CMI Graduate Student Projects: Volume 1

Characterizing Bacterial Communities in Beaufort Sea Sediments 

in a Changing Arctic

Alexis Walker

College of Fisheries and Ocean Science, University of Alaska Fairbanks

Chukchi-Beaufort Seas Storms and Their Influence on Surface Climate

Yang Yang

International Arctic Research Center, University of Alaska Fairbanks

Using Genotyping-by-Sequencing (GBS) Population Genetics Approaches to

Determine the Population Structure of Tanner Crab (Chionoecetes bairdi) in Alaska

Genevieve Johnson

College of Fisheries and Ocean Science, University of Alaska Fairbanks

March 2018

OCS Study BOEM 2018-021
Contact Information:

Email: UAF-CMI@alaska.edu

phone: 907.474.6782

Coastal Marine Institute

College of Fisheries and Ocean Sciences

University of Alaska Fairbanks

P. O. Box 757220

Fairbanks, AK 99775-7220

These studies were funded in part by the U.S. Department of the Interior, Bureau of Ocean Energy Management (BOEM) 

under cooperative agreements M16AC00004, M16AC00007, and M16AC00011, between BOEM, Alaska Outer 

Continental Shelf Region, and the University of Alaska Fairbanks. This report, BOEM 2018-021 is available through

the Coastal Marine Institute, select federal depository libraries, and electronically from https://www.boem.gov/BOEMNewsroom/Library/Publications/Alaska-Scientific-and-Technical-Publications.aspx

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

representing the opinions or policies of the U.S. Government. Mention of trade names or commercial products does not

constitute their endorsement by the U.S. Government.
Table of Contents

Characterizing Bacterial Communities in Beaufort Sea Sediments in a Changing Arctic ....... 

Alexis Walker (BOEM M16AC00004).......................................................................................... 1 

Contents............................................................................................................................................. 1 

Abstract ............................................................................................................................................. 3 

Introduction....................................................................................................................................... 4 

Methods............................................................................................................................................. 7 

Results............................................................................................................................................... 9 

Discussion ...................................................................................................................................... 15

Acknowledgments.......................................................................................................................... 17

References....................................................................................................................................... 17

Appendices...................................................................................................................................... 23

Chukchi-Beaufort Seas Storms and Their Influence on Surface Climate .................................... 

Yang Yang (BOEM M16AC00007)...............................................................................................27

Contents........................................................................................................................................... 27

Abstract ........................................................................................................................................... 29

Introduction..................................................................................................................................... 30

Methods........................................................................................................................................... 31

Results............................................................................................................................................. 33

Conclusions..................................................................................................................................... 61

Acknowledgments.......................................................................................................................... 61

References....................................................................................................................................... 61

Using Genotyping-by-Sequencing (GBS) Population Genetics Approaches to Determine

the Population Structure of Tanner Crab (Chionoecetes bairdi) in Alaska.................................. 

Genevieve Johnson (BOEM M16AC00011) ...............................................................................64

Contents........................................................................................................................................... 64

Abstract ........................................................................................................................................... 65

Introduction..................................................................................................................................... 66

Methods........................................................................................................................................... 67

Results............................................................................................................................................. 70

Discussion ...................................................................................................................................... 73

Acknowledgments.......................................................................................................................... 74

References....................................................................................................................................... 75

iii
Characterizing Bacterial Communities in Beaufort Sea Sediments in a

Changing Arctic

Principal Investigator: Alexis Walker

College of Fisheries and Ocean Sciences

University of Alaska Fairbanks

Cooperative Agreement Number: M16AC00004

Period of Performance: 6/16-1/18

Contents 

List of Figures....................................................................................................................................... 2 

List of Tables ........................................................................................................................................ 2 

Abstract ................................................................................................................................................. 3 

Introduction........................................................................................................................................... 4 

Objectives ....................................................................................................................................... 7 

Methods................................................................................................................................................. 7 

Sample Collection .......................................................................................................................... 7 

Sediment Characteristics................................................................................................................ 7 

Genomics Approach....................................................................................................................... 7 

DNA Sequence Processing ............................................................................................................ 8 

Statistical Analyses......................................................................................................................... 8 

Results................................................................................................................................................... 9 

Environmental Variables................................................................................................................ 9 

Alpha-diversity ............................................................................................................................. 10

Beta-diversity................................................................................................................................ 12

Environmental Correlations......................................................................................................... 12

Putative Oil-degraders.................................................................................................................. 14

Discussion ........................................................................................................................................... 15

Acknowledgments.............................................................................................................................. 17

References........................................................................................................................................... 17

Appendices.......................................................................................................................................... 23

1
List of Figures

Figure 1. Map of the study area including sample stations and regions defined by certain 

oceanographic characteristics .............................................................................................................. 5 

Figure 2. Map of the 79 sampling locations across the Beaufort Sea and the Amundsen Gulf 

relative to oil and gas potential ............................................................................................................ 6 

Figure 3. Correlations between environmental variables using Spearman’s Rho..........................10

Figure 4. Diversity of bacteria from the Beaufort Sea to the Amundsen Gulf...............................11

Figure 5. Correlations between diversity and environmental variables..........................................11

Figure 6. Bacterial community structure across the Beaufort Sea and the Amundsen Gulf in 

nMDS ordination space...................................................................................................................... 12

Figure 7. Correlation between environmental variables and bacterial community structure in 

nMDS ordination space with fitted vectors....................................................................................... 14 

Figure 8. Putative oil-degrading taxa found in Beaufort Sea sediments......................................... 14 

List of Tables

Table 1. Inherent characteristics of environmental variables ............................................................ 9 

Table 2. Correlations between environmental variables and bacterial community structure ........ 13 

Table A1. Collection data for all sediment samples.........................................................................23

Table A2. R2 and p-values from non-transformed and transformed variables in linear regression 

model ................................................................................................................................................... 25 

Table A3. Rs and p-values from Spearman’s Rho investigation of correlations between 

environmental variables. .................................................................................................................... 26 

2
Abstract

Reduced sea ice cover has shifted marine productivity patterns in the Arctic. These shifts affect 

the quality and quantity of organic matter (OM) deposited to the seafloor and available to the 

benthic organisms that rely on sinking material from overlying water masses for sustenance. 

Additionally, processes regulating the local remineralization and degradation of OM may be 

impacted. Sediment bacteria are the biogeochemical engineers of the benthos and play a vital 

role in OM and biogeochemical budgets. Although bacterial diversity and community structure

can reflect local biogeochemical processes, environmental characteristics, physical disturbances, 

and chemical contaminants, they are largely understudied in the Beaufort Sea benthos. The

overall goals of this study were to characterize broad-scale patterns in the diversity and 

community structure of benthic bacteria from the western Beaufort Sea into the Amundsen Gulf

and to elucidate the environmental drivers of these patterns. To achieve these goals, sediment 

bacterial communities were characterized via the 16S rRNA gene using a next-generation DNA 

sequencing (NGS) approach. Diversity, community structure, and associated environmental 

correlates were assessed for 79 sampling sites across the Alaskan and Canadian regions of the 

Beaufort Sea shelf and slope to the Amundsen Gulf. Bacterial alpha-diversity exhibits overall 

positive correlations with depth, salinity, and δ15N and negative correlations with chl-a, chla/phaeopigments, and C/N across all sediments analyzed. Patterns of beta-diversity are not

highly definitive, and bacterial communities appear to be loosely clustered with high overlap in 

nMDS ordination space. Overall, bacterial communities are more similar within Beaufort subregions including the Alaskan Beaufort Shelf, the Mackenzie Shelf, Amundsen Gulf, and Banks 

Island. Environmental variables identified as drivers of community structure differences were 

depth, salinity, δ15N, chl-a, C/N, and total organic carbon (TOC). As previously ice-covered 

Arctic waters become more accessible for human activities, particularly petroleum and gas 

exploration, there is an increased likelihood of contaminant exposure. Our study area included 

samples within and adjacent to areas with high oil and gas resource potential. In order to gain a 

general sense of oil biodegradation potential across the study area, bacterial community data 

generated were queried for putative oil-degrading taxa. Of the 14 taxa investigated, the majority 

were found sporadically throughout the study area, with more taxa found on the Alaskan 

Beaufort Shelf and the Amundsen Gulf than on the Mackenzie Shelf. Only three taxa were 

ubiquitously distributed across all samples. 

3
Introduction

Bacteria represent a biological link between the abiotic and biotic realms because their diverse 

metabolic pathways make them key mediators of biogeochemical processes. Unlike higher 

trophic level organisms, bacteria can metabolize both organic and inorganic compounds, making 

them essential in the replenishment and removal of nutrients to surrounding organisms. For 

example, benthic organisms that feed on sinking material must deal with the challenge of 

obtaining nutrition from a food source that has been degraded during transport through the 

overlying water column (Arndt et al., 2013; Lopez & Levinton, 1987). Bacteria in sediments 

metabolize these degraded compounds and produce more labile material for neighboring microand meio-fauna (as byproducts of metabolism or by becoming food for benthic organisms). 

Additionally, bacteria can sequester labile organic material and remove nutrients from the 

benthic system via assimilation and mortality. Thus, bacteria play an integral role in nutrient and 

OM degradation and cycling and contribute to fluxes of energy throughout the benthic food web 

(Bienhold, Boetius, & Ramette, 2012a; Deming & Baross, 1993). Particular bacterial taxonomic 

groups metabolize specific compounds. For example, in Arctic marine sediments, the bacterial 

taxa Flavobacteria, Marinilabiaceae, and Pseudoalteromonadaceae exhibit a strong positive 

correlation and increase in relative abundance with increased nutrients and fresh phytodetritus. 

Moreover, bacterial diversity generally increases with increased phytodetritus flux (Bienhold, 

Boetius, & Ramette, 2012a). Bacterial taxa such as Acidobacteria can also indicate nutrient-poor 

conditions (Bienhold, Boetius, & Ramette, 2012a; Teske et al., 2011). Therefore, bacterial 

diversity and community composition may provide insight into the quantity and quality of 

material deposited from surface primary production and the local pool of OM and nutrients 

available to benthic consumers (Judd, Crump, & Kling, 2006). 

The study area covers a broad geographical range from the western Beaufort Sea near the 

Colville River to the Amundsen Gulf and Banks Island. This Arctic sector encompasses three 

large regions: the Alaskan Beaufort Shelf (ABS), the Mackenzie Shelf (MS), and the Amundsen 

Gulf (AG) (Figure 1). The Amundsen Gulf is a large channel connecting the Canadian Arctic 

Archipelago with the southeast Beaufort Sea, bordered by the Mackenzie Shelf to the southwest

and Banks Island (BI) to the north. The Amundsen Gulf is characterized by a narrow shelf, 

several peripheral bays, straights, and inlets, and a central basin with an average depth of 300 m 

and a maximum depth of 630 m (Forest et al., 2010; Gamboa et al., 2017; Stokes, Clark, & 

Winsborrow, 2006). The Mackenzie Shelf is characterized by inner, middle, and outer shelf 

regions with depths 0–45, 45–80, and 80–100 m, respectively (Carmack & Macdonald, 2002). 

Bordered by the Mackenzie Canyon to the west and the Amundsen Gulf trough to the east, the 

Mackenzie Shelf has been described as a vast estuary because its water properties are greatly 

influenced by both coastal (Mackenzie River) and marine sources (Arctic Ocean) (Carmack & 

Macdonald, 2002; Magen et al., 2010). The Mackenzie River, fourth largest in the Arctic in 

terms of freshwater discharge and the largest in terms of sediment transport, greatly influences 

the water and sediment characteristics of the Mackenzie Shelf and, to a lesser extent, the easternmost section of the Alaskan Beaufort Shelf and Amundsen Gulf (Rachold et al., 2004). Several 

4
smaller sporadic rivers flowing from the Alaskan coastline also influence the Alaskan Beaufort 

Shelf. The Alaskan Beaufort Shelf is bordered by Point Barrow to the west and the Mackenzie 

Canyon to the east, with a shelf break at ~80 m water depth. The water properties of the Alaskan 

Beaufort Shelf are also influenced by warmer Bering Strait water, referred to as the Beaufort 

shelf-break jet, which flows due east from Barrow Canyon (Dunton, Weingartner, & Carmack, 

2006; Pickart, 2004). 

Figure 1. Map of the study area including sample stations and regions defined by certain oceanographic 

characteristics. 

The environmental gradients exhibited in the study area provide a natural experimental setting 

for exploration of linkages between environmental characteristics and bacterial community 

structure. Identifying links between bacterial community composition and function is a growing 

area of research aimed at identifying ecologically important bacterial groups (Fuhrman, 2009; 

Teske et al., 2011). Arctic benthic bacterial communities have only been explored in a handful of 

studies, with the majority conducted prior to 1980 or in deep-sea sediments in Greenland and 

Norway (e.g., Bienhold, Boetius, & Ramette, 2012b; Buttigieg & Ramette, 2015; Jacob et al., 

2013; Teske et al., 2011). Multiple studies have characterized pelagic bacteria in the Beaufort 

Sea; however, pelagic and benthic bacterial communities are significantly different with respect 

to composition, diversity, and function (e.g., Ortega-Retuerta et al., 2013; Zinger et al., 2011). 

Biogeographical patterns of community composition and diversity have already been established 

for bacterial communities in deep-sea Arctic sediments where environmental conditions are 

relatively uniform and constant (Buttigieg & Ramette, 2015; Jacob et al., 2013). Therefore, it is 

likely that the wide range of benthic habitat conditions across the Beaufort Sea and the 

Amundsen Gulf will be reflected in sediment bacterial community structure and diversity. 

Benthic bacteria are valuable bioindicators because they reflect both short-term and long-term 

environmental changes. In the long term, community structure remains relatively constant, 

mirroring trends in environmental characteristics such asfood availability, hydrography, and 

5
bathymetry (Zinger et al., 2011). Bacteria can also be considered the microbial equivalent to the 

“canary in the coal mine” due to high turnover rates and immediate and successive shifts in 

community structure following a disturbance. Shifts in bacterial community structure may also 

indicate the presence of contaminants such as heavy metals and hydrocarbons, which are 

chemical components of crude oil, in the environment. A variety of bacterial taxa involved in oil 

biodegradation, including some obligate oil-degraders, occur naturally in low or undetectable 

numbers prior to a large oil exposure event. These taxa, which typically "bloom" following oil 

exposure, can rapidly break down the chemical components of oil in situ (Head, Jones, & Röling, 

2006; McKew et al., 2007; Yakimov, Timmis, & Golyshin, 2007). The effectiveness and 

efficiency with which these bacteria can degrade oil increases significantly with diversity 

(Dell’Anno et al., 2012; McKew et al., 2007; Röling et al., 2002). Higher bacterial diversity 

translates to more metabolic strategies available to break down complex compounds in oil and 

the resulting degradation products. Establishing the presence and distribution of known oildegrading taxa and associated community diversity provides valuable insights into how 

microbial communities might respond to oil exposure in a given location (Dell’Anno et al., 

2012). This information is particularly relevant in the study area, which incorporates regions 

within and adjacent to areas with current and historical exploratory oil and natural gas wells and 

areas designated as having high oil and gas resource potential in the Beaufort Sea (Figure 2) 

(BOEM, 2012; CAFF/USGS, 2017). 

Figure 2. Map of the 79 sampling locations across the Beaufort Sea and the Amundsen Gulf relative to oil 

and gas potential. Sediments for bacterial community analyses were collected in 2012, 2013, and 2014. 

The areas of oil and gas potential represent the percent likelihood of the presence of oil/gas and gas 

reserves, with 100% being areas where the presence of oil/gas has been established through exploratory 

drilling (CAFF/USGS, 2017). 

6
Objectives

1. Assess the diversity and community structure of sediment bacteria at 79 locations 

distributed across the Beaufort Sea continental shelf and slope.

2. Examine correlations between environmental parameters and corresponding 

bacterial diversity and community composition.

