Chukchi Offshore Monitoring in Drilling Area 

(COMIDA): Factors Affecting the Distribution and 

Relative Abundance of Endangered Whales and Other 

Marine Mammals in the Chukchi Sea

Final Report of the Chukchi Sea Acoustics, Oceanography, and 

Zooplankton Study

National Marine Mammal Laboratory

Alaska Fisheries Science Center

National Marine Fisheries Service

National Oceanic and Atmospheric Administration

7600 Sand Point Way NE, Seattle, WA 98115-6349

 Funding Agency: 

 Bureau of Ocean Energy Management 

 Alaska Outer Continental Shelf Region 

 U.S. Department of the Interior 

 3801 Centerpoint Drive, Suite 500

 Anchorage, AK 99503-5823

Contract No. M09PG00016

August 2015

OCS Study BOEM 2015-034

This report does not constitute a publication and is for information only. All data herein are to be considered provisional.
OCS Study

BOEM 2015-034

This report has been reviewed by the Department of Interior and approved for publication. 

Approval does not signify that the contents necessarily reflect the views and policies of the 

Department, nor does mention of trade names or commercial products constitute endorsement or 

recommendation for use. 
OCS Study

BOEM 2015-034

Chukchi Offshore Monitoring in Drilling Area 

(COMIDA): Factors Affecting the Distribution and 

Relative Abundance of Endangered Whales and Other 

Marine Mammals in the Chukchi Sea

Final Report of the Chukchi Sea Acoustics, Oceanography, and 

Zooplankton Study

Prepared for:

Environmental Studies Program

Alaska Outer Continental Shelf Region

Bureau of Ocean Energy Management

U.S. Department of Interior

3801 Centerpoint Drive, Suite 500

Anchorage Alaska 99503-5823

Submitted through:

National Marine Mammal Laboratory

Alaska Fisheries Science Center

National Marine Fisheries Service, NOAA

U.S. Department of Commerce

7600 Sand Point Way NE

Seattle, Washington 98115-6349

 
OCS Study

BOEM 2015-034

This study was initiated and supported by the U.S. Department of Interior, Bureau of Ocean 

Energy Management (BOEM), Alaska Outer Continental Shelf Region, Anchorage, Alaska, 

through an Interagency Agreement between BOEM and the National Marine Mammal 

Laboratory (M09PG00016), as part of the BOEM Alaska Environmental Studies Program.
OCS Study

BOEM 2015-034

REPORT AVAILABILITY

This document is available to the public through:

National Technical Information Service

5285 Port Royal Road

Springfield, Virginia 22161

FAX: (703) 605-6900

www.ntis.gov

CITATION

May be cited as: Berchok, C.L., J.L. Crance, E.C. Garland, J.A. Mocklin, P.J. Stabeno, J.M. 

Napp, B.K. Rone, A.H. Spear, M. Wang, and C.W. Clark. 2015. Chukchi Offshore Monitoring 

In Drilling Area (COMIDA): Factors Affecting the Distribution and Relative Abundance of 

Endangered Whales and Other Marine Mammals in the Chukchi Sea. Final Report of the 

Chukchi Sea Acoustics, Oceanography, and Zooplankton Study, OCS Study BOEM 2015-034. 

National Marine Mammal Laboratory, Alaska Fisheries Science Center, NMFS, NOAA, 7600 

Sand Point Way NE, Seattle, WA 98115-6349.

Email of corresponding editors: Catherine.Berchok@noaa.gov and Jessica.Crance@noaa.gov
OCS Study

BOEM 2015-034

KEY PERSONNEL (IN ALPHABETICAL ORDER)

Catherine L. Berchok1, Ph.D. – Program Coordinator, Passive acoustics P.I.

Christopher Clark5, Ph.D. – Noise Modeling and Auto-detection Buoy P.I.

Jessica L. Crance1, M.S. – Project Manager

Ellen C. Garland7, Ph.D. – Statistical advisor

Julie A. Mocklin1, M.S. – Final Report Editor

Sue E. Moore2, Ph.D. – Science advisor (ecosystems)

Jeff Napp3, Ph.D. – Zooplankton P.I.

James E. Overland4, Ph.D. – Science advisor (climate)

Brenda K. Rone1, M.S. – Visual Survey P.I. 

Adam H. Spear3, M.S. – Zooplankton Acoustics

Phyllis Stabeno4, Ph.D. – Oceanography P.I.

Muyin Wang6, Ph.D. – Climate Modeling P.I.

1 National Marine Mammal Lab (NMML), Alaska Fisheries Science Center, NOAA Fisheries 

Service, 7600 Sand Point Way NE, Seattle, WA 98115-6349 

2 National Marine Fisheries Service, Office of Science & Technology, 7600 Sand Point Way 

NE, Seattle, WA 98115-6349

3 Resource Assessment and Conservation Engineering (RACE) Division, Alaska Fisheries

Science Center, NOAA Fisheries Service, 7600 Sand Point Way NE, Seattle, WA 98115-6349 

4 Pacific Marine Environmental Laboratory (PMEL), Ocean Environment Research Division, 

7600 Sand Point Way NE, Seattle, WA 98115-6349 

5 Bioacoustics Research Program (BRP), Cornell University, 159 Sapsucker Woods Road, 

Ithaca, NY 14850

6

Joint Institute for the Study of the Atmosphere and Ocean (JISAO), University of Washington, 

7600 Sandpoint Way NE, Seattle, WA 98195

7 School of Biology, University of St. Andrews, Fife, KY16 9TH, UK
OCS Study

BOEM 2015-034

i

I. LIST OF ACRONYMS

AB-2012: Auto-detection buoy deployed and operated in 2012

ACC: Alaska Coastal Current 

ADCP: Acoustic Doppler Current Profiler

AFSC: Alaska Fisheries Science Center

AIC: Akaike Information Criterion

AMSR: Advanced Microwave Scanning Radiometer

ARCWEST: Arctic Whale Ecology Study

ARGOS: Advanced Research and Global Observation Satellite

ARS: Area Restricted Search

ARTS: Air Rocket Transmitter System

ASAMM: Aerial Surveys of Arctic Marine Mammals

AURAL: Autonomous Underwater Recorder for Acoustic Listening

AW: Anadyr Water

BCB: Bering-Chukchi-Beaufort

BOWFEST: Bowhead Whale Feeding Ecology Study

BS: Bering Sea 

BW: Bering Water

BWASP: Bowhead Whale Aerial Survey Project

CCSM3: Community Climate System Model ver3

CCSM4: Community Climate System Model ver4

CDS: Conventional Distance Sampling

CESM: Community Earth System Model

CHAOZ: Chukchi Sea Acoustics, Oceanography, and Zooplankton Study

CHAOZ-X: Chukchi Sea Acoustics, Oceanography, and Zooplankton Study Extension

CMIP5: Coupled Model Intercomparison Project, phase 5

COMIDA: Chukchi Offshore Monitoring In Drilling Area

CPA: Closet Point of Approach

Cornell-BRP: Cornell-Bioacoustics Research Program

CSESP: Chukchi Sea Environmental Studies Program

CTD: Conductivity, Temperature, Depth sensor instrument package
OCS Study

BOEM 2015-034

ii

CV: Coefficient of Variation

DBO: Distributed Biological Observatory 

DELMA: Detection Classification and Machine Learning Algorithms

DiFAR: Directional Frequency Analysis and Recording

DTAG: Suction Cup Acoustic Tag

DVM: Diel Vertical Migration

DWBA: Distorted-wave Born Approximation

EcoFOCI: Ecosystems and Fisheries-Oceanography Coordinated Investigations

ECS: Eastern Chukchi Sea

ESW: Effective Strip Width

FFT: Fast Fourier Transform

FM: Frequency Modulated

GAM: Generalized Additive Model

GUI: Graphical User Interface

HEE: Hermes Electronics

HMM: Hidden Markov Model 

IACUC: International Animal Care and Use Committee

JISAO: Joint Institute for the Study of the Atmosphere and Ocean

LFDCS: Low-frequency Detection and Classification System

MAG: Magnavox

MARU: Marine Autonomous Recording Unit

MGCV: Mixed GAM Computation Vehicle

MW: Melt Water

NCEP: National Centers for Environmental Prediction

NCAR: National Center for Atmospheric Research

NEPA: National Environmental Policy Act

NMML: National Marine Mammal Laboratory

NOAA: National Oceanic and Atmospheric Administration

NRC: National Research Council

NSF: National Science Foundation

NSIDC: National Snow and Ice Data Center

PAR: Photosynthetically Active Radiation
OCS Study

BOEM 2015-034

iii

PCMDI: Program for Climate Model Diagnosis and Intercomparison

PMEL: Pacific Marine Environmental Laboratory

PRIEST: Pacific Right Whale Ecology Study

PTT: Platform Terminal Transmitter

RCP4.5: Representative Concentration Pathway4.5

RCP8.5: Representative Concentration Pathway8.5

RHIB: Rigid-Hulled Inflatable Boat

RMS: Root-mean-squared 

ROC: Receiver Operating Characteristic

SBE 19plus: SeaBird SeaCAT CTD

SBE 49: SeaBird FastCAT CTD

SDA: Speed-Distance-Angle 

SPOT 5: Smart POsition and Temperature tag ver5

SPW: Sparton sonobuoy

SSSM: Switching State-Space Model

TAPS6-NG: Tracor Acoustic Profiling System 6 - Next Generation

TEK: Traditional Ecological Knowledge

TFS: Truncated Fluid Sphere

TIROS: Television Infrared Observation Satellite

USS: Undersea Sensor Systems

UTC: Coordinated Universal Time

WARC: Western Alaska Resident Catalog

WATC: Western Alaska Transient Catalog

WHOI: Woods Hole Oceanographic Institution

WW: Winter Water
OCS Study

BOEM 2015-034

iv

II. LIST OF FIGURES

Figure 1. Map showing general study area ..................................................................................... 3

Figure 2. Location of long-term passive acoustic recorder moorings .......................................... 20

Figure 3. Bowhead whale calling activity..................................................................................... 27

Figure 4. Gunshot call activity...................................................................................................... 28

Figure 5. Beluga whale calling activity ........................................................................................ 30

Figure 6. Gray whale calling activity............................................................................................ 31

Figure 7. Walrus calling activity................................................................................................... 34

Figure 8. Bearded seal calling activity.......................................................................................... 36

Figure 9. Seismic airgun noise activity......................................................................................... 39

Figure 10. Vessel noise activity.................................................................................................... 40

Figure 11. Ice noise activity.......................................................................................................... 41

Figure 12. Location of CHAOZ passive acoustic moorings off Icy Cape, AK in relation to those 

deployed by Hannay et al. (2013)................................................................................................. 43

Figure 13. Ice cover in northeastern Chukchi Sea ........................................................................ 52

Figure 14. Location of passive acoustic recorders (red dots) in relation to areas of industrial 

activity. Figure modified from Blees et al. ................................................................................... 59

Figure 15. Location of passive acoustic recorders (red dots) in relation to areas of industrial 

activity. Figure modified from Hartin et al................................................................................... 60

Figure 16. Modifications of a 77C sonobuoy ............................................................................... 67

Figure 17. Sonobuoy monitoring station ...................................................................................... 68

Figure 18. Omnidirectional and Yagi antenna placement ............................................................ 69

Figure 19. Marine mammal observer using 25x “big-eye” binoculars......................................... 70

Figure 20. Summary of combined visual and acoustic effort, 2010-2012.................................... 71

Figure 21. Bowhead whale acoustic and visual detections........................................................... 73

Figure 22. Gray whale acoustic and visual detections.................................................................. 74

Figure 23. Walrus acoustic and visual detections......................................................................... 74

Figure 24. Bearded seal acoustic and visual detections................................................................ 75

Figure 25. Fin whale acoustic and visual detections..................................................................... 76

Figure 26. Humpback whale acoustic and visual detections ........................................................ 76

Figure 27. Miscellaneous cetacean acoustic and visual detections............................................... 77

Figure 28. Miscellaneous marine mammal acoustic and visual detections.................................. 77

Figure 29. An example of a SPOT 5 satellite transmitter that was deployed in 2012. ................. 87

Figure 30. Locations (red dots) of the gray whale tagged off Wainwright................................... 88

Figure 31. Switching state-space modeled locations.................................................................... 89

Figure 32. Bathymetry in the study area, the three mooring sites, and the six hydrographic 

transects occupied ......................................................................................................................... 92

Figure 33. TAPS6-NG .................................................................................................................. 95

Figure 34. Cross section of temperature and salinity at Icy Cape ................................................ 97

Figure 35. Time series from the inshore mooring (C1), deployed August 2010-2011................. 99

Figure 36. Time series from the midshore mooring (C2), deployed August 2010-2011............ 100

Figure 37. Time series from the offshore mooring (C3), deployed August 2010-2011 ............. 101

Figure 38. Time series from the inshore mooring (C1), deployed August 2011-2012............... 102

Figure 39. Time series from the midshore mooring (C2), deployed August 2011-2012............ 103

Figure 40. Time series from the offshore mooring (C3), deployed August 2011-2012 ............. 104

Figure 41. Ice keel depth for both deployment years.................................................................. 105
OCS Study

BOEM 2015-034

v

Figure 42. Plots of daily bottom currents.................................................................................... 106

Figure 43. Transport calculated at Icy Cape for deployments.................................................... 107

Figure 44. Transport calculated at the Icy Cape line (Transect D) using the mooring data ....... 108

Figure 45. The warmer, more saline Atlantic water is evident in the Temperature-Salinity (T-S) 

diagram ....................................................................................................................................... 109

Figure 46. Percent areal ice cover on January 3, 2011 ............................................................... 110

Figure 47. Temperature (A), salinity (B) measured in August 2011 .......................................... 111

Figure 48. Plots of temperature and salinity on a density grid ................................................... 112

Figure 49. ADCP estimated zooplankton volume backscatter (Sv) late August to late September 

2011............................................................................................................................................. 113

Figure 50. Wavelet analysis of 2010 - 2011 near surface data (7-8 m) zooplankton volume 

backscatter data from the Icy Cape transect ............................................................................... 114

Figure 51. Wavelet analysis of 2010 - 2011 at depth data (27-28 m) zooplankton volume 

backscatter data from the Icy Cape transect ............................................................................... 115

Figure 52. Wavelet analysis of 2011-2012 near surface data (11-12 m) zooplankton volume 

backscatter data from the Icy Cape transect ............................................................................... 116

Figure 53. Wavelet analysis of 2011-2012 at depth data (28-32 m) zooplankton volume 

backscatter data from the Icy Cape transect ............................................................................... 117

Figure 54. Estimated abundance at 25 m for two types of zooplankton scatterers..................... 118

Figure 55. Estimated abundance at 10 m for two types of zooplankton scatterers..................... 119

Figure 56. 1 m2 Tucker Sled on the icy deck of the F/V Aquila................................................. 125

Figure 57. Map of currents over the Chukchi shelf .................................................................... 127

Figure 58. Hydrographic measurements at Point Hope (Line A) in September 2010................ 129

Figure 59. Hydrographic measurements at Point Hope (Line A) in August 2011 ..................... 130

Figure 60. Hydrographic measurements at Point Hope (line A) in August 2012....................... 131

Figure 61. Hydrographic measurements at Cape Lisburne (Line B) in September 2010........... 132

Figure 62. Hydrographic measurements at Cape Lisburne (Line B) in August 2011 ................ 133

Figure 63. Hydrographic measurements at Cape Lisburne (Line B) in August 2012 ................ 134

Figure 64. Hydrographic measurements at Point Lay (Line C) in September 2010................... 135

Figure 65. Hydrographic measurements at Point Lay (Line C) in August 2011 ........................ 136

Figure 66. Hydrographic measurements at Point Lay (Line C) in August 2012 ........................ 137

Figure 67. Hydrographic measurements at Icy Cape (Line D) in September 2010.................... 138

Figure 68. Hydrographic measurements at Icy Cape (Line D) in August 2011 ......................... 139

Figure 69. Hydrographic measurements at Icy Cape (Line D) in August 2012 ......................... 140

Figure 70. Hydrographic measurements at Wainwright (Line E) in September 2010 ............... 141

Figure 71. Hydrographic measurements at Wainwright (Line E) in August 2011..................... 142

Figure 72. Hydrographic measurements at Wainwright (Line E) in August 2012..................... 143

Figure 73. Hydrographic measurements at Barrow Canyon (Line F) in August 2011............... 144

Figure 74. Hydrographic measurements at Barrow Canyon (Line F) in August 2012............... 145

Figure 75. Trajectory of satellite-tracked drifters deployed in August 2011.............................. 146

Figure 76. Concentration of euphausiid calyptopae in the water column, 2010 - 2012. ............ 149

Figure 77. Concentration of euphausiid furcilia in the water column, 2010 - 2012................... 150

Figure 78. Concentration of euphausiid furcilia along the bottom, 2010 – 2012. ...................... 151

Figure 79. Concentration of euphausiid furcilia day and night, 2010 – 2012. Purple symbols 

indicate day, pink symbols indicate night................................................................................... 152

Figure 80. Concentration of euphausiid juvenile and adults in the water column, 2010 – 2012.153
OCS Study

BOEM 2015-034

vi

Figure 81. Concentration of euphausiid juvenile and adults in the bottom layer, 2010 – 2012. 154

Figure 82. Concentration of hyperiid amphipods in the water column, 2010 – 2012. ............... 155

Figure 83. Concentration of gammarid amphipods in the water column, 2010 – 2012. ............ 156

Figure 84. Concentration of gammarid amphipods near bottom, 2010 – 2012. ......................... 157

Figure 85. Concentration of Pseudocalanus spp. in the water column, 2010 – 2012. ............... 158

Figure 86. Concentration of Calanus glacialis in the water column, 2010 – 2012. ................... 159

Figure 87. Concentration of Calanus glacialis near bottom, 2010 – 2012................................. 160

Figure 88. Concentration of Calanus hyperboreus in the water column, 2010 – 2012.............. 161

Figure 89. Concentration of larvaceans in the water column, 2010 – 2012. .............................. 162

Figure 90. Concentration of pteropods in the water column, 2010 – 2012. ............................... 163

Figure 91. Concentration of pteropods along the bottom, 2010 – 2012. .................................... 164

Figure 92. Concentration of chaetognaths in the water column, 2010 – 2012. .......................... 165

Figure 93. Relationship between zooplankton displacement volume and mean water column 

volume backscatter in 2011 ........................................................................................................ 167

Figure 94. Relationship between zooplankton displacement volume and mean water column 

volume backscatter in 2012 ........................................................................................................ 168

Figure 95. Icy Cape 2010 transect sections of physical properties and zooplankton ................. 169

Figure 96. Icy Cape 2011 transect sections of physical properties and zooplankton ................. 170

Figure 97. Icy Cape 2012 transect sections of physical properties and zooplankton ................. 171

Figure 98. Duration of ice cover at each grid point based on Hadley Sea Ice analysis.............. 177

Figure 99. Time series of zonally averaged number of sea ice-free months .............................. 178

Figure 100. Time series of monthly sea ice extent for the Chukchi Sea .................................... 180

Figure 101. CESM1.0 simulated monthly ice thickness averaged over a small box around the 

moorings in the Chukchi Sea ...................................................................................................... 182

Figure 102. Annual mean ocean current simulated by CESM.................................................... 183

Figure 103. Ocean current at 30 m depth.................................................................................... 184

Figure 104. Ocean temperature (left; C) and salinity (right; psu) at 40 m depth ...................... 185

Figure 105. Model simulated ocean temperature (left; C) and salinity.................................... 186

Figure 106. Bowhead whale calling activity as it relates to oceanographic variables at the inshore 

location, 2010-2012 .................................................................................................................... 194

Figure 107. Bowhead whale calling activity as it relates to oceanographic variables at the 

midshore location, 2010-2012 .................................................................................................... 195

Figure 108. Bowhead whale calling activity as it relates to oceanographic variables at the 

offshore location, 2010-2012...................................................................................................... 196

Figure 109. Marine mammal calling activity (blue line) vs. winds (m/s; black line) at the 

midshore location........................................................................................................................ 197

Figure 110. Gunshot calling activity as it relates to oceanographic variables at the inshore 

location, 2010-2012 .................................................................................................................... 199

Figure 111. Gunshot calling activity as it relates to oceanographic variables at the midshore 

location, 2010-2012 .................................................................................................................... 200

Figure 112. Gunshot calling activity as it relates to oceanographic variables at the offshore 

location, 2010-2012 .................................................................................................................... 201

Figure 113. Beluga whale calling activity as it relates to oceanographic variables at the inshore 

location, 2010-2012. ................................................................................................................... 203

Figure 114. Beluga whale calling activity as it relates to oceanographic variables at the midshore 

location, 2010-2012 .................................................................................................................... 204
OCS Study

BOEM 2015-034

vii

Figure 115. Beluga whale calling activity as it relates to oceanographic variables at the offshore 

location 2010-2012 ..................................................................................................................... 205

Figure 116. Gray whale calling activity as it relates to oceanographic variables at the inshore 

location, 2010-2012 .................................................................................................................... 207

Figure 117. Barrow transect line oceanographic, zooplankton, and gray whale survey results, 

2011............................................................................................................................................. 208

Figure 118. Point Hope transect line oceanographic, zooplankton, and gray whale survey results, 

2012............................................................................................................................................. 209

Figure 119. Walrus calling activity as it relates to oceanographic variables at the inshore 

location, 2010-2012 .................................................................................................................... 212

Figure 120. Walrus calling activity as it relates to oceanographic variables at the midshore 

location, 2010-2012 .................................................................................................................... 213

Figure 121. Walrus calling activity as it relates to oceanographic variables at the offshore 

location, 2010-2012 .................................................................................................................... 214

Figure 122. Marine mammal calling activity (blue line) vs. winds (m/s; black line) at the 

midshore location........................................................................................................................ 215

Figure 123. Wainwright transect line oceanographic, zooplankton, walrus, and bearded seal 

survey results, 2010 .................................................................................................................... 216

Figure 124. Wainwright transect line oceanographic, zooplankton, walrus, and bearded seal 

survey results, 2011 .................................................................................................................... 217

Figure 125. Wainwright transect line oceanographic, zooplankton, walrus, and bearded seal 

survey results, 2012 .................................................................................................................... 218

Figure 126. Icy Cape transect line oceanographic, zooplankton, walrus, and bearded seal survey 

results, 2010 ................................................................................................................................ 219

Figure 127. Icy Cape transect line oceanographic, zooplankton, walrus, and bearded seal survey 

results, 2011 ................................................................................................................................ 220

Figure 128. Icy Cape transect line oceanographic, zooplankton, walrus, and bearded seal survey 

results, 2012 ................................................................................................................................ 221

Figure 129. Bearded seal calling activity as it relates to oceanographic variables at the inshore 

location, 2010-2012. ................................................................................................................... 224

Figure 130. Bearded seal calling activity as it relates to oceanographic variables at the midshore 

location, 2010-2012. ................................................................................................................... 225

Figure 131. Bearded seal calling activity as it relates to oceanographic variables at the offshore 

location, 2010-2012. ................................................................................................................... 226

Figure 132. Ice noise presence as it relates to oceanographic variables at the inshore location, 

2010-2012 ................................................................................................................................... 228

Figure 133. Ice noise presence as it relates to oceanographic variables at the midshore location, 

2010-2012 ................................................................................................................................... 229

Figure 134. Ice noise presence as it relates to oceanographic variables at the offshore location, 

2010-2012 ................................................................................................................................... 230

Figure 135. Water masses and circulation in the Chukchi Sea................................................... 244

Figure 136. Schematic of ecosystems and possible future scenarios.......................................... 245

Figure 137. Detector performance curves as a function of different detection threshold settings

..................................................................................................................................................... 258

Figure 138. Relative classifier performance of training and test datasets. ................................. 259

Figure 139. Locations of the AB-2012 (red) and MARU-DB recorders.................................... 261
OCS Study

BOEM 2015-034

viii

Figure 140. Diagram showing the schema and components of the Acoustic Ecology system... 264

Figure 141. Example of Noise Report Browser results .............................................................. 265

Figure 142. Example frame from one of the types of output movies computed by DELMA’s 

spatial analyzer............................................................................................................................ 266

Figure 143. Examples of another type of output from DELMA’s communication space visualizer

..................................................................................................................................................... 267

Figure 144. Spectrogram example of a frequency-modulated sound automatically detected by 

AB-2012...................................................................................................................................... 268

Figure 145. Daily detections of potential bowhead whale sound clips transmitted from AB-2012

..................................................................................................................................................... 269

Figure 146. Examples of 12 bowhead sounds ............................................................................ 270

Figure 147. Example of a long-term spectrogram ...................................................................... 270

Figure 148. Example of time-varying median noise level plot from MARU-DB_2010-11....... 272

Figure 149. Three-panel plot for MARU-DB_2011-12 showing an example of noise from an ice 

movement event.......................................................................................................................... 273

Figure 150. Three-panel plot for MARU-DB_2013-14 which shows an example of noise from a 

ship passing close........................................................................................................................ 274

Figure 151. Example of a diel plot of hourly median noise levels for the bowhead frequency 

band............................................................................................................................................. 275

Figure 152. Comparison of ambient noise level in the bowhead frequency band...................... 277

Figure 153. Times-series of noise levels and surface wind speeds............................................. 278

Figure 154. Regressed wind speed plot: the linear regression of ambient noise level ............... 279

Figure 155. Time series comparison of bowhead-band level noise............................................ 280

Figure 156. Monthly time series comparisons of daily bowhead-band level noise.................... 281

Figure 157. On-effort transects and the area of analysis within Arctic waters to estimate relative 

abundance. .................................................................................................................................. 288
OCS Study

BOEM 2015-034

ix

III. LIST OF TABLES

Table 1. List of all passive acoustic recorders.............................................................................. 21

Table 2. Summary of results for bowhead, beluga, and gray whale, walrus, and bearded seal 

calling activity, 2010-2012. .......................................................................................................... 24

Table 3. Key timing events for bowhead whale calling activity................................................... 25

Table 4. Key timing events for beluga whale calling activity. ..................................................... 29

Table 5. Key timing events for gray whale calling activity.......................................................... 31

Table 6. Key timing events for walrus calling activity................................................................. 33

Table 7. Key timing events of bearded seal calling activity. ........................................................ 35

Table 8. Key timing events of airgun, vessel, and ice noise activity............................................ 38

Table 9. Total number of sonobuoys deployed per year in the Arctic and the success rate, 2010-

2012............................................................................................................................................... 71

Table 10. Summary of visual trackline effort ............................................................................... 72

Table 11. Summary of sightings................................................................................................... 78

Table 12. Summary of mooring locations and measurements taken ............................................ 93

Table 13. Material properties and other parameters used in the scattering models...................... 96

Table 14. Maximum keel depth measured at the mooring sites ................................................... 98

Table 15. The number of hydrographic stations occupied in the Chukchi Sea .......................... 123

Table 16. The identifying number of the drifter and the latitude and longitude......................... 123

Table 17. Net configuration and sampling strategy for all transect lines ................................... 124

Table 18. Temperature and salinity ranges for different water masses in the Chukchi Sea. ...... 127

Table 19. Mean concentration of selected zooplankton taxa...................................................... 147

Table 20. Measurements of ice thickness and bottom oxygen used in the GAM runs............... 190

Table 21. Summary of the top variables, AIC, % R-squared values, and sample size............... 192

Table 22. Bowhead whale results of the GAM model................................................................ 193

Table 23. Gunshot call results of the GAM model ..................................................................... 198

Table 24. Beluga whale results of the GAM model.................................................................... 202

Table 25. Gray whale results of the GAM model....................................................................... 206

Table 26. Walrus results of the GAM model.............................................................................. 211

Table 27. Bearded seal results of the GAM model..................................................................... 223

Table 28. Ice noise results of the GAM model ........................................................................... 227

Table 29. Summary of the effects of Scenario 1 on key marine mammal species. .................... 249

Table 30. Summary of effects of Scenario 2 on key marine mammal species. .......................... 252

Table 31. Listing of the Cornell auto-detection buoy and MARU recording information......... 260

Table 32. The number of sightings (groups) used in the estimation of density (after truncation) of 

cetaceans within Arctic waters, 2010-2012. ............................................................................... 289

Table 33. Estimates of encounter rate (groups/km) and CV (in parenthesis) across all years for 

cetaceans within Arctic waters, 2010-2012. ............................................................................... 290

Table 34. Estimates of density (individuals/km2) and CV (in parenthesis) across all years for 

cetaceans within Arctic waters, 2010-2012. ............................................................................... 290
OCS Study

BOEM 2015-034

x

IV. TABLE OF CONTENTS

I. List of Acronyms...................................................................................................................... i

II. List of Figures ........................................................................................................................ iv

III. List of Tables.......................................................................................................................... ix

V. Executive Summary ................................................................................................................ 1

VI. Introduction ........................................................................................................................... 16

A. Background ........................................................................................................................ 16

B. Objectives of study............................................................................................................. 17

C. Summary of research effort................................................................................................ 18

D. Structure of report .............................................................................................................. 18

VII. Marine mammal distribution ................................................................................................. 19

A. Moored Observations......................................................................................................... 19

1. Methods.......................................................................................................................... 19

2. Results............................................................................................................................ 23

3. Discussion...................................................................................................................... 42

4. Conclusions.................................................................................................................... 62

5. Recommendations.......................................................................................................... 64

B. Shipboard Observations ..................................................................................................... 66

1. Methods.......................................................................................................................... 66

2. Results............................................................................................................................ 70

3. Discussion...................................................................................................................... 79

4. Conclusions.................................................................................................................... 84

5. Recommendations.......................................................................................................... 85

C. Photo-Identification............................................................................................................ 85

1. Methods.......................................................................................................................... 85

2. Results............................................................................................................................ 85

3. Discussion...................................................................................................................... 86

4. Conclusions.................................................................................................................... 86

5. Recommendations.......................................................................................................... 86

D. Satellite Telemetry ............................................................................................................. 86

1. Methods.......................................................................................................................... 86

2. Results............................................................................................................................ 88

3. Discussion...................................................................................................................... 89
OCS Study

BOEM 2015-034

xi

4. Conclusions.................................................................................................................... 89

5. Recommendations.......................................................................................................... 90

VIII. Biophysical Patterns and trends........................................................................................... 91

A. Moored Observations......................................................................................................... 91

1. Methods.......................................................................................................................... 91

2. Results............................................................................................................................ 96

3. Discussion.................................................................................................................... 119

4. Conclusions.................................................................................................................. 121

5. Recommendations........................................................................................................ 121

B. Shipboard Observations ................................................................................................... 122

1. Methods........................................................................................................................ 122

2. Results.......................................................................................................................... 126

3. Discussion.................................................................................................................... 172

4. Conclusions.................................................................................................................. 173

5. Recommendations........................................................................................................ 174

IX. Climate modeling.................................................................................................................. 175

1. Methods........................................................................................................................ 175

2. Results.......................................................................................................................... 175

3. Discussion.................................................................................................................... 185

4. Conclusions.................................................................................................................. 186

5. Recommendations........................................................................................................ 187

X. Correlation of marine mammal distribution to biophysical parameters................................ 188

1. Methods........................................................................................................................ 188

2. Results.......................................................................................................................... 190

3. Discussion.................................................................................................................... 231

4. Conclusions.................................................................................................................. 241

5. Recommendations........................................................................................................ 242

XI. Long-range predictions of habitat use by arctic and subarctic marine mammal species..... 243

1. Discussion.................................................................................................................... 243

2. Conclusions.................................................................................................................. 252

3. Recommendations........................................................................................................ 253

XII. Noise modeling and impact mitigation ............................................................................... 255

1. Methods........................................................................................................................ 255

2. Results.......................................................................................................................... 268
OCS Study

BOEM 2015-034

xii

3. Discussion.................................................................................................................... 282

4. Conclusions.................................................................................................................. 285

5. Recommendations........................................................................................................ 286

XIII. Estimating relative abundance of marine mammals .......................................................... 288

A. Visual................................................................................................................................ 288

1. Methods........................................................................................................................ 288

2. Results.......................................................................................................................... 289

3. Discussion.................................................................................................................... 290

4. Conclusions.................................................................................................................. 291

5. Recommendations........................................................................................................ 291

B. Acoustic............................................................................................................................ 291

XIV. Summary........................................................................................................................... 295

A. Overall summary .............................................................................................................. 295

B. Recommendations for future work................................................................................... 295

XV. Literature cited.................................................................................................................. 297

XVI. Acknowledgements ......................................................................................................... 316

XVII. List of publications and presentations............................................................................. 317

XVIII. Appendices...................................................................................................................... 321

A. Field survey summary table. ............................................................................................ 321

B. Mooring diagrams. ........................................................................................................... 322

C. Long-term passive acoustic data by species..................................................................... 326

D. Sonobuoy and visual survey data ..................................................................................... 335

E. Passive acoustics table showing percentage of days with calls for each species by 

month................................................................................................................................ 353

F. Photo-identification results within the Bering Sea and Gulf of Alaska. .......................... 354

G. Long-term passive acoustic data showing timespans included in the GAMs for each 

species or signal................................................................................................................ 356

H. GAM results including TAPS6-NG variables.................................................................. 360

I. List of attached electronic files ........................................................................................ 362
OCS Study

BOEM 2015-034

1

V. EXECUTIVE SUMMARY

The Chukchi Sea Acoustics, Oceanography, and Zooplankton (CHAOZ) study was 

initiated in September 2009 through an Interagency Agreement (formal title: Chukchi Offshore 

Monitoring in Drilling Area (COMIDA): Factors Affecting the Distribution and Relative 

Abundance of Endangered Whales) between the Bureau of Ocean Energy Management (BOEM)

and the National Marine Mammal Laboratory (NMML). The goal of this study was to document 

the distribution and relative abundance of bowhead, humpback, right, fin, gray, and other whales 

in areas of potential seismic surveying, drilling, construction, and production activities and relate 

changes in those variables to oceanographic conditions, indices of potential prey density, and 

anthropogenic activities. The scope of this study was expanded to include more marine 

mammals than just the cetaceans listed above. The final list included bowhead, gray, beluga, 

killer, minke, humpback, right, and sperm whales, bearded and ribbon seals, and walrus. Not all 

the species in this study area are listed under the Endangered Species Act (i.e., gray, beluga, 

killer, and minke whales, ribbon and bearded seals, and walrus are not listed).

The study had ten principal objectives:

1. Assess the year-round seasonal occurrence of bowhead, gray, and other whale calls 

in the northeastern Chukchi Sea.

2. Estimate relative abundance of these whales.

3. Obtain two full years of biophysical measurements on the shallow Chukchi shelf 

utilizing moorings at three sites, and collect hydrographic and lower trophic level 

data during deployment/recovery of the moorings.

4. Evaluate the extent to which variability in environmental conditions such as sea ice, 

oceanic currents, water temperature and salinity, and prey abundance influence 

whale distribution and relative abundance.

5. Run the National Center for Atmospheric Research (NCAR) climate model 

(Community Earth System Model: CESM1.0) for future projections using the sea ice 

extents from 2007/2008 as initial conditions.

6. Analyze multiple ensemble members CESM as well as the group of CMIP5 models

to assess the future variability of sea ice cover and extended sea ice free seasons 

during fall for the Chukchi Sea.

7. Evaluate whether changes in seasonal sea ice extent are resulting in a northward shift 

of Bering Sea cetacean species such as fin, humpback, and North Pacific right 

whales.

8. Provide long-term estimates of habitat use for large whale species and compare this 

with predictions about annual ice coverage to establish predictive variables that 

describe large whale occurrence.

9. Develop a quantitative description of the Chukchi Sea’s noise budget, as contributed 

by biotic and abiotic sound sources, and continuous, time-varying metrics of 

acoustic habitat loss for a suite of arctic marine mammal species.
OCS Study

BOEM 2015-034

2

10. Develop a near-real-time passive acoustic monitoring system that can be used as an 

impact mitigation tool.

The objectives of CHAOZ were addressed using multiple research disciplines. The study 

area was mainly in the northeastern Chukchi Sea, but also included research in the Bering and 

western Beaufort Seas. Data were collected both over the short-term (roughly, one month),

during ship surveys, and long-term, from year-round passive acoustic and oceanographic 

moorings. Data were collected in two year-long mooring deployments (2010-11 and 2011-12), 

as well as during three field surveys in August and September of 2010, 2011, and 2012. 

Research efforts during the field season included visual surveys, tagging studies, photoidentification studies, zooplankton and oceanographic sampling (CTD and Tucker sled 

zooplankton tows), passive acoustic monitoring (sonobuoys), drifter deployments, and a nearreal-time auto-detection buoy (2012 only) that provided acoustic detections and ambient noise 

data via an Iridium satellite link. Research that occurred in the lab during the rest of the year, 

included long-term analysis from over-wintering moorings (passive acoustic and biophysical) 

located at 40, 70, and 120 nm off Icy Cape, noise modeling to establish baseline data and 

predictions on the low-frequency acoustic environment (<1 kHz), and climate modeling with a 

focus on future ice projections. Figure 1 depicts the general study area and the main locations 

for data collection among the various research disciplines; also shown are the study areas for the 

industry-sponsored Chukchi Sea Environmental Studies Program (CSESP) within the lease area.
OCS Study

BOEM 2015-034

3

FIGURE 1. MAP SHOWING GENERAL STUDY AREA FOR THE CHAOZ PROJECT WITH CSESP STUDY AREAS DISPLAYED.

Results of this research help explain the distribution of marine mammals in the Chukchi 

Sea Planning Area in relation to oceanographic conditions and potential prey availability. 

Important products of this work are the creation of integrated biophysical (including 

oceanography, zooplankton indices, and marine mammal distribution) databases, a near-real-time 

passive acoustic monitoring system which can be used as an impact mitigation tool in an 

increasingly noisy environment, and predictions on the variability of ice conditions in a warming 

Arctic, including an extended sea ice free season. Information from this study will be used by 

BOEM for pre- and post-lease analysis and documentation under the National Environmental 

Policy Act (NEPA) for Beaufort Sea and Chukchi Sea Lease Sales. 

This report is organized into seven areas of research (Sections VII – XIII) which address 

the objectives; summaries for each section are presented herein. The major highlights and 

recommendations from this research precede the section summaries here. 
OCS Study

BOEM 2015-034

4

Key Findings

The Chukchi Sea ecosystem is complicated: landscape ecology, and regional and local 

forcing all combine to determine whether or not there will be favorable conditions for both the 

permanent and transitory residents. The residents of interest in this study, marine mammals, 

belong to several different feeding guilds, further complicating our goal of understanding how 

climate change and other anthropogenic forcing will affect them. In this study, one species of 

interest is planktivorous (bowhead), another predominantly piscivorous (beluga), and two are 

obligate benthic feeders (walrus and bearded seal) and one is a facultative benthic feeder (gray 

whale). Another factor that challenges our comprehension of Chukchi Sea dynamics is the 

degree to which each species depends on or coexists with seasonal sea ice. Listed below are 

some key findings of this research.

 Dense, nutrient-rich Bering Sea water is advected into the Chukchi Sea shelf, together 

with warm, fresh water from the Alaska Coastal Current. Ice algal blooms occur below 

the ice and as the ice melts this production is exported to the bottom, where it continues 

to produce oxygen into the summer. During summer, subsurface phytoplankton blooms 

are common, and fuel secondary productivity.

 30-50% of the transport through Bering Strait goes along the coast past Icy Cape, and 

variations in flow are highly correlated with local winds. Also, Atlantic water can be seen 

as far south as Icy Cape, indicating that slope water can intrude > 200 km onto the 

shallow Chukchi Sea shelf. 

 There was large interannual variability, with 2010-11 having stronger flow, more 

polynyas, and more incidents of flow up from Barrow Canyon, than were observed in 

2011-12 and 2012-13. Ice keels on the Icy Cape line often exceed 20 m in depth and 

some were greater than 25 m. Deep keels are usually found in the spring.

 High concentrations of ammonium can be seen on the Pt. Hope line and in Barrow 

Canyon, indicating an active microbial loop of converting detritus into ammonium. 

Ammonium is the preferred nitrogen form for many phytoplankton.

 In a shallow, benthic dominated system such as the Chukchi, sinking of the heavy 

refractory components of crude oil have the potential to impact food webs. Three of the 

six marine mammals in this study feed on the bottom; the species of krill fed upon by 

bowheads and Arctic cod may feed on detrital material in the nepheloid layer just above 

the bottom.

 Zooplankton community composition showed great variability among years and there 

was evidence for events such as on-shelf advection which introduced Arctic basin species 

to the shelf. There was also physical evidence for up-canyon transport along Transect E 

(Wainwright).

 Both bowhead and beluga whales undergo consistent, predictable seasonal migrations 

that are strongly correlated with both month and ice concentration. Bowhead calling 

activity showed a strong positive correlation with winds heading SSW. These winds 

could perhaps serve as a tailwind - helping them to conserve energy during their fall 

migration. These winds could also force ice into the area, and this may serve as a cue to 

start their migration. In the fall, bowhead calling ceased once ice thickness was > 0.5m. 
OCS Study

BOEM 2015-034

5

 Bowhead whale calling activity during the fall 2010 migration was tri-modal, and may be 

the result of age/sex segregation. The gunshot call type occurred at end of these peaks, 

possibly suggesting its use as a migration cue to assemble and move. The gunshot calls 

were also strongly associated with the formation of ice. It is possible they use this call 

type to navigate through the ice and determine its thickness.

 Walrus were detected over winter at the offshore location, which was very unexpected, as 

most walrus overwinter on the Bering Sea pack ice. One ribbon seal call was also 

detected in April, when this species is said to be in the Bering Sea as well. In addition, 

vessels were also detected overwinter in both years, which we are attributing to Russian 

ice breakers. 

 Gray whales were strongly correlated to prey availability, and were detected in large 

clusters either near the mouth of Barrow Canyon, or off Point Hope. Both of these 

locations showed large numbers of larvaceans and Pseudocalanus (proxies for high 

plankton biomass and productivity) as well as high amounts of nitrate and ammonium. 

Bearded seals and walrus tended to prefer the same shallow offshore locations near 

Hanna Shoal, an area known to have high biomass of benthic epifauna and infauna, but 

there were few overlapping sightings between the species. 

 The photo-documentation of a cow/calf humpback pair within Arctic waters and the 

acoustic detection of fin whale calls (on sonobuoys) near Barrow Canyon were important 

achievements as they provided evidence of subarctic species in the Arctic. Also, satellite 

telemetry analysis of one gray whale indicated foraging behavior within a limited area 

during the tag’s entire transmission period, providing insight into summer feeding ground 

habitat use.

 This study illustrates the importance of utilizing multiple survey methods. Comparison 

of visual survey versus passive acoustic monitoring during the fall cruises found 

comparable results for bowhead and beluga whales and walrus, better results visually for 

gray and minke whales, bearded seals, and Dall's and harbor porpoise, and better results 

acoustically for humpback, fin, and killer whales. Ringed and spotted seals were not 

differentiated acoustically and so comparisons cannot be made. 

 Our results are consistent with recent publications predicting a shift in ecosystem regimes 

from a benthic-dominated system to a more pelagic-dominated system. That shift may 

already have begun.

 The first auto-detection buoy was successfully deployed in the Arctic approximately 55 

nm off Icy Cape. From 01 September - 01 November 2012, the system detected and 

transmitted biotic and abiotic signals via satellite in near-real time. 

 As a result of predicted increases in surface wind speeds, median minimum ambient noise 

levels in the 71-708 Hz frequency band (bandwidth utilized by most marine mammals) 

are predicted to increase by at least 10 dB, from approximately 85 dB to approximately 

95 dB, with overall median noise level increasing from approximately 95 dB to 

approximately 105-110 dB, which is equivalent to levels in today’s shipping lanes off 

Boston.

 Despite small sample sizes, results from the visual survey provided a measure of relative 

densities of four species (bowhead, fin, humpback and gray whale) in the Chukchi Sea 

during August and September.
OCS Study

BOEM 2015-034

6

 This study collected biophysical, marine mammal, and passive acoustic data in regions 3-

5 (long-term for regions 4 and 5) of the Distributed Biological Observatory (DBO).

Section VII: Marine Mammal Distribution

Two year-long deployments of three long-term passive acoustic recorder arrays (moored 

AURALs) deployed off Icy Cape were made over the course of this study. A total of 8,054 days 

of acoustic data were collected from the long-term passive acoustic recorders. One recorder 

from each array (inshore, midshore, and offshore) for each year was analyzed fully for all 

species/signals (total of 1,744 days of acoustic data). Bowhead whales were regularly detected 

on the AURALs and both the fall and spring migrations were detected as pulses in the calling 

activity levels. The fall migration was captured on all three moorings and the spring migration 

was detected primarily at the inshore location, which is consistent with open leads in the 

nearshore ice in the spring and a fanning out of the fall migration over the Chukchi shelf. The 

fall 2010 migration pulse was trimodal, perhaps corresponding to age/sex segregation in the 

migration. Gunshot calls coincided with the end of most peaks in bowhead calling activity. This 

correlation may be useful for distinguishing between right whales and bowheads where their 

distributions overlap in the Bering Sea. Beluga whales also had strong calling peaks consistent 

with fall and spring migrations; both migrations are seen at all locations, with the highest levels 

of calling activity seen at the inshore location. Two populations of beluga whales migrate 

through the area at overlapping times; passive acoustics may be a useful tool for distinguishing 

between these populations. Very little calling activity was seen for gray whales, which is 

consistent with other research in the area and likely indicative of the placement of recorders 

outside their core summering areas and low calling rates, rather than an absence of gray whales 

in the study area. Fall and spring pulses in calling activity at the inshore location seem to suggest 

that those migrations were detected. Walrus were detected at all locations in both years, but 

limited to the summer/fall at the inshore and midshore locations. Calling activity was nearly 

year-round at the offshore location, with high peaks seen in February/March in both years; this 

was an unexpected finding and has not been described in any publication to date. Bearded seal 

calling activity was nearly ubiquitous for both years at all locations and showed slight 

differences in calling activity levels between locations and years. A large escalation to saturated 

calling levels coincided with the whelping/mating/molting season.

Killer whale calling activity was detected on several days, mostly inshore. Minke whale 

and ribbon seal calling activity were each detected on one day at the inshore location. The 

ribbon seal calling activity occurred in April which was outside the known range of this species 

in the Arctic. It is likely that spotted and ringed seals were detected, but we did not classify 

these to species. Instead they were categorized as unidentified pinnipeds, with calling activity 

occurring in both years and at all locations, with the majority at the inshore location. There was

no calling activity for humpback, fin, right, and sperm whales from acoustic moorings; this was 

expected for the first two species given placement of the recorders (and expected in general for 

the latter two). The long-term distribution of vessel, airgun, and ice noise activity was also 

analyzed, with expected results. Two cases of vessel noise activity present in the middle of the 

winter are thought to be from distant Russian icebreakers.

In addition to moored AURALs, sonobuoys were deployed every three hours throughout 

the entire cruise to obtain an evenly sampled cross-survey census of marine mammal calling. 
OCS Study

BOEM 2015-034

7

Concurrent with sonobuoy deployments, visual surveys, limited to daylight hours, were 

conducted to document the presence and distribution of all marine mammals encountered 

throughout the survey. A total of 630 sonobuoys were deployed and 4,250 nm of trackline were 

visually surveyed. Acoustic surveys detected six cetacean species (bowhead, gray, humpback,

fin, minke, and killer whales) and two pinniped species (walrus and bearded seal). Visual 

surveys detected seven cetacean species (bowhead, gray, humpback, fin, minke, harbor porpoise, 

Dall’s porpoise), five pinniped species (walrus, bearded, ringed, spotted, and northern fur seals)

and a polar bear within the survey area. Photographs were opportunistically collected of 

humpback, killer, and gray whales, and North Pacific right whales (Bering Sea only) but no 

matches were made to existing catalogs. The most commonly sighted and/or acoustically 

detected species were bowhead whales, gray whales, walrus, and bearded seals. There were 

comparable visual and acoustic results for bowhead whales and walrus, (and a lack thereof for 

beluga whales). Gray whales and bearded seals were detected more often visually than 

acoustically, and believed to be a result of low calling rates at this time of year. Porpoise were 

only detected visually; frequency limitations of the sonobuoy system prevent their echolocation 

clicks from being detected. Fin, humpback, and killer whales were heard more often than they 

were seen. Fin whale calls were detected near the mouth of Barrow Canyon, which is at least 

280 km farther north than the previous known record in the Alaskan Arctic. This study 

illustrates the importance of utilizing multiple survey methods, as certain methods are better at 

detecting certain species than others. Although limited in time, these data provide extensive 

spatial coverage to complement the long-term, but point sampled, data collected from the 

moored recorders.

The final method utilized to assess marine mammal distribution was satellite telemetry. 

One juvenile gray whale was embedded with a satellite tag which transmitted positional data for 

48 days. The animal remained within 140 km of the deployment area and occupied relatively 

shallow waters (20-50 m in depth) to the south of Hanna Shoal. Models indicate it was most 

likely foraging in the area. 

Section VIII: Biophysical Patterns and Trends

Each year, three, year-long biophysical moorings were deployed in the middle of the 

passive acoustic arrays at the three sites (inshore, midshore, offshore) off Icy Cape. To avoid ice 

keels, each mooring was only ~10 meters above the sea floor. These instruments collected data 

on over 15 different oceanographic parameters. Data were collected at least hourly and CTD and 

Niskin bottle casts were conducted following or preceding mooring recoveries and deployments 

to calibrate instruments on the moorings. Hydrographic surveys were also conducted yearly on 

six (five in 2010) hydrographic transect lines. CTD deployments measured water column 

properties, and Niskin bottles collected water samples at various depths to measure oxygen, 

chlorophyll, nutrients (nitrate, phosphate, silicate, nitrite and ammonium), and salinity. 

Bottom currents were generally northeastward following bathymetry, and variability in 

currents was primarily wind-driven. Approximately 40% of the flow through Bering Strait 

passes the Icy Cape line. Bottom temperature ranged from approximately -1.8 to < 5.0 °C, with 

maximum temperatures occurring in late August or September. Salinity ranged from < 31 to 

~34.5 psu and was highly variable, as a result of different water types, and the melting and 

freezing of sea ice. The highest turbidities occurred in fall when the winds began to increase and 
OCS Study

BOEM 2015-034

8

before the sea ice areal coverage became >80%. The spring phytoplankton bloom was evident in 

each time series. Nitrate ranged from 0 – 20 μM; concentrations decreased from mid-spring 

through July or August and then increased during late winter and early spring. During the time 

of the shipboard surveys, the surface was largely depleted of nutrients along all lines. Sea ice 

arrived in early to mid-November, increased quickly to near 100% areal coverage and then

declined precipitously in late May or June. Ice thickness increases to an average of ~4 m in 

March, with the thickest ice generally seen late in spring. The position of the ice influenced the 

water properties; the Alaska Coastal Current and Winter Water appeared in both years, but Melt 

Water only appeared in 2012. 

It was evident from the mooring deployments that there were more polynyas in 2010-

2011 than 2011-2012. In addition, the transport was stronger in 2010-2011 than in 2011-2012 

and 2012-2013. Ice arrived at approximately the same time in 2011 and 2012, but it retreated 

almost a month earlier in 2011 than 2012. This was reflected in the bottom mooring 

temperatures, which warmed to above -1.8 °C in early to mid-June in 2011 and in late June to 

late July in 2012. Shipboard transects showed that vertical stratification was strongest along the 

Point Hope transect (DBO3) in 2010, primarily due to lower surface salinities in 2010. Farther 

north at the Point Lay and Icy Cape lines, the ACC appeared to be more confined in a narrow 

band along the coast. The biggest difference between the years was at the Wainwright and 

Barrow Canyon lines. In 2012, intrusions of high silicate and nitrate were observed on the 

western Barrow Canyon and the shelf west of the canyon. We hypothesize that this is slope 

water intruding up the canyon and onto the shelf, perhaps a result of upwelling. This may have 

affected the ACC as well.

An Acoustic Doppler Current Profiler and a TAPS6-NG were used to estimate 

zooplankton volume backscatter and concentration. Inverse methods were used to estimate the 

abundance of scatterers as a function of size in two depth bins (near bottom and near surface).

Near-bottom abundance estimates for euphausiid-shaped scatterers showed that the 14-18 mm 

size range had the highest concentrations (30-40 per m3), with even higher concentrations of this 

class near the surface (100-200 per m3). As expected, abundance was highest in the fall and 

declined during winter. Near-bottom abundance estimates for the 2-6 mm size range copepodshaped scatterers were approximately 1500 per m3

, and abundances did not decline with time 

from fall to winter. Adult and juvenile euphausiids had the highest concentration in the study 

area in the cold year, 2012. Concentrations of younger stages of euphausiids, principally furcilia, 

were at least an order of magnitude higher than adult and juvenile concentration with no 

consistent difference among cold and warm years. Low concentrations of the youngest 

euphausiid stage, calyptopis, were captured in 2010, the presence of which suggests recent 

spawning in the region. Net-based estimates of juvenile and adult euphausiid concentrations 

were low and did not yield evidence for the conveyor belt hypothesis. Concentrations of the 

furcilia stage were much higher, and in 2010 and 2011 were present at all stations across the 

shelf. In one warm year, evidence for euphausiid reproduction in the Chukchi was found. 

Hyperiid and gammarid amphipods were found in similar concentrations to euphausiids. Among 

the copepod taxa, Oithona spp. had the highest concentrations and Pseudocalanus spp., was very 

abundant in all years. Both net and acoustic estimates indicated that zooplankton concentrations 

are often as high or higher near the bottom than they are in the rest of the water column on the 

Chukchi shelf in summer.
OCS Study

BOEM 2015-034

9

Diel vertical migration by zooplankton was not regular (non-stationary) during the year. 

In the Chukchi Sea, a diel signal was not always detectable, but a semi-diurnal signal was often 

statistically significant at depth, particularly at the midshore and offshore moorings. Temporal 

patterns at the three mooring stations were often very different, indicating great variability in 

zooplankton behavior across this relatively broad, flat shelf. The shallow water column and 

difficulty predicting where the zooplankton spend most of their time may make it difficult to 

understand the exposure of plankton to oil, should there be an oil spill in the region. It is 

possible to moor single and multi-frequency instruments on the bottom to assess water column 

zooplankton throughout the year. As the instruments become more reliable, our knowledge of 

what happens during the winter and early spring will increase. 

Section IX: Climate Modeling

The goal of the climate modeling component of the CHAOZ project was to provide 

projections of future sea ice and ocean conditions in the Chukchi Sea based on coupled climate 

models. There were two parts to the climate modeling aspect: conduct a climate model 

assessment, and study the impact of changing initial sea ice conditions on future projections. 

Based on evaluations, we found that all of the selected 12 CMIP5 models are doing decent jobs 

in terms of simulating the sea ice cover over the Chukchi Sea. According to these 12 models, the 

length of open water duration will be prolonged over the entire Chukchi Sea under the RCP8.5 

emission scenario, although there is an evident north-south gradient. The changes in sea ice 

from 2010 to 2020 are small in the Alaskan Arctic, except near the coast in the Beaufort Sea and 

in the east Siberian Sea under the RCP8.5 (high) emission scenarios. From 2030 to 2040, the 

change is obvious with most of the northern Chukchi Sea (defined as 80° or north) and the 

Beaufort Sea having 11 months of ice coverage instead of 12. By the decade centered in 2050, 

the northern Chukchi Sea and the Beaufort Sea would have ice presence only up to 10 months. 

This implies that the northern Chukchi Sea (near latitude 80° N) would shift from current yeararound sea ice cover to seasonal open water of 1-2 months. An acceleration of ice reduction can 

be inferred in the middle of the 21st century. In the southern Chukchi Sea (north of the Bering 

Strait to 70° N), sea ice cover will be reduced from 8-9 months coverage at present to 5-6 months

of coverage by 2040, i.e., more than half a year of open water as predicted by these models. 

Currently there are 0-4 months of open water duration in the Chukchi Sea. As time progresses, 

the differences between the north and south will be reduced, and the entire Chukchi Sea will 

have more than 7 months of open water by the end of 21st century. Although the models

indicate that ice can rebound from an extreme low condition within a year, in the past decade, we 

have been losing thick, multi-year sea ice in the Arctic, and therefore the quality of the ice has 

been reduced. Model simulations indicate that there is large interannual variability of ice 

thickness in the Chukchi Sea. At the beginning of ice formation, the averaged ice is less than 0.5 

m thick (December), and ice gradually grows to 0.5-1 m thick in January. From February to 

June the average ice thickness is between 1-2 m thick with relatively large interannual 

variability. There is no significant trend in the next 20-30 years according to the CESM under 

the more moderate RCP6.0 emission scenario.

Besides sea ice condition, we also investigated the ocean conditions simulated by 

CESM1.0, and the modeling results are very encouraging. The monthly mean ocean currents at 

30 m depth in the model agree well with observations. Both the directions and the magnitude of 
OCS Study

BOEM 2015-034

10

ocean currents were well captured by the model runs. The ocean temperature and salinity from 

the CESM1.0 model runs and those observed at moorings were also compared. The model 

captured the seasonal variation of the ocean temperature well, but the models seem to show 

larger temperature gradients in the spring and fall seasons compared with the observations. The 

model results for salinity were less satisfactory; they underestimated salinity values year round at 

all three mooring locations. Running these models revealed that anthropogenic forcing is more 

important than the initial conditions on the decadal scales. The sea ice will remain at its current 

level up to 2040 in the Chukchi Sea in winter, but a larger decline in the fall is expected. This 

indicates that sea ice will arrive in the Southern Chukchi Sea later and later; this pattern has 

already been occurring in the past decade. Although the ice cover seems to be relatively stable in 

the spring as shown in the ensemble means, there are episodic events of early retreat. The 

increasing frequency of these early retreat events will especially become evident after 2050. The 

change of projected sea ice condition is consistent with model projected ocean temperature 

change, in that fall has the largest temperature increase compared with other seasons. We may 

see ice form later in the fall than at present, particularly in the southern Chukchi Sea. In any 

given year, the long-term ice thickness trend is overwritten by the large interannual variability, 

which is shown by our new, reduced ice cover initialized runs. 

Section X: Correlation of Marine Mammal Distribution to Biophysical Variables

Generalized Additive Models (GAMs) were run to assess the effects of oceanographic 

conditions on the distribution of marine mammal calling activity. All possible combinations of 

the seven marine mammal species/sound sources at the three mooring locations and with the 19 

biophysical variables were used in the GAM runs. In addition to the GAMs, calling presence for 

each species/sound source was plotted against eight oceanographic variables (ice concentration, 

ice thickness, chlorophyll, oxygen, nitrate, salinity, wind speed, and transport) at all three 

mooring locations to determine if any positive or negative correlations existed on a temporal 

scale. Furthermore, biophysical results from the shipboard transect lines were correlated with 

the visual and passive acoustic (sonobuoy) data. Measurements of temperature, salinity, nitrate, 

and ammonium were plotted with zooplankton (Pseudocalanus, C. glacialis, larvaceans, and 

pteropods) concentration. These were then plotted with sonobuoy effort and detections as well 

as visual sightings for gray whales, walrus, and bearded seals.

Both bowhead and beluga whales undergo consistent, predictable seasonal migrations 

that are strongly correlated with both month and ice concentration in the GAM models. 

Bowheads also showed a positive correlation with winds to the south-southwest in the fall, which 

may serve as a migration cue. Bowhead whale fall migrations were also found to be linked to 

several variables which may serve as proxies for prey availability. There was a strong 

correlation between gunshot call activity and both ice presence and thickness. We suggest that 

bowhead whales use this particular call type to assess ice thickness, to cue migration, or both. 

Beluga whales are positively correlated with the presence of polynyas. Because our recording 

system is frequency limited we are unable to detect the echolocation clicks beluga whales 

produce while feeding, however, our recorders were not placed in the prime beluga feeding area, 

and so a correlation with prey and calling activity was not expected.

Gray whale calling activity was sparse throughout the two years of long-term recordings 

most likely due to placement of the recorders along with low calling rates. This calling activity, 
OCS Study

BOEM 2015-034

11

however, showed a significant correlation with ice concentration and a weak one (in 2011-12) 

with chlorophyll. There is also a tentative correlation between increased calling activity during 

the high transport deployment year. Gray whales were also sighted in large clusters near the 

mouth of Barrow Canyon and in the middle of the transect line off Point Hope; areas known to 

have high biomass of benthic infauna and epifauna. Both of these locations also showed high 

concentrations of zooplankton (larvaceans and Pseudocalanus) as well as high amounts of nitrate 

and ammonium at the Point Hope location. 

Month, ice (thickness and concentration respectively), and several variables that can 

serve as proxies for prey availability, showed significant correlation with walrus and bearded 

seal calling activity. Possible presence of a polynya and prey availability make the surprising 

result of overwintering walrus in the northeastern Chukchi Sea plausible; we suggest these are 

juvenile males that did not migrate to the Bering Sea mating grounds. Results from the GAM 

analyses suggest prey availability might be the reason the ramp-up of bearded seal calling levels 

varies interannually and among locations. Both walrus and bearded seals were found along the 

Icy Cape and Wainwright transect lines near the shoals, an area known to have high biomass of 

benthic epifauna and infauna, but there were few overlapping sightings between the species. Ice 

noise activity levels were highest inshore and seemed to be influenced by wind direction. At all

locations ice concentration was positively correlated and ice thickness was negatively correlated 

with ice noise activity levels.

Section XI: Long Range Predictions of Habitat Use by Arctic and Subarctic Marine 

Mammal Species

Currently, the Chukchi Sea shelf is a benthic-dominated ecosystem, with a large amount 

of nutrients being advected into the area from the highly productive Bering Sea. This dense 

water, modified by ice melt and summer heating, results in a highly stratified water column. In 

addition, near the coast, the warm low-salinity ACC overlays this denser Bering water, 

stratifying the coastal waters. Subsurface phytoplankton blooms are found at and below this 

interface, and fuel secondary productivity in the benthos. As a result, there is tight benthicpelagic coupling that sustains the higher trophic levels. 

We used the climate modeling results from this project to predict future changes in Arctic 

environmental conditions and the possible response of Arctic and subarctic marine mammals to

these changes. The models predict an earlier retreat in seasonal sea ice and suggest that ice will 

form later in the fall season, creating longer open-water seasons. Based on these predictions, we 

would like to discuss two possible scenarios, both of which are dependent on the winds. If the 

winds remain strong as the ice retreats/melts, then the water column will be well mixed, delaying 

the spring phytoplankton bloom. This scenario will result in a loss of carbon flux to the benthos, 

and an increased volume of Bering Sea Summer Water and ACC into the Chukchi, and thus 

higher transport of nutrients into the system. However, an increase in ocean storms combined 

with weaker stratification will cause mixing of the water column, injection of nutrients into the 

sunlit surface waters, and result in a shift to a regime where more of the production remains in 

the water column. Our climate model predictions estimate that the magnitude and direction of 

the currents will remain similar to present day levels. If this holds true, then this scenario of 

shifting more production to the pelagic zone is highly likely. 
OCS Study

BOEM 2015-034

12

The other scenario includes an earlier ice retreat/melt, a decrease in winds (e.g., weaker 

winds at the time of ice retreat), and continued warming of the atmosphere. This will result in 

early salinity stratification from the melting ice, supporting an early surface phytoplankton 

bloom which uses up all nutrients in that layer. This surface layer will warm via solar heating, 

resulting in a strong vertically stratified water column during the summer, which combined with 

a weakening of nutrient advection would limit surface blooms. However, the increased 

penetration of light below the pycnocline (due to low phytoplankton biomass) may allow 

subsurface phytoplankton blooms to form. This scenario will result in the early export of ice 

algae to the benthos, but the magnitude of that export will likely decrease with earlier ice 

retreats/melt. Thus, benthic secondary production will decrease unless the flux of phytoplankton 

carbon from the subsurface blooms is enough to compensate for the loss of carbon to the benthos 

when the ice retreats earlier. The amount of production in the subsurface layer under relatively 

low light, but high nutrient conditions remains unknown. 

Bowhead, beluga, and gray whales are the most adaptive, and are best suited to adjust to 

a regime or ecosystem shift. However, ecosystem changes may result in population or migration 

re-distribution, which could have severe negative effects on native subsistence hunting. Walrus 

are predicted to be the most vulnerable to climate change or ecosystem shifts. They may be 

forced to haul out on the coast, which will impact population health; they may also just move out 

of the Chukchi Sea, which some studies show is already happening. Bearded seals are more 

adaptable than walrus, and will probably adjust accordingly. Finally, longer open-water seasons 

will result in more subarctic species (including pinnipeds) moving farther north into the Chukchi 

Sea and creating increased competition with resident Arctic species. 

It is important to remember that regardless of which scenario actually occurs, most 

marine mammal species have innate migration patterns and reproductive cycles, as seen in the 

strong correlation between most of the marine mammals and month in the GAM integrative 

results. As such, migration timing and patterns either may not change significantly, or those 

changes may be considerably delayed. Furthermore, given their status in the highest trophic 

level, indirect effects of climate change on apex predators often involve several trophic levels 

and therefore have a delayed response on the population. This illustrates the importance of 

continued long-term passive acoustic monitoring, which is ideally suited to documenting 

migration patterns. Finally, both scenarios predict an increase in low-frequency ambient noise

levels of about 10-15 dB, due to both natural causes as well as increased shipping traffic. This 

increase could have negative effects on acoustic communication with conspecifics, foraging, 

navigation, or evading predators. 

Section XII: Noise Modeling and Impact Mitigation

This objective had two primary goals: a) develop methods to report occurrences of 

acoustically active marine species and ocean noise metrics in near-real-time; and b) quantify and 

assess the Chukchi Sea “noise budget” (including biotic and abiotic sound sources) and assess 

the influences of individual source types and the aggregate of multiple sources, including 

different source types, on the overall acoustic environment and on the acoustic habitats of 

selected marine mammal species. As a result we now have a method by which to quantify the 

acoustic contributions from vessels and seismic airgun surveys to the aggregate noise budget (see 

Clark et al., 2009; Hatch et al., 2012).
OCS Study

BOEM 2015-034

13

An auto-detection buoy was utilized to address the first objective. This was the first autodetection buoy to be deployed in the Arctic. The buoy was deployed approximately 55 nm off 

Icy Cape between 01 September and 01 November 2012, and in that time detected and 

transmitted via satellite, 762 audio clips of possible bowhead whales. Of these 762 audio clips 

identified by the auto-detector as bowhead whales, 351 were confirmed as bowheads by 

experienced analysts (46%). In addition to bowhead whales, the auto-detection buoy detected a 

period of seismic airgun activity from approximately 12 - 18 September, 2012. A bidirectional 

communications feature was developed and successfully allowed analysts to request selected 

portions of data from the buoy. This enabled us to double check, in greater detail, any biotic or 

abiotic detection event recorded on the onboard data storage system. At this point, the AB’s 

fundamental system infrastructure has been established and validated. One potential future 

utilization of this instrument would be to use this to monitor ambient noise levels in real-time for 

mitigation purposes.

In the summers of 2010 and 2011, a single marine autonomous recording unit (a.k.a., 

MARU) supplemented with a second glass sphere containing additional batteries was deployed 

in the Chukchi Sea between the inshore and midshore locations. The purpose of these 

instruments was to collect and utilize empirical data to determine current and predict future 

ambient noise levels in the Chukchi Sea. These MARU buoys recorded data throughout the 

year, on a 50% duty cycle in 2010, and continuously in 2011.

A computer analytical system called DELMA was used to process all recorded data and 

basic sound analyses were used to compute, illustrate, and compare a suite of acoustic measures 

for both the broadband and bowhead-band frequency ranges. Results were used to calculate the 

spatial-temporal-spectral variability of the acoustic environment. In particular, an effort was 

made to analyze the data for possible structure in the relationships between noise metrics, wind 

level, and percent ice coverage. This was undertaken in order to inform models to predict future 

ambient noise levels under reduced ice concentration conditions. Data on percent ice coverage 

and wind speeds were used to test for relationships between wind speed, percentage of ice cover,

and ambient noise metrics.

A high negative correlation between noise and ice in the fall was discovered as the ice 

concentration is increasing, and a high positive correlation existed between noise and ice in late 

spring as the ice concentration is decreasing. In the future, with the expected decreases in ice 

concentration and the duration of the ice season, we expect an increase in ocean storms. The 

increase in the spatial and temporal extent of open water combined with the increase in storms 

will lead to a dramatic increase in ambient noise conditions. In terms of the spatio-temporal 

change, we should expect noise level increases to follow the seasonal and geographic decrease in 

loss of ice. A reasonable expectation is that median minimum noise levels in the 71-708 Hz 

frequency band will shift up by at least 10 dB from approximately 85 dB to approximately 95 dB 

and overall median noise level increase from approximately 95 dB to approximately 105-110 dB. 

These predicted median noise levels are equivalent to levels in today’s shipping lanes off Boston 

(Hatch et al., 2012).
OCS Study

BOEM 2015-034

14

Section XIII: Estimating Relative Abundance of Marine Mammals

To estimate relative abundance within Arctic waters, encounter rates (groups/km) and 

densities (individuals/km2) from sightings collected during “on-effort” status were computed. 

The area of analysis (198,677 km2) was defined as U.S. waters just north of the Bering Strait to 

the easternmost extent of effort, east of Barrow, AK. Detection probability was estimated using 

the hazard-rate within Conventional Distance Sampling framework. Perpendicular distances 

were pooled across all “large whale” species (bowhead, fin, humpback, and gray whales). 

Pooling is beneficial in that it provides a greater sample size for fitting the detection function, it 

enables density to be computed for species with relatively low numbers of detections, and it

improves the precision of these estimates. Modeling of perpendicular distance was conducted 

with ungrouped data truncated at 3 km for all whale species. 

On-effort trackline used to estimate densities totaled 1,230 nm (2,278 km). Estimated 

parameters for the hazard rate model (the model most supported by AIC) across all species were: 

average detection probability (P) – 0.32, CV (P) – 0.23, and effective strip width (ESW) – 0.96 

km. From 2010 to 2012, there were 16 sightings of bowhead whales (.0059 encounter rate; 

density = .0043), 5 sightings of fin whales (.0022 encounter rate; density = .0019), 2 sightings of 

humpback whales (.0007 encounter rate; density = .0004), and 15 sightings of gray whales 

(.0051 encounter rate; density = .0030). Visual estimates of relative abundance presented in this 

study assumed that no cetaceans were missed on the trackline (g[0]=1). Visual survey methods 

for this project differed from standard line-transect or abundance surveys due to constraints of 

the overall project. In the future we recommend conducting a dedicated shipboard survey if the 

goal is to compute densities in a relatively small area such as the lease area, or aerial transect 

surveys for large areas. 

Recommendations

The data collected for this study demonstrate the utility and benefit of concurrent 

zooplankton, oceanography, and marine mammal/noise monitoring, combined with climate 

modeling. These data, along with those currently being collected for the ongoing BOEM-funded 

ARCWEST and CHAOZ-X projects represent the only long-term integrated dataset of its kind 

from the Chukchi Sea lease area and Alaskan Arctic in general. We therefore recommend 

continuation of the long-term mooring deployments. With current modifications to the moored 

TAPS6-NG instruments, we will be able to collect data for a full year, allowing for assessment of 

trophic interactions on an annual time scale. It will also be possible to establish multi-year 

patterns in marine mammal distributions as they relate to indices of zooplankton and 

oceanographic conditions. Moorings should be deployed not only in locations where the biggest 

changes in oceanographic and marine mammals and prey distribution are expected to occur, but 

also across a broad spatial range (as is the case with the ARCWEST/CHAOZ-X projects). This 

will ensure that critical migration timing and distribution patterns are fully documented. 

We also recommend continuation of the integrated biophysical shipboard surveys

conducted during this study. These surveys provide data on the fine-scale vertical resolution of 

zooplankton abundance as they correlate with oceanographic indices, nutrients, and distribution 

of marine mammals. To maximize marine mammal detections during shipboard surveys, it is 

essential to have both passive acoustic monitoring and visual survey components. Since each 
OCS Study

BOEM 2015-034

15

method is well-suited to particular species, together they provide a more complete picture of 

marine mammal distribution. In addition, joint passive acoustic/visual survey focal follows 

enable future calculations of relative abundance. Addition of a benthic ecology component 

would help to address prey availability for those mammals that feed on benthic epifauna and 

infauna.

Because this area is predicted to undergo rapid climate change, it is critical to know what 

is happening to currents and ice cover during the crucial spring and fall months. Unfortunately, 

because of the ice cover these seasons are currently inaccessible with present technologies, 

excepting passive acoustic recorders. To help increase our understanding and knowledge of 

oceanographic conditions and to collect the necessary suite of data, investments in new 

technology are necessary, perhaps in the form of advanced moorings or autonomous subsurface 

gliders/AUVs. Furthermore, animal-borne sensors should be utilized to take advantage of realtime discrete sampling and gain valuable information on marine mammal habitat utilization 

during these dynamic seasons.
OCS Study

BOEM 2015-034

16

VI. INTRODUCTION

A. Background

The western Arctic physical climate is rapidly changing. The summer Arctic minimum 

sea ice extent in September 2012 reached a new record of 3.61 million square kilometers, a 

further 16% reduction from a record set in 2007 (4.30 million square kilometers), and a more 

than 50% reduction than that of two decades ago (Parkinson and Comiso, 2013). The speed of 

these changes was unexpected, as the consensus of the climate research community just a few 

years ago declared such changes would not occur for another thirty years (Wang and Overland,

2009). As sea temperature, oceanographic currents, and prey availability are altered by climate 

change, changes in marine mammal species composition, abundance, and distribution are 

expected (and evidenced already by local knowledge and opportunistic sightings). In addition, 

the observed northward retreat of the minimum extent of summer sea ice has the potential to 

create opportunities for the expansion of oil- and gas-related exploration and development into 

previously closed seasons and localities in the Alaskan Arctic. It may also open maritime 

transportation lanes across the Arctic adding to the ambient noise in the environment. Timing 

and location of whale migrations may play an important role in assessing where, when, or how 

exploration or access to petroleum reserves may be conducted to mitigate or minimize the impact 

on protected species. Moreover, several species are used, or potentially used, for subsistence by 

native communities in both Russia and the US. Whales form an important part of the diet and 

cultural traditions of most people in villages along the coasts of the Chukchi Sea. Detailed 

knowledge of large whale migration and movement patterns is essential for effective population 

monitoring. Because all marine mammal species are subjected to changes in environmental 

variables such as oceanographic currents, sea temperature, sea ice cover, prey availability, and 

anthropogenic impacts, more complete information on the year-round presence of these species

as they relate to these variables is needed in the Chukchi Sea planning area. 

Passive acoustic monitoring is currently the best tool for large scale population 

monitoring and assessment of baleen whales in Alaskan seas (Moore et al., 2006; Hannay et al.,

2013). Specifically, acoustic detection has proven a key addition to the census of bowhead 

whales (Balaena mysticetus) during their spring migration past Barrow (Clark and Ellison, 2000; 

Blackwell et al., 2007; Delarue et al., 2009) and in relation to oil and gas development activities 

offshore of Prudhoe Bay (Greene et al., 2004; Blackwell et al., 2013). Gray whale (Eschrichtius 

robustus) calls have also been detected year-round near Barrow on long-term recorders deployed 

in collaboration with the NSF/Shelf-Basin Interaction Study (Stafford et al., 2007b). These longterm passive acoustic data, when integrated with data from concurrent monitoring of 

oceanographic conditions, can contribute strongly to explaining finer-scale variability in whale 

occurrence and relative abundance (e.g., Bluhm and Gradinger, 2008; Laidre et al., 2008; 

Stafford et al., 2013).

The CHAOZ study combined arrays of long-term passive acoustic recorders with 

collocated biophysical and oceanographic moorings deployed in the Chukchi Sea at 40, 70, and 

120 nm off Icy Cape, AK over the course of two years (2010-11 and 2011-12). The passive 

acoustic arrays monitored for the presence of baleen whales, as well as odontocete whales, 

pinnipeds, and environmental and anthropogenic noise. The biophysical and oceanographic 

moorings estimated zooplankton size and abundance, ice concentration and thickness, 

fluorescence (chlorophyll), nitrate, oxygen, PAR, temperature, and salinity, as well as current 
OCS Study

BOEM 2015-034

17

speed and direction. Moorings permit observations during long periods when ice covers the 

region, especially during the critical spring and early summer periods when spring phytoplankton 

blooms occur. Such measurements are virtually impossible to obtain from ships because of their

limited ability to work in ice-covered seas. To complement these data and provide a more 

immediate assessment of whale presence, a short-term real-time auto-detection buoy was 

deployed during the summer of 2012 to monitor for bowhead whales; it transmitted data via 

satellite whenever a vocalization was detected. In addition, climate modeling was utilized to 

predict future sea ice and oceanographic conditions; results from these models were then used to 

predict future marine mammal distribution based on estimated sea ice and climate conditions.

Noise modeling was also included to determine the current ambient noise levels in the Chukchi 

Sea based on anthropogenic and environmental sources, as well as predict increases in those 

levels due to sea ice retreat, increased shipping, or other factors. To complement and calibrate 

the long-term data, shipboard observations and measurements were collected during the fall field 

surveys. These included sampling stations that conducted CTD casts and zooplankton net tows, 

24-hour passive acoustic monitoring using sonobuoys, opportunistic visual surveys for marine 

mammals, and satellite-tracked drifters.

B. Objectives of study

The overall goal of this multi-year, interdisciplinary study was to document the 

distribution and relative abundance of bowhead, humpback, right, fin, gray, and other whales in 

areas of potential seismic surveying, drilling, construction, and production activities and relate 

changes in those variables to oceanographic conditions, indices of potential prey density, and 

anthropogenic activities. This study had five component projects: marine mammal distribution 

(passive acoustics and visual methods), oceanography, zooplankton, climate modeling, and noise 

modeling/impact mitigation. 

The specific objectives were:

1. Assess the year-round seasonal occurrence of bowhead, gray, and other whale calls in the 

Chukchi Sea.

2. Estimate relative abundance of these whales.

3. Obtain two full years of biophysical measurements on the shallow Chukchi shelf utilizing 

moorings at three sites, and collect hydrographic and lower trophic level data during 

deployment/recovery of the moorings.

4. Evaluate the extent to which variability in environmental conditions such as sea ice, oceanic 

currents, water temperature and salinity, and prey abundance influence whale distribution 

and relative abundance.

5. Run the National Center for Atmospheric Research (NCAR) climate model (Community 

Earth System Model: CESM1.0) for future projections using the sea ice extents from 

2007/2008 as initial conditions.

6. Analyze multiple ensemble members CESM as well as the group of CMIP5 models to assess 

the future variability of sea ice cover and extended sea ice free seasons during fall for the 

Chukchi Sea.

7. Evaluate whether changes in seasonal sea ice extent are resulting in a northward shift of 

Bering Sea cetacean species such as fin, humpback, and North Pacific right whales.
OCS Study

BOEM 2015-034

18

8. Provide long-term estimates of habitat use for large whale species and compare this with 

predictions about annual ice coverage to establish predictive variables that describe large 

whale occurrence.

9. Develop a quantitative description of the Chukchi Sea’s noise budget, as contributed by 

biotic and abiotic sound sources, and continuous, time-varying metrics of acoustic habitat 

loss for a suite of arctic marine mammal species.

10. Develop a near-real-time passive acoustic monitoring system that can be used as an impact 

mitigation tool.

C. Summary of research effort

The CHAOZ project consisted of three field seasons during the months of August and 

September on board three different vessels: the 2010 survey occurred from 24 August to 20 

September 2010 on the F/V Alaskan Enterprise; the 2011 survey occurred from 12 August to 11 

September 2011 on the F/V Mystery Bay; and the 2012 survey occurred from 8 August to 7 

September 2012 on the R/V Aquila. Over the span of three field seasons, a total of 67 passive 

acoustic and 32 oceanographic moorings were deployed, a combined total of 190 hydrographic 

and 169 zooplankton sampling stations were conducted, and three drifters were deployed. A 

total of 630 sonobuoys were deployed during the 24-hour passive acoustic monitoring, and 4,250 

nm were surveyed for marine mammal and bird observations. A total of 29 scientists from 15

organizations/institutions participated in the cruises.

D. Structure of report

This report is divided into a number of sections, each designed to be read as a stand-alone 

report. Sections VII-IX deal with the marine mammal (both Arctic and subarctic), oceanography 

and zooplankton, and climate modeling components, respectively. Section X integrates the first 

two of these individual components to examine how variability in environmental parameters and 

prey abundance affect marine mammal distribution. Future predictions of marine mammal 

distribution based on the predicted ice and climate conditions will then be provided in the form 

of two potential scenarios (Section XI); data from Sections VII-IX are used in this section. The 

report culminates with a section on noise modeling and impact mitigation (Section XII) and one 

on estimating relative abundance (Section XIII). Section XIV contains a summary of this study 

and recommendations for the future. 
OCS Study

BOEM 2015-034

19

VII. MARINE MAMMAL DISTRIBUTION

A. Moored Observations

1. Methods

Equipment

Two deployments of three, long-term passive acoustic recorder arrays were made over 

the course of this study (Figure 2a, Table 1). The arrays were located at 40, 70, and 120 nm off 

Icy Cape, AK. The moorings for each array were arranged in a pentagonal shape, with 2-4 km 

spacing between units, and recorded for one year per deployment. These bottom-mounted 

moorings were comprised of an anchor, chain, acoustic release, passive acoustic recorder, and 

30” steel subsurface float arranged in a linear configuration (Figure 2b, total length of mooring 

~8 m; hydrophone ~6 m off the sea-floor). Autonomous Underwater Recorders for Acoustic 

Listening1(AURAL, Multi-Électronique, Rimouski, QC, Canada) were used on these subsurface 

moorings. The AURALs sampled at 16,384 Hz (nominal bandwidth from 10 Hz to 8 kHz), on a 

duty cycle of 95 min of recording every 5 hours in the 2010-2011 deployments (32% duty cycle). 

