Data_Sheet_1_Evaluating the Efficacy of Acoustic Metrics for Understanding Baleen Whale Presence in the Western North Atlantic Ocean.docx

• Main Authors: Nicole Pegg, Irene T. Roca, Danielle Cholewiak, Genevieve E. Davis, Sofie M. Van Parijs
• Format: Dataset
• Language: unknown
• Published: 2021
• Subjects: Oceanography; Marine Biology; Marine Geoscience; Biological Oceanography; Chemical Oceanography; Physical Oceanography; Marine Engineering; acoustic metrics; soundscapes; baleen whales; random forest classification model; species richness; baleen whale; North Atlantic
• Online Access: https://doi.org/10.3389/fmars.2021.749802.s001; https://figshare.com/articles/dataset/Data_Sheet_1_Evaluating_the_Efficacy_of_Acoustic_Metrics_for_Understanding_Baleen_Whale_Presence_in_the_Western_North_Atlantic_Ocean_docx/16852696

Soundscape analyses provide an integrative approach to studying the presence and complexity of sounds within long-term acoustic data sets. Acoustic metrics (AMs) have been used extensively to describe terrestrial habitats but have had mixed success in the marine environment. Novel approaches are needed to be able to deal with the added noise and complexity of these underwater systems. Here we further develop a promising approach that applies AM with supervised machine learning to understanding the presence and species richness (SR) of baleen whales at two sites, on the shelf and the slope edge, in the western North Atlantic Ocean. SR at both sites was low with only rare instances of more than two species (out of six species acoustically detected at the shelf and five at the slope) vocally detected at any given time. Random forest classification models were trained on 1-min clips across both data sets. Model outputs had high accuracy (>0.85) for detecting all species’ absence in both sites and determining species presence for fin and humpback whales on the shelf site (>0.80) and fin and right whales on the slope site (>0.85). The metrics that contributed the most to species classification were those that summarized acoustic activity (intensity) and complexity in different frequency bands. Lastly, the trained model was run on a full 12 months of acoustic data from on the shelf site and compared with our standard acoustic detection software and manual verification outputs. Although the model performed poorly at the 1-min clip resolution for some species, it performed well compared to our standard detection software approaches when presence was evaluated at the daily level, suggesting that it does well at a coarser level (daily and monthly). The model provided a promising complement to current methodologies by demonstrating a good prediction of species absence in multiple habitats, species presence for certain species/habitat combinations, and provides higher resolution presence information for most ...