3. Query bacterial community composition from 79 sediment samples for potential 

oil-degrading taxa.

Methods

Sample Collection

A total of 79 archived sediment samples were processed and sequenced using next-generation 

sequencing (NGS). The resulting data were analyzed using multivariate statistics. Samples were 

collected opportunistically in 2012–2014 across a broad section of the Beaufort Sea and 

Amundsen Gulf (~151–119°W) as part of two collaborative field programs, the USTransboundary Fish and Lower Trophic Communities Project (USTB) and the Beaufort Regional 

Environmental Assessment Project (BREA). Sampling efforts included a series of shelf-slope 

transects starting near the Colville River and extending to the Amundsen Gulf and northward 

near Banks Island at 20 –1200 m depths (Figure 1). All sediment samples, environmental and 

genomic, were collected from the top 1 cm surface layer of a double van Veen grab (USTB 

2014) or a 0.25 m2 box core sample (USTB 2012 and BREA) using a 60 cc (2.5 cm) sterilized 

syringe. Following collection aboard respective vessels, the samples were immediately frozen at 

-20o

C and stored at -80oC upon arrival at UAF. 

Sediment Characteristics

Seven sediment characteristics were used to investigate correlations with bacterial diversity and 

community structure: Chlorophyll-a concentration (chl-a), phaeopigments concentration (phaeo), 

chl-a: phaeopigments (Ch/Ph), total organic carbon (TOC), carbon:nitrogen (C/N), δ13C, and 

δ15N. These characteristics include commonly used proxies for the quality and quantity of 

organic matter in sediments. All parameters were measured on sediment samples taken from the 

upper 1 cm of either the same box core or replicate grab samples from the same stations as the 

bacterial genomic samples. Additional environmental variables included depth, bottom water 

temperature, and bottom water salinity recorded from CTD profiles at each station. All 

environmental data were collected as part of the USTB and BREA field programs (S. HardyUSTB, S. MacPhee-BREA, unpublished data) and provided as a courtesy for use in the analyses 

presented here.

Genomics Approach

16S ribosomal (rRNA) marker gene surveys were conducted on freeze-dried sediments in order 

to assess the diversity and community structure of bacterial populations in the Beaufort Sea and 

Amundsen Gulf benthos. Sample preparation for sequencing had three steps: (1) total genomic 

7
DNA was extracted from sediment samples using the Qiagen PowerSoil kit, (2) the V4 region of 

the 16S rRNA gene was amplified using revised forward and reverse primers from the Earth 

Microbiome Project (EMP) standard protocol, and (3) library preparation was completed with 

iTru adapters for sequencing. This last step was completed following the one-step polymerase 

chain reaction (PCR) protocol with indexed primers, which is the current standard protocol used 

by the EMP (Apprill et al., 2015; J. G. Caporaso et al., 2011; J. G. Caporaso et al., 2012; Parada, 

Needham, & Fuhrman, 2016; Walters et al., 2016). The processed samples were sequenced on 

the Illumina MiSeq platform in the DNA Core Laboratory facility at the University of Alaska 

Fairbanks (UAF).

DNA Sequence Processing

Once raw sequencing data were obtained, sequences were demultiplexed using the Mr. Demuxy 

package (Cock et al., 2009). To obtain a taxonomy table with operational taxonomic units 

(OTUs) for downstream analyses, sequences were run through the mothur toolkit on a high 

performance-computing cluster (through UAF Research Computing Systems) using a modified 

MiSeq standard operating procedure. OTUs were clustered at 99% similarity (see Appendix) 

(Schloss et al., 2009). Taxonomy was assigned to OTUs using the newest version of the SILVA 

reference database, SILVA 128 (Glöckner et al., 2017; Wang et al., 2007). The table comprising 

the relative abundance of OTUs identified for each sample was used to investigate the alphadiversity (species richness) and beta-diversity (community structure) of bacterial assemblages 

from all 79 study sites and to assess correlations between environmental variables of these 

patterns. All analyses involving environmental data with OTU data were conducted with 77 of 

the 79 samples, as two samples did not have the associated environmental characteristics. 

Finally, a taxonomy table generated with mothur was searched for putative oil-degrading taxa to 

establish their presence or absence across all sample sites. 

Statistical Analyses

All environmental variables were checked for normality using Q-Q plots and the Shapiro Wilkes 

test for normal distribution. Correlation between individual environmental variables was 

investigated using a combination of pairwise plots, linear regression models, and Spearman’s 

Rho correlation coefficients in R (R Core Team, 2017). This was done to elucidate covarying

environmental parameters. Alpha-diversity was investigated using the Inverse Simpson Index, 

which is generally preferable when trying to differentiate between multiple sites with varying 

environmental characteristics (Morris et al., 2014). Inverse Simpson indices were calculated 

using the vegan package in R (Oksanen et al., 2017; R Core Team, 2017). An nMDS plot using 

Bray-Curtis indices was constructed to visualize broad-scale patterns of overall bacterial 

community structure across the Beaufort Sea and the Amundsen Gulf. In order to accommodate 

such a large dataset, the OTU table was square root transformed and Wisconsin doublestandardized, which is necessary to fit these data into 2-dimensional space with low-stress (< 

0.15) (Link et al., 2013; Oksanen, 2015). Correlations between community structure and 

environmental variables were evaluated using the envfit function in the vegan R package to fit 

8
environmental variables to nMDS plots with Bray-Curtis (Oksanen et al., 2017; R Core Team, 

2017). Envfit calculates the vectors of environmental variables and fits these vectors to an nMDS

ordination through QR decomposition, which is similar to linear regression, (Oksanen, 2015). 

The nMDS ordination constructed here is based on a distance matrix (Bray-Curtis); however, as 

long as the stress value of the nMDS ordination is low (< 0.15), the resulting linear 

representation of Bray-Curtis distances in nMDS space provides reasonable goodness of fit (R2

) 

values (Oksanen, 2015; Oksanen et al., 2017). Envfit also uses permutations to assess the 

significance of fitted vectors (i.e., environmental variables). 

Results

Environmental Variables 

The majority of environmental variables (80%) exhibited a non-normal distribution as indicated 

by Q-Q plots and significant values (p<0.05) yielded from the Shapiro-Wilkes tests for 

normality. These included depth, temperature, salinity, chl-a, phaeopigments, chl-a/ 

phaeopigments, δ13C, and C/N. A significant Shapiro-Wilkes p-value rejects the null of normally 

distributed data. Two variables, δ15N and TOC, exhibited normal distributions. However, the 

Shapiro-Wilkes p-value for δ15N was very close to significant (p=0.053; Table 1). 

Transformations appropriate to individual non-normal distributions were tested and applied. 

After transformation, many of the Q-Q plots exhibited normal distributions; however, many of 

the variables still failed the Shapiro-Wilkes test (i.e., transformed data exhibited closer to normal 

distributions but not necessarily normally distributed). For two of the environmental variables, 

salinity and δ13C, a transformation yielding a normal distribution could not be reached.

Table 1. Inherent characteristics of environmental variables. Normal distribution (ND), Potential outliers 

(PO), Transformation (Tf), Shapiro-Wilkes p-values before transformation (p-value) and after 

transformation (T p-values) NTf indicates that all transformations applied did not satisfy either the Q-Q 

plot or Shapiro-Wilkes test for normality, and N indicated normal distribution.

*A transformation for δ

15N was investigated due to the close to significant p=value prior to transformation. 

9
Given the highly variable distributions of the environmental data explored here and the difficulty 

in achieving normal distribution through transformation for all variables, the downstream

analyses herein are non-parametric. R2 and p-values from linear regression models investigating 

correlations between environmental variables, with non-transformed and transformed data are 

presented in Appendix Table A2. These data may be useful for future parametric analyses such 

as principal components analysis (PCA) or canonical correspondence analysis (CCA). 

Correlations between environmental variables were investigated using the Spearman’s Rho (Rs) 

correlation coefficient. A visual representation of the Rs and p-values from correlation analyses

is depicted in Figure 3. The strongest correlations exhibited among these environmental variables 

are between depth and salinity (Rs=0.94) and depth and chl-a/ phaeopigments (Rs= -0.76). There 

are a few moderate correlations (Rs=0.69–0.41) exhibited between variables and depth and 

salinity measures as well as between variables within the sediment pigments, isotopes, and 

organic matter characteristics (Figure 3). The remainder of correlation strengths are relatively 

low (Rs=0.39–0.02) and average to an Rs ~ 0.11. Appendix Table A3 provides a complete list of 

Rs and associated p-values. 

Figure 3. Correlations between environmental variables using Spearman’s Rho. Empty grid-cells indicate 

correlations that were not significant (p>0.05). The presence of a sphere indicates a significant correlation 

(p<0.05) and size and color of the spheres illustrate the strength and nature (positive/negative) of the 

correlation, respectively. 

Alpha-diversity 

Inverse Simpson diversity indices calculated were organized in a bar plot form from west to east 

to investigate the overall trend in diversity across the study area (Figure 4). Bacterial diversity

exhibits an increasing trend from west to east with mean inverse Simpson values of 236 for 

samples located west of the Mackenzie River and 298 for samples located east of the Mackenzie 

River. The mean Inverse Simpson value was 364 for the four samples collected directly north of 

the Mackenzie River.

10
Figure 4. Diversity of bacteria from the Beaufort Sea to the Amundsen Gulf. The bar plot is organized on 

the x-axis from west to east. Samples are colored by their location relative to the Mackenzie River (MR) 

(west=WMR and east=EMR), which is a natural and influential divide in the study area. 

Spearman’s Rho was used to investigate correlations between environmental variables and 

diversity (Figure 5). The overall correlation strengths between diversity and environmental 

variables are moderate (Rs=0.5~0.4) to low (Rs<0.4). Diversity is correlated most strongly with 

salinity, depth (positive), and chl-a (negative). 

Figure 5. Correlations between diversity and environmental variables. Empty grid-cells indicate 

correlations that were not significant (p>0.05). The presence of a sphere and bold text indicates a 

significant correlation (p<0.05), sphere size and color illustrate the strength and nature (positive/negative) 

of the correlation, respectively.

11
Beta-diversity

Loose clustering and overall nMDS plot distribution indicate that there are inherent 

dissimilarities in bacterial community structure; however, but grouping variables are more 

difficult to discern (Figure 6). In spite of a large amount of overlap, clustering of Alaskan 

Beaufort Shelf communities appears on the lower portion of the nMDS and of Mackenzie Shelf 

communities on the upper portion of the nMDS. Except for one sample, the Banks Island 

samples cluster together. The Amundsen Gulf communities are distributed more sporadically. A 

PERMANOVA indicates that the bacterial communities between these regions are significantly 

different (p=0.001) but with a low R2 (0.1). The variable ‘year’ was also investigated using a 

PERMANOVA and showed no significant effect on bacterial community structure.

Figure 6. Bacterial community structure across the Beaufort Sea and the Amundsen Gulf in nMDS 

ordination space colored by region 

Environmental Correlations

Potential environmental drivers of community structure were examined using the envfit function 

in the R package vegan (Oksanen et al., 2017). The best results from envfit are produced when 

the nMDS ordination to be fitted exhibits low stress. According to Clarke, stress value

designations are as follows: >0.20=unacceptable, <0.20=acceptable, <0.10=good, and 

<0.05=ideal (Clarke, 1988). The nMDS ordination depicted in Figure 6 only has acceptable

stress (0.11) in 2-D space (k=2). Therefore, to ensure more accurate vector fitting and R2 values 

and associated p-values, environmental variables were fitted with an nMDS in 3-D space (k=3), 

which has good stress (0.07). R2 and p-values from applying envfit with 999 permutations to the 

12
nMDS ordination (bacterial community structure) and fitted environmental variables are listed in 

Table 2. Depth, chl-a, δ15N, C/N, TOC, and salinity are significantly correlated, i.e., explain

some portion (R2

) of the variation in bacterial community structure across the Beaufort Sea and 

Amundsen Gulf benthos. Variables that are significantly (p<0.05) correlated with bacterial 

community structure explain relatively low percentages of variance (10–42%). The output from 

envfit, nMDS axes, and associated R2 and p-values were plotted with the nMDS ordination with 

the lowest stress (Figure 7). This plot indicates that the significant correlates of bacterial 

community structure are not equally affecting all samples in the study area. Depth, salinity, and 

δ15N have maximum correlations with samples on the left side of the nMDS, while chl-a, C/N, 

and TOC exhibited maximum correlations with samples on the right side of the nMDS.

Table 2. Correlations between environmental variables and bacterial community structure. Significant

correlation values are bold. 

13
Figure 7. Correlation between environmental variables and bacterial community structure in nMDS 

ordination space with fitted vectors. Environmental vectors that are stronger predictors have longer 

arrows than weaker predictors. Arrow directions indicate the area (refer to Figure 6) in which the 

environmental vectors have maximum correlations and change most rapidly in ordination space (Oksanen 

et al., 2017)

Putative Oil-degraders

All of the potential oil-degrading taxa, except Microbulbifer, were present in low relative 

abundances within the study area (Figure 8). Alcanivoracaceae, Colwellia, and Shewanella were 

present at a majority of the 77 sights.

Figure 8. Putative oil-degrading taxa found in Beaufort Sea sediments. Each verticle stack of 14 blocks 

represents one sample.

sample

14
Discussion

The goals of this study were to identify large-scale patterns of bacterial diversity and community 

structure and to identify environmental correlates of community structure in a highly dynamic 

sector of the Arctic. This sector, from the western Beaufort Sea to the Amundsen Gulf, 

encompasses a vast array of varying oceanographic characteristics including bathymetry, 

hydrology, terrigenous input, and annual sea ice dynamics. The majority of oceanographic and 

biological studies from this sector in the Arctic are region-specific (i.e., the Alaskan Beaufort 

Shelf, the Mackenzie Shelf, and the Amundsen Gulf), in part, due to the distinct oceanographic 

qualities that characterize each of these regions. In light of this, results from this study are 

interpreted using both broad-scale patterns for the entire study area and regional patterns 

reported in the literature.

The presence of depth-associated water masses is a broad-scale characteristic of this Arctic 

sector. In general, a fresher Polar Mixed layer is found from ~0–50 m, a Pacific layer from ~50–

200 m, and a warmer, more saline Atlantic layer at depths greater than 200 m (Lansard et al., 

2012; Miquel et al., 2015; Smoot & Hopcroft, 2017). Salinity and temperature characteristics 

demarcate the depth-distribution of these water masses. It is likely that patterns of bacterial 

diversity and community structure reflect these characteristics as benthic bacterial diversity 

significantly increases with depth (Rs=0.41) and salinity (Rs=0.43) across the Beaufort Sea and 

the Amundsen Gulf. Additionally, bacterial community structure is significantly correlated with 

depth and salinity. Although depth and salinity are highly correlated (Rs=0.94), similar and even 

higher R-values of salinity over depth may indicate that both influence bacterial communities. 

Hamdan et al. (2013) found water masses, and salinity, in particular, to be a driving force in 

benthic bacterial and archaeal communities from Alaskan Beaufort Shelf sediments (Hamdan et 

al., 2013). Studies characterizing benthic macro-organisms from the Mackenzie Shelf to the 

Amundsen Gulf also found salinity to be significantly correlated with community structure 

(Cusson, Archambault, & Aitken, 2007; Darnis et al., 2012). 

Sediment δ15N, which is also positively correlated with depth and salinity (Rs~0.4), is 

significantly correlated with bacterial diversity (Rs=0.33) and community structure. Bulk δ15N 

sediment values generally increase with water depth and can be used as a proxy for alteration of 

nitrogenous compounds throughout the water column and in the benthos. It is not surprising that 

δ15N and bacterial community structure and diversity are significantly linked because bacteria

and archaea dominate nitrogen-related processes in marine sediments and are capable of 

denitrification, nitrification, nitrogen fixation, and annamox (Devol, 2015). The significant 

correlation between bacteria and δ15N highlights the importance of δ15N as a proxy over δ13C 

with respect to sediment bacteria on the scale of this study. This theory is supported by the lack 

of a significant correlation between δ13C and bacterial diversity or community structure. 

