OCS Study MMS 2005-062 

Final Report 

Title: Susceptibility of sea ice biota to disturbances in the shallow Beaufort 

Sea: Phase 1: Biological coupling of sea ice with the pelagic and benthic 

realms 

Rolf R Gradinger – Principal Investigator 

Bodil A Bluhm – Co-principal Investigator 

November 2005
 

Contact information 

e-mail: carter@sfos.uaf.edu 

 phone: 907.474.1811 

 fax: 907.474.1188 

 postal: Coastal Marine Institute 

 School of Fisheries and Ocean Sciences 

 University of Alaska Fairbanks 

 Fairbanks, AK 99775-7220

ii
 

Table of Contents 

Summary........................................................................................................ 4

Background ................................................................................................... 5

Study area...................................................................................................... 8

Results.......................................................................................................... 12

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

Acknowledgements..................................................................................... 37

Data appendix I........................................................................................................... 53

Data Appendix II ........................................................................................................ 61

3
 

Summary 

We assessed the impact of sediment load on the abundances and composition of 

biological communities in near-shore fast ice and underlying waters close to Barrow, 

Alaska. Based on existing scientific evidence we hypothesized that any disturbance of the 

sea ice habitat, e.g. by enhanced sediment load, construction of ice roads and gas or oil 

spills, would likely impact the biological links between ice, water column and sea floor 

and may, hence, affect the biomass and productivity in all realms. Sampling was 

conducted in April 2002 and February, April and May 2003 with ice corers, water 

samplers, plankton nets and sediment corers at two locations which differed mainly in 

terms of sea ice sediment load. In 2003 a strong ice algal bloom developed in the 

sediment-free fast ice, reaching a maximum pigment concentration of 330 μg Chl a l-1 in 

the bottom 10cm of the ice in May while it remained below 1μg Chl a l-1 in the water 

column. With increasing ice algal biomass, the δ

13C ratio of sea ice POM increased from 

an initial average value of -25‰ in February to -16‰ in May, while no enrichment was 

observed in pelagic POM. The abundance of ice metazoans increased with the 

progressing season to a maximum of 276,000 animals m-2, dominated by nematodes and 

merosympagic polychaete juveniles. Abundances of meroplanktic stages of benthic 

polychaetes in the water column were consistently at least one order of magnitude below 

abundances in the ice, suggesting sea ice as an important feeding habitat for young life 

stages. The sediment loaded sea ice had a total particle load of 106 g m-2. Light was 

reduced by more than 99% compared to the clean ice site, which had a particle load of 

only 6 g m-2. In the dirty ice we observed no substantial increase, over time, for any of 

the studied biological parameters. Our observations demonstrate that sea ice sediments 

have a profound impact on sea ice biology, suppressing biological spring bloom 

formation by orders of magnitude compared to clean ice. Stable isotope analyses of ice 

meiofauna and gut/stomach content analyses on amphipods supported the concept of sea 

ice algae being important food in Arctic coastal food webs in early spring. The study 

progress and results were communicated to the community in Barrow through public 

presentations, radio and newspaper contributions. The science community was informed 

through conference presentations, publications and web pages. 

4
 

Background

Sea ice is a key component in structuring polar environments (Eicken 1992, 

Sakshaug 1991, Gradinger & Spindler 1997, Gradinger 2002). Beside its important role 

as a platform for marine mammals and birds, it serves as a habitat for a unique highly 

specialized community of bacteria, algae, protozoa and metazoa, which contribute to the 

biogeochemical cycles of the Arctic and Antarctic seas. Early seal hunters had already 

discovered the close relationship between ice algae production and higher trophic levels 

when they found numerous seals associated with brownish-colored ice floes which they, 

therefore, named seal-ice (Nansen 1897). This coloration is caused by billions of 

unicellular algae living within the sea ice. The ice algal primary production in seasonally 

ice-covered waters contributes 4 to 26% of total primary production (Legendre et al. 

1992) and may even be above 50% in the permanently ice-covered central Arctic 

(Gosselin et al. 1997) due to the low amount of short wave radiation penetrating into the 

water column (Gosselin et al. 1997). The few available time series data reported a 

seasonal variability by a factor of ~10 for algal and bacterial biomass, and >50 for 

primary and bacterial production (Horner 1980, Smith et al. 1988, Gradinger et al. 1991, 

Haecky & Andersson 1999). A significant fraction of the spatial and temporal variability 

is directly linked to environmental variables, in particular light availability and nutrient 

supply. These are modulated by snow cover, ice morphology and ice microstructure 

(Sullivan et al. 1985, Gosselin et al. 1986, Eicken et al. 1991, Gradinger et al. 1991, 

Legendre et al. 1991, Gradinger 1999a). The locally enormous sediment load of so-called 

“dirty ice” (e.g., Reimnitz et al. 1987, Nürnberg et al. 1994, Eicken et al. in press) is 

assumed to have an additional, profound impact on the ice biota through alteration of the 

available light energy, but this impact has not been quantified yet. 

In the coastal Chukchi and Beaufort Seas, mass developments of unicellular algae of 

maximum concentrations of 43*106 cells l-1 ice (Horner 1976) were reported in the 

bottom layers of the fast ice in spring (May/June). Both the plant biomass and plant 

productivity inside the ice out-reach the water column values during this period of the 

year. The only available estimate of annual ice algal primary production is 5gC m-2 for 

the shallow Beaufort Sea (Alexander 1974), which is in the range of data from other 

coastal locations with an annual ice cover (Arrigo 2003). Horner’s summary (1984) of the 

knowledge of ice algal biomass and production is still the most recent publication on the 

topic in the Beaufort Sea area. 

Recent changes were observed in Arctic ice extent and thickness (e.g. Parkinson et al. 

1999, Rothrock et al. 1999), and model results (e.g., Gordon & O’Farrell 1997) predict a 

60% loss in summer sea ice extent in the northern hemisphere by the time atmospheric 

CO2 has doubled. Results from the SHEBA ice drift experiment already point toward 

substantial changes in the ice community composition during ice melt with freshwater 

algae being dominant and sea ice fauna completely missing, compared to earlier data 

(Melnikov et al. 2001). The general scarcity of ice algal biomass data highlights the need 

for comparative and supplementary new data on ice algal biomass in the Beaufort Sea as 

acknowledged in the ACIA (2004) report. 

Sea ice algae not only contribute significantly to the overall primary production of the 

Arctic, but they also form the basis for the sea-ice related food web which extends to 

higher trophic levels, such as sea floor dwellers, seals and polar bears. Carey and coworkers (e.g. Carey & Boudrias 1987, Carey 1992) found high abundances of amphipods, 

5
 

especially Pseudalibrotus (=Onisimus) litoralis, under the Beaufort Sea fast ice. The 

authors estimated that about 1–10% of the annual ice algal production is grazed by these 

amphipods and sink, as fecal pellets, to the sea floor where they become available to 

benthic species. Apart from amphipods, a large assemblage of meiofauna inhabits the sea 

ice and the ice/water interface, e.g. copepods, copepod nauplii, nematodes, turbellarians, 

and larvae of benthic polychaetes and gastropods (Carey & Montagna 1982, Cross 1982, 

Kern & Carey 1983, Grainger et al. 1985, Gradinger 1999b). Grainger and Hsiao (1990) 

observed that several of these taxa fed on a variety of ice algae in the Canadian Frobisher 

Bay in early and late spring. The authors concluded that herbivory on ice algae is the 

dominant feeding mode for ice related meiofauna. To what extent these taxa utilize or 

even depend on the ice as a food source in the Beaufort Sea and transfer, via the food 

web, energy from the sea ice to the benthic realm is unknown, especially as to the spatial 

and temporal variability. The sea ice, pelagic and benthic realms are, moreover, coupled 

through the complex life cycles of their inhabitants. The studies in the shallow coastal 

Beaufort Sea (summarized by Carey 1992) suggest that larvae of benthic copepods, 

polychaetes and gastropods use sea ice as a nursery ground, whereas the adults of these 

taxa inhabit the benthos. 

Ratios of naturally occurring stable isotopes of carbon δ13C and nitrogen (δ15N) are 

widely used in identifying food web connections in Arctic and other marine ecosystems 

(e.g. Hobson & Welch 1992, Iken et al. 2005). Both carbon and nitrogen ratios increase 

from one trophic level to the next in known steps (Fry & Sherr 1988) – for δ13C with a 

typical increase of 1‰, for δ15N with an increase of 3–4‰. This enrichment, an 

increasing contribution of the heavier isotope 13C and 15N, is caused by preferred 

physiological processing (e.g. respiration, excretion) of the lighter isotopes (12C and 14N). 

Identifying trophic interactions based on field stable isotope data is dependent on 

knowing the isotopic signature of the carbon source, the marine primary producers. 

Isotopic ratios for marine primary producers can change on large scales (e.g. with 

latitude) but also within one region depending on habitat. The differences between 

isotopic ratios of, for example, primary producers in the benthic environment and in the 

pelagic environment can be used to follow the fate of these production units in the marine 

food web. Large differences in the isotopic signature have been reported for sea ice algae 

and polar phytoplankton in general, with ice algae being isotopically heavier (higher δ13C 

ratios) than phytoplankton (Hobson et al. 1995). The heavier δ13C isotopic signature of 

ice algae is caused by the limited availability of inorganic carbon within the brine channel 

network (Kennedy et al. 2002, Thomas & Papadimitriou 2003). In conclusion, the 

variation in isotopic ratios of organic material in polar waters can be attributed to two 

major factors: a) habitat specific ratios for primary producers (e.g. ice algae versus 

phytoplankton) and b) changes due to food web interactions (e.g. herbivores versus 

carnivores). 

The previously described studies provide windows of insight into some of the 

principle coupling processes between sea ice, water column and benthos. Although many 

questions remain open, the results show that a wide variety of taxa spend at least a certain 

part of their lives in or associated with sea ice and may depend on the provided food 

source and habitat. Hence, any disturbance of the sea ice habitat, e.g. by enhanced 

sediment load, construction of ice roads and gas or oil spills, would likely impact the 

biological links between ice, water column and sea floor and may, hence, affect the 

6
 

biomass and productivity in all realms. Some of these disturbances operate by decreasing 

the amount of light available to primary producers while others affect the water 

chemistry. The possibility of such disturbances is increasing with the present acceleration 

of oil and gas exploration and development in the Arctic shelf seas, such as the Beaufort 

Sea. 

Over the last 15 years, several studies focused on the disturbance under sea ice by an 

oil spill. In Antarctic sea ice, crude oil and diesel fuel negatively impacted ice algal 

biomass (Fiala & Delille 1999) whereas no adverse effects were observed in an ice algae 

community after a short-term exposure to oil during the Baffin Island Oil Spill (BIOS) 

experiment (Cross 1987). While the risk of larger oil spills in the shallow Beaufort Sea is 

considered to be low (MMS 2001), an increase in sediment load in the water column and 

the sea ice, and eventually on the sea floor, is undoubtedly linked to exploration-related 

construction work. Trenching for oil pipelines, hauling gravel for road or island 

constructions and water discharge, including particulate matter, are typical activities that 

will enhance the load of particulate matter in the marine environment (MMS 2001). 

Suspended sediments are incorporated into sea ice by various naturally occurring 

processes during ice formation, similar to the incorporation of ice organisms (Reimnitz et 

al. 1992, Gradinger & Ikävalko 1998) and are released at ice melt. Light, obviously, is the 

dominant factor controlling the seasonality of plant production in polar environments. 

Field and experimental data showed a strong decrease of ice algal abundance and 

productivity with increasing snow thickness caused by light reduction (e.g. Gradinger et 

al. 1991). Incorporated sea ice sediments will further reduce light levels by increased 

attenuation, which will most likely further reduce production and accumulation rates of 

ice algae. High turbidity will also reduce planktonic and, thus, benthic primary and 

secondary production, but quantitative studies on this topic are scarce. MMS (2001) 

assessed the potential negative impact on the production of macrophytes in the Beaufort 

Sea boulder patch to be in the order of 6%. 

Our proposed work aimed at contributing to “better understanding coastal marine 

environments affected by offshore oil and gas exploration” (CMI framework issue 1) in 

coastal Alaskan waters. 

