OCS Study

BOEM 2024-056

US Department of the Interior

Bureau of Ocean Energy Management

Gulf of Mexico Regional Office

New Orleans, LA

Nearshore Sound Propagation of and 

Species Response to Active-Source 

Seismic Surveys
OCS Study

BOEM 2024-056

US Department of the Interior

Bureau of Ocean Energy Management

Gulf of Mexico Regional Office

New Orleans, LA

Nearshore Sound Propagation of and 

Species Response to Active-Source 

Seismic Surveys

September 2023 

Authors:

Sarah K Henkel, Lauren Borland, Grace Chang, Taylor K Chapple, Katherine Heal, Scott A Heppell, 

Kaus Raghukumar, Leif Rasmuson

Prepared under BOEM Interagency Agreement Number M21PG0008

by 

Oregon State University

Hatfield Marine Science Center

2030 SE Marine Science Dr.

Newport, OR 97365
iii

DISCLAIMER

This study was funded, in part, by the US Department of the Interior, Bureau of Ocean Energy 

Management (BOEM), Environmental Studies Program, Washington, DC, through Interagency 

Agreement Number M21PG0008 with the National Science Foundation (Award Number 2118594). This 

report has been technically reviewed by BOEM, and it has been approved for publication. The views and 

conclusions contained in this document are those of the authors and should not be interpreted as 

representing the opinions or policies of BOEM, nor does mention of trade names or commercial products 

constitute endorsement or recommendation for use.

REPORT AVAILABILITY

Download a PDF file of this report at https://espis.boem.gov/Final%20Reports/BOEM_2024-056.pdf. 

To search for other Environmental Studies Program ongoing and completed studies, visit

https://www.boem.gov/environment/environmental-studies/environmental-studies-information/. 

CITATION

Henkel SK, Borland LK, Chang G, Chapple TK, Heal K, Heppell SA, Raghukumar K, Rasmuson L.

2024. Nearshore sound propagation of and species response to active-source seismic surveys. 

New Orleans (LA): US Department of the Interior, Bureau of Ocean Energy Management. 30 p. 

Obligation No.: M21PG0008. Report No.: OCS Study BOEM 2024-056. 

ABOUT THE COVER

Graduate student L Borland and fisherman collaborator J Purkey offloading data from InnovaSea acoustic 

receiver. Photo credit: S Henkel.

ACKNOWLEDGMENTS

We acknowledge additional financial support from Oregon Sea Grant and additional material support 

from the Oregon Department of Fish & Wildlife Marine Reserves Program. We thank Tom Calvanese for 

logistical support in Port Orford and assistance at sea. We thank Captains Purkey and Eaton for vessel 

support.

Author affiliations: S.K. Henkel & L.K. Borland, Department of Integrative Biology, Oregon State 

University, Corvallis, OR, USA; S.A. Heppell & T.K. Chapple, Department of Fisheries, Wildlife, and 

Conservation Sciences, Oregon State University, Corvallis, OR; K. Raghukumar & K. Heal, Integral 

Consulting Inc., Santa Cruz, CA; L. Rasmuson, Oregon Department of Fish & Wildlife, Newport, OR.
iv

Contents

List of Figures ............................................................................................................................................. v

List of Abbreviations and Acronyms .......................................................................................................vi

1 Background ......................................................................................................................................... 7

2 Motivation for the Study..................................................................................................................... 7

3 Objectives............................................................................................................................................ 9

4 Approach ...........................................................................................................................................10

4.1 Sound Measurements .................................................................................................................10

4.1.1 NoiseSpotter Deployment .......................................................................................................10

4.1.2 NoiseSpotter Data Analysis ....................................................................................................10

4.2 Organism Responses..................................................................................................................12

4.2.1 Array Design............................................................................................................................12

4.2.2 Animal Tagging .......................................................................................................................13

4.2.3 Acoustic Tag Data Analysis ....................................................................................................13

5 Results ...............................................................................................................................................15

5.1 Acoustic Conditions.....................................................................................................................15

5.2 Animal Reponses ........................................................................................................................18

5.2.1 Black Rockfish.........................................................................................................................18

5.2.2 China Rockfish ........................................................................................................................21

5.2.3 Lingcod....................................................................................................................................22

5.2.4 Dungeness crab ......................................................................................................................23

6 Conclusions .......................................................................................................................................25

References.................................................................................................................................................27

 
v 

List of Figures

Figure 1. NoiseSpotterTM vector sensor array consisting of three vector sensors and a conductivitytemperature-depth sensor........................................................................................................ 9

Figure 2. Locations of the 38 hydrophones deployed and recovered for the study within the Redfish Rocks 

Marine Reserve off southern Oregon, USA. ..........................................................................12

Figure 4a. Long-term spectral average of acoustic pressure measured 70 cm above the seabed during 

the pre-survey (top), during (middle) and post-survey (bottom) analysis periods. ................16

Figure 4b. Long-term spectral average of x-particle velocity measured 70 cm above the seabed during the 

pre-survey (top), during (middle) and post-survey (bottom) analysis periods........................17

Figure 5. Power spectral densities for x-particle velocity during the pre-survey (left), during (middle) and 

post-survey (right) analysis periods. ......................................................................................17

Figure 6. (Left) Example of acoustic pressure and azimuthal bearing estimated from particle velocity. 

(Right) Locations of R/V Marcus G. Langseth and NoiseSpotterTM for data period shown 

above......................................................................................................................................18

Figure 7. Distributions of black rockfish acceleration values on each of the selected study days. ............19

Figure 8. Calculated area used by black rockfish individuals in each study period....................................19

Figure 9. Area within the study area used by each black rockfish in the three periods of the study..........20

Figure 10. Black rockfish depth for each of the selected study days..........................................................20

Figure 11. Distributions of China rockfish acceleration values within each of the selected study days. ....21

Figure 12. Calculated area used by China rockfish individuals in each study period.................................22

Figure 13: Lingcod individuals detected by period of our study.................................................................. 22

Figure 14. Lingcod acceleration by study period. .......................................................................................23

Figure 15. Dungeness crab individuals detected by period of our study....................................................23

Figure 16. Dungeness crab acceleration by period of study.......................................................................24
vi