However, δ13C data exhibit a bimodal or even trimodal distribution, which may interfere with 

identifying it as a significant correlate in an area with such a large range of δ13C values.

δ13C was not significantly correlated with bacterial communities, but C/N and TOC were 

15
correlated. C/N is negatively correlated with bacterial diversity and significantly correlated with 

bacterial community structure. C/N reflects the degree of degradation that has occurred in 

organic matter and may be affected by similar processes that influence the δ15N of sediments, 

leading both variables to be significantly correlated with bacterial community structure. This is 

further supported by the relationship between C/N and diversity being negative. TOC is not 

significantly correlated with bacterial diversity but is significantly correlated with community 

structure. These findings, with respect to carbon, indicate that the source is not as important as 

the amount of carbon present in structuring bacterial communities. The “freshness" of organic 

carbon is also a potential driver in structuring bacterial communities, as there is a significant 

correlation between bacterial diversity and community structure with chl-a. Chl-a is negatively 

correlated with bacterial biodiversity. Chl-a/phaeopigments are also negatively correlated with 

bacterial diversity but are not significantly correlated to bacterial community structure. The 

findings with respect to correlations between diversity and sediment pigments are in contrast to 

findings reported from the Laptev Sea shelf edge and slope (Eurasian Arctic), which showed 

significant increases in bacterial diversity with increasing chl-a and phaeopigments (Bienhold et 

al., 2012b). 

An investigation of contaminant degradation potential is relevant because the study area is 

located in an environment where both natural gas/oil seeps and anthropogenic resource 

exploration occur. To that end, the presence and absence of putative oil-degrading taxa were 

investigated across all sediment samples. Contaminant degradation studies have shown that 

increased diversity of bacteria is linked to higher degradation potential of contaminants in situ 

and in the laboratory (e.g., Head et al., 2006; Röling et al., 2002). In addition, a study looking at 

how natural and anthropogenic oil exposure affects diversity found that small amounts of oil 

(natural levels) were correlated with increased diversity while increasing oil concentrations, as 

might be associated with an exposure event, corresponded in a decrease in bacterial diversity

(Orcutt et al., 2010), likely due to enrichment of oil-degrading taxa.

The presence of putative oil-degrading taxa, coupled with diversity estimates, indicates a 

capacity of oil biodegradation exists in these sediments. Putative oil-degrading bacteria generally

occur naturally in the marine system and are more prevalent in areas exposed to natural or 

anthropogenic inputs of oil and petroleum product. The putative oil-degrading bacteria

investigated here comprised a list of 13 genera and one family that have been identified through 

oil degradation studies in Arctic seawater (McFarlin et al., 2014; Prince et al., 2013). These taxa 

are not definitively linked with oil degradation but are capable of multiple modes of metabolism. 

They are implicated as playing a role in oil degradation because they have been found to increase 

significantly in relative abundance following oil exposure. Three taxa exhibited an almost 

ubiquitous distribution across the region: Alcanivoracaceae, Colwellia, and Shewanella. 

Members of the Alcanivoracaceae family are widespread in marine environments and include 

species that are directly linked to oil biodegradation (Silveira & Thompson, 2014). Colwellia are

also commonly found in marine environments, though many of the species within this genus are 

psychrophilic and found more commonly in polar regions (Bowman et al., 1998). The genus 

16
Shewanella is commonly found in low temperature and high-pressure (deeper) marine systems. 

This genus is highly metabolically versatile and has been identified in the biodegradation of 

heavy metals, such as iron and uranium, in addition to oil (Tiedje, 2002). Microbulbifer was the

only taxa absent from all sites. The remaining ten taxa were sporadically distributed with more 

taxa present on the Alaska Beaufort Shelf and the Amundsen Gulf than on the Mackenzie Shelf. 

This overview of the broad-scale patterns exhibited by benthic bacteria from the western 

Beaufort Sea to the Amundsen Gulf provides a solid foundation for additional analyses, with this 

and similar datasets, and can inform future field and laboratory research. Additional investigation 

of the environmental variables presented here, by region (i.e., the Alaskan Beaufort Shelf, 

Mackenzie Shelf, and the Amundsen Gulf), would provide insight into the processes shaping 

bacterial communities on a regional scale. Furthermore, this study highlights the need to explore 

additional environmental correlates of bacterial communities in Arctic sediments. 

Acknowledgments

I would like to sincerely thank CMI and BOEM for providing students with the opportunity to 

follow their research passions and participate as principal investigators. A huge thank you to my 

advisors Dr. Sarah Hardy and Dr. Mary Beth Leigh for all of their help and guidance throughout 

this process and for the matched funding that they provided. I would like to extend my gratitude 

to Dr. Eric Collins and the UAF Research Computing Systems for providing me with matching

funds, valuable assistance, and a platform on which to analyze my data. This work was supported 

in part by the high-performance computing and data storage resources operated by the Research 

Computing Systems Group at the University of Alaska Fairbanks, Geophysical Institute and 

BOEM under (M16AC00004).

References

Apprill, A., McNally, S., Parsons, R., & Weber, L. (2015). Minor revision to V4 region SSU 

rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquatic 

Microbial Ecology, 75(2), 129–137. doi.org/10.3354/ame01753

Arndt, S., Jørgensen, B. B., Larowe, D. E., Middelburg, J. J., Pancost, R. D., & Regnier, P. 

(2013). Earth-Science Reviews Quantifying the degradation of organic matter in marine 

sediments: A review and synthesis. Earth Science Reviews, 123, 53–86. 

doi.org/10.1016/j.earscirev.2013.02.008

Bienhold, C., Boetius, A., & Ramette, A. (2012a). The energy-diversity relationship of complex 

bacterial communities in Arctic deep-sea sediments. The ISME Journal, 6(4), 724–32. 

doi.org/10.1038/ismej.2011.140

Bienhold, C., Boetius, A., & Ramette, A. (2012b). The energy–diversity relationship of complex 

bacterial communities in Arctic deep-sea sediments. The ISME Journal, 6(4), 724–732. 

doi.org/10.1038/ismej.2011.140

17
Bowman, J. P., Gosink, J. J., McCammon, S. A., Lewis, T. E., Nichols, D. S., Nichols, P. D., 

Skerratt, J. H., Staley, J. T., & McMeekin, T. A. (1998). Colwellia demingiae sp. nov., 

Colwellia hornerae sp. nov., Colwellia rossensis sp. nov. and Colwellia psychrotropica sp. 

nov.: psychrophilic Antarctic species with the ability to synthesize docosahexaenoic acid. 

International Journal of Systematic Bacteriology, 48(4), 1171–1180. 

doi.org/10.1099/00207713-48-4-1171

Bureau of Ocean Energy Management (BOEM). (2012). Arctic Ocean Oil and Gas Lease 

Planning. Retrieved October 2017 from 

http://www.arcgis.com/home/item.html?id=ec25cf340e8b42bda6cb03c9aa512c8a

Buttigieg, P. L., & Ramette, A. (2015). Biogeographic patterns of bacterial microdiversity in 

Arctic deep-sea sediments (HAUSGARTEN, Fram Strait). Frontiers in Microbiology, 

5:660. doi.org/10.3389/fmicb.2014.00660

Conservation of Arctic Flora and Fauna/United States Geological Survey (CAFF/USGS). (2017). 

Circumpolar distribution and probability of potential petroleum reserves in the North. 

Retrieved August 11, 2017, from 

http://geo.abds.is/geonetwork/srv/eng/catalog.search#/metadata/8b1233d0-96cb-476b-a8c6-

7c3609f7e8a5

Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Lozupone, C. A., Turnbaugh, P. 

J., Fierer, N., & Knight, R. (2011). Global patterns of 16S rRNA diversity at a depth of 

millions of sequences per sample. Proceedings of the National Academy of Sciences, 

108(Supplement_1), 4516–4522. doi.org/10.1073/pnas.1000080107

Caporaso, J. G., Lauber, C. L., Walters, W. A , Berg-Lyons, D., Huntley, J., Fierer, N., Owens,

S. M., Betley, J., Fraser, L., Bauer, M., Gormley, N. Gilbert, J. A., Smith, G.,

 & Knight, R. (2012). Ultra-high-throughput microbial community analysis on the Illumina 

HiSeq and MiSeq platforms. The ISME Journal, 6(8), 1621–1624.

doi.org/10.1038/ismej.2012.8

Carmack, E. C., & Macdonald, R. W. (2002). Oceanography of the Canadian Shelf of the 

Beaufort Sea: A Setting for Marine Life. Arctic, 55(5), 29–45. doi.org/10.14430/arctic733

Clarke, K. R. (1988). Non-parametric multivariate analyses of changes in community structure. 

Australian Journal of Ecology, 18(1988), 117–143. doi.org/10.1111/j.1442-

9993.1993.tb00438.x

Cock, P. J. A., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., Friedberg, I.,

Hamelryck, T., Kauff, F., Wilczynski, B., & de Hoon, M. J. L. (2009). Biopython: freely 

available Python tools for computational molecular biology and bioinformatics. 

Bioinformatics, 25(11), 1422–1423. doi.org/10.1093/bioinformatics/btp163

Cusson, M., Archambault, P., & Aitken, A. (2007). Diversity of benthic assemblages on the 

Arctic continental shelf: Historical data from Canada. Marine Ecology Progress Series, 331, 

291–304. doi.org/10.3354/meps331291

18
Darnis, G., Robert, D., Pomerleau, C., Link, H., Archambault, P., Nelson, R. J., Geoffroy, M., 

Tremblay J.-E., Lovejoy, C., Ferguson, S. H., Hunt, B. P. V., & Fortier, L. (2012). Current 

state and trends in Canadian Arctic marine ecosystems: II. Heterotrophic food web, pelagicbenthic coupling, and diversity. Climatic Change, 115(1), 179–205. 

doi.org/10.1007/s10584-012-0483-8 

Dell’Anno, A., Beolchini, F., Rocchetti, L., Luna, G. M., & Danovaro, R. (2012). High bacterial 

diversity increases degradation performance of hydrocarbons during bioremediation of 

contaminated harbor marine sediments. Environmental Pollution, 167, 85–92. 

doi.org/10.1016/j.envpol.2012.03.043

Deming, J. W., & Baross, J. A. (1993). The early diagenesis of organic matter: bacterial activity. 

In Engel, M. & Mackel, S. (eds.), Organic Geochemistry. Plenum Press, New York.

Devol, A. H. (2015). Denitrification, Anammox, and N2 Production in Marine Sediments. 

Annual Review of Marine Science, 7(1), 403–423. doi.org/10.1146/annurev-marine010213-135040

Dunton, K. H., Weingartner, T., & Carmack, E. C. (2006). The nearshore western Beaufort Sea 

ecosystem: Circulation and importance of terrestrial carbon in arctic coastal food webs. 

Progress in Oceanography, 71, 362–378. doi.org/10.1016/j.pocean.2006.09.011

Forest, A., Bélanger, S., Sampei, M., Sasaki, H., Lalande, C., & Fortier, L. (2010). Three-year 

assessment of particulate organic carbon fluxes in Amundsen Gulf (Beaufort Sea): Satellite 

observations and sediment trap measurements. Deep-Sea Research Part I: Oceanographic 

Research Papers, 57(1), 125–142. doi.org/10.1016/j.dsr.2009.10.002

Fuhrman, J. A. (2009). Microbial community structure and its functional implications. Nature, 

459(7244), 193–199. doi.org/10.1038/nature08058

Gamboa, A., Montero-Serrano, J.-C., St-Onge, G., Rochon, A., & Desiage, P.-A. (2017). 

Mineralogical, geochemical, and magnetic signatures of surface sediments from the 

Canadian Beaufort Shelf and Amundsen Gulf (Canadian Arctic). Geochemistry, 

Geophysics, Geosystems, 18(2), 488–512. doi.org/10.1002/2016GC006477

Glöckner, F. O., Yilmaz, P., Quast, C., Gerken, J., Beccati, A., Ciuprina, A., Bruns, G., Yarza, 

P., Peplies, J., Westram, R., & Ludwig, W. (2017). 25 years of serving the community with 

ribosomal RNA gene reference databases and tools. Journal of Biotechnology, 261, 169–

176. doi.org/10.1016/j.jbiotec.2017.06.1198

Hamdan, L. J., Coffin, R. B., Sikaroodi, M., Greinert, J., Treude, T., & Gillevet, P. M. (2013). 

Ocean currents shape the microbiome of Arctic marine sediments. The ISME Journal, 7(4), 

685–696. doi.org/10.1038/ismej.2012.143

Head, I. M., Jones, D. M., & Röling, W. F. M. (2006). Marine microorganisms make a meal of 

oil. Nature Reviews Microbiology, 4(3), 173–182. doi.org/10.1038/nrmicro1348

Jacob, M., Soltwedel, T., Boetius, A., & Ramette, A. (2013). Biogeography of Deep-sea benthic 

bacteria at a regional scale (LTER HAUSGARTEN, Fram Strait, Arctic). PLoS ONE, 8(9), 

e72779. doi.org/10.1371/journal.pone.0072779

19
Judd, K. E., Crump, B. C., & Kling, G. W. (2006). Variation in dissolved organic matter controls 

bacterial production and community composition. Ecology, 87(8), 2068–2079. 

doi.org/10.1890/0012-9658(2006)87[2068:VIDOMC]2.0.CO;2

Lansard, B., Mucci, A., Miller, L. A., Macdonald, R. W., & Gratton, Y. (2012). Seasonal 

variability of water mass distribution in the southeastern Beaufort Sea determined by total 

alkalinity and δ18O. Journal of Geophysical Research: Oceans, 117, C03003.

doi.org/10.1029/2011JC007299

Link, H., Chaillou, G., Forest, A., Piepenburg, D., & Archambault, P. (2013). Multivariate 

benthic ecosystem functioning in the Arctic-benthic fluxes explained by environmental 

parameters in the southeastern Beaufort Sea. Biogeosciences, 10, 5911–5929. 

doi.org/10.5194/bg-10-5911-2013

Lopez, G. R., & Levinton, J. S. (1987). Ecology of deposit-feeding animals in marine sediments. 

The Quarterly Review of Biology, 62(3), 235–260.

Magen, C., Chaillou, G., Crowe, S. A., Mucci, A., Sundby, B., Gao, A., Macabe, R., & Sasaki, 

H. (2010). Origin and fate of particulate organic matter in the southern Beaufort Sea - 

Amundsen Gulf region, Canadian Arctic. Estuarine, Coastal and Shelf Science, 86(1), 31–

41. doi.org/10.1016/j.ecss.2009.09.009

McFarlin, K. M., Prince, R. C., Perkins, R., & Leigh, M. B. (2014). Biodegradation of dispersed 

oil in Arctic seawater at -1°C. PLoS ONE, 9(1), 1–8. doi.org/10.1371/journal.pone.0084297

McKew, B. A., Coulon, F., Osborn, A. M., Timmis, K. N., & McGenity, T. J. (2007). 

Determining the identity and roles of oil‐metabolizing marine bacteria from the Thames 

estuary, UK. Environmental Microbiology, 9(1), 165–176.

Mincks, S. (2005). Persistence of labile organic matter and microbial biomass in Antarctic shelf 

sediments: evidence of a sediment food bank. Marine Ecology Progress Series, 300, 3–19. 

doi.org/10.2307/24869726

Miquel, J. C., Gasser, B., Martín, J., Marec, C., Babin, M., Fortier, L., & Forest, A. (2015). 