Based on existing knowledge of the linkages between sea ice and adjacent habitats as 

well as on potential effects of disturbances, we addressed the following major 

hypotheses: (1) Sea ice biota contributes significantly to the biogeochemical cycle in the 

fast ice-covered shallow Beaufort Sea in terms of production of organic material and also 

as a seasonal habitat and food source for pelagic and benthic invertebrates, (2) Certain 

life stages of a number of benthic taxa depend on the ice algal biomass as a food source 

early in the year prior to the occurrence of phytoplankton blooms, and (3) Disturbances 

of the linkages between sea ice, water column and benthos will reduce the abundance of 

ice associated biota. 

Our objectives were: 

a) to determine the diversity, abundance and biomass of ice related fauna in 

comparison to planktonic and benthic communities in relation to ice sediment 

load, 

b) to determine algal biomass in the sea ice, water column and benthos to assess the 

absolute and relative amount of available food in sea ice in relation to sediment 

load and 

7
 

c) to identify the relevance of sea ice-produced organic material for the nearshore 

food web, based on stable isotope analysis. 

Study area 

The fast ice along the Alaskan coastline forms in November/December and reaches a 

thickness of 1.5–1.8m by April (Macdonald et al. 1999, Gradinger & Bluhm 2003). 

Break-up usually occurs between late June and mid-July. Attached to the shore and 

anchored to the sea floor by up to 20–25 m deep keels (Macdonald 2000), the ice extends 

several km out onto the Chukchi and Beaufort shelves. We chose the fast ice cover close 

to Barrow, Alaska as the study area (Fig. 1), as the Barrow Arctic Science Consortium’s 

(BASC) logistics provide easy access to the ice, and earlier studies had demonstrated the 

formation of pronounced algal blooms in sediment-free fast ice in this area (Horner & 

Schrader 1982). 

Figure 1: Location map at the northernmost tip of Alaska. BASC = Barrow Arctic Science 

Consortium. Site 1: no sediment, Site 2: substantial sediment load in sea ice. 

Sampling was conducted April 24–28, 2002 and February 12–17, April 1–5 and May 

27–30, 2003. The fast ice was sampled in level, un-ridged areas. The first sampling in 

April 2002 was used to collect sediment-free ice at both locations to detect potential sitespecific differences. Site 1 (Fig 1, 2a and c) was chosen based on its easy access in all 

weather conditions, while site 2 (Fig. 1, 2b) provided a higher likelihood of encountering 

sediment in the sea ice. Site 1 (without visible sediment incorporation in both years) was 

located in the Chukchi Sea just offshore of the BASC facilities (71° 20′N, 156° 42′W). 

Site 2, located around Point Barrow in the Beaufort Sea (71° 22′N, 156° 24′W) exhibited 

a substantial amount of sediment in the top 40 to 44 cm in 2003 (Fig 2b: gray layer at top 

of ice core). Both sites were approximately 200 m offshore in a water depth of 5 to 5.6 m. 

8
 

 c 

Figure 2: Field view of sites 1 (a) and 2 (b). Note the substantial amount of sediment in the top 

40–44 cm of the ice core at site 2. Both pictures were taken during the February 2003 

field campaign. c) Examples of the sampling gear used for the study.

The fieldwork is documented on the project’s web page: 

http://www.sfos.uaf.edu/research/seaicebiota/cmi/barrow2002/index.html. 

9
 

Material and Methods

During each field phase, we collected samples from the sea ice, the water column 

and the sea floor at two fast ice locations close to Barrow (Fig. 2a, c for examples of 

sampling gear used). 

At each location, snow thickness was determined at ten locations in a perimeter of 

1 m around the site. Air, snow surface, snow/ice interface and ice temperatures were 

measured with a Traceable thermometer (accuracy of 0.05°K). Ice cores were collected 

with a KOVACS-type ice auger (10 cm diameter) and the total ice thickness was 

recorded from a minimum of four cores per site. One complete core from each site was 

melted to determine total sediment load. Ice temperature was measured immediately 

after coring on one ice core per site in 10 to 20 cm intervals over the entire ice thickness. 

Temperature and salinity (T/S) of the water column were determined with a YSI 85 

sensor at 1 m intervals. Freezing within the T/S sensor head due to cold surface 

temperatures (down to -30°C) caused malfunction of the salinity readings in February 

and April 2003. 

At each station and date, the PAR (photosynthetically active radiation: 400–700 nm 

wavelength) intensity was measured with a LI-COR underwater 4π spherical sensor at 

1 m intervals through a separate core hole. A 2π planar sensor that remained on the ice 

surface provided a reference. To ensure accurate depth readings, for both light and T/S, 

the sensors were attached to marked fiberglass rods. Light measurements were conducted 

around solar noon. Both sensors measured the photon quantum flux density (number of 

photons per unit area per unit time, units: μmol photons m-2

 s

-1). 

In May 2003, the spectral composition of the incoming radiation on the surface of the 

sea ice and the downwelling radiation under the sea ice were determined using a 

calibrated OceanOptics radiometer equipped with a 3 m long fiber-optic cable, to a 

spectral resolution of 2 nm. 

Biological samples were taken from sea ice, water column and sea floor sediment in 

replicates of four per site, season and parameter, unless otherwise specified. 

A minimum of eight ice cores were taken at each site, comprising four replicates of 

two core sets (A and B). The distance between replicate cores varied between 0.3–2 m. 

For further processing, we used the bottom 10 cm of the ice cores, as these generally 

contain the highest ice algal and ice meiofauna abundances (Horner 1985), which was 

consistent with our visual inspection of all cores sampled. We also verified this 

assumption in April 2003: 93% of total ice algal biomass (as estimated by the 

concentrations of the pigment chlorophyll a) was located in the bottom 10 cm section of 

one core taken at site 1 (for details see results section). 

The bottom sections of set A of the replicate cores were melted directly in the dark. 

After complete melt, 50 ml sub-samples were filtered onto Whatman GF/F filters and 

subsequently frozen for ice algal pigment analysis (Chl a and phaeophytin) to estimate 

biomass. Phaeophytin is a break-down product of chlorophyll and high relative 

contributions of phaeophytin are indicative of cell mortality/aging of organic material due 

to grazing or extreme environmental stress, for example. Another set of 50 ml subsamples was filtered onto pre-combusted and pre-weighed GF/F filters, rinsed and frozen 

10
 

for later determination of stable isotope composition (δ13C, δ15N) and amount of 

particulate organic carbon and nitrogen (POC, PON). 

The bottom sections of core set B were melted in the dark after addition of 1 l each 

of 0.2 μm-filtered seawater to avoid osmotic stress for the biota. After complete melt, 

sub-samples of 50 to 200 ml were fixed with 1% formaldehyde for ice algal counts to 

estimate abundances (cell count per ml). The remaining samples were concentrated over 

20 μm gauze. Metazoan ice meiofauna was sorted and counted fresh or after fixation in 

4% formaldehyde using a Wild M3 dissecting microscope to estimate abundances. All 

counts were corrected for dilution factors. 

Water samples were taken through the core holes from an intermediate water depth 

(3 m) with a Kemmerer water sampler. For analysis of phytoplankton biomass (Chl a and 

phaeophytin), POC, PON and stable isotope composition (δ13C, δ15N), 0.2–0.5 l each was 

filtered on GF/F filters and treated like the ice samples. For phytoplankton cell counts, 

subsamples of 200 ml were fixed with 1% formaldehyde to estimate phytoplankton 

abundances. 

The major tool for processing the particle samples in the sea ice algae and 

phytoplankton size range was a FLOWCAM, an image analysis-based flow-cytometer. 

Three digital pictures per second were recorded from the continuous sample stream and 

were simultaneously analyzed for particle abundance and size. Absolute particle 

abundances were calculated based on the mean number of particles per image and a 

minimum count of 200 particles per sample. The digital images of the individual particles 

were used to identify the particle composition within each selected size fraction (5 to <10 

μm, 10 to < 20 μm and ≥20 μm). Four samples per site and sampling period were 

analyzed from the sea ice and the water column. 

Vertical zooplankton hauls were taken with 20 μm and 200 μm nets. Samples were 

sorted either live or after preservation in formaldehyde (4% final concentration), and 

specimens were counted by taxon using a dissecting microscope to estimate zooplankton 

abundance. 

Benthic sediment samples were collected with a benthic corer (6 cm diameter) with 

an attached extension deployed through a core hole. The top 5 cm of the cores were 

collected, thoroughly mixed, and sub-samples were frozen for determination of algal 

biomass in sea floor sediments. 

Algal pigments were extracted from samples from all three realms. Ice algal and 

phytoplankton samples were extracted with 7 ml of 90% acetone for 24 hours in the 

freezer (Gradinger et al. 2005). In the case of the sea floor sediment algae samples, 15 ml 

of 90% acetone were added to approximately 1 g (wet weight) of sediment (Conde et al. 

1999). After extraction of the sediment samples, 7 ml of acetone were transferred into a 

borosilicate tube and centrifuged for 20 minutes at 1500 min-1. Pigment concentrations 

(Chlorophyll a and phaeophytin) were determined fluorometrically with a Turner Designs 

fluorometer (Arar & Collins 1992). Following the pigment extraction, the sediment was 

dried at 60oC for 24 hours and weighed. Chlorophyll (Chl) a concentrations in sediment 

are therefore presented per g dry weight sediment. 

11
 

Filters for stable isotope analysis were dried in a drying oven at 65°C for 1–2 days 

and subsequently weighed for total dry weight to determine total load of particulate 

matter (POC, PON). All filters were then HCl-fumed (to remove carbonates) in a vacuum 

chamber for 24 hours and dried again. The filters were folded into tin cups and delivered 

to the University of Alaska Fairbanks (UAF) Stable Isotope Facility where they were run 

on ThermoFinnigan Delta mass spectrometers for their δ

13C and δ15N values and 

particulate organic carbon (POC) and nitrogen (PON) masses. Sample isotopic ratios are 

expressed in the conventional notation as parts per thousand (‰) according to the 

following equation:

 δX = [(R sample/R standard) – 1] • 1000 

where X is 13C or 15N of the sample and R is the corresponding ratio 13C/12C or 15N/14N. 

Terminology being used when presenting isotope data is: enriched=heavier (containing 

more of the heavy isotope) and depleted=lighter (containing less of the heavy isotope). 

Enrichment in δ13C results in fewer negative numbers; enrichment in δ15N results in more 

positive numbers. For a single sample, 0.2–0.4 mg dry mass of faunal tissue are required 

at current equipment sensitivity. Depending on the body mass of a species/taxon, this 

amount required pooling up to several hundred individuals, e.g. 30–50 turbellarians, 50–

80 polychaete juveniles, 150–300 copepod nauplii or nematodes. Due to the considerable 

effort required to pick this amount from a sample and due to low faunal densities in the 

winter and/or site 2, samples could only be obtained from taxa that were reasonably 

common at a site at any given time. 

Gut and stomach content analyses were performed on amphipods collected in traps 

(Fig. 2a), that had been deployed under the sea ice for time periods of up to 24 hours in 

February, April and May 2003. Stomachs and guts of three randomly selected animals 

from each sampling date and site were placed on a microscope slide and microscopically 

examined at a magnification of 400x using a ZEISS compound microscope. The particle 

composition in the stomachs/guts was qualitatively analyzed in terms of size of particles 

and occurrence of identifiable biological material. Digital pictures were taken with a 

Canon Rebel digital camera at highest resolution and were enhanced using 

GraphicConverter.

Means, standard deviations, Kendall and Spearman rank correlations and various 

tests for differences between the observations at site 1 and site 2 were calculated using 

the following software packages: Excel, Kaleidagraph, Systat and Statview. 

Results 

Physical properties 

Over the course of the study, the snow thickness varied within and between 

sampling events and stations between 3 and 7 cm. A major ice break-off had occurred at 

site 1 on December 23, 2002. Therefore, ice thickness at that site was lower (0.8 m) than 

at site 2 (1.2 m) in February 2003. Ice thickness increased over time to maximum values 

of 1.3 m at site 1 and 1.7 m at site 2 in May 2003. The variability of ice thickness at each 

site at each sampling event was less than 5 cm. 

12
 

4

3

2

1

0

5

6

4

3

2

1

0

5

6

Figure 3: Environmental parameters (temperature and irradiance) at site 1 (with no sediment, 

left) and site 2 (high sediment load, right) in sea ice cover in 2003. a and b: Sea ice 

temperatures. Horizontal line indicates top of ice floe; measurements above the line were 

made in air and snow. c and d: Sub-ice irradiance (ratio 4π under-water to 2π surface 

sensor). e and f: Spectral light composition in May 2003. Thick gray lines along the xaxis indicate ranges of maximum chlorophyll absorption. Note the different scales for site 

1 and 2 for some parameters.