List of Abbreviations and Acronyms

Short Form Long Form

ANOVA analysis of variance

BOEM Bureau of Ocean Energy Management

cSEL cumulative sound exposure level

CPUE catch per unit effort

FFTs fast Fourier transforms 

KUD kernel use density

LTSA long-term spectral average

NMFS National Marine Fisheries Service

NOAA National Oceanographic and Atmospheric Administration

NSF National Science Foundation

RRMR Redfish Rocks Marine Reserve

R/V Research Vessel

TL total length

VSA vector sensor array 
7 

1 Background

Sound is a crucial aspect of the underwater environment for fishes and invertebrates–various species use 

sound to communicate, identify predators, navigate, and many other activities needed for survival in their 

habitat (Montgomery et al. 2006, Carroll et al. 2017). Disruptions and disturbances in the natural 

soundscape can have important impacts on all these activities and likely have important implications for 

the functioning of the ecosystem at large (Rossi et al. 2017). Human-generated noise has become 

increasingly prevalent in the past few decades (Slabbekoorn et al. 2010; Duarte et al. 2021) as humans 

explore more of the oceans, extract resources, and increase global commerce. In the northeast Pacific, 

marine species are impacted by ever increasing levels of acute and chronic anthropogenic noise 

(Hildebrand 2009) from increasing human activities offshore including pile driving, offshore renewable 

energy development, and seismic surveying (Frisk 2012). This human-generated noise is recognized as a 

form of pollution in the ocean, affecting most animals in the ecosystem, although there is a broad range of 

responses to this pollution. There are also many forms, intensities, and frequencies of noise, ranging from 

small boat noise to airgun pulses from seismic surveying.

Mitigations placed on offshore activities that aim to minimize or avoid the potential impacts of 

anthropogenic underwater noise to marine life are becoming more prevalent and rely on the best available 

science. However, data on the potential impacts of underwater noise to fishes and invertebrates is limited, 

and most of the available research is focused on charismatic and well-studied marine mammals (Hawkins 

and Popper 2017). This is largely due to their protected status either under the Marine Mammal Protection 

Act, Endangered Species Act, or both. Current regulatory decisions regarding the impacts of noisegenerating activity are being made using information from a relatively limited amount of environmental 

studies that have reported a broad range of potential effects. The conflicting effects reported in the 

scientific literature creates a significant level of uncertainty when attempting to discern the potential 

short-term and, in particular, long-term impacts of underwater noise. This paucity of data on the potential 

impacts of sound to fish and invertebrates has been a point of concern for recreational anglers, 

commercial fishers, as well as resource managers and further research on this issue is needed. 

2 Motivation for the Study

The motivation for this study is a need to assess the short-term effects of seismic airgun sound on the 

behavior of commercially and recreationally important fishes and invertebrates in order to better inform 

environmental analyses and resource management decisions. This is due to the general paucity of 

literature assessing the impacts of seismic air gun noise to fishes important to fisheries on the continental 

shelf in the Pacific Northwest. Rockfish observed in a field enclosure showed startle and alarm responses 

during 10-min exposures to sounds from a single 1639-cm3air gun; the general threshold for the alarm 

responses was about 180 dB re 1 μPa while regression analyses of changes in depth distribution and shifts 

to active behaviors suggested that more subtle behavioral responses to sounds might become evident at 

161 dB re 1 μPa (Pearson et al. 1992). Those initial responses were sustained only for a few minutes and 

may differ from those of unconfined fish. In another experiment a single 1639-cm3air gun with a source 

level of 223 dB re 1μPa was used to produce peak pressures above 186 dB re 1 μPa at the base of 

rockfish aggregations to test effects on CPUE in blind experimental trials; CPUE declined for three of the 

five central California rockfish species targeted (Skalaski et al. 1992). Very little is known about the 

responses of northeast Pacific fishes to in situ, loud, repetitive noises such as they would experience with 

a seismic survey. Based on the limited information to date, there seems to be potential for behavioral 

changes of rockfish, and that effects are likely to be species specific. Shifts in spatial use in movement 

can impact population dynamics and ecosystem structure, so assessing how movement or habitat use 
8 

changes in response to anthropogenic noise is critical, for example when considering the effectiveness 

marine reserves meant to serve as refuge habitat for harvested fish species (Ogburn et al. 2017).

More studies have been conducted on crustacean response to noise from seismic survey noise; however, 

the only semi-local crustacean that has been studied in response to airgun surveys is the snow crab, where 

CPUE was assessed in response to actual 2D seismic surveys in two years, but no effects were found 

(Morris et al. 2018). Similarly, commercial catch rate of Australian rock lobster coinciding with seismic 

surveys suggests no effect of airgun exposure on their distribution (Parry & Gason 2006), and La Bella et 

al. (1996) found no significant differences in catches and body sizes of Norway lobster in response to 

seismic airgun operations in situ. In contrast, rock lobster exhibited responses ranging from physiological 

stress and reduction in nutritional condition (Fitzgibbon et al. 2017) to reduced ability to reflexively right 

bodies and even damaged sensory systems (Day et al. 2019). Christian et al. (2003) attempted a telemetry 

study with six tagged crabs; however, technical difficulties made them unable to discern fine scale 

movement. However, responses to seismic surveys have not been investigated for Dungeness crab

(Cancer magister): the highest value fishery in California, Oregon, and Washington [Pacific Fisheries 

Information Network (PacFIN), pacfin.psmfc.org], and, in 2021, the highest value crab fishery in Alaska 

after the collapse of other crab stocks in the Bering Sea (Bevaart 2022, Nichols & Saishnikoff 2022).

A National Science Foundation (NSF)-sponsored geophysical seismic survey was conducted in summer 

2021 using the R/V Marcus G. Langseth to collect data along the Cascadia Subduction Zone off Oregon 

and Washington. The survey commenced in Newport, Oregon, proceeded southward until near the 

Oregon-California border on June 17, and then continued northward until offshore of British Columbia on 

July 8 before concluding the survey on July 11, 2021. The survey collected 2-D marine seismic data via

discharge of a 36-airgun array as an energy source and a total discharge volume of approximately 6600

in3

per burst. This presented an opportunity to conduct investigations on the potential responses of cold 

temperate marine species to this soundscape-altering seismic airgun survey. Monitoring and mitigation 

measures for this project were developed for protected species; however, marine mammals, birds, and 

turtles are not the only organisms that could be affected. As full-scale seismic surveys are quite expensive 

and labor intensive, this was a novel opportunity on which we capitalized to better understand both 

soundscape changes in the nearshore environment and potential organismal impacts.