Downward particle flux and carbon export in the Beaufort Sea, Arctic Ocean; the role of 

zooplankton. Biogeosciences, 12(16), 5103–5117. doi.org/10.5194/bg-12-5103-2015

Morris, D. J., O’Connell, M. T., & Macko, S. A. (2015). Assessing the importance of terrestrial 

organic carbon in the Chukchi and Beaufort Seas. Estuarine, Coastal and Shelf Science, 

164, 28–38. doi.org/10.1016/j.ecss.2015.06.011

Morris, E. K., Caruso, T., Buscot, F., Fischer, M., Hancock, C., Maier, T. S., Meiners, T., 

Müller, C., Obermaier, E., Prati, D., Socher, S. A., Sonnemann, I.,Wäschke, N., Wubet, T., 

Wurst, S., & Rillig, M. C. (2014). Choosing and using diversity indices: insights for 

ecological applications from the German Diversity Exploratories. Ecology and Evolution, 

4(18), 3514–3524. doi.org/10.1002/ece3.1155

Oksanen, J. (2015). Multivariate Analysis of Ecological Communities in R: vegan tutorial. 

Retrieved from http://cc.oulu.fi/~jarioksa/opetus/metodi/vegantutor.pdf

20
Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., Mcglinn, D., & Oksanen, M. 

J. (2017). Title Community Ecology Package. Retrieved from 

https://github.com/vegandevs/vegan/issues

Orcutt, B. N., Joye, S. B., Kleindienst, S., Knittel, K., Ramette, A., Reitz, A., Samarkin, V., 

Treude, T., & Boetius, A. (2010). Impact of natural oil and higher hydrocarbons on 

microbial diversity, distribution, and activity in Gulf of Mexico cold-seep sediments. DeepSea Research Part II: Topical Studies in Oceanography, 57(21), 2008–2021. 

doi.org/10.1016/j.dsr2.2010.05.014

Ortega-Retuerta, E., Joux, F., Jeffrey, W. H., & Ghiglione, J. F. (2013). Spatial variability of 

particle-attached and free-living bacterial diversity in surface waters from the Mackenzie 

River to the Beaufort Sea (Canadian Arctic). Biogeosciences, 10, 2747–2759. 

doi.org/10.5194/bg-10-2747-2013

Parada, A. E., Needham, D. M., & Fuhrman, J. A. (2016). Every base matters: assessing small 

subunit rRNA primers for marine microbiomes with mock communities, time series and 

global field samples. Environmental Microbiology, 18(5), 1403–1414. 

doi.org/10.1111/1462-2920.13023

Pickart, R. S. (2004). Shelfbreak circulation in the Alaskan Beaufort Sea: Mean structure and 

variability. Journal of Geophysical Research C: Oceans, 109(4), 1–14. 

doi.org/10.1029/2003JC001912

Prince, R. C., McFarlin, K. M., Butler, J. D., Febbo, E. J., Wang, F. C. Y., & Nedwed, T. J. 

(2013). The primary biodegradation of dispersed crude oil in the sea. Chemosphere, 90(2), 

521–526. doi.org/10.1016/j.chemosphere.2012.08.020 

Rachold, V., Eicken, H., Gordeev, V. V, Grigoriev, M. N., & Hubberten, H. (2004). The Organic 

Carbon Cycle in the Arctic Ocean. doi.org/10.1007/978-3-642-18912-8 

R Core Team. (2017). R: A language and environment for statistical computing. Vienna, Austria: 

R Foundation for Statistical Computing. Retrieved from https://www.r-project.org/

Robinson, R. S., Kienast, M., Albuquerque, A., Altabet, M., Contreras, S., De Pol Holz, R., & 

Yang, J. -Y. (2012). A review of nitrogen isotopic alteration in marine sediments. 

Paleoceanography, 27(4). doi.org/10.1029/2012PA002321

Röling, W. F. M., Milner, M. G., Jones, D. M., Lee, K., Daniel, F., Swannell, R. J. P., & Head, I. 

M. (2002). Robust hydrocarbon degradation and dynamics of bacterial communities during 

nutrient-enhanced oil spill bioremediation. Applied and Environmental Microbiology, 

68(11), 5537–5548.

Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. 

B.,Lesniewski, R. A., Brian B. Oakley, B. B., Parks, D., Robinson, C. J., Sahl, J. W.,

Stres, B., Thallinger, G., Van Horn, D. J., & Weber, C. F. (2009). Introducing mothur: 

Open-source, platform-independent, community-supported software for describing and 

comparing microbial communities. Applied and Environmental Microbiology, 75(23), 

7537–7541. doi.org/10.1128/AEM.01541-09

21
Silveira, C. B., & Thompson, F. (2014). The Family Alcanivoraceae. In: Rosenberg, E., 

DeLong, E.F., Lory, S., Stackebrandt, E., & Thompson, F. (eds), The Prokaryotes. 

Springer, Berlin, Heidelberg. doi.org/10.1007/978-3-642-38922-1_369

Smoot, C. A., & Hopcroft, R. R. (2017). Depth-stratified community structure of Beaufort Sea 

slope zooplankton and its relations to water masses. Journal of Plankton Research, 39(1), 

79–91. doi.org/10.1093/plankt/fbw087

Stokes, C. R., Clark, C. D., & Winsborrow, M. C. M. (2006). Subglacial bedform evidence for a 

major palaeo-ice stream and its retreat phases in Amundsen Gulf, Canadian Arctic 

Archipelago. Journal of Quaternary Science, 21(4), 399–412. doi.org/10.1002/jqs.991 

Tesdal, J.-E., Galbraith, E. D., & Kienast, M. (2013). Nitrogen isotopes in bulk marine sediment: 

linking seafloor observations with subseafloor records. Biogeosciences, 10(1), 101–118. 

doi.org/10.5194/bg-10-101-2013

Teske, A., Durbin, A., Ziervogel, K., Cox, C., & Arnosti, C. (2011). Microbial community 

composition and function in permanently cold seawater and sediments from an Arctic fjord 

of Svalbard. Applied and Environmental Microbiology, 77(6), 2008–2018. 

doi.org/10.1128/AEM.01507-10

Tiedje, J. M. (2002). Shewanella—the environmentally versatile genome. Nature Biotechnology, 

20(11), 1093–1094. doi.org/10.1038/nbt1102-1093

Walters, W., Hyde, E. R., Berg-Lyons, D., Ackermann, G., Humphrey, G., Parada, A., & Knight, 

R. (2016). Improved bacterial 16S rRNA gene (V4 and V4-5) and fungal internal 

transcribed spacer marker gene primers for microbial community surveys. mSystems, 1(1), 

e00009-15. doi.org/10.1128/mSystems.00009-15

Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007). Naive Bayesian classifier for rapid 

assignment of rRNA sequences into the new bacterial taxonomy. Applied and 

Environmental Microbiology, 73(16), 5261–5267. doi.org/10.1128/AEM.00062-07

Yakimov, M. M., Timmis, K. N., & Golyshin, P. N. (2007). Obligate oil-degrading marine 

bacteria. Current Opinion in Biotechnology, 18(3), 257–266. 

doi.org/10.1016/j.copbio.2007.04.006

Zinger, L., Amaral-Zettler, L. A., Fuhrman, J. A., Horner-Devine, M. C., Huse, S. M., Welch, D. 

B. M., Martiny, J. B. H., Sogin, M., Boetius, A., & Ramette, A. (2011). Global patterns of 

bacterial beta-diversity in seafloor and seawater ecosystems. PLoS ONE, 6(9), 1–11. 

doi.org/10.1371/journal.pone.0024570

22
Appendices

Table A1. Collection information for all sediment samples.

Sample Latitude Longitude Year Cruise Depth (m)

B2.50A 71.167 -151.100 2012 USTB 50

B1.200A 71.230 -150.084 2012 USTB 200

B1.200B 71.230 -150.084 2012 USTB 200

B1.200C 71.230 -150.084 2012 USTB 200

B1.500B 71.250 -150.084 2012 USTB 500

B1.1000 71.283 -150.084 2012 USTB 1000

A6.37 70.451 -146.102 2014 USTB 37

A6.20 70.274 -146.093 2014 USTB 20

A5.35 70.334 -145.110 2014 USTB 35

A5.20 70.119 -145.107 2014 USTB 20

A5.50 70.547 -145.081 2014 USTB 51

A5.100 70.715 -145.075 2014 USTB 101

A5.350 70.840 -145.062 2014 USTB 350

A4.100 70.582 -144.153 2014 USTB 107

A4.20 70.201 -144.100 2014 USTB 20

A4.50 70.462 -144.082 2014 USTB 54

A4.35 70.290 -144.072 2014 USTB 37

A2.20 69.979 -142.216 2014 USTB 21.6

A2.50 70.303 -142.139 2014 USTB 52.5

A2.350 70.540 -142.077 2014 USTB 349

A2.100 70.476 -141.926 2014 USTB 109

A2.200 70.497 -141.902 2014 USTB 200

A1.200 70.366 -141.152 2014 USTB 201

A1.20 69.719 -141.139 2014 USTB 20

A1.02 70.333 -141.117 2013 BREA 75

A1.100 70.333 -141.058 2014 USTB 99

A1.04 70.400 -141.050 2013 BREA 350

A1.350 70.406 -141.034 2014 USTB 350

A1.50 70.031 -141.032 2014 USTB 50

A1.06 70.533 -141.017 2013 BREA 750

TBS.350 70.342 -140.391 2014 USTB 347

TBS.04 70.334 -140.367 2013 BREA 350

TBS.200 70.270 -140.306 2014 USTB 203

TBS.100 70.246 -140.303 2014 USTB 101

GRY.06 70.433 -138.983 2013 BREA 750

GRY.04 70.250 -138.367 2013 BREA 350

DWT.01 70.584 -138.317 2013 BREA 1200

GRY.02 70.000 -137.667 2013 BREA 75

KUG.04 70.977 -134.786 2014 BREA 327

KUG.03 70.932 -134.745 2014 BREA 201

23
Sample Latitude Longitude Year Cruise Depth (m)

KUG.02 70.849 -134.668 2014 BREA 77.8

KUG.01 70.009 -133.844 2014 BREA 20.5

AGK.02 70.047 -133.560 2014 BREA 29.5

ESC.04 70.088 -132.139 2014 BREA 19

ESC.03 70.338 -131.005 2014 BREA 17

BNK.03 72.092 -129.974 2014 BREA 364

BNK.04 72.099 -128.817 2014 BREA 379

BNK.05 72.928 -127.791 2014 BREA 252

BNK.02 72.098 -127.481 2014 BREA 350

BNK.07 72.101 -127.143 2014 BREA 192

FRK.07 70.088 -126.919 2014 BREA 37

NTC.01 71.314 -126.883 2014 BREA 352

FRK.06 70.084 -126.768 2014 BREA 126

SMO.01 69.860 -126.743 2014 BREA 42

FRK.05 70.082 -126.426 2014 BREA 225

BNK.06 72.929 -126.396 2014 BREA 55

FRK.04 70.087 -126.044 2014 BREA 202

FRK.03 70.084 -125.861 2014 BREA 126

FRK.02 70.085 -125.420 2014 BREA 73

FRK.01 70.081 -125.199 2014 BREA 32

WIS.01 70.102 -125.067 2014 BREA 54

WIS.02 70.175 -124.832 2014 BREA 44

CPY.03 70.444 -124.522 2014 BREA 173

CPY.02 70.259 -124.509 2014 BREA 69.1

CPY.01 70.227 -124.494 2014 BREA 42.7

BPT.01 69.705 -123.821 2014 BREA 41

BPT.02 69.701 -123.785 2014 BREA 71

BPT.04 69.697 -123.239 2014 BREA 68.1

ALX.01 71.617 -120.334 2014 BREA 46.9

AMN.01 70.836 -119.772 2014 BREA 257

PWS.01 72.109 -119.433 2014 BREA 105.3

DOL.01 69.380 -118.764 2014 BREA 184.7

WLK.01 71.480 -118.692 2014 BREA 100

MNT.01 71.228 -118.373 2014 BREA 214.0

WLK.02 71.548 -118.260 2014 BREA 59.4

DOL.03 69.537 -118.220 2014 BREA 437

WLK.03 71.596 -118.008 2014 BREA 45

MNT.04 71.397 -117.191 2014 BREA 75

24
Table A2. R2 and p-values from non-transformed and transformed variables in linear regression models.

*A transformation for δ15N was investigated due to the close to significant p=value prior to

transformation. Linear regression models were used to investigate the correlation between variables. Both 

transformed and non-transformed data (Table A2) were used because linear regressions require a normal 

distribution of the resulting linear model, not necessarily the objects being investigated. Variables that 

were not transformed were treated as such, either because they were normally distributed (N) or an 

appropriate transformation wasn’t identified NTf (No Transformation) in Table A2. 

25
Table A3. Rs and p-values from Spearman’s Rho investigation of correlations between environmental 

variables. 

Note that “abs”= absolute value.

Rs Dep(m) Chl-a Phaeo Ch/Ph δ 15N absδ13C C/N TOC Temp Sal

Depth 1 -0.39 0.17 -0.76 0.42 -0.05 -0.52 -0.19 0.37 0.94

Chl-a -0.39 1 0.69 0.43 -0.36 0.49 0.18 0.37 0.03 -0.33

Phaeo 0.17 0.69 1 -0.3 -0.12 0.47 -0.13 0.15 0.13 0.2

Ch/Ph -0.76 0.43 -0.3 1 -0.25 0.02 0.48 0.34 -0.2 -0.7

δ15N 0.42 -0.36 -0.12 -0.25 1 -0.55 -0.4 -0.02 0.14 0.39

abs(δ13C) -0.05 0.49 0.47 0.02 -0.55 1 -0.2 0.36 0.15 0.02

C/N -0.52 0.18 -0.13 0.48 -0.4 -0.2 1 0.09 -0.31 -0.47

TOC -0.19 0.37 0.15 0.34 -0.02 0.36 0.09 1 0.01 -0.1

Temp 0.37 0.03 0.13 -0.2 0.14 0.15 -0.31 0.01 1 0.41

Salinity 0.94 -0.33 0.2 -0.7 0.39 0.02 -0.47 -0.1 0.41 1

pvaluesDep(m) Chl-a Phaeo Ch/Ph δ 15N absδ13C C/N TOC Temp Sal

Depth 0.00 0.14 0.00 0.00 0.68 0.00 0.09 0.00 0.00

Chl-a 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.80 0.00

Phaeo 0.14 0.00 0.01 0.28 0.00 0.26 0.19 0.28 0.09

Ch/Ph 0.00 0.00 0.01 0.03 0.85 0.00 0.00 0.08 0.00

δ15N 0.00 0.00 0.28 0.03 0.00 0.00 0.85 0.22 0.00

abs(δ13C) 0.68 0.00 0.00 0.85 0.00 0.08 0.00 0.21 0.85

C/N 0.00 0.11 0.26 0.00 0.00 0.08 0.43 0.01 0.00

TOC 0.09 0.00 0.19 0.00 0.85 0.00 0.43 0.91 0.37

Temp 0.00 0.80 0.28 0.08 0.22 0.21 0.01 0.91 0.00

Salinity 0.00 0.00 0.09 0.00 0.00 0.85 0.00 0.37 0.00

26
Chukchi-Beaufort Seas Storms and Their Influence on Surface Climate

Principal Investigator: Yang Yang

International Arctic Research Center

University of Alaska Fairbanks

Cooperative Agreement Number: M16AC00007

Period of Performance: 6/16-1/18

Contents 

List of Figures..................................................................................................................................... 28

Abstract ............................................................................................................................................... 29

Introduction......................................................................................................................................... 30

Methods............................................................................................................................................... 31

Results................................................................................................................................................. 33 

Storm Seasonality...........................................................................................................................33 

 Composite Analysis of Surface Climate ....................................................................................... 35 

Sea-level pressure (SLP)........................................................................................................ 35 

10 m wind ............................................................................................................................... 38 

2 m surface air temperature ................................................................................................... 41 

Sea surface temperature (SST) .............................................................................................. 44 

Sea ice concentration ............................................................................................................. 47 

Downward longwave radiation ............................................................................................. 49 

Downward shortwave radiation............................................................................................. 52 

Surface sensible heat influx................................................................................................... 55 

 Surface latent heat influx ....................................................................................................... 58 