13
 

Th

temper perature gradient and its seasonal change within the 

ice w

-2 s-1) to April (393 and 642 μmol 

phot

ay. 

d 

03. The 

anal

), 

Seasonal changes in ice algal biomass (given as mean Chl a units ± SD) were 

ling locations. At site 1 (without sediment), algal pigment content 

in th ary 

on 

, 

 

e temperatures in the ice cores were coldest at the top and decreased to seawater 

ature at the bottom. The tem

ere very similar at the two sites (Fig. 3a and b). The coldest air and ice temperatures

were encountered in February 2003, with minimum ice temperatures of -25°C at the ice 

surface and air temperatures of -30°C. Ice temperatures in April 2003 ranged from -13°C 

at the top of the ice cores to -1.9°C in the lowermost 10cm. In late May, ice temperatures 

had increased to -0.2 to -2°C at the ice surface. A slight temperature increase with season 

was also detectable in the temperature readings closest to the ice-water interface from 

-4.5°C in February to -1.9°C in April and -1.4°C in May 2003. The temperature profiles 

in the water column (not shown) exhibited neither seasonal patterns nor any indication of 

vertical stratification. Values ranged between -1.4 and -2°C. Salinity (not shown) in May 

varied between 30.6 and 31.9. 

The incident short wave radiation in the PAR range increased with season at sites 1 

and 2 from February (34 and 138 μmol photons m

ons m-2

 s

-1) to May (886 and 1402 μmol photons m-2

 s

-1). Light intensities at the 

ice/water interface under sediment-loaded sea ice (site 2) were two orders of magnitude 

below those under sediment-free ice (site 1; Fig. 3c and d), and never exceeded an 

absolute value of 0.4 μmol photons m–2 s

–1. At site 1, the relative irradiance levels in the 

ice/water interface region decreased from February to April and remained low in M

Distinct differences between sea ice with a high sediment load (site 2) versus low 

sediment load (site 1) were evident in the spectral light composition (Fig. 3e and f). At 

site 1, attenuation was highest in the blue and red wave bands, where the algal pigment 

chlorophyll has its absorbance maxima. In contrast, the spectral composition at site 2 di

not indicate any preferential absorption in the PAR range (400 to 700 nm). 

The dry mass (DM) of particulate matter over the entire ice thickness was 15.3 times 

higher at site 2 (102.41 g DM m-2) than at site 1 (6.67 g DM m-2) in April 20

ysis of the four replicates of the bottom sections revealed that approximately 0.9% of 

the total particle load was localized in the bottom 10 cm at site 2 (0.9 ± 0.1 g DM m-2

while the fraction at site 1 was considerably higher with 46.5% (3.1 ± 0.9 g DM m-2). 

Biological properties 

observed at both samp

e bottom 10 cm of the sea ice increased by two orders of magnitude from Febru

(7.4 ± 0.7 μg Chl a l–1) to May 2003 (329.3 ± 42.0 μg Chl a l–1 sea ice) (Fig. 4a). In the 

dirty sea ice at site 2, pigment concentration increased slightly from 1.9 ± 0.0 μg Chl a l–1

in February to 8.3 ± 7.0 μg Chl a l–1 in May 2003. Pigment levels at site 2 had been 

higher than at site 1 in April 2002, when only clean ice had been collected from both 

sites. The Chl a/phaeophytin ratios decreased at both locations with progressing seas

from February (13.3 ± 1.7 at site 1, and 10.3 ± 4.9 at site 2) to May (6.3 ± 1.3 at site 1

and 2.7 ± 0.8 at site 2). Analysis of algal pigment concentration at site 1 over the entire 

ice thickness in April 2003 demonstrated that the bottom 10 cm contained 93% (7.72 mg

14
 

09

08

07

05

04

00

06

03

02

01

Figure 4: Particulate organic carbon (POC), and algal pigment concentrations and ratios at two 

coastal fast ice sites with no sediment (site 1) and high sediment load (site 2) during sea 

ice cover in 2003. Algal pigment concentrations (a and c) and pigment ratios (Chl 

a/phaeophytin; b and d) in ice, water and sediment. POC and δ13C (e and f) in sea ice and 

water samples.

15
 

Chl a m-2) of the entire integrated pigment content (8.31 mg Chl a m-2) (Fig. 5). Visual 

inspection revealed no indication of internal or surface pigment maxima in any of the 

other cores taken throughout the project. The chlorophyll increase at site 1 over the 103 

days between the February and May sampling converts into a net algal doubling time of 

18.8 days, while the algal doubling time at site 2 (with sediment, 49 days) was higher 

compared to site 1 by a factor of 2.6.

Compared to sea ice algal concentrations, phytoplankton pigment levels remained 

low (< 2 μg Chl a l–1) throughout the 2003 study period at both locations (Fig. 4a). The 

Chl a/phaeophytin ratios at the two sites were lowest in April 2003 (0.7 ± 0.1) and 

highest in late May 2003 (1.8 ± 0.6) with no significant site-specific differences (Fig. 4b). 

The ratio of the mean Chl a concentrations in sea ice relative to the water column 

increased with the progressing season from 150 (February) to 370 (May) at site 1, while it 

decreased from 47 to 9 at site 2. 

Mean benthic sediment chlorophyll concentrations varied between <0.2 and >1.0 μg 

Chl a g

–1 DM (dry mass) sediment throughout the sampling period at both sites, with no 

clear seasonal trend at site 2, but a mean increase at site 1 in May (Fig. 4c). The pigment 

concentrations in the sea floor sediment varied considerably between replicates at site 1. 

Reasons for the high variability may relate to the difficulty in sampling the coarse sea 

floor sediment. Chl a/phaeophytin ratios remained below 0.9 ± 0.4 at site 2 with no clear 

seasonal pattern. A dramatic increase occurred in May at site 1 (5.2 ± 3.8, Fig. 4d). 

Ice algae and phytoplankton are the main contributors to living particulate organic 

carbon (POC) together with detritus, bacteria and proto- and metazoans. The sea ice POC 

concentrations increased at site 1 by a factor of 12 during the 2003 sampling period (Fig. 

4e) from 1.3 ± 0.6 mg l-1 in February to 15.2 ± 1.9 mg POC l-1 in May. At site 2, POC 

decreased from February (9.8 ± 2.2 mg l-1) to May (5.7 ± 4.3 mg l-1). POC concentrations 

in the water column remained below 0.2 mg l-1 at both sites throughout the study period. 

At site 1, δ

13C became increasingly enriched (less negative) with the progressing 

season from -24.9 ± 1.6‰ in February to -15.5 ± 0.8‰ in May (Fig. 4f). In contrast, the 

isotopic signature remained below -25‰ at site 2 in February and April and got slightly 

enriched to -24.2 ± 0.2 ‰ in May. The positive relationship between δ

13C (‰) and POC 

(μg/l) was highly significant: 

(1) δ

13C = -25.964 + 0.0007*POC (r2

 = 0.899, n=18, p<0.0001). 

At both sites, stable isotope ratios in the water column samples were depleted in 

April (-26.6 ± 0.7‰ at site 1, -28.3 ± 1.7‰ at site 2) relative to February (site 1: -23.8 ± 

0.3‰, site 2: -24.9 ± 0.3‰) and May (site 1: -24.3 ± 2.7‰, site 2: -22.5 ± 1.2‰). 

The abundance of ice algae in cells/ml (for examples of FLOWCAM pictures see 

Fig. 6) increased strongly at site 1 from February to May in 2003 (mean values ± 

16
 

standard deviations: Fig. 7). Large single-celled ice diatoms such as the one shown in 

Fig. 8a dominated the size fraction ≥20 μm in May 2003 in all clean ice cores (Fig. 6f), 

while they were nearly absent from all other samples (e.g., Fig. 6c). The growth (=cell 

division) of ice diatoms caused the significant increase in particle numbers in this size 

fraction at site 1 between February and May, while no such change was seen in the dirty 

sea ice at site 2 or in the phytoplankton samples (Fig. 7). This observation is consistent 

with the relative changes in chlorophyll a and particulate organic carbon concentrations. 

The abundance of meiofauna (examples in Fig. 8) within the bottom 10cm section of the 

sea ice, cores followed the changes of the ice algal biomass (Fig. 9a and b). At site 1, 

mean total abundance increased from 18,000 individuals m–2 sea ice in February to 

276,000 individuals m–2 in May. In contrast, mean total abundances at site 2 remained 

below 17,000 individuals m–2 throughout the study period in 2003 with no seasonal trend. 

At both locations, ice meiofauna was dominated by polychaete juveniles, nematodes, 

copepods (calanoid, cyclopoid and harpacticoid), copepod nauplii and turbellaria (Fig. 8). 

Taxa less frequently found (listed as others, Fig. 9) were mainly unidentified larvae, 

rotifers and cnidarians. Unicellular meiofauna taxa (ciliates and flagellates) occurred 

regularly but were not quantified. High abundance of nematodes in May at site 1 was a 

result of reproduction: we observed juveniles hatching out of egg cases in the laboratory. 

We kept a few ice polychaete juveniles from site 1 in culture (12:12h light:dark, -1oC) in 

the lab in Fairbanks, which as subadults were identified as Scolelepis squamata

(Spionidae). 

Zooplankton abundances in the water column showed a seasonal increase of similar 

magnitude at both sites in 2003 (Fig. 9c and d). Average values increased from 2,140 

individuals m-2 at site 1 and 4730 individuals m-2 at site two in February to 28,360 

individuals m-2 at site one and 56,300 individuals m-2 at site 2 in May. At all sampling 

dates, the zooplankton (Fig. 9c and d) was dominated by copepods, with nauplii starting 

to occur in late April (2002) and May (2003). The total abundance of polychaete 

juveniles in the water column was <700 individuals m–2 at any of the sampling events 

and, thus, at least one order of magnitude below polychaete juvenile abundances in the 

sea ice in April and May (3,520 to 136,600 individuals m–2) at site 1. 

Collecting quantitative benthic samples was extremely difficult through the ice 

sheet. The coarse sea floor sediment at site 1 frequently did not stay in the corer during 

retrieval. We were able to analyze the relative meiofauna composition in one benthic 

sediment core collected at site 1 in May 2003. The composition was dominated by 

harpacticoid copepods (28%), other copepods (21%), copepod nauplii (27%), and 

nematodes (7%); we did not observe polychaete larvae and juveniles in this sample. 

Using the means for all parameters of the complete sea ice data set from both 

sampling sites and all dates, the correlation analysis (Table 1) revealed significant 

relationships between various ice biota biomass and abundance parameters. Ice algal Chl 

a and POC were significantly correlated as well as ice meiofauna abundance and ice algal 

Chl a and POC, respectively. The δ

13C ratios were significantly correlated with Chl a and 

POC values. 

17
 

Figure 5: Algal pigment concentration in April 2003 at site 1. Note that the vertical resolution 

changes from 10 cm sections (top 112 cm) to 1 cm sections in the bottom 10 cm of the 

core (indicated by the horizontal line). Depth represents the midpoint between the upper 

and lower end of a section.

δ13C and δ15N means (± standard deviations) of all analyzed ice meiofauna and 

zooplankton for all sampling periods are compiled in tables 2 and 3, respectively. Faunal 

densities, and therefore the number of isotope samples, were too low to observe any 

trends in stable isotope data at site 2. Figure 10 shows the distribution of the δ13C values 

of the more common taxa by taxonomic groups and sampling periods. At site 1 (Fig. 10a, 

11), several ice meiofauna taxa showed progressively enriched δ13C signatures with 

progressing season, e.g., by around 7‰ in nematodes and turbellaria. 

18
 

Figure 6: Examples of FLOWCAM images from sea ice samples collected at sites 1 and 2 in 

May 2003.

A slight enrichment in δ13C occurred in several zooplankton taxa at site 1 (Fig. 10b, 

11b), e.g., in nauplii and cyclopoid copepods, while the other sampled taxa revealed no 

obvious trends. Partial feeding on enriched particles that were released from the sea ice 

during the first melting processes might explain the enrichment in selected zooplankton 

taxa late in the sea ice season. Pelagic POM, in contrast, remained rather stable over the 

time sampled with respect to its carbon isotopic signature and would, therefore, not 

trigger any isotopic enrichment in its grazers. At site 2, pelagic POM isotope values were 

more variable between sampling periods while zooplankton signatures showed very little 

variability. 

