USAP-DC

This project is co-funded by a collaboration between the Directorate for Geosciences and Office of Advanced Cyberinfrastructure to support Artificial Intelligence/Machine Learning and open science activities in the geosciences. Machine learning model will be used in this project to predict the distributions of five zooplankton species in the western Antarctic Peninsula (wAP) based on oceanographic properties. The project will take advantage of a long-term series collected by the Palmer Long-Term Ecological Research (LTER) program that collects annual data on physics, chemistry, phytoplankton (or food), zooplankton and predators (seabirds, whales and seals). By analyzing this dataset and combining it with other data collected by national and international programs, this project will provide understanding and prediction of zooplankton distribution and abundance in the wAP. The machine learning models will be based on environmental properties extracted from remote sensing images thus providing ecosystem knowledge as it decreases human footprint in Antarctica. The relationship between species distribution and habitat are key for distinguishing natural variability from climate impacts on zooplankton and their predators. This research benefits NSF mission by expanding fundamental knowledge of Antarctic systems, biota, and processes as well as aligning with data and sample reuse strategies in Polar Research. The project will benefit society by supporting two female early-career scientists, a post-doctoral fellow and a graduate student. Polar literacy will be promoted through an existing partnership with Out Of School activities that target Science, Technology, Engineering and Mathematics (STEM) education, expected to reach 120,000 students from under-represented minorities in STEM annually. The project will also contribute to evaluate the ecosystem in the proposed Marine Protected Area in the wAP, subject to krill fishery. Results will be made available publicly through an interactive web application. The Principal Investigators propose to address three main questions: 1) Can geomorphic features, winter preconditioning and summer ocean conditions be used to predict the austral summer distribution of zooplankton species along the wAP? 2) What are the spatial and temporal patterns in modeled zooplankton species distribution along the wAP? And 3) What are the patterns of overlap in zooplankton and predator species? The model will generate functional relationships between zooplankton distribution and environmental variables and provide Zooplankton Distribution Models (ZDMs) along the Antarctic Peninsula. The Palmer LTER database will be combined with the NOAA AMLR data for the northern wAP, and KRILLBASE, made public by the British Antarctic Survey’s Polar Data Center. This project will generate 1) annual environmental spatial layers on the Palmer LTER resolution grid within the study region, 2) annual species-specific standardized zooplankton net data from different surveys, 3) annual species-specific predator sightings on a standardized grid, and 4) ecological model output. Ecological model output will include annual predictions of zooplankton species distributions, consisting of 3-dimensional fields (x,y,t) for the 5 main zooplankton groups, including Antarctic krill, salps and pteropods. Predictions will be derived from merging in situ survey data with environmental data, collected in situ or by remote sensing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Person	Role
Cimino, Megan	Investigator and contact
Jack, Conroy	Researcher
Steinberg, Deborah	Co-Investigator

Antarctic Organisms and Ecosystems	Award # 2203177
Antarctic Organisms and Ecosystems	Award # 2203176

USAP-2203176_1

DATA_MANAGEMENT_PLAN_-zdm.pdf