Conclusions......................................................................................................................................... 61 

Acknowledgments.............................................................................................................................. 61 

References........................................................................................................................................... 61 

27
List of Figures 

Figure 1. Extent of CBHAR and HIRHAM domains.......................................................................31 

Figure 2. Storm track clusters of the S2NP and the W2E storms from 1979 to 2009....................32

Figure 3. Seasonal cycle of intensity, duration, and count for the W2E and S2NP storms from

1979 to 2009........................................................................................................................................ 34 

Figure 4. Seasonal cycle of intensity for the Pan-Arctic storms in the HIRHAM domain from 

1979 to 2014........................................................................................................................................ 34 

Figure 5. Climatological and W2E storm composite SLP, 2 m surface air temperature, and 10 m 

wind vector for August....................................................................................................................... 36 

Figure 6. Climatological and the S2NP storm composite SLP, 2 m surface air temperature, and 

10 m wind vector for August ............................................................................................................. 37 

Figure 7. Climatological and W2E storm composite wind speed ................................................... 39 

Figure 8. Climatological and S2NP storm composite wind speed ..................................................40

Figure 9. Climatological and W2E storm composite 2 m surface temperature.............................. 42 

Figure 10. Climatological and S2NP storm composite 2 m surface temperature........................... 43 

Figure 11. Climatological and W2E storm composite SST............................................................. 45 

Figure 12. Climatological and S2NP storm composite SST............................................................ 46 

Figure 13. Difference between the storm composite and climatological sea ice concentration for 

W2E and S2NP storms....................................................................................................................... 48 

Figure 14. Climatological and W2E storm composite downward longwave radiation ................50

Figure 15. Climatological and S2NP storm composite downward longwave radiation ................ 51 

Figure 16. Climatological and W2E storm composite downward shortwave radiation................. 53 

Figure 17. Climatological and S2NP storm composite downward shortwave radiation................ 54 

Figure 18. Climatological and W2E storm composite surface sensible heat flux .......................... 56 

Figure 19. Climatological and S2NP storm composite surface sensible heat flux......................... 57 

Figure 20. Climatological and W2E storm composite surface latent net heat flux ........................ 59 

Figure 21. Climatological and S2NP storm composite surface latent net heat flux.......................60

28
Abstract

The storms that enter the Chukchi Sea and western Beaufort Sea region originate predominantly

from the Pacific Ocean, possibly as far south as the sub-tropical Pacific region, or from the East

Siberian Sea region. These storms take two primary pathways into the Chukchi and western 

Beaufort Seas: south to north from the Pacific Ocean (S2NP storms) and west to east from the 

East Siberian Sea (W2E storms). These storm types have distinct characteristics and can produce

substantial local and regional changes in surface atmospheric and oceanic properties. In this 

study, the climatology of these two types of storms was analyzed using the Chukchi-Beaufort 

High-Resolution Atmospheric Reanalysis (CBHAR) dataset. The results show that the W2E and 

S2NP storms have the most prominent influences on surface climate and ocean properties in 

August and October, respectively, and that the S2NP storms are generally stronger than the W2E 

storms. The composite analysis of 2 m temperature fields, based on the number of S2NP storms, 

indicates a strong thermal contrast along the coast and suggests the occurrence of a baroclinic 

zone that supports storm development. The composite analysis of surface winds demonstrates 

that W2E storms change prevailing weak northeasterly winds to strong southerly winds over the 

Bering and Chukchi Seas and southerly surface winds occur on the northern coast of Alaska 

when the S2NP storms enter the study domain. The S2NP storms bring warm and moist air from 

the Pacific Ocean into the Arctic. In climatology, downward longwave and shortwave radiation 

at the surface are larger than surface turbulent heat fluxes. There are large differences in surface 

climate variables between the climatological mean and the composite analysis based on the 

number of storms. The composite analysis also indicates that both surface latent and sensible 

heat fluxes decrease when storms occur in the study area. It is suggested that storms can reduce 

sea ice concentration substantially in the ice areas through a combination of thermodynamic and 

dynamic factors. Sea ice retreat (northward) and sea ice advancement (southward) occur with 

southerly and northerly winds induced by the storms entering the study domain. Possible 

linkages were suggested between sea ice anomalies and frequency of the S2NP storms over the 

region.

29
Introduction

The Arctic has experienced significant environmental changes including rapidly decreased sea 

ice cover and considerably increased ocean and air temperatures (Comiso et al., 2008; Steele et 

al., 2008; Screen and Simmonds, 2010). Recent research has found an intensification of synoptic 

storm activity over the Arctic Ocean (e.g., Zhang et al., 2004; Serreze et al., 2008; Simmond et 

al., 2008; Woods and Caballero, 2016; Graham et al., 2017). As one outstanding example, a 

superstorm invaded the Beaufort-Chukchi Seas from eastern Siberia in August 2012, 

contributing to record low summer sea ice coverage (Simmonds and Rudeva, 2012). In 2016, the 

Korean icebreaker Araon observed an extreme storm moving from the East Siberian Sea into the 

Chukchi Sea, rapidly reducing the sea ice extent in that area. With diminishing sea ice cover and 

more open water, we will likely observe increased frequency and intensity of storms, changes in 

surface wind patterns, and increased surface wind speeds and precipitation. The communities

along the coast of Alaska would be subject to the threat of extreme weather events caused by 

intense storms, which increase the potential for coastal erosion and flooding. 

The spatial resolution in previous Arctic storm studies ranged from 50 km to 200 km, which may 

not resolve high-resolution geometrical features of storms. Little research has been undertaken 

using fine-resolution data to document the climatological characteristics of storms in the Chukchi 

and Beaufort Seas or to investigate how the storms influence surface climate. The ChukchiBeaufort High-Resolution Atmospheric Reanalysis (CBHAR) has horizontal resolutions of 10 

km for each hour from 1979 to 2009, which makes it possible to analyze high-resolution 

climatology features of synoptic storms and their influences on surface climate and ocean 

properties. 

Though the climatological wind direction in the Chukchi and Beaufort Seas region is easterly, 

storms propagate into the study domain moving south to north from the Pacific Ocean (S2NP) 

and west to east (W2E) from eastern Siberia. The S2NP storms are most likely to migrate up 

through the Bering Sea. In this study, we assessed how these two storm groups influenced the 

surface climate. This study aimed to document climatological features of the storms that travel 

along different pathways.

 

30
Methods

This study was based on the 31-year, 10-km horizontal, hourly Chukchi-Beaufort HighResolution Atmospheric Reanalysis (CBHAR) (Liu et al., 2014; Zhang et al., 2013). The 

Advanced Research Weather Research and Forecasting model (WRF-ARW; Skamarock et al. 

2008) was the main tool for generating this reanalysis dataset. CBHAR is a three-dimensional 

observationally constrained and thermodynamically and dynamically consistent gridded hindcast 

reanalysis. It covers the Chukchi and Beaufort Seas, the Arctic Slope of Alaska and the Brooks 

Range, the western part of the Canadian Yukon Territory, and the East Siberian Sea coastal area 

(Figure 1). The initial and lateral boundary conditions are from The European Centre for 

Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim; Dee et al. 2011). The 

high spatial resolution allows CBHAR to resolve fine scale synoptic storm processes and their 

impact on the surface variables and ocean properties. 

HIRHAM-NAOSIM is a regional coupled atmospheric-ocean-ice model that covers the Arctic 

region north of approximately 60°N (Rink et al., 2003, 2013) (Figure 1). HIRHAM is a regional 

atmospheric climate model based on a subset of the High-Resolution Limited Area Model

(HIRLAM; Unden et al. 2002) and the ECHAM climate model (Roeckner at al. 2003). HIRHAM 

is the atmospheric component of the coupled model and has a horizontal resolution of 50 km. 

The North Atlantic-Arctic Ocean Sea Ice Model (NAOSIM) is the ocean-ice component of the 

coupled model and has a horizontal resolution of approximately 25 km. We compared the 

CBHAR results with output from the HIRHAM-NAOSIM to show that characteristics of 

Chukchi Sea and Beaufort Sea storm climatology are rather different from the HIRHAMNAOSIM results. 

Figure 1. Extent of CBHAR (blue) and HIRHAM (red) domains.

31
We applied and refined the automatic storm identification and tracking algorithm developed by 

Zhang et al. (2004) using 6-hourly sea-level pressure (SLP) fields to track the storms. The 

criteria for identifying and tracking storms included the following:

(1) A storm center was identified if the SLP at the grid point was lower than all surrounding grid 

points within a 50 km radius using the CBHAR data.

(2) The minimum SLP between the center of the storm and every surrounding grid point was at 

least 0.01 hPa.

(3) Two adjacent storm centers appearing concurrently within 500 km of each other were 

identified as the same storm system with the lowest storm center as the center of the 

combined storm system.

(4) The lifetime of the storm was at least 12 hours, and each storm track was at least 100 km 

long. The latter criteria eliminated minor disturbances generated by orographic effects. 

Using the criteria above, we identified 451 storms traveling into the Chukchi and Beaufort Seas 

from 1979 to 2009: 290 individual storms as S2NP and 161 as W2E. Figure 2 depicts the 

composite tracks associated with the S2NP storms and the W2E storms. 

(a) (b)

Figure 2. Storm track clusters of (a) the S2NP and (b) the W2E storms from 1979 to 2009. The blue lines 

are individual storm paths.

To investigate the characteristics of storms moving into the Chukchi and Beaufort Seas from 

these two pathways, we performed a composite analysis for each set of the S2NP and W2E 

storms. Using CBHAR data, we calculated the average SLP fields for each storm and used that 

data to calculate the average for the S2NP and W2E storms, respectively, for the period 1979–

2009. 

Storm intensity, duration, and track number were calculated. The storm intensity was obtained by 

calculating the mean absolute values of the difference between the central SLP of the storm and 

the climatological monthly mean SLP at the corresponding grid points over the cyclone duration. 

Intensities of storms in the month were averaged to get monthly mean absolute values. The storm 

32
track count was the number of storms moving in a particular geographical region in a month. The 

storm duration was the length of time between the first appearance of the storm and its 

subsequent disappearance. The average storm duration was obtained by averaging the storm 

durations within each region.

Climatologically, Arctic storms are not active in summer, and it is widely acknowledged that 

Arctic storms are typically more intense in winter. However, we found the S2NP and W2E 

storms were most dominant in October and August, respectively, so these months are the focus 

of this composite analyses performed for 2 m surface air temperatures, 10 m winds, sea surface 

temperature (SST), sea ice concentrations, and the following energy budget terms: downward 

longwave radiation, downward shortwave radiation, surface sensible heat flux, and surface latent 

heat flux. Composite analysis eliminates the details of individual storms from the dataset, leaving 

only the general features for storms along a specific path.

Results

Storm Seasonality

Significant differences exist in the seasonal cycle of the S2NP and W2E storms (Figure 3). W2E 

storms have a pronounced seasonal cycle. Over the 31-year period, the number of W2E storms 

averaged 34 and 2 for August and January, respectively, and storm intensity, duration, and track 

number all peaked in August. The lifetime of the W2E storms is longer in summer months and

shorter in the other seasons when compared to the S2NP storms. A seasonal cycle of storm 

duration and track numbers is not as evident for the S2NP storms. The number of the S2NP 

storms ranged between 20–30 per month and was relatively consistent throughout the year. 

Storm intensity was greater for the S2NP compared with the W2E storms throughout the year; 

however, S2NP storm intensities reflected only a moderate seasonal cycle, higher in winter and 

lower in summer, which is consistent with the Pan-Arctic storm intensity seasonal cycle from the 

HIRIHAM-NAOSIM output (Figure 4). The highest values in the S2NP storm intensity, 

duration, and track number all occurred in October.

33
Figure 3. Seasonal cycle of intensity, duration, and count for the W2E (blue line) and the S2NP (red line) 

storms from 1979 to 2009

Figure 4. Seasonal cycle of intensity for the Pan-Arctic storms in the HIRHAM domain from 1979 to 

2014.

34
Composite Analysis of Surface Climate

Sea-level pressure

The sea-level pressure (SLP) difference is notably large between the climatology and both storm 

composites. Climatologically, the SLP field is characterized by a weak high-pressure system 

over the Beaufort Sea near the Alaskan coast in August (Figure 5a). In contrast, the W2E storm 

composite SLP field is characterized by a low-pressure system near 73°/180°E in the East 

Siberian Sea, which produces a strong pressure gradient parallel to the coast favorable for 

northwesterly flows. Lower than normal pressure covers the western Beaufort Sea and the East 

Siberian Sea. Figure 5b reveals clear distinctions between the climatology and the S2NP storm 

composite SLP field in October. The main passage of the S2NP storms approaching the Chukchi 

and Beaufort Seas is through Bering Strait, where storm occurrences are persistent throughout 

the year (Figure 3). A closed contour of low pressure is evident in the Bering Strait in the S2NP 

storm composite SLP field, and the wind changes direction when storms occur. As shown in 

Figure 6, the easterly wind parallel to the Alaska coast changed to southerly offshore wind. This 

indicates that offshore flow is more frequent with S2NP storms passing through Bering Strait in 

October. Cyclonic flow over the East Siberian Sea is conducive for warm and moist air advection 

in the Bering Strait and possibly onshore over the Brooks Range. The wind resulting from the 

intense S2NP storms would be southwesterly through the Bering Strait and southerly along the 

Beaufort Sea coast, increasing open water near shore.

35
(a)

 

(b)

 

Figure 5. (a) Climatological and (b) W2E storm composite SLP (black contour), 2 m surface air 

temperature (color fill), and 10 m wind vector for August.

36
(a)

 

(b)

 

Figure 6. (a) Climatological and (b) S2NP storm composite SLP (black contour), 2 m surface air 

temperature (color fill) and 10 m wind vector for August.

37
10 m wind

Climatological and storm composites of the 10 m wind speed fields for the W2E storms are 

shown in Figure 7. Climatologically, the August wind is rather calm in the Chukchi and Beaufort 

Seas area (Figure 5a). W2E storms can bring strong southwesterly winds into the Chukchi and 

Bering Seas. This strong offshore flow blowing from the east Eurasian landmass can advect 

positive temperature anomalies, contributing to warming over the coastal area of the East 

Siberian and Bering Seas. The W2E storms can cause intense wind events in Bering Strait and 

the adjacent Chukchi Sea. Instead of a climatological weak northeasterly wind along the coast of 

the East Siberian Sea, Figure 7b shows a region of increased westerly flows along the coast and 

enhanced wind speeds. The difference between the climatological mean wind speed field and

that of the storm composite field is shown in Figure 7c. For both storm types, the maximum 10 m 

surface wind speed increase is found along the coast of the East Siberian Sea and in the Bering 

Strait. 

Climatological and storm composites of the 10 m wind speed fields for the S2NP storms and 

their difference are shown in Figure 8. The S2NP composite 10 m wind field is also quite 

different from the climatological wind field (Figure 6). The prevailing wind in October is 

easterly in Chukchi and Beaufort region. Strong easterly flows dominating the October Chukchi 

and Beaufort wind fields are no longer present in the S2NP storm composite wind field (Figure

8b). Instead, there are strong southerlies and south-westerlies in the regions blowing in the 

offshore direction. There is a moderate increase in wind speed in the East Siberian Sea and the 

Chukchi Sea and a decrease in wind speed in the Bering Strait. 

38
 (a)

 (b)

(c)

 

Figure 7. (a) Climatological and (b) W2E storm composite wind speed. (c) Difference between the W2E 

storm composite and climatological wind speed field.

39
 (a)

 (b)

(c)

 

Figure 8. (a) Climatological and (b) S2NP storm composite wind speed. (c) Difference between the S2NP

storm composite and climatological wind speed field.

40
2 m surface air temperature

The climatological 2 m surface air temperature and 10 m surface winds in August are shown in 

Figure 9a. The August 2 m surface air temperatures over the north part of Chukchi and Beaufort 

Seas are around the freezing point due to the melting of sea ice. There is sharp discontinuity of 2 

m temperature stretching from the Siberian coast across the Chukchi and Beaufort Seas due to 

differential heating of the atmosphere between the land and the ice-free ocean (Figure 9a). The 

large temperature gradient in August suggests the presence of a baroclinic zone along the coast. 

