DOI: 10.5061/dryad.c2fqz619z
title: Changes in sea ice and range expansion of sperm whales in the eclipse sound region of Baffin Bay, Canada

description: Sperm whales (Physeter macrocephalus) are a cosmopolitan species but are
 only found in ice-free regions of the ocean. It is unknown how their
 distribution might change in regions undergoing rapid loss of sea ice and
 ocean warming like Baffin Bay in the eastern Canadian Arctic. In 2014 and
 2018, sperm whales were sighted near Eclipse Sound, Baffin Bay: the first
 recorded uses of this region by sperm whales. In this study, we
 investigate the spatiotemporal distribution of sperm whales near Eclipse
 Sound using visual and acoustic data. We combine several published
 open-source, data sets to create a map of historical sperm whale presence
 in the region. We use passive acoustic data from two recording sites
 between 2015 and 2019 to investigate more recent presence in the region.
 We also analyze regional trends in sea ice concentration dating back to
 1901 and relate acoustic presence of sperm whales to the mean sea ice
 concentration near the recording sites. We found no records of sperm whale
 sightings near Eclipse Sound outside of the 2014/2018 observations. Our
 acoustic data told a different story, with sperm whales recorded yearly
 from 2015-2019 with presence in the late summer and fall months. Sperm
 whale acoustic presence increased over the 5-year study duration and was
 closely related to the minimum sea ice concentration each year. Sperm
 whales, like other cetaceans, are ecosystem sentinels, or indicators of
 ecosystem change. Increasing number of days with sperm whale presence in
 the Eclipse Sound region could indicate range expansion of sperm whales as
 a result of changes in sea ice. Monitoring climate change-induced range
 expansion in this region is important to understand how increasing
 presence of a top-predator might impact the Arctic food web.
description: Historical Sperm Whale Distribution
 in Baffin Bay Sighting data was compiled
 from eight published datasets and studies to gain a better understanding
 of the historical distribution of sperm whales in Baffin Bay. Sampling
 effort for the sighting data was only available for two of the eight data
 sets. First, for the Programme Intégré de recherches sur les oiseaux
 pélagiques (PIROP) data set (CWS 2021), effort was calculated by plotting
 the boundaries of where sperm whales were not sighted during their surveys
 between 1970-2000 (Gjerdrum et al. 2012). Second, data points for sperm
 whale sightings and survey effort area from Shell’s marine mammal visual
 monitoring and mitigation program from 2012-2014 were extracted and
 replicated from Frouin-Mouy et al. (2017) using WebPlotDigitizer (Rohatgi
 2017). Although there was no sampling effort available
 for the remaining six data sets they were included in the analysis as
 opportunistic sightings. Commercial whaling records for sperm whales were
 accessed on the Ocean Biodiversity Information System (OBIS) from the
 History of Marine Animal Populations (HMAP) Data Set 04: World Whaling
 (Smith 2021). Opportunistic and incidental sightings of sperm whales were
 also retrieved from the Maritimes Region Whale Sightings Database (WSDB)
 from Fisheries and Oceans Canada (Whalesightings Database 2021), the
 Incidental Sightings of Marine Mammals data set from the Institute of
 Marine Research (IMR) (Hartvedt 2020), and from environmental surveys done
 by the Northwest Atlantic Fisheries Organization (NAFO 2014).
 Opportunistic sightings from citizen scientists were included from the
 Happywhale database (Happywhale 2021). One additional opportunistic
 sighting of a sperm whale in Eclipse Sound and tag data from sperm whales
 in Baffin Bay reported in Lefort et al. (2022) were also used.