Upon retrieval of the 2010-2011 instruments, it was discovered that the recorders stopped three 

months early. To compensate for this extremely low battery life, the duty cycle was reduced to 

85 min of recording every 5 hours for the 2011-2012 deployments (28% duty cycle). After the 

cruise, examination of the battery packs revealed a diode problem which limited their voltage 

output, and so only two of the 2011-2012 instruments recorded for a full year. A new model of 

battery pack was developed and has been successfully used for all AURAL deployments since 

2012. In addition to the passive acoustic data, each AURAL has built-in sensors that measure 

temperature and pressure. Detection ranges, or the distance at which a calling animal or signal 

can be detected on a recorder, are highly variable. They depend on several factors, including the 

source level of the signal (how loud the call or noise is), ambient noise levels, and the sound 

speed profile of the water column. The sound speed profile varies depending on the 

oceanographic conditions (e.g., temperature, salinity, currents, fronts, etc.) at that time (Stafford 

et al., 2007a). Underwater sounds travel greater distances when the region is ice-covered (Urick, 

1983); thus, we would expect greater detection ranges in the winter ice-covered months. 

However, if ice moves or shifts, this creates an increase in ambient noise levels (sometimes 

substantially), further illustrating the highly variable nature of detection ranges. Table 1 lists the 

deployment and recording information for these moorings.

 

1 Reference to trade names does not imply endorsement by the National Marine Fisheries Service or NOAA.
OCS Study

BOEM 2015-034

20

FIGURE 2. LOCATION OF LONG-TERM PASSIVE ACOUSTIC RECORDER MOORINGS IN THE CHUKCHI SEA (A). PASSIVE 

ACOUSTIC RECORDER MOORING DIAGRAM (B).
OCS Study

BOEM 2015-034

21

TABLE 1. LIST OF ALL PASSIVE ACOUSTIC RECORDERS AND DEPLOYMENT INFORMATION, 2010-2012. * = INSTRUMENT INCLUDED IN DETAILED ACOUSTIC ANALYSES.

Mooring Mooring 

Cluster Latitude Longitude Water depth (m)

Recorder 

Start Date

Recorder

End Date

# Days 

with Data

Sampling 

Rate (Hz)

Duty Cycle

(min on/5 hours)

Deployment

Date

Retrieval

Date

CZ10_AU_A01 Inshore 70.90715 -163.23940 42.5 9/10/2010 7/8/2011 302 16384 95 8/29/2010 8/25/2011

CZ10_AU_A02 Inshore 70.87172 -163.04417 44.3 9/10/2010 7/7/2011 301 16384 95 8/28/2010 8/25/2011

CZ10_AU_A03* Inshore 70.79838 -163.08112 43.4 9/10/2010 6/27/2011 291 16384 95 8/28/2010 8/25/2011

CZ10_AU_A04 Inshore 70.78260 -163.28725 44.3 9/10/2010 6/10/2011 274 16384 95 8/29/2010 8/25/2011

CZ10_AU_A05 Inshore 70.85360 -163.36985 42.5 9/10/2010 6/8/2011 272 16384 95 8/29/2010 8/25/2011

CZ10_AU_B01 Midshore 71.25275 -164.27702 42.5 9/10/2010 6/7/2011 271 16384 95 8/29/2010 8/22/2011

CZ10_AU_B02 Midshore 71.23573 -164.18980 42.5 9/10/2010 6/11/2011 275 16384 95 8/29/2010 8/22/2011

CZ10_AU_B03* Midshore 71.20203 -164.19223 42.5 9/10/2010 6/21/2011 285 16384 95 8/29/2010 8/22/2011

CZ10_AU_B04 Midshore 71.19615 -164.29698 43.4 9/10/2010 6/25/2011 289 16384 95 8/29/2010 8/22/2011

CZ10_AU_B05 Midshore 71.22858 -164.34443 43.4 9/10/2010 6/5/2011 269 16384 95 8/29/2010 8/22/2011

CZ10_AU_C01 Offshore 71.85340 -165.99580 44.3 9/10/2010 5/28/2011 261 16384 95 8/31/2010 8/22/2011

CZ10_AU_C02* Offshore 71.83395 -165.90330 44.3 9/10/2010 6/8/2011 272 16384 95 8/31/2010 8/22/2011

CZ10_AU_C03 Offshore 71.80242 -165.92203 44.3 9/10/2010 5/23/2011 256 16384 95 8/31/2010 8/22/2011

CZ10_AU_C04 Offshore 71.79822 -166.03020 44.3 9/10/2010 6/3/2011 267 16384 95 8/31/2010 8/22/2011

CZ10_AU_C05 Offshore 71.82873 -166.07077 44.3 9/10/2010 5/23/2011 256 16384 95 8/31/2010 8/22/2011

CZ11_AU_A01 Inshore 70.87347 -163.22113 44.0 9/3/2011 4/22/2012 233 16384 85 9/1/2011 8/21/2012

CZ11_AU_A02 Inshore 70.85723 -163.11833 43.5 9/3/2011 4/22/2012 233 16384 85 9/1/2011 8/21/2012

CZ11_AU_A03* Inshore 70.81677 -163.13612 43.5 9/3/2011 9/7/2012 371 16384 85 9/1/2011 8/21/2012

CZ11_AU_A04 Inshore 70.80977 -163.24780 43.7 9/3/2011 12/11/2011 100 16384 85 9/1/2011 8/21/2012

CZ11_AU_A05 Inshore 70.84755 -163.30945 43.8 9/3/2011 9/30/2012 394 16384 85 9/1/2011 8/21/2012

CZ11_AU_B01 Midshore 71.25242 -164.27443 43.5 8/29/2011 5/8/2012 254 16384 85 8/24/2011 8/22/2012

CZ11_AU_B02 Midshore 71.23530 -164.19293 43.9 8/29/2011 5/3/2012 249 16384 85 8/24/2011 8/22/2012

CZ11_AU_B03* Midshore 71.20163 -164.19792 44.3 8/29/2011 5/19/2012 265 16384 85 8/24/2011 8/22/2012

CZ11_AU_B04 Midshore 71.19627 -164.29440 45.2 8/29/2011 5/24/2012 270 16384 85 8/24/2011 8/22/2012

CZ11_AU_B05 Midshore 71.22860 -164.34398 44.4 8/29/2011 5/4/2012 250 16384 85 8/24/2011 8/22/2012

CZ11_AU_C01 Offshore 71.85365 -165.99832 43.5 8/29/2011 5/29/2012 275 16384 85 8/26/2011 8/22/2012

CZ11_AU_C02* Offshore 71.83190 -165.90055 42.6 8/29/2011 5/14/2012 260 16384 85 8/26/2011 8/22/2012

CZ11_AU_C03 Offshore 71.80225 -165.92727 42.7 8/29/2011 6/5/2012 282 16384 85 8/26/2011 8/22/2012

CZ11_AU_C04 Offshore 71.79858 -166.02878 43.3 8/29/2011 4/6/2012 222 16384 85 8/26/2011 8/22/2012

CZ11_AU_C05 Offshore 71.82903 -166.07145 43.7 8/29/2011 5/9/2012 255 16384 85 8/26/2011 8/22/2012
OCS Study

BOEM 2015-034

22

Data Processing

After the recorders were retrieved, the hard drives were removed and the raw data was 

immediately backed up onto an external hard drive. The original drives were saved as master 

copies of the data. The data were then processed in two steps. First the raw sound files were 

converted into ten-minute files, renamed with intuitive filenames containing mooring name, date, 

and time information. Image files (.png) of spectrograms were then pre-generated from 

recordings (FFT 1024, 0.85 overlap, Hamming window). These image files displayed either 300 

s of data from 0 to 250 Hz (low-frequency signals), 225 s of data from 0 to 800 Hz (midfrequency signals), or 90 s of data from 0 to 8.192 kHz (high-frequency signals).

Data Analysis

All acoustic data (100% of the image files) were analyzed manually for presence of the 

following: fin whales in the low frequency band; bowhead, right, humpback, gray, and minke 

whales, walrus, unidentified pinnipeds, as well as vessel noise and seismic airguns in the midfrequency band; and beluga, killer whale, minke whale (boing call), bearded and ribbon seals, 

and environmental noise (ice) in the high frequency band. Bowhead whales were identified 

mainly by their frequency modulated moans and complex notes forming the basis of song, as per 

Hannay et al. (2013). Beluga vocalizations were classified as whistles, pulsed calls, noisy calls,

combined calls, and echolocation clicks (Sjare and Smith, 1986). Generally, whistles and pulsed 

calls are easily identifiable; most echolocation clicks exceeded the frequency range that was 

recorded for this study. Gray whales were identified by their low-frequency moans, pulses, and 

bongo sounds (Cummings et al., 1968; Stafford et al., 2007b). Walrus were identified using their 

stereotypical knock and bell calls (Stirling et al., 1987) along with various grunts. Bearded seals 

were identified by their characteristic trills (Risch et al., 2007). Ribbon seals produce distinct 

vocalizations during the spring mating season, including downsweeps, roars, and grunts 

(Watkins and Ray, 1977). The call used to identify ribbon seals within this study was an intense 

downward frequency sweep. Representative repertoires obtained from literature were used for 

all other species included in the report. 

An in-house, MATLAB-based program (SoundChecker) was used for the analysis. The 

SoundChecker program was developed in response to the sheer magnitude of the data, the 

enormous overlap of the acoustic repertoires of many Alaskan marine mammal species, and the 

lack of stereotyped calls for most species, which resulted in poor auto-detection performance. 

SoundChecker operates on the pre-generated image files, which reduces the computational time 

needed to generate spectrograms during analysis. The image files are indexed to allow for zoom 

and playback functioning during analysis. For each image file, the analyst selects one of four 

options: yes, no, maybe, and no-with-noise to indicate whether a species was detected in that 

file. The no-with-noise option is selected when the presence of high levels of noise mask 

potential calls from that species. It is important to note that analysts were highly conservative 

when assigning yes designations; if there was any doubt as to the source of the calls within an 

image file, that image file was marked as maybe. The results below use only those image files 

marked as yes. Furthermore ‘maybe’ was also used to mark any signals that could potentially be 

calls of that species; future studies using these data will be expedited as only the image files 

marked with yeses and maybes will need to be included and the full data set will not need to be 

re-analyzed.
OCS Study

BOEM 2015-034

23

For the 2011-2012 fin whale analysis, a low-frequency detection and classification 

system (LFDCS; Mark Baumgartner, WHOI) was used to automatically detect fin whale 

vocalizations. The LFDCS is an IDL-based program that uses manually created call libraries to 

apply discriminant function analysis across seven measurements, called call attributes, taken 

from each auto-detected call. The analyst selects exemplary calls, in this case fin whale calls, to 

create a call library. The LFDCS is then run on novel data sets and uses this comprehensive call 

library for comparison in discriminant function analysis to classify all of its auto-detections. 

Over two-hundred exemplars were carefully selected for the fin whale call library. The call 

library was then put through comprehensive and iterative logistical regression analysis, in order 

to determine its efficacy for application on novel data sets. Once the library was deemed robust 

enough for real application, the LFDCS was run on each mooring data set, and the resulting fin 

whale auto-detections were checked for accuracy by a manual analyst.

2. Results

Over the course of the CHAOZ study, a total of 8,054 days of acoustic data were 

collected from the long-term passive acoustic recorders. One recorder from each array (inshore, 

midshore, and offshore) for each year was analyzed fully for all species/signals (total of 1,744 

days of acoustic data). 

Because of the staggered duty cycle used for the recordings, there was differing sampling 

effort among days. This was normalized by dividing the number of image files with calls 

detected for that day by the number of available image files for that day. The results that follow 

are presented for each mooring as the percentage of time intervals with calls for each day. This 

will be referred to as calling activity for the remainder of this report. It is important to note that 

calling activity indicates the duration of sustained calling for that day, not the number of call 

detections or number of animals vocalizing. For example, if a day shows 100% beluga calling 

activity that means that 100% of the 335 ninety second time bins in that day contained at least 

one beluga call.

The percentage of days with calling for each species/signal by month is presented in 

Appendix E (see Section XVIII.E). The results for the species/signals analyzed were divided into 

those that had a lot of calling activity and those that had little to none. The species that had the 

greatest amount of calling activity were bowhead and beluga whales, bearded seals, and walrus. 

These species, along with gray whales, are good proxies for Arctic ecosystem change because 

they represent a variety of differing habitat and dietary niches. As such, this report will focus on 

these five species (Table 2). However, plots of the calling activity for all species analyzed are 

presented in Appendix C. In addition to all the species mentioned below, analysts also marked 

the presence of airgun, vessel, and ice noise. 
OCS Study

BOEM 2015-034

24

TABLE 2. SUMMARY OF RESULTS FOR BOWHEAD, BELUGA, AND GRAY WHALE, WALRUS, AND BEARDED SEAL CALLING ACTIVITY, 2010-2012.

Species Year Mooring First date 

with calling

Last date 

with calling

# days w/ 

calls

# days w/ 

recordings

% days 

w/ calls

# intervals 

w/ calls

# intervals w/ 

recordings

% intervals 

w/ calls

Bowhead 

whale

2010-

2011

Offshore 9/22/2010 4/21/2011 62 272 22.8% 3278 36791 8.9%

Midshore 9/14/2010 6/18/2011 85 285 29.8% 4186 38925 10.8%

Inshore 9/14/2010 6/27/2011* 129 291 44.3% 7731 38175 20.3%

2011-

2012

Offshore 9/20/2011 12/3/2011 40 260 15.4% 2063 32498 6.3%

Midshore 8/29/2011* 5/19/2012* 65 265 24.5% 3548 33114 10.7%

Inshore 9/14/2011 8/14/2012 131 371 35.3% 5762 45919 12.5%

Beluga 

whale

2010-

2011

Offshore 11/8/2010 4/21/2011 17 272 6.3% 128 87221 0.1%

Midshore 10/20/2010 6/7/2011 29 285 10.2% 262 91510 0.3%

Inshore 9/15/2010 6/19/2011 71 291 24.4% 3211 46222 6.9%

2011-

2012

Offshore 10/3/2011 5/7/2012 25 260 9.6% 340 74654 0.5%

Midshore 10/10/2011 5/19/2012* 39 265 14.7% 763 76114 1.0%

Inshore 10/5/2011 7/18/2012 81 371 21.8% 2827 106874 2.6%

Gray 

whale

2010-

2011

Offshore N/A N/A 0 272 0.0% 0 36927 0.0%

Midshore 10/8/2010 10/8/2010 1 285 0.4% 10 38915 0.0%

Inshore 10/1/2010 11/9/2010 6 291 2.1% 71 38165 0.2%

2011-

2012

Offshore N/A N/A 0 260 0.0% 0 32498 0.0%

Midshore N/A N/A 0 265 0.0% 0 33114 0.0%

Inshore 9/20/2011 7/21/2012 15 371 4.0% 52 45919 0.1%

Walrus

2010-

2011

Offshore 9/10/2010* 6/7/2011 107 272 39.3% 1984 36928 5.4%

Midshore 9/10/2010* 6/21/2011* 41 285 14.4% 1149 38915 3.0%

Inshore 9/10/2010* 6/27/2011* 87 291 29.9% 1781 38165 4.7%

2011-

2012

Offshore 8/29/2011* 5/10/2012 102 260 39.2% 2460 32498 7.6%

Midshore 8/29/2011* 5/11/2012 67 265 25.3% 1178 33114 3.6%

Inshore 9/3/2011* 8/13/2012 111 371 29.9% 5718 45922 12.5%

Bearded 

seal

2010-

2011

Offshore 10/1/2010 6/8/2011* 157 272 57.7% 20485 87221 23.5%

Midshore 9/10/2010* 6/21/2011* 222 285 77.9% 41473 91510 45.3%

Inshore 9/25/2010 6/27/2011* 241 291 82.8% 38471 74044 52.0%

2011-

2012

Offshore 8/30/2011 5/14/2012* 222 260 85.4% 21610 74900 28.9%

Midshore 8/29/2011* 5/19/2012* 222 265 83.8% 34610 76364 45.3%

Inshore 9/8/2011 8/20/2012 279 371 75.2% 33636 106725 31.5%

*= Date recorder limited.
OCS Study

BOEM 2015-034

25

Bowhead whales

Bowhead whale fall migration was detected at all three locations in both deployment 

years (Figure 3, Table 3). In fall 2010, calling activity began in mid- to late September 

(inshore/midshore and offshore, respectively) and ended mid-December at all locations (Table 

3). The main pulse of calling activity was divided into three distinct peaks centered on early 

October, early November, and early December for all three locations. In contrast, the main pulse 

of calling activity for the fall 2011 migration was more continuous, with no multiple calling 

peaks seen. For the inshore and offshore locations, calling activity began at approximately the 

same time as in 2010 (mid- to late September, respectively), but the midshore location saw 

calling activity in late August, two weeks earlier than in 2010. For all locations, calling activity 

ended within a few days of each other and approximately ten days earlier than in 2010.

TABLE 3. KEY TIMING EVENTS FOR BOWHEAD WHALE CALLING ACTIVITY.

Year Mooring

Fall Migration Spring Migration

Date 

Range Peaks Date Range Peaks

2010-

2011

Offshore 09/22/10-

12/12/10

Three: early Oct, 

early Nov, early 

Dec

N/A None, but recorder 

failed early

Midshore 09/14/10-

12/15/10

Three: early Oct, 

early Nov, early 

Dec

03/29/11-

06/18/11

Small one midJune, but recorder 

failed early

Inshore 09/14/10-

12/12/10

Three: early Oct, 

early Nov, early 

Dec

03/04/11-

06/27/11*

Main: May; others 

mid-Apr, mid-Jun

2011-

2012

Offshore 09/20/11-

12/03/11

Main: late Nov; 

others: early Oct, 

early Nov

N/A None, but recorder 

failed early

Midshore 08/29/11*-

12/01/11

One very broad: 

centered on early 

Nov; other early Oct

04/23/12-

05/19/12*

One day: mid-May, 

but recorder failed 

on that day.

Inshore 09/14/11-

12/01/11 Main: early Nov. 04/11/12-08/14/12

Main: early May; 

others late May, 

mid-Jun. 

* = Date recorder limited

Detection of the bowhead spring migration in both deployment years was greatest at the 

inshore and least at the offshore locations (Figure 3, Table 3). The spring 2011 pulse in calling 

activity at the inshore location began in late March, ending just before the recorder failed in late 

June. There is a slight indication of three peaks in calling activity in the spring of 2011, though 

these are less defined than the fall 2010 peaks. Very low levels of calling activity were present 

at the midshore locations from April through mid-June, with a small peak occurring right before 

the recorder stopped in mid-June. No calling activity was detected at the offshore location. The 
OCS Study

BOEM 2015-034

26

spring calling activity in 2012 at the inshore location was more skewed. Although calling 

activity started much later (mid-April) than in 2011, the bulk of calling activity occurred in late 

April/early May, with scattered activity continuing until mid-August. Calling activity on the 

midshore recorder also began later than in 2011. There was one day with elevated calling 

activity at the midshore location (mid-May); however, this was also the day the recorder failed. 

As in 2011, there was no calling activity during the spring 2012 migration at the offshore 

location. There was no overwinter bowhead calling activity at any location in any study year.

Analysts also flagged image files containing gunshot calls, an impulsive call type 

produced by both bowhead and right whales (Clark, 1983; Würsig and Clark, 1993; Parks et al.,

2005). Although it is attributed to bowhead whales in the Arctic, this call type is flagged 

separately from the other bowhead calls because of our ongoing effort in the Bering Sea to 

differentiate bowhead and right whale gunshot calls. Gunshot call activity (Figure 4, green) 

coincided with general bowhead calling activity, although there were considerably fewer days 

with gunshot call activity and lower levels of calling activity on those days. Interestingly, the 

peaks in gunshot call activity occur near the end of the peak in bowhead calling activity in nine 

out of fourteen cases. Gunshot call activity also coincided with the last days of general bowhead 

calling activity for both of the fall, and the 2011 spring, migrations.

In the fall of 2010, the offshore location showed generally higher and more sustained 

peaks in gunshot call activity than the midshore or inshore locations (Figure 4, green). In the fall 

of 2011, the opposite was true; the inshore location showed the highest peak in gunshot call 

activity. No gunshot call activity was detected at the midshore or offshore locations during the 

spring migrations of 2011 and 2012, although it is possible those migrations were missed due to 

recorder failure. For the inshore location, the 2012 spring calling activity was slightly greater 

than in 2011, and it aligned more closely with the end of the main pulse of calling activity.
OCS Study

BOEM 2015-034

27

FIGURE 3. BOWHEAD WHALE CALLING ACTIVITY (PRESENTED AS THE PERCENTAGE OF TIME INTERVALS WITH 

CALLS) FOR INSHORE (LOWER PANEL), MIDSHORE (MIDDLE PANEL), AND OFFSHORE (UPPER PANEL) LOCATIONS, 

2010-2012. DARK GRAY SHADING INDICATES NO DATA, AND TEAL SHADING INDICATES DAYS WHERE DETECTIONS 

WERE MASKED BY NOISE.
OCS Study

BOEM 2015-034

28

FIGURE 4. GUNSHOT CALL ACTIVITY (GREEN) OVERLAID ON BOWHEAD WHALE CALLING ACTIVITY. CALLING

ACTIVITY IS PRESENTED AS THE PERCENTAGE OF TIME INTERVALS WITH GUNSHOT CALLS FOR INSHORE (LOWER 

PANEL), MIDSHORE (MIDDLE PANEL), AND OFFSHORE (UPPER PANEL) LOCATIONS, 2010-2012. DARK GRAY SHADING 

INDICATES NO DATA, AND TEAL SHADING INDICATES DAYS WHERE DETECTIONS WERE MASKED BY NOISE.

Beluga whales

Both the fall and spring beluga whale migrations were detected in both deployment years 

at all three locations, with higher and more sustained peaks in calling activity found inshore 

(Figure 5, Table 4). For the 2010 fall migration, calling activity began earliest inshore (midSeptember), and latest offshore (early-November). The end of fall 2010 calling activity was also 

staggered the same way, ending in early December inshore, and mid-to-late December offshore. 

The peak in calling activity occurred in approximately late November at all three locations, 

although no peak exists at the offshore location. The fall 2011 migration showed a more uniform 

start of calling activity (early October in all locations); however, the end varied among locations, 

ranging from late November (offshore) to mid-December (inshore/midshore). The calling 

activity was also not as pronounced as in 2010, although it seems that the calling activity was 

distributed among locations in 2011, whereas it was more concentrated inshore in 2010. The 

peak in calling activity was slightly earlier (mid-November) in 2011 than in 2010. 
OCS Study

BOEM 2015-034

29

TABLE 4. KEY TIMING EVENTS FOR BELUGA WHALE CALLING ACTIVITY.

Year Mooring

Fall Migration Spring Migration

Date 

Range Peaks Date Range Peaks

2010-

2011

Offshore 11/08/10-

12/21/10

Very small 

cluster mid-Nov

04/03/11-

04/21/11 Mid-Apr

Midshore 10/20/10-

12/15/10

Small one late 

Nov

03/04/11-

06/07/11

Very small cluster 

mid-Apr

Inshore 09/15/10-

12/02/10 Late Nov 03/27/11-06/19/11

Two: early and late 

May

2011-

2012

Offshore 10/03/11-

11/23/11

Small one midNov04/12/12-

05/07/12 Early May

Midshore 10/10/11-

12/12/11

Small one midNov04/02/12-

05/19/12* Early May

Inshore 10/05/11-

12/18/11

Early to midNov04/11/12-

07/18/12

Two: early and midMay

* = Date recorder limited

For all locations and years, there was generally more beluga whale calling activity in the 

spring than in the fall (with the exception of the midshore mooring in 2010-2011). As in the fall, 

there was considerably less calling activity at the midshore and offshore locations than at the 

inshore location. The start of spring 2011 calling activity varied among locations, the earliest 

being early March (midshore), and the latest being late-March/early April (inshore/offshore, 

respectively). Two large peaks in calling activity were seen at the inshore location (early and 

late May). The end of spring 2011 calling varied quite a bit among locations, ranging from lateApril to mid-June. The spring pattern in 2012 was similar to that of the fall with calling activity 

less concentrated at the inshore location. The start of spring 2012 calling activity ranged from 

early to mid-April, and the end ranged from early May to mid-July, both similar to that of 2011, 

though this may be recorder-limited at the midshore and offshore locations. The peak in calling 

activity was early-May for all locations (although the inshore location had another peak in midMay), slightly later than spring 2011. There were very few, scattered, overwinter instances of 

calling activity at all sites over both years.
OCS Study

BOEM 2015-034

30

FIGURE 5. BELUGA WHALE CALLING ACTIVITY (PRESENTED AS THE PERCENTAGE OF TIME INTERVALS WITH 

CALLS) FOR INSHORE (LOWER PANEL), MIDSHORE (MIDDLE PANEL), AND OFFSHORE (UPPER PANEL) LOCATIONS, 

2010-2012. DARK GRAY SHADING INDICATES NO DATA, AND TEAL SHADING INDICATES DAYS WHERE DETECTIONS 

WERE MASKED BY NOISE.

Gray whales

In contrast to bowhead and beluga whales, gray whale calls were detected infrequently in 

both years. With the exception of one day of calling activity at the midshore location, all calling 

activity was confined to the inshore location (Figure 6, Table 5). 

For the 2010 fall migration, gray whale calling activity was detected on October 8th at 

the midshore location, and early October through early November, with a peak in early October, 

at the inshore location. The 2011 fall migration occurred approximately in the same time range 

(late September – early November). Calling activity was so low that no peak exists, although 

most of the calling activity is centered on early November.

No spring migration was detected at the midshore or offshore locations in either year, 

however the recorders ended early and it is possible that the migration was just missed. For the 

inshore location, the only location with year-round data, spring 2012 had calling activity from 
OCS Study

BOEM 2015-034

31

mid-May through late July, with a very small peak in mid-July. There was no overwinter calling 

activity at any site during any year.

TABLE 5. KEY TIMING EVENTS FOR GRAY WHALE CALLING ACTIVITY.

Year Mooring

Fall Migration Spring Migration

Date

Range Peaks Date Range Peaks

2010-

2011

Offshore N/A None N/A None 

Midshore 10/8/2010 One day: early Oct N/A None 

Inshore 10/01/10-

11/19/10 Early Oct N/A None 

2011-

2012

Offshore N/A None N/A None 

Midshore N/A None N/A None 

Inshore 09/20/11-

11/05/11

Very small cluster: 

early Nov

05/16/12-

07/21/12

Very small one 

in mid-July

FIGURE 6. GRAY WHALE CALLING ACTIVITY (PRESENTED AS THE PERCENTAGE OF TIME INTERVALS WITH CALLS) 

FOR INSHORE (LOWER PANEL), MIDSHORE (MIDDLE PANEL), AND OFFSHORE (UPPER PANEL) LOCATIONS, 2010-2012. 

DARK GRAY SHADING INDICATES NO DATA, AND TEAL SHADING INDICATES DAYS WHERE DETECTIONS WERE 

MASKED BY NOISE.
OCS Study

BOEM 2015-034

32

Walrus

Walrus were detected at all locations in both years (Figure 7, Table 6). Their calling 

activity was nearly year-round at the offshore location, but limited to the summer/fall at the 

inshore and midshore locations. The lack in recording effort between June and September of 

2011 makes clear interpretation of the results difficult. However, it seems likely, given the high 

percentages of calling activity immediately before and after this time period, especially at the 

inshore and midshore locations, that calling continued throughout this time period. Unlike the 

previous three species, with their distinct fall and spring migrations, walrus calling activity 

appears to have two time periods, over-summer (roughly May to November) and over-winter 

(November to May). Bouts of low level calling activity distributed throughout the year blurs the 

lines between these time periods, and so the date ranges presented in Table 6 should be 

considered approximate.

There are two complete sets of results for the overwinter time period: 2010-2011 and 

2011-2012. In both cases there is extensive calling activity present at the offshore location, 

sporadic and low levels of calling activity at the inshore location, and minimal calling activity at 

the midshore location. This is contrary to the bowhead, beluga, and gray whale results where the 

offshore location had the least amount of calling activity. Walrus, unlike the cetaceans, had 

consistent calling activity detected overwinter. Peaks in the calling activity offshore occurred in 

mid-February 2011, and one month later (mid-March) in 2012. The calling activity in 2012 is 

more skewed than that in 2011. Inshore, the peaks were present for just a few days in late 

November 2010 and 2011 and mid-February 2012.

There are no complete sets of data for the over-summer time period. Calling activity was 

present from the start of the recordings at all three locations on September 10, 2010. This calling 

activity did end abruptly, however, in Mid-October at all three locations. For 2011, 

approximately two months of data is missing from the inshore and midshore locations. Calling 

activity is seen to increase to near-constant levels in June at both these locations before the 

recordings end. When the recordings for the subsequent deployment resume in early September, 

calling activity is still at high levels. It is impossible to know what the overall distribution in 

calling activity was during this time period, but it does appear that there is a peak in early 

November at both the inshore and midshore locations. The offshore recorder failed almost a 

month earlier than at the other locations and so there are not enough data in June to determine if 

a similar increase in calling activity occurred in that month. Again, there seems to be a peak in 

calling activity in early November at the offshore location. The 2012 May-November time 

period lasted only a couple of weeks in the midshore and offshore locations before those 

recorders failed. However, the recorder at the inshore location continued for the full deployment 

and data are available into September. Here, a very strong and lengthy pulse of calling activity 

was present, peaking in early July. No second peak in calling activity was seen. Determination 

of whether calling activity during the over-summer time period is typically bimodal will have to 

wait until analyses are completed on subsequent years.
OCS Study

BOEM 2015-034

33

TABLE 6. KEY TIMING EVENTS FOR WALRUS CALLING ACTIVITY.

Year Mooring Over-Summer Over-Winter

Date 

Range Peaks Date Range Peaks

2010-

2011

Offshore 9/10/10* -

10/10/10 Maybe mid-Oct 11/18/10-4/22/11 Mid-Feb & late Apr

Midshore 9/10/10*-

10/10/10 Maybe mid-Oct 11/12/10-4/18/11 None

Inshore 9/10/10*-

10/17/10 -

11/8/10-

4/27/11 One day: late Nov

2010-

2011 

& 

2011-

2012

Offshore 5/2/11**-

10/28/11** Maybe early Oct 11/25/11 -4/30/12 Mid-Mar

Midshore

5/31/10** 

-

11/4/11**

Maybe mid-Jun & 

early Oct

11/20/11 -

4/27/12

Small ones late Nov & 

early Apr

Inshore 5/7/11** -

11/3/11**

Maybe mid-Jun & 

early Oct

11/16/11 -

4/14/12 Late Nov & mid-Feb

2011-

2012

Offshore 5/1/12-

5/10/12* - - -

Midshore 5/11/12 -

5/11/12* - - -

Inshore 6/14/12 -

8/13/12 Early Jul - -

* = Date recorder limited

** = Dates missing in middle of range
OCS Study

BOEM 2015-034

34

FIGURE 7. WALRUS CALLING ACTIVITY (PRESENTED AS THE PERCENTAGE OF TIME INTERVALS WITH CALLS) FOR 

INSHORE (LOWER PANEL), MIDSHORE (MIDDLE PANEL), AND OFFSHORE (UPPER PANEL) LOCATIONS, 2010-2012. 

DARK GRAY SHADING INDICATES NO DATA, AND TEAL SHADING INDICATES DAYS WHERE DETECTIONS WERE 

MASKED BY NOISE.

Bearded seals

Bearded seal calling activity was nearly ubiquitous for both years at all locations (Figure 

8, Table 7). Low levels of calling activity were present throughout the year, gradually increasing 

from late fall through winter, reaching near saturated levels (calling present on 100% of time 

intervals per day) by late spring. 

In 2010-2011, calling activity started earliest at the inshore location (late September), 

followed one to two weeks later at the offshore and midshore locations, respectively. Calling 

activity at the inshore location had the most gradual ramp-up, increasing from early October 

through early April. The ramp-up of calling activity at the midshore location occurred in a 

shorter timespan (early November through mid-March). At the offshore location, calling activity 

increased to saturated levels in just two months (early March to early April). Saturated calling 

levels lasted from early April through late June at the inshore location. It appears that the calling 

activity was beginning to decrease right before the recordings ended, but this cannot be known 

for certain. Saturated calling levels lasted somewhat longer at the midshore location (mid-March 

to mid-June), and again, it appears that calling activity may have begun to decline immediately 
OCS Study

BOEM 2015-034

35

preceding the end of the recording. The offshore location has the shortest sustained period of 

saturated calling levels, but there was still 100% calling activity on the last day of the recordings, 

and so it looks like that calling activity period was truncated by the reduced recording effort.

Calling activity in the 2011-2012 data showed similar patterns; calls were detected year 

round, with calling activity increasing from the fall through the spring. Calling activity started 

earlier in 2011 than in 2010 with bearded seal calling activity present from the beginning of the 

recordings at all three locations. Ramp-up of calling was also more drawn out in 2011 and all 

three locations saw the start of this ramp-up in early October instead of the staggered start seen 

in 2010. Inshore, the increase continued until mid-April when extremely consistent and 

saturated levels of calling activity extended until late June, at which point they plummeted 

sharply, returning to low levels for the rest of the recording (early Sept.). The midshore location 

had a shorter ramp up period which extended until late February, when saturated calling activity 

levels were reached, albeit inconsistently at times. Saturated levels were artificially truncated by 

the early failure of the recorder. Offshore, the ramp up period was as long as the one inshore, 

however there was a less consistent (and more peaked) increase in calling activity offshore. Near 

saturation levels did not continue for long before the recorder stopped.

TABLE 7. KEY TIMING EVENTS OF BEARDED SEAL CALLING ACTIVITY.

Year Mooring Date 

Range

> 90% Calling Activity 

Range Peaks

2010-

2011

Offshore 10/1/10-

6/8/11* Mid-Apr to early Jun* Late Dec

Midshore 10/7/10-

6/21/11* Mid-Mar to mid-Jun Late Jan

Inshore 9/25/10-

6/27/11* early Apr to late Jun Early Oct and late Dec

2011-

2012

Offshore 8/30/11*-

5/14/12* mid-Apr to mid-May* Early Oct and early Dec

Midshore 8/29/11*-

5/19/12* late Feb to mid-May* Early Oct

Inshore 9/8/11*-

6/23/12 mid-Apr to late Jun Early Oct and mid-Jan

* = Date recorder limited
OCS Study

BOEM 2015-034

36

FIGURE 8. BEARDED SEAL CALLING ACTIVITY (PRESENTED AS THE PERCENTAGE OF TIME INTERVALS WITH CALLS) 

FOR INSHORE (LOWER PANEL), MIDSHORE (MIDDLE PANEL), AND OFFSHORE (UPPER PANEL) LOCATIONS, 2010-2012. 

DARK GRAY SHADING INDICATES NO DATA, AND TEAL SHADING INDICATES DAYS WHERE DETECTIONS WERE 

MASKED BY NOISE.

Other species

The rest of the species analyzed had little to no calling activity detected. Killer whales 

(Appendix C. 7) were detected on the 2011-2012 deployments at the inshore (6 days in 

September/October of 2011 and May/June of 2012) and midshore (1 day in April 2012) 

locations. Less than 10% of time intervals on those days had calling activity. Minke whale boing 

calls (Rankin and Barlow, 2005) were detected on the inshore location on only one day (19 

October 2011; Appendix C. 8). No minke whale pulsed calls (Winn and Perkins, 1976) were 

detected at any location on any year (Appendix C. 9). A variety of pinniped grunts, yelps, and 

barks were detected but not identified to species (Appendix C. 10). These detections are lumped 

together as unidentified pinnipeds and most likely include species such as ringed and spotted 

seals as well as less common calls types from bearded and ribbon seals and walrus. These 

unidentified pinniped calls were detected most often at the inshore location, although there was 

sporadic low-level calling activity at the midshore location. Very sparse and low-level calling 

activity was present at the offshore location. The main peak in calling activity for these 

unidentified pinnipeds was in early December, with smaller peaks in early May and mid-June. 
OCS Study

BOEM 2015-034

37

For all years, there was no calling activity at any location for the following species: humpback, 

right, fin, and sperm whales, and ribbon seals (Appendix C). 

The LFDCS analysis for fin whales on the 2011-2012 data set flagged over 2,000 signals, 

all of which were checked manually and determined to be either mooring noise or airgun signals. 

Environmental and anthropogenic sources

While reviewing the data for marine mammal calling activity, analysts also noted the 

presence of anthropogenic (seismic airguns and vessel) and environmental (ice) noise. Although 

not directly related to marine mammal presence, the results for these signals are reported here, as 

they were analyzed and presented in a similar manner. We use noise activity here as the 

equivalent of calling activity for these non-biological signal types. The presence of seismic 

airguns, vessel noise, and ice noise from the long-term AURAL recorders are shown in Figure 9-

11. 

Seismic airguns

Seismic airgun noise activity was ubiquitous at all three locations in the fall of 2010 and 

2011 (Figure 9, Table 8). The start of the seismic airgun surveys in 2010 is unknown, as airgun 

noise activity was present on 100% of the time intervals for the first day of the recording period. 

This noise activity remained at high levels for most of September, decreasing in late September 

to zero on 25 September (offshore) or 26 September (inshore, midshore) before resuming the 

following day and quickly reaching 100% saturation. Shooting ceased abruptly on the last day of 

September at the offshore location, and the next day at the inshore and midshore locations. Due 

to recorder failure at the end of the 2010-2011 deployment, the start of the 2011 seismic surveys 

is again unknown. Airgun noise activity again was present on the first day of the recording 

period, although not at 100% saturation levels. In fact, airgun noise activity remained at much 

lower levels in fall of 2011 than in fall of 2010. Furthermore, the inshore location in 2011 had 

approximately one third the level of noise activity than at either the midshore or offshore 

locations. Airgun noise activity ceased in mid-September for approximately two weeks before 

resuming at the beginning of October. The last day with airgun noise activity differed among 

locations ranging from early (inshore) to mid-October (mid- and offshore). 
OCS Study

BOEM 2015-034

38

TABLE 8. KEY TIMING EVENTS OF AIRGUN, VESSEL, AND ICE NOISE ACTIVITY.

Year Mooring Date Range

Airguns

Date Range

Vessel Noise

Date Range

Ice Noise

2010-

2011

Offshore 9/10/10*-9/30/10 -

11/1/10-11/17/10 

12/2/10-4/22/11

Midshore 9/10/10*-10/1/10 10/11/10-10/12/10 

2/14/11-2/18/11

11/4/10-11/21/10 

12/3/11-3/4/11

Inshore 9/10/10*-10/1/10 9/14/10-11/11/10 11/8/10-11/16/10 

12/3/10-5/21/11**

2011-

2012

Offshore 8/29/11*- 9/17/11 

10/3/11-10/10/11

8/29/11*-10/5/11 

12/7/11-1/10/12 10/15/11-5/7/12

Midshore 8/29/11*-9/17/11 

10/3/11-10/13/11 

8/29/11*-9/13/11 

11/15/11-11/25/11 11/21/11-5/6/12

Inshore 9/3/11*- 9/17/11 

10/3/11-10/5/11 9/4/11*-10/6/11 11/21/11-7/22/12

* = Date recorder limited

** = Date bearded seal limited
OCS Study

BOEM 2015-034

39

FIGURE 9. SEISMIC AIRGUN NOISE ACTIVITY (PRESENTED AS THE PERCENTAGE OF TIME INTERVALS WITH 

SIGNALS) FOR INSHORE (LOWER PANEL), MIDSHORE (MIDDLE PANEL), AND OFFSHORE (UPPER PANEL) LOCATIONS, 

2010-2012. DARK GRAY SHADING INDICATES NO DATA, AND TEAL SHADING INDICATES DAYS WHERE DETECTIONS 

WERE MASKED BY NOISE.

Vessel noise

Vessel noise presence is shown in Figure 10 and Table 8. Vessel noise activity was 

present at only the inshore and midshore locations during the 2010-2011 deployment; starting 

mid-September and lasting for two months at the inshore location and starting mid-October and 

lasting for two days at the midshore location. In the spring there were low levels of vessel noise 

activity over four days in mid-February. Vessel noise activity was present at all three locations 

during the 2011-2012 deployment. The starting date of this noise activity in 2011 is unknown 

due to recorder failure at the end of the 2010-2011 deployment. Noise activity was detected on 

the first day of the 2011-2012 recordings at the midshore and offshore locations and on the 

second day at the inshore location. Noise activity continued for approximately one month at the 

inshore and offshore locations and a half-month at the mid-shore location. A second bout of 

vessel noise activity was seen at the midshore location the second half of November, and at the 

offshore location for about a month starting in early-December. 
OCS Study

BOEM 2015-034

40

FIGURE 10. VESSEL NOISE ACTIVITY (PRESENTED AS THE PERCENTAGE OF TIME INTERVALS WITH SIGNALS) FOR 

INSHORE (LOWER PANEL), MIDSHORE (MIDDLE PANEL), AND OFFSHORE (UPPER PANEL) LOCATIONS, 2010-2012. 

DARK GRAY SHADING INDICATES NO DATA, AND TEAL SHADING INDICATES DAYS WHERE DETECTIONS WERE 

MASKED BY NOISE.

Ice noise

A substantial source of noise on the year-long recordings was from ice, primarily caused 

by cracking and rubbing (Xie and Farmer, 1992). Ice noise activity was present at all locations 

during both deployment years (Figure 11, Table 8). For the 2010-2011 deployment, ice noise 

activity occurred in two pulses: a small pulse of activity during approximately the first half of 

November followed about a half-month later by a long pulse of consistent, albeit varying, levels 

of noise activity throughout winter and the following spring. Ice noise activity was most 

prevalent at the inshore location, with several days in winter and early summer reaching 100% 

saturation levels. The last day with confirmed ice noise for the inshore location was in late May, 

however the actual end date may have been masked by other acoustic signals (e.g., bearded seals, 

Figure 8). Ice noise activity ended three months earlier at the midshore location, in early March, 

with levels staying below 50%. The offshore location showed the lowest levels of ice noise 

activity, not exceeding 25%. Noise activity at the offshore location ended in late April.

Overall, there were higher levels of ice noise activity at all three locations during the 

2011-2012 deployment (Figure 11). Ice noise activity at the inshore location in 2011 started 
OCS Study

BOEM 2015-034

41

nearly a month later than in 2010. It was also present two months longer, ending in late July, 

although there was no masking noise present like in 2010. In addition, even though the noise 

activity was present throughout this time period, there appears to be a main initial pulse ending 

in early-May, where levels frequently reached saturation, followed by a second pulse which 

contained more random presence and more random levels of noise activity. At the midshore 

location, noise activity began on the same day as the inshore location and continued at consistent 

moderate levels until early May, similar to the first pulse at the inshore location. Noise activity 

started the earliest at the offshore location (mid-October), and ended at a similar time to that at 

the midshore location. Both the mid- and offshore locations, however, had recorder failures that 

limit our ability to know the true end date of the noise activity.

FIGURE 11. ICE NOISE ACTIVITY (PRESENTED AS THE PERCENTAGE OF TIME INTERVALS WITH SIGNALS) FOR 

INSHORE (LOWER PANEL), MIDSHORE (MIDDLE PANEL), AND OFFSHORE (UPPER PANEL) LOCATIONS, 2010-2012. 

DARK GRAY SHADING INDICATES NO DATA, AND TEAL SHADING INDICATES DAYS WHERE DETECTIONS WERE 

MASKED BY NOISE.
OCS Study

BOEM 2015-034

42

3. Discussion

Bowhead whales

The patterns of bowhead whale calling activity described in the results above 

complement what is currently known about their spatio-temporal distribution in the scientific 

literature. The bowhead whales detected on the long-term recorders are part of the BeringChukchi-Beaufort (BCB) stock that migrate through the Chukchi Sea annually between their 

wintering grounds in the Bering Sea and their summer feeding grounds in the Canadian Beaufort 

Sea (see Quakenbush et al., 2010 for an extensive literature review of this migration). 

In both years of this study, the fall migration was detected as a strong pulse in calling 

activity between September and December. This is consistent with past studies (mainly aerial 

and some shipboard surveys) which have described the fall migration as beginning in September 

and continuing through November/December, when the whales pass through Bering Strait 

(Moore and Reeves, 1993). Current data from satellite tagging (Quakenbush et al., 2010) and 

other passive acoustic studies (Hannay et al., 2013) have described a similar time period. More 

specifically, the fall migration in 2010 ended in mid-December, the same as that reported by 

Hannay et al. (2013) at their central Chukchi mooring locations in 2007 and 2010 (Figure 12). In 

addition, our data show that the 2011 fall migration ended in late-November; although Hannay et 

al. (2013) do not report on the 2011 fall migration, this timing corresponds with what they found 

for the fall migrations of 2008 and 2009. The reasons for these interannual differences in timing 

will be discussed in Section X below.

The fall migration pulse in calling activity was seen at all recording locations, and at 

similar levels, suggesting an even distribution of bowhead whales across the Icy Cape line. The 

bowhead migration is known to diverge once past Point Barrow, AK; some whales head west 

toward Wrangel Island and others head southwest toward the northern Chukotka coast (Moore 

and Reeves, 1993; Moore and Laidre, 2006). In fact, the fall migration pathway in the Chukchi 

Sea fans out so much that it cannot be designated as a Biologically Important Area (BIA, Clarke 

et al., 2015) migratory corridor.

An interesting result of the bowhead whale call analysis was the presence of three distinct 

peaks in calling activity during the fall migration at all three sites in 2010 (Figure 3). Traditional 

Ecological Knowledge (TEK) asserts that bowheads are segregated by age class during their fall 

migration; smaller whales leading the migration, followed by large adults including cow/calf 

pairs (Braham et al., 1984). Recent work by Koski and Miller (2009) using calibrated vertical 

photography on bowhead whales during their fall migration in the Beaufort Sea, found that small 

subadults do precede the adults, with cow/calf pairs the last to leave. Ljungblad et al. (1987) also 

detected three peaks of calling activity in the fall from migrating bowhead whales. While they 

interpret the three peaks as representing aggregations or pulses of whales passing Barrow, they 

do not speculate as to the age/sex classes of the pulses.
OCS Study

BOEM 2015-034

43

FIGURE 12. LOCATION OF CHAOZ PASSIVE ACOUSTIC MOORINGS OFF ICY CAPE, AK IN RELATION TO THOSE

DEPLOYED BY HANNAY ET AL. (2013) AND THE CSESP STUDY AREAS. JASCO SUMMER MOORINGS RECORDED 

GENERALLY FROM AUGUST TO OCTOBER, AND WINTER MOORINGS RECORDED FROM OCTOBER TO AUGUST.

It is interesting that Hannay et al. (2013) state they did not find three distinct pulses of 

calling activity in their data set; however, it is unclear as to whether they intended this statement 

to apply to just the spring migration. There are several examples in their plot of bowhead whale 

daily call presence (Hannay et al., 2013, see their Figure 5) that show three pulses during the fall 

migration (e.g., W50, W35, CL50, PL85), and they do describe the bowhead calling as occurring 

in pulses. But it is clear that the triple pulse is not a consistent feature in the data collected 

throughout their study area. It was also not consistent throughout their study period, a result we 

also saw between our 2010 and 2011 fall migration data. Taken as a whole, these data suggest 

that if these pulses do represent temporal separation between age classes, this separation varies 

interannually. Barrow whalers report that the segregation of migration pulses in the fall is 

tenuous (Huntington and Quakenbush, 2009), which may explain the differences seen here.

A more detailed acoustic analysis of these three peaks is needed to determine whether 

there is a difference in call characteristics among them, which would suggest differences in 

calling among the age/sex classes. Results from this analysis could then be applied to the 2011 

fall migration pulse to determine whether similar, but blurred, patterns exist in those data. We 
OCS Study

BOEM 2015-034

44

have begun this finer scale analysis with the inclusion of the bowhead whale gunshot call. As 

shown in Figure 4, this call type occurs near the end of each of the fall migration pulses in 

calling activity for both 2010 and 2011. One definitive case of gunshot calls being produced 

during the spring ice census in Barrow could not be associated with any visible surface activity 

(Würsig and Clark, 1993); but current recollection of this event is that it was associated with 

adults and not cow/calf pairs (C. Clark, pers. comm.). Further discussion of the gunshot call and 

its potential function can be found in Section X.3.

In both years of this study, the spring migration was detected as a strong pulse in calling 

activity between April and July. This again agrees with past (Moore and Reeves, 1993) and 

current (Quakenbush et al., 2012; Hannay et al., 2013) literature, as well as from the TEK 

acquired from centuries of springtime bowhead whale subsistence hunts (Braham et al., 1980). 

The calling activity detected during the spring migration was concentrated at the inshore 

location, with just low-level activity detected at the midshore, and none at the offshore locations. 

A very small possibility exists that the lack of spring calling activity at the midshore and offshore 

locations is due to early recorder failure at those locations, although the inshore recorder also 

failed early in the spring of 2011 and the spring pulse was still seen. Furthermore, this skewed 

spatial distribution of calling activity fits well with what is known about the spring migration of 

bowhead whales. In general, they remain close to shore and use leads in the ice to migrate 

northward from the northwestern Bering Sea along the Chukotka or Alaskan coasts through the 

Bering Strait, and then along the Alaskan coast toward their summering grounds in the Beaufort 

Sea (Braham et al., 1980; Moore and Reeves, 1993; Quakenbush et al., 2012). However, 

satellite tag data (Quakenbush et al., 2013) and passive acoustics (Clark et al., 1986) have shown 

that not all whales are confined to the lead system in the spring. The spring 2011 migration was 

detected in its entirety at the inshore location. Unlike the fall migration pulse in calling activity, 

there was no clear end to the spring calling pulse; rather, it gradually decreased starting mid-May 

with consistent calling activity until mid-August (Figure 3). Although the CHAOZ data are 

unable to determine if this gradual decline in calling activity is common across years, recorders 

deployed in the same locations for the subsequent BOEM-funded ARCWEST study have lasted 

a full year for several years, thus an answer will be available once those analyses are complete. 

In addition, Hannay et al. (2013) have reported similar decreases in detections after the main 

pulse of spring calling activity at other locations in the Chukchi Sea. 

At the present (post-whaling era) time, the Chukchi Sea is used primarily as a migratory 

corridor by the BCB (Bering-Chukchi-Beaufort) stock. It is also identified as a Biologically 

Important Area (BIA) for reproduction (Clarke et al., 2015), but this is based on sightings of 

cow/calf pairs (including neonates) during the spring and fall migrations, and so it still has a 

migratory context. 

Bowhead whales are planktivorous, feeding mainly on copepods and euphausiids, 

although they can also eat other crustaceans and fish (Lowry, 1993; Lowry et al., 2004). They 

can feed in the water column, at the surface, and epibenthically (Würsig et al., 1989). Recent 

work by Mocklin et al. (2012) has shown that epibenthic feeding is more prevalent than 

previously thought.

Whether bowhead whales use the Chukchi Sea to feed is unclear. As stated by Clarke et 

al. (2015), despite extensive aerial survey effort, very few observations of feeding bowhead 

whales exist for the northeastern Chukchi Sea to be designated as a BIA for feeding, although 
OCS Study

BOEM 2015-034

45

they also mention the limitations in identifying feeding behavior during aerial surveys. 

Nevertheless, feeding has been observed in the Chukchi Sea (Lowry and Frost, 1984; Ljungblad 

et al., 1986), and old whaling catch records have shown that bowhead whales historically used 

the Chukchi Sea in the summer/fall months (Dahlheim et al., 1980). Several authors have also 

suggested feeding during the spring migration is more common than previously thought (Lowry 

et al., 2004; Moore and Laidre, 2006, Mocklin et al., 2012). Furthermore, recent data from 

satellite tags have shown that bowhead whales sometimes turn around mid-migration 

(Quakenbush et al., 2013), and so it is important to note that they most likely are influenced by 

multiple motivators while in the Chukchi Sea. Further investigation of this feeding behavior will 

be described in Section X.3.

Finally, as noted in Hannay et al. (2013) it is also possible that periods of low calling 

activity levels are due to low calling rates and not necessarily from low whale presence (Würsig 

and Clark, 1993). However, they also counter with the fact that periods with low calling rates 

also correspond to periods with low numbers of visual observations. The data presented here 

agree strongly with those obtained from visual observations, TEK, and satellite tag data, and so 

we conclude that calling activity is a good proxy for the spatio-temporal distribution of bowhead 

whales.

Beluga whales

Like bowheads, beluga whales are a migratory species that move between the Bering Sea 

and the Arctic annually. The story for beluga whales, however, is complicated by the fact that 

two populations of whales, the eastern Chukchi Sea (ECS) and eastern Beaufort Sea (BS), are 

migrating through the study area at overlapping times (Hauser et al., 2014). As summarized in

Suydam et al. (2001), these populations were identified based on the areas that they use for 

calving, molting, and feeding, and confirmed through genetic analysis. The BS population 

concentrates in the Canadian Beaufort Sea, with core areas near the Mackenzie Delta and in 

Viscount Melville Sound, while the ECS population concentrates on the continental shelf and 

slope in the northeastern Chukchi and western Beaufort seas with core areas near Kasegaluk 

Lagoon and Barrow Canyon (Hauser et al., 2014).

Beluga whale acoustic presence was seen as pulses of calling activity in the late fall and 

spring at all locations and in both deployment years (Figure 5). This follows with what is known 

about ECS and BS beluga whale migration and their movements within the Arctic (Braham et 

al., 1984; Lowry et al., 1985; Moore et al., 2000; Suydam et al., 2001; Suydam, 2009; Delarue et 

al., 2011; Citta et al., 2013; Hauser et al., 2014; Clarke et al., 2015; Garland et al., 2015). 

After overwintering in the northern Bering/southern Chukchi Seas, both populations 

begin their migration north to their feeding grounds in the Arctic. The spring migration occurs 

from March to early July. In the current study, calling activity was present on all three recorders 

from early March to mid-July, with peaks ranging from mid-April to late May, and with much 

greater levels occurring at the inshore location (Figure 5). The timing, location, and level of the 

inshore calling activity fits well with the migratory route of the larger BS population. These 

beluga whales begin their migration first, following leads in the ice until reaching their feeding 

grounds in the Canadian Beaufort Sea no later than July. They cross the Chukchi Sea in mid- to 

late April and the Beaufort Sea from May to June. Their migration corresponds well with that of 
OCS Study

BOEM 2015-034

46

the bowhead whale, possibly because they might rely on the breathing holes left behind by the 

bowhead whales. However, the information on the BS population does not necessarily exclude 

the ECS population from contributing to the patterns of calling activity seen in this study. The 

smaller ECS population is thought to begin its migration later (D. Hauser, unpublished data). 

They arrive at Kasegaluk Lagoon by late June – early July, to calve, feed, and molt, and leave by 

mid- to late July as they spread out to feed further offshore of Kasegaluk Lagoon, near Barrow 

Canyon, or up to the ice edge (Hauser et al., 2014; Suydam et al., 2001). Although spring calling 

activity was highest at the inshore location, it was also detected at the mid- and offshore 

locations. This fits with results from other passive acoustic studies (Delarue et al., 2011; Moore 

et al., 2012; Hannay et al., 2013) that have also found high levels of beluga calling on offshore 

recorders in the Chukchi Sea in May, and suggests that not all beluga whales are traveling 

northeast along the inshore lead in the Chukchi Sea at this time of year. In fact, Suydam et al. 

(2001) have shown with satellite tags that beluga whales do not seem to be limited by high ice 

concentrations. 

For both populations, calving and mating occur May-August, although young calves have 

been seen as early as March and as late as September in the Arctic. Braham et al. (1984) list 

Peard Bay (between Barrow and Wainwright) as a prime mating location, but there is no 

contemporary evidence to support this.

Only one recorder functioned during the summer (inshore 2011-2012), but only very low 

levels of calling activity were found into July. Results from aerial surveys place the inshore 

mooring far outside the core feeding area (Clarke et al., 2015), suggesting that our lack of calling 

activity is due to low whale densities. However, satellite tag results show that the summer core 

area for the ECS population off Kasegaluk Lagoon does extend to this inshore mooring (Hauser 

et al., 2014; Figure 1). It is unknown if belugas are feeding at the mooring locations, or while 

passing through the area toward the ice edge. Beluga whales are highly vocal during most 

behavior states (e.g. during social interactions, or directional swimming/migration), however, 

studies have shown that beluga whales rely almost entirely on echolocation clicks when foraging 

(Castellote et al., 2011; Panova et al., 2012; Castellote et al., in review); although see Stafford et 

al. (2013) for a summary of evidence to the contrary. Due to sampling rate limitations, the 

passive acoustic recorders used in this (and the Hannay et al., 2013) study would not be able to 

detect echolocation clicks, which have peak frequencies between 40-60 kHz (Au et al., 1985). 

However, there are instruments available (CPOD echolocation loggers, Chelonia Ltd., Cornwall, 

UK) that are designed to detect and record high frequency echolocation clicks. Although these 

instruments are currently unable to record for a full year, the addition of CPODs at our mooring 

locations would facilitate the identification of foraging behavior, possibly increasing both the 

number of detections (Castellote et al., 2013) and our knowledge of beluga whale spatiotemporal distribution.

Beluga whales are benthic and pelagic feeders (Seaman et al., 1982; Braham et al., 1984). 

The diet of the BS population has been said to be primarily Arctic cod, along with other fish, 

cephalopods, and shrimp (Moore et al., 2000). The diet of the ECS population is less well

known but is thought to consist of saffron cod, cephalopods, crustaceans, and marine worms 

(Braham et al., 1984). Point Lay hunters have reported the stomachs of whales harvested in 

Kasegaluk Lagoon to contain shrimp, cephalopods, and small fish (Lowry et al., 1985). The 

most current data is from Quakenbush et al. (2015) who analyzed the stomach contents of 67 

ECS whales and 62 BS whales. They found that shrimp were the predominant prey type of both 
OCS Study

BOEM 2015-034

47

populations, with the most predominant fish species being saffron cod for the ECS and Arctic 

cod for the BS. Although other studies suggest that even the ECS population feeds on Arctic cod 

(Stafford et al., 2013; Hauser et al., 2014). Worms and octopus were still more common prey 

items than fish for the ECS and BS populations, respectively. It is important to note that the ECS 

whales analyzed by Quakenbush et al. (2015) were found near Point Lay in June and July, when 

they are concentrating in Kasegaluk for their molt/calving period. Most of the BS population 

were collected during their spring migration, presumably before they have reached their prime 

feeding grounds. Therefore, these results may not reflect the true composition of their diet. In 

fact, dive data from Citta et al. (2013) shows that the ECS beluga whales dive to depths of 200-

300 m, where the boundary layer between water masses aggregates Arctic cod.

Beluga calling activity in the fall was less prominent than in the spring. This result 

follows that of Hannay et al. (2013). Belugas are distributed typically further offshore (Moore et 

al., 2000); it is not necessary for the whales to follow leads in the fall, since the waters are icefree, and so they most likely fan out over a much wider area than during the spring migration. 

The fall migration has been said to split once past Point Barrow (Clarke et al., 1993), similar to 

the bowhead whale migration, with one migratory path continuing southwest through the 

Chukchi Sea and another remaining north of 72°N and heading west. In September, the BS 

population moves west past the ECS population and they hold this west-east positioning for the 

rest of the fall migration to the Bering Sea (Hauser et al., 2014). This bifurcation is the best 

explanation for the lack of calling activity at the mid- and offshore locations. In fact, the number

of days in October with calling on a recorder located at 75°N (Moore et al., 2012) equaled that of 

the inshore mooring location of this study (Appendix E), and was 5-10 times greater than that of 

the mid- and offshore locations. 

There were also a few instances of calling activity at all locations. This calling activity

occurred when polynyas formed, creating leads in the ice. The association between belugas and 

ice conditions is discussed in detail in Section X.3.

In addition to the work on the seasonal distribution of beluga whales at the three 

locations, a separate study is currently being conducted to determine if the two populations of 

beluga whales can be discriminated based on passive acoustic techniques. Dr. Ellen Garland, an 

NRC post-doc at NMML from 2012-2014, analyzed the beluga acoustic data for the inshore and 

offshore recorders for 2010-2011 as part of a larger data set including archival data from two 

past BOEM –funded projects (PRIEST and BOWFEST). She determined that the different peaks 

in calling activity were indicative of distinct beluga stocks migrating through the Chukchi Sea at 

different times (Garland et al., 2015), which supports the current population stock delineation 

suggested by satellite telemetry, aerial surveys, and other acoustical studies (e.g., Suydam et al., 

2001; Hauser et al., 2014; Clarke et al., 2015). Garland et al. (2015) concluded that the migration 

of beluga populations can be discriminated when temporal differences between calling peaks are 

large enough to be identified as independent events. She has begun to establish call repertoires 

for the two populations; see Garland et al. (2015) for the BS population repertoire. Alexandra 

Ulmke has taken over the analysis, under Dr. Garland’s guidance and with NOAA S&T funding, 

and is to develop the call repertoire for the ECS population. When completed, the two 

repertoires will be compared and the results applied to the entire data set to hopefully 

differentiate between the two populations using call characteristics alone.
OCS Study

BOEM 2015-034

48

Note on spring migration of the BS and ECS populations of beluga whales:

Spring calling activity on the inshore recorder, as mentioned above, exhibited a bimodal 

pattern. This pattern was evident in both deployment years, and was also seen in the Hannay et 

al. (2013) data (e.g., their Figure 6, W35). This bimodal pattern might be caused by the two 

populations moving by at different times, it might also be due to sex/age segregation of the BS 

population (which was shown to occur in the fall migration (Hauser et al., 2014)). Braham et al.

(1984) reported that beluga whales are in big groups in the winter, splitting into smaller groups 

of 2-4 animals during the spring migration (although bigger groups of 100+ are seen) that reform into large groups on the feeding grounds, dispersing once more into smaller groups as they 

migrate to the Bering Sea; the larger groups being led by large adult males followed by 

cow/calves along with subadults. It also could be due to just separate groups passing by at 

different times, or by oceanographic conditions temporally halting or diverting the migration. No 

bimodal pattern was seen in either the mid- or offshore spring data. 

Although it is not clear from the literature how the two populations have been 

differentiated during the spring migration, unpublished satellite tag data support this population 

separation (D. Hauser, pers. comm.). Evidence from aerial surveys and passive acoustic 

recordings also seems to support that the BS population is the one passing through the Chukchi 

Sea at this time. Aerial surveys conducted during the spring migration have shown the whales 

swimming predominantly northeast in the Chukchi Sea and east in the Beaufort Sea (Moore et 

al., 1993). Timing of the spring bimodal peak in calling activity at the inshore location seems to 

align with a slightly delayed bimodal peak on a recorder (deployed through BOEM-funded 

BOWFEST project) located to the east off Barrow, AK (Garland et al., 2015). These again do 

not remove the ECS population from consideration, as there does not seem to be any evidence 

that the ECS whales always move directly from the Bering Sea to Kasegaluk Lagoon. They 

therefore could be part of the stream of whales moving to the northeast. In fact, the only ECS 

beluga with a functioning satellite tag during its spring migration left the Bering Sea and 

travelled NW into Russian waters off the Chukotka Peninsula then east toward Barrow Canyon 

and the ice edge before turning around and heading toward Icy Cape near Kasegaluk Lagoon

(see tag #22149; 

http://www.alaskafisheries.noaa.gov/protectedresources/whales/beluga/ptlay.htm). As suggested 

by Delarue et al. (2011), it would seem logical for the migrating whales to replenish their energy 

stores before arriving in the lagoon, especially since they may not feed there. 

Although Pt. Lay hunters have found evidence of feeding from whales taken within 

Kasegaluk Lagoon, they also say that whales taken from the large aggregations near the passes 

typically have empty stomachs (Lowry et al., 1985); new data from Quakenbush et al. (2015) 

also show a lack of feeding from whales harvested in the lagoon2. In addition, the hunters assert

that the beluga move back and forth between the lagoon for molting and the ice edge for feeding 

(Frost et al., 1993). Both Barrow Canyon and the ice edge are prime locations for foraging 

 

2 Quakenbush et al. (2015) do mention that beluga have the tendency to regurgitate food when being chased, so 

empty stomachs may not be a reliable indicator of feeding habits. They also cite evidence of fasting from tissue 

sample analysis (Woshner, 2000 in Quakenbush et al. (2015)); however, the lag times of the prey signatures in the 

tissue samples may be too great to be useful in determining whether beluga whales fast around Kasegaluk Lagoon.
OCS Study

BOEM 2015-034

49

(Delarue et al., 2011; Hauser et al., 2014), and so a direct migration path to Kasegaluk Lagoon 

seems unlikely. 

Gray whales

The lack of gray whale calling activity at the three recording locations (Figure 6) was 

expected. The inshore location, where almost all the calling activity was detected, is still 40 nm 

off the coast, while aerial surveys have found that most gray whales remain within 

approximately 25 nm from shore between Point Barrow and Point Lay, AK or in the area 

between Wainwright, AK and Hanna Shoal which was to the east of our recording locations 

(Clarke and Ferguson, 2010). Our results also fit those from Hannay et al. (2013); although their 

recorders covered a much wider area they still found low levels of calling. Their results are even 

more surprising, however, because they had several recorders located right where the aerial 

surveys documented concentrations of gray whales. 

The low levels of gray whale calls that Hannay et al. (2013) found in 2010 were limited 

to their recorder (hereafter referred to as industry recorders) locations within 35 nm of Point Lay 

and 5 nm of Wainwright. They detected calling in 2010 from late July to late August while we 

saw calling activity much later (between early-October to mid-November) on our inshore 

recorder. The timing of the two study results suggests that perhaps the calling heard on the 

industry recorders was from whales that shifted up to our inshore recorder location before 

migrating out of the Arctic. Our inshore location is ~75 km away from either their W50 or PL50 

locations (Figure 12), so a small cluster of whales could make this move without being detected 

on either of the industry recorders. It is also possible that the migration happened along the coast 

(reaching our inshore location), after the inshore industry recorders were retrieved (last out midOctober). However, this does not explain why industry recorders the same distance from shore 

(e.g., W50, PL50) had no detections. It is likely that since only a small portion of the industry 

recorders were manually analyzed, gray whale calls, with their low calling rate, were missed. 

The randomness of the spatial distribution of the calling activity is probably due to a 

combination of two factors: a low calling rate, and calling behavior that is context dependent. 

Crane and Lashkari (1996), found that gray whales do call along their migration route, but the 

calling rate is extremely low (mean: 20 hr. between calls). This means, assuming a swim speed 

of 6 km/hr (Rugh et al., 2001), that there could be ~65 nm between calls; so the chances that a 

recorder will be recording when a whale is calling nearby are low. We have found from the 

work we’ve done with joint visual and passive acoustic surveys (see Shipboard Observations

below), that the same concentrations of whales in the same area at different times during a single 

cruise, can have vastly different calling rates, due to differences in behavior (the presence of 

courtship behavior in this case). Gray whales are in the Arctic to feed. They are typically 

benthic feeders, but will also feed in the water column (Swartz et al., 2006). Their prey include a 

variety of invertebrates including gammaridean amphipods (their preferred prey) which 

aggregate on the Chukchi and Bering shelves, worms, bivalves, pelagic mysids, crab larvae, and 

herring eggs (Nerini, 1984; Darling et al., 1998). Although there is information on gray whale 

calling behavior on their breeding grounds and during their migration, little exists on the sounds 

they make while feeding. We have noticed an interesting lack of gray whale calling behavior 

from sonobuoys during times when gray whales are obviously feeding, as evidenced by mud 

plumes.
OCS Study

BOEM 2015-034

50

The timing of the calling activity on our inshore recorder fits the timing of the migration. 

Eastern North Pacific gray whales make the long roundtrip migration from their feeding grounds 

in the Arctic to the breeding/calving lagoons off Baja California, Mexico annually (but note that 

not all whales migrate fully (see Rugh et al., 2001; Stafford et al., 2007b)). The start of the 

southbound migration varies; starting as early as mid-August, but the majority of whales start to 

leave during September/October, and most are out of northern waters mid-October to November 

(Rugh et al., 2001). The timing of this migration coincides with the breeding season, which is a 

three-week period from late November to early December when most females are in estrus

(Swartz et al., 2006). Therefore, the fall peaks in calling, which occurred in early October 2010 

and early November 2011, coincide with both this migration and breeding season. No gray 

whales were heard past November during this study or that of Hannay et al. (2013). This is in 

contrast to the results presented by Stafford et al. (2007b) who found that gray whale calls were 

present from October 2003 to May 2004 at a mooring located northeast of Barrow, AK. 

The southbound migration takes about two months to complete (Rugh et al., 2001) and is 

segregated with pregnant females leading, mature adults next, with immature animals last 

(Swartz et al., 2006). In mid-February the migration shifts from southbound to northbound, a 

time period that has been relatively consistent since the 1960’s (Rugh et al., 2001; Rugh et al.,

2005; DeJesús et al., 2014). The northbound migration takes three months and occurs in two 

waves (Swartz et al., 2006). The first, in February, is made up of newly pregnant females 

followed two weeks later by adults; immature whales follow after another week. The second 

contains the cow/calf pairs that migrate between March and May arriving in the Arctic feeding 

grounds between May and June. Again, the calling activity seen at the inshore location in 2012 

coincides with this spring migration. It seems plausible, therefore, to assume that the inshore 

mooring location is located in part of the migratory path taken by the gray whales in both the fall 

and spring. It remains to be seen whether there is a reason they are calling at that location 

instead of those with the overwintering industry recorders, or whether it was just a coincidence. 

The last confounding factor that may influence both the calling behavior and the 

detection of those calls is the presence of ambient noise. As mentioned in both Crane and 

Lashkari (1996) and Hannay et al. (2013), ambient noise can make the low frequency calls of 

gray whales hard to detect. In addition, the latter paper describes improvements in recorder 

design that increased the detection of the gray whale calls by reducing noise generated by the 

recorder itself. Improvements were seen in their 2011 data, a year that saw a reduced amount of 

noise activity from airguns (Figure 9); however, this reduced presence of airgun noise did not 

result in more gray whale calling activity on our recorders (Figure 6). It is unknown what effect 

airgun noise has on the calling behavior of gray whales. Many studies exist (see Moore and 

Clarke, 2002 for summary) that show gray whales react to anthropogenic noise sources by 

changing their course to avoid it. Only one of these studies (Dahlheim, 1987) examined the 

effects of these noise sources on the calling behavior of gray whales. Its findings included 

increased calling rates with playback signals such as boat noise, gray whale calls, and pure tones, 

but a cessation of calling altogether when a test tone was played. It is possible that the presence 

of the impulsive signals from airguns might have an effect on gray whale calling rates, but no 

information is available from the literature to support or refute this. 

Walrus
OCS Study

BOEM 2015-034

51

One of the biggest surprises of this study was the high level of mid-winter walrus calling 

activity at our offshore location, a result which was not found by Hannay et al. (2013). Calling 

was present from November through April in both deployment years and at all three locations, 

however, calling was most pronounced at the offshore location with peak levels reaching close to 

100% saturation mid-February 2011 and mid-March 2012. 

This is an unexpected result because of what is known about their migrations and 

subsequent seasonal distribution of Pacific walrus. Most winter on Bering Sea pack ice (Fay et 

al., 1984a), to the south of St. Lawrence Island (the majority of the population) and in outer 

Bristol Bay near Round Island, usually around some form of open water (e.g., polynyas). The 

mating season occurs mid-winter. Male fertility is highest between November and February; 

subadult males become fertile later in the year and may mate with the late ovulating females (Fay 

et al., 1984b). With ovulation occurring from December to May and fertilization occurring by 

March, the mating season is December through March. Because walrus calve on drifting ice, the

males do their reproductive display (which includes acoustic displays (Miller, 1975)) in small 

areas near the nursing cow/calf pairs for short periods of time; it would be hard for a male to 

defend a herd in such an unstable environment (Van Opzeeland et al., 2008).

As described in Fay (1982) walrus begin to disperse from these wintering sites in April, 

and many move through Bering Strait in May (some reaching as far as Barrow, AK). However, 

historical sightings of walrus off Point Hope from January through April are not uncommon. It 

is not unreasonable to assume that the walrus heard overwintering on the offshore recorder are 

subadults that do not have any reason to expend the energy required to migrate to the breeding 

ground in the Bering Sea. Indeed, subadults seem ‘the most inclined to wander or to be diverted 

by irregular ice movements’ (Fay, 1982). In addition, young male walrus tend to remain at the 

periphery of the areas where the adults aggregate in the winter (Fay et al., 1984b). Miller (1975) 

describes instances of subadult males engaging in reproductive displays and suggests that 

practice sessions occur; this would explain the presence of calling activity if the animals are, in 

fact, subadults. At any rate, some form of open water (e.g., polynya, leads) has to be present 

throughout the time period with this calling activity. Jay et al. (2012) reported large amounts of 

open water accompanied by high numbers of walrus in the Chukchi Sea in November of 2008-

2011, so it is not unreasonable to assume that some pockets of open water existed overwinter in 

the years of this study. A Modis ice image from mid-Feb 2011 shows evidence of leads offshore 

in the study area (Figure 13a). The Modis ice image from mid-March 2012 (Figure 13b) 

provides compelling evidence that cracks forming in the Bering Strait progressed toward the 

CHAOZ study area by mid-March, 2012. 
OCS Study

BOEM 2015-034

52

FIGURE 13. ICE COVER IN NORTHEASTERN CHUKCHI SEA. A) MODIS VISIBLE-BAND IMAGE WITH HEAVY CLOUD 

COVER FROM MID-FEBRUARY 2011. BLACK DOT SHOWS APPROXIMATE LOCATION OF ICY CAPE. B). MODIS 

INFRARED-BAND IMAGE FROM MID-MARCH 2012. THE THREE RED DOTS MARK THE LOCATIONS OF OUR INSHORE, 

MIDSHORE, AND OFFSHORE MOORING LOCATIONS.

Walrus time their northern migration based on ice movements from wind and sea surface 

currents – essentially hitching a ride on the moving icepack; however, they spend most of the 

migration swimming (Fay, 1982). They begin extending up into the northeastern Chukchi Sea 

starting in May. Most reach their summering grounds in July/August which corresponds to the 

beginning of the summer pulse of calling activity detected at all locations (Fay, 1982). The 

timing of the start of this pulse varies slightly between locations and deployment years, and also 

between our results and those from Hannay et al. (2013). Reasons for the differences seen within 

our data set will be discussed in Section X. Hannay et al. (2013) have shown that walrus are not 

uniformly distributed in the Chukchi Sea. They also found that areas of concentration shift 

throughout the summer season, with a gradual increase in detections near Hanna Shoal occurring 

in late July and August. For these reasons, variations in timing among locations are to be 

expected. 

Females with calves are the most migratory, and tend to stay with the ice edge as it 

moves north in the Chukchi Sea. Jay et al. (2012) found that June/July is currently a time period 

with walrus ranging further north than in the past. Adult males are the least migratory, hauling 

out along the Chukchi coast in the summer. In addition, many thousands of males remain in the 

Bering Sea for the summer (Fay et al., 1984a). Walrus are benthic feeders and prefer to remain 

in areas where the water depth does not exceed 100 m (Fay, 1982). Their diet varies spatiotemporally, and they forage opportunistically (Seymour et al., 2014), but feed primarily on 

bivalve mollusks (Fay, 1982; Jay et al., 2014) and other invertebrates such as worms, snails, and 

crabs. The advantage for the walrus hauling out onto ice is that they can rest while it carries 

them around to new feeding grounds. Because of the high energetic demands of nursing (which 

lasts for approximately two years), it is logical that the females remain ice associated. It is 

unclear why males do not also remain with the ice, but Miller (1976) suggests it is because they 

do not have any high-energy demands in the summer, they save additional energy by lying 

closely in groups, and the extra heat generated from neighboring bodies aids with their molt. 

Their preference for haul out sites that are out of the wind further supports this argument. The 
OCS Study

BOEM 2015-034

53

molting period is long, happening anywhere from March to October with a peak in July/August 

(Fay, 1982), it appears that it takes at least a month for an individual to completely molt. Trips 

into the water will impede the molt as that will cut off circulation to their skin, so the hair 

follicles cannot regenerate (Fay, 1982). It is important to note, however, that this model of 

age/sex class segregation is changing as a result of climate change. When the ice leaves Hanna 

Shoal early in the season, large aggregations of walruses of all ages and sex classes form 

enormous haul-outs (summarized in Hannay et al., 2013). These combined haul-outs are 

dangerous for young walrus who can get trampled and killed during stampedes; the resulting calf 

mortality can have compounding effects on the population (Udevitz et al., 2013).

It is assumed that underwater calls are produced by male walrus (Kastelein et al., 2002), 

so it would be expected that the largest levels of calling activity would occur closest to the coast 

where the males are hauled out. Although we are missing recording time in the middle of this 

summer pulse of calling activity, what remains seems to show a reduction in calling activity from 

the inshore to the offshore locations. However, the results from Hannay et al. (2013, their Figure

11) show the opposite result. It is possible that the assumption that these underwater calls can be 

produced only by males is incorrect, as Shusterman and Reichmuth (2008) have shown that 

females are capable of producing them as well. Analyses are currently underway for the BOEMfunded ARCWEST and CHAOZ-X projects; which have complete years of data and so can 

better describe the spatio-temporal trends in the data. Comparison with satellite tracks of tagged 

individuals might help determine if female walrus typically make underwater sounds in the wild, 

but tagging females with acoustic tags (e.g., DTAGs) would be, by far, the best method to 

quickly verify that the original, male-only, assumption is correct. 

In the past, the southbound migration coincided with the rapid advance of the ice pack in 

October; possibly occurring in waves through the Bering Strait, with the animals that summered 

on the northern Chukotka coast preceding those that summered in Barrow and Wrangel Island. 

The pulse of calling activity seen from the start of the recordings until around 10 October at all 

three locations and in both years (Figure 7) might correspond to this fall migration. Most of the 

population remains south of the icepack, which reaches Bering Strait by November. The thin ice 

that develops in the fall is not strong enough to support the animals, and they swim ahead of it. 

The passive acoustic data from Hannay et al. (2013) and radio tag data from Jay et al. (2012) 

suggest that the walrus are moving out of the Chukchi Sea, not based on ice advance, but on the 

retreat of the ice edge. Also Jay et al. (2012) found that walrus are moving to the Chukotka coast 

prior to heading down through the Bering Strait. What has been known about walrus distribution 

is likely to continue to change as climate change progresses.

Bearded seals

Before the recent changes in sea ice extent, bearded seals spent a majority of their time in 

the Arctic and subarctic closely associated with the sea ice. This association still holds, but data 

from aerial surveys, tagging and passive acoustics show that many individuals now spend their 

summer in open water.

Braham et al. (1977), Burns and Eley (1978), and Allen and Angliss (2013) provide a 

thorough description of the past and current distribution and ecology of the Alaskan stock of the 

bearded seal (Erignathus barbatus nauticus) which is summarized below (with additional 
OCS Study

BOEM 2015-034

54

references as needed). Bearded seals spend most of their time associated with the drifting pack 

ice, rarely hauling out on land (and even avoiding areas with continuous landfast ice). They can, 

but rarely do, maintain breathing holes, and so avoid areas with high ice concentrations, 

preferring areas where constantly moving ice helps to keep leads open. However, they also 

prefer heavier pack ice (70-90% ice cover) than other phocid seals and therefore tend to be 

distributed further north (they are most abundant 20-100 nm from shore, rather than within 20 

nm from shore, with the exception of a group nearshore to Kivalina, AK). They are typically 

found in groups of 1-2, although groups of 30 seals have been seen during the molting season in 

the summer.

Like walrus, bearded seals tend to prefer areas where water depths are less than 200 m

(Burns and Frost, 1979; Burns, 1981). They are primarily benthic feeders and eat mainly 

crustaceans, mollusks, cephalopods, worms, and fish. Their diet composition is strongly sitedependent. For example, crustaceans are the most common prey item near St. Lawrence Island, 

while bivalves and shrimp are the most common prey in Norton Sound. Males and females eat 

the same items, but a higher proportion of the diet is composed of shrimp for the younger seals 

(Lowry et al., 1980). Their ability to forage for a variety of organisms gives them an advantage 

over the more bivalve-centric walrus when feeding in the same areas (Lowry et al., 1980). 

However, as sea ice retreats farther away from the continental shelf into deeper waters, benthic 

foraging opportunities will diminish. This is most likely the reason for the recent uptick in 

bearded seal sightings and acoustic detections in the open water. 