Migrating storms can bring temperature changes through horizontal temperature advection 

(Figure 9b). The dominant features for the storm composite 2 m surface temperature are

warming along the coastal areas of the East Siberian Sea and the Alaskan sector southern 

boundary of the Beaufort Sea and cooling over the Canadian sector southern Beaufort Sea. The 

August offshore southerlies yield warmer than climatological temperatures over the northeastern 

East Siberian Sea, southern Chukchi Sea, and the Bering Sea. The difference between the 

climatological and storm composite 2 m surface temperature field is relatively small (Figure 9c). 

Climatologically, easterly winds in October are associated with cold air advection over the 

coastal area of northern Alaska. Onshore flow tends to advect cool air to land, and cold air 

advection is dominant along the Alaskan Beaufort Sea coastal area. The land is colder than the 

open water area along the Beaufort coast, and the North Slope area is dominated by temperatures 

below -10°C (Figure 10a). Northeasterly winds advect cold air over the Beaufort Sea toward the 

Alaskan coastal region, yielding strong negative temperature anomalies along the coast. Figure

10b provides the S2NP storm October composite of the 2 m surface air temperature field with 10 

m wind vectors. There is a substantial increase of surface temperature in the North Slope region 

in comparison to climatological values. This indicates that the S2NP storms can advect warm air 

from the south into the North Slope regions. Prominent warm temperature anomalies are also 

present in Bering Strait. Most of the Beaufort Sea is characterized by warming of 2 m surface 

temperatures when S2NP storms occur (Figure 10b). There are smaller temperature contrasts 

between the land and ocean when the S2NP storms bring southerly flow over the Brooks Range. 

There is a clear correspondence between the area of negative temperature anomalies associated 

with northerly winds in the East Siberian Sea. Temperature increases caused by the October 

S2NP storms along the Beaufort coast are over 2.5°C higher than the increases brought by the 

W2E storms in August (Figure 10c).

41
 (a)

 (b)

(c)

Figure 9. (a) Climatological and (b) W2E storm composite 2 m surface temperature. Black arrows 

indicate wind vectors. (c) Difference between the W2E storm composite and climatological 2 m surface 

temperature. 

42
 (a)

 (b)

(c)

Figure 10. (a) Climatological and (b) S2NP storm composite 2 m surface temperature. Black arrows 

indicate wind vectors. (c) Difference between the S2NP storm composite and climatological 2 m surface 

temperature. 

43
Sea surface temperature 

In general, the pattern for the climatological sea surface temperature (SST) field is similar to that 

for the storm composite SST field (Figures 11a and 11b, respectively). A common feature of the 

climatological and storm composite SST fields is the warm water in Bering Strait and the sharp 

SST gradient in the Bering Sea in August. During the W2E storm events, slight increases of SST 

are observed along the Beaufort coast of Alaska and eastern Siberia coast (Figure 11c). The SST 

change in the Bering Sea is very small, even though this region corresponds to wind speed 

increases and wind direction changes when the W2E storms occur. Similar to the W2E storms, 

the S2NP storm composite SST fields resemble that of the October climatological pattern (Figure 

12b and 12a, respectively) in that the highest SST is centered in the Bering Strait with a large 

SST gradient present. In contrast to the W2E, the SST decreases along the coast of the East 

Siberian Sea in the S2NP storm scenario (Figure 12c). 

44
 (a)

 (b)

(c)

Figure 11. (a) Climatological and (b) W2E storm composite SST. (c) Difference between the W2E storm 

composite and climatological SST. 

45
 (a)

 (b)

(c)

 

Figure 12. (a) Climatological and (b) S2NP storm composite SST. (c) Difference between the S2NP storm 

composite and climatological SST. 

46
Sea ice concentration

Changes in temperature, wind speed, and wind direction caused by storms can have direct 

impacts on the changes of sea ice. Serreze et al. (1995) proposed linkages between large 

anomalies in summer sea ice extent and a high frequency of cyclone activity. In this section, we 

will examine the change of sea ice concentration when different storms occur. 

The southern Beaufort Sea coastal areas are normally ice-free in August, but northern areas in 

deeper water and off the shelf are consistently covered by sea ice. This has obviously changed 

over the last ten years. When the W2E storms dominate the Chukchi Sea in August, the 10% sea 

ice concentration contour slightly advances southward near the coast of the East Siberian Sea and 

retreats northward in the eastern Chukchi Sea (Figure 13a), which corresponds to the northerly 

and southerly wind vectors of the W2E storms (Figure 9b). Wendler (1973) showed that sea ice 

conditions depend on the surface wind direction. Strong southwesterly wind can bring the warm 

continental air to the East Siberian Sea and the Chukchi Sea and melt the sea ice. In early 

October, the sea ice begins to freeze up. There is a sharp decrease of sea ice when the S2NP 

storms progress into the Chukchi and Beaufort Seas area. These decreases are greatest along the 

Chukchi Sea coast of Alaska and in the eastern Bering Sea (Figure 13b). The pronounced 

October sea ice concentration reduction is located within 100 km offshore of the Alaska Coast in 

the eastern Chukchi Sea. This area favors polynyas and thin ice, which can be pushed away from 

the coast by the southerly to southeasterly offshore winds of the S2NP storms even though the 

prevailing climatological wind direction in this area is from the northeast (Figure 10a). There is 

no significant 2 m surface air temperature difference between the continent and the adjacent icecovered ocean (Figure 10b), which suggests wind, not temperature, is influencing sea ice 

concentration. The S2NP storms over the eastern Chukchi Sea promote warm and moist 

southerly flows that can enhance melting of sea ice and reduction of surface albedo. This may 

explain the retreat of the 10% sea ice concentration contour when the S2NP storms are present 

(Figure 13b). 

47
 (a)

 (b)

Figure 13. Difference between the storm composite and climatological sea ice concentration for (a)W2E 

and (b) S2NP storms. The black and red lines are the storm composite and climatological 10% sea ice 

concentration contour respectively.

48
Downward longwave radiation

We examined the atmospheric energy budget of the of the Chukchi and Beaufort Seas for

changes in downward longwave radiation linked to W2E and S2NP storm activity (Figures 14 

and 15, respectively). Downward longwave radiation is emitted by the atmosphere and is 

influenced by clouds and water vapor. All positive values are in the upward direction, from 

ocean to the atmosphere. The increase of downward longwave radiation at the surface is in 

response to the increase in cloud cover. Increased longwave radiation favors more radiative 

warming and, therefore, ice melt and heat uptake in the ocean. October is the transition time 

from summer to winter conditions. Large upward fluxes occur over open water and the coastal 

Chukchi and Beaufort Seas where thin ice is growing, with smaller upward fluxes occurring over 

the northern Beaufort Seas where thick sea ice is present. The increase of downward longwave 

radiation with the S2NP storms in October (Figure 15c) is twice that due to the W2E storms in 

August (Figure 14c). 

49
 (a)

 (b)

(c)

Figure 14. (a) Climatological and (b) W2E storm composite downward longwave radiation. (c) Difference 

between the W2E storm composite and climatological downward longwave radiation. 

50
 (a)

 (b)

(c)

 

Figure 15. (a) Climatological and (b) S2NP storm composite downward longwave radiation. (c) 

Difference between the S2NP storm composite and climatological downward longwave radiation. 

51
Downward shortwave radiation

We examined the atmospheric energy budget of the of the Chukchi and Beaufort Seas for

changes in downward shortwave radiation linked to W2E and S2NP storm activity (Figures 16 

and 17, respectively). Downward shortwave radiation decreases due to cloud cover associated 

with storms, reflecting more solar energy back to space (Figure 16a). The greatest reduction of 

downward shortwave radiation, caused by the W2E storms in August, covers most of the 

Chukchi Sea region (Figure 16c). Figure 17c illustrates a widespread decrease of downward 

shortwave radiation due to October S2NP storms, especially in the eastern Beaufort Sea. This 

area corresponds to the regions with peak values of downward longwave radiation (Figure 15c). 

There is also a substantial increase of downward shortwave radiation along the coast of the East 

Siberian Sea, possibly due to the cold air advection from the north clearing the sky over the 

region.

52
 (a)

 (b)

(c)

 

Figure 16. (a) Climatological and (b)W2E storm composite downward shortwave radiation. (c) Difference 

between the W2E storm composite and climatological downward shortwave radiation. 

53
 (a)

 (b)

(c)

Figure 17. (a) Climatological and (b)S2NP storm composite downward shortwave radiation. (c) 

Difference between the S2NP storm composite and climatological downward shortwave radiation. 

54
Surface sensible heat flux

The ocean transfers heat absorbed from both longwave and shortwave radiation into the 

atmosphere primarily by surface sensible and latent heat flux. Sensible heat is transported from 

warm to cold subjects. Sensible heat flux can be found when there are differences in temperature

between the atmosphere and the ocean surface below. The values of the surface sensible flux are 

usually much smaller than the radiation terms. Summertime absorption of solar radiation in open 

water can melt sea ice and increase the surface sensible heat content of the upper ocean. In 

August, the climatological surface heat flux is upward (positive) in the southern Chukchi Sea 

over open water areas south of the sea ice margin where low albedo can promote strong solar 

heating and increase the oceanic sensible heat (Figure 18a). When the W2E storms occur, the 

upward sensible heat flux decreases most along the western coast of Alaska, the eastern coast of 

East Siberian Sea, and in the southern Chukchi Sea (Figures 18b and 18c). This corresponds to 

the region with the strongest southwesterly offshore wind, which can advect warm air from the 

East Siberian Sea to the Chukchi Sea. In October, S2NP storms reduce surface sensible heat flux 

(Figure 19) by increasing the 2 m surface air temperature in the southeastern Chukchi Sea and 

the southern Beaufort Sea (Figure 10b). Decreases of the sensible heat flux in the Bering Strait 

and along the coast of the East Siberian Sea can be affected by the decrease of SST in that 

region. 

55
 (a)

 (b)

(c)

 

Figure 18. (a) Climatological and (b) W2E storm composite surface sensible heat flux. (c) Difference 

between the W2E storm composite and climatological surface sensible net heat flux. 

56
 (a)

 (b)

(c)

 

Figure 19. (a) Climatological and (b) S2NP storm composite surface sensible heat flux. (c) Difference 

between the S2NP storm composite and climatological surface sensible net heat flux. 

57
Surface latent heat flux

We examined changes in surface latent heat flux linked to W2E and S2NP storm activity

(Figures 20 and 21, respectively). In the Arctic, surface latent heat flux transfers of energy 

between ice and the atmosphere, i.e., when sea ice melts, latent heat is absorbed, and when sea 

ice forms, latent heat is released. Surface latent heat flux followed the same pattern as surface 

sensible heat flux; the maximum decrease caused by W2E storms occurred in August along the 

northwestern Alaska coast and East Siberian Sea coast (Figure 20c), and the maximum decrease 

caused by S2NP storms occurred in October along the Alaskan coast in the Chukchi and 

Beaufort Seas and in the Bering Strait (Figure 21c). 

58
 (a)

 (b)

(c)

 

Figure 20. (a) Climatological and (b) W2E storm composite surface latent net heat flux. (c) Difference 

between the W2E storm composite and climatological surface latent net heat flux.

59
 (a)

 (b)

(c)

 

Figure 21. (a) Climatological and (b) S2NP storm composite surface latent net heat flux. (c) Difference 

between the S2NP storm composite and climatological surface latent net heat flux.

60
Conclusions 

This study characterizes the climatology of the S2NP and the W2E storms and their impact on 

the ocean and regional climate. The W2E storms have a pronounced seasonal cycle with the 

highest intensity in August and lowest intensity in winter, while the S2NP storms occur more 

regularly throughout the year and reach the highest intensity in October. The S2NP storms in 

October are generally more intense than the W2E storms in August. Differences between the 

storm composite and climatological surface conditions are large and substantial. S2NP and W2E 

storms affect the Chukchi and Beaufort Seas surface climate. For example, the August W2E 

storms developed in the East Siberian Sea are associated with higher than normal southwesterly 

wind speed in the Bering Strait and the Chukchi Seas, which can advect warm continental air 

over the ocean and help melt the sea ice. The October S2NP storms can cause significant surface 

temperature increase over the eastern Chukchi Sea and change the prevailing easterly flow in the 

Beaufort Sea to southeasterly flow. The offshore flow associated with the S2NP winds can help 

push sea ice away from the coast and decrease the sea ice concentration. The W2E and S2MP 

storms both increase cloudiness and, accordingly, decreases in downward shortwave radiation

decreases and increases in downward longwave radiation. 

Acknowledgments

This study was funded by Coastal Marine Institute and Bureau of Ocean Energy Management, 

Alaska OCS Region. Special thanks go to Dr. Seth Danielson of College of Fisheries and Ocean 

Sciences at the University of Alaska Fairbanks for his insightful comments and review on this 

work. We also thank Dr. Jing Zhang of North Carolina A&T State University, who provided 

valuable information related to the CBHAR data. We would also like to express special thanks to 

Warren Horowitz, Project Officer, and Heather Crowley at BOEM for their excellent 

recommendations and feedback on the quarterly and final reports. We also thank and Guillermo 

Auad, who provided valuable recommendations and comments to the quarterly and final reports. 

References

Comiso, J. C., C. L. Parkinson, R. Gersten, and L. Stock (2008), Accelerated decline in the 

Arctic sea ice cover. Geophys. Res. Lett., 35, L01703. 

Dee, D. P., S. M. Uppala, A. J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. A. 

Balmaseda, G. Balsamo, P. Bauer, P. Bechtold, A. C. M. Beljaars, L. Van de Berg, J. 

Bidlot, N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S. B. 

Healy, H. Hersbach, E. V. Hólm, L. Isaksen, P. Kållberg, M. Köhler, M. Matricardi, A. P. 

McNally, B. M. Monge‐Sanz, J.‐J. Morcrette, B.‐K. Park, C. Peubey, P. De Rosnay, C. 

Tavolato, J.‐N Thépaut, and F. Vitart (2011), The ERA-Interim reanalysis: Configuration 

and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–

 597. doi:10.1002/qj.828

61
Graham, R. M., L. Cohen, A. A. Petty, L. N. Boisvert, A. Rinke, S. R. Hudson, M. Nicolaus, and 

M. A. Granskog (2017), Increasing frequency and duration of Arctic winter warming 

events. Geophys. Res. Lett., 44, 6974–6983.

Liu F., J. R. Krieger, and J. Zhang (2014), Toward Producing the Chukchi–Beaufort HighResolution Atmospheric Reanalysis (CBHAR) via the WRFDA Data Assimilation 

System. Mon. Wea. Rev., 142, 788–805. 

Roeckner, E; G. Bäuml, L. Bonaventura, R. Brokopf, M. Esch, M. Giorgetta, S. Hagemann, I. 

Kirchner, L. Kornblueh, E. Manzini, A. Rhodin, U. Schlese, U. Schulzweida, and A. 

Tompkins (2003), The atmospheric general circulation model ECHAM5. Part 1. Model 

description. Report no. 349, Max-Planck-Institut für Meteorologie (MPI-M). 

Rinke, A., R. Gerdes, K. Dethloff, T. Kandlbinder, M. Karcher, F. Kauker, S. Frickenhaus, C. 

Köberle, and W. Hiller (2003), A case study of the anomalous Arctic sea ice conditions

during 1990: Insights from coupled and uncoupled regional climate model simulations. 

J. Geophys. Res., 108(D9), 4275. doi:10.1029/2002JD003146.

Rinke, A., K. Dethloff, W. Dorn, D. Handorf, and J. C. Moore (2013), Simulated Arctic

atmospheric feedbacks associated with late summer sea ice anomalies. J. Geophys. Res.