Virtually all studies of airgun survey sound impacts in both open ocean and shallow-water environments 

focus on the pressure component of sound propagation (e.g., Cato 1976, Dahl et al. 2007, Martin and 

Popper 2016, Haxel et al. 2013, Haxel et al. 2019), but few have focused on the accompanying particle 

motion to which many animals have sensitivity (Nedelec et al. 2016, Popper and Hawkins 2018). 

Regulatory guidelines to assess the effects of sound on aquatic life largely describe hearing thresholds in 

terms of sound pressure. More comprehensive guidelines have been proposed that incorporate state-ofthe-science to consider the effects of particle motion (Popper et al. 2014); however, much research 

remains to be done before these guidelines can be updated to include particle motion effects. 

 
9 

3 Objectives

Objective 1: Simultaneously measure particle motion and pressure components of the seismic survey 

activity, including both signals from airgun pulses and ensuing reverberation between pulses. 

We used an acoustic vector sensor array (VSA) developed by Integral Consulting Inc. known as the 

NoiseSpotterTM (Figure 1; Raghukumar et al. 2020). Vector sensors were located approximately 35 cm, 

50 cm, and 75 cm above the seabed. Acoustic propagation models are less reliable in the nearshore 

environment, resulting from challenges with complex bathymetry and sea surface/seafloor reflections. By 

taking in situ measurements instead of relying on imprecise modeling techniques, we will be able to 

directly characterize seismic survey sound pressure and particle motion where the animals are exposed. 

Figure 1. NoiseSpotterTM vector sensor array consisting of three vector sensors and a 

conductivity-temperature-depth sensor. 

Objective 2: Measure behavioral responses of rockfishes (genus Sebastes), Dungeness crab (Cancer 

magister), and Lingcod (Ophiodon elongatus) to the airgun pulses using acoustic telemetry. 

We conducted an acoustic telemetry tagging study to characterize organism behavior before, during, and 

after the seismic surveys. We anticipated the potential of at least two different behavioral responses to the 

presence of airgun discharge: (1) Initial, high energy but brief pulse disturbances may result in startle 

responses akin to a predator escape event: animals may increase horizontal velocity which would be 

detected as changes in acceleration, and semi-pelagic animals may dive towards a reef, which would be 

detected as changes to height off bottom; (2) Repeated (every 20 seconds) intense pulses of the airguns 

over several hours pushes the acoustic stressor from a pulse to a press disturbance which may result in a 

change in the spatial use of the animal as they seek refuge from the sound. 

 
10

4 Approach

The study was carried out in the Redfish Rocks Marine Reserve (RRMR). We chose this site not only

because there were multiple passes of the seismic survey vessel by this location, but also because 

previous work on rockfish movement using acoustic telemetry tags shows site fidelity of rockfish within 

this marine protected area (Calvanese 2016). 

4.1 Sound Measurements

The Geospectrum Technologies Inc. acoustic vector sensors (models M20-40 and M20-100) were

mounted on the NoiseSpotter 35 cm, 50 cm, and 75 cm above the seabed and horizontally separated by 1 

m. Particle motion measurements are comprised of three-dimensional particle velocity vectors (u, v, w), 

measured in a sensor reference frame. The sensor at 50 cm contains an integrated inertial motion unit 

(IMU), whose measurements are used to translate the particle velocity vectors into a true earth-referenced 

frame.

The sensors are sensitive to acoustic frequencies in the 50 Hz–3 kHz range. Omnidirectional pressure 

measurements have a flat frequency response, while the particle velocity measurements have a peak

sensitivity at 1 kHz. The 50 Hz-3 kHz frequency range is particularly well suited to measurement of 

sounds for fishes, invertebrates, and baleen whales whose hearing sensitivities fall within this frequency 

range (Nedwell et al., 2004) as well as anthropogenic sounds like those from seismic surveys and ships 

(frequency range 10–500 Hz). The nominal pressure sensitivity of the pressure channel at 1 kHz is−79 dB

re V/μPa, which, along with the saturation level of the sensors, allows for measurement of sound pressure 

levels up to 240 dB re 1 μPa at a distance of 100 m from the sensor. Sensitivity of the velocity channels 

range from 24-80 dB re 1 V/m/s over 10 -3000 Hz, with a peak in sensitivity at 1 kHz. To address 

hydrodynamically induced non-acoustic pressure fluctuations, particularly common in energetic 

environments, flow noise suppression shields were installed around each vector sensor unit to reduce 

contamination effects, thereby improving data quality and signal detectability (Raghukumar et al. 2019).

4.1.1 NoiseSpotterTM Deployment

The acoustic vector sensor array was deployed within the Redfish Rocks Marine Reserve (RRMR)

amongst the Vemco receivers used to track the fish. Thus, the NoiseSpotterTM was receiving the same 

sound levels as the tagged fish. The NoiseSpotterTM was first deployed on May 18, 2021, and finally 

recovered on July 21, 2021. Two equipment turnarounds occurred within this deployment period on June 

7-8 and June 28-30, 2021. 

4.1.2 NoiseSpotterTM Data Analysis

Initial data processing of the vector sensor data consisted of applying manufacturer-provided calibration 

curves to transform voltage time series to pressure and velocity time series in units of micro-Pascal and 

meters/second, respectively. Acoustic pressure and particle velocity time series data were Hanningwindowed and calibrated in the frequency-domain using sensor-specific calibration curves, and then 

Fourier-transformed back into the time-domain. Phase offsets between the pressure and velocity channels 

were also corrected using sensor-specific phase calibration curves. The pressure sensitivity of the sensors 

is flat in the 0–3 kHz frequency range, while the velocity sensitivity peaks at 1 kHz with an 

approximately 23 dB/decade rolloff in sensitivity on either side of the 1 kHz peak. All pressure and 

particle velocity time series are then bandpass filtered in the 50-3000 Hz frequency band that the vector 

sensors are most sensitive to. 
11

Airgun surveys were active off southern Oregon June 10–12 and June 16–19, 2021. Booms were most 

apparent in the dataset on 11 June when the survey vessel ranged from 27 km to 81 km away from the 

NoiseSpotterTM and 18 June when the survey vessel ranged from 21 km to 97 km away from of the 

NoiseSpotterTM while pulsing. Booms were somewhat audible on June 17 but interspersed with a lot of 

background noise, and the vessel averaged over 100 km away from the NoiseSpotterTM. While the second 

NoiseSpotter deployment collected data from June 8 to June 20, and airgun pulses were detected on June 

11, 17, and 18, interference from other instrument components made acoustic analysis beyond June 16 on 

the second deployment untenable. Thus, for the purposes of NoiseSpotterTM analysis, the three 

deployment periods are categorized as “pre-survey” (May 18–29), “during” (June 8–16), and “postsurvey” (July 3–11, at which point the R/V Marcus G. Langseth was over 500 km away from RRMR). 