Bearded seals winter in the northern and central Bering Sea shelf and in the Strait. In the 

past they aggregated north of St. Lawrence Island in March, and began to disperse (both north 

and south) in April, crossing into the Arctic by June. Currently, it is thought that most of the 

north-bound seals pass through Bering Strait between April and June. They are widely 

distributed in the summer with some (mostly juveniles) remaining near the coast in the Bering 

and Chukchi Seas. Their whelping/mating/molting (in that order) seasons occur from March and 

late June (Burns and Eley, 1978). Pups are born in April on small drifting floes in shallow 

waters, and weaned in May. Male bearded seals produce long (> 1 min) trills (Ray et al., 1969) 

during the mating season which starts just prior to the weaning of pups in May. As summarized 

in Jones et al. (2014) these males return to the same breeding locations each year, and have been 

shown to use either a roaming or territorial mating strategy; the duration of the trill call used 

between the two strategies varies. Most seals head south through the Bering Strait in the fall, 

ahead of the advancing ice. This southbound migration is said to be less predictable and 

noticeable than the northbound leg. In late winter/early spring they are dispersed in the broken 

and drifting pack ice from the Chukchi Sea to the ice edge in the Bering.

Bearded seal calling vocalizations were ubiquitous throughout the study area for both 

years. The level of bearded seal calling activity increased in the fall to near saturated levels that 

started between late February and mid-April, depending on the deployment year, and location 

(Figure 8), which coincides with the start of the whelping/mating/molting time period. For most 

locations, the end of the peak calling could not be determined due to recorder failure. However, 

the inshore data for both years shows a sharp decrease in calling activity around the end of June,

which matches well with the end of the whelping/mating/molting time period. These results are 

consistent with those reported by Hannay et al. (2013), who reported an increase in calling 

activity in fall-winter with a peak in April and an abrupt end of calling activity in late June. This 
OCS Study

BOEM 2015-034

55

also fits with the results from Moore et al. (2012), who reported a pulse in calling activity MayJuly 2009 on a mooring located far north on the Chukchi Plateau.

The data collected for this study also shows that bearded seals are present in the 

northeastern Chukchi Sea year-round. Calling activity was seen year round, with low levels after 

the end of the whelping/mating/molting season until early October when a small peak was seen 

at all locations and in both years. Again, this is in agreement with the data presented by Hannay 

et al. (2013), as well as others (e.g., MacIntyre et al., 2013; Jones et al., 2014). From October to 

when calling activity reached near-saturated levels, calling activity levels fluctuated within each 

individual location/deployment and varied among locations/deployments. The environmental 

factors contributing to this variation will be discussed in Section X below.

Other species

The northward encroachment of subarctic species into habitats historically occupied 

solely by Arctic species is a serious concern. Clarke et al. (2013) suggest their intrusion into the 

Arctic may be due to either post-whaling population growth, or to climate change extending the 

open water season. Having the ability to monitor year-round for these species is important as we 

try to sort out what changes are happening and their subsequent effects on the Arctic species. 

For this reason, analysis of the passive acoustic recordings extended to a number of other 

subarctic marine mammal species. Some like fin, killer, minke, and humpback whales and 

ribbon seals, have been sighted or detected in the Arctic before, and therefore would be expected 

to have at least some calling activity. We will discuss each of these species below. Other species, 

such as right and sperm whales, are not expected to be present on the northeastern Chukchi shelf. 

Although we did analyze the data for these species, the fact that we did not find any calling 

activity is expected and therefore no discussion follows.

Fin whales

One surprising result was the lack of fin whale calling activity at any of the locations for 

either year. At first glance, this seems to be in direct contrast with the results from other passive 

acoustic studies (Delarue et al., 2013a; Hannay et al., 2013), as well as the short-term sonobuoy 

results (see Section VII.B.2; Crance et al., 2015) collected during the field season for this project. 

However, closer examination of the timing and locations for the detections made during these 

studies reveal that our effort was not comparable. Although the industry recorders, deployed by 

the two studies mentioned above, were spread throughout the northeastern Chukchi Sea, fin 

whale detections were limited to those to the southwest of our mooring line. These detections 

were rare, occurring on a handful of days per recorder in 2009 and 2010 (see Delarue et al., 

2013a, Table 1), although 2007 saw a few recorders with approximately 30 days with calls 

detected. The majority of detections, in the years closest to those of our study, were made on a 

recorder that was ~100 nm away from our midshore location. The only detections made close to 

our mooring line, and in the same year as our study, were those from a recorder located ~ 35 nm 

away, and were made on 14 August 2010 and 3 October 2010. Given our recorders did not begin 

recording until 10 September 2010, only one day was missed. Fin whales detected on sonobuoys 

during the research cruises were also mainly concentrated in the area off Cape Lisburne and 

south. The one exception is a series of about 30 fin whale calls detected on a sonobuoy deployed 
OCS Study

BOEM 2015-034

56

in late August 2012, approximately 50 nm off the coast near Barrow, AK (Crance et al., 2015). 

This detection, although to the east of the mooring line, was still made a long distance from our 

recorders, and it is much closer to the coast than our inshore mooring location, so it is possible 

the whale did not pass by any of our mooring locations.

The other possibility is that fin whales are present (albeit in low numbers), but are not 

vocalizing. However, it is generally not expected that fin whales would be present in large 

numbers in the Chukchi Sea. Fin whales are a subarctic species that, in Alaskan waters, are 

common throughout the Gulf of Alaska (Watkins et al., 2000; Stafford et al., 2007a) and Bering 

Sea shelf (Moore et al., 2002). Historically they ranged in these locations as well as in the 

Western Chukchi Sea (Mizroch et al., 2009). However, fin whale sightings in the southern 

Chukchi Sea from aerial surveys conducted since the 1980’s have been rare (Moore et al., 2000; 

Clarke et al., 2013). Vessel surveys conducted since 2008 (Aerts et al., 2013) and 2010 (this 

study, see Section VII.B.2) have had no sightings of fin whales in the Arctic.

Although we did not have satisfactory performance from our attempts at fin whale autodetection via the LFDCS, the full 2010-2011 dataset was analyzed manually, still without a 

single fin whale call detection. Further iterative testing and call library manipulation is 

underway to try and improve the LFDCS in correctly detecting fin whale calls.

Killer whales

Not much is known about killer whales in the Arctic other than it seems likely they are 

probably of the transient ecotype. See Clarke et al. (2013) for references that support this 

assumption. The transient ecotypes are the mammal eaters, who stalk their prey silently, and so 

it is unlikely that many calls would be detected in the study area. However, they are typically 

very noisy just after a kill (Deecke et al., 2005; C. Berchok pers. comm), so perhaps information 

on their feeding frequency might be able to be obtained from these data with additional analysis 

on the characteristics of post-meal calling bouts.

Killer whale calling activity was infrequent, occurring on only seven days out of the two 

years the recorders were in the water. All but one of these days occurred early September to 

early October and from mid-May to late June at the inshore mooring location. The first time 

period fits with the results from Hannay et al. (2013) who had occasional detections of killer 

whales in the Point Lay/Cape Lisburne recorders between late July and October annually. Their 

lack of call detections in the May/June time period is most likely due to the low calling rate of 

transient whales. In addition, sightings from shipboard (Aerts et al., 2013; this study - see 

Section VII.B.2) and aerial surveys (Clarke et al., 2013) are rare, so it is not just a matter of them 

being present and not heard, but rather a combination of low presence and low calling activity.

Humpback whales

Humpback whales are another subarctic species that is uncommon in the Arctic (Aerts et 

al., 2013; Clarke et al., 2013; this study - see Section VII.B.2). We detected no humpback whale 

calling activity on the long term moorings. Only two detections of humpback whale calls were 

reported by Hannay et al. (2013). These detections were made off Cape Lisburne in August 

2010, ~100 nm from our mooring line, and a month before our recorders were deployed.
OCS Study

BOEM 2015-034

57

Minke whales

The story for the minke whale mirrors that of the humpback; they are sighted infrequently 

by visual and vessel surveys (Aerts et al., 2013; Clarke et al., 2013; this study - see Section 

VII.B.2), and passive acoustic detections are rare. We had only one day with calling activity 

from minke whales, 19 October 2011. The call type detected was the boing call (Rankin et al.,

2005). Delarue et al. (2013b) found minke whale boing calls around the same time period in the 

same October/November time frame in 2009 and 2011 to the west of our moorings.

Ribbon seals

Although there was a lack of ribbon seal calling activity on our moorings, there was one 

detection found on 6 April 2011 (inshore). This is surprising due to what is currently known 

about their seasonal distribution in the Arctic. As summarized in Boveng et al. (2013), ribbon 

seals are strongly associated with sea ice in the Bering and Okhotsk Seas for reproduction 

(including whelping and nursing) and molting. The season for these activities extends from midMarch through June. Molting periods are segregated by age and reproductive status (Burns, 

1981), with mature seals molting during the breeding season (beginning in late-April to early 

May and extending as late as July (Tikhomirov, 1961)). Ribbon seals do not form dense breeding 

aggregations, as females tend to be solitary and their breeding location is within the shifting ice 

front of the pack ice. They are not well adapted for maintaining breathing holes in the winter sea 

ice, and this restricts them to the part of the ice edge containing new, clean ice floes less than 20 

m wide and of medium thickness. This part is never coastal but instead can extend up to 150 km 

from the southern edge of the ice. Ribbon seals are deep divers and prefer feeding on the 

continental shelf slope in the pelagic and demersal zones, when not limited by the distance of the 

ice edge from the slope. They prefer to feed on fish such as pollock and cod (Arctic, Pacific, and 

saffron), cephalopods such as squid and octopus, and crustaceans. 

After they are finished with their reproductive/molting activities, ribbon seals leave the 

ice and spend the rest of their year at sea (Burns, 1981); remaining highly dispersed during the 

open-water season. To reiterate: ribbon seals do not remain on the ice until it recedes – they 

leave it once they are through with whelping, nursing, mating, and molting. There seems to be 

conflicting information as to where the ribbon seals go once the Bering and Okhotsk Seas are ice 

free. One view is that some of the seals move from the Bering into the southern Chukchi (with 

some of the Okhotsk seals moving into the Bering). Another view is that very few ribbon seals 

move through Bering Strait; they move instead to areas near the Bering Sea slope with high 

productivity such as the Pribilof Islands (Lowry, 1985). Recent satellite tagging efforts have 

found the real situation to be somewhere in between: about 30% of ribbon seals tagged in the 

central Bering Sea moved into the Arctic with the ice retreat and, during July-October, spent 

about 10% of their time budget there. Most of the tagged seals stayed in the Bering Sea, but not 

just near the slope: seals were tracked both on the shelf (including coastal areas) and in the basin, 

leading Boveng et al. (2013) to suggest that ribbon seals can thrive in a diversity of habitats and 

environmental conditions outside their ice-obligate activities time period.

Passive acoustics has detected ribbon seals on both the Bering Sea shelf and in the Arctic. 

Miksis-Olds and Parks (2011), found that the peak in ribbon seal calling in the southern half of 
OCS Study

BOEM 2015-034

58

the Bering Sea shelf occurred from April to May, coinciding with the mating season. On both 

the Chukchi Sea shelf (Hannay et al., 2013) and basin (Moore et al., 2012) ribbon seal calling 

was detected in the late fall (October and November), which seems to fit timing-wise with their 

southbound return to the Bering Sea ice edge. Here we present calling activity in the 

northeastern Chukchi Sea that coincides with the timing of Bering Sea breeding season. As with 

the mid-winter walrus detection, it seems likely that the source of these calls are subadults that 

had no motivation to migrate down to the Bering Sea breeding grounds. As these calls are 

thought to be part of a reproductive/territorial display (Watkins and Ray, 1977), with this 

assumption it also seems likely that the presence of these sounds in the April Chukchi Sea are 

indicative of juvenile male practice sessions.

As for overall lack of ribbon seal calling activity on our recordings, if these calls are truly 

associated with reproductive displays, and these displays are limited to a small time period and 

limited to waters south of the Bering Strait, then it is logical to conclude that not many of these 

calls will be heard in the Arctic. In fact, Hannay et al. (2013) found only three ribbon seal 

detections between July and October over four years of recordings at 10-44 mooring locations 

per year. Although their overwinter detections of ribbon seals were greater, they were still 

confined to a small time window of less than ten days in 2008 and only four days in 2009. Their 

other two years (2007 & 2010) of overwinter recordings contained no detections of ribbon seals, 

the latter fitting within the time frame of the results presented here. Although Hannay et al. 

(2013) included puffing sounds in their analysis that we did not, Moore et al. (2012) also did not 

include puffing sounds, and so any differences in October/November call detections between 

studies is coincidental.

Other pinnipeds

Arctic pinnipeds make a variety of yelping, barking, grunting, growling, hissing, and 

roaring sounds (Jones et al., 2014). We did note the presence of these as unidentified pinniped, 

without unnecessarily delaying the rest of the analysis with trying to identify these sounds to 

species. Because these data are now flagged, it would not take much time to reprocess the data 

set and extract information on any of the additional pinniped species that may be of interest and 

have repertoires that are defined in the literature (i.e., ringed seals). The seasonal distribution of 

our unidentified pinniped sounds is near-year-round (see Figure C.10 in Appendix C), and 

occurred on all three mooring locations in both deployment years.

Environmental and anthropogenic sources

Seismic airguns 

There were no surprises in the seasonal distribution of seismic airgun noise activity; these 

activities were confined to the open water season in both years of this study. In 2010 seismic 

airguns were detected on the long-term recordings at all three locations from the time the 

recordings started on 10 September to 1 October 2010. The timing of noise activity coincides 

with Statoil’s 2D and 3D seismic exploration activities. The M/V Geo Celtic, under the direction 

of seismic contractor Fugro, completed almost 3,000 miles of seismic data acquisition (using a 

pair of 3000 in3three-string arrays) in the Chukchi Sea in 2010, including participation in sound 

source verification trials (mitigation source gun: one 60 in3airgun) conducted by JASCO 
OCS Study

BOEM 2015-034

59

Research Ltd (Blees et al., 2010). The area they surveyed during these operations (Figure 14) 

was in very close proximity to our mooring line, and therefore the saturated levels of seismic 

airgun noise activity was expected. Shell did not conduct any deep water exploration in 2010 in 

either the Chukchi or Beaufort Seas, but did conduct some shallow hazard work in Harrison Bay 

in the Beaufort Sea September 13 through 9 October 2010, although some sound source 

verification work (again through JASCO Research Ltd) was conducted mid-August. It is not 

possible to distinguish between this survey and the one run on the M/V Geo Celtic with our 

current results; however, if doing so is of interest, this could be accomplished with further 

analysis. As with the unidentified pinniped, the data has already been flagged for the presence of 

seismic airgun noise, and so that time-consuming step in the process is already completed. There 

is a mention in Reiser et al. (2011) that the R/V Mt Mitchell conducted seismic airgun work in 

the Chukchi Sea from 10 October 2010 before reaching Dutch Harbor on 20 October 2010. This 

activity was not present on our recorders. 

FIGURE 14. LOCATION OF PASSIVE ACOUSTIC RECORDERS (RED DOTS) IN RELATION TO AREAS OF INDUSTRIAL 

ACTIVITY. FIGURE MODIFIED FROM BLEES ET AL., 2010 (FIGURE 2.1).

Although the time period (beginning of recordings at beginning of September to midOctober) where airgun sounds were detected were the same between 2010 and 2011, the levels of 

seismic airgun noise activity in 2011 were much more sporadic and lower. The seismic 

surveying conducted in 2011 was limited to shallow hazard site surveys in the Statoil lease 
OCS Study

BOEM 2015-034

60

blocks (Figure 15), to the northeast of our offshore and midshore recording locations (Hartin et 

al., 2011). The shallow hazards work was conducted using two clusters (one single and one 4-

airgun) of 10 in3airguns and covering nearly 3000 miles; this reduced effort explains the 

reduction in seismic airgun noise activity levels from 2010 to 2011, and also the higher levels at 

the offshore and midshore locations as compared with the inshore location. The second (smaller) 

pulse of airgun noise activity in early October 2011 cannot be explained by the timeline of 

Statoil activities, but could be due to other industry efforts in the Beaufort Sea or from scientific 

research efforts in the Chukchi or Beaufort Seas (Cameron et al., 2012).

FIGURE 15. LOCATION OF PASSIVE ACOUSTIC RECORDERS (RED DOTS) IN RELATION TO AREAS OF INDUSTRIAL 

ACTIVITY. FIGURE MODIFIED FROM HARTIN ET AL., 2011 (FIGURE 2.1).

Vessel noise

The seasonal distribution of vessel noise coincides with the open water season, with a 

few notable exceptions. Most of the vessel noise occurred in September and October, consistent 

with the seismic airgun activities discussed above as well as with the field seasons of the various 

scientific studies (such as CHAOZ and CSESP) conducted in 2010 and 2011 (Hopcroft and Day, 

2013). Vessel noise activity levels were much lower than those from the seismic activity, most 

likely because the lower level, less distinct, vessel noise signals were masked by the louder, more 
OCS Study

BOEM 2015-034

61

ubiquitous, seismic noise. A second bout of vessel noise activity was seen at the midshore 

location the second half of November 2011, which corresponds to the presence of the USCGC 

Healy (C. Ashjian, pers. comm.). 

There are two other instances with vessel noise activity detected outside of the expected 

open water season, 14-18 February 2011 at the midshore location and 7 December 2011 to 10 

January 2012 at the offshore location. The first time period coincides with the peak in walrus 

calling activity at the offshore location. A Modis visible-band ice image (Figure 13a) shows the 

presence of long leads. Although no U.S. icebreakers were operating in the area at that time, the 

fact remains that there are 16 Russian icebreakers in operation (S. Moore, pers. comm.). The 

acoustic propagation conditions in the Arctic Ocean are well studied (Urick, 1983), with a 

surface duct present which permits long-distance propagation of sound, especially from sources 

near the surface. For these reasons, and from the fact that the sounds did not appear to be from 

nearby sources (C. Berchok opinion based on lack of distinct internal machinery sounds) we 

suspect that the vessel noise heard was from distant Russian icebreakers. As for the second bout 

of vessel noise at the end of 2011/beginning of 2012, there is no evidence to suggest this 

situation was any different. 

Ice Noise

A very good summary of the characteristics of ambient noise from ice is provided in 

Urick (1983). Ice conditions, wind speed, snow cover, and air temperature are all factors that 

contribute to different qualities of the ice noise. For example, impulsive sounds are prominent 

during periods of cooling air temperature, while the noise has more of a Gaussian distribution 

during periods of warming air temperatures. Wind and currents can move the ice – causing 

collisions and sliding of the ice which can be impulsive or very tonal (e.g., Xie and Farmer, 

1992). These tonal sounds may sometimes contain enough frequency modulation to be confused 

with bowhead and beluga whales unless care is taken to examine the sound within its full context 

– and by listening closely to the nuances in its character. Wind can also generate sound, even 

under full ice cover, through the pelting of ice granules on the ice surface.

In both deployment years, ice noise activity levels were highest at the inshore location. 

As this is the site closest to the fast ice boundary zone – a high energy area of constant upheaval 

between the landfast ice and the drifting pack ice. Other than a small pulse of ice noise activity in 

the first half of November, 2010, at all three locations, the rest of the seasonal distribution of the 

ice noise seems to be randomly patterned. Similar seasonal distributions were seen in both 

deployment years, with the ice noise typically starting in November and ending around May. 

Exceptions included an earlier end date at the midshore location in 2011 and a later end date at 

the inshore location in 2012. However the 2012 inshore recorder actually lasted for the entire 

deployment and may be better representative of the real seasonal patterning of the ice noise. 

Analysis of data from these locations from the BOEM-Funded ARCWEST and CHAOZ-X 

projects (with their full years of recordings) will help accurately determine this seasonal 

distribution. In addition to Section X.3, further discussion of ice noise can be found in the Noise 

Modeling Section (Section XII.3).
OCS Study

BOEM 2015-034

62

4. Conclusions

Generally, the seasonal and spatial distributions of sounds from the five main Arctic 

marine mammal species (bowhead, beluga, and gray whales, walrus, and bearded seals), the five 

subarctic species (fin, killer, humpback, and minke whales, and ribbon seals), anthropogenic 

sources (airguns and vessel), and environmental (ice) sources were within the expected ranges 

and were in agreement with past aerial survey and/or other passive acoustic results. The 

implication of this, for most of the biological sources, is that calling rates are sufficiently high 

enough that calling activity levels can be used as a proxy for species presence. The recording 

period for this study was September 2010 through September 2012.

The fall and spring migrations were detected for both bowhead and beluga whales. The 

fall bowhead migration was seen at equal levels of calling activity at all three recording 

locations, while calling activity from the spring migration was constrained mainly to the inshore 

location. Bowhead whales are known to follow leads as they migrate east toward their summer 

feeding grounds in the Canadian Arctic. Their westward migration in the fall is well known to 

fan out through the Chukchi shelf. In addition to describing this general migration timing we 

found two other aspects worth noting for the bowhead whales. First, we saw three distinct peaks 

in the fall migration that agree with the age/sex segregation of migrating bowheads known from 

traditional knowledge and aerial biometric studies. Second, our more fine-scale analysis of 

bowhead calling has revealed that the gunshot calling activity occurs near the end of each of the 

peaks of regular bowhead calling activity. Further analyses are planned to examine these results 

in more detail.

The fall and spring migrations for beluga whales were detected at all three recording 

locations. In general, calling activity levels for each recording location were slightly higher in 

the spring; higher levels were seen at the inshore location than at either the midshore or offshore 

locations. Beluga whales are known to associate with bowhead whales during the spring 

migration east, although the presence of calling activity at the midshore and offshore locations 

agrees with results from satellite tagging and other passive acoustic studies that show that they 

are not limited to the inshore leads in the spring. Calling activity from their fall migration is 

present at all three locations, but skewed to the inshore location, showing that although they do 

travel throughout the Chukchi shelf, they are not quite as evenly distributed across the shelf as 

are bowhead whales. Peaks in beluga calling activity have been noted and work is well 

underway to define the acoustic repertoires within each peak, with the eventual goal of using 

passive acoustics to discriminate between the eastern Beaufort Sea and eastern Chukchi Sea 

populations.

The very low calling rates of gray whales make them a difficult species to study using 

passive acoustic techniques. However, because we analyzed our data fully, we were able to see 

small pulses of low-level calling activity corresponding to their fall and spring migrations that 

were not seen on the results from the industry recorders. This calling activity was limited mostly 

to the inshore recording location, which was about 15 nm further offshore than the bulk of aerial 

survey sightings from the Chukchi Sea. We did not find any overwinter calling activity. With 

the placement of additional long-term recorders along the Chukchi coast for the BOEM-funded 

ARCWEST project, we should be able to build upon the results presented here and form a 

clearer picture of the movements of this species.
OCS Study

BOEM 2015-034

63

The high levels of walrus calling activity overwinter at our offshore location in both years 

was highly unexpected and have not been described in any published study to date. We suggest 

that the animals heard during this time period are subadults that remained in the Arctic for the 

winter instead of migrating to the mating grounds. Further investigation of ice images and walrus 

satellite tagging results will be pursued in the near future. Otherwise, the timing of the other 

calling activity on our recorders corresponds well with what is known about their spring and fall 

migrations.

Bearded seal calling activity was so ubiquitous and at such high levels that it occasionally 

masked other sounds and frankly was the bane of our analysts’ existence, especially during the 

analysis of recordings for bowhead and beluga calling. Our results agree well with those from 

other passive acoustic studies. Year-round calling activity is present, with low levels in the 

summer, a small peak in the fall, and a large escalation to saturated levels between late February 

and mid-April (corresponding to the whelping/mating/molting season). 

Several subarctic species had very low levels and short periods of calling activity, as was 

expected. Killer whale calling activity was found on seven days of recordings over the two years 

of this study. The timing of this calling activity, in early fall and late-spring, suggests a possible 

connection to migration. However, the ecotype most likely making these calls is the transient 

(marine mammal eating) ecotype, which is known to range throughout Alaskan waters, and so 

the timing found here might be tied to the gray whale migration. We had only one day with 

minke whale calling activity, the timing and location of which is in good agreement with that 

from other passive acoustic studies. Although we had only one day with ribbon seal calling 

activity, this activity occurred in April. This is surprising as this population should be south of 

the Bering Strait at this time of year. As with the walrus, it is most likely that this calling activity 

represents subadult animals that were not motivated to migrate south to breed.

No fin or humpback whale calling activity was detected during this study. Detections of 

these species, as reported from other passive acoustic studies, have been limited to the region off 

Cape Lisburne which is far to the west of our recorders. Aerial and ship-based studies have also 

found these species to be rare in the study area. We did not detect any calling activity from 

North Pacific right whales or sperm whales, as expected. Finally – we did not have any 

detections of ringed seals because we did not include that species in our analysis. We have, 

however, flagged results for unidentified seals in the data, and could produce seasonal 

distribution for this species with just a small amount of additional time.

 This study illustrates the utility of passive acoustics to monitor marine mammal 

populations both spatially and temporally over large geographic regions. The results obtained 

from this study were in good agreement with those from aerial and vessel surveys, satellite 

tagging efforts, and other passive acoustic studies, as well as the natural history of these species 

obtained from TEK. The position of this mooring line between the Burger, Klondike, and Statoil 

lease areas, and relative to the industry recorders is optimal. The fact that we obtained such 

comparable results using just three mooring locations demonstrates the importance of this Icy 

Cape mooring line for monitoring marine mammals (as well as anthropogenic sources) in the 

Chukchi Sea lease area.
OCS Study

BOEM 2015-034

64

5. Recommendations

Long-term, year-round, monitoring of marine mammal populations is essential for 

understanding their distribution and behavioral ecology, particularly in the Alaskan Arctic where 

the environment is undergoing rapid modification as a result of climate change. Continuing to 

challenge what is currently known about marine mammal distribution in this area is vital, as 

assumptions - based on data obtained before the dramatic changes in sea ice extent were seen –

may be outdated. Passive acoustic monitoring provides an excellent platform for monitoring 

marine mammals year-round, especially given the inaccessibility of the area for the majority of 

the year. Not only can we monitor year-round, we can (with careful placement of recorders) 

cover a large geographic region, allowing large-scale migration and movement patterns to be 

documented for the majority of marine mammal species present in the Arctic. Furthermore, the 

cost of supplies for turning around our recorders is incredibly inexpensive, making continued 

maintenance of this very valuable long-term dataset quite cost effective. Even if funds are not 

available for analysis at the current time, there is always the chance they will be in the future. 

Passive acoustic data do not have an expiration date; the more passive acoustic data that are

available the better that trends can be identified. Therefore, our strongest recommendation is to 

continue to fund deployments and retrievals of these recorders, as well as facilitating vessel 

sharing to keep sea time costs at an equally reasonable level. 

The addition of CPOD echolocation loggers to the passive acoustic moorings would 

allow us to detect echolocation clicks of foraging belugas. Although these instruments are 

currently unable to last a full year on a duty cycle, further advancements in their development 

may eventually allow for year-round recording. This would not only increase beluga whale 

detectability, but also enhance our knowledge of beluga habitat use. 

One thing that was apparent during analyses of this data set, is that not much is known 

about the current ecology of these species in their wintering grounds in the Bering Sea. 

Recorders that have been deployed for the BOEM-funded ARCWEST project during our transits 

between Nome and Dutch Harbor, AK have collected a robust data set that can be analyzed to 

obtain more information from this area and season. We recommend making analysis of these 

data a priority so that better inferences can be made for the migratory patterns of these species.

We have developed a method for manually analyzing these acoustic data fully, and in as 

short a time period as possible. This effort is still time-consuming, but necessary, given the poor 

performance of auto-detection algorithms with the chaos3of Arctic species sounds present in the 

Chukchi Sea. With the inevitable encroachment of subarctic species, the auto-detection problem 

becomes increasingly more difficult. Still, if auto-detectors can be developed that perform 

reasonably well, passive acoustic analyses will become orders of magnitude less expensive. 

These auto-detectors are also of critical importance for passive acoustic monitoring from other 

platforms such as auto-detection buoys (see Section XII) and autonomous gliders. For these 

reasons we recommend further funding of auto-detection techniques and equally importantly –

comparison of these results with data sets fully reviewed by experienced analysts. We will 

continue to collaborate with M. Baumgartner (LFDCS, WHOI), C. Clark (Cornell Bioacoustics 

 

3 This cacophony of Arctic sounds was the reason behind the naming of the CHAOZ project.
OCS Study

BOEM 2015-034

65

Research Program), and X. Mouy (JASCO Applied Sciences) to further develop our autodetectors. 

Great strides in the use of passive acoustics to determine the relative abundance of marine 

mammals have been made in the past several years (see Section XII). We recommend that these 

techniques be made a priority so that more information can be obtained from these archival 

passive acoustic recordings. 

Finally, as mentioned in the conclusions above, there are a few interesting results from 

this study that should be examined further, namely, the multiple peaks seen in the bowhead and 

beluga whale migrations, and the timing of the bowhead gunshot call type within the main 

bowhead calling peaks. These analyses are either well underway for the BOEM-funded

ARCWEST and CHAOZ-X projects, or they will be relatively soon.
OCS Study

BOEM 2015-034

66

B. Shipboard Observations

1. Methods

Sonobuoys

During the 2010-2012 CHAOZ field survey cruises, sonobuoys were deployed every 

three hours to obtain an evenly sampled cross-survey census of marine mammal calling. 

However, when in areas of high whale density, or when trying to localize on a calling species of 

interest, multiple sonobuoys were deployed more frequently to obtain near-continuous recording. 

A sonobuoy is a free-floating, expendable, short-term passive acoustic listening device 

that transmits signals in real time via VHF radio waves to a receiver on a vessel or aircraft. The 

hydrophone is suspended down from the surface float at a programmable depth. Given that the 

minimum programmable deployment depth (61 m) of the sonobuoy exceeds that of the shallow 

Chukchi Sea shelf (~40 m), modifications were made by tying up sections of the sonobuoy 

housing to prevent the main wire spool from deploying (Figure 16). These modifications, which 

do not impact the signal transmission, resulted in a deployment depth of approximately 24 m, 

placing the hydrophone array at approximately 22 m, or mid-water column. Additional 

modifications involved replacement of the 9V display battery so that the sonobuoys could be 

programmed prior to deployment.
OCS Study

BOEM 2015-034

67

FIGURE 16. MODIFICATIONS OF A 77C SONOBUOY (TOP ROW, LEFT TO RIGHT): CUTTING OFF THE BOTTOM SECTION 

OF STRING AND REATTACHING IT TO THE WEIGHT; TAPING UP THE BOTTOM ARRAY OF SENSORS; A 77C SONOBUOY 

FULLY MODIFIED. (BOTTOM ROW) MODIFICATIONS OF 53E SONOBUOYS BY TYING UP CABLE TO SHORTEN THE 

DEPLOYMENT DEPTH: (LEFT) SPARTON 53E; (RIGHT) USS 53E. 

Three types of sonobuoys were used over the three field seasons: omnidirectional only 

(57B), DiFAR only (77C), and programmable DiFAR/Omnidirectional (53D, 53E, 53F, 57B). 

DiFAR (Directional Frequency Analysis and Recording) capable sonobuoys transmit signal 

bearing information along with the acoustic signal. If two or more DiFAR sonobuoys are 

deployed, cross-fixes can be obtained on a calling animal to determine its location. When in 

DiFAR mode, the maximum frequency is limited to 2.5 kHz, thus the 53F sonobuoys were 

deployed occasionally in omnidirectional mode to achieve full bandwidth when it was not 

important to get a bearing to the calling animal. 

The sonobuoy monitoring station (Figure 17a) was located in the bridge of the vessel, 

which allowed the acoustic technician to interact with the captain and visual observation team, 

and to make simultaneous visual and acoustic observations when needed. The signals 

transmitted from the sonobuoys were received by one of two preamplified antennas, an 

omnidirectional and a directional (Yagi) antenna. Both antennas (and preamps) were placed up
OCS Study

BOEM 2015-034

68

FIGURE 17. SONOBUOY MONITORING STATION (A). CUSTOM DESIGNED DIFAR TRACKING AND MONITORING 

PROGRAM (B).

in the crow’s nest of the vessel with the directional antenna facing astern (Figure 18). The Yagi 

was used primarily during transit when the sonobuoy was guaranteed to be behind the vessel, and 

the omnidirectional antenna was used for monitoring multiple sonobuoys simultaneously, or 

when other shipboard scientific operations caused the sonobuoy to not be directly behind the 

vessel. A switch located in the bridge next to the acoustic station was used to alternate between 

antennas depending on the direction of travel. In-air reception range was approximately 10-12 

miles when using the omnidirectional antenna. Reception range increased with the directional 

Yagi antenna, with an average of 14 miles, and a maximum of 18 miles. The age of the 

sonobuoy, its transmitting RF channel, and sea state ultimately determined reception range. It is 

important to note the difference between the in-air reception range (sonobuoy to antenna) and 

underwater sound propagation range (animal to sonobuoy). The underwater sound transmission 

range to the sonobuoy was estimated to be (at this time of year and in this study area) a radius of 

approximately 10-15 nm. Under the best conditions, with an average cruising speed of 9 kts, the 

15 nm radius around the deployed sonobuoy could be monitored for up to an hour and a half. 

When the next sonobuoy was deployed three hours later, its 15 nm radius would just touch that 

of the previous one. So although there are temporal gaps in the sonobuoy coverage, the spatial 

coverage was near-complete.
OCS Study

BOEM 2015-034

69

FIGURE 18. OMNIDIRECTIONAL AND YAGI ANTENNA PLACEMENT (A) IN RELATION TO THE R/V AQUILA AND (B) IN 

RELATION TO EACH OTHER ON THE CROW’S NEST.

The signals received by the shipboard antennas were then pre-amplified (15dB; 

PV160VDA, Advanced Receiver Research, Burlington, VT), before being sent via cabling to up 

to three G39WSBe WinRadio sonobuoy receivers, then inputted into a MOTU brand Ultralite 

mk3 multi-channel external soundcard. The soundcard digitized the signal at a sampling rate of 

48 kHz, which resulted in an audio frequency range up to 2.5 kHz for DiFAR and 24 kHz for 

omnidirectional sonobuoys. The external soundcard was connected to a laptop computer where 

the recordings were monitored in real-time using ISHMAEL (Mellinger 2001) software. 

Directional bearing information of calls was obtained using DiFAR demultiplexing software and 

a custom MATLAB interface (Greeneridge Sciences, Inc. and Mark McDonald, Whale 

Acoustics). A GPS feed into the computer provided the ship’s position every minute, as well as 

the sonobuoy deployment location information, and time. A custom tracking and plotting 

program implemented in MATLAB (Catherine Berchok; Figure 17b) allowed for real-time 

plotting of the vessel and sonobuoy locations, as well as bearing and location coordinates of 

calling whales. All data were simultaneously recorded to an external hard drive.

Visual surveys

Vessel surveys were conducted in the Bering, Chukchi, and Beaufort Seas during the 

summers of 2010-2012. Visual operations were conducted to document the presence and 

distribution of all marine mammals encountered throughout the survey when transiting to 

mooring locations and sampling stations. Photographs were collected (namely North Pacific 

right, humpback, gray, and killer whales) on an opportunistic basis. Given the remote location 

and paucity of survey effort in a large portion of the areas, any information on distribution would 

provide an invaluable contribution to existing scientific knowledge.

Shipboard visual survey methods were applied during daylight hours and under 

acceptable survey conditions (Beaufort Sea state 5 or lower). A rotating team of three scientists 

(two on watch, one at rest position) collected sighting data using standard line-transect methods 
OCS Study

BOEM 2015-034

70

during on-effort status. Operations began at 08:00 and ceased at 20:00, or as long as conditions 

would allow. A full observation period lasted 80 minutes (40 minutes in each position) and was 

followed by a 40 min rest period. One observer (port) was stationed on the ship’s bridge wing. 

The observer used 25x ‘big-eye’ binoculars (Figure 19) with reticles to scan from 90° port to 90° 

starboard. The data recorder was positioned on the bridge and surveyed the trackline with 7x50 

binoculars while scanning through the viewing area of the primary observer. When a sighting 

was detected, the primary observer conveyed to the recorder the horizontal angle and number of 

reticles from the horizon to the initial sighting. Additional information collected was sighting 

cue, course and speed, species identity, and best, low, and high estimates of group size. The 

computer program WINCRUZ 

(https://swfsc.noaa.gov/uploadedFiles/Divisions/PRD/WinCruz.pdf) was used to record all 

sighting and environmental data (e.g., cloud cover, wind speed and direction, and sea 

conditions).

On-effort status was defined as a visible horizon, Beaufort Sea state 5 or lower, and 

survey speed of ~9 knots. Under unacceptable weather conditions (no visible horizon and/or sea 

state 6 or greater), surveying continued in an off-effort status. When weather deteriorated 

(visibility ≤ 0.5 nautical miles (nm) and/or taking spray over the bow), off-effort watches were 

conducted on the bridge by one observer/recorder. This was mainly to monitor weather changes 

and notify when conditions improved as well as to record off-effort sightings.

FIGURE 19. MARINE MAMMAL OBSERVER USING 25X “BIG-EYE” BINOCULARS.

2. Results

A summary of the combined visual and passive acoustic effort during the 2010-2012 

CHAOZ field surveys is shown in Figure 20 and Table 9-10. The results presented here are 

centered on the Alaskan Chukchi Sea, from Point Barrow, AK down to the Bering Strait. This 

area also extends slightly eastward into the western Beaufort Sea to Cape Halkett, AK. For full 
OCS Study

BOEM 2015-034

71

survey coverage results, which include the visual and acoustic effort undertaken on the transit 

legs through the Bering Sea, please see Appendix D.

FIGURE 20. SUMMARY OF COMBINED VISUAL AND ACOUSTIC EFFORT, 2010-2012. GRAY LINES = VISUAL EFFORT, 

BLACK DOT = SUCCESSFUL SONOBUOY DEPLOYMENT.

TABLE 9. TOTAL NUMBER OF SONOBUOYS DEPLOYED PER YEAR IN THE ARCTIC AND THE SUCCESS RATE, 2010-2012.

Year Deployed in Arctic Successfully deployed Success rate

2010 110 102 92.7%

2011 104 91 87.5%

2012 101 91 90.1%

Total 315 284 90.2%
OCS Study

BOEM 2015-034

72

TABLE 10. SUMMARY OF VISUAL TRACKLINE EFFORT FOR ARCTIC WATERS ONLY AND COMBINED ARCTIC WATERS 

AND BERING SEA, CHAOZ, 2010-2012.

The total number of sonobuoys deployed per year and their success rate is shown in 

Table 9, and sonobuoy deployment locations and species detected are presented in Figure 20-28. 

A total of 259 sonobuoys were deployed during the 2010 cruise, of which 110 were deployed in 

the Arctic. Of these, 57 were modified (taped and tied) Sparton (SPW) 77C’s, 48 were SPW 

53F’s, and 5 were 57B omnidirectional buoys. A total of 246 sonobuoys were deployed during 

the 2011 cruise, of which 104 were deployed in the Arctic. Of these, 19 were modified (taped 

and tied) SPW 77C’s, 40 were SPW 53F’s, 40 were modified (tied and battery replaced) 

Undersea Sensor Systems (USS) 53E’s, and 5 were Hermes Electronics (HEE) 57B 

omnidirectional buoys. A total of 227 sonobuoys were deployed during the 2012 field survey, of 

which 101 were deployed in the Arctic. Of these, 22 were modified SPW 77C’s, 23 were 53D’s 

(21 USS, 1 SPW, 1 Magnavox [MAG]), 11 were 53E’s (10 HEE, 1 SPW), 41 were UND 53F’s, 

and 4 were 57B (3 MAG, 1 SPW). In total, six cetacean species (bowhead, gray, humpback, fin, 

minke, and killer whales), and two pinniped species (walrus and bearded seal) were detected in 

the study area (Figure 21-26; Appendices C, D).

Over the three year study, a total of 1,563 nm (2,728 km) of on-effort trackline was 

surveyed in Arctic waters and a total of 2,901 nm (5,374 km) for Arctic and Bering Sea 

combined (Table 10, Figure 20). Seven cetacean species, five pinniped species and a polar bear 

were documented within the survey area (Figure 20-26).

We present the results here in order of most commonly sighted to least commonly 

sighted. The most commonly sighted and/or acoustically detected species were bowhead whales, 

gray whales, walrus, and bearded seals (Figure 21-28). Bowhead whales were seen or 

acoustically detected in all three years, although there were no visual sightings in 2011 (Figure 

21). There were no acoustic or visual detections south of Wainwright, and the majority of visual 

detections were concentrated around the Barrow Arch area. Gray whales were sighted in two 

main concentrations: off Barrow, AK in all years, and in the southern Chukchi in 2010 (Figure 

22). Most acoustic detections of gray whales occurred between Icy Cape and the Barrow Arch, 

with one acoustic detection near the Bering Strait in 2010. Walrus were ubiquitous throughout 

the study area for all three years, with most sightings and acoustic detections in the northeastern 

Chukchi Sea, offshore of Icy Cape and Wainwright (Figure 23). Acoustic detections occurred as 

far south as the Bering Strait. In 2012, there was a concentration of walrus sightings off the 

Barrow Arch. Bearded seals were sighted regularly in all three years, although there were no 

Year Arctic waters only Arctic waters and Bering Sea

Km Nm Km Nm

2010 1,061 573 1,582 854

2011 1,118 694 2,566 1,385

2012 549 296 1,226 662

Total 2,728 1,563 5,374 2,901
OCS Study

BOEM 2015-034

73

acoustic detections (Figure 24). Most sightings were in the northeastern Chukchi Sea between 

Icy Cape and the Barrow Arch, although there was a concentration of sightings in 2011 between 

Point Hope and the Bering Strait.

FIGURE 21. BOWHEAD WHALE ACOUSTIC AND VISUAL DETECTIONS DURING THE 2010-2012 CHAOZ SURVEYS.
OCS Study

BOEM 2015-034

74

FIGURE 22. GRAY WHALE ACOUSTIC AND VISUAL DETECTIONS DURING THE 2010-2012 CHAOZ SURVEYS.

FIGURE 23. WALRUS ACOUSTIC AND VISUAL DETECTIONS DURING THE 2010-2012 CHAOZ SURVEYS.
OCS Study

BOEM 2015-034

75

FIGURE 24. BEARDED SEAL ACOUSTIC AND VISUAL DETECTIONS DURING THE 2010-2012 CHAOZ SURVEYS.

Fin and humpback whales were often sighted and acoustically detected in the southern 

Chukchi; however, they were detected only acoustically north of Point Hope (Figure 25-26). One 

fin whale was detected acoustically off Barrow Canyon in 2012; this detection currently 

represents the farthest northeast report of a fin whale in the Alaskan Arctic (Figure 25; Crance et 

al., 2015). Killer whales were detected acoustically on two occasions: once in 2010 near Bering 

Strait, and once in 2012 off Point Hope (Figure 27, circles). They were never sighted in the 

Chukchi Sea in any of the three years of this study. Minke whales were sighted and acoustically 

detected on three occasions in 2010 in the southern Chukchi (two visual sightings, one acoustic 

detection), and were sighted only once in 2012 off Icy Cape (Figure 27, triangles). Harbor 

porpoise were sighted from the Bering Strait to north of Cape Lisburne in 2010, with one 

sighting off Icy Cape in 2011 and one off Barrow in 2011 (Figure 27, squares). There was one 

sighting of Dall’s porpoise in 2010 near the Bering Strait. Harbor porpoise and Dall’s porpoise 

were not acoustically detected; their only vocalization type, echolocation clicks, are too high in 

frequency (110-150 kHz) to be detected on sonobuoys. 
OCS Study

BOEM 2015-034

76

FIGURE 25. FIN WHALE ACOUSTIC AND VISUAL DETECTIONS DURING THE 2010-2012 CHAOZ SURVEYS.

FIGURE 26. HUMPBACK WHALE ACOUSTIC AND VISUAL DETECTIONS DURING THE 2010-2012 CHAOZ SURVEYS.
OCS Study

BOEM 2015-034

77

FIGURE 27. MISCELLANEOUS CETACEAN ACOUSTIC AND VISUAL DETECTIONS DURING THE 2010-2012 CHAOZ 

SURVEYS: KILLER AND MINKE WHALE, HARBOR AND DALL’S PORPOISE.

FIGURE 28. MISCELLANEOUS MARINE MAMMAL ACOUSTIC AND VISUAL DETECTIONS DURING THE 2010-2012 CHAOZ 

SURVEYS: SPOTTED SEAL, RINGED SEAL, AND POLAR BEAR.
OCS Study

BOEM 2015-034

78

Spotted seal visual sightings were ubiquitous throughout the study area in 2010, but were 

only sighted once in 2011 off Barrow and in one cluster off Icy Cape in 2012 (Figure 28, circles). 

There were only two sightings of ringed seals near Barrow, one in 2010 and one in 2011 (Figure 

28, squares). However, a majority of the unidentified seals are likely either spotted or ringed; 

species identification was difficult to determine for animals in the water. One polar bear was 

sighted offshore of Wainwright in 2012 (Figure 28, star). 

No belugas were detected either visually or acoustically, during any of the CHAOZ field 

surveys. There were also no sightings or acoustic detections of North Pacific right whales, ribbon 

seals, northern fur seals, or Stellar sea lions in the Alaskan Arctic.

TABLE 11. SUMMARY OF SIGHTINGS (NUMBER OF INDIVIDUALS) FOR ARCTIC WATERS, 2010-2012. ON, OFF = EFFORT 

STATUS.

2010 2011 2012

Grand Total

On Off Total On Off Total On Off Total

Bowhead whale 7(12) 1(2) 8(14) 0 0 0 9(11) 1(3) 10(14) 18(28)

Bearded seal 36(39) 12(12) 48(51) 34(36) 0 34(36) 7(7) 4(4) 11(11) 93(98)

Dall’s porpoise 1(1) 0 1(1) 0 0 0 0 0 0 1(1)

Fin whale 1(1) 0 1(1) 5(9) 0 5(9) 0 0 0 6(10)

Gray whale 15(17) 6(10) 21(27) 1(1) 2(3) 3(4) 0 4(4) 4(4) 28(35)

Harbor porpoise 15(20) 2(3) 17(23) 1(1) 0 1(1) 0 0 0 18(24)

Humpback whale 0 1(1) 1(1) 1(1) 2(2) 3(3) 0 0 0 4(4)

Minke whale 1(1) 1(1) 2(2) 0 0 0 0 0 0 2(2)

Northern fur seal 0(0) 1(1) 1(1) 0 0 0 0 0 0 1(1)

Polar bear 0 0 0 0 0 0 0 1(1) 1(1) 1(1)

Ringed seal 1(4) 0 1(4) 0 1(1) 1(1) 0 0 0 2(5)

Spotted seal 18(21) 5(5) 23(26) 0 1(1) 1(1) 1(1) 0 1(1) 25(28)

Walrus 2(4) 6(19) 8(23) 33(52) 0 33(52) 4(6) 52(340) 56(346) 97(421)

Unid large whale 10(12) 1(1) 11(13) 2(2) 0 2(2) 0 0 0 13(15)

Unid porpoise 2(2) 0 2(2) 0 0 0 0 0 0 2(2)

Unid seal 67(96) 10(11) 77(107) 36(36) 7(8) 43(44) 38(39) 10(10) 48(49) 168(200)

Unid small whale 2(2) 1(1) 3(3) 0 0 0 0 0 0 3(3)
OCS Study

BOEM 2015-034

79

3. Discussion

The three research cruises conducted for this study (2010-2012) took place in August and 

September; the survey results therefore represent just a snapshot of marine mammal distributions

in the study area. However, the primary benefit of the survey data is the extensive spatial 

coverage they are able to achieve. These nicely complement the long-term, but point-sampled 

data, collected by the passive acoustic recorder moorings. In this section we will discuss results 

from the marine mammal data that was collected during the three survey cruises and how they tie 

in with the long-term passive acoustic recorder results. For this reason we will address the 

species in the order they were presented previously for the long-term recorder data. We will not 

repeat information already contained in the discussion for the long-term moorings (Section 

VII.A.3), and instead will refer the reader back to that section when needed.

Bowhead whales

There were comparable visual and acoustic results for bowhead whales, which suggests 

that their calls are a good proxy for presence, at least during this early fall time period. Clark et 

al. (1986) present results from multiple studies conducted during the spring ice survey off 

Barrow, AK that seem to suggest that comparable results are obtained from visual and acoustic 

survey methods when the visual observers had an unimpeded view of the area. Bowhead whale 

sightings and detections were concentrated near Barrow, AK, as is expected from numerous 

studies (e.g., Moore et al., 2000; Shelden et al., 2013; Clarke et al., 2015). The field survey 

happened later in in 2010 than in the other two years, and occurred the earliest in 2012. The 

distribution of the bowheads from these surveys, with whales seen/heard further to the east than 

in 2010, suggests that they were just beginning their fall migration south during this time period. 

In fact, these data were collected during the period with low calling activity preceding the first 

peak in bowhead whale calling activity seen on the long-term recorder. The lack of any 

sightings/detections along the Icy Cape line strengthen the argument that the bowhead whales 

were just not present that far west at that time of the year. Based on the long-term results (Figure 

4) which show that the gunshot calling occurs within the pulses of regular bowhead calling 

activity, it would not be expected that any gunshot calls would be detected during any of the field 

seasons. Indeed, no gunshot calling was detected on the sonobuoys deployed on any of the 

cruises.

Beluga whales

There were no visual or acoustic detections of belugas during any of the three field 

seasons. This does not correspond with satellite tagging results that show that the Barrow 

Canyon area is a core area in August and September for both male and female beluga whales 

(Hauser et al., 2014). However, aerial survey data (summarized in Clarke et al., 2015) have 

found beluga whale sightings to be infrequent and widely distributed throughout the Chukchi Sea 

in the fall, with a sharp decline in sightings by September/October. This assumption is also 

supported by long-term passive acoustic recorder results from Hannay et al. (2013) and those 

from this study (Figure 5). Clarke et al. (2015) suggest that the beluga whales are north of our 

study area as they migrate west in the fall. This is supported by data from Moore et al. (2012) 
OCS Study

BOEM 2015-034

80

that show a large pulse of beluga whale calling activity from May to August on a recorder 

located far north on the Chukchi Plateau. It is important to note that the satellite tagging results 

are from 40 BS whales and 24 ECS whales out of a total estimated population size of ~40,000 

and 4,000 whales, respectively. Therefore, the data are not contradictory; all methods support 

the assumption that low numbers of animals are present in the Chukchi Sea in August and 

September. It is expected that these low densities would result in low sighting and detection 

rates during our surveys. Hannay et al. (2013), suggest that the lack of call detections in their 

data reflect a possible reduction in calling for the purpose of predator (i.e., killer whale) 

avoidance. Although these data cannot be used to link calling activity to whale presence, the 

lack of both call detections and visual sightings during our three years of field surveys suggests 

that the low levels of calling activity, for this highly vocal species, correspond to low beluga 

whale densities in that area.

Gray whales

Gray whales were more often detected visually than acoustically in August/September, a 

finding that supports the low calling rate reported by Crane and Lashkari (1996) for migrating 

gray whales and assumed throughout the discussion on the long-term recorder results (Section 

VII.A.3). The vast majority of sightings occurred in areas deemed gray whale Biologically 

Important Areas (BIAs) for feeding and reproduction for the summer and fall (Clarke et al., 

2015). In the northeastern Chukchi/western Beaufort Seas, most sightings/detections occurred 

close to shore. This is expected from the narrow extent of the defined BIAs, and fits extremely 

well with the lack of gray whale calling activity along our Icy Cape mooring line (and the calling 

being mainly concentrated at the inshore location). Only two offshore occurrences of gray 

whales in the Arctic were found. One acoustic detection occurred close to Hanna Shoal, which 

used to be an area with high concentrations of feeding gray whales in the 1980s, but aerial 

surveys flown there since have not found many whales (Clarke and Ferguson, 2010). Low levels 

of acoustic detections, however, have been reported for the Hanna Shoal area by Hannay et al. 

(2013). The other acoustic detection occurred ~40nm off Icy Cape on August 28th, 2011. 

Although this calling activity was not detected at the inshore location because the recorder failed 

in late-June, no additional gray whale calling activity was found at this mooring location until 

late-September, further emphasizing the very low acoustic detectability and/or low densities of 

this species in the Chukchi Sea in the fall. In addition, studies have shown that gray whales are 

silent when feeding (Ljungblad et al., 1983), with sounds heard only when socializing was 

observed (S. Moore, pers. comm.). This was also observed during a 2013 field survey, in which 

gray whales that were feeding near Point Hope (evidenced by extensive mud plumes) were 

predominantly silent, while gray whales that were exhibiting presumed reproductive behavior 

were very vocal (C. Berchok, pers. observation). Given that the Chukchi is a known feeding 

ground, it is expected that the vocal activity of gray whales would be low. The other two areas 

of high gray whale concentrations were encountered in the southern Chukchi Sea off Point Hope 

and just north of Bering Strait. These areas are well known gray whale hotspots (Moore et al., 

2003; Bluhm et al., 2007) and as such, are also designated as a BIA for gray whale feeding.
OCS Study

BOEM 2015-034

81

Walrus

There was good consistency between the visual and acoustic results for walrus detections

in the northeastern Chukchi Sea, and also between the shipboard surveys and long-term mooring 

results (see Section VII.A.2). This supports the statement by Hannay et al. (2013) that walrus 

calling activity can serve as a proxy for walrus presence in the northeastern Chukchi Sea. The 

walrus reported from Cape Lisburne and south were detected acoustically but not visually 

sighted. The acoustic detections were made in high seas (2 sonobuoys), in the rain (1 sonobuoy), 

at night (2 sonobuoys), and while at station (1 sonobuoy) when visual operations cease; passive 

acoustic monitoring is a nice complement to traditional visual surveys in that it provides 

information on calling animals in a variety of unworkable visual survey conditions. Most 

sightings/detections occurred offshore between Icy Cape and Wainwright, near Hanna Shoal. 

Again, these results are consistent with what is currently known about walrus distribution (Jay et 

al., 2012). Walrus distributions determined from aerial survey data varied among the three field 

seasons of this study. In 2010 walrus were associated with sea ice in early August and moved to 

open water and coastal haul-outs near Pt. Lay and Cape Lisburne in late August and September 

(Clarke et al., 2011); the 2010 acoustics detections south of Cape Lisburne fall within this later 

time frame. In 2011 there was a larger walrus haul-out located near Pt. Lay that was discovered 

earlier (mid-August) and that lasted for a longer period of time (until early October) than in 2010 

and contained 1,000 to 20,000 walruses at various times throughout the season (Clarke et al., 

2012).Walrus sighted in open water offshore in 2011 showed a preference for the Hanna Shoal 

area in August/September. Indeed, our detections/sightings were predominantly offshore of 

Wainwright and near Hanna Shoal in 2011. In 2012, walruses were observed in the water and 

hauled out on ice, particularly near Hanna Shoal; walrus haul-outs on land were not seen in 2012 

(Clarke et al., 2013). The walruses seen and heard during our 2012 research cruise were found 

further offshore than in the other two years of the study.

Bearded seals

Bearded seals, while commonly seen, were not acoustically detected during any of the 

three field seasons. This is consistent with the long-term results presented in Section VII.A.2, 

which had only sporadic detections in August and September. These also correspond with the 

results reported by Hannay et al. (2013) on their long-term recorders, who reported an abrupt 

decrease in detections from the end of June to late August. The authors suggested that this 

decrease was due to a lack of calling and not an absence of animals, which is supported by the 

visual and acoustic data presented here. This also agrees with aerial survey sightings in the 

Chukchi Sea in 2010-2012 of 25, 82, and 3 individuals respectively; bearded seals do not appear 

to be present in the northeastern Chukchi Sea in large numbers in the August/September time 

period.

Other species

There were several records of subarctic cetaceans detected and/or sighted during our 

survey cruises in the Chukchi Sea. Most were located in the southern Chukchi Sea, but a few 

(with the exception of the spotted seal) were found north of Point Hope. Two species, fin whales 

(Figure 25) and humpback whales (Figure 26), were both visually sighted and acoustically 
OCS Study

BOEM 2015-034

82

detected; however, passive acoustic monitoring performed better than the visual surveys, while 

one species, minke whales (Figure 27), were seen more often than heard. Harbor and Dall’s 

porpoise (Figure 27), had only visual sightings and killer whales (Figure 27) had only acoustic 

detections. This illustrates the importance of utilizing multiple survey methods, as certain 

methods are better at detecting certain species than others. For example, harbor and Dall’s 

porpoise vocalizations are very high frequency, and therefore undetectable on sonobuoys due to 

sampling rate restrictions. On the other hand, fin whale calls are very low in frequency, and very 

loud; as a result, they have the potential to travel larger distances, and are therefore theoretically

easier to detect acoustically. More generally, visual methods are restricted to good sea 

conditions, visibility, and daylight hours, while acoustic methods are limited to just the animals 

that are making calls. By combining visual and acoustic surveys, we can obtain a more complete 

picture of marine mammal distribution within the study area. 

Fin whales

The low number of detections of fin and humpback whales in the northeastern Chukchi 

are consistent with results presented by other passive acoustic studies (Delarue et al., 2013a; 

Hannay et al., 2013), and from the results obtained from our long-term recorder data (Section 

VII.A.2). Most records of fin whale presence in the Chukchi Sea were obtained with passive 

acoustic monitoring instead of during the visual survey. All but one of the sightings were located 

to the west of the Icy Cape mooring line, and most of those were detected in the southern 

Chukchi Sea. Therefore, detections of fin whale calling activity at those three mooring locations 

were not expected (Section VII.A.2). There have been very few sightings of fin whales in the 

Chukchi Sea over the years from aerial survey efforts and none from vessel surveys. The 

increased presence of fin whales in the Arctic is most likely due to using passive acoustics to 

monitor for this species. Although it is not unreasonable to assume that there are increasing 

numbers of fin whales present in the Chukchi Sea, more long-term data is needed in more 

locations to determine if such a trend exists. 

The acoustic detection of fin whale calling activity so far to the east (off Barrow Canyon)

in 2012 suggests the possibility that this species may be encroaching on more northeasterly 

territories (Crance et al., 2015). This could be a result of post-whaling recovery, or it could be a 

response to the changing climate and ecosystem (Clarke et al., 2013; Crance et al., 2015). In 

either case, a greater presence of this species in the northeastern Chukchi could have potentially 

devastating impacts on the ecosystem (Moore and Huntington, 2008). Fin whales are 

opportunistic feeders, capable of thriving on zooplankton as well as fish (Mizroch et al., 1984; 

Perry et al., 1999; Flinn et al., 2002). The impact of this increased resource competition on 

feeding specialists such as bowhead whales could be substantial (Perry et al., 1999), particularly 

in this area where the zooplankton community is moderated by sea ice and temperature (Questel 

et al., 2013). 

Humpback and minke whales

Like fin whales, humpback whales are another subarctic species that were detected 

infrequently during the survey cruise. Most of the detections/sightings occurred to the south of 

Cape Lisburne. None were close enough to the Icy Cape mooring line to have had any chance of 
OCS Study

BOEM 2015-034

83

being detected on the long-term recordings, which fits with the lack of detections at those 

locations. As mentioned previously (Section VII.A.2), aerial survey efforts have also determined 

that humpback whales occur infrequently in the northeastern Chukchi Sea. However, they are 

opportunistic feeders, just like fin whales, and are currently well positioned to penetrate into the 

Biologically Important feeding areas of bowhead and gray whales, if conditions continue to 

change. There were only three records of minke whales during our field survey cruises, two in 

the southern Chukchi in 2010 and one just east of Icy Cape. 

Killer whales

The only killer whale sightings were located south of Point Hope, in approximately the 

same areas as the concentrations of gray whales (Figure 27). As discussed in Section VII.A.2

above, these killer whales are most likely the transient ecotype, which eats marine mammals like 

gray whales. The transient ecotype tend to be more quiet than the other ecotypes (Deecke et al., 

2005), likely as a means of reducing auditory cues to potential prey. Furthermore, they were 

found to be silent when chasing or hunting gray whales (Ljungblad and Moore, 1983). While the 

possibility that killer whales are present but not vocalizing cannot be eliminated, the lack of 

sightings/detections during the three years of survey cruises supports the long-term recorder 

findings that killer whales are rare in the northeastern Chukchi Sea.

Harbor and Dall’s Porpoise

These small odontocete whale species were sighted in three areas: one close to Barrow 

Canyon (Harbor only), one cluster of sightings due north of Cape Lisburne (Harbor only), and 

another cluster to the northeast of Bering Strait (both species). The only sounds produced by 

porpoise are echolocation clicks that are too high to be detected on our sonobuoys or long-term 

recorders. Both species of porpoise are also difficult animals to identify during aerial surveys 

due to their small size; vessel surveys are therefore a good method for collecting information on 

their distribution. It is suggested that harbor porpoise are undergoing a range expansion and 

being seen more frequently in the Chukchi Sea (Aerts, 2012); more data should be collected so 

that these trends can be better identified. 

Small ice seals

Spotted seals were sighted throughout the survey area, but the majority are concentrated 

from the Icy Cape line to the east. A few sightings of ringed seals were also made in the 

Beaufort Sea and in the eastern Chukchi Sea near Barrow Canyon. No sightings or acoustic 

detections of ribbon seals were made during the vessel surveys. It is difficult to distinguish 

between spotted and ringed seals during vessel surveys as they often rest vertically at the surface

and have similar pelage. More often than not, they are lumped into a combination ringed/spotted 

seal category, or into the unidentified pinniped category. As such, their sightings do not appear 

on the species distribution maps. Like the two species of porpoise, small ice seals are difficult to 

sight during aerial surveys and these records are also saved as ‘unidentified pinnipeds’. As 

mentioned with analysis of the long-term recorder data, Arctic pinnipeds make a variety of 

sounds in the snort/bark/yelp/etc. category. As the original objectives of this project did not 
OCS Study

BOEM 2015-034

84

focus on ice seals, we just flagged any instances of this calling as ‘unidentified pinniped’. A 

combination of visual and acoustic survey methods should be used to help distinguish between 

the various species of ice seals in order to obtain a more accurate idea of distribution in the 

Chukchi Sea in the August/September time period.

Polar bears

One sighting of one polar bear was made up near the end of the Wainwright sampling 

line in 2012. 2012 was the only year in which we encountered ice during the survey, which may 

explain why there were no visual detections in the previous years. Polar bears do not make any 

underwater sounds that can be detected on passive acoustic recordings.

4. Conclusions

Shipboard visual and passive acoustic surveys conducted while the ship is underway 

provide an inexpensive way to leverage on the sea time needed to service the long-term 

moorings and conduct the biophysical sampling stations. The cruise track needed to complete 

this mooring/sampling work is extensive, covering a wide spatial area at an important time of the 

year for many of the marine mammal species. The results of this three year shipboard survey 

have shown that the northeastern Chukchi Sea is an important area for several resident species in 

the August/September time period, including bowhead and gray whales, walrus, and bearded 

seals. Although there was some interannual variability in detection locations, all four of these 

species were detected visually or acoustically in large numbers in all three years of surveys. The 

southern Chukchi Sea also appears to be an important area for both Arctic species (i.e., gray 

whales) and subarctic species (e.g., fin and humpback whales, and harbor porpoise). Clarke et al. 

(2013) suggest there may be an increase in these cetaceans within this region, which could be 

either a result of post-commercial whaling recovery and seasonal changes, a response to climate 

change, or both.

The combination of visual and acoustic surveys is essential to maximize the detection 

potential for each species. Either method alone runs the risk of missed detections and 

underestimating the importance of an area to a particular species. In addition, having this 

combination of methods on the same survey cruise allows comparisons to be made in situ. We 

have found that bowhead whales, fin whales, humpback whales, and walrus are equally likely (or 

for beluga and killer whales – equally unlikely) to be sighted or detected during the 

August/September time period of these cruises. For gray whales, bearded seals, minke whales, 

and the two porpoise species, call detections cannot be used as a proxy for presence of these 

species at this time of the year. It is important to note that the season over which these 

statements are valid must be defined so that the data are not misinterpreted during other times of 

the year.

In addition to the benefits listed above, having dedicated visual observers working 

concurrently with passive acoustics allows for focal follows to be conducted. These focal follows 

are crucial for several reasons. First, they allow for cross-validation of each method. They also 

are very important for attributing call types to species and to certain behaviors for those species, 
OCS Study

BOEM 2015-034

85

adding to their known calling repertoire. Finally, they play a critical role in creating a database 

of call counts for each species which is necessary for eventually being able to estimate their 

relative abundance (see Section XIII). Information obtained on these call repertoires and call 

counts could then possibly be applied to the data collected from our long-term recorders, 

providing not only year-round seasonal distribution of the various species, but year-round 

seasonal distribution of their behaviors, and, eventually, accurate estimates of their year-round 

relative abundance.

5. Recommendations

While out at sea, we make every attempt to have a dedicated visual observation team 

working concurrently with someone using sonobuoys for real-time passive acoustic monitoring. 

In the event that we do not have a dedicated field season in the upcoming years, it is important 

that we ensure at least one visual observer and one passive acoustic technician are included in 

any opportunistic field surveys we may conduct. This ensures that we take full advantage of any 

opportunity to conduct combined visual/acoustic surveys, increase our knowledge of the calling 

repertoires of each marine mammal species, and increase the sample size of our database of call 

counts. Furthermore, the bearing information from the DiFAR sonobuoys will allow, with 

multiple sonobuoys deployed, the localization of calling animals (see Section VII.B.2). This 

then allows us to obtain estimates of call detectability that are necessary for calculations of 

relative abundance (XIII.B).

C. Photo-Identification

1. Methods

At the cruise leader’s discretion, survey effort was temporarily suspended to allow closer 

approaches to sightings for photo-identification. Photographs were obtained to help evaluate the

movements of animals during the survey and for comparison to existing catalogs. Photographs 

were taken using Canon 50D, 7D, and Nikon D200 digital cameras equipped with a 100-400 and 

80-200 mm zoom lens set to autofocus. All photographs were reviewed, and the highest quality 

identification photograph(s) of each animal were selected to be compared to existing photoidentification catalogs.

2. Results

Over the three year study, opportunistic photographs were collected of humpback, killer, 

gray and North Pacific right whales. One humpback (mother/calf pair) and one juvenile gray 

whale were photographed in the Chukchi Sea in 2012. The humpback (mother) was compared to 

the NMML catalog. There was no match. The gray whale photographed in 2012 was a juvenile 

animal and not matched given the low likelihood of being in the catalog. All other photographs 

were collected in the Bering Sea and along the Alaskan Peninsula in the Gulf of Alaska (see 

Appendix F for plots of sighting and sonobuoy locations of these species detected outside the 

official study area). 
OCS Study

BOEM 2015-034

86

3. Discussion

Photo-identification is an invaluable tool to understand seasonal and temporal habitat use 

and to understand population structure. Although there were no matches to date (ARCWEST or 

CHAOZ-X may still find matches), the documentation of a cow/calf humpback pair within 

Arctic waters was an important achievement as it provides insight into the distribution and

habitat use of this crucial demographic for this species. Furthermore, with the changing climate, 

it is important to obtain visual documentation of species that are sighted infrequently within 

these waters.

4. Conclusions

Given the objectives of this project, we did not have sufficient time to dedicate to photoidentification studies. Only a few individuals were documented opportunistically, and there were 

zero matches to the catalogs.

5. Recommendations

Obtaining photographs for photo-identification purposes typically requires the survey to 

suspend operations when feasible and approach the animal(s). As a rule, we attempt to collect 

photographs opportunistically as the vessel continues on its course when we don’t have time to 

stop. However, dedicated time would need to be allocated to conduct robust photo-identification 

studies. 

D. Satellite Telemetry

1. Methods

In 2012, satellite tagging operations were conducted on an opportunistic basis to assess 

the feasibility of deploying tags on large whales in the Arctic for the BOEM-funded ARCWEST 

project that commenced in 20134. Satellite telemetry was conducted at the discretion of the chief 

scientist after considering weather, time of day, and planned oceanographic operations. Once a 

tagging candidate (humpback, fin or gray whale) was located, a 24’ rigid-hulled inflatable boat 

(RHIB) was launched with a coxswain, tagger, data recorder and photographer on board. 

Satellite transmitters were attached to the body of the whales using the Air Rocket Transmitter 

System (ARTS, Heide-Jørgensen et al., 2001), which is a modified marine safety pneumatic line 

thrower. Tagging took place from a bow platform with the RHIB positioned approximately 6-10 

m perpendicular from the animal.

Whales were tagged with the implantable configuration of the SPOT 5 transmitters 

produced by Wildlife Computers (Redmond, WA) (Figure 29). These instruments are cylindrical 

 

4 Although the satellite tags were paid for by ARCWEST funds, the ship time was paid for by CHAOZ funds and 

the results are included in this report.
OCS Study

BOEM 2015-034

87

in shape and contain an ARGOS satellite PTT. When deployed, approximately 4 cm of the tag 

remains external to the body of the whale, with an antenna extending out of the distal end of the 

tag. The tags were duty-cycled to record from 02:00-08:00 and 14:00-20:00 GMT daily in order 

to maximize battery life and transmission rate. This sampling design was expected to provide 

extensive data while the whales were on their feeding grounds. Beginning in November, when 

migration likely begins, transmitters were programmed to transmit every other day, following the 

same alternating 6 hr. on/off periods. Follow-up photo-documentation of tag placement and 

animal behavior was attempted for 20-30 min after deployment. Tag deployment and follow-up 

photo-documentation were performed according to regulations and restrictions specified in the 

existing permits issued by the National Marine Fisheries Service to the National Marine 

Mammal Laboratory (permit #14245) and the International Animal Care and Use Committee 

(IACUC) assurance issued to NMML.

FIGURE 29. AN EXAMPLE OF A SPOT 5 SATELLITE TRANSMITTER THAT WAS DEPLOYED IN 2012.

Satellite tags were monitored by Argos Data Collection and Location Service receivers 

on NOAA TIROS-N weather satellites in sun-synchronous polar orbits (Argos, 1990). Locations 

were calculated by Argos from Doppler-shift data when multiple messages were received during 

a satellite’s passage overhead. Argos codes locations into quality classes (LQ) labeled B, A, 0, 1, 

2, 3, in order of increasing accuracy. Fadely et al. (2005) verified accuracies of 0.4 km (±0.3) for 

LQ3, 0.7 km (±0.6) for LQ2, 1.5 km (±1.5) for LQ1, 4.9 km (±5.3) for LQ0, 2.9 km (±5.2) for 

LQA, and 17.4 km (±26.2) for LQB.

The SDA Argos filter (Freitas et al., 2008) was applied to all location qualities in 

software R in order to remove locations that implied unlikely deviations from the track’s path as 

well as unrealistic travel rates. This filter requires two main parameters: turning angles and 

maximum speed of travel. The default value of turning angles (Freitas et al., 2008) was used and 

the maximum speed was assumed to be 15 km/h. Exploratory analysis showed that the use of 

different maximum speed limits (12 and 18 km/h) did not influence the filter results. Distances 

between filtered locations were calculated assuming a great circle route. A Bayesian switching 
OCS Study

BOEM 2015-034

88

state-space model was then applied to SDA filtered data to estimate a position and behavioral 

state every 6 hours (Jonsen et al., 2005; Kennedy et al., 2014).

2. Results

One gray whale was tagged during this study on 25 August, 2012. The deployment 

occurred 16 miles offshore from Wainwright, Alaska. The whale was judged to be a juvenile 

based on size. The tag transmitted for 48 days, until 11 October 2012 (Figure 30). The animal 

remained within 140 km of the deployment site for the duration of the tag and occupied 

relatively shallow waters (20-50 m in depth) to the south of Hanna Shoal. Results from the 

switching state-space model show that all positions fell within the area restricted search (ARS) 

criteria threshold (Figure 31).

FIGURE 30. LOCATIONS (RED DOTS) OF THE GRAY WHALE TAGGED OFF WAINWRIGHT DURING 2012. THE GREEN 

STAR MARKS THE TAGGING LOCATION (70.8°N, 160.5°W).
OCS Study

BOEM 2015-034

89

FIGURE 31. SWITCHING STATE-SPACE MODELED LOCATIONS (6 HOUR TIME-STEP) OF THE GRAY WHALE TAGGED 

OFF WAINWRIGHT DURING 2012. RED TRIANGLES INDICATE AREA RESTRICTED SEARCH (ARS). NO OTHER 

BEHAVIORAL STATES APPEARED IN THE MODEL RESULTS.

3. Discussion

Results from the gray whale satellite tagged off Wainwright, AK indicate the animal was 

most likely foraging in this area. Several gray whales were concentrated in the area and mud 

plumes were observed (indicative of foraging) (Rugh and Fraker, 1981). The switching statespace model showed that this animal was in ARS for the entire time the tag transmitted, which 

further suggests this animal was foraging. ARS is often synonymous with foraging behavior 

(Kareiva and Odell, 1987; Mayo and Marx, 1990; Kennedy et al., 2014). This animal will be 

included in a larger dataset of tagged individuals as part of ARCWEST which commenced in 

2013.

4. Conclusions

Using a combination of visual surveys and satellite telemetry, results provided an 

understanding of the distribution of marine mammals during the months of August and 

September as well as information on habitat use within the summer feeding grounds during the 

summer and fall foraging periods.
OCS Study

BOEM 2015-034

90

5. Recommendations

Conducting satellite telemetry operations in Arctic waters is a challenging but not 

impossible endeavor. Inclement weather, locating concentrations of whales, and the 

approachability of the species, all factor into the amount of time needed to successfully deploy a 

satellite tag. From our experience, gray whales in the Arctic are highly sensitive to boat 

presence. Humpback and fin whales generally appear to be passing through the area, spending 

little time at the surface. Dedicated operations with sufficient time around large groups of 

whales are essential to successful telemetry projects in the Arctic.
OCS Study

BOEM 2015-034

91

VIII. BIOPHYSICAL PATTERNS AND TRENDS

A. Moored Observations

1. Methods

Mooring Sites and Instrument Configuration

Each year, three, year-long biophysical moorings were deployed at each of three sites 

(Figure 32; C1-inshore, C2-midshore, and C3-offshore). To avoid ice keels, the top of each 

mooring was only ~10 meters off the bottom (or ca. 30 m from the ocean surface). Mooring 

designs were identical for each year and the instruments which successfully collected data are 

listed in Table 12. Mooring deployment locations and parameters measured are presented in

Table 12. Data were collected at least hourly and all instruments were calibrated prior to 

deployment. The physical and chemical data were processed according to manufacturers’ 

specifications. All current time series were low-pass filtered with a 35 hour, cosine-squared, 

tapered Lanczos filter to remove tidal and higher-frequency variability, and re-sampled at 6 hour 

intervals. CTD and water bottle casts were conducted following or preceding mooring 

recoveries and deployments to provide quality control of the data collected by some of the 

instruments on the moorings (e.g., temperature, salinity, PAR, dissolved oxygen, and nitrate).
OCS Study

BOEM 2015-034

92

FIGURE 32. BATHYMETRY IN THE STUDY AREA, THE THREE MOORING SITES, AND THE SIX HYDROGRAPHIC 

TRANSECTS OCCUPIED.
OCS Study

BOEM 2015-034

93

TABLE 12. SUMMARY OF MOORING LOCATIONS AND MEASUREMENTS TAKEN FOR THE CHAOZ STUDY. ALL 

MOORINGS WERE TAUT-WIRE MOORINGS, MEASURING TEMPERATURE (SEACAT, RCM-9), SALINITY (SEACATS, RCM9), CURRENTS (RCM-9, ACOUSTIC DOPPLER CURRENT PROFILER: ADCP) AND CHLOROPHYLL FLUORESCENCE 

(WETLABS ECO-FLUOROMETER). NITRATE CONCENTRATIONS WERE MEASURED USING SATLANTIC ISUS. OXYGEN 

WAS MEASURED USING AANDERAA OXYGEN OPTODE 3835 AND TURBIDITY WAS ALSO MEASURED ON THE RCM-9. 

THE ASL INSTRUMENT ACOUSTICALLY MEASURES ICE KEEL DEPTH. THE TAPS-8 (8 FREQUENCY TRACOR ACOUSTIC 

PROFILING SYSTEM) IS AN OLDER VERSION OF THE TAPS-6NG (TAPS-6 NEXT GENERATION) THAT WAS BUILT FOR 

SUBSEQUENT BOEM PROJECTS.

Zooplankton Volume Backscatter Estimates Derived From ADCP Measurements

Estimates of zooplankton volume backscatter (Sv) were also derived from the upward 

looking, 600 kHz Teledyne RDI Workhorse Sentinel ADCP (Table 12). Measurements of echo 

counts from each bin and time point were used to estimate volume backscatter. 

MatlabTM (R2012b) was used to process all data. The ADCP echo intensities (counts) 

were converted to Sv according to Gostiaux and van Haren’s (2010) modified version of the 

commonly used Deines (1999) sonar equation:

Sv = C + 10log10((Tx+273.16)R2) – LDBM – PDBW + 2αR + 10log10(10KcE/10 – 10KcEr/10),

where C is a transducer/system noise constant provided by the manufacturer (-139.3 dB for the 

Workhorse Sentinel), Tx (°C) is the variable temperature at the transducers, LDBM is the 10log10

(transmit pulse length constant in meters), PDBW is the 10log10 (variable transmit power in Watts), 

Mooring name Latitude Longitude Instrument Parameters Measured

10CKP-1A 70.839°N 163.197°N 600 KHz ADCP

T, PAR, Fl, Currents (WC), 

Pres, Nitrate

10CKIP-1A 70.840°N 163.205°N RCM9, ASL 

T, S, O2, Currents (B), Turb, 

Pres, Keel Depth

10CKP-2A 71.220°N 164.250°N

SeaCat, 600 KHz 

ADCP 

T, S, PAR, Fl, Currents (WC), 

Pres, Nitrate

10CKIP-2A 71.223°N 164.252°N RCM9, ASL

T, O2, Currents (B), Turb, Pres, 

Keel Depth

10CKP-3A 71.826°N 165.975°N

SeaCat, 600 KHz 

ADCP 

T, S, PAR, Currents (WC), Pres

10CKIP-3A 71.820°N 165.982°N RCM9, ASL

T, S, O2, Currents (B), Turb, 

Pres, Keel Depth

11CKIP-1A 70.840°N 163.209°N

SeaCat, 600 KHz 

ADCP

T, S, PAR, Fl, Currents (WC), 

Pres

11CKP-1A 70.839°N 163.194°N RCM9 T, O2, Currents (B), Turb, Pres

11CKP-2A 71.221°N 164.241°N

SeaCat, 600 KHz 

ADCP 

T, S, PAR, Fl, Currents (WC), 

Pres, Nitrate

11CKIP-2A 71.223°N 164.257°N RCM9, ASL

T, S, O2, Currents (B), Turb, 

Pres, Keel Depth

11CKT-2A 71.218°N 164.248°N TAPS-8 Zoop Biovolume

11CKP-3A 71.825°N 165.975°N

SeaCat, 600 KHz 

ADCP 

T, S, Par, Fl, Currents (WC), 

Press, Nitrate

11CKIP-3A 71.819°N 165.982°N RCM9, ASL

T, S, O2, Currents (B), , Press, 

Keel Depth

C2 2010 2011

C1

C3

C2

C1

C3
OCS Study

BOEM 2015-034

94

α (dB/m) is the sound absorption coefficient of seawater, R (m) is the slant range along the beam 

to the scatterers, E (counts) is the echo intensity, Er (counts) is the reference noise level 

determined from the lowest echo intensity value over the whole water column during the entire 

deployment period, and Kc (dB/count) is the conversion factor provided by the manufacturer to 

convert ADCP counts to dB. Sv was calculated separately for each beam, then the average of all 

beams was computed in the linear domain before being converted back to log units.

Wavelet analysis (Torrence and Compo, 1998) was applied to standardized ADCP data 

((x – mean)/standard deviation) to examine the dominant modes of temporal variation and to 

determine strength of these modes across the observation period. Software to accomplish the 

analyses was written in Python using information at https://github.com/aaren/wavelets as a 

resource. Wavelet transforms are similar to Fourier transforms in that they convert information 

in the time domain into the frequency domain. They are particularly informative when a signal 

is non-stationary, which is the case with our data. The orthogonal basis functions used here were 

sine and cosines. The rapid ascent and descent of zooplankton during diel vertical migration 

result in a “square” shaped migration. In addition, the length of time zooplankters remain in the 

upper water column also varies (see below).

