Gray whale data
All methods were carried out in accordance with relevant guidelines and regulations. This project was approved by the Oregon State University Institutional Animal Care and Use Committee (IACUC-2019-0008) and complies with the ARRIVE guidelines. All gray whale data collection was carried out under a research permit from NOAA/NMFS (#16011 and #21678, issued to John Calambokidis).
We used a small research vessel (5.4 m rigid-hulled inflatable boat) to collect gray whale data over the course of three foraging seasons (May to October from 2016-2018) along the central coast of Oregon, USA, including near the ports of Newport and Depoe Bay (Fig. 1). Whale photographs were taken for individual photo-identification and drone-based videos were recorded for photogrammetry analysis when weather conditions and whale behavior were suitable (methods are fully described in54). We also opportunistically collected fecal samples at the whale sightings using two 300 µm nylon mesh dipnets (methods are fully described in29). Samples were transferred to sterile plastic jars and placed on ice until stored in a freezer (-20° C) for later analysis. Date, time, and location were documented for each fecal sample, as well as the matching photo for the specific individual.
We used Adobe Bridge software (version 18.104.22.1682) for photo-identification analysis. Photographs were compared to long-term gray whale catalogs held by the Marine Mammal Institute at Oregon State University and Cascadia Research Collective (Olympia, WA, USA) to obtain individual sex and minimum age information based on date of first sighting. If sex was unknown, sex was determined through fecal genetic analyses (methods are fully described in28). Given the unknown movements of our study whales prior to fecal sample collection, we assessed the residency time (in days) of these whales within the study area to justify our assumption that the whales were exposed to the measured soundscape levels and vessel counts. Through photo-identification comparison, we summed the number of days a sampled whale was in the study area (1) prior to sample collection in that year, and (2) in total that year.
Images of whales flat and straight at the surface were extracted from drone video recordings using VLC software (version 2.2.8) and scored as good or poor quality based on pre-defined attributes28. Only images scored as good were measured in custom MATLAB (version 22.214.171.124, release 2017b) software, producing a series of ten morphometric attributes that describe the whale’s body condition. These metrics were assessed in R (version 3.5.055) to calculate a final metric called Body Area Index (BAI), which is a unitless and length-standardized metric of body condition that allows comparisons among individuals of different lengths and demographic units (e.g., calves and adults, or males and females28). We applied a coefficient of variance threshold of 5% for both whale length and BAI measurements to improve accuracy. Whales were assigned to a demographic unit based on sex and maturity status in each year28 based on fieldwork observations, photo-identification, and photogrammetry results. The BAI metric has been successfully implemented to document variation in body condition in this specific gray whale population across foraging seasons28.
Fecal samples were filtered, desalted, and freeze-dried29. Dried, processed samples were mixed and weighed to the nearest 0.2 g; samples below 0.02 g were excluded from the analysis to avoid inflated values ["small sample effect"; see 33,43]. Fecal hormone metabolites were extracted29 and quantified using commercial Enzyme-linked Immunosorbent Assay kits for cortisol (#ADI-900-071), progesterone (#ADI-900-011), and testosterone (#ADI-900-065) from Enzo Life Sciences, following the manufacturer's protocols (https://www.enzolifesciences.com). Since fecal samples reflect the metabolized products of the parent hormones, the cortisol kit quantifies fGC, the progesterone kit quantifies progestin metabolites (fP), and the testosterone kit quantifies androgen metabolites (fA).
Samples were run in duplicate in 2016-2017, and in triplicate in 2018. All samples were analyzed within 11 months of collection. Our quality assurance and quality control protocols include full standard curves in each assay, re-run of any sample with >15% coefficient of variation (CV) between replicates, and re-run of any sample outside the percent-bound range of 15-85%. Samples not conforming to these standards were analyzed again until suitable values were obtained. Values below the limit of detection (<LOD) were excluded from the analysis57. When multiple fecal samples were collected from the same individual in the same day, we applied the values from the sample with higher mass56. Gray whale fecal hormone assays have been validated for all hormones described in this study, and results exhibited excellent parallelism and accuracy as well as good match to known physiological state (age, sex, reproductive state)29.
