1

BOEM 2019-053

ANIMIDA III Boulder Patch and Other Kelp 

Communities in Development Area

Final Report

Prepared By:

Kenneth H. Dunton, Principal Investigator

University of Texas Marine Science Institute

750 Channel View Drive, Port Aransas, Texas 78373

e-mail of corresponding author: ken.dunton@utexas.edu

Authors:

Kenneth H. Dunton1, Christina E. Bonsell1, Katrin Iken2, Brenda Konar2, Arley F. Muth1, 

and Susan V. Schonberg1

1University of Texas Marine Science Institute

2University of Alaska Fairbanks

Prepared For:

Cathy Coon

Chief, Environmental Sciences Management

Alaska Office Bureau of Ocean Energy Management 

3801 Centerpoint Drive, Suite 500

Anchorage, Alaska 99503

Cooperative Agreement No. M12AC00007

15 January 2020
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Table of Contents

Acknowledgements...............................................................................................................................................3

Executive Summary ..............................................................................................................................................4

Background and Setting ..............................................................................................................................5

Directed Experimental and Observational Studies..........................................................................7

Background and Setting................................................................................................................. 10

1. Seasonality and change near the mouth of an Arctic river: Environmental 

variability in Stefansson Sound, Alaska............................................................................................10

Introduction ..................................................................................................................................................10

Methods...........................................................................................................................................................12

Results .............................................................................................................................................................15

Discussion ......................................................................................................................................................26

Appendix.........................................................................................................................................................31

2. Spatial and temporal trends in community composition in the Arctic Boulder 

Patch .....................................................................................................................................................................35

Introduction ..................................................................................................................................................35

Methods...........................................................................................................................................................35

Results .............................................................................................................................................................40

Discussion ......................................................................................................................................................57

3. Patterns and drivers of benthic community structure and early succession in an 

estuarine Arctic kelp bed..........................................................................................................................63

Introduction ..................................................................................................................................................63

Methods...........................................................................................................................................................66

Results .............................................................................................................................................................68

Discussion ......................................................................................................................................................76

Appendix.........................................................................................................................................................83

Directed Experimental and Observational Studies .........................................................................85

4. Within- and between-basin population connectivity in the Arctic endemic 

kelp Laminaria solidungula.................................................................................................... 85

Glossary...........................................................................................................................................................85

Introduction ..................................................................................................................................................85

Methods...........................................................................................................................................................88

Results .............................................................................................................................................................92

Discussion ......................................................................................................................................................97

Conclusions................................................................................................................................................. 101

5. Long-term patterns of benthic irradiance and kelp production in the central 

Beaufort Sea reveal implications of warming for Arctic inner shelves........................ 106

Introduction ............................................................................................................................................... 106

Methods........................................................................................................................................................ 108

Results .......................................................................................................................................................... 112

Discussion ................................................................................................................................................... 122
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6. Physiological responses of an Arctic crustose coralline alga (Leptophytum 

foecundum) within variations of salinity....................................................................................... 128

Introduction ............................................................................................................................................... 128

Methods........................................................................................................................................................ 129

Results .......................................................................................................................................................... 132

Discussion ................................................................................................................................................... 136

7. Carbonate chemistry of the nearshore Arctic: shifts and future implications.... 138

Introduction ............................................................................................................................................... 138

Methods........................................................................................................................................................ 139

Results .......................................................................................................................................................... 140

Discussion ................................................................................................................................................... 149

8. Freshwater input effects on Laminaria solidungula microscopic stages (short 

report) .............................................................................................................................................................. 152

Introduction ............................................................................................................................................... 152

Methods........................................................................................................................................................ 153

Results .......................................................................................................................................................... 154

Discussion ................................................................................................................................................... 156

References...................................................................................................................................................... 158

Acknowledgements

This study was funded by the U.S. Department of Interior, Bureau of Ocean Energy and 

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

BOEM Cooperative Agreement No. M12AC00007. The studies of the Boulder Patch 

have benefited greatly from BOEM support. We are deeply grateful to Dick Prentki, our 

former Project Officer (PO) who launched the ANIMIDA III Boulder Patch program

before his retirement in 2012. Cathy Coon followed as PO with the same level of 

enthusiasm and dedicated support. Despite her promotion to a leadership role at BOEM 

during this project, she always made time in her busy schedule to continue providing

critical guidance. Cathy was especially receptive to our logistic requests and assisted 

with our outreach activities at Kaktovik. We thank John and Ted Dunton, the crew of the 

R/V Proteus, who in addition to providing a safe and reliable diving platform, helped

design and fabricate equipment for deployment and assisted with every aspect of our field

work. Tim Whiteaker (University of Texas at Austin) assisted with the creation and 

maintenance of a dedicated Boulder Patch web site that is linked to our BOEM work in 

the western Arctic. Finally, special thanks to Arley Muth and Susan V. Schonberg who 

handled the giant task of extracting chapters from all of us and organizing this eightchapter final report.
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Executive Summary

Arley F. Muth, Christina E. Bonsell and Kenneth H. Dunton

In the nearshore Alaskan Beaufort Sea, discrete, isolated rocky habitats 

harbor biological communities that are unusually diverse for the region, with 

markedly different fauna and flora than found in the surrounding soft sediment 

areas. The boulder fields also support tightly linked food webs, with connections to 

higher trophic levels such as fishes and seals. These systems are highly vulnerable to 

disturbances, including sedimentation from human activities and coastal erosion, 

fluctuations in salinity from seasonal river discharge, and temperature increases 

from heat influx originating at lower latitudes and from atmospheric warming. 

Spatial isolation of these boulder fields and the slow development of benthic 

communities, limit ecosystem recovery from disturbance. To assess the possible 

ecological effects of such environmental disturbances, this field program undertook 

an investigation of the biological communities and environmental conditions across 

the Boulder Patch, the largest of the Beaufort Sea nearshore boulder fields. 

The overlying objectives of this program were to: 1) monitor incident and in 

situ photosynthetically active radiation (PAR), salinity, temperature, currents, and 

pH to define the spatial variability in annual kelp productivity and biomass; 2)

characterize benthic community structure changes across environmental gradients 

in the Boulder Patch and 3) collect annual measurements of kelp growth in the 

Boulder Patch to provide a nearly continuous record of kelp growth since 1977. 

Within and in addition to these objectives, individual research projects were 

completed, using experimental approaches, genetic analysis and historical data to 

gain an in depth understanding of mechanisms (past and present) that resulted in 

the Boulder Patch community we see today and how this community may vary in 

future conditions. This report is composed of eight chapters. The first three 

chapters focus on the larger scale setting of the Boulder Patch, building on the 

earlier work conducted by Dunton, Iken, Konar and Schonberg. The second five 

chapters address specific and directed experimental and observational studies that 

were performed by UT-Austin Ph.D. students Christina Bonsell and Arley Muth. Our 

combined effort work reveals that the Boulder Patch remains a dynamic, productive, 

biologically diverse environment within Stefansson Sound well after its initial 

discovery over 50 years ago. Seasonal environmental fluctuations, particularly of

salinity and light levels, create the current spatial species distributions. Through 

long-term observations, benthic monitoring and experimental work, we continue to 

better understand how environmental factors shape this ecosystem and it may

respond to changes associated with regional climate warming.
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BACKGROUND AND SETTING

1. Seasonality and change near the mouth of an Arctic river: Environmental variability 

in Stefansson Sound, Alaska

On Arctic inner shelves, seasonal spatial gradients in abiotic conditions 

caused by bathymetry and distance from freshwater inputs influence biological 

characteristics and biogeochemical processes. Spatial variations were examined in 

near-benthic temperature, salinity, currents, pH and light over seasonal and annual 

timescales (2011-2017) in Stefansson Sound, a shallow embayment (<15 m) at the 

mouth of the Sagavanirktok River on the North Slope of Alaska. Stable temperature 

and salinity conditions prevailed in the study area throughout the winter (-1.9 to -

1.8oC, salinity 33-35). However, spatial abiotic differences developed from MayOctober. During these months, temperature and salinity exhibited strong cross-shelf 

gradients, especially in June-September when nearshore sites reached 6oC or more 

and exhibited salinities below 20, while offshore sites averaged 2oC and salinities of 

28-29. Under the cover of landfast ice and coincident with the spring freshet, coastal 

waters near the mouth of the Sagavanirktok River experienced salinities below 10 at 

4 m depths in one of the sampled years. Currents are generally alongshore and bidirectional from November-May, but flow direction becomes wind-driven and 

variable following ice break-up. In all seasons, currents were faster and more 

variable at sites adjacent to deep, inter-island passes, reflecting influence by strong 

offshore currents. Underwater light levels were highest in July and August, but 

extremely variable in space and time (0.0-12.6 mol photon m-2 day-1) in response to 

wind-driven resuspension events. The timing of ice break-up appears to determine 

mean annual conditions, as it separates the cold, dark, high salinity, low variance 

winter from the warmer, fresher, high variance summer. These measurements 

imply that a considerable portion of the benthos is regularly exposed to low salinity 

conditions before the beginning of ice break-up. Our results reinforce the estuarine 

nature of Arctic nearshore systems and provides the hydrographic setting that 

determines the strong seasonal nature of the coupled benthic and pelagic ecosystem 

of the Beaufort Sea.

2. Spatial and temporal trends in community composition in the Arctic Boulder Patch

Epilithic communities were investigated over four study years, 2005, 2006, 

2013, and 2015, at seven locations across the Boulder Patch. Macroalgal biomass 

overwhelmed invertebrate biomass throughout all sampling years and locations. 

Macroalgal biomass was consistently dominated by four species: the kelp Laminaria 

solidungula and the red algae Phycodrys sp., Coccotylus truncatus and Dilsea sp.

These species (along with Rhodomela, which is frequent but has low biomass owing 

to its filamentous morphology) are common in the Arctic and are prominent in the 

Boulder Patch (Dunton et al., 1982). A comparison of the biomass of these four 

genera between 1978-80 at site DS-11 and this study yielded similar patterns in 

abundance over a span of nearly 40 years. Biomass values in this study ranged from

162 g m-2 for L. solidungula, to 51 g m-2 for Phycodrys, 42 g m-2 for C. truncates, and 
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25 g m-2 for Dilsea. Dunton et al. (1982) reported biomass values for these same 

genera as 262 g m-2, 45 g m-2, 33 g m-2, and 31 g m-2, respectively. In this study, 

overall biomass varied greatly across sites and years, although relatively high 

biomass consistently occurred at sites DS11 and W3, indicating dependably 

favorable algal growth conditions. At DS-11, We observed overall slightly lower 

algal biomass and changes in composition in 2013, which was also reflected in 

higher dispersion in community structure in that year at some sites, possibly in 

response to a disturbance. Macroalgal biomass was positively correlated, although 

not significantly, with invertebrate biomass, which also varied across sites and 

years. Invertebrate biomass was consistently dominated by sponges, bryozoans, and 

hydrozoans, which matches patterns from the late 1970s. Bryozoans and hydroids 

are typical Arctic coastal community components, but sponges seem to be more 

unique to the Boulder Patch, where they act as dominant space competitors. Distinct 

differences in invertebrate community composition in 2013 matched observations 

for the macroalgal communities, emphasizing potential disturbances in that year. 

Temporal differences were driven by small and mobile species such as amphipods 

and polychaetes, which are fast to respond to changes in environmental conditions.

Patterns in epilithic communities were compared with those in Camden Bay 

to the east, sampled in 2005, 2007, and 2017. The main macroalgal taxa in Camden 

Bay were relatively like those in the Boulder Patch but invertebrate taxa differed 

considerably, with significant differences in both assemblage types between the two 

regions. Overall average algal biomass was higher in Camden Bay while overall 

average invertebrate biomass was higher in the Boulder Patch. Invertebrate 

composition in Camden Bay was dominated by a few highly abundant taxa within 

the crustaceans and mollusks, but some groups regularly occurring in the Boulder 

Patch, such as echinoderms, decapods, and chitons, were completely absent in 

Camden Bay. This could indicate environmental constraints in Camden Bay or 

distribution limits and bottlenecks to dispersal and recruitment. 

3. Patterns and drivers of benthic community structure and early succession in an 

estuarine Arctic kelp bed

Benthic community structure in Arctic nearshore ecosystems may result 

from both seasonal abiotic variability and the processes of recruitment and 

succession, all of which could be mediated by physiographic and bathymetric 

characteristics. Spatial differences were compared in community structure to 

environmental conditions (temperature, salinity, current speed, underwater light), 

and to bathymetry and physiography (depth, distance from river inputs) in a 

shallow Arctic kelp bed. Additionally, because propagule delivery and recruitment 

can overcome strictly abiotic effects on ecosystem structure, we monitored spatial 

and temporal patterns of recruitment and community development to assess the 

timescale and trajectory of ecological succession. The benthic community varied 

among sites but was dominated by red algae (47-79% average cover at each site), 

prostrate kelps (2-19% average cover), and crustose coralline algae (0-19% average 

cover). Strong spatial distinctions among sites were particularly due to the positive 

correlation between distance to river inputs and cover by crustose coralline algae (ρ 
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= 1.0), and larger proportions of invertebrates at a high flow site. However, there 

were no significant relationships between any single environmental characteristic 

and any functional group. Recruitment patterns indicated that successional 

processes are important to structuring the community, with foliose red algae as a 

key early successional group (up to ~60% of recruits at shallow sites). Greater 

variance in successional trajectory at sites near deep passes compared to more 

sheltered sites indicated that differential flow patterns affect community 

development by altering the local propagule pool. Succession also occurred 

extremely slowly, which would hinder ecosystem recovery after catastrophic 

disturbances. These results suggest that community development in the nearshore 

Beaufort Sea occurs over many years and is influenced by combinations of abiotic 

and biotic factors. The benthic community, therefore, reflects an integration of 

abiotic conditions over timescales longer than most ecological studies. While 

seasonality exerts strong influence on Arctic systems, ecologically important

environmental variability in the coastal zone can also occur over extended time 

periods. Climate-driven changes in Arctic Ocean wind dynamics, freshwater 

budgets, sea ice extent, and erosional inputs will alter the community structure and 

distribution of shallow kelp beds.

DIRECTED EXPERIMENTAL AND OBSERVATIONAL STUDIES

4. Within- and between-basin population connectivity in the Arctic endemic kelp 

Laminaria solidungula

Pathways of dispersal and post-glacial recolonization patterns across the 

Arctic Ocean may provide insight into climate-driven borealization of Arctic 

ecosystems. To assess connectivity and dispersal in this rapidly-changing region, we 

analyzed population genetic structure in the Arctic endemic kelp Laminaria 

solidungula at two scales: 1) among populations in the Alaskan Beaufort Sea using 

microsatellites, and 2) among populations from across the Arctic using ribosomal 

large sub-unit (LSU) sequence data. The Beaufort Sea populations appear to be 

largely panmictic (global FST: 0.01), though sampling stations as close as 6 km could 

exhibit significant pairwise differentiation. Apparent inbreeding was extremely low 

(FIS<0) and genetic diversity was high (He>0.5) at all well-sampled stations, 

indicative of rare self-recruitment. These results suggest that sub-polar benthic 

species migrating into the Beaufort Sea will quickly spread across the U.S. Arctic 

coast, likely assisted by alongshore transport during fall storms. Conversely, LSU 

analysis revealed two genotypes that represent strong population differentiation 

between Beaufort Sea L. solidungula and those from the Canada and Svalbard, 

indicating that phylogeography of this species reflects Arctic recolonization after the 

Last Glacial Maximum. LSU topology within Laminariales demonstrates that this 

partition represents a significant subdivision within L. solidungula. 

5. Long-term patterns of benthic irradiance and kelp production in the central 

Beaufort Sea reveal implications of warming for Arctic inner shelves
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A multidecadal dataset of annual growth was synthetized of the Arctic 

endemic kelp Laminaria solidungula and corresponding measurements of in situ

benthic irradiance from Stefansson Sound in the central Beaufort Sea. We 

incorporate long-term data on sea ice concentration (National Sea Ice Data Center) 

and wind (National Weather Service) to assess how ice extent and summer wind 

dynamics affect the benthic light environment and annual kelp production. We find 

evidence of significant changes in sea ice extent in Stefansson Sound, with an 

extension of the ice-free season by approximately 17 days since 1979. Although kelp 

elongation at 5-7 m depths varies significantly among sites and years (3.8 to 49.8 cm 

yr-1), there is no evidence for increased production with either earlier ice break-up 

or a longer summer ice-free period. This is explained by very low light 

transmittance to the benthos during the summer season (mean daily percent 

surface irradiance ±SD: 1.7±3.6 to 4.5±6.6, depending on depth, with light 

attenuation values ranging from 0.5 to 0.8 m-1), resulting in minimal potential for 

kelp production on most days. Additionally, on month-long timescales (35 days) in 

the ice-free summer, benthic light levels are negatively related to wind speed. The 

frequent, wind-driven resuspension of sediments following ice break-up 

significantly reduce light to the seabed, effectively nullifying the benefits of an 

increased ice-free season on annual kelp growth. Instead, benthic light and primary 

production may depend substantially on the 1-3-week period surrounding ice 

break-up when intermediate sea ice concentrations reduce wind-driven sediment 

resuspension. These results suggest that both benthic and water column primary 

production along the inner shelf of Arctic marginal seas may decrease, not increase, 

with reductions in sea ice extent.

6. Physiological responses of an Arctic crustose coralline alga (Leptophytum 

foecundum) within variations of salinity

Arctic crustose coralline algae (CCA) persist in an environment of high 

seasonal variability. In addition to anthropogenic seawater chemistry changes 

(ocean acidification), calcifying organisms in the Arctic face naturally low pH ocean 

water and high magnitude freshwater pulses in the spring. The effects of salinity on 

the CCA Leptophytum foecundum were observed through a series of laboratory and 

field experiments in Stefansson Sound, Alaska. Laboratory experiments 

demonstrated through varying parameters (photosynthetic yield, pigments, and 

calcium carbonate dissolution), that salinity (treatments of 10, 20 and 30), 

independent of pH, affected L. foecundum physiology and dissolution. These 

experiments also revealed that L. foecundum was resilient to salinity down to 20, as 

individuals in the 20 and 30 salinity treatments were not significantly different for 

any of the parameters tested. This result was driven by aragonite saturation (Ω

aragonite) levels of the culture media; the 20 and 30 salinity treatments had levels 

of equilibrium or saturation (precipitation favored), while the 10 salinity treatments 

were undersaturated in regard to aragonite (dissolution favored). Reciprocal 

transplants and recruitment patterns between areas dominated by CCA and areas 

where CCA were absent, illustrate that in-shore locations receiving large pulses of 

freshwater are not suitable for the persistence CCA. Ultimately, spatially and 
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temporally varying salinity regimes and the resultant Ω arag levels determine 

distribution of CCA in the near shore Arctic.

7. Carbonate chemistry of the nearshore Arctic: shifts and future implications

World-wide, oceans are threatened by increased atmospheric carbon dioxide 

(CO2) levels that result in increased CO2 levels absorbed into seawater. This process, 

known as ocean acidification, leads to physiological disruption of species processes 

and the dissolution of calcifying marine organisms. The Arctic Ocean is more

susceptible to ocean acidification due to natural factors that lower pH in addition to 

anthropogenic climate change. In order to quantify pH and other calcium carbonate 

parameters, pH, salinity, and temperature sensors were deployed at inshore near 

the mouth of the Sagavanirktok River (E-1) and 6 km offshore (DS-11) in 2016 and 

2017 for one year. In addition, invertebrate and algal assemblages were quantified 

and compared between sites. Aragonite (the most common calcium carbonate 

mineral) saturation levels (Ω arag) were also calculated, due to the direct impact on 

calcifying organisms. Levels above one favor precipitation, while saturation levels 

below one favor dissolution. Seasonal and annual variations were detected within 

and between sites. In general, the inshore site (E-1) experienced lower salinity 

levels during the spring freshet and the freshwater input resulted in lower Ω arag 

levels. However, pH was slightly higher at the inshore site, and this pH signal was 

associated with runoff from the Sagavanirktok River, with waters slightly more 

basic than ocean water. Likely associated with low Ω arag levels, crustose coralline 

algae were absent from the inshore site. Red algal biomass increased at the inshore 

site and inshore invertebrate communities were bryozoan-dominated. Salinity

changes, total alkalinity, and runoff from the Sagavanirktok River strongly 

influenced ocean water carbonate chemistry and these values resulted in different 

benthic communities. The inshore communities have the potential to expand 

throughout the Boulder Patch as freshwater runoff increases and ocean acidification 

continue to decrease Ω arag levels.

8. Freshwater input effects on Laminaria solidungula microscopic stages

The Arctic endemic kelp species, Laminaria solidungula, is an important 

contributor to nearshore food webs of the Alaska Arctic. Population density 

differences among areas of the Stefansson Sound Boulder Patch led to questions of 

effects of abiotic factors on population persistence. Although many factors affect 

sporophyte densities, microscopic stages of the life history are known to be more 

susceptible to environmental conditions. Laminaria solidungula were cultured 

under varying light (10, 20 and 40 μmol photons m2 s

-1) and salinity (10, 20, 30) 

treatments in order to explore how these factors interactively and separately affect 

microscopic sporophyte production. Ultimately, cultures in the low salinity 

treatment were not able to produce sporophytes regardless of light level, however, 

light levels did affect final densities. As freshwater influence from the Sagavanirktok 

River continues to increase, lower salinity levels and light reducing sediments may 

reach further offshore could decrease sporophyte production and L. solidungula

population persistence.
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BACKGROUND AND SETTING

1. Seasonality and change near the mouth of an Arctic river: 

Environmental variability in Stefansson Sound, Alaska

Christina E. Bonsell and Kenneth H. Dunton

INTRODUCTION

Shallow, predominantly soft sediment habitats, which characterize the 

benthic nearshore areas of the U.S. Arctic, are important settings for marine life and 

ecological processes. These ecosystems are also locations of ongoing and proposed 

industrial activity. Numerous river and stream inputs define the inner shelf area as 

an estuarine environment (Carmack and Wassman 2006). Benthic organisms on the 

inner shelf include key consumers that assimilate both autochthonous and 

allochthonous (mostly terrestrial) sources of carbon. Consumer dependence on 

terrestrial sources is now well documented by isotopic evidence and gut analyses of 

fauna collected in lagoon systems along the Beaufort Sea coast (Craig et al. 1984, 

Dunton et al. 2012, Harris et al. 2018). The high productivity within the linked 

barrier island lagoon environment allow migratory birds, marine and anadromous 

fishes, and marine mammals to take advantage of the food and habitat provided by 

these protected lagoon systems (Craig et al. 1984, Craig 1984, Dickovy 1984, Frost 

and Lowry 1984). In addition, the strong seasonality of Arctic coastal ecosystems 

defines the timing and magnitude of freshwater inflows, sea ice dynamics, and water 

exchange with the ocean, which in turn shapes their estuarine nature.

Seasonal changes in the physiochemical environment of these systems is 

largely event driven, dominated by annual cycles of river inputs and ice cover 

(Mcclelland et al. 2012, Weingartner et al. 2017). Rivers draining the North Slope of 

Alaska discharge over half of their annual flow in a short, two-week spring melt 

period between late May and early June (i.e. the “spring freshet”; McClelland et al.

2012, McClelland et al. 2014). This flow initially occurs above and beneath the sea 

ice, creating a relatively stable stratified layer of river water that can reach up to ~ 3 

m deep (Reimnitz 2000, Alkire and Trefry 2006, Kasper and Weingartner 2015)

until sea ice break-up commences and wind mixing strengthens (Weingartner et al. 

2017). Depending on winds, currents, and flow conditions, ice break-up can occur 

over a period of days or weeks. In the Beaufort Sea, the ice free summer is then 

characterized by wind mixing and higher current speeds, especially late in the 

season (Weingartner et al. 2017). Ice formation begins as the amount solar energy 

that reaches Arctic latitudes decreases in the fall, further homogenizing the water 

column. Landfast ice forms along the shore, then spreads offshore, with freeze-up 

complete in nearshore areas by late-October (Barry et al. 1979, Mahoney et al.

2014). As ice formation continues, both salinity and inorganic nutrient levels 

increase within the underlying waters through the respective processes of brine 
11

rejection and remineralization (Chapman and Lindley 1980, Matthews 1981,

Dunton et al. 1982, Macdonald and Yu 2006).

In the marine realm of Arctic nearshore systems, bathymetry and proximity 

to river inputs mediate seasonal variation in abiotic conditions, and thereby impart 

“zones” of ecological characteristics and biogeochemical processes. For example, 

greater sediment resuspension at shallow depths due to summer wind mixing can 

reduce benthic primary productivity relative to deeper, more offshore areas 

(Aumack et al. 2007). Additionally, stratification in the spring and early summer 

exposes nearshore, near river benthos to a distinct, river derived water mass. 

Associated organic matter, freshwater, and heat inputs may result in profound 

differences between nearshore and offshore benthic diversity and biogeochemical 

processes (Bonsell 2019, sections 1.2 and 2.4.).

Understanding year round in situ conditions is a critical knowledge gap 

linking Arctic abiotic factors to biological characteristics and biogeochemical 

processes. As seasonal transitions can vary from year to year (e.g., Harris et al. 

2017), multiple years of data are necessary to assess the magnitude and spatial 

extent of abiotic changes. Logistical constraints and ice risks discourage long-term 

mooring deployments in shallow areas, meaning that underwater conditions during 

the freshet and ice break-up are not well characterized for most Arctic estuaries. 

Recent technological advances have made affordable in situ instrumentation more 

readily available, and researchers more willing to risk equipment for the chance of 

acquiring valuable environmental data. 

The goals of this study are to: 1) Define the seasonal cycle in temperature, 

salinity, currents, and underwater light in the nearshore environment of Stefansson 

Sound; 2) Determine the influence of bathymetric and physiogeographic 

characteristics (distance from river inputs and coastline orientation) on spatial 

differences in the marine environment; 3) Discuss the influence of regional wind 

patterns on temporal variability and current dynamics; and 4) Assess evidence for 

long-term change in temperature and salinity in Stefansson Sound. This work 

depicts the environmental setting for nearshore biological processes on Arctic inner 

shelves. 

Weingartner et al. (2017) recently described seasonal abiotic conditions in 

the nearshore landfast ice zone of the central Beaufort Sea coast, with moorings as 

shallow as 7 m in Stefansson Sound. Their study provides important environmental 

context for ecological studies and proposed development scenarios related to oil 

and gas extraction. Our work extends shoreward to 4 m within Stefansson Sound, 

where stratified fluvial inputs interact with benthic processes that affect local 

production, diversity, and biogeochemical cycling. Observations of temperature, 

salinity, and currents collected in this study were compared to offshore 

measurements, as well as to data collected in previous decades (1970s and 1980s). 

Comparisons to historical datasets are important since the changes occurring over 

sub-decadal time scales can be masked by innate interannual variability. We
12

consider the effects of bathymetric and physiogeographic characteristics on the 

abiotic environment, and how those may impact marine life. Our dataloggers were 

within 60 cm of the benthos, providing an opportunity to describe how benthic 

biota experience seasonality adjacent to river inputs. 

METHODS

Study Area

Stefansson Sound is a shallow (<10 m) embayment on the central Beaufort 

Sea coast, partially protected by barrier islands. The Sagavanirktok River, with 

headwaters in the Brooks Range, empties into the Stefansson Sound via numerous 

delta channels (Fig. 1.1). Like most of the Beaufort Sea coast, the benthos is 

primarily composed of silty sediments, but a large deposit of boulders and cobbles, 

termed the “Boulder Patch”, gives rise to a unique benthic bed community in the 

eastern portion (Fig. 1.1; Dunton et al. 1982). The Endicott Causeway, completed in 

1985, extends into the sound past the Sagavanirktok River delta. Development of 

the adjacent Prudhoe Bay oilfield resulted in intense study of Stefansson Sound 

relative to other Arctic inner shelf habitats. As a result, changes to the marine 

environment over time can be evaluated from historical in situ datasets. 

Over the course of the study period (2011-2017), we occupied five long-term 

monitoring sites within the Boulder Patch area of Stefansson Sound. Sites differed in 

depth and distance from Sagavanirktok River channels (Fig. 1.1, Table 1.1). These 

included three nearshore sites (E1, L1, and W1), and two sites further offshore 

(DS11, W3).
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Figure 1.1. Map of the Stefansson Sound area, with the five long-term study sites used in 

this study: W1, W3, E1, L1, and DS11. The green area symbolizes the Boulder Patch. 

Mooring description

Benthic moorings, composed of a variety of in situ data loggers (described 

below), collected continuous environmental data at each of our sites. These were 

retrieved each summer (July or August) and redeployed within two weeks after 

downloading the data. Photosynthetically active radiation (PAR) was recorded ~ 50 

cm above the seabed using a LI-COR LI-193SA spherical quantum sensor (accuracy: 

±5%) connected to LI-1000, LI-1400, or LI-1500 dataloggers. Some of these data 

have been incorporated in previous studies to examine multidecadal underwater 

light patterns in the Boulder Patch (Bonsell and Dunton 2018). Continuous in situ

measurements of conductivity and temperature were collected with HOBO U24 

Conductivity/Temperature Loggers (hourly sample interval) and Star Oddi DST

loggers (30 min sample interval; accuracy for both loggers: ± 0.1 oC, ±1.0 salinity). 

Instantaneous measurements of temperature and conductivity were measured upon 

deployment and retrieval of loggers using a YSI Data Sonde to calibrate records at 

each site. Temperature and conductivity were used to calculate salinity using the 

1983 UNESCO equation. Measurements of temperature, current velocity and current 

bearing within ~1.2 m of the bottom were collected using SeaHorse Tilt Current 

meter fitted with a MAT-1 accelerometer or with a Lowell MAT-1 Tilt Current Meter 

(30 min sample intervals, accuracy: ± 1o).
14

In addition, Sea-bird (Satlantic) SeaFETs and SBE 37-SM MicroCATs C-T (P) 

were deployed July 2016-July 2018 at two sites within the Boulder Patch, 

Stefansson Sound, Alaska (Fig. 1.1); Site E1 (70°18.8665 N, 147°44.0413 W; 4 m 

depth) and site DS-11 (70°19.3248 N, 147°34.8816; 7 m depth). 

Our mooring design placed conductivity cells within 30 cm of the benthos. 

This exposed them to fouling by resuspended sediments during fall storms, a known 

problem in these environments (Weingartner et al. 2017). As a result, conductivity 

data was removed for portions of the record at some sites. Additionally, some 

moorings were lost to damage by deep draft ice, though the large majority survived 

deployment (Fig. 1.2).

Data analyses

After undergoing quality control, summary statistics were generated for each 

data type at each site overall and separated by season: ice-covered winter 

(December through April), pre ice break-up spring (May through June), open water 

summer (July through September), and fall freeze-up (October through November). 

This definition corresponds with the timing of key hydrologic events: the spring 

freshet (late May to early June), landfast ice break-up (July), and sea ice freeze-up 

(early to mid-November). The four-season model has been used by others to 

describe Arctic annual cycles in connection to biological activity (Carmack & 

Macdonald, 2002). To directly assess how abiotic conditions differed among sites 

over time daily mean values for each continuous variable (temperature, salinity, 

PAR, current velocity) were calculated for each day where there was data for >1 site 

and compared using a blocked-ANOVA approach using time (day) as a block. Posthoc pairwise comparisons assessed differences in least-squared means, with Tukeyadjusted p-values for multiple comparisons. Daily mean current direction and 

summary statistics were determined to the nearest degree using the R package 

‘circular’ (Agostinelli and Lund 2013). Average daily wind speed and fastest fiveminute wind direction from the Deadhorse Airport were downloaded from the 

National Climate Data Center (National Weather Service) and plotted against mean 

daily current conditions at each site. All data analysis was carried out using R v. 

3.3.3 (R Core Team, 2016). 

Table 1.1. Site locations, bathymetric, and physiographic information.

Site

Latitude 

(DD)

Longitude 

(DD) Depth (m)

Dist. to river 

input (km)

DS11 70.32228 -147.579 6.1 9.03

E1 70.31495 -147.732 4.4 3.54

L1 70.28993 -147.613 5.5 7.20

W1 70.37003 -147.873 6.0 3.54

W3 70.37627 -147.794 6.6 7.31
15

RESULTS

Our data show the strong seasonal pattern of environmental conditions in 

the Arctic, with relatively consistent temperature, salinity, light, and currents during 

the winter and a much more variable environment during the rest of the year (Table 

1.2, Figs. 1.2-1.5). Generally, sites experienced synchronous variations in all 

parameters except salinity, though the magnitude of change could vary significantly 

between sites (Figs. 1.2 and 1.5) Signatures of the annual sea ice cycle and the 

spring freshet are apparent in the underwater physiochemical record.

Salinity on the seabed reached its highest average (33-36) in the winter, and 

its lowest average in summer (below 30) at all sites (Table 1.2). Winter 

temperatures were stable at ~-1.9 to -1.8°C but reached upwards of 5°C in the 

summer (Table 1.2, Figs. 1.2-1.4). Shallow, inshore sites experienced the greatest 

range in salinities, with E1 and W1 fresher than the other sites in the spring and 

summer (Table 1.2, Figs. 1.2 and 1.3). The site closest to the river mouth (E1) also 

exhibited the greatest riverine influence: temperatures rose earlier and a springtime 

pulse of low salinity was noted in some years that was not seen at the offshore sites 

(Figs. 1.2 and 1.4). E1 also had significantly higher temperatures than the other sites 

during spring and summer and was the warmest site overall (Tables 1.2-1.3). While 

the freshet signal was strongest at E1, it could also be seen at other sites as a small 

(<1oC) rise in temperature in the spring (Fig. 1.2). The subsequent onset of sea ice 

melt was marked by a rapid, large (> 2°C) increase in temperature and a decrease in 

salinity (>3) across Stefansson Sound (Fig. 1.2). Temperatures were maximal in 

August, then decreased gradually through the early fall until dropping rapidly by 

more than 2°C leading up to sea ice freeze-up (Fig. 1.2).

Photon flux was extremely variable across sites in the summer, but most 

notably at E1 (Table 1.2, Figs. 1.2 and 1.3). Underwater light levels hit their annual 

maximum in July (above 5 mol photons m-2 day-1), with the rest of the summer 

characterized by smaller, sporadic spikes of measurable light levels (Fig. 1.2). In 

summer, DS11 and E1 tended to have higher underwater light levels than other sites 

(Table 1.3). PAR dropped to zero in the fall, then increased briefly in the spring in 

two of the four years (2014 and 2017), before dropping to zero again (Fig. 1.2). 

At all sites, near bottom current velocities were much lower and less variable 

under ice cover during the winter and spring (<2 cm/s, Table 1.2, Figs. 1.2,1.3 and 

1.5). Like temperature, current speeds accelerated slightly (<2 cm s-1) with the 

freshet, then strengthened considerably as sea ice cover decreased in the Beaufort 

Sea during summer (Fig. 1.2). Average velocity at all sites was highest in the 

summer and fall (average >3 cm s-1, Table 1.2, Figs. 1.2, 1.3 and 1.5). The shallow, 

within bay sites E1 and L1 experienced their maximum seasonal current velocity in 

the summer, while currents were fastest in the fall at the more exposed and deeper 

sites (DS11, W1, W3; Table 1.2, Figs. 1.2, 1.3 and 1.5). On average, velocity was 

greater at these three sites, while E1 and L1 had significantly lower velocities, and 

lower variability (as measured by standard deviation) across seasons (Tables 1.2 
16

and 1.3, Fig. 1.3). The onset of sea ice freeze-up in October (2014-2015) or 

November (2016) caused current speed to plummet (Figs. 1.2 and 1.5).

Current direction data failed to meet the assumptions for statistical 

comparisons between sites and seasons as it was not Von Mises distributed (Watson 

test for continuous probability distribution on the circle, α=0.1). Currents were 

primarily bi-directional: east-west for W1 and W3, northwest-southeast for L1 and 

DS11 (Figs. 1.5-1.7). Unlike the other sites, currents at E1 were more unidirectional 

and tended to be toward the south (Figs. 1.5-1.7, Table 1.3). At all sites, the 

strongest currents typically had a northwest bearing (Figs. 1.5 and 1.7). During 

summer and fall, strong along-shore flows developed throughout the Boulder Patch 

(Fig. 1.6). Currents bearing south became more frequent during the winter and pre 

break-up spring (Figs. 1.5 and 1.6). 
17

Table 1.2. Mean and standard deviation of daily benthic environmental conditions 

measured at each site, split by season, of entire data record (Fig. 1.2). Subscripts refer to 

number of observations (days). Superscripts indicate that there were significant 

differences between sites, with letter indicating group affinity within each season 

(α=0.05, Supp. Table 1.1). * Differences in mean current direction could not be analyzed 

statistically.