Zooplankton Volume Backscatter Estimates Derived From Multi-Frequency Measurements

A 6-frequency (50, 78, 115, 200, 420, 735 kHz) Tracor Acoustic Profiling System - Next 

Generation (TAPS6-NG) was used to estimate the size and abundance of zooplankton from late 

August 2011, until late January 2012. Deployment of TAPS6-NG instruments in August 2010 

was not possible due to the late arrival of funds and the difficulties of developing this new line of 

instruments. The new TAPS6-NG is comprised of a PVC block containing the 6 individual 

transducers (Transonics, Inc.) mounted on the top of an Acoustic Doppler Current Profiler 

(ADCP) syntactic foam float (Deep Water Buoyancy, Inc., Figure 33). The controller electronics 

case is clamped inside the float where the ADCP instrument would normally reside, and several 

pressure cases containing lithium ion batteries are mounted below the float in a custom-designed,

stainless steel frame. The instrument collects measurements between the range of 1-35 m, with 

data bin centers every 0.37 m. Sample volumes for each frequency were from ca. 0.5-50 m3at a 

range of 2-30 m, respectively, from the transducer faces. Raw data from each frequency of the 

TAPS6-NG were recorded during these intervals as mean integrated echo intensities (W/m2) 

computed over 24 individual pings per ensemble. System electronics optimization was obtained 

by tuning each transducer in the freshwater dive tank (30’ x 15’) at the NOAA Western Regional 

Center. System calibration consisted of determining the source and receiver levels for each 

frequency before and after deployments, using a standard calibrated transducer. Calibration was 

accomplished at the Hydroacoustic Technology Inc. calibration barge in Seattle, Washington.
OCS Study

BOEM 2015-034

95

FIGURE 33. TAPS6-NG. A. A PVC BLOCK CONTAINING THE 6 INDIVIDUAL TRANSDUCERS IS MOUNTED ON THE TOP OF 

AN ACOUSTIC DOPPLER CURRENT PROFILER (ADCP) SYNTACTIC FOAM FLOAT. B. THE ENTIRE ASSEMBLY 

SHOWING THE TRANSDUCER BLOCK ABOVE THE FLOAT AND THE BATTERY CASES BELOW THE FLOAT.

Our first TAPS6-NG instrument was deployed at the midshore mooring site C2 in 2011

(Figure 32; Mooring 11CKT2B;) and successfully recovered one year later. The acoustic 

instrument was programmed to sample every 30 minutes April-September, and at 60 minute 

intervals October-March. This mooring was placed nearby two other moorings, one of which 

contained a 600 kHz ADCP, ISUS nitrate analyzer, chlorophyll fluorometer, CTD, and 

Photosynthetically Active Radiation (PAR) light meter (Table 12). The other mooring contained 

a mechanical current meter and an ice profiler

(http://www.pmel.noaa.gov/foci/operations/mooring_plans/2011/aug2011_contVes_moorings.ht

ml).

MatlabTM (R2012b) was used to process the acoustic data. Background and instrument 

noise was defined as the weekly minimum intensities for each frequency. Those values were 

then subtracted from each measurement for that week. The intensities were then converted to 

volume-scattering strength (Sv, dB re 1 m-1) followed by correction with calibration constants. 

Signal-to-noise ratios of <10 dB were used as a threshold to reject Sv values that were not used in 

further analyses.

Inverse methods were used to estimate the abundance of scatterers as a function of size

(Holliday, 1977; Greenlaw, 1979; Greenlaw and Johnson, 1983). Abundances were estimated 

for near-bottom (10 meters from the transducer head) and near-surface (25 meters from the 

transducer head). Near-surface data were only analyzed until the end of September due the 

possibility of ice affecting the backscatter.

The truncated fluid sphere (TFS) and distorted-wave Born approximation (DWBA) 

scattering models were used in the inverse calculation to estimate scattering from small, 

spherical organisms (e.g., copepods, eggs, nauplii) and elongate organisms (e.g., euphausiids, 

mysids), respectively (Holliday, 1992; Holliday et al., 2003). The assumed values used in the 

models, included the animal orientation, sound speed (h) and density contrast (g), LevenbergMarquardt factor, number of size classes, and size range, are provided in Table 13. Euclidian 
OCS Study

BOEM 2015-034

96

norms were computed as a goodness-of-fit statistic between measured Sv and the inverse model 

fit to verify that the inversion could adequately explain the measured Sv values.

TABLE 13. MATERIAL PROPERTIES AND OTHER PARAMETERS USED IN THE SCATTERING MODELS AND INVERSE 

SOLUTIONS.

Parameter TFS DWBA

G 1.00 1.018

H 1.003 1.006

Levenberg-Marquardt 1.0x10-3 1.0x10-3

Orientation random broadside/horizontal

Size classes 48 48

Size Range 1.0-11.25 mm 10.0-24.0 mm

2. Results

Time Series of Physical and Chemical Data from Biophysical Moorings

Except for the ADCP (which measures throughout the water column) and the ice profiler 

(which measures keel depth), all measurements are in the bottom 10 meters of the water column. 

The inshore mooring (C1) was in Alaska Coastal Current (ACC) water, while ~ 70 km farther 

offshore the midshore mooring, C2, was in winter water (Figure 34). 
OCS Study

BOEM 2015-034

97

FIGURE 34. CROSS SECTION OF TEMPERATURE AND SALINITY AT ICY CAPE SHOWING THE LOCATIONS OF THE 

MOORINGS. NOTE THAT C1 IS IN THE ACC WATER.

Time series of all mooring data are shown in Figure 35-40. Only a few instruments failed 

completely, including the turbidity at the offshore location (C3, 2011-2012), the nitrate sensors at 

the offshore location (C3, 2010-2011) and inshore location (C1, 2011-2012), and after analysis, 

one fluorometer at the offshore location (C3, 2010-2011) proved to be inconsistent. The rest of 

the data are discussed below.

Bottom currents were generally northeastward following bathymetry. Daily net speeds 

were < 45 cm s-1. The low-pass filtered time series were variable, with reversals lasting typically 

2-4 days. The variability in currents was forced by winds. Vertically, the currents were well 

correlated (typically r > 0.7; 95% significance level ~0.1 between surface and bottom currents).

Bottom temperature ranged from approximately -1.8 to <5.0 °C, with maximum 

temperatures occurring in late August or September. Salinity ranged from <31 to ~34.5. The 

variability in salinity was the result of different water types, and the melting (and mixing) of sea 

ice, and the freezing of surface waters. 
OCS Study

BOEM 2015-034

98

The highest turbidities occurred in fall when the winds began to increase and before the 

sea ice areal coverage became >80%. Evidence of wind mixing can be seen in the winter when 

polynyas occur (e.g., Figure 35). 

The spring phytoplankton bloom is evident in each time series. The bloom (initially ice 

algae) likely occurs under and in the ice before evidence of it appears at the bottom of the water 

column where our fluorometer was located. This bloom consumes near-surface nutrients. Once 

the ice begins to melt, chlorophyll is exported to the benthos. Associated with the increase in

primary production is an increase of percent oxygen saturation (e.g., Figure 35), suggesting that 

primary production continues at depth or that advection continually replenish oxygen (~40 m). 

PAR is an indication of how much light available for photosynthesis reaches the bottom of the 

water column. Typically, measureable light reaches the bottom from May through September. 

A common feature in the PAR time series is a decrease in PAR to almost zero during the height 

of the bloom (e.g., Figure 36 in June). This is likely a result of shading by phytoplankton above 

the instrument.

Nitrate ranges from 0 – 20 μM at depth. It decreases from mid-spring through July or 

August and then increases during late winter and early spring. The source of the water 

replenishing nutrients in the winter may be the continental slope or water originating in the 

Bering Sea and flowing up Central Channel joining the more coastal flow from Point Lay.

Sea ice arrives in early to mid-November, increasing quickly to near 100% areal coverage 

and declines precipitously in late May or June. The areal ice concentration was more variable in 

2010-2011, than it was in 2011-2012; there were two periods of nearly open water occurring in 

2010-2011, while none occurred in 2011-2012. Ice thickness (Figure 41) increases to an average 

of ~4 m in March. The thickest ice is generally seen late in spring (Table 14). 

TABLE 14. MAXIMUM KEEL DEPTH MEASURED AT THE MOORING SITES. THE DATE OF MEASUREMENT IS GIVEN IN 

PARENTHESIS. THE ICE PROFILER SENSOR (ASL) AT C1 FAILED IN 2011/2012.

Maximum Keel 

Depth

C1

(m)

C2

(m)

C3

(m)

2010-2011 29.8 (April 1) 24.3 (June 4) 20.1 (April 19)

2011-2012 No data 27.6 (March 30) 28.7 (April 16)
OCS Study

BOEM 2015-034

99

FIGURE 35. TIME SERIES FROM THE INSHORE MOORING (C1), DEPLOYED AUGUST 2010-2011. FROM TOP PANEL TO

BOTTOM PANEL: NEAR-BOTTOM VELOCITY (ROTATED TO ALONG SHELF); TEMPERATURE (GREEN), SALINITY

(RED); TURBIDITY; CHLOROPHYLL FLUORESCENCE CONCENTRATION (GREEN), DISSOLVED OXYGEN PERCENT 

SATURATION (BLUE), AND PHOTOSYNTHETICALLY ACTIVE RADIATION (PAR, ORANGE); NITRATE CONCENTRATION; 

AND AREAL ICE CONCENTRATION IN A 50 KM X 50 KM BOX AROUND C1 (DARK BLUE), DAILY AVERAGE DEPTH OF 

THE ICE KEELS (LIGHT BLUE).
OCS Study

BOEM 2015-034

100

FIGURE 36. TIME SERIES FROM THE MIDSHORE MOORING (C2), DEPLOYED AUGUST 2010-2011. FROM TOP PANEL TO 

BOTTOM PANEL: NEAR-BOTTOM VELOCITY (ROTATED TO ALONG SHELF); TEMPERATURE (GREEN), SALINITY 

(RED); TURBIDITY; CHLOROPHYLL FLUORESCENCE CONCENTRATION (GREEN), DISSOLVED OXYGEN PERCENT 

SATURATION (BLUE), AND PHOTOSYNTHETICALLY ACTIVE RADIATION (PAR, ORANGE); NITRATE CONCENTRATION; 

AND AREAL ICE CONCENTRATION IN A 50 KM X 50 KM BOX AROUND C2 (DARK BLUE), DAILY AVERAGE DEPTH OF 

THE ICE KEELS (LIGHT BLUE).
OCS Study

BOEM 2015-034

101

FIGURE 37. TIME SERIES FROM THE OFFSHORE MOORING (C3), DEPLOYED AUGUST 2010-2011. FROM TOP PANEL TO 

BOTTOM PANEL: NEAR-BOTTOM VELOCITY (ROTATED TO ALONG SHELF); TEMPERATURE (GREEN), SALINITY 

(RED); TURBIDITY; DISSOLVED OXYGEN PERCENT SATURATION (BLUE), PHOTOSYNTHETICALLY ACTIVE 

RADIATION (PAR, ORANGE); AND AREAL ICE CONCENTRATION IN A 50 KM X 50 KM BOX AROUND C3 (DARK BLUE),

DAILY AVERAGE DEPTH OF THE ICE KEELS (LIGHT BLUE). NOTE: NITRATE IS MISSING IN THIS PLOT DUE TO FAILED 

SENSORS.
OCS Study

BOEM 2015-034

102

FIGURE 38. TIME SERIES FROM THE INSHORE MOORING (C1), DEPLOYED AUGUST 2011-2012. FROM TOP PANEL TO 

BOTTOM PANEL: NEAR-BOTTOM VELOCITY (ROTATED TO ALONG SHELF); TEMPERATURE (GREEN), SALINITY 

(RED); TURBIDITY; CHLOROPHYLL FLUORESCENCE CONCENTRATION (GREEN), DISSOLVED OXYGEN PERCENT 

SATURATION (BLUE), AND PHOTOSYNTHETICALLY ACTIVE RADIATION (PAR, ORANGE); AND AREAL ICE 

CONCENTRATION IN A 50 KM X 50 KM BOX AROUND C1 (DARK BLUE). NOTE: NITRATE IS MISSING IN THIS PLOT DUE 

TO FAILED SENSORS.
OCS Study

BOEM 2015-034

103

FIGURE 39. TIME SERIES FROM THE MIDSHORE MOORING (C2), DEPLOYED AUGUST 2011-2012. FROM TOP PANEL TO 

BOTTOM PANEL: NEAR-BOTTOM VELOCITY (ROTATED TO ALONG SHELF); TEMPERATURE (GREEN), SALINITY 

(RED); TURBIDITY; CHLOROPHYLL FLUORESCENCE CONCENTRATION (GREEN), DISSOLVED OXYGEN PERCENT 

SATURATION (BLUE), AND PHOTOSYNTHETICALLY ACTIVE RADIATION (PAR, ORANGE); NITRATE CONCENTRATION; 

AND AREAL ICE CONCENTRATION IN A 50 KM X 50 KM BOX AROUND C2 (DARK BLUE), DAILY AVERAGE DEPTH OF 

THE ICE KEELS (LIGHT BLUE).
OCS Study

BOEM 2015-034

104

FIGURE 40. TIME SERIES FROM THE OFFSHORE MOORING (C3), DEPLOYED AUGUST 2011-2012. FROM TOP PANEL TO 

BOTTOM PANEL: NEAR-BOTTOM VELOCITY (ROTATED TO ALONG SHELF); TEMPERATURE (GREEN), SALINITY 

(RED); CHLOROPHYLL FLUORESCENCE CONCENTRATION (GREEN), DISSOLVED OXYGEN PERCENT SATURATION 

(BLUE), AND PHOTOSYNTHETICALLY ACTIVE RADIATION (PAR, ORANGE); NITRATE CONCENTRATION; AND AREAL 

ICE CONCENTRATION IN A 50 KM X 50 KM BOX AROUND C3 (DARK BLUE), DAILY AVERAGE DEPTH OF THE ICE KEELS 

(LIGHT BLUE). NOTE: TURBIDITY IS MISSING IN THIS PLOT DUE TO FAILED SENSORS.
OCS Study

BOEM 2015-034

105

FIGURE 41. ICE KEEL DEPTH FOR BOTH DEPLOYMENT YEARS. THE TOP PANELS ARE THE MEDIAN ICE KEEL DEPTH 

WITH AN 11 DAY RUNNING FILTER. THE BOTTOM PANELS ARE THE MAXIMUM DEPTH OF THE ICE KEEL OVER AN 11 

DAY PERIOD. THE ICE PROFILER FAILED ON THE INSHORE MOORING (C1) DURING THE 2011-2012 DEPLOYMENT.

Currents and Transport

The along shelf currents were well correlated for each deployment (Figure 42). The flow 

is strongest along the coast at the inshore mooring (C1), and weakens offshore, but does not go 

to zero at the offshore mooring (C3). In general the flow is toward the northeast, with multiple 

events of reversal in flow forced by wind events. 
OCS Study

BOEM 2015-034

106

FIGURE 42. PLOTS OF DAILY BOTTOM CURRENTS (ROTATED TO ALONG SHORE) AT THE THREE MOORING SITES IN 

2010-2011. THE TIME SERIES HAVE BEEN LOW PASS FILTERED. CORRELATIONS BETWEEN AND AMONG MOORING 

SITES REFLECTED BY RED ARROWS.

The current records can be integrated to obtain along shore transport (Figure 43). Since 

the flow at the offshore current meter does not go to zero, these estimates of transport may be an 

underestimation. The transports are significantly correlated with winds from the National 

Centers for Environmental Prediction (NCEP) (r=0.68, at 1 day lag) and dominated with 2-4 day 

variability. Mean transport for the 2010-2011 deployment was 0.42 x 106 m3s

-1

, and in 2011-

2012 it was 0.23 x 106 m3s

-1

, indicating significant year-to-year variability (transport measured 

in 2012-2013 was similar to 2011-2012).
OCS Study

BOEM 2015-034

107

FIGURE 43. TRANSPORT CALCULATED AT ICY CAPE FOR DEPLOYMENTS IN 2010-2011 AND 2011-2012.

By calculating monthly averages of transport, the seasonal pattern of flow becomes 

clearer (Figure 44). The monthly mean transports during winter and fall were highly variable, 

but transport was northeastward with less variability in April through July. Using the 

measurements of transport through Bering Strait (Woodgate et al., 2005) indicates that the 

monthly mean transport on the Icy Cape line ranges from 25-50% of the transport through 

Bering Strait.
OCS Study

BOEM 2015-034

108

FIGURE 44. TRANSPORT CALCULATED AT THE ICY CAPE LINE (TRANSECT D) USING THE MOORING DATA. IN 2012-

2013 ONLY THE MIDSHORE MOORING (C2) WAS DEPLOYED AND TRANSPORT WAS CALCULATED USING A 

REGRESSION DEVELOPED FOR THE 2010-2011 AND 2011-2012 DATA SETS. THE BLACK CIRCLES ARE THE LONG-TERM 

MONTHLY AVERAGE TRANSPORTS THROUGH BERING STRAIT (WOODGATE ET AL., 2005).

Upwelling Events and Intrusion of Atlantic Water onto the Shelf

Polynyas often occur on the eastern side of the Chukchi Sea. In 2010-2011, there were 

three events where the salinity increased markedly at the inshore mooring site (C1; Figure 35 and 

Figure 45). The first two events, which occurred in late-October and early January, respectively, 

were accompanied by an increase in temperature. The third event in mid-February, was 

associated with a slight decrease in temperature. Ice had not arrived at the mooring site at the 

time of the first event, but the second occurred when there was a reduction in areal ice cover. 

The open water is evident in Figure 46. Both events were associated with southwestward flow 

and a slight decrease in percent oxygen saturation, especially the second event (Figure 35). 

These two events are likely caused by intrusion of Atlantic water from Barrow Canyon. The 

third event is very different. It was associated with northeastward flow, and no apparent change 

in ice coverage. The slight cooling of temperature (Figure 45, green data) supports the 

conclusion that the event is a result of brine rejection.

In contrast, during the 2011 – 2012 deployment at the inshore mooring site (C1), salinity 

does not show the same extremes (>34.2), although there is some evidence of higher salinity and 

warmer water in and around January 1, 2012 (Figure 38).
OCS Study

BOEM 2015-034

109

FIGURE 45. THE WARMER, MORE SALINE ATLANTIC WATER IS EVIDENT IN THE TEMPERATURE-SALINITY (T-S)

DIAGRAM FROM THE TIME SERIES MEASURED AT THE INSHORE MOORING SITE, C1. THE COOLING OF THE 

FEBRUARY EVENT IS EVIDENT IN GREEN.

Atlantic 

Water
OCS Study

BOEM 2015-034

110

FIGURE 46. PERCENT AREAL ICE COVER ON JANUARY 3, 2011. THE DARK BLUE IS 100% ICE COVER, WHILE WHITE IS 

<10% COVER. ICE COVER SCALE IS AT THE BOTTOM OF THE PLOT.

Water Masses and year-to-year variability

Hydrographic data were collected in 2011 and 2012 along the Icy Cape line. In August 

2011, the ice edge had retreated beyond the shelf break into the Beaufort Sea while in 2012 the 

ice persisted on the shelf, just to the north of the hydrographic transect. The position of the ice 

influences the water properties (Figure 47), with Alaska Coastal Current (ACC) and Winter 

Water (WW) appearing in both years. Melt Water (MW) only appeared in our 2012 data. The 

plots for 2010 are not shown (but see Figure 67) because in 2010 we were unable to collect deep 

data without a winch and real-time CTD. The presence/absence of melt water is even more 

clearly evident in the Temperature-Salinity (T-S) plot (Figure 48). 

January	3	
OCS Study

BOEM 2015-034

111

FIGURE 47. TEMPERATURE (A), SALINITY (B) MEASURED IN AUGUST 2011, TEMPERATURE (C), SALINITY (D) 

MEASURED IN AUGUST 2012 ALONG THE ICY CAPE TRANSECT. ACC, WW AND MW WATER ARE INDICATED IN THE 

TEMPERATURE PANEL.
OCS Study

BOEM 2015-034

112

FIGURE 48. PLOTS OF TEMPERATURE AND SALINITY ON A DENSITY GRID FOR (A) AUGUST 2011 AND (B) AUGUST 2012.

NOTE IN 2011 THERE WAS NO MELT WATER IN IN THE T-S PLOT (EMPTY OVAL WITHOUT POINTS).

Time Series of Zooplankton Backscatter Estimated From ADCP Instruments 

We first examined the temporal patterns in ADCP-derived zooplankton volume 

backscattering from the fall 2011 echograms at the offshore (C3) and midshore (C2) moorings 

(Figure 49). One can see evidence for diel vertical migration (the cyclic banding of colors 

demonstrating daily highs and lows in volume backscatter) during this period as well as 

difference in Sv at the two sites along the same transect line.

a b 
OCS Study

BOEM 2015-034

113

FIGURE 49. ADCP ESTIMATED ZOOPLANKTON VOLUME BACKSCATTER (SV) LATE AUGUST TO LATE SEPTEMBER 2011. 

TOP PANEL = MIDSHORE MOORING; BOTTOM PANEL = OFFSHORE MOORING; MONTH/DAY IS ON ABSCISSA AND 

CENTERED AT 1300 HRS ALASKA DAYLIGHT SAVING TIME (ADT, GMT+8 HRS).

At the midshore location (Figure 49, top), Sv appears to be low in late August and high at 

the very end of September. The midshore station also has a layer of scattering that remains close 

to the bottom for most of the short time series. Zooplankton backscatter at the surface is very 

high in late September, but the signal may be contaminated by air bubbles. At the offshore 

station (C3) we see strong scattering in late August throughout the water column diminishing 

with time so that by late September, Sv has declined and there is a clear pattern in the diel 

vertical distribution. 

We next examined the patterns of temporal variability near the surface (7 – 12 m depth) 

and at depth (27 – 31 m depth) during the 2010 and the 2011 deployments for each of the 3 sites 

(inshore C1, midshore C2, and offshore C3) to examine the strength of diel vertical migration 

and to learn if there were other strong temporal patterns in the data. In 2010-2011, near the 

surface there was very little evidence of any significant variability in either the diurnal or semidiurnal periods (24- and 12-hr periods) in either the temporal plots (Figure 50, left panel) or the 

averages over the deployment (Figure 50, right panel). The contoured values of variability are 

highest for the longest period, > 64 hrs, however the averages over the entire deployments were 

not high enough to be statistically significant at any of the three mooring sites. The result was 

slightly different at depth. Again, the variability was highest at the longer periods in the contour 

plot (left panel > 64 hrs), however when averaged over the course of the entire deployment, there 

was statistically significant variability (P < 0.05) in the semi-diurnal (12-hr) period at the 
OCS Study

BOEM 2015-034

114

midshore (C2) and offshore (C3) mooring sites and a peak in the variability at the diel (24-hr)

period (Figure 51).

FIGURE 50. WAVELET ANALYSIS OF 2010 - 2011 NEAR SURFACE DATA (7-8 M) ZOOPLANKTON VOLUME BACKSCATTER 

DATA FROM THE ICY CAPE TRANSECT. FROM TOP PANEL TO BOTTOM PANEL: OFFSHORE, C3; MIDSHORE, C2; 

INSHORE, C1. LEFT PLOTS SHOW CONTOURED WAVELET VALUES AS A FUNCTION OF DATE. RIGHT PLOTS SHOW

THE AVERAGE MAGNITUDE OF TEMPORAL SIGNAL AS A FUNCTION OF PERIOD. DOTTED LINE IS P = 0.05 SUCH THAT 

WHEN THE PEAKS ON THE BLUE LINE ARE TO THE RIGHT OF THE DOTTED LINE, VARIABILITY IN THAT PERIOD IS 

CONSIDERED TO BE STATISTICALLY SIGNIFICANT. 
OCS Study

BOEM 2015-034

115

FIGURE 51. WAVELET ANALYSIS OF 2010 - 2011 AT DEPTH DATA (27-28 M) ZOOPLANKTON VOLUME BACKSCATTER 

DATA FROM THE ICY CAPE TRANSECT. FROM TOP PANEL TO BOTTOM PANEL: OFFSHORE, C3; MIDSHORE, C2; 

INSHORE, C1. DETAILS OF PLOTS ARE THE SAME AS IN THE PREVIOUS PLOT.

The temporal variability of zooplankton near the surface in 2011-2012 was somewhat 

similar to that observed during the previous year, although the peak diel variability was stronger 

at the midshore (C2) and offshore (C3) than it had been the previous year (Figure 52). Diel 

variability was still not significant (P < 0.05) when examined over the entire deployment. 

Results at depth were similar to the previous year with a significant (P< 0.05) amount of 

temporal variability at the semi-diurnal period and a non-significant peak in the diel period 

(Figure 52). Peaks in semi-diurnal variability occur at different times at the different locations. 

For example, much of the semi-diurnal signal occurred after May at the inshore mooring, while 

the levels of temporal variability were more evenly distributed over the time of deployment at 

the other two locations.
OCS Study

BOEM 2015-034

116

FIGURE 52. WAVELET ANALYSIS OF 2011-2012 NEAR SURFACE DATA (11-12 M) ZOOPLANKTON VOLUME 

BACKSCATTER DATA FROM THE ICY CAPE TRANSECT. FROM TOP PANEL TO BOTTOM PANEL: OFFSHORE, C3;

MIDSHORE, C2; INSHORE, C1. DETAILS OF PLOTS ARE THE SAME AS IN THE PREVIOUS PLOT.
OCS Study

BOEM 2015-034

117

FIGURE 53. WAVELET ANALYSIS OF 2011-2012 AT DEPTH DATA (28-32 M) ZOOPLANKTON VOLUME BACKSCATTER 

DATA FROM THE ICY CAPE TRANSECT. FROM TOP PANEL TO BOTTOM PANEL: OFFSHORE, C3; MIDSHORE, C2; 

INSHORE, C1. DETAILS OF PLOTS ARE THE SAME AS IN THE PREVIOUS PLOT.

Time Series of Zooplankton Volume Backscatter Estimated From Multi-Frequency Acoustic 

Measurements (TAPS6-NG)

We successfully obtained measurements of echo intensity at all 6 frequencies from 

deployment until September 30th 2011 and then again from early December until late January

2012 at the midshore mooring, C2. The first interruption was due to an error in the instrument’s 

software code. The instrument ceased taking measurements after the attempt to change sample 

intervals from 30 to 60 minutes at the end of September (Figure 54-55; missing data indicated by 

red arrow on time axis). It started acquiring measurements again in early December and 

continued to operate until it depleted its batteries in late January 2012. Early depletion of battery 

power was corrected in later years by switching from alkaline to lithium batteries and increasing 

the capacity (amp hrs) of the battery packs.

Near-bottom abundance estimates for euphausiid-shaped scatterers showed that the 14-18 

mm size range were most abundant at 30-40 per m3(Figure 54A). Lower abundances for 

euphausiid-shaped scatterers of around 10 per m3 were apparent in the smaller (~10-11 mm) and 
OCS Study

BOEM 2015-034

118

larger (>20 mm) size ranges. All size ranges, with the exception of the 20 mm individuals, were 

most abundant in the fall and declined in number in the winter months, consistent with 

expectations. Near-bottom abundance estimates for the 2-6 mm size range copepod-shaped 

scatterers were approximately 1500 per m3(Figure 54B). Estimates of the abundance of 

copepod-shaped organisms did not decline with time from fall to winter.

Near-surface abundance estimates (25 m from transducer, ca 10 m depth) for euphausiidshaped scatterers 14-18 mm were higher than those deeper in the water column (100-200 per m3

Figure 55A). Lower abundances of approximately 10-20 per m3 were estimated for the larger 

(>20 mm) sizes. Abundances stayed consistent throughout August and September. Near-surface 

abundances of approximately 1500 per m3for the copepod-shaped, 2-6 mm size range were only 

briefly detected in early September (Figure 55B). Echo intensities during the remainder of the 

deployment were below the signal-to-noise ratio threshold used in our analyses. 

FIGURE 54. ESTIMATED ABUNDANCE AT 25 M FOR TWO TYPES OF ZOOPLANKTON SCATTERERS DERIVED FROM THE 

MULTI-FREQUENCY TAPS6-NG (A) EUPHAUSIIDS AND (B) COPEPODS. RED ARROW ON TIME AXIS INDICATES A GAP 

IN DATA COLLECTION CAUSED BY A PROBLEM WITH INSTRUMENT SOFTWARE.
OCS Study

BOEM 2015-034

119

FIGURE 55. ESTIMATED ABUNDANCE AT 10 M FOR TWO TYPES OF ZOOPLANKTON SCATTERERS DERIVED FROM THE 

MULTI-FREQUENCY TAPS6-NG (A) EUPHAUSIIDS AND (B) COPEPODS.

3. Discussion

Time Series of Physical and Chemical Data from Biophysical Moorings

The Arctic has changed markedly in the last decade, entering a new phase sometimes 

referred to as the “new normal” - thinner ice, earlier ice retreat in spring and late ice arrival in the 

fall, warmer ocean temperatures during summer, and changes in weather patterns (Wood et al., 

2015). These physical changes will continue to have profound impacts on this ecosystem. 

Long-term observations are necessary to quantify the changes in the ocean.

Time Series of Zooplankton Backscatter Estimated From ADCP Instruments

Single-frequency acoustic data does not allow users to attribute the volume 

backscattering (Sv) to specific components of the plankton community. Only multi- or broadband acoustics can discriminate among the large diversity of sizes of plankton and target 

strengths of different types of scatterers in the plankton. However, ADCPs are a common tool of 

physical oceanographers and require little extra effort to capture the echo counts. These echo 

counts can be used to identify the period of maximum variability in zooplankton backscatter 

(e.g., diel, fortnightly, seasonal, etc.). Therefore, they are a useful tool for examining periodicity 

in bulk zooplankton and future investigations should include them.

The patterns of temporal variability in zooplankton volume backscatter were very 

different from what we’ve observed in the southeastern Bering Sea. In the southeastern Bering 

Sea, wavelet analysis of scattering at 18 m measured from spring to late summer, showed a 

strong diel periodicity. In that region it was strongest at the end of the summer in 2007 and 2009 

and was strongest in the middle of the summer in 2010. In all three years, the strength of the diel 
OCS Study

BOEM 2015-034

120

signal was much stronger than the semi-diurnal signal. In the Chukchi Sea, a diel signal was not 

always detectable, but a semi-diurnal signal was often statistically significant at depth, 

particularly at the midshore (C2) and offshore (C3) mooring sites. Temporal patterns at the three 

stations were often very different indicating great variability in zooplankton behavior across this 

relatively broad, flat shelf. 

We attribute these results to differences in the zooplankton community and photoperiod 

between the Bering and Chukchi Seas. At mooring site M2 in the southeastern Bering Sea, the 

water column is much deeper than that of the Chukchi Sea (72 m versus 40 m) and the 

zooplankton biomass is dominated by late stage Calanus spp. and juvenile and adult 

Thysanoessa spp. In the Chukchi Sea there were very few adult or juvenile euphausiids.

In addition, the photoperiods (hours of sunlight) and sun angle vary affecting the light 

stimulus to the zooplankton. At M2 in the eastern Bering Sea there are a minimum of 6 3/4 hrs 

of sunlight in the winter and a maximum of almost 18 hrs at the summer solstice, while in the 

Chukchi Sea the zooplankton will experience 0 hrs of light from mid-November to mid-January

and 24 hrs of light from mid-May to early August. Factors such as ice thickness and ice snow 

cover, further modify the light penetrating the surface waters and hinder or remove the light 

intensity cue. The intermittently detectable diel signal in the Chukchi Sea is consistent with 

previous Arctic research (Cottier et al., 2006; Berge et al., 2009; Wallace et al., 2010). Previous 

Arctic studies have confirmed that because of the extreme environment, DVM can become 

intermittently synchronized or even completely unsynchronized, where individuals vertically 

migrated in an uncoordinated way. Note that food cues and the onset of diapause may also 

modify migration behavior at high latitudes.

Time Series of Zooplankton Volume Backscatter Estimated From Multi-Frequency 

Measurements

The newly developed TAPS6-NG acoustics system was successful in obtaining two, 

short-term records of zooplankton volume backscatter at the midshore mooring site, C2 (AugustSeptember & December-January). Zooplankton backscatter from both copepod-shaped and 

elongate scatterers was detected during those periods. The size of the euphausiids resolved by 

the inverse method generally agreed with net samples collected in August which indicated that 

the number of adult euphausiids was very low and that most euphausiids were from much 

younger stages. The system software did not automatically take measurements of background 

electronic system noise as intended and we had to rely on using minimum echo intensities 

measured for each depth bin each week. This resulted in signal-to-noise ratio for many 

ensembles that were below our 10 dB cutoff, and is the likely reason why we were unable to 

detect more zooplankton scatters during winter in the near-surface estimates. Copepods are 

weak scatterers, thus may be difficult to detect above noise thresholds the further they are from 

the transducer face. 

A net sampling program is important to help capture a near complete picture of the 

zooplankton community (minus the delicate jelly plankton), and can be used to guide which 

scattering models are appropriate to interpret zooplankton volume backscatter from acoustic 

instruments. Unfortunately, the amount of net sampling is currently limited to the ice-free 

periods and the times when an ice-breaker is in the area. Net samples also require a large 
OCS Study

BOEM 2015-034

121

amount of analyst time to process. More effort should be expended on optical and other 

technologies with automated image analysis that can capture data on zooplankton community 

structure during times of the year when net sampling is not possible.

4. Conclusions

The currents in the Chukchi Sea are well correlated both horizontally and vertically, and

are generally northeastward, but with events of southwestward flow. The currents are also well 

correlated with the local winds. The flow patterns on the shelf are fairly well known, and results 

from this study (hydrography, current measurements, and satellite-tracked drifter trajectories) 

contributed to the refinement of the map of currents shown in Figure 57.

Approximately 40% of the flow through Bering Strait passes the Icy Cape line. 

Furthermore, monthly mean total transport, measured from moorings, is highly variable in fall 

and winter, with less variability in summer. Transport was stronger as measured on the 2010-

2011 mooring deployments than on those in 2011-2012 and 2012-2013. Intrusions of slope water 

up Barrow Canyon could be seen at Icy Cape. However, these intrusions have high interannual 

variability, as the two years differed markedly in the number and strength of the intrusions.

Although average keel depth was ~4-5 m, deep ice keels (>27 m) occurred each year, generally 

in late spring. There were more polynyas seen on the 2010-2011 deployment than on the one 

from 2011-2012. Ice arrived at approximately the same time in 2011 and 2012, but it retreated 

almost a month earlier in 2011 than 2012. This was reflected in the bottom mooring 

temperatures, which warmed above -1.8°C in early to mid-June in 2011 and in late June to late 

July in 2012. 

The Chukchi shelf is shallow and chlorophyll exported to the bottom continues to be 

productive, as evidenced by enhanced oxygen concentrations. At the end of summer, nitrate is 

usually low, indicating little or no nitrate on the shelf at that time. The bottom nitrate is 

replenished in September, but decreases in the fall likely because of dilution by vertical mixing. 

By May or June the nitrate supply has been replenished.

Diel vertical migration by zooplankton was not regular (non-stationary) during the year. 

The shallow water column and difficulty predicting where the zooplankton spend most of their 

time may make it difficult to understand the exposure of plankton to oil, should there be an oil 

spill in the region. Upward looking zooplankton acoustics holds promise for determining yearround abundance and distribution, however the instruments used for these measurements must be 

very robust.

5. Recommendations

It is critical that we continue to moor physical and biological instruments in close 

proximity to better conduct inter-disciplinary studies. Bio-physical moorings have been deployed 

each year since 2010 on the Icy Cape line (inshore, midshore, and offshore), thus it is essential 

that we maintain this long-term dataset. The measurements of currents provide an estimate of 

transport along the Alaskan coast in the Chukchi Sea. These, combined with measurements of 
OCS Study

BOEM 2015-034

122

temperature, salinity, chlorophyll fluorescence, nutrients and PAR will provide indices to better 

understand how the ecosystem is changing, especially during the ice cover seasons. 

We must work to improve the reliability of the TAPS6-NG controller board and update 

system memory so that individual ping data can be stored. Improvements will also include 

examining predicted signal to noise ratios to see if instrument performance for measurements far 

from the transducer face can be improved.

Ultimately, we must utilize new and emerging technologies to better sample this remote 

and harsh region. New instrumentation such as acoustic fish finders, new fluorometers which 

provide information on the health of phytoplankton, and “lab on a chip” which measures nitrate, 

phosphate, silicate, and ammonium are being developed to be deployed on moorings and 

vehicles. Furthermore, we need to collect or find better estimates of the material properties of 

euphausiids, particularly the relationship between lipid content and target strength. We also need 

to investigate ways to combine acoustic and optical methods so that analysts know which 

plankton scattering models to apply at different times of the year. Finally, it is important to 

conduct modeling and other experiments to arrive at methods to optimize our ability to detect 

plankton close to the underside of ice.

B. Shipboard Observations

1. Methods

Hydrography-- Physical and Chemical Variables

Hydrographic data were collected during cruises in 2010, 2011 and 2012 (Table 15). The 

primary design of the hydrographic survey was twofold: to collect samples of temperature, 

salinity, chlorophyll fluorescence, oxygen and PAR using a CTD, and to collect samples for 

analysis of oxygen, chlorophyll, nutrients (nitrate, phosphate, silicate, nitrite and ammonium), 

and salinity. Water samples were collected at selected depths on six hydrographic transect lines 

(Figure 32; A, B, C, D, E, F). Line F was not occupied in 2010. The primary purpose of the 

water samples collected for salinity and oxygen was to calibrate the instruments on the CTD. 

In 2010, due to the failure of our winch and of the SeaBird SBE 911plus CTD deck unit, 

we were unable to complete hydrographic casts using our high resolution CTD and rosette. 

Therefore, the CTD/water bottle casts were conducted with a lower resolution SeaBird SeaCAT 

(SBE19plus) lowered on a mechanical (non-conducting) wire to 30 meters. Niskin bottles were 

attached to the wire at three depths (1, 20 and 30 meters). The bottles were tripped using 

messengers and the data were downloaded from the SBE 19 after each cast. In 2011 and 2012, 

sampling was fully successful, with a Sea-Bird SBE 911plus system with dual temperature and 

salinity sensors, and oxygen (SBE-43) sensors, a photosynthetically active radiation (PAR) 

sensor (Biospherical Instruments QSP-200 L4S or QSP-2300), and a chlorophyll fluorescence 

(WET Labs WETStar WS3S) sensor. Nutrients and chlorophyll samples were collected at the 

surface, at 10 meter intervals, and at the bottom of the cast.

Nutrients and chlorophyll samples were taken from each Niskin bottle, processed and 

frozen in the – 80°C freezer for analysis in the laboratories at PMEL and AFSC, respectively, in 

Seattle, Washington. Salinity calibration samples were taken on approximately half the casts and 
OCS Study

BOEM 2015-034

123

analyzed using a laboratory salinometer at PMEL. Oxygen samples were taken on most casts 

and titrated using the Winkler method. The number of CTD stations and the number of nutrient 

and chlorophyll samples collected are shown in Table 15.

TABLE 15. THE NUMBER OF HYDROGRAPHIC STATIONS OCCUPIED IN THE CHUKCHI SEA (INCLUDING BERING 

STRAIT), TOGETHER WITH THE NUMBER OF NUTRIENT SAMPLES AND THE NUMBER OF CHLOROPHYLL SAMPLES 

COLLECTED AND PROCESSED.

CTDs Nutrients Chl.

2010: 24 Aug – 20 Sep 57 159 176

2011: 12 Aug – 11 Sep 56 286 276

2012: 8 Aug – 7 Sep 55 297 276

Satellite-tracked drifters

In 2011, three satellite-tracked drifters were deployed (funded by NOAA) in the Chukchi 

Sea (Table 16). The drogues were “holey socks” centered at a depth of 25 m, which was below 

the summer mixed layer depth. Each drifter was instrumented with a temperature sensor at the 

bottom of a float (i.e., just below the sea surface). At these high latitudes, more than 14 positionfixes per day were obtained from Argos, until the drifter was caught in the ice at which time the 

fixes became erratic. Once the data were received from Argos, spurious data were deleted from 

the time series. Data collected after the drogue was lost or entered into ice (determined from 

maps of ice extent) were noted.

TABLE 16. THE IDENTIFYING NUMBER OF THE DRIFTER AND THE LATITUDE AND LONGITUDE WHERE IT WAS 

DEPLOYED.

Argos Drifter Latitude Longitude 

106694 66.008°N 168.797°N

106699 67.776°N 168.580°N

106698 72.798°N 161.009°N

Satellite remote sensing and ice data

The Advanced Microwave Scanning Radiometer EOS (AMSR; 

http://nsidc.org/data/amsre/) was used in this report. AMSR is a data set of sea-ice extent and 

areal concentration, consisting of daily ice concentration data at 12.5 km resolution, which are 

available from the National Snow and Ice Data Center. The time series of percent areal coverage 

were calculated in a 50 km x 50 km box around each of the mooring sites.

Zooplankton Net Data

Zooplankton was collected on each cruise using a multiple-opening and closing 1 m2

Tucker Sled trawl equipped with sled-like runners at the bottom so that samples could be taken 

in close proximity to the bottom (Dougherty et al., 2010; Figure 56). The majority of the 
OCS Study

BOEM 2015-034

124

sampling was accomplished during daylight hours, however on several occasions we were 

unable to complete an entire transect during daylight and some stations were completed in 

darkness. The Tucker Sled was equipped with 1 m2, 333 μm Nitex mesh nets for collection of 

“large” zooplankters and a 500 μm mesh drogue net that was open when the sled was deployed. 

Inside the mouths of the 333 μm mesh Tucker nets was a 20 cm diameter net of smaller mesh 

(153 μm) to capture the smaller-sized zooplankton species. A calibrated General Oceanics 

mechanical flowmeter was situated along the centerline of the 1 m2net to measure distance 

traveled and a small CTD was mounted just behind the top bar of the Tucker Sled to determine 

temperature, salinity, and depth during and after each cast. A SeaBird SeaCAT (SBE 19plus) or 

SeaBird FastCAT (SBE 49) was attached to the top of the frame immediately behind the net to 

telemeter depth data and record the in situ temperature and conductivity. Net configurations, 

sampling strategy, and instruments for individual years are described in Table 17. 

When two 333 μm mesh nets were used, the bottom net was fished for 2 minutes along 

the bottom before being closed. At closure, the frame was retrieved at approximately 20 m/min 

so that the second net sampled the entire water column. Plankton captured by the nets was 

washed into the cod ends, sieved through identically-sized wire mesh screens and preserved in 

glass jars with sodium borate-buffered 5% Formalin. Samples were inventoried at the end of the 

cruise and then sent to the Polish Sorting Center in Szczecin, Poland for processing. During the

first two years we used the standard FOCI zooplankton protocol (Napp et al., 1996). In the third 

year we instituted a revised protocol that provided better species-specific identifications of some 

of the non-copepod taxa and was more closely aligned with protocols used by other scientists 

working in the region. Taxa removed from the samples for enumeration were returned to Seattle 

in small glass vials. The remainder of the sample is archived at the Polish National Marine 

Fisheries Research Institute in Gdynia, Poland and will be archived there for 20 years from the 

date of sample collection, under an existing Joint Studies Agreement between the U.S. NOAAFisheries and the Polish National Marine Fisheries Research Institute. After 20 years the 

samples will be destroyed. Zooplankton data from this project are stored in the NOAAFisheries, Alaska Fisheries Science Center, Recruitment Processes Program relational database, 

EcoDAT.

TABLE 17. NET CONFIGURATION AND SAMPLING STRATEGY FOR ALL TRANSECT LINES EXCEPT THE BARROW 

CANYON LINE (TRANSECT F). ON THAT TRANSECT LINE NO ALONG-BOTTOM NET TOW WAS ATTEMPTED.

Year Large Mesh Nets Small Mesh Nets CTD

2010 1, 333 μm mesh; double oblique tow 1, 153 μm mesh; double oblique 

tow

SBE 

19plus

2011 2, 333 μm mesh nets; 1 towed along the 

bottom and 1 from bottom to the surface

2, 153 μm mesh nets; 1 towed 

along the bottom and 1 from 

bottom to the surface

SBE 

19plus

2012 2, 333 μm mesh nets; 1 towed along the 

bottom and 1 from bottom to the surface

1, 153 μm mesh net; towed from 

bottom to the surface

SBE 

49
OCS Study

BOEM 2015-034

125

Comparison of Zooplankton Estimates From the Tucker Net and a TAPS-6

An older, 6-frequency Tracor Acoustic Profiling System (TAPS-6) was used to estimate 

volume-scattering strength (Sv, dB re 1 m-1) of zooplankton. The instrument and approach has 

been used in other subarctic and arctic ecosystems to examine patterns in the temporal and 

spatial distribution of zooplankton (Holliday et al., 2009). The six frequencies were: 265, 420, 

700, 1100, 1850, and 3000 kHz. Note that the frequencies and instrument design are

fundamentally different from the TAPS6-NG instruments designed and moored for this study 

and described in the previous section. The TAPS-6 was attached to the top of the epibenthic 

Tucker Sled, with the transducers angled towards the center of the net opening (Figure 56).

FIGURE 56. 1 M2 TUCKER SLED ON THE ICY DECK OF THE R/V AQUILA. THE KNEELING SCIENTIST HAS HIS HEAD IN 

THE NET MOUTH. THE TAPS-6 (BLACK CANISTER) IS MOUNTED ON THE TOP BAR OF THE TUCKER FRAME AND IS 

POINTED DOWN INTO THE TOW PATH OF THE NET. THE TRANSDUCER FACES POINT TO THE RIGHT AND DOWN IN 

THIS PICTURE.

The instrument was used in a small volume (ca. 2.5 liters) measurement mode which 

collects Sv data at a range of 1.5-m from the transducer face. The TAPS-6 averages multiple 

ping cycles prior to storing the data. The number of ping cycles per average used during these 

deployments was 6, which gives a new data ensemble every 2.6 seconds. Since each ping 

averages 5 independent samples, each data set results in 30 degrees of freedom. TAPS-6 

calibration was accomplished by determining the source and receiver levels for each frequency, 

before and after each field season, using a standard calibrated transducer.

For abundance comparisons of zooplankton between the nets and the TAPS-6, species 

abundance from the water column and bottom net samples were separated and then summed into 

copepod-shaped and elongate/euphausiid-shaped categories. For displacement volume

comparisons, the water column Tucker large-mesh and 25-cm small-mesh net sample 

displacement volumes were summed for all species.
OCS Study

BOEM 2015-034

126

MatlabTM (R2012b) was used to process the TAPS-6 acoustic data. Raw data from each 

frequency of the TAPS-6 was recorded as Sv. Background noise was defined as the minimum 

intensities (W/m2) for the entire cast for each frequency. Noise was then removed by subtracting 

it from each measurement. Signal-to-noise ratios of <10 dB were used as a threshold to reject Sv

values that were not acceptable for further analysis.

Inverse methods were used to estimate the abundance of plankton scatterers in 1-m depth 

bins as a function of size (Holliday, 1977; Greenlaw, 1979; Greenlaw and Johnson, 1983). The 

truncated fluid sphere (TFS), distorted-wave Born approximation (DWBA) and hard elastic 

scattering models were used in the inverse calculation to estimate scattering from small, 

spherical organisms (e.g., copepods, eggs, nauplii), elongate organisms (e.g., euphausiids, 

mysids), and planktonic shelled molluscs, respectively (Holliday, 1992; Stanton, 1994; Holliday 

et al., 2003). Assumed values for the material properties and the assumed orientation of these 

scatterers are provided in Table 13. In this application, however, the Levenberg-Marquardt 

factors for the nonlinear regression was 1.0 x 10-3as opposed to 1.0 x 10-4that was used to 

process the moored acoustics data (Table 13). Euclidian norms were computed as a goodnessof-fit statistic between Sv and the inverse model, fit to verify that the inversion could adequately 

explain the measured Sv values.

MatlabTM (R2012b) was used for linear regression analysis. The TAPS-6 inverseestimated abundance of copepods, pteropods, and euphausiids was compared to net sample 

estimates for those 3 taxonomic categories (copepods, euphausiids, and shelled molluscs) at all 

available stations in 2010, 2011, and 2012. Finally, mean volume backscatter in the water 

column for each of the 6 frequencies measured along Transect D (Icy Cape line) in 2011 and 

2012 was compared to zooplankton displacement volume from the water column net from the 

same tows.

Contour section plots of zooplankton Sv at 420 kHz along transect D were created using 

Surfer Plot (Version 10.7.972) and compared to temperature and salinity data collected during 

the CTD casts at the same station.

2. Results

Hydrography -- Physical and Chemical Variables

The Chukchi Sea consists of a broad shallow shelf (Figure 32, Figure 57), which is 

incised by two major canyons at the slope – Barrow Canyon in the east and Herald Canyon in the 

west. The flow on the eastern part of the shelf is generally northward and follows bathymetry 

(Figure 57). Three types of water enter onto the shelf from Bering Strait: Alaska Coastal 

Current (ACC), Bering Water (BW) and Anadyr Water (AW). In addition, intrusions of water 

from the Bering Sea basin onto the shelf can occur either through Bering Canyon or over the 

shelf break to the west of Bering Canyon. Thus the physical, chemical, and biological properties 

over the shelf are the sum total of advective and in situ processes. Table 18 summarizes the range 

of properties expected for each water type. Data collected as part of CHAOZ provides insight 

into the magnitude of transport and flow pathways.
OCS Study

BOEM 2015-034

127

TABLE 18. TEMPERATURE AND SALINITY RANGES FOR DIFFERENT WATER MASSES IN THE CHUKCHI SEA. (FROM 

DANIELSON ET AL., SUBMITTED).

FIGURE 57. MAP OF CURRENTS OVER THE CHUKCHI SHELF (MODIFIED FROM WOOD ET AL., 2015).

Water Mass Temperature 

(°C)

Salinity 

Alaska Coastal Water (ACW) 7 - 12 20 - 32

Winter Water (WW) -2 - 0 30 - 33.5

Bering Shelf Summer Water (BSSW) 0 - 8 30 - 33.5

Atlantic Water (AtlW) -2 - 1 33.5 - 35
OCS Study

BOEM 2015-034

128

Interannual and spatial variability among hydrographic sections

Line A: Point Hope (Figure 32, Figure 58-60)

The warmer, fresher water of the ACC was confined to the innermost and shallower 20 to 

30-km, portions of this line. In 2010, waters of the ACC were vertically mixed to ~30-m, while 

in 2011, a shallow pycnocline was observed at 5-m. In 2012, waters of the ACC were weakly 

stratified. Nitrate tended to be low near shore both in the surface and at depth for all years.

In the offshore portion of this transect, the bottom layer was saltier and rich in nutrients 

(including ammonium), conditions that typify AW. Nitrite is an intermediate compound in 

several important biological reactions, and concentrations are generally low. While sections of 

nitrite are shown for completion, these will not be discussed. Bottom temperatures were < 3°C 

in 2012, and > 3°C in 2011. In 2010, offshore water was weakly stratified, and the pycnocline 

was > 10-m thick, while in 2011 and 2012, there was a strong two-layer system with a sharp 

pycnocline between 5-10 m. Continuous depth distributions of chlorophyll and oxygen were not 

measured in 2010 due to the failure of the winch and SeaBird 911plus CTD deck unit. In 2011, 

maximum chlorophyll was confined to the coast, while in 2012 there was a sub-surface 

chlorophyll maximum farther offshore. In both years, surface waters were generally 

supersaturated with oxygen, while deeper waters were undersaturated, likely due to respiration.
OCS Study

BOEM 2015-034

129

FIGURE 58. HYDROGRAPHIC MEASUREMENTS AT POINT HOPE (LINE A) IN SEPTEMBER 2010. (A.) CONTOURS OF 

TEMPERATURE, SALINITY, SIGMA-T, AND BOTTOM DEPTH. (B.) LINE PLOTS OF NUTRIENTS (SILICATE, PHOSPHATE, 

NITRATE, NITRITE AND AMMONIUM) MEASURED AT THREE DEPTHS (1 M; RED, 20 M; GREEN AND 30 M; BLUE). THE 

COASTLINE IS ON THE RIGHT SIDE OF EACH PLOT.
OCS Study

BOEM 2015-034

130

FIGURE 59. HYDROGRAPHIC MEASUREMENTS AT POINT HOPE (LINE A) IN AUGUST 2011. (A.) TEMPERATURE,

SALINITY, SIGMA-T OXYGEN (PERCENT SATURATION) AND CHLOROPHYLL FLUORESCENCE. (B.) THE FIVE 

NUTRIENTS (SILICATE, PHOSPHATE, NITRATE, NITRITE AND AMMONIUM).
OCS Study

BOEM 2015-034

131

FIGURE 60. HYDROGRAPHIC MEASUREMENTS AT POINT HOPE (LINE A) IN AUGUST 2012. LEFT: TEMPERATURE, 

SALINITY, SIGMA-T OXYGEN (PERCENT SATURATION) AND CHLOROPHYLL FLUORESCENCE. RIGHT: THE FIVE 

NUTRIENTS (SILICATE, PHOSPHATE, NITRATE, NITRITE AND AMMONIUM).

Line B: Cape Lisburne (Figure 32, Figure 61-63)

While warmer, fresher water of the ACC was still observed along the inner portions of 

this hydrographic line, the saltier nutrient rich Anadyr water was no longer evident on the

offshore portion of the line. Thus, horizontal gradients in physical and chemical properties were 

relatively weak. Once again, in 2011, bottom temperatures were warmer and stratification was 

stronger than in 2012. In 2011 and 2012, the pycnocline was deeper than, and not as sharp as,
OCS Study

BOEM 2015-034

132

the pycnocline observed along Line A. An offshore lens of fresher water was evident in 2010 

resulting in an upper stratified layer. Nutrients were generally low, but in 2011 and 2012, higher 

silicate was observed along the coast, and nutrients were elevated offshore in association with 

higher salinities at depth. This may be an indication of water from the Pt. Hope transect entering 

the shelf through Central Channel. A sub-surface chlorophyll maximum was observed in 2011 

and 2012; however, in 2011 oxygen remained undersaturated (perhaps indicating that a bloom 

was in an early stage or that the cells at depth had higher chlorophyll/biomass ratios than the 

cells near the surface), while in 2012 large portions of the water column were supersaturated.

FIGURE 61. HYDROGRAPHIC MEASUREMENTS AT CAPE LISBURNE (LINE B) IN SEPTEMBER 2010. (A.) CONTOURS OF 

TEMPERATURE, SALINITY AND SIGMA-T. (B.) LINE PLOTS OF NUTRIENTS (SILICATE, PHOSPHATE, NITRATE, NITRITE 

AND AMMONIUM) MEASURED AT THREE DEPTHS (1 M; RED, 20 M; GREEN AND 30 M; BLUE).
OCS Study

BOEM 2015-034

133

FIGURE 62. HYDROGRAPHIC MEASUREMENTS AT CAPE LISBURNE (LINE B) IN AUGUST 2011. (A.) TEMPERATURE,

SALINITY, SIGMA-T OXYGEN (PERCENT SATURATION) AND CHLOROPHYLL FLUORESCENCE. (B.) THE FIVE 

NUTRIENTS (SILICATE, PHOSPHATE, NITRATE, NITRITE AND AMMONIUM).
OCS Study

BOEM 2015-034

134

FIGURE 63. HYDROGRAPHIC MEASUREMENTS AT CAPE LISBURNE (LINE B) IN AUGUST 2012. (A.) TEMPERATURE,

SALINITY, SIGMA-T OXYGEN (PERCENT SATURATION) AND CHLOROPHYLL FLUORESCENCE. (B.) THE FIVE 

NUTRIENTS (SILICATE, PHOSPHATE, NITRATE, NITRITE AND AMMONIUM).

Line C: Point Lay (Figure 32, Figure 64-66)

In 2010 and 2011, the ACC was slightly cooler and saltier at line C than on Lines A and 

B. In 2012, the ACC had a larger seaward extent, and was vertically mixed along the coast. 

Most notably in 2012, the deeper, offshore water was less saline than in 2010 and 2011. 

Silicate and ammonium concentrations were generally higher in bottom water at Line C than at 

Line B indicating remineralization and nutrient regeneration, while nitrate and phosphate 

remained low. A subsurface chlorophyll maximum was observed in 2011; however, oxygen 

remained undersaturated.
OCS Study

BOEM 2015-034

135

FIGURE 64. HYDROGRAPHIC MEASUREMENTS AT POINT LAY (LINE C) IN SEPTEMBER 2010. (A.) CONTOURS OF 

TEMPERATURE, SALINITY AND SIGMA-T. (B.) LINE PLOTS OF NUTRIENTS (SILICATE, PHOSPHATE, NITRATE, NITRITE 

AND AMMONIUM) MEASURED AT THREE DEPTHS (1 M; RED, 20 M; GREEN AND 30 M; BLUE).
OCS Study

BOEM 2015-034

136

FIGURE 65. HYDROGRAPHIC MEASUREMENTS AT POINT LAY (LINE C) IN AUGUST 2011. (A) TEMPERATURE, SALINITY, 

SIGMA-T OXYGEN (PERCENT SATURATION) AND CHLOROPHYLL FLUORESCENCE. (B) THE FIVE NUTRIENTS

(SILICATE, PHOSPHATE, NITRATE, NITRITE AND AMMONIUM).
OCS Study

BOEM 2015-034

137

FIGURE 66. HYDROGRAPHIC MEASUREMENTS AT POINT LAY (LINE C) IN AUGUST 2012. (A) TEMPERATURE, SALINITY, 

SIGMA-T OXYGEN (PERCENT SATURATION) AND CHLOROPHYLL FLUORESCENCE. (B) THE FIVE NUTRIENTS

(SILICATE, PHOSPHATE, NITRATE, NITRITE AND AMMONIUM).

Line D: Icy Cape (Figure 32, Figure 67-69)

Along this transect, the warmer fresher signature of the ACC observed to the southwest

continued to weaken. Along the bottom, there was colder and saltier water than along Line C, 

except for the nearshore stations in 2012 where stratification was weak. In each year at the 

bottom near the middle of the hydrographic line was a small concentration of winter water. Not 

only was silicate and ammonium elevated along the bottom as observed along Line C, but nitrate 

and phosphate concentrations were also higher in association with elevated salt content along the 

bottom. There were strong subsurface chlorophyll maximums in 2011 and 2012, with oxygen 

supersaturation at or above these features, and undersaturation and high ammonium 

concentrations below.
OCS Study

BOEM 2015-034

138

FIGURE 67. HYDROGRAPHIC MEASUREMENTS AT ICY CAPE (LINE D) IN SEPTEMBER 2010. (A.) CONTOURS OF 

TEMPERATURE, SALINITY AND SIGMA-T. (B.) LINE PLOTS OF NUTRIENTS (SILICATE, PHOSPHATE, NITRATE, NITRITE 

AND AMMONIUM) MEASURED AT THREE DEPTHS (1 M; RED, 20 M; GREEN AND 30 M; BLUE).
OCS Study

BOEM 2015-034

139

FIGURE 68. HYDROGRAPHIC MEASUREMENTS AT ICY CAPE (LINE D) IN AUGUST 2011. (A) TEMPERATURE, SALINITY, 

SIGMA-T OXYGEN (PERCENT SATURATION) AND CHLOROPHYLL FLUORESCENCE. (B) THE FIVE NUTRIENTS

(SILICATE, PHOSPHATE, NITRATE, NITRITE AND AMMONIUM).
OCS Study

BOEM 2015-034

140

FIGURE 69. HYDROGRAPHIC MEASUREMENTS AT ICY CAPE (LINE D) IN AUGUST 2012. (A) TEMPERATURE, SALINITY, 

SIGMA-T OXYGEN (PERCENT SATURATION) AND CHLOROPHYLL FLUORESCENCE. (B) THE FIVE NUTRIENTS

(SILICATE, PHOSPHATE, NITRATE, NITRITE AND AMMONIUM).

Line E: Wainwright (Figure 32, Figure 70-72)

Warmer fresher water, (the ACC), was observed at the innermost portion of Transect E 

each year. In 2011, the region of warmer fresher water separated from the coast and was found 

over the center of the canyon. Further inshore, cooler, saltier water in the canyon is consistent 

with upwelling along the edge of the canyon. This transect was well stratified, especially in 

2010 and 2011, with weaker stratification in 2012. Bottom salinities were relatively high, with 

the highest salinities observed in 2012. These salinities were comparable to Line A and higher

than at Lines B, C and D. Nutrient concentrations were moderate in 2010 and 2011, but in 2012, 

concentrations were high, and resembled concentrations in AW that were observed along Line A.

The cold temperatures (<0°C) and salinities (>33) is indicative of either onshore flow of slope 

water or AW/WW that was transported to the region. Once again a subsurface bloom is evident 

(especially in 2012) and the oxygen was supersaturated near the thermocline in both 2011 and 

2012. Percent oxygen saturation was highest in the middle of the water column in 2011 and 
OCS Study

BOEM 2015-034

141

2012 and extended for most of the length of the transect. In 2012 the supersaturated oxygen 

layer was shallower than the fluorescence maximum.

FIGURE 70. HYDROGRAPHIC MEASUREMENTS AT WAINWRIGHT (LINE E) IN SEPTEMBER 2010. (A.) CONTOURS OF 

TEMPERATURE, SALINITY, AND SIGMA-T. (B.) LINE PLOTS OF NUTRIENTS (SILICATE, PHOSPHATE, NITRATE, 

NITRITE AND AMMONIUM) MEASURED AT THREE DEPTHS (1 M; RED, 20 M; GREEN AND 30 M; BLUE).
OCS Study

BOEM 2015-034

142

FIGURE 71. HYDROGRAPHIC MEASUREMENTS AT WAINWRIGHT (LINE E) IN AUGUST 2011. (A) TEMPERATURE, 

SALINITY, SIGMA-T OXYGEN (PERCENT SATURATION) AND CHLOROPHYLL FLUORESCENCE. (B) THE FIVE 

NUTRIENTS (SILICATE, PHOSPHATE, NITRATE, NITRITE AND AMMONIUM).
OCS Study

BOEM 2015-034

143

FIGURE 72. HYDROGRAPHIC MEASUREMENTS AT WAINWRIGHT (LINE E) IN AUGUST 2012. (A) TEMPERATURE,

SALINITY, SIGMA-T OXYGEN (PERCENT SATURATION) AND CHLOROPHYLL FLUORESCENCE. (B) THE FIVE 

NUTRIENTS (SILICATE, PHOSPHATE, NITRATE, NITRITE AND AMMONIUM).

Line F: Barrow Canyon (Figure 32, Figure 73-74)

This Distributed Biological Observatory (DBO) transect was occupied in 2011 and 2012. 

On the northern side of the canyon, bottom waters were very cold, salty, and relatively high in 

nutrient concentrations. In 2011, stratification was relatively weak, and the cold, saltier, 

nutrient-rich waters were confined to depths > 50 m. In 2012, stratification was stronger, and the 

base of the pycnocline was ~20 m; hence the cold, saltier and nutrient-rich bottom waters 

occupied the bottom ~40 m of the water column. As observed on Line E, salinities and nutrients 

were higher in 2012 than in 2011. Along the northern flank of the canyon, there was a 

chlorophyll fluorescence maximum and regions of oxygen supersaturation. As in previous years 

the area of oxygen supersaturated was shallower than the region of high chlorophyll 

fluorescence. On the southern portion of the transect, stratification weakened, and nutrients were 

depleted throughout the water column. In 2011, ammonium concentrations were elevated in 
OCS Study

BOEM 2015-034

144

bottom waters, with the concentration gradient shoaling at the southern end of the transect. In 

2012, the ammonium maximum was confined to the northern portion of the transect.

FIGURE 73. HYDROGRAPHIC MEASUREMENTS AT BARROW CANYON (LINE F) IN AUGUST 2011. (A) TEMPERATURE, 

SALINITY, SIGMA-T OXYGEN (PERCENT SATURATION) AND CHLOROPHYLL FLUORESCENCE. (B) THE FIVE 

NUTRIENTS (SILICATE, PHOSPHATE, NITRATE, NITRITE AND AMMONIUM) AT THE DISTRIBUTED BIOLOGICAL 

OBSERVATORY LINE.
OCS Study

BOEM 2015-034

145

FIGURE 74. HYDROGRAPHIC MEASUREMENTS AT BARROW CANYON (LINE F) IN AUGUST 2012. (A) TEMPERATURE, 

SALINITY, SIGMA-T OXYGEN (PERCENT SATURATION) AND CHLOROPHYLL FLUORESCENCE. (B) THE FIVE 

NUTRIENTS (SILICATE, PHOSPHATE, NITRATE, NITRITE AND AMMONIUM) AT THE DISTRIBUTED BIOLOGICAL 

OBSERVATORY LINE.

Satellite-tracked drifters

Three satellite-tracked drifters were deployed in collaboration with EcoFOCI/PMEL

(Figure 75). Their drogue was centered below the wind mixed layer, so until the beginning of 

the fall mixing period the drifters were not strongly influenced by the winds. The trajectories of 

the two drifters released between Bering Strait and Pt. Hope showed generally northward flow. 

The other drifter, released near Hanna Shoal, traveled counterclockwise and then to the west. 

After being caught in the ice field (and likely losing their drogues) the drifter trajectories showed 

limited movement indicating that at least through December the ice was resident over the 

Chukchi Sea.
OCS Study

BOEM 2015-034

146

FIGURE 75. TRAJECTORY OF SATELLITE-TRACKED DRIFTERS WITH 25-M-DEEP DROGUES DEPLOYED IN AUGUST 

2011. THE STARS INDICATE THE STARTING LOCATIONS, AND CIRCLES ARE PLOTTED EVERY 10 DAYS. THE DRIFTERTRACK COLORS AND TIME SPANS ARE AS FOLLOWS: DRIFTER 106694, RED, 19 AUGUST–12 DECEMBER 2011; DRIFTER 

106698, GREEN, 27 AUGUST–27 NOVEMBER 2011; DRIFTER 106699, BLUE, 19 AUGUST–11 DECEMBER 2011. ENDING DATES 

INDICATE THE LAST TRANSMISSION OR WHEN THE DRIFTER WAS CAUGHT IN SEA ICE. THE DEPTH CONTOURS ARE 

AT 30, 50, 100, 200, 500 AND 1000 M.

Zooplankton Net Data

The mean net-estimated concentration of adult and juvenile euphausiids was below 1 m-3

with the highest mean concentration occurring during the cold year, 2012 (Table 19). 

Concentrations of younger stages of euphausiids, principally furcilia, were at least an order of 

magnitude higher than adult and juvenile concentrations with no consistent difference among 

cold and warm years. The concentrations of the youngest euphausiid stage captured, calyptopis, 

were low in all years, but in the first year the concentrations were on the order of 1 m-3. The 

presence of calyptopis stage euphausiids suggests recent spawning in the region. Other plankters 

whose acoustic scattering would also be approximated by the elongate scattering model, hyperiid 

and gammarid amphipods, were found in similar concentrations to euphausiids. The mean 

concentration of gammarid amphipods was also highest during 2012, the coldest year; the 

highest concentration of hyperiid amphipods was in 2011. 

Among the copepod taxa, Oithona spp. (not shown) had the highest concentrations. 

Pseudocalanus spp., was very abundant in all years (O 102-103individuals m-3, but the mean 

concentration in the cold year (2012) was about 70% that in the two warmer years). This species 
OCS Study

BOEM 2015-034

147

complex is often identified as important for producing prey for larval fish. Pseudocalanus spp. 

production is thought to be more closely tied to temperature than food availability. Calanus 

glacialis, a “medium-sized” copepod (females 3.5 – 5.2 mm), was found at lower concentrations 

than Pseudocalanus spp. (101– 102individuals m-3), and had its highest concentrations during 

the final cold year, 2012. A congener, Calanus hyperboreus, which is significantly larger and 

normally found in deeper water, was present only in 2011.

We also examined trends in three non-copepod taxa: planktonic molluscs (pteropods) for 

their high acoustic reflectance, chaetognaths for their predatory impact on the younger copepod 

and euphausiid stages, and larvaceans. Under the initial protocol, shelled and naked pelagic 

molluscs were counted together. In 2012, however, they were separated and the shelled 

pteropods dominated. Concentrations of chaetognaths were O 101, and somewhat lower in 2012 

than the other two years. Larvaceans are often considered important for transferring energy from 

small-sized phytoplankton to higher trophic levels. Their mean concentrations spanned three 

orders of magnitude (101– 103) with the highest concentrations observed during the first two 

years. Barnacle nauplii (not shown) were the most abundant taxa captured by our nets.

TABLE 19. MEAN CONCENTRATION OF SELECTED ZOOPLANKTON TAXA (NUMBER PER M3).

Mean No. Per M3 Year

Taxon 2010 2011 2012

Euphausiids

 calyptopis stage 1.18 0.03 0.04

 furcilia stage 9.75 6.61 8.38

 juvenile and adult stages 0.19 0.22 0.95

Organisms with similar acoustic scattering as euphausiids

 Hyperiid amphipods 0.12 2.68 0.52

 Gammarid amphipods 0.11 0.01 1.76

Copepods

 Pseudocalanus spp. 1052.69 1025.19 737.56

 Calanus glacialis 82.08 63.25 290.48

 Calanus hyperboreus 0.00 2.71 0.00

Other non crustacean taxa

 Larvaceans 781.22 1096.01 8.10

 Pteropods (with and without shells) 126.95 11.64 5.05 (4.89 T)

 Chaetognaths 46.71 47.00 27.57

Areal distribution of selected taxa

The distribution of euphausiid calyptopae was very patchy in space and time (Figure 76). 

The highest concentrations were sampled in 2010 and were most often located in the eastern 

portion of the Chukchi shelf. Despite one large sample at the end of Transect A (Pt. Hope line),

most of the youngest stage of euphausiids were found on either Transect D (Icy Cape) or 

Transect E (Wainwright). In 2011 and 2012, calyptopae were found either in or adjacent to 
OCS Study

BOEM 2015-034

148

Transect F (Barrow Canyon). The distribution of the furcilia stage was much less patchy with 

individuals found along each transect in 2010 and 2011 (Figure 77). In 2012, individuals were 

absent from the entire Transects E & F and mostly absent from Transect D. This latter 

observation could be an indication of lower physical transport during the cold year. 

Concentrations of furcilia along the bottom layer were determined in 2011 and were found to be 

comparable and sometimes higher in magnitude to those found throughout the water column 

(Figure 78). Concentrations of furcilia captured at night were not appreciably higher than those 

captured during the day along the same transect lines (Figure 79). The distribution of juvenile 

and adult euphausiids was also very patchy, particularly in the first two years (Figure 80). A 

higher proportion of stations had non-zero concentrations in 2012, with the highest 

concentrations found at the western (Transect A) and eastern (Transect F) extremes of the region. 

Concentrations along the bottom were of the same magnitude as those in the water column, but 

they were found in the bottom layer less often than in the water column (Figure 81). 

The distributions of both hyperiid and gammarid amphipods was also very patchy. The 

number of stations at which we found hyperiids was much greater than that for gammarids. In 

2010, hyperiids were most often found at the offshore extent of our transects (Figure 82). They 

were found both inshore and offshore in other years. Gammerid amphipods were much more 

frequently encountered in 2012, with the highest concentrations along Transect E (Wainwright 

line) and one station on each of Transects A & C (Figure 83). They were frequently found in the 

net fished near the bottom (Figure 84).

Copepods were ubiquitous across the shelf. Pseudocalanus spp. was found at almost 

every station in every year (Figure 85). In 2010, the concentrations were somewhat higher in the 

eastern part of the region on Transects D & E. In 2011 it appeared that the concentrations at the 

western and eastern edges of the sampling region were comparable. In 2012, we found high 

concentrations along Transect E, but much lower concentrations along the other lines. Calanus 

glacialis was also found at almost every station in all years (Figure 86). The lowest 

concentrations were often found in the central transect lines (B & C). The concentrations in 

2012 were higher and much more uniform across the sampling region. Calanus glacialis was 

also found along the bottom, but in lower concentrations than in the water column (Figure 87). 

C. hyperboreus was found only in 2011 and only along Transects E & F in the water column 

(Figure 88). This indicates an intrusion of oceanic waters onto the shelf. During that one year,

C. hyperboreus was found more frequently along the bottom on Transect E than it was found in 

the water column, however, the concentrations were much lower along the bottom.

The non-copepod taxa also had patchy distributions. Larvaceans were abundant and 

more frequently captured in the first two years of the study (Figure 89). They were present at 

almost every station in those years, although their concentrations were low along Transects B & 

C in 2010. In 2012, they were infrequently encountered and their concentrations were low with 

few positive stations in the middle of the region (Transects B, C, and D). Pteropods were most 

frequently encountered in the first year of the study when we also had the highest concentrations 

(Figure 90). 2011 had low concentrations and a low frequency of occurrence. Two of the three 

years they were found along Transect D where our acoustic instruments were deployed. They 

were rarely encountered in the net towed along the bottom (Figure 91; data only for 2012). 

Chaetognaths were also ubiquitous and found at all stations in all years (Figure 92). They had 

high concentrations along Transects A & D in 2010, Transects E & F in 2011, and Transect C in 

2012. In 2012, after changing our protocol, we determined that the majority of the chaetognaths 
OCS Study

BOEM 2015-034

149

were of the genus Parasagitta. Eukrohnia spp. were only found along the western-most line 

(Transect A), except for a single station at the end of Transect B (Cape Lisburne) and in the 

middle of Transect E (not shown).

FIGURE 76. CONCENTRATION OF EUPHAUSIID CALYPTOPAE IN THE WATER COLUMN, 2010 - 2012.
OCS Study

BOEM 2015-034

150

FIGURE 77. CONCENTRATION OF EUPHAUSIID FURCILIA IN THE WATER COLUMN, 2010 - 2012.
OCS Study

BOEM 2015-034

151

FIGURE 78. CONCENTRATION OF EUPHAUSIID FURCILIA ALONG THE BOTTOM, 2010 – 2012.
OCS Study

BOEM 2015-034

152

FIGURE 79. CONCENTRATION OF EUPHAUSIID FURCILIA DAY AND NIGHT, 2010 – 2012. PURPLE SYMBOLS INDICATE 

DAY, PINK SYMBOLS INDICATE NIGHT.
OCS Study

BOEM 2015-034

153

FIGURE 80. CONCENTRATION OF EUPHAUSIID JUVENILE AND ADULTS IN THE WATER COLUMN, 2010 – 2012.
OCS Study

BOEM 2015-034

154

FIGURE 81. CONCENTRATION OF EUPHAUSIID JUVENILE AND ADULTS IN THE BOTTOM LAYER, 2010 – 2012.
OCS Study

BOEM 2015-034

155

FIGURE 82. CONCENTRATION OF HYPERIID AMPHIPODS IN THE WATER COLUMN, 2010 – 2012.
OCS Study

BOEM 2015-034

156

FIGURE 83. CONCENTRATION OF GAMMARID AMPHIPODS IN THE WATER COLUMN, 2010 – 2012.
OCS Study

BOEM 2015-034

157

FIGURE 84. CONCENTRATION OF GAMMARID AMPHIPODS NEAR BOTTOM, 2010 – 2012.
OCS Study

BOEM 2015-034

158

FIGURE 85. CONCENTRATION OF PSEUDOCALANUS SPP. IN THE WATER COLUMN, 2010 – 2012.
OCS Study

BOEM 2015-034

159

FIGURE 86. CONCENTRATION OF CALANUS GLACIALIS IN THE WATER COLUMN, 2010 – 2012.
OCS Study

BOEM 2015-034

160

FIGURE 87. CONCENTRATION OF CALANUS GLACIALIS NEAR BOTTOM, 2010 – 2012.
OCS Study

BOEM 2015-034

161

FIGURE 88. CONCENTRATION OF CALANUS HYPERBOREUS IN THE WATER COLUMN, 2010 – 2012.
OCS Study

BOEM 2015-034

162

FIGURE 89. CONCENTRATION OF LARVACEANS IN THE WATER COLUMN, 2010 – 2012.
OCS Study

BOEM 2015-034

163

FIGURE 90. CONCENTRATION OF PTEROPODS IN THE WATER COLUMN, 2010 – 2012.
OCS Study

BOEM 2015-034

164

FIGURE 91. CONCENTRATION OF PTEROPODS ALONG THE BOTTOM, 2010 – 2012.
OCS Study

BOEM 2015-034

165

FIGURE 92. CONCENTRATION OF CHAETOGNATHS IN THE WATER COLUMN, 2010 – 2012.
OCS Study

BOEM 2015-034

166

Comparison of Zooplankton Estimates From the Tucker Net and a TAPS-6

There was a modest relationship between Sv and zooplankton displacement. Data from 

four of the six frequencies showed a positive relationship between the two variables (Figure 93-

94). The variance, explained by a linear relationship, was highest for the three lowest 

frequencies (r2values ~0.5-0.7, p-values ~ 0.003-0.03) for the 265, 420, and 700 kHz 

frequencies. The amount of variation explained for the three highest frequencies: 1100, 1850, 

and 3000 kHz, was poor. 

This may have been due, in part, to the presence of pteropods in the samples. Shelled 

pelagic molluscs, are hard elastic scatterers and have much higher target strength relative to their

displaced volume compared to other organisms. An example of this can be seen in Figure 93 and 

Figure 94, as indicated by outlier points circled in red on the 1850 and 3000 kHz plots. These 

plots show a large increase in Sv values without a commensurate change in displacement volume.

The inverse-estimated zooplankton abundance from TAPS-6 data using data from all six 

frequencies did not show a significant correlation with the net sample abundance (not shown). 

The inverse-estimated abundance was also several orders of magnitude greater than net 

abundance. This could be the result of incorrect model assumptions, or the inability to model all 

types of scatterers that are captured by the nets. For example, the calcareous-shelled pteropod 

has much stronger target strength as opposed to a fluid-like copepod. Larvaceans were also 

present in high numbers, but are difficult to model because they may or may not be surrounded 

by a mucous mesh house. Additionally, there was no significant correlation between inverseestimated zooplankton abundance and net abundance after the 1100, 1850 and 3000 kHz 

frequencies were removed from inverse analysis. Removing these frequencies showed a 

decrease in estimated abundance with the final result being that the estimated and net caught 

abundances were of the same order of magnitude, but the correlation between the variables was 

still poor.
OCS Study

BOEM 2015-034

167

FIGURE 93. RELATIONSHIP BETWEEN ZOOPLANKTON DISPLACEMENT VOLUME AND MEAN WATER COLUMN 

VOLUME BACKSCATTER IN 2011 AT DIFFERENT FREQUENCIES ALONG THE ICY CAPE TRANSECT. RED CIRCLES 

INDICATE OUTLIERS DUE TO THE PRESENCE OF SHELLED PTEROPODS.
OCS Study

BOEM 2015-034

168

FIGURE 94. RELATIONSHIP BETWEEN ZOOPLANKTON DISPLACEMENT VOLUME AND MEAN WATER COLUMN 

VOLUME BACKSCATTER IN 2012 AT DIFFERENT FREQUENCIES ALONG THE ICY CAPE TRANSECT. RED CIRCLES 

INDICATE OUTLIERS DUE TO THE PRESENCE OF SHELLED PTEROPODS.

We used the acoustic volume backscatter at 420 kHZ as a proxy or index for zooplankton 

biomass and contoured Sv at 420 kHz as a function of depth along the Transect D (Icy Cape) for 

each year. The daytime sections of acoustically estimated zooplankton biovolume were plotted 

along with temperature and salinity (Figure 95-97). In 2010 and 2011, the highest volume 

backscatter was observed within 10-15 m of the bottom where the coldest, most saline waters 

were found.

In 2012, high values of volume backscatter were found in the pycnocline (Figure 97). 

The pycnocline was strongest that year along this transect and zooplankton values decreased 

inshore of a front that separated stratified and well mixed waters. Inshore values of volume 

backscatter in 2010 and 2011 tended to be lower than other parts of the transect, although the 

structure of the water column in those years was very different. In 2010 and 2011, the thickness 

of the bottom layer of zooplankton between 80 and 150 km from the transect origin was greater 

than it was during 2012.
OCS Study

BOEM 2015-034

169

FIGURE 95. ICY CAPE 2010 TRANSECT SECTIONS OF PHYSICAL PROPERTIES AND ZOOPLANKTON. DISTANCE (KM) 

FROM SHORE. TOP – TEMPERATURE (°C, COLOR CONTOURS) AND SALINITY (PSU, LINE CONTOURS); BOTTOM –

ZOOPLANKTON VOLUME BACKSCATTER (SV).
OCS Study

BOEM 2015-034

170

FIGURE 96. ICY CAPE 2011 TRANSECT SECTIONS OF PHYSICAL PROPERTIES AND ZOOPLANKTON. DISTANCE (KM) 

FROM SHORE. TOP – TEMPERATURE (°C, COLOR CONTOURS) AND SALINITY (PSU, LINE CONTOURS); BOTTOM –

ZOOPLANKTON VOLUME BACKSCATTER (SV).
OCS Study

BOEM 2015-034

171

FIGURE 97. ICY CAPE 2012 TRANSECT SECTIONS OF PHYSICAL PROPERTIES AND ZOOPLANKTON. DISTANCE (KM) 

FROM SHORE. TOP – TEMPERATURE (°C, COLOR CONTOURS) AND SALINITY (PSU, LINE CONTOURS); BOTTOM –

ZOOPLANKTON VOLUME BACKSCATTER (SV).
OCS Study

BOEM 2015-034

172

3. Discussion

Our hydrographic data set with transects normal to the coast line provide new insight into 

the physical and chemical oceanography of the Chukchi shelf. These data and those from the 

moorings enable us to refine previous summaries of Chukchi shelf currents, and in particular 

those waters that flow over the lease area. They also allow us to quantify transport and flux of 

heat, salt, and nutrients during the sampling period. For example, we determined that between 

30 - 50% of the transport through Bering Strait, eventually travels along the coast past Icy Cape. 

