In the summer of 2020, a team led by Drew Harvell, an ecology and evolutionary biology professor, along with postdoc Lillian Aoki and Ph.D. student Olivia Graham, embarked on a research expedition to the San Juan Islands in Washington state to investigate the causes behind seagrass wasting disease. The team shared their marine adventures and findings on Twitter through accounts such as @DrewHarvell, @lillian_aoki, and @o_jgraham.
Similar to how COVID-19 impacts society, marine diseases caused by agents like viruses, bacteria, and protists can profoundly affect ecosystems. The team aims to understand the triggers of these epidemics in nature, which involve various factors such as host stress, environmental conditions, and shifts in biological communities.
Collaborating with six other universities and the Smithsonian Institution, Cornell CALS’ Harvell Lab investigates a disease outbreak affecting West Coast eelgrass. Eelgrass, a crucial underwater plant, offers valuable ecological, economic, and cultural benefits by serving as habitat for young fish, sequestering carbon, stabilizing shorelines, and filtering pathogens.
However, seagrass wasting disease threatens eelgrass meadows, creating necrotic lesions that hinder photosynthesis and result in extensive die-offs. The team’s goal is to understand how climate change and biological diversity impact this disease, exploring whether invertebrate herbivores within the eelgrass community contribute to its spread.
During their fieldwork in Oregon and Washington, the team conducted surveys and utilized drones for mapping eelgrass meadows, aiming to track their growth or decline over time. This mapping, alongside field surveys, aids in understanding climate change’s effects on eelgrass health.
Back in Ithaca, the team collaborates with experts in computing science, including Carla Gomes and Ph.D. student Brendan Rappazzo, to develop an artificial intelligence-driven algorithm. This innovative tool rapidly and accurately measures disease levels on eelgrass blades by analyzing thousands of images, enabling the team to quantify lesions more efficiently than manual methods, significantly advancing their research.