Atmos., 118, 7698–7714. doi:10.1002/jgrd.50584.

Screen, J. A., and I. Simmonds (2010), The central role of diminishing sea ice in recent Arctic

temperature amplification. Nature., 464, 1334–1337. 

Serreze, M. C. (1995), Climatological aspects of cyclone development and decay in the Arctic. 

Atmos.–Ocean., 33, 1–23. 

Serreze, M. C., and A. P. Barrett (2008), The summer cyclone maximum over the central Arctic 

Ocean. J. Climate., 21, 1048–1065. 

Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker. M. G. Duda, X.-Y Huang, 

W. Wang, and J. G. Powers (2008), A description of the Advanced Research WRF 

version 3. NCAR Tech Note NCAR/TN–4751STR, 113 pp. 

Simmonds, I., C. Burke, K. Keay (2008), Arctic climate change as manifest in cyclone behavior. 

J. Climate., 21, 5777–5796. 

Simmonds, I., and I. Rudeva (2012), The great Arctic cyclone of August 2012. Geophys. Res. 

Lett., 39, L23709. 

Steele, M., W. Ermold, and J. Zhang (2008), Arctic Ocean surface warming trends over the past 

100 years. Geophys. Res. Lett., 35, L02614. 

Undén, P; L. Rontu, H. Järvinen, P. Lynch, J. Calvo, G. Cats, J. Cuxart, K. Eerola, C. Fortelius,

J. A. Garcia-Moya, C. Jones, G. Lenderlink, A. McDonald, R. McGrath, B. Navascues, 

N. Woetman, V. Ødegaard, E. Rodrigues, M. Rummukainen, R. Rõõm, K. Sattler, 

B. Hansen-Sass, H. Savijärvi, B. Wichers-Schreur, R. Sigg, H. The, and A. Tijm (2002), 

 HIRLAM-5 Scientific Documentation. Scientific Report. http://hirlam.org. 

Wendler, G. (1973), Sea ice observation by means of satellite. J. Geophys. Res., 78(9), 1427–

1448. 

62
Woods, C. and R. Caballero (2016), The Role of Moist Intrusions in Winter Arctic Warming and 

Sea Ice Decline. J. Climate., 29, 4473–4485. 

Zhang, X., J. E. Walsh, J. Zhang, U. S. Bhatt, and M. Ikeda (2004), Climatology and interannual

variability of Arctic cyclone activity 1948–2002. J. Climate., 17, 2300– 2317. 

Zhang, X., J. Zhang, J. Krieger, M. Shulski, F. Liu, S. Stegall, W. Tao, J. You, and B. Potter, 

(2013), Beaufort and Chukchi Seas mesoscale meteorology modeling study. Final Project 

Report. Bureau of Ocean Energy Management, BOEM 2013-0119, 204 pp.

63
Using Genotyping-by-Sequencing (GBS) Population Genetics Approaches to

Determine the Population Structure of Tanner Crab (Chionoecetes bairdi) in

Alaska

Principal Investigator: Genevieve Johnson

College of Fisheries and Ocean Sciences

University of Alaska Fairbanks

Cooperative Agreement Number: M16AC00011

Period of Performance: 6/16-1/18

Contents 

List of Figures and Tables.................................................................................................................. 64

List of Tables ...................................................................................................................................... 64

Abstract ............................................................................................................................................... 65

Introduction......................................................................................................................................... 66

Objectives................................................................................................................................. 67 

Methods............................................................................................................................................... 67 

Sample Collection.................................................................................................................... 67

DNA Extraction, ddRAD Library Preparation, and Sequencing.......................................... 68 

Read Filtering, SNP Calling, and Genotype Assembly......................................................... 69 

Genetic Diversity and Population Genetic Analyses.............................................................70

Results................................................................................................................................................. 70

Discussion .......................................................................................................................................... 73 

Acknowledgments.............................................................................................................................. 74 

References........................................................................................................................................... 75 

List of Figures and Tables

Figure 1: Map of sampling regions....................................................................................................68

Figure 2: A representation of ddRAD sequencing method..............................................................69

Figure 3: Plot of locus coverage across individuals. ........................................................................69

Figure 4: Set 1 PCA plot of first and second principal components using the adgenet package in 

R ........................................................................................................................................................... 72

Figure 5: Set 2 PCA plot using the adgenet package in R ...............................................................72

Figure 6: Set 2 PCA plot, with C. opilio removed, using the adgenet package in R ..................... 73 

List of Tables

Table 1: Pairwise genetic distance calculations for Set 1 ................................................................71

Table 2: Pairwise genetic distance calculations for Set 2 ................................................................71

64
Abstract

Information on the extent of migration and structure of wild populations is an important

consideration in the development of natural resource management policies. Genomic studies can

increase our ability to the detect differences in population structure. Until recently, use of such

studies was limited by the genomic resources and databases available for a particular species.

However, novel and continuously refined sequencing technology offers the potential to

circumvent this barrier and provide access to genomic-scale datasets of genetic variation, even

for species without available reference genomes. This project focused on developing baseline

estimates of the genome-wide variation and population structure of Tanner crab (Chionoecetes

bairdi), a commercial species that is important to Alaska’s economy and society. The Tanner

crab is targeted in commercial, personal use, and subsistence fisheries in Alaska. Previously

strong crab fisheries in Prince William Sound, Kodiak, and the Bering Sea have faced major

uncertainties over the past few decades, including declines and mixed recoveries that may be

linked to environmental changes and both anthropogenic and biological factors. Here, we

attempted to apply genomic sequencing techniques to investigate the population structure of C.

bairdi in Alaska, focusing on the connectivity of Cook Inlet and Shelikof Strait populations with

populations in Southeast Alaska, Prince William Sound, Kodiak Island, the Alaska Peninsula, the

Aleutian Islands, and the Bering Sea shelf. Technical issues limited the sample sizes from

Southeast Alaska, Prince William Sound, and the Bering Sea shelf and restricted the

geographical regions that could be statistically compared. No significant distinction between

regions could be detected based on individuals’ genotypes through measurements of FST or by

Principal Components Analysis (PCA). We are continuing to test population structure models

and estimates of genome-wide variation and diversity.

65
Introduction

An understanding of population boundaries is an important consideration in the development of

natural resource management policies (Reiss et al. 2009; Webb & Woodby 2011). Determining

these boundaries for marine organisms can pose a special challenge, particularly for species that

disperse during pelagic larval life stages. Due to lack of observable physical boundaries and

obstacles to migration in marine environments, it was long assumed that marine populations

were largely homogenous (Caley et al., 1996). However, while marine populations may have less

variation than freshwater species, which have obvious population barriers, studies have shown

low-level variation and evidence of subpopulations in marine organisms (e.g., Ward et al., 1994;

Merkouris et al., 1998; Hutchinson et al., 2001; Reiss et al., 2009). Even minor levels of

differentiation can indicate a lack of connectivity between regions (Palumbi, 2003; Reiss et al.,

2009). Physical mechanisms including tidal currents, water stratification, shore topography,

wind, and other oceanographic features may affect larval dispersal and retention (Cowen & 

Sponaugle, 2009). In addition, biological constraints to successful larval propagation may also

exist.

Tanner crab (Chionoecetes bairdi) is important species in commercial, personal use, and

subsistence fisheries in Alaska. Tanner crab ranges from the southern Bering Sea across

Southcentral Alaska to Southeast Alaska. Stocks throughout the Gulf of Alaska are managed by

the Alaska Department of Fish and Game (ADF&G) (Otto & Pengilly 2002; Woodby et al. 2005)

and stocks in the Bering Sea are jointly managed by ADF&G and the National Marine Fisheries

Service (NMFS). The Bering Sea commercial fishery was once the most substantial crab fishery

in the state by landed weight. The Prince William Sound fishery historically comprised the

majority of shellfish harvested in the Gulf of Alaska region (NPFMC 2016; Trowbridge 1993).

Tanner crab abundance has faced major declines, probably linked to environmental change

among other factors (Armstrong et al. 1998; Zheng & Kruse 2006). Population recovery has been

variable and is not well understood. More information on the genetic structure of Alaskan Tanner

crab populations may help us understand the extent of migration and mixing across regions of

Alaska and explain some of the variability observed in recovery. It is important to know whether

Tanner crab stocks around Cook Inlet and Shelikof Strait (Bureau of Ocean Energy Management

[BOEM] study areas) are isolated and distinct or substantially connected to other populations

throughout Alaska. If the former is the case, management of offshore activities should address

the potential of impacting an isolated stock that would not be supplemented by other populations.

If the latter is the case, potential impacts from offshore activities might be mediated by recruits

entering affected areas from elsewhere. 

Genome sequencing tools are increasingly being applied to assess population structure and

connectivity in marine species and have detected more complex spatial structures than previously

identified (Jørstad, 2004; Hauser & Carvalho, 2008). Reduced-representation genome-wide scans

involve simultaneous discovery and genotyping of hundreds to thousands of single nucleotide

polymorphisms (SNPs). The datasets generated by these approaches are useful for resolving fine66
scale structure and detecting low levels of variation (Peterson et al. 2012; Rodrigues-Ezpeleta et

al., 2015). Double-digest restriction-associated DNA sequencing (ddRAD) is a reducedrepresentation sequencing approach that samples a subset of regions rather than sequencing all

regions of the entire genome. ddRAD sequencing provides a cost-effective way to analyze the

genomes of non-model organisms with little reference data because deep coverage of reads can

be obtained from genome regions adjacent to restriction-enzyme cut sites. Restriction-enzyme

digestion is based on occurrences of restriction-enzyme recognition sites that are relatively

consistent throughout the genomes of closely related individuals, so there is no primeridentification step required (Peterson et al. 2012). 

Here, we applied these techniques to investigate the population structure of C. bairdi in Alaska,

focusing on the connectivity of Cook Inlet and Shelikof Strait populations with populations in

Southeast Alaska, Prince William Sound, Kodiak Island, the Alaska Peninsula, the Aleutian

Islands, and the Bering Sea shelf. This study will increase our understanding of marine

organisms that could be affected by offshore development activities. Insight into population

genetic structure may be able to uncover multi-generational movement patterns that are not

otherwise distinguishable and may indicate why the stock abundances are so variable. 

Objectives

1. Test the null hypothesis of genetic homogeneity across all sampled populations

using an extensive multi-locus genotype dataset. If genetic heterogeneity is detected,

test alternative models of population structure.

2. Assess whether populations in BOEM special project areas of Cook Inlet and

Shelikof Strait are distinct or contiguous with nearby populations and assess the

extent of their natural genetic variability.

3. Test for evidence of extreme population size changes in the extent and nature of

observed standing genetic variation.

Methods

Sample Collection

From 2009–2016, a total of 1204 C. bairdi crabs were sampled over nine regions including

Southeast Alaska, Prince William Sound, Kachemak Bay, Shelikof Strait, Kodiak Island, the

Alaska Peninsula, the Aleutian Islands, the Bering Sea west of 166°W, and the Bering Sea east

of 166°W (Figure 1). Crabs were collected by pots or trawl during annual stock assessment

surveys conducted by NMFS and ADF&G. Samples of tissue (dactyl segment of walking leg) or

hemolymph (0.2 mL) were stored in 95–100% ethanol at -15°C. A subset of these samples (~28 

per region) was selected for DNA extraction and sequencing. Sampling efforts were not uniform

across regions, so each region was treated as a cluster and samples were selected systematically

to get approximately uniform coverage across each geographic region. An additional ten

Beaufort Sea C. opilio samples were sequenced to detect introgression and to use as an outgroup.

67
Figure 1. Map of sampling regions. Southeast Alaska (1), Prince William Sound (2), Kachemak Bay (3),

Shelikof Strait (4), Kodiak Island (5), Alaska Peninsula (6), Aleutian Islands (7), SE eastern Bering Sea

(8), and NW eastern Bering Sea (9).

DNA Extraction, ddRAD Library Preparation, and Sequencing

Total genomic DNA was extracted using the Gentra Puregene Tissue Kit by QIAGEN

(QIAGEN, Valencia, CA, USA). DNA extractions were assessed for quality (long fragment

lengths) by gel electrophoresis. DNA quantity and protein contamination were checked with a 

Qubit 2.0 fluorometer and a Nanodrop 1000 spectrophotometer, respectively. 

Samples were processed according to the double-digest restriction-associated DNA (ddRAD)

sequencing protocol developed by Peterson et al., 2012 (Figure 2). This method allocates

sequencing effort to a subset of restriction digest fragments and ensures that the same genomic

regions or loci are sequenced from the different individuals being genotyped. DNA extracts were

sent to Research and Testing Laboratory (RTL; Lubbock, TX, USA) and Admera Health (South

Plainfield, NJ, USA) for digestion, size selection, library preparation, and sequencing. Genomic

DNA from each sample was fragmented using a combination of restriction enzymes (EcoRI-HF

and MspI) to generate pools of variable length fragments flanked by the restriction-enzyme

recognition sites. The tagged genomic fragment pools were size selected at 350–400 bp, using a 

BluePippin (Sage Science, Beverly, MA, USA). The size-selected pools were then sequenced on

an Illumina Hi-Seq platform (Illumina Inc, San Diego, CA, USA) to generate >1 million

sequence reads from each individual. 

68
Figure 2. A representation of ddRAD sequencing method. 

Read Filtering, SNP Calling, and Genotype Assembly

Demultiplexed FASTQ files were run through FASTQC (Andrews, 2010) to assess overall

sequence quality and to obtain summary statistics. On average, the sequence quality dropped

below moderate thresholds for the last 20 bases of each read, so reads were trimmed by 20 bp

from the 3’ end during the second step of assembly in the ipyrad toolkit (Eaton, 2016). The

demultiplexed FASTQ reads were filtered, clustered, and aligned in ipyrad in order to call

variant sites and assemble a dataset of individual genotypes. A range of inputs for the parameters

of minimum read depth and clustering thresholds were tested to check for potential bias in SNP

calls. 

Figure 3. Plot of locus coverage across individuals. The x-axis represents all loci that were retained in the 

final genotype assembly. The y-axis represents each individual that was genotyped. Along the y-axis, the 

lower 100 individuals were part of the library preparation and sequencing serviced by RTL Genomics. 

The upper 170 individuals were part of the set of samples that were serviced by Admera Health. Very few

loci were genotyped across both groups.

69
Genetic Diversity and Population Genetic Analyses

Genotype assemblies were added using the vcfR package (Knaus & Grunwald, 2016). The

adegenet package (Jombart & Ahmed, 2011) was used to plot summary information for genotype

assemblies and perform Principal Components Analysis (PCA) on SNPs to identify potential

clustering patterns among individuals. We also used it to calculate heterozygosity within

individuals, subpopulations, and within the total population. FIS and FST were calculated

according to the Weir & Cockerham (1984) method implemented in hierfstat (Goudet, 2005). 

Results

Of the 100 DNA extraction samples that were processed and sequenced by RTL Genomics, 93

individual samples yielded >0.5 M reads. The ddRAD libraries from the seven individuals that

had <0.5 M were sequenced again but failed to meet the 0.5 M threshold and were dropped from

the analysis. After quality filtering, reads per individual in the final dataset ranged 605,824 – 

4,793,785 with an average of 1,897,727. On average, only 0.88% of reads were filtered out due

to low quality. 

Our overall dataset was compromised because very few loci were genotyped across all samples.

By looking at the locus coverage plot (Figure 3), it is apparent that there was a strong

discrepancy between loci that were recovered from the samples processed by RTL Genomics and

those processed by Admera Health. Due to uncontrollable circumstances, we were not able to

work with the same sequencing provider for all stages of this project. Although we specified

particular restriction enzymes and size-selection thresholds for each service provider, minor

differences in instruments or site-specific protocols may have led to this issue of locus coverage

bias. Since the datasets do not overlap, we analyzed each group separately. From this point on,

we refer to the samples that were processed by RTL Genomics as “Set 1.” Set 1 was composed

of 25 individuals from Southeast Alaska, 25 individuals from Prince William Sound, 25

individuals from the SE eastern Bering Sea, and 25 individuals from the NW eastern Bering Sea.