Cumulative Sound Exposure Levels (cSELs, Southall et al. 2007) are typically computed using 24-hour 

exposures, but in general, can be computed over any predefined period of time. Here, they are computed 

on hour-long data segments for sound pressure measured at the sensor 70 cm above the seabed. cSELs are 

a useful acoustic metric, particularly for examining behavioral changes since it calculates exposure to 

sound over sustained periods, as opposed to peak sound pressure which is an instantaneous measure. 

A long-term spectral average of the acoustic pressure was calculated using the methods described by 

Wiggins et al. (2007). Long-term spectral average gives a spectrogram over long time periods and 

provides a coarse map or table of contents to groups of events in the finer time scale data. Instead of 

inspecting short duration spectrograms for individual events, successive spectra are calculated and 

averaged together. These averaged-spectra are arranged sequentially to provide a time series of the 

spectra. The averaging time determines the resolution of the resulting plot and the data compression 

factor. Depending on the number of samples used for the spectra and the averaging time used, data 

compression factors of 4000 or more are possible while still providing enough resolution to observe shortterm events above the ambient noise. Here, LTSAs are computed using 1024-point Fast Fourier 

Transforms (FFTs), Hanning windowed with no overlap between windows, and averaged over successive 

5 s periods. 

Next, we used statistics of the frequency spectra (x-component of particle velocity) to discern the 

contribution of seismic airgun pulses to the particle velocity soundscape. Frequency spectra are computed 

using 1024-point Hanning-windowed FFTs with no overlap between windows for the entire dataset. The 

statistics (min, max, mean, 10-, 50-, 90- and 95-percentil confidence intervals) are then computed for each 

of the 1024 frequency bins, and partitioned into the three periods of interest (before, during, and after the 

seismic surveys).

Finally, we applied directional processing to the vector sensor data following the methods described by 

Thode et al. (2019). These directional processing algorithms provide an “azigram” for each minute of 

data, which shows the conventional spectrogram in addition to the frequency- and time-dependent 

azimuthal and elevation angles1

. Azimuth and elevation angles obtained from the particle motion sensor 

data are corrected using digital compass data such that the bearings displayed in the azigrams are in true 

earth coordinates, i.e. 0°, 90°, 180° and -90° indicate true north, east, south and west respectively. 

Frequency-domain bearing angles are computed by calculating 4096-point complex frequency spectra of 

the pressure and particle velocity time series. Active acoustic intensities are computed along each axis (x-, 

y-, z-), following which bearing and elevation angles are computed as either the ratio of the two 

1 Elevation angles were computed but not included in the analysis as this quantity can vary extensively in shallow 

water waveguides where standing acoustic waves are present.
12

horizontal active intensities, or the ratio between the horizontal and vertical active intensities. Successive 

spectra of bearing angles are then overlapped by 90% to come up with the azigram. Elevation angles are 

computed, but not included in the analysis since this quantity can vary extensively in shallow water 

waveguides where standing acoustic waves are present.

4.2 Organism Responses

We hypothesized that behavioral responses to these seismic surveys depend on the morphology (e.g., type 

of swim bladder or lack thereof) and physiology of the species, which are also reflected in their depth 

preferences (e.g., demersal or pelagic). We selected black rockfish (Sebastes melanops; semi-pelagic, 

swim bladder), China rockfish (S. nebulosus; demersal, swim bladder), lingcod (Ophiodon elongatus; 

semi-pelagic, no swim bladder), and Dungeness crab (Cancer magister; crustacean, no swim bladder). 

4.2.1 Array Design 

An array of Vemco VR2W acoustic receivers (InnovaSea Systems, Nova Scotia) was deployed within the 

RRMR, with overlapping coverage across the study site to enable triangulation of acoustically tagged 

animal positions. The configuration of the array was developed in consultation with InnovaSea Systems 

to reduce any impacts that acoustic shadows from rock formations may have on tag detection. 

The array covered approximately 4.7 km2

at water depths of 17.6 to 38.5 m. The main array of 35 

hydrophones and the NoiseSpotterTM were deployed 5 May 2021 with an additional 10 hydrophones 

around the perimeter of the main array deployed on 17 May 2021. Receiver data were downloaded on 9 

September 2021 following conclusion of the experiment. Seven hydrophones were lost during the study 

period; thus, data reported here are derived from a total of 38 hydrophones (Figure 2). 

Figure 2. Locations of the 38 hydrophones deployed and recovered for the study within the 

Redfish Rocks Marine Reserve off southern Oregon, USA. 

White polygons in the midst of the array are emergent rocks.
13

4.2.2 Animal Tagging

All 15 black rockfish (TL = 43.0 ± 2.3 cm) and 13 of the 15 China rockfish (TL = 32.7 ± 3.0 cm) were 

captured on 5 May 2021, using hook and line gear with baited 3/0-6/0 circle hooks to minimize potential 

injury to animals. All 15 lingcod (TL = 57.3 ± 8.6 cm) and the last two China rockfish (TL = 36.7 ± 3.3 

cm) were captured on May 19, 2021, using longline gear with baited circle hooks set overnight.

We anesthetized fish in a solution of 40 mg/L Aqui-S 20E in a 58-L static water bath supplied with air 

stones for 3–23 minutes and monitored each individual throughout. Once stage 2 anesthesia was noted, 

we placed each fish ventral side up in a padded surgical cradle, wrapping a damp cloth around the head to 

protect the eyes. A hose with running seawater was placed in the mouth and oriented toward the ventral 

buccal cavity to ensure ventilation to the gills. A longitudinal incision, approximately 2 cm long, was 

made anterior and lateral to the cloaca, and the acoustic tag was inserted into the peritoneal cavity. We 

then used 23 interrupted skin sutures to close the incision. Fish were then externally tagged with a 25 mm 

long floy tag to indicate to potential anglers that they were research animals and should be released. After 

surgery was completed, fish were placed in a post-surgery tank to recover from anesthesia for between 7 

and 82 minutes. Once observed to be swimming upright in the recovery tank, fish were released as close 

to capture location as possible. Lingcod and China rockfish were tagged with Vemco V13A acoustic tags 

to capture acceleration and horizontal position while the semi-pelagic black rockfish were tagged with 

V13AP tags to capture vertical position as well.