Season Site Temp. (

oC) Salinity

Current vel. 

(cm s-1)

Current 

direction 

(degrees)*

PAR (mol photons 

m-2 day

-1)

Icecovered 

(DecApr)

DS11 -1.8±0.1 a

908 34±1 a908 1.7±1.3 a151 180±83 151 0.013±0.025 605

E1 -1.9±0.1 b

605 34±2 b598 1.3±0.6 bd454 236±64 454 0.014±0.034 454

L1 -1.8±0.1 a

605 36±1 c240 0.7±0.4 c454 194±108 454 0.012±0.025 302

W1 -1.9±0.1 b

454 33±1 d151 1.3±1.0 b303 144±48 303

Instrument 

failed

W3 -1.8±0.1 c

485 34±1 ab302 1.6±0.8 ad151 162±100 302 0.017±0.024 151

Prebreak-up 

spring 

(MayJun)

DS11 -1.6±0.2 a

366 33±1 a366 1.7±1.2 a61 96±83 61 0.082±0.178 244

E1 -1.3±0.7 b

244 31±6 b243 1.5±0.8 b183 199±69 183 0.070±0.186 183

L1 -1.5±0.2 c

244 35±1 c132 0.7±0.5 c183 198±91 183 0.065±0.113 122

W1 -1.6±0.2 

ad183

31±0 b

77 1.4±0.9 b122 149±47 122

Instrument 

failed

W3 -1.7±0.2 d

183 34±1 a122 1.6±0.7 a61 166±68 122 0.082±0.103 61

Open 

water 

summer 

(Jul-Sep)

DS11 2.0±2.2 a

519 29±3 a519 7.3±4.9 ab108 353±97 109 1.062±1.878 a336

E1 3.2±2.5 b

377 27±3 b377 5.3±3.7 c248 266±90 248 0.812±1.789 ab247

L1 2.0±2.1 a

326 29±4 c246 3.9±2.6 c250 279±87 250 0.620±1.314 b222

W1 1.7±2.2 a

232 26±4 d148 7.7±5.5 a215 193±75 217 0.102±0.236 b27

W3 1.6±2.1 a

334 28±3 a270 6.5±4.0 b201 206±71 219 0.534±1.188 b126

Fall 

freeze-up 

(OctNov)

DS11 -1.4±1.0 366 31±2 ab322 8.8±8.4 a

61 235±80 61 0.014±0.053 244

E1 -1.3±1.1 244 31±2 bc221 4.3±4.8 b

183 271±73 183 0.013±0.040 183

L1 -1.4±0.9 244 32±2 c

124 3.5±4.3 b183 285±86 183 0.002±0.005 124

W1 -1.4±0.9 183 29±2 a

49 7.7±7.5 a142 215±68 142

Instrument 

failed

W3 -1.4±0.9 244 31±3 abc98 8.0±5.9 a

108 228±97 132 0.001±0.002 70
18

Table 1.3. Daily, year-round mean (±SD) benthic environmental conditions at each site of 

entire data record (Fig. 1.2). Superscripts indicate that there were significant differences 

between sites, with letter indicating group affinity (α=0.05, Supp. Table 1.1). * 

Differences in mean current direction could not be analyzed statistically.

Site

Temperature 

(

oC) Salinity

Current velocity 

(cm s-1)

Current 

direction 

(degrees)*

PAR (mol photons 

m-2 day-1)

DS11 -0.78±1.96 a

2159 32±3 a2115 4.4±5.3 a381 181±77 382 0.271±1.013 1429

E1 -0.41±2.50 b

1470

31±4

b

1439

2.8±3.2 b

1068 239±66 1068 0.208±0.925 1067

L1 -0.82±1.89 a

1419 33±4 a742 1.9±2.7 c1070 251±76 1070 0.194±0.756 772

W1 -0.95±1.84 a

1052 30±4 c425 4.3±5.4 d782 164±61 784 0.102±0.236 27

W3 -0.80±1.88 a

1246 32±3 b792 4.8±4.6 a521 189±71 775 0.184±0.700 408
19

mol 

phot

ons

m

-2

day

-1

	

mol	photons	m-1	day

-1	

Figure 1.2. Timeseries of daily mean temperature, salinity, photon flux, and current 

velocity at each site.
20

	

mol photons m-2 day

-1

Figure 1.3. Distribution of measurements for each variable by site and season for entire 

data record (Fig. 1.2). Sites are ordered by increasing distance offshore. Central 

horizontal line indicates the median, upper and lower limits of the box indicate 

interquartile range; vertical lines indicate the overall range of values. See Table 1.2 for 

significant group affinities. This figure differs from Table 1.2 in that it shows median 

values, so less weight is given to rare observations at the edge of the range.
21

Figure 1.4. Temperature vs. salinity plots at each site for entire data record (Fig. 1.2), 

color-coded by season. Purple: ice-covered; blue: spring; green: summer; teal: fall. Black 

line indicates the freezing point of water.
22

Figure 1.5. Timeseries of daily direction and relative speed of wind at Deadhorse Airport 

(NWS) and currents at each site. North is positive in the y-axis; east is positive in the xaxis. 
23

Figure 1.6. Seasonal flow patterns over the Boulder Patch. Vectors display the principal 

uni- or bi-directionality and relative velocity, based on circular histograms of current 

data. 
24

Figure 1.7. Distribution of mean daily current velocity and direction for each site for the 

entire dataset. Arc sections indicate the frequency of currents of a particular velocity and 

bearing, binned by 20o

pH values

pHT values were recorded hourly for 2016 deployments, however, upon 

retrieval both pH sensors had failed (E-1: July 2016-June2017; DS-11: July 2016-

February 2017). Sensors were reset for the 2017 deployments to record pHT every 

two hours and were actively recording when retrieved in July 2018. Values and 

trends were similar between sites for each year, but drastic variations were seen 

between years. For more detailed pH and carbonate chemistry results and 

discussion see section 2.4.

2016 Results

Coastal ocean pHT followed seasonally distinct transitions that relate to four 

main events: the summer open water, fall freeze-up, the winter ice covered season, 

and the spring freshet. For the 2016-2017 deployment, pHT during the freshet was 

not captured due to sensor failure. pHT varied from 7.83 – 8.70 at E1 (error 0.042)

and 7.79 – 8.69 at DS-11 (0.046), both sites showed a unique pH spike during 

freeze-up (Fig. 1.8).
25

7.6	

7.8	

8	

8.2	

8.4	

8.6	

8.8	

215	 265	 315	 365	 415	 465	 515	

E-1	pH	internal	

DS-11	pH	internal	

open	water	

freeze	

up	

ice	season	 freshet	

	

Jul	 Aug	 Sep	 Oct	 Nov	 Dec	 Jan	 Feb	 Mar	 Apr	 May	 Jun	 Jul	

2016	

pHint	E-1	

pHint	DS-11	

Figure 1.8. pHT values for 2016 at E-1 (black) and DS-11 (red). Data can be 

observed in four distinct periods, based on ice formation and run-off from the 

adjacent Sagavanirktok River; open water, freeze-up, ice season, and freshet.

2017 results

pHT varied from 7.67 – 8.67 at E1 (error 0.085) and 7.75 – 8.34 at DS-11 

(0.028), both sites showed pH spikes when salinity declined due to run-off from the 

Sagavanirktok River; open water season (DS-11-July 2017) and the spring freshet 

(E1-July 2018), but spikes were not detected during freeze-up as they were in 2016 

for both sites (Figs. 1.8 and 1.9). Heterotrophy during the ice season and low light 

season likely increased pCO2 and caused a slight decrease in pH as seen in 2016-

2017. 
26

7.6	

7.8	

8	

8.2	

8.4	

8.6	

8.8	

214	 264	 314	 364	 414	 464	 514	 564	

E-1	pH	

DS-11	pHext	

open	water	

freeze	

up	

ice	season	 freshet	

	

Jul	 Aug	 Sep	 Oct	 Nov	 Dec	 Jan	 Feb	 Mar	 Apr	 May	 Jun	 Jul	

2017	

pHint	E-1	

pHext	DS-11	

Figure 1.9. pHT values for 2016 at E-1 (black) and DS-11 (red). Data can be 

observed in four distinct periods, based on ice formation and run-off from the 

adjacent Sagavanirktok River; open water, freeze-up, ice season, and freshet.

DISCUSSION

Spatiotemporal variability

Temporal abiotic variability in Stefansson Sound is primarily driven by seaice dynamics. On annual timescales, the timing of ice break-up and freeze-up 

determines the magnitude of current velocity, as well as temperature and salinity 

variation (Figs. 1.2 and 1.5). This timing also significantly influences underwater 

light levels (Dunton et al. 1992). Since tides along the Beaufort Sea coast are 

minimal, higher-frequency variations are caused by atmospheric forcing, which 

drives nearshore water mass movement (Dunton et al. 2006, Weingartner et al.

2017). In the absence of ice cover, this effect is amplified, leading to higher 

environmental variability in the summer. Much of the temperature and salinity 

variation at offshore sites (DS11 and W3) from July-October can be explained by 

upwelling due to easterly winds, which transports cold, saline shelf bottom waters 

further onto the inner shelf ( Dunton et al. 2006). Westerly winds, on the other 

hand, entrain warm, fresh nearshore water into Stefansson Sound (Sellmann et al. 

1992). These drivers are strengthened in the absence of sea ice, so most of the major 

spatial abiotic variability exists outside of the ice-covered winter months. 

Spatial abiotic variation is linked to bathymetry and distance from the 

Sagavanirktok River. While our salinity record is inconsistent due to sediment 

fouling, some key trends emerge. By mid-November, after fall freeze-up, equivalent 

bottom temperature and salinity conditions prevail throughout Stefansson Sound. 
27

Winter minimum temperatures of ~-1.9, just above the freezing point (Fig. 1.4), as 

reported by Matthews (1981) and Sellmann et al. (1992), persist until May. Salinity 

increases to above 33 through the beginning of winter as a result of ion exclusion by 

the growing sea ice (Matthews 1981). With the advancement of spring, river runoff 

and sea ice melt begin to influence salinity and temperature in shallow areas (Figs. 

1.2-1.3). Low salinities (mean ≤ 32) at E1 and W1 suggest greater riverine influence 

at these sites through the spring and summer (Table 1.3, Figs. 1.3 and 1.4). This is 

especially evident at E1, where the freshet was documented each year in June (Figs.

1.2-1.4), as predicted by modelling studies which showed that stratified fluvial 

plumes are retrained in the nearshore during ice cover (Kasper and Weingartner

2015). Elevated bottom temperatures at the shallow, nearshore site (E1; annual 

mean -0.47 oC) indicate frequent position above the thermocline, and exposure to 

warmer, inshore waters throughout the summer, when the mean temperature at 

this site is ~ 1 oC above that of the other sites (Tables 1.2 and 1.3, Figs. 1.3 and 1.4; 

Sellmann et al. 1992). While the timing of these events corresponds to Weingartner 

et al. (2017), comparison of the two studies emphasizes the cross shore estuarine 

gradient in Stefansson Sound, with nearshore waters generally warmer by a few 

degrees and fresher than those further offshore. 

pH was also linked to distance from the Sagavanirktok River, timing of ice 

formation and the spring freshet. This dataset marks the first, continuous dataset 

for the Arctic Ocean. Although previous studies have reported pH and associated 

carbonate chemistry parameters for this area (Mathis et al. 2015), the 

measurements were not continuous and represented specific time periods over 

multiple areas. Section 2.4 of this report discusses in depth pH values for the 

Boulder Patch and the associated carbonate chemistry parameters.

Underwater light levels in the Boulder Patch are negligible for most of the 

year. In the winter, ice cover and the polar night keep the benthos in darkness. 

Sediments in the sea ice, which become entrained during fall storms (Osterkamp 

and Gosnik 1984) can further attenuate light at the sea surface. In some years (2014 

and 2017), underwater light briefly rose in the spring, likely due to the presence of 

an ice canopy relatively free of these sediments, which allows light to penetrate into 

the water column. In spring of 2014, the year with the highest under ice light levels, 

the return to darkness corresponded with the pulse of low salinity at E1 (Fig. 1.2), 

indicating that sediment and debris accompanying spring runoff either wash over or 

spread under the ice, causing light levels to drop again. Underwater light levels 

increase dramatically during ice break-up, the time when they reach their maximum 

annual value (Bonsell and Dunton 2018). After ice break-up, wind and runoff events 

suspend sediments in the water column and light levels decrease until the 

sediments settle (Aumack et al. 2007, Bonsell and Dunton, 2018). Comparisons 

between sites agree with past studies that showed daily underwater light levels are 

often elevated at the deep, offshore sites (Table 1.3) where concentrations of 

suspended sediment are reduced (Aumack et al. 2007, Bonsell and Dunton 2018).
28

Unlike other variables, current velocity and direction show large spatial 

variability in both summer and into winter, connected to the bathymetry and 

physiography of Stefansson Sound (Tables 1.2 and 1.3, Figs. 1.3, 1.5 and 1.6). In 

winter, currents are minimal at most shallow sites (E1, L1; <2 cm s-1). Offshore 

circulation patterns may influence sites adjacent to deep areas (W1, DS11 and W3, 

Fig. 1.1), and result in larger variability in winter current velocities, though overall 

velocities remained low (<3 cm s-1), similar to winter measurements made by 

Matthews (1981) at DS11. Without direct influence from surface winds, currents 

appear more tidally influenced during winter and spring (Figs. 1.5 and 1.6). After ice 

break-up, flow direction becomes much more variable, as water motion becomes 

influenced by surface winds (Weingartner et al., 2017). Alongshore currents 

dominate across the Boulder Patch (Figs. 1.5 and 1.6). Wind driven flows 

periodically reach above 15 cm s-1, usually associated with easterly winds (Figs. 1.5 

and 1.7). Spatial variability in current velocity and direction is related to 

geomorphology and bathymetry: W1, which is less protected and closer to a deep 

inter island pass than the other shallow sites (Fig. 1.1), has the highest mean current 

velocity in summer and fall (> 8 cm s-1; Table 1.2, Fig. 1.3). The nearby deep site, W3, 

also experiences elevated flow velocity during summer and fall, as does DS11 

(means >6 cm s-1). Flow speeds are less than those measured north of Stefansson 

Sound at 17 m depth, which can reach above 50 cm s-1 in the fall (Weingartner et al.

2017), and the shallow sites closer to Foggy Island Bay have even lower mean 

current speeds (≤5 cm s-1). Strong east wind events during late summer and fall 

result in elevated current velocity, often bearing northwest, until freeze-up is 

complete (Figs. 1.2 and 1.5). The W3 site mooring was lost to ice scour during one 

such fall storm in 2015. We were able to reconstruct the event to the day (11 

October 2015) by a sudden increase in current speeds across Stefansson Sound 

leading up to instrument loss, which corresponded to a days-long easterly wind 

event (Figs. 1.2 and 1.5), which would have pushed drifting ice into and across 

Stefansson Sound (Fig. 1.6). This annual cycle of current velocity, driven by ice cover 

and wind, reflects previous studies in Stefansson Sound (Weingartner et al. 2017). 

Long-term change

In Stefansson Sound, the ice free season has lengthened by ~17 days over the 

past four decades (Bonsell and Dunton 2018). Comparisons to previous studies 

suggest that Stefansson Sound is warming as well: the average temperature at DS11 

for August 1987-August 1988 was -1.25±1.42oC (Sellmann et al. 1992), compared to 

our August 2011-July 2017 average of -0.76±1.97oC (Fig. 1.10). The spring freshet 

signal was also absent from the 1988 temperature record, leading to a difference of 

0.3 degrees in average May-June temperature (2012-2017 mean±SD: -1.58±0.20, 

1988 mean±SD: -1.82±0.08, Fig. 1.10). Long-term warming may be confined to the 

spring and summer months, since our winter measurements of benthic temperature 

and salinity at DS11 mirror those collected at midwater depths from November 

1978- February 1979 (-1.98--1.80 oC, salinity 32.5-34.3; Matthews, 1981a). The 

longer open-water season in the landfast ice zone during the recent decade is a 
29

result of temperatures increasing earlier and decreasing later in the year (Fig. 1.10; 

Mahoney et al. 2014). While the range of salinities appears similar, summertime 

salinities dropped later in the year in 1987 due to a later melt season.

A longer ice-free season would profoundly impact annual current dynamics. 

Of the variables we measured, currents responded most immediately to ice breakup and freeze-up, which confines the interaction between wind forcing and water 

motion (Figs. 1.2 and 1.5; Weingartner et al. 2017). Earlier melt and later freeze-up 

would raise annual mean current velocity. Additionally, strengthened easterly winds 

in the Beaufort Sea region (Wood et al. 2013, 2015) would intensify advection from 

Stefansson Sound. This would potentially increase summer salinities at the benthos 

by transporting buoyant freshwater offshore.

Figure 1.10. Temperature and salinity at DS11 for 2011-2017 (colored lines, this study) 

compared to 25 August 1987 to 11 August 1988 (black line, Sellmann et al. 1992).

Implications for the benthic ecosystem

In river fed estuaries, the flow of freshwater can create gradients that affect 

ecological and biogeochemical processes. The extent and character of these 

gradients impact the ecosystem function of the estuary. In this study, we have 

demonstrated that the freshwater plume associated with the freshet can bathe 

shallow portions of Stefansson Sound in low salinity water, reaching down to ~4 m 

in certain years (Figs. 1.2 and 1.3). Arctic river water differs from seawater not only 

in salinity, but in other chemical constituents, which could affect the physiology of 
30

benthic organisms underlying the buoyant layer. Nutrient depleted river water 

displaces seawater high in inorganic nutrients, which have built up over the winter, 

which may result in significant nutrient stress for benthic autotrophs. Additionally, 

the organic composition of freshwater could create zones of differential biological 

activity and biogeochemical processes. For example, the dissolved organic matter 

associated with the freshet is highly labile (Holmes et al. 2008). If mixing is minimal 

(as it is under the ice), a nearshore zone with high concentrations of labile DOM 

could impact sediment microbial diversity and activity.

Current dynamics also influence the benthic realm by determining rates of 

sedimentation, as well as the delivery of food and nutrients to benthic organisms 

(e.g., Pequegnat 1964, Sebens 1984). High flow speeds can also mechanically stress 

or remove attached flora and fauna. Additionally, current dynamics determine the 

advection and transport of planktonic propagules, thereby impacting population 

connectivity and persistence within the region (e.g., Cowen and Sponaugle 2009,

Coleman et al. 2011). The annual cycle of minimal currents in the winter and 

maximal currents in the fall imply that propagule transport for any given species 

greatly depends on the timing of its life cycle. Differences in current regime between 

the eastern Boulder Patch sites and the deep and western sites (Figs. 1.5-1.7) 

suggest that these locations may have differing rates of propagule advection and 

delivery, which could affect benthic community structure and genetic diversity. 

Intensified easterly winds (Wood et al. 2013, 2015) and a longer open-water season 

(Mahoney et al. 2014) in the Beaufort Sea would increase the advection of terrestrial 

material, nutrients, plankton, and propagules away from the coastal zone. 

Studies that focus on estuarine processes within the Arctic are rare, and even 

fewer studies cover time periods outside of mid- to late summer (McClelland et al.

2012). However, as demonstrated here, environmental variability occurs during

periods outside the summer months that could profoundly affect nearshore 

ecosystems. Knowledge of the range and spatial patterns of abiotic conditions 

provide the conceptual basis to evaluate and understand ecosystem processes and 

function on Arctic inner shelves. 
31

APPENDIX

Supplementary Table S1.1. Results table for comparing each abiotic 

factor among sites via a blocked ANOVA (blocked by day) for the 

entire data record.

Season Variable Sig diff. by site?

All Temperature Yes

Sum Sq. DF F

Intercept 153.0 1 37.08

Site 188.5 4 11.42

Day 210.3 1 50.95

Residuals 30295.3 7340

Season Variable Sig diff. by site?

Ice covered winter Temperature Yes

Sum Sq. DF F

Intercept 12.62 1 1326.85

Site 2.21 4 58.18

Day 0.15 1 15.77

Residuals 29.01 3051

Season Variable Sig diff. by site?

Spring break-up Temperature Yes

Sum Sq. DF F

Intercept 2.38 1 19.29

Site 18.51 4 37.57

Day 0.03 1 0.24

Residuals 149.55 1214

Season Variable Sig diff. by site?

Open water summer Temperature Yes

Sum Sq. DF F

Intercept 137.8 1 27.90

Site 486.5 4 24.62

Day 82.2 1 16.65

Residuals 8802.7 1782

Season Variable Sig diff. by site?

Fall freeze-up Temperature No

Sum Sq. DF F

Intercept 20.17 1 22.17

Site 2.72 4 0.75

Day 8.88 1 9.76
32

Residuals 1160.22 1275

Season Variable Sig diff. by site?

All Salinity Yes

Sum Sq. DFF

Intercept 48861 364.16

Site 32784 61.08

Day 1711 12.72

Residuals 73888 5507

Season Variable Sig diff. by site?

Ice covered winter Salinity Yes

Sum Sq. DFF

Intercept 3738.81 2016.10

Site 609.34 82.14

Day 24.21 13.06

Residuals 4066.9 2193

Season Variable Sig diff. by site?

Spring break-up Salinity Yes

Sum Sq. DFF

Intercept 519.91 46.53

Site 1600.54 35.81

Day 116.61 10.43

Residuals 10437.6 934

Season Variable Sig diff. by site?

Open water summer Salinity Yes

Sum Sq. DFF

Intercept 2367.71 211.53

Site 1432.74 31.97

Day 133.81 11.94

Residuals 17408.8 1554

Season Variable Sig diff. by site?

Fall freeze-up Salinity Yes

Sum Sq. DFF

Intercept2060.61 428.57

Site 131.24 6.82

Day 191.11 39.74

Residuals 3885.0 808

Season Variable Sig diff. by site?

All PAR No

Sum Sq. DFF

Intercept 0.011 0.01

Site 4.814 1.46

Day 0.271 0.33

Residuals 3000.75 3654
33

Season Variable Sig diff. by site?

Ice covered winter PAR No

Sum Sq. DF F

Intercept 0.0005 1 0.60

Site 0.0030 3 1.28

Day 0.0001 1 0.09

Residuals 1.1660 1507

Season Variable Sig diff. by site?

Spring break-up PAR No

Sum Sq. DF F

Intercept 0.04 1 1.37

Site 0.02 3 0.30

Day 0.02 1 0.70

Residuals 16.16 605

Season Variable Sig diff. by site?

Open water summer PAR Yes

Sum Sq. DF F

Intercept 9.29 1 3.40

Site 61.27 4 5.60

Day 15.10 1 5.52

Residuals 2491.96 911

Season Variable Sig diff. by site?

Fall freeze-up PAR No

Sum Sq. DF F

Intercept 0.045 1 29.19

Site 0.003 3 0.70

Day 0.460 1 26.60

Residuals 0.940 615

Season Variable Sig diff. by site?

All Current velocity Yes

Sum Sq. DF F

Intercept 3835 1 243.08

Site 6335 4 100.38

Day 3353 1 212.49

Residuals 54293 3441

Season Variable Sig diff. by site?

Ice covered winter Current velocity Yes

Sum Sq. DF F

Intercept 10.40 1 14.78

Site 198.55 4 70.58

Day 5.97 1 8.49

Residuals 953.65 1356
34

Season Variable Sig diff. by site?

Spring break-up Current velocity Yes

Sum Sq. DF F

Intercept 21.55 1 32.73

Site 91.89 4 34.90

Day 17.24 1 26.19

Residuals 357.47 543

Season Variable Sig diff. by site?

Open water summer Current velocity Yes

Sum Sq. DF F

Intercept 160.8 1 9.65

Site 2097.0 4 31.45

Day 78.8 1 4.72

Residuals 15235.2 914

Season Variable Sig diff. by site?

Fall freeze-up Current velocity Yes

Sum Sq. DF F

Intercept 2.8 1 0.08

Site 2680.2 4 18.81

Day 25.3 1 0.71

Residuals 21733.6 610
35

2. Spatial and temporal trends in community composition in the Arctic 

Boulder Patch

Katrin Iken and Brenda Konar

INTRODUCTION

The Alaskan Beaufort Sea seafloor is, like most Arctic shelves, typically 

dominated by soft sediments (Carmack and MacDonald 2002). In isolated nearshore 

locations, however, sea ice deposited rocks create hard substratum that can be 

colonized by a markedly different fauna and flora than found in the surrounding 

low-diversity soft sediment areas (Dunton and Schell 1986). These rocky areas are 

biodiversity hotspots that support tightly linked food webs, based on benthic 

primary producers (macroalgae) that typically do not occur on Arctic shelves 

(Dunton et al. 1982). The high biodiversity associated with rocky areas lies primarily 

with the availability of hard substratum, providing structural habitat for an epilithic 

community, and with the presence of large macroalgae that serve as foundation 

species for associated organisms (Steneck et al. 2002, Christie et al. 2003, FilbeeDexter et al. 2018). The communities inhabiting these nearshore rocky areas are 

structured by physical factors, e.g., sea ice, presence of substratum, light, and 

sedimentation (Dunton and Dayton 1995, Aumack et al. 2007, Konar 2007, Zacher et 

al. 2009, Bonsell and Dunton 2018) as well as by complex biological interactions, 

such as competition (Barnes 2002, Barnes and Kuklinski 2004, Konar and Iken 

2005, Campana et al. 2009).

The Boulder Patch in Stefansson Sound is considered a regional biodiversity 

hotspot with higher macroalgal and invertebrate diversity than in the surrounding 

regions (Dunton and Schonberg 2000). While the overall area of the Boulder Patch 

is approximately 63 km2, environmental conditions such as light availability can 

differ considerably among different locations within the Boulder Patch region. Given 

that biological communities are driven strongly by the physical environment 

(Bonsell and Dunton 2018), it would stand to reason that community composition 

and biodiversity of the epilithic community differs among the different locations 

within the Boulder Patch. We here explored whether epilithic community patterns 

differed among various sites in the Boulder Patch and if these spatial differences 

were consistent over time. In addition, we tested whether community patterns in a 

smaller boulder region in Camden Bay were like those in the Boulder Patch and how 

these regional differences compared to local spatial differences. 

METHODS

Epilithic communities associated with deposited boulders were sampled at 

six sites (DS11, E1, E2, E3, W1, W2, W3) in the summers (early August) of 2005, 
36

2006, 2013, and 2015. All sampling was done using SCUBA, following protocols 

established by the Census of Marine Life ‘NaGISA’ Project (Natural Geography in 

Shore Areas; Rigby et al. 2007). Macroalgal and invertebrate assemblages were

sampled from 10 randomly placed 0.25 m2 quadrats. All macroalgal and invertebrate 

samples were collected and placed in fine mesh bags in situ. All samples were 

carefully washed over 500 μ mesh and the retained fraction sorted under a 

compound microscope. Macroalgae were identified to species level where possible 

and wet weight determined to 1 g precision. Macroalgal identifications were assisted 

by taxonomic experts (Sandra Lindstrom, University of British Colombia, Canada; 

Robert Wilce, University of Massachusetts Amherst (emeritus), USA; Christian 

Wiencke, Alfred Wegener Institute, Germany). In several cases it was unclear if 

different species identifications were due to regional differences or to different 

specialists identifying algae from different regions. As to not overestimate regional 

differences in the algal community, we therefore performed these comparisons 

based on the genus level. Invertebrates were sorted and identified to species or 

higher taxonomic level in those cases where species identifications were impossible 

(taxonomic assistance by Susan Schonberg, University of Texas, USA). Invertebrate 

wet biomass was determined to 0.0001 g accuracy. All invertebrates were preserved 

in 10% buffered formalin and later transferred into 50% isopropanol for long-term 

storage at the University of Alaska Fairbanks.

In addition to collections in the Boulder Patch, we performed similar 

collections in Camden Bay in 2005 (macroalgae only), 2007, and 2017. Except for 

2017, collections in Camden Bay were done on five replicate quadrats per site. 

Macroalgal samples were done at site DSE, DSC and DSW in 2005, and sites DSW and 

DS4 were sampled for macroalgae and invertebrates in 2007 and 2017. Samples 

were processed as described for those in the Boulder Patch. 

Community data were analyzed using multivariate statistics in the software 

package PRIMERTM version 7 (Clarke et al. 2014). Macroalgal and invertebrate 

assemblages were analyzed separately to account for the vast differences (by >3-4 

orders of magnitude) larger algal biomass than invertebrate biomass and to assess if 

the two groups showed similar patterns. Both datasets were first dispersionweighted to account for the large variance in some of the taxa (e.g., kelp in 

macroalgal and sea stars in invertebrate datasets, respectively, could have very small 

or very large biomass in different replicates, leading to large variance). Distribution 

of taxa across replicates was then assessed via shade plots and both datasets squareroot transformed, which reduced the weight of outlier values and led to comparable 

data ranges in both datasets. A resemblance matrix using Bray-Curtis similarities 

was created including a 0.1 dummy variable to account for the lack of variance 

because some replicates did not contain any biological material (all macroalgal and 

invertebrate biomass was zero). The effects of time (year) and location (site) was 

tested using PERMANOVA, with year as a random factor and site as fixed factor. A 

SIMPER analysis was conducted on both macroalgal and invertebrate datasets to 

identify taxa that contributed to differences in the assemblages among sites and/or 

years. Shannon’s Diversity index [H’ = SUM(Pi*ln(Pi))] and Pielou’s Evenness index 

[J’ = H’-Log(S)] were calculated, where Pi is the proportion of species i relative to the 
37

total number of species, and S is the total number of species. The distance to 

centroids in the data matrix was used to assess dispersion of communities at the 

seven study sites over time, measuring the “spread” or heterogeneity of data within 

the assemblages. Differences in distance to centroid for each site across study years 

were statistically tested using ANOVA. A Euclidean distance matrix was applied to 

dispersion measures from a PERMDISP analysis to test for significant differences in 

a PERMANOVA design with year as a random factor and site as fixed factor. 

We also sampled epilithic communities in Camden Bay during some of the 

study years (2005, 2007, 2017). Sampling protocols were like those employed in the 

Boulder Patch although replication was typically limited to 5 replicates instead of 10 

but level of analysis was typically the same (except that in 2005, invertebrate 

assemblages were not identified for biomass in Camden Bay). Also, sampling site 

selection in Camden Bay was more haphazard than in the Boulder Patch and sites 

were not possible to consistently revisit over sampling years, making the 

assessment of temporal trends more difficult. Only one site (DSW) was sampled in 

all three years, and site DS4 was sampled in 2007 and 2017. Data for Camden Bay 

(macroalgae and invertebrates considered separately) were treated as described for 

the Boulder Patch data. Camden Bay assemblages were assessed via diversity 

measures (Pielou’s and Shannon), hierarchical cluster analysis, and nMDS plots. 

Assemblage composition was compared between the two regions (Boulder Patch 

and Camden Bay) using Analysis of Similarity (ANOSIM) and by hierarchical cluster 

analysis. A SIMPROF test examined statistical differences (p=0.05) among clusters 

and a SIMPER analysis identified taxa that contributed most to differences in 

groupings.
38

Table 2.1. Macroalgal biomass (g wet weight 0.25 m-2) at seven sites in the Boulder 

Patch over four study years. 

Site Year

sp.Phycodrys 

truncatusCoccotylus Dilsea sp.

dentataOdonthalia 

sp.Rhodomela 

melagoniumChaetomorpha 

solidungulaLaminaria 

latissimaSaccharina 

esculentaAlaria 

plumosaChaetopteris 

arcticaBattersia 

plicataAhnfeltia 

DS11 2005 13.7 10.6 6.3 0.5 0.5 0.0 40.6 0.0 0.0 0.0 0.0 0.0E1 2005 30.3 29.8 4.5 1.1 0.5 0.0 2.6 0.0 0.0 0.3 0.0 0.0E2 2005 26.6 17.0 0.5 0.5 0.2 0.0 7.4 0.0 0.0 0.1 0.0 0.0E3 2005 4.0 7.8 1.1 0.1 0.0 0.0 7.5 0.0 0.0 0.0 0.0 0.0W1 2005 9.6 4.8 0.2 1.1 0.0 0.0 6.3 0.0 0.0 0.0 0.0 0.0W2 2005 13.1 11.5 0.0 0.0 0.0 0.0 8.6 0.0 0.0 0.0 0.0 0.0W3 2005 22.9 17.7 16.0 3.5 5.5 0.0 23.3 0.0 0.0 0.0 0.0 0.0DS11 2006 25.0 15.2 26.5 0.8 1.8 0.0 28.8 18.0 2.7 0.0 0.0 0.0E1 2006 42.0 19.0 1.6 0.5 0.1 0.1 8.5 0.0 0.0 0.4 0.0 0.0E2 2006 22.9 9.6 0.3 0.2 0.0 0.0 35.5 0.0 0.0 0.0 0.0 0.7E3 2006 17.3 7.6 12.0 0.0 0.0 0.0 24.9 0.0 0.0 0.0 0.0 0.0W1 2006 18.7 4.6 1.4 0.3 0.0 0.0 9.1 0.0 0.0 0.0 0.0 0.0W2 2006 44.7 43.8 0.3 0.0 0.0 0.0 43.8 0.0 0.0 0.2 0.0 0.0W3 2006 68.0 42.2 49.6 2.5 3.3 0.0 128.6 14.3 0.0 0.0 0.0 0.0DS11 2013 15.4 7.2 16.7 0.3 0.4 0.0 14.9 19.6 0.0 0.0 0.0 0.2E1 2013 13.5 6.8 3.0 0.5 0.5 0.0 19.6 4.4 0.0 3.4 1.1 0.0E2 2013 54.5 14.5 1.4 1.0 0.1 0.0 11.3 0.4 0.0 0.3 0.3 0.0E3 2013 14.7 7.9 16.2 0.1 0.4 0.0 18.4 0.0 0.0 0.0 0.0 0.0W1 2013 12.6 5.0 15.1 0.2 2.3 0.0 7.7 0.2 0.0 0.0 0.0 0.0W2 2013 4.0 4.3 0.1 0.0 0.1 0.0 7.0 0.0 0.0 0.0 0.0 0.0W3 2013 14.4 15.9 8.6 0.0 3.4 0.0 15.6 0.2 0.0 0.0 0.0 0.0DS11 2015 35.0 31.5 27.7 1.4 1.1 0.0 22.7 58.1 7.3 0.0 0.0 0.6E1 2015 21.3 29.5 1.4 1.6 0.2 0.0 1.0 0.7 0.0 3.8 0.4 0.0E2 2015 28.7 18.0 5.7 2.7 0.1 0.0 18.4 0.0 0.0 1.8 0.1 0.0E3 2015 31.1 16.3 16.0 0.0 0.5 0.0 14.3 0.9 0.0 1.3 0.0 1.2W1 2015 15.4 13.0 6.9 0.0 0.0 0.0 18.4 0.0 0.0 0.0 0.0 0.0W2 2015 10.3 11.2 0.1 0.3 0.0 0.0 2.2 0.0 0.0 0.0 0.0 0.0W3 2015 34.4 19.5 17.4 1.9 5.1 0.0 32.2 6.4 0.0 0.0 0.0 0.0
39

Table 2.2. Invertebrate biomass (g wet weight 0.25 m-2) at seven sites in the Boulder 

Patch over four study years. 