We refer to the samples processed by Admera Health as “Set 2.” Set 2 samples consisted of 28

individuals from Kachemak Bay, 28 individuals from Shelikof Strait, 28 individuals from the

Alaska Peninsula, 28 individuals from the Aleutian Islands, 28 individuals from Kodiak Island, 5 

individuals from the NW eastern Bering Sea, 4 individuals from the SE eastern Bering Sea, 7 

individuals from Prince William Sound, 4 individuals from Southeast Alaska, and 10 C. opilio

samples from the Beaufort Sea.

FST is a measure of variation within a subpopulation relative to the total. A value close to zero 

indicates high connectivity between regions or large populations with several migrants. As 

values approach one, they indicate more isolated populations, with less migration between the 

subpopulations. None of the populations included in Set 1 had large FST values, indicating that 

there is some connectivity between the regions (Table 1). 

70
All of the regions in Set 2 showed some differentiation from the C. opilio samples from the 

Beaufort, with the Bering Sea paired samples having the lowest FST values (Table 2). This is 

expected because there is hybridization between C. opilio and C. bairdi in the Bering Sea. It is 

important to note that the Set 2 sample sizes from Southeast Alaska, Prince William Sound, SE 

eastern Bering Sea, and NW eastern Bering Sea were not large enough to make accurate 

comparisons. All of the pairwise FST values between Set 2 regions with adequate sample sizes 

were close to zero, indicating substantial population connectivity. 

Table 2. Pairwise genetic distance calculations for Set 2, based on Weir & Cockerham FST (Weir & 

Cockerham, 1984).

Beaufort EBS_NW EBS_SE KBay Shelikof Kodiak AKpen Aleutian PWS

EBS_NW -0.009667613

EBS_SE 0.027705318 -0.18514276

KBay 0.469531293 0.286409066 0.24392811

Shelikof 0.467471239 0.282299457 0.23903579 -0.0026346198

Kodiak 0.467133515 0.283081511 0.24737442 0.0005767091 0.0021622383

AKpen 0.47297505 0.291525695 0.24680543 -0.0010437049 -0.0014327386 -0.0025865687

Aleutian 0.471789604 0.286322364 0.24465215 -0.0007037092 -0.0011546424 0.0019111874 -0.0001890493

PWS 0.41211025 0.208712148 0.20543012 0.0041447305 0.0044290488 0.0110118635 0.0042518539 -0.0010508976

SEAK 0.370109603 0.165464122 0.1714497 0.0020573475 0.0007170724 0.0034855329 0.0057633322 0.0043821051 0.009953875

PCA plots (Figures 4–6) demonstrate the lack of strong population genetic structure. The C. 

opilio samples showed greater diversity and did not cluster strongly with the C. bairdi samples. 

This indicates high gene flow or very low genetic diversity between our regions.

Table 1. Pairwise genetic distance calculations for Set 1, based on Weir & Cockerham FST (Weir &

Cockerham, 1984).

EBS_NW EBS_SE PWS

EBS_SE 0.0005085823

PWS 0.0058412348 0.0050166246

SEAK -0.0022553837 0.0058942658 0.0019089325

71
Figure 4. Set 1 PCA plot of first and second principal components using the adgenet (Jombart, 2008)

package in R. Points are colored according to collection region. There is no clear grouping, and no

patterns emerged amongst these regions based on this result.

Figure 5. Set 2 PCA plot using the adgenet (Jombart, 2008) package in R. Points are colored according to

collection region, and eclipses are drawn around groups. The Beaufort samples are C. opilio. We expect

them to cluster away from the C. bairdi samples. 

72
Figure 6. Set 2 PCA plot, with C. opilio removed, using the adgenet (Jombart, 2008) package in R. Points

are colored according to collection region, and eclipses are drawn around groups. There is no clear

grouping, and no patterns emerged amongst these regions based on this result.

Discussion 

Instances of bias in locus coverage across multiple library preparation events have been reported

(DaCosta & Sorenson, 2014) but not to this scale. The stark contrast in locus coverage across the

samples from this project (Figure 3) could be due to offsets in size-selection specificity or an

incorrect enzyme pair used in library preparation. We are following up with the service providers

to check for errors in protocols. 

When calling SNPs on datasets that contained samples from the Bering Sea, we observed a 

frequency distribution of SNPs-per-locus that was indicative of a recent mixing between two

historically isolated groups (Travis Glenn University of Georgia, personal communication).

These results are likely due to hybridization between C. opilio and C. bairdi in the Bering Sea

and introgressed C. opilio alleles observed in our Bering Sea samples. The frequency distribution

of SNPs-per-locus from a dataset of only Southeast Alaska samples does not produce this same

pattern, indicating that introgressed C. opilio alleles are not prevalent in Gulf of Alaska samples.

This is consistent with the introgression patterns noted by Merkouris et al. (1998). It also

supports the idea that population connectivity is closely tied with larval advection in the direction

of the Alaska Coastal Current and not against it. We plan to explore the extent of this

hybridization in greater detail by characterizing variants that are exclusively characteristic of

each species.

73
Pairwise FST estimates between regions indicated that there is gene flow. We are further

characterizing this connectivity by testing models of isolation by distance and comparing

directional migration estimates. We are testing other methods to investigate the genetic

population structure. Moving forward, we plan to use Bayesian methods in STRUCTURE

(Pritchard et al., 2003) to determine the number of subpopulations and perform assignment tests

on individuals. Thus far, we are not able to reject the null hypothesis of our first objective,

homogeneity across the populations, and there is not sufficient evidence of distinct populations

in the study areas to address our second objective. These results may change or be clarified once

we can pool together Set 1 and Set 2 data. The low levels of variation we observed could provide

support for our third hypothesis, historical bottleneck or selection events, but they have not been

statistically confirmed. Based on these initial analyses, the genome of C. bairdi is characterized

by low levels of variation, and there are no clear differences in variation between regions across

Alaska. 

These results are not consistent with existing estimates of low but significant genetic

differentiation among C. bairdi subpopulations detected with allozyme markers (Merkouris et

al., 1998); however, we made comparisons at a different spatial scale. We only compared genetic

diversity at the regional scale of our nine sampling regions, and our estimates suggest gene flow

between each pair of regions. We did not pool the data over a larger regional scale (i.e.,

Southeast Alaska, Gulf of Alaska, and Bering Sea) as Merkouris et al. (1998) did. We also had

an estimate of moderate to high gene flow between the northwest and southeast eastern Bering

Sea regions. This does not align with previous allozyme findings or current models of larval

advection patterns of C. bairdi in the eastern Bering Sea, which indicate some isolation in the

southeast region near Bristol Bay (Merkouris et al., 1998; Richar et al., 2015). Our observations

are more similar to the panmictic genetic population structure observed in C. opilio throughout

Alaska (Albrecht et al., 2012). Additional analyses should help us determine which framework 

best represents the Tanner crab genetic population structure in the Bering Sea.

Acknowledgments

This research was supported by the University of Alaska Coastal Marine Institute (CMI) with

funds from the Bureau of Ocean Energy Management (cooperative agreement M16AC00011). 

Funds were matched by the University of Alaska Fairbanks. Research reported in this publication

was supported by the National Institute of General Medical Sciences of the National Institutes of

Health under Award Numbers UL1GM118991, TL4GM118992, or RL5GM118990. The content

is solely the responsibility of the authors and does not necessarily represent the official views of

the National Institutes of Health. Collections for this project were supported by the Alaska

Department of Fish and Game (Permit No. CF-16-102) and the National Marine Fisheries

Service. The content is solely the responsibility of the authors and does not necessarily represent

the official views of the ADF&G or NMFS. Thesis committee members included J. Andrés 

López, Ph.D.; Sarah Hardy, PhD.; and Ginny Eckert, Ph.D.

74
References

Albrecht, G. T., Valentin, A. E., Hundertmark, K. J., & Hardy, S. M. (2014). Panmixia in

Alaskan populations of the snow crab Chionoecetes opilio (Malacostraca: Decapoda) in

the Bering, Chukchi, and Beaufort Seas. Journal of Crustacean Biology, 34(1), 31–39.

Andrews, S. (2010). FastQC: A quality control tool for high throughput sequence data.

Babraham Institute, Cambridge, UK.

Armstrong, J., Armstrong, D., & Hilborn, R. (1998). Crustacean resources are vulnerable to

serial depletion–the multifaceted decline of crab and shrimp fisheries in the Greater Gulf

of Alaska. Reviews in Fish Biology and Fisheries, 8(2), 117–176.

Caley, M. J., Carr, M. H., Hixon, M. A., Hughes, T. P., Jones, G. P., & Menge, B. A. (1996).

Recruitment and the local dynamics of open marine populations. Annual Review of

Ecology and Systematics, 477–500.

Cowen, R. K., & Sponaugle, S. (2009). Larval dispersal and marine population

connectivity. Annual Review of Marine Science V1, 443–466.

DaCosta, J. M., & Sorenson, M. D. (2014). Amplification biases and consistent recovery of loci

in a double-digest RAD-seq protocol. PLoS One, 9(9), e106713.

Eaton, D. (2016). ipyrad documentation. http://ipyrad.readthedocs.io/index.html

Goudet, J. (2005). Hierfstat, a package for R to compute and test hierarchical F‐statistics. 

Molecular Ecology Resources, 5(1), 184–186.

Hauser, L., & Carvalho, G. R. (2008). Paradigm shifts in marine fisheries genetics: ugly

hypotheses slain by beautiful facts. Fish and Fisheries, 9(4), 333–362.

Hutchinson, W. F., Carvalho, G. R., & Rogers, S. I. (2001). Marked genetic structuring in

localized spawning populations of cod Gadus morhua in the North Sea and adjoining

waters, as revealed by microsatellites. Marine Ecology Progress Series, 223, 251–260.

Jombart, T., & Ahmed, I. (2011). adegenet 1.3-1: new tools for the analysis of genome-wide

SNP data. Bioinformatics, 27(21), 3070–3071.

Jørstad, K. E. (2004). Evidence for two highly differentiated herring groups at Goose Bank in the

Barents Sea and the genetic relationship to Pacific herring, Clupea allasi. Environmental

Biology of Fishes, 69(1–4), 211–221.

Knaus, B. J., & Grunwald, N. J. (2016). VcfR: an R package to manipulate and visualize VCF 

format data. bioRxiv, 041277.

Merkouris, S. E., Seeb, L. W., & Murphy, M. C. (1998). Low levels of genetic diversity in

highly exploited populations of Alaskan Tanner crabs, Chionoecetes bairdi, and Alaskan

and Atlantic snow crabs, C. opilio. Fishery Bulletin: National Oceanic and Atmospheric

Administration, 96, 525–537.

North Pacific Fishery Management Council (NPFMC). (2016). 2016 Crab SAFE. Contributors:

The Plan Team for the King and Tanner Crab Fisheries of the Bering Sea and Aleutian

Islands, Bush, K., Dorn, M., Eckert, G., Foy, R.J., Garber-Yonts, B., Hamazaki, T.,

Ianelli, J. N., Punt, A.E., Rugolo, L., Siddeek, M.S.M., Stockhausen, W., Slater, L.,

Stram, D., Szuwalski, C., Turnock, B. J., Weber, D., & Zheng, J.

75
Otto, R. S., & Pengilly, D. (2002). Spatiotemporal trends in Tanner crab (Chionoecetes bairdi) 

size at maturity. Crabs in cold water regions: biology, management, and economics. A. 

J. Paul, E. G. Dawe, R. Elner, G. S. Jamieson, G. H. Kruse, R. S. Otto, B. Sainte-Marie,

T. C. Shirley, and D. Woodby (eds.). University of Alaska Sea Grant, AK-SG, 02-01. 

Palumbi, S. R. (2003). Population genetics, demographic connectivity, and the design of marine

reserves. Ecological Applications, 13(1) Supplement, S146–S158.

Peterson, B. K., Weber, J. N., Kay, E. H., Fisher, H. S., & Hoekstra, H. E. (2012). Double digest

RADseq: an inexpensive method for de novo SNP discovery and genotyping in model

and non-model species. PLoS One, 7(5), e37135.

Pritchard, J. K., Wen, W., & Falush, D. (2003). Documentation for structure software: version 2.

Reiss, H., Hoarau, G., Dickey‐Collas, M., & Wolff, W. J. (2009). Genetic population structure of

marine fish: mismatch between biological and fisheries management units. Fish and

Fisheries, 10(4), 361–395.

Richar, J. I., Kruse, G. H., Curchitser, E., & Hermann, A. J. (2015). Patterns in connectivity and

retention of simulated Tanner crab (Chionoecetes bairdi) larvae in the eastern Bering

Sea. Progress in Oceanography, 138, 475–485.

Rodríguez‐Ezpeleta, N., Bradbury, I. R., Mendibil, I., Álvarez, P., Cotano, U., & Irigoien, X. 

(2016). Population structure of Atlantic mackerel inferred from RAD‐seq‐derived SNP 

markers: effects of sequence clustering parameters and hierarchical SNP 

selection. Molecular Ecology Resources, 16(4), 991–1001. 

Trowbridge, C. E. (1993). Prince William Sound Management Area 1992 Shellfish Annual

Management Report. Alaska Division of Commercial Fisheries, Central Region.

Ward, R. D., Woodwark, M., & Skibinski, D. O. F. (1994). A comparison of genetic diversity

levels in marine, freshwater, and anadromous fishes. Journal of Fish Biology, 44(2), 

213–232.

Webb, J. B., & Woodby, D. (2011). Long-term Alaska crab research priorities, 2011. Alaska

Department of Fish and Game, Division of Commercial Fisheries, Regional Information

Report 5J11-04, Juneau, AK.

Weir, B. S., & Cockerham, C. C. (1984). Estimating F‐statistics for the analysis of population

structure. Evolution, 38(6), 1358–1370.

Woodby, D., Carlile, D., Siddeek, S., Funk, F., Clark, J. H., & Hulbert, L. (2005). Commercial

fisheries of Alaska. Alaska Department of Fish and Game, Special Publication, (05–09),

74 pp. 

Zheng, J., & Kruse, G. H. (2006). Recruitment variation of eastern Bering Sea crabs: Climateforcing or top-down effects? Progress in Oceanography, 68(2), 184–204.

76
As the Nation’s principal conservation agency, the Department of the Interior has 

responsibility for most of our nationally owned public lands and natural 

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providing for the enjoyment of life through outdoor recreation.The Department 

assesses our energy and mineral resources andworks to ensure that their 

development is in the best interests of all our people by encouraging stewardship 

and citizen participation in their care. The Department also has a major 

responsibility for American Indian reservation communities and for people who 

live in island communities.

The Bureau of Ocean Energy Management 

The Bureau of Ocean Energy Management (BOEM) works to manage the 

exploration and development of the nation's offshore resources in a way that 

appropriately balances economic development, energy independence, and 

environmental protection through oil and gas leases, renewable energy 

development and environmental reviews and studies.

The Department of the Interior Mission
As the Nation’s principal conservation agency, the Department of the Interior has 

responsibility for most of our nationally owned public lands and natural 

resources. This includes fostering the sound use of our land and water 

resources, protecting our fish, wildlife and biological diversity; preserving the 

environmental and cultural values of our national parks and historical places; and 

providing for the enjoyment of life through outdoor recreation.The Department 

assesses our energy and mineral resources andworks to ensure that their 

development is in the best interests of all our people by encouraging stewardship 

and citizen participation in their care. The Department also has a major 

responsibility for American Indian reservation communities and for people who 

live in island communities.

The Bureau of Ocean Energy Management 

The Bureau of Ocean Energy Management (BOEM) works to manage the 

exploration and development of the nation's offshore resources in a way that 

appropriately balances economic development, energy independence, and 

environmental protection through oil and gas leases, renewable energy 

development and environmental reviews and studies.

The Department of the Interior Mission