Concurrent with gray whale data collection, acoustic data were recorded off the coast of Newport, Oregon, from 15 June to 8 October of 2017 and 5 June to 1 October of 2018 (no acoustic data is available from 2016). A passive acoustic monitoring (PAM) hydrophone system was deployed outside the Newport harbor entrance at 44.5932 N, -124.1029 W, in 20 m water depth, and 1.25 km from the coastline (Fig. 1). The custom PAM system consisted of an omni-directional hydrophone (International Transducer Corporation transducer model ITC1032) with sensitivity - 192 dB re μPa V-1 @ 1m combined with a low-power 16-bit data acquisition system and preamplifier housed in a fiberglass composite pressure housing58. The PAM system was mounted on a weighted, semi-trawl protected aluminum frame 0.5 m above the seafloor with no sea surface expression. Data were recorded at 32 kHz sample rate on a 20% duty cycle (12 minutes of every hour). A low frequency cutoff was applied to avoid aliasing around the Nyquist frequency, resulting in acoustic measurements that included energy up to 13 kHz. Data analysis followed previously described methods58. Root mean square (rms) sound pressure levels (SPL) were calculated from 50 Hz – 1,000 Hz frequency band. This frequency range captures low frequency vessel-generated noise <1,000 Hz typical of vessels using the ports of Newport and Depoe Bay59, and the sound energy from wind-related processes with a lower bound of 400 Hz60 while avoiding the peak of surface wind noise near 8,000 Hz . This range is also relevant for the acoustic sensitivity of gray whales and overlaps with their most frequent call observed in the northeastern Pacific (known as “M3 call”)61–63. A daily median rms sound pressure level (SPL; 50Hz - 1 kHz) calculated from 6 a.m. to 7 p.m. Pacific Daylight Time provides a measure of the 50th percentile, or typical sound levels, associated with vessel activity at the harbor entrance during the busiest daytime hours of each day.
Vessel traffic data
Daily vessel counts at the ports of Newport and Depoe Bay during our three field seasons were obtained from the Oregon Department of Fish and Wildlife (ODFW) through video analysis at the ports. These daily vessel counts consist only of recreational craft, including commercial charters on fishing and crabbing trips, and private boats (e.g., private fishing trips, kayaks, row boats, and jet skis). Therefore, vessel activity for other purposes (e.g., whale watching, research, funerals, maintenance trips, commercial fishing, Coast Guard, and dredging) are not tracked by these counts and thus, are not accounted for in this analysis. However, during the time period of this study, no major seismic, sonar or marine construction occurred in our study area, and recreational vessel traffic in coastal areas has been found to correlate strongly with ambient noise levels 5,7.
Wind speed data
Wind-generated surface noise also contributes to ocean soundscapes64. Therefore, we compiled local wind speed data during our study periods to assess and compare the contributions of wind and vessel traffic to recorded underwater ambient noise levels. Hourly wind speed data from an anemometer station located near the hydrophone on the South Beach jetty entrance to the port of Newport (station NWPO3, Newport, OR, -44.613 N, 124.067 W; Fig. 1) during our three field seasons were obtained from the NOAA National Data Buoy Center (NDBC). Times were converted to local Pacific Daylight Time. Hourly median wind speed (m/s) and a daily median wind speed value (m/s) from 6 a.m. to 7 p.m. were calculated to match noise level measurements from the deployed hydrophones.
Our goal was to assess if and how gray whale fGC concentrations vary relative to vessel counts as a proxy for ambient ocean noise conditions, while simultaneously accounting for the effects of year, demographic unit, other hormone metabolites, and body condition (BAI). Therefore, every fecal sample was matched with the BAI measurement of that individual from the same day or within ± 14 days of the fecal sample collection (no change in body condition within this window was detected; paired t-test using all BAI values of individuals assessed within 14 days in 2016, 2017 and 2018: n = 61, p = 0.86, df= 60, t = - 0.174).