19
 

Figure 7: Abundance (N/ml) of particles larger than 20 μm in sea ice and water samples from 

sites 1 and 2 in 2003. The increase at site 1 is due to growth of sea ice diatoms (see Fig. 6 

and 8a). 

20
 

Figure 8: Examples of typical ice inhabiting flora and fauna in the coastal fast ice taken at 

different magnifications: a) pennate diatom, b) turbellarian, c) polychaete species 1 

juvenile (Feb 2003, d) polychaete species 2 juvenile (May 2003), e) copepod Oithona 

similis, f) amphipod Onisimus glacialis (sub-ice fauna).

21
 

Figure 9: Abundance and composition of metazoans in the sea ice (a and b) and water column (c 

and d) from April 2002 to May 2003 in and under sediment-free ice (site 1) and 

sediment-loaded ice (site 2).

Fig. 11 shows the δ

13C isotopic signatures for sea ice meiofauna and zooplankton as 

dependent of the δ

13C values of sea ice and pelagic POM (= the carbon sources), 

respectively. Figure 11a demonstrates a significant positive correlation between sea ice 

fauna δ

13C signatures and ice POM δ13C signatures (Kendall rank correlation test, 

p=0.04), which, again, suggests a strong dependence of ice fauna on sea ice POM as a 

food source. At the dirty ice site and for the zooplankton of two sites (Fig. 11), there may 

also be a positive correlation of faunal δ

13C signatures with POM δ13C signatures, at least 

for zooplankton, but the progressive enrichment of both components with season is not 

obvious. 

22
 

Figure 10: Distribution of δ13C values of sea ice fauna and zooplankton from the Barrow area by 

taxonomic groups. Sea ice fauna from site 1 (clean ice) (a) and from site 2 (c). 

Zooplankton from site 1 (b) and from site 2 (d).

The gut content analyses demonstrated a clear shift in the composition of the 

amphipod food sources. Pennate diatoms, typical for sea ice habitat, were observed in 

100% of all fields of view of all animals examined during our study in May, while we 

observed only one single diatom cell in 60 fields of view studied in three animals 

collected in February. February stomach and gut contents were dominated by very small 

particles (<5μm in diameter) with no recognizable biological features. The observations 

for the April animals revealed intermediate conditions with two animals containing 

substantial amounts of ice diatoms in its stomach/gut, while the gut/stomach of a third 

animal was nearly empty with no diatoms observed (Fig. 12). 

23
 

Site 1 Site 2

Figure 11: δ13C isotopic signatures for sea ice meiofauna and zooplankton from sampling sites 1 

and 2 for 2002 and 2003 as dependent of the δ13C values of sea ice and pelagic POM, 

respectively. Sea ice fauna from site 1 (clean ice) (a) and from site 2 (dirty ice) (c). 

Zooplankton from site 1 (b) and from site 2 (d). Only the values collected in 2003 are 

presented in (c) as this was the time of dirty ice at this location. The dashed line indicates 

a 1:1 relationship. 

24
 

Figure 12: Stomach/gut contents of three randomly selected amphipods collected in either 

February, April or May 2003. Note the high relative abundance of pennate diatoms in the 

May samples. All pictures taken at 400x magnification. 

25
 

Figure 13: Microphotograph of cnidarian observed in sea ice at site 1. Picture taken at a 

magnification of 400x, size of body about 70μm. 

Discussion 

Our study demonstrates that sediment within the ice has a pronounced impact on the 

seasonal development of the sea ice biota. In the region of high sediment load, the 

biological development of the spring algal bloom was suppressed, and abundance of sea 

ice fauna was greatly reduced compared to a clean ice location. 

Seasonal development of ice biota through a spring bloom cycle at a clean ice site 

Seasonal development of the sea ice primary producers 

With increasing day length and, hence, increasing incident light intensity and 

temperatures in both fast and pack ice, ice algae develop a spring bloom in the bottom cm 

of the sea ice (Horner & Schrader 1982, Horner 1985, Welch & Bergmann 1989, 

Gradinger et al. 1991, Welch et al. 1991, Haecky & Andersson 1999, this study). 

Within the fast ice, spring ice algal blooms, dominated by diatoms, occur within a 

time window defined by available light intensity (start) and the seasonal ice melt (end). In 

general, the timing and the amplitude of ice algal spring bloom (in terms of Chl a) 

observed at our clean ice site 1 followed the pattern observed in earlier Alaskan fast-ice 

studies (Alexander 1974, Horner & Schrader 1982). With progressing season, under-ice 

PAR intensities increased from 1.5 μmol photons m-2 s-1 in February to 7.7 μmol photons 

26
 

m-2 s-1 in late May. Ice algae, in general, are adapted to low-light conditions (Cota & 

Smith 1991, Cota et al. 1991, Kirst & Wiencke 1995, Mock & Gradinger 1999), and the 

onset of ice algal spring bloom formation in the field occurs after PAR exceeds threshold 

values of about 2 μmol photons m-2 s

-1 (Horner & Schrader 1982). Thus, light alone has a 

sufficiently large limiting effect to explain the low concentrations of algae in February 

2003, when light intensities were just approaching the threshold for ice algal primary 

production. PAR values exceeded the threshold later in the season, which coincided with 

the observed biomass increase. 

Besides light, temperature has a major impact on ice algal growth. Firstly, low ice 

temperatures, down to -25oC at the surface of Barrow fast ice in February 2003, directly 

reduce physiological process rates. One of the very few studies on temperature effects on 

ice biota revealed Q10 values of about 1 to 5 for Antarctic ice algae (Kottmeier & Sullivan 

1988). Secondly, low temperatures result in high brine salinities and, therefore, osmotic 

stress for sympagic biota (Kirst & Wiencke 1995, Gradinger 2002). The salinity within 

the brine channel network of ice algae is a direct function of the ice temperature; for 

example, salinities above 100 ppt occur at temperatures below -6oC (Assur 1958). 

Experimental studies with cultures of Arctic and Antarctic ice algae showed slower or 

completely inhibited growth with decreasing temperatures and increasing salinity (Grant 

& Horner 1976, Zhang et al. 1999, Arrigo 2003). We measured such low temperatures 

(and consequently high brine salinities) in the upper parts of the ice cores taken in 

February and April (Fig. 2). The relatively warm and constant temperatures in less than 

10cm distance from the bottom of the sea ice (above -2oC in April and May) put – at that 

time of the year – no thermal and salinity restrictions on the formation of ice-bottom 

community, which contributed 93% to the total integrated algal pigment content in April 

2003 at site 1. In addition to light and temperature, limited supply of nutrients from the 

water column into the ice restricts high algal biomass accumulation to the bottom cm of 

the ice/water interface region (e.g., Cota et al. 1987). The strong increase in algal pigment 

concentration within the lowermost decimeter of the fast ice at site 1 (Fig. 5) can be 

explained by constant nutrient advection from the water into the ice which allows for 

favorable growth conditions (and in the end elevated biomass values) closest to the icewater interface. We doubt that differences in nutrient availability between site 1 and site 

2 caused the differences that we observed between ice algal biomass at the sites in 2003, 

as the control data from April 2002 revealed high algal abundances and biomass values at 

both sites Similar to earlier investigations, the ice algal bloom community was 

dominated by diatoms; we did not observe any indications of blooms by freshwater algae 

as Melnikov et al. (2001) found during the pack ice observations of the SHEBA study. 

 

We conclude that the combination of seasonally varying gradients of light, 

temperature and nutrient availability led to a bottom ice algal bloom in the spring (Fig. 4) 

that, in April 2003, resulted in 20-fold higher algal biomass than in February. The 

observed temporal increase in Chl a is in the same range as the published factors of about 

10-fold for algal and bacterial biomass in comparable studies (Horner 1980, Smith et al. 

1988, Gradinger et al. 1991, Haecky & Andersson 1999). By April 2003, the integrated 

algal biomass at site 1 (8.3 mg Chl a m

-2 for total ice thickness) was higher than in pack 

ice of the transpolar drift system in the spring (Gradinger 1999a and unpublished data; 

27
 

median = 1.4 mg Chl a m-2), but lower than in other Arctic near-shore studies, where 

maximum values exceeded 300 mg Chl a m

-2 (Smith et al. 1990). By the end of May 

2003, a total POC concentration of 15.2 ± 1.9 mg C l-1 was reached within the bottom 10 

cm of the ice core. This value converts to about 1.5 g C m-2 and represents the net 

biomass accumulation over the entire spring bloom. This accumulation is in the same 

order of magnitude as yearly primary production estimates for fast ice off Barrow (5 g C 

m

-2; Alexander 1974, Horner 1984) and off Narwhal Island (coastal Beaufort Sea, 0.7 g C 

m

-2, Horner & Schrader 1982). 

The chlorophyll increase over the 103 days between the February and May sampling 

converts into an algal growth rate of 0.037 d-1, or an algal doubling time of 18.8 d, 

assuming that loss terms such as grazing or sedimentation are minor during the build up 

of the ice algal spring bloom (Gradinger 1999b, Nozais et al. 2001, Michel et al. 2002). 

This estimate is in accordance with data from other field studies on Arctic (Gradinger et 

al. 1991: 10 d) and Antarctic pack ice (Grossi et al. 1987: 10 d) under natural snow-cover 

conditions. All field data, however, are well below the empirical potential growth rate 

maximum of 0.92 d-1 for a temperature of -2oC (Eppley 1972), indicating ignored 

substantial loss terms, or, more likely, periodically sub-optimal growth conditions (e.g. 

light limitation) under in-situ conditions. 

Small-scale spatial patchiness (<2 m) was assessed based on the variability 

(SD/mean = 9 to 23%) of the Chl a concentrations between the four replicates at each 

sampling date. The results agree with other studies that found small-scale patchiness of 

<15% in comparable environmental settings, especially similar snow thicknesses. In 

contrast, larger scale variability on scales of hundreds of meters to hundreds of kilometers 

between stations in a geographical area can exceed 50% in the same season (Gosselin et 

al. 1997, Gradinger 1999a, Haecky & Andersson 1999). Again, a significant fraction of 

these spatial and the above discussed temporal variabilities are related to environmental 

variables, in particular light availability and nutrient supply modulated by the snow 

cover, ice morphology and microstructure (Sullivan et al. 1985, Gosselin et al. 1986, Cota 

& Horne 1989, Eicken et al. 1991, Gradinger et al. 1991, Legendre et al. 1991). 

Seasonal development of sea ice fauna 

Total ice meiofauna abundance increased, with increasing algal biomass, from 

17,600 Ind m-2 in February to 276,210 Ind m-2 in late May at site 1. At site 1, polychaetes 

dominated in April. Later in spring, polychaete abundance decreased and nematodes were 

the dominant taxa. At site 2, nematode abundance decreased from February to May, 

while polychaetes increased slowly to a maximum of 5900 Ind m-2. Copepods (mainly 

harpacticoids and cyclopoids) and their nauplii, turbellarians, nematodes and polychaete 

juveniles formed the dominant metazoan ice meiofauna taxa. These findings are similar 

to observations from other near-shore Arctic studies with respect to seasonality and 

relative composition (e.g. Cross, 1982, Kern & Carey 1983, Carey 1985, Grainger & 

Hsiao 1990, Nozais et al. 2001, Michel et al. 2002). Ciliates, which can numerically 

dominate the sea ice fauna >20μm (Gradinger et al. 1991, Gradinger 1999b, Nozais et al. 

2001) were observed regularly, but were not quantitatively documented. The ice 

meiofauna composition closely resembles that of the Arctic benthic meio- and 

28
 

macrofauna in near-shore waters (this study, Carey & Montagna 1982, Feder & Schamel 

1976, Szymelfenig et al. 1995, Schizas and Shirley 1996); the linkage between sea ice 

and benthic realm is discussed below. The dramatic changes in Arctic sea ice meiofauna 

composition, specifically the lack of nematodes, as observed in offshore pack ice, and 

suggested to be related to climate change (Melnikov et al. 2001), were not detected in our 

study. 

