DOI: 10.6076/d1t59h
title: Daily totals of odontocete acoustic presence at western North Atlantic acoustic monitoring sites

description: A combination of machine learning and expert analyst review was used to
 detect odontocete echolocation clicks, identify dominant click types, and
 classify clicks in 32 years of acoustic data collected at 11 autonomous
 monitoring sites in the western North Atlantic between 2016 and 2019.
 Previously-described click types for eight known odontocete species or
 genera were identified in this data set: Blainville’s beaked whales
 (Mesoplodon densirostris), Cuvier’s beaked whales (Ziphius cavirostris),
 Gervais’ beaked whales (Mesoplodon europaeus), Sowerby’s beaked whales
 (Mesoplodon bidens), and True’s beaked whales (Mesoplodon mirus), Kogia
 spp., Risso’s dolphin (Grampus griseus), and sperm whales (Physeter
 macrocephalus). Six novel delphinid echolocation click types were
 identified and named according to their median peak frequencies.
 Consideration of the spatiotemporal distribution of these unidentified
 click types, and comparison to historical sighting data, enabled
 assignment of the probable species identity to three of the six types, and
 group identity to a fourth type. UD36, UD26, and UD28 were attributed to
 Risso’s dolphin (G. griseus), short-finned pilot whale (G. macrorhynchus),
 and short-beaked common dolphin (D. delphis), respectively, based on
 similar regional distributions and seasonal presence patterns. UD19 was
 attributed to one or more species in the subfamily Globicephalinae based
 on spectral content and signal timing. UD47 and UD38 represent distinct
 types for which no clear spatiotemporal match was apparent. This approach
 leveraged the power of big acoustic and big visual data to add to the
 catalog of known species-specific acoustic signals and yield new
 inferences about odontocete spatiotemporal distribution patterns. The
 tools and call types described here can be used for efficient analysis of
 other existing and future passive acoustic data sets from this region.
description: Acoustic data were collected using High-frequency Acoustic
 Recording Packages (Wiggins & Hildebrand 2007) deployed at deep
 water sites along the continental shelf break and slope in the western
 North Atlantic, between ~30ºN to ~40°N. An energy detector was used to
 identify odontocete echolocation clicks in the raw timeseries data, and
 then an unsupervised clustering algorithm was used to cluster clicks in
 5-minute bins, generating summary spectra, inter-click-interval
 distributions, and mean waveform envelopes for resultant clusters. This
 clustering step was taken to identify recurring patterns in a naturally
 variable signal type, and also to reduce the size of the data set to
 improve computational efficiency. The summary spectra were classified to
 one of 20 classes, identified via a second clustering step, using a neural
 network-based classifier. Daily acoustic presence of each class, given in
 hours, was computed per deployment.
description: These .mat (Matlab) files correspond to consecutive acoustic
 device deployments at 11 acoustic monitoring sites, given by the file
 name. Each file contain data from a single deployment, with 4
 variables: spNameList - 21x1 cell array; contains class
 names for known species, novel putative delphinid click types (UD), and
 common noise sources included in this analysis.
 labeledBins - 1x21 cell array; cell {1,i} contains an Nx2 double
 matrix of the the start (first column) and end (second column) datetimes
 of all 5-minute bins containing clicks labeled as class spNameList{i,1} in
 this deployment. N varies by class, depending on how prevalent that class
 was during this deployment. Datetimes are in the Matlab serial date number
 format. dailyTots - Nx22 double matrix; first column
 contains the dates of days for which there was recording effort during
 this deployment; columns 2-22 correspond to the classes in spNameList. The
 values in index (j,i) give the total hours of acoustic presence of class
 spNameList{i-1,1} on day dailyTots(j,1). Minimum temporal resolution is
 presence in 5-minute bins, so the minimum acoustic presence recorded is
 0.0833 hours. Datetimes are in the Matlab serial date number
 format. binFeatures - 3x21 cell array; cells {1:3,i}
 each contain an Nx1 double, where N corresponds to the number of
 5-minute bins containing clicks which were labeled as spNameList{1,i} in
 this deployment. Cell {1,i} gives the classifier confidence associated
 with the labels for each 5-minute bin; cell {2,i} give the mean
 peak-to-peak received level of all clicks labeled as spNameList{1,i} in
 each 5-minute bin; cell{3,i} gives the number of clicks labeled as
 spNameList{1,i} in each 5-minute bin.