ARTHROPODA MOLLUSCA ANNELIDA ECHINODERMATA CNIDARIA ASCIDIACEA BRYOZOA PORIFERA NEMERTEA

Site Year Amphipoda Isopoda Decapoda Crustacea other Pycnogonida Gastropoda Polyplacophora Bivalvia Polychaeta Asteroidea Holothuroidea Anthozoa Hydrozoa Cnidaria other Ascidiacea Bryozoa Porifera Nemertea

DS11 2005 0.00 0.00 0.00 0.00 0.00 0.07 0.52 0.00 0.13 0.04 0.00 0.00 0.49 0.00 0.00 0.20 0.56 0.00E1 2005 0.00 0.31 0.00 0.03 0.00 0.00 0.00 0.10 0.01 0.00 0.00 0.00 0.01 0.00 0.00 2.44 0.17 0.00E2 2005 0.00 0.08 0.00 0.00 0.00 0.01 0.00 0.01 0.02 0.00 0.00 0.00 0.53 0.00 0.00 0.54 0.29 0.00E3 2005 0.01 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.03 0.00 0.00 0.00 0.50 0.00 0.00 0.07 1.88 0.00W1 2005 0.00 0.00 0.00 0.00 0.00 0.02 0.01 0.00 0.01 0.00 0.00 0.03 1.60 0.00 0.00 0.19 1.71 0.00W2 2005 0.02 0.39 0.00 0.00 0.00 1.13 0.11 0.00 0.05 8.40 0.00 0.34 0.86 0.00 0.00 0.60 4.88 0.00W3 2005 0.00 0.00 0.00 0.00 0.00 0.01 0.24 0.00 0.00 0.01 0.00 0.71 0.17 0.00 0.01 0.18 0.24 0.00DS11 2006 0.02 0.00 0.00 0.00 0.01 0.02 0.12 1.37 0.06 0.01 0.00 0.00 0.39 0.00 0.00 0.84 6.42 0.00E1 2006 0.01 0.07 0.00 0.25 0.00 0.00 0.00 0.25 0.00 0.00 0.00 0.00 0.15 0.00 0.03 3.02 1.39 0.00E2 2006 0.02 0.12 0.00 1.39 0.00 0.00 0.77 0.18 0.00 0.00 0.00 0.00 0.51 0.00 0.01 1.57 0.69 0.00E3 2006 0.00 0.59 0.00 0.00 0.00 0.02 0.15 0.01 0.05 0.00 0.00 0.00 1.02 0.00 0.03 0.47 0.44 0.00W1 2006 0.09 0.00 0.00 0.00 0.02 0.00 0.07 0.00 0.04 0.00 0.00 0.18 1.11 0.00 0.02 0.76 0.39 0.00W2 2006 0.01 0.00 0.00 0.00 0.00 0.02 0.00 0.01 0.14 0.02 0.00 0.70 0.82 0.00 0.03 2.17 0.66 0.00W3 2006 0.00 0.00 0.00 0.00 0.02 0.05 0.42 0.01 0.04 0.12 0.71 0.30 0.10 0.00 0.00 0.55 3.37 0.00DS11 2013 0.01 0.00 0.31 0.00 0.00 0.06 0.38 0.57 0.05 0.01 0.00 0.02 0.67 0.01 0.00 0.11 1.38 0.00E1 2013 0.04 0.41 0.00 0.06 0.00 0.03 0.00 0.11 0.00 0.00 0.00 0.58 0.36 0.00 0.00 0.17 0.55 0.00E2 2013 0.07 0.69 0.00 0.00 0.01 0.00 0.00 0.10 0.02 0.00 0.00 0.00 0.12 0.00 0.00 1.10 1.11 0.00E3 2013 0.03 0.00 0.83 0.00 0.01 0.03 0.36 0.03 0.02 0.01 0.00 0.00 0.19 0.00 0.00 0.05 0.24 0.00W1 2013 0.00 0.00 0.00 0.00 0.03 0.20 0.42 0.00 0.02 0.20 0.00 1.45 0.34 0.00 0.33 0.08 0.33 0.00W2 2013 0.01 0.36 0.00 0.00 0.02 0.03 0.67 0.00 0.06 0.09 0.00 1.99 0.06 0.00 0.00 0.16 0.63 0.00W3 2013 0.00 0.00 0.00 0.00 0.01 1.17 0.16 0.20 0.04 0.00 1.22 0.04 0.12 0.00 0.05 0.45 0.05 0.00DS11 2015 0.00 0.00 0.00 0.00 0.00 0.13 0.49 0.00 0.20 0.00 0.00 0.00 0.51 0.00 0.01 6.26 11.79 0.00E1 2015 0.13 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.06 0.00 0.00 0.72 0.49 0.00E2 2015 0.11 0.43 0.00 0.31 0.00 0.01 0.00 0.29 0.02 0.00 0.00 0.00 0.46 0.00 0.00 0.33 2.66 0.00E3 2015 0.09 0.91 0.00 0.00 0.01 0.03 0.27 0.01 0.08 0.02 0.00 0.00 0.05 0.00 0.00 2.72 4.81 0.00W1 2015 0.05 0.74 0.00 0.00 0.01 0.09 0.11 0.00 0.08 0.19 0.00 1.54 0.15 0.00 0.00 0.32 8.32 0.00W2 2015 0.00 0.00 0.00 0.00 0.01 0.02 0.00 0.00 0.01 17.69 0.00 2.17 0.05 0.00 0.00 0.15 3.00 0.00W3 2015 0.07 0.00 1.18 0.00 0.02 0.09 0.17 0.00 0.02 0.10 0.00 0.93 0.02 0.00 0.03 5.64 10.68 0.00
40

RESULTS

Boulder Patch

Over the four investigation years, we observed 12 macroalgal species and 

249 invertebrate taxa across the Boulder Patch. Not all taxa occurred in collections 

in all years, but some taxa were common and typically dominated the community in 

terms of biomass (Tables 2.1 and 2.2). Macroalgal biomass ranged from 15.6 – 308.5 

g wet weight 0.25 m-2 per sites and year (Fig. 2.1). Among the macroalgae, the kelp 

Laminaria solidungula and the red algae Phycodrys sp., Coccotylus truncatus and 

Dilsea sp. were typically the largest biomass contributors (Fig. 2.1). Kelps 

contributed much to the variability in total biomass at particular sites per year. 

Invertebrate biomass was an order of magnitude lower than that of macroalgae and 

ranged from 1.4 – 19.4 g wet weight 0.25 m-2 per sites and year (Fig. 2.1). Most 

invertebrate biomass was contributed by Porifera (39% of total invertebrate 

biomass), Bryozoa (18%), Echinodermata (esp. Asteroidea, 15%), Hydrozoa and 

Cnidaria (both 6%). Among those, biomass of Cnidaria and Asteroidea was highly 

clumped at some sites (Fig. 2.2). 

Phycodrys sp.

Coccotylus truncatus

Dilsea sp.

Odonthalia dentata

Rhodomela sp.

Chaetomorpha melagonium

Laminaria solidungula

Saccharina latissima

Alaria esculenta

Chaetopteris plumosa

Battersia arctica

Ahnfeltia plicata

0

100

200

300

400

2005 2006 2013 2015

DS11

E1E2E3

W1W2W3

DS11

E1E2E3

W1W2W3

DS11

E1E2E3

W1W2W3

DS11

E1E2E3

W1W2W3

Biomass g wet weight * 0.25 m-2

Figure 2.1. Macroalgal species biomass (g wet weight 0.25 m-2) at seven sites in the 

Boulder Patch over four study years. 
41

Biomass g wet weight * 0.25 m-2

2005 2006 2013 2015

DS11

E1E2E3

W1W2W3

DS11

E1E2E3

W1W2W3

DS11

E1E2E3

W1W2W3

DS11

E1E2E3

W1W2W3

0

5

10

15

20

25

Amphipoda

Isopoda

Decapoda

Crustacea other

Pycnogonida

Gastropoda

Polyplacophora

Bivalvia

Polychaeta

Asteroidea

Holothuroidea

Anthozoa

Cnidaria other

Ascidiacea

Bryozoa

Hydrozoa

Porifera

Nemertea

Figure 2.2. Invertebrate taxon biomass (g wet weight 0.25 m-2) at seven sites in the 

Boulder Patch over four study years.

The interaction term year x site in the PERMANOVA of the macroalgal dataset 

was significant, indicating that the change in macroalgal assemblage among sites 

was dependent on the year of investigation, with the interaction term explaining 

about 14% of total variability (Table 2.3a). Site itself had a stronger effect on 

macroalgal assemblage composition than year in this analysis (15% versus 5% of 

the variance explained, respectively). The year x site effect (Table 2.3a) was also 

illustrated in the nMDS plot where especially the assemblage in 2013 was very 

different from the assemblages in other years for most sites (strong effects at DS11, 

E1, W2 and W3) while the 2013-year effect was less noticeable at the other sites 

(Fig. 2.3). The residual effect was large (65%), pointing towards other drivers of 

patterns in macroalgal assemblages, not explained by sites and years. Strong site 

differences were mostly driven by greater biomass of Rhodomela sp. at DS11 and 

W3, Dilsea sp. being generally higher at DS11, E3 and W3, and Odonthalia dentata

particularly low at several sites in 2013 (Fig. 2.4). Shannon diversity was variable 

across sites and years but tended to be higher in later years (Fig. 2.5). Evenness also 

was variable, but generally slightly lower at sites E1 and E2. Similar to assemblage 

structure, dispersion within the assemblages was significantly affected by the year x 

site interaction (19% of variance), although the effect of “site” alone was negligible 
42

in this analysis (Table 2.3b). Dispersion, measured as the distance to centroid, was 

significantly larger in 2013 at sites DS11, E1, and W2 (Fig. 2.6). 

Table 2.3. PERMANOVA results of Boulder Patch macroalgal assemblage 

composition by year and site (a) and of dispersion of Boulder Patch macroalgal

assemblages by year and site (b). 

Source df SS MS Pseudo-F P(perm)

 Unique 

perms

year 3 21600 7200 6.4453 0.001 9905

site 6 84169 14028 3.9108 0.001 9913

year x site 18 64566 3587 3.211 0.001 9809

Residual 252 2.82E+05 1117.1 

Total 279 4.52E+05 

Estimates of components of variation

Source Estimate Sq.root % Variance

V(year) 86.898 9.3219 5.1

S(site) 261.03 16.156 15.2

V(yexsi) 246.99 15.716 14.4

V(Res) 1117.1 33.423 65.3

Source df SS MS Pseudo-F P(perm)

 Unique 

perms

year 3 5707 1902.3 18.922 0.0001 9946

site 6 1335.1 222.51 0.58501 0.7393 9947

year x site 18 6846.4 380.36 3.7833 0.0001 9906

Residual 252 25335 100.54 

Total 279 39224 

Estimates of components of variation

Source Estimate Sq.root % Variance

V(ye) 25.74 5.0734 17.1

S(si) -3.9461 -1.9865 -2.6

V(yexsi) 27.982 5.2898 18.6

V(Res) 100.54 10.027 66.9

a

b
43

site

DS11

E1

E2

E3

W 1

W 2

W 3

2005

2005

2005

2005

2005

2005

2005

2006

2006

2006

2006

2006

2006

2006

2013

2013

2013

2013

2013

2013

2013

2015

2015

2015 2015

2015

2015

2015

2D Stress: 0.14

Figure 2.3. nMDS (non-metric Multidimensional Scaling) plot of macroalgal 

assemblages at seven sites in the Boulder Patch over four study years. Sites are 

represented as different symbols and years are indicated in the plot. Trajectories 

indicate direction of change in macroalgal assemblage structure over time per site. 

Dispersion weighting, Square-root transform

Resemblance: Bray-Curtis similarity (+d)

A 

B 

C 

A. Dilsea sp.

0 - 50

B. Odonthalia dentata

0 - 4

C. Rhodomela sp.

0 - 6 DS11

E1

E2

E3

W 1

W 2

W 3

DS11

E1

E2

E3

W 1

W 2

W 3

DS11

E1

E2

W 2

W 1

W 3

E3

E1

E2

DS11 E3

W 1

W 2

W 3

2D Stress: 0.14

Figure 2.4. Macroalgal species contributing to the differences in assemblage 

structure among sites in the Boulder Patch. Species are indicated as pie slices (A-C) 

with pie pieces proportional to the range of biomass within each taxon.
44

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Evenness

Shannon

DS112006 2013 2015 2005 2006 2013 2015 2005 2006 2013 2015

E1 E2 2005 2006 2013 2015

E3 2005 2006 2013 2015

W1 2005 2006 2013 2015

W2 2006 2013 2015

W3 2005 2005

Figure 2.5. Shannon Wiener diversity index (H’) and Pielou’s evenness index (J’) for 

macroalgal assemblages at seven sites in the Boulder Patch over four study years.

0

20

40

60

DS11 2005 2006 2013 2015

2005200620132015

2005200620132015

E1 E2

2005200620132015

E3

2005200620132015

W1

2005200620132015

W2

200620132015

W3

2005

*

*

ns ns

* Distance to centroid

ns

ns

Figure 2.6. Mean dispersion, measured as the distance to centroid, for macroalgal 

assemblages at seven sites in the Boulder Patch over four study years. Dispersion is 

a measure of the scatter within the multivariate assemblage. Asterisks above means 

indicate significant (ANOVA, p≤0.05) differences among years within each site; 
45

placement of asterisk in the graph identifies the years that are different within each 

respective site. 

Invertebrate assemblages also were driven by a significant year x site 

interaction term in the PERMANOVA, explaining about 13% of the variance (Table 

2.4a). Among the individual terms, year (16%) had a much larger effect than site 

(5%). While all sites clustered relatively closely in 2005 and 2006, invertebrate 

assemblage composition was distinctly different in 2013 and then again in 2015 

(Fig. 2.7). Invertebrate assemblages in 2005 and 2006 were characterized by higher 

biomass of especially the chiton Stenosemus albus, the bryozoan Eucratea loricata

and other Bryozoa, the hydroid Sertularia cupressoides, while assemblages in 2013 

were particularly rich in the polychaete Exogone sp., and those in 2015 contained 

high biomass of Amphipoda (Fig. 2.8). Shannon diversity within sites and years was 

higher but much more variable than for macroalgae, with a general trend to lower 

values in 2015 (Fig. 2.9). Evenness was generally lower than for macroalgae with no 

discernable trend among sites or years. Like overall assemblage structure, the year x 

site interaction term and the individual year term were significant when assessing 

dispersion within sites across years, accounting for 16% and 19% of the variance, 

respectively (PERMANOVA, Table 2.4b). Dispersion, measured as the distance to 

centroid, was significantly greater at E1 and W3 in 2013 compared to other years, 

but was significantly lower in 2015 at DS11, in 2006 at W2, and in 2005 and 2006 at 

W1 compared with other years at those sites (Fig. 2.10). Total invertebrate biomass 

was positively correlated to total macroalgal biomass and different morphological 

groups of macroalgae (bushy morphology of red algae, erect blade morphology of 

kelps), but none of these relationships were significant (all p>0.05). 
46

Table 2.4. PERMANOVA results of Boulder Patch invertebrate assemblage

composition by year and site (a) and of dispersion of Boulder Patch invertebrate 

assemblages by year and site (b). 

a Source df SS MS Pseudo-F P(perm)

 Unique 

perms

si 6 1.07E+05 17822 2.1105 0.0002 9827

ye 3 1.52E+05 50593 17.463 0.0001 9804

sixye 18 1.52E+05 8444.4 2.9147 0.0001 9475

Res 252 7.30E+05 2897.2 

Total 279 1.14E+06 

Estimates of components of variation

Source Estimate Sq.root % Variance

S(si) 234.44 15.311 5.4

V(ye) 681.37 26.103 15.6

V(sixye) 554.73 23.553 12.7

V(Res) 2897.2 53.825 66.3

b Source df SS MS Pseudo-F P(perm)

 Unique 

perms

si 6 1072 178.66 0.79333 0.5944 9960

ye 3 4164.8 1388.3 20.529 0.0001 9947

sixye 18 4053.8 225.21 3.3303 0.0001 9898

Res 252 17041 67.623 

Total 279 26332

Estimates of components of variation

Source Estimate Sq.root % Variance

S(si) -1.1636 -1.0787 -1.2

V(ye) 18.866 4.3435 18.7

V(sixye) 15.759 3.9697 15.6

V(Res) 67.623 8.2233 66.9
47

Transform: Dispersion weighting, Square root

Bray-Curtis similarity (+d)

site

DS11

E1

E2

E3

W 1

W 2

W 3

site

DS11

E1

E2

E3

W 1

W 2

W 3

2005

2005

2005

2005

2005

2005

2005

2006

2006

2006

2006

2006

2006

2006

2013

2013

2013

2013

2013

2013

2013

2015

2015

20152015

2015

2015

2015

2D Stress: 0.21

Figure 2.7. nMDS plot of invertebrate assemblages at seven sites in the Boulder 

Patch over four study years. Sites are represented as different symbols and years are 

indicated in the plot. Trajectories indicate direction of change in macroalgal 

assemblage structure over time per site.

A 

B 

D C 

E 

F 

A. Stenosemus albus

0 - 0.8

B. Eucratea loricata

0 - 20

C. Sertularia cupressoides

0 - 6

D. Bryozoa

0 - 2

E. Amphipoda

0 - 0.2

F. Exogone

0 - 0.002

2005

2005

2005

2005

2005

2005

2005

2006

2006

2006

20062006

2006

2006

2013

2013

2013

2013

2013

2013

2013

2015 2015

20152015

2015

2015

2015

2D Stress: 0.21

Transform: Dispersion weighting, Square root

Bray-Curtis similarity (+d)

Figure 2.8. Invertebrate species contributing to the differences in assemblage 

structure among years in the Boulder Patch. Species are indicated as pie slices (A-F) 

with pie pieces proportional to the range of biomass within each taxon.
48

DS112006 2013 2015 2005 2006 2013 2015 2005 2006 2013 2015

E1 E2 2005 2006 2013 2015

E3 2005 2006 2013 2015

W1 2005 2006 2013 2015

W2 2006 2013 2015

W3 2005 2005

0

0.5

1.0

1.5

2.0

2.5

3.0

Evenness

Shannon

Figure 2.9. Shannon Wiener diversity index (H’) and Pielou’s evenness index (J’) for 

invertebrate assemblages at seven sites in the Boulder Patch over four study years.

0

20

40

60

Distance to centroid

DS11 2005 2006 2013 2015

2005200620132015

2005200620132015

E1 E2

2005200620132015

E3

2005200620132015

W1

2005200620132015

W2

200620132015

W3

2005

*

*

ns

ns

* *

*

Figure 2.10. Mean dispersion, measured as the distance to centroid, for invertebrate 

assemblages at seven sites in the Boulder Patch over four study years. Dispersion is 

a measure of the scatter within the multivariate assemblage. Asterisks above means 

indicate significant (ANOVA, p≤0.05) differences among years within each site; 

placement of asterisk in the graph identifies the years that are different within each 

respective site.
49

Camden Bay (and comparison with Boulder Patch)

The most common macroalgal taxa in Camden Bay were Coccotylus truncatus, 

Dilsea sp., Phycodrys sp., and Laminaria solidungula, with Saccharina latissima and 

Alaria esculenta also common at some sites (Fig. 2.11, Table 2.5). Total biomass 

varied strongly among sites and years, with unusually high biomass at site DS4 in 

2007. 

Table 2.5. Macroalgal biomass (g wet weight 0.25 m-2) in Camden Bay over three 

study years. 

Sample Phycodrys sp.

Coccotylus 

truncatus Dilsea sp.

Odonthalia 

dentata Rhodomela sp

Chaetomorpha 

melagonium L. solidungula S. latissima

Alaria 

esculenta

Chaetopteris 

plumosa

Battersia 

arctica Rhodophyta

DSE2005 23.36 18.54 1.00 1.42 0.06 0.05 0.09 0.80 0.00 0.74 0.00 0.22

DSC2005 13.24 7.66 21.64 3.42 1.68 0.28 0.32 0.70 0.00 1.10 0.10 0.23

DSW2005 3.36 7.48 6.84 1.62 0.94 0.03 0.00 1.76 0.00 0.44 0.00 0.12

DSW2007 1.40 1.41 70.40 37.20 3.00 0.40 4.40 43.80 18.00 5.00 0.00 0.00

DS42007 59.40 45.80 15.80 45.00 0.21 0.01 115.20 87.00 11.20 2.80 0.00 0.00

DSW2017 9.97 18.19 13.25 3.63 1.29 0.15 0.10 0.23 39.57 6.58 0.16 0.14

DS42017 2.65 9.30 0.00 1.59 0.14 0.03 42.81 20.86 6.37 0.60 0.00 0.00 Biomass g wet weight * 0.25 m-2

0

100

200

300

400 Phycodrys sp

Coccotylus truncatus

Dilsea sp.

Odonthalia dentata

Rhodomela sp

Chaetomorpha melagonium

L. solidungula

S. latissima

Alaria esculenta

Chaetopteris plumosa

Battersia arctica

Rhodophyta

DSE 2005

DSC 2005

DSW 2005DSW 2007

DS4 2007

DSW 2017

DS4 2017

Figure 2.11. Macroalgal species biomass (g wet weight 0.25 m-2) at four sites at 

Camden Bay over three study years. Not all sites were surveyed during each year. 
50

Shannon diversity and Pielou’s Evenness were slightly lower in 2017 

compared with other years but there were no significant differences (ANOVA, 

p>0.05 for all comparisons; Fig. 2.12). Macroalgal assemblage composition fell into 

two distinct clusters on a 45% similarity level (Fig. 2.13). Among the taxa causing 

this separation were mostly the kelps Laminaria solidungula and Saccharina 

latissima (Fig. 2.14). 

a

b

0

0.2

0.4

0.6

0.8

1.0

J’

0

0.5

1.0

1.5

2.0

H’(ln)

DSE 2005

DSC 2005

DSW 2005

DSW 2007

DS4 2007

DSW 2017

DS4 2017

DSE 2005

DSC 2005

DSW 2005

DSW 2007

DS4 2007

DSW 2017

DS4 2017

Figure 2.12. Box plots of macroalgal assemblage Shannon diversity (a) and Pielou’s 

evenness (b) four sites at Camden Bay over three study years (not all sites were 

surveyed each year). There were no significant differences in any of the measures. 
51

2005

2005

20052017

201720072007

100

80

60

40

Similarity

Dispersion weighting

Transform: Square root

Resemblance: Bray-Curtis similarity

Site

DSE

DSC

DSW

DS4

Figure 2.13. Hierarchical cluster analysis of the macroalgal assemblages at Camden 

Bay. Results of a SIMPROF test are displayed by colored histogram branches, where 

red branches signify no significant difference between connecting samples. 

Dispersion weighting, square root transform.

Resemblance: Bray-Curtis similarity

B A 

A. L. solidungula

0 - 2

B. S. latissima

0 - 2

Similarity

45% DSE2005

DSC2005

DSW2005

DSW2007

DS42007

DSW2017

DS42017

2D Stress: 0.03

Figure 2.14. Macroalgal species contributing to the differences in assemblage 

structure among sites and years at Camden Bay. Species are indicated as pie slices 

(A-C) with pie pieces proportional to the range of biomass within each taxon. Green 

circles represent non-significant groupings from the cluster analysis (Fig. 2.13) at a 

45% similarity level. 

Overall average biomass at Camden Bay was higher than at the Boulder 

Patch, mostly driven my higher biomass of the kelps Saccharina latissima and Alaria 

esculenta (Fig. 2.15). Shannon diversity was higher in Camden Bay, although this 

difference was not statistically significant (t-test, p=0.093) and there also was no 
52

regional statistical difference in Pielou’s evenness (Fig. 2.16). Macroalgal 

assemblage structure in the two regions (combining sites and years within each 

region) was significantly different (AMOSIM test, global R = 0.445, p = 0.001) (Fig. 

2.17). In addition to the differences in kelp mentioned above, these regional 

differences were also driven by the complete absence of Ahnfeltia plicata in Camden 

Bay.

0

50

100

150

Phycodrys sp.

Coccotylus truncatus

Dilsea sp.

Odonthalia dentata

Rhodomela sp.

Chaetomorpha melagonium

Laminaria solidungula

Saccharina latissima

Alaria esculenta

Chaetopteris plumosa

Battersia arctica

Ahnfeltia plicata

Biomass g wet weight * 0.25 m

Rhodophyta

-2

BP CB

Figure 2.15. Overall average macroalgal biomass (g wet weight 0.25 m-2) in the 

Boulder Patch and at Camden Bay. Overall biomass per region is separated by taxa 

indicated in the legend. 

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.2

1.4

1.6

1.8

J’

H’(ln)

BP CB BP CB

a b

Figure 2.16. Box plots of macroalgal assemblage Shannon diversity (a) and Pielou’s 

evenness (b) in the Boulder Patch and at Camden Bay. There were no significant 

differences in any of the measures.
53

Dispersion weighting

Transform: Square root

Resemblance: Bray-Curtis similarity

region

BP

CB

2D Stress: 0.16

Figure 2.17. nMDS plot of macroalgal assemblages between the two regions. Each 

symbol represents the assemblage at a certain site and year. 

Invertebrate assemblages in Camden Bay were mostly composed of 

amphipods, bivalves, and polychaetes (Fig. 2.18, Table 2.6). Overall, invertebrate 

assemblages were less taxon rich than in the Boulder Patch, although the more 

limited sampling in Camden Bay could contribute to some of those differences. 

Camden Bay invertebrate assemblages differed strongly between the two sampling 

years (only about 10% similarity between years, ANOSIM global R for year effect: 

0.725, p=0.001), but also between the two sites sampled in both years (ANOSIM 

global R for site effect: 0.477, p=0.001). Differences in invertebrate assemblages in 

Camden Bay between sampling years were mostly driven by the gastropods 

Rissoidae, the amphipods Atylus carinatus and Apherusa megalops, and differences 

between sites were influenced mostly by the bivalve Musculus discors and the 

barnacle Balanus crenatus (Fig. 2.19). Shannon diversity and Pielou’s evenness were 

higher in 2007 than 2017 but only the 2007 to 2017 evenness comparison for DSW 

was statistically significant (Fig. 2.20; ANOVA, Tukey post-hoc comparison p=0.046). 

Table 2.6. Invertebrate biomass (g wet weight 0.25 m-2) in Camden Bay over three 

study years. 

Sample Amphipoda Isopoda Crustacea otherGastropoda Bivalvia Polychaeta Ascidiacea Bryozoa Hydrozoa Porifera Nemertea

DS42017 1.907 0.100 0.816 1.159 2.050 1.368 0.167 0.166 0.135 0.200 0.243

DSW2017 5.161 0.000 0.470 1.359 0.079 0.314 0.000 0.216 0.200 0.000 0.000

DS42007 1.667 0.000 1.236 0.746 2.150 2.579 0.000 1.013 0.807 0.748 0.199

DSW2007 1.288 0.200 0.471 0.679 0.898 0.276 0.200 0.985 0.200 0.227 0.000
54

0

5

10

15 Amphipoda

Isopoda

Crustacea other

Gastropoda

Bivalvia

Polychaeta

Ascidiacea

Bryozoa

Hydrozoa

Porifera

Nemertea

DS4-2017

DSW-2017

DS4-2007

DSW-2007

Biomass g wet weight * 0.25 m-2

Figure 2.18. Invertebrate taxon biomass (g wet weight · 0.25 m-2) by taxa at two 

sites and two years.

Dispersion weighting, Square root transform

Resemblance: Bray-Curtis similarity

A 

B 

C 

D 

E 

A. Apherusa megalops

0 - 2

B. Atylus carinatus

0 - 2

C. Musculus discors

0 - 2

D. Rissoidae

0 - 2

E. Balanus crenatus

0 - 0.9

DS4-2017

DSW-2017

DS4-2007

DSW-2007

2D Stress: 0.0

Figure 2.19. Invertebrate species contributing to the differences in assemblage 

structure among sites and years at Camden Bay. Species are indicated as pie slices 

(A-E) with pie pieces proportional to the range of biomass within each taxon.
55

0

0.2

0.4

0.6

0.8

ab

b

ab

a

0

0.5

1.0

1.5

2.0 DS4-2017 DSW-2017 DS4-2007 DSW-2007 J’

H’(ln) DS4-2017 DSW-2017 DS4-2007 DSW-2007

a b

Figure 2.20. Box plots of invertebrate assemblage Shannon diversity (a) and Pielou’s 

evenness (b) at Camden Bay. There were no significant differences in Shannon 

diversity; statistical differences (ANOVA, p<0.05) in Pielou’s evenness are indicated 

by different letters above boxes.

The invertebrate assemblages between the two regions separated 

significantly (ANOSIM global R = 0.557, p = 0.01; Fig. 2.21). There were much fewer 

Porifera, Bryozoa, and Cnidaria in Camden Bay than in the Boulder Patch, and 

Echinodermata were missing completely in Camden Bay. In contrast, Arthropoda 

(mostly based on Amphipoda) were much more prominent in Camden Bay (Fig. 

2.22). Overall, invertebrate assemblages were less taxon rich than in the Boulder 

Patch, with significantly lower Shannon diversity in Camden Bay (Fig. 2.23; t-test, 

p=0.046), although the more limited sampling in Camden Bay could contribute to 

some of those differences.
56

.

region

BP

CB

2005

2005

2005

2005 2005

2005 2005

2006

2006

2006

2006

2006

2006

2006

2013

2013

2013

2013

2013

2013

2013

2015

2015

2015

2015 2015 2015

2015

2017

2017

2007 2007

2D Stress: 0.19

Dispersion weighting, Square root transform

Resemblance: Bray-Curtis similarity

Figure 2.21. nMDS plot of invertebrate assemblages between the two regions. Each 

symbol represents the assemblage at a certain site and year.

Biomass g wet weight * 0.25 m-2

0

5

10

15

Cnidaria

Arthropoda

Annelida

Mollusca

Bryozoa

Chordata

Echinodermata

Porifera

Nemertea

BP CB

Figure 2.22. Overall average invertebrate biomass (g wet weight · 0.25 m-2) in the 

Boulder Patch and at Camden Bay. Overall biomass per region is separated by taxa 

indicated in the legend.
57

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.5

1.0

1.5

2.0

2.5

J’

H’(ln)

BP CB BP CB

*

a b

Figure 2.23. Box plots of invertebrate assemblage Shannon diversity (a) and Pielou’s 

evenness (b) in the Boulder Patch and at Camden Bay. Shannon diversity was 

statistically different (ANOVA, p<0.05) between regions but not Pielou’s evenness.

DISCUSSION

Epilithic assemblage patterns in the Boulder Patch

The Boulder Patch is considered a high biodiversity environment in the 

Beaufort Sea, dominated by macroalgal biomass and supporting a rich invertebrate 

assemblage (Dunton et al. 1982, Dunton and Schonberg 2000). Our investigations 

support these earlier assessments despite large interannual variability in overall 

biological biomass and differences in community composition. A comparison with 

adjacent boulder regions in Camden Bay establishes that these also may serve as 

biodiversity hotspots in the region, while being distinct in their macroalgal and 

invertebrate assemblage structures compared with the Boulder Patch. 

Macroalgae growing on the rock deposits form the foundation of the epilithic 

communities, forming habitat as well as provide a food source for the system, either 

via direct grazing or through particulate organic matter (POM; Dunton and Schell 

1987, Christie et al. 2007, Renaud et al. 2015, Filbee-Dexter et al. 2019). Macroalgal 

assemblages are dominated by red and brown algae, specifically kelp, with 12 

common taxa. This number of species was much lower than a recent compilation of 

algae in the Boulder Patch (Wilce and Dunton 2014) but included all biomass 

dominant taxa. Average algal biomass was highly variable among sites and years 

with little overall consistency, although sites DS11 and W3 had among the highest 

biomass in most study years, indicating consistently favorable algal growth 
58

conditions. Both these sites had high rock cover (unpublished data), which may 

favor algal recruitment and subsequent growth. 

The prominent species across sites and years were very consistent with the 

red algae Phycodrys sp., Coccotylus truncatus and Dilsea sp. as well as the kelp 

Laminaria solidungula. Spatial consistency of prominent species was also a 

characteristic of coastal ecosystems the European Arctic (Włodarska-Kowalczuk et 

al. 2009) and likely reflects the competitive advantage of some key species in each

environment (Konar and Iken 2005). Among the red algae, Phycodrys sp. and 

Coccotylus truncatus were the most abundant and regularly occurring species. These 

two taxa alone comprised between 34-89% of the average macroalgal biomass 

across sites and years. Both taxa have been the most abundant rhodophytes in the 

Boulder Patch since early studies in the area (Martin and Gallaway 1994). A red 

alga reported as very common in the Boulder Patch in the past are encrusting 

coralline red algae (e.g., Martin and Gallaway 1994), which we also observed but 

were not quantified using epilithic scrapes. However, their distribution among sites 

was assessed in a different portion of this study (see section 1.3). The two fleshy red 

algal genera are also known from other Arctic regions, including several transarctic 

exchanges (van Oppen et al. 1995), confirming the species’ adaptation to a wide 

range of arctic temperature, nutrient, and light conditions for growth and 

reproduction (Schoschina 1996), likely rendering these taxa as relatively resilient to 

environmental variation. Other red algal species were less abundant, yet with Dilsea

sp. contributing noticeable biomass. This is different from slightly higher biomass of 

O. dentata over Dilsea sp. found in biomass scrapes in the Boulder Patch in the late 

1970s (Dunton and Schonberg 2000); in addition, although methods cannot be 

directly compared, our finding is also distinctly different from the frequency of 

occurrence in photo quadrats established in the Boulder Patch between 1984 and 

1991 (Martin and Gallaway 1994), where O. dentata was about 3-5 times more 

frequent than Dilsea sp. The distinct morphology of the two species makes 

misidentifications as a cause for these differences unlikely. Rather, the consistency 

of higher Dilsea sp. biomass in our study since at least 2005 may indicate that a shift 

in the relative abundance of these two species may have occurred sometime in the 

late 1990s or early 2000s. This shift may have been location specific as Dilsea sp. 

biomass was highest in most years at sites DS11, E3 and W3 but only marginally 

present at some other sites (e.g., E2, W2). 

The most abundant brown alga in the Boulder Patch was the kelp Laminaria 

solidungula, comprising between 2 – 56% of the average macroalgal biomass. The 

kelp Saccharina latissima, was occasionally abundant at some sites and years, 

especially DS11 and W3, but not as consistent as L. solidungula. Lowest kelp 

abundance consistently over the years, except in 2013, was at site E1. The Arctic 

kelp L. solidungula is particularly well adapted to the arctic light and nutrient 

conditions as it is able to photosynthesize in the summer under good light 

conditions but not use the stored photosynthate for actual growth until winter time, 

when nutrient concentrations are replenished (Henley and Dunton 1995, Bartsch et 

al. 2008). In contrast, growth of S. latissima is more closely tied to photosynthesis in 

the summer, which may explain its lesser abundance in the highly seasonal light and 
59

nutrient conditions in the Boulder Patch (Henley and Dunton 1995, Bonsell and 

Dunton 2018), although it has been found to also be able to mobilize storage 

laminarin into mannitol for metabolic processes in the winter (Scheschonk et al. 

2019). The seasonally adaptive growth strategy in L. solidungula is likely beneficial 

under the typical low temperature regimes of the Arctic based on reduced 

respiration rates (Borum et al. 2002) but may not be a competitive strategy should 

water temperatures increase in the coastal Arctic. Warmer water temperatures may 

favor physiological processes (e.g., growth) in S. latissima (Scheschonk et al. 2019), 

although the continued prolonged darkness of the polar night, and possibly even 

further reduced light availability from increasing sediment load in the water column 

with increased fetch at reduced sea ice cover (Bonsell and Dunton 2018) may offset 

any positive effect on S. latissima. Hence, the combined trajectories of temperature 

and light will be instrumental in determining the performance and possible 

resilience of these two main kelp species to future environmental conditions. 

The macroalgal assemblage structure was specific to the different sites 

although there was a significant interaction with the factor year in the analysis, 

meaning that within each site, macroalgal assemblages also changed among study 

years. The year that had the greatest influence at the macroalgal assemblages at 

several of the sites was 2013. This difference in macroalgal assemblage structure in 

2013 was also evident from the dispersion, basically a measure of the homogeneity 

of community assemblage, which was significantly higher in 2013 at several sites. 

This increased dispersion indicates that the assemblages responded to some 

disturbance that created higher variability in the distribution and biomass of 

macroalgae within the affected sites. In most cases, algal assemblages returned 

closer to previous assemblage structures in 2015, indicating some recovery from 

the 2013 disturbance. This suggests that the 2013 disturbance was not catastrophic 

as full recovery of the assemblages takes years, probably decades in the Boulder 

Patch (Konar 2013). 

Macroalgae create habitat for associated invertebrate assemblages. This 

association can in part be driven by algal morphology, where a bushy thallus 

morphology close to the substrate like in Phycodrys sp. and Coccolytus truncatus

provides good habitat structure for associated invertebrates, as found in other arctic 

and subarctic systems (Lippert et al. 2001, Christie et al. 2007). Similarly, erect kelp 

blades provide suitable habitat for an increased abundance of invertebrates, 

although distinct from those associated with bushy growth forms (Lippert et al. 

2001, Christie et al. 2007), although studies in other systems found that general 

landscape features rather than individual algal morphologies may influence 

associated invertebrates (Cacabelos et al. 2010, Torres et al. 2015). We did not 

investigate relationships between invertebrate biomass and individual algal species 

or algal morphologies but the generally positive relationship (albeit not significant) 

between macroalgal and invertebrate biomass suggests that in the Boulder Patch,

macroalgae are important habitat providers. 

