CSCR’s eelgrass team, consisting of middle school and high school students, has been researching eelgrass since 2013. The main goal of the eelgrass research team is to survey eelgrass in our harbor.
In 2018 they created an ArcGIS map of the type of underwater sediment in the area, and presented it as a poster at Zosterapalooza, a professional eelgrass conference hosted by the EPA, in March of 2019 where they fielded questions from professors, graduate students, and environmental regulatory professionals. After snorkeling, mapping, and measuring water turbidity in summer 2019, they presented that summer’s field findings to a 2020 virtual audience of local town officials, non-profit and state representatives at CSCR’s State of the Harbor conference.
In the Spring of 2020, Lucy Clay and Andrew Hoadley were inspired to apply techniques from Laura Humphrey’s statistics course at Cohasset High School and wrote a grant to propose to conduct a survey of the Cohasset Harbor in which they would be collecting data on the correlation between eelgrass health and sediment type. Upon winning the grant from the Marjot Foundation, Hoadley and Clay planned to evaluate the underwater situation in 30 different sites per each sediment type. The team has been working on grant research since May and has been going on expeditions since June on our new 24’ Carolina Skiff, generously donated by Aaron Hassan and Fran and Gail Collins. Within Covid capacity restrictions, they were able to incorporate additional students into the project — varsity researchers Brendan Burke and Beck LaBash, and other middle school and high school students.
Marjot Mid-year Report
Center for Student Coastal Research
Lucy Clay and Andrew Hoadley
- Introduction: (who, where, when)
We are very lucky to have been given this opportunity by the Marjot Foundation to conduct eelgrass research this summer. Our team consists of our leaders Lucy Clay and Andrew Hoadley along with two additional teammates, Beck LaBash and Brendan Burke, who have joined us and contributed greatly to the project at the “varsity level” as our mentor puts it. Additionally, other junior teammates have significantly helped us with our workload. We are thankful that our research was not greatly affected by the Covid guidelines and are pleased to report that no one at CSCR has contracted Covid. Despite wearing masks, gloves, and staying six feet apart, we had an extremely productive, research-heavy summer!
Our eelgrass team started outdoor expeditions in June. With a newly donated 24’ Carolina skiff and approval from the Board of Health, we were able to proceed with our original plan of having 3 expeditions a week at low tide (about 2-4 hours each) while staying 6’ apart. We finished our sample collection for our Marjot grant in early August, with a total of 88 sites. We are now beginning to work on data management and analysis as the snorkeling and reliable boating season is drawing to a close.
Because we found better, more efficient ways of collecting and classifying data, which saved time and improved the quality of our data, our expedition procedures evolvfrom the original procedure stated in our proposal.
In terms of sediment type, we changed our sediment classifications: [sand, shells, solid granite rock, mud, gravel, stoney cobble, clay, or “other”] to adhere to the USDA’s: [loam, sand, sandy gravel, gravel, gravelly cobble, and cobble]. This allowed us to focus on observed particle size, make interpolation maps more feasible, and organize our data more efficiently in the future.
We decided to define eelgrass health solely in terms of coverage density instead of defining health primarily in terms of leaf length and coverage density, as stated in our proposal. We concluded that defining eelgrass health in terms of leaf length would be very hard to analyze and correlate, considering that the leaves grow significantly throughout the summer. If we had defined health in terms of leaf length, a possible confounding variable would have been that the sun and other external factors were making the eelgrass grow longer, and not necessarily the sediment type it was growing in.
We refined our procedures to determine the coverage density at each site, as unbiased as possible, by deploying what we’ve named “our democratic quadrat”, which is quite large at 1.5 x 1.5 meters. Once at an eelgrass site/area, this quadrat is thrown into the water randomly and each person on the research vessel classifies the coverage density of eelgrass inside the quadrat as none, 1% (to represent one solo colonizing plant), <25%, <50%, <75%, or <100%. The majority opinion is then entered into both Survey 123 (our ARCGIS app) and our field notebook.