Variations in flow were highly correlated with the winds. With three years of data we can begin 

to look at variability across the shelf; however, more years of data will be necessary to fully 

capture the inherent variability in the system. The years 2010-2011 had stronger flow, more

polynyas, more incidents of flow up Barrow Canyon, than were observed in 2011-2012 and 

2012-2013. Hydrographic data also revealed that Atlantic water can be seen as far south and 

west as Icy Cape, indicating that slope water can intrude > 200 km onto the shallow Chukchi 

shelf. The Atlantic water was the source of the C. hyperboreus present only in 2011. Of 

particular interest from the hydrographic data were the pools of nutrient-rich waters often found 

offshore in the Bering Shelf waters and other non-Alaska Coastal Current waters. High 

ammonium concentrations between Pt. Hope and Barrow Canyon indicate the effects of active 

remineralization and regeneration of nutrients in the cold Arctic. Subsurface maxima of 

chlorophyll fluorescence were common during the late summer cruises. It was not determined 

whether or not these regions were due to higher fluoresence per cell (photo-adaptation) or if 

there was increased phytoplankton biomass there. However these regions were sometimes 

associated with high percent saturation of oxygen. In those cases it is presumed that the cells 

were actively photosynthesizing and contributing to the total primary production. Sinking cells 

from this subsurface region would continue to seed the benthos and add carbon to support 

secondary production. 

Zooplankton Net Data

The Tucker Sled data showed great interannual and spatial variability. The hydrography 

(temperature, salinity, and location of different water masses) varied among years and the 

zooplankton distributions may reflect this to a large degree. Our data also provided insight into 

“event-scale” phenomena. For example, the warmest year, 2010, had low, but detectable 

concentrations of an early developmental stage of euphausiids, indicating reproduction over the 

Chukchi Sea shelf. Previous work hypothesized that euphausiids in the Chukchi are expatriate 

populations that do not reproduce. Similarly, the presence of low, but detectable concentrations 

of C. hyperboreus, an arctic basin species, are indicative of a major advective event that 

delivered water and organisms onto the Chukchi shelf. The documented variability in 

zooplankton community indicates that the arctic strongly responds to those forces that drive the 

summer physics, chemistry, and biology of this region.

The lack of high concentrations of juvenile and adult euphausiids away from Barrow 

canyon is puzzling. Bowhead and other baleen whales transit through this region in spring and 

fall, and our expectation was that we would find concentrations of euphausiids over the 

continental shelf. While the concentrating mechanism around Barrow Canyon is now well 

described (Ashjian et al., 2010), previous authors hypothesized that there was a “conveyor belt” 
OCS Study

BOEM 2015-034

173

of euphausiids that originated in the Bering Sea and were transported into the Chukchi Sea 

(Berline et al., 2008). Net-based estimates are known to underestimate actual euphausiid 

abundance due to avoidance of the sampler (e.g., Clutter and Anraku, 1968; Sameoto et al., 

1993). Although we did not include a light to “blind” the targets, we did tow our nets on the 

bottom in anticipation that euphausiids would be hard on bottom as observed in the eastern 

Bering Sea (Napp, unpublished data). While many of the taxa we captured had abundances on 

bottom that were greater than or equal to water column abundance, there was no clear evidence 

of a euphausiid conveyor belt. 

Comparison of Zooplankton Estimates From the Tucker Net and a TAPS-6

This exercise was a valuable test to see what, if any, information the older TAPS-6 units 

could provide on plankton abundance, biovolume, and size distribution in an Arctic environment. 

The older TAPS-6 instruments were designed with relatively high acoustic frequencies and low 

sample volume for vertical casts or moored deployment in regions where scattering was 

dominated by relatively small (down to 1 mm ESR), highly abundant, taxa like copepods. In our 

use of the instrument, the inverse-modeling analytical approach using two or three, simple 

scattering models (copepod, euphausiid, and pteropod) did not provide estimates of taxonspecific plankton abundance that closely approximated net caught plankton abundance. We 

attribute this to multiple factors: 1. the complexity of the zooplankton community that includes 

high abundances of organisms such as shelled molluscs, appendicularia, and chaetognaths that 

are difficult to model; 2. the potential contribution of marine snow; 3. the instrument 

configuration which averages multiple pings and saves the average value rather than the raw 

pings; 4. lack of true noise measurements. Comparison of the TAPS-estimated biovolume with 

net-captured biomass was not possible because we lacked wet weight information on the species 

retained by the net. 

There was, however, good agreement between the number of organisms captured by the 

net and the Sv at 420 kHz (R2 = 74; 0.68). Although the water column is shallow throughout 

most of the Chukchi shelf (30-40 m) there is physical structure to the water column with the 

interleaving of different water masses. The structure was different among years with 2012 

showing the highest degree of stratification. Zooplankton can recognize the differing 

temperatures and salinities of these water masses and may align themselves according to their 

preferences. Thus, in the absence of other information, using the Sv at 420 kHz may provide 

insight into the vertical distribution of the zooplankton community, in general, in ways that could 

not be observed with the Tucker sled where a single sample is collected over the entire water 

column.

4. Conclusions

There was no evidence of Bering Sea Water or Anadyr Water along Cape Lisburne 

(Transect B), but some evidence of it at the seaward edge of Point Lay and Icy Cape (Transects 

C & D). The greatest interannual variability was along the Wainwright line (Transect E). 

Subsurface (sub-pycnocline) blooms of phytoplankton were common, but the waters were not 

always supersaturated. Oxygen saturations are likely confounded by rates of primary production, 

respiration, vertical mixing, and warming of the water column. During the time of our surveys, 
OCS Study

BOEM 2015-034

174

the surface was largely depleted of nutrients along all lines. Ammonium concentrations along the 

bottom were often >2 μM. Ammonium is the result of decomposition of organic matter, and is 

the preferred nitrogen source (over nitrate) for some phytoplankton. It can also be converted into 

nitrate through nitrification (ammonium  nitrite  nitrate). Nitrite as an intermediary product 

is usually found at low concentrations, as is observed in this data set.

At the Point Hope transect line (Transect A; which is the same as the Distributed 

Biological Observatory [DBO] Line 3) the high concentrations of ammonium could be regionally

formed or advected from the Bering Sea where high (>6 μM) concentrations are observed during 

summer and fall. Further investigation is necessary to examine timing of the bloom, rates of 

ammonification and regional advection to quantify the sources of ammonium. Vertical 

stratification along this line was strongest in 2010, primarily due to lower surface salinities in 

2010. Farther north on the Chukchi plateau at lines C and D, the ACC appeared to be more 

confined in a narrow band along the coast. The biggest difference between the years was at 

Lines E and F. In 2012, intrusions of high silicate and nitrate were observed on the western 

Barrow Canyon and the shelf west of the canyon. We hypothesize that this is slope water 

intruding up the canyon and onto the shelf, perhaps a result of upwelling; perhaps affecting the 

ACC as well.

Zooplankton community composition showed great variability among years, as well as

evidence for physical events such as advection, which introduced Arctic basin species to the 

shelf. Net-based estimates of juvenile and adult euphausiid concentrations were low and did not 

yield evidence for the conveyor belt hypothesis. Concentrations of the furcilia stage were much 

higher, and in 2010 and 2011 were present at all stations across the shelf. In warm years with 

low summer areal ice extent, euphausiids may reproduce. However, the fate of those progeny is 

not known, and the question of endemic versus expatriate populations still exists. Finally, both 

net and acoustic estimates indicated that zooplankton concentrations are often as high or higher 

near the bottom than they are in the rest of the water column on the Chukchi shelf in summer.

5. Recommendations

It is important to utilize new and varied technologies to better sample this remote and 

difficult region. These include towed vehicles, and autonomous and semi-autonomous vehicles 

such as wave gliders and profiling gliders. The use of multiple-frequency, hull mounted acoustic 

transducers during spring and summer would help us to better map distribution and biomass of 

euphausiids (e.g., De Robertis et al., 2010; Ressler et al., 2012). At the end of ARCWEST, when 

we have additional years of zooplankton data, we should use multi-variate statistical analyses to 

examine variability in zooplankton community composition and the relationship among different 

taxa and the water masses present at the time of sampling. Further investigation is necessary to 

examine timing of the phytoplankton blooms, rates of ammonification, and regional advection to 

quantify the sources of ammonium
OCS Study

BOEM 2015-034

175

IX. CLIMATE MODELING

1. Methods

The speed of changes in Arctic sea ice cover in the last decade were unexpected, as the 

consensus of the climate research community just a few years ago was that such changes would 

not be seen for another thirty years. The modeling component of this project aims to provide 

projections of future sea ice and ocean conditions in the Chukchi Sea based on coupled climate 

models. There are two parts to the climate modeling aspect in this study.

The first part is climate models assessment. Models that contributed to the Coupled 

Model Intercomparison Project, Phase 5 (CMIP5) were used in this study. We obtained all 

available sea ice concentration simulations from the Program for Climate Model Diagnosis and 

Intercomparison (PCMDI) at the Lawrence Livermore National Laboratory. Model simulations 

of historical sea ice extent were used for assessment purposes. We compared the climatological 

mean and the magnitude of the seasonal cycle of the Arctic sea ice extent with observations 

following Wang and Overland (2009, 2012). Models that passed both criteria were further 

evaluated for the Chukchi Sea region, and used for future projections. Projections under two 

emission scenarios, named Representative Concentration Pathways (RCP4.5 and RCP8.5) are 

used for future projections. These represent low and high emission scenarios, respectively. The 

reason we did not include the RCP6.0 in the ensemble discussion is because not all the models 

submitted their RCP6.0 simulations, and we wanted to keep the number of models consistent. 

The second part is to study the impact of changing initial sea ice conditions to the future 

projections. One of the better CMIP5 models, the National Center for Atmospheric Research 

(NCAR) CESM1.0 (a newer version of CCSM4) was used in this part of the study. From 

CCSM4 (RCP6.0) future runs, we selected one September with low sea ice extent in the Arctic 

resembling the lowest value observed (4.23 million km2in model) in recent years. Taking this as 

current sea ice condition (year 2011), we then re-ran CESM1 for another 30 years under the 

RCP6.0 emission scenario starting from Sept. 1, 6, and 11, 2011, respectively. One of the 

reasons to choose the RCP6.0 scenario is because this is a medium emission scenario. The new 

30-year CESM runs (2010-2040) represent projections of the ice conditions after the new record 

low was reached.

2. Results

Based on our careful evaluations, we found that all of the selected 12 CMIP5 models are 

doing decent jobs in terms of simulating the sea ice cover over the Chukchi Sea (Wang and 

Overland, 2015). According to these 12 models, the length of open water duration will be 

prolonged over the entire Chukchi Sea, under the RCP8.5 emission scenario, although there is an 

evident north-south gradient. As shown in Figure 98, the shading indicates the number of 

months with sea ice present at each grid point within a calendar year averaged over the 12 

selected models. A grid box is considered to be ice-covered when its sea ice concentration is 

more than 15%, following normal convention. In the past decade (2004 – 2013) north of 75° N, 

there were 12 months of ice cover at almost all grid points in the Alaskan Arctic based on Hadley 
OCS Study

BOEM 2015-034

176

sea ice analysis (http://www.metoffice.gov.uk/hadobs/hadisst/data/download.html). Near the 

Bering Strait, the average sea ice cover lasted about 7 months/year in the last decade. The mean 

sea ice durations for the decade centered on 2010 from the selected 12 climate models (all 

interpolated to the same 0.5° x 1° resolution latitude-longitude grid; Figure 98), show similar 

structure compared with observations (i.e., the overall feature of ice presence is well captured by 

these models). As time progresses, the changes from 2010 to 2020 are small in the Alaskan 

Arctic, except near the coast in the Beaufort Sea and in the east Siberian Sea under the RCP8.5 

(high) emission scenarios. From 2030 to 2040, the change is obvious with most of the northern 

Chukchi Sea and the Beaufort Sea having 11 months of ice coverage instead of 12 (Figure 98). 

By the decade centered in 2050, the northern Chukchi Sea and the Beaufort Sea would have ice 

presence only up to 10 months (Figure 98). This implies that the northern Chukchi Sea (near 

latitude 80° N) would shift from current year-round sea ice cover to seasonal open water of 1-2 

months. An acceleration of ice reduction can be inferred from these plots in the middle of the 

21st century. In the southern Chukchi Sea, north of the Bering Strait to 70° N, sea ice cover will 

be reduced from eight to nine months coverage at present to five to six months of coverage by 

2040 (Figure 98), i.e., more than half a year of open water as predicted by these models.
OCS Study

BOEM 2015-034

177

FIGURE 98. DURATION OF ICE COVER AT EACH GRID POINT BASED ON HADLEY SEA ICE ANALYSIS (TOP PANEL) AND 

MODEL SIMULATIONS FOR PRESENT (2010) AND FUTURE UNDER RCP8.5 EMISSION SCENARIOS (REMAINING PANELS). 

RESULTS ARE SMOOTHED WITH A 9-YR MEAN WITH THE LABELED YEAR INDICATING THE CENTER OF THE DECADE. 

MODEL RESULTS ARE BASED ON THE ENSEMBLE MEANS OF THE 12 SELECTED MODELS.
OCS Study

BOEM 2015-034

178

The change in the north-south gradients of the open water duration can be clearly seen 

from Figure 99, which shows the zonally averaged length of open water duration at each latitude. 

Currently there are 0-4 months of open water duration in the Chukchi Sea. As time progress, the 

difference between the north and south will reduce, and the entire Chukchi Sea will have more 

than 7 months of open water by the end of the 21st century. Around the 2040s, we may see one

month of open water in the northern Chukchi Sea by 80° N, where there is none at present.

Similarly, there is one more month of open water duration in the Chukchi Sea from north to 

south at other latitudes.

FIGURE 99. TIME SERIES OF ZONALLY AVERAGED NUMBER OF SEA ICE-FREE MONTHS OVER THE CHUKCHI SEA

BASED ON THE 12 MODELS’ PROJECTIONS UNDER EMISSION SCENARIO RCP8.5.

Figure 100 shows the historical and future projected monthly sea ice extent time series 

averaged over the Chukchi Sea (bounded by 65° - 80° N, and 175° E - 157° W). As shown in 

the historical time series, the Chukchi Sea is now completely covered by sea ice for nearly seven 

months of the year, from December to June. Sea ice starts to retreat in June, though more than 

90% of the Chukchi Sea is still covered by sea ice during that month. This retreat progresses 

slowly, and by August, the ice cover can still be as much as 60% of the Chukchi Sea (except in 

2007, which had record low ice cover). The mean sea ice cover in September has had a large 
OCS Study

BOEM 2015-034

179

reduction in recent years (2007-2012), with some recovery in 2013 (Figure 100; the black curve 

in right panel of 3rd row). In recent years, there has been ice cover only up to 21% of its 

climatological mean of 0.60 million km2. As a result, the following October also shows ice 

reduction in recent years, as severe as September. The Chukchi Sea was nearly ice free during 

September 2007 and 2012 in the observed sea ice extents, but the recovery in October 2012 was 

faster than 2007. Some of the individual runs from these 12 models seem to catch this kind of 

sudden drop. Since what actually happened is only one single realization of the model, we 

should not expect the ensemble mean to represent the observed time-series. At the beginning of 

the 21st century, the slope of the ensemble mean (red and blue) is relatively flat, but it shows 

increased decline near the middle of the 21st century. Near the end of the 21st century, a clear 

departure between the ensemble mean of the selected 12 models (red/blue) and the mean of all 

members of 37 models (magenta/green) is seen. The selected models show faster decline under 

both emission scenarios (red for RCP8.5 and blue for RCP4.5).
OCS Study

BOEM 2015-034

180

FIGURE 100. TIME SERIES OF MONTHLY SEA ICE EXTENT FOR THE CHUKCHI SEA (65° - 80° N, AND 175° E - 157° W). 

THE THIN COLORED CURVES ARE COMBINATIONS OF HISTORICAL RUNS AND PROJECTIONS UNDER EMISSION 

SCENARIOS RCP4.5 (LIGHT BLUE), AND RCP8.5 (PINK). THE SOLID BLACKCURVES ARE BASED ON THE HADLEY SEA 

ICE ANALYSIS, HADISST_ICE. THE THICK SOLID RED/MAGENTA CURVES ARE THE ENSEMBLE MEANS OF THE 

SELECTED 12 MODELS (ALL MODELS) UNDER EMISSIONS SCENARIOS RCP8.5, AND THE SOLID BLUE/GREEN CURVES 

ARE CORRESPONDING MEANS UNDER EMISSIONS SCENARIO RCP4.5.
OCS Study

BOEM 2015-034

181

The CCSM4 (slightly older version of CESM1.0) sea ice simulation shows some 

improvement compared to its precursor, CCSM3 (part of CMIP3), as we found at the beginning 

of the project. This is rather encouraging. By the time we were ready to execute our own 

simulations with the computational support obtained from the NCAR super computer, NCAR 

had upgraded their model to CESM version 1.0 and removed the support of CCSM4. We 

therefore did our simulations using the most updated version, CESM1.0. Three runs were 

carried out and each lasted 30 years under the emission scenarios RCP6.0. Even though we 

started with low sea ice cover at the beginning of the simulation (September 2010), soon there 

were no differences between our runs (blue dashed lines) and the original CCSM4 runs (purple 

lines) as shown in the ice thickness time series in Figure 101. This indicates that ice can rebound 

from an extremely low condition within a year. However, as several studies have pointed out, in 

the past decade, we have been losing thick, multi-year sea ice in the Arctic (Kwok et al., 2009;

Kwok and Untersteiner, 2011). In other words, the quality of the ice has been reduced. 

Although the overall ice thickness has been reduced, the projected ice thickness in the small 

region around the mooring locations seems to be relatively stable as projected by CESM1, shown 

in Figure 101. In order to validate the model results, ice thickness obtained from C1-C3 

moorings deployed in the Chukchi Sea (C1: 70.8°N, 163.2°W, C2: 71.2°N, 164.2°W, and C3: 

71.8°N, 166.0°W) are also plotted in Figure 101 for a comparison. By converting ice draft 

observed to ice thickness (colored squares/circles in Figure 101), we found that the CESM1.0 

model actually simulated the ice thickness reasonably well (Figure 101). Model simulations 

indicate that there is quite large interannual variability of ice thickness in the Chukchi Sea. At 

the beginning of ice formation, the averaged ice is less than a half-meter thick (December), and 

ice gradually grows to 0.5-1 m thick in January. From February to June the average ice 

thickness is between 1-2 m thick with relatively large interannual variability. There is no 

significant trend in the next 20-30 years according the CESM under RCP6.0 emission scenario.
OCS Study

BOEM 2015-034

182

FIGURE 101. CESM1.0 SIMULATED MONTHLY ICE THICKNESS AVERAGED OVER A SMALL BOX AROUND THE 

MOORINGS IN THE CHUKCHI SEA. COLORED CIRCLES ARE OBSERVED MEDIAN ICE THICKNESS AND COLORED 

SQUARES ARE OBSERVED MEAN ICE THICKNESS AT THE 3 CHUKCHI SEA MOORINGS (C3-OFFSHORE, BLUE; C2-

MIDSHORE, GREEN; AND C1-INSHORE, RED).
OCS Study

BOEM 2015-034

183

Besides sea ice conditions, we also investigated the ocean conditions simulated by 

CESM1.0, and the results are very encouraging. Figure 102 shows the spatial distribution of 

annual mean ocean currents simulated by the model. The broad features of the northward flow 

through the Bering Strait, as well as the splits of the currents into the Chukchi Sea are clearly 

seen from Figure 102.

FIGURE 102. ANNUAL MEAN OCEAN CURRENT SIMULATED BY CESM. RED CIRCLES ARE THE C1-INSHORE, C2-

MIDSHORE, AND C3-OFFSHORE MOORING LOCATIONS.

The monthly mean ocean currents at 30 m depth agree well with observations as shown 

in Figure 103. The closest points of the model simulated ocean currents are chosen to be 

compared with those observed at the three mooring locations. Apparently, both the directions 

and the magnitude of ocean currents are well captured by the model runs.
OCS Study

BOEM 2015-034

184

FIGURE 103. OCEAN CURRENT AT 30 M DEPTH FROM OBSERVATION (RED) AND MODEL SIMULATION (BLACK). EACH 

BLACK STICK REPRESENTS 1-YR SIMULATION FROM THE FIRST DECADE RUN (2010-2020). TOP PANEL CORRESPONDS 

WITH THE C3-OFFSHORE; C2-MIDSHORE; C1-INSHORE MOORINGS.

The ocean temperature and salinity from the CESM1 model runs and those observed at 

moorings were also compared. Figure 104 shows that the model captures the seasonal variation 

of the ocean temperature well, especially at the C3 mooring (top left panel of Figure 104). Close 

to shore, the temperature variations are also captured well by the model runs, but the summer 

temperature is higher than the observed (left middle panel). Models seem to show larger 

temperature gradients in the spring and fall seasons compared with the observations. As for the 

salinity, the model results are less satisfactory. The model underestimates the salinity values 

year round at all three mooring locations (right panels of Figure 104), and the twin peak feature 

of the seasonal salinity variation is missed by the model runs. Yet, the model is able to capture 

the May salinity peak even though the value is still less than the observations.
OCS Study

BOEM 2015-034

185

FIGURE 104. OCEAN TEMPERATURE (LEFT; C) AND SALINITY (RIGHT; PSU) AT 40 M DEPTH FROM OBSERVATION 

(RED) AND MODEL SIMULATION (GREEN/BLUE) AT THE C3-OFFSHORE MOORING (TOP); C2-MIDSHORE MOORING 

(MIDDLE); AND C1-INSHORE MOORING (BOTTOM).

3. Discussion

Over the past decade there has been rapid decline of Arctic sea ice cover, yet the largest 

changes happened in the Alaskan Arctic, i.e., the Chukchi Sea and the Beaufort Sea (Wang and 

Overland, 2015). The reduced sea ice cover will impact not only the climate system, but also the 

components of the ecosystem in the region, and people who live along its shores. Climate model 

simulations show that there will be reduced ice cover, particularly in the fall season in the 

southern Chukchi Sea. This is consistent with the model projected ocean temperature changes in 

the next 20 years, shown in Figure 105. Generally speaking, the ocean temperature in the 2020s 

(magenta dashed lines; left panels) will be higher than that in the 2010s (green solid lines; left 

panels, Figure 105). As discussed earlier, the CESM1 model generally captures the seasonal 

cycle of the ocean temperature changes at the three moorings (C1-C3) near Icy Cape. Yet it 

shows that the temperature in the second decade is slightly higher, although there is quite large 

interannual variability. As for the salinity (right panels of Figure 105), there are no obvious 

changes from the 2010s to 2020s as all the magenta lines and green lines overlaid each other. 

We also found that the direction and magnitude of ocean currents will stay about the same at 
OCS Study

BOEM 2015-034

186

these locations (figure is not shown) compared to present day. Large interannual variability 

dominates the flow in the region.

FIGURE 105. MODEL SIMULATED OCEAN TEMPERATURE (LEFT; C) AND SALINITY (PSU; RIGHT) IN CURRENT 

DECADE, 2010-2019 (GREEN) AND NEAR FUTURE, 2020-2029 (MAGENTA) BASED ON CESM1 UNDER EMISSION SCENARIO 

RCP6.0. SOLID RED LINES ARE OBSERVED VALUES OBTAINED FROM ICY CAPE MOORINGS: TOP = C3-OFFSHORE,

MIDDLE = C2-MIDSHORE, BOTTOM = C1-INSHORE.

4. Conclusions

We discovered that the anthropogenic forcing is more important than the initial 

conditions on the decadal scales. The sea ice extent declines fast in the fall (August to 

November) and will continue to decline in the future as simulated by the models. This indicates 

that sea ice will arrive the Southern Chukchi Sea later and later. This pattern has already been 

occurring in the past decade or so (Wood et al., 2015). Although the ice cover seems to be 

relatively stable in the spring (May-June) as shown in the ensemble means, there are episodic 

events of early retreat (e.g., the thin red lines in Figure 100, in June). The increasing frequency of 

these early retreat events will especially become evident after 2050.

There is a weak declining trend in the simulated Chukchi sea ice extent in the fall, but not 

in the spring in the next 20-30 years. The sea ice will remain at its current level up to 2040 in the 

Chukchi Sea in winter, but a larger decline in the fall is expected. The change in projected sea 

ice conditions is consistent with model projected ocean temperature change; that is, fall has the 
OCS Study

BOEM 2015-034

187

largest temperature increase compared with other seasons. We may see ice form later, 

particularly in the southern Chukchi Sea. Based on selected CMIP5 models, the future duration 

of the open water season will be longer by about 1-month around the 2040s in the Chukchi Sea, 

and the gradients of the length of the open water season will be reduced between north and south. 

For the ice thickness, at any given year, the long term trend is overwritten by the large 

interannual variability, which is shown by our new, reduced ice cover initialized runs. We found 

that they are almost indistinguishable between our new CESM1 runs and CCSM4 runs. The 

CESM1.0 model is able to capture the ocean current well in both direction and magnitude. The 

ocean temperature was simulated well by CESM1, yet improvements are needed in the ocean 

salinity simulations, as the model missed the fall peak at all three mooring locations.

5. Recommendations

Models are built upon physical rules and are the only tools we have to make objective 

projections for the future climate state. Although not perfect, models are able to capture the 

main features of the ocean circulation, ocean temperature, and the sea ice cover and thickness in 

good agreement with observations. The ice-ocean-atmosphere is a dynamically coupled system, 

and we need to rely on models to help us understand the physical processes. In situ observations 

are important sources for us to validate model results, and can help us to further improve our 

models. We need to keep both teams working together.
OCS Study

BOEM 2015-034

188

X. CORRELATION OF MARINE MAMMAL DISTRIBUTION TO 

BIOPHYSICAL PARAMETERS

1. Methods

Moored observations

Gaussian distribution Generalized Additive Models (GAMs) were utilized to assess the 

effects of oceanographic conditions on marine mammal distribution and the presence of ice 

noise. GAMs were chosen because they are particularly well suited to multi-variate, nonnormally distributed and nonparametric datasets, and because of their flexibility with non-linear 

relationships. Our data contain a considerable amount of zero values (especially for calling 

activity) making the use of GAMs a necessity. One drawback of GAMs, however, is that their 

interpretation is not as straightforward as that from linear models.

Analyses were run in the R programming language (R Development Core Team 2012), 

and the models were fitted using the gam function from the mgcv package (Wood 2006). Large 

differences in calling/noise activity were seen among the three mooring locations (Section: 

VII.A.2), indicating each location should be analyzed separately. For each of the three locations 

– inshore, midshore, and offshore – a GAM was fit to seven passive acoustic data results: the 

seasonal calling activity data for each of the five key Arctic marine mammal species, to the 

seasonal calling activity data specific to bowhead whale gunshot calls, and to the seasonal ice 

noise activity data. This resulted in 21 full GAM runs in total. 

There were 32 different biophysical measurements available to include for each GAM 

run (XVIII.H), which would have resulted in the generation of 4.3 billion models for each of the 

21 runs. To reduce this to a more practical and manageable (i.e., non-supercomputer) size, a 

number of assumptions and protocols were enacted to keep the process consistent and logical.

First, we eliminated the 8 TAPs measurements from the base set of runs. There was only 

one mooring location (midshore) where these data were collected and at that location the 

instruments operated for just 36 days. This TAPS6-NG data set was run separately: 36 days at 

midshore location for the 7 sound sources, including the following additional variables (full 

column and bottom for each): total zooplankton biovolume (mm3/m3), euphausiid abundance

(No./m3), total volume backscatter (420 kHz, Sv, full column: dB re 1 (m2 m-2); bottom: dB re 1 

m-1), and total volume backscatter (50kHz, Sv, full column: dB re 1 (m2 m-2); bottom: dB re 1 m1

). Further details on the methods, and the results are presented in Appendix H (see Section

XVIII.H).

Second, we eliminated the two measurements of surface currents (u-vector and v-vector) 

because of the near-uniformity of the currents throughout the water column, and the strong 

correlation between the winds and surface currents (Stabeno et al., in prep). To ensure this 

assumption was valid, we completed a third set of runs replacing bottom currents with surface 

currents; the results of this comparison are included in Section X.2 below.

Lastly, there were two sets of variables, ice thickness and bottom oxygen levels, where 

the differences among the variables were just differences in measurement type, not in the 

variables themselves. That is, the three measures of ice thickness, for example, were directly 

correlated and would bias the model outcome if all three were included in the GAM runs. To 
OCS Study

BOEM 2015-034

189

eliminate this bias, we ran a set of single-variable GAM runs for each of the location/sound 

source combinations. For each combination we selected the measurement type that provided the 

highest explanatory power for the variance in seasonal calling (or noise) activity based on the

lowest AIC (Akaike’s Information Criterion) value. The measurements selected for each of the 

21 location/sound source combinations are listed in Table 20.

These steps reduced the number of variables to a total of 19. In addition to the ice 

thickness (m) and oxygen (either % saturation or mMol kg-1) variables selected from Table 20, 

the following 17 variables (with their units in parentheses) were included: month, year, 

zooplankton volume backscattering [bottom- ADCP, Sv], zooplankton volume backscattering

[full column-ADCP, Sv], ice concentration (%), chlorophyll (μg/L), PAR (mEin cm-2s-1), 

temperature (°C), salinity (psu), bottom currents (u-vector, m/s), bottom currents (v-vector, m/s), 

transport (Sv), bottom nitrate, turbidity (FNU), winds (u-vector, m/s), winds (v-vector, m/s), and 

windspeed (m/s). For the u and v vectors listed above, u is in the east/west direction and v is in 

the north/south direction. These 19 variables resulted in the generation of a more manageable 

500,000 models for each of the 21 GAM runs.

For each of the 21 GAM runs, we ranked the resulting half-million models as follows. 

First, we sorted the models using their AIC values (lowest to highest) and their R-squared values 

(highest to lowest). Then we looked at sample size used for each model (i.e., the number of days 

included in that model), and eliminated any that were less than 100 on the assumption that these 

models were not representative of the data. Finally, we chose the top AIC and top R-squared 

models from those remaining on these lists. These are identified in Table 21, along with their Rsquared values (%) and the sample size of data used for that model. The best model for each 

GAM run provided the best fit to the data (AIC) and explained the most variance in the data (Rsquared).

The differences in sample size are the result of the GAM protocol: any instance of an 

“NA” in the dataset (meaning data are not available for that variable for that day) led to the 

removal of that entire day from the GAM. Because our dataset is made up of measurements 

collected from multiple instruments at multiple locations with various failure dates, some models 

contained larger datasets than others. A caveat of our analysis is that the overlap between the 

passive acoustic results and the oceanographic measurements is sometimes patchy (especially at 

the inshore and offshore locations); as a result the top models selected may not be the best suited 

to explain the variability seen in the calling activity distributions, but they are the best models for 

these data at this time. As the BOEM-funded ARCWEST and CHAOZ-X data are added to the 

dataset, this patchiness will be reduced and the GAM results can then be updated. The actual 

time periods of data used in the top AIC model for each of the location/sound source runs are 

included as yellow bars in the figures included in Appendix G, Section XVIII.G.

Because of the large number of possible models for each GAM run (i.e., a half-million), 

we also wanted to look at what variables consistently came out on top, not necessarily just in the 

top AIC model. To this end, we selected the top five models from the AIC and R-squared lists 

(Cerchio et al., 2014), and compiled a list of variables which had P-values that were significant 

to < 0.01 and those that had P-values that were significant to < 0.05. To make these lists, a 

variable had to show that level of significance for three or more of the top AIC models as well as 

three or more of the top R-squared models.
OCS Study

BOEM 2015-034

190

For the results that follow, we’ve included a table that shows both the variables that

contributed to the top AIC model, color-coded by significance, and the common significant 

variables for that location/sound source combination (Table 22-27).

In addition to the GAMs, calling presence for each species was plotted against eight 

oceanographic variables (ice concentration, ice thickness fluorescence, oxygen, nitrate, salinity, 

wind speed, and transport) to determine any positive or negative correlations on a temporal scale. 

When individual variables had more than one possible measurement (e.g., ice thickness: average, 

median, or standard deviation), the measurement that was included in the GAM was the 

measurement plotted with marine mammal calling presence. Also indicated on these plots (e.g., 

Figure 106) are times where there were no data.

TABLE 20. MEASUREMENTS OF ICE THICKNESS AND BOTTOM OXYGEN USED IN THE GAM RUNS

Inshore Midshore Offshore

Ice 

Thickness Oxygen

Ice 

Thickness Oxygen

Ice 

Thickness Oxygen

Gunshot Call Average % Sat. Average % Sat. Median % Sat.

Bowhead Median % Sat. Average % Sat. Average mMol kg-1

Gray Std. Dev. % Sat. Std. Dev. % Sat. Average % Sat.

Walrus Std. Dev. mMol kg-1 Std. Dev. mMol kg-1 Average % Sat.

Beluga Median % Sat. Std. Dev. % Sat. Median mMol kg-1

Bearded Std. Dev. % Sat. Average % Sat. Median % Sat.

Ice Noise Std. Dev. mMol kg-1 Average % Sat. Median mMol kg-1

Shipboard observations

Oceanographic and zooplankton results from the shipboard transect lines were correlated 

with the visual and passive acoustic (sonobuoy) data. Measurements of temperature, salinity, 

nitrate, and ammonium were plotted with zooplankton (Pseudocalanus, C. glacialis, larvaceans, 

and pteropods) concentration. These were then plotted with sonobuoy effort and detections as 

well as visual sightings for gray whales, walrus, and bearded seals. Only those transects and 

years where marine mammal species were detected on transect were included, thus some species 

may not have plots of transect line summaries (e.g., bowhead, beluga). As noted earlier, CTD 

data collected in 2010 were a more coarse spatial resolution than the two following years, with 

data collected only in the upper 30 m, and discrete samples collected at three depths: surface (1 

m), 20 m, and 30 m.

2. Results

While the results of the GAM models were run for all three locations, the midshore data 

had the largest sample sizes spanning the longest timeframes, and had the most consistent RSquared values between the top AIC and top R-Squared models for each run. Furthermore, the 

midshore location is positioned between the Burger and Klondike study areas, and as such is best 

suited for representing the area of interest for this study. Therefore, the GAM results for all 

locations will be listed in the tables below, but the description of these results will focus on the 
OCS Study

BOEM 2015-034

191

midshore location unless otherwise stated; any significant differences between the three locations 

will be described. It is also important to note that reference to any correlations regarding the 

long term plots are qualitative in nature, winds were estimated from the midshore location only, 

and transport was averaged between the three locations.

Table 21 represents a summary of the top models (lowest AIC and greatest R-squared),

their AIC and R-squared values and sample size, as well as the variables with significant (i.e. P < 

0.01 and P < 0.05) values that were consistent between the top five AIC and top five R-squared 

models. As mentioned in the methods section above, because of the patchiness in the available 

passive acoustic and biophysical data sets, it is important to understand which time periods were 

used for the top AIC models selected by the GAMs. Plots indicating these time periods in the 

long-term passive acoustic data are included in Appendix G, Section XVIII.G. 

The results below are organized by species/sound source. For each species/sound source 

we present the GAM results, followed by the long-term qualitative comparison plot, and the 

short-term transect line qualitative comparison plot (if applicable). Finally, the GAM results for 

the surface vs. bottom current comparison and TAPS6-NG-only runs are briefly described.

Bowhead whale

Table 22 presents the results of the GAM runs for bowhead whales. For all three 

locations, month and ice concentration were significant, with month extremely significant. At 

the midshore location, which had the highest sample size included in the GAM, month was 

significant to p < 5.16 E-29. The common significant variables between the top AIC and top RSquared models were chlorophyll, bottom currents (V), ice concentration, month, turbidity, and 

wind speed for the midshore location. The best model for the midshore GAM run included ten 

variables, of which chlorophyll, bottom currents (V), ice concentration, month, turbidity, and 

wind speed were all highly significant. It is important to note that because the GAM models 

exclude all days where data is missing for any variable, only certain time spans were included for 

each of the GAM runs. While most of the midshore bowhead calling activity data were included, 

the two spring peaks were excluded for both years. Only the fall migration was included for both 

the inshore and offshore data, a result of large gaps among oceanographic variables (See 

Appendix G, Section XVIII.G, for time spans included).

Bowhead whale calling activity as it relates temporally to ice, oceanographic variables, 

nutrients, transport, and wind speed is presented in Figure 106-108. There was a strong 

association with ice; calling activity would peak just before the ice concentration reached 100% 

and while ice thickness was less than 0.5 m. However, bowhead whale calling activity at the 

inshore location in the spring began when ice concentration started to decrease, even when ice 

thickness was still over 1 m (Figure 106). Furthermore, calling activity would also increase 

when polynyas formed (Figure 106). There is a strong correlation between bowhead whale 

calling activity and chlorophyll, most notably at the midshore location (Figure 107). There is 

also a strong correlation between bowhead whale calling activity and wind speeds, with high 

winds in fall and low winds in spring. This is most evident when bowhead whale calling activity 

is plotted against wind vectors for the midshore location (Figure 109). In the fall of both years, 

bowheads have an association with strong winds to the SSW, with the one exception of the third 

pulse in the fall 2010 migration.
OCS Study

BOEM 2015-034

192

TABLE 21. SUMMARY OF THE TOP VARIABLES, THE AIC AND % R-SQUARED VALUES, AND THE SAMPLE SIZE, FOR 

BOTH THE TOP AIC MODEL AND THE TOP R-SQUARED MODEL, AND THE COMMON SIGNIFICANT VARIABLES AMONG 

THE TOP FIVE AIC AND R-SQUARED MODELS FOR EACH SPECIES OR SIGNAL.

While no clear associations are present with the salinity and nitrate data, it is interesting 

to note that both salinity and nitrate were increasing during the fall 2010 migration, and 

decreasing in the fall 2011 migration. It is possible this may have some relationship with the 

three migration pulses that are evident in fall 2010 but not 2011; however, more data are needed 

to determine this. Only two reports of bowhead whales occurred along the transect lines, one 

visual sighting and one acoustic detection along Wainwright in 2010. As a result, bowheads 

were not included in the transect line plots.

Inshore Midshore Offshore

Top AIC I + S + U + R + N + t + n + M + G A + I + F + P + S + V + N + C + n + M A + I + P + C + t + M + H + O

AIC, % R2, n 1664, 39.8%, 182 3926, 50.0%, 452 961, 46.8%, 103

Top R2 Q + I + F + T + U + R + C + n + M + G Q + I + F + V + N + C + u + n + M + H + G Q + I + T + S + U + N + u + n + M + O

AIC, % R2, n 1788, 56.9%, 200 4283, 50.3%, 492 1832, 74.2%, 246

Sig < 0.01 I n M I V C n M M

Sig < 0.05 I R n M I F V C n M I M

Top AIC P + N + n + G A + S + N + C + n + M I + U + V + N + M

AIC, % R2, n 750, 8.7%, 182 2398, 7.0%, 452 549, 70.0%, 247

Top R2 I + S + V + C + n + M A + S + V + N + C + n + M I + S + U + V + N + M

AIC, % R2, n 1064, 12.5%, 235 2399, 7.0%, 452 549, 70.0% 247

Sig < 0.01 - S M I M

Sig < 0.05 - S n M I U V M

Top AIC I + F + T + C + n + M A + I + F + P + T + S + N + C + n + M A + I + P + S + C

AIC, % R2, n 1279, 21.9%, 200 2180, 15.1%, 452 37, 9.3%, 103

Top R2 A + I + T + U + N + u + n + M A + I + F + P + T + S + N + M A + I + P + S + N + n

AIC, % R2, n 2261, 46.1%, 288 2271, 17.8%, 460 1109, 12.3%, 247

Sig < 0.01 M F P M A 

Sig < 0.05 I T M F P T M N A

Top AIC I + F + T + S + C + t + n I + T No suitable models

AIC, % R2, n 148, 17.2%, 200 303, 0.6%, 550 -

Top R2 T + S + R + N + u + n + M I + T + S + U + V + R + N + C + G No suitable models

AIC, % R2, n 811, 20.7%, 182 335, 2.7%, 453 -

Sig < 0.01 T - -

Sig < 0.05 T I -

Top AIC P + T + N + n + K A + Q + F + P + S + N + u + n + M + O P + C + M

AIC, % R2, n 1097, 26.8%, 182 2882, 41.5%, 452 696, 12.5%, 103

Top R2 I + F + T + S + u + t + n + M A + Q + I + T + S + t + n + M + O Q + R + N + M + H + G

AIC, % R2, n 3921, 66.5%, 497 3521, 50.4%, 495 2053, 32.5%, 246

Sig < 0.01 n Q n O M

Sig < 0.05 n Q S M n O M

Top AIC Q + P + S + U + V + N + t + n + M A + Q + I + S + V + R + N + C + n + M + G A + Q + I + P + C + t + M

AIC, % R2, n 1492, 79.4%, 182 3771, 85.0%, 452 215, 14.2%, 103

Top R2 Q + V + R + N + n + M + K Q + I + T + R + t + n + M + H A + S + V + C + t + M + J

AIC, % R2, n 2273, 88.9%, 279 4380, 86.7%, 530 2063, 92.0%, 272

Sig < 0.01 V n M Q R n M -

Sig < 0.05 V n M Q I R n M A M

Top AIC Q + I + S + U + R + N + u + n A + I + F + P + S + N + C + u + t + n + M I + P + S + C + J

AIC, % R2, n 1539, 33.6%, 182 3002, 24.1%, 452 568, 25.6%, 103

Top R2 S + U + R + C + u + n + M A + I + F + P + U + N + C + u + t + n + M + H I + P + C + M + J

AIC, % R2, n 2152, 48.5%, 235 3243, 25.9%, 491 569, 26.1%, 103

Sig < 0.01 - I F M -

Sig < 0.05 U I F M J

Top 5 Models: 

Common Vars* Ice NoiseTop 1 Models

Top 5 Models: 

Common Vars* Gray Whale

Top 1 Models

* To be included, variable had to be in at least 3 of each set of models. I = ice concentration (%); S = salinity (psu); U = U bottom currents (cm s-1); R = transport (Sv, 

averaged across all locations); N = nitrate (bottom); t = V winds at midshore location; n = wind speed at midshore location; M = month; G = bottom O2 (mMol kg-1); A = 

ADCP (600) column Sa (area backscattering dB re 1 m-1); F = chlorophyll (fluorescence); P = PAR (mE in cm-2 s-1); V = V bottom currents (cm s-1); C = Turbidity 

(FNU); H = average ice thickness; O = % oxygen saturation; Q = ADCP (600 kHz) bottom Sv (volume backscattering dB re 1 m-1); T = temperature (°C); u = U winds at 

midshore location; K = standard deviation ice thickness; J = median ice thickness Bowhead Whale Top 1 Models

Top 5 Models: 

Common Vars* WalrusTop 1 Models

Top 5 Models: 

Common Vars* Gunshot Call Top 1 Models

Top 5 Models: 

Common Vars* Beluga Whale Top 1 Models

Top 5 Models: 

Common Vars*

Top 5 Models: 

Common Vars* Bearded Seal

Top 1 Models
OCS Study

BOEM 2015-034

193

TABLE 22. BOWHEAD WHALE RESULTS OF THE GAM MODEL. RESULTS REPRESENT THE TOP AIC MODEL (LOWEST 

AIC VALUE), AND THE COMMON SIGNIFICANT (P<0.05) VARIABLES AMONG THE TOP AIC AND THE TOP R-SQUARED

MODELS. GREEN = HIGHLY SIGNIFICANT, P<0.01. YELLOW = SIGNIFICANT, P<0.05. RED = NOT SIGNIFICANT.

Inshore Mid-Shore Offshore

Top AIC model

ADCP_colSa ADCP_colSa

Chlorophyll

Currents_botU

Currents_botV

Ice_conc Ice_conc Ice_conc

IceThick_avg

Month Month Month

Nitrate Nitrate

O2_mmol

O2_%sat

PAR PAR

Salinity Salinity

Transport

Turbidity Turbidity

Wind_spd Wind_spd

Wind_v Wind_v

Common sign. variables

Chlorophyll

Currents_botV

Ice_conc Ice_conc Ice_conc

Month Month Month

Transport

Turbidity

Wind_spd Wind_spd
OCS Study

BOEM 2015-034

194

FIGURE 106. BOWHEAD WHALE CALLING ACTIVITY AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE 

INSHORE LOCATION, 2010-2012.BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS. TOP ROW: PERCENT ICE 

CONCENTRATION (BLUE LINE) AND ICE THICKNESS (M; ORANGE LINE). SECOND ROW: CHLOROPHYLL (μG/L; GREEN 

LINE) AND OXYGEN (M X 10; PURPLE LINE). THIRD ROW: NITRATE (μM; RED LINE) AND SALINITY (PSU; TAN LINE). 

BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; TEAL LINE). HORIZONTAL BARS ABOVE EACH

ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND SPEED ARE PRESENTED AS A 3-DAY MOVING 

AVERAGE.
OCS Study

BOEM 2015-034

195

FIGURE 107. BOWHEAD WHALE CALLING ACTIVITY AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE 

MIDSHORE LOCATION, 2010-2012.BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS. TOP ROW: PERCENT ICE 

CONCENTRATION (BLUE LINE) AND ICE THICKNESS (M; ORANGE LINE). SECOND ROW: CHLOROPHYLL (μG/L; GREEN 

LINE) AND OXYGEN (M X 10; PURPLE LINE). THIRD ROW: NITRATE (μM; RED LINE) AND SALINITY (PSU; TAN LINE). 

BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; TEAL LINE). HORIZONTAL BARS ABOVE EACH

ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND SPEED ARE PRESENTED AS A 3-DAY MOVING 

AVERAGE.
OCS Study

BOEM 2015-034

196

FIGURE 108. BOWHEAD WHALE CALLING ACTIVITY AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE 

OFFSHORE LOCATION, 2010-2012.BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS. TOP ROW: PERCENT ICE

CONCENTRATION (BLUE LINE) AND ICE THICKNESS (M; ORANGE LINE). SECOND ROW: CHLOROPHYLL (μG/L; GREEN 

LINE) AND OXYGEN (M X 10; PURPLE LINE). THIRD ROW: NITRATE (μM; RED LINE) AND SALINITY (PSU; TAN LINE). 

BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; TEAL LINE). HORIZONTAL BARS ABOVE EACH

ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND SPEED ARE PRESENTED AS A 3-DAY MOVING

AVERAGE.
OCS Study

BOEM 2015-034

197

FIGURE 109. MARINE MAMMAL CALLING ACTIVITY (BLUE LINE) VS. WINDS (M/S; BLACK LINE) AT THE MIDSHORE 

LOCATION, 2010-2012. ARROWS (USING THE OCEANOGRAPHIC CONVENTION) INDICATE THE DIRECTION THE WINDS 

ARE BLOWING TOWARD, (I.E., AN ARROW POINTING TO THE TOP OF THE PAGE INDICATES WINDS BLOWING FROM 

THE SOUTH AND TO THE NORTH). LENGTH OF WIND VECTOR INDICATES SPEED. TOP ROW: BOWHEAD WHALE. 

MIDDLE ROW: GUNSHOT CALL. BOTTOM ROW: BELUGA WHALE. NOTE CALLING ACTIVITY Y-AXIS IS NOT ON THE 

SAME SCALE.

Gunshot calls (Bowheads)

Gunshot calling activity occurred near the end of most of the peaks in regular bowhead 

whale calling activity (Figure 4). It was therefore expected that gunshot calls would be 

correlated with similar variables as bowhead whales, however this was not the case. The results 

for the GAM runs for gunshot calls are presented in Table 23. There were no consistent 

significant variables in the best models across the three locations, although nitrate appeared in all 

three top models. The common significant variables were month, salinity, and wind speed for the 

midshore location. Six variables were included in the best model for the midshore location, of 

which three (month, salinity, and wind speed) were significant. Only four significant variables

were shared between bowheads and gunshot calls: ice concentration (offshore only), month 

(highly significant, midshore and offshore), oxygen (inshore only) and wind speed (inshore and 

midshore). While most of the midshore calling activity data were included in the GAMS (spring 

missing in both years), only 2010-11 fall data were included for the inshore location, and only 

2011-12 data were included for the offshore location due to a lack of coincidental oceanographic

data (see Appendix G, Section XVIII.G).
OCS Study

BOEM 2015-034

198

Gunshot call presence was correlated with ice presence at all locations (Figure 110-112). 

Gunshot calling would peak immediately after ice concentration hit 100%, but while thickness 

was still less than 0.5 m. They are also strongly associated with polynyas, with increased calling 

activity occurring during polynyas. In the spring, gunshot calling resumes as the ice retreats. 

Like the general bowhead whale calling activity, gunshot calling activity is also slightly 

associated with wind speed, albeit to a lesser degree (Figure 110-112). For the midshore location, 

gunshot calling activity was slightly correlated with winds to the NNW in 2010-11, whereas they 

were associated with consistent winds to the SSW in 2011-12 (Figure 109, middle panel).

TABLE 23. GUNSHOT CALL RESULTS OF THE GAM MODEL. RESULTS REPRESENT THE TOP AIC MODEL (LOWEST AIC 

VALUE), AND THE COMMON SIGNIFICANT (P<0.05) VARIABLES AMONG THE TOP AIC AND THE TOP R-SQUARED

MODELS. GREEN = HIGHLY SIGNIFICANT, P<0.01. YELLOW = SIGNIFICANT, P<0.05. RED = NOT SIGNIFICANT.

Inshore Mid-Shore Offshore

Top AIC model

ADCP_colSa

Currents_botU

Currents_botV

Ice_conc

Month Month

Nitrate Nitrate Nitrate

O2_mMol

PAR

Salinity

Turbidity

Wind_spd Wind_spd

Common sign. 

variables

Ice_conc

Currents_botU

Currents_botV

Month Month

Salinity

Wind_spd
OCS Study

BOEM 2015-034

199

FIGURE 110. GUNSHOT CALLING ACTIVITY AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE INSHORE 

LOCATION, 2010-2012.BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS. TOP ROW: PERCENT ICE

CONCENTRATION (BLUE LINE) AND ICE THICKNESS (M, ORANGE LINE). SECOND ROW: CHLOROPHYLL (μG/L; GREEN 

LINE) AND OXYGEN (M X 10; PURPLE LINE). THIRD ROW: NITRATE (μM; RED LINE) AND SALINITY (PSU; TAN LINE). 

BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; TEAL LINE). HORIZONTAL BARS ABOVE EACH

ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND SPEED ARE PRESENTED AS A 3-DAY MOVING

AVERAGE.
OCS Study

BOEM 2015-034

200

FIGURE 111. GUNSHOT CALLING ACTIVITY AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE MIDSHORE 

LOCATION, 2010-2012.BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS. TOP ROW: PERCENT ICE

CONCENTRATION (BLUE LINE) AND ICE THICKNESS (M; ORANGE LINE). SECOND ROW: CHLOROPHYLL (μG/L; GREEN 

LINE) AND OXYGEN (M X 10; PURPLE LINE). THIRD ROW: NITRATE (μM; RED LINE) AND SALINITY (PSU; TAN LINE). 

BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; TEAL LINE). HORIZONTAL BARS ABOVE EACH

ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND SPEED ARE PRESENTED AS A 3-DAY MOVING 

AVERAGE.
OCS Study

BOEM 2015-034

201

FIGURE 112. GUNSHOT CALLING ACTIVITY AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE OFFSHORE 

LOCATION, 2010-2012.BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS. TOP ROW: PERCENT ICE

CONCENTRATION (BLUE LINE) AND ICE THICKNESS (M; ORANGE LINE). SECOND ROW: CHLOROPHYLL (μG/L; GREEN 

LINE) AND OXYGEN (M X 10; PURPLE LINE). THIRD ROW: NITRATE (μM; RED LINE) AND SALINITY (PSU; TAN LINE). 

BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; TEAL LINE). HORIZONTAL BARS ABOVE EACH 

ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND SPEED ARE PRESENTED AS A 3-DAY MOVING 

AVERAGE.

Beluga whale

The results of the GAM runs for beluga whales are represented in Table 24. The 

common significant variables among the top AIC and top R-Squared models selected by the 

GAM runs were ice concentration, month, and temperature at the inshore location, chlorophyll, 

month, nitrate, PAR, and temperature for the midshore location. The midshore model included 

ten different variables, of which seven (ADCP volume backscatter (full column), chlorophyll, 

month, nitrate, PAR, temperature, wind speed) were significant. It is important to note that while 

the midshore location included most of the calling activity in the GAMs, the inshore location 

only included the 2011-12 data (about 75%), and only the fall of 2010 for the offshore location, 

again a result of insufficient data in the oceanographic variables dataset.

Plotting beluga calling activity with ice conditions and nutrients revealed a strong 

association with ice at the inshore and midshore, but not the offshore, locations (Figure 113-

115). Peak vocal activity occurred when ice was forming in the fall, and as it began breaking up 

(decreased ice concentration and decreasing thickness) in the spring. Belugas are also strongly 
OCS Study

BOEM 2015-034

202

associated with polynyas, where a sharp decrease in ice concentration is correlated with a peak in 

vocal activity. There may be a slight association with nitrate levels in the spring, however more 

data are needed for confirmation. Belugas were only marginally associated with winds (Figure 

109, bottom row), with a slight correlation between calling activity and moderate winds to the

SSW, most notably in 2011-12. There were no visual or acoustic detections of beluga during the 

shipboard surveys, therefore they are not included in the transect line plots.

TABLE 24. BELUGA WHALE RESULTS OF THE GAM MODEL. RESULTS REPRESENT THE TOP AIC MODEL (LOWEST AIC 

VALUE), AND THE COMMON SIGNIFICANT (P<0.05) VARIABLES AMONG THE TOP AIC AND THE TOP R-SQUARED

MODELS. GREEN = HIGHLY SIGNIFICANT, P<0.01. YELLOW = SIGNIFICANT, P<0.05. RED = NOT SIGNIFICANT.

Inshore Mid-Shore Offshore

Top AIC model

ADCP_colSa ADCP_colSa

Chlorophyll Chlorophyll

Ice_conc Ice_conc Ice_conc

Month Month

Nitrate

PAR PAR

Salinity Salinity

Temperature Temperature

Turbidity Turbidity Turbidity

Wind_spd Wind_spd

Common sign. variables

ADCP_ColA

Chlorophyll

Ice_conc

Month Month

Nitrate

PAR

Temperature Temperature
OCS Study

BOEM 2015-034

203

FIGURE 113. BELUGA WHALE CALLING ACTIVITY AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE INSHORE 

LOCATION, 2010-2012. BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS. TOP ROW: PERCENT ICE

CONCENTRATION (BLUE LINE) AND MEDIAN ICE THICKNESS (M; ORANGE LINE). SECOND ROW: CHLOROPHYLL 

(μG/L; GREEN LINE) AND PERCENT OXYGEN SATURATION (PURPLE LINE). THIRD ROW: NITRATE (μM; RED LINE) AND 

SALINITY (PSU; TAN LINE). BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; TEAL LINE). 

HORIZONTAL BARS ABOVE EACH ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND SPEED ARE 

PRESENTED AS A 3-DAY MOVING AVERAGE.
OCS Study

BOEM 2015-034

204

FIGURE 114. BELUGA WHALE CALLING ACTIVITY AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE 

MIDSHORE LOCATION, 2010-2012. BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS TOP ROW: PERCENT ICE

CONCENTRATION (BLUE LINE) AND STANDARD DEVIATION ICE THICKNESS (M; ORANGE LINE). SECOND ROW: 

CHLOROPHYLL (μG/L; GREEN LINE) AND PERCENT OXYGEN SATURATION (PURPLE LINE). THIRD ROW: NITRATE 

(μM; RED LINE) AND SALINITY (PSU; TAN LINE). BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; 

TEAL LINE). HORIZONTAL BARS ABOVE EACH ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND

SPEED ARE PRESENTED AS A 3-DAY MOVING AVERAGE.
OCS Study

BOEM 2015-034

205

FIGURE 115. BELUGA WHALE CALLING ACTIVITY AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE 

OFFSHORE LOCATION 2010-2012. BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS; TOP ROW: PERCENT ICE 

CONCENTRATION (BLUE LINE) AND STANDARD DEVIATION ICE THICKNESS (M; ORANGE LINE). SECOND ROW: 

CHLOROPHYLL (μG/L; GREEN LINE) AND PERCENT OXYGEN SATURATION (PURPLE LINE). THIRD ROW: NITRATE 

(μM; RED LINE) AND SALINITY (PSU; TAN LINE). BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; 

TEAL LINE). HORIZONTAL BARS ABOVE EACH ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND 

SPEED ARE PRESENTED AS A 3-DAY MOVING AVERAGE. 

Gray whale

Results for the gray whale GAM runs are presented in Table 25. Ice concentration, 

temperature and wind speed were the only significant variables in the best model selected from 

the inshore GAM run. The only consistent significant variable among the top AIC and top RSquared models from the inshore GAM run was temperature. Gray whale calling activity was 

limited to the inshore location; the midshore location had only one day of calling activity, and 

the offshore location had none. It is important to note that only the fall 2011 gray whale calling 

peak was included in the GAM for the inshore location; no data were included for 2010-11 at the 

inshore location, a result of the low number of coincident data from the oceanographic variables

dataset.

Only the inshore calling activity data were plotted with the other variables (Figure 116).

Gray whale calling activity showed a negative correlation with ice presence. Calling activity in 

the fall ceased before the ice formation began, and did not resume in spring until the 

concentration started to decrease. There was an association with chlorophyll in 2011-2012, 
OCS Study

BOEM 2015-034

206

though not in 2010-2011. It is possible that gray whales are associated with either low nitrate 

levels, or occur just before a peak in nitrate levels. Unfortunately, there are no nitrate data for 

2011-2012, so we cannot confirm this association.

Gray whale visual and acoustic detections during the shipboard survey were plotted 

against results from the transect line sampling (zooplankton concentration), temperature, salinity, 

nitrate, and ammonium), the results of which are presented in Figure 117. Overall, gray whales 

were associated with high levels of Pseudocalanus, C. glacialis, and larvaceans, as well as 

nitrate and ammonium. In 2011 along the Barrow transect (Line F), all gray whale sightings 

were inshore near the start of the transect line. There were no acoustic detections. The location 

of the gray whale sightings corresponds with high levels of Pseudocalanus and moderate levels 

of larvaceans, though there were no obvious correlations between gray whales and the other 

variables. Generally, there were high levels of larvaceans and Pseudocalanus associated with 

high levels of salinity, nitrate, and ammonium. In 2012 at the Point Hope line (Line A), there 

were high numbers of gray whale sightings near high levels of Pseudocalanus and C. glacialis. 

These were also associated with high levels of nitrate and ammonium.

TABLE 25. GRAY WHALE RESULTS OF THE GAM MODEL. RESULTS REPRESENT THE TOP AIC MODEL (LOWEST AIC 

VALUE), AND THE COMMON SIGNIFICANT (P<0.05) VARIABLES AMONG THE TOP AIC AND THE TOP R-SQUARED

MODELS. GREEN = HIGHLY SIGNIFICANT, P<0.01. YELLOW = SIGNIFICANT, P<0.05. RED = NOT SIGNIFICANT.

Inshore Mid-Shore Offshore

Top AIC model

Chlorophyll

Ice_conc Ice_conc

Salinity

Temperature Temperature

Turbidity

Wind_spd

Wind_v

Common sign. 

variables

Ice_conc

Temperature
OCS Study

BOEM 2015-034

207

FIGURE 116. GRAY WHALE CALLING ACTIVITY AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE INSHORE 

LOCATION, 2010-2012. BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS. TOP ROW: PERCENT ICE

CONCENTRATION (BLUE LINE) AND AVERAGE ICE THICKNESS (M; ORANGE LINE). SECOND ROW: CHLOROPHYLL 

(μG/L; GREEN LINE) AND PERCENT OXYGEN SATURATION (PURPLE LINE). THIRD ROW: NITRATE (μM; RED LINE) AND 

SALINITY (PSU; TAN LINE). BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; TEAL LINE). 

HORIZONTAL BARS ABOVE EACH ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND SPEED ARE 

PRESENTED AS A 3-DAY MOVING AVERAGE.
OCS Study

BOEM 2015-034

208

FIGURE 117. BARROW TRANSECT LINE OCEANOGRAPHIC, ZOOPLANKTON, AND GRAY WHALE SURVEY RESULTS, 

2011. X-AXIS REFERS TO DISTANCE ALONG TRANSECT, WHERE 0 KM = STATION 1 ALONG TRANSECT. TOP ROW = 

SONOBUOY EFFORT AND DETECTIONS AND VISUAL SIGHTINGS (INCLUDING # INDIVIDUALS) ALONG TRANSECT

LINE; OPEN CIRCLE = SONOBUOY DEPLOYED BUT NO DETECTIONS, CLOSED CIRCLE = ACOUSTIC DETECTION, 

BLACK DIAMOND = SIGHTING. SECOND ROW = ZOOPLANKTON DENSITY AT EACH TRANSECT STATION; 

PSEUDOCALANUS (ABBREVIATED P. CALANUS) = PINK SQUARE, C. GLACIALIS = RED DIAMOND, LARVACEANS = 

TURQUOISE DIAMOND. THIRD ROW = TEMPERATURE (°C). FOURTH ROW = SALINITY (PSU). FIFTH ROW = NITRATE 

(μM). BOTTOM ROW = AMMONIUM (μM).
OCS Study

BOEM 2015-034

209

FIGURE 118. POINT HOPE TRANSECT LINE OCEANOGRAPHIC, ZOOPLANKTON, AND GRAY WHALE SURVEY RESULTS, 

2012. X-AXIS REFERS TO DISTANCE ALONG TRANSECT, WHERE 0 KM = STATION 1 ALONG TRANSECT. TOP ROW = 

SONOBUOY EFFORT AND DETECTIONS AND VISUAL SIGHTINGS (INCLUDING # INDIVIDUALS) ALONG TRANSECT

LINE; OPEN CIRCLE = SONOBUOY DEPLOYED BUT NO DETECTIONS, CLOSED CIRCLE = ACOUSTIC DETECTION, 

BLACK DIAMOND = SIGHTING. SECOND ROW = ZOOPLANKTON DENSITY AT EACH TRANSECT STATION; 

PSEUDOCALANUS (ABBREVIATED P. CALANUS) = PINK TRIANGLE, C. GLACIALIS = RED SQUARE, PTEROPODS = ORANGE 

CIRCLE. THIRD ROW = TEMPERATURE (°C). FOURTH ROW = SALINITY (PSU). FIFTH ROW = NITRATE (μM). BOTTOM 

ROW = AMMONIUM (μM).
OCS Study

BOEM 2015-034

210

Walrus

The results of the GAM runs for walrus calling activity are presented in Table 26. The 

common significant variables for the top AIC models and the top R-Squared models are ADCP 

volume backscatter (bottom), month, oxygen saturation, salinity, and wind speed for the 

midshore location. Ten variables contributed to the best model selected by the GAM run at the 

midshore location, of which six (ADCP volume backscatter (bottom), chlorophyll, month, 

oxygen saturation, salinity, wind speed) were highly significant. It is important to note that only 

very little of the main peaks in walrus calling activity were included in the GAM runs. Just the 

fall end of the summer pulse in calling activity was included for all locations in 2010 and at the 

midshore location in 2011-12. There was just one instance (2010-11; offshore) where the 

beginning portion of the winter pulse in calling activity was included (Appendix G., Section 

XVIII.G).

When plotted with ice and nutrients, a strong negative correlation with ice was evident at 

the inshore and midshore locations, though not at the offshore location (Figure 119-121). For the 

inshore and midshore locations, walrus calling activity began to increase when the ice was 

breaking up in the early summer, peaked during the ice-free summer months, and decreased 

when the ice began forming again; however, there was low levels of calling activity overwinter 

at both of these locations. The offshore location had two calling peaks, one during the ice-free 

summer months, and one over winter when ice was present. There was a strong correlation with 

chlorophyll in 2011-2012. Peak calling activity followed the peak in chlorophyll at the midshore 

and offshore locations in fall 2011, and aligned well in spring 2012 at the inshore location. 

There is also a possible association with oxygen saturation at the inshore and midshore locations. 

The results correlating the shipboard walrus visual and acoustic detections with the 

transect line data are presented in Figure 123-128. Generally, walrus were strongly associated 

with high levels of nitrate and ammonium, as well as high concentrations of Pseudocalanus and 

larvaceans. At the Wainwright transect line, walrus were consistently sighted in areas that had 

high concentrations of Pseudocalanus and larvaceans. These also corresponded with areas of 

high nitrate and ammonium levels (Figure 123-128). In 2012, interestingly, walrus were not 

associated with high zooplankton levels, although there were still high nitrate levels (Figure 

125). Even though walrus do not eat zooplankton, high zooplankton concentrations are usually 

indicative of high productivity, and high benthic biomass. At the Icy Cape transect line, walrus 

were again associated with high levels of nitrate and ammonium (Figure 126-128). In 2010, 

walrus were also strongly associated with salinity (Figure 126). In 2011 there was a strong 

correlation with zooplankton, though this was less obvious in the other years (Figure 127). 

Overall, there were many visual sightings of walrus, though only a handful of acoustic 

detections.
OCS Study

BOEM 2015-034

211

TABLE 26. WALRUS RESULTS OF THE GAM MODEL. RESULTS REPRESENT THE TOP AIC MODEL (LOWEST AIC VALUE), 

AND THE COMMON SIGNIFICANT (P<0.05) VARIABLES AMONG THE TOP AIC AND THE TOP R-SQUARED MODELS. 

GREEN = HIGHLY SIGNIFICANT, P<0.01. YELLOW = SIGNIFICANT, P<0.05. RED = NOT SIGNIFICANT.

Inshore Mid-Shore Offshore

Top AIC model

ADCP_botSv

ADCP_colSa

Chlorophyll

IceThick_std

Month Month

Nitrate Nitrate

O2_%sat

PAR PAR PAR

Salinity

Temperature

Turbidity

Wind_spd Wind_spd

Wind_u

Common sign. 

variables

ADCP_botV

Month Month

O2_%sat

Salinity

Wind_spd Wind_spd
OCS Study

BOEM 2015-034

212

FIGURE 119. WALRUS CALLING ACTIVITY AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE INSHORE 

LOCATION, 2010-2012. BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS. TOP ROW: PERCENT ICE

CONCENTRATION (BLUE LINE) AND STANDARD DEVIATION ICE THICKNESS (M; ORANGE LINE). SECOND ROW: 

CHLOROPHYLL (μG/L; GREEN LINE) AND PERCENT OXYGEN SATURATION (PURPLE LINE). THIRD ROW: NITRATE 

(μM; RED LINE) AND SALINITY (PSU; TAN LINE). BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; 

TEAL LINE). HORIZONTAL BARS ABOVE EACH ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND

SPEED ARE PRESENTED AS A 3-DAY MOVING AVERAGE.
OCS Study

BOEM 2015-034

213

FIGURE 120. WALRUS CALLING ACTIVITY AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE MIDSHORE 

LOCATION, 2010-2012. BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS. TOP ROW: PERCENT ICE

CONCENTRATION (BLUE LINE) AND STANDARD DEVIATION ICE THICKNESS (M; ORANGE LINE). SECOND ROW: 

CHLOROPHYLL (μG/L; GREEN LINE) AND PERCENT OXYGEN SATURATION (PURPLE LINE). THIRD ROW: NITRATE 

(μM; RED LINE) AND SALINITY (PSU; TAN LINE). BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; 

TEAL LINE). HORIZONTAL BARS ABOVE EACH ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND

SPEED ARE PRESENTED AS A 3-DAY MOVING AVERAGE.
OCS Study

BOEM 2015-034

214

FIGURE 121. WALRUS CALLING ACTIVITY AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE OFFSHORE 

LOCATION, 2010-2012. BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS. TOP ROW: PERCENT ICE 

CONCENTRATION (BLUE LINE) AND AVERAGE ICE THICKNESS (M; ORANGE LINE). SECOND ROW: CHLOROPHYLL 

(μG/L; GREEN LINE) AND PERCENT OXYGEN SATURATION (PURPLE LINE). THIRD ROW: NITRATE (μM; RED LINE) AND 

SALINITY (PSU; TAN LINE). BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; TEAL LINE). 

HORIZONTAL BARS ABOVE EACH ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND SPEED ARE 

PRESENTED AS A 3-DAY MOVING AVERAGE.
OCS Study

BOEM 2015-034

215

FIGURE 122. MARINE MAMMAL CALLING ACTIVITY (BLUE LINE) VS. WINDS (M/S; BLACK LINE) AT THE MIDSHORE 

LOCATION, 2010-2012. ARROWS (USING THE OCEANOGRAPHIC CONVENTION) INDICATE THE DIRECTION THE WINDS 

ARE BLOWING TOWARD, (I.E., AN ARROW POINTING TO THE TOP OF THE PAGE INDICATES WINDS BLOWING FROM 

THE SOUTH AND TO THE NORTH). LENGTH OF WIND VECTOR INDICATES SPEED. TOP ROW: WALRUS. MIDDLE ROW: 

BEARDED SEAL. BOTTOM ROW: ICE NOISE. NOTE CALLING ACTIVITY Y-AXIS IS NOT ON THE SAME SCALE.
OCS Study

BOEM 2015-034

216

FIGURE 123. WAINWRIGHT TRANSECT LINE OCEANOGRAPHIC, ZOOPLANKTON, WALRUS, AND BEARDED SEAL 

SURVEY RESULTS, 2010. X-AXIS REFERS TO DISTANCE ALONG TRANSECT, WHERE 0 KM = STATION 1 ALONG 

TRANSECT. TOP ROW = SONOBUOY EFFORT AND DETECTIONS AND VISUAL SIGHTINGS (INCLUDING # OF 

INDIVIDUALS) OF WALRUS AND BEARDED SEALS; OPEN CIRCLE = SONOBUOY DEPLOYED BUT NO DETECTIONS, 

CLOSED CIRCLE = ACOUSTIC DETECTION, BEARDED SEALS = RED SQUARE. SECOND ROW = ZOOPLANKTON DENSITY 

AT EACH TRANSECT STATION; PSEUDOCALANUS (ABBREVIATED P. CALANUS) = PINK SQUARE, C. GLACIALIS = RED

TRIANGE, LARVACEANS = TURQUOISE CIRCLE, PTEROPODS = BLUE CIRCLE. THIRD ROW = TEMPERATURE (°C). 

FOURTH ROW = SALINITY (PSU). FIFTH ROW = NITRATE (μM). BOTTOM ROW = AMMONIUM (μM).
OCS Study

BOEM 2015-034

217

FIGURE 124. WAINWRIGHT TRANSECT LINE OCEANOGRAPHIC, ZOOPLANKTON, WALRUS, AND BEARDED SEAL 

SURVEY RESULTS, 2011. X-AXIS REFERS TO DISTANCE ALONG TRANSECT, WHERE 0 KM = STATION 1 ALONG 

TRANSECT. TOP ROW = SONOBUOY EFFORT AND DETECTIONS AND VISUAL SIGHTINGS (INCLUDING # OF 

INDIVIDUALS) OF WALRUS AND BEARDED SEALS; OPEN CIRCLE = SONOBUOY DEPLOYED BUT NO DETECTIONS, 

CLOSED CIRCLE = ACOUSTIC DETECTION, WALRUS = BLUE TRIANGLES. SECOND ROW = ZOOPLANKTON DENSITY AT 

EACH TRANSECT STATION; PSEUDOCALANUS (ABBREVIATED P. CALANUS) = PINK DIAMOND, C. GLACIALIS = RED

SQUARE, LARVACEANS = TURQUOISE CIRCLE, PTEROPODS = ORANGE CIRCLE. THIRD ROW = TEMPERATURE (°C). 

FOURTH ROW = SALINITY (PSU). FIFTH ROW = NITRATE (μM). BOTTOM ROW = AMMONIUM (μM).
OCS Study

BOEM 2015-034

218

FIGURE 125. WAINWRIGHT TRANSECT LINE OCEANOGRAPHIC, ZOOPLANKTON, WALRUS, AND BEARDED SEAL 

SURVEY RESULTS, 2012. X-AXIS REFERS TO DISTANCE ALONG TRANSECT, WHERE 0 KM = STATION 1 ALONG 

TRANSECT. TOP ROW = SONOBUOY EFFORT AND DETECTIONS AND VISUAL SIGHTINGS (INCLUDING # OF 

INDIVIDUALS) OF WALRUS AND BEARDED SEALS; OPEN CIRCLE = SONOBUOY DEPLOYED BUT NO DETECTIONS, 

CLOSED CIRCLE = ACOUSTIC DETECTION, WALRUS = BLUE TRIANGLES, BEARDED SEALS = RED SQUARES. SECOND 

ROW = ZOOPLANKTON DENSITY AT EACH TRANSECT STATION; PSEUDOCALANUS (ABBREVIATED P. CALANUS) = PINK 

TRIANGLE, C. GLACIALIS = RED SQUARE, EUPHAUSIIDS = BLUE CIRCLE. THIRD ROW = TEMPERATURE (°C). FOURTH

ROW = SALINITY (PSU). FIFTH ROW = NITRATE (μM). BOTTOM ROW = AMMONIUM (μM).
OCS Study

BOEM 2015-034

219

FIGURE 126. ICY CAPE TRANSECT LINE OCEANOGRAPHIC, ZOOPLANKTON, WALRUS, AND BEARDED SEAL SURVEY 

RESULTS, 2010. X-AXIS REFERS TO DISTANCE ALONG TRANSECT, WHERE 0 KM = STATION 1 ALONG TRANSECT. TOP 

ROW = SONOBUOY EFFORT AND DETECTIONS AND VISUAL SIGHTINGS (INCLUDING # OF INDIVIDUALS) OF WALRUS 

AND BEARDED SEALS; OPEN CIRCLE = SONOBUOY CIRCLE = SONOBUOY DEPLOYED BUT NO DETECTIONS, CLOSED 

CIRCLE = ACOUSTIC DETECTION, WALRUS = BLUE TRIANGLES. SECOND ROW = ZOOPLANKTON DENSITY AT EACH 

TRANSECT STATION; PSEUDOCALANUS (ABBREVIATED P. CALANUS) = PINK TRIANGLE, C. GLACIALIS = RED SQUARE, 

FURCILIA = BLUE CIRCLE, PTEROPODS = ORANGE CIRCLE. THIRD ROW = TEMPERATURE (°C). FOURTH ROW = 

SALINITY (PSU). FIFTH ROW = NITRATE (μM). BOTTOM ROW = AMMONIUM (μM).
OCS Study

BOEM 2015-034

220

FIGURE 127. ICY CAPE TRANSECT LINE OCEANOGRAPHIC, ZOOPLANKTON, WALRUS, AND BEARDED SEAL SURVEY 

RESULTS, 2011. X-AXIS REFERS TO DISTANCE ALONG TRANSECT, WHERE 0 KM = STATION 1 ALONG TRANSECT. TOP 

ROW = SONOBUOY EFFORT AND DETECTIONS AND VISUAL SIGHTINGS (INCLUDING # OF INDIVIDUALS) OF WALRUS 

AND BEARDED SEALS; OPEN CIRCLE = SONOBUOY DEPLOYED BUT NO DETECTIONS, CLOSED CIRCLE = ACOUSTIC 

DETECTION, WALRUS = BLUE TRIANGLE, BEARDED SEALS = RED SQUARES. SECOND ROW = ZOOPLANKTON DENSITY 

AT EACH TRANSECT STATION; PSEUDOCALANUS (ABBREVIATED P. CALANUS) = PINK TRIANGLE, C. GLACIALIS = RED

SQUARE, LARVACEANS = TURQUOISE CIRCLE. THIRD ROW = TEMPERATURE (°C). FOURTH ROW = SALINITY (PSU). 

FIFTH ROW = NITRATE (μM). BOTTOM ROW = AMMONIUM (μM).
OCS Study

BOEM 2015-034

221

FIGURE 128. ICY CAPE TRANSECT LINE OCEANOGRAPHIC, ZOOPLANKTON, WALRUS, AND BEARDED SEAL SURVEY 

RESULTS, 2012. X-AXIS REFERS TO DISTANCE ALONG TRANSECT, WHERE 0 KM = STATION 1 ALONG TRANSECT. TOP 

ROW = SONOBUOY EFFORT AND DETECTIONS AND VISUAL SIGHTINGS (INCLUDING # OF INDIVIDUALS) OF WALRUS 

AND BEARDED SEALS; OPEN CIRCLE = SONOBUOY DEPLOYED BUT NO DETECTIONS, CLOSED CIRCLE = ACOUSTIC 

DETECTION, BEARDED SEALS = RED SQUARES. SECOND ROW = ZOOPLANKTON DENSITY AT EACH TRANSECT 

STATION; PSEUDOCALANUS (ABBREVIATED P. CALANUS) = PINK CIRCLE, C. GLACIALIS = RED CIRCLE, LARVACEANS = 

TURQUOISE DIAMONDS, PTEROPODS = ORANGE CIRCLE. THIRD ROW = TEMPERATURE (°C). FOURTH ROW = 

SALINITY (PSU). FIFTH ROW = NITRATE (μM). BOTTOM ROW = AMMONIUM (μM).
OCS Study

BOEM 2015-034

222

Bearded seal

The bearded seal GAM results are presented in Table 27. Of the common significant 

variables for the top AIC models and the top R-Squared models, only month was consistent for 

all locations. There were three common significant variables for the midshore location: ADCP 

volume backscatter (bottom), ice concentration, month, transport, and wind speed. Eleven 

variables were included in the top midshore model, of which seven (ADCP volume backscatter 

(bottom), bottom currents (V), ice concentration, month, salinity, transport, wind speed) were 

significant. Again, while most of the midshore data were included in the GAM (with the 

exception of the time period with the rapid drop in calling activity), the inshore and offshore data 

only included the first year, and stopped well before calling activity neared 100%.

The plots correlating bearded seal calling activity with the oceanographic variables at all 

locations are presented in Figure 129-131. Bearded seal calling activity was positively 

associated with ice. Peak calling activity occurred with ice cover, and calling activity increased 

simultaneously with ice thickness. Calling stopped shortly after ice concentration dropped. At 

the offshore location in 2011-12, there was an unusually deep, persistent ice keel, with a 

simultaneous drop in bearded seal calling activity (Figure 131). There is a loose association 

between calling activity and salinity, with both increasing simultaneously. Nitrate also increases 

as bearded seal calling activity increases, though with a slight delayed effect. There were no 

evident correlations with winds, despite wind speed being a common significant variable

contributing to the top AIC and R-squared models selected by the GAM runs for both the inshore 

and midshore locations. There was only a slight association in 2011-12 with winds to the SSW

(Figure 122, middle row).

The results of the shipboard bearded seal visual and acoustic data with the transect line 

sampling data are combined with the walrus data, and are presented in Figure 123-128. 

Generally, bearded seal results were similar to walrus. Bearded seals were associated with high 

densities of Pseudocalanus and larvaceans, and somewhat associated with nitrate and 

ammonium. At the Wainwright transect line, bearded seals were associated with high densities 

of Pseudocalanus (Figure 123-128). There were no bearded seals seen or acoustically detected 

on the transect in 2011. Interestingly, in 2012, bearded seals were generally detected at the 

inshore stations, unlike the walrus (Figure 125). There were high amounts of Pseudocalanus at 

these stations, though no obvious associations with the other measurements. At the Icy Cape 

line, there were no bearded seals seen or acoustically detected in 2010. In 2011, bearded seals 

were sighted all along the transect line. They were associated with high levels of Pseudocalanus

and larvacean densities, and somewhat associated with nitrate and ammonium peaks (Figure 

127). In 2012, bearded seals were associated with high ammonium levels, though interestingly 

there were no obvious correlations with zooplankton (Figure 128). Similar to walrus, bearded 

seals were visually sighted numerous times, but were rarely acoustically detected.
OCS Study

BOEM 2015-034

223

TABLE 27. BEARDED SEAL RESULTS OF THE GAM MODEL. RESULTS REPRESENT THE TOP AIC MODEL (LOWEST AIC 

VALUE), AND THE COMMON SIGNIFICANT (P<0.05) VARIABLES AMONG THE TOP AIC AND THE TOP R-SQUARED

MODELS. GREEN = HIGHLY SIGNIFICANT, P<0.01. YELLOW = SIGNIFICANT, P<0.05. RED = NOT SIGNIFICANT.