In terms of taxononomic composition, we made one unusual find: the first record of 

Cnidaria (Fig. 13) as a regular component of the sympagic fauna. This taxon was 

observed in abundances of 40 to 1500 individuals m-2 in the live counts and occurred 

throughout the study period. We observed mainly very small medusae (approximately 

1mm), which were gliding slowly along the bottom of the counting chamber. Polyps were 

larger, but rare. We assume that previous studies have overlooked the cnidarians due to 

their transparent nature, their small size and the negative effects of direct ice melt and/or 

fixation, similar to the case of ciliates (e.g., Gradinger 1999b). A benthic origin, common 

for other coastal fast-ice meiofauna taxa (Carey 1985) is also likely for these cnidarians. 

The immigration of meroplanktic larvae of benthic copepods, polychaetes and 

gastropods into Arctic sea ice has previously been reported for near-shore Chukchi and 

Beaufort waters (Carey & Montagna 1982, Cross 1982, Kern & Carey 1983, Nozais et al. 

2001). In this study, the major immigration of larval and juvenile polychaetes into the sea 

ice occurred between February (40 individuals m-2) and April (137,000 individuals m-2). 

Food availability, mainly ice algae, is likely the major reason for the increase in 

polychaete numbers, as herbivory appears to be the dominant feeding strategy for ice 

meiofauna in general (Grainger & Hsiao 1990). The observed dominance of diatoms (Fig. 

7) at site 1 provided a favorable food source for meiofauna (Grainger & Hsiao 1990) at 

site 1, which was not available for meiofauna in the ice at site 2 or for zooplankton at 

either site during our study period. At the time of the polychaete abundance maximum in 

the ice, the Chl a concentration of the ice bottom community was 760 times or two orders 

of magnitude higher than in the water column (see also Alexander 1974, Horner & 

Schrader 1982). Our data support the hypothesis that polychaete larvae and juveniles use 

sea ice as a nursery ground (Carey 1992), whereas the adults inhabit the benthos. 

Polychaete juveniles from our study showed a positive phototactic behavior known from 

several polychaete species (e.g. McCarthy et al. 2002). This may provide a mechanism 

for moving from the plankton into the brighter sea ice, a change from a meroplanktic to a 

lifestyle we define analogously as merosympagic. The density decrease of the iceassociated polychaete juveniles between April and late May suggests either active 

downward migration towards the sea floor or passive release through ice melt, as 

indicated by the warm ice temperatures in May. The slightly enriched δ13C values in the 

seawater (Fig. 3f) and higher Chl a/phaeophytin ratios (Fig. 3d) at the sea floor in May 

compared to April also point towards a loss of organic matter from the ice into the water 

column and to the sea floor. On the other hand, polychaete juveniles might have outgrown the brine channel diameters by late May, forcing them out of the sea ice or they 

changed to negative phototaxis (McCarthy et al. 2002). The brine channel network, with 

diameters of less than 1mm, sets an upper size limit for its inhabitants (Krembs et al. 

29
 

2000). Although we did not measure the polychaetes, we observed that the majority of 

polychaetes were larger by late spring. 

Nematodes are another well-documented major component of the Arctic sea ice 

meiofauna. In coastal areas, several investigators noted this taxon to be especially 

abundant in late spring/early summer (May–June; Kern & Carey 1983, Grainger et al. 

1985, Carey 1992, this study). Very similar to our observations, Carey (1992) observed a 

seasonal shift in dominance from polychaetes (67% in March) to nematodes later in the 

season (77% of total abundance in May) in fast ice in Stefansson Sound near Prudhoe 

Bay. The relatively late increase of nematode abundances could be related to their 

nutritional ecology: Tschesunov and Riemann (1995) proposed dissolved organic 

material (DOM), which can reach very high concentrations within the ice matrix 

(Thomas and Papadimitriou 2003), likely at the end of the bloom, as a potential food 

source for ice nematodes. In the present study, the increase in nematode abundance was 

the result of major reproductive activity during May sampling. It remains unclear whether 

the observed association of the nematode egg cases with microalgal aggregates was an 

artifact introduced by sampling processing, or whether it reflects a natural preference. 

 

The abundances of sympagic copepod nauplii, copepodites and adult copepods 

followed the temporal trend of the sympagic polychaetes with peak abundances of 24,300 

and 1,370 individuals m-2, respectively, in April 2003. This is contrary to Carey’s (1992) 

finding of a continuous increase in copepod abundance until the end of May. The 

comparatively high ratio of nauplii can result either from immigration of early nauplii 

stages into the ice or reproduction of adults within the sea ice. Examples for both 

strategies are documented: pelagic Antarctic copepods use coastal sea ice as a nursery 

ground (e.g. Kurbjeweit et al. 1993, Schnack-Schiel 2003), while Arctic and Antarctic 

harpacticoid sympagic copepods reproduce within the sea ice (Kern & Carey 1983, Carey 

1992). 

The abundance of zooplankton increased by a factor of 13 from February to May. 

The total zooplankton abundance remained below max. 24,100 individuals m-2 and 

reached only 10–12% of the abundances of the sea ice meiofauna at site 1. Copepods, by 

far, dominated the zooplankton with copepodites and adults contributing >90% in 

February and April and nauplii contributing 69% in May. The predominance of copepods 

in the epipelagic zones of the Arctic in general is well established (e.g. Smith & SchnackSchiel 1990, Mumm 1993, Mumm et al. 1998). The seasonal increase in abundance, 

which is mainly due to an increase in larval stages in May, is reflected in a significant 

correlation (p <0.01) between zooplankton abundance and surface light, a proxy for 

progressing season, and coincides with the onset of ice melt. In our study, however, 

phytoplankton concentrations remained low in late May. For many Arctic copepod 

species, reproduction (and thus occurrence of nauplii) is not synchronized with the onset 

of the spring bloom, but is based on storage products (Smith & Schnack-Schiel 1990); the 

first nauplii stages are typically non-feeding. Ice melt following the end of our study 

period will lead to increased water column algal concentrations, mainly diatoms, through 

the increase of light intensities, water column stratification and release of ice algae, and 

30
 

thus improved feeding conditions for later stages of pelagic fauna in the following 

months (e.g., Horner & Schrader 1982, Smith & Sakshaug 1990). 

Impact of sediment load on ice algal biomass 

The occurrence of considerable amounts of sediment within sea ice is a welldocumented phenomenon in the Arctic in general (e.g. Nürnberg et al. 1994), and in our 

study area, the Chukchi and Beaufort Seas, in particular (Barnes & Reimnitz 1973, 

Barnes et al. 1982, Osterkamp & Gosink 1984). Up to 50% of the entire Arctic ice cover 

can contain visually detectable amounts of sediment (Pfirman et al. 1989, Reimnitz et al. 

1993, Nürnberg et al. 1994), which can be transported across the offshore Arctic with the 

ice drift. Ice gouging and the lifting of anchor ice can lead to incorporation of coarse 

material. This incorporation can include animal relicts like ostracod or bivalve shells, 

from the sea floor, in up to about 60 m water depth (Barnes et al. 1984, Reimnitz et al. 

1987). Fine-grained sediments are incorporated during frazil ice formation in turbulent 

conditions and tend to be evenly distributed on a horizontal and vertical scale (Osterkamp 

& Gosink 1984, Kempema et al. 1989, Reimnitz et al. 1993, Eicken et al. 1997). In the 

present study, the homogenous distribution of fine-grained sediment in the top 40 cm of 

the ice cores at site 2 suggests suspension-freezing as the mechanism for sediment 

incorporation. The total seston (sediment and other particles) content of the sedimentloaded ice at site 2 (102 g DM m-2) was within the range of data reported from visibly 

discolored ice, “dirty ice”, in the Transpolar Drift, and the Laptev and Beaufort Seas (100 

to 200 g DM m-2; Reimnitz et al. 1993, Nürnberg et al. 1994, Eicken et al. 1997, Eicken 

et al. 2000). “Clean ice” has a particle load that is about 20 times lower (6 g DM m-2 in 

this study at site 1, see also references above).

Until now, the role of sediment on the light regime had only been studied from the 

perspective of ice albedo and attenuation, whereas biological consequences had not been 

evaluated. High seston concentrations in dirty sea ice have profound impacts on the light 

regime experienced by the sea ice biota underneath (Osterkamp & Gosink 1984, Light et 

al. 1998), similar to the shading effect of the snow cover (Maykut 1985, Gradinger et al. 

1991). Over the period of time of our observations, available light at the sea ice/water 

interface at the dirty ice location (site 2) was reduced by 99.8–100% compared to the 

clean ice site (site 1), and photon flux densities ranged between 0 and 0.5 μmol photons 

m-2 s-1. The spectral attenuation at the clean ice site had a minimum at around 580 to 600 

nm, which indicates that the high concentrations of ice algae affected the light spectrum 

at the clean ice site but not at the dirty ice site, where sediment likely shaped the 

spectrum (Beeler SooHoo et al. 1987, Light et al. 1998, this study). The shading effect of 

ice algae also explains the decline in the ratios of transmitted light at site 1 from 4.4% in 

February to 1.4% in April with increasing algal biomass. 

The reduced light availability in the PAR range in dirty sea ice explains the slow algal 

growth relative to the sediment-free site (for summary of data see Table 4). Even in May, 

the photon flux densities in the sediment-laden ice were still just approaching the 

threshold light intensity required for ice algal growth as determined by Mock and 

Gradinger (1999) in in-situ primary production measurements of Arctic pack ice samples. 

The photon flux density was below levels where biomass build-up was observed in the 

31
 

field (Horner & Schrader 1982). At comparable photon flux density levels of 0.3–0.4 

μmol photons m-2

 s

-1, no algal growth was observed over 19 days in an artificially 

darkened Arctic pack ice area (Gradinger et al. 1991). Over the four months of our 

observations, slow algal growth at an estimated doubling time of 49 days occurred at the 

dirty ice site; this is 31 days longer or 2.6 times slower than at the clean ice site. We 

largely exclude site-specific differences other than sediment as a cause for this difference 

as the snow load was similar at both sites and a rich algal bloom had formed at this site 

when it was sediment-free in April 2002. The break-up event that occurred in December 

2002 at site 1 certainly increased the differences between site 1 and 2, as it resulted in 

thinner ice at site 1 (0.4m difference in Feb 2003). This thinner ice subsequently allowed 

for relatively higher light intensities and thus faster algal growth at site 1. However, this 

effect is minor compared to the sediment impact on the light regime at site 2, where the 

observed reduction of PAR was equivalent to the combined effect of a 50 cm snow load 

on 3 m thick sea ice (Maykut 1985). These values exceed by far any observed differences 

in the physical properties between site 1 and 2. The strong site-to-site variability in 

sediment load provides an example of the already mentioned larger-scale patchiness of 

ice properties. The two sites we studied can be seen as two naturally occurring extremes 

in the spatial patchwork within the Alaskan fast ice zone. 

We conclude that the influence of sea ice sediment is as important as snow cover, if 

not more important, in controlling ice algal growth in Arctic sea ice by modulating the 

available light intensities. It was not one of our objectives to study the spatial 

characteristics of the sea ice sediments in our study area. Experiments with graded snow 

cover (see citation above) had revealed that manipulations of snow cover on scales of 

10x10 m are sufficient to alter the light climate and algal growth. We, therefore, assume 

that the minimum spatial dimension needed for sediment patches to influence ice algal 

growth is on the order of several meters. The relative contribution of clean to sediment 

laden fast ice and the variability of sediment load are currently not known and might vary 

from year-to-year. We recommend that interannual comparisons of algal production 

incorporate detailed evaluations of the boundary conditions (e.g. light, snow and ice 

thickness, sediment load throughout the ice column), as year-to-year changes in such 

boundary conditions will have pronounced impacts on algal growth characteristics. 

The reduction of algal growth caused by entrained sediment within sea ice has several 

broader implications. Previous studies did not sample dirty sea ice but focused primarily 

on clean ice (e.g. Gosselin et al. 1997, Gradinger 1999a, Gradinger et al. 1999). This 

suggests an overestimation of Arctic-wide ice algal biomass and primary production 

estimates as sea ice sediments occur in fast and pack ice in any part of the Arctic, 

independent of water depth (Nürnberg et al. 1994). While sediment incorporation occurs 

mainly in water depth less than 30m (see various cited studies of the working groups of 

Eicken and Reimnitz), the sediment laden sea ice is distributed over the Arctic by the ice 

drift patterns. Currently, the ice algal primary production in seasonally ice-covered 

waters is estimated to contribute 4 to 26% to total primary production (Legendre et al. 

1992) and above 50% in the permanently ice-covered central Arctic (Gosselin et al. 