Our initial sampling approach was superimposing a grid of equal-sized sectors over the study area and using our existing data to categorize the soil type such that we could, upon numbering the grid squares, randomly choose 15 in each soil area and take two random samples in each sector. Instead, we developed procedures to survey the whole harbor by recording tracks of underwater video footage along with the geolocation of the track. To do this, we took a gopro camera and clamped it onto a 2.2 meter stick so that the gopro is faced downward towards the sediment as the boat is moving. As GoPro’s can’t record their location underwater, we created an app built on ArcGIS’s Quickcapture to record streaming GPS points. By doing these tracks, we were able to create an extensive grid of our harbor with our gopro/GPS tracks. Once we have organized the video and gps files, every point on the GPS track can be matched to a frame of the gopro footage using a script that our teammate Beck wrote in Python. Since we had covered the whole harbor, we were able to successfully sample our whole area of study.
In addition to making moving tracks, we studied 88 stationary sites. Another ArcGIS app, Survey 123 was used to input our data at each site. With this app we recorded coverage density, location, date/time, sediment type, the organisms we see, and more. Additionally, we deployed the Democratic Quadrat, which was previously mentioned. At most sites, we usually sent Lucy, our “sediment sampler” to collect soil and eelgrass samples using various equipment like snorkel gear, jars, and shovels. We may find that these samples can’t be concatenated with our larger geotagged picture dataset.
Inspired by Forest Schenke a graduate student we’d met at Zosterapalooza who was using image-J to analyze eelgrass leaves, we tapped the expertise of our colleague, Beck LaBash to experiment with using google’s teachable machine programs to train the computer to identify eelgrass in underwater photos. Our initial tests were successful, indicating fewer human hours of watching and categorizing the undulating underwater video footage. We will have to confirm that it can differentiate Ulva (sea lettuce) seaweed from eelgrass.
In addition, Beck collected over-the-water drone imagery at different cloud coverages and tides to try to create photogrammetry that provides a high quality aerial view of the eelgrass in the harbor to corroborate our underwater footage.
Findings so far
Initial analysis of Survey 123 site data for 2020 revealed a -0.09 correlation (a weak correlation) between coverage density and sediment type. However, this data set is only from our specific anchored sites, so is significantly smaller than the dataset that is yet to be generated from our moving video footage tracks.
In early August, we made a discovery. Lucy had noticed that despite what the surface looked like, some areas under the eelgrass were springier to walk on than others. She and Brendan wondered whether that might be more indicative of eelgrass health than the observable particle size differences in the surface sediments she was collecting. Brendan found that the sediment she collected was different from the soil that the anchor brought up. The soil that the anchor brought up was usually thicker, and varied in color and texture from site to site. We began investigating this curiosity more towards the end of the summer, and would like to continue. We hope that we will be able to find a partner with a furnace to assess the organic matter of the many soil samples we currently have in the CSCR refrigerator.
Beck’s preliminary drone footage, the updated basemap he found, and our Survey 123 findings all appear to line up well in regards to presence of eelgrass.
- Next steps
We have our handwritten notebooks, Survey 123 data, Quickcapture tracks, and hours of underwater GoPro footage to organize. We are beginning to sort through this footage and data with the main focus of finding effective and efficient methods of data management. From there, we will create geotagged images with Beck’s script then classify these images by coverage density and average particle size to calculate the correlation coefficient between eelgrass health and sediment type.
Thank you again for this amazing opportunity!
Lucy, Andrew, Brendan, Beck
In the future:
Within this team with its seamless collaborative spirit, we exceed expectations, we learn by doing, and even inspire other researchers. Over the last two months, CSCR’s eelgrass team presented updates to a local group of Seagrass researchers (from DMF, EPA, WHOI, etc.), to the Marjot Foundation, and to the New England Estuarine Research Society. In the Spring of 2021, they will present their grant deliverables and their enhancements to the Marjot Foundation at the High School Marine Science Fair, to the EPA-hosted eelgrass conference, and to local stakeholders at CSCR’s State of the Harbor of 2021.
Why we care/ why eelgrass is important
Eelgrass is a very valuable plant
(1) it is an important primary producer in the food chain
(2) it is also a defense against climate change, absorbing greenhouse gases
(3) it provides a habitat for many organisms
(4) it helps lessen the effects of ocean acidification
(5) it helps fight coastal erosion