Dungeness crabs were collected were captured on May 19, 2021, using crab pots deployed overnight. 

Only female Dungeness were used for the experiment because previous studies (Stone and O’Clair 2001

in Alaska and Henkel & Roegner unpublished data in Oregon) indicate females move less than males (so 

more likely to stay in our detection area), and females are not retained by fishers. Selected crabs (carapace 

width = 16.5 ± 0.4 cm) were placed in shallow trays with seawater so the backs of their carapaces could

dry. The center of the carapace was wiped with an alcohol wipe and the tag was applied using 

SuperGlueTM. Crabs were then released in the middle of the array. 

4.2.3 Acoustic Tag Data Analysis

Three days with detectable air gun pulses were identified–June 11, 17, and 18–within the study period 

(May 20–July 11). For the black and China rockfish we had sufficient sample size throughout the study to 

make statistical comparisons of acceleration, depth (black rockfish only), and spatial use across the study 

periods. To standardize the total number of observations for comparisons of behavioral patterns, 3 full 

days before (June 1, 3, and 8) and after (June 21, 25, and July 1) were randomly chosen within 10-day 

periods before and after the passage of the survey vessel (when it was > 160 km from the RRMR) to 

compare to the three days with the seismic survey vessel presence. While these dates fall between the data 

availability dates of the NoiseSpotterTM, they were chosen to have the most similar environmental 

conditions but not have the vessel in range and avoid capture or tagging effects that might have persisted 

through the end of May 2021. 

Violin plots with means were used to visually assess differences in acceleration (and depth for black 

rockfish) values for each rockfish species. Differences among periods were tested with a mixed-effects 

model, with study period (five levels: before, each of the passage days, and after) as a fixed effect and 

transmitter (i.e., individual), water temperature (degrees Celsius), and salinity as random effects. All 

analyses were conducted using RStudio; important packages included lubridate for working with dates 

and times (Grolemund and Wickham 2011), ggplot2 for data visualization (Wickham 2016), and lme4 for 

analyzing mixed-effects models (Bates et al. 2015).

To evaluate trends in movement metrics with respect to specific sound levels received by animals within 

the study site and evaluate whether other environmental factors had a significant explanatory effect on 
14

rockfish acceleration, we used generalized additive mixed models (GAMMs, mgcv package in R; Wood 

2011) for a non-linear approach to assessing relationships following methods used in Rasmuson et al. 

(2021), using the restricted maximum likelihood method. We tested for temporal autocorrelation among 

factors using the DHARMa package (Hartig 2022). GAMMs remove the assumption of linearity of 

responses (here, acceleration) and independence of points (useful for a temporally autocorrelated dataset). 

Here we used GAMMs to assess if continuous sound exposure level (SEL; measured in 30-second 

intervals) or root mean squared sound pressure levels (SPLrms) had a significant effect on acceleration 

values of study animals over the periods for which we had high quality noise data, while taking 

environmental variables into account (salinity, water temperature, wind speed, wind gust speed, time of 

day) as well as animal ID, as individual fish can display differing levels of activity overall. All 

observations of fish available during the periods of NoiseSpotterTM data availability were used in these 

models but summarized to the nearest 30-second interval to ensure compatibility with measurement of 

other input variables into the model. Environmental variables that were measured less frequently 

(temperature and salinity every 10 minutes, for example) were imputed using the R package mice (van 

Buuren and Groothuis-Oudshoorn 2011) to ensure we did not miss the complexity of the high temporal 

resolution data from fish tags, as any empty values in the GAMM would not be included. We used 

forward selection to add in environmental factors, although tag ID as a random effect variable was 

included in all models to account for individual variation. Further, a first-order continuous autoregressive 

correlation structure was included in all models to take the temporal autocorrelation of the time series into 

account (Zuur et al. 2009). We used Akaike Information Criterion (AIC) for selecting the most 

appropriate model with the lowest AIC (Burnham and Anderson 2004).

To assess use of the study site and evaluate changes over time and in response to survey noise, we 

calculated the geographic activity center for each, using 95% kernel density (KUD) estimates during each 

period of the study (R packages: raster, rgeos (Bivand and Rundel 2022), sf (Pebesma 2018), 

adehabitatHR (Calenge 2006), rgdal (Bivand et al. 2023)). We then used repeated measures ANOVA and 

post-hoc pairwise comparisons with Bonferroni adjusted p-values for estimates for shifts in activity use 

area by study period for each black rockfish to account for any differences in individual behavior using 

the same R packages as described in the movement metric analysis.

 
15

5 Results

5.1 Acoustic Conditions

During all three analysis periods, hour-long cumulative sound exposure levels (cSELs) seemed to be 

strongly influenced by wind speeds (measured at the National Data Buoy Center Station PORO3 

meteorological buoy located roughly 3 km north of RRMR) with a general increase in cSELs during the 

postsurvey period when overall wind speeds were highest (Figure 3). A diel modulation of cSELs due to 

diel variability in wind speeds is observed, most prominently in the “pre-survey” period. Measured cSELs 

were highest (approximately 145 dB re 1 μPa) on June 11, at which time the R/V Marcus G. Langseth

was at its second closest point of approach (27 km to 81 km away from the NoiseSpotterTM while 

pulsing.). No acoustic data were analyzed for the day of the closest absolute approach: June 18 (21 km to 

97 km away from of the NoiseSpotterTM), although the average range of the R/V Marcus G. Langseth

from the NoiseSpotterTM was similar for both days. Measured wind speeds were also highest on June 11, 

suggesting that the high cSELs measured are a combination of sound from seismic airgun pulses and high 

wind-generated ambient noise.

Figure 3. Cumulative Sound Exposure Level and wind speeds. 

(Top) Cumulative Sound Exposure Level (cSEL) over the NoiseSpotter® analysis periods (black dots) and distance 

between the R/V Marcus G. Langseth and the NoiseSpotter® (green line). (Bottom) Wind speeds over the 

NoiseSpotter analysis periods, measured at the Port Orford meteorological buoy. 