 Acoustic Data
 Collection We used passive acoustic
 recordings from two sites in the eastern Canadian Arctic near Eclipse
 Sound between July 2015 and September 2019 over the span of five
 deployments (Fig. 1, Table 1). The temporal coverage among sites and
 between deployments varied as a result of field work timing to
 retrieve/deploy the instruments, the battery life and storage space of the
 instruments, and different duty cycle regimes discussed in further detail
 in the last Methods subsection. The two sites were approximately 23 km
 apart and have an approximate maximum detection range of 20 km for sperm
 whales based on previous studies (Tran et al. 2014). The Guys Bight (GB)
 instrument had one deployment at a depth of ~100 m in 2015. The depth of
 the instrument is not ideal for recording a deep-diving animal such as the
 sperm whale, but the recordings from 2015 were still included in the
 analysis as an opportunistic data set, in the sense that it was not
 necessarily acquired for this specific study. The instrument at Pond Inlet
 (PI) was at depths between 670 and 800 m over four deployment periods from
 2016-2019 (Table 1). We used data from two types of autonomous,
 bottom-mounted recording devices to collect passive acoustic recordings:
 Song Meter SM2M (SM2M; Wildlife Acoustics Inc, Concord, MA, USA) and
 High-frequency Acoustic Recording Package (HARP; Wiggins and Hildebrand,
 2007). The SM2M was deployed at the GB site and recorded at a sampling
 rate of 192 kHz. The HARPs were deployed at the PI site and collected
 recordings at a sampling rate of 200 kHz. These two sampling rates were
 chosen to detect the high-frequency echolocation clicks of marine mammals,
 including but not limited to, sperm whales. Acoustic Data Analysis at the Guys
 Bight Recording Site Sperm whales
 regularly produce high-frequency echolocation clicks (2 - 32 kHz) with a
 duration of 100 μs and are distinguishable from other high-frequency
 odontocetes (Weilgart & Whitehead 1988; Goold & Jones
 1995; Møhl et al. 2000, 2003) (Fig. 2). Trained analysts (NP and CS)
 manually screened the acoustic recordings from the GB site for sperm whale
 echolocation clicks using long-term spectral averages (LTSAs; averaged
 over 5 s and 100 Hz bins), which provide a compressed spectrogram view
 allowing large time series data sets to be analyzed (Wiggins &
 Hildebrand 2007) (Fig. 2a). Data were manually scanned in the custom
 software program <em>Triton </em>(Wiggins & Hildebrand
 2007) developed in <em>MATLAB </em>(MathWorks, Natick, MA).
 Analysts viewed 1-hour LTSA segments across a frequency range of 0 to 40
 kHz to identify potential sperm whale encounters. Spectrograms of 10 s in
 length were used to confirm species identification (Fast Fourier transform
 length 2000 points, 75% overlap, bandwidth 40 kHz). A sperm whale
 encounter was defined as a series of clicks within a recording period of 5
 minutes. The start and end times of these encounters were logged
 in <em>Triton </em>(Wiggins & Hildebrand 2007) and
 used for further analysis. Since all LTSA detections
 were visually verified by trained analysts, we assumed there were no false
 positives. And given the distinguishable echolocation clicks of sperm
 whales and the short duration of the data, we assumed negligible missed
 detections because of the feasibility of listening to and viewing all
 spectrograms of interest. Acoustic Data Analysis at the Pond
 Inlet Recording Site Clicks at the PI
 site were automatically detected using a multi-step approach (Roch et al.
 2011; Soldevilla et al. 2011; Solsona-Berga et al. 2020). This approach
 was developed for acoustic data that was sampled at 200 kHz (PI recording
 site), which is why manual analysis was conducted for the GB site that was
 sampled at 196 kHz (Table 1). Since sperm whale clicks
 are characterized by multiple pulses approximately 5 ms apart (Møhl et al.
 2000), clicks closer than 30 ms apart were merged. Band passing all
 acoustic data (5-95 kHz) minimized the effects of low-frequency noise from
 vessels or weather. Calculating the spectra of detected signals required 4
 ms of data and a 512-point Hann window centered on the click with 50%
 overlap. To account for the frequency dependent instrument response of
 each deployment, spectra were corrected for the hydrophone transfer
 function. Sperm whale echolocation clicks are similar
 to the impulsive signals from ship propeller cavitation, so an automated
 detector was used to exclude periods of ship passages in the PI data to
 reduce the number of false positive detections. The detector, developed by
 Solsona-Berga et al. (2020) identified potential ship passages from LTSAs.