Invertebrate biomass was most consistently dominated by three groups of 

sessile taxa: sponges, bryozoans, and followed by hydrozoans. All three taxa also 
60

have been the dominant epilithic fauna in the late 1970s, even in the species being 

represented such as Halichondria panicea, Choanites lutkenii , and Semisuberites 

cribrosa within Porifera, Eucratea loricata within Bryozoa, and Sertularia 

cupressoides within Hydrozoa (Dunton et al. 1982, Martin and Gallaway 1994, 

Dunton and Schonberg 2000). This temporal consistency indicates that the overall 

dominance structure of the assemblage on the phylum or class level has remained 

for several decades. Among these taxa, bryozoans and hydrozoans are typical 

members of Arctic and other high latitude hard substrate assemblages, often 

dominating frequency of occurrence or biomass (Lippert et al. 2001, Kuklinski and 

Barnes 2008, Włodarska-Kowalczuk et al. 2009). Bryozoan biomass in the Boulder 

Patch is contributed by both, those associated with rocky substrate (Kuklinski et al. 

2006) but also those encrusting on blades of the red alga Coccotylus truncates. In 

contrast, sponges are often present but not dominant in other Arctic boulder or hard 

substrate regions (e.g., Beuchel and Gullikson 2008). Yet, in the Boulder Patch, 

persistence of sponges may be supported by the fast-vegetative regrowth of sponges 

after biomass removal through a disturbance (Konar 2013) and their competitive 

advantage over other space occupiers (Konar and Iken 2005). It is unclear why 

sponges are so successful in the Boulder Patch because of the high inorganic 

sedimentation rates there but their dominance may make the Boulder fields unique 

even within the Arctic. But similarly to some of the bryozoans and hydrozoans, 

sponges may be a rather unusual Arctic K-strategist that thrives in the Boulder 

Patch environment. 

Invertebrate assemblages were more strongly influenced by study year than 

macroalgal assemblages, and distinct assemblage changes were observed at all sites 

from the earlier study years (2005 and 2006) to 2013, before assemblages changed 

again to a yet different structure in 2015. Some of these temporal differences were 

driven by small and mobile species that contribute much to diversity and 

assemblage structure but do not contribute much to overall biomass (e.g., 

amphipods, polychaetes). Those species are often highly adaptable and quickly 

respond to environmental changes, and may be a result of the possible increase in 

disturbance in the later study years already discussed above for macroalgal 

assemblage structure. The effect in 2013 was also noticeable as a general decline of 

invertebrate biomass compared with other study years. The concurrent reduction in 

biomass in some of the sessile key bryozoans (Eucratea loricata) or hydroids 

(Sertularia cupressoides) in later years supports the idea that disturbance took place 

that changed some of the character taxa from earlier study years. The overall effect 

of this presumed higher disturbance on the homogeneity of the invertebrate 

assemblage was less unified than in the macroalgal assemblage but at most sites 

manifested as higher dispersion in 2013 (or 2013 and 2015) compared with other 

study years (note that DS11 was an exception in this trend). 

Comparisons with Camden Bay boulder fields

Overall, the main macroalgal taxa in Camden Bay were relatively similar to 

those in the Boulder Patch but invertebrate taxa differed considerably, and both 

assemblage compositions were significantly different between the two regions. 
61

Camden Bay had higher average biomass and diversity in macroalgae but overall 

lower average invertebrate biomass and diversity. Hence, it seems that while the 

overall species present in the two regions may derive from a regional species pool, 

that local conditions in either region then drive the specific assemblage 

compositions.

Macroalgal biomass in Camden Bay differed greatly among years, with very 

low biomass in 2005, highest biomass in 2007, and intermediate biomass in 2017. 

Much of this difference was driven by differences in kelp biomass, possibly 

reflecting better and worse growth years for kelps in Camden Bay. Kelp growth is 

especially dependent on light availability (Dunton and Schell 1986, Dunton 1990), 

and like the situation in the Boulder Patch, summer light availability can be 

extremely low based on suspended sediment load (Bonsell and Dunton 2018). Since 

sampling years in Camden Bay were not the same as in the Boulder Patch, we cannot 

compare if 2007 may have been a particularly prosperous year for kelps across both 

regions. If sampling years and sites are averaged for each region, Camden Bay had 

about a third higher macroalgal biomass than the Boulder Patch, although these 

comparisons have to be interpreted with caution as differences in sampling 

(number of replicates, sites, and years) will contribute to differences in average 

biomass. Other than different biomass proportions in macroalgal species between 

the two regions, a noteworthy observation was that Ahnfeltia plicata was absent in 

all years in Camden Bay. While this distinct red algal species is not a large biomass 

contributor in the Boulder Patch either, it is a regular member of the macroalgal 

assemblage there. Ahnfeltia plicata is a broadly distributed species across a wide 

temperature range in the temperate and arctic regions of the Atlantic and Pacific 

(Lüning 1984); hence, some other local factor must be limiting this species in 

Camden Bay. This may not have a large effect on the overall biodiversity 

considerations in these two regions, but it would be informative for our 

understanding of the region if the bottleneck in Camden Bay is in dispersal, 

settlement, or growth. 

Invertebrate assemblages in Camden Bay differed distinctly between the 

Boulder Patch and Camden Bay. Overall average invertebrate biomass in Camden 

Bay was only about a third of the average biomass in the Boulder Patch. Given that 

the macroalgal assemblage was distinct but composed of largely the same taxa, the 

differences in invertebrate assemblages may not be due to physical habitat 

differences provided by macroalgae but may be more related to other 

environmental factors (substrate composition, food availability, disturbance, etc.). 

On the phylum level, Boulder Patch invertebrate assemblages were dominated by 

Porifera, Bryozoa, and Cnidaria (Hydrozoa), while in Camden Bay they were 

dominated by Arthropoda and Mollusca. Maybe most striking was the complete 

absence of some groups in Camden Bay, such as all Echinodermata, Polyplacophora, 

Decapoda (i.e., Pagurus). Some of these differences are almost certainly driven by 

the much lower sampling effort in Camden bay as, for example, echinoderms in the 

Boulder Patch occurred infrequently but then with high biomass (e.g., a large sea 

star), which these taxa with a more clumped distribution can easily be missed with 

lower sampling effort. Other taxa, such as chitons, seem to indicate at a more 
62

reliable difference between the two regions and may be related to possible 

differences in the abundance of encrusting coralline red algae. Higher biomass of 

some taxa (e.g., the amphipod Atylus carinatus and the barnacle Balanus crenatus, or 

the bivalve Musculus spp. and the gastropod Boreocingula martyni) accounted for 

the high presence of Arthropoda and Mollusca, respectively, but also explained the 

lower invertebrate Shannon diversity index than at the Boulder Patch. Overall, these 

findings seem to indicate that the Camden Bay boulder fields provide excellent 

habitat for some species, which then thrive, but that a possible lack of microhabitat 

or challenging environmental challenges limit the diversity of invertebrate taxa in 

Camden Bay. A detailed study of environmental conditions in Camden Bay, as has 

been done in the Boulder Patch, could possibly provide some indication of these 

environmental limitations. 
63

3. Patterns and drivers of benthic community structure and early 

succession in an estuarine Arctic kelp bed

Christina E. Bonsell and Kenneth H. Dunton

INTRODUCTION

Kelp bed communities in the Arctic are expected to change dramatically as 

climate warming reduces ice cover and warms temperatures. Boreal species are 

expected to shift distributions poleward into the Arctic realm and contribute to 

predicted increases in primary productivity and biomass in rocky nearshore 

habitats (Müller et al. 2009, Krause-Jensen et al. 2012, Krause-Jensen and Duarte 

2014). However, the future of kelp beds adjacent to erosional coasts and rivers may 

be more uncertain than those along stable, rocky coasts (Filbee-Dexter et al. 2019). 

In these inner shelf areas, salinity stress from augmented freshwater inputs can 

reduce kelp photosynthetic capacity and recruitment (Karsten et al. 2001,

McClelland et al. 2006, Fredersdorf et al. 2009). Increased sediments (Gordeev 

2006, Lantuit et al. 2012, Fritz et al. 2017) that attenuate light (Van Duin et al. 2001, 

Aumack et al. 2007) and hinder recruitment (Devinny and Volse 1978, Zacher et al.

2016, Lind and Konar 2017, Traiger and Konar 2017) may further obstruct 

persistence of local kelps. As these habitats characterize a considerable portion of 

Arctic kelp beds, especially in Alaska and Russia (Lantuit et al. 2012, Filbee-Dexter 

et al. 2019), baseline data on the physical environment and biological patterns of 

shallow, river influenced areas is vital to assess and evaluate the redistribution of 

Arctic biota and ecosystems. 

The Boulder Patch is the largest of the discrete kelp beds scattered along the 

Alaskan Beaufort Sea coast, located in shallow (~4-7 m), nearshore waters of 

Stefansson Sound, east of Prudhoe Bay and adjacent to the Sagavanirktok River 

delta (Fig. 3.1). Influence from the Sagavanirktok River, and other large rivers 

(including the Mackenzie River) along the Beaufort Sea coast, make Stefansson 

Sound considerably estuarine in character (Section 1.1). Boulder and cobble 

substrates that characterize the Boulder Patch support a rich epibenthic community 

with greater diversity than the surrounding soft sediment habitat (Dunton et al. 

1982, Dunton and Schonberg 2000). Diversity and structure of this community 

varies across the kelp bed (Martin and Gallaway 1994). The community is 

dominated by the Arctic endemic kelp, Laminaria solidungula. The kelps Saccharina 

latissima and Alaria esculenta are also present, though the latter is rare. Foliose red 

algae, including Phyllophora truncatus, Dilsea socialis, and Phycodrys rubens form a 

dense turf community throughout much of the Boulder Patch. Nestled amongst the 

turf are filter feeders: sponges (such as Halichondria panacea and Semisuberites 

cribrosa), bryozoans (such as Eucratea loricata), and hydroids (Sertularia spp.). The 

soft coral Gersemia rubiformis is a charismatic community member, and common in 

certain areas. At some sites, crustose coralline algae (likely Leptophytum laeve and L. 

foecundum) coats much of the rock surface unoccupied by the preceding groups, 

though it is completely absent in other areas. While the relationship between 
64

underwater light levels and kelp growth in the Boulder Patch is well established 

(Dunton 1990, Aumack et al. 2007, Dunton et al. 2009, Bonsell and Dunton 2018), 

the causes of these spatial variations in community structure are unknown. 

The Arctic coastal ocean is distinctively seasonal, with strong influence from 

the cycles of terrestrial freshwater inflow and sea ice extent, the physiochemical 

marine environment transforms considerably from ice covered winter, to spring 

melt, to open water summer. The range of conditions experienced over the year 

could have strong effects on ecosystem characteristics and processes. It is especially 

pertinent to establish linkages between abiotic variability and biological ecosystem 

characteristics because seasonal patterns are rapidly shifting in the Arctic due to 

increased surface air temperatures (~1oC/decade, Christiansen et al. 2013) and the 

expansion of the summer open water season (Markus et al. 2009, Parkinson, 2014). 

These changes have already altered community structure and ecosystem function in 

Arctic marine habitats (e.g., Wassmann 2011, Kortsch et al. 2012). Shifts in sea ice 

extent and sea surface temperature – which can be measured remotely – have 

demonstrable effects on benthic community composition (Beuchel et al. 2006, 

Grebmeier et al. 2006, Kortsch et al. 2012). Linkages between year-round in situ 

environmental conditions and ecosystem characteristics, such as community 

structure and recruitment, allows for stronger predictions of Arctic ecosystem 

change in the face of climactic warming and increased industrial activity. 

Additionally, the drivers and spatiotemporal patterns of benthic recruitment 

and succession are currently understood via a handful of studies carried out in 

Svalbard and Eastern Greenland (Beuchel and Gulliksen 2008, Fricke et al. 2008, 

Kuklinski et al. 2013, Meyer et al. 2017), and one in the Beaufort Sea (Konar 2013). 

In some habitats, recruitment can occur relatively quickly, such as in shallow, 

Atlantic influenced Svalbardian fjords (Meyer et al. 2017), while in other areas, 

recruitment and succession can proceed very slowly, taking over a decade for 

cleared experimental plots to resemble the benthic community (Beuchel and 

Gulliksen 2008, Konar 2013). Kortsch et al. (2012) hypothesized that climate driven 

shifts in temperature and ice cover had reduced the resilience of benthic 

communities in Svalbard by altering patterns of recruitment and succession. These 

processes are a key component in the future persistence of Arctic kelp beds.

The goal of the present study is to determine how seasonal variation in the 

physiochemical environment, spatial variation in benthic community structure, and 

community development are related in a shallow (< 9 m), river influenced Arctic 

kelp bed. We paired long-term environmental data (Section 1.1) with benthic 

surveys to assess how benthic community composition may reflect spatial 

environmental variability. We also used in situ settlement tiles to evaluate whether 

community structure was simply an effect of larval supply. We examined if recruited 

community composition was different from established benthic community 

composition but became more similar to the benthic community over time. The 

progression of ecological succession can reveal mechanisms that relate abiotic 

differences to community dissimilarity (Kroeker et al. 2012, Kroeker et al. 2013). 
65

Additionally, we were interested in the timescale of community development. 

Previous work indicates that recovery from disturbance can take a decade or more 

in the Boulder Patch (Konar 2013). However, climax community composition could 

be determined early on in succession due to abiotic restraints on species 

composition. This would especially be the case if the strength of competitive biotic 

interactions is low due to abiotic stress (Bertness and Callaway 1994). Therefore, 

this study also indirectly addresses the role of abiotic versus biotic variables in 

determining community structure in Arctic nearshore ecosystems. 

Figure 3.1. Map of study sites (W1, W3, E1, L1, and DS11) in the Boulder Patch with 

rock cover indicated in green. Adapted from Bonsell and Dunton 2018. Arrows indicate 

channel inputs from the Sagavanirktok River
66

METHODS

Study sites and environment

We used five long-term study sites that vary in their depth, distance to shore, 

and proximity to the mouth of the Sagavanirktok River. (Fig. 3.1). Three sites (DS11, 

E1, L1) lie between a set of barrier islands (including Narwhal Island) and the 

mainland, while the other two sites (W1 and W3) are more exposed, located near 

deep channels between a shoal and barrier islands (Fig. 3.1).

Annual and seasonal means of temperature, salinity, underwater light levels 

(photosynthetically active radiation, PAR), and current velocity at each site were 

derived from Section 1.1. Briefly, in situ benthic loggers were deployed at each site 

for multiple years, bounded by the period of August 2011-August 2017 (methods 

described in Section 1.1). Distance from each site to nearest river input (Fig. 3.1) 

was estimated in ArcGIS by drawing a line between each site and the nearest land 

boundary associated with a river channel mouth. Depth per site is from Aumack et 

al. (2007) and Bonsell and Dunton (2018).

Benthic community structure and succession

To characterize established benthic community structure at each site, 0.05 

m2 photoquadrats were taken by a diver using a Nikon 1 AW1 waterproof digital 

camera in July and August 2016 and 2017. Due to the predominantly poor visibility 

and heterogeneous rock cover in the Boulder Patch, the diver would take the photos 

haphazardly while working in a spiral pattern by rising above the benthos, kicking 

twice and ‘landing’ with the photoquadrat. If rock cover within the photoquadrat 

appeared to be >75%, a photo was taken. Photos were analyzed for benthic percent 

cover to the lowest possible taxonomic group using point intercept of 56 stratified 

random points using the online program CoralNet (coralnet.ucsd.edu). Photos with 

>25% soft sediment or >20% unclear points were removed from the analysis, with 

24 to 43 photos used in final analysis for each site (n: E1=36, DS11=31, L1=24, 

W1=43, W3=35). All data were normalized to percent rock cover in each 

photoquadrat.

To assess recruitment and succession patterns at each site, settlement tiles 

(10x10 cm fiber cement) were deployed in July 2015. Each subsequent year, a

subset of these tiles was removed, and a new set was deployed by divers. This gave 

us data from plates that had been deployed for durations of one (2015-2016, 2016-

2017, and 2017-2018), two (2015-2017 and 2016-2018), and three years (2015-

2018), with each plate representing an independent unit. Not all deployed plates 

were recovered due to loss by ice scour or adverse diving conditions (Supp. Table 

3.1). After retrieval by divers, plates were placed in individual sealed containers 

with seawater and transported live to the lab where they were examined under a 

dissecting microscope. Organisms were identified to the lowest taxonomic level 

possible and counted. 
67

Five functional groups were used to evaluate differences in community 

structure among sites: kelp, crustose coralline algae (hereafter abbreviated as CCA), 

fleshy red algae, filter feeders, and suspension feeders (Table 3.1). There are various 

definitions for “filter” and “suspension” feeding, and the two are sometimes used 

synonymously. Here, these terms are used to separate bryozoans, corals, and 

hydroids (suspension feeders) from sponges and ascidians (filter feeders). For the 

species present in this study, the former extends appendages (cilia or tentacles) into 

the water column to acquire food particles, while the latter actively pump water 

through their bodies to feed. These groups also differ in their gross morphology: the 

filter feeders are generally robust while the suspension feeders are more 

filamentous. Differences in feeding behavior and morphology may lead to differing 

responses to variation in flow and suspended sediments, and use of multiple traits 

to group biota leads to higher sensitivity when assessing differences in community 

composition (Bremner et al. 2003).

Data analysis

To determine differences in established benthic communities among sites, a 

Bray-Curtis matrix was calculated from square-root transformed percent cover data, 

then analyzed statistically using PERMANOVA as well as visually using an nMDS 

plot. Square root transformation decreased the number of pairwise differences in 

homogeneity, but the transformation did not change PERMANOVA results.

Differences in total abundance of biota on settlement tiles within sites and within 

ages (one, two, and three years) was assessed with Welsh one-way tests, or Welsh 

two-sample t-test, depending on the number of groups. To compare the benthic 

community to settlement tile communities, percent cover (photoquadrats) and 

abundance (tiles) data were transformed into proportional abundance of each 

functional group, then square-root transformed, and a Bray-Curtis matrix was 

calculated. The trajectory of community development on the tiles was evaluated 

using NMDS plots that grouped mean benthic community and means of each age of 

tile community, as well as by plotting the Bray-Curtis dissimilarity to the benthic 

community over time at each site. Additionally, mean and variance in Bray-Curtis 

dissimilarity between one- and two-year old tiles for each site was plotted. 

The relationship between mean environmental variables and percent cover 

by each functional group was assessed with Spearman’s rank-correlations. The use 

of correlations to connect the physical environment to biological characteristics is 

meant to springboard future field studies and experiments. While low sample 

number (five sites) prevents more rigorous statistical tests, we aim to characterize 

broad patterns which link abiotic factor to community structure and present a 

baseline in the growing field of Arctic kelp bed ecology.
68

Table 3.1. Common species and genera from the Boulder Patch associated with each 

functional group category used in community analysis.

Functional group Common Boulder Patch 

examples

Foliose red algae Phyllophora truncata

Dilsea socialis

Phycodrys rubens

Crustose coralline 

algae (CCA)

Leptophytum foecundum

Leptophytum laevae

Kelp Laminaria solidungula

Saccharina latissima

Alaria esculenta

Suspension feeders Phylum Bryozoa

Alcyonidium sp.

Eucratea loricata

Phylum Cnidaria

Sertularia sp.

Calicella sp.

Obelia sp.

Gersemia rubiformis

Phylum Crustacea

Balanus sp.

Filter feeders Phylum Porifera:

Semisuberites cribrosa

Halichondria panicea

Phylum Urochordata

Rhizomogula sp.

RESULTS

Benthic community structure

Benthic community composition (%), as recorded by photoquadrats, varied 

among sites (Figs. 3.2-3.4, Tables 3.2 and 3.3). The community at the deep, offshore 

site DS11 prominently featured kelp (mostly Laminaria solidungula, though 

Saccharina latissima and Alaria esculenta were common) and CCA (Figs. 3.3 and 3.4, 

Table 3.2). The foliose red Dilsea socialis was more common at this site than at the 

shallower sites. D. socialis was also relatively common at the other deep site, W3, 

along with Phyllophora truncata and CCA. Gersemia rubiformis was also present in at 

W3, though other invertebrate groups were rare (Figs. 3.3 and 3.4, Table 3.2). This 

contrasts with and adjacent but shallow site (W1), where G. rubiformis, sponges, 

hydroids, and bryozoans were relatively common, though the community was 
69

dominated by foliose reds (especially Phycodrys rubens) (Figs. 3.3 and 3.4, Table 

3.2). The shallowest site, E1, exhibited the highest cover by foliose reds, dominated 

by P. rubens, though P. truncata was also abundant (Figs. 3.3 and 3.4, Table 3.2). This 

was the only site where barnacles were recorded in photoquadrats. Neither CCA nor 

G. rubiformis were recorded at this site. Gersemia rubiformis was also absent from L1 

and DS11 photoquadrats, though it was observed at DS11. As with the other shallow 

sites, L1 was dominated by foliose reds, but also had the second-highest kelp cover 

(Figs. 3.3 and 3.4, Table 3.2). CCA was present, but rare at this site. All sites had 

>10% cover by bare rock, with sites W3 and W1 having over 30% bare rock 

coverage (Table 3.2).

For E1 and W1, where photoquadrats were taken over two consecutive 

years, community structure did not vary by year (PERMANOVA, p>0.05), so analysis 

was done by site only. Benthic community structure based on functional groups was 

significantly different among sites (Table 3.3, Fig. 3.2). Pairwise comparisons

demonstrated that all sites were unique from each other, except E1 and L1 (Supp. 

Table 3.2).

Relationship to environmental variables 

Comparisons between functional group percent cover and environmental 

variables at each site (Supp. Table 3.3) revealed only one significant relationship – a 

positive correlation between cover of CCA and distance from Sagavanirktok River 

channels (ρ =1.0, p<0.05; Fig. 3.5A-B). Distance from river channels had stronger 

correlations with functional group percent cover than depth (Fig. 3.5A). There were 

negative, but non-significant, relationships between mean salinity and cover by both 

invertebrate groups (Fig. 3.5A). Temperature and velocity had no strong 

correlations with any functional group (Fig. 3.5A). Interestingly, PAR also had no 

strong correlations with any algal group but was negatively related to filter feeder 

cover (Fig. 3.5A). 

Settlement tiles and comparisons to benthic community

Abundance of individual biota could be extremely variable among settlement 

tiles (Table 3.4). In general, mean overall abundance increased over time (Table 

3.4). Deployment duration had a significant effect on total abundance of tile biota at 

sites W1 (Welch one-way test, F2,19=21.033, p<0.05) and W3 (Welch two-sample ttest, t17.38 =-2.67, p<0.05). Mean abundance was greatest for tiles deployed at DS11, 

though not significant due to high variability (Table 3.4).

Settlement tile communities significantly varied by site and age 

(PERMANOVA, p>0.05). Within each site, tile communities significantly differed 

among specific years of deployment (e.g. at DS11, the community on one-year-old 

tiles from 2015-2016 was significantly different from one year-old tiles from 2016-

2017) as well as age (Pairwise PERMANOVAs, p>0.05; Fig 3.6A-E). Within durations, 

communities were also different among sites (PERMANOVA, p>0.05), except for 
70

one-year-old tiles from L1 and W1. Microscopic red algae recruits and juveniles 

were common on many of the tiles, though they were too small to identify species 

(Fig. 3.4). CCA recruits and juveniles were also common on tiles at most sites and

were even present at E1 (on tiles deployed in 2017-2018 only) despite the absence 

of this group from the adjacent established benthic community (Fig. 3.4). Notably, 

no juvenile kelp ever recruited to the tiles.

At DS11, tiles were numerically dominated by CCA, while other sites were 

dominated by both CCA and/or red algae and suspension feeders (Fig. 3.4). Many

filter feeders also recruited to W3 tiles in year one (Fig. 3.4). Comparison of tile 

communities (proportional abundance) to the benthic community (proportional 

percent cover) demonstrates that tile communities were generally dissimilar to the 

benthic community, and only became more similar to the benthic community in year 

two at site W3, and in year three at W1 (Fig. 3.6F). Of all the sites, tiles at DS11 were 

the most like the benthic community and had the least change between ages (Figs. 

3.6A and F and 7). Tile communities at W1 and W3 were very dissimilar between 

years one and two, with high variation in how the community developed (Figs. 3.6DF and 7).

Table 3.2. Mean and standard deviation of each functional group in the established 

benthic community at each site.

Site CCA Red algae Kelp Filter 

feeders

Suspension 

feeders

Rock

E1 0 ± 0 79.31 ± 

0.51

2.9 ± 0.25 0.77 ± 

0.11 6.12 ± 0.21 10.91 ± 0.34

L1 0.64 ± 0.06 71.67 ± 

1.19 9.85 ± 0.93 0.48 ± 0.05 2.79 ± 0.21 14.57 ± 0.82

W1 0.48 ± 0.03 47.32 ± 

0.76 4.4 ± 0.24 2.12 ± 0.12

14.87 ± 

0.39

30.81 ± 

0.59

W3 4.67 ± 0.15 58.01 ± 

0.63 1.65 ± 0.14 0.77 ± 0.05 0.24 ± 0.02 34.66 ± 0.61

DS11 18.67 ± 0.46 38.01 ± 

0.78

19.03 ± 

0.75

0.44 ± 

0.03 4.08 ± 0.19 19.77 ± 0.59
71

Figure 3.2. Non-metric multidimensional scaling plot (Bray-Curtis matrix, square 

root transform) of benthic community structure by functional group at each site. 

Center of ellipses indicate median community structure; ellipse area indicates 

standard deviation. Crosses indicate individual photoquadrats. 2D stress=0.14.

Table 3.3. PERMANOVA summary table for comparing benthic community structure 

by site (square root transformed data).

Df Sums 

of 

Sqs

Mean 

Sqs

F. 

Model

R2 Pr(>F)

Site 4 6.01 1.50 23.45 0.36 0.01

Residuals 164 16.51 0.06 0.64
72

Figure 3.3. Example photoquadrats from each site.
73

Figure 3.4. Mean proportion of each functional group at each site recorded for 

benthic photoquadrats and settlement tiles. This value represents proportion of 

total biotic percent cover for the benthic community and proportional abundance 

for tile communities. Note that no kelp recruited to settlement tiles.
74

Table 3.4. Mean total abundance (± standard deviation) of individual biota per 100 

cm2 settlement tile and number of tiles per deployment duration. * Indicates that 

abundances were significantly different between or among deployment durations 

for that site.

Site 1 Year 2 Years 3 Years 

Mean ± SD N Mean ± SD N Mean ± SD N

DS11 69.9 ± 61.7 24 233.0 ± 316.3 16 - -

E1 17.8 ± 17.8 24 61.6 ± 108.5 16 - -

L1 3.3 ± 2.1 16 - - - -

W1* 15.0 ± 18.9 24 14.1 ± 4.7 8 35.9 ± 8.2 8

W3* 22.3 ± 19.6 16 42.0 ± 15.6 16 - -

Figure 3.5. A) Correlation matrix between percent cover of functional groups and 

mean environmental variables at each site. Number denotes Spearman rank 

correlation (ρ), with ρ=1 being statistically significant in these cases (p<0.05). Color 

indicates correlation value, with deeper blue meaning more positively correlated 

and deeper red meaning more negatively correlated. B) Mean percent cover of 

crustose coralline algae (CCA) in the benthic community at each site compared to 

distance to nearest river input. See Fig. 3.1 for site locations.
75

Figure 3.6. Development on tile communities over time as compared to the benthic 

community at each site. A-E) Non-metric multidimensional scaling plot of benthic 

and tile communities (from Bray-Curtis matrix on square-root transformed 

proportional data) broken up by site showing the trajectory of community 

development over time. Ellipses represent the mean and standard deviation of each 

community. 2D stress=0.11. F) Mean development of settlement tile communities 

over time compared to benthos, represented as mean BC dissimilarity. The leftmost 

points represent the mean dissimilarity between photoquadrats within the benthic 

community at each site. 
76

Figure 3.7. Development of settlement tile communities at each site between years 

one and two represented as mean (± standard deviation) Bray-Curtis dissimilarity. 

Standard deviation increased with distance to inter-island passes (Fig. 3.1). 

DISCUSSION

Spatial heterogeneity in benthic community structure

Freshwater that flows from the Sagavanirktok River in the spring and early 

summer strongly influences Boulder Patch benthic community structure. In general, 

there were more invertebrates closer to the river and at lower mean salinities, while 

kelp and CCA showed the opposite pattern (Fig. 3.5A). This relationship was 

strongest with cover by CCA, which increased with distance from river inputs (Fig. 

3.5B). During the spring freshet, Stefansson Sound can become extremely stratified 

(Weingartner et al. 2017), with shallow, inshore sites above the halocline (Section 

1.1). The site closest to the river (E1) is distinct in its lack of CCA, relatively high 

cover by foliose red algae (79%), and low cover by kelp (3%) relative to the other 

sites. CCA are ecologically important members of subtidal habitats (Steneck 1986, 

Mccoy and Kamenos 2015) and are competitive dominants in the Boulder Patch 

(Konar and Iken 2005) and other Arctic epilithic communities (Beuchel and 

Gulliksen 2008, Kuklinski 2009), so their complete absence at E1 represents an 

significant ecological pattern. Although CCA are usually dominant in low light 

environments (Vadas and Steneck 1988), the lack of CCA at E1 does not seem to be 

related to light levels, as this site is comparable, or darker than other sites (Dunton 

et al. 2009, Bonsell and Dunton 2018, Section 1.1). The environmental data indicate 

that freshwater influence may preclude the establishment of CCA at this site (Fig. 

3.5A and B). While CCAs are prone to low pH, Sagavanirktok River water is 

considerably basic (Craig and McCart 1975, A. Muth pers. comm.). Instead, low 
77

salinities may be responsible for the absence of macroscopic CCA (King and 

Schramm 1982, Schoenrock et al. 2018). Settlement tile data demonstrates that 

recruitment is not limiting, as CCA can recruit to E1 in certain years (Fig. 3.4). 

Furthermore, the low rate of growth of all recruited taxa prevents competitive 

exclusion early in succession. Instead, osmotic stress and mortality caused by 

exposure to the buoyant freshwater layer in certain years prevent slow growing 

CCA from becoming part of the established community. 

Salinity stress may also explain the low kelp abundance at E1. Karsten 

(2008) demonstrated that L. solidungula exhibits decreased photosynthetic 

performance after two days of exposure to salinities less than 20, conditions that 

often occur at E1 during the freshet (Section 1.1). Salinity gradients structure kelp 

forests globally (e.g., Sundene 1953, Buschmann et al. 2004, Spurkland and Iken 

2011), and low salinities resulted in the first published instance of kelp habitat 

deforestation (Yendo 1914, Steneck and Erlandson 2002). In Stefansson Sound, 

warmer, fresher waters during the spring and summer may give a competitive 

advantage to red algae over CCA and kelp (King and Schramm 1982, Harley et al. 

2012, Schoenrock et al. 2018), and thereby amplify the negative effect of foliose red 

algae on kelp (Filbee-Dexter and Wernberg 2018). This relationship is also implied 

by the high kelp cover observed at the site with the lowest foliose red algae cover 

(DS11, Table 3.2).

Current dynamics are an important structuring mechanism for benthic 

communities because they alter the delivery of propagules, food, and nutrients, and 

can cause mechanical stress or removal of entire organisms. Current direction also 

determines the source of propagules delivered to any particular area. W1 had a 

large percent bare rock cover (31%) and a greater abundance of invertebrates (Fig. 

3.4, Table 3.2). The greater current velocities from the nearby passes (Section 1.1) 

may favor filter and suspension feeders by providing higher delivery rates of 

particulate food (e.g., Pequegnat 1964, Sebens 1984), though further investigation is 

needed. Community composition at L1 was not statistically different from that at E1, 

with both sites dominated by foliose red algae (Figs. 3.2 and 3.4, Table 3.2, Supp. 

Table 3.2). Although these locations experience different regimes of temperature 

and salinity, their seasonal current dynamics are very similar (Section 1.1). These 

sites may be protected from strong currents due to their shallow position between 

barrier islands and the shore, which may provide a competitive advantage to algae 

over invertebrates (Fig. 3.1). W3 and DS11 both exhibit relatively elevated mean 

light levels and current speeds (Section 1.1), but have dissimilar benthic 

communities, with less CCA and kelp at W3 (Figs. 3.2 and 3.4, Supp. Table 3.2). 

Prominent bare rock cover at W3 (average 35%) implies that this difference may be 

due to more frequent disturbance. The proximity of this site to a deep channel pass 

into Stefansson Sound may expose it not only to stronger currents, but also to more 

frequent ice scour, a common disturbance in benthic polar habitats (Conlan et al. 

1998, Beuchel and Gulliksen 2008) that has been observed extensively at W3 

(Section 1.1). This would preclude dominance by slow growing, but competitively 
78

dominant Arctic CCA (Konar and Iken 2005, Kuklinski 2009, Schoenrock et al. 2018)

and slow to establish kelp (this study). 

The influence of light levels on benthic community composition are not 

straight forward, likely due to interactions between environmental variables 

influencing benthic biota. The most nearshore site (E1) and the most offshore site 

(DS11) had similar light levels, but disparate salinity regimes over the time period 

of data collection (Section 1.1). Elevated light levels at DS11 in the absence of low 

salinities (Section 1.1) may contribute to the abundance of kelp at this site. It is well 

established, and confirmed by our results, that elevated underwater irradiance at 

this site supports large kelp biomass (Dunton 1990, Aumack et al. 2007, Dunton et 

al. 2009, Bonsell and Dunton 2018). Kelp are well known foundation species which 

alter patterns of water motion (e.g., Kitching et al. 1934) and chemistry (e.g., 

Krause-Jensen et al. 2016), so community structure at this site could reflect the 

direct and indirect interspecific effects of high L. solidungula biomass. The negative 

relationship between light levels and filter feeders (Fig. 3.5A) similarly indicates 

that invertebrates are outcompeted by macroalgae in locations with elevated light 

levels. Suspended sediments in the water column, the main source of underwater 

light variability in the Boulder Patch, can also have strong negative effects on filter

feeders by reducing feeding efficiency and pumping rate (e.g., Bell et al. 2015).

Patterns of recruitment and succession

Recruitment varied both spatially and interannually. The benthic community 

seems to recruit from a propagule pool generally derived from adjacent biota at 

certain locations (E1, W1, and DS11; Fig. 3.6F). At sites towards the edges of the 

main Boulder Patch area (L1 and W3; Fig. 3.6F), propagule input from other 

locations could result in a community of recruits that is substantially different from 

the established epilithic community. These spatial differences likely derive from the 

influence of hydrodynamic patterns across Stefansson Sound on larval transport 

and retention, as locations at the edge of the Boulder Patch may receive 

proportionally greater propagule input from adjacent soft bottom habitats (socalled “edge effects”). Abundant recruits and juveniles at DS11 may result from 

elevated local propagule production due to advantageous environmental conditions, 

or simply enhanced priority effects (numerical dominance of local propagules 

leading to local recruits) due to the overall high epilithic cover at this site. 

Rhodophytes dominated tile communities (Fig. 3.4). Foliose red algae, which 

readily settled in great numbers, appear to be key early successional species in this 

community. Investigations of overgrowth interactions in the Boulder Patch 

demonstrated that foliose red algae often act as a substrate for bryozoan, hydroid, 

and sponge growth (Konar and Iken 2005). Consequently, the interactions between 

this group and other functional groups, including its negative effect on kelps (FilbeeDexter and Wernberg 2018), may shape the trajectory of community development. 

CCA was also numerically dominant on tiles, and was even present at E1 where 

adults were absent, indicating that these algae produce abundant propagules, as has 

been found in shallow areas of Arctic fjords (Meyer et al. 2017). Also similar for 
79

these two habitats is dominance by algal recruits compared to invertebrates (Meyer 

et al. 2017).

Although kelp is a dominant benthic biota at these sites and readily recruits 

to tiles in large numbers in the lab (C. Bonsell, pers. obs.), it did not recruit to 

settlement tiles at all during the study period. In a previous study from the Boulder 

Patch, the first kelp to recruitment to bare boulders only occurred after seven years 

(Martin and Gallaway 1994). The low apparent recruitment of kelps does not prove 

that kelp are not present on the tiles, a “seed bank” of gametophytes may be present 

that have not formed sporophytes yet due to lack of some environmental cue (

Carney and Edwards 2010, Carney et al. 2013;). Alternatively, kelp spore 

chemotaxis may lead to settlement on alternative habitats in the environment 

(Amsler et al. 1992, Reed et al. 1992). However, if kelp are slow to recruit, 

establishment may be inhibited by faster growing foliose red algae and benthic 

invertebrates in habitats where these groups are abiotically favored, such as turbid 

or high flow areas (Witman and Dayton 2001, Filbee-Dexter and Wernberg 2018).