1997). According to the results of this study, these estimates need to be corrected 

downward. Based on the observed biomass accumulation in terms of POC, net production 

32
 

in dirty sea ice was reduced to 4% of the clean ice values. Assuming that 40% of the sea 

ice in the Arctic may be sediment-laden at similar concentrations (Pfirman et al. 1989, 

Reimnitz et al. 1993, Nürnberg et al. 1994), the impact of dirty ice would lead to a 

reduction of total annual sea ice primary production by 38%. Global climate change 

might further enhance the sea ice sediment load and its transport in the Arctic by the 

increasing frequency and strength of storms and increasing coastal erosion (Proshutinsky 

et al. 1999, Stierle & Eicken 2002). Anthropogenically generated increase in suspended 

sediments through coastal and shelf exploration activities may add to this effect and 

cause further reduction of ice algal productivity on regional to basin-wide scales. 

Impact of sediment load on ice meiofauna 

Ice meiofauna abundance differed greatly between the two sites in 2003. The 

observed correlation between meiofauna density and POC and pigment concentrations, 

respectively, suggests that the ice is primarily used as a habitat in which to feed and may 

be less important as a habitat per se. Both functions have previously been discussed 

(Carey 1985, Grainger & Hsiao 1990, Krembs et al. 2000, Gradinger 2002, SchnackSchiel 2003). The relevance as a food-bearing habitat is supported by the differences in 

absolute densities between the two sites in 2003: few individuals were attracted to dirty 

ice that provided protection from potential predators, but little food. Again, we consider 

the location per se unlikely as a cause for these differences, even though one could argue 

that fewer organisms may have been available to recruit to the ice. Ice meiofauna 

concentrations, however, were very high at site 2 in April 2002 and zooplankton 

abundance in the water column was similar to that at the clean ice site. 

In addition to the limited food availability in dirty ice, the reduced light under the ice 

may have had an impact on faunal abundance in itself. McCarthy et al. (2002) reported 

ontogenetic changes in the phototaxis of meroplanktic polychaete larvae. One day old 

larvae were positively attracted, while older larvae (28 days) responded negatively to 

light. We propose that the observed positive phototactic behavior of the polychaete 

juveniles in our study may cause an avoidance of dirty ice patches. Attraction to the 

brightest spots of sea ice in early spring (February – March) may select for best initial 

conditions for later algal growth. 

Analysis of the ice based food web using stable isotope data 

The analysis of stable carbon isotopes provides information on the carbon sources

used in a food web (Fry & Sherr 1988). In our case, organic matter produced in the water 

and in the sea ice comprised the major primary carbon sources throughout the icecovered period, and these can have distinctly different isotopic signatures. A typical high 

latitude marine phytoplankton community, depending on location and time of the year, 

has a δ13C signature of -27‰ to -25‰ (Hobson & Welch 1992, Iken et al. 2005). Ice 

algae have been found to be generally isotopically heavier (=more enriched) than 

phytoplankton reflected in less negative δ13C ratios (Hobson et al. 1995, Naidu et al. 

2000, Schubert & Calvert 2001). As a mechanism, restricted CO2 supply into the ice is 

suggested to cause CO2 limitation and, consequently, isotopic enrichment (Kennedy et al. 

2002, Thomas & Papadimitriou 2003). Our data reflect the wide range of possible δ13C 

33
 

ratios in ice algae, from -24.9 ± 1.6 ‰ in February to -15.5 ± 0.8 ‰ in late May. This 

change is, therefore, not caused by any food web interactions but by the growth 

conditions that ice algae experience in their unique environment. While the February ice 

algal signatures were close to those of Arctic phytoplankton (this study, Hobson et al. 

1995, Schubert & Calvert 2001), the signatures became significantly enriched with 

increasing POC concentration in the ice. The water column POM δ13C ratios, in contrast, 

showed little enrichment over the time of our study. The degree of similarity between ice 

algal and phytoplankton signatures may be an indicator for the successional state of an 

ice algal bloom. The previously unappreciated dynamic nature of natural δ13C ice algal 

signatures stresses the need to apply them with caution in food web studies (Hobson et al. 

1995), because the values may vary greatly depending on the state and magnitude of the 

algal bloom at the time of release of this food source to the pelagic and benthic realms. 

Our unprecedented findings demonstrate that it is imperative for field studies to identify 

the isotopic signatures of the end members (primary producers) of a particular food web 

for that particular study region and time. The not uncommon approach of choosing one 

single value for such an end member for a whole study and large region appears to be 

justifiable for phytoplankton only (if at all), where seasonal changes may be minor. In the 

case of sea ice, the profound changes in the δ13C ratios caused by CO2 limitation suggest 

that the use of one single number to characterize the isotopic composition of sea ice algae 

is not justified. In contrast, the entire range of observed data needs to be considered when 

developing a food web model for a particular region. 

Stable isotope ratios show a stepwise enrichment between trophic levels of about 1‰ 

for δ13C (DeNiro & Epstein 1981, Hobson & Welch 1992, Post 2002) assuming balanced 

steady-state conditions, thus allowing identification of relative trophic positions among 

members of a food web. Our study is the first to generate any stable isotopic data on sea 

ice meiofauna and to document the changes of isotopic ratios in sea ice meiofauna and 

sympagic amphipods in relation to their food. A shortcoming of the stable isotope 

approach for ice meiofauna applications is the high number of small-sized animals 

required for one single sample, which consequently restricted our observations of stable 

isotope ratios mainly to site 1 where faunal abundances were sufficiently high for 

collections. Despite this limitation, the correlation between the increasingly enriched ice 

particulate organic matter and the meiofauna values at site 1 (Fig. 11) provides further 

strong evidence for the dominance of herbivory in ice metazoans as suggested by 

Grainger and Hsiao (1990). These data also support the notion of sea ice as a nursery 

ground for larvae and juveniles of pelagic and benthic biota as their stable isotope ratios 

changed synchronously with ice particulate matter. This is substantiated by the few 

observations from site 2: At site 2, isotopic ratios of the sea ice derived POC remained 

nearly constant and, consequently, no increase in the ratios of the meiofauna were found 

(Fig. 11). The scarce fauna that was present in the dirt sea ice at site 2 apparently still fed 

upon the scarce ice algae. Those, however, were not CO2 limited enough to have to select 

the heavy carbon isotope; ice algae and ice fauna at site 2, therefore, remained 

isotopically depleted relative to the algae and fauna at site 1. 

 Only in some cases did we observe the expected trophic level difference of about 1‰ 

for δ13C in the ice meiofauna values at site 1 relative to the ice POC data for the same 

time period. This inconsistency is likely due the relatively slow and probably taxon34
 

dependent turnover of newly ingested carbon in the body tissue, which will cause the 

isotopic ratio of animals (both herbivores and carnivores) to lag behind the changing 

isotopic signature of the ice particulate organic carbon. These previously unappreciated 

complexities of the matter necessitate a detailed analysis of body turn-over rates for the 

dominant ice inhabiting taxa (e.g. amphipods, polychaetes, nematodes) to fully resolve 

relative contributions of different end members for the nutrition of a particular taxon at a 

given time of the year. 

The lack of isotopic enrichment in the ice associated amphipods from February 2003 

to April 2003, when ice algae got progressively enriched, may support Carey and 

Boudrias’s (1987) observation from stomach contents analysis that the amphipod O. 

litoralis utilized ice algae when abundant, while also feeding on other prey at other times. 

This is in accordance with our own observations. Less fresh material consumed earlier in 

the season (e.g. detritus, carcasses) has a more enriched δ13C signature. Ice algae appear 

to be a more important food source for the amphipods later in the season (May/June) 

when increased ice porosity and release of material from the ice makes the ice-produced 

organic matter accessible for larger animals like the amphipods. 

 

The broader role of sea ice in the Arctic marine food web: an integrative summary 

and outlook 

The coastal fast ice biota is fueled by ice algae that accumulate considerable amounts 

of biomass with seasonally increasing light intensities. The overall contribution of ice 

algae appears to be small in annually ice covered areas compared to its higher 

contribution in multi-year ice regimes (4 to 26% versus >50% of total primary 

production; Legendre et al. 1992). Several studies, however, emphasized the relevance of 

ice algal production for the growth and survival of sea ice associated, pelagic and benthic 

animals as the first source of freshly produced organic material after the polar winter 

(Carey & Boudrias 1987, Carey 1992, Runge et al. 1991, this study). We measured 

substantial accumulation of algal biomass in sediment-free sea ice between February and 

May, resulting in a net primary production of 1.5g C m-2 over the spring period until end 

of May. Our stable isotope data revealed that the ice algal biomass is consumed by ice 

fauna (turbellarians, polychaetes and others). Gut content analyses showed that under-ice 

amphipods feed mainly on ice diatoms in May. 

The role of sea ice for merosympagic biota 

The high biomass within the ice is only available to taxa small enough to enter the 

brine channel network, such as merosympagic larvae of polychaetes, gastropods and 

crustaceans. This community enters the ice realm for short time periods to graze on the 

ice algae, mainly diatoms before they return to the benthic and pelagic realms either after 

ice melt or when their body size exceeds that of the typical dimensions of the brine 

channels (<1mm in diameter). To what extent these merosympagic forms depend on the 

early availability of high food concentrations in the spring remains to be resolved in 

experimental studies. Such studies should include investigations on the quality and 

quantity of the food, changes in the food composition (e.g. from diatoms to flagellates 

with different sizes) and its effect on growth rates. Inappropriate food concentrations 

might lead to reproduction failure of the polychaetes and gastropods. Earlier ice melt, an 

ongoing Arctic warming consequence, together with low phytoplankton concentration, 

35
 

dominated by small flagellates, might lead to such a situation. Our current observations in 

the coastal Beaufort Sea show that polychaete larvae and juveniles clearly have a 

preference for the high biomass, diatom dominated sea ice situation that occurs from 

early April to late May. 

The dominance of polychaetes in soft sediment ecosystems of coastal areas, shelf seas 

and the deep-sea worldwide is well documented (Gage & Tyler 1991, Flach & Heip 

1996, Paterson et al. 1998, Glover et al. 2001). This also holds true for Arctic shelf and 

deep-sea soft sediments (Feder & Schamel 1976, Carey & Ruff 1977, Grebmeier et al. 

1988, Kröncke 1994, Bluhm et al. 2005) and is, in part, a consequence of the diverse 

feeding habits of polychaetes, which allow them to exploit available resources very 

efficiently (Iken et al. 2005). The majority of benthic polychaete species have a 

planktonic larval stage, defined as so-called meroplankton. Experiments in non-polar 

marine environments revealed that (i) survival, growth and development of larval 

polychaetes can be correlated with phytoplankton concentrations (Qian & Chia 1990), (ii) 

planktotrophic polychaete larvae may be food limited at times (Hansen 1999), (iii) size of 

the food items matter (Hansen 1999), and that (iv) larval polychaetes are strongly 

associated with high food concentrations, specifically marine snow (Shanks & del 

Carmen 1997). These four observations are applicable to the nearshore Alaskan 

environment, where only sea ice provides high concentrations of food for herbivorous 

polychaete larvae and juveniles in the time period from April to the beginning of June. 

Polychaetes are ecologically important components of benthic communities with respect 

to, for instance, bioturbation (Hutchings 1998) and as prey for various fish and crab 

species. In the Arctic, several fishes such as sculpins and eel pouts as well as vertebrates, 

such as fulmars and walrus, rely on polychaetes for part of their energy supply (Lydersen 

et al. 1989, Fisher and Stewart 1997, Green et al. 1997, Chiperzak et al. 2003). Thus, 

variability in the reproductive success of polychaetes and potentially other taxa, using sea 

ice as a nursery ground, has the potential to impact higher trophic levels. 

Our results also show that the sea ice meiofauna followed a certain succession pattern 

driven by a) reproduction of animals within the ice in the case of nematodes and b) 

migration into and out of the ice in the case of meroplanktic larvae. These processes lead 

to distinct changes in the relative composition of the sea ice meiofauna over the course of 

the spring bloom prior to the ice melt. In contrast to Melnikov et al. (2001), we did not 

find any evidence for major species changes in both algal and meiofauna taxa relative to 

earlier studies (see citations above). 