Inshore Mid-Shore Offshore

Top AIC model

ADCP_botSv ADCP_botSv ADCP_botSv

ADCP_colSa ADCP_colSa

Currents_botU

Currents_botV Currents_botV

Ice_conc Ice_conc

Month Month Month

O2_mmol

Nitrate Nitrate

PAR PAR

Salinity Salinity

Transport

Turbidity Turbidity

Wind_spd Wind_spd

Wind_v Wind_v

Common sign. variables

ADCP_botV

ADCP_ColA

Currents_botV

Ice_conc

Month Month Month

Transport

Wind_spd Wind_spd
OCS Study

BOEM 2015-034

224

FIGURE 129. BEARDED SEAL CALLING ACTIVITY AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE INSHORE 

LOCATION, 2010-2012. BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS. TOP ROW: PERCENT ICE

CONCENTRATION (BLUE LINE) AND AVERAGE ICE THICKNESS (M; ORANGE LINE). SECOND ROW: CHLOROPHYLL 

(μG/L; GREEN LINE) AND OXYGEN (M X 10; PURPLE LINE). THIRD ROW: NITRATE (μM; RED LINE) AND SALINITY (PSU; 

TAN LINE). BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; TEAL LINE). HORIZONTAL BARS 

ABOVE EACH ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND SPEED ARE PRESENTED AS A 3-DAY 

MOVING AVERAGE.
OCS Study

BOEM 2015-034

225

FIGURE 130. BEARDED SEAL CALLING ACTIVITY AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE 

MIDSHORE LOCATION, 2010-2012. BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS. TOP ROW: PERCENT 

ICE CONCENTRATION (BLUE LINE) AND AVERAGE ICE THICKNESS (M; ORANGE LINE). SECOND ROW: 

CHLOROPHYLL (μG/L; GREEN LINE) AND OXYGEN (M X 10; PURPLE LINE). THIRD ROW: NITRATE (μM; RED LINE) AND 

SALINITY (PSU; TAN LINE). BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; TEAL LINE). 

HORIZONTAL BARS ABOVE EACH ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND SPEED ARE 

PRESENTED AS A 3-DAY MOVING AVERAGE.
OCS Study

BOEM 2015-034

226

FIGURE 131. BEARDED SEAL CALLING ACTIVITY AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE 

OFFSHORE LOCATION, 2010-2012. BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS. TOP ROW: PERCENT 

ICE CONCENTRATION (BLUE LINE) AND AVERAGE ICE THICKNESS (M; ORANGE LINE). SECOND ROW: 

CHLOROPHYLL (μG/L; GREEN LINE) AND OXYGEN (M X 10; PURPLE LINE). THIRD ROW: NITRATE (μM; RED LINE) AND 

SALINITY (PSU; TAN LINE). BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; TEAL LINE). 

HORIZONTAL BARS ABOVE EACH ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND SPEED ARE 

PRESENTED AS A 3-DAY MOVING AVERAGE.

Ice noise

The results of the GAMs for ice noise are presented in Table 28. Again, the midshore 

location contained the largest amount of noise activity data, only the beginning of the 2010-11 

pulse of ice noise activity was included in the offshore GAM run. The inshore location did a 

little better with over half the 2010-11 pulse included. The midshore location had three common 

significant variables: chlorophyll, ice concentration, and month. The top AIC model results for 

all three locations had only one variable that was both consistent and significant among all 

locations: ice concentration. Salinity was consistent among all locations, but was not significant 

at the midshore location. The top AIC model selected by the GAM run at the midshore location 

included eleven variables, of which chlorophyll, ice concentration, month, PAR, turbidity, and 

winds (U, V) were significant. 

The long-term correlation plots for all locations are presented in Figure 132-134. Ice 

noise activity required the presence of ice, but the presence of ice did not guarantee ice noise 

activity, indicating that other factors are involved. There was a slight delay of approximately 

five days between the ice beginning to form (>0% ice concentration) and the first instance of ice 
OCS Study

BOEM 2015-034

227

noise activity. Ice noise activity is present for only the first half of the total ice presence 

(determined by >0% ice concentration at the midshore and offshore location, but present 

throughout the ice period at the inshore location. Ice noise dropped dramatically when a polynya 

was formed in the fall of 2010 (best seen at the offshore location, Figure 134), resuming once the 

polynya was gone and ice concentration reached 100% again. There was no ice noise detected in 

spring of 2011 at the midshore and offshore locations, when ice concentration levels were 

decreasing. Although the midshore passive acoustic recorder failed before ice concentration 

reached zero, it was still collecting data when ice concentration dropped from 100% to less than 

5%. Noise activity was present at the inshore location during the rapid decrease in ice noise in 

spring of both years – which can be seen clearly during the 2011-12 deployment. Overall, there 

was more ice noise activity in 2011-2012 than in the previous year. Ice noise activity also 

persisted longer in the spring of 2012 than the spring of 2011. There was a strong positive 

correlation between ice noise and wind (Figure 122, bottom row). In 2010-11, there was a slight 

association with winds to the north, and in 2011-12 there was a strong correlation with high 

winds to the SSW. There was a negative correlation between over-winter ice noise and 

chlorophyll at all locations in 2011-12, this trend seems to be present in the 2010-11 data as well.

TABLE 28. ICE NOISE RESULTS OF THE GAM MODEL. RESULTS REPRESENT THE TOP AIC MODEL (LOWEST AIC 

VALUE), AND THE COMMON SIGNIFICANT (P<0.05) VARIABLES AMONG THE TOP AIC AND THE TOP R-SQUARED

MODELS. GREEN = HIGHLY SIGNIFICANT, P<0.01. YELLOW = SIGNIFICANT, P<0.05. RED = NOT SIGNIFICANT.

Inshore Mid-Shore Offshore

Top AIC models

ADCP_botSv

ADCP_colSa

Chlorophyll

Currents_botU

Ice_conc Ice_conc Ice_conc

IceThick_med

Month

Nitrate Nitrate

PAR PAR

Salinity Salinity Salinity

Transport

Turbidity Turbidity

Winds_spd Winds_spd

Winds_u Winds_u

Winds_v

Common sign. 

variables

Chlorophyll

Currents_botU

Ice_conc

IceThick_med

Month
OCS Study

BOEM 2015-034

228

FIGURE 132. ICE NOISE PRESENCE AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE INSHORE LOCATION, 

2010-2012. BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS. TOP ROW: PERCENT ICE CONCENTRATION 

(BLUE LINE) AND STANDARD DEVIATION ICE THICKNESS (M; ORANGE LINE). SECOND ROW: CHLOROPHYLL (μG/L; 

GREEN LINE) AND OXYGEN (M X 10; PURPLE LINE). THIRD ROW: NITRATE (μM; RED LINE) AND SALINITY (PSU; TAN 

LINE). BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; TEAL LINE). HORIZONTAL BARS ABOVE 

EACH ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND SPEED ARE PRESENTED AS A 3-DAY MOVING 

AVERAGE.
OCS Study

BOEM 2015-034

229

FIGURE 133. ICE NOISE PRESENCE AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE MIDSHORE LOCATION, 

2010-2012. BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS. TOP ROW: PERCENT ICE CONCENTRATION 

(BLUE LINE) AND STANDARD DEVIATION ICE THICKNESS (M; ORANGE LINE). SECOND ROW: CHLOROPHYLL (μG/L; 

GREEN LINE) AND OXYGEN (M X 10; PURPLE LINE). THIRD ROW: NITRATE (μM; RED LINE) AND SALINITY (PSU; TAN 

LINE). BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; TEAL LINE). HORIZONTAL BARS ABOVE 

EACH ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND SPEED ARE PRESENTED AS A 3-DAY MOVING 

AVERAGE.
OCS Study

BOEM 2015-034

230

FIGURE 134. ICE NOISE PRESENCE AS IT RELATES TO OCEANOGRAPHIC VARIABLES AT THE OFFSHORE LOCATION, 

2010-2012. BLACK LINE = PERCENT OF TIME INTERVALS WITH CALLS. TOP ROW: PERCENT ICE CONCENTRATION 

(BLUE LINE) AND STANDARD DEVIATION ICE THICKNESS (M; ORANGE LINE). SECOND ROW: CHLOROPHYLL (μG/L; 

GREEN LINE) AND OXYGEN (M X 10; PURPLE LINE). THIRD ROW: NITRATE (μM; RED LINE) AND SALINITY (PSU; TAN 

LINE). BOTTOM ROW: WIND SPEED (M/S; PINK LINE) AND TRANSPORT (SV; TEAL LINE). HORIZONTAL BARS ABOVE 

EACH ROW INDICATE TIMES WITH NO DATA. ALL DATA EXCEPT WIND SPEED ARE PRESENTED AS A 3-DAY MOVING 

AVERAGE.

Note on GAM comparison of surface vs. bottom currents

In general, there were very few differences between the models produced from the

bottom and surface current iterations of the 21 separate GAM runs; the R-Squared values and 

sample sizes changed only slightly, especially at the midshore location where the sample sizes 

were high. Therefore, there is good evidence to support our initial assumptions of water current 

uniformity in the water column.

Note on GAM runs including TAPS6-NG variables: 

The results from these runs showed that a variety of the TAPS6-NG variables provided 

high explanatory power for the variance in bowhead whale, bearded seal, and walrus calling 

activity over short time scales. In the future, every effort to collect these data simultaneous with 

passive acoustic recordings should be attempted to evaluate their explanatory power in 

understanding marine mammal distributions. 
OCS Study

BOEM 2015-034

231

3. Discussion

While the results of the GAM models were run for all three locations, the midshore data 

had the largest sample sizes spanning the longest timeframes, and had the most consistent RSquared values between the top AIC and top R-Squared models for each run. Furthermore, the 

midshore location is positioned between the Burger and Klondike study areas, and as such is best 

suited for representing the area of interest for this study. Thus, the discussion that follows will 

focus on the midshore location unless otherwise stated. Any significant differences between the 

three locations will be described. It is also important to note that reference to any correlations 

regarding the long term plots are qualitative in nature, winds were estimated from the midshore 

location only, and transport was averaged between the three locations.

Bowhead whale

Given that bowheads follow a regular migration pattern with fall migration occurring 

ahead of the advancing ice edge and the spring migration taking place in nearshore leads, the 

significance of both ice concentration and month in the GAMs are not surprising. Month was 

extremely significant (p values ranged from 1.8E

-5 to 5.2E-29) for all models and all variables, 

indicating that the timing of the bowhead migration is driven mainly by endogenous cycles, and 

then secondarily by ice conditions. In the fall, bowhead calling activity is greatest just before ice 

forms (or in the early formation of ice), and ceases once the ice concentration reaches 100% or 

the ice thickness is greater than 0.5 m (see Figure 103-105). In spring, bowhead calling activity 

occurs in ice thicknesses greater than 0.5 m; however, it is important to note the scale of the 

measurements. The instrument measuring ice thickness measures a surface area of approximately 

1m2above the instrument, whereas ice concentrations are calculated in a 50 km2area around 

each mooring site, and bowheads can potentially be acoustically detected up to 20 kilometers 

away from the recorder. Thus, in the spring, bowheads are calling in what appears to be very 

thick ice; rather, it is more likely that they are using a lead that is nearby, but not immediately 

over the ice profiling instrument. This preference for thin ice is supported by Moore (2000) who 

demonstrated that bowhead whales prefer shallow, coastal waters in light ice years, but preferred 

outer shelf/trough waters (where an open lead develops) in heavy ice years. However, other 

studies (Clark et al., 1986; Moore and Laidre, 2006; Quakenbush et al., 2012) do show that 

bowhead whales migrate through areas with heavy ice conditions and no evidence of leads. 

Bowheads also showed a positive correlation with winds in the fall (Figure 109), with 

wind speed being one of the significant variables in the best model selected for both the inshore 

and midshore GAM runs. The direction and strength of the winds strongly influence ice 

concentration and movement throughout the Arctic (Weingartner et al., 2013). With the 

exception of the third peak in bowhead calling activity in fall 2010, all calling activity at the 

midshore location was associated with strong, consistent winds to the south-southwest. Strong 

persistent winds from one direction tend to push ice in that same direction (Weingartner et al., 

2013). Thus, the strong winds may have been an indicator to the bowheads that ice was about to 

be forced southward, thus potentially cuing their migration. Winds also force surface currents, 

which may help the whales to conserve energy by serving as a tailwind during their fall 

migration to the Bering Sea.
OCS Study

BOEM 2015-034

232

Chlorophyll was also a highly significant variable in the best model selected from the 

midshore GAM run (just fall data were included) and showed a strong correlation in the longterm plots. This may be a coincidence, given that chlorophyll levels increase once the ice begins 

to open, allowing both ice-associated algae to fall to the bottom and light to penetrate the sea 

surface. It could also indicate that chlorophyll may serve as a proxy for prey availability, which 

is an additional factor influencing bowhead distribution. Several authors have shown that 

bowhead migrations may also be driven by prey aggregations and availability, rather than by ice 

presence, and that feeding during the spring migration is more common than previously thought 

(Lowry et al., 2004; Moore and Laidre, 2006; Mocklin et al., 2012). The longer tail in spring 

calling activity described in Section VII.A.3 may be indicative of this spring feeding. In the 

spring, as ice breaks up and creates leads, ice-associated algae fall to the bottom and a water 

column phytoplankton bloom occurs until nutrients are completely stripped from the surface 

waters. This is then followed by increases in zooplankton abundance, the predominant prey item 

for bowhead whales (Carroll et al., 1987). In fact, some authors suggest that a reduction in sea 

ice may enhance feeding opportunities for bowhead whales in the short-term (e.g., Moore and 

Laidre, 2006; Moore and Huntington, 2008). Because bowhead whales rely on the larger, older 

stages of zooplankton, the exact relationship between spring production of ice and water column 

phytoplankton and the immediate feeding of migratory bowheads is not entirely clear. If sea ice 

recedes much earlier and results in decreases of carbon flux to the bottom, will that result in a 

reduction of energy transfer to planktivorous baleen whales? The answer is not certain, as some 

of the phytoplankton production lost to the benthos will be transformed into primary productivity 

that remains in the water column. We do know that euphausiids in the Bering Sea live for up to 

3-4 years and that the euphausiid Thysanoessa raschii may feed in the nepheloid layer (layer 

above the bottom that contains significant amounts of suspended sediment), particularly over 

winter (Hunt et al., in review). Therefore the observed relationship between chlorophyll and 

bowheads may also reflect a change in the distribution and availability of euphausiids during the 

onset of the spring bloom, rather than to the carbon contained in the phytoplankton. Although 

the long-term effects of reduced sea ice on bowhead whale populations remain unknown, the 

BCB population has increased steadily at 3.4% during two decades of sea ice loss in the Arctic 

(Walsh, 2008), therefore this population does not appear to be hindered by sea ice reduction at 

present. 

Aerts et al. (2013) analyzed marine mammal (cetacean and pinniped) distribution among 

the Burger, Klondike, and Statoil study areas during the 2008-2010 open-water season. With the 

exception of one sighting of two bowheads in Statoil in September, all bowhead whales were 

sighted in the Burger study area in October (Aerts et al., 2013). These authors attribute this to 

the migration paths of bowheads across the northern Chukchi to the Chukotka coast that cross

the Burger and Statoil study areas more often than the Klondike area. Although the Chukchi Sea 

is not listed as a Biologically Important Area for bowheads, feeding bowheads have been 

observed in the Chukchi Sea (see Section VII.A.3). It is also possible that the migrating 

bowheads are taking advantage of potential concentrations of epibenthic prey along their 

migratory route, and in particular in the benthic-driven Burger ecosystem. Further work (i.e.,

using the methods of Mocklin et al., 2012 to examine feeding behavior) is needed to determine if 

this is in fact the case.

Transport was a common significant variable for all top models at the inshore location. 

Transport in 2010-11 was very high for the Chukchi Sea, with moderate levels in 2011-12. 
OCS Study

BOEM 2015-034

233

Positive correlations were also seen between transport and calling activity in the fall on the long 

term plots (Figure 106-108). Moore (2000) indicated that during fall in the northern Chukchi

Sea, bowhead whales preferred coastal/shoal habitats (where our recorders are located) in high 

transport years, but preferred outer shelf/trough waters in years with low transport. She 

attributed this relationship to the advection of zooplankton through the Bering Strait into the 

Chukchi Sea each summer, as highly productive Bering Sea water flows into the Chukchi, 

driving primary productivity and high benthic biomass at the shoals. This may help explain why 

there is some evidence of feeding during the fall migration (see Section VII.A.3).

Nitrate and salinity both shared a similar pattern, in which they were increasing in the fall 

of 2010, but decreasing in the fall of 2011 at the inshore and midshore locations (Figure 35-39).

It is possible that this may be related to the distinct peaks seen in the fall migration of 2010 that 

was not evident in fall 2011. Perhaps there was some cue in the oceanographic conditions to 

which different age or sex classes responded. Additional data would help confirm this 

hypothesis.

Gunshot calls (bowheads)

Gunshot call activity is strongly correlated to both bowhead whale calling activity and ice 

conditions. It was determined that peak gunshot calling occurs near the end of the peaks in 

bowhead whale vocal activity (Figure 110 -112). However, peak gunshot calling is also tightly 

associated with ice cover. Gunshot calling would often begin once ice concentration reached 

100%, but while ice thickness was still less than 0.5 m. Once ice thickness increased, gunshot 

calling ceased, suggesting the migration of the bowheads had passed through the area. It is 

interesting that gunshot calls occur almost entirely in near 100% ice cover. Little is known about 

the function of a gunshot call, though it was first reported for this species by Würsig and Clark 

(1993). Perhaps this particular vocal signal is utilized by the bowheads to navigate through the 

ice, locate leads or openings, or possibly determine keel depths. 

In fact, several authors have suggested that bowhead whales use their frequency 

modulated (FM) calls to estimate ice thickness (Ellison et al., 1987; George et al., 1989). Ellison 

et al. (1987) determined that the echoes of bowhead FM calls off thick pack ice are up to 20 dB 

greater than the echoes off new ice. These authors suggest that bowheads can use the echoes to 

determine ice thickness and thus help navigate through the ice and find areas thin enough to 

break through. Although the calls analyzed in those studies were FM modulated tonal calls, the 

impulsive nature of gunshots would make them an ideal call type for perceiving relative levels 

from echoes off ice keels. Given the tight correlation between gunshot calls and ice, it is likely 

that they are using these calls to determine ice thickness and to navigate. If so, the importance of 

maintaining low ambient noise in this environment during the migration period may be critical, 

as an increase in noise may hinder their ability to both navigate around ice, and find an ice 

thickness suitable for breaking. Alternatively, the positioning of the peaks of gunshot calling 

activity near the end of each of the regular peaks in bowhead calling activity possibly suggests it 

may be used as a migration cue to assemble and move.

Beluga whale
OCS Study

BOEM 2015-034

234

Not surprisingly, the two significant common variables among the best models obtained 

from the GAM runs for the inshore location were ice concentration and month for the inshore 

location, and month for the midshore location. Like bowheads, beluga whale migrations are 

likely driven primarily by endogenous rhythms and secondarily by ice. Garland et al. (2015) 

also found a strong positive correlation between beluga whale calling activity and both month 

and ice on recorders spread from the northern Bering Sea to off Barrow, AK. However, as 

mentioned in Section VII.A.3, not all beluga whales migrate through the inshore leads in the 

Chukchi Sea in the spring; this may be the reason for the lack of correlation between ice 

concentration and calling activity at the offshore location. Prey availability may be the driving 

force behind this diversion from inshore leads. As ice melts and breaks up in the spring, iceassociated algae falls to the bottom, which creates a spike in chlorophyll measurements in the 

water column and a subsequent increase in zooplankton, the prey of cod. Both Arctic and 

saffron cod, the primary fish species for the BS and ECS populations of belugas, respectively, 

are planktivorous; however, Arctic cod are associated with the ice edge (Gradinger and Bluhm,

2004) while saffron cod are more coastal. It could be that only the BS population travels 

offshore, or it could be that sampling biases exist in stomach content analyses. Since evidence is 

available (Section VII.A.3) to show that the ECS population does travel to the ice edge to feed, it 

seems likely that both populations can feed on Arctic cod. Chlorophyll was a highly significant 

variable in the best model selected from the midshore GAM run and showed a high qualitative 

correlation in the long-term plots (Figure 113-114). In the majority of cases the peak in beluga 

whale calling activity preceded the spike in chlorophyll levels in the spring, but lagged during 

the fall. This may be because ice edge was not located near our recorder locations in the spring. 

In fact Figure 113-114 do not appear show the ice edge over the mooring locations during the 

spring migrations; the calling activity ceased prior to the majority of the ice break up. It is also 

possible, however, that the beluga were there and feeding; our recording system is unable to 

detect the high frequency echolocation signals they produce while feeding (see Section VII.A.3).

Belugas are also positively associated with the presence of polynyas. As seen in Figure 

113-115 (most evidently in the 2010-2011 deployment data and at the inshore and midshore 

locations) whenever ice concentration drops markedly (indicative of a polynya formation), 

beluga calling activity increases. Again, this may be due to high productivity at the ice edge, and 

thus high prey availability, or just availability of breathing holes. The correlation with wind 

speed is also likely a result of their association with the ice edge in the fall, as strong winds have 

a significant impact on the sea ice edge (Weingartner et al., 2013). However, no clear pattern 

with wind direction was seen (Figure 109).

Moore (2000) found that belugas in the fall preferred slope habitat in the northern 

Chukchi, rather than coastal habitat. Although we did not have any moorings located in slope 

waters, our results showed the opposite results of greater calling activity at the inshore recorder. 

This can be explained by the bifurcation in the beluga whale fall migration discussed in Section 

VII.A.3. She also reported that transport had little effect on habitat preference for beluga whales; 

they preferred slope habitats in all transport conditions. This is supported by the data presented 

here, in which transport was never listed as a variable in any of the top GAM models. However, 

Stafford et al. (2013) found higher concentrations of belugas along the Beaufort Sea slope when 

the Alaska Coastal Current was well-developed. These authors attribute the higher numbers to 

enhanced foraging opportunities, a result of the well-defined front that promotes an aggregation 

of prey species. Thus, movements of water masses do appear to have an effect on beluga whale 
OCS Study

BOEM 2015-034

235

distribution. There appears to be a slight association with nitrate in the spring, however there are 

only nitrate data for one year at that location; more data are needed for confirmation.

Gray whale

Although gray whales were included in the GAM models, there are very few data for this 

species (only the inshore location had more than one day of calling activity) as they tend to 

remain closer to shore and have low calling rates. Therefore, the top models selected may not be 

the best suited to explain the variability seen in the calling activity distributions, but they are the 

best models for these data at this time. There was only one variable that was consistent among 

the top models for the midshore location ice concentration. Not surprisingly, the long-term plot 

at the inshore location showed a negative association with ice (Figure 116). Gray whale calling 

activity occurred before the ice formed in the fall, and did not resume in the spring until ice 

concentration dropped considerably, which is consistent with what is known about gray whale 

distribution (e.g., Moore et al., 2000; Clarke et al., 2012). Gray whales spend the summer in the 

northeastern Chukchi Sea, feeding on benthic infauna primarily in the shallow, productive shelf 

waters between Wainwright and Barrow, (Moore et al., 2000; Clarke et al., 2015) but also in the 

waters between Wainwright and Hanna Shoal – but not on the Shoal itself (Clarke and Ferguson, 

2010; Hannay et al., 2013). Most gray whales begin their southern migration before the ice 

forms. Moore et al. (2000) found that the average percent ice cover at gray whale sightings was 

only 1%, indicating that gray whales prefer open water. 

A weak association with chlorophyll was present in the 2011-2012 data, though this was 

not evident in 2010-2011. However, our acoustic recorder failed in late June of 2011, and the 

peak in gray whale calling in 2012 occurred in late July, so we may have missed the peak vocal 

activity for summer 2011. Furthermore, gray whales tend to be less vocally active while feeding 

and migrating than when socializing or on their breeding grounds (Rasmussen and Head, 1965; 

Crane and Lashkari, 1996; Section VII.B this study), so low levels of calling activity were 

expected. 

Transport was not included as a variable in any of the models. Most likely this is due to 

recorder placement away from prime feeding areas. Moore (2000) found that gray whales in the 

northern Chukchi were strongly associated with coastal/shoal feeding areas when transport was 

high. Gray whales prey on benthic infauna that occur along the coast between Icy Cape and 

Barrow (Feder et al., 1994). In the Moore (2000) study, gray whales preferred these habitats 

exclusively in years of high transport when productivity was likely increased by the advection of 

nutrients from the Bering Strait. Although transport was not included as a significant variable in 

any of the models, it is interesting to note that 2010-11, a high transport year, also had 

considerably more gray whale calling activity than 2011-12, a year with moderate transport

(Section VIII.A). Perhaps the lower amount of calling activity in 2011-12 is a result of gray 

whales preferring offshore waters in moderate to low transport years (Moore, 2000), positioning 

them further from our recording locations. This correlation with transport (and therefore high 

biomass) is also evident in the transect line sampling data. 

Gray whales were visually sighted or acoustically detected in areas with high 

concentrations of zooplankton, particularly Pseudocalanus, C. glacialis, and larvaceans (Figure 

117-118). High zooplankton concentrations may serve as a proxy for benthic biomass. At the 

Barrow Canyon transect line, gray whales were concentrated at the inshore stations, where there 

were high concentrations of Pseudocalanus and C. glacialis. Schonberg et al. (2014) also report 
OCS Study

BOEM 2015-034

236

large concentrations of gray whales inshore between Wainwright and Barrow, a region with high 

densities of benthic amphipods. Furthermore, at the Point Hope line there was a strong 

correlation between gray whales and nitrate and ammonium, at the same location that there were 

extremely high concentrations of Pseudocalanus. Bluhm et al. (2009) suggest that most 

epifaunal biomass is between Bering Strait and Point Hope. They also correlate high relative 

abundances of gray whales in this area to an oceanographic front between the Bering Shelf 

Water and the Alaska Coastal Water (Bluhm et al., 2007). Fronts are known to support elevated 

biomass of hyper-benthic communities as well as marine mammal aggregations (Dewicke et al., 

2002; Mendes et al., 2002). In this region, the tight benthic-pelagic coupling is driven by high 

sedimentation rates allowing for high benthic biomass (Grebmeier et al., 2006a; Bluhm et al., 

2007).

Walrus

Month was again a highly significant variable that occurred in both the best model 

selected by the GAM for the midshore and offshore locations, as well as in the majority of the 

top five AIC and top five R-squared selected models. This again hints at endogenous cycles, 

which is not surprising for this species where many individuals are known to migrate between 

feeding grounds in the Arctic and mating grounds in the Bering Sea. Although they use the 

northward movement of the icepack to conserve energy on their migration, they still spend most 

of their time swimming (Fay, 1982), suggesting that internal timing trumps environmental 

factors.

Timing of the start of the summer pulse of calling activity varied slightly between 

mooring locations (Section VII.A.3). Although the best models obtained from the GAM run do 

not include ice concentration as a variable, the negative correlation between this variable (which 

is slightly different between locations) and the timing of the start of the summer calling pulse is 

clear in Figure 119-121. This lack of an ice concentration contribution is due to the patchiness in 

the overlap of the available datasets (see Appendix G, Section XVIII.G); no data from this 

summer calling pulse were used in the GAM runs. The only location to have overlapping ice 

thickness measurements (midshore), did show this variable to contribute significantly to the best 

model selected by that GAM run for the end of the summer calling pulse, indicating that the 

walrus move out of the area ahead of the ice front. 

The presence of high levels of walrus calling throughout the winter at the offshore 

location, as discussed in Section VII.A.3, was an unexpected result. In addition to the sea ice 

images (Figure 13) that show evidence of open water near this recording location, winter calling 

activity appears to start immediately after a dip in ice concentration (indicating a polynya) in the 

2010-11 data (Figure 121); there is a slight correlation in the 2011-12 data. These data indicate 

that there was, in fact, enough open water to explain the presence of this calling activity. With 

the presence of these open waters may come high productivity (Stirling, 1997), suggesting these 

individuals may prefer to remain in the Arctic instead of migrating south (see Section VII.A.3). 

ADCP volume backscatter was also a highly significant variable in the best model 

selected from the midshore GAM run. This is indicative of a highly productive area where both 

plankton and walrus benthic prey are available. Several authors have shown a tight correlation 

between primary production and coastal benthic infauna in the northeastern Chukchi Sea (e.g., 
OCS Study

BOEM 2015-034

237

Dunton et al., 1989; Grebmeier et al., 2006a). High zooplankton biomass (measured by ADCP 

volume backscatter) may further contribute to the rain of detrital flux that fuels the benthic 

community, the predominant food source for walrus (Sheffield and Grebmeier, 2009). This is 

also supported by the inclusion of chlorophyll and oxygen as a highly significant variable in the 

best model obtained by the midshore GAM run. Chlorophyll, along with oxygen and salinity,

were shown to peak during the summer pulse in calling activity at this location (Figure 120). 

This suggests productivity continued there in the fall; with oxygen present, any ice-associated 

algae that fell to the bottom during the spring would be able to thrive.

This correlation was also shown by Aerts et al., (2013), who compared marine mammal 

distributions among Burger, Klondike, and Statoil study areas. They found walrus in 

significantly higher densities around Burger than either Klondike or Statoil – likely a result of 

the more benthic-driven ecosystem and high biomass of benthos (Blanchard et al., 2013; Day et 

al., 2013) better suited to walrus foraging (Klondike is a more pelagic system, with currents 

coming from the Bering Sea bringing higher biomasses of zooplankton, and Statoil has both 

pelagic and benthic characteristics). This is also supported by Schonberg et al. (2014), who 

found that walrus aggregated in an area south of Hanna Shoal with high concentrations of 

bivalves and worms, although they state that this aggregation only took place when sea ice was 

present.

The results of the transect line sampling data with walrus visual and acoustic data also 

reveal a tight coupling between walrus presence and local production. At both the Wainwright 

and the Icy Cape transect lines, walrus were repeatedly sighted or detected at locations that had 

high concentrations of Pseudocalanus, C. glacialis, and larvaceans. There was also a strong 

correlation with high levels of nitrate and ammonium. Our results also coincide with Aerts et al. 

(2013) and Schonberg et al. (2014), in that there were more walrus detected on the Wainwright 

line near Hanna Shoal and the Burger study area than the Icy Cape line, which is situated 

between the two. As with the gray whales, walrus were found in high numbers at areas with 

tight benthic-pelagic coupling and high biomass. 

Walrus were not correlated with transport, which is shown to have a direct influence on 

primary productivity. However, this is likely due to the summer calling activity data being left 

out of the GAM runs (see Appendix G, Section XVIII.G). The inclusion of the full year 

ARCWEST and CHAOZ-X datasets will provide opportunities to examine this relationship in 

the near future.

Wind speed was also a highly significant variable in the best model selected from the 

midshore GAM run, which is likely a result of the effect of wind on ice movements. Wind speed 

has been shown to strongly influence ice cover (Niebauer and Day, 1989; Weingartner et al.,

2013), which would affect habitat availability for walrus. Walrus, especially females nursing 

pups, conserve energy by using the ice to move to potential areas of high biomass. However, 

walrus are not deep divers and prefer to feed in waters less than 100m deep (Fay, 1982). If this 

holds true, then the retreating sea ice may impact walrus populations in the future. This is 

explained in greater detail in Section XII.3. No consistent trends were seen between wind 

direction and walrus calling activity (Figure 122), although the majority of winds during the 

summer pulses of calling activity were heading to the southwest.
OCS Study

BOEM 2015-034

238

Bearded seal

It is important to note that calling activity is not a good proxy for presence of bearded 

seals outside of the mating season (see Section VII.B.3). Males produce elaborate vocal displays 

to advertise breeding condition or establish territories during the mating season (see Section 

VII.A.3). It is not surprising, therefore, that month was a highly significant variable that 

occurred in both the best model selected by the GAM as well as in the majority of the top five 

AIC and top five R-squared selected models for all locations. However, we did find year-round 

low-level bearded seal calling presence at all three locations. Little is currently known about the 

vocalizations of female bearded seals, therefore it is unknown what proportion of the detected 

calls in the non-breeding season are from each sex. Although this is in agreement with other 

passive acoustic studies (see Section VII.A.3), it is counter to the thought that most bearded seals 

winter in the Bering Sea and pass into the Arctic between April and June. Bearded seals are said 

to migrate south ahead of the ice edge in the fall. Figure 129-131 show that the small pulse of 

calling activity in October of both years and all locations precedes the increase in ice 

concentration by about a month. 

The midshore location had the best coverage of dates included in the GAM runs (see 

Appendix G, XVIII.G). For this location, ice concentration was a significant variable in best

model selected by the GAM and was a variable consistent among the top models (5 AIC/5 Rsquared). Bearded seals spent most of their time in the drifting pack ice, preferring areas with 

heavier ice concentrations (70-90%) than other phocid seals, but light enough that breathing 

holes do not need to be maintained. Currently, however, many bearded seals spend the summer 

in open water. This is because, like walrus, bearded seals are benthic feeders that prefer shallow 

feeding areas 100-150m deep. However, unlike walrus, bearded seals rarely haul-out, and 

instead just distribute themselves throughout the open water (see Section VII.A.3 for more 

details).

Ice thickness was not included as a significant variable in the model. It is important to 

note, however, that the scale of resolution is important (see bowhead whale discussion above; 

this section). In one case (at the offshore 2011-2012 location) there was a marked decrease in 

bearded vocal activity that corresponds with an unusually deep and persistent ice keel (Figure 

131), suggesting that the presence of thick, concentrated, multiyear ice pushed the seals out of 

the area temporarily, further supporting the hypothesis that bearded seals prefer areas with a 

specific ice thickness. 

Bengtson et al. (2005) suggest that bearded seal densities may also be influenced by 

levels of primary productivity and benthic biomass. Volume backscatter from the ADCP 

(bottom), indicative of zooplankton, was a highly significant variable that occurred in both the 

best model selected by the GAM (for midshore and offshore locations) as well as in the majority 

of the top five AIC and top five R-squared selected models at the midshore location. 

Zooplankton are prey for many species that bearded seals rely on (e.g., larval cod, capelin, 

shrimp; Lowry et al., 1980; Bluhm and Gradinger, 2008). Aerts et al. (2013) also found higher 

densities of bearded seals at the benthic-driven Statoil and Burger study areas than at the pelagicdriven Klondike study area. Our results suggest that the benthic-driven area extends westward 

from the Statoil and Burger study areas. In fact, bearded seal calling activity occurred at the 

highest levels at our midshore recorder location, which is situated between Burger and Klondike.

Transport was also highly significant variable contributing to the best model selected by the 
OCS Study

BOEM 2015-034

239

GAM run at the midshore location. High transport levels are linked with high productivity 

(Grebmeier et al., 2006a). Transport has been shown to be wind forced (Roach et al., 1995), so it 

is not surprising that wind speed was also a significant variable contributing to the best model 

selected by the GAM runs at both the inshore and midshore locations. Finally, the presence of 

salinity, as a variable of high significance in the best model selected by the GAM run at the 

inshore and midshore locations suggest an influx of water up Barrow Canyon that can possibly 

bring zooplankton into the area.

Our shipboard survey results also coincide with the results presented by Aerts et al. 

(2013), indicating that beaded seals are in areas of high overall productivity (both benthic and 

pelagic). Along the Icy Cape and Wainwright transect lines, bearded seals were associated with 

high densities of Pseudocalanus and larvaceans, and to a lesser extent, C. glacialis. Bearded 

seals were also associated with high nitrate and ammonium levels, though not to the same degree 

as walrus. These differences may be a result of varying diets (see Section VII.A.3). Walrus are 

specialist benthic feeders, while bearded seals are more generalist feeders. This generalist diet 

may provide bearded seals with more habitat opportunities, as evidenced by the broader spatial 

distribution seen both in the long-term moorings (see Section VII.A.2) as well as the short-term 

shipboard surveys (Section VII.B.2). Their diet may also make the bearded seal more adaptive, 

and thus less vulnerable to ecosystem change (See Section XI.1 ). Although bearded seals were 

found in the same areas as walrus, bearded seals are usually not found in the immediate vicinity 

of large concentrations of walrus (Burns, 1970). Our study presents similar results, in which 

walrus and bearded seals are frequently sighted along the Icy Cape and Wainwright transects 

near the shoal areas, but there were few overlapping sightings between the two species.

Lastly, this correlation of bearded seals and high productivity may help to explain the 

variation in the ramp up of calling activity among locations and between years (Section VII.A.3). 

Figure 130-129 suggest that the difference in the timing of high ice concentrations among 

locations/years did not coincide with the increase in bearded seal calling leading up to the mating 

season, although it was a significant variable in the best model selected by the GAM run at the 

midshore location. In fact, with the exception of the ice keel mentioned above, ice thickness also 

did not correlate well with the differences seen among the locations/years. However, nitrate and 

salinity seem to show a positive correlation with the different increases of calling activity among 

locations/years. These variables are proxies for productivity (increases in salinity reflect possible 

flow up Barrow Canyon which may bring increased zooplankton into the study area). For 

example, the 2010-2011 inshore levels of nitrate seem to increase slightly earlier (Figure 129), 

than those at the midshore location (Figure 130); this follows the earlier start of calling activity at 

the inshore location. Furthermore, the 2010-2011 nitrate levels are delayed compared with those 

from 2011-2012, again mimicking the trend in calling activity between the two years. With the 

addition of the full-year datasets from the BOEM-funded ARCWEST and CHAOZ-X projects, 

we should be better able to determine if such correlations exist. For the few locations/years 

where recordings exist, it appears that the sharp decrease in calling activity at the end of the 

mating season is positively correlated with the rapid reduction in ice concentration, which is also 

marked by a sharp increase in chlorophyll levels (Figure 129 - 131).
OCS Study

BOEM 2015-034

240

Ice noise

For the midshore location, that had the greater seasonal coverage included in the GAM 

runs, month was a highly significant variable that occurred in both the best model selected by the 

GAM as well as in the majority of the top five AIC and top five R-squared selected models. This 

speaks to the seasonal nature of ice in the Arctic. 

As expected, ice concentration was a highly significant variable contributing to the best 

model of ice noise activity selected by the GAM runs at all locations. Simply put, you cannot 

have ice noise without ice. Ice noise activity, however, cannot be used as a proxy for 

determining the presence of ice, since there are periods of time where ice is present but ice noise 

activity is absent (Figure 132-134). Nonetheless, it might be possible to determine whether 

certain environmental conditions exist during the periods of time while the Arctic is ice covered.

It appears that ice thickness is negatively correlated with ice noise activity at the 

midshore and offshore locations (Figure 132-134). This is intuitive, as thicker, more extensive 

ice would be less prone to fracturing and colliding than thinner, more irregular ice. Although it 

was a significant variable contributing to the best model selected by the offshore GAM run, the 

section of data with high ice thickness and low ice noise activity was not included in the GAM 

run; it did not contribute at all to any of the top AIC or top R-squared models selected by the 

midshore GAM run.

Although ice noise activity is present during ice formation in all locations/years,

detection of the ice retreat was possible only at the inshore location because of recorder failure at 

the other two locations (Figure 132-134). The only location to have ice noise activity data 

included in the GAM runs for this spring retreat is the midshore location. The significant 

variables contributing to the best model selected by the GAM run, in addition to month and ice 

concentration, were chlorophyll, PAR, turbidity, and wind direction.

Niebauer and Day (1989) showed that shifts in wind regimes have strong effects on ice 

cover. Interannual differences in ice noise activity at the midshore location are shown in Figure 

122. It appears as though ice noise activity is reduced with winds from the south, and increased 

with winds from the northeast. For the 2010-2011 deployment, wind direction shifted back and 

forth frequently and ice noise activity was at low levels. The 2011-2012 deployment saw much 

greater ice noise activity levels, while the winds remained fairly consistent from the northeast. 

Petrich et al. (2012), found that for an 11 year record of ice break up at Barrow, AK (a location 

they say is representative of the Chukchi coast), ice break up happened with all wind directions 

but those toward the north or northwest. So our results may indicate that the winds to the SSW 

are forcing the ice to move, while winds to the north are not. If this is the case, it is then 

plausible to suggest that these ice noise activity levels would be higher at the inshore location, 

closest to the high energy zone between the pack ice and shorefast ice.

The other variables that contributed significantly to the top model obtained from the 

midshore GAM run are those that are coincident with ice conditions. During the spring as the ice 

breaks up and melts, the ice-associated algae fall off the ice, causing bottom chlorophyll levels to 

spike. PAR and turbidity are measures of light levels and suspended material in the water 

column, and are expected to increase as the ice breaks up, melts, or is blown out of the area. 

Chlorophyll is the only of these variable that was plotted against ice noise activity levels in
OCS Study

BOEM 2015-034

241

Figure 132-134. There it is apparent that a negative correlation exists; no ice, and therefore no 

ice noise, is present when chlorophyll levels peak.

It is important to note, however, that the ice noise activity levels described here reflect 

the percentage of 90 s time intervals per day when ice noise was present. They do not indicate 

actual ambient noise levels. Please refer to Section XII.3 for a discussion on how ambient noise

levels correlate to environmental parameters.

4. Conclusions

The Chukchi Sea ecosystem is complicated: landscape ecology, and regional and local 

forcing all combine to determine whether or not there will be favorable conditions for both the 

permanent and transitory residents. For example, northern Bering Sea ice dynamics and 

transport through the Strait via the Bering and Anadyr currents, transport nutrients and plankton 

to the Chukchi. The nutrient-rich currents enable local primary production on the shallow 

Chukchi shelf that nourishes many different trophic levels. Should the sea ice dynamics on the 

northern Bering Sea shelf change, this may alter nutrient concentrations in the Bering current and 

impact the Chukchi shelf. Similarly, terrestrial events far interior to Alaska, determine the 

timing and heat content of the Yukon River. The Yukon River has a strong influence on the 

amount of heat and salt transported to the inshore region of the Chukchi Sea as far north as 

Barrow. As a result, changes to the flux of Yukon River water into the Bering will impact the 

Chukchi. While mean transport is northeastward along the Alaskan Coast and mean winds are 

out the northeast, events of strong northward winds increase coastal transport and provide 

opportunities for residents in the form of creation of polynyas, on-shelf transport of Arctic water, 

and up-canyon flow at Barrow Canyon. 

The Chukchi Sea is predominantly a benthic-driven ecosystem, with nutrient-rich waters 

being advected from the Bering Strait and episodically from the Arctic basin, augmenting 

primary production up to the Barrow Canyon. The current balance of benthic-pelagic production 

favors the benthos, but still results in high biomass of zooplankton and high biomass of benthic 

epifaunal and infaunal invertebrates that comprise the diets of resident Arctic benthic feeding 

species such as gray whales, walrus, and bearded seals. Both bowhead and beluga whales 

undergo consistent, predictable seasonal migrations that are strongly correlated with both month 

and ice concentration, but both also had several variables that can serve as proxies for prey 

availability show up as significant contributors to the best models selected by the GAM runs. 

There was a strong correlation between gunshot call activity and both ice presence and thickness, 

We suggest that bowhead whales use this particular call type to assess ice thickness, to cue 

migration, or both. Gray whale calling activity was sparse throughout the two years of long-term 

recordings most likely due to placement of the recorders along with low calling rates. This 

calling activity, however, showed a significant correlation with ice concentration and a weak one 

(in 2011-12) with chlorophyll. There is also a tentative correlation between increased calling 

activity during the high transport deployment year. Gray whales were also sighted in large 

clusters near the mouth of Barrow Canyon and in the middle of the transect line off Point Hope

(Figure 1); areas known to have high biomass of benthic infauna and epifauna. Both of these 

locations also showed high concentrations of zooplankton (larvaceans and Pseudocalanus) as 

well as high amounts of nitrate and ammonium. 
OCS Study

BOEM 2015-034

242

Month and ice thickness, along with several variables that can serve as proxies for prey 

availability, were significant variables contributing to the top model selected by the GAM runs 

for walrus calling activity, but ice concentration seems to be negatively correlated with the 

timing of the start of the summer pulse in calling activity. Possible presence of a polynya and 

prey availability make the surprising result of overwintering walrus in the northeastern Chukchi 

Sea plausible; we suggest these are juvenile males that did not migrate to the Bering Sea mating 

grounds. Month and ice concentration were significant variables contributing to the top model 

selected by the GAM runs for bearded seals, as well as several variables that serve as proxies for 

prey availability that might be the reason the ramp-up of bearded seal calling levels varies 

interannually and among locations. Both walrus and bearded seals were found along the Icy

Cape and Wainwright transect lines near the shoals, an area known to have high biomass of 

benthic epifauna and infauna, but there were few overlapping sightings between the species. Ice 

noise activity levels were highest inshore and seemed to be influenced by wind direction. At all 

locations ice concentration was positively correlated and ice thickness was negatively correlated 

with ice noise activity levels.

5. Recommendations

The data collected for the CHAOZ project demonstrate the utility and benefit of 

concurrent zooplankton, oceanography, and marine mammal monitoring. These data, combined 

with those currently being collected for the ongoing BOEM-funded ARCWEST and CHAOZ-X 

projects represent the only long-term integrated dataset of its kind from the Chukchi Sea lease 

area and Alaskan Arctic in general. We therefore recommend continuation of the long-term 

mooring deployments. With current modifications to the moored TAPS6-NG instruments, we 

will be able to collect data for a full year, allowing for assessment of trophic interactions on an 

annual time scale. It will also be possible to establish multi-year patterns in marine mammal 

distributions as they relate to indices of zooplankton and oceanographic conditions. As 

mentioned earlier, the addition of CPODs on the passive acoustic moorings would allow for 

determining foraging efforts of belugas, which would aid in understanding habitat utilization as 

well as correlations with potential prey.

We also recommend continuation of the integrated biophysical shipboard surveys 

conducted during this study. These surveys provide data on the fine-scale vertical resolution of 

zooplankton abundance as they correlate with oceanographic indices, nutrients, and distribution 

of marine mammals. To maximize marine mammal detections during shipboard surveys, it is 

essential to have both passive acoustic monitoring and visual survey components. Since each 

method is well-suited to particular species, together they provide a more complete picture of 

marine mammal distribution. Integration of recent benthic ecology studies supported by BOEM

(e.g., Grebmeier et al., 2015) will help to address prey availability for those mammals that feed 

on benthic epifauna and infauna.
OCS Study

BOEM 2015-034

243

XI. LONG-RANGE PREDICTIONS OF HABITAT USE BY ARCTIC AND 

SUBARCTIC MARINE MAMMAL SPECIES 

1. Discussion

Current situation

The high benthic biomass and productivity in the Chukchi is driven by a combination of 

highly productive waters being advected through the Bering Strait and locally produced primary 

productivity. It is estimated that approximately 40% of the productivity in the Chukchi Sea shelf 

is a result of advection from the Bering Sea (Springer et al., 1989; Grebmeier et al., 2006b; 

Grebmeier, 2012). Dense, nutrient-rich Bering Sea water/Anadyr water flows north through 

Bering Strait, driven by a pressure head between the Pacific and Arctic Oceans and modified by 

strong north-south winds (Figure 135). This dense Bering Sea water transports high levels of 

nutrients to the bottom of the Chukchi Sea shelf, which then drives primary, and thus secondary,

productivity. While surface nutrients are depleted, a rich supply of bottom nutrients are available 

to support subsurface blooms. Nutrients are recycled, providing significant concentrations of 

ammonium, a preferred form of nitrate for phytoplankton. Episodic wind events from the 

northeast also result in up-canyon flow from Barrow Canyon, introducing nutrients onto the 

shelf.

Locally, ice-associated algae fall to the bottom as the ice breaks up and melts. As these 

phytoplankton fall to the bottom, they are “trapped” in the subsurface by a strong pycnocline and 

the absence of strong winds. Carbon that becomes incorporated into the sediment fuels benthic 

secondary production. Some of the plant cells may remain suspended and be photosynthetically 

active depending on light concentration. 

Finally, the Alaska Coastal Current consists of warm, fresh water from the Yukon and 

other river runoff close to shore. The predominant, but weak, winds from the southwest during 

summer support transport of this water mass northeast along the coast. This strengthens the front 

that occurs between the ACC and Bering Sea water. 

In the discussion below, we use this project’s regional ocean atmosphere circulation 

model to forecast future conditions in the Chukchi shelf ecosystem. The models predict late 

arrival of the sea ice and longer open-water seasons in the Chukchi Sea (Section IX.2). Although 

not much changes before 2050 in the ensemble mean predictions for spring, there are episodic 

early sea ice retreat events predicted by the models beginning in 2020. 

Although the models predict a late arrival of sea ice in the fall, this element has no 

bearing on future predictions. Ice is a defining characteristic of the Arctic system; it is extensive 

during late fall, winter, and into spring. The timing of ice retreat in the spring is important in 

determining ocean temperatures; an early ice retreat permits greater solar heating and results in 

warmer sea surface temperatures. An early ice retreat also likely results in an earlier export of 

chlorophyll to bottom. The arrival of ice in November is primarily a result of atmospheric 

conditions, with ocean temperatures playing a minor role – if the sea surface temperature is very 

warm, the arrival of ice may be delayed by a few days. Therefore habitat predictions that follow 

are based on ice extent projections of just the spring ice retreat.
OCS Study

BOEM 2015-034

244

Based on the strong correlation with ice that was evidenced in the GAM results (Section 

X), marine mammal species will likely be impacted by reductions or changes in sea ice cover.

Given this, and the current state of water mass circulation in the Alaskan Arctic, we foresee two 

possible scenarios, both determined by wind patterns. In the first scenario, with earlier ice retreat 

or melting and stronger spring/ summer winds, stratification of the water column would be 

delayed until solar heating effected stratification and the spring phytoplankton bloom would be 

delayed. Weaker stratification throughout the summer and fall (due to weaker salinity gradients) 

would enable more summer blooms and an earlier fall bloom. In the second scenario, ice retreat 

is again earlier (due to melting or advection), but now the winds are weak and salinity gradients 

are strong. The spring phytoplankton bloom occurs earlier, but summer blooms may be less 

common and the fall bloom occurs later or not at all. These two scenarios, and the possible 

outcomes of each, are discussed below. Figure 136 is a schematic representing the current 

conditions in the Chukchi, as well as the two different scenarios. It is important to note that the 

timing of ice melt relative to the solar cycle is critical. If the ice melts early then potential ice 

algal production will be lost.

FIGURE 135. WATER MASSES AND CIRCULATION IN THE CHUKCHI SEA. FIGURE MODIFIED FROM QUESTEL ET AL. 

2013.
OCS Study

BOEM 2015-034

245

FIGURE 136. SCHEMATIC OF ECOSYSTEMS AND LOWER TROPHIC LEVELS DURING TWO POSSIBLE FUTURE 

SCENARIOS. A) CURRENT CONDITIONS IN THE CHUKCHI SEA. AN ICE ALGAE BLOOM OCCURS UNDER THE ICE IN 

LATE WINTER AND EARLY SPRING. THIS IS EXPORTED TO THE BENTHOS WITH THE MELTING ICE. IN SUMMER, A 

SUBSURFACE BLOOM OCCURS BELOW THE PYCNOCLINE. AFTER THE RETREAT OF ICE AND STABILIZATION OF

THE WATER COLUMN, A SURFACE PHYTOPLANKTON BLOOM OCCURS. IN THE FALL, WITH THE MIXING OF 

NUTRIENTS INTO THE SURFACE A FALL PHYTOPLANKTON BLOOM CAN OCCUR. IN BOTH SCENARIOS ICE WILL 

RETREAT EARLIER B) STRONG WINDS RESULT IN A MIXING OF THE WATER COLUMN AND LOW VERTICAL SALINITY 

GRADIENTS AND A DELAYED SPRING PHYTOPLANKTON BLOOM. C) SCENARIO 2. LOW WINDS RESULT IN STRONG 

STRATIFICATION DUE TO HIGH VERTICAL SALINITY GRADIENTS AND AN EARLIER SPRING PHYTOPLANKTON

BLOOM. 

Scenario 1: Early ice retreat and strong winds

In the first scenario, early ice retreat is coupled with strong winds. This leads to longer 

open-water seasons. The strong winds mix the water column, introducing more nutrients from 

the bottom to the surface and erasing any vertical salinity gradient. The mixing also delays the 

spring bloom. Early retreat would shorten the period during which ice and associated algae 

cover the area and shorten the growing season for the ice algae, reducing the flux of carbon to 

the benthos. The spring phytoplankton bloom is then delayed until the system stratifies due to 

solar heating. The weaker stratification during summer would allow winds to mix into the 

bottom layer, introducing nutrients into the surface layer and thus supporting increased summer 

phytoplankton production. This would provide more food for the pelagic consumers during the 

short summer. Weakened stratification would also promote an earlier fall bloom. This scenario 

presently applies to the southern Bering Sea. The scenario predicts a shift from a benthic 

towards a more pelagic-dominated ecosystem. Such a regime shift may already be occurring in 
OCS Study

BOEM 2015-034

246

the northern Bering Sea. The retreat of ice cover led to a decline in the flux of carbon to the 

sediments, which caused a decrease in benthic prey populations, an increase in pelagic species, 

and geographic displacement of marine mammal populations (Grebmeier et al., 2006b; 

Grebmeier, 2012). 

In addition, an earlier and longer period of winds from the SW would also have the effect 

of forcing the ACC to remain close to shore, strengthening the northeast current and increasing

advection of coastal plankton from the Bering Strait. This strengthening also tends to 

concentrate prey near Barrow Canyon (e.g., Stafford et al., 2013), creating enhanced foraging 

opportunities for higher trophic levels.

The effects of this shift to a more pelagic system on marine mammal distributions are 

somewhat disparate based on feeding strategies and are summarized in Table 29. Those species 

that are generalist feeders and prey upon both benthic and pelagic species will fare better than the 

specialist feeders that rely only on the benthos. Bowheads have the ability to assume a generalist 

diet. Although they rely heavily on energy-rich epibenthic zooplankton, they feed throughout the 

entire water column, and therefore are not limited to epibenthic prey (Lowry et al., 2004). While 

they feed predominantly in the eastern Beaufort Sea, they also engage in feeding along their 

migration route, taking advantage of any prey aggregations in the Chukchi Sea or near Barrow 

Canyon (increased in this scenario). In addition, the strengthened ACC in this scenario, 

combined with a slight shifting of winds from the northeast could result in localized upwelling 

along the coast, and potentially create “krill traps” along the Chukchi coast similar to those seen 

near Barrow (Okkonen et al., 2011). Although their additional resilience to changes in sea ice 

(as a physical structure) and ability to utilize multiple habitats increase their ability to adapt, they 

have still been identified as “moderately sensitive’ to the effects of climate change (Laidre et al., 

2008), however, because of their slow population growth and stable innate migration patterns.

Perhaps of more immediate concern, changes to the spring ice extent could have a 

negative impact on subsistence hunting along the Chukchi coast. Bowheads historically have 

relied heavily on leads that open in shallow shelf waters during their spring migration. However, 

as mentioned above, they have been observed in all ice conditions and a variety of habitats. 

Therefore, a reduction in ice in the spring may reduce the need for the bowheads to remain close 

to shore, allowing them to migrate further offshore. This will have a substantial effect on spring 

native subsistence hunting, which has already been affected due to a thinning of the ice which 

restricts the hunters’ access to the leads. A migration shift offshore would reduce harvest 

success (e.g., exceeding time limit to land whale before it spoils) while increasing risk to the 

hunters (e.g., rough seas, ice movements, decreased communication abilities) if they have to 

travel further from shore.

Beluga whale diets are even more diverse than that of bowhead whales. They prey on 

benthic, epibenthic, and pelagic organisms (Section VII.A.3), making them excellent feeding 

generalists. Currently, the ECS population feeds on benthic prey in Kasegaluk Lagoon while 

molting and calving, before dispersing further offshore to forage. In this scenario, they may shift 

their diet to take advantage of the increased pelagic prey availability and remain closer to their 

core summer areas, potentially increasing their presence in the lease area. They may also 

concentrate near Barrow Canyon, where a strong ACC causes prey aggregation, resulting in 

higher numbers of belugas (Stafford et al., 2013; Hauser et al., 2014). Alternatively, if the

inshore pelagic prey is not ideal, they may disperse farther offshore to remain near the ice edge 
OCS Study

BOEM 2015-034

247

and its associated productivity. On par with bowheads as far as resiliency to sea ice conditions, 

beluga whales can also utilize multiple habitats. In the Laidre et al. (2008) study, beluga whales 

were regarded as the least sensitive cetacean to climate changes; however, gray whales, which 

have been referred to as the most versatile and adaptable cetacean species (Moore and 

Huntington, 2008), were not included in the study. Finally, because the spring beluga hunt in the 

Chukchi Sea occurs when the whales are in Kasegaluk Lagoon for their annual molt, changes in 

prey distribution will likely not affect the subsistence harvest. 

As mentioned above, gray whales are extremely adaptive and versatile (Moore and 

Huntington, 2008). Although predominantly benthic feeders, in the Chukchi, they feed on a 

wide variety of both benthic and pelagic invertebrates (Section VII.A.3), using several different 

foraging strategies (see Darling et al., 1998 for summary). As such, they are even better suited to 

adapting to ecosystem regime shifts than beluga whales. A shift to a more pelagic ecosystem 

will likely have one of two effects. Gray whales may opt to take advantage of the available 

pelagic prey. Their diets near Vancouver Island are predominated by pelagic invertebrates 

(Darling et al., 1998), so a dietary shift is not unreasonable. Another possibility is that gray 

whale distribution will shift farther north or east into the Beaufort Sea where there may still be a 

high benthic biomass. Such redistribution based on regime shift has already been reported in the 

northern Bering Sea (Rugh et al., 2001).

Walrus are the most specialized feeders of any Arctic marine mammal included in this 

study. They are benthic specialist feeders that prefer to eat bivalves, and so the decline in 

benthic biomass predicted by this scenario would be detrimental to the population. In fact, 

several authors suggest that walrus will decline if the benthic-pelagic coupling declines (e.g., 

Bluhm and Gradinger, 2008). In addition, Laidre et al. (2008) suggested that walrus are highly 

sensitive to changes in sea ice; without ice cover, exploiting such a large foraging area would be 

physiologically taxing. Walrus have their calves in the Bering Sea; this is therefore not affected 

by an early Arctic spring ice retreat as the two seas are not coupled in that way. Females nursing 

pups prefer to haul out on ice to rest near areas with high productivity; however, walrus are 

limited to shallow water depth (~100m). Unlike beluga whales that can follow the ice edge 

north, walrus will abandon the ice and begin hauling out on land, far from the best foraging 

grounds. Furthermore, a lack of sea ice haul-out locations will result in extremely large 

aggregations at the shore haul-outs. This has already been witnessed along the coast of Point 

Lay, where walrus were sighted hauled out in unprecedented numbers (ASAMM flight report 

240). On September 27, 2014 an estimated 35,000 walrus were hauled out on the shore at Point 

Lay, whereas just four days earlier, there were only an estimated 1,500 individuals. A similar 

event was also reported on the far eastern Russian coast. Female walruses tend to avoid hauling 

out on land, perhaps due to the increased risk to calves in the presence of large herds (Laidre et 

al., 2008). These large haul outs not only lead to pups being separated from their mothers or 

perhaps crushed by large males, but also to increased local competition, exhaustion, and possible 

starvation. The passive acoustic data from Hannay et al. (2013) and radio tag data from Jay et al.

(2012) suggest that the walrus are already moving out of the Chukchi Sea, not based on ice 

advance, but on the retreat of the ice edge. The tag data also found that walrus are moving to the 

Chukotka coast (where colder temperatures have resulted in a slower ice retreat than that seen in 

the Alaskan Chukchi) prior to heading down through the Bering Strait. Therefore, while the 

actual effect of sea ice reduction on walrus populations remains speculative, it is likely to have a 

negative impact (Moore and Huntington, 2008). 
OCS Study

BOEM 2015-034

248

Bearded seal responses to ice reduction are perhaps more difficult to predict, although 

they have been regarded as the least sensitive marine mammal species to sea ice reduction 

(Laidre et al., 2008). They are generalist feeders, preying on both benthic invertebrates and 

pelagic fishes, and therefore, are likely to be unaffected by the pelagic shift in this scenario. They 

prefer moderately heavy ice cover, and so can occur further north. However, like walrus, 

bearded seals tend to prefer to feed in shallow water depths (< 200 m) and are limited to how far 

north they can go. Bearded seals rarely haul out on land, instead remaining pelagic in the open 

water. Although this is more energy intensive than remaining with the ice, the issues 

surrounding walrus young being trampled in large land haul-outs is therefore not a concern for 

bearded seals. Bearded seals also use the ice to whelp/mate/molt between March and late June. 

Weaning is complete by May, so any small changes in spring sea ice retreat will not have an 

immediate impact on their reproductive success.

For those Arctic species that can thrive in a pelagic-driven ecosystem, the increased open 

water period may allow for a longer feeding season. However, a shift to a longer feeding season 

may be delayed due to endogenous migratory behavior (see Section X). The lack of sea ice and 

longer open-water season, plus increased phytoplankton production, will also open up new 

habitats and provide increased foraging opportunities for subarctic species. There is already 

evidence of these species being reported farther north into the Chukchi (e.g., Clarke et al., 2013; 

Delarue et al., 2013b; Crance et al., 2015). This influx of subarctic species into the northern 

Chukchi may lead to increased competition with resident Arctic species. In addition to cetaceans

(fin, humpback, minke, and killer whales), the longer open water season may open up new 

foraging habitat to ribbon seals as well. Although ribbon seals generally remain in the Bering 

Sea during the ice-free months, a shift to a pelagic system may provide new foraging 

opportunities. 

As mentioned previously, longer open-water seasons and less ice cover could result in 

more ocean storms, and storms with higher surface wind speeds. In addition to mixing the water 

column, another effect of increased storms will be an increase in ambient noise. Winds and 

surface waves have a substantial effect on ambient noise levels. Therefore, an increase in the 

number and magnitude of the storms will likely result in increased ambient noise levels by an 

estimated 10-15 dB (see Section XII). This would negatively impact the communication 

efficiency of marine mammals, and increase the potential for masking of acoustic signals 

between conspecifics.
OCS Study

BOEM 2015-034

249

TABLE 29. SUMMARY OF THE EFFECTS OF SCENARIO 1 ON KEY MARINE MAMMAL SPECIES.

Species Impacts

Bowhead 

whales

 Potential changes to timing and migration routes may impact subsistence harvest

 Increased water column production and transport may increase plankton prey 

availability

Beluga whales  Subsistence harvest will likely not be affected

 Increased water column production and transport may favor pelagic fish prey, but not 

Arctic cod

Gray whales  Decrease in ice may increase access to foraging habitat

 Declining benthic prey availability; may switch to alternative foraging strategies

Walrus  Decreased access to ice over shallow feeding grounds; increased haul-outs on shore and 

increased risk to adult females and calves

 Declining benthic prey availability

Bearded Seal  Decreased access to ice over shallow feeding grounds; increased energy expenditure 

while foraging

 Declining benthic prey availability may result in a shift in foraging strategies to take 

advantage of increased pelagic prey availability

Ambient Noise  Increases due to greater ocean storm noise as well as increased ship traffic. Estimated 

10-15 dB increase in ambient noise levels

 Increased ambient noise levels could result in decreased communication or foraging 

efficiency for all marine mammal species

Scenario 2: Early ice retreat and weak winds

The second scenario presents a very different possibility, but also includes the interaction 

of ice melt and winds. Ice is predicted to leave the region earlier in the year, but with weaker 

winds, a “cap” of low salinity water forms on the surface, which will not mix without substantial 

wind energy (i.e., there is strong vertical stratification). Subsequent solar heating is limited to 

the upper layer, which enhances the stratification. Earlier stratification will support an early

surface phytoplankton bloom, which will utilize the remaining nutrients in the upper layer. This 

cap will then limit further surface phytoplankton blooms. Although the increased penetration of 

light below the pycnocline may allow subsurface phytoplankton blooms to form, the extent of 

the productivity of these blooms is unknown. In addition, the strong stratification can delay the 

mixing of the water column in the fall, thus reducing the fall phytoplankton bloom. 
OCS Study

BOEM 2015-034

250

In addition, weak winds would allow the warm, fresh ACC water to spread out over the 

Chukchi shelf rather than remain along the coast. This would add to the melt water cap, further 

stratify the water column, and slow the transport of ACC water from the northern Bering Sea. 

Similar changes have already occurred near the MacKenzie River Delta (Wood et al., 2013). 

Studies have shown that an increase in fresh, warm water runoff into the Beaufort Sea causes the 

ice in that region to melt and retreat earlier than adjacent coasts with minimal runoff (Wood et 

al., 2013). These studies were conducted over a decade ago, and ice retreat and fresh water 

runoff have increased considerably since then. This increased input of fresh water will lead to 

larger amounts of fresh/brackish water at the coastal nearshore areas. Studies have shown that 

areas with brackish water at river deltas have decreased biomass, diversity, and productivity; a 

result of decreased light levels in the water due to suspended sediments and dissolved organic 

matter (Bluhm and Gradinger, 2008). Thus, with decreased ice inshore and lower light levels, 

less production falls to the bottom and inshore benthic prey populations may decline.

A similar situation occurring in the northern Bering Sea could have a large impact on the 

productivity of the Chukchi shelf. If the winds weaken on the northern Bering Sea shelf, then it 

will leave a stratified water column in the northern Bering Sea, which may reduce the flow of 

nutrient-rich Anadyr water into the Chukchi Sea. With a decreased influx of nutrients, 

phytoplankton production will have to rely to a greater extent on remineralization of locallyproduced organic matter to produce nutrients. Furthermore, without the strong ACC moving 

north along the coast, there would be a lessening of the front that aggregates prey for marine 

mammals.

As in Scenario 1, the timing of ice melt relative to the solar cycle (light availability) is 

important. If the ice melts earlier, then the ice algal contribution to vertical carbon flux will be 

less. Similarly, the primary production in the surface waters may decline because the low 

salinity surface waters and weak winds prevent mixing of nutrients into the surface waters. 

These predicted events would reduce the amount of carbon reaching the benthos and secondary 

production. The effects may be most intense nearshore where the influence of the warm, fresh 

water is the greatest, thus the nearshore benthic biomass may decline relative to historical levels 

with increases in relative biomass farther offshore around the lease area or farther to the north as 

the ice retreats. 

The effects of these changes on key marine mammal species are presented in Table 30. In 

this scenario, benthic production and biomass are predicted to decline, and the extent of pelagic 

production is unknown. As in Scenario 1, feeding generalists should fare better than feeding 

specialists. Bowhead whales are generalist feeders, feeding on both planktonic and benthic prey, 

although they rely heavily on epibenthic zooplankton. Declines in benthic biomass (under this 

scenario) would result in decreased in foraging opportunities, especially nearshore; however, we 

do not know if there will be an increase in the production of their pelagic prey. If there is an 

increase in pelagic prey, they will likely shift to a pelagic-dominated diet. Otherwise, they will 

need to shift their migration offshore to take advantage of foraging opportunities. In either case, 

the weakened ACC will result in an increase in brackish water, decreasing overall productivity 

nearshore. This could negatively affect native subsistence hunting, which is currently restricted 

to nearshore waters, and could bring the whales much closer to the lease areas.

Beluga whales are generalist feeders, and as such may begin (or continue) relying more 

heavily on pelagic prey as their predominant food source. As such, their distribution and 
OCS Study

BOEM 2015-034

251

migration patterns may shift slightly. Although they will still go to Kasegaluk lagoon to molt and 

calve, their distribution patterns after molting may change as a result of prey availability. If 

sufficient pelagic prey are available, they may shift their diet and remain closer to their summer 

core areas; however, insufficient pelagic biomass inshore may cause them to disperse farther 

offshore, following the ice edge and its associated productivity. Given the reduced nearshore 

biomass, gray whales may have to move farther offshore (closer to the lease area), or potentially 

into the Beaufort Sea to a more suitable habitat with increased foraging opportunities. 

Nevertheless, the versatility of the gray whale and their generalist feeding strategy make them 

the best suited to adapt to ecosystem shifts. 

As feeding specialists, walrus are the most vulnerable under this scenario. With a lack of 

ice, walrus (especially females nursing calves) would have to haul out on land, then go further 

offshore, where benthic productivity is higher, to feed. This would be energetically unfavorable, 

potentially affecting survival and reproductive success. As in Scenario 1, walrus would be 

hauling out in extremely large numbers, which would be detrimental to calves and the overall 

health of the population (Udevitz et al., 2013), so they may choose instead to relocate to the 

Chukotka coast. Bearded seals may fare better than walrus, given both the short amount of time 

it takes to wean their pups, as well as their flexible foraging strategies. Although bearded seals 

are limited to foraging at depths less than 200 m, given the shallow depth and broad expanse of 

the Chukchi Sea shelf, the only limiting factor for this species would be the extent of the 

decreased productivity offshore. 

As with Scenario 1, any decrease in sea ice will lead to an increase in subarctic species 

(e.g., fin, humpback, minke, and killer whales) encroaching into Arctic waters. Longer openwater seasons may increase foraging opportunities for non-resident species, and create an 

increase in competition with resident Arctic species. Moreover, given there is already evidence 

of subarctic species moving farther north into the Arctic in the current benthic-dominated system 

(e.g., Clarke et al., 2013; Delarue et al., 2013b; Crance et al., 2015), it is reasonable to assume 

that if benthic productivity is already depressed, pelagic productivity does not increase, and 

foraging effort outpaces primary or secondary productivity, any increase in competition will put 

strain on the higher trophic levels. This strain may be more than expected in Scenario 1, given 

that this scenario does not necessarily include an increase in pelagic biomass.

In regards to noise levels, a strong pycnocline created by the cap of fresh warm water 

would create a dramatic change in typical sound transmission properties such that sounds would 

tend to be trapped within the layer in which they were produced and be poorly transmitted 

between layers. This would then affect communication between vocalizing animals, reducing 

the communication range for individuals in different layers. As with Scenario 1, the ambient 

noise levels are expected to increase by an estimated 10-15 dB.
OCS Study

BOEM 2015-034

252

TABLE 30. SUMMARY OF EFFECTS OF SCENARIO 2 ON KEY MARINE MAMMAL SPECIES.

Species Impacts

Bowhead whales  Potential changes to timing, migration routes, and Chukchi residency to impact 

subsistence harvest

 Decreased production and transport may decrease prey availability

Beluga whales  Subsistence harvest will likely not be affected

 Decreased production and transport may result in a shift in diet or further dispersal 

offshore

Gray whales  Decreased production and transport may shift distribution farther offshore

 Shift in distribution may bring them closer to lease area

Walrus  Decreased access to ice over shallow feeding grounds; increased frequency of haul-outs 

on shore

 Declining benthic prey availability, particularly nearshore

Bearded Seal  Decreased access to ice over shallow feeding grounds

 Declining benthic prey availability, particularly nearshore; their generalist foraging 

strategy may give them an advantage if pelagic prey increases

Ambient Noise  Increased noise levels due to strong water column stratification and increased shipping 

traffic. Ambient levels may increase by 10-15 dB

 Increased ambient noise levels could result in decreased communication or foraging 

efficiency for all marine mammal species

2. Conclusions

We predict two possible scenarios, both of which are dependent on the winds. If the 

winds remain strong and persist from the southwest, then sea ice may be forced out of the area 

before substantial melting and the spring ice algal bloom occurs. This will result in a loss of 

carbon flux to the benthos, a strengthening of the Bering Sea water and ACC into the Chukchi, 

and higher transport of nutrients into the system. However, an increase in ocean storms will 

cause mixing of the water column, resulting in a shift to a pelagic regime. Our climate model 

predictions estimate that the magnitude and direction of the currents will remain similar to 

present day levels. If this holds true, then this pelagic-shifting scenario is highly likely. 
OCS Study

BOEM 2015-034

253

The second scenario suggests that a decrease in winds and continued warming of the 

atmosphere will cause the ice to melt earlier in the year, resulting in a more heavily stratified 

water column. Reduced winds will result in a weakening of the ACC, with reduced advection of 

nutrients from the Bering Sea. Benthic productivity will decrease, particularly nearshore, 

although effects on pelagic productivity remain unknown. 

Both scenarios predict an increase in low-frequency ambient noise levels, due to both 

environmental as well as anthropogenic (e.g., seismic airguns, increased shipping traffic) 

sources, both of which are a result of predicted longer open-water seasons. The predicted 

increase of 10-15 dB in low-frequency ambient noise would dramatically change the acoustic 

environment and acoustic habitats for species that rely on the low-frequency band for basic life 

functions such as communication, foraging, navigating, and evading predators. This would 

include bowhead, beluga, and gray whales, bearded seals, and walrus, all of which could 

experience chronic reductions in communication space and disruptions in the adaptive benefits of 

lower ambient noise conditions. 

Bowhead, beluga, and gray whales are the most adaptive, and are best suited to adjust to 

a regime or ecosystem shift. However, ecosystem changes may result in population or migration 

re-distribution for some species, which could have severe negative effects on native subsistence 

hunting. Walrus are predicted to be the most vulnerable to climate change or ecosystem shifts. 

They may be forced to haul out on the coast, which will impact population health; they may also 

move out of the Chukchi Sea, which some studies show is already happening. Bearded seals are 

more adaptable than walrus, and will probably adjust accordingly. Finally, longer open-water 

seasons will result in more subarctic species moving farther north into the Chukchi Sea and 

creating increased competition with resident Arctic species. 

It is important to remember that no matter the scenario, most marine mammal species 

have innate migration patterns and reproductive cycles, as seen in the strong correlation between 

most of the marine mammals and month in the GAM integrative results (see Section X). As 

such, migration timing and patterns either may not change significantly, or those changes may be 

considerably delayed. This illustrates the importance of continued long-term passive acoustic 

monitoring, which is ideally suited to documenting migration patterns.

3. Recommendations

While we have some ideas of future predictions and ecosystem changes, these are based 

largely on only two full years of data. Thus, it is essential to continue collecting oceanographic, 

biophysical, and passive acoustic data to augment our current knowledge about the Alaskan 

Arctic ecosystem. Moorings should be deployed not only in locations where the biggest 

oceanographic, marine mammal, and prey distribution changes are expected to occur, but also 

across a range of locations and predictions. Furthermore, it is critical to know what is happening 

with the environmental parameters in the crucial spring and fall months. However, because the 

Arctic is ice-covered for 8-9 months a year, these seasons are currently inaccessible with most 

present technologies, the exception being passive acoustic recorders. To collect the necessary 

suite of crucial data, we need investments in technologies that will allow us to collect those data 

during time periods and in places that we are currently unable to access. This may be in the form 
OCS Study

BOEM 2015-034

254

of new advanced moorings, subsurface gliders or AUV’s, etc. In addition, it is critical that we 

maintain a large spatial sampling scale with the acoustic recorders to monitor changes in marine 

mammal distribution and ambient noise levels. Passive acoustics is currently the best means of 

assessing these distributions and noise levels year-round and is best suited for determining if 

there are changes in species composition, distribution, or migration patterns in this rapidly 

changing ecosystem (Hannay et al., 2013; Clark et al., 2015). Finally, to understand how whales 

relate to indicators from oceanographic and prey studies, it is imperative that technologies such 

as satellite telemetry and other animal-borne sensors fixed with dive profile instruments be 

included as a key component in ecosystem research. This will provide information during 

discrete real time sampling as well as provide long term information on habitat utilization as it 

relates to mooring data. This in turn will aid in strengthening future predictions.
OCS Study

BOEM 2015-034

255

XII. NOISE MODELING AND IMPACT MITIGATION

1. Methods

There were two different methods developed for each of the two primary objectives. The 

primary objectives were to develop and implement methods to: a) report occurrences of 

acoustically active marine species and ocean noise metrics in near-real-time, and b) quantify and 

assess the Chukchi Sea’s “noise budget” (including biotic and abiotic sound sources) and assess 

the influences of individual source types and the aggregate of multiple sources, including 

different source types, on the overall acoustic environment and on the acoustic habitats of 

selected marine mammal species. As a result we now have a method by which to quantify the 

acoustic contributions from vessels and seismic airgun surveys to the aggregate noise budget (see 

Clark et al., 2009; Hatch et al., 2012). Throughout this report the term impact mitigation will be 

used to refer to the method of near-real-time monitoring and the term noise modeling will be 

used to refer to the method of assessing the influences of sound sources on ambient sound levels, 

the acoustic environment, and species-specific acoustic habitats. 

Impact Mitigation

Auto-detection Buoy

Throughout this report, the term AB-2012 will be used to refer to the auto-detection buoy 

deployed and operated in 2012. The AB-2012 system transmitted bowhead whale detection data

and ambient noise data to Cornell-BRP in near-real-time via Iridium satellite (see 

(http://www.listenforwhales.org, http://stellwagen.noaa.gov/protect/whalealert.html for an 

operational example of this system). The methods implemented by this system provided the 

mechanism by which timely information was delivered on the presence of an endangered species 

in a zone leased for industrial development and on ambient noise levels in the 10-4000 Hz 

frequency band. 

The AB-2012 was programmed to sample continuously at 8 kHz (10 - 4000 Hz effective 

bandwidth), detect potential bowhead sounds and compute ambient noise spectral distribution 

measurements. The detection process ran continuously and was maintained using an on-board 

data management system. The system transmitted the top 10, 2-s acoustic detections, referred to 

as sound clips, every hour via Iridium satellite to Cornell (Spaulding et al., 2010). A sound 

clip’s detection rating was based on how well its acoustic features matched the features of 

modeled bowhead frequency-modulated calls derived from >10,000 bowhead call examples. 

The on-board noise analysis process computed a 1024-point discrete Fourier transform ensemble 

every 30 seconds and transmitted the resultant spectral data every 2 hours. The process of 

computing spectral distribution data was specifically motivated by the expectation that such data 

would provide timely information on the occurrence and received levels of abiotic acoustic 

events (e.g., from seismic airgun activity, vessel traffic, weather, ice) and biotic events (e.g., 

choruses of singing bowheads or bearded seals). It was not intended to enable observation of 

sparse events such as single marine mammal calls (e.g., from bowheads).

Both the detection and spectral data were available via a web-based, on-line system and 

visualized with a basic GUI 
OCS Study

BOEM 2015-034

256

(http://test.nrwbuoys.org/ab/clip/?position=Chukchi&search=pos:Chukchi). Several of the many 

enhancements of the system included ruggedization of the mooring by Woods Hole 

Oceanographic Institution (WHOI) ocean engineers for the demanding arctic environment; 

modularization of the onboard data acquisition, signal conditioning, processing, management, 

and transmission sub-systems by Cornell-BRP engineers; and implementation of bi-directional 

communications by Cornell-BRP engineers. The latter improvement provided a mechanism by 

which, for example, Cornell-BRP could reprogram onboard code and request transmissions of 

selected portions of acoustic data. The buoy was outfitted with a Xeos tracker, which provided a 

mechanism by which WHOI and Cornell-BRP could observe the GPS position of the system in 

the event that it drifted from its mooring location.

Auto-detection performance evaluation

Prior to deployment of AB-2012, Cornell-BRP developed a methodology for evaluating 

the performance of the automated bowhead whale call detector. Since bowhead acoustic 

detections are relatively rare, we chose to use Precision/Recall curves to measure performance, 

because such curves are not subject to the issues of uneven class size that can be a problem when 

using Receiver Operating Characteristic (ROC) curves. The general procedure started by 

running the detector/classifier with a low detection threshold, yielding a large number of 

candidate detections, most of which would be false. We then computed a posteriori the 

Precision and Recall at various intermediate thresholds, yielding various performance curves as 

shown in Figure 137. We measured performance in this way for several different methods of 

sound classification. As a control, we scored detected events according to the maximum signalto-noise ratio during the event.

Not surprisingly, as shown in Figure 137 (Amplitude Only, blue “x” values), precision 

did not change significantly as the score increased, since a higher score simply corresponds to a 

louder call event. Based on this performance we estimated that we could improve the threshold 

so as to detect about 30% of the potential calls, 80% of which would be classified as true calls.

Onboard the autobuoy deployed in 2012, we used a simple Discrete Hidden Markov 

Model (HMM) classifier. A short-time Fourier Transform (STFT) spectrogram was computed 

using a DFT size of 256 points, and an overlap of 192 point, and then a bank of four binary 

image filters was applied, each intended to enhance ridges in one of four “directions”, vertical, 

horizontal, and both diagonals. Finally, a measure of local ridge energy was computed as the 

max of the directional filtered energies, and a local directional estimate was assigned according 

to which filter produced the maximum energy output. The HMM observed symbols were 

represented by a pair of variables per spectrogram time slice, the modal (max) frequency index, 

and the estimated contour direction at the modal frequency. This gave 4 * 256 possible symbols, 

but we further reduced this by integer-dividing the frequency index by 4, leaving 256 symbols. 

The HMMs used 16 hidden states, and were trained as “forward” models, allowing only 

increasing internal state index. 

Using this pair of models, we were able to improve upon the trivial classifier. Some 

operating points offered a precision of about 80%, while keeping more than 30%. This curve is 

represented in Figure 137 by the red “x” marks labeled “Discrete HMM (deployed)”. 
OCS Study

BOEM 2015-034

257

This discrete HMM was not optimal, and we later did some experiments with a vectorquantized HMM which seemed likely to improve performance. We used a standard k-means 

procedure to estimate the k=256 most representative frequency-direction pairs. We used the full 

set of true and false detected events in the training set to compute these optimal symbols, and 

used the full-resolution frequency values. 

This evaluation is shown by the sequence of light blue “x” marks labeled as “VectorQuantized HMM” in Figure 137. It can also be seen that when we used the vector-quantized 

feature set, the number of false positive events was reduced by roughly 50%, giving us more than 

90% precision at 30% recall. We also ran the detector with a lower threshold to produce a 

greater number of candidate events for classification. The performance of the lower threshold 

detector/classifier is shown in Figure 137 by the two sequences of “x” marks labeled as 

“Amplitude Only, Lower Threshold” (green) and “Vector-Quantized HMM, Low Threshold” 

(purple).