Tile communities only become more similar to the benthic community over 

time at two sites, W1 and W3. However, both sites were still 40% dissimilar to the 

benthic community at the end of the study (Fig 3.6F). Community development at 

these two sites was also highly variable (Fig. 3.7). Their location adjacent to a deep 

pass connecting Stefansson Sound and the Beaufort Sea, and the associated strong 

currents at these sites (Section 1.1.) may lead to temporal patchiness in the 

propagule pool, contributing to the variability in the recruited community. Tiles at 

DS11 were the most like the benthic community, with the smallest shift in 

community between years one and two (Figs. 3.6 and 3.7), again indicating the 

importance of priority effects at this site. These results overall indicate that 

propagule pressure resulting from the interaction between local production and 

hydrodynamics leads to initial recruited community, then environmental filtering 

shapes the trajectory of early succession. 

Succession occurred very slowly, in agreement with past studies in the 

Boulder Patch (Martin and Gallaway 1994, Konar 2013). Even after three years in 

situ, the large majority of individuals on the tile remained <1 mm. Consequentially, 

very few instances of overgrowth were observed, unlike similar studies in Arctic 

fjords (Beuchel and Gulliksen 2008, Meyer et al. 2017). Overall, community 

development in the Boulder Patch appears to be very slow, which makes it difficult 

to parse out successional patterns over timescales less than a decade (Martin and 

Gallaway 1994, Beuchel and Gulliksen 2008, Konar 2013, Meyer et al. 2017). These 

results bolster conclusions that the Boulder Patch community would be very 

sensitive to any catastrophic disturbance event (Konar 2013). 

Scaling up: links to Arctic climate

Understanding the links between climatic changes and shifts in Arctic kelp 

bed community structure and distribution requires knowing the connections 

between climate, the abiotic environment, and biota. The body of research 
80

surrounding Arctic kelp ecology is small compared to that of lower latitudes but

drawing upon knowledge of the physical environment of Arctic coasts can 

illuminate relationships between climate and community structure (Fig. 3.8). The 

majority of the proposed relationships shown in Figure 3.8B have not been 

empirically tested. Rather, we present them as hypotheses for future scientific 

inquiry. 

First, coastal salinity reflects freshwater inputs from both Arctic rivers and 

sea ice melt. Downwelling winds entrain freshwater along Arctic inner shelves, 

while upwelling winds advect it offshore and bring in cool, salty oceanic water 

(Sellmann et al. 1992, Weingartner et al. 2017). Low salinities exert considerable 

influence on community structure by causing osmotic stress in certain algal groups, 

particularly CCA (King and Schramm 1982, Schoenrock et al. 2018), which is 

reflected in Boulder Patch community structure (Fig. 3.4). Through the negative 

effect of CCAs on invertebrate groups and foliose red algae (Konar and Iken 2005, 

Beuchel and Gulliksen 2008, Kuklinski 2009, Kortsch et al. 2012), low salinity can 

give a competitive advantage to these groups. The adverse effects of salinity on kelp 

(Fredersdorf et al. 2009, Karsten 2007) is amplified by the negative effects of red 

algae on kelp (Filbee-Dexter and Wernberg 2018). As the Arctic Ocean freshens due 

to runoff and sea ice melt (McClelland et al. 2006, McPhee et al. 2009, Morison et al.

2012), kelp beds in shallow, river influenced areas may face increasingly adverse 

conditions for kelps and CCA to persist. However, intensification of easterly winds, 

such as what is occurring along the Beaufort Sea coast (Wood et al. 2013, 2015) may 

ameliorate salinity conditions through upwelling.

Second, fluvial inputs and coastal erosion introduce suspended sediments to 

nearshore habitats, which increases light attenuation (Van Duin et al. 2001, Aumack 

et al. 2007, Fritz et al. 2017). Wind driven currents and mixing in the open water 

season resuspend benthic sediments on Arctic inner shelves, and further degrade 

the underwater light environment (Aumack et al. 2007 Bonsell and Dunton 2018). 

While all algae need light to grow, Arctic kelps are particularly sensitive to low light 

conditions caused by sediment resuspension (Dunton 1990, Aumack et al. 2007, 

Bartsch et al. 2016, Bonsell and Dunton 2018), while red algae are generally more 

tolerant of low-light conditions (Vadas and Steneck 1988). Sediments also have a 

negative effect on filter feeders (Fig. 3.4B), such as sponges (Bell et al. 2015). 

Identical to freshwater inputs, downwelling winds entrain water masses with high 

concentrations of suspended sediments along the coastal margin (Dunton et al.

2006). Climate driven lengthening of the open water season, intensified coastal 

erosion, and increased fluvial sediment inputs would thereby have a negative effect 

on abundance and persistence of nearshore kelps and filter feeders (Fig. 3.8).

Finally, wind dynamics can considerably impact patterns of benthic 

recruitment by altering the speed and direction of prevalent current flow. Faster 

currents and currents that advect water masses out of the coastal zone decrease 

local propagule retention (e.g., Gaylord et al. 2012). The community of recruits 

would potentially be more varied (Fig. 3.6), and the established community would 
81

reflect the relative ability of each individual to withstand the local environment 

(Leibold et al. 2004). The resultant decrease in priority effects (wherein current 

community composition strongly influences future composition by dominating the 

propagule pool) would strengthen the importance of abiotic effects on benthic 

community structure (Fig. 3.8). 

The community structure of Arctic kelp beds reflects an integration of local 

environmental conditions over decadal timescales due to the long successional cycle 

and longevity of many of the taxa. While this study highlights the effects of seasonal 

abiotic variation on benthic community structure, it also demonstrates that 

variability within a single year (or even >5 years) cannot account for the spatial 

distribution of taxa. Researchers must account for long-term environmental 

variability to fully comprehend and predict the impact of climate variation on kelp 

bed structure and distribution.
82

Figure 3.8. A) Positive and negative impacts (represented as arrows) of climatic 

drivers (in grey box) on environmental parameters/processes and functional 

groups on Arctic inner shelves. Biotic interactions are represented by blue arrows. 

A decrease in priority effects would strengthen the impacts of abiotic variables on 

functional group abundance (bolded arrows). See in-text citations (p 80). B) Overall 

effect of climatic drivers on functional group abundance. Indirect effects that occur 

through biological interactions are in blue.
83

Appendix

Supplemental Table S3.1. Number of tiles (and tile pieces) retrieved from each site 

for each deployment period. *indicates plates lost to ice scour.

Site One year Two 

years

Three 

years

2015-

2016

2016-

2017

2017-

2018

2015-

2017

2016-

2018

2015-

2018

DS11 10 8 8 8 8 0*

E1 8 8 8 8 8 0

L1 0 0 8 0 0 0

W1 8 8 8 8 0 8

W3 0* 8 8 0* 8 0*

Supplementary Table S3.2. Pairwise PERMANOVA summary table for comparing 

benthic community structure at each site (square root transformed data). *Denotes 

significant difference.

Df SS MS F. 

Model R2 Pr(>F)

DS11|E1*

N:31|36

Site 1 3.23 3.23 69.59 0.52 0.01

Residuals 65 3.02 0.05 0.48

DS11|L1*

N:31|24

Site 1 1.91 1.91 26.83 0.34 0.01

Residuals 53 3.76 0.07 0.66

DS11|W1*

N:31|43

Site 1 2.67 2.67 29.80 0.29 0.01

Residuals 72 6.45 0.09 0.71

DS11|W3*

N:31|35

Site 1 1.71 1.71 30.45 0.32 0.01

Residuals 64 3.58 0.06 0.68
84

E1|L1

N:36|24

Site 1 0.11 0.11 2.42 0.04 0.12

Residuals 58 2.57 0.04 0.96

E1|W1*

N:36|43

Site 1 0.89 0.89 12.98 0.14 0.01

Residuals 77 5.26 0.07 0.86

E1|W3*

N:36|35

Site 1 0.98 0.98 28.23 0.29 0.01

Residuals 69 2.40 0.03 0.71

L1|W1*

N:24|43

Site 1 0.82 0.82 8.91 0.12 0.01

Residuals 65 6.00 0.09 0.88

L1|W3*

N:24|35

Site 1 0.44 0.44 8.06 0.12 0.01

Residuals 57 3.14 0.06 0.87

W1|W3*

N:43|35

Site 1 2.00 2.00 26.14 0.26 0.01

Residuals 76 5.83 0.08 0.74

Supplementary Table S3.3. Environmental characteristics of each site (Section 1.1). 

Values for physiochemical parameters represent mean±SD.

Site Lat. (DD)

Long. 

(DD)

Depth 

(m)

Dist. 

to 

river 

input 

(km)

Temp. 

(

oC) Salinity

Current 

velocity 

(cm s-1)

PAR 

(mol 

photons 

m-2 day1)

DS11 70.32228 -

147.579 6.1 9.03 -0.78 ±1.96 32 ±3 4.4 ±5.3

0.271 

±1.013 

E1 70.31495 -

147.732 4.4 3.54 -0.41 ±2.50 31 ±4 2.8 ±3.2

0.208 

±0.925

L1 70.28993 -

147.613 5.5 7.20 -0.82 ±1.89 33 ±4 1.9 ±2.7

0.194 

±0.756

W1 70.37003 -

147.873 6.0 3.54 -0.95 ±1.84 30 ±4 4.3 ±5.4

0.102 

±0.236

W3 70.37627 -

147.794 6.6 7.31 -0.80 ±1.88 32 ±3 4.8 ±4.6

0.184 

±0.700
85

DIRECTED EXPERIMENTAL AND OBSERVATIONAL STUDIES

4. Within- and between-basin population connectivity in the Arctic 

endemic kelp Laminaria solidungula

Christina E. Bonsell and Kenneth H. Dunton

GLOSSARY

Microsatellites: DNA loci containing repeated short sequences of nucleotides. In 

genetic studies, comparisons are made using microsatellite length. 

LSU: The ribosomal large sub-unit. In Phaeophyceae, the LSU has a size of 26S. In 

genetics studies, comparisons are made between LSU ribosomal DNA sequences 

(which code for the ribosomal RNA that makes up the LSU). 

Ho: Observed heterozygosity.

He: Expected heterozygosity.

FST: Fixation index. The mean reduction in heterozygosity of subpopulations 

compared to the total population. Subpopulations that are not differentiated have a 

value close to zero and subpopulations that are completely differentiated have a 

value closer to 1. If FST is significantly different from 0, then there is significant 

(sub)population differentiation.

GST: Similar to FST, but explicitly developed for >2 alleles. However, GST can never 

reach 1 if there are >2 alleles.

FIS: Inbreeding coefficient. Mean reduction of individual heterozygosity due to 

inbreeding within a subpopulation. Has a value of 1 if all individuals are 

homozygous (an indication of inbreeding), and -1 if all individuals are heterozygous.

GIS:Similar to FIS, but explicitly developed for >2 alleles.

INTRODUCTION

Contemporary dispersal pathways and post-glacial recolonization into and 

across the Arctic are not well understood (Hardy et al. 2010), but are necessary to 

quantitatively forecast Arctic ecosystem changes (Krause-Jensen and Duarte 2014). 

The Arctic is warming at a faster rate than any other region in the world (IPCC 

2014). As a result, a suite of ecosystem changes are occurring, including changes in 

species distributions (Bluhm et al. 2011, Grebmeier 2012, Michel et al. 2012). The 

northward range expansion of species of diatoms (Reid et al. 2007), bivalves (Berge, 

et al. 2005), and fish demonstrate that “the coming Arctic invasion” (Vermeij and 

Roopnarine 2008) is occurring across many taxonomic groups. These changes 

reflect increases in suitable habitats for subpolar species, but invasion success also 

requires adequate vectors to disperse individuals to new areas (Sakai et al. 2001). 

These vectors are changing as well, since ships traversing the increasingly navigable 

Northwest Passage transport non-native species into Arctic waters (Smith and 

Stephenson 2013, Chan et al. 2016). The spatial spread of species moving poleward 

as the Arctic warms will likely reflect the dispersal pathways used by contemporary 
86

Arctic species (Vermaij and Roopnarine 2008). Understanding how populations are 

connected through the region allows for accurate prediction and sound 

management of species’ persistence and the borealization of polar ecosystems.

In the changing Arctic, dispersal among populations and the resultant genetic 

connectivity can determine a population’s capacity to withstand change or recover 

from disturbance. Healthy populations, with enough genetic diversity, can be selfsustaining (Hamilton 2009). However, immigration from outside populations may

play an important role in ‘rescuing’ populations dwindled by ecosystem change or 

decimated by disturbance (Brown and Kodric-Brown, 1977). Persistence of 

foundation species (sensu Dayton 1972), such as kelps, in the face of warmer 

temperatures, increased turbidity, and lower salinities is central to the 

redistribution of habitats and ecosystem functions in the emerging Arctic.

In rocky subtidal Arctic areas, kelps provide habitat and a source of primary 

production that can increase local biodiversity and production, and alter local 

biogeochemistry (Dunton et al. 1982, Dunton and Schell 1987, Krause-Jensen et al.

2016, Filbee-Dexter et al. 2019). Shifts in kelp distributions are occurring globally 

(Krumhansl et al. n.d.), and are recorded in a few Arctic areas (Bartsch et al. 2016). 

Sub-polar kelps are expected to expand their range northward due to increasing 

temperatures and alleviated underwater light conditions (Müller et al. 2009, 

Krause-Jensen and Duarte 2014). However, baseline understanding of Arctic kelp 

biology and ecology is limited, making it challenging to predict and anticipate 

change under future climate and development scenarios (Filbee-Dexter et al. 2019). 

Currently, fourteen species of kelp inhabit the Arctic region (Filbee-Dexter et al.

2019). Only one of these species, Laminaria solidungula, is defined as an “Arctic 

endemic” due to its evolutionary history and its association with dark, freezing 

temperature waters. This species is also common in deep waters of cold, subarctic 

areas, including Svalbard (Lüning 1990). Consequently, the distributional pattern of 

L. solidungula provides a valuable opportunity to examine mechanisms of physical 

dispersal and kelp evolutionary history, due to the trans-Arctic spread of 

Laminariales into the Atlantic Ocean from the North Pacific (Stam et al. 1988).

Furthermore, as this species represents the most cold tolerant kelp (tom Dieck

1993), it is of interest for comparative biogeography studies and predicting future 

kelp distributions.

Due to their requirement of rocky substrate, L. solidungula can exist in 

patches separated by 100s of kms, as they do in the Alaskan Beaufort Sea (Dunton et 

al. 1982; Fig. 4.1). Genetic data indicate that marine macroalgae disperse on scales 

of 100s to 1000s of m year-1 on average, either through their free swimming 

microscopic stage or their macroscopic stage via drift (Kinlan and Gaines 2003). A 

lack of habitat continuity may limit the amount of genetic exchange between L. 

solidungula patches, and decreased within patch genetic diversity, as shown in other 

kelp species (Alberto et al. 2010, Durrant et al. 2018). Genetic diversity of a 

population can be linked to the probability that a declining population could be 

“rescued” by migration from other populations, as well as linked to the capacity of a 
87

population to adapt to change or disturbance (Brown and Kodric-Brown, 1977, 

Sakai et al. 2001). Recently, work by Wernberg et al. (2018) highlighted the link 

between higher genetic diversity (expected heterozygosity) in temperate kelp 

populations and their resilience to extreme climatic events – kelp forests with 

higher genetic diversity showed greater capacity to regrow after experimental 

disturbance and a marine heatwave. Genetic diversity, therefore, may then provide 

an important measure to evaluate the vulnerability of Beaufort Sea L. solidungula

populations to the environmental perturbations associated with climate change and 

coastal development.

Spatial genetic data can be used to infer information about dispersal and 

population connectivity. Closely related populations of individuals are generally 

characterized by having higher connectivity through propagule dispersal. The 

timescale of connectivity conferred by genetic data varies by genetic marker. For 

example, microsatellite markers, which evolve relatively quickly, are used to infer 

population connectivity over scales of decades to thousands of years while 

mitochondrial cytochrome C oxidase subunit I gene (COI), internal transcribed 

spacer (ITS), and other ribosomal markers are used to infer population connectivity 

and species relationships over longer periods, from tens of thousands to millions of 

years. Mitochondrial and ribosomal markers have been used to infer the 

recolonization history of the Arctic by macroalgae after the Last Glacial Maximum 

(McDevit and Saunders 2010, Coyer et al. 2011, Bringloe and Saunders 2018). 

However, data from more than a few high Arctic sites within these studies are rare 

(but see Nieva et al. 2018). 

The aim of this study is to evaluate genetic connectivity of Laminaria 

solidungula populations across two different scales in the Arctic: 1) among stations 

within the Alaskan Beaufort Sea, including six stations within the Stefansson Sound 

Boulder Patch, Camden Bay, and Barter Island, and 2) among sites in the Western 

Arctic Basin, including the Beaufort Sea, Arctic Canada, Newfoundland, and 

Svalbard. We used two different markers, corresponding to each scale. For the 

regional scale study, we used microsatellite markers to infer population 

connectivity and genetic diversity in the Alaskan Beaufort Sea. These are putatively 

neutral and rapidly evolving, often used to determine population differentiation 

across geographic scales of 10s-1000s of m. For the basin scale assessment, we 

sequenced the D1/D2 region of the large subunit (26S) ribosomal RNA gene (rDNA). 

The 26S rDNA sequence is generally highly conserved in Phaeophyceae and has 

been used in combination with other molecular markers to resolve taxonomic 

relationships in brown algae at the level of order and above (Phillips et al. 2008, 

Lane 2006), but its use in determining genetic divergence within kelp species is 

limited. However, the more divergent D1/D2 hypervariable regions within the LSU 

rDNA gene, which have been excluded from large phylogenetic analysis of 

Phaeophyceae due to high variability (Draisma et al. 2001), are often used to 

barcode species of taxa such as dinoflagellates (Scholin et al. 1994) and ciliates 

(Stoeck et al. 2014) and have been suggested as a barcode gene for all metazoans 

(Sonnenberg et al. 2007). We therefore expected subspecies level variability in the 
88

D1/D2 region within Laminariales, reflecting population differentiation over longer 

timescales than microsatellites.

METHODS

Adult sporophytes of L. solidungula were collected from six sites: Stefansson 

Sound Boulder Patch (Beaufort Sea), Camden Bay (Beaufort Sea), Barter Island 

(Beaufort Sea), Finlayson Islands (Canadian Archipelago), Bonne Bay 

(Newfoundland), and Kongsfjorden (Svalbard; Fig. 4.1). Within the Beaufort Patch

there were six distinct sampling stations: B1, DS11, E1, L1, W1, and W3 (Fig. 4.1). 

Therefore, there were eight Beaufort Sea stations total, among three sites. 

Stefansson Sound, Bonne Bay, and Kongsfjorden samples were collected by divers. 

Camden Bay samples were obtained by dragging for plant material. Barter Island 

samples were collected from fresh drift algae. Finlayson Island samples were 

acquired incidentally via benthic trawl. Tissue samples were collected from the 

meristem (when possible), then dried in silica desiccant and frozen at -80oC. 

Figure 4.1. A) Location of three Beaufort Sea sampling sites. B) Location of sampling 

stations within the Boulder Patch. Benthic cover and long-term environmental data 

exist for sites DS11, E1, L1, W1, and W3.

For DNA extraction, ~10-20 mg of dry material from each sporophyte was 

ground using an MPBio FastPrep tissue lyser (Lysing Matrix A). Extraction was 

carried out using a cetyltri-methylammonium bromide (CTAB) protocol (Zuccarello 

and Lokhorst 2005). Briefly, ground kelp material was added to a microcentrifuge 

tube with 500-700 μL of CTAB extraction buffer and incubated overnight at 65 oC 

with gentle shaking. Chloroform: isoamyl alcohol (24:1) was added then briefly 
89

vortexed, and the phases were separated by centrifugation at 12000g for 10 min. 

The aqueous phase was removed to a new centrifuge tube, and the extraction step 

was repeated in that tube. The twice extracted aqueous phase was again placed in a 

new tube along with an equal volume of 100% isopropanol, then mixed by 

inversion. The sample was incubated at room temperature for 30 min to 1 hr. 

Precipitated nucleic acids were collected by centrifugation for 30 min at 12000g at 

20oC, after which the supernatant was decanted. The DNA pellet was washed in 70% 

ethanol, concentrated by centrifugation for 5 min at 12000g, drained, and air-dried. 

Depending on the size of the pellet, we added 50-150 μL of 0.1 X TE buffer (145 M 

Tris, 1 μM EDTA). Samples were frozen at -20oC. 

Microsatellite analysis

From population genetic studies in other kelp species, we selected five 

primer pairs that amplified microsatellite sequences in L. solidungula sampled in the 

Boulder Patch (Table 4.1, Supp. Table 4.1). PCR reactions consisted of 0.25 mM 

dNTPs, 0.5 mM MgCl, 1X Dream Taq buffer, 2U Dream Taq polymerase, 0.4 μM of 

each reverse primer and fluorescently labeled forward primer, and 1 μL DNA 

template (10-20 ng) in 20μL total volume. Capillary electrophoresis fragment 

analyses were performed at Texas A&M University Corpus Christi Genomics Core 

Lab on an Advanced Analytical Fragment Analyzer. Peaks were scored in Geneious 

(http://geneious.com). 

Table 4.1. Information for loci used in this study. *Marker development reference

Locus AT

Sequence 

motif

L. solidungula

fragment length

Cross 

amplification 

species References

Ld-3 51 (aag)6 251-263 S. japonica, 

L. hyperborea

Liu et al. 2012*

Ld-13 54 (ggaa)10 159-231 S. japonica Liu et al. 2012*

Ld2/520 53 (at)6(gt)7 366-388 L. digitata,

L. nigrecens

Billot et al. 1998*, 

Martinez et al. 2015

LolVVIV-15 60 (aac)21 102-144 L. hyperborea, 

L. ochroleuca

Coelho et al. 2014*

LolVVIV -23 60 (aac)14 105-111 L. digitata, 

L. hyperborea, 

L. ochroleuca

Coelho et al. 2014*

Microsatellite loci only amplified consistently for samples collected in 

Beaufort Sea, so this analysis excluded the other sites. Data were filtered to only 

include samples with one or fewer missing loci. We used MICRO-CHECKER (Van 

Oosterhout et al. 2004) to scan for evidence of null alleles and scoring errors. Per 
90

locus FST and FIS were determined in GENEPOP (Rousset 2008), along with tests for 

linkage disequilibrium, deviations from Hardy Weinberg equilibrium, and 

heterozygote excess. We used Fisher’s exact test in GENEPOP to determine pairwise 

population differentiation on genotypes. Per population F statistics and AMOVA 

were analyzed using GENODIVE (Meirmans and Van Tienderen 2004). All other data 

analysis and visualization for microsatellite DNA variation was carried out in R (R 

Core Team 2016).

To assess the role of environmental factors on genetic differentiation among 

stations within the Boulder Patch, we compared biotic and abiotic environmental 

variables (Section 1.1. and 1.2) to genetic diversity using a distance based 

redundancy analysis (db-RDA; Legendre and Anderson 1999). Benthic percent 

cover data of functional groups (kelp, crustose coralline algae, foliose red algae, 

filter feeders, suspension feeders) were arcsine transformed. To homogenize the 

mean and standard deviation for all environmental data for the db-RDA, including 

benthic cover, raw values were normalized by subtracting the grand mean and 

dividing by the standard deviation. Dissimilarity between kelp stations based on 

microsatellite data were calculated using a principal coordinate analysis (PCoA, R 

package adgenet; Jombart 2008). The relationship between PCoA coordinates and 

normalized environmental variables was determined using a db-RDA, with a 

stepwise model building approach (R package vegan; Oksanen et al. 2016). 

Differences among stations were assessed using one-way ANOVAs on each 

environmental variable, with posthoc Tukey HSD tests.

Ribosomal large subunit (LSU) DNA

The lack of microsatellite amplification in individuals collected outside of the 

Beaufort Sea necessitated the use of a different marker to investigate population 

differences among geographic regions in the Western Arctic Ocean Basin. Partial 

sequences of LSU ribosomal RNA genes covering the D1/D2 region (~550 bp) were 

acquired for samples from Boulder Patch stations DS11 (n=4) and B1 (n=5), Barter 

Island (n=3), Camden Bay (n=8), Newfoundland (n=3), Svalbard (n=7), and the 

Finlayson Islands (n=4). Amplification was carried out in 20 μL total volume using 

0.4 μM D1R/D2C primers (Scholin et al. 1994), 1X Myfi mix (Bioline), and 10-20 ng 

DNA. Cycling conditions consisted of 2 min at 94oC, then 30 cycles of 1 min at 94 oC, 

40 sec at 51 oC, 1.5 min at 72 oC, with a final elongation step of 5 min at 72 oC.

Sequences were visually inspected, trimmed, and aligned using Geneious. To 

assess the relative phylogenetic significance of variation in the LSU, we aligned L. 

solidungula sequences with other sequences from the Order Laminariales available 

on GenBank (Table 4.2). A neighbor-joining algorithm was used to visualize LSU 

variation within and between species.
91

Table 4.2. List of samples used for LSU analysis and accession numbers.

Species Collection information

No. 

samples LSU Genbank and reference

Laminaria 

solidungula

Boulder Patch, AK, USA 

(collector: C. Bonsell)

9

Camden Bay, AK, USA 

(collectors: J. Dunton and T. 

Dunton)

8

Barter Island, AK, USA

(collector: C. Bonsell)

3

Bonne Bay, NL, Canada 

(collector: G. Bishop)

3

Hansneset, Svalbard, Norway 

(collector: I. Bartsch)

7

Finlayson Islands, NU, Canada 

(collector: B. Bluhm)

4

Laminaria 

yezoensis

L. Druehl culture 1 AY851518 (Lane et al. 2006)

Laminaria 

sinclarii

Mud Cove, Bamfield, BC, 

Canada

1 AY851516 (Lane et al. 2006)

Laminaria 

ochroleuca

Roscoff, Brit., France 1 AF071154 (Rousseau et al. 2000)

Laminaria 

digitata

Roscoff, Brit., France

Green Pt., Lepreau, NB, Canada

Hornbaek, Sjaelland, Denmark

1

1

1

AF071153 (Rousseau and De 

Reviers 1999)

AY851517 (Lane et al. 2006)

AY441778 (Erting et al. 2004)

Laminaria 

hyperborea

Roscoff, Brit., France

Deget, Denmark

1

1

AF071155 (Rousseau and De 

Reviers 1999)

AY441779 (Erting et al. 2004)

Saccharina 

sessilis

Cape Beale, Bamfield, BC, 

Canada

1 AY851513 (Lane et al. 2006)

Saccharina 

nigripes

Green Pt., Lepreau, SB, Canada 1 AY851514 (Lane et al. 2006)

Saccharina 

latissima

Helsinborg, Denmark 

Hirsholm, Denmark

Drelnes, Denmark

1

1

1

AY441780 (Erting et al. 2004)

AY441781 (Erting et al. 2004)

AY441782 (Erting et al. 2004)

Saccharina 

gyrate

L. Druehl culture 1 AY851526 (Lane et al. 2006)

Saccharina 

augustata

L. Druehl culture 1 AY851515 (Lane et al. 2006)

Cymathaere 

triplicate

Wiffen Spit, Sooke, BC Canada 1 AY851519 (Lane et al. 2006)

Egregia 

menziezii

Boiler Bay, OR, USA 1 AY851506 (Lane et al. 2006)

Lessonia 

corrugata

Gov. Is. Reserve, Tas., Australia 1 AY851532 (Lane et al. 2006)

Lessonia 

flavicans

Rookery Bay, Falkland Islands 1 AY851531 (Lane et al. 2006)

Lessonia 

nigrescens

Las Cruces, Chile 1 AY851530 (Lane et al. 2006)
92

RESULTS

Microsatellite analyses: Beaufort Sea populations

For the 119 individuals analyzed, the five polymorphic loci had a total of 49 

alleles, ranging from 5 to 13 alleles per locus (Table 4.3). No significant linkage 

disequilibrium was detected (p<0.05). Heterozygosity varied among loci, and 

overall heterozygosity was moderate (Ho=0.67, Table 4.3). No difference between 

observed and expected heterozygosity was observed (Bartlett’s K2=0.217, p>0.05). 

Three out of five loci deviated significantly from Harvey-Weinberg Equilibrium 

(p<0.05, Table 4.3). Genetic diversity (number of alleles per individual) varied 

among sites, depending on sample size (Table 4.4). 

Table 4.3. Summary statistics for microsatellite loci. Bold indicates significant deviance 

from Harvey-Weinberg Equilibrium (p<0.05). * indicates significant deviance from 

HWE after Bonferroni correction.

Locus # alleles Ho He Fis Fst

Ld-3 8 0.75 0.66 -0.132 0.001

Ld-13 13 0.25 0.24 -0.018 0.016

Ld2/520 12 0.85 0.72 -0.188 0.006

LolVVIV-15

11 0.82 0.68 -0.208* 0.017

LolVVIV -23

5 0.80 0.49 -0.636 0.013

Global 0.69 0.56 -0.244* 0.010

Table 4.4. Summary statistics for samples from each location. Bold indicates sig after 

Bonferroni correction.

Site or 

Station N # alleles/ind

Effective # 

of alleles Ho He Fis

p (Heterozygote 

excess)

BTI 12 2.083 2.61 0.627 0.533 -0.138 0.766

CAM 2 6.000 2.47 0.625 0.425 0.167 1.000

Boulder Patch Stations:

B1 16 1.438 2.66 0.689 0.537 -0.240 0.091

DS11 19 1.421 2.45 0.800 0.571 -0.378 0.000

E1 13 1.692 2.40 0.662 0.517 -0.243 0.258

L1 18 1.389 2.71 0.696 0.569 -0.198 0.741

W1 20 1.450 2.39 0.667 0.530 -0.227 0.010

W3 19 1.421 2.50 0.703 0.547 -0.260 0.004
93

The likely total number of alleles per individual for these stations and loci is 

~1.5. Overall heterozygosity at each station (Ho) was moderate to high (0.625-0.800, 

Table 4.4). There were significant departures from Hardy-Weinberg Equilibrium at 

three out of the eight sampling stations, and overall (FIS≠0; Tables 4.4 and 4.5). Chisquared tests demonstrated that heterozygote excess was present at two sites, as 

well as within the global population (Table 4.4). There was no significant population 

structure given by sites (AMOVA, p<0.05). Similarly, overall population 

differentiation (FST = 0.010, 0.006 excluding Camden Bay samples, Table 4.5) and 

pairwise FST (0-0.107, Table 4.6) were low, meaning that populations are not well 

differentiated by site. However, some pairwise genetic differentiation was 

statistically significant, with site DS11 showing the most consistent genetic 

differentiation from the other sites (Table 4.6). DS11 had significantly higher kelp 

cover than the other sites (α=0.05; one way ANOVA: F(1,4)=9.70, Supp. Tables 4.2 and 

4.3) and comparison to environmental parameters via dbRDA revealed that benthic 

cover by kelp significantly influenced the genetic differences between sites (Table 

4.7, Fig. 4.2). 

Table 4.5. Overall differentiation statistics for Beaufort Sea stations. Values in brackets 

include the two Camden Bay individuals. *statistically significant.

Fst 0.006 (0.010*)

Fis -0.248 (-0.243)

Gst 0.005 (0.028*)

Gis -0.200* (-0.241*)

Table 4.6. Pairwise FST values between Beaufort Sea sampling locations. Sampling sites 

are in bold, sampling stations within the Boulder Patch site are in normal font. * indicates 

significant differentiation (p<0.05). **indicates significant differentiation after 

Bonferroni correction.

CAM B1 DS11 E1 L1 W1 W3

BTI 0.059 0 0.019* 0.009 -0.008 -0.002 0

CAM 0.18* 0.1* 0.107* 0.052 0.089 0.069

B1 0.022* 0.014 -0.003 -0.004 0

DS11 0.031** 0.003 0.019* 0.016**

E1 0.009 0.005 -0.006

L1 -0.003 -0.001

W1 -0.01
94

Table 4.7. Stepwise model building results for the effect of environmental variables on 

the distance between stations given by PCoA on genetic data. Bold indicates significance. 

Units: Temperature, daily mean (oC); Light (photons m2 day-1); Salinity, daily mean; 

Current velocity, daily mean (cm sec-1)

Df AIC F p

% Kelp cover 1 -15.705 4.7264 0.025

% CCA cover 1 -13.585 2.057 0.15

Temperature (Ice cover) 1 -13.131 1.6179 0.2333

Temperature (Year-round) 1 -13.355 1.829 0.2417

Light (Open water) 1 -12.64 1.1858 0.3833

Temperature (Open water) 1 -12.9 1.4095 0.3917

% Red algae cover 1 -12.573 1.1298 0.4167

Light (Year-round) 1 -12.617 1.1664 0.425

Salinity (Open water) 1 -12.157 0.8 0.5417

Salinity (Year-round) 1 -12.173 0.8124 0.575

% Filter feeder cover 1 -11.957 0.6516 0.6167

Salinity (Ice break-up) 1 -12.048 0.7185 0.6333

Temperature (Ice break-up) 1 -11.914 0.6202 0.6583

Salinity (Ice freeze-up) 1 -11.608 0.4055 0.75

Salinity (Ice cover) 1 -11.517 0.3436 0.7833

Current velocity (Ice break-up) 1 -11.339 0.227 0.8083

Current velocity (Ice cover) 1 -11.341 0.2282 0.825

Current velocity (Ice freeze-up) 1 -11.301 0.2021 0.9

Temperature (Ice freeze-up) 1 -11.037 0.0377 0.9083

% Suspension feeder cover 1 -11.135 0.0978 0.9167

Current velocity (Open water) 1 -11.108 0.0811 0.9417

Current velocity (Year-round) 1 -11.023 0.0292 1
95

Figure 4.2. Ordination plot of stations within the Boulder Patch based on genetic 

distance compared to kelp cover.

LSU analysis: Laminaria solidungula across the Western Arctic Ocean Basin 

and comparisons to other Laminariales

LSU rDNA sequences were ~550 bases long after trimming, with two 

variable sites: C-T polymorphism and a G-A polymorphism. All individuals had 

either the C and G or T and A combination, resulting in two genotypes. One genotype 

was found exclusively in the Beaufort Sea individuals, and the other was restricted 

to the Canadian/Atlantic Arctic, with a single exception in Camden Bay (Fig 4.3). The 

Beaufort Sea genotype was 1 base different from the L. yezoensis genotype (Fig. 4.4). 

These two species had fewer deviations from Saccharina species and C. triplicata

than from other Laminaria species (Fig. 4.4). 
96

Figure 4.3. Map of L. solidungula sampling locations (triangles) and percent of LSU 

genotypes, represented as pie graphs. Black = “Beaufort” genotype, grey = “Atlantic 

Arctic” genotype. Number of samples in parentheses. Cross shows location of the 

pole.
97

Figure 4.4. Topology of LSU sequences from kelps in the families Arthromenceae 

(blue), Lessonaceae (pink), and Laminaraceae (green). Laminaria solidungula

sequences, generated in this study are bolded. All other sequences from GenBank 

(Table 4.2). Branch numbers indicate percent difference between sequences. Adding 

the differences along the path between any two species will give the total percent 

difference in sequence between those two species. This tree does not represent a 

phylogeny.

DISCUSSION

Evidence for panmixia in the Beaufort Sea

When compared to other microsatellite studies on kelps, the level of 

population differentiation (FST) we found in the Beaufort Seas corresponds to the 

low end of differentiation found over scales of <20 km (Billot et al. 2003, Alberto et 

al. 2010, Brennan et al. 2014, Robuchon et al. 2014). As the majority of our samples 

came from within the Boulder Patch, our results fit into the model of weak 

differentiation over smaller scales for most kelp species (Billot et al. 2003, Coleman 

et al. 2011, Brennan et al. 2014). However, we found that drift kelp on Barter Island 
98

were not differentiated from attached kelp within the Stefansson Sound Boulder 

Patch, approximately 150 km away. This indicates that the Beaufort Sea population 

of Laminaria solidungula has the capacity for panmixia (random mating within the 

region), despite the lack of habitat continuity. Other studies have reported strong 

differentiation over similar spatial scales, which was attributed to complex coastline 

or hydrographic barriers (Billot et al. 2003, Breton et al. 2018, Durrant et al. 2018). 

Coleman et al (2011) demonstrated that genetic connectivity of kelps is related to 

the strength of currents that connects those systems. The lack of genetic structure in 

the Beaufort Sea indicates high connectivity among habitats, which may be assisted 

by the regional circulation regime.

Kelp dispersal in the nearshore Beaufort Sea is likely enhanced by fall storm 

events, which can transport large numbers of reproductive individuals, as evidenced 

by the extensive biomass that can wash onshore after a large wind event (Fig. 4.5). 

Experiments with seabed drifters show that neutrally buoyant, near bottom drift 

(which would transport kelp), can transport materials alongshore by 10s of km 

under ice cover and 100s of km in the summer (Barnes and Reimnitz 1982). 