Only mature males and non-pregnant, non-lactating mature females were included in this analysis to minimize the known impact of normal variation in fGC concentrations due to life history phases 29,30,65. All statistical analyses were conducted in R software 55 with a significance level of 0.05. Normality of all variables was tested using the Shapiro-Wilk normality test, with non-normal variables log-transformed (log-normal [value +1]) before further analysis.
Linear regressions were performed using the lm function in R to test for correlations between (1) ambient noise (daily median SPLrms) in 2017 and 2018 and daily vessel count data from both ports, and (2) ambient noise in 2017 and 2018 and daily median wind speed. To further explore the temporal patterns in underwater sound levels and correlation with local wind patterns, the median noise levels for each hour of each day recorded by the Newport hydrophone (50 Hz – 10,000 Hz) in 2017 and 2018 were plotted in MATLAB (version 126.96.36.1990202 – R2019b) alongside the hourly median wind speed over the same time period (Fig. 3).
Once the relationship between ambient noise and vessel counts was established (Fig. 2), and analyses of wind speed data indicated wind was not the dominant source of underwater noise, (Fig. 3), we conducted linear mixed models (LMM), using the lme4 package in R66, to assess the effects of vessel counts, year, demographic units, BAI, and other hormone metabolites on fGC concentrations. Hence, we use vessel counts as a proxy of ambient noise exposure. This approach allows us to use whale data collected in 2016 when a hydrophone was not deployed concurrently with whale data collection. The ports of Newport and Depoe Bay are 22 km apart, which is within the daily travel range of a gray whale. As daily vessel counts at Newport and Depoe Bay were positively correlated (rate of change = 3.176, F1,233 = 520.6, R2 = 0.689, p < 0.001), we assumed that vessel activity from both ports influences acoustic conditions within the study area. Therefore, we summed vessel counts from the two ports for analysis relative to fGC concentrations.
Due to uncertainty regarding gut transit time (which cannot be determined experimentally in mysticetes40), different time lags between vessel count and fecal collection were assessed in the LMMs, including the sum of vessel counts on the same day as fecal sample collection, and on the previous 1-7 days (based on known gut transit times in large mammals of ~12 hours to 4 days39,67,68). All models included the whale identification, day, and month as random effects to account for pseudoreplication and variations in sampling per day and month, respectively. Model selection was based on the lowest Akaike’s information criterion (AIC69). After the most influential temporal scale for vessel counts was determined, additional LMMs were run with varied combinations of the fixed effects. Model fit was evaluated by assessing the marginal R2 (R2m: variance explained by fixed effects) and the conditional R2 (R2c: variance explained by both fixed and random effects) using the MuMIn package in R70,71. F-statistics and p-values were obtained using the lmerTest package72. Additional linear regressions between fGC concentrations and factors identified as significant in the LMMs were conducted as post-hoc analyses to verify linear correlations and describe the direction of the association.
In an effort to directly assess the impact of variable ambient noise levels on gray whale stress responses, without the intermediary of vessel counts, we conducted additional linear regressions between fGC concentrations and acoustic levels. For these analyses we had a smaller sample size based on only 2017 and 2018 data because no hydrophone data were available for 2016.
Sex specific spatial distribution patterns may have pre-disposed a demographic group to different noise exposures (e.g., closer to ports). Hence, to assess any sex specific distribution pattern relative to distance from port, the Euclidean distance between the initial whale sighting and the nearest port (Newport or Depoe Bay) was calculated using ArcGIS (version 10.8) for all whale sightings in this project (2016-2018). A Mann-Whitney U test was used to compare the difference in distance to port by whale sex.
The code and associated data to run the LMMs, linear regressions and Mann-Whitney U test are deposited in the Dryad Digital Repository: http://doi.org /10.5061/dryad.n5tb2rbvn73.