A long-term spectral average (Figure 4a and 4b) of the acoustic pressure and x-particle velocity shows 

periods of elevated acoustic energy between 0 and 500 Hz associated with the seismic surveys between 

June 11 and 13, 2021. Similar to Figure 3, a temporal banding of acoustic energy associated with diel 

changes in wind speeds is seen, with an increase in acoustic energy levels observed toward the end of 

May 2021, likely due to increased wind speeds. Occasional boat passages (short-duration broadband 

spectral lines) are seen during all three data periods, with the most frequent boat passages occurring 

during the pre-survey period. The post-survey period shows higher acoustic energy across the 0–1000 Hz 
16

frequency band considered, with the exception of a dip in acoustic energy on July 7, 2021, that coincides 

with lower wind speeds on that day as compared to the rest of the post-survey analysis period. 

The contribution of seismic airgun pulses to the particle velocity soundscape is discerned in statistics of 

the frequency spectra (Figure 5, x-component of particle velocity) for each of the analysis periods. The 

mean, maximum, and 99-percentile curves show an elevation in particle velocity levels due to the 

presence of seismic airgun pulses in the during period. In contrast, the remaining percentile curves 

(minimum, 1%, 10%, 50%, and 90%) show the highest levels during the postsurvey period, likely due to 

sustained higher wind speeds during this period.

An example of directional processing applied to the particle velocity data is shown in Figure 6, for a 

minute-long data segment during which seismic airgun pulses were measured at a distance of 35.1 km 

from the R/V Marcus G. Langseth. The true bearing of the R/V Marcus G. Langseth relative to the 

NoiseSpotterTM was -55° with respect to true north (i.e., to the northwest). Frequency-specific bearings 

were calculated to be in the range of -55–50° with respect to true north for the primary arrivals associated 

with the seismic pulses. While the pressure data show additional arrivals associated with multipath 

propagation, the signal-to-noise ratio was insufficient to effectively resolve the associated bearings for 

these multipath arrivals.

Figure 4a. Long-term spectral average of acoustic pressure measured 70 cm above the seabed 

during the pre-survey (top), during (middle) and post-survey (bottom) analysis periods. 
17

Figure 4b. Long-term spectral average of x-particle velocity measured 70 cm above the seabed 

during the pre-survey (top), during (middle) and post-survey (bottom) analysis periods. 

Figure 5. Power spectral densities for x-particle velocity during the pre-survey (left), during 

(middle) and post-survey (right) analysis periods. 
18

Figure 6. (Left) Example of acoustic pressure and azimuthal bearing estimated from particle 

velocity. (Right) Locations of R/V Marcus G. Langseth and NoiseSpotterTM for data period shown 

above. 

Bathymetric data from https://www.gebco.net/.

5.2 Animal Reponses

5.2.1 Black Rockfish

Black rockfish had more incidences of higher acceleration (Figure 7) on the survey dates with detected 

airgun pulse noise (mixed-effects model, p < 0.001 for survey period) as compared to days in the before 

period. Post-hoc Tukey tests showed a significant difference for “before” compared to June 11 (p = 

0.0002, coefficient estimate = 0.547 m/s2

); “before” to June 17 (p = 0.0096, coefficient estimate = 0.378 

m/s2

); and “before” to June 18 (p = 0.0036, coefficient estimate = 0.374 m/s2). This behavioral response 

aligns with what is known about black rockfish behavior; individuals of this species are more mobile than 

other rockfish species, and startle responses are what we might expect in response to a noise disturbance. 

We noticed potential habituation of behavior over the three passes of the seismic survey vessel as the 

coefficient of the increase in acceleration values decreased for each of the dates with detected airgun 

pulse noise. The acceleration in the after period was also significantly greater than the before period (p < 

0.0001, coefficient estimate = 0.34 m/s2

), but the acceleration in the after period was not different than 

any of the during dates. As the highest measured wind speeds during the study period were recorded on 

June 11 it could be challenging to discern whether the increased acceleration was due to the seismic 

pulses or the high winds. However, the randomly chosen “after” dates at the end of June and July 1 had 

much lower wind speeds (average 3.77 m/s) as compared to the during (8.57 m/s) and the before (5.4 m/s) 

dates. As the “after” dates had high acceleration and low wind, it does not appear from this categorical 

analysis that increased acceleration can be attributed to high wind speeds. 

The best-fit GAMM model for black rockfish acceleration, which explained only 13.2% of the variation 

observed, included transmitter ID number (individual fish) as a random factor, as well as water 

temperature, salinity, hour of the day, depth (as a proxy for tide), and an interaction between pressure 

measurements of noise and the distance to the R/V Marcus G. Langseth. Wind speed was included as a 

possible model term but just missed being statistically significant as an explanatory variable, reinforcing 

that other factors are contributing to the observed variation in acceleration. 
19

Figure 7. Distributions of black rockfish acceleration values on each of the selected study days. 

Numbers above the plots depict the number of fish include in each date. Each of the study dates with noise from the 

seismic survey had higher acceleration values than the average of the before days. The average of the after days 

was also higher than before but not different than during.

Black rockfish spatial use of the study area varied almost significantly among study periods (p = 0.059; 

repeated measures ANOVA: average differences in sizes of KUD areas by period). In pairwise 

comparisons of the means only the after period was significantly different than before in average area 

used (p = 0.012). However, Figure 8 indicates a lower range of KUDs and many more observations of 

smaller KUDs in the during period as compared to before. Furthermore, three black rockfish were 

detected but did not relocate the necessary five or more times June 11 or 18 to calculate KDEs, suggesting 

that these three individuals changed position very little, which further supports the observed contraction 

of space use. Figure 9 suggests black rockfish are clustering closer to the reef in the middle of the study 

area in the “during” period.

Figure 8. Calculated area used by black rockfish individuals in each study period.
20

Figure 9. Area within the study area used by each black rockfish in the three periods of the study.

