This morning we had our first lab meeting of the Fall 2020 semester! First, we had a quick round of introductions as we welcomed two new students to the lab – Ana-Belen (Ph.D. Student) and Sam (M.Env.Sci. Student). Ana-Belen will be working on mapping biodiversity of deep-sea cliffs in the Galapagos, and Sam is undertaking the course-based Master of Environmental Science program!
A quick recap of the summer followed; highlights included
1. Collecting ground-truth data for Placentia Bay habitat maps (read more)
2. Habitat mapping in Conception Bay (read more)
3. UAV drone mapping of eelgrass beds around Placentia Bay (blog post coming soon!)
4. Snazzy website updates
Last week we said farewell to Ben Misiuk as he makes his way to Dalhousie to start his second post-doc with Craig Brown (https://oceanfrontierinstitute.com/research/become). Hopefully you’ll pop into our lab meetings occasionally to share some wisdom - we wish you all the best Ben!
Since everyone has been busy this summer, over the next few weeks we will be presenting thesis updates to the lab group so we can learn about what everyone has been up to!
Today’s thesis updates were by Poppy and Xiaodong.
Poppy’s talk, Drivers of the megabenthic communities of the Charlie-Gibbs Fracture Zone, covered a lot of ground! We learned how the geomorphology and location of the CGFZ makes it an important biogeographic boundary in the North Atlantic Ocean. Poppy’s work using ROV surveys to identify Vulnerable Marine Ecosystems and Species (VME/VMS), and the factors that drive their distribution, fills a crucial knowledge gap and will contribute to the development of a marine protected area network on the high seas!
In Xiaodong’s talk, Seafloor substrate classification using self-adaptive analysis scale method, he walked us through the methods he developed to automate analysis scale determination for substrate classification in habitat mapping. He used bathymetric and backscatter features to build a prediction model for classifying seafloor substrates; ground-truth data was used to assess prediction accuracy. Xiadong’s work demonstrates that self-adaptive scale analysis gives more accurate predictions than single and multi-scale analysis and highlights the importance of choosing analysis scale relevant to the features – Fine scale tends to introduce artifacts, while broad scale tends to overlook details – and different features are sensitive to scale in different ways. Xiaodong is nearly finished writing up the manuscript – we are all looking forward to reading it!
Next week, Rylan and Shreya will present their thesis updates!