water column by fish. As a result, small, shy, and nocturnal fish that remain in a single habitat would be more likely to be detected by eDNA analysis than active fish that may not remain in the rocky reefs or sea grass habitats long enough to leave measurable amounts of DNA. Comparatively, the active fish would likely be attracted to a BRUVs by the presence of bait. The combination of these two methods being complimentary to one another allowed for further in-depth comparisons between habitats and the park itself.

Despite being done over a relatively small area, this study was able to successfully distinguish fish diversity in and between seagrass and rocky reef habitats. As the authors noted both eDNA and BRUVs were selective enough to only be able to detect a fish known to use rocky reefs in the rocky reef habitats and not in the seagrass. Looking at these findings from a broader scale, one might predict that the authors would find more kinds of, and individual fish in the protected park than outside of it. However, there was not a difference in fish density or abundance inside or outside of the park. This is consistent with the findings of other studies, but there are innumerable factors that could have caused this. 

Figure 4. The species detection curves for eDNA, BRUVs, and combined together in seagrass and reefs. The percentage indicates the increase in detections between single and combined methods (Stat et al., 2019)



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