My name is Ella Crotty, and I am an undergraduate research intern through the NOAA Ernest F. Hollings Scholarship Program. I chose to work with OME for my internship because I didn't know very much about environmental DNA (eDNA), and I wanted to learn! eDNA refers to genetic material that organisms leave behind in the environment by shedding skin, releasing mucus, etc. It can be used to detect if species are present in an area of the ocean using a water sample. For my project, I worked with an automated sampler that sits in the water and collects eDNA samples at regular intervals by filtering water samples through super-fine filters.
My internship included fieldwork in the Olympic Coast National Marine Sanctuary (OCNMS). I went out to the northwest coast of Washington twice to help with sampler recovery and redeployment. I helped collect and filter eDNA samples on the boat, then after the sampler was recovered, I helped store the samples from the sampler, which had already been filtered underwater. We returned to land and spent a day sterilizing the sampler before redeploying it, which was a complicated process. I had never worked with such a big and complicated instrument before, and it was interesting to learn about the logistics of setting up and maintaining something like that without access to a full lab! We brought a lot of tools and extra parts, in case anything broke and needed to be fixed on the coast between boat days.
The main goal of my research project was to determine how the presence of different species in OCNMS changes when the amount of oxygen in the water is low. Low oxygen, also known as hypoxia, can cause stress and death in marine animals. Two mg/L of oxygen is often used as the upper limit of what counts as hypoxic. I compared the data from our eDNA samples, which told me which species were detected in the sanctuary on certain dates, with dissolved oxygen data sampled from CTD casts and moorings. In OCNMS during the summer, a process called coastal upwelling causes oxygen-poor deep water to move up from the bottom of the Pacific Ocean, which decreases oxygen levels near the coast. However, some species are more sensitive than others, and some, like larger fish, are mobile and can move away from the low-oxygen areas. So, our main research question was: which species stick around when the oxygen drops, and which can no longer be detected? This question will help us understand how seasonal changes in oxygen are impacting OCNMS waters.
It required quite a few stages of data analysis and quality-checking in order to make the environmental data and eDNA data compatible with each other. Using the programming language R, I cleaned the data and matched the species detections to the temperature and oxygen at the date and time when the eDNA samples were taken. I implemented a lot of checkpoints to ensure that the code was doing what I wanted it to. For example, I checked to make sure that everything was in the same time zone, but between the CTDs, mooring, eDNA sampler, and handwritten sample data, I had to be very vigilant about converting the time zones before combining data.
First, I focused on species that OCNMS and the coastal treaty tribes consider high priority for monitoring and management and that were detected at least 10 times. Of the 64 priority species, we detected 24 and four were detected at least 10 times. The two species with the most detection data were Pacific herring and the copepod Acartia longiremis. I didn't find any correlation between oxygen levels and the presence of these species, which suggests that seasonal hypoxia is not impacting these commonly detected taxa. Herring are known to be pretty hypoxia-tolerant, which agrees with my findings. I also investigated species outside the priority list by calculating a bunch of statistics and filtering for significant results. I found a few species that did have observable relationships with hypoxia, including two species of copepod, tiny crustaceans that are used as indicators of ecosystem health. Through this internship, I developed a code workflow that combines environmental data with eDNA data, which is useful as we continue to study how different species are affected by environmental conditions.