Overall survey period was not significant for variability in black rockfish depth positions, although posthoc tests show significant differences in pairwise comparisons between before and June 11 (p = 0.0186, 

coefficient estimate = -2.24 m) and June 11 and after (p = 0.0058, coefficient estimate = 2.25 m). These 

estimates show that black rockfish traveled up in the water column (negative depth indicates closer to the 

surface) on the first day of the pass of the survey vessel compared to the before period, and the fish were 

found further down in the water column on average in the after period as compared to the initial survey 

period (Figure 10). Further exploration into the bathymetry of the site should be conducted to convert 

distance from surface to height off the bottom, as the distance from the seafloor may be more ecologically 

relevant than distance from the water’s surface due to the highly rugose seafloor, including emergent 

rocks, within the study area. 

Figure 10. Black rockfish depth for each of the selected study days.

 

Before During After 
21

5.2.2 China Rockfish

China rockfish acceleration values were higher on the first day of seismic survey noise and in the after 

period as compared to the before period (Figure 11) (mixed-effects model, p = 0.0093 for survey period). 

Post-hoc Tukey tests showed a significant difference for before to June 11 (p = 0.0022, coefficient 

estimate = 0.413 m/s2

) and before to after (p < 0.0001, coefficient estimate = 0.171 m/s2). Higher 

accelerations in the after period as compared to before are consistent with results for black rockfish. 

Figure 11. Distributions of China rockfish acceleration values within each of the selected study 

days. 

Numbers above the plots depict the number of fish include in each date. The first date with noise from the seismic 

survey (June 11) had higher acceleration values than the average of the before days. The average acceleration 

across the after days was also higher than before.

Overall, China rockfish demonstrated a smaller use area within the RRMR than the black rockfish. While 

there were no statistically significant differences in the mean KUDs among study periods (p = 0.385), in 

the during period, there were only three China rockfish with enough relocations to calculate 95% KDEs 

(potentially contributing to the lack of statistical significance) as compared to nine in the before and after 

(as well as the June 12-16, data not shown). The contrast of these low number of relocations with those 

immediately before and after the seismic surveying days suggests that seismic survey noise likely had an 

effect on spatial use of this species. Presumably, the individuals that were not relocating enough times to 

be included in the analysis were moving so little that they were not detected on other acoustic receivers 

within our array, suggesting that the impulsive noise did provoke a response in most China rockfish to use 

even less space than observed in other times of the study; akin to that observed for black rockfish. In 

contrast, for the individuals for which we did have enough relocations to include in the analysis there 

appeared to be more incidences of larger space use in the during and after periods as compared to before

(Figure 12). Together, these observations indicate a disruption of space use by China rockfish in response 

to the airgun pulses.
22

Figure 12. Calculated area used by China rockfish individuals in each study period.

5.2.3 Lingcod

Most of the tagged lingcod individuals in this study were undetected within the acoustic array during the 

days of the seismic survey noise presence (Figure 13). Here rather than random days before and after, we 

have plotted each week before and after the arrival of the vessel along with days in between the days of 

detectable seismic pulses when the R/V Marcus G. Langseth was out of range. Most fish were detected in 

the array in the week immediately preceding the arrival of the vessel. In the days after the first pass of the 

vessel, more fish were detected in the array. Likewise, after the last pass of the vessel, increasing numbers 

of individuals were detected in the study area. It is possible that the lack of detection of almost all of our 

study animals is a response to seismic survey activity; however, we cannot determine whether this a 

movement outside of the array in response to the seismic survey noise (or other reasons) or a lack of 

detections due to lingcod sheltering in crevices. For the few observations of lingcod during the period of 

seismic surveying, trends show that lingcod accelerated less on two of the days with the seismic survey

operating in range, with both mean and maximum values lower than those observed in the before, in 

between, and after periods (Figure 14). 

Figure 13: Lingcod individuals detected by period of our study. 

Text above each bar indicates the total number of detections (all individuals) for each period. 
23

Figure 14. Lingcod acceleration by study period. 

Violin plots show the distribution of all acceleration values observed in sampled periods of time.

5.2.4 Dungeness crab

Dungeness crab presence in the array declined leading up to the first pass of the survey vessel (Figure 15) 

suggesting these animals were dispersing beyond the detection range of our array even before the seismic 

survey vessel arrived. However, like the lingcod, still fewer crab were detected on the days of the survey 

vessel pass than the days in between. For the three individuals that were detected on the day of the first 

pass, the range of values in instantaneous acceleration contracted, but the mean acceleration value 

increased as compared to values in the before period (Figure 16). This might suggest that there was less 

resting time on this day with fewer low acceleration values and more higher acceleration values. Four 

crab were detected in the middle period with more observations, so perhaps the individuals that remained 

within the acoustic array during the time of seismic surveying were not moving around as much and thus 

detected less as compared to June 12–16. Surprisingly, the two individuals that were detected on June 18 

moved slower than those observed in the middle period and the week immediately before. There were not 

enough individuals or observations throughout the rest of the study to draw conclusions on the behavioral 

impacts of impulsive anthropogenic noise. 

Figure 15. Dungeness crab individuals detected by period of our study. 

Text above each bar indicates the total number of detections (all individuals) for each period.
24

Figure 16. Dungeness crab acceleration by period of study.

 
25

6 Conclusions

Acoustic pressure and particle motion were measured pre-, during and post- a seismic airgun survey of the 

Cascadia Subduction Zone. Measurements were made inside the Redfish Rocks Marine Reserve in 

southern Oregon, and were accompanied by in situ tagging of rockfish, lingcod, and Dungeness crabs. 

The acoustic environment was dominated by wind noise, with occasional contributions from boats. We 

were able to detect seismic airgun pulses from the R/V Marcus G. Langseth during the seismic survey on 

June 11, 2021, that exceeded 160 dB re 1 μPa (pressure) or 95 dB re 1 nm/s (particle velocity) with 

cumulative sound exposure levels approximately 145 dB when the R/V Marcus G. Langseth was at its 

second closest point (27 km to 81 km away from the NoiseSpotterTM while pulsing.). [No acoustic data 

were analyzed for the day of the closest absolute approach: June 18 (21 km to 97 km away from of the 

NoiseSpotterTM), although the average range of the R/V Marcus G. Langseth from the NoiseSpotterTM 

was similar for both days.]