The role of sea ice for under-ice biota 

Amphipods are the best studied consumers of ice algal production at the ice-water 

interface in all parts of the Arctic (e.g. Poltermann 2000, Werner and Gradinger 2002, 

Gradinger and Bluhm 2004). Several species of amphipods are endemic to the multi-year 

sea ice cover and exploit the ice cover year-round, while benthic species like Onisimus 

litoralis are common in nearshore seasonal ice regimes. We observed substantial 

ingestion of ice algae by the amphipod Onisimus litoralis in May 2003 at site 1 in 

accordance with Carey and Boudrias (1987). Amphipods in general are an important food 

for Arctic birds and, maybe most important, for Arctic cod (Boreogadus saida). This fish 

species is frequently observed in close association with fast ice and pack ice year-round, 

and appears to use the ice both for protection against potential predators and as a feeding 

36
 

habitat, ingesting under-amphipods and zooplankton (e.g. Gradinger and Bluhm 2004 and 

references therein). Arctic cod, major prey for seals and birds, plays a major role in the 

entire Arctic marine food web as major link from ice related primary production to birds, 

seals and finally polar bears. In recent years, the Arctic ice cover has been shrinking and 

thinning with minimum ice coverage in the Beaufort and Chukchi Seas in 2002–2004 

(Serreze et al. 2003, Stroeve et al. 2005). The impact of such changes in the duration and 

characteristics of the ice cover on the biology in near-shore fast ice and off-shore pack ice 

regimes has been identified from various Arctic regions including altered primary 

production patterns, changes in species composition and alterations in the food web 

structure (e.g. Stirling & Derocher 1993, Gradinger 1995, Melnikov et al. 2001). 

Sediment in sea ice 

Sediment load in nearshore fast ice changes with time and location due to various 

naturally occurring processes. Studies over the last 30 years showed that suspension 

freezing, as a main physical process, causes varying incorporation rates of sediments into 

the growing ice sheets (Reimnitz et al. 1987) causing year-to-year variability in the 

location and the extent of sediment incorporation in the sea ice. In near-shore regions, the 

potential increase of sediment load due to increased coastal erosion (Stierle and Eicken 

2002) and various human activities can additionally modify the sediment input. In any 

case, sediment in sea ice plays a pivotal role in controlling these biological processes in 

the sea ice in that “dirty fast ice” is drastically slowing the build-up of the ice algal 

biomass. In our case study, algal doubling times were increased by a factor of 2.6 in dirty 

sea ice relative to clean sea ice. The reduced light intensities caused a domino effect 

along the sympagic food chain starting with reducing the build-up of ice algal biomass, 

followed by diminished development of ice meiofauna densities. We did not observe a 

notable increase in the abundance of primary producers in dirty ice with increasing light 

intensities, i.e. no algal group was able to efficiently grow at the very low available light 

intensities. This finding is consistent with many other observations in the Arctic and 

Antarctic. The reduced biomass accumulation of primary producers, moreover, altered 

the cryo-pelagic-benthic coupling processes in two ways: The input of organic matter 

from the sea ice to the realms below was reduced and the sediment laden ice provided 

less food for the merosympagic and meroplanktic larvae and juveniles. Future studies 

should focus on providing regional estimates of light availability at the bottom of the ice 

(e.g. by ROV, AUV or divers) together with field sampling identifying the role of ice, 

snow, ice algae and ice sediment in the attenuation of light. Such a study would provide 

the basis for a reasonably accurate estimate of the impact of sea ice sediment load on the 

primary production in the Chukchi and Beaufort Seas. Our case study clearly exemplifies 

the significance of the previously ignored parameter sediment for sea ice biological 

processes. 

Acknowledgements

We wish to thank Barrow Arctic Science consortium for the excellent logistical 

support during our stays in Barrow in 2002 and 2003. Hajo Eicken, Geophysical Institute 

at the University of Alaska Fairbanks (UAF), and Peter McRoy and Alan Springer, 

Institute of Marine Science (IMS) at UAF, were very helpful in providing sampling 

equipment. Thanks are due to Sang Lee, IMS, Klaus Meiners, Yale University, and Kate 

37
 

Wedemeyer, Minerals Management Service Anchorage, for assistance during field 

sampling. Tim Howe and Norma Haubenstock ran the stable isotope samples at the 

Alaska Stable Isotope Facility, UAF. This project was funded through a research grant 

from the Coastal Marine Institute (CMI Task Order 85242) and by the School of 

Fisheries and Oceans Sciences, UAF. 

38
 

39

Table 1: Spearman rank correlation matrix (rs) for the entire sea ice data set based on mean values 

of each parameter for each sampling date. Significant relationships in bold (p-level <0.05*

). Light 

4 π (%) is the ratio of the PAR level at the ice-water interface compared to the surface 2π reading. 

Chl a

(μg l-1) 

Meiofauna 

abundance 

(Ind m-2) 

POC 

(μg l-1) 

C/N Light 

4π (%) 

Light 2π

(μmol 

photons m-2

s

-1) 

Meiofauna 

abundance (Ind 

m

-2) 

.851* 

POC (μg l-1) .810* .929*

C/N -.571 -.750 -.643 

Light 4π (%) .571 -.595 -.524 -.393 

Light 2π (μmol 

photons m-2

 s

-1) 

.476 .119 .048 -.250 -.238 

δ

13C (‰) .905* .810 .833* -.607 -.452 .357 
Table 2: δ13C isotopic signatures for sea ice meiofauna and zooplankton from sampling sites 1 (sediment-free sea ice) and 

ice) for 2002 and 2003. a) Mean δ13C values (‰) for n=2 to n=9 and individual δ13C values for n=1. b) Standard deviations 

species/taxa are sorted in taxonomic order. No samples were taken at site 2 in May 2002. Note that site 2 had clean ice in 22003. POM=particulate organic matter.

ASite/Realm Site 1: sea ice Site 2: sea ice Site 1: water column Site 2: wat

Date Apr 02 May 02 Feb 03 Apr 03 May 03 Apr 02 Feb 03 Apr 03 May 03 Apr 02 May 02 Feb 03 Apr 03 May 03 Apr 02 FTaxonPOM -20.42 -13.39 -24.92 -23.83 -15.52 -21.50 -25.54 -25.83 -24.17 -25.95 -24.24 -24.69 -27.14 -24.30 -24.53 Planula -21.82Ctenophora -21.36Turbellaria -19.30 -17.08 -21.40 -15.36 -20.16 -16.86 -21.52 

Rotifers -21.95 -22.98Nematoda -16.85 -23.25 -15.63Polychaeta -22.17 -16.20 -22.73 -22.31 -19.59 -18.02 -23.36 

Trochophora -20.08 -21.77Nauplii -25.19 -23.21 -24.05 -20.37 -25.61 -22.28 Calanoidea -24.32 -22.11 -21.19 -23.57 -25.21 -21.33 -21.83 Calanus glacialis -24.71 -23.42Harpacticoidea -22.58 -17.01 -20.47 -22.87 -23.20 

Cyclopoidea -24.15 -21.65 -19.77 -21.37 

Onisimus litoralis -19.34 -22.02Gammaracanthus loricatus -19.24 -20.40Chaetognatha -21.27 BSite/RealmDate Apr 02 May 02 Feb 03 Apr 03 May 03 Apr 02 Feb 03 Apr 03 May 03 Apr 02 May 02 Feb 03 Apr 03 May 03 Apr 02 FTaxonPOM 0.20 2.15 1.64 0.95 0.76 0.11 0.44 0.19 0.15 3.79 0.17 1.85 1.24 2.72 0.15 Turbellaria 1.44 0.38Nematoda 1.56 0.62Polychaeta 0.66 0.33 1.09

Nauplii 0.76 Calanoidea 2.88 0.46 0.13 Calanus glacialis 2.02 0.94

Harpacticoidea 1.06 1.44 Cyclopoidea 0.21Onisimus litoralis 1.78 1.42Gammaracanthus loricatus 0.97 0.47

Site 1: sea ice Site 2: sea ice Site 1: water column Site 2: wat

40
Table 3: δ15N isotopic signatures for sea ice meiofauna and zooplankton from sampling at sites 1 (sediment-free 

(sediment-laden sea ice) for 2002 and 2003. a) Mean δ15N values (‰) for n=2 to n=9 and individual δ15N values deviations (‰) for n≥3 only. The species/taxa are sorted in taxonomic order. No samples were taken at site 2 in Msite 2 had clean ice in 2002 and dirty ice in 2003. POM=particulate organic matter. This table contains fewer datsome samples had sufficient mass for δ13C measurements, but not for δ15N measurements. 

A

Site/Realm Site 1: sea ice Site 2:sea ice Site 1: water column Site 2: water column

Date Apr 02 May 02 Feb 03 Apr 03 May 03 Apr 02 Feb 03 Apr 03 May 03 Apr 02 May 02 Feb 03 Apr 03 May 03 Apr 02 Feb 03 APOM 7.74 7.52 6.34 7.90 10.07 8.43 5.61 11.90 10.19 5.92 8.07 15.14 7.28 6.96 

Planula 11.50Ctenophora 12.96Turbellaria 8.50 9.69RotifersNematoda 6.00 12.00 9.13 11.66Polychaeta 7.74 5.71 13.89 8.24 8.49 10.27 7.44 14.63 10.44 10.37 

Trochophora 11.22 11.39Ostracoda 13.97Nauplii 10.70 9.62 14.04 10.04 10.48 15.42 8.81 

Calanoidea 12.95 12.56 10.45 15.47 13.61 13.26 

Calanus glacialis 11.40 11.87 

Harpacticoidea 7.60 7.39 9.95 11.85 9.50 

Cyclopoidea 11.84 10.58 10.88 10.57 12.06Onisimus litoralis 12.90 12.73Gammaracanthus loricatus 12.59 11.34Chaetognatha 14.44 Copepoda 9.93 9.18

BSite/Realm Site 1:sea ice Site 2: sea ice Site 1:water column Site 2: water column

Date Apr 02 May 02 Feb 03 Apr 03 May 03 Apr 02 Feb 03 Apr 03 May 03 Apr 02 May 02 Feb 03 Apr 03 May 03 Apr 02 Feb 03 APOM 0.38 1.03 1.73 0.65 0.56 0.41 0.61 1.89 0.98 0.39 1.30 0.43 0.92 0.80 Turbellaria 0.78Nematoda 1.54Polychaeta 2.03 2.03 1.92 1.14

Trochophora 0.89Nauplii 1.90 

Calanoidea 0.77 0.78 0.48 Calanus glacialis 1.62 0.91 

Harpacticoidea 0.91Cyclopoidea 0.34Onisimus litoralis 1.24 0.54Gammaracanthus loricatus 1.73 0.95

41
 

Table 4: Summary of key findings (mean values or range) regarding the differences in the 

development of biota in the sea ice, water and at the sea floor in relation to sea ice 

sediment load. 

Site 1 Without sediment Site 2: With sediment 

Total Seston (g DM m-2) 6.7 102.4 

Light (% surface 

irradiance) 

0.01–0.05 0–0.0003 

Chl amax (μg Chl a l–1) in 

ice

329.3 8.3 

Doubling time (d) of ice 

algae 

18.8 49 

POC (g C m-2) in ice (May) 1.5 0.05 

Ratio Chl conc. ice/water 370 9 

Sea ice meiofauna (May, 

Ind l-1) 

276,000 12,000 

Abundance ratio for 

metazoans water/sea ice 

(May 03) 

10% 471% 

Sea ice polychaetes, (April 

03, Ind l-1) 

136,600 3,100 

Chl amax (μg Chl a l–1) in 

water 

0.9 0.9 

Chl amax (μg Chl a g 

sediment) in surface 

sediment 

5.2 0.4 

Organic carbon sediment 

(% dry weight) 

0.4 1.8 

Final POC (g C m-3) in 

water 

0.1 0.1 

42
 

Study products 

Presentations: 

R. Gradinger and B.A. Bluhm 2001. Susceptibility of sea ice biota to disturbances in the 

shallow Beaufort Sea. Phase 1: Biological coupling of sea ice with the pelagic and 

benthic realms. CMI Annual Review, Fairbanks. 

R. Gradinger, M. Nielson, and B. Bluhm 2002. Cryo-benthic coupling in coastal sea ice 

off Barrow, Alaska–concept and preliminary results. AAAS meeting, Fairbanks. 

R. Gradinger and B.A. Bluhm 2002. Cryo-benthic coupling in coastal sea ice off Barrow, 

Alaska–concept and preliminary. IARC, Fairbanks. 