The vector-quantized HMMs were trained using hold-out cross-validation, where each 

model was trained on 80% of the data and tested on the remaining 20%. At the lower threshold, 

it would be possible to have a higher recall (around 60%) at our original 70% precision. As 

shown in Figure 138, the performance was only slightly degraded between the training and test 

sets, indicating that we were most likely not over-fitting the models. For future deployments, we 

could use the vector-quantized models to improve detector performance. In both cases, the 

HMM training and test data were drawn from the same recording, so our performance estimates 

here are likely to be optimistic.
OCS Study

BOEM 2015-034

258

FIGURE 137. DETECTOR PERFORMANCE CURVES AS A FUNCTION OF DIFFERENT DETECTION THRESHOLD SETTINGS

(SEE TEXT FOR DETAILS).
OCS Study

BOEM 2015-034

259

FIGURE 138. RELATIVE CLASSIFIER PERFORMANCE OF TRAINING AND TEST DATASETS.

AB-2012 Deployment and Recovery 

On 9 August 2012, AB-2012 was successfully deployed and began operating in the 

Chukchi Sea in an area of oil and gas development: 71.17411o N by 161.558o W. The initial 

recovery attempt on 21-22 Oct 2012 by WHOI on board the USCGC Healy was unsuccessful 

due to high sea-state conditions. Shortly thereafter, the Xeos tracker indicated that AB-2012 was 

adrift. Despite this condition, the buoy continued to function properly, and even continued to 

report some interesting biological detections during this time. On 09 Nov 2012, with logistical 

help from Dr. Michael Macrander and the Shell Exploration & Production Company, the entire 

AB-2012 system was successfully recovered, including the mooring and anchor. Cornell-BRP’s

onboard data processing and recording module was eventually delivered to Ithaca, NY in 

January, 2013.

Upon arrival in Ithaca, the FLASH memory data from AB-2012 was downloaded into the 

CHAOZ-2012 acoustic data system along with the sound clips received from AB-2012 while it 

was operating. At this time we determined that AB-2012’s on-board FLASH drive had filled to 

capacity on 22 October at 00:37 h local. 
OCS Study

BOEM 2015-034

260

Noise Modeling

MARU-DB Recorders

In the summers of 2010 , 2011, and 2012 a single marine autonomous recording unit (aka 

MARU: Calupca et al., 2000, Clark et al., 2002, Parks et al., 2009) supplemented with a second 

glass sphere containing additional batteries was deployed in the Chukchi Sea (Table 31, Figure 

139). Throughout this report, the term MARU-DB will be used to refer to this “double-bubble” 

configuration, and a suffix will be used to refer to the start and end year during which the 

recorder operated (e.g., MARU-DB-2010-11 refers to the recorder that started recording in 2010 

and ended in 2011). This passive acoustic recording method addressed a core task of the second 

objective, which was to use empirical data to calculate the spatial-temporal-spectral variability of 

the acoustic environment in the Chukchi Sea. An expected secondary benefit of this method was 

that it would provide additional data on bowhead whale acoustic occurrence to compare with AB 

detection results. As it turned out, this secondary benefit was never realized because MARUDB-2012-13 failed to record. 

Each MARU-DB was programmed to record continuously at a 2 kHz sampling rate. In 

the first year’s deployment, MARU-DB-2010-11 recorded on a 50% duty cycle (30-min on, 30-

min off) over a 12-month recording period. In 2011-12, the MARU-DB recorder was 

programmed to record continuously. 

A MARU-DB recorder consisted of an HTI-94-SSQ hydrophone with a sensitivity of -

168 dB re 1 V/μPa, an amplifier with a gain of 23.5 dB and an A/D converter with a sensitivity 

of 103 Bit/V. The final transformation coefficient used for calculating sound level metrics was -

151.2 dB re 1 μPa. The system had a flat (± 1.0 dB) frequency response between 10 – 585 Hz 

(Parks et al., 2009).

TABLE 31. LISTING OF THE CORNELL AUTO-DETECTION BUOY AND MARU RECORDING INFORMATION. MARU ID

REFERS TO THE ACTUAL ID NUMBER OF THE MARU IN THE CORNELL-BRP FLEET. “GB DATA” REFERS TO THE 

TOTAL AMOUNT OF DATA RECORDED IN GIGABYTES. “SR” REFERS TO THE SAMPLING RATE. 
OCS Study

BOEM 2015-034

261

FIGURE 139. LOCATIONS OF THE AB-2012 (RED) AND MARU-DB RECORDERS (YELLOW) RELATIVE TO THE NORTH 

SLOPE, AK COASTLINE. NOTE THAT THE POSITIONS OF THE EQUIPMENT WERE INTENTIONALLY LOCATED IN 

NEARLY THE SAME SPOT YEAR AFTER YEAR.

Acoustic Detections

Data from MARU-DB -2010-11 and MARU-DB-2011-12 were analyzed for the acoustic 

occurrence of seismic airgun array events and bowhead sounds. Analysts with experience in 

both types of sound events used Raven software (Charif et al., 2004) to review and annotate the 

data at a 12 h resolution. By this procedure, two daily 12 h time periods (00:00 – 12:00 and 

12:00 – 24:00 UTC) were scrutinized for the non-occurrence (0) or occurrence (1) of bowhead 

calls and bowhead songs. Given that the primary objectives for the Cornell-BRP efforts were 

directed at quantifying the ambient noise environment and real-time detection of bowhead 

sounds, the bowhead detection results from the analysis of the MARU-DB data are not included 

here. 

Acoustic Ecology Analytical System

A combination of existing analytical acoustic technologies and newly developed 

analytical methods were applied to meet project objectives. Initially in the project, the analytical 

components were developed independently. Over the course of the project we began to convert 
OCS Study

BOEM 2015-034

262

these components into object-oriented modules and integrate them into a more comprehensive 

system, which, for the sake of simplicity, we refer to as DELMA5.

The DELMA system provides a quantitative mechanism by which to analyze, aggregate, 

quantify, estimate, assess and visualize potential influences of different sound source types on 

the overall acoustic environment. It provides the mechanisms by which to engage the same suite 

of processes for either individual sound source types or aggregations of different types. When 

applied to a particular species of interest, it provides the mechanisms by which to assess the 

impact of individual or aggregated sound source types on the species’ acoustic habitat. This 

process can be thought of as a mechanism by which to quantify the “acoustic budget” for an area 

of interest, where measures are calculated as functions of time, space and acoustic spectra (e.g.,

sound level in dB in a frequency band of interest).

DELMA was used to process all recorded data into root-mean-squared (RMS) sound 

level values (decibels, dB re 1 μPa) within the 10 – 1000 Hz frequency band at 1 sec and 1 Hz 

resolutions, rounded to the nearest dB. These basic sound level metrics were computed using

customized sound analysis software MatLab module in DELMA. This system operated on either 

a high-performance computing platform, which greatly facilitated data processing speed and 

provided the capability to efficiently reprocess data as needed (Dugan et al., 2011), or a multicore stand-alone computer system. 

Acoustic measurement analysis was performed on all recorded data and stored as MatLab 

*.mat files. Additional Matlab software was developed to enable a variety of ways to visualize 

these *.mat files, and these visualizations were stored as Matlab *.fig files, *.png files, and *.eps 

files, as needed. 

For comparative noise analysis, 3rd-octave sound levels were calculated for the 21 3rd-

octave bands spanning the 9-891 Hz frequency band. Sound level measurements within this 9-

891 Hz band, regardless of the time period over which a measurement was calculated, are 

referred to as broadband levels. A limited portion of the 9-891 Hz frequency band was further 

used to quantify noise levels for a frequency range in which bowheads produce many, if not 

most, of their calls and a large portion of their song notes. This band consisted of 11 3rd-octave 

bands spanning the 71 – 708 Hz frequency range and is referred to as the bowhead-band. 

Results from the basic sound analyses were used to compute, illustrate, and compare a 

suite of acoustic measures for both the broadband and bowhead-band. This analytical suite 

included narrowband spectrograms, 1/3-octave spectrograms, and sound level percentiles for 

each 1 h period (5th, 25th, 50th, 75th, and 95th, where 50th percentile is the median noise level, and 

25th percentile and 75th percentile levels are used to illustrate variability), and cumulative 

distributions of median levels. These results were also used to assess the spatial-temporalspectral variability of the acoustic environment. 

Figure 140 shows a diagrammatic schema of the components of the acoustic ecology 

analysis system. This system takes advantage of recently developed methodologies to calculate 

the noise budget contributions from different types of anthropogenic sound sources and their 

influences on the acoustic environment (e.g., Clark et al., 2009; Hatch et al., 2012; Williams et 

al., 2014). The basic input components in the system include such items as: estimates on the 

 

5

In the initial years of this project we used a customized software code referred to as SEDNA, but this evolved into 

a more comprehensive system now referred to as DELMA.
OCS Study

BOEM 2015-034

263

numbers and distributions of different marine mammal species of interest (i.e., Biological Data); 

time-varying positions, empirical and/or estimated sound fields generated by each type of 

anthropogenic activity (i.e., Vessel Database and Vessel AIS/GPS); acoustic contributions from 

abiotic environmental sources such as wind and ice (i.e., Weather Data); sound propagation 

models informed by site-specific parameter values (i.e., Propagation Modeling); and empirical 

acoustic data (i.e., MARU-DB Data); and empirical or proxies for species specific sound source 

levels and auditory thresholds.

All DELMA data analysis products are archived in a Cornell “Box.” All data and data 

products will be fully available. Some examples of the types of results, in this case shown as 

figures, generated from different components of this system as diagramed in Figure 140 include: 

1. Noise Report Browser: showing the analysis of MARU-DB data visualized as an annual 

4-channel plot (Figure 141);

2. Spatial Analyzer: a single frame from an animated integration of a seismic survey vessel 

and acoustic data from the Beaufort Sea as received on an array of MARUs (Figure 142); 

and 

3. Communication Space Visualizer: a map surface (latitude by longitude) showing 

communication space for a species of interest based on the integration of noise fields 

generated by different sound sources and a modeled sub-population of animals. In this 

four-panel example (Figure 143) the sound sources are noise generated by surface winds

under low wind and high wind conditions and noise from a ship (R/V Cape Flattery).
OCS Study

BOEM 2015-034

264

FIGURE 140. DIAGRAM SHOWING THE SCHEMA AND COMPONENTS OF THE ACOUSTIC ECOLOGY SYSTEM.
OCS Study

BOEM 2015-034

265

FIGURE 141. EXAMPLE OF NOISE REPORT BROWSER RESULTS, DISPLAYED AS A 4-PANEL PLOT BASED ON OUTPUT FROM THE NOISE ANALYZER (A MATLAB *.FIG 

FILE) (SEE FIGURE 140). THE RESULTS HERE ARE BASED ON THE ANALYSIS OF THE ENTIRE MARU-DB 2010-11 ACOUSTIC DATA RECORDED FROM 11 SEPTEMBER 

2010 TO 02 SEPTEMBER 2011. THE TOP PANEL SHOWS A SPECTROGRAM AT 1-HZ RESOLUTION. THE SECOND FROM TOP PANEL SHOWS A SPECTROGRAM AT 3RD-

OCTAVE BOWHEAD-BAND RESOLUTION (71-708 HZ). THE SECOND FROM BOTTOM PANEL SHOWS THE AMBIENT NOISE LEVEL FOR THE BOWHEAD-BAND 

RESOLUTION (71-708 HZ) AND BROADBAND (9-891 HZ) FREQUENCY BANDS. THE BOTTOM PANEL SHOWS THE SPECTRAL PROBABILITY DENSITY (SPD; MERCHANT 

ET AL., 2013) AND THE 5TH, 25TH, 50TH, 75TH AND 95TH PERCENTILE STATISTICAL DISTRIBUTIONS.
OCS Study

BOEM 2015-034

266

FIGURE 142. EXAMPLE FRAME FROM ONE OF THE TYPES OF OUTPUT MOVIES COMPUTED BY DELMA’S SPATIAL ANALYZER PROCESS (FIGURE 137). THIS 

EXAMPLE SHOWS A MAP WITH THE LOCATIONS OF NINE MARUS (COLORED CIRCLES), THE TRACK OF A SEISMIC RESEARCH VESSEL (WHITE LINE), AND THE 

BROADBAND NOISE LEVEL AT EACH OF THE MARUS. IN ACTUAL OPERATION, THE DELMA OUTPUT IS DYNAMIC AND ANIMATED, SUCH THAT THE USER CAN 

STEP FORWARDS OR BACKWARDS THROUGH THE MOVEMENT OF THE VESSEL WHILE OBSERVING THE CHANGES IN THE RECEIVED LEVELS AT EACH OF THE 

MARUS. THE COLOR OF A MARU CIRCLE INDICATES THE MEASURED SOUND LEVEL (DB RE 1 μPA) AT THE RECORDER (SEE THE COLOR BAR ON THE RIGHT)

ACCORDING TO THE FREQUENCY BAND AND TIME SCALE OF THE USER-SELECTED ANALYSIS PARAMETERS.
OCS Study

BOEM 2015-034

267

FIGURE 143. EXAMPLES OF ANOTHER TYPE OF OUTPUT FROM DELMA’S COMMUNICATION SPACE VISUALIZER

PROCESS (FIGURE 140) AS APPLIED TO THE CHUKCHI SEA AB-2012 DEPLOYMENT AREA. THIS 4-PANEL EXAMPLE 

SHOWS THE RELATIVE DIFFERENCES IN COMMUNICATION SPACE FOR THREE BOWHEAD WHALES UNDER FOUR 

DIFFERENT ENVIRONMENTAL ACOUSTIC CONDITIONS AS MEASURED IN THE BOWHEAD-BAND. A: TOP LEFT– LOW 

SURFACE WIND (2 MPS, 85 DB). B: TOP RIGHT – HIGH SURFACE WIND (14 MPS, 102 DB). C: BOTTOM LEFT – LOW WIND 

(2 MPS, 85 DB) AND SINGLE SHIP (172 DB). D: BOTTOM RIGHT HIGH WIND (14 MPS, 102 DB) AND SINGLE SHIP (172 DB). 

(SEE CLARK ET AL., 2009 AND HATCH ET AL., 2012 FOR DETAILS OF THIS ANALYTICAL PROCESS).

Noise, Ice and Wind

DELMA data processing results (e.g., spectrogram figures, noise distribution plots, see 

Figure 141-138) for different time scales revealed a variety of acoustic events. These, in 

combination with additional analysis and careful listening were used to identify obvious biotic 

(e.g., bowhead whales and bearded seals) and abiotic types of sound sources (e.g., vessels, 

seismic airguns, wind noise and ice) that were contributors to the ambient environmental noise 

measurements and scenes. In particular, an effort was made to analyze the data for possible 

Low Wind Hi Wind

Ambient Ambient + Vessel
OCS Study

BOEM 2015-034

268

structure in the relationships between noise metrics, wind level and percent ice coverage. This 

was undertaken in order to inform models to predict future ambient noise levels under reduced 

ice concentration conditions. Data on percent ice coverage and wind speeds were used to test for 

relationships between wind speed, percentage of ice cover and ambient noise metrics (see Roth 

et al., 2012). For this analysis we combined the satellite wind data (6 hr resolution), satellite sea 

ice concentration data, ambient noise metrics (6 hr resolution), while accounting for known 

anthropogenic source occurrence (e.g., seismic airgun survey) and bowhead whale occurrence. 

We have completed an initial correlations analysis between a) daily noise level in the 

bowhead frequency band and daily wind speed and b) daily noise level in the bowhead frequency 

band and daily ice concentration. We conducted a preliminary evaluation to predict future noise 

levels by combining daily wind speed, sea ice concentration and both broadband and bowheadband noise level data in a multivariate regression analysis. Results from the regression analysis 

can be used to estimate noise levels under future open water conditions.

2. Results

Impact Mitigation

Auto-detection Buoy, Near-real-time Bowhead Sound Detections 

Of the total audio clips (N=762) detected and transmitted via satellite by AB-2012 over 

the duration of the deployment (between 01 September 2012 and 01 November 2012), 351 were 

noted as being of biological origin by Cornell-BRP research analysts with expertise in bowhead 

and other arctic marine mammal sound recognition (Figure 144). Figure 145 shows the daily 

comparison between the number of sounds detected by AB-2012 and the number of those 

validated as bowhead sounds (46%). While this is a significantly lower value of precision than 

we saw when testing the performance of the detector against the Beaufort Sea training data, it 

was not unexpected, since the training and test data sets were not exactly equivalent.

FIGURE 144. SPECTROGRAM EXAMPLE OF A FREQUENCY-MODULATED SOUND AUTOMATICALLY DETECTED BY AB2012 OPERATING IN THE CHUKCHI SEA; 19 SEPTEMBER 2012. ANALYSTS IDENTIFIED THIS AS A BOWHEAD WHALE 

SOUND.
OCS Study

BOEM 2015-034

269

FIGURE 145. DAILY DETECTIONS OF POTENTIAL BOWHEAD WHALE SOUND CLIPS TRANSMITTED FROM AB-2012

(BLUE) OVER THE ENTIRE CHUKCHI DEPLOYMENT (N = 762) AND THOSE SOUND CLIPS IDENTIFIED AS BOWHEAD 

SOUNDS BY EXPERIENCED ANALYSTS UPON MANUAL REVIEW (RED, N = 351).

Auto-detection Buoy, Noise Spectral Distribution 

All of the added auto-buoy features were successful and provided valuable mechanisms 

for observing major types of acoustic events in near-real-time. In particular, two important 

features included Iridium transmissions of potential bowhead sound clips of various durations (as

opposed to the original fixed duration of two seconds) (Figure 146), and computing and sending 

spectral distribution data (Figure 147). For example, in Figure 147, a period of seismic airgun 

activity is evident from approximately 12 September into 18 September. The bidirectional 

communications feature that allowed us to request selected portions of data from the buoy was 

also successful. This enabled us to double check, in greater detail, any detection event (e.g., a 

sound that was similar to a bowhead but might have been a humpback whale) or spectral event 

(e.g., something that appeared to be manmade, but in fact, was noise due to weather) recorded on 

the AB-2012’s onboard data storage system. 
OCS Study

BOEM 2015-034

270

FIGURE 146. EXAMPLES OF 12 BOWHEAD SOUNDS AS FIRST DETECTED AT AND TRANSMITTED FROM THE AB-2012 

AND THEN VALIDATED AT CORNELL BY EXPERIENCED ANALYSTS (DATA SAMPLES FROM 20 OCTOBER 2012, 1024 PT. 

FFT, 50% OVERLAP, HAMMING WINDOW).

FIGURE 147. EXAMPLE OF A LONG-TERM SPECTROGRAM FOR THE PERIOD FROM 11 SEPTEMBER 2012 AT 20:29:26Z TO 

18 SEPTEMBER 2012 AT 23:57:43Z. THE SPECTRAL DATA WERE COMPUTED ON THE AB AND TRANSMITTED VIA 

IRIDIUM SATELLITE AS PART OF THE REGULAR DATA PACKAGE.
OCS Study

BOEM 2015-034

271

Noise Modeling

MARU-DB Recorders

Over the two-year field season, two MARU-DBs and one AB were successfully deployed 

(Table 31). MARU-DB-2012-13 failed to record, so is not considered further. MARU-DB2010-11 was programmed to only record at a 50% duty cycle, while MARU-DB-2011-12 

recorded continuously at a 100% duty cycle. For the two years for which MARU-DB data 

collection was successful (2010-11, 2011-12), recordings were made on 718 days, resulting in a 

total of 184 GB of data. 

Sound Analysis Measurements

Acoustic measurement analysis for all the MARU-DB recorded data were stored as 

Matlab *.mat files. A variety of visualizations of these *.mat files were stored as Matlab *.fig 

files, and some were converted into *.png files or *.mov files for illustrative purposes. Where 

appropriate, Matlab *.fig files were made using both bowhead-band and broadband data. These 

various visualization files included:

1. Annual 4-channel figures (Figure 141)

2. Daily 4-channel figures;

3. Daily summary noise statistical distributions;

4. Annual summary noise statistical distributions;

5. Time-varying noise distribution figures;

6. Time-varying noise level figures (Figure 148);

7. Customized figures (Figure 149 and Figure 150);

8. Diel plots (Figure 151);
OCS Study

BOEM 2015-034

272

FIGURE 148. EXAMPLE OF TIME-VARYING MEDIAN NOISE LEVEL PLOT FROM MARU-DB_2010-11 FOR 29 SEPTEMBER FROM 00:00 – 24:00 (UTC) DURING A SEISMIC 

AIRGUN SURVEY. LEVELS ARE FOR THE BOWHEAD FREQUENCY BAND (71-708 HZ) EVERY 10 MINUTES. IN THIS EXAMPLE, THE HIGH LEVELS FROM 

APPROXIMATELY 00:00 – 17:00 ARE WHEN THE FULL ARRAY IS OPERATING AND THE LOWER LEVELS ARE WHEN THE MITIGATION AIRGUN IS OPERATING. NOTE 

THAT FOR MARU-DB-2010-11 DATA WERE RECORDED ON A 50% DUTY CYLCE, SO THE GAPS WITH FLAT LINES REPRESENT “OFF” PERIODS WHEN NO DATA WERE

RECORDED.
OCS Study

BOEM 2015-034

273

FIGURE 149. THREE-PANEL PLOT FOR MARU-DB_2011-12 SHOWING AN EXAMPLE OF NOISE FROM AN ICE MOVEMENT EVENT ON 12 DECEMBER 2012 FROM 17:00 

THROUGH 23:00 (UTC).
OCS Study

BOEM 2015-034

274

FIGURE 150. THREE-PANEL PLOT FOR MARU-DB_2013-14 WHICH SHOWS AN EXAMPLE OF NOISE FROM A SHIP PASSING CLOSE TO THE RECORDER ON 4 

SEPTEMBER 2013 FROM 01:30 TO 02:30 (UTC). THIS TYPE OF EVENT WHEN AN ACOUSTIC SOURCE APPROACHES, PASSES BY, AND MOVES AWAY FROM A 

RECORDER IS REFERRED TO AS A CLOSEST POINT OF APPROACH (CPA).
OCS Study

BOEM 2015-034

275

FIGURE 151. EXAMPLE OF A DIEL PLOT OF HOURLY MEDIAN NOISE LEVELS FOR THE BOWHEAD FREQUENCY BAND

(71 – 708 HZ) BASED ON OUTPUT FROM THE SPATIAL ANALYZER (SEE FIGURE 140). THE RESULTS HERE ARE BASED 

ON THE ANALYSIS OF THE ENTIRE 2010-2011 MARU-DB_2010-11 DATA RECORDING PERIOD FROM 02 SEPTEMBER 2010 

TO 11 SEPTEMBER 2011 (FIGURE 141). THE RELATIVE CHANGES IN AMBIENT NOISE LEVEL FROM FALL INTO SPRING 

AND FROM SPRING INTO SUMMER ARE EVIDENT AS CHANGES IN THE COLOR REPRESENTATION OF NOISE LEVEL.

Noise, Ice and Wind

Comparisons were made between ambient noise levels, wind speed and ice concentration.

Satellite wind data for 2010-2012 were downloaded from NOAA’s Atlas FLK v1.1 derived 

surface winds (level 3.0) site 

(http://coastwatch.pfeg.noaa.gov/erddap/griddap/jplCcmpL3Wind6Hourly.html), while sea ice 
OCS Study

BOEM 2015-034

276

concentration data were obtained from the NOAA/NSIDC Climate Data Record of Passive 

Microwave Sea Ice Concentration, Version 2 site 

(https://nsidc.org/data/docs/noaa/g02202_ice_conc_cdr/). For each MARU-DB, the ambient 

noise level, surface wind and ice concentration data were plotted, compared and stored as Matlab

*.mat files. A variety of visualizations of these *.mat files were stored as Matlab *.fig files, and 

some were converted into *.png files or *.mov files for illustrative purposes. Where appropriate, 

Matlab *.fig files were made using both bowhead-band and broadband data. These various 

visualization files include (with some illustrative examples):

1. Comparisons of daily noise levels and wind speed (Figure 152);

2. Time-series comparison of noise levels and surface wind speed (Figure 153);

3. Regression of hourly median lowest noise level and surface wind speed (Figure 154);

4. Comparisons of hourly median lowest noise level, daily satellite wind speed and sea 

ice concentration (Figure 155);

5. Monthly Spearman Rank correlations between daily noise level and wind speed 

(Figure 156).

For the correlations analysis of daily noise levels, wind speed and ice concentration 

(Figure 155) we used hourly broadband noise levels from the Noise Report Browser (71-708 Hz, 

6 h resolution), satellite derived surface wind data (6 h resolution, level 3.0, derived from Atlas 

FLK v1.1), and daily satellite sea ice concentration data (NOAA/NSIDC Climate Data Record of 

Passive Microwave Sea Ice Concentration). Noise data were converted to daily median levels, 

and 6 h wind speed values were converted to average daily values. A Spearman/Pearson nonparametric test of association (right tail; assumed a positive correlation) between noise and wind 

speed and noise and ice concentration was run for each month (Figure 156). For noise and wind, 

the results revealed a strong positive relationship (Spearman’s rho) between noise and wind from 

October 2010 through August 2011. For noise and ice concentration, the results revealed a flat 

relationship (Spearman’s rho) between noise and ice concentration for September 2010, and for 

December 2010 through May 2011, but a strong negative association in November 2010 and a 

strong positive association in June 2011.
OCS Study

BOEM 2015-034

277

FIGURE 152. COMPARISON OF AMBIENT NOISE LEVEL IN THE BOWHEAD FREQUENCY BAND (71 – 708 HZ, MARU-DB-2010-11) AND WIND SPEED METRICS FROM THE 

“RED DOG” BUOY AND SATELLITE (ATLAS) DATA SOURCES.
OCS Study

BOEM 2015-034

278

FIGURE 153. TIMES-SERIES OF NOISE LEVELS AND SURFACE WIND SPEEDS DURING THE 11 SEPTEMBER - 27 OCTOBER 2010 PERIOD. NOISE LEVELS ARE 6 HR

MEDIAN LEVELS IN DB IN THE BOWHEAD FREQUENCY BAND (71-708 HZ, MARU-DB_2011-12). SURFACE WIND SPEEDS ARE DERIVED FROM SATELLITE DATA (6 H 

RESOLUTION, LEVEL 3.0, DERIVED FROM ATLAS FLK V1.1).
OCS Study

BOEM 2015-034

279

FIGURE 154. REGRESSED WIND SPEED PLOT: THE LINEAR REGRESSION OF AMBIENT NOISE LEVEL IN THE BOWHEAD FREQUENCY BAND (71 – 708 HZ, 6 HR

RESOLUTION) AND WIND SPEED METRICS FROM SATELLITE (ATLAS) DATA SOURCES (6 HR RESOLUTION, LEVEL 3.0, DERIVED FROM ATLAS FLK V1.1) FOR THE 12 

SEPTEMBER THROUGH 27 OCTOBER 2010 PERIOD.
OCS Study

BOEM 2015-034

280

FIGURE 155. TIME SERIES COMPARISON OF BOWHEAD-BAND LEVEL NOISE (71-708 HZ, 1-SEC RESOLUTION, MARU-DB_2011-12: NOTE HIGH LELVEL OF 

VARIABILITY), “REGRESSED WIND SPEED” (BLUE), AND SEA ICE CONCENTRATION (BLACK). “REGRESSED WIND SPEED” WAS CALCULATED BY REGRESSING

SURFACE WIND SPEED VALUES (6 HR RESOLUTION, LEVEL 3.0, DERIVED FROM ATLAS FLK V1.1 DATA) WITH NOISE LEVEL, AND APPLYING THE LINEAR 

REGRESSION EQUATION TO CONVERT WIND SPEED INTO ESTIMATED WIND NOISE (I.E., “REGRESSED WIND SPEED”) VALUES.
OCS Study

BOEM 2015-034

281

FIGURE 156. MONTHLY TIME SERIES COMPARISONS OF DAILY BOWHEAD-BAND LEVEL NOISE (71-708 HZ, 1 HR RESOLUTION, MARU-DB_2011-12) AND DAILY 

SURFACE WIND (6 HR RESOLUTION, DERIVED FROM ATLAS FLK V1.1); TOP PANEL. DAILY BOWHEAD-BAND LEVEL NOISE (71-708 HZ, 1 HR RESOLUTION, MARUDB_2011-12) AND DAILY SEA ICE CONCENTRATION (NOAA/NSIDC CLIMATE DATA RECORD OF PASSIVE MICROWAVE SEA ICE CONCENTRATION); BOTTOM PANEL.
OCS Study

BOEM 2015-034

282

3. Discussion

This report represents an initial synthesis of the acoustic data collected from two MARUDBs (718 days, 184 GB), and a single AB system (65 days, 26.7 GB) from 11 September 2010 

through 3 November 2012 (Table 31). 

Considerable effort and resources were devoted to achieving the two primary objectives, 

impact mitigation via the AB system for near-real-time monitoring, and noise modeling via the 

development of the DELMA acoustic analysis system. The overarching motivation for this 

research was to increase understandings of the spatial and temporal dynamics of the Chukchi Sea 

acoustic environment and the quantification of the various sound sources that contribute to that 

environment (i.e., the region’s acoustic budget). Of particular concern are the short-term and 

long-term influences of anthropogenic activities on the marine acoustic environment and the 

possible impacts of those influences on the regions ecological health. An example of a shortterm influence would be noise from a seismic airgun survey or drilling operation and the 

resultant changes in the acoustic habitats of bowhead whales, beluga whales or ice seals. An 

example of a long-term influence would be changes in the marine acoustic environment as a 

result of lower sea ice concentration and increased surface winds coupled with the expected 

increase in background noise due to increased commercial activities.

Impact Mitigation

Auto-detection Buoy

The task of developing an actual mechanism by which to mitigate acoustic impact 

focused on the application of a short-term, area-specific system, referred to as an auto-detection 

buoy, to monitor for sounds from bowhead whales and sounds generated by activities from the 

exploration and development of offshore oil and gas resources in the Chukchi Sea. Our ability to 

undertake this component of the project was made technically and economically possible 

because Cornell-BRP and WHOI had already developed the technology, and there were very few 

associated non-recoverable engineering costs. Furthermore, critical improvements to the 

technology, for example increased sampling rate and simultaneous detection algorithms, were 

accomplished and paid for by a totally separate industry-based whale monitoring project off 

Massachusetts (see http://www.listenforwhales.org), and were readily adapted to this Chukchi 

Sea context at very little cost to the project. 

The mechanics for deploying, operating and recovering an auto-detection buoy in the 

Chukchi Sea during the open-water season were achieved in summer-fall 2012 with the 

successful deployment, operation and recovery of AB-2012 and with generous logistical support 

from Dr. Michael Macrander and the Shell Exploration & Production Company. Potential 

bowhead whale calls were detected, and packages of acoustic clips were transmitted back to 

Cornell for expert review. Acoustic data were processed in situ and resultant spectral density 

distribution data were transmitted back to Cornell and used to scan for natural and anthropogenic 

events (e.g., increased broadband noise during high wind weather events and seismic airguns). 

The buoy-to-Cornell bi-directional communication system was exercised and successfully used 

to modify buoy behavior: for example, to modify on-board detection code, request transmissions

of specific sections of acoustic data, change the transmission schedule or change the data 
OCS Study

BOEM 2015-034

283

contained in the transmission package. By exercising these technical activities, system field 

operational protocols were established, and on-board data processing, data management and 

telemetry sub-system capabilities were confirmed. 

At this point, the AB’s fundamental system infrastructure has been established and 

validated. System infrastructure has been modularized so that the firmware and software can be 

easily reconfigured. For example, the system’s sampling rate can be increased with very little 

impact on power consumption and data storage. It can now simultaneously run multiple detectors 

for multiple species. 

Some further improvements are still needed in detection capability and performance, and 

in the system’s graphical user interface (GUI). Not surprisingly, a significant remaining 

challenge is the implementation of effective and efficient onboard detectors, whereby effective 

and efficient refers to the achievement of low false positive and false negative rates. A primary 

goal for the GUI is to make it more user friendly, flexible and devoid of technical idiosyncrasies 

such that almost anyone, not just those of us familiar with the system, can readily obtain and see 

information about local noise conditions, the occurrences of anthropogenic acoustic activities, 

and detections of marine mammals.

Noise Modeling

Sound Analysis Measurements

A significant level of effort was devoted to building the first version of the acoustic 

ecology analysis system (Figure 140). We applied this system to quantify and evaluate 

contributions of biotic and abiotic sources to the ambient noise budget. To tailor this system for 

this project we iterated through a series of exercises using data collected with the MARU-DB 

autonomous seafloor recorders. This effort took advantage of some of Cornell-BRP’s existing 

code and ongoing development of applications of this code for other acoustic ecology projects. 

This effort is ongoing and has transitioned seamlessly into CHAOZ-X, the continuation of this 

original CHAOZ project.

A significant specification for the acoustic ecology analysis system includes having the 

capability to rapidly and efficiently ingest, manage and process large amounts of acoustic data. 

These speed and efficiency requirements have taken advantage of an existing technology 

development project in Cornell-BRP, called DELMA, that uses a parallelized, distributed, and 

multi-core high-performance-computer. A major benefit of this technology is that we can now 

complete the basic acoustic analysis task very rapidly: for example, we can process a year’s 

worth of MARU-DB data in a few tens of minutes, rather than many days. As a result, during 

this project it became more efficient to reprocess the raw data than to store and manage all the 

various iterations of the analysis. At some point after we had completed a fairly large number of 

data analysis runs, we did save the analysis products. 

The acoustic ecology analysis system includes the software infrastructure and 

mechanisms by which we now integrate, analyze and visualize different data types and data 

products. The conceptualized motivation for this system was that it would enable us to quickly 

explore and discover features of the analytical results to answer questions about and explain the 

observed structures of the ambient noise environment. In the results section above we provide 

some examples of the different types of data analysis products. These data analysis products 
OCS Study

BOEM 2015-034

284

have been produced for almost all the data and will be openly available. In some cases the 

example was included to represent a special type of visualization product, and we have not 

produced figures for all the data (e.g., Figure 149-155).

Seismic Airgun Surveys and Vessel Noise

Seismic airgun survey impulses from multiple sources detected on MARU-DB-2010-11 

and MARU-DB-2011-12 raised noise levels by approximately 14 dB (Guerra et al., 2013). We 

did not systematically annotate the acoustic data for the occurrence of vessels (Figure 150). 

However, analysts did notate vessel CPAs, and from these we know that vessels were only 

infrequently detected within the vicinity of a MARU-DB. We now have a method by which to 

quantify the contributions from seismic airgun surveys and vessels to the aggregate noise budget 

(see Clark et al., 2009; Hatch et al., 2012).

Noise, Ice and Wind

The exploration and discovery of relationships between noise level, wind speed and ice 

concentration are just getting underway, but some interesting results are beginning to emerge. As 

shown in Figure 152, there is an obvious positive relationship between low-frequency noise level 

and surface wind speed (but not between noise level and wind speed at the NOAA land station at 

Red Dog Dock, a separation distance of almost 380 km). As shown in Figure 153, when the 6-h 

median values of the 5th percentile noise level (i.e., representing the lowest noise levels as shown 

in Figure 152, top panel, and referred to as the median minimum noise level) and surface wind 

speed are plotted together, this association between noise and wind is even more apparent. The 

correlation between these 6-h median values and surface wind speed is 0.89 indicating that a 1 

m/s increase in wind speed results in a 1.4 dB increase in noise level within the 71-708 Hz 

frequency band (Figure 154). 

This resultant relationship between noise and wind is not all that surprising as the 

relationship between wind and noise is well known (Wenz, 1962; Urick, 1983). We recognize 

that the relationship presented here is for a limited subset of the data (23 days) so further analysis 

is warranted. Unfortunately, the satellite that provided these surface wind data stopped operating 

in 2011, so this source of surface wind data for the AB-2010 site was not available for further 

analysis. Meteorological data from buoys deployed by BOEM and CSESP programs will be 

incorporated in future development of noise models and statistical analyses in the BOEM-funded 

CHAOZ-extension study.

Our initial explorations of a relationship between wind, ice noise and ice conditions are 

still very preliminary. To begin, we wanted to have data for each variable over the entire year of

recording, so we needed to extend the surface wind data past the expiration date of the satellite 

data. Therefore, we applied the correlation between noise and wind to estimate surface wind 

speed for the entirety of the MARU-DB-2010-12 recording period, and combined noise data with 

these wind estimates and ice concentration measures (Figure 155). There are several interesting 

features evident in this three-variable figure that are worth noting. 

1. When ice concentration is above approximately 0.8, median minimum noise levels in the 

bowhead-band are consistently and persistently low (ca. 83-88 dB). 
OCS Study

BOEM 2015-034

285

2. The period during the onset of ice in late October through early November 2010 was 

preceded by a multi-week period with high surface winds and high noise levels.

3. The period during the loss of ice in early June 2011 was coincident with a multi-week 

period with high surface winds and high noise levels. 

We assume, but need to test the assumption, that when ice concentration is high (e.g., > 

0.8) surface winds are not reliably correlated with ambient noise. Under these ice conditions we 

know that ice events can produce extremely loud noise events as well as a wide variety of sounds 

that have biological-like features (e.g., frequency-modulation, amplitude-modulation, broad-band 

bangs) such that ice events can sound like wolf howls, bee buzzes, whale calls and bowhead 

gunshot sounds. 

Results from the application of the Spearman Rank correlation (Figure 156) reflect the 

interesting features as noted above for Figure 155 in that we find a high negative correlation 

between noise and ice in the fall as the ice concentration is increasing and a high positive 

correlation between noise and ice in the late spring as the ice concentration is decreasing. 

What do these preliminary results tell us about future noise conditions in the Arctic? As 

stated above in Section IX: Climate Modeling, the model predicts two possible scenarios, both of 

which are dependent on the winds. In one model the winds are strong and persistent, in the other 

the winds decrease. Both scenarios predict decreases in ice concentration and an increase in the 

spatial and temporal extent of the open-water season. In the first scenario, there will be an 

increase in ocean storms, which in combination with the increase in the open-water season will 

lead to an increase in ambient noise conditions. In the second scenario, with the increase in the 

spatial and temporal extent of the open-water season and the calmer wind conditions there will 

be an additional increase in shipping traffic and other types of human activities, which will lead 

to an increase in anthropogenic background noise. Some preliminary estimates of the expected 

change in noise conditions can be gleaned from Figure 154 and Figure 155. In terms of the 

spatial and temporal change, we should expect noise level increases to follow the seasonal and 

geographic decrease in loss of ice. Using Figure 155 as the template, one would expect that the 

median minimum noise levels in the 71-708 frequency band would shift up by at least 10 dB 

from approximately 85 dB to approximately 95 dB. This is based on results indicating a 1.4 dB 

increase in noise level within the 71-708 Hz frequency band for each 1 m/s increase in wind 

speed (see Guerra et al. 2011). In terms of noise dynamics in any one location under the 

expected mid-winter, open-water condition with the expected increase in surface wind speeds

one would expect to see the overall median noise level increase from approximately 95 dB to 

approximately 105-110 dB. These predicted median noise levels are equivalent to levels in 

today’s shipping lanes off Boston (see Hatch et al., 2012).

4. Conclusions

Auto-detection Buoy

At this point, the AB’s fundamental system infrastructure is established, but some further 

improvements are still needed in detection capability and performance, and in its graphical user 
OCS Study

BOEM 2015-034

286

interface (GUI). The system’s sampling rate can be increased with very little impact on power 

consumption and data storage. The detection software can now run multiple detectors. A 

significant remaining challenge is the implementation of effective and efficient onboard 

detectors, whereby effective and efficient refers to the achievement of low false positive and 

false negative rates. A primary goal for the GUI is to make it very user friendly, flexible and 

devoid of technical idiosyncrasies such that anyone, not just those of us familiar and patient with 

the system, can readily obtain and see information about local noise conditions and the 

occurrences of anthropogenic acoustic activities and various marine mammals.

Sound analysis system

In this first phase of the CHAOZ project we’ve made significant progress in the 

analytical tools. We can now process large amounts of acoustic data very rapidly. The DELMA 

system has specifically been designed to be scalable (i.e., the same code can run efficiently on a 

laptop or a high performance computer). The acoustic ecology system’s flexibility has proven to 

be extremely valuable for visualizing analysis results at user selectable spatial, temporal and 

spectral scales. It is modularized and the code is nearly all converted into an object oriented 

format.

Biologically and scientifically, despite the rapid improvements in the technology, we are 

really just beginning to learn how to comprehend the very large amounts of analyses results. We 

are learning how to animate results to take advantage of our brains, which are so well adapted for 

seeing patterns through motion. We are learning how to rigorously test for the biological 

significance of numerical patterns, but we are still at the beginning when it comes to assimilating

the complexity and scales of our collective, aggregated data sets.

5. Recommendations

Auto-detection Buoy

The GUI by which users obtain and interact with an operational AB can be and should be 

easier to use than any online weather buoy or satellite application. The system should be so 

comfortable for the user that it is viewed as an ocean acoustic “weather channel”. 

Functionally and specific to the issues surrounding near-real-time acoustic monitoring 

and mitigation, there is no technical reason impeding the deployment of an AB network in the 

Arctic. Weather and ice conditions are challenging, but buoy design can overcome many of these

challenges. On-board power is a limitation, but harvesting solar and/or wind energy could help 

mitigate this limitation. Such a network would dramatically increase empirical evidence of 

biological impact and ecological risk, thereby reducing uncertainties inherent in modeled 

estimates of biological impact and ecological risk. This in turn would improve the use of aerial 

and vessel surveys, by allowing them to focus on areas and times of greatest uncertainty and

when acoustic monitoring is not a viable option.
OCS Study

BOEM 2015-034

287

Sound analysis system

The basic system has been successfully developed but has not been fully integrated with 

DELMA. As expected, as we pushed to make the entire process more efficient, from data 

ingestion to end products, we discovered areas for improvement. These included inefficiencies in 

a wide range of places, from inconsistencies in data file naming conventions and file folder 

structures, to a lack of seamless connections between Matlab code and Java code. Some of these 

glitches were expected to happen as we moved to higher levels of automation, but at the time we 

had to accept those future difficulties in exchange for the immediate need to get something 

specific done. Many of these specific, late-project inefficiencies were not surprising, but the level 

of effort to fix any one of them has proven difficult to predict. There is also the tradeoff between 

a software fix that works but is a kluge and one that is robust. We are now at the point in the 

project where most kluge fixes are unacceptable. It cannot be understated how much mental 

effort and time is needed to really build a system that is scalable and capable of reliably

processing large data sets.

A very significant technical issue that has enormously important implications for moving 

the science forward is the lack of a scalable, comprehensive data system by which to integrate all 

the various types of data and data formats into a data discovery environment. Without real 

investment in such a system, we will simply continue to spend increasing time and resources on 

logistical issues (e.g., moving data around, reformatting data, trying to find data, etc.). As such

this is the most significant recommendation. The data scene has become far too large and 

complex, obscuring the very scale and detail that we seek to comprehend in our data.
OCS Study

BOEM 2015-034

288

XIII. ESTIMATING RELATIVE ABUNDANCE OF MARINE MAMMALS

A. Visual

1. Methods

To estimate relative abundance within Arctic waters, encounter rates (groups/km) and 

densities (individuals/km2) from sightings collected during “on-effort” status only (defined in 

Section VII) were computed. The area of analysis (198,677 km2) was defined as U.S. waters just 

north of the Bering Strait to the easternmost extent of effort, east of Barrow, AK (Figure 157).

FIGURE 157. ON-EFFORT TRANSECTS AND THE AREA OF ANALYSIS WITHIN ARCTIC WATERS TO ESTIMATE 

RELATIVE ABUNDANCE.

Detection probability was estimated using the hazard-rate within Conventional Distance 

Sampling (CDS) framework (Buckland et al., 2001, 2004). For bowhead, fin, humpback, and 

gray whales, perpendicular distances were pooled across all “large whale” species. Pooling 

provided greater sample size for fitting the detection function and allowing for density to be 

computed for species with relatively low number of detections and for improving precision of 

these estimates. Pooling across species with insufficient sample sizes is a relatively common 
OCS Study

BOEM 2015-034

289

practice (e.g., Barlow et al., 1997; Forney and Barlow, 1998) as long as pooling includes species 

with similar detection characteristics. In this study, perpendicular distance data were combined 

across species detected at similar average distances (e.g, Barlow et al., 2001). Modeling of 

perpendicular distance was conducted with ungrouped data truncated at 3 km for all whale 

species. Truncation was set at these distances after exploratory analyses were conducted to 

assess best truncation points (i.e., balance between sample size and appropriate fit model). The 

procedures and values selected for truncation are consistent with the literature (e.g., Buckland et 

al., 2001; Zerbini et al., 2006; Friday et al., 2013).

Model selection was conducted following the Akaike Information Criterion (AIC;

Akaike, 1973). A model with species as a covariate was run and was not well supported by AIC; 

therefore, no covariates were included in this analysis. The detection function was used to 

estimate species-specific density for all years combined (that is, an average across years). 

Encounter rate and its variance were estimated empirically from the data (Innes et al., 2002). For 

the purpose of this analysis, detection probability on the trackline was assumed to be (g[0] = 1). 

Detection probability and density estimates were computed using the R package mrds version 

2.1.0 (Laake et al., 2012).

2. Results

On-effort trackline used to estimate densities totaled 1,230 nm (2,278 km) (Figure 157).

Estimated parameters for the hazard rate model (most supported by AIC) across all species were: 

average detection probability (P) – 0.32, CV(P) – 0.23, and effective strip width (ESW) – 0.96

km. The number of sightings used in the estimation of density (after truncation) are shown in

Table 32. Estimates of encounter rate (Table 33) and density (

Table 34) were calculated for cetaceans with adequate sample sizes only. Estimates were not 

corrected for the proportion of animals missed on the trackline. The effort included for these 

estimates was as follows: August 24, 2010-September 15, 2010, August 19, 2011 – September 

3, 2011, and August 15-31, 2012.

TABLE 32. THE NUMBER OF SIGHTINGS (GROUPS) USED IN THE ESTIMATION OF DENSITY (AFTER TRUNCATION) OF 

CETACEANS WITHIN ARCTIC WATERS, 2010-2012. THE EFFORT INCLUDED FOR THESE ESTIMATES WAS: AUGUST 24, 

2010-SEPTEMBER 15, 2010, AUGUST 19, 2011 – SEPTEMBER 3, 2011, AND AUGUST 15-31, 2012.

Species Sightings (groups)

Bowhead whale 16

Fin whale 5

Humpback whale 2

Gray whale 15
OCS Study

BOEM 2015-034

290

TABLE 33. ESTIMATES OF ENCOUNTER RATE (GROUPS/KM) AND CV (IN PARENTHESIS) ACROSS ALL YEARS FOR 

CETACEANS WITHIN ARCTIC WATERS, 2010-2012. THE EFFORT INCLUDED FOR THESE ESTIMATES WAS: AUGUST 24, 

2010-SEPTEMBER 15, 2010, AUGUST 19, 2011 – SEPTEMBER 3, 2011, AND AUGUST 15-31, 2012.

Species Encounter Rate

Bowhead whale 0.0059 (0.59)

Fin whale 0.0022 (0.79)

Humpback whale 0.0007 (0.94)

Gray whale 0.0051 (0.86)

TABLE 34. ESTIMATES OF DENSITY (INDIVIDUALS/KM2) AND CV (IN PARENTHESIS) ACROSS ALL YEARS FOR 

CETACEANS WITHIN ARCTIC WATERS, 2010-2012. THE EFFORT INCLUDED FOR THESE ESTIMATES WAS: AUGUST 24, 

2010-SEPTEMBER 15, 2010, AUGUST 19, 2011 – SEPTEMBER 3, 2011, AND AUGUST 15-31, 2012.

Species Density

Bowhead whale 0.0043 (0.65)

Fin whale 0.0019 (0.88)

Humpback whale 0.0004 (0.97)

Gray whale 0.0030 (0.86)

3. Discussion

Visual estimates of relative abundance presented in this study assumed that no cetaceans 

were missed on the trackline (g[0]=1). Failure to meet this assumption (referred to as ‘visibility 

bias’) is common in marine mammal surveys and causes negative biases in density estimates 

(Laake, 1999; Buckland et al., 2001). Marsh and Sinclair (1989) coined the terms ‘perception’ 

and ‘availability’ bias to differentiate two forms of visibility bias. Perception bias occurs when 

marine mammal groups are available to be seen but are missed by the observers while 

availability bias corresponds to animals that are missed because they are submerged. Except for 

long-diving species such as sperm whales and beaked whales and for species that are only seen at 

close ranges such as harbor porpoise, the latter is typically not as important an issue as 

perception bias in ship surveys (see discussion in Barlow, 1995); animals are often at the surface 

and within the visual range of observers due to the relatively slow speed of the vessel. The 

magnitude of visibility bias is possibly small in the estimates of large whales with visible bodies 

and conspicuous blows such as humpback, bowhead, fin and gray whales. Studies to assess the 

proportion of whales missed on the trackline have shown that nearly 90-100 percent of 

humpback whales are detected during ship surveys, depending on visibility conditions and group 

size (Barlow and Gerrodette, 1996; Calambokidis and Barlow, 2004). On the other hand, 
OCS Study

BOEM 2015-034

291

visibility bias is more substantial for species that are more difficult to detect (e.g., porpoise) or 

for deep-diving species (e.g., sperm whales) leading to more severe negative bias in their 

estimates; neither were analyzed here. The magnitude of the bias is unknown in this study.

Surveys with two independent observer teams would allow for experiments to estimate g(0) and 

should therefore be considered in future surveys for which abundance and density estimates are a 

priority.

 Summer and fall density estimates for bowhead and gray whales in the Chukchi Sea have 

been calculated using the 2008-2010 COMIDA aerial survey data (Clarke et al., in review).

However, estimates are not comparable to estimates presented here given differences in 

platforms and geographic coverage. Small sample sizes typically result in large uncertainty 

around the estimates. Estimates were calculated for bowhead, gray, humpback and fin whales 

despite their small numbers of visual detections (Table 32) in order to provide predictions on 

presence within the Chukchi Sea during August/September. The effects of the small sample 

sizes are reflected in the high CVs for these species.

4. Conclusions

Despite the small sample sizes, results from this survey did provide a measure of 

densities of four species within the Chukchi Sea during August and September. Furthermore, 

identifying species within the area provided invaluable information for the other key components 

of this study. Visuals provided species identification for calls detected by acoustics as well as 

providing information on species and numbers within areas of oceanographic moorings and 

sampling stations. Additionally, visuals provided information on interannual differences in the 

spatial distribution of these key Arctic species.

5. Recommendations

Visual survey methods for this project differed from standard line-transect or abundance 

surveys due to constraints of the overall project. To improve estimates, surveys should be 

designed with density and abundance estimates as a main objective. This would require 

additional ship time, observers, and ideally, be designed with predetermined tracklines. 

Depending on the objective, shipboard surveys may or may not be appropriate to fulfill 

requirements. If the question is density within the Chukchi Sea, an aerial survey (i.e., Aerial 

Surveys of Arctic Marine Mammals, ASAMM) may be a more appropriate platform to meet 

objectives given the ability to cover greater distances within a large survey area. However, 

calculating densities within smaller regions (e.g., the lease area) could be achieved in a 

reasonable amount of time with a shipboard survey.

B. Acoustic

No relative abundance estimates were calculated from the passive acoustic data collected 

during the CHAOZ project. As detailed below, the field is still in its infancy, but we now have a 

plan as to how to begin to collect the field data necessary to mine our long-term passive acoustic 

datasets and eventually estimate relative abundance for all marine mammal species included in 

this study.
OCS Study

BOEM 2015-034

292

In recent years, considerable effort has been spent developing methods for incorporating 

towed passive acoustic arrays into survey designs to improve estimates of animal density and/or 

abundance. These methods have proved successful at obtaining density estimates on par with 

those from visual methods (e.g., Barlow and Taylor, 2005) for species such as sperm whales that 

vocalize at high calling rates and source levels. However, for the shallow Chukchi Sea shelf 

(~40 m), towed arrays are not feasible as they require at least 100m depth so that masking from 

the tow ship, and the risk that they would become tangled on something on the bottom, would be 

minimized. Towed arrays are even less practical than visual surveys for cruises that stop at 

numerous stations for biophysical sampling and mooring deployments. Furthermore, they are 

better suited to species with higher frequency call repertoires, since lower frequencies on the 

recordings are masked by flow noise. Finally, as with visual line transect surveys, they are 

limited to the short time window of the open water season. 

As shown in Section VII.A above, passive acoustic moorings are an excellent platform 

for obtaining long-term datasets on the year-round seasonal distribution of multiple species of 

cetaceans and pinnipeds. There are even certain subarctic species where passive acoustic 

monitoring methods surpassed visual survey techniques for detecting individuals (see Section 

VII.B.3). Is it possible to use these data to obtain relative abundance estimates for Arctic and 

subarctic marine mammal species? Marques et al. (2009) answer concisely: “... estimating 

absolute density or abundance from such data is considerably more difficult, although 

considerably more useful too.” Their paper describes the steps needed to accomplish this task: 

cue counting, estimating average detection probability, and determining the false positive 

proportion.

The first two methods (cue counting and average detection probability) provide 

information on the ‘availability bias’ defined in Section XIII.A.3 above. The main problem with 

passive acoustic methods is that they are only capable of detecting animals that are making 

sounds. Like animals that are below the surface when the visual survey passes by, silent animals 

cannot be counted. Cue counting is a method that estimates the probability that an animal is 

calling. For the upcoming field season, we plan to begin building a dataset of these probability 

estimates with the following method6. The visual team will conduct their line transect survey as 

usual (Section VII.B.1). Sightings will then be randomly selected for a focused cue count 

session. The visual team will take detailed notes on all animals within their field of view, while 

the passive acoustics team deploys and monitors a sonobuoy and notes the presence of any calls 

for that species. At the end of the ten-minute session, the cue count will be entered into the 

database as ‘calling’ or ‘not calling’. The bigger the sample size, the more accurate the resulting 

estimate of calling probability will be. 

It is important that the selected sightings be obtained from the visual sightings as opposed 

to the sonobuoy detections so as to not bias the cue counts by leaving out silent animals. It is 

equally important to conduct these cue counts under a variety of situations; e.g., at various times 

of the day, at different locations, with different group sizes, and during a variety of behaviors, so 

that corrections for these factors can be applied. It would also be beneficial to collect these data 

throughout the year. Although impractical on a shipboard survey, training of local native hunters 

 

6 Another way to obtain cue counts is to use acoustic recorder tags (e.g., DTAGs). These instruments are able to 

collect call counts from individual whales quite accurately, but the amount of extra ship time needed to conduct this 

type of effort will be cost-prohibitive (and also limited to the open water season).
OCS Study

BOEM 2015-034

293

and participation on platforms of opportunity (see www.aoos.org for more information) could 

provide the means of obtaining these data beyond the open water season. Since the visual survey 

method (i.e., Big Eyes) and passive acoustic method (sonobuoys) can be used at five mile 

distances – the influence of the survey vessel on the calling behavior of the animal can be 

reduced. 

If two or more sonobuoys are deployed during these cue counts, cross-fixes to the calling 

animals, and therefore detection distance, can be calculated. This can then be used to estimate 

the average detection probability for that species. As mentioned above (Section VII.A.1), and by 

other studies (Stafford et al., 2007a), the detection range is influenced by a variety of 

oceanographic factors as well as the presence of ambient noise (of biological, environmental, and 

anthropogenic sources) that can mask the calls of interest. Again, this is a source of availability 

bias that has to be determined before relative density and abundance estimates can be obtained. 

However, on our multi-disciplinary cruises, oceanographic sampling equipment is on board and 

available for measuring many of the environmental variables that contribute to sound 

propagation.

Call localization techniques from moored recorder arrays are another way of obtaining 

these average detection probabilities (although not cue counts). For the CHAOZ study we 

deployed five-element arrays positioned with the elements spaced 2-4 nm apart. The critical 

component of any array localization study is time synchronization of all recorders in the array. 

Typically, high precision crystals are used to maintain the time synchronization, but temperature 

differences can cause fluctuations among times. A linear time drift is generally assumed, with the 

difference in start and end times smoothed linearly over the entire length of the deployment. 

Less than perfect drifts (e.g., 5 minutes over 3-4 months) have been seen with recorders that 

were expected to perform better (C. Berchok, pers. observation). For this reason we equipped 

each mooring with an acoustic pinger that sent a small (<7 s) series of chirps every half hour. 

Localization of these chirps with all elements of the array would provide an estimate of the 

localization error correction for that location that would account for all oceanographic conditions 

at that time of day/year. However, in an attempt to limit the amount of sounds produced, the 

source level of the pings was not sufficient in the 2010-11 deployment for the signal to be 

detected on any of the other recorders in the array. For the 2011-12 deployment, the signal was 

boosted slightly and the duty cycle reduced to hourly transmissions. These data exist and can be 

used to determine detection distances of the calling animals within the arrays (localizations 

always have smaller error bounds within the array (Berchok et al., 2006)). The current limiting 

factor is the lack of accurate auto-detection algorithms that can quickly compile the detections on 

the other four recorders of each array and provide the core data needed to perform these 

localizations. 

The last piece of the puzzle is obtaining an estimate of the false positive rate of the 

acoustic analysis methods (i.e., the percentage of times a signal was falsely identified as 

belonging to a certain species). This provides information on the perception bias. Work has been 

done (Caillat et al., 2013) on other data sets that suggests these techniques are appropriate. 

However, in speaking with the author about the current level of performance for Arctic autodetectors it was clear that the variance in any resulting abundance estimates would be 

impractically high. Therefore, more work is needed to develop auto-detectors that can perform 

well in the Arctic environment where very few species have stereotyped calls, and many species 

(and environment sources such as ice noise) make similar, often overlapping sounds. In the 
OCS Study

BOEM 2015-034

294

meantime, we will continue with our manual analysis while concurrently working on improving 

our auto-detectors (Section VII.A.5). With our manual analysis method, a random blind crosscheck by a couple of experienced analysts can provide the metrics needed to obtain this false 

detection rate; however, manual analysts tend to be conservative with their detections (e.g.,

Hannay et al., 2013), and a low false detection rate is expected. After these components are in 

place, estimates of relative density and abundance can then be calculated following the methods 

described in Marques et al. (2009). 

Finally, it is very important to emphasize at this point that although it will most likely 

take some time to establish an Arctic database of cue counts and detection probabilities, the longterm passive acoustic data sets collected during the CHAOZ project will be available and ready 

for the suitable abundance techniques (Marques et al., 2009) to be applied. The field methods 

needed to build this database can be readily integrated into multidisciplinary cruises, and 

therefore will not require the substantial ship time nor personnel of a traditional visual survey.

Furthermore, the sample size in this Arctic database will only increase over time, leading to more 

accurate recalculations of relative density and abundance, which will then be available for 

comparisons between years, and eventually decades.
OCS Study

BOEM 2015-034

295

XIV. SUMMARY

A. Overall summary 

This integrative five year study was able to correlate marine mammal distributions with 

oceanographic parameters, and indices of potential prey availability. The technologies utilized in 

this study allowed us to assess complex trophic interactions, and illustrated the benefit of these 

complex analyses being conducted on an annual scale. Furthermore, by including climate and 

ocean atmosphere modeling, we were able to not only predict ecosystem-wide changes as a 

result of a reduction in sea ice, but also to assess potential impacts to marine mammal 

populations and distribution. Although the current marine mammal dataset was too brief to 

determine if there was a northward shift of subarctic species into the Arctic, anecdotal evidence 

(i.e., walrus and ribbon seals detected in the Chukchi during off-season months, the acoustic 

detection of a fin whale off Barrow Canyon) possibly suggests that such a northward shift of 

subarctic species may be underway.

This project included two years of continuous year-round data at a location that is well 

suited to document changes to the ecosystem. However, with this limited spatio-temporal scale, 

it is difficult to determine if our results are representative of true annual patterns, or merely 

reflect the high interannual variability in this region. By including additional data that are 

currently being collected in the ongoing BOEM-funded ARCWEST and CHAOZ-X (CHAOZ 

extension) projects, we will not only increase the temporal time scale of our dataset, but greatly 

expand our spatial coverage to include other areas that are critical to marine mammal migrations 

as well. This will allow us to more fully assess year-round distributions as well as quantify 

interannual variation, better predict future oceanographic conditions and ecosystem shifts, and 

evaluate potential impacts of climate change on both lower and upper trophic levels.

B. Recommendations for future work

The data collected for the CHAOZ project demonstrate the utility and benefit of 

concurrent zooplankton, oceanography, and marine mammal monitoring. These data, combined 

with those currently being collected for the ongoing BOEM-funded ARCWEST and CHAOZ-X 

projects represent the only long-term integrated dataset of its kind from the Chukchi Sea lease 

area and Alaskan Arctic in general. We therefore recommend continuation of the long-term 

mooring deployments. With current modifications to the moored TAPS6-NG instruments, we 

will be able to collect data for a full year, allowing for assessment of trophic interactions on an 

annual time scale. It will also be possible to establish multi-year patterns in marine mammal 

distributions as they relate to indices of zooplankton and oceanographic conditions. 

We also recommend continuation of the integrated biophysical shipboard surveys 

conducted during this study. These surveys provide data on the fine-scale vertical resolution of 

zooplankton abundance as they correlate with oceanographic indices, nutrients, and distribution 

of marine mammals. To maximize marine mammal detections during shipboard surveys, it is 

essential to have both passive acoustic monitoring and visual survey components. Since each 

method is well-suited to particular species, together they provide a more complete picture of 
OCS Study

BOEM 2015-034

296

marine mammal distribution. In addition, joint passive acoustic/visual survey focal follows 

enable future calculations of relative abundance. Addition of a benthic ecology component 

would help to address prey availability for those mammals that feed on benthic epifauna and 

infauna.
OCS Study

BOEM 2015-034

297

XV. LITERATURE CITED

Aerts, L.A.M., A. Kirk, C. Schudel, B. Watts, P. Seiser, A. Mcfarland, and K. Lomac-Macnair. 

2012. Marine Mammal Distribution and Abundance in the Northeastern Chukchi Sea, 

July-October 2008-2011. Report prepared by LAMA Ecological for ConocoPhillips 

Alaska, Inc., Shell Exploration and Production Company and Statoil USA E&P, Inc. 69 

pp.

Aerts, L.A.M., A.E. McFarland, B.H. Watts, K.S. Lomac-MacNair, P.E. Seiser, S.S. Wisdom, 

A.V. Kirk, and C.A. Schudel. 2013. Marine mammal distribution and abundance in an 

offshore sub-region of the northeastern Chukchi Sea during the open-water season. 

Continental Shelf Research 67: 116-126.

Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. In 

B. N. Petran and F. Csáaki (eds.), International symposium on information theory, 2nd 

edition, pp. 267-281. Budapest, Hungary: Acadèemiai Kiadi.

Allen, B.M. and R.P. Angliss. 2013. Alaska marine mammal stock assessments: Bearded Seal 

(Erignathus barbatus nauticus). NOAA-TM-AFSC-277: 55-61.

Angliss, R.P., D.J. Rugh, D.E. Withrow and R.C. Hobbs. 1995. Evaluations of aerial 

photogrammetric length measurements of the Bering-Chukchi-Beaufort Seas stock of 

bowhead whales (Balaena mysticetus). Reports of the International Whaling Commission 

45: 313-324. 

Argos. 1990. User’s manual. Service Argos, Inc. Landover, MD, USA.

ASAMM (Aerial Surveys of Arctic Marine Mammals) daily flight report. Chukchi Sea Flight 

240, 27 September 2014. 

http://www.afsc.noaa.gov/nmml/cetacean/bwasp/2014/ASAMMChukchi_Flight240_27September2014.pdf

Ashjian, C.J., R.G. Campbell, J.C. George, J. Kruse, W.Maslowski, S.E. Moore, C.R. Nicolson, 

S.R. Okkonen, B.F. Sherr, E.B. Sherr, and Y.H. Spitz. 2010. Climate variability, 

oceanography, bowhead whale distribution, and Inupiat subsistence whaling near Barrow, 

Alaska. Arctic 63: 179-194.

Au, W.W.L., D.A. Carder, R.H. Penner, and B.L. Scronce. 1985. Demonstration of adaptation in 

beluga whale echolocation signals. Journal of the Acoustical Society of America 77(2): 

726-730.

Barlow, J. 1995. The abundance of cetaceans in California waters. Part I: Ship surveys in the 

summer and fall of 1991. Fishery Bulletin 93: 1-14.

Barlow, J. and T. Gerrodette. 1996. Abundance of cetaceans in California waters based on 1991 

and 1993 ship surveys. NOAA Technical Memorandum NMFS-SWFSC-233. La Jolla, 

CA: National Marine Fisheries Service. 15 pp.

Barlow, J., T. Gerrodette, and G. Silber. 1997. First estimates of vaquita abundance. Marine 

Mammal Science 13: 44-58.

Barlow, J., T. Gerrodette, and T. Forcada. 2001. Factors affecting perpendicular sighting 

distances on shipboard line-transect surveys for cetaceans. Journal of Cetacean Research 

and Management 3: 201-212.
OCS Study

BOEM 2015-034

298

Barlow, J. and B.L. Taylor. 2005. Estimates of sperm whale abundance in the northeastern 

temperate Pacific from a combined acoustic and visual survey. Marine Mammal Science 

21(3): 429-445.

Bengtson, J.L., L.M. Hiruki-Raring, M.A. Simpkins, and P.L. Boveng. 2005. Ringed and 

bearded seal densities in the eastern Chukchi Sea, 1999-2000. Polar Biology 28: 833-845.

Berchok, C.L., G.L. D’Spain, and J.A. Hildebrand. 2006. Reducing source localization errors: a 

visualization method to help guide the design, calibration, and use of widely-separated 

acoustic sensor arrays. Journal of the Acoustical Society of America. 120: 2999.

Berge, J., F. Cottier, K.S. Last, Ø.Varpe, E. Leu, J. Søreide, K. Eiane, S. Falk-Petersen, K. 

Willis, H. Nygård, D. Vogedes, C. Griffiths, G. Johnsen, D. Lorentzen, and A.S. Brierley. 

2009. Diel vertical migration of Arctic zooplankton during the polar night. Biology 

Letters 5: 69–72. 

Berline, L., Y.H. Spitz, C.J. Ashjian, R.G. Campbell, W. Maslowski, S.E. Moore. 2008. 

Euphaushiid transport in the western Arctic Ocean. Marine Ecology Progress Series 360: 

163-178.

Blackwell, S.B., W.J. Richardson, C.R. Greene, Jr., and B. Streever. 2007. Bowhead whale 

(Balaena mysticetus) migration and calling behavior in the Alaskan Beaufort Sea, 

Autumn 2001-04: an acoustic localization study. Arctic 60: 255-270.

Blackwell, S.B., C.S. Nations, T.L. McDonald, C.R. Greene, Jr., A.M. Thode, M. Guerra, and A. 

M. Macrander. 2013. Effects of airgun sounds on bowhead whale calling rates in the 

Alaskan Beaufort Sea. Marine Mammal Science 29: E342-E365. DOI: 

10.1111/mms.12001

Blanchard, A.L., C.L. Parris, A.L. Knowlton, and N.R. Wade. 2013. Benthic ecology of the 

northeastern Chukchi Sea. Part I. Environmental characteristics and macro faunal 

community structure 2008-2010. Continental Shelf Research 67: 52-66.

Blees, M.K., K.G. Hartin, D.S. Ireland, and D. Hannay. (eds.) 2010. Marine mammal monitoring 

and mitigation during open water seismic exploration by Statoil USA E&P Inc. in the 

Chukchi Sea, August–October 2010: 90-day report. LGL Rep. P1119. Rep. from LGL 

Alaska Research Associates Inc., LGL Ltd., and JASCO Research Ltd. for by Statoil 

USA E&P Inc., Nat. Mar. Fish. Serv., and U.S. Fish and Wild. Serv. 102 pp, plus 

appendices.

Bluhm, B.A., K.O. Coyle, B. Konar, and R. Highsmith. 2007. High gray whale relative 

abundances associated with an oceanographic front in the south-central Chukchi Sea. 

Deep-Sea Research II 54: 2919-2933.

Bluhm, B.A. and R. Gradinger. 2008. Regional variability in food availability for Arctic marine 

mammals. Ecological Applications 18: S77-S96.

Bluhm, B.A., K. Iken, S. Mincks Hardy, B.I. Sirenko, and B.A. Holladay. 2009. Community 

structure of epibenthic megafauna in the Chukchi Sea. Aquatic Biology 7: 269-293.
OCS Study

BOEM 2015-034

299

Boveng, P.L., J.L. Bengtson, M.F. Cameron, S.P. Dahle, E.A. Logerwell, J.M. London, J.E. 

Overland, J.T. Sterling, D.E. Stevenson, B.L. Taylor, and H.L. Ziel. 2013. Status review 

of the ribbon seal (Histriophoca fasciata). U.S. Department of Commerce, NOAA 

Technical Memorandum NMFS-AFSC-225. 175 pp.

Braham, H.W., C. Fiscus, and D. J. Rugh. 1977. Marine mammals of the Bering and southern 

Chukchi seas. Environmental Assessment of Alaska Outer Continental Shelf 1: 1-99.

Braham, H.W., M.A. Fraker, and B.D. Krogman. 1980. Spring Migration of the Western Arctic 

Population of Bowhead Whales. Marine Fisheries Review 42: 36-46.

Braham, H.W., B.D. Krogman, and G.M. Carroll. 1984. Bowhead and white whale migration, 

distribution, and abundance in the Bering, Chukchi, and Beaufort Seas, 1975-78. NOAA 

Technical Report NMFS SSRF-778.

Buckland, S.T., D.R. Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers, and L. Thomas. 2001. 

Introduction to distance sampling estimating abundance of biological populations. 

Oxford, UK: Oxford University Press. 432 pp.

Buckland, S.T., D.R. Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers, and L. Thomas. 2004. 

Advanced distance sampling. Oxford, UK: Oxford University Press. 411 pp.

Burns, J.J. 1967. The Pacific bearded seal. Alaska Department of Fish and Game, PittmanRobertson Project Report W-6-r and W-14-R.

Burns, J.J. 1970. Remarks on the distribution and natural history of pagophilic pinnipeds in the 

Bering and Chukchi Seas. Journal of Mammalogy 51: 445-454.

Burns, J.J. 1981. Bearded seal, Erignathus barbatus. In S.H. Ridgeway and R.J. Harrison, F.R.S. 

(eds.), Handbook of Marine Mammals, pp.145-170. London: Academic Press.

Burns, J.J. and T.J. Eley. 1978. The Natural History and Ecology of the Bearded Seal 

(Erignathus Barbatus) and the Ringed Seal (Phoca Hispida). Environmental Assessment 

of the Alaskan Continental Shelf Annual Reports of Principal Investigators for the Year 

Ending March 1, 1978.

Burns, J.J. and K.J. Frost. 1979. The natural history and ecology of the bearded seal, Erignathus 

barbatus. Alaska Department of Fish and Game. 77pp.Caillat, M., L. Thomas, and D. 

Gillespie. 2013. The effects of acoustic misclassification on cetacean species abundance 

estimation. Journal of the Acoustical Society of America 134: 2469-2476.

Caillat, M., L. Thomas, and D. Gillespie. 2013. The effects of acoustic miscalculation on 

cetacean species abundance estimation. Journal of the Acoustical Society of America. 

134: 2469-2476.

Calambokidis, J. and J. Barlow. 2004. Abundance of blue and humpback whales in the eastern 

North Pacific estimated by capture-recapture and line-transect methods. Marine Mammal 

Science 20: 63-85.

Calupca T.A., K.M. Fristrup, and C.W. Clark. 2000. A compact digital recording system for 

autonomous bioacoustic monitoring. Journal of the Acoustical Society of America. 108:

2582-2582.
OCS Study

BOEM 2015-034

300

Cameron, D., E. Ellis, A. Harrison, H. Ingram, and M. Piercy. 2012. Plate boundaries around the 

Chukchi borderland: An integrated geophysics cruise to test models for the formation of 

the Canada Basin. Protected Species Mitigation and Monitoring Report of the Coakley 

Marine Geophysical Survey in the Arctic Ocean. Prepared for Lamont-Doherty Earth 

Observatory of Columbia University, Palisades, NY, and Nat. Mar. Fish. Serv., Silver 

Spring, MD. 54 pp, plus appendices. 

Carroll, G.M., J.C. George, L.F. Lowry, and K.O. Coyle. 1987. Bowhead whale (Balaena 

mysticetus) feeding near Point Barrow, Alaska, during the 1985 spring migration. Arctic 

40: 105-110.

Castellote, M., R.J. Small, M.O. Lammers, J. Jenniges, J. Mondragon, and S. Atkinson. In 

review. Dual instrument passive acoustic monitoring of belugas in Cook Inlet, Alaska. 

Submitted to Journal of the Acoustical Society of America.

Castellote, M., T. McGuire, C. McKee, and M. Lammers. 2011. Can we hear Cook Inlet beluga 

whales feeding? Poster presentation at the Alaska Marine Science Symposium, 17-21 

January, 2011. Anchorage, AK.

Castellote, M., R.H. Leeney, G. O’Corry-Crowe, R. Lauhakangas, K.M. Kovacs, W. Lucey, V. 

Krasnova, C. Lydersen, K.M. Stafford, and R. Belikov. 2013. Monitoring white whales 

(Delphinapterus leucas) with echolocation loggers. Polar Biology 36: 493-509.

Castellote, M., T.A. Mooney, L. Quakenbush, R. Hobbs, C. Goertz, and E. Gaglione. 2014. 

Baseline hearing abilities and variability in wild beluga whales (Delphinapterus leucas).

Journal of Experimental Biology 217(10): 1682-1691.

Cerchio, S., S. Strindberg, T. Collins, C. Bennett, and H. Rosenbaum. 2014. Seismic surveys 

negatively affect humpback whale singing activity off Northern Angola. PLoS ONE 9: 

e86464. doi: 10.1371/journal.pone.0086464

Charif, R.A., C.W. Clark, and K.M. Fristrup. 2004. Raven 1.2 User’s Manual. Cornell 

Laboratory of Ornithology, Ithaca, NY.

Citta, J.J., R.S. Suydam, L.T. Quakenbush, K.J. Frost, and G.M. O’Corry-Crowe. 2013. Dive 

behavior of Eastern Chukchi beluga whales (Delphinapterus leucas), 1998-2008. Arctic 

66: 389-406.

Clapham, P.J., A.S. Kennedy, B.K. Rone, A.N. Zerbini, J.L. Crance, and C.L. Berchok. 2012. 

North Pacific right whales (Eubalaena japonica) in the southeastern Bering Sea. Final 

Report. OCS Study BOEM 2012-074. National Marine Mammal Laboratory, Alaska 

Fisheries Science Center, NOAA. 175pp.

Clark, C.W. 1983. Acoustic communication and behavior of the southern right whale. In R.S. 

Payne (ed.), Communication and Behavior of Whales, pp. 163-198. American 

Association for the Advancement of Science Selected Symposium 76. Boulder, Colorado: 

Westview Press.

Clark, C.W., W.T. Ellison, and K. Beeman. 1986. A preliminary account of the acoustic study 

conducted during the 1985 spring bowhead whale, Balaena mysticetus, migration off 

Point Barrow, Alaska. Report of the International Whaling Commission 36: 311-316.
OCS Study

BOEM 2015-034

301

Clark, C.W. and W.T. Ellison. 2000. Calibration and comparison of the acoustic location 

methods used during the spring migration of the bowhead whale, Balaena mysticetus, off 

Pt. Barrow, Alaska, 1984-1993. Journal of the Acoustical Society of America. 106: 3509-

3517.

Clark, C. W., J.F. Borsani, and G. Notarbartolo-di-Sciara. 2002. Vocal activity of fin whales, 

Balaenoptera physalus, in the Ligurian Sea. Marine Mammal Science 18: 286–295.

Clark, C.W., W.T. Ellison, B.L. Southall, L. Hatch, S. Van Parijs, A. Frankel, and D. Ponirakis. 

2009. Acoustic Masking in Marine Ecosystems: Intuitions, Analysis, and Implications. 

Marine Ecology Progress Series 395: 201-222.

Clark, C.W., C.L. Berchok, S.B. Blackwell, D.E. Hannay, J. Jones, D. Ponirakis and K.M. 

Stafford. 2015. A year in the acoustic world of Western Arctic bowhead whales in the 

Bering, Chukchi and Beaufort seas. Progress in Oceanography 136: 223-240 

http://dx.doi.org/10.1016/j.pocean.2015.05.007.

Clarke, J.T., S.E. Moore, and M.M. Johnson. 1993. Observations on beluga fall migration in the 

Alaskan Beaufort Sea, 1982-87, and northeastern Chukchi Sea, 1982-91. Report of the 

International Whaling Commission 43: 387-396.

Clarke, J.T. and M.C. Ferguson. 2010. Aerial surveys of large whales in the northeastern 

Chukchi Sea, 2008-2009, with review of 1982-1991 data. IWC Scientific Committee, pp. 

1-18.

Clarke, J.T., M.C. Ferguson, C.L. Christman, S.L. Grassia, A.A. Brower, and L.J. Morse. 2011. 

Chukchi Offshore Monitoring in Drilling Area (COMIDA) Distribution and Relative 

Abundance of Marine Mammals: Aerial Surveys. Final Report, OCS Study BOEMRE 

2011-06. National Marine Mammal Laboratory, Alaska Fisheries Science Center, NMFS, 

NOAA, 7600 Sand Point Way NE, F/AKC3, Seattle, WA 98115-6349.

Clarke, J., C.L. Christman, A.A. Brower, and M.C. Ferguson. 2012. Distribution and relative 

abundance of marine mammals in the Alaskan Chukchi and Beaufort Seas, 2011. Annual 

Report, OCS Study BOEM 2012-009. National Marine Mammal Laboratory, Alaska 

Fisheries Science Center, NMFS, NOAA, 7600 Sand Point Way NE, F/AKC3, Seattle, 

WA 98115-6349.

Clarke, J.T., C.L. Christman, A.A. Brower, and M.C. Ferguson. 2013. Distribution and Relative 

Abundance of Marine Mammals in the Northeastern Chukchi and Western Beaufort Seas, 

2012. Annual Report, OCS Study BOEM 2013-00117. National Marine Mammal 

Laboratory, Alaska Fisheries Science Center, NMFS, NOAA, 7600 Sand Point Way NE, 

F/AKC3, Seattle, WA 98115-6349.

Clarke, J., K. Stafford, S.E. Moore, B.K. Rone, L. Aerts, and J.L. Crance. 2013. Subarctic 

cetaceans in the southern Chukchi Sea: Evidence of recovery or response to a changing 

ecosystem. Oceanography 26: 136-149. doi: 10.5670/oceanog.2013.81

Clarke, J., M.C. Ferguson, C. Curtice, and J. Harrison. 2015. Biologically Important Areas for 

cetaceans within U.S. waters – Arctic region. Aquatic Mammals 41: 94-103. doi: 

10.1578/AM.41.1.2015.94
OCS Study

BOEM 2015-034

302

Clarke, J.T., A.S. Kennedy, and M.C. Ferguson. Bowhead (Balaena mysticetus) and gray whale 

(Eschrichtius robustus) distribution, relative abundance and habitat selection in summer 

and fall in the northeastern Chukch Sea, 2009-2012. Arctic, in review.

Cleator, H.J., I. Stirling, and T.G. Smith. 1989. Underwater vocalizations of the bearded seal 

(Erignathus barbatus). Canadian Journal of Zoology 67: 1900-1910.

Clutter, R.I. and M. Anraku. 1968. Avoidance of samplers. In Zooplankton Sampling, UNESCO 

Monograph on Oceanographic Methodology, vol. 2, pp. 57-643. Paris, France: UNESCO 

Press.

Cottier, F.R., G.A. Tarling, A. Wold, and S. Falk-Petersen. 2006. Unsynchronised and 

synchronised vertical migration of zooplankton in a high Arctic fjord. Limnology and 

Oceanography 51: 2586–2599. 

Crance, J.L., C.L. Berchok, J. Bonnel, and A.M. Thode. 2015. Northeasternmost record of a 

north Pacific fin whale (Balaenoptera physalus) in the Alaskan Chukchi Sea. Polar 

Biology doi: 10.1007/s00300-015-1719-7

Crane, N.L. and K. Lashkari. 1996. Sound production of gray whales, Eschrichtius robustus, 

along their migration route: a new approach to signal analysis. Journal of the Acoustical 

Society of America 100: 1878-1886.

Cummings, W.C., P.O. Thompson, and R. Cook. 1968. Underwater Sounds of Migrating Gray 

Whales, Eschrichtius glaucus. Journal of the Acoustical Society of America 44 (5): 1278-

1281.

Dahlheim, M.E. 1987. Bio-acoustics of the gray whale (Eschrichtius robustus). Ph.D. 

Dissertation, University of British Columbia.

Dahlheim, M., T. Bray, and H. Braham. 1980. Vessel survey for bowhead whales in the Bering 

and Chukchi Seas, June-July 1978. Marine Fisheries Review 42: 51-57.