The ambient noise floor (100–120 dB re 1 μPa range for pressure, 60 dB re 1 nm/s range for particle 

velocity) appeared to scale with wind speeds, with the highest ambient noise levels occurring during the 

post-survey period. Episodic contributions from the seismic airgun pulses were most evident in the mean, 

maximum and 99 percentile levels. The highest measured wind speeds during the study period coincided 

with the first pass and second closest point of approach of the R/V Marcus G. Langseth; thus, the high 

cumulative sound exposure levels during this period contain contributions from both wind noise and the 

airgun pulses. It will therefore be necessary in subsequent analyses to separate the contribution to the 

acoustic environment from each source. Efforts are ongoing to implement automated detectors for a better 

quantitative characterization of sound levels from the seismic pulses to inform a more granular 

understanding of behavioral responses to acute periods of elevated sound levels.

Our tagging study demonstrated differences in the behavior of both black and China rockfish on at least 

the first pass of the seismic survey, indicating detection of and response either to high noise overall or 

specifically to the airgun pulses. Overall, black and China rockfish exhibited significantly different 

instantaneous acceleration values across periods of the study with higher acceleration on the first pass of 

the seismic survey vessel compared to the before period, supporting our hypothesized response to the 

pulse disturbance. Although the average instantaneous acceleration rates for black rockfish were higher 

than the before period, on each of the seismic survey pass days, the average acceleration declined with 

subsequent passes. There are two possible explanations for this pattern: (1) black rockfish demonstrated 

habituation to the presence of the survey noise or (2) black rockfish responded more strongly to the 

combined effects of airgun pulses and high wind on the first day of the survey pass as compared to 

subsequent passes. As compared to the magnitude of black rockfish results, estimates for differences in 

China rockfish acceleration are low, as expected for this species that moves very little on average (Love et 

al. 2002). However, compared to black rockfish results, changes in China rockfish acceleration were a 

greater proportion of estimated means; thus China rockfish exhibited more pronounced increases in 

movement on the days of seismic survey noise. We also hypothesized that black rockfish would dive in 

response to the seismic survey noise. This hypothesis was not supported as they actually moved shallower 

on the day of the first seismic survey pass. It is likely that both rockfish species restricted their spatial use 

of the study area in response to the seismic survey we hypothesized. 

Lingcod appeared to change movement behavior in response to the presence of the seismic survey noise: 

either moving out of our detection array or hiding in crevices such that the tag pings could not be detected 

by our receivers, making it difficult to determine if they increased or decreased their spatial use. 

However, those lingcod we did detect on the days of the seismic survey pass had lower acceleration 

values than before, after, and in between. This response is contrary to our hypothesis and the results for 

the rockfish. We are not able to draw conclusions for Dungeness crab due to most crabs leaving the array 

prior to the arrival of the R/V Marcus G. Langseth. 
26

Our study provides evidence for behavioral responses to elevated noise levels associated with the seismic 

survey. However, the magnitude of the changes in acceleration (and depth for black rockfish) were quite 

small, and declined across the three days of the seismic survey. Thus, the ecological implications of these 

behavioral responses seem limited, similar to Pearson et al.’s (1992) findings of initial startle and depth 

responses in rockfish being sustained only for a few minutes. In contrast, the variation in space use might 

have implications for other aspects of their performance. The near lack of movement of the China 

rockfish could have short- term impacts to their foraging. The aggregation of black rockfish could have 

implications for competition for food and could have positive or negative short-term effects on 

catchability. Skalaski et al. (1992) found CPUE declined for three of the five central California rockfish 

species targeted during airgun pulses, so uncertainty on how acoustic disturbances immediately affect 

catchability remains. However, since the rockfish in our study returned to being broadly distributed in the 

after period, any impacts to foraging or catchability are likely limited in duration. 

While we did detect behavioral responses in both black and China rockfish, they did not leave the study 

area on days they were exposed to the airgun pulses, which is not surprising for these species with small 

home ranges (Parker et al. 2007, Hannah and Rankin 2011, Calvanese 2016). This lack of displacement is 

similar to the findings of Meekan et al. (2021) for tropical demersal fishes of Australia and McQueen et 

al. (2022) for spawning Atlantic cod. In contrast, the majority of the lingcod apparently moved out of our 

study area on the days the seismic survey was pulsing within range. These species-specific responses also 

are evident in the behavior as both rockfish species increased their acceleration on the day of the first 

survey pass while the lingcod decreased. We encourage further assessment for other species that occupy 

different ecological roles, have different physiologies (such as elasmobranchs), or display different 

movement behaviors such as highly migratory species. Finally, our results show that animal response to 

varying sound levels and environmental factors in the nearshore environment is complex and may not 

simply show a direct relationship between sound magnitude and activity. Both species of rockfish had 

higher accelerations in the after period (without seismic survey noise) as compared to before. (This is not 

a sustained response to the survey noise as the acceleration actually increased on each of the after days.)

Importantly, we did not assess physiological responses (e.g. cortisol or blood glucose levels; Kight & 

Swaddle 2011) which may not necessarily mirror behavioral changes. In fact, some authors hypothesize 

that disturbances first lead to physiological followed by behavioral changes (Gedamke et al. 2016). 

Despite our conclusion of no lasting changes in movement behavior of either rockfish species in response 

to seismic survey noise, it may not be appropriate to assume that there are no long-term biological 

responses and impacts on fish species. A reduced behavioral response that occurs after continued 

exposure to noise may be the result of habituation, but long-term physiological and behavioral changes 

can occur, even if instantaneous startle responses appear lowered or reduce over time (Bejder et al. 2009).

Quantifying how anthropogenic noise contributes to the soundscape is important for understanding 

potential effects on marine species. This work contributes to our understanding of Oregon nearshore 

communities, characterizing how they may respond to increasing human activity and noise, and is broadly 

applicable to the Northeast Pacific shelf as our study species range from California to the Aleutian 

Islands. The results of this study can be used by Federal and State managers to inform future mitigation 

strategies for noise-producing activities and for environmental studies that assess the potential long-term 

impacts to affected species, including indirect impacts to commercial and recreational fishing industries. 

These findings will contribute to NOAA’s Ocean Noise Strategy and inform the Pacific Fishery 

Management Council about acoustic impacts on Essential Fish Habitat and managed resources. While we 

may not yet be able to draw conclusions about the direct effects of noise on the lingcod and Dungeness 

crab, data on their movement patterns and behavior before and after the seismic surveys (as well as the 

behavior of the rockfishes in those periods) can be used by resource managers and fisheries researchers to 

estimate potential effectiveness of spatial management strategies such as marine reserve placement or 

various other fishery closure areas. 
27

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