 Average power spectral densities (APSD) were computed in 2-hour data
 blocks over low (1-5 kHz), medium (5-10 kHz), and high (10-50 kHz)
 frequency bands. Using received sound levels, transient signals such as
 odontocetes, ship passages, and weather, were compared in the three
 frequency bands. Trained acousticians (NP and CS) manually verified
 identified ship passages in <em>Triton </em>(Wiggins &
 Hildebrand 2007; Solsona-Berga et al. 2020). Ship passage times were
 removed from further analysis and considered time periods with no
 recording effort. Noise produced by the instruments and
 clicks produced by non-sperm whale odontocetes were also removed in the PI
 data to reduce the number of false positive detections. A basic classifier
 using spectral click shape and peak frequency was implemented, taking
 advantage of a sperm whale click’s distinctive lower frequency spectral
 shape to remove clicks by delphinids and beaked whales which occur at
 higher frequencies (Solsona-Berga et al. 2020). The remaining acoustic
 encounters at site PI, containing presumed sperm whale echolocation
 clicks, were manually reviewed with <em>DetEdit
 </em>(Solsona-Berga et al. 2020). <em>DetEdit
 </em>provides users with interactive data visualizations that aid in
 efficiently annotating data, allowing deletion of false detections. Sperm
 whale clicks were binned into 5-minute intervals, and the number of
 5-minute bins with sperm whale detections per day was considered for
 further analysis. Daily averages of 5-minute bins were calculated only for
 days with sperm whale presence since the time series was
 zero-inflated. Only clicks exceeding a peak-to-peak
 received level (RL) of 125 dBpp re 1 µPa were included to provide a
 consistent detection threshold. This step of the detection process
 excluded clicks with low RLs. However, by binning clicks into 5-minute
 intervals, the chances of missing presence within a bin was low for sperm
 whales who click regularly. Accounting for the Duty
 Cycle Duty cycle regimes, or the process
 of turning an acoustic recorder on at specified intervals, are implemented
 to maximize the deployment duration by conserving battery power and
 storage space of the instrument (Wiggins & Hildebrand 2007; Au et
 al. 2013). Duty cycle regimes can widely vary based on the desired
 deployment duration, the sampling rate, and the recording instrument. Two
 of the five deployments in this study had a duty cycle that was adjusted
 for. The GB deployment had a duty cycle with a 5 minute
 recording duration in a 60 minute cycle (Table 1). Because there was only
 one deployment at this site, we had no continuous data to quantify the
 impact of the duty cycle. Instead, the number of 5-minute bins with sperm
 whale detections per day was linearly adjusted based on the recording
 effort in the duty cycle. The third deployment at the
 PI site also had a duty cycle with a 15 minute recording duration in a 20
 minute cycle (Table 1). Since there was continuous data from three other
 deployments at this site, the duty cycle was evaluated on continuously
 sampled data from the first deployment (less presence of sperm whales) and
 the fourth deployment (more presence of sperm whales). Random samples of
 the 15/20 duty cycle were taken from the deployments, shifting the
 listening period by one minute to find the proportion of overall recording
 effort. Sperm whale clicks in the 2017 PI deployment were linearly
 adjusted by 0.0839, resulting from the mean of forced duty cycles on the
 2016 and 2019 deployments (0.0939 and 0.0738 respectively).
 Changes in Sea Ice
 Concentration A monthly gridded sea ice
 concentration (SIC) product ranging from 1850 to 2017 was used to evaluate
 historical changes of sea ice within a 20 km radius around the PI
 recording site (Walsh et al. 2019). Only data from 1901 and beyond was
 included to avoid data sets that had non-observed data which were
 supplemented with estimates. The 20 km radius was selected as a maximum
 detection range for sperm whales clicks based on a previous study (Tran et
 al. 2014) and distance within which most interactions with the surface
 would likely occur. Six ¼ degree latitude by ¼ degree longitude grid cells
 were within 20 km of the recording site and included in the analysis. This
 sea ice product merges 18 different data sources and provides a single
 mid-month day (MMD) average from each available source. The MMD average
 for the six grid cells near the recording site were averaged across the
 multiple sources (when available) to produce a single MMD average for the
 20 km radius around the site. A linear regression was used to model the
 relationship between year and the median MMD SIC average by fitting a
 linear equation to the observed data in R Statistical Software (R Core
 Team 2013). A finer resolution of mean daily SIC was
 used to compare with daily presence of sperm whales during our recording
 period from 2015-2019. Daily Advanced Microwave Scanning Radiometer 2
 (AMSR2) sea ice maps were obtained from the University of Bremen (Spreen
 et al. 2008) and processed using Windows Image Manager (WIM) and Windows
 Automation Module (WAM) software (Kahru 2001) to produce an annual time
 series of mean daily SIC within a 20 km radius mask around the recording
 site. WAM software was used to compute the daily arithmetic mean,
 variance, and median of the SIC as a percent of the total mask area. The
 data excludes locations within 1 km from land to reduce edge effects and
 influence of snow on land.
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