Danielson, S.L., L. Eisner, C. Ladd, C. Mordy, L. Sousa and T.J. Weingartner. Submitted (July 

2015). A comparison between late summer 2012 and 2013 water masses, macronutrients, 

and phytoplankton standing crops in the northern Bering and Chukchi Seas. Deep-Sea 

Res. II.

Darling, J.D, K.E. Keogh, and T.E. Steeves. 1998. Gray whale (Eschrichtius robustus) habitat 

utilization and prey species off Vancouver Island, B.C. Marine Mammal Science 14: 692-

720.

Day, R.H., T.J. Weingartner, R.R. Hopcroft, L.A.M. Aerts, A.L. Blanchard, A.E. Gall, B.J. 

Gallaway, D.E. Hannay, B.A. Holladay, J.T. Mathis, B.L. Norcross, J.M. Questel, and 

S.S. Wisdom. 2013. The offshore northeastern Chukchi Sea, Alaska: A complex highlatitude system. Continental Shelf Research 67: 147–165.

Deecke, V.B., J.K.B. Ford, and P.J.B. Slater. 2005. The vocal behaviour of mammal-eating killer 

whales (Orcinus orca): Communicating with costly calls. Animal Behaviour 69: 395-405.

Deines, K.L. 1999. Backscatter estimation using Broadband acoustic Doppler current profilers. 

Proceedings of the IEEE Sixth Working Conference on Current Measurement, pp. 249 –

253. San Diego, California.
OCS Study

BOEM 2015-034

303

De Jesus, M., G. Heckel, J.M. Breiwick, and S.B. Reilly. 2014. Migration timing and distance 

from shore of southbound eastern Pacific gray whales (Eschrichtius robustus) off 

Ensenada, Baja California, Mexico. Marine Mammal Science 30: 674-690.

Delarue, J., M. Laurinolli, and B. Martin. 2009. Bowhead whale (Balaena mysticetus) songs in 

the Chukchi Sea between October 2007 and May 2008. Journal of the Acoustical Society 

of America 126: 3319-3328.

Delarue, J., M. Laurinoll, and B. Martin. 2011. Acoustic detections of beluga whales in the 

Northeastern Chukchi Sea, July 2007 to July 2008. Arctic 64: 15-24.

Delarue, J., B. Martin, D. Hannay, and C.L. Berchok. 2013a. Acoustic occurrence and affiliation 

of fin whales detected in the northeastern Chukchi Sea, July to October 2007-10. Arctic 

66: 159-172.

Delarue, J., B. Martin, and D. Hannay. 2013b. Minke whale boing sound detections in the 

northeastern Chukchi Sea. Marine Mammal Science 29(3): E333-E341.

DeRobertis, A., D. McKelvey, and P.H. Ressler. 2010. Development and application of 

empirical multi-frequency methods for backscatter classification. Canadian Journal of 

Fisheries and Aquatic Sciences 67: 1459-1474.

Dewicke, A., V. Rottiers, J. Mees, and M. Vincx. 2002. Evidence for an enriched hyperbenthic 

fauna in the Frisian front (North Sea). Journal of Sea Research 47: 121–139.

Dougherty, A., C. Harpold, and J. Clark. 2010. Ecosystems and Fisheries-Oceanography 

Coordinated Investigations (EcoFOCI) field manual. AFSC Processed Report 2010-02, 

213 pp. Alaska Fisheries Science Center, NMFS, NOAA, 7600 Sand Point Way NE, 

Seattle WA 98115. http://www.afsc.noaa.gov/Publications/ProcRpt/PR2010-02.pdf

Dugan, P.J., D.W. Ponirakis, J.A. Zollweg, M.S. Pitzrick, J.L. Morano, A.M. Warde, A.N. Rice, 

and C.W. Clark. 2011. SEDNA - Bioacoustic Analysis Toolbox Matlab Platform to 

Support High Performance Computing, Noise Analysis, Event Detection and Event 

Modeling. IEEE Xplore, vol. OCEANS-11, Kona, Hawaii.

Dunton, K.H., S.M. Saupe, A.N. Golikov, D.M. Schell, and S.V. Schonberg. 1989. Trophic 

relationships and isotopic gradients among arctic and subarctic marine fauna. Marine 

Ecology Progress Series, Oldendorf 56: 89-97.

Ellison, W.T., C.W. Clark, and G.C. Bishop. 1987. Potential use of surface reverberation by 

bowhead whales, Balaena mysticetus, in under-ice navigation: Preliminary 

considerations. Report of the International Whaling Commission 37: 329-332.

Fadely, B.S., B.W. Robson, J.T. Sterling, A. Greig, and K.A. Kall. 2005. Immature Steller sea 

lion (Eumetopias jubatus) dive activity in relation to habitat features of the eastern 

Aleutian Islands. Fisheries Oceanography 14: 243-258.

Fay, F.H. 1982. Ecology and biology of the Pacific walrus, Odobenus rosmarus divergens

Illiger. North American Fauna 74: 1-279.

Fay, F.H., Y.A. Bukhtiyarov, S.W. Stoker, and L.M. Shults. 1984a. Foods of the Pacific walrus 

in winter and spring in the Bering Sea. NOAA Technical Report NMFS, 12: 81-88.
OCS Study

BOEM 2015-034

304

Fay, F.H., G.C. Ray, and A.A. Kibal’chich. 1984b. Time and location of mating and associated 

behavior of the Pacific walrus, Odobenus rosmarus divergens Illiger. Soviet-American 

cooperative research on marine mammals 1: 89-99.

Feder, H.M., A.S. Naidu, S.C. Jewett, J.M. Hameedi, W.R. Johnson, and T.E. Whitledge. 1994. 

The northeastern Chukchi Sea: benthos-environmental interactions. Marine Ecology 

Press Series 111: 171-190.

Flinn, R.D., A.W. Trites, E.J. Gregr, and R.I. Perry. 2002. Diets of fin, sei, and sperm whales in 

British Columbia: An analysis of commercial whaling records, 1963-1967. Marine 

Mammal Science 18: 663-679.

Freitas, C., C. Lydersen, M.A. Fedak, and K.M. Kovacs. 2008. A simple new algorithm to filter 

marine mammal Argos locations. Marine Mammal Science 24: 315-325.

Forney, K.A. and J. Barlow. 1998. Seasonal patterns in the abundance and distribution of 

California cetaceans, 1991-1992. Marine Mammal Science 14: 460-489.

Friday, N.A., A.N. Zerbini, J.M. Waite, S.E. Moore, and P.J. Clapham. 2013. Cetacean 

distribution and abundance in relation to oceanographic domains on the eastern Bering 

Sea shelf in June and July of 2002, 2008, and 2010. Deep Sea Research II 94: 244-256. 

doi: 10.1016/j.dsr2.2013.03.011

Frost, K.J., L.F Lowry, and G. Carroll. 1993. Beluga whale and spotted seal use of a coastal 

lagoon system in the northeastern Chukchi Sea. Arctic 46: 8-16.

Garland, E.C., M. Castellote, and C.L. Berchok. 2015. Beluga whale (Delphinapterus leucas) 

vocalizations and call classifications from the eastern Beaufort Sea population. Journal of 

the Acoustical Society of America 137:3054-3067 doi: 10.1121/1.4919338.

Garland, E.C., C.L. Berchok, and M. Castellote. 2015. Temporal peaks in beluga whale 

(Delphinapterus leucas) acoustic detections in the northern Bering, northeastern Chukchi, 

and western Beaufort Seas: 2010-2011. Polar Biology. doi: 10.1007/s00300-014-1636-1

George, J.C., C.W. Clark, G.M. Carroll, and W.T. Ellison. 1989. Observations on the icebreaking and ice navigation behavior of migrating bowhead whales (Balaena mysticetus) 

near Point Barrow, Alaska, Spring 1985. Arctic 42: 24-30.

Gostiaux, L. and H. Van Haren. 2010. Extracting meaningful information from uncalibrated 

backscattered echo intensity data. Journal of Atmospheric and Oceanic Technology 27: 

943-949.

Gradinger, R.R. and B.A. Bluhm. 2004. In-situ observations on the distribution and behavior of 

amphipods and Arctic cod (Boreogadus saida) under the sea ice of the High Arctic 

Canada Basin. Polar Biology 27: 595-603. 

Grebmeier, J.M., L.W. Cooper, H.M. Feder, and B.I. Sirenko. 2006a. Ecosystem dynamics of the 

Pacific-influenced Northern Bering and Chukchi Seas in the Amerasian Arctic. Progress 

in Oceanography 71: 331-361.

Grebmeier, J.M., J.E. Overland, S.E. Moore, E.V. Farley, E.C. Carmack, L.W. Cooper, K.E. 

Frey, J.H. Helle, F.A. McLaughlin, and S.L. McNutt. 2006b. A major ecosystem shift in 

the northern Bering Sea. Science 311: 1461-1464.
OCS Study

BOEM 2015-034

305

Grebmeier, J.M. 2012. Shifting patterns of life in the Pacific Arctic and sub-Arctic Seas. Annual 

Review of Marine Science 4. doi: 10.1146/annurev-marine-120710-100926.

Grebmeier, J.M., B.A. Bluhm, L.W. Cooper, S.L. Danielson, K.R. Arrigo, A.L. Blanchard, J.T. 

Clarke, R.H. Day, K.E. Frey, R.R. Gradinger, M. Kedra, B. Konar, K.J. Kuletz, S.H. Lee, 

J.R. Lovvorn, B.L. Norcross, and S.R. Okkonen. 2015. Ecosystem characteristics and 

processes facilitating persistent macrobenthic biomass hotspots and associated benthivory 

in the Pacific Arctic. Progress in Oceanography 136: 92-114. doi: 

10.1016/j.pocean.2015.05.006

Greene, C.R., M.W. McLennan, R.G. Norman, T.L. McDonald, R. Jakubczak, and W.J. 

Richardson. 2004. Directional frequency and recording (DIFAR) sensors in seafloor 

recorders to locate calling bowhead whales during their fall migration. Journal of the 

Acoustical Society of America. 116: 799-813.

Greenlaw, C.F. 1979. Acoustical estimation of zooplankton populations. Limnology and 

Oceanography 24: 226–242.

Greenlaw, C.F. and R.K. Johnson. 1983. Multiple-frequency acoustical estimation. Biological 

Oceanography 2: 227–252.

Guerra, M., A.M. Thode, S.B. Blackwell, and A.M. Macrander. 2011. Quantifying seismic 

survey reverberation off the Alaskan North Slope. Journal of the Acoustical Society of 

America 130: 3046-3058.

Guerra, M., A.N. Rice, D.W. Ponirakis, and C.W. Clark. 2013. Characterization of the 

interannual ambient noise baseline off Icy Cape, AK and its primary noise sources (2010-

2012). Alaska Marine Science Symposium, Anchorage, AK, January 2013.

Hannay, D.E., J. Delarue, X. Mouy, B.S. Martin, D. Leary, J.N. Oswald, and J. Vallarta. 2013. 

Marine mammal acoustic detections in the northeastern Chukchi Sea, September 2007-

July 2011. Continental Shelf Research 67: 127-146.

Hartin K.G., L.N. Bisson, S.A. Case, D.S. Ireland, and D. Hannay (eds.). 2011. Marine mammal 

monitoring and mitigation during site clearance and geotechnical surveys by Statoil USA 

E&P Inc. in the Chukchi Sea, August–October 2011: 90-day report. LGL Rep. P1193. 

Rep. from LGL Alaska Research Associates Inc., LGL Ltd., and JASCO Research Ltd. 

for Statoil USA E&P Inc., Nat. Mar. Fish. Serv., and U.S. Fish and Wild. Serv. 202 pp, 

plus appendices.

Hatch, L.T., C.W. Clark, S. Van Parijs, A.S. Frankel, and D.W. Ponirakis. 2012. Quantifying 

loss of acoustic communication space for right whales in and around a U.S. National 

Marine Sanctuary. Conservation Biology 26: 983-994.

Hauser, D.D.W., K.L. Laidre, R.S. Suydam, and P.R. Richard. 2014. Population-specific home 

ranges and migration timing for Pacific Arctic beluga whales (Delphinapterus leucas). 

Polar Biology 37: 1171-1183. doi: 10.1007/s00300-014-1510-1

Heide-Jørgensen, M.P., L. Kleivane, N. Øien, K.L. Laidre, and M.V. Jensen. 2001. A new 

technique for deploying satellite transmitters on baleen whales: tracking a blue whale 

(Balaenoptera musculus) in the North Atlantic. Marine Mammal Science 17: 949-953.
OCS Study

BOEM 2015-034

306

Holliday, D.V. 1977. Extracting bio-physical information from the acoustic signatures of marine 

organisms. In N.R. Anderson and B.J. Zahuranec (eds.), Oceanic Sound Scattering 

Prediction, pp. 619-624. New York: Plenum Press.

Holliday, D.V. 1992. Zooplankton Acoustics. In B.N. Desai (ed.), Oceanography of the Indian 

Ocean, pp. 733–740. New Delhi: Oxford-IBH.

Holliday D.V., P.L. Donaghay, C.F. Greenlaw, D.E. McGehee, M.M. McManus, J.M. Sullivan, 

and J.L. Miksis. 2003. Advances in defining fine- and micro-scale patterns in marine 

plankton. Aquatic Living Resources 16: 131–136.

Holliday, D.V., P.L., Donaghay, C.F. Greenlaw, J.M. Napp, and J.M. Sullivan. 2009. Highfrequency acoustics and bio-optics in ecosystems research. ICES Journal of Marine 

Science 66: 974-980.

Hopcroft, R.R. and R.H. Day (eds.). 2013. Seasonal and interannual dynamic s of the 

northeastern Chukchi Sea ecosystem. Continental Shelf Research, Special Issue 67: 1-

166.

Hunt, G.L., Jr., P. Ressler, G. Gibson, A. De Robertis, K. Aydin, M.F. Sigler, I. Ortiz, E. 

Lessard, B.C. Williams, A. Pinchuk, and T. Buckley. Euphausiids of the eastern Bering 

Sea: A synthesis of recent studies of euphausiid production, consumption, and 

population control. Deep Sea Res., II, submitted.

Huntington, H.P. and L.T. Quakenbush. 2009. Traditional knowledge of bowhead whale 

migratory patterns near Kaktovik and Barrow, Alaska. Report to the Alaska Eskimo 

Whaling Commission.

Innes, S., M.P. Heide-Jørgensen, J. Laake, K.L. Laidre, H. Cleator, P. Richard, and R.E.A. 

Stewart. 2002. Surveys of belugas and narwhals in the Canadian High Arctic in 1996. 

Scientific Publications of the North Atlantic Marine Mammal Commission 4: 169-190.

Jay, C.V., A.S. Fischbach, and A.A. Kochnev. 2012. Walrus areas of use in the Chukchi Sea 

during sparse sea ice cover. Marine Ecology Progress Series 468: 1-13.

Jay, C.V., J.M. Grebmeier, A.S. Fischbach, T.L. McDonald, L.W. Cooper, and F. Hornsby. 

2014. Pacific Walrus (Odobenus rosmarus divergens) resource selection in the 

northern Bering Sea. PLoS ONE 9: e93035. doi:10.1371/journal.pone.0093035

Jones, J.M., B.J. Thayre, E.H. Roth, M. Mahoney, I. Sia, K. Merculief, C. Jackson, C. Zeller, M. 

Clare, A. Bacon, S. Weaver, Z. Gentes, R.J. Small, I. Stirling, S.M. Wiggins and J.A. 

Hildebrand. 2014. Ringed, bearded, and ribbon seal vocalizations north of Barrow, 

Alaska: Seasonal presence and relationship with sea ice. Arctic 67: 203-222.

Jonsen, I.D., J.M. Flemming, and R.A. Myers. 2005. Robust state-space modeling of animal 

movement data. Ecology 86: 2874-2880.

Kareiva P. and G. Odell. 1987. Swarms of predators exhibit ‘prey-taxis’ if individual predators 

use area-restricted search. American Naturalist 130: 233-270.
OCS Study

BOEM 2015-034

307

Kastelein, R.A., P. Mosterd, B. Van Santen, M. Hagedoorn, and D. de Haan. 2002. Underwater 

audiogram of a Pacific walrus (Odobenus rosmarus divergens) measured with narrowband frequency-modulated signals. The Journal of the Acoustical Society of America

112: 2173-2182.

Kennedy, A.S., D.R. Salden, and P.J. Clapham. 2011. First high‐to low‐latitude match of an 

Eastern North Pacific right whale (Eubalaena japonica). Marine Mammal Science. doi: 

10.1111/j.1748-7692.2011.00539.x.

Kennedy, A.S., A.N. Zerbini, B.K. Rone, and P.J. Clapham. 2014. Individual variation in 

movements of satellite-tracked humpback whales in the eastern Aleutian Islands and 

Bering Sea. Endangered Species Research 23: 187-195.

Koski, W.R. and G.W. Miller. 2009. Habitat use by different size classes of bowhead whales in 

the central Beaufort Sea during late summer and autumn. Arctic 62: 137-150.

Kwok R., G.F. Cunningham, M. Wensnahan, I. Rigor, H.J. Zwally, and D. Yi. 2009. Thinning 

and volume loss of Arctic sea ice: 2003-2008. Journal of Geophysical Research: Oceans 

114: C07005. doi: 10.1029/2009JC005312

Kwok, R. and N. Untersteiner. 2011. The thinning of Arctic sea ice. Physics Today 64: 36-41.

Laake, J. 1999. Distance sampling with independent observers: Reducing bias from 

heterogeneity by weakening the conditional independence assumption. In G.W. Garner, 

S.C. Amstrup, J.L. Laake, B.J.F. Manly, L.L. McDonald, and D.G. Robertson (eds.), 

Marine mammal survey and assessment methods, pp. 137-148. Rotterdam, Netherlands: 

A. A. Balkema.

Laake, J.L., D.L. Borchers, L. Thomas, D. Miller, and J. Bishop. 2012. Mark-recapture distance 

sampling (MRDS). Version 2.1.0. 

Laidre, K.L., I. Stirling, L.F. Lowry, Ø. Wiig, M.P. Heide-Jørgensen, and S.H. Ferguson. 2008. 

Quantifying the sensitivity of Arctic marine mammals to climate-induced habitat change. 

Ecological Applications 18: S97-S125.

Ljungblad, D.K. and S.E. Moore. 1983. Killer whales (Orcinus orca) chasing gray whales 

(Eschrichtius robustus) in the northern Bering Sea. Arctic: 361-364.

Ljungblad, D.K., S.E. Moore, and D.R. Van Schoik. 1983. Aerial Surveys of Endangered Whales 

in the Beaufort, Eastern Chukchi, and Northern Bearing Seas, 1982. No. NOSC/TD-605. 

Naval Ocean Systems Center, San Diego, CA.

Ljungblad, D.K., S.E. Moore, J.T. Clarke, and J.C. Bennett. 1986. Aerial surveys of endangered 

whales in the northern Bering, eastern Chukchi, and Alaskan Beaufort Seas, 1985: with a 

seven year review, 1979-85. Naval Ocean Systems Center, San Diego, Technical Report 

1111, pp. 440. (NTIS PB87-115929/AS.) 

Ljungblad, D.K., S.E. Moore, J.T. Clarke, and J.C. Bennett. 1987. Distribution, Abundance, 

Behavior, and Bioacoustics of Endangered Whales in the Alaskan Beaufort and Eastern

Chukchi Seas, 1979-86. No. NOSC/TR-1177. Naval Ocean Systems Center, San Diego, 

CA.
OCS Study

BOEM 2015-034

308

Lowry, L. F. 1985. The ribbon seal (Phoca fasciata). in J. J. Burns, K. J. Frost, and L. F. Lowry

(eds.), Marine Mammals Species Accounts, pp. 71-78. Alaska Department Fish and 

Game, Juneau, AK.

Lowry, L.F. 1993. Foods and feeding ecology. In J.J. Burns, J.J. Montague, and C.J. Cowles, 

(eds.), The Bowhead Whale, pp. 201-238. Lawrence, Kansas: Allen Press.

Lowry, L.F., K.J. Frost, and J.J. Burns. 1980. Feeding of bearded seals in the Bering and 

Chukchi Seas and trophic interaction with Pacific walruses. Arctic 33: 330-342.

Lowry, L.F., and K.J. Frost. 1984. Foods and feeding of bowhead whales (Balaena mysticetus) in 

western and northern Alaska. Scientific Reports of the Whales Research Institute 35: 1-

16.

Lowry, L.F., G.A. Seaman, and K.J. Frost. 1985. Investigations of belukha whales in coastal 

waters of western and northern Alaska – I: Distribution, abundance, and movements. U.S. 

Department of Commerce, NOAA OCS Final report NA-81-RAC-00049, pp. 60.

Lowry, L.F., G. Sheffield, and J.C. George. 2004. Bowhead whale feeding in the Alaskan 

Beaufort Sea, based on stomach contents analyses. Journal of Cetacean Research and 

Management 6: 215-223.

MacIntyre, K.Q., K.M. Stafford, C.L. Berchok, and P.L. Boveng. 2013. Year-round acoustic 

detection of bearded seals (Erignathus barbatus) in the Beaufort Sea relative to changing 

environmental conditions, 2008–2010. Polar Biology 36: 1161-1173.

Marques, T.A., L. Thomas, J. Ward, N. DiMarzio, and P.L. Tyack. 2009. Estimating cetacean 

population density using fixed passive acoustic sensors: an example with Blainville’s 

beaked whales. Journal of the Acoustical Society of America 125(4): 1982-1994.

Marsh, H. and D.F. Sinclair. 1989. Correcting for visibility bias in strip transect aerial surveys of 

aquatic fauna. Journal of Wildlife Management 53: 1017-1024.

Mayo, C.A. and M.K. Marx. 1990. Surface foraging behavior of the north Atlantic right whale, 

Eubalaena glacialis, and associated zooplankton characteristics. Canadian Journal of 

Zoology 68: 2214-2220.

Mellinger, D.K. 2001. Ishmael 1.0 User’s Guide. NOAA Technical Memorandum OAR PMEL120, available from NOAA/PMEL, 7600 Sand Point Way NE, Seattle, WA 98115, USA.

Mendes, S., W. Turrell, T. Lu ̈tkebohle, and P. Thompson. 2002. Influence of the tidal cycle and 

a tidal intrusion front on the spatio-temporal distribution of coastal bottlenose dolphins. 

Marine Ecology Progress Series 239: 221–229.

Merchant, N.D., T.R. Barton, P.M. Thompson, E. Pirotta, D.T. Dakin, and J. Dorocicz. 2013. 

Spectral probability density as a tool for ambient noise analysis. Journal of the Acoustical 

Society of America. 133: 262-267, DOI:10.1121/1.4794934

Miller, E.H. 1975. Walrus ethology. I. The social role of tusks and applications of 

multidimensional scaling. Canadian Journal of Zoology 53: 590-613. 

Miller, E.H. 1976. Walrus ethology. II. Herd structure and activity budgets of summering males. 

Canadian Journal of Zoology 54: 704-715.
OCS Study

BOEM 2015-034

309

Miksis-Olds, J.L. and S.E. Parks. 2011. Seasonal trends in acoustic detection of ribbon seal 

(Histriophoca fasciata) vocalizations in the Bering Sea. Aquatic Mammals 37(4): 464-

471.

Mizroch, S.A., D.W. Rice, and J.M. Breiwick. 1984. The fin whale, Balaenoptera physalus. 

Marine Fisheries Review 46: 20-24.

Mizroch, S.A., D.W. Rice, D. Zwiefelhofer, J. Waite, and W.L. Perryman. 2009. Distribution 

and movements of fin whales in the North Pacific Ocean. Mammal Review 39: 193-227.

Mocklin, J.A., D. J. Rugh, S.E. Moore, and R.P. Angliss. 2012. Using aerial photography to 

investigate evidence of feeding by bowhead whales. Marine Mammal Science 28: 602-

619.

Moore, S.E. and J.T. Clarke. 1992. Distribution, abundance and behavior of endangered whales 

in the Alaskan Chukchi and western Beaufort seas, 1991: with a review of 1982-91. 

Report of Science Applications International Corporation (SAIC), Maritime Services 

Division, for Minerals Management Service, Anchorage, Alaska. 

Moore, S.E. and R.R. Reeves. 1993. Distribution and movement. In J.J. Burns, J.J. Montague, 

and C.J. Cowles (eds.), The Bowhead Whale, pp. 313-386. Lawrence, Kansas: Allen 

Press.

Moore, S.E., J.T. Clarke and M.M. Johnson. 1993. Beluga Distribution and movements offshore 

northern Alaska in spring and summer, 1980-1984. Report of the International Whaling 

Commission 43: 375-386.

Moore, S.E. 2000. Variability of cetacean distribution and habitat selection in the Alaskan 

Arctic, Autumn 1982-91. Arctic 53: 448-460.

Moore, S.E., D.P. DeMaster, and P.K. Dayton. 2000. Cetacean habitat selection in the Alaskan 

Arctic during summer and autumn. Arctic 53(4): 432-447.

Moore, S.E. and J.T. Clarke. 2002. Potential impact of offshore human activities on gray whales 

(Eschrichtius robustus). Journal of Cetacean Research and Management 4: 19-25.

Moore, S.E., J.M. Waite, N.A. Friday, and T. Honkalehto. 2002. Cetacean distribution and 

relative abundance on the central-eastern and southeastern Bering Sea shelf with 

reference to oceanographic domains. Progress in Oceanography 55: 249–261, 

http://dx.doi.org/10.1016/S0079-6611(02)00082-4.

Moore, S.E. and K.L. Laidre. 2006. Trends in sea ice cover within habitats used by bowhead 

whales in the western Arctic. Ecological Applications 16: 932-944.

Moore, S.E., K.M. Stafford, D.K. Mellinger, and J.A. Hildebrand. 2006. Listening for large 

whales in the offshore waters of Alaska. Bioscience 56: 49-55.

Moore, S.E. and H.P. Huntington. 2008. Arctic marine mammals and climate change: impacts 

and resilience. Ecological Applications 18: S157-165.

Moore, S.E., K.M Stafford, H. Melling, C. Berchok, O. Wiig, K.M Kovacs, C. Lydersen, and J. 

Richter-Menge. 2012. Comparing marine mammal acoustic habitats in Atlantic and 

pacific sectors of the High Arctic: year-long records from Fram Strait and the Chukchi 

Plateau. Polar Biology 35: 475-480.
OCS Study

BOEM 2015-034

310

Napp, J. M., L.S. Incze, P.B. Ortner, D.L.W. Siefert, and L. Britt. 1996. The plankton of Shelikof 

Strait, Alaska: Standing stock, production, mesoscale variability and their relevance to 

larval fish survival. Fisheries Oceanography 5: 19-38.

Nerini, M. 1984. A review of gray whale feeding ecology. In M.L. Jones, S.L. Swartz, and S. 

Leatherwood (eds.), The Gray Whale, Eschrichtius robustus, pp. 423-450. Orlando, 

Florida: Academic Press Inc. 

Niebauer, H.J. and R.H. Day. 1989. Causes of interannual cariability in the sea ice cover of the 

eastern Bering Sea. GeoJournal 18: 45-59.

Okkonen, S.R., C.J. Ashjian, R.G. Campbell, J.T. Clarke, S.E. Moore, and K.D. Taylor. 2011. 

Satellite observations of circulation features associated with a bowhead whale feeding 

‘hotspot’ near Barrow, Alaska. Remote Sensing of Environment 115: 2168-2174. doi: 

10.1016/j.rse.2011.04.024

Panova, E.M., R.A. Belikov, A.V. Agafonov, and V.M. Bel’Kovich. 2012. The relationship 

between the behavioral activity and the underwater vocalization of the beluga whale 

(Delphinapterus leucas). Oceanology 52(1): 79-87.

Parkinson, C.L. and J.C. Comiso. 2013. On the 2012 record low Arctic sea ice cover: Combined 

impact of preconditioning and an August storm. Geophysical Research Letters 40(7): 

1356-1361.

Parks, S.E., P.K. Hamilton, S.D. Kraus, and P.L. Tyack. 2005. The gunshot sound produced by 

male North Atlantic right whales (Eubalaena glacialis) and its potential function in 

reproductive advertisement. Marine Mammal Science 21: 458-475.

Parks, S., I. Urazghildiiev, and C.W. Clark. 2009. Variability in ambient noise levels and call 

parameters of North Atlantic right whales in three habitat areas. Journal of the Acoustical 

Society of America 125: 1230-1239.

Perry, S.L., D.P. DeMaster, and G.K. Silber. 1999. The great whales: History and status of six 

species listed as endangered under the US Endangered Species Act of 1973. Marine 

Fisheries Review 61: 1-74.

Petrich, C., H. Eicken, J. Zhang, J. Krieger, Y. Fukamachi, and K. I. Ohshima. 2012. Coastal 

landfast sea ice decay and breakup in northern Alaska: Key processes and seasonal 

prediction, J. Geophys. Res. 117 C02003 doi:10.1029/2011JC007339.

Quakenbush, L.T., J.J. Citta, J.C. George, R.J. Small, and M.P. Heide- Jørgensen. 2010. Fall and 

winter movements of bowhead whales (Balaena mysticetus) in the Chukchi Sea and 

within a potential petroleum development area. Arctic 63: 289-307.

Quakenbush, L.T., J.J. Citta, J.C. George et al. 2012. Seasonal movements of the BeringChukchi-Beaufort stock of bowhead whales: 2006-2011 satellite telemetry results. 

International Whaling Commission Science Committee Report SC/64/BRG1. 

Quakenbush, L.T., R.J. Small, and J.J. Citta. 2013. Satellite tracking of bowhead whales: 

Movements and analysis from 2006 to 2012. US Deptartment of the Interior, Bureau of 

Ocean Energy Management, Alaska Outer Continental Shelf Region, Anchorage, Alaska. 

OCS Study BOEM 2013-01110, pp. 56. 
OCS Study

BOEM 2015-034

311

Quakenbush, L.T., R.S. Suydam, A.L. Bryan, L.F. Lowry, K.J. Frost, and B.A. Mahoney. 2015. 

Diet of Beluga Whales, Delphinapterus leucas, in Alaska from stomach contents, March–

November. Marine Fisheries Review doi: dx.doi.org/10.7755/MFR.77.1.7.

Questel, J.M., C. Clarke, and R.R. Hopcroft. 2013. Seasonal and interannual variation in the 

planktonic communities of the northeastern Chukchi Sea during the summer and early 

fall. Continental Shelf Research 67: 23-41.

Rankin, S. and J. Barlow. 2005. Source of the North Pacific “boing” sound attributed to minke 

whales. Journal of the Acoustical Society of America 118: 3346-3351.

Rasmussen, R.A. and N.E. Head. 1965. The quiet gray whale Eschrichtius glaucus. Deep-Sea 

Research 12: 869–877.

Ray, C., W.A. Watkins, and J. Burns. 1969. The underwater song of Erignathus (Bearded seal). 

Zoologica 54: 79-83.

R Development Core Team (2012) R: A language and environment for statistical computing. 

2.11.0. R Foundation for Statistical Computing, Vienna.

Reiser, C.M, D.W. Funk, R. Rodrigues, and D. Hannay (eds.). 2011. Marine mammal monitoring

and mitigation during marine geophysical surveys by Shell Offshore, Inc. in the Alaskan 

Chukchi and Beaufort seas, July–October 2010: 90-day report. LGL Rep. P1171E–1. 

Rep. from LGL Alaska Research Associates Inc., Anchorage, AK, and JASCO Applied 

Sciences, Victoria, BC for Shell Offshore Inc, Houston, TX, Nat. Mar. Fish. Serv., Silver 

Spring, MD, and U.S. Fish and Wild. Serv., Anchorage, AK. 240 pp, plus appendices.

Ressler, P.H., A. De Robertis, J.D. Warren, J.N. Smith, and S. Kotwicki. 2012. Developing an 

acoustic index of euphausiid abundance to understand trophic interactions in the Bering 

Sea ecosystem. Deep-Sea Research II 65-70: 184-195.

Richard, P.R., M.P. Heide-Jorgensen, J.R. Orr, R. Dietz, and T.G. Smith. 2001. Summer and 

autumn movements and habitat use by belugas in the Canadian High Arctic and adjacent 

areas. Arctic 54: 207–222.

Risch, D. R., C.W. Clark, P.J. Corkeron, A. Elepfandt, K.M Kovacs, C. Lydersen, I. Stirling, 

S.M Van Parijs. 2007. Vocalizations of male bearded seals, Erignathus barbatus: 

classification and geographical variation. Animal Behaviour. 73: 747-762.

Roach, A.T., K. Aagaard, C.H. Pease, S.A. Salo, T. Weingartner, V. Pavlov, and M. Kulakov. 

1995. Direct measurements of transport and water properties through the Bering Strait. 

Journal of Geophysical Research 100: 18443–18457. doi:10.1029/95JC01673

Roth, E.H., J.A. Hildebrand, and S.M. Wiggins. 2012. Underwater ambient noise on the Chukchi 

Sea continental slope from 2006-2009. Journal of the Acoustical Society of America 131: 

104-110. doi: 10.1121/1.3664096

Rugh, D.J. and M.A. Fraker. 1981. Gray whale (Eschrichtius robustus) sightings in the eastern 

Beaufort Sea. Arctic 34: 186-187.

Rugh, D.J., K.E.W. Shelden, and A. Schulman-Janiger. 2001. Timing of the gray whale 

southbound migration. Journal of Cetacean Research and Management 3: 31-39.
OCS Study

BOEM 2015-034

312

Rugh, D.J., R.C. Hobbes, J.A. Lerczak, and J.M. Breiwick. 2005. Estimates of abundance of the 

eastern North Pacific stock of gray whales (Eschrichtius robustus) 1997-2002. Journal of 

Cetacean Research and Management 7: 1-12.

Sameoto, D., N. Cochrane, and A. Herman. 1993. Convergence of acoustic, optical, and netcatch estimates of euphausiid abundance – use of artificial light to reduce net avoidance. 

Canadian Journal of Fisheries and Aquatic Sciences 50: 334-346.

Schonberg, S.V., J.T. Clarke, and K.H. Dunton. 2014. Distribution, abundance, biomass and 

diversity of benthic infauna in the Northeast Chukchi Sea, Alaska: Relation to 

environmental variables and marine mammals. Deep Sea Research II 102: 144-163.

Schusterman, R.J. and C. Reichmuth. 2008. Novel sound production through contingency 

learning in the Pacific walrus (Odobenus rosmarus divergens). Animal cognition 11: 319-

327.

Seaman, G.A., L.F. Lowry, and K.L. Frost. 1982. Foods of belukha whales (Delphinapterus 

leucas) in western Alaska. Cetology 44: 1-19.

Seymour, J., Horstmann-Dehn, L., & Wooller, M. J. (2014). Interannual variability in the 

proportional contribution of higher trophic levels to the diet of Pacific walruses. Polar 

biology, 37(5), 597-609.

Sheffield, G. and J.M. Grebmeier. 2009. Pacific walrus (Odobenus rosmarus divergens): 

Differential prey digestion and diet. Marine Mammal Science 25: 761-777.

Shelden, K.E.W. and J.A. Mocklin, Editors. 2013. Bowhead Whale Feeding Ecology Study 

(BOWFEST) in the western Beaufort Sea. Final Report, OCS Study BOEM 2013-0114. 

National Marine Mammal Laboratory, Alaska Fisheries Science Center, NMFS, NOAA, 

7600 Sand Point Way NE, Seattle, WA 98115-6349.

Simpkins, M.A., L.M. Hiruki-Raring, G. Sheffield, J.M. Grebmeier, and J.L. Bengtson. 2003. 

Habitat selection by ice-associated pinnipeds near St. Lawrence Island, Alaska in March 

2001. Polar Biology 26: 577-586.

Sjare, B.L. and T.G. Smith. 1986. The vocal repertoire of white whales, Delphinapterus 

leucas, summering in Cunningham Inlet, Northwest Territories. Can. J. Zool. 64, 407-415

Spaulding, E., M. Robbins, T. Calupca, C.W. Clark, C. Tremblay, A. Waack, A. Warde, J. 

Kemp, and K. Newhall. 2010. An autonomous, near-real-time buoy system for automatic 

detection of North Atlantic right whale calls. Proceedings of Meetings on Acoustics 6: 

010001-01000122. 

Springer, A.M., C.P. McRoy, and K.R. Turco. 1989. The paradox of pelagic food webs in the 

northern Bering Sea – II. Zooplankton communities. Continental Shelf Research 9: 359–

386.

Stabeno, P.J., N. Kachel, C. Ladd, K. Martini, and C. Mordy. In prep. Currents and transport on 

the Chukchi Shelf: 2010 – 2014. To be submitted to Continental Shelf Research.

Stafford, K.M., D.K. Mellinger, S.E. Moore, and C.G. Fox. 2007a. Seasonal variability and 

detection range modeling of baleen whale calls in the Gulf of Alaska, 1999–2002. Journal 

of the Acoustical Society of America 122: 3378–3390.
OCS Study

BOEM 2015-034

313

Stafford, K.M., S.E. Moore, M. Spillane, and S. Wiggins. 2007b. Gray whale calls recorded near 

Barrow, Alaska, throughout the winter of 2003-04. Arctic 60: 167-172. 

Stafford, K.M., S.R. Okkonen, and J.T. Clarke. 2013. Correlation of a strong Alaska Coastal 

Current with the presence of beluga whales Delphinapterus leucas near Barrow, Alaska. 

Marine Ecology Progress Series 474: 287-297. doi: 10.3354/meps10076

Stanton, T.K., P.H. Wiebe, D. Chu, M.C. Benfield, L. Scanlon, L. Martin, and R.L. Eastwood. 

1994. On acoustic estimates of zooplankton biomass. ICES Journal of Marine Science: 

51: 505-512.

Stirling, I., W. Calvert, and C. Spencer. 1987. Evidence of stereotyped underwater vocalizations 

of male Atlantic walruses (Obodenus rosmarus rosmarus). Canadian Journal of Zoology 

65: 2311-2321.

Stirling, I. 1997. The importance of polynyas, ice edges, and leads to marine mammals and birds. 

Journal of Marine Systems 10: 9-21.

Suydam, R.S., L.F. Lowry, K.J. Frost, G.M. O’Corry-Crowe, and D. Pikok, Jr. 2001. Satellite 

tracking of eastern Chukchi Sea beluga whales into the Arctic Ocean. Arctic 54: 237-243.

Suydam, R.S. 2009. Age, growth, reproduction, and movements of beluga whales 

(Delphinapterus leucas) from the eastern Chukchi Sea. Dissertation, University of 

Washington.

Swartz, S.L., B.L. Taylor, and D.J. Rugh. 2006. Gray whale Eschrichtius robustus population 

and stock identity. Mammal Review 36: 66-84.

Tikhomirov, E.A. 1961. Distribution and migration of seals in waters of the far east. Pages 199‐

210 in Conference on Pelagic Mammals, 1959. Ichthyological Commission of the 

Academy of Sciences of the USSR, Moscow, Russia. (Translated from Russian by L.V. 

Sagen, U.S. Fish and Wildlife Service, Marine Mammal Biological Laboratory, Seattle, 

WA, 26 p.)

Torrence, C. and G.P. Compo. 1998. A practical guide to wavelet analysis. Bulletin of the 

American Meteorological Society 79: 61-78.

Udevitz, M.S., R.L. Taylor, J.L. Garlich-Miller, L.T. Quakenbush, and J.A. Snyder. 2013. 

Potential population-level effects of increased haul-out-related mortality of Pacific walrus 

calves. Polar biology 36: 291-298.

Urick, R.J. 1983. Principles of Underwater Sound, 3rd edition. New York, NY: McGraw Hill.

Van Opzeeland, I., L. Kindermann, O. Boebel, and S. Van Parijs. 2008. Insights into the acoustic 

behaviour of polar pinnipeds—current knowledge and emerging techniques of study. In 

E.A. Weber and L.H. Krause (eds.), Animal Behavior: New Research, pp. 133-161. 

Hauppage, New York: Nova Science Publishers, Inc. 

Van Parijs, S.M., K.M. Kovacs, and C. Lydersen. 2001. Spatial and temporal distribution of 

vocalizing male bearded seals: Implications for male mating strategies. Behaviour 138: 

905-922. 
OCS Study

BOEM 2015-034

314

Wallace, M.I., F.R. Cottier, J. Berge, G.A. Tarling, C. Griffiths, and A.S. Brierley. 2010. 

Comparison of zooplankton vertical migration in an ice-free and a seasonally ice-covered 

Arctic fjord: An insight into the influence of sea ice cover on zooplankton behaviour. 

Limnology and Oceanography 55: 831-845.

Walsh, J.E. 2008. Climate of the Arctic marine environment. Ecological Applications 18: S3-

S22.

Wang, M. and J.E. Overland. 2009. An ice free summer Arctic within 30 years? Geophysical 

Research Letters 36: L07502. doi: 10.1029/2009GL037820

Wang, M. and J.E. Overland. 2012. A sea ice free summer Arctic within 30 years—an update 

from CMIP5 models. Geophysical Research Letters 39: L18501. doi: 

10.1029/2012GL052868 

Wang, M. and J.E. Overland. 2015. Projected future duration of the sea-ice-free season in the 

Alaskan Arctic. Progress in Oceanography 136: 50-59.

doi:10.1016/j.pocean.2015.01.001.

Watkins, W.A. and G.C. Ray. 1977. Underwater sounds from ribbon seal, Phoca (Histriophoca) 

fasciata. Fishery Bulletin 75: 450-453.

Watkins, W.A., M.A. Daher, G.M. Reppucci, J.E. George, D.L. Martin, N.A. DiMarzio, and D.P. 

Gannon. 2000. Seasonality and distribution of whale calls in the North Pacific. 

Oceanography 13: 62–67.

Weingartner, T., E. Dobbins, S. Danielson, W. Winsor, R. Potter, and H. Statscewich. 2013. 

Hydrographic variability over the northeastern Chukchi Sea shelf in summer-fall 2008-

2010. Continental Shelf Research 67: 5-22.

Wenz, G.M. 1962. Acoustic ambient noise in the ocean: spectra and sources. Journal of the 

Acoustical Society of America 334: 1936-1956.

Williams, R., C.W. Clark, D. Ponirakis, and E. Ashe. 2014. Acoustic quality of critical habitats 

for three threatened whale populations. Animal Conservation 17: 174-185. 

doi: 10.1111/acv.12076

Winn, H.E. and P.J. Perkins. 1976. Distribution and sounds of the minke whale, with a review of 

mysticete sounds. Cetology 19: 1-12.

Wood, S.N. 2006. Generalized additive models: An introduction with R. Chapman and Hall, 

Boca Raton, Florida.

Wood, K.R., J.E. Overland, S.A. Salo, N.A. Bond, W.J. Williams, and X. Dong. 2013. Is there a 

“new normal” climate in the Beaufort Sea? Polar Research 32: 19552, doi: 

http://dx.doi.org/10.3402/polar.v32i0.19552.

Wood, K.R., N.A. Bond, S.L. Danielson, J.E. Overland, S.A. Salo, P.J. Stabeno and J. 

Whitefield. 2015. A decade of environmental change in the Pacific Arctic region. 

Progress in Oceanography 36: 12-31. 

Woodgate, R.A., K. Aagaard, and T.J. Weingartner. 2005. Monthly temperature, salinity, and 

transport variability of the Bering Strait through flow. Geophysical Research Letters 32: 

L04601. doi: 10.1029/2004GL021880
OCS Study

BOEM 2015-034

315

Würsig, B. E. M. Dorsey, W. J. Richardson, and R. S. Wells. 1989. Feeding, aerial and play 

behavior of bowhead whales, Balaena mysticetus, summering in the Beaufort Sea. 

Aquatic Mammals 15:27-27.

Würsig, B. and C.W. Clark. 1993. Behavior. In J.J. Burns, J.J. Montague, and C.J. Cowles (eds.),

The Bowhead Whale, pp. 157-200. Lawrence, Kansas: Allen Press.

Xie, Y. and D.M. Farmer. 1992. The sound of ice break-up and floe interaction. Journal of the 

Acoustical Society of America 91: 1423-1428.

Zerbini, A.N., J.M. Waite, J.L. Laake, and P.R. Wade. 2006. Abundance, trends and distribution 

of baleen whales off western Alaska and the central Aleutian Islands. Deep Sea Research 

I 53: 1772-1790.
OCS Study

BOEM 2015-034

316

XVI. ACKNOWLEDGEMENTS

This project would not have been possible without the help of a great number of people. First and 

foremost, we thank Heather Crowley (BOEM) for her endless support. Chuck Monnett was 

instrumental in starting the CHAOZ project. The authors would also like to acknowledge the 

following people: 

North Slope residents

Craig George, Robert Suydam, and Lesley Pierce (North Slope Borough) for helping us develop 

and distribute our cruise information flyers; Sheyna Wisdom (Fairweather Science, LLC) for 

providing information on contacting the village communication centers during the field surveys; 

the villages of Barrow, Point Hope, Point Lay, Wainwright, Kivalina, Kotzebue; Gay Sheffield 

(UAF); and the whaling captains. 

Vessel captains and crew

Captain Atle Remme, F/V Alaskan Enterprise; Captain Fred Roman, F/V Mystery Bay; and 

Captain Kale Garcia, R/V Aquila, as well as all crew members.

Sonobuoys

Theresa Yost (Naval Operational Logistics Support Center); Jeffrey Leonhard, Todd Mequet, 

and Edward Rainey (Naval Surface Warfare Center, Crane Division); and Robin Fitch (I&E 

Director Marine Science, Office of the Assistant Secretary of the Navy) for providing the 

sonobuoys used during the field surveys, and Don Ljungblad for teaching us about sonobuoy 

modifications.

At sea science crew

William Floering, Sigrid Salo, Adam Spear, Sam Denes, Dan Naber, Stephanie Grassia, Amy S. 

Kennedy, Dana Wright, Laura Morse, Andy Bankert, Jason Michalec, Jessica Thompson, Misty 

E. Niemeyer, Lisa DeForest, Heather Riley, Elizabeth Küsel, Colleen Harpold, Chris TessagliaHymes, Robert Ambrose, Steve Porter, John Kemp

Analysts/technicians

Ellen Garland for her assistance with the GAMs, Eliza Ives for incorporating the LFDCS on our 

dataset and for assistance with this report, Charles Greenlaw for his guidance and expertise, 

Benjamin Bloss and David Strausz for helping construct, tune, and calibrate the TAPS6-NG, 

Stephanie Grassia, Alexandra Ulmke, and Dawn Grebner for their acoustic analysis, Sigrid Salo, 

Nancy Kachel, Alexandre N. Zerbini, Amy S. Kennedy, Janice M. Waite
OCS Study

BOEM 2015-034

317

XVII. LIST OF PUBLICATIONS AND PRESENTATIONS

Publications

Cheng, W., E. Curchitser, C. Ladd, P. Stabeno, and M. Wang. 2014. Ice-Ocean Interactions in 

the Eastern Bering Sea: NCAR CESM Simulations and Comparison with Observations. 

Deep Sea Research II. doi: 10.1016/j.dsr2.2014.03.002.

Clark, C.W., C.L. Berchok, S. Blackwell, J. Citta, D. Hannay, J. Jones, L. Quakenbush, and 

K.M. Stafford. 2015-accepted. A year in the acoustic world of Western Arctic bowhead 

whales. Progress in Oceanography.

Crance, J.L., C.L. Berchok, J. Bonnel, and A.M. Thode. 2015. Northeastern-most record of a 

north Pacific fin whale (Balaenoptera physalus) in the Alaskan Chukchi Sea. Polar 

Biology, doi: 10.1007/s00300-015-1719-7.

Garland, E.G., C.L. Berchok, and M. Castellote. 2015. Temporal peaks in beluga whale 

(Delphinapterus leucas) acoustic detections in the northern Bering, northeastern Chukchi, 

and western Beaufort Seas: 2010-2011. Polar Biology, doi: 10.1007/s00300-014-1636-1.

Garland, E.C., M. Castellote, and C.L. Berchok. 2015. Beluga whale (Delphinapterus leucas) 

vocalizations and call classification from the eastern Beaufort Sea population. Journal of 

the Acoustical Society of America, 137: 3054-3067, doi: 10.1121/1.4919338. .

Overland, J.E. and M. Wang. 2013. When will the summer Arctic be nearly sea ice free?

Geophysical Research Letters 40, DOI: 10.1002/grl.50316.

Overland, J.E., M. Wang, J. Walsh, and J.C. Stroeve. 2014. Future Arctic climate changes: 

Adaptation and mitigation timescales. Published online, Earth’s Future. doi: 

10.1002/2013EF000162.

Wang, M. and J.E Overland. 2012. A sea ice free summer Arctic within 30 years-an update from 

CMIP5 models. Geophysical Research Letters 39, DOI: 10.1029/2012GL052868. 

Wang, M., J.E. Overland, and P.J. Stabeno. 2012. Future climate of the Bering and Chukchi Seas 

projected by global climate models. Deep-Sea Research II, 65-70: 46-57.

Wang, M. and J.E. Overland. 2015. Projected future duration of the sea-ice-free season in the 

Alaskan Arctic. Progress in Oceanography. doi:10.1016/j.pocean.2015.01.001

Oral Presentations

Berchok, C.L., J. Crance, B. Rone. 2010. North Pacific Right Whale Survey Acoustics. 26 May 

2010. Sonobuoy conference at the Naval Air Station, Whidbey Island, Oak Harbor, WA.

Berchok, C.L. and J. Crance. 2015. Chukchi Sea whale ecology. 28 May 2015. Sonobuoy 

conference at the Naval Air Station, Whidbey Island, Oak Harbor, WA.

Clark, C.W., Berchok, C.L., Blackwell, S.B., Hannay, D.E., Jones, J., Ponirakis, D., and 

Stafford, K.M. 2015. A year in the acoustic world of Western Arctic bowhead whales. 

Oral presentation at the Alaska Marine Science Symposium, 21-25 January 2013. 

Anchorage, AK.

Delarue, J., D.K. Mellinger, K.M Stafford, and C.L. Berchok. 2010. Where do the Chukchi Sea 

fin whales come from? Looking for answers in the structure of songs recorded in the 

Bering Sea and Western N. Pacific. Oral presentation at the 159th meeting of the 

Acoustical Society of America, 19-23 April 2010. Baltimore, MD.
OCS Study

BOEM 2015-034

318

Garland, E.C., C.L. Berchok, and M. Castellote. 2012. Acoustic monitoring of belugas 

(Delphinapterus leucas) in the eastern Chukchi Sea. Oral presentation at the 164th

Meeting of the Acoustical Society of America, 22-26 October 2012. Kansas City, MO.

Guerra, M., A.N. Rice, and C.W. Clark. 2013. Acoustic environment as context to understand 

patterns in Arctic marine biodiversity. Invited talk at the Zoological Society of London. 

17 May 2013. 

MacIntyre, K. 2013. Acoustic detection of bearded seals (Erignathus barbatus) in the Bering, 

Chukchi, and Beaufort Seas 2008-2011. Oral presentation at the Alaska Marine Science 

Symposium, 21-25 January 2013. Anchorage, AK.

Moore, S., K. Kuletz, and J. Murphy. 2011. Fish, seabird, and marine mammal observations 

during the 2010-2011 Pilot DBO. Distributed Biological Observatory (DBO) workshop 

and Pacific Arctic Group (PAG) meeting, 15-16 November 2011. Sidney, BC.

Moore, S.E. 2012. Science Lecture aboard the USCGC HEALY entitled “Marine Mammals in 

the ‘New Normal’ Pacific Arctic”, where sampling protocols and mammal distributions 

from the CHAOZ cruises were highlighted. August 15, 2012.

Napp, J.M., C.L. Berchok, P.J. Stabeno, and S.E. Moore. 2013. An integrated ocean observing 

approach to understanding the effects of climate variability in the NE Chukchi Sea. Oral 

presentation at the Alaska Marine Science Symposium, 21-25 January 2013. Anchorage, 

AK. 

Napp, J.M. and P.J. Stabeno. 2012. CHAOZ in the Arctic: Go North Young Scientist! EcoFOCI 

Seminar, 10 April 2012. Seattle, WA. 

Napp, J.M., C.L. Berchok, P.J. Stabeno, and S.E. Moore. 2013. Steps toward an integrated 

ocean observing approach to understanding the effects of climate variability in the NE 

Chukchi Sea. Oral presentation by Moore at the 28th Lowell Wakefield Symposium, 26-

29 March 2013. Anchorage, AK.

Overland, J.E. 2010. Hot Arctic-Cold Continents: Climate Impacts of the Newly Open Polar 

Sea. Rapid changes in Arctic sea ice: Assessing drivers and future trajectories workshop, 

6-8 October 2010. Fairbanks, AK.

Stabeno, P.J., N.B. Kachel, C. Ladd, and J.M. Napp. 2014. The CHAOZ project: Influence of 

climate variability on the northeastern Chukchi ecosystem. Oral presentation at the 

American Geophysical Union Ocean Sciences Meeting, February 26, 2014. Honolulu, 

HI.

Stabeno, P.J., C.L. Berchok, S.E. Moore, C. Mordy, J.M. Napp, and S. Salo. 2012. Chukchi 

Acoustics, Oceanography, and Zooplankton (CHAOZ): Observations on the Chukchi 

Sea. Oral presentation at the Alaska Marine Science Symposium, 16-20 January 2012. 

Anchorage, AK. 

Stabeno, P.J. and J.E. Overland. 2012. The New Normal in U. S. Arctic. Presented to the NOAA 

Senior Res. Council. July 19, 2012. Seattle, WA.

Stabeno, P.J. and J.M. Napp. 2012. Observations in the Chukchi Sea. Oral presentation for 

Pacific Marine Environmental Laboratory (PMEL) seminar series, April 10, 2012.

Wang, M. 2010. Melting pond in the Arctic Sea ice: How much can we learn from the NPEO? 

Rapid changes in Arctic sea ice: Assessing drivers and future trajectories workshop, 6-8 

October 2010. Fairbanks, AK.

Wang, M. 2013. A sea ice free summer Arctic within 30 years: An update from CMIP5 models. 

Oral presentation at the Alaska Marine Science Symposium, 21-25 January 2013. 

Anchorage, AK.
OCS Study

BOEM 2015-034

319

Poster Presentations

Berchok, C.L., S. Moore, J. Napp, J. Overland, and P. Stabeno. 2011. Bringing CHAOZ to the 

Arctic. Poster presentation at the Alaska Marine Science Symposium, 17-21 January 

2011. Anchorage, AK.

Crance, J.L., C.L. Berchok, A. Kennedy, B. Rone, E. Küsel, J. Thompson, and P.J. Clapham. 

2011. Visual and acoustic survey results during the 2010 CHAOZ cruise. Poster 

presentation at the Alaska Marine Science Symposium, 17-21 January 2011. Anchorage, 

AK.

Crance, J.L., C.L. Berchok, B. Rone, A. Kennedy, E. Küsel, L. Morse, J. Thompson, and P.J. 

Clapham. 2011. Short-term trends in the summer distribution of cetaceans in the 

Chukchi Sea. Poster presentation at the 19th Biennial Conference on the Biology of 

Marine Mammals, Nov 28 – Dec 2, 2011. Tampa, FL.

Crance, J.L., C.L. Berchok, B. Rone, A. Kennedy, E. Küsel, L. Morse, J. Thompson, and P.J. 

Clapham. 2012. Short-term trends in the summer distribution of Cetaceans in the Chukchi 

Sea. Poster presentation at the Alaska Marine Science Symposium, 16-20 January 2012. 

Anchorage, AK. 

Garland, E.C., C.L. Berchok, and M. Castellote. 2013. Spatio-temporal distribution of Alaskan 

beluga (Delphinapterus leucas) populations based on acoustic monitoring. Poster 

presentation at the Alaska Marine Science Symposium, 21-25 January 2013. Anchorage, 

AK.

Garland, E.G., C.L. Berchok, and M. Castellote. 2014. Spatio-temporal movement patterns of 

Alaskan beluga whale (Delphinapterus leucas) populations based on vocal peaks from 

passive acoustic monitoring. Poster presentation at the Alaska Marine Science 

Symposium, January 20-24, 2014. Anchorage, AK.

Grassia, S.L., C.L. Berchok, Crance, J.L., B.K. Rone, A. Kennedy, and P.J. Clapham. 2013. 

Short- and long-term distribution of marine mammals in the Chukchi Sea. Poster 

presentation at the Alaska Marine Science Symposium, 21-25 January 2013. Anchorage, 

AK. 

Guerra, M. A.N. Rice, D.W. Ponirakis, and C.W. Clark. 2013. Characterization of the 

interannual ambient noise baseline off Icy Cape, Alaska, and its primary sources (2010-

2012). Poster presentation at the Alaska Marine Science Symposium, 21-25 January 

2013. Anchorage, AK.

Napp, J. and P. Stabeno. 2011. Summer 2010 hydrography and zooplankton on the Chukchi Sea 

shelf (the CHAOZ project). Poster presentation at the Alaska Marine Science 

Symposium, 17-21 January 2011. Anchorage, AK.

Napp, J.M., A.H. Spear, and P.J. Stabeno. 2012. Acoustic detection of zooplankton in the 

Chukchi Sea. Poster presentation at the Alaska Marine Science Symposium, 16-20 

January 2012. Anchorage, AK. 

Wang, M., J.E. Overland, and P. Stabeno. 2011. Future status of the Chukchi Sea seen from 

global climate models. Poster presentation at the Alaska Marine Science Symposium, 

17-21 January 2011. Anchorage, AK.

Wang, M., W. Cheng, J.E. Overland, and P.J. Stabeno. 2012. Chukchi Sea climate variability 

seen from CESM. Poster presentation for Community Earth System Model (CESM) 

annual workshop. Breckenridge, CO, June 18-21, 2012. 
OCS Study

BOEM 2015-034

320

Wang, M. and J.E. Overland. 2014. Projected future duration of the sea-ice-free season in the 

Alaskan Arctic. Poster presentation at the Alaska Marine Science Symposium, January 

20-24, 2014. Anchorage, AK.
OCS Study

BOEM 2015-034

321

XVIII. APPENDICES

A. Field survey summary table 

Year Start date End date Start port 

location

End port 

location Vessel Captain Chief Scientist

2010 8/24/2010 9/20/2010 Nome, AK Dutch Harbor, 

AK

F/V Alaskan 

Enterprise Atle Remme Dr. Catherine Berchok

2011 8/12/2011 9/11/2011 Dutch Harbor, 

AK

Dutch Harbor, 

AK F/V Mystery Bay Fred Roman Dr. Catherine Berchok

2012 8/8/2012 9/7/2012 Dutch Harbor, 

AK

Dutch Harbor, 

AK R/V Aquila Kale Garcia Dr. Catherine Berchok
OCS Study

BOEM 2015-034

322

B. Mooring diagrams.

APPENDIX B. 1. MOORING DIAGRAM FOR PASSIVE ACOUSTIC RECORDERS.
OCS Study

BOEM 2015-034

323

APPENDIX B. 2. MOORING DESIGN FOR CKP1A, CKP2A, AND CKP3A. IN ADDITION TO THE 600 KHZ ADCP (CURRENTS), 

THIS MOORING CONTAINS INSTRUMENTS TO MEASURE NITRATE (ISUS), TEMPERATURE AND SALINITY (SEACAT), 

FLUORESCENCE (ECOFLUOROMETER) AND PHOTOSYNTHETICALLY ACTIVE RADIATION (PAR).
OCS Study

BOEM 2015-034

324

APPENDIX B. 3. MOORING DESIGN FOR CKIP1A, CKIP2A AND CKIP3A. IN ADDITION TO THE ASL ICE INSTRUMENT 

(MEASURES ICE THICKNESS), THIS MOORING CONTAINS RCM9 THAT MEASURES CURRENTS AT ONE DEPTH, 

TEMPERATURE, OXYGEN, AND TURBIDITY.
OCS Study

BOEM 2015-034

325

APPENDIX B. 4. MOORING DESIGN FOR CKT. THE TAPS-8 IS AN INSTRUMENT THAT ACOUSTICALLY MEASURES 

ZOOPLANKTON BIO-VOLUME.
OCS Study

BOEM 2015-034

326

C. Long-term passive acoustic data by species

Data are presented as the percentage of time intervals with calls) for inshore (lower panel), 

midshore (middle panel), and offshore (upper panel) locations 2010-2012. Dark gray shading 

indicates no data and teal shading indicates days where detections were masked by noise.

APPENDIX C. 1. BOWHEAD WHALE ACOUSTIC DETECTIONS.
OCS Study

BOEM 2015-034

327

APPENDIX C. 2. GUNSHOT CALL ACOUSTIC DETECTIONS.

APPENDIX C. 3. BELUGA ACOUSTIC DETECTIONS.
OCS Study

BOEM 2015-034

328

APPENDIX C. 4. GRAY WHALE ACOUSTIC DETECTIONS.

APPENDIX C. 5. WALRUS ACOUSTIC DETECTIONS.
OCS Study

BOEM 2015-034

329

APPENDIX C. 6. BEARDED SEAL ACOUSTIC DETECTIONS.

APPENDIX C. 7. KILLER WHALE ACOUSTIC DETECTIONS.
OCS Study

BOEM 2015-034

330

APPENDIX C. 8. MINKE “BOING” ACOUSTIC DETECTIONS.

APPENDIX C. 9. MINKE WHALE ACOUSTIC DETECTIONS (NONE).
OCS Study

BOEM 2015-034

331

APPENDIX C. 10. UNIDENTIFIED PINNIPED ACOUSTIC DETECTIONS.

APPENDIX C. 11. HUMPBACK WHALE ACOUSTIC DETECTIONS (NONE).
OCS Study

BOEM 2015-034

332

APPENDIX C. 12. RIGHT WHALE ACOUSTIC DETECTIONS (NONE).

APPENDIX C. 13. SPERM WHALE ACOUSTIC DETECTIONS (NONE).
OCS Study

BOEM 2015-034

333

APPENDIX C. 14. RIBBON SEAL ACOUSTIC DETECTIONS (NONE).

APPENDIX C. 15. ICE NOISE ACOUSTIC DETECTIONS.
OCS Study

BOEM 2015-034

334

APPENDIX C. 16. VESSEL NOISE ACOUSTIC DETECTIONS.

APPENDIX C. 17. SEISMIC AIRGUN ACOUSTIC DETECTIONS.
OCS Study

BOEM 2015-034

335

D. Sonobuoy and visual survey data

Visual sightings (left) and acoustic detections (right) of each species in the Bering, Chukchi, and Beaufort Seas by month for 2010-

2012. DBO regions outlined in black.

APPENDIX D. 1.SUMMARY OF TOTAL VISUAL AND SONOBUOY EFFORT, 2010-2012.
OCS Study

BOEM 2015-034

336

APPENDIX D. 2. BOWHEAD WHALE SIGHTINGS AND ACOUSTIC DETECTIONS, 2010-2012.
OCS Study

BOEM 2015-034

337

APPENDIX D. 3. GRAY WHALE SIGHTINGS AND ACOUSTIC DETECTIONS, 2010-2012.
OCS Study

BOEM 2015-034

338

APPENDIX D. 4. HUMPBACK WHALE SIGHTINGS AND ACOUSTIC DETECTIONS, 2010-2012.
OCS Study

BOEM 2015-034

339

APPENDIX D. 5. FIN WHALE SIGHTINGS AND ACOUSTIC DETECTIONS, 2010-2012.
OCS Study

BOEM 2015-034

340

APPENDIX D. 6. NORTH PACIFIC RIGHT WHALE SIGHTINGS AND ACOUSTIC DETECTIONS, 2010-2012.
OCS Study

BOEM 2015-034

341

APPENDIX D. 7. MINKE WHALE SIGHTINGS AND ACOUSTIC DETECTIONS, 2010-2012.
OCS Study

BOEM 2015-034

342

APPENDIX D. 8. KILLER WHALE SIGHTINGS AND ACOUSTIC DETECTIONS, 2010-2012.
OCS Study

BOEM 2015-034

343

APPENDIX D. 9. DALL’S PORPOISE SIGHTINGS, 2010-2012.
OCS Study

BOEM 2015-034

344

APPENDIX D. 10. HARBOR PORPOISE SIGHTINGS, 2010-2012.
OCS Study

BOEM 2015-034

345

APPENDIX D. 11. SPERM WHALE ACOUSTIC DETECTIONS, 2010-2012.
OCS Study

BOEM 2015-034

346

APPENDIX D. 12. BEARDED SEAL SIGHTINGS AND ACOUSTIC DETECTIONS, 2010-2012.
OCS Study

BOEM 2015-034

347

APPENDIX D. 13. WALRUS SIGHTINGS AND ACOUSTIC DETECTIONS, 2010-2012.
OCS Study

BOEM 2015-034

348

APPENDIX D. 14. NORTHERN FUR SEAL SIGHTINGS, 2010-2012.
OCS Study

BOEM 2015-034

349

APPENDIX D. 15. RINGED SEAL SIGHTINGS, 2010-2012.
OCS Study

BOEM 2015-034

350

APPENDIX D. 16. SPOTTED SEAL SIGHTINGS, 2010-2012.
OCS Study

BOEM 2015-034

351

APPENDIX D. 17. STELLER SEA LION SIGHTINGS, 2010-2012.
OCS Study

BOEM 2015-034

352

APPENDIX D. 18. POLAR BEAR SIGHTINGS, 2010-2012.
OCS Study

BOEM 2015-034

353

E. Passive acoustics table showing percentage of days with calls for each species by 

month.

Species Mooring September October November December January February March April May June July August

Inshore 21.4 72.6 85.0 21.0 0 0 8.1 51.7 85.5 43.9 58.1 16.1

Midshore 25.5 80.6 85.0 25.8 0 0 1.6 10.0 8.0 33.3 n/a 33.3

Offshore 11.8 53.2 78.3 22.6 1.6 0 0 1.7 0 0 n/a 0

Inshore 1.8 9.7 33.3 9.7 0 0 0 3.3 14.5 17.5 0 0

Midshore 0 4.8 18.3 17.7 0 0 0 0 0 0 n/a 0

Offshore 0 1.6 38.3 43.5 8.1 3.5 0 0 0 0 n/a 0

Inshore 3.6 25.8 58.3 6.5 4.8 1.8 6.5 46.7 67.7 14.0 12.9 0

Midshore 0 6.5 36.7 14.5 6.5 0 14.5 31.7 28.0 4.8 n/a 0

Offshore 0 3.2 23.3 3.2 4.8 3.5 0 26.7 4.4 0 n/a 0

Inshore 71.4 43.5 23.3 14.5 8.1 7.0 12.9 25.0 21.0 56.1 93.5 12.9

Midshore 68.6 37.1 20.0 4.8 1.6 0 3.2 15.0 4.0 81.0 n/a 100.0

Offshore 47.1 24.2 11.7 41.9 33.9 71.9 58.1 38.3 20.0 50.0 n/a 100.0

Inshore 21.4 67.7 66.7 82.3 96.8 96.5 100.0 100.0 100.0 93.0 41.9 22.6

Midshore 19.6 33.9 73.3 91.9 100.0 96.5 100.0 100.0 100.0 100.0 n/a 33.3

Offshore 54.9 40.3 31.7 71.0 77.4 75.4 90.3 100.0 100.0 100.0 n/a 66.7

Inshore 1.8 9.7 6.7 0 0 0 0 0 3.2 5.3 16.1 0

Midshore 0 1.6 0 0 0 0 0 0 0 0 n/a 0

Offshore 0 0 0 0 0 0 0 0 0 0 n/a 0

Inshore 1.8 4.8 0 0 0 0 0 0 1.6 1.8 0 0

Midshore 0 0 0 0 0 0 0 1.7 0 0 n/a 0

Offshore 0 0 0 0 0 0 0 0 0 0 n/a 0

Inshore 0 0 0 0 0 0 0 0 0 0 0 0

Midshore 0 0 0 0 0 0 0 0 0 0 n/a 0

Offshore 0 0 0 0 0 0 0 0 0 0 n/a 0

Inshore 0 0 0 0 0 0 0 0 0 0 0 0

Midshore 0 0 0 0 0 0 0 0 0 0 n/a 0

Offshore 0 0 0 0 0 0 0 0 0 0 n/a 0

Inshore 0 0 0 0 0 0 0 0 0 0 0 0

Midshore 0 0 0 0 0 0 0 0 0 0 n/a 0

Offshore 0 0 0 0 0 0 0 0 0 0 n/a 0

Inshore 0 1.6 0 0 0 0 0 0 0 0 0 0

Midshore 0 0 0 0 0 0 0 0 0 0 n/a 0

Offshore 0 0 0 0 0 0 0 0 0 0 n/a 0

Inshore 0 0 0 0 0 0 0 0 0 0 0 0

Midshore 0 0 0 0 0 0 0 0 0 0 n/a 0

Offshore 0 0 0 0 0 0 0 0 0 0 n/a 0

Inshore 12.5 8.1 16.7 11.3 17.7 7.0 24.2 23.3 50.0 28.1 12.9 3.2

Midshore 5.9 3.2 6.7 1.6 0 0 3.2 13.3 34.0 19.0 n/a 66.7

Offshore 0 0 1.7 0 1.6 0 0 6.7 0 0 n/a 0

Inshore 0 0 0 0 0 0 0 1.7 0 0 0 0

Midshore 0 0 0 0 0 0 0 3.3 0 0 n/a 0

Offshore 0 0 0 0 0 0 0 0 0 0 n/a 0

Inshore 0 0 0 0 0 0 0 0 0 0 0 0

Midshore 0 0 0 0 0 0 0 0 0 0 n/a 0

Offshore 0 0 0 0 0 0 0 0 0 0 n/a 0

Inshore 0 0 30.0 83.9 80.6 75.4 69.4 66.7 53.2 36.8 61.3 0

Midshore 0 0 38.3 75.8 45.2 59.6 29.0 35.0 4.0 0 n/a 0

Offshore 0 4.8 28.3 71.0 64.5 70.2 25.8 21.7 6.7 0 n/a 0

Inshore 12.5 12.9 1.7 0 0 0 0 0 0 0 0 0

Midshore 13.7 3.2 6.7 0 0 7.0 0 0 0 0 n/a 33.3

Offshore 5.9 1.6 0 6.5 3.2 0 0 0 0 0 n/a 66.7

Inshore 55.4 6.5 0 0 0 0 0 0 0 0 0 0

Midshore 66.7 9.7 0 0 0 0 0 0 0 0 n/a 100.0

Offshore 66.7 6.5 0 0 0 0 0 0 0 0 n/a 100.0

Gray whale

Bowhead 

whale

Gunshot call

Beluga whale

Walrus

Bearded seal

Airguns

Killer whale

Humpback 

whale

Fin whale

Minke whale

Minke boing

Right whale

Unidentified 

pinniped

Ribbon seal

Sperm whale

Ice noise

Vessel
OCS Study

BOEM 2015-034

354

F. Photo-identification results within the Bering Sea and Gulf of Alaska.

Individuals (Appendix F. 1) were matched to NMML catalogs (humpback and right 

whale), and to the Western Alaska Transient and Resident Catalogs (killer whale, 

WATC/WARC). Of the three North Pacific right whales photographed, two were previously 

sighted and one was a new addition. NMML-09 has been sighted in five years (1996, 2000, 

2008, 2009, 2010). NMML-85 has been sighted in three years (2009, 2010, 2011). In 2010, 

NMML-85 was sighted with a new individual designated as NMML-94. There were no matches 

found for humpback whales or the one adult male killer whale.

APPENDIX F. 1. SUMMARY OF PHOTOS COLLECTED AND ANALYSIS RESULTS, 2010-2012.

Year Species No. Individuals Catalogs Matches

2010 North Pacific right whale 1 NMML NMML-09

2011 North Pacific right whale 2 NMML NMML-85, NMML-94*

All Humpback whale 27 NMML No matches

2012 Killer whale 1 WATC/WARC No match

Photo-identification of North Pacific right whales, although opportunistically collected, 

provided an invaluable contribution to insights into this critically endangered species. Most 

notably, the sightings of NMML-09 reinforce the importance of the Bering Sea Critical Habitat. 

Over five years, NMML-09 was sighted within the same general location with multiple sightings 

occurring within 2008 (Clapham et al., 2012) (Appendix F. 2). This animal has provided a 

significant contribution to understanding summertime foraging habitat use as well as providing 

the first high- to low-latitude match of a North Pacific right whale (Kennedy et al., 2011). Photo 

contributions spanning all of the North Pacific date back to 1979. In over 30 years, only 23 

individuals have been added to the NMML North Pacific right whale catalog. The documentation 

of a new individual in 2011 provided a significant contribution to this catalog.
OCS Study

BOEM 2015-034

355

APPENDIX F. 2. SIGHTING HISTORY OF A NORTH PACIFIC RIGHT WHALE (NMML-09) OVER FIVE YEARS WITHIN THE 

BERING SEA CRITICAL HABITAT.
OCS Study

BOEM 2015-034

356

G. Long-term passive acoustic data showing timespans (orange) included in the 

GAMs for each species or signal.

APPENDIX G. 1. LONG-TERM PASSIVE ACOUSTIC RESULTS SHOWING TIMESPANS INCLUDED IN GAM FOR BOWHEAD 

WHALES.

APPENDIX G. 2. LONG-TERM PASSIVE ACOUSTIC RESULTS SHOWING TIMESPANS INCLUDED IN GAM FOR BOWHEAD 

WHALES AND GUNSHOT CALLS.
OCS Study

BOEM 2015-034

357

APPENDIX G. 3. LONG-TERM PASSIVE ACOUSTIC RESULTS SHOWING TIMESPANS INCLUDED IN GAM FOR BELUGA

WHALES.

APPENDIX G. 4. LONG-TERM PASSIVE ACOUSTIC RESULTS SHOWING TIMESPANS INCLUDED IN GAM FOR GRAY

WHALES.
OCS Study

BOEM 2015-034

358

APPENDIX G. 5. LONG-TERM PASSIVE ACOUSTIC RESULTS SHOWING TIMESPANS INCLUDED IN GAM FOR WALRUS.

APPENDIX G. 6. LONG-TERM PASSIVE ACOUSTIC RESULTS SHOWING TIMESPANS INCLUDED IN GAM FOR BEARDED 

SEALS.
OCS Study

BOEM 2015-034

359

APPENDIX G. 7. LONG-TERM PASSIVE ACOUSTIC RESULTS SHOWING TIMESPANS INCLUDED IN GAM FOR ICE NOISE.
OCS Study

BOEM 2015-034

360

H. GAM results including TAPS6-NG variables

Additional Methods

The TAPS6-NG GAM runs were limited to the calling activity of three species: bearded 

seal, bowhead whales, and walrus. The remainder of the species/sound sources that were 

included in the main GAM models had no calling/noise activity for any of the days with TAPS6-

NG measurements and were not included in the TAPS6-NG analysis. In addition, not all of the 

oceanographic variables from the main GAM models were included in the TAPS6-NG GAM 

runs. Salinity was excluded as no data were available. Month and year variables were excluded 

as the data came from a single month and year. Ice concentration and ice thickness were 

excluded, as there was no ice present during TAPS6-NG data collection. 

Two separate sets of runs were conducted for the TAPS6-NG analysis; one included the 

four full water column variables (Type I), and the second one included the four bottom layer 

(Type II) variables. This was necessary, as the computing power limit would have been reached 

if all eight variables had been included (i.e., 22 variables would have produced 4.1 million 

models; all attempts at running a 20-variable GAM crashed after 24 hours of running). For all 

runs, the entire two-year midshore dataset was used. This allowed examination of how strongly 

correlated the TAPS6-NG variables were to the calling data. If a TAPS6-NG variable rose to the 

top of the GAM runs, this would indicate the variable provided a high level of explanatory power 

to explain the variance in calling activity during the short period of time that the TAPS6-NG was 

collecting data, higher than other models containing more data. 

Results 

Because there were only 36 days of data available from the TAPS6-NG instrument (25 

Aug - 29 Sep 2011), and four of these days occurred before the passive acoustic recordings 

began, there is a very limited dataset (32 days) available. Typically models having this sample 

size would be automatically excluded from being ranked high in the GAM analysis. In this case, 

however, the motivation was to see if any of the TAPS6-NG variables contributed significantly 

to the variability seen in the seasonal distributions of calling activity. Therefore, no models were 

excluded due to a low (n < 100) sample size. A summary table of the results of the two runs is 

presented in Appendix H. Table 1.

Discussion

The results from this set of GAM runs indicate a variety of the TAPS6-NG variables 

provided high explanatory power for the variance in bowhead whale, bearded seal, and walrus 

calling activity. In the future, every effort to collect these data simultaneous with passive 

acoustic recordings should be attempted to evaluate their explanatory power in understanding 

marine mammal distributions. 
OCS Study

BOEM 2015-034

361

APPENDIX H. TABLE 1. TAP6-NG GAM RESULTS SUMMARY OF THE TOP VARIABLES, THE % R-SQUARED VALUE, AND 

THE SAMPLE SIZE, FOR BOTH THE TOP AIC MODEL AND THE TOP R-SQUARED MODEL, AND THE COMMON 

SIGNIFICANT VARIABLES AMONG THE TOP FIVE AIC AND R-SQUARED MODELS FOR EACH SPECIES OR SIGNAL.

Type I Midshore Type II Midshore

Bearded Seal

Top 1 Models

Top AIC Q + t + a Q + R + t + f

AIC, % AIC, n 9, 48.5%, 32 14, 41.0%, 32

Top Rsqr A + Q + P + T + V + R + N + C + t + n + G A + Q + P + T + V + R + N + C + t + n + G

AIC, % R2, n 4625, 59.6%, 495 4625, 59.6%, 495

Top 5 Models: 

Common Vars*

Sig < 0.01 Q Q 

Sig < 0.05 Q t Q R t 

Bowhead Whale

Top 1 Models

Top AIC F + p P + T + N + d + h

AIC, % AIC, n 28, 28.0%, 32 32, 25.3%, 29

Top Rsqr F + p A + Q + P + T + U + R + N + C + t + n

AIC, % R2, n 28, 28.0%, 32 4489, 26.8%, 495

Top 5 Models: 

Common Vars*

Sig < 0.01 p -

Sig < 0.05 F p -

Walrus

Top 1 Models

Top AIC N + u + a + r Q + V + R + N + d + f + h + O

AIC, % AIC, n 244, 48.2%, 29 241, 58.1%, 29

Top Rsqr Q + P + T + U + V + R + N + a + O Q + V + R + N + d + f + h + O

AIC, % R2, n 245, 52.5%, 29 241, 58.1%, 29

Top 5 Models: 

Common Vars*

Sig < 0.01 - Q h O

Sig < 0.05 a Q d h O

* To be included, variable had to be in at least 3 of each set of models; U = U bottom currents (cm s-1); R = transport (Sv, averaged 

across all locations); N = nitrate (bottom); t = V winds at midshore location; n = wind speed at midshore location; G = bottom O2 

(mMol kg-1); A = ADCP (600) column Sa (area backscattering dB re 1 m-1); F = chlorophyll (fluorescence); P = PAR (mE in cm-2 

s-1); V = V bottom currents (cm s-1); C = Turbidity (FNU); O = % oxygen saturation; Q = ADCP (600 kHz) bottom Sv (volume 

backscattering dB re 1 m-1); T = temperature (°C); u = U winds at midshore location; f = TAPS 420 kHz Volume backscatter (Sv) 

bottom (dB re 1 m-1); p = TAPS Euphausiid abundance (/m3) full column; d = TAPS Euphausiid abundance (/m3) bottom; h = 

TAPS 50 kHz Volume backscatter (Sv) bottom (dB re 1 m-1); a = TAPS Total BioVolume (mm3/m3) full column; r = TAPS 50 kHz 

Volume backscatter (Sv) full column (dB re 1(m2))
OCS Study

BOEM 2015-034

362

I. List of attached electronic files:

1. Sonobuoy deployment tables. These are tables showing every sonobuoy deployed 

during the CHAOZ cruises, as well as species detected, for 2010-2012. File name: 

“CHAOZ 2010-12 sonobuoy deployments.pdf”

2. Table of GAM daily averaged variables across disciplines. This table contains 

daily average values for each variable that was included in the GAMs. This 

spreadsheet also contains one tab per mooring location (inshore, midshore, and 

offshore). File name: “CHAOZ_GAM_AllComponents_FINAL.xls”

3. CTD and plankton reports, detailing samples and measurements collected at each 

transect sampling station for the 2010-2012 field surveys.

a. 2010: “CHAOZ_2010_CTD&planktonReport.pdf”

b. 2011: “CHAOZ_2011_CTD&planktonReport.pdf”

c. 2012: “CHAOZ_2012_CTD&planktonReport.pdf”

4. Archived Samples list. This document contains a list of all data samples, their 

approximate file size, and their location, for all data collected during the CHAOZ 

study. File name: “CHAOZ Archived Samples List.pdf”

5. Technical Summary. This is a brief (3-4 page) summary report of the CHAOZ 

project. File name: “CHAOZ M09PG00016 Technical Summary.pdf”
The Department of the Interior Mission

Protecting America's Great Outdoors and Powering Our Future 

The U.S. Department of the Interior protects America’s natural resources and heritage, honors 

our cultures and tribal communities