Summer circulation patterns along the Alaska Arctic coast, including over the 

Boulder Patch, are wind driven and primarily alongshore, with dominant westward 

flows (Barnes and Reimnitz 1982, Weingartner et al. 2017, Section 1.1). In the 

Beaufort Sea, L. solidungula releases meiospores under the cover of 1.8 m thick 

landfast sea ice in late winter or early spring (K. Dunton, pers.comm.). Currents are 

minimal during this time, so the transport of mature adults during the summer and 

fall is more important for maintaining population connectivity, since kelp 

meiospores, whose small size exposes them to boundary layer effects, can only 

travel 10s of m without the aid of strong currents (Gaylord et al. 2006). The low 

pairwise genetic differentiation demonstrates that each sampling site is highly 

connected to the others. The fact that this population exhibits low genetic 

differentiation among geographically distinct locations suggests that range 

expansion by boreal species may occur rather quickly within the Beaufort Sea, given 

suitable habitat. 

Additional evidence of a panmictic L. solidungula population in the Beaufort 

Sea is high observed heterozygosity, as well as heterozygote excess at two Boulder 

Patch stations (DS11 and L1, Table 4.4), and negative inbreeding coefficients (FIS<0, 

Tables 4.4 and 4.5). Outbreeding is rare in kelps (Valero et al. 2001) and isolated 

kelp patches should display prominent selfing and inbreeding (Gaylord et al. 2012). 

However, despite the habitat isolation of the Boulder Patch, the kelp are apparently 

not isolated genetically. While connectivity to a wider kelp population is not 

unexpected, the level of heterozygosity in the Beaufort Sea population is unusual. 

Interestingly, this suggests that very little sexual reproduction occurs within the 

Boulder Patch itself; in other words, the Boulder Patch is not self-recruiting. 

Concurrent observations in the Boulder Patch show that sporophyte recruitment 

happens over spans of >5 yrs. (Martin and Gallaway 1994, Konar 2013, Section 1.2). 

Furthermore, kelp establishment on hard substrate, rather than to algae or 

invertebrates that will become dislodged, is extraordinarily rare considering the 
99

relative availability of rock surface (Bonsell and Muth unpub. data). Additionally, L 

solidungula in the Boulder Patch can be considerably long lived: Dunton (1990) was 

able to relocate individuals more than two decades after they had been tagged for 

growth measurements. The genetic structure may reflect establishment in the 

Boulder Patch by drift kelp from various sources and breeding populations followed 

by little to no self-recruitment. Submergence of the Beaufort Sea coast after the last 

glacial maximum, and subsequent recruitment of kelp to the Boulder Patch, may 

have effectively occurred relatively recently compared to the apparent long L. 

solidungula generation time. As a result, Boulder Patch genetic structure may show 

heterozygote excess associated with recent population mixing (i.e., isolate 

breaking). This may act in combination with a selective benefit to heterozygosity, as 

seen in other kelp species (Raimondi et al. 2004, Johansson et al. 2013).

Heterozygote excess and high variability in inter-locus FIS (Table 4.3) are also 

an indicator of clonal reproduction (Balloux et al. 2003). However, the kelp haploiddiploid life cycle would require apomeiosis and the production of diploid zoospores 

instead of the normal haploid to increase population heterozygosity. Regular 

production of diploid zoospores has only been reported from a single wild 

subpopulation of L. digitata (Oppliger et al. 2014). In that case, the irregular 

zoospores did not significantly contribute to population dynamics. Therefore, it 

would be extremely unusual if our results reflected L. solidungula clonal 

reproduction.

Smaller scale differentiation within the Beaufort Sea mainly consisted of

pairwise differentiation of the Boulder Patch station DS11 compared to other 

stations. This station has the highest kelp density (Supp. Table 4.2; Dunton and Iken 

unpubl. data), and high annual kelp growth (Bonsell and Dunton 2018; K. H. Dunton 

et al. 2009). It is notable that there was significant differentiation between kelps 

from DS11 and W3, which have comparable depths and environmental regimes 

(Section 1.2), similar levels of genetic diversity, and similarly high heterozygosity 

(Fig. 4.2, Table 4.4). Ordination analysis also revealed high dissimilarity among 

three spatially adjacent stations: E1, L1, and DS11 (Figs. 4.1 and 4.2). These stations 

all have distinct environmental regimes (Section 1.2); however, ordination analysis 

indicated that genetic dissimilarity between sites was related to kelp cover rather 

than to any particular abiotic factor (Table 4.7). Given the long-lived nature of L. 

solidungula, our multi-year environmental data may not have covered enough 

timespan to capture selective forces, such as multidecadal disturbance and 

environmental variability. Furthermore, spatial patterns in benthic cover are related 

to environmental variables in the Boulder Patch (Section 1.2). Significant 

outbreeding at DS11 and at W3 suggest that these areas may exhibit high genetic 

exchange. These two sites exhibit especially strong along shore currents during the 

open water season (Section 1.2). Flow patterns demonstrate regular physical 

connectivity with areas outside the Boulder Patch. The deeper, offshore areas of the 

Boulder Patch could therefore function as sources of L. solidungula propagules or 

entrap kelp drift from the eastern Beaufort Sea and act as sinks.
100

Divergence between Arctic basins: outcome of glacial cycles

Despite the high connectivity within the Beaufort Sea, LSU sequences 

indicated that there is far less gene flow occurring between the Canadian Arctic and 

the U.S. Arctic. Two distinct genotypes were identified, which occurred almost 

exclusively in different basins except for one Canadian genotype present in Camden 

Bay. The dominance of one genotype in each basin indicates long-term evolutionary 

divergence between L. solidungula lineages from the Canadian and Alaskan Artic. 

This observation is supported by results from sequencing for COI in the same 

species collected from the North American Arctic (McDevit and Saunders 2009, 

Bringloe 2018 Fig. E58). It also resembles the pattern in Saccharina latissima, a 

circumpolar species, which shows strong population differentiation between Pacific 

and Atlantic populations, with the Canadian Arctic as an area of interbreeding 

(McDevit and Saunders 2010). Based on various lines of genetic evidence, S. 

latissima recolonized the Arctic after the Last Glacial Maximum from at least two 

different populations, likely from a Pacific and Atlantic population, or a local refuge 

population (McDevit and Saunders 2010, Bringloe and Saunders 2018). We propose 

that a similar process occurred for L. solidungula, recolonization from allopatric 

source populations, with mixing between the two occurring in the eastern Beaufort. 

The population structure of S. latissima displays within basin admixture at high 

Arctic sites (Neiva et al. 2018). This reflects our microsatellite analysis findings of 

admixture within the Beaufort population of L. solidungula and suggests that cross 

basin range expansion by boreal species will occur much more slowly than within 

basin. 

Polymorphism within L. solidungula LSU is interesting given the lack of 

within species variation in other investigated kelp species (L. digitata, S. latissima), 

and the overall low genetic divergence among kelp species. Whole LSU sequences 

are <3% divergent among species in the Laminariales, while other markers (ITS, 

RUBISCO, mitochondrial) are much more variable (Lane et al. 2006). Significant 

divergence, beyond the LSU locus, may also explain why microsatellite markers 

developed from Boulder Patch samples failed to amplify in all samples from outside 

the Beaufort Sea, and most samples from Camden Bay. The degree of genetic (COI) 

differentiation in S. latissima among Arctic basins led Nieva et al. (2018) to conclude 

that the phylogenetic lineages represented incipient species. Although our work 

only involves one genetic marker, the scale of genetic differentiation within the 

Laminariales suggests similar emerging divergence for L. solidungula. L. solidungula

is hypothesized to have first colonized the Arctic after the opening of the Bering 

Strait ~ 5.32 mya (Rothman et al. 2017). Glacial growth and retreat then lead to 

allopatric divergence and phylogeographic population structure (McDevit and 

Saunders 2010, Neiva et al. 2018).

Relationship to other kelp species

LSU topology grouped L. solidungula with L. yezoensis, separate from other 

Laminaria, in agreement with the findings of Rothman et al. (2017) for concatenated 
101

alignment of rbcL and ITS. McDevit and Saunders (2010) also found high 

relatedness of L. solidungula and L. yezoensis based on COI sequences, though 

neither study included Beaufort Sea samples. Interestingly, the divergence between 

Beaufort L. solidungula and L. yezoensis is less than the divergence between Beaufort 

and Atlantic Arctic genotypes of L. solidungula (Fig. 4.4). The rbcL topology of 

Rothman et al. (2017) placed Canadian L. solidungula between L. yezoensis from 

Japan and L. yezoensis from British Columbia. Their time calibrated phylogeny 

indicated that these two species started diverging 16.4 to 5.6 mya, but these species 

are not monophyletic for LSU (this study) or rbcL (Rothman et al. 2017). These two 

species may have continued to interbreed during glacial maxima when L. 

solidungula would have been pushed south of the Arctic basin. The genetic 

relationships within and between these two species would be an interesting 

investigation based on the priorities to resolve kelp phylogenies with molecular 

data (e.g., Lane et al. 2006; McDevit and Saunders 2009; Jackson et al. 2016; 

Rothman et al. 2017). Taxonomic investigation of L. solidungula and L. yezoensis may 

uncover hidden diversity in the Arctic and North Pacific and unravel Pacific-Atlantic 

migration histories in Laminaria. 

CONCLUSIONS

The phylogeographic history of kelp and the origin of kelp in the Arctic 

remains highly debated (Adey et al. 2008, Bolton 2010, Rothman et al. 2017). Our 

LSU results reveal significant and previously unknown subspecies diversity in L. 

solidungula, the only Arctic endemic kelp. Further investigation via multiple genetic 

markers may uncover whether the two genotypes require taxonomic consideration. 

Laminaria solidungula, an Arctic endemic with relatively wide distribution, but 

distinct substrate requirements, is a model organism for testing the various 

hypothesis of recolonization and potential glacial refugia. Significant subspecies 

differentiation, tied to geography, demonstrate the substantial impact of glacial 

cycles on current Arctic diversity. While our results shed some light upon those 

processes, additional details of trans-Arctic population connectivity could be 

elucidated with further genetic investigation. 

Kelp are thought to be poor dispersers in their zoospore stage (Santelices 

1990, Norton 1992, Gaylord et al. 2006), but as sporophytes they can dislodged by 

storms carried long distances (e.g., Saunders 2014). In the Alaskan Beaufort Sea, fall 

storm cause large masses of kelp to wash ashore along exposed area of the coast, 

from Pt. Barrow to Demarcation Bay (Fig. 4.4). These storms coincide with the time 

that the local kelp develop reproductive structures and become fecund and may 

therefore be a key mechanism for kelp dispersal within the Beaufort Sea and 

contribute to the high gene flow shown in this study. As the timing of sea ice freezeup becomes later due to warming, dislodgement by waves and transport by storm 

driven currents will become more frequent (Thomson et al. 2016). This would 

enhance the connectivity of existing Arctic benthic populations, as well as 

potentially assist the spread of boreal species. 
102

The genetic structure of Laminaria solidungula in the Beaufort Sea suggests 

that this area is one of high gene flow and genetic diversity. However, it also 

indicates that the Boulder Patch kelps are not self-seeding, but are instead a part of 

a large, mixed population in the Beaufort Sea. This may be connected to relatively 

recent population mixing in the current inter glacial period compared to long L. 

solidungula generation times. Along with the low apparent recruitment of kelps 

(Dunton et al. 1982, Konar 2013, Section 1.2), it appears that kelp removal or die off 

would be followed by a decade or more of recovery, with kelp recruiting from the

larger regional population. While the recovery time of a disturbed local kelp 

population would take many years (Konar 2013, Section 1.2), the high rates of 

genetic exchange indicate that population connectivity ultimately lends resilience to 

the L. solidungula population of the Boulder Patch. The Boulder Patch is the largest 

kelp bed in the Beaufort Sea, and kelp inhabiting alternative habitats are clearly 

important to regional genetic patterns and population persistence. In the face of 

further development in the nearshore Beaufort Sea, more work is needed to 

investigate the spatial distribution, size, and mating patterns of the regional kelp 

population to accurately assess the consequences of anthropogenic modification to 

kelp habitats such as the Boulder Patch.

Figure 4.5. Reproductive Laminaria solidungula (main species), Saccharina 

latissima, and Alaria esculenta drift algae on the northwest side of Endicott 

Causeway. 15 August 2017.
103

Supplemental Table S4.1. All primers tested, species for which there are multiple 

alleles at that locus, and citations. 

Primers Species Citation

Lo4-24 L. ochroleuca, L. hyperborea Coelho et al. 2014

LoIVVIV-13 F

L. ochroleuca, L. hyperborea, 

L. digitata Coelho et al. 2014

LoIVVIV-15 F L. ochroleuca, L. hyperborea Coelho et al. 2014

LoIVVIV-17 F

L. ochroleuca, L. hyperborea, 

L. digitata Coelho et al. 2014

LoIVVIV-23 F

L. ochroleuca, L. hyperborea, 

L. digitata Coelho et al. 2014

LoIVVIV-24 F

L. ochroleuca, L. hyperborea, 

L. digitata Coelho et al. 2014

LoIVVIV-27 F L. ochroleuca Coelho et al. 2014

LoIVVIV-28 F

L. ochroleuca, L. hyperborea, 

L. digitata Coelho et al. 2014

Ld1-124 F

L. digitata, Lessonia 

nigrescens

Billot et al 1998, Martinez et 

al. 2005

Ld2-148 F

L. digitata, L. hyperborea, 

Lessonia nigrescens

Billot et al. 1998, Martinez et 

al. 2005, Robuchon et al.

2014

Ld2-158 F

L. digitata, L. hyperborea, 

Lessonia nigrescens

Billot et al. 1998, Martinez et 

al. 2005, Robuchon et al.

2014

Ld2-167 F

L. digitata, L. hyperborea, 

Lessonia nigrescens

Billot et al. 1998, Martinez et 

al. 2005, Robuchon et al.

2014

Ld2-371 F

L. digitata, Lessonia 

nigrescens

Billot et al. 1998, Martinez et 

al. 2005

Ld2-520 F

L. digitata, Lessonia 

nigrescens

Billot et al 1998, Martinez et 

al 2005

Ld2-531 F

L. digitata, Lessonia 

nigrescens

Billot et al. 1998, Martinez et 

al 2005

Ld2-704 F

L. digitata, Lessonia 

nigrescens

Billot et al 1998, Martinez et 

al. 2005

Ld-2 F S. japonica Liu et al. 2012

Ld-3 F S. japonica, L. hyperborea

Liu et al. 2012, Evankow 

2015

Ld-6 F S. japonica, L. hyperborea

Liu et al. 2012, Evankow 

2015

Ld13-F S. japonica Liu et al. 2012
104

Supplementary Table S4.2. Daily, year-round mean (±SD) benthic environmental 

conditions (originally from Bonsell 2019) and mean benthic percent cover of functional 

groups (originally from Bonsell 2019) at each site. Bold indicates significant differences 

between sites (Supp. Table 4.3). Superscripts indicate grouping from Tukey HSD test. 

Site

Temp. 

(

oC) Sal.

Current 

vel. (cm 

s

-1

)

PAR (mol 

photons 

m-2 day-1)

Crustose 

coralline

algae

Foliose 

red 

algae Kelp

Filter 

feeders

Suspen

-sion 

feeders

DS11 -0.78

±1.96a

32

±3a

4.4

±5.3a

0.271

±1.013

18.67 

± 0.46a

38.01 

± 0.78a

19.03 

± 0.75a

0.44 

± 0.03ab

4.08 

± 0.19a

E1 -0.41

±2.50b

31

±4b

2.8

±3.2b

0.208

±0.925 0± 0b

79.31 

± 0.51b

2.9 

± 0.25b

0.77 

± 0.11 b

6.12 

± 0.21a

L1

-0.82

±1.89 a

33

±4a

1.9

±2.7c

0.194

±0.756

0.64 

± 0.06c

71.67 

± 1.19bc

9.85 

± 0.93b

0.48 

± 0.05 ab

2.79 

± 0.21ab

W1 -0.95

±1.84a

30

±4c

4.3

±5.4a

0.102

±0.236

0.48 

± 0.03d

47.32 

± 0.76ad

4.4 

± 0.24b

2.12 

± 0.12 a

14.87 

± 0.39d

W3 -0.80

±1.88a

32

±3b

4.8

±4.6a

0.184

±0.700

4.67 

± 0.15a

58.01 

± 0.63cd

1.65 

± 0.14b

0.77 

± 0.05 ab

0.24 

± 0.02c
105

Supplementary Table S4.3. Results table for comparing benthic cover by each 

functional group among sites via ANOVA. *Indicates significant differences among 

sites (α=0.05).

Variable:

Crustose coralline algae % 

cover*

Sum Sq. DF F

Intercept 5.55 1 472.18

Site 4.00 4 84.99

Residuals 1.93 164

Variable:

Foliose red algae % cover*

Sum Sq. DF F

Intercept 2.06 1 199.01

Site 0.51 4 12.43

Residuals 1.70 164

Variable:

Kelp % cover*

Sum Sq. DF F

Intercept 4.16 1 74.30

Site 2.17 4 9.70

Residuals 9.19 164

Variable:

Filter feeder % cover*

Sum Sq. DF F

Intercept 0.031 1 3.65

Site 0.085 4 2.49

Residuals 1.393 164

Variable:

Suspension feeder % cover*

Sum Sq. DF F

Intercept 0.20 1 22.40

Site 0.64 4 17.78

Residuals 1.48 164
106

5. Long-term patterns of benthic irradiance and kelp production in the 

central Beaufort Sea reveal implications of warming for Arctic inner 

shelves

Christina E. Bonsell and Kenneth H. Dunton

INTRODUCTION

Seasonal sea ice cover plays a prominent role in marine primary productivity 

in high latitude ecosystems, as it can set the timing of peak production and 

determine annual light budgets (Clark et al. 2013, Ji et al. 2013, Kahru Manzano et al. 

2011, Post et al. 2013). In the Arctic Ocean, there has been a striking decline in sea 

ice extent since the onset of observations via satellite measurements, at a rate of 

approximately 13.3% loss in area per decade (Serreze and Stroeve 2015). Despite 

on-going efforts by scientists to investigate the effects of sea ice loss on pelagic 

production (reviewed in Wassmann and Reigstad 2011), only a few studies to date 

have addressed the direct consequences on benthic production (Krause-Jensen et al.

2012, Clark et al. 2013, Krause-Jensen and Duarte 2014). In coastal Arctic systems, 

benthic primary production by macro- and microalgae in Arctic waters is important 

to ecosystem production, elemental cycling, and food web dynamics, especially 

during times of limited pelagic production (Dunton and Schell 1987, Glud et al. 

2009, McMeans et al. 2015, Renaud et al. 2015). Additionally, biophysical processes 

in shallow, nearshore Arctic areas, where much of this production takes place, 

remain understudied due to logistical constraints (e.g., Fritz et al. 2017). Changes to 

production in these areas would have broad consequences for Arctic ecosystem 

function. 

Because of the strong annual cycle of solar irradiance in polar regions, 

seasonal sea ice and solar energy models predict that earlier dates of ice break-up 

will result in exponential increases in benthic light budgets (Clark et al. 2013). For 

instance, Krause-Jensen et al. (2012) and Clark et al. (2013) used existing gradients 

in seasonal ice cover in Greenland and Antarctica, respectively, to link lengthened 

ice free seasons with increases in macroalgal production and hypothesized that 

future warming driven reductions in seasonal sea ice extent and duration will 

enhance annual production by benthic macrophytes. These predictions contribute 

to the idea that Arctic coastal habitats will become increasingly macrophytedominated as Arctic warming continues, with consequences for Arctic food webs 

and seawater chemistry (Clark et al. 2013, Krause-Jensen and Duarte 2014, KrauseJensen et al. 2016). However, variations in underwater optical properties, which 

have a profound influence on light transmittance to the benthos and demonstrable 

impacts on benthic primary production (Van Duin et al. 2001), are largely 

overlooked in these analyses. Bartsch et al. (2016) hypothesized that enhanced 

sediment inputs from glacial melt caused a narrower euphotic zone during the open 

water season, leading to observed shallowing of peak biomass and shallower depth 

limit of kelps in Svalbard over the past two decades. The links between ice loss, 
107

irradiance at depth, and primary production appear to be multifaceted and warrant 

further investigation.

While ice cover determines irradiance at the water’s surface, irradiance at 

depth under an ice-covered ocean also depends on light attenuation of the water 

column. In the coastal Arctic Ocean, summer water transparency is influenced by 

concentrations of phytoplankton and sediments suspended in the water column. 

Concentrations of suspended sediments during the open water summer in the 

coastal Beaufort Sea have been directly linked to increased light attenuation and 

decreased annual production by benthic macroalgae (Aumack et al. 2007). Many 

Arctic inner shelf areas (depth <10 m), such as the Alaskan Beaufort Sea coast, have 

particularly high suspended sediment concentrations due to shallow depth, 

persistence of unconsolidated sediments, and significant inputs by numerous rivers 

and streams (notably, the Arctic Ocean receives 11% of global river discharge, but 

only constitutes 1% or global ocean volume; McClelland et al. 2011). These 

sediments originate from coastal erosion, resuspension due to water motion, and 

fluvial inputs by Arctic rivers, which discharge the large majority of their annual 

suspended sediments loads by the end of the spring melt (O’Brien et al. 2006, 

Walker et al. 2008, Wegner et al. 2003). Because most river fluxes occur before the 

end of ice break-up, changes in wind direction and/or speed during the open water 

period are the main drivers of temporal variability in underwater irradiance in the 

nearshore areas, as they are in other shallow aquatic systems (Van Duin et al. 2001). 

Annual benthic light budgets may consequently have a negative relationship with 

wind speeds during the ice-free season. 

The primary objective of this paper is to examine how sea ice extent and 

wind dynamics affect variation in the annual benthic light budget and production by

the Arctic endemic kelp Laminaria solidungula in the central Beaufort Sea. Since 

1979, sea ice duration in the Beaufort Sea has decreased at an accelerating rate, 

while summertime easterly winds have increased in speed and frequency across the 

coastal region (Frey et al. 2015, Wood et al. 2013, 2015). These long-term 

environmental changes may have significant, but opposing, effects on long-term 

primary production patterns. Although lengthened ice-free season results in 

increased irradiance at the waters’ surface, enhanced summer winds may degrade 

the underwater light climate. Frond elongation in L. solidungula, is entirely 

dependent on the utilization of photosynthetically derived carbon reserves 

produced the previous summer (Dunton and Schell 1986). The resulting annual 

growth has a strong correlation with the light budget of the preceding ice free 

season (Dunton 1990). This species is ideal for assessing the biological effects of 

changes to the Arctic underwater light environment due to its enhanced capacity to 

respond to small changes in irradiance compared to other kelp species, particularly 

evident in its low saturating irradiance for photosynthesis (38 μmol photons sec-1; 

Dunton and Jodwalis 1988). 

Multidecadal time series that document biological responses to variations in 

regional climate in Arctic marine systems are rare, but critical for the development 
108

of accurate projections of future ecosystem change (Wassmann et al. 2011). Here, 

we synthesize a multidecadal dataset (collected from 1977-1992 and 2002-2008) 

and incorporate previously unpublished data (2012-2016), on benthic irradiance 

and kelp growth from Stefansson Sound in the central Beaufort Sea (Dunton 1990, 

Aumack et al. 2007, Dunton et al. 1992, Dunton et al. 2009) to demonstrate the 

combined influence of seasonal ice extent and wind dynamics on the annual light 

budget, and annual kelp production. Additionally, we assess whether annual

variations in L. solidungula growth relate to seasonal ice extent and summer wind 

dynamics. In doing so, this work highlights the importance of including factors that 

affect underwater light transmittance in projecting changes in primary productivity 

and ecosystem structure in Arctic marine ecosystems. 

METHODS

Study Site

The Stefansson Sound Boulder Patch (hereafter ‘the Boulder Patch’) is an

isolated rocky zone of boulders and cobbles covering an area of approximately 63

km2 in a region dominated by soft sediment (Barnes and Reimnitz 1974; Fig. 5.1). 

Located in relatively shallow water (4-8 m) within 15 kilometers of the coast, and 

directly adjacent and north of the Sagavanirktok River Delta, the Boulder Patch 

remains a non-depositional environment. Geologists have long maintained that the 

Boulder Patch, which lies within the Gubik Formation, is an erosional environment 

that is largely free of Pleistocene sediment accumulation. High wind-driven water 

currents that persist throughout the summer open-water period are thought to 

transport sediments to deeper inner shelf areas, especially during the 1-2 week 

period of peak discharge from the Sagavanirktok in late May and early June (Dunton

et al. 1982, Rember and Trefry 2004).

The epilithic community in the Boulder Patch is dominated by the kelp L. 

solidungula and represents a regional biodiversity hotspot. Research conducted in 

the area since the 1970s has focused primarily on characterizing the underwater 

light environment and the biological production of L. solidungula (Dunton 1985, 

Dunton and Schell 1986, Dunton and Schell 1987, Dunton et al. 1992, Henley and 

Dunton 1995, Aumack et al. 2007, Dunton et al. 2009). Field studies have been 

nearly continuous since 1978 except for a single seven-year lapse (1993-2000), with 

ten long-term study sites occupied since 1984 (Fig. 5.1).

Kelp production

Laminaria solidungula individuals from long-term study sites were collected 

by SCUBA divers at one- or two-year intervals. The thallus of this species consists of 

a single blade with multiple ovate growth sections, each representing one year of 

production. The blade section closest to the stipe represents production from the 

most recent year and the immediate distal section represents growth from the 
109

previous year. Because multiple years of production can be measured from a single 

individual, this dataset spans from 1976 to 1990 and 1996 to 2015.

Figure 5.1. The Stefansson Sound Boulder Patch, showing location of long-term study 

sites in relation to percent rock cover in Stefansson Sound. Inset shows location of 

Stefansson Sound (blue star) in reference to Alaska.

Benthic and surface irradiance

Spherical quantum sensors (LI-193SA, LI-COR Inc.), placed ~0.5 m above the 

benthos, were deployed for measurements of photosynthetically active radiation 

(PAR) at sites across the Boulder Patch (Fig. 5.1, Supp. Table 5.1). Sensors were 

deployed in conjunction with either CR21 (Campbell Scientific), LI-1000, LI-1400, or 

LI-1500 dataloggers (LI-COR Inc.), depending on the site and study year (Supp. 

Table 5.1). Cosine PAR sensors (LI-192SA, LI-COR Inc.) deployed in line with a LI1000 datalogger collected continuous surface light measurements at East Dock in 

Prudhoe Bay (1986-1987) and Endicott Island (1987-2016; Fig. 5.1). Sensors were

cleaned between deployments (once a year), as biofouling in this environment is 

negligible. Sensors made instantaneous measurements every minute and logged the 

average every 1 or 3 hours, depending on site and study year (Supp. Table 5.1). All 

PAR measurements were converted into total daily photon flux rate (mol m-2 day-1) 
110

for analysis. Daily hours of saturating irradiance for L. solidungula (Hsat: hours with 

average photon flux rate ≥38 μmol photon m-2 sec-1; Dunton and Jodwalis 1988)

were also calculated, as this metric is more closely related to annual production 

than photon flux rate (Dunton 1990). For years with irradiance data for >90% of the 

year, annual Hsatwas calculated at each site. 

Sea Ice

Sea ice concentration from 1979-2016, measured via passive microwave 

data, was obtained from the National Sea Ice Data Center via the Arctic Data 

Integration Portal (http://portal.aoos.org) for the two 25 km2 grid areas that 

contain the Boulder Patch. Values from the two areas mirrored each other closely 

over time and were therefore averaged daily. Dates of key events in the ice season 

(break-up start, break-up end, freeze-up start, and freeze-up end) were calculated 

using the algorithms described by Johnson and Eicken (2016). These events define 

the seasonality of direct incident solar radiation to the sea surface. The length of the 

ice-free season was calculated as freeze-up start minus break-up end. 

Wind 

Wind speed and direction for West Dock in western Stefansson Sound 

(National Climate Data Center ID 9497645), spanning 1993-2016, were obtained 

from the National Weather Service Cooperative Observer Program (COOP) via the R 

package “r-noaa” (Chamberlain et al. 2016). Wind speed and direction for 

Deadhorse Airport, spanning 1973-2016, were obtained from the Alaska Airport 

Weather Observations Network (mesonet.agron.iastate.edu/ASOS/) and underwent 

quality control procedures before analysis. Coastal wind speed was estimated from 

the Deadhorse data using the linear relationship between wind speed at Deadhorse 

and West Dock (see Results). For consistency, we only use the estimated coastal 

wind speed in analyses relating to irradiance and kelp growth. To examine changes 

in summer winds over time, we used data confined by the dates of start of break-up 

and start of freeze-up for each year; for analyses relating wind to benthic light or 

kelp growth, we focused on wind data confined by the dates of end of break-up and

start of freeze-up for each year. In concurrence with other studies in the region, 

storm events were defined as >6 consecutive hours of average wind speeds over 10 

m s

-1, with direction defined by primary cardinal direction (e.g., winds from NNE 

were defined as N winds; Manson and Solomon 2007).

To assess the effect of wind speed on underwater light transmittance, we 

plotted the relationship between mean daily coastal wind speed (as estimated from 

Deadhorse) and daily percent surface irradiance. Due to noisy data, wind speeds 

were binned by rounding to the nearest whole number. 

Production model

To assess the effects of ice extent and wind speed on annual kelp production, 

we used the model developed by Aumack et al. (2007) to calculate daily production 
111

rates under given depth and suspended sediment concentrations. Briefly, this 

model uses incident radiation and a suspended sediment concentration specific light 

attenuation coefficient to estimate benthic irradiance to calculate L. solidungula 

production over the open water season. We estimated incident radiation on each 

day of the year by averaging values for each day from all the years of surface 

irradiance measurements. We incorporated daily wind speeds by using estimates of 

suspended sediment concentrations under given wind speeds obtained in this 

region (Trefry et al. 2009, Aumack and Dunton unpublished data; Supp. Table 5.2). 

We confined the model to ice free dates only and used daily average coastal wind 

speed. Annual carbon production was estimated for the average size of individuals 

from our long-term dataset (22.5 cm basal blade length). Modelled carbon 

production was compared to the long-term kelp growth dataset, with measured kelp 

growth converted into g C yr

-1 based on previously derived relationships (Dunton 

and Jodwalis 1988, Aumack 2003).

Statistical analysis

Trends in ice events, wind speed, and kelp growth over time were assessed 

using linear regression analyses. The relative effects of year and site, and the 

interaction between the two terms, on kelp growth were determined using ANOVA.

We tested the relationships between kelp growth (linear elongation in centimeters) 

and date of ice break-up and length of ice-free season by linear regression.

For years and sites with irradiance data for >90% of the summer period

(July-September), we tested if ice break-up date and length of ice-free season 

predicted the annual benthic irradiance budget using linear models. For these data, 

cumulative Hsat was calculated for each day by inclusively summing Hsat from all 

previous days to determine the seasonality of potential kelp production. 

For all years of irradiance data, the longest portion of the year with 

representative data for multiple sites and years from multiple decades is 27 July –

31 August. There were not adequate redundancies in sites across years for these 

dates, so separate one-way ANOVAs were conducted to test the individual effects of 

site and year on three irradiance variables: a) total annual irradiance, b) mean 

irradiance, and c) total Hsat. The relative effects of mean daily sea ice concentration 

and mean daily maximum wind speed, and the interaction between the two terms, 

on the three irradiance variables for this 36-day period were determined by linear 

regressions. As westerly winds entrain turbid coastal water in the nearshore in this 

region, we also tested the effects of wind direction and proportion of westerly winds 

on the three irradiance variables using linear regression. We defined westerly wind 

as within 45o of due west (270o). To test the relative importance of this period of 

time to annual kelp production, the relationships between site specific annual kelp 

growth and total irradiance, as well as between site specific annual kelp growth and 

total Hsat, were each tested by linear regression. 
112

All analyses were carried out using R (R Core Team, 2016). Directional data 

was analyzed and plotted using the R package “circular” (Lund and Agostinelli 

2015). 

RESULTS

Physical drivers: sea ice and winds

We found that the ice-free season in Stefansson Sound (end of break-up to 

start of freeze-up) lengthened during the period of this study. Dates between ice 

break-up and freeze-up increased from 74 days in 1979 to 132 days in 2016 (Fig. 

5.2-5.3, Supp. Table 5.3). Duration of ice break-up, however, does not show any 

change (Fig. 5.3, Supp. Table 5.3). 

Coastal wind speeds directly measured at West Dock and estimated from 

Deadhorse show no trends over time (linear regressions on daily summer wind 

speed and fraction of hours with wind speeds > 10 m/s, p>0.05, R2<0.1). We also did 

not find evidence for a strengthening of easterly or westerly winds over time (linear 

regressions, p>0.05, R2<0.1). From the 1993-2016 data, we calculated the linear 

relationship between West Dock and Deadhorse daily wind speeds as

(Eq. 1) SpeedWest Dock= 1.40 +1.04*SpeedDeadhorse

where speeds are in m s-1 (linear regression, p<0.05, R2=0.63). Wind direction data 

for both stations are not vonMises distributed and therefore could not be analyzed 

statistically for trends over time. We discerned that mean wind direction at each 

station varies between years (particularly at Deadhorse), but typically originates

from the east at West Dock and from the northeast at Deadhorse (Fig. 5.4).
113

Figure 5.2. Sea ice concentration by Julian Day in Stefansson Sound over a 38-year 

period (January 1979-2017) illustrates the lengthening of the open water season and the 

decrease in summer ice concentration over time.

Figure 5.3. Key events related to freeze-up and break-up by Julian Day (A-B, D-E) or 

duration of event in days (C, F). Generally, break-up is occurring earlier and freeze-up 

later over the 38-year period. Blue line represents the linear relationship ±SE (grey 

shading). 
114

Figure 5.4. Daily summer wind variables over time for West Dock (A, C) and Deadhorse 

Airport (B, D). The distributions of daily mean wind direction (A, B) show that winds 

tend to originate from the east. The earliest year of measurement is the most interior 

circle of points, the latest year is the most exterior. Semi-transparent points indicate daily 

average wind direction, colored by year. Opaque red squares indicate annual mean 

direction. Black line indicates overall mean direction for all years. Daily wind speeds (C, 

D) are shown as annual means ±SD (black line and grey shading). Wind speed varies 

between summers but shows no long-term trends.
115

Figure 5.5. Mean Laminaria solidungula growth (cm) varies between sites and years, 

with some years showing strong annual patterns (e.g. 2001 and 2003).

In situ kelp growth and benthic irradiance: relationships to sea ice and wind 

Analysis of the long-term data shows that kelp growth (as measured by blade 

elongation) in the Boulder Patch fluctuates over time and between sites (Fig. 5.5), 

although there is more variation in growth between sites than between years 

(SSSite=80127, FSite(9,8936)=105, p<0.01 SSYear=442890, FYear(34,8936)=153, -

p<0.01). The relationship between annual kelp growth across the Boulder Patch and 

the length of the open water season is not significant (linear regression, p>0.05, 

R2<0.1).

Benthic irradiance at all sites displays strong seasonality, with some 

variation in maximum daily photon flux per year (Fig. 5.6). We only have data for 

the entire summer for a couple of years, confined mostly to the late 1980s and early 

1990s, but we did not find any trends over time in the annual maximum daily 

photon flux rate (linear regressions performed for each site, p>0.05, R2<0.1). Under 

ice cover, no measurable light occurs except at some sites in some years in the 

spring under “clean ice” (Dunton 1984), until around the time of the freshet when 

irradiance levels return to zero (Fig. 5.6). The first significant light transmittance 

occurs after ice break-up has begun (Figs. 5.6 and 5.7). Overall summer light levels 

are low, with most days lacking light levels that approach saturation irradiance for 

Laminaria solidungula (Table 5.1). Interestingly, these data show that the days 

surrounding ice break-up contribute strongly to annual Hsat for a given site, 

sometimes over 50% of the annual value (Fig. 5.7). 

Our analysis of data from 27 July – 31 August showed no significant 

differences between sites for mean daily benthic irradiance (MDI), total benthic 
116

irradiance (TBI), or total Hsat(TH). However, we detected significant differences 

between years for this period (SSMDI~Year=10.58, FMDI~Year (6,18)=9.95, p <0.01;

SSTBI~Year=12102, FTBI~Year (6,18)=10.54, p<0.01; SSTH~Year=85591, FTH~Year

(6,18)=11.8, p<0.01). TBI and TH from this period do not predict annual kelp growth

(linear regressions, p>0.05, R2<0.1). We did not find significant effects of mean daily 

sea ice concentration, or the interaction between ice concentration and mean daily 

maximum wind speed on MDI, TBI, or TH. However, mean daily maximum wind 

speed alone significantly affects MDI (linear regression, p<0.05, R2=0.24), TBI 

(linear regression, p<0.05, R2=0.24), and TH (linear regression, p<0.05, R2=0.19). 

