Abstract
The Mid-Atlantic region is set to be one of the first and largest contributors to the offshore wind energy goals of the United States. Yet, the same region is home to a diverse marine ecosystem comprising important marine species such as the critically endangered North Atlantic right whale (NARW). To support the responsible development and operation of the planned offshore wind farms, there is a need for high-resolution modeling of NARW presence, i.e., at the spatial and temporal resolutions relevant to farm-level operations. Towards this, we leverage highly localized observations from nine glider deployments in the Mid-Atlantic to propose a machine learning approach for modeling NARW presence conditioned on a diverse set of glider- and satellite-based oceanic, physical, and contextual information. We find that tree- and ensemble-based models achieve the highest levels of accuracy, while maintaining a sensible balance of missed and false alarms. Interpretation of the machine-learnt features reveals interesting insights on the relative value of well-resolved satellite surface measurements to well-resolved vertical information from glider sampling in explaining the species-habitat patterns of NARWs. We then discuss the value of the models proposed herein to offshore wind developers and operators in the United States and elsewhere. Our work constitutes the first machine learning attempt to jointly leverage glider- and satellite-based information for modeling of NARWs. Data and codes for producing the results of this work have been made freely available to promote the research on this timely topic.