This effect of wind is always negative. Plots of binned mean daily wind speed 

compared to percent surface irradiance for all ice-free dates at each site echo this 

negative relationship (Fig. 5.8). In contrast, there is no linear relationship between 

daily percent surface irradiance and sea ice concentration in the summer, with high 

irradiance values possible at high ice concentrations (Fig. 5.9). Although there is no 

relationship between mean wind direction and MDI, TBI, or TH for the 27 July – 31 

August data, the proportion of westerly winds has a significant, negative effect on 

each of these irradiance variables (linear regressions, p<0.05, R2MDI=0.21, 

R2TBI=0.20, R2TH=0.19).

Modelled kelp production based on wind during the ice-free summer is more 

variable between sites than measured production and shows a general increase 

over time in annual production (Fig. 5.10). Compared to measured production, the 

model is conservative, often underestimating annual kelp production, and has a high 

incidence of predicting zero production, especially for shallower sites.
117

Table 5.1. Summary of underwater daily light values for ice free summer (end of 

break-up to the start of freeze-up). Differences in mean daily surface irradiance 

between sites are due to differences in benthic light data coverage over time (Supp. 

Table 5.1).

Site

Dept

h (m)

Mean daily 

surface 

irradiance

(mol 

photons 

m-2 day-1)

Mean 

daily 

percent 

surface 

irradiance 

±SD

Mean light 

attenuatio

n, k (m-1)

Percent of 

days with 

benthic 

photon 

flux of 0

Mean 

daily 

hours Hsat

Percent of 

days with 

0 hours 

Hsat

DS11 6.1 15.5 4.1±6.2 0.52 20.9 1.9±4.0 77.8

E1 4.4 15.1 3.2±6.4 0.78 29.9 1.7±4.0 81.5

E2 4.3 13.8 3.1±6.2 0.81 38.1 1.5±3.6 82.8

E3 5.5 12.7 3.6±6.6 0.60 35.4 1.7±3.9 78.8

L1 5.5 17.0 1.9±3.8 0.72 35.3 1.4±3.7 82.7

W1 6.0 12.5 1.9±3.7 0.66 39.3 0.8±2.5 87.0

W2 6.2 13.3 2.2±4.2 0.62 41.2 0.9±2.9 87.9

W3 6.6 12.2 2.3±4.3 0.57 43.9 1.1±2.8 84.2
118

Figure 5.6. Seasonal pattern of both incident (Endicott Island) and underwater irradiance 

(mol photons m-2day-1) on the seabed at eight locations in Stefansson Sound by year. 

Note change in scale for surface irradiance.
119

Figure 5.7. Cumulative Hsat, as proportion of annual Hsat, illustrates the rapid 

accumulation of hours of saturating irradiance during and directly following the period of 

ice break-up, based on available in situ irradiance data from each site from July through 

September. Pairs of black vertical lines frame the period between start of break-up and 

when sea ice concentration reaches 15%. 
120

Figure 5.8. Mean daily underwater irradiance, as percent surface irradiance, at each site 

in relation to coastal wind speed calculated from the Deadhorse Airport station (Eq. 1). 
121

Figure 5.9. Distribution of daily underwater irradiance values (as percent of surface 

irradiance) as a function of ice concentration during the period from the start of ice breakup to the start of freeze-up. Note that high irradiance values are possible at relatively high 

ice concentrations.
122

Figure 5.10. Modelled kelp production (white circles), calculated for an average sized 

individual (22.5 cm basal blade length) based on the relationship between wind velocity,

total suspended solids, and light attenuation during the ice free period, compared to

measured mean kelp production (black circles ±SE) calculated from blade elongation. 

DISCUSSION

Although the ice-free season has lengthened over time in Stefansson Sound, 

we found no evidence of increased kelp production. Low light conditions prevail 

during the summer, suggesting that suspended sediments, often derived from wind 

driven water motion, are integral in controlling light transmittance in shallow, 

nearshore regions of the Arctic. However, annual kelp production and long-term 

trends were not accurately predicted by our model, which relied on established 

relationships between wind speed and light attenuation during the ice-free summer. 

Instead, annual benthic light budgets – and therefore annual kelp production –

appear to rely heavily on enhanced light transmittance in the days surrounding ice 

break-up. Overall, our results suggest that sea ice presence in these systems has a 

net positive effect on underwater light transmittance and marine primary 

production by attenuating swell and wave action. 

Laminaria solidungula in Stefansson Sound survive at impressively low 

quantum budgets, reported previously by Dunton et al. (1990) at 45 mol photons m2 yr-1, the lowest measured for any kelp population (Lüning and Dring 1979 reported 

boreal Laminaria hyperborea at 71 mol photons m-2 yr-1). In addition, L. solidungula 

exhibits saturation (Ek) and compensation (Ec) irradiance at 38 μmol photons m-2 

sec-1 and 0.5-3 μmol photons m-2 sec-1, respectively (Chapman and Lindley 1980, 
123

Dunton and Jodwalis 1988), among the lowest reported for marine macroalgae 

(Wiencke et al. 2006), and well below that of other cold water kelp species 

(Laminaria digitata: Ek and Ec =150 μmol photons m-2 sec-1 and 6 μmol photons m-2 

sec-1, respectively; Saccharina latissima: Ek and Ec =150 μmol photons m-2 sec-1 and 5

μmol photons m-2 sec-1, respectively; L. hyperborea Ek and Ec =90 μmol photons m-2 

sec-1 and 9 μmol photons m-2 sec-1, respectively; Lüning 1971, King and Schramm 

1976). Consequently, because L. solidungula is very responsive to the low irradiance 

with an annual growth pattern strongly linked to summer open water conditions 

(Dunton 1990), the species is an excellent indicator of interannual variations in the 

underwater light climate.

Ice concentration, wind dynamics, benthic irradiance, and kelp growth

Summer sea ice concentrations in Stefansson Sound have declined 

considerably since the 1980s (Fig. 5.2). Our analysis indicates that the open water

season (break-up end to freeze-up start) of the Boulder Patch area has gained 

approximately 17 days between 1979 and 2016 (Fig. 5.3). This rate of change is 

considerably less than that measured for the western coastal Beaufort Sea (~54 

days over 34 years, Johnson and Eicken 2016) and the Beaufort Sea as a whole (~36 

days over 30 years, Markus et al. 2009; ~31 days over 34 years, Stroeve et al. 2014; 

~41 days over 33 years Frey et al. 2015). However, the Beaufort Sea is a region 

characterized by considerable variability compared to other Arctic regions (Markus 

et al. 2009, Stroeve et al. 2014), and the rate of change for Stefansson Sound is 

similar to the trend for the entire Arctic (~20 days over 30 years, Markus et al. 

2009; ~15 days over 34 years, Parkinson 2014). While some of these differences 

may be attributed to varied definitions of “ice covered”, geomorphology is likely the 

main cause of disparity. Stefansson Sound is largely protected by a chain of barrier 

islands, which affects the dynamics of landfast ice compared to pack ice. Landfast 

ice forms first in protected areas, such as lagoons and sheltered bays, and the timing 

of its freeze and melt are closely tied to the onset of freezing and thawing 

temperatures, respectively (Barry et al. 1979, Mahoney et al. 2014). As a result, ice 

presence in sheltered coastal waters such as Stefansson Sound may respond to 

different forcing and exhibit more moderate long-term trends than in exposed and 

offshore waters.

One expected outcome of decreased seasonal ice is an increase in underwater 

light and annual production in L. solidungula over time (i.e., Krause-Jensen et al. 

2012, Clark et al. 2013), as demonstrated by our productivity model (Fig. 5.10). Our

data, however, do not show such a trend (Figs. 5.4 and 5.10). Previous research has 

demonstrated that while there is a strong correlation between annual benthic 

irradiance and L. solidungula production, there is no correlation between daily 

surface and underwater irradiance in Stefansson Sound (1990). Additionally, the 

timing of ice dynamics has no direct impact on annual kelp production. Instead,

annual benthic light budgets in Stefansson Sound are primarily limited by 

resuspension of sediments by wind induced water motion (Aumack et al. 2007). 

This is partially demonstrated by the considerable accumulation of Hsat during the
124

dates surrounding ice break-up (Fig. 5.7), when wind driven water motion is limited 

by high sea ice concentrations. The negative relationship between wind speed and 

benthic irradiance over month long periods, and the prevalence of low light 

conditions during the ice-free season (Table 5.1), further emphasize the link 

between wind and light attenuation.

The lack of a significant relationship between ice concentration and benthic 

light during late July through August indicates that the negative effects of wind 

outweigh the potentially remediating effects of any sea ice that lingers in the 

nearshore area after the open water season is underway. As in other shallow aquatic 

systems, rates of particle settlement and transport of suspended sediments 

confound the connection between benthic irradiance and wind (Van Duin et al. 

2001). The effect of wind direction on the entrainment or advection of turbid coastal 

waters adds additional complexity to this relationship. It appears that the frequency 

of short-term increases in underwater light transmittance, under favorable wind 

and ice conditions ultimately determine the annual benthic light budget in this

system.

The temporal accumulation of Hsat suggests that the days surrounding ice 

break-up contribute significantly (as much as 75-100%; Fig. 5.7) to annual benthic 

light budgets and annual kelp production. Underwater irradiance during this time 

depends on the amount of sediments contained within the ice (Dunton et al. 1982; 

Kempema et al. 1989), as well as ice dynamics during break-up, which are difficult 

to predict, and therefore not included in our production model. The omission of the 

break-up period may explain why the ice and wind-based production model often 

underestimated kelp growth (Fig. 5.10). The drastically reduced light levels during 

the ice-free summer and the lack of relationship to annual kelp growth demonstrate 

that light conditions during the ice-free summer are usually poor. Consequently,

contracted sea ice extent may simply contribute to wind driven mixing of the water 

column and increased suspended sediments earlier in the year, effectively 

diminishing benthic irradiance and kelp production during the ice-free summer.

Reduced sea ice coverage in the coastal Beaufort Sea has increased wave 

fetch and intensified wave energy (Thomson and Rogers 2014, Thomson et al. 2016)

that can substantially increase suspended sediments (Trefry 2009). Other studies in 

this region show that wave height is related to wind speed, as well as ice 

concentration (Manson and Solomon 2007). Therefore the strengthening of easterly 

winds in the region since the mid-2000s may also contribute to enhanced sediment 

resuspension (Wood et al. 2013, 2015), though this trend was not reflected in the 

wind measurements recorded at the Prudhoe Bay weather stations.

Current projections for macrophyte communities in Arctic marine systems 

assume that benthic irradiance will increase as sea ice extent contracts (i.e., Clark et 

al. 2013, Krause-Jensen and Duarte 2014). The long-term data presented here show 

that this assumption may not apply to inner shelf regions, especially marginal Arctic 

seas that receive substantial river inputs during the summer open water period. 
125

This also agrees with results from a glacially influenced site, where sediment laden 

glacial meltwater is thought to have shrunk the euphotic zone over time (Bartsch et 

al. 2016). Light attenuation by suspended sediments demonstrably affects 

irradiance available for primary production, both in the water column (Van Duin et 

al. 2001) and in the benthos (Anthony et al. 2004, Aumack et al. 2007), and should 

be considered for Arctic ecosystem change scenarios.

The Arctic inner shelf under future climate change

Shallow inner shelf zones make up 20% of the area of Arctic shelves (Fritz et 

al. 2017). Outside of Greenland and the Canadian Archipelago, these systems 

experience considerable inputs from Arctic rivers during the melt season that 

contribute to total suspended sediment (TSS) concentrations (Gordeev et al. 1996, 

Gordeev 2006; Holmes et al. 2002, McClelland et al. 2012). Coastal erosion also 

significantly contributes to elevated TSS levels in inner shelf zones (Reimnitz et al. 

1988, Hill and Nadeau 1989), which are often maintained by wind events that 

resuspend unconsolidated sediments on shallow shelves (Hill and Nadeau 1989). 

The reduction in water transparency in response to increased TSS, which is 

accentuated by expanded fetch and diminished ice extent, demonstrates the effects 

of a warming Arctic on primary production, especially on the coast and inner shelf. 

Consequently, we should expect that coastal ecosystems will respond differently to 

climate change than deeper, offshore seasonal ice zones.

As warming continues, multiple environmental factors in nearshore Arctic 

areas will cause average summer light attenuation to surpass current levels (~0.8 

m-1; Table 5.1). The persistence of these high attenuation conditions, which match 

the turbid conditions previously measured in Stefansson Sound (Dunton 1990,

Dunton et al. 2009), will alter regimes of underwater light transmittance and 

primary productivity (Fig. 5.11). First, the reduction of sea ice will increase the 

incident sunlight to the ocean, leading to enhanced capacity for primary 

productivity at the surface. However, this reduction in sea ice will also lead to 

lengthened fetch, larger swell, stronger wave activity in the nearshore, and higher 

incidence of sediment resuspension (Wegner et al. 2003, Walker et al. 2008, 

Thomson and Rogers 2014, Thomson et al. 2016). Nearshore wave activity also 

intensifies coastal erosion, aided by melting permafrost and sea level rise (ACIA 

2005, Overeem et al. 2011). Currently, both sheltered mainland lagoon and open 

ocean exposed coastlines of the Beaufort Sea are eroding at rates of 0.9 to 1.8 m y-1, 

respectively (Gibbs and Richmond 2015). Globally, rates have been measured as 

high as 25 m y-1 (Jones et al. 2009, Gunther et al. 2015). Beaufort Sea Coast erosion 

rates appear to have increased, or even doubled over the past five decades (Jones et 

al. 2008, Jones et al. 2009). As the climate continues to warm, most Arctic coastlines 

are expected to experience accelerated coastal erosion (ACIA 2005, Fritz et al. 

2017). River discharge to the Arctic Ocean is also increasing, with measured 

increase of ~11% from 1964-2000 (McClelland et al. 2006). This will contribute to 

intensified fluvial sediment flux, estimated to rise by 30-122% for the six largest 

Arctic rivers by the end of the century (Gordeev 2006).
126

The additive effects of sea ice loss, coastal erosion, and enhanced fluvial 

inputs will ultimately increase suspended sediments in Arctic inner shelf waters 

during the open water season (Fig. 5.11). Our research suggests that as summer ice 

concentrations continue to decrease, the annual benthic light budget in nearshore 

areas will progressively depend on light transmitted through the ice and water 

before and during break-up. It also shows that primary production may shift in 

unexpected ways in the future, depending on local characteristics. In areas with high 

suspended sediments, benthic production may not be enhanced by ice loss, but will 

increasingly rely on light transmittance during break-up. Phytoplankton

productivity in the nearshore Arctic will also depend on the balance between light 

attenuation by suspended sediments and nutrient concentrations in the upper 

water column, which are similarly determined by sediment resuspension, erosion, 

and fluvial inputs. Decreased light transmission during summer will shallow the 

critical depth of growth, altering depth distributions of benthic and planktonic 

primary producers. Our work demonstrates that environmental factors that 

characterize Arctic inner shelves, particularly suspended sediments, should be 

considered in projections that evaluate the response of Arctic ecosystems to climate 

change. 
127

Figure 5.11. Conceptual diagram of the effects of a) summer ice cover versus b) reduced 

summer ice cover on processes affecting the underwater light environment in shallow 

inshore (<10 m) Arctic ecosystems. Reduced ice will increase coastal erosion, lead to 

larger swell, and increase sediment resuspension, which will combine with enhanced 

fluvial sediment inputs to ultimately increase light attenuation (k). This will decrease 

annual growth of benthic macrophytes, and shift depth distributions of pelagic and 

benthic primary producers by shallowing the critical depth.
128

6. Physiological responses of an Arctic crustose coralline alga 

(Leptophytum foecundum) within variations of salinity

Arley F. Muth and Kenneth H. Dunton

INTRODUCTION

Dissolution of marine calcifying organisms is of broad and current interest in 

ocean climate change studies. Lower pH levels, driven by carbon dioxide (CO2) 

uptake into ocean water, causes a reduction in calcium carbonate (also other 

carbonate minerals e.g., aragonite and calcite) saturation levels leading to increased 

dissolution rates of calcifying species. However, pH is only one parameter of the 

saturation level equation and other mechanisms and factors have the potential to 

affect calcium carbonate saturation levels. At high latitudes, aragonite saturation 

levels are low (~2) when compared to ocean averages (~3.5) or low latitude values 

(~4; Gangstø et al., 2008; Jiang et al., 2015) due to processes such as freshwater 

input, rapid seasonal ice melt, upwelling, and relatively high respiration rates of 

organic matter (Mathis et al., 2015a). In the Arctic Ocean, natural factors in addition 

to anthropogenic factors have the potential to decrease aragonite saturation levels 

below natural variations (Ω aragonite 3.5-0.8) by 2025 (Mathis et al. 2015a). 

Decreased rates of productivity, growth, and net calcification have been 

shown to occur when coralline algal species are exposed to lower than ambient pH 

conditions for short time periods (~21 days; Diaz-Pulido et al. 2012, Mccoy 2013, 

Büdenbender et al. 2015). CCA species are often the first species affected by lower 

pH because they precipitate magnesium calcite, which is about 20% more soluble 

than aragonite (Kamenos et al. 2008). CO2 vents in Italy decrease pH locally and 

coralline species disappeared near the vents while turf algal biomass increased 

60%, including certain invasive species (Hall-Spencer et al. 2008). Low salinities 

also decrease calcification rates and productivity in CCA species (King and Schramm

1982). Schoenrock et al. (2018) saw decreased calcification rates, photosynthetic 

efficiency, and density in Lithothamnion glaciale in southwest Greenland as waters

became fresher in fjord environments. Marine invertebrates are also susceptible to 

dissolution as juvenile oysters cultured under decreased salinity and pH conditions 

showed increased rates of mortality in response to both factors, but that low 

salinities alone had the most impact (Dickinson et al. 2012). 

Within the Beaufort Sea, a diverse benthic community attached to boulders 

and cobbles occurs in an area known as the Boulder Patch (for a detailed 

description see (Dunton et al. 1982, Wilce & Dunton 2014). Three kelp species 

(Laminaria solidungula, Saccharina latissima and Alaria esculenta) and various red 
129

algal species (e.g., Phycodrys fimbriata and Coccotylus truncatus) are common on 

rock surfaces (Wilce and Dunton 2014). CCA species, including Leptophytum 

foecundum and L. lavae, cover 77% of the hard substratum in some areas and are 

completely absent in others (see section 1.2 and 1.3). Arctic species are adapted to 

high seasonal changes but in a relatively small geographic area (~ 6 km) within the 

Boulder Patch. Benthic surveys show patterns of decreasing CCA coverage at 

inshore areas as well as changes to algal diversity, composition and biomass. Within 

the Boulder Patch, a complete loss of CCA can be seen at inshore sites, and this study 

aims to understand the mechanisms preventing CCA from occurring at inshore 

locations.

The goals of this study were to understand the role of salinity and related 

abiotic parameters (pH, total alkalinity) on CCA physiology and distribution. Do 

changes in salinity affect CCA physiology independent of pH? Are seawater 

chemistry conditions influencing CCA distributions? Laboratory experiments were 

used to focus on short-term changes to CCA physiological mechanisms in response 

in alterations in water chemistry, and field studies allowed for real time, long-term 

observations of recruitment and persistence in natural conditions. 

METHODS

Study site and species background

Within the Boulder Patch, Stefansson Sound, Alaska, there are varying 

distributions of CCA and the most striking of these patterns is between an inshore 

site, located near the mouth of the Sagavanirktok River (E-1) and an offshore site 

(DS-11; Fig. 6.1). The substrate of cobbles and boulders at the offshore site is 

covered with CCA (Leptophytum foecundum, determined by Paul Gabrielson of 

University of North Carolina Chapel Hill), while CCA is absent at the inshore site.

Salinity measurements throughout the study area from past years have shown that 

salinity can drop to 15-20 at both sites but can reach as low as 0 at the inshore site 

(see section 1.1). Total alkalinity (AT) is often lower in fresh and brackish waters, 

which reduces pH buffering capacities and results in lower pH values, leading to 

dissolution of the calcium carbonate skeletons of calcifying organisms. However, 

continuous pHT for the Boulder Patch show consistently higher pH values at the 

inshore site, where CCA are absent. AT values did decrease with lower salinity 

measurements as expected, and mesocosm experiments were conducted to observe 

if changes in salinity and AT, with a constant pH, could drive dissolution and/or 

lower photosynthetic efficiency in L. foecundum. 

Mesocosm Experiments

Culture conditions

Cobbles were collected in July of 2016 at DS-11 and transported to the 
130

University of Texas Marine Science Institute (UTMSI) and kept in a 0°C chamber 

until experiments began. Leptophytum foecundum cobbles were cultured in 

complete darkness at three salinities (10, 20 and 30) and 0°C. A no-specimen 

control treatment (salinity of 30) was also monitored to determine the effect of the 

medium on water chemistry. Samples (n=4) were placed in salinity treatments for 5 

weeks, after which all samples recovered at 30 for 5 weeks. Growth media (pH 8) 

was created using Gulf of Mexico (GOM) offshore water (low in nutrients) and 

distilled water, and all treatments were supplemented with Provasoli’s Enriched 

Seawater, ensuring sufficient supply of macro and micronutrients. Media was 

replaced weekly and water quality parameters (pH, temperature, salinity) were 

recorded before and after each water change using a data sonde (YSI 6920V2-2). 

Water samples were also taken before and after each water change for titratable 

alkalinity measurements (values were log transformed to meet ANOVA assumptions 

of normality and equal variances). 

Photosynthetic efficiency

Samples were monitored weekly for photosynthetic efficiency, dark-adapted 

yield values (Fv/Fm) using a pulse amplitude modulation (PAM) fluorometer (Walz, 

diving-PAM model). Three areas per cobble (n=4 for each salinity treatment) were 

measured for yield values each week at the same location on the cobble and time of 

day.

Calcium carbonate dissolution

Calcium carbonate dissolution was estimated by analyzing culture medium 

AT when the medium was first replaced (initial) and after one week (final). An 

automated open cell Gran titration system (ASALK2; Apollo SciTech) coupled to a 

thermostated water bath was used to measure AT (Lonthair et al. 2017) at 

experimental temperature. AT values were combined with temperature, pH and 

salinity measurements to estimate aragonite saturation levels using the software 

program CO2SYS. Saturation levels equal to one are at equilibrium, greater than one 

indicate precipitation and less than one indicates dissolution. 

Pigments

Cobble photographs were taken at the start of the experiment, after 5 weeks 

in salinity treatments and after the 5-week recovery period, then analyzed using 

ImageJ to estimate percent pigment. 

CCA recruit survivorship

In July 2017, fiber cement tiles (10 x 10 cm) were retrieved from DS-11 (Fig. 

6.1) following a 12-month deployment on the seabed. Tiles/samples were 

transported to UTMSI and kept in a 0°C chamber until experiments began. CCA 

recruit (1-2 yr. old individuals) survivorship was monitored in salinity treatments of 

10, 20 and 30. Tiles (n=5) were placed in each salinity treatment (10, 20 and 30) 

and five CCA recruits were identified and marked to ensure the same individuals 

were monitored throughout the experiment. Tiles were kept within the salinity 
131

treatments for three months (Sept. 27 – 23 Dec. 23, 2017) and monitored weekly for 

survivorship. 

In situ field experiments

Adults

To observe natural effects of abiotic factors and spatial changes in carbonate 

chemistry on L. foecundum in situ, reciprocal transplants between the inshore (E-1) 

and offshore site (DS-11) were conducted. Cobbles from offshore (n=6) with CCA 

present were photographed and transferred to the inshore site and remained on the 

seabed from July 2016 – July 2017. Cobbles were retrieved and kept in cool, dark 

conditions while transferred to the laboratory on Endicott Island, Alaska. In 

addition to the transplanted cobbles, control cobbles from each site were also 

collected for comparison in July 2017. In the laboratory, all cobbles with CCA 

present were measured for dark-adapted yield using PAM fluorometry (same 

methods as above). Tissue samples of the transplanted and control CCA were 

collected for pigment analysis, stored at -80°C and analyzed using the methods 

described above. 

Recruits

Fiber-cement settlement tiles deployed in the Boulder Patch seafloor in July 

2016 (see section 1.3) were retrieved from sites E-1 and DS-11 in July 2018 were

transported to UTMSI and kept in a 0°C chamber prior to quantifying and comparing 

estimates of density and size of CCA recruits between sites. Tiles analyzed from E-1 

the previous year had no CCA recruits present at the inshore (E-1) site, however, in 

July 2017, recruits were observed on the inshore settlement tiles (Section 1.2). 

Using a uniform grid, density of CCA individuals was counted for 50 FOV per tile at 

40x. Pictures were taken at 40x, capturing 5 individuals per tile and ImageJ was 

used to analyze the size of the recruits from both sites. 

Statistics

Mesocosm parameters were compared by calculating differences in AT and 

pH in new media and week-old media, using 2-way ANOVAs (salinity treatment and 

treatment/recovery period) and values were log transformed in order to meet test 

normality and equal variance assumptions. Fv/Fm among treatments were 

compared using repeated measures 2-way ANOVAs (salinity and time) while 

cobbles were in salinity treatments and after all cobbles had been placed in recovery 

conditions. Changes in visual pigments were compared using 2-way ANOVAs 

(salinity treatment and treatment/recovery period) and ANOVAs were used to 

compare recruit size (values were square root transformed in order to meet ANOVA 

assumptions of normality and equal variances and density (values were log 

transformed in order to meet ANOVA assumptions of normality and equal 

variances) between sites. All statistics were run using R Version 3.3.1. 
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RESULTS

Culture Conditions

Salinity and temperature treatments remained consistent over the 5-week 

treatment period (control: 30.9 ± 0.2; 30: 31.5 ± 0.2; 20: 20.9 ± 0.1; 10: 10 ± 0.1) and 

salinity values in the recovery treatments were 31.2 ± 0.1. pH values decreased in 

all treatments over each week (start pH 8.06 ± 0.02, end pH 7.84 ± 0.02), but the 

changes did not differ among treatments or between periods (treatment/recovery; 

2-way ANOVA: period F1=0.004, p=0.949; salinity treatment F4=1.07, p=0.388; 

interaction F4=0.661, p=0.623). 

Mesocosm Experiments

Photosynthetic Efficiency

Yield values were significantly different between the salinity of 10 treatment 

and the salinity of 20 and 30 treatments and interaction of time and salinity was 

also significant, as yield values dropped in the 10 salinity treatment throughout the 

5 week period, specifically weeks 3 and 5 (Fig. 6.1; Repeated measures 2-way 

ANOVA: salinity F1=23.60, p<0.001, time F5=44.33, salinity*time F5=5.47, p<0.001). 

After cobbles were all placed in a recovery salinity of 30, the previous salinity 

treatments did not affect yield values, however yield values for all treatments did 

decrease initially and then recover over time (Fig. 6.1; Repeated measures 2-way 

ANOVA: salinity F1=0.50, p=0.49, time F4=56.88, p<0.001, salinity*time F4=2.14, 

p=0.07).

Figure 6.1. Fv/Fm values across 5 weeks of salinity treatments (10, 20 and 30) and 

5 weeks in a recovery treatment of 30. Error bars represent SE.

Calcium Carbonate Parameters 

Discrete water samples analyzed for AT fluctuations each week of the

experiment showed a significant increase in AT in the salinity of 10 treatment 
133

compared to the control, and salinity of 20 and 30 treatments during the 5-week 

period. Samples placed in a recovery salinity of 30 showed not significant 

differences in AT (Fig. 6.3; 2-way ANOVA period F1=5.39, p=0.027; treatment 

F4=6.39, p<0.001; interaction F4=4.50, p=0.006). Media aragonite saturation levels 

for each treatment (Table 6.1), showed only the salinity of 10 treatment was under 

saturation (Ω aragonite = 0.5), with 20 and 30 treatments at equilibrium and 1.6 

respectively. The 20 and 30 treatments also did not differ in respect to AT (Fig. 6.2).

Table 6.1. Carbonate chemistry parameters for salinity treatments used in 

laboratory experiments. When Ω = 1, the seawater is exactly in equilibrium

(saturation) with respect to aragonite. Values in parentheses are SE.

Salinity	

Treatment

pH	

(NBS	scale)

HCO3	in	

(mmol/kgSW)

CO3	in	

(mmol/kgSW)

CO2	in	

(mmol/kgSW)

WCa out WAr out

10 8.07	(0.05) 1131.1	(107.2) 32.4	(9.3) 17.2	(3.1) 0.9	(0.3) 0.5	(0.1)

20 8.07	(0.04) 1711.8	(100.1) 64.9	(13.0) 21.7	(3.6) 1.7	(0.3) 1.0	(0.2)

30 8.06	(0.03) 2293.0	(65.9) 105.4	(2.4) 25.9	(1.1) 2.6	(0.1) 1.6	(0.1)

Figure 6.2. Top: Average AT differences over 5 weeks at varying salinities (10, 20, 30 

and control without any cobbles) and 5 weeks of recovery, all treatments at salinity 

of 30. Bottom: Average pigment loss over 5 weeks of salinity treatments (10, 20 and 

30) and 5 weeks of recovery, all treatments at a salinity of 30. Error bars are SE.
134

Pigments

Percent changes in visual pigments of the cobbles in the salinity of 10 

treatment lost significantly more pigments during the treatment period (Fig. 6.3; 2-

way ANOVA: period F1=4.4, p=0.046; salinity treatment F3=15.56, p<0.001, 

interaction F4=5.92, p=0.024) while the salinity of 20 and 30 cobbles remained 

unaffected and lost little to no pigmentation throughout the experiment (Fig. 6.3). 

Recruit survivorship

CCA recruits (n=25) were monitored for 3 months in salinity treatments of 

10, 20 and 30. Throughout the study recruits were lost (salinity of 10 = 5 lost, 

salinity of 20 = 2 lost and salinity of 30 = 2 lost), however the number of individuals 

lost did not significantly differ among treatments (ANOVA F1,13 = 1.09, p=0.31). 

Field Experiments

Adults

Photosynthetic efficiency and Pigments

Field transplants with adult CCA showed high amounts of variability. Yield 

(Fv/Fm) values were not significantly different, however, non-pigmented areas had 

yield values as high or higher than pigmented areas, likely due to endolithic algal 

species (transplanted 0.45 ± 0.01, control 0.43 ± 0.01, ANOVA F1,34=2.79, p=0.103). 

Transplanted cobbles from the offshore to the inshore site pigmentation, but 

percent lost was highly variable (Fig. 6.4, a-d; 23.03% ± 15.0%).
135

Figure 6.3. a) cobble photographed in 2016 pre-transplant b) same cobble as a,

photographed in 2017 after a one year transplant to an inshore site, high pigment 

loss c) cobble photographed in 2016 pre-transplant d) same cobble as c, 

photographed in 2017 after a one year transplant to an inshore site, low pigment 

loss e) one year recruits at the offshore site at 40x f) one year recruits at the inshore 

site at 40x.

Recruits

Size and Density

Recruit size and density were compared between the offshore and nearshore

site. Recruits were present at both sites but were significantly larger at the offshore 
136

site (Fig. 6.3, e and f; offshore size 12.89 ± 1.91 mm2, inshore size 1.35 ± 0.209 mm2;

ANOVA F1,78=64.91, p<0.001). Densities of CCA recruits were significantly higher at 

the offshore (site offshore density 1.16 ± 0.40 per cm2, inshore density 9.02 ± 3.76 

per cm2; ANOVA F1,14=7.02, p=0.01). 

DISCUSSION

Arctic coralline algae survive in an environment of high seasonal variability 

and drastic water chemistry changes (see section 2.4). Within the Boulder Patch of 

Stefansson Sound, Alaska, CCA distributions vary from dominant benthic space 

holders to complete absence. Influence of the Sagavanirktok River and associated 

freshwater input into the sound drives carbonate chemistry parameter changes, 

causing seawater variations that affect CCA and their ability to persist (section 2.4).

Results from mesocosm laboratory experiments illustrate that Leptophytum 

foecundum, the dominant CCA species at the offshore site (DS-11), was able to 

tolerate salinities in the range of 20 without any significant physiological impacts. 

Photosynthetic efficiency (Fig. 6.1), visual pigments and dissolution of calcium 

carbonate (Fig. 6.2) were not significantly different between the 20 and 30 salinity 

treatments. However, at a salinity of 10, CCA experienced reduced photosynthetic 

efficiency, decreased visual pigments and increased calcium carbonate dissolution 

(Figs. 6.1 and 6.2). Interestingly, all parameters measured (Fv/Fm, pigments and 

AT) were able to recover very quickly once specimens were placed in a recovery 

salinity of 30. These results highlight that L. foecundum is likely very resilient to 

lower salinities and changes in carbonate chemistry and has acclimated to periods 

of stress, occurring each year during break-up. 

The significant increase in AT in the 10-salinity treatment is most likely 

driven by the 0.5 Ω aragonite value. When the saturation level is below one, 

dissolution of calcium carbonate is favored and in the closed systems of the 

mesocosms, dissolution is the only process that would affect AT. CCA precipitate 

magnesium calcite, which is 20% more soluble than aragonite, therefore CCA are 

likely susceptible to dissolution when Ω aragonite levels are less than one. 

Cobbles transplanted in the field were extremely variable in their response 

to transferring to the inshore site (Fig. 6.3, a-d). On average, 23% of the visual 

pigments were lost, but the error associated with that value was 15%. During the 

year the cobbles were transplanted (2016-2017), salinity levels at the inshore site 

did not fall below 20, while the following year (2017-2018), salinity at the inshore 

site was near zero (see section 2.4). These annual variations and periods of low 

salinity likely affect CCA attempting to recruit to the inshore sites, and extreme low 

salinity events prevent CCA from persisting. 

Recruits of CCA were not shown to be more susceptible to lower salinities 
137

than adults in mesocosm studies and after 3 months in salinity treatments of 10, 20 

and 30 there was no significant difference in recruit survivorship. However, within 

the 3-month period we may not have been able to detect difference in growth or 

calcification rates due to the small size of the individuals tracked. Recruits size and 

density were significantly larger and denser at the offshore site than the inshore site 

(Fig. 6.3, e and f). The recruitment of CCA onto settlement tiles was observed at the 

inshore site (Section 1.2), which shows that the absence of CCA at the inshore site is 

not due to propagule dispersal limitations. CCA propagules are reaching the inshore 

site, but they are unable to persist and grow into adults, likely due to water 

chemistry changes associated with low salinity.

During the years of this study (2016-2018), Ω aragonite saturation levels 

were found to remain around one for much of the year (see section 2.4), even at the 

offshore site where CCA cover the benthos. Studies have predicted that Arctic 

waters could reach Ω aragonite levels of one by 2100 (Gangstø et al. 2008), 

however, these levels are currently occurring in nearshore environments. CCA 

species in the Boulder Patch survive at Ω aragonite levels near one, but during 

break-up, Ω aragonite levels drastically drop and these events drive CCA 

distributions.

Corallines are known to grow laterally rather than vertically when 

colonizing space, allowing them to cover the benthos (Airoldi 2000). However, the 

roles, which corallines play in algal and sessile invertebrate succession, vary 

drastically across communities. Studies have shown that corallines enhance 

biodiversity (Asnaghi et al. 2015), while other work has focused on the lack of 

further algal recruitment once coralline secure dominance (Bulleri et al. 2002). 

Johnson and Man (1986) found Phymatolithon in Nova Scotia to suppress the 

recruitment of turf algal species. A similar pattern has been observed in the Boulder 

Patch of Stefansson Sound, however, a few algal species seem to possess the ability 

to recruit to the CCA species present (Leptophytum foecundum and L. lavae). The 

mechanism that allows for this recruitment is unknown, but slower growing crusts 

(e.g., Lithothamnion phymatodeum; (Dethier and Steneck 2001) have shown 

resilience to turf overgrowth and shading. In Stefansson sound, red algal biomass is 

reduced when CCA are present, but kelp biomass increases (see section 1.2 and 1.3). 

The mechanism driving this pattern needs to be explored; but as CCA coverage 

varies, algal and invertebrate assemblages co-vary. 

Due to susceptibility of CCA to variations in carbonate chemistry, these 

species also serve as bioindicators for changes in ocean chemistry in nearshore

environments. As freshwater input into the Arctic Ocean increases, areas devoid of 

CCA could increase, causing changes to the epilithic communities. CCA are 

conspicuous benthic species, and their presence and absence could serve as an 

effective tool for assessing water quality and changes in the nearshore Arctic that 

will affect not only CCA, but also other marine calcifying organisms. 
138

7. Carbonate chemistry of the nearshore Arctic: shifts and future 

implications

Arley F. Muth and Kenneth H. Dunton

INTRODUCTION

Arctic marine species are tolerant to extreme seasonal shifts in 

environmental conditions, but high latitude systems are also predicted to be the 

most impacted by climate change. These impacts include increased temperature, 

freshwater input, changes in ice coverage and higher susceptibility to ocean 

acidification. As carbon dioxide (CO2) increases in the atmosphere, more CO2 is 

absorbed by the world’s ocean, causing a decrease in pH. High latitude waters are 

colder; making CO2 more soluble and prolonged periods of time dominated by 

heterotrophy (respiration) cause higher amounts of carbon dioxide partial pressure 

(pCO2) within the water column and decrease ocean pH. In the Arctic, this process is 

exacerbated by large amounts of freshwater input, decreasing nearshore salinities 

and total alkalinity (AT; Mathis et al., 2015)

The Arctic Ocean receives 11% of the world’s freshwater run-off, but only 

comprises 1% of the world’s oceans (McClelland et al. 2012). Furthermore, most of 

this water is released during a short period each spring during break-up, causing 

drastic changes to the nearshore water composition and chemistry. The 

Sagavanirktok River, which is the second largest river draining the North of Alaska 

(1.6 km3 annual discharge; McClelland et al. 2014), drains the Brooks Range into 

Stefansson Sound. These rapid and drastic changes to nearshore waters directly

affect the saturation level of aragonite (Ω aragonite), the most commonly reported 

calcium carbonate mineral, and calcifying organisms ability to persist (Gangstø et al.

2008, Diaz-Pulido et al. 2012). 

The goal of this study was to capture the first continuously measured ocean 

total pH (pHT) for the nearshore Arctic Ocean. Spatial placement of sensors in 

varying proximity to freshwater input allowed for comparisons to be made between 

areas of differing water masses (marine dominated vs. seasonal freshwater 

influenced). This seasonally dynamic region of the coast is of great interest and has 

ecological implications for fisheries, subsistence hunting, and is under great flux due 

to anthropogenic climate change. Additionally, by capturing pHT , salinity, 

temperature and AT from discrete water samples, aragonite saturation levels Ω

aragonite were estimated in order to further understand water chemistry effects on 

calcifying organisms. Benthic species (invertebrate and algal) were catalogued and 

compared between sites in order to assess water chemistry effects on species 

composition.
139

METHODS

Study Sites and Background

Sea-bird (Satlantic) SeaFETs and SBE 37-SM MicroCATs C-T (P) were 

deployed July 2016-July 2018 at two sites within the Boulder Patch, Stefansson 

Sound, Alaska (Fig. 7.1); Site E1 (70o18.8665 N, 147o44.0413 W; 4 m depth) and site 

DS-11 (70o19.3248 N, 147o34.8816; 7 m depth). Sites varied in proximity to the 

Sagavanirktok River, as such, we expected marked spatial and temporal gradients in 

salinity and carbonate chemistry moving from near shore of the Sagavanirktok 

River to offshore in open Beaufort 

Sea.

Figure 7.1. Map of the Boulder Patch and instrument deployment sites. 

Calibration

Water samples were collected when instruments were deployed and 

retrieved. Single point calibration techniques were used to ensure sensor accuracy 

throughout deployment period. Samples were preserved with mercuric chloride 

and stored as directed by Dickson et al. (2007) for pH total (pHT) and total alkalinity 

(AT) analysis. Spectrophotometric pH was measured at 25oC for 2016 deployment 

samples and 20oC for 2017 retrieval and deployment samples, using m-cressol 

purple. An automated open cell Gran titration system (ASALK2; Apollo SciTech) was 

used to measure AT (Lonthair et al. 2017). Due to logistical constraints, water 
140

samples were taken at time of deployment and applied to values the following day, 

keeping time of day consistent. Sensors were conditioned before deployment;

however, values became steady after one day on the seafloor, and applying 

calibration values the following day was more accurate to the data. 

Error Calculations

Retrieval water samples were used to calculate sensor error over the yearlong deployment for the 2016-2017 dataset. New sensors were deployed in 2018, 

and retrieval samples were not collected, however, several pH (NBS) values were 

measured at the retrieval sites. In order to estimate a conversion factor between 

pHNBS and pHT, spectrophotometric pH was measured at 25oC from a certified 

reference material (CRM, Dickson Laboratory, UC Scripps, Batch 171) that was 

diluted to create five salinity samples (33.43, 20, 15, 10, 5). pHT was corrected using 

AT, temperature and salinity of the samples. A calibrated PRO DSS YSI sonde (same 

model used in original field measurements) was then used to measure pHNBS for 

each salinity sample created. Although there is greater error associated with the 

data sonde (pH ± 0.01), this calibration protocol allowed the use of the pHNBS values 

to estimate sensor error (pHNBS + 0.118 = pHT).

 

Data processing and carbonate chemistry parameter calculations

pH values reported are all calibrated pHT values. Internal pH was reported 

for both 2016-2017 datasets and 2017-2018 E-1, while external pH was reported 

for 2017-2018 DS-11. Aragonite saturation levels were estimated for each data set 

using pH, salinity, and temperature values, and estimated AT from several discrete 

water samples taken throughout the Boulder Patch in July of 2018 (n=13; CO2Calc, 

USGS). AT was measured using the techniques described above and regressed 

against salinity values (y=31.032x + 1653.3; r

2=0.61). 

Temperature and salinity values were plotted against the water freeze line to 

ensure the values were valid measurements. Any values that fell below the freeze 

line were not included in the analysis. 

Cobble Analysis

In 2016, cobbles were collected haphazardly along ordinal direction 

transects (180 ̊) at dive sites DS-11, E-1 and W-3. Cobbles were photographed with 

and without algal biomass, image analysis software ImageJ was used to estimate 

percent cover of encrusting algal and rock surface area. Algal species were 

separated and wet weight of each was recorded. A t-test was used to compare red 

algal biomass wet weights between DS-11 and E-1. Invertebrate species were 

identified, catalogued and preserved for further identification and biomass 

measurements at the University of Texas at Austin, Marine Science Institute.
141

RESULTS

pHT values were recorded hourly for 2016 deployments, however, upon 

retrieval both pH sensors had failed (E-1: July 2016-June2017; DS-11: July 2016-

February 2017). Sensors were reset for the 2017 deployments to record pHT every 

two hours and were actively recording when retrieved in July 2018. Values and 

trends were similar between sites for each year, but drastic variations were seen 

between years. 

2016 Results

Coastal ocean pHT, salinity, and temperature follow seasonally distinct 

transitions that relate to four main events: the summer open water, fall freeze-up, 

the winter ice covered season, and the spring freshet. All variables were stable 

when ice forms in the fall but become more variable during the open water period 

(Fig. 7.2). However, for the 2016-2017 deployment, pHT during the freshet was not 

captured due to sensor failure. Ranges in temperature (E1: -1.87 – 7.08°C; DS-11: -

1.88 – 6.08°C) and salinity (E1:19.07 – 34.35; DS-11: 21.93 – 34.51) were similar 

between sites (Fig. 7.3), especially when compared to 2017 data (see below). While 

pHT varied from 7.83 – 8.70 at E1 (error 0.042) and 7.79 – 8.69 at DS-11 (0.046), 

both sites showed a unique pH spike during freeze-up (Fig. 7.2).

The temporal separation of the salinity/pH relationships showed two distinct 

“water masses” or “chemistry regimes” that occur before and after Oct 25 (for EI) 

and Nov 5 (for DS-11). These anomalous shifts are likely associated with changes in 

the physicochemical environment with the freezing of coastal waters and/or the 

influence freshwater discharge from the Sagavanirktok River (Fig. 7.2). These dates 

also correspond to the first spike in pHT seen at each site (E1: ~October 25; DS-11: 

~November 5th). Both sites show a slight decrease in pH November through 

February as heterotrophy and respiration increase pCO2 in the water column, and 

an increase from March until break-up (only seen at E-1), due to the presence of 

light and primary production (Fig. 7.2). 
142

DS-11 DS-11

sal temp

25.8086 3.823

25.8496 3.8222

25.8712 3.8386

25.8747 3.8875

25.8944 3.8418

25.8815 3.8075

7.7	

7.8	

7.9	

8	

8.1	

8.2	

8.3	

8.4	

8.5	

8.6	

8.7	

8.8	

215	 265	 315	 365	 415	 465	 515	

E-1	pH	internal	

DS-11	pH	internal	

open	water	

freeze	

up	

ice	season	 freshet	

	

8.05477287

8.04079751

8.03188967

8.02703228

25.8086 3.823 8.02642768

25.8496 3.8222 8.03143497

25.8712 3.8386 8.0350445

25.8747 3.8875 8.03563954

25.8944 3.8418 8.03028519

25.8815 3.8075 8.02087338

25.8692 3.8114 8.01802397

25.8183 3.9871 8.01599091

7.8	

7.9	

8	

8.1	

8.2	

8.3	

8.4	

8.5	

8.6	

8.7	

8.8	

18	 20	 22	 24	 26	 28	 30	 32	 34	

post	Oct	25	

pre	Oct	25	

E-1 DS-11 DS-11 DS-11 E-1 DS-11

temp pH	internal sal temp pH	internal pH	internal

4.9575

4.9161

4.8865

5.2362

5.0968

5.3305

5.4831

5.3827

5.3988

5.3682

5.746

5.776 8.05477287

5.5945 8.04079751

5.6773 8.03188967

5.7606 8.02703228

5.6918 25.8086 3.823 8.02642768

5.5749 25.8496 3.8222 8.03143497

5.3664 25.8712 3.8386 8.0350445

5.324 25.8747 3.8875 8.03563954

5.3945 25.8944 3.8418 8.03028519

7.8	

7.9	

8	

8.1	

8.2	

8.3	

8.4	

8.5	

8.6	

8.7	

8.8	

18	

7.7	

7.8	

7.9	

8	

8.1	

8.2	

8.3	

8.4	

8.5	

8.6	

22	 24	 26	 28	 30	 32	 34	

pre	Nov.	5th	

post	Nov.	5th	

pHint	E-1	

pHint	DS-11	

E-1	

Pre	Oct	25	

Post	Oct	25	

Pre	Nov	5	

Post	Nov	5	

DS-11	

Jul	 Aug	 Sep	 Oct	 Nov	 Dec	 Jan	 Feb	 Mar	 Apr	 May	 Jun	 Jul	

Nov	5	

Oct	25	

Figure 7.2. pHT for DS-11 (red) and E-1 (black) 2016-2017 as ocean conditions vary 

from open water, freeze-up, ice season and the spring freshet. Spikes of pH in OctNov correspond to freeze-up. Bottom graphs show salinity vs. pH relationships for 

each site before and after Oct 25, 2016 (E-1) and Nov 5, 2016 (DS-11). Dates 

correspond to freeze-up, peaks in pH and a change in the relationship of pH and 

salinity. 

Temperature and salinity measurements also followed temporal patterns as 

seen in pHT that correspond to open water, freeze-up, ice cover, and the spring 

freshet (Fig. 7.3). The 2016 dataset highlighted (more exaggerated in the 2017 

dataset below) the influence of the Sagavanirktok River on changes in pHT, salinity 

and temperature. Although salinity levels were lower at E-1, the inshore site, during 

times in the spring, salinity ranges between sites were similar (E1: 19.07 – 34.35; 

DS-11 21.93-34.51). 
143

pH	internal sal temp pH	internal pH	internal

8.05477287

8.04079751

8.03188967

8.02703228

25.5039 3.823 8.02642768

17	

19	

21	

23	

25	

27	

29	

31	

33	

35	

200	 300	 400	 500	

E-1	sal	

DS-11	sal	

pH	internal sal temp pH	internal pH	internal

8.05477287

8.04079751

8.03188967

8.02703228

25.5039 3.823 8.02642768

-3	

-1	

1	

3	

5	

7	

200	 300	 400	 500	

E-1	temp	

DS-11	temp	

open	

water	

freeze	

up	

ice	season	 fresh	

-et	

	

Salinity	E-1	

Salinity	DS-11	

Jul	Aug	Sep	Oct	Nov	Dec	Jan	Feb	Mar	Apr	May	Jun	Jul	

Jul	Aug	Sep	Oct	Nov	Dec	Jan	Feb	Mar	Apr	May	Jun	Jul	

Temperature	E-1	

Temperature	DS-11	

Figure 7.3. Salinity (top) and temperature (bottom) for 2016-2017, E-1 (black) and 

DS-11 (red) as ocean conditions vary from open water, freeze-up, ice season and the 

spring freshet.

2017 Results

Annual variations of pHT, salinity, and temperature were apparent between 

2016-2017 and 2017-2018 datasets. The 2017-2018 values also centered on ice 

formation and the spring freshet. As in 2016-2017, factors stabilized during the ice 

season and temperature (E1: -1.86 – 9.73°C; DS-11: -1.91 – 9.05°C) and salinity 

(E1:0.001 – 34.02; DS-11: 12.72 – 34.86) varied drastically during July 2017 and July 

of 2018, between sites, but did follow seasonal patterns (Fig. 7.4).

pHT varied from 7.67 – 8.67 at E1 (error 0.085) and 7.75 – 8.34 at DS-11 

(0.028), both sites showed pH spikes when salinity declined due to run-off from the 

Sagavanirktok River; open water season (DS-11-July 2017) and the spring freshet 

(E1-July 2018), but spikes were not detected during freeze-up as they were in 2016 
144

for both sites (Fig. 7.5). Heterotrophy during the ice season and low light season 

likely increased pCO2 and caused a slight decrease in pH as seen in 2016-2017. 

Salinity/pH relationships continued to be temporally separated for both sites, 

however, they were not related to freeze-up, but rather related to freshwater input, 

and ice cover. These chemical signatures were seen at both sites for both years and 

signaled a change between ice covered stabilized ocean water and ocean water 

mixed with freshwater from the Sagavanirktok River.

0	

5	

10	

15	

20	

25	

30	

35	

40	

214	 314	 414	 514	

E-1	Sal	

DS-11	Sal	

12.7695 7.1776

12.7838 7.1652

12.7507 7.393

12.7963 7.5753

12.7652 7.6292

12.8552 7.6681

12.9232 7.6868

12.9341 7.5797

12.9288 7.7353

12.9433 7.3854

12.938 7.3535

12.9471 7.359

12.9461 7.578

12.9724 7.6562

12.9802 7.8486

12.9642 7.7701

7.6	

7.8	

8	

-4	

-2	

0	

2	

4	

6	

8	

10	

12	

214	 314	 414	 514	

E-1	Temp	

DS-11	temp	

Salinity	E-1	

Salinity	DS-11	

Temperature	E-1	

Temperature	DS-11	

Jul	Aug	Sep	Oct	Nov	Dec	Jan	Feb	Mar	Apr	May	Jun	Jul	

Jul	Aug	Sep	Oct	Nov	Dec	Jan	Feb	Mar	Apr	May	Jun	Jul	

open	

water	

freeze	

up	

fresh	

-et	

	

ice	season	

Figure 7.4. Salinity (top) and temperature (bottom) for 2017-2018, E-1 (black) and 

DS-11 (red) as ocean conditions vary from open water, freeze-up, ice season and the 

spring freshet. Note the salinity declines in July 2017 at DS-11 and Jun/Jul 2018 at 

E-1.
145

Aug-28

3829-4167

7.6	

7.8	

8	

8.2	

8.4	

8.6	

8.8	

214	 264	 314	 364	 414	 464	 514	 564	

E-1	pH	

DS-11	pHext	

15	

20	

25	

30	

35	

40	

E-1	Sal	

DS-11	Sal	

6	

8	

10	

12	

E-1	Temp	

7.7	

7.8	

7.9	

8	

8.1	

8.2	

8.3	

8.4	

0	 10	 20	 30	 40	

pre	Aug	28	

post	Aug	28	

7.6	

7.8	

8	

8.2	

8.4	

8.6	

8.8	

0	 10	 20	 30	 40	

pre	June	18	

post	June	18	

Jul	 Aug	 Sep	 Oct	 Nov	 Dec	 Jan	 Feb	 Mar	 Apr	 May	 Jun	 Jul	

open	water	

freeze	

up	

ice	season	 freshet	

	

E-1	 DS-11	

Figure 7.5. pHT for DS-11 (red) and E-1 (black) 2017-2018 as ocean conditions vary 

from open water, freeze-up, ice season and the spring freshet. Spikes of pH in 

Jun/Jul of 2018 at E-1 correspond to low salinity levels and the spring freshet. 

Bottom graphs show salinity vs. pH relationships for each site before and after June 

18th, 2018 for E-1 and before and after August 28th, 2017 for DS-11.

Ω Aragonite Levels

Estimated Ω aragonite levels ranged from 0.91 – 4.96 at E-1 and 0.91 to 4.88 

at DS-11 for 2016-2017, with values near equilibrium (Fig. 7.6a, blue line) for much 

of the ice season at both sites. Peaks in Ω aragonite levels corresponded to peaks in 

pHT (Fig. 7.2). The 2017-2018 dataset exhibited more variability in Ω aragonite

levels between sites than 2016-2017 (Fig. 7.6b) and this was also reflected in other 

parameters measured (as seen in Figs. 7.4 and 7.5). DS-11 2017-2018 Ω aragonite

levels fluctuated from 0.56 – 3.14 and 0.00 - 4.66 at E-1. Lowest levels for both sites 

were reached during the spring freshet in July of 2018. 
146

Figure 7.6. Ω aragonite levels for 2016-2017 (top) and 2017-2018 (bottom). The 

blue line shows equilibrium with respect to aragonite, values in June/July of 2018 

fell well below equilibrium. 

Cobble Analysis

Crustose coralline algae (CCA) covered on average of 77.5% (±3.8%) of the 

cobbles at DS-11 and was not present at E-1. DS-11 was dominated by one CCA 

species, Leptophytum foecundum, but samples of Leptophytum leave were also found. 

When CCA were absent (E-1), red algal biomass was significantly greater (T-test: 

t20.72=-2.2762, p=0.03; Fig. 7.7), but was dominated by two species (Fig. 7.8). Overall, 
147

red algal species on the cobbles from DS-11 and E-1 are similar (Table 7.1), but DS11 was more diverse and more species contributed to the overall biomass (Fig. 7.8). 

Branching invertebrates also showed a conspicuous pattern in the presence and 

absence of CCA.

Invertebrate species differed between sites (Table 7.2). Sertularia 

cupressoides (hydroid) was only found at DS-11 and recruited to the edges of the 

CCA (~0.0009 g per cm2). Eucratea loricata (bryozoan) covered the tops of the 

cobbles along with red algae species at E-1 where CCA was not present (0.01 g cm-2).

Figure 7.7. Red algal biomass per cm2 of cobble surface at DS-11 with CCA present 

and E-1 with CCA absent. Error bars represent SE.
148

Figure 7.8. Red algal biomass composition at DS-11 with CCA present and E-1 with 

CCA absent.

Table 7.1. Red algal species present on cobbles collected at DS-11 with CCA present 

and E-1 with CCA absent. 

CCA No	CCA

Phycodrys	fimbiata x x

Coccotylus	truncatus x x

Rhodomela	confervoides x x

Odonthalia	dentata x x

Dilsea	socialis x x

Ahnfeltia	borealis x

Chlorophyta Chaetomorpha	sp. x

Laminaria	solidungula x x

Sphacelaria	plumosa x

Ecotocarpus	siliculosus x

Rhodophyta

Ochrophyta
149

Table 7.2. Invertebrate species present at DS-11 and E-1

E-1 DS-11

Eucratea	loricata Eucratea	loricata

Alcyonidium	gelatinosum Carbasea	carbasea

Flustrella	gigantea

Crisiidae	sp.

Unknown	Bryozoan

Lafornia	maxima Lafornia	maxima

Abietinaria	sp. Sertularia	cupressoides

Sertularia	albimaris Sertularia	albimaris

Nereimyra	aphroditoides unknown	Nereis

Exogone	sp. Exogone	sp.

Harmothoe	imbricata unknown	Serpulidae

unknown	Polydora

unknown	Spionidae

Porifera Haliclona	rufescens

Nematoda unknown	Nematoda unknown	Nematoda

Foraminifera unknown	Foramnifera

Chiton Stenosemus	albus

Caprella	sp.

Atylus	carinatus

Cirripedia Balanus	crenatus

unknown	Ascidian

Chelysoma	sp.

Dendrodoa	aggregata

Amphipoda

Ascidian

Bryozoa

Hydrozoa

Polychaeta

DISCUSSION

Due to environmental factors in the Arctic (e.g., cold water, respiration, 

freshwater influence), low aragonite saturation levels have been reported for these 

waters (Yamamoto-Kawai et al. 2009); however, values for near shore areas are 

unknown. Capturing the first continuous pHT dataset with associated carbonate 

chemistry parameters is critical for the nearshore Arctic, in order to obtain baseline 

values, and to disseminate factors or interactions of factors affecting water 

chemistry.

Annual variability in pHT corresponded to distinct time periods associated 

with open water, freeze-up, ice cover, and the spring freshet. Other parameters 

measured (salinity, temperature) also have specific patterns within these time 

periods (Figs. 7.3 and 7.4). In 2016, pH increases were observed at E-1 and DS-11 

(Fig. 7.2), near freeze-up. Additional work is needed to describe the exact reason for 
150

these spikes, but likely the spikes are driven by an increase in primary production, 

drawing down water pCO2, and causing pH to rise. These increases can occur due to 

several factors, but because the spikes were seen at both sites, roughly around the 

same time as freeze-up, the increases could be a result of brine formation and water 

column turnover. As ice forms, high salinity waters form at the surface below the 

ice, however, this water is dense and it ultimately migrates to the bottom, causing an 

overturn of the water column. This process can disturb the bottom, increase 

sedimentation, and re-suspend benthic phytoplankton. We also see conductivity 

sensors fail during this time period, and it is believed that this is due to an increase 

in sedimentation that obstructs the sensors. Further work needs to be done (chl a

data, exact ice formation, etc.) in order to be sure this is the process causing pH 

changes. Before freeze-up pH was variable (Fig. 7.2), however, once freeze-up 

occurred and ice formed, values stabilized. There was a slight reduction in pH 

during the winter months, possibly due to the lack of photosynthesis and an 

increase in CO2 production due to heterotrophic processes. This decrease was seen 

at both sites for 2016 and 2017 (Figs. 7.2 and 7.5), and as spring approached pH 

increased. For 2016, sensor failure resulted in no data for the spring freshet, 

however this data was captured in the 2017 dataset.

The 2017 dataset patterns were different than 2016 and the sites also 

differed from one another (Fig. 7.5). The fall pH anomalies related to freeze-up 

were not detected in 2017, but there was a very strong signal for E-1 during the 

spring freshet and pH increased dramatically with the associated freshwater pulse 

from the Sagavanirktok River. 

There was a distinct temporal separation with the relationship between 

salinity and pH before and after freeze-up from both sites in 2016 (Fig. 7.2).

Separations were seen in 2017 as well, but due to timing of the separation, it 

appears different events, other than freeze-up, caused the separation. DS11, the

offshore site separated very soon after deployment, and this is likely driven by the 

reduction of river water into the system after late summer and this water shows a 

different pH and salinity relationship than under ice seawater. The inshore site, E-1, 

has the opposite pattern, the separation happens during the spring freshet. 

Although a number of processes can drive these water mass changes, the differences 

likely represent ocean water that is mixed with run-off from the Sagavanirktok 

River (lower salinity, higher pH vs. higher salinity, lower pH) and ocean water that 

is stable and static under the ice. Aragonite saturation levels were calculated in 

order to understand how these seasonal water chemistry variations affect benthic 

assemblages. 

By calculating Ω aragonite, salinity, pH, AT and temperature are integrated 

and calcification process of biological organisms is better understood. Saturation 

level patterns for aragonite showed that for much of the year, the saturation levels 

were at equilibrium (=1), but during the spring freshet of 2018, the levels dropped 

to zero at the inshore site. A value below one causes dissolution of aragonite, and 

experimentally this was observed with the crustose coralline algae (CCA) species 
151

found in the Boulder Patch, Leptophytum foecundum (section 2.3). Low Ω aragonite

levels associated with the near shore site, likely drive crustose coralline algae 

distribution (see section 2.3) and cause the absence of this species at the near shore 

sites (see sections 1.2 and 1.3).

Other systems under saturated with respect to aragonite, CCA are also 

absent. Studies of CO2 vents in the Mediterranean show a simplification, less 

canopy-forming algal species, and an increase in turf algae in areas of close 

proximity to the vents, where saturation levels are low (Kroeker et al., 2013). We 

see similar patterns in the Boulder Patch, in areas where CCA are not present, there 

was greater red algal biomass (Fig. 7.7), and the biomass was dominated by two 

species (Fig. 7.8). From this study, we see that the near shore site, that is under 

saturated in respect to aragonite, especially during spring break-up, is devoid of CCA 

and the benthic assemblage was distinct from the offshore site where CCA are 

present.

Further investigation is needed to understand the changes in invertebrate 

distributions, but there were different assemblages seen at each site. One striking 

pattern was the dominance of Eucratea loricata (bryozoan) at the in-shore site. This 

bryozoan, along with a few red algal species covered the rocks and cobbles at this 

site but was found in very low densities at the offshore site and only in areas where 

CCA were not present. 

There are many factors that may cause a decrease in Ω aragonite and many 

of these factors naturally occur in the Arctic Ocean (cold temperatures, 

heterotrophy, freshwater input etc.; Mathis et al. 2015). In addition, these waters 

are susceptible to ocean acidification, a process caused by the anthropogenic 

increase in CO2 in the atmosphere. These processes combined have the potential 

alter the benthic states that are now present in the Boulder Patch offshore sites to 

resemble the inshore sites without CCA and a less diverse benthic community. In 

addition, Laminaria solidungula densities are lower at the inshore sites (sections 1.2 

and 1.3) and it is unclear if this deficiency is due to changes in the benthic structure 

(i.e., presence or absence of CCA) or if some other abiotic factor (see section 2.5). 

Independent of the cause, a benthic shift of species in the Boulder Patch would affect 

the biodiversity and productivity of the system and the myriad organisms these 

systems sustain.
152

8. Freshwater input effects on Laminaria solidungula microscopic stages

(short report)

Arley F. Muth, Christina E. Bonsell and Kenneth H. Dunton

INTRODUCTION

Brown seaweeds of the order Laminariales (kelps) play important roles in 

the ecosystems they inhabit. Laminaria solidungula, the only Arctic endemic kelp, 

provides a food source and habitat for benthic communities along the north coast of 

Alaska. This kelp species is perennial and persists beneath the ice in polar night 

light conditions, providing the basis of the food chain when phytoplankton is unable 

to survive. In winter months, up to 50% of mysid body carbon was derived from L. 

solidungula detritus (Dunton and Schell 1987).

Kelps use a diplohaplontic life history strategy for reproduction. 

Macroscopic sporophytes release microscopic zoospores at a 50:50 male to female 

ratio. Zoospores settle onto the substrate, undergo gametogenesis and once 

gametophytes are formed, gametes are released. Male gametophytes produce 

spermatozoids that are released and fertilize eggs that protrude from the female 

gametophytes. Once fertilization occurs, a microscopic sporophyte is produced (Fig. 

8.1). Often, microscopic stages are more vulnerable to changes in environmental 

conditions (Deysher and Dean 1986a, reviewed in Graham et al. 2007, Harley et al. 

2012), which ultimately affects adult population persistence (Graham et al. 2007). Why only substrate effect in kelp?

microscopic 

sporophyte

sporophyte

zoospores 

gametophytes 

♀ ♂

Figure 8.1. Laminaria solidungula (general kelp) life history. Macroscopic 

sporophytes (2n) release zoospores (1n) at a 50:50 sex ratio. Zoospores develop 

into male and female gametophytes, and the female egg is fertilized by the male 

spermatozoids, producing a microscopic sporophyte (2n).
153

Many abiotic factors affect reproductive success in kelps, such as 

temperature (Deysher and Dean 1986b, Matson and Edwards 2007), nutrients 

(Santelices and Ojeda 1984), light (Lüning and Dring 1979, Lüning 2018) and 

settlement densities of zoospores (Reed et al. 1991, Muth 2012). In the U.S. Arctic, 

salinity and light levels differ drastically among seasons (section 1.1), but 

interactive effects on L. solidungula are unknown.

The Stefansson Sound Boulder Patch is an area of rocky substrate composed 

of boulders and cobbles that creates an ideal habitat for kelp populations. Kelp 

densities within this area decrease with proximity to the Sagavanirktok River (see 

sections 1.2 and 1.3) and we were interested in exploration of potential mechanisms 

affecting kelp density and distributions within the Boulder Patch. Light levels and 

salinity levels are reduced at sites closer to the Sagavanirktok River (see section 1.1) 

due to river run-off and increased sedimentation. This experiment was designed to 

explore the effects of salinity and light separately and interactively on L. solidungula 

microscopic stages and sporophyte production.

METHODS

Reproductive Laminaria solidungula individuals were collected from Endicott 

Island, Alaska and shipped to the University of Texas Marine Science Institute. 

Samples were kept at 0°C in aerated filtered seawater. Reproductive sori were 

placed in between layers of damp paper towels and kept in darkness for 24 h. Then 

sori were placed in 10°C seawater to induce sporulation (Dec. 7th, 2017). After 

sporulation, 10 mL of the solution was placed in 50 x 10 mm Petri Dishes. The 

zoospore solution remained in the dishes for 1 week to allow for maximum 

settlement, the medium was then replaced with Gulf of Mexico seawater and RO 

water to attain salinity treatments of 10, 20 and 30, and Provasoli’s Enriched 

Seawater (PES) to ensure all micro and macro nutrients were available. After 

settlement, dishes were divided into 3 light treatments within the salinity 

treatments (10, 20 and 40 μmol photons m2 s

-2), creating nine salinity/light 

treatments and each treatment was replicated 5 times. Medium was changed 

weekly. Dishes were kept at 0°C in a 10:14 light:dark regime.

Dishes were monitored weekly for microscopic stage development. Initial 

settlement densities were quantified (Dec. 15th, 2017) to ensure all treatments 

initially had similar settlement. Gametophyte densities were quantified Jan. 26th, 

2018 and finally sporophyte densities over one month (Feb. 28th, Mar. 16th and Mar. 

27th), in order to track any delays in sporophyte production. Each dish was 

observed under 400x magnification for 10 fields of view. 

Densities at each stage (settlement and gametophytes) were compared using 

2-way ANOVAs among light and salinity levels. Sporophyte densities were 

compared using a repeated measures 2-way ANOVA (time, light, salinity, and light x 

salinity) to assess sporophyte densities over time. The interaction of light and 

salinity was not significant and was removed from the model. All statistics were run 
154

using R Version 3.3.1. 

RESULTS

Zoospore settlement densities were not significantly different among salinity 

and light treatments (2-way ANOVA, light vs. salinity F3,41 = 0.3293, p=0.80; Table 

8.1, Fig. 8.2). Any differences in gametophyte or sporophyte densities were a result 

of environmental conditions after settlement. Gametophyte densities were 

significantly different among salinity treatments, however, there was no significant 

difference among light treatments or the salinity light interactions (2-way ANOVA, 

salinity F3,41 = 21.67, p<0.001).

Table 8.1. Treatments (salinity:10, 20, 30 and light: 10, 20, 40) and average densities for 

zoospores, gametophytes and sporophytes.

Salinity

Light	(umol	

photons	m2	s-2)

zoospores	

(mm2)	Dec.	

15th,	2017

zoospore	

standard	

error

gametophytes	

(mm2)	Jan.	

26th,	2018

gametophyte	

standard	

error

sporophytes	

(mm2)	Feb.	

28th,	2018

sporophyte	

standard	

error

sporophytes	

(mm2)	Mar.	

16th,	2018

sporophyte	

standard	

error

sporophytes	

(mm2)	Mar.	

27th,	2018

sporophyte	

standard	

error

10 10 1.05 0.34 0.13 0.13 - - - - - -

10 20 1.05 0.16 - - - - - - - -

10 40 1.18 0.86 - - - - - - - -

20 10 1.32 0.36 1.58 0.16 0.01 0.00 0.03 0.01 0.01 0.00

20 20 0.79 0.48 0.66 0.29 0.02 0.00 0.03 0.01 0.03 0.01

20 40 1.18 0.25 0.66 0.21 0.04 0.01 0.04 0.02 0.07 0.03

30 10 1.45 0.64 2.37 0.49 0.02 0.00 0.02 0.01 0.04 0.01

30 20 1.32 0.21 1.45 0.44 0.03 0.01 0.06 0.02 0.04 0.01

30 40 1.32 0.21 2.37 0.34 0.04 0.01 0.04 0.01 0.03 0.01

0

0.5

1

1.5

2

zoospores

Zoospores	per	mm2	

Light	 10	 20	 40	 10	 20	 40	 10	 20	 40	

Salinity	 10	 20	 30	

Figure 8.2. Zoospores per mm2 after a 1-week settlement period. No significant 

difference among salinity or light treatments.

Gametophytes were the first stage to show an effect of salinity. The low 

salinity treatment (10) was significantly lower than the 20 and 30 treatments, 

however, light was not significant and the interaction between light and salinity was 

not significant either.
155

Figure 8.3. Gametophytes per mm2. Salinity treatments (10 vs. 20 and 30) were 

significantly different. Light and salinity x light interaction were not significant. 

Sporophytes densities were quantified over one month, but time was not 

significant (p=0.127). Sporophyte densities between 40 and 10 μmol photons m-2 s

-1 

light treatments were significantly different (Fig. 8.4, Table 8.1) and densities 

between the salinity of 10 treatment and the 20 and 30 were significantly different 

(repeated measures ANOVA: light p=0.01, salinity p<0.001).

Figure 8.4. Sporophyte densities per mm2 over 1 month. Top (Feb. 28th, 2018), 

center (March 16th, 2018) bottom (Mar. 27th, 2018). Densities were significantly 

lower in the 10 vs. 40 μmol photons m2 s

-1. Sporophytes were not detected in the 

low salinity treatment (10), and densities were not significantly different between 

the 20 and 30 treatments.
156

DISCUSSION

Laminaria solidungula is a conspicuous kelp species that occurs on the north 

coast of Alaska and dominant seaweed in the Boulder Patch of Stefansson sound 

(see sections 1.2 and 1.3). Understanding population and distribution dynamics of 

this species is an integral, if not primary goal of our Boulder Patch monitoring 

program. Through this preliminary experiment, we were able highlight the effects 

variable salinity and light play on L. solidungula microscopic stages that are critical 

for adult population persistence. 

Data from the Boulder Patch has shown that light and salinity values are 

lowest at sites in closer proximity to the Sagavanirktok River (E-1, see section 1.1). 

These sites also have lower kelp abundances (see section 1.2 and 1.3), even when 

standardized to less available substrate (i.e., rock cover). This data illustrates how 

light and salinity can affect successful kelp recruitment (sporophyte production), 

but that low salinity levels (10) prevent sporophytes from forming. The current 

experiment did introduce salinity treatments at the settlement stage, so although 

settlement was equal at the start of the experiment, salinity and light lowered 

gametophyte densities in certain treatments (Fig. 8.3, Table 8.1). A lack of mature 

gametophytes would also contribute to lower sporophyte densities. 

Microscopic stages were cultured under optimal temperature and nutrient 

regimes; however, settlement densities were very close to one (Fig. 8.2; Reed et al. 

1991). Lower settlement densities resulted in lower gametophyte and sporophyte 

densities but were high enough for fertilization to occur and sporophytes to form. 

Because settlement density did not differ among treatments from day one of the 

experiment, initial densities did not affect results. 

In addition, L. solidungula microscopic stages develop at a much slower pace 

than most other kelps (Reed et al. 1991, Muth et al. 2019). Using Macrocystis 

pyrifera as a guide, settlement is counted after 24 h, germination after 48 h, 

gametophytes after 7 days and sporophytes within 2-5 weeks (Reed et al. 1991). 

However, L. solidungula was left to settle for one week in the dishes, germinated 

zoospores may take up to 3 weeks to observe, gametophytes in general were seen 

after 6 weeks and sporophytes after 10 weeks. Laminaria solidungula from the 

Canadian Arctic had similar developmental times, but in general produces 

reproductive material earlier and zoospore release is later than the populations of 

the Boulder Patch (Roleda 2016). This slower process may be due to the timing of 

zoospore release (January-March) and the timing of break-up and light availability 

(June-July). This delayed recruitment process subjects the sensitive microscopic 

stages to abiotic conditions for longer periods of time than most kelp species, but 

might also be an adaptation for commencing their macroscopic form under the ice. 

Future studies will explore how each stage (settlement, gametophytes, and 

sporophytes) respond to variations in salinity. Pin pointing stages that are more 
157

vulnerable to low salinity events will allow us to predict how earlier spring breakup events and increased periods of freshwater flow may affect future L. solidungula

populations.
158

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