R. Gradinger and B.A. Bluhm 2003. From the shoreline across the Arctic shelves: 

Biological properties of sea ice ecosystems. SEARCH open science meeting, Seattle. 

R. Gradinger and B.A. Bluhm 2003. Susceptibility of sea ice biota to disturbances in the 

shallow Beaufort Sea. Phase 1: Biological coupling of sea ice with the pelagic and 

benthic realms. CMI Annual Review, Fairbanks. 

B.A. Bluhm and R. Gradinger 2003. Evidence for cryo-benthic coupling in coastal 

Alaskan waters, based on community and stable isotope (13C, 15N) analysis–

preliminary results. Gordon Research Conference, “Polar Science”, Ventura. 

R. Gradinger and B. Bluhm (M. Nielson, presenter) 2004. Susceptibility of sea ice biota 

to disturbances in the shallow Beaufort Sea. Phase 1: Biological coupling of sea ice 

with the pelagic and benthic realms. CMI Annual Review, Fairbanks. 

C.P. McRoy, R. Gradinger, A. Springer, B. Bluhm, S. Iverson, S. Budge 2004. Stable 

carbon isotopes reveal food web shifts due to Arctic sea ice decline. AGU Montreal. 

R. Gradinger and B. Bluhm (presenter) 2005. Susceptibility of sea ice biota to 

disturbances in the shallow Beaufort Sea. Phase 1: Biological coupling of sea ice with 

the pelagic and benthic realms. CMI Annual Review, Fairbanks. 

B.A. Bluhm, R. Gradinger, M.R. Nielson 2005. Biological coupling of sea ice with the 

pelagic and benthic realms: the effect of sediment in sea ice. ASLO Conference, Salt 

Lake City. Feb 20–25, 2005. 

R. Gradinger and B.A. Bluhm (presenter) 2005. Susceptibility of sea ice biota to 

disturbance in the shallow Beaufort Sea. Phase 1: Biological coupling of sea ice with 

pelagic and benthic realms. Minerals Management Service Information Transfer 

Meeting, Anchorage. Mar 14–16, 2005. 

B.A. Bluhm, R.R. Gradinger, and M.R. Nielson 2005. The role of sea ice sediments in the 

seasonal development of near-shore Arctic fast ice biota of Barrow, Alaska. Gordon 

Research Conference, Polar Science, Ventura. Mar 13–18. 

43
 

Public outreach 

Gradinger, R. and B. Bluhm, ongoing since 2002. Project related webpage: 

http://www.sfos.uaf.edu/research/seaicebiota/cmi/barrow2002/index.html

Gradinger, R. and B. Bluhm 2003. Hidden Life in the Arctic Sea Ice: From Barrow’s 

Shore-Fast Ice to the Central Arctic Ocean. Public lecture, Barrow Alaska Feb 15 

2003.

Bluhm B. and R. Gradinger 2003. Explorations under the Pack-Ice of the Arctic Ocean. 

Public lecture, Barrow Alaska. May 31 2003.

Arctic Sounder 2003. “Ice life” plays major role in ecosystem by Earl Finkler. Featuring 

the CMI project. 

Barrow public radio station 2003. “Ice life” plays major role in ecosystem by Earl 

Finkler. Featuring the CMI project. 

Student training 

Mette Nielson, Graduate student at SFOS – participated in project activities since 2002 

Reports and publications 

R. Gradinger and B.A. Bluhm 2002. First field trip of new CMI project completed. SFOS 

Newsletter 6. 

R. Gradinger and B.A. Bluhm 2003. Susceptibility of sea ice biota to disturbances in the 

shallow Beaufort Sea. Phase 1: Biological coupling of sea ice with the pelagic and 

benthic realms. CMI Annual Report 9: 81–88. 

R. Gradinger and B.A. Bluhm 2004. Susceptibility of sea ice biota to disturbances in the 

shallow Beaufort Sea. Phase 1: Biological coupling of sea ice with the pelagic and 

benthic realms. CMI Annual Report No. 10: 70–78. 

R. Gradinger, B.A. Bluhm and M.R. Nielson (in revision). The pivotal role of sea ice 

sediments for the seasonal development of near-shore Arctic fast ice biota off Barrow, 

Alaska. Mar. Ecol. Prog. Ser. 

44
 

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Data appendix I 

Table 1: Mean abundances with standard deviations (STDV) of four replicates for phytoplankton and sea ice 

samples collected at site 1 and/or 2 

Location Date size classes (ESD) total number of cells/ml meantotal number of cells/ml STDV

Site 1: Sediment-free ice 2/13/03 

10 38193 7249 

20 4736 899 

100 941 179 

200 25 5 

1000 0 0 

Site 1: Sediment-free ice 5/28/03 size classes (ESD) total number of cells/ml meantotal number of cells/ml STDV

10 153312 18355 

20 41323 4947 

100 10725 1284 

200 0 0 

1000 0 0 

Site 1:Phytoplankton 2/13/03 size classes (ESD) total number of cells/ml meantotal number of cells/ml mean

10 4353 1734 

20 1226 488 

100 262 104 

200 0 0 

1000 0 0 

Site 1:Phytoplankton 5/28/03 size classes (ESD) total number of cells/ml meantotal number of cells/ml mean

10 10037 3325 

20 1855 614 

100 340 112 

200 28 9 

1000 0 0 

Site 2: Sediment-laden ice 2/15/03 size classes (ESD) total number of cells/ml meantotal number of cells/ml STDV

10 84438 28036 

20 10964 3640 

100 1200 399 

200 0 0 

1000 0 0 

Site 2: Sediment-laden ice 5/30/03 size classes (ESD) total number of cells/ml meantotal number of cells/ml STDV

10 150922 31261 

20 44996 9320 

100 2093 433 

200 0 0 

1000 0 0 

53
 

54

Site 2: Phytoplankton 2/15/03 size classes (ESD) total number of cells/ml meantotal number of cells/ml STDV

10 13630 3136 

 20 3163 728 

100 347 80 

200 0 0 

 1000 0 0 

Site 2: Phytoplankton 5/30/03 size classes (ESD) total number of cells/ml meantotal number of cells/ml STDV

 10 11225 1534 

 20 2871 392 

100 265 36 

200 0 0 

 1000 0 0 
Table 2: Mean abundance (Ind m-2) with standard deviation (SD) of sea ice meiofauna based on four replicate ice cores 

Site 1: Sediment-free ice Taxon Apr-02 Feb-03 Apr-03 May-03 SD values Apr-02 Feb-03 Apr-03 Copepoda 34078 582 1371 629 6923 608 1272 Nauplii 3250 1027 24327 6867 2594 668 12214 Polychaetes 3524 36 136694 12283 3263 71 8985 Trochophora 0 0 0 229 0 0 0 Turbellaria 4425 5163 17946 8968 2415 1532 2110 Nematoda 41493 9752 3269 232352 37766 2209 1570 Others 0 1093 1495 14878 0 1322 1410 Site 2: Sediment-laden ice Taxon Apr-02 Feb-03 Apr-03 May-03 SD values Apr-02 Feb-03 Apr-03 Copepoda 165049 848 576 989 27258 544 1038 Nauplii 15462 3339 784 3880 12689 748 547 Polychaetes 16998 1957 3101 5924 15939 1316 3451 Turbellaria 22003 708 58 299 12413 398 115 Nematoda 197227 9112 790 53 179434 3294 639 Others 0 627 142 813 0 498 176 

55
Table 3: Mean and standard deviation (STDV) of zooplankton abundance (Ind/m2) at site 1 and site 2 based on four replicates each. 

Site 1 Site 1 

Mean STDV 

Taxon Apr 02 Feb 03 Apr 03 May 03 Apr 02 Feb 03 Apr 03 Copepoda 11087 2003 23625 8013 12347 769 6338 Nauplii 2314 104 207 19480 992 132 265 Polychaetes 69 0 138 242 80 0 195 Trochophora 207 0 0 35 138 0 0 Turbellaria 35 0 35 207 69 0 69 Nematoda 207 0 0 138 178 0 0 Others 69 35 104 242 80 69 207 

Site 2 Site 2 

Mean STDV 

Taxon Apr 02 Feb 03 Apr 03 May 03 Apr 02 Feb 03 Apr 03 Copepoda 15404 4179 4697 9844 5699 534 1996 Nauplii 14195 0 35 44624 3867 0 69 Polychaetes 725 0 0 656 306 0 0 Trochophora 2590 207 0 104 1994 178 0 Turbellaria 69 0 0 276 138 0 0 Nematoda 69 35 0 207 80 69 0 Others 829 311 104 587 529 207 132 

56
Table 4: Light intensity (umol photons m-2 s-1) and temperature measurements in the water at site 1 and 2. Light 

intensities were measured with a surface 2π and a submerged 4π sensor 

The 4π under-water sensor values were normalized (light 4π corrected) for fluctuations of the 

surface 2π reference sensor 

Site 1 Site 2 

Date 

Water depth (m) 

Temperature (C) 

Light 2 π

Light 4 π

Light 4π , corrected 

Water depth (m) 

Temperature (C) 

Light 2 π

Light 4 π

Lc

 April 2002 0 -1.9 513.3 82.2 82.2 0.0 -1.7 956.4 164.2 

1 -1.9 500.9 29.8 30.5 1.0 -1.8 966.2 80.6 2 -1.8 480.7 1.9 2.0 2.0 -1.8 930.5 4.6 3 -1.8 471.2 1.5 1.7 3.0 -1.8 860.1 2.5 4 -1.8 457.2 0.9 1.0 4.0 -1.8 835.1 1.7 5 -1.8 452.2 0.6 0.7 5.0 -1.8 765.1 1.1 

5.6 -1.8 424.2 0.5 0.6 

 February 2003 0 -2 31.3 93.7 93.7 0.0 -1.6 162.9 3.7 

1 -1.9 31.0 1.7 1.7 1.0 -1.6 164.7 0.0 2 -1.6 32.7 1.5 1.4 2.0 -1.6 185.8 0.0 3 -1.6 34.6 1.3 1.1 3.0 -1.8 186.1 0.0 4 -1.6 35.7 0.9 0.8 4.0 -1.9 186.7 0.0 

5 -1.5 37.6 0.8 0.6 5.0 187.1 0.0 

 April 2003 0 -1.7 383.1 191.4 191.4 0.0 -2.0 646.2 2.2 

1 -1.7 384.3 30.4 30.3 1.0 -1.9 642.4 0.1 2 -1.7 393.5 5.6 5.5 2.0 -1.8 640.3 0.0 3 -1.7 393.1 4.1 4.0 3.0 -1.8 634.5 0.0 4 -1.7 397.3 2.6 2.5 4.0 -1.8 645.0 0.0 5 -1.7 404.9 1.8 1.7 5.0 -1.7 640.3 0.0 

 May 2003 0 -1.3 666.4 202.2 202.2 0.0 -1.4 1696.0 48.7 

1 -1.6 660.9 108.4 109.3 1.0 -1.6 1539.0 1.5 2 -1.7 655.2 7.7 7.9 2.0 -1.7 1544.0 0.4 3 -1.7 653.3 5.7 5.8 3.0 -1.7 1548.0 0.3 4 -1.7 647.7 4.4 4.6 4.0 -1.7 1208.0 0.2 

57

5 -1.7 642.2 3.5 3.6 5.0 -1.7 1103.0 0.1 
Table 5: Mean and standard deviation (STDV) for chlorophyll concentration, chlorophyll/phaeophytin ratio and %organic matter in sea floor sediment at site 1 and 2. 

Chlorophyll (μg/l) Mean STDV time Site 1 Site 2 Site 1 S Feb 03 0.5 0.4 0.6  April 03 0.2 0.4 0.0  May 03 1.0 0.3 0.7 Chlorophyll/phaeophytin ratio time Site 1 Site 2 Site 1 S Feb 03 0.6 0.2 0.1  April 03 0.8 0.2 0.1  May 03 5.2 0.4 3.8 % Organic matter time Site 1 Site 2 Site 1 S Feb 03 1.12 6.86 0.27  April 03 0.53 5.58 0.07  May 03 0.37 1.82 N/A 

58
59

Table 6: Mean and standard deviation (STDV) for chlorophyll, particulate organic carbon (POC), d13C ratio chlorophyll/phaeophytin ratio in sea ice and water column 

on four replicates each. 

Chlorophyll (μg/l) Mean STDV time 

Site 1: sediment-free ice 

Site 1: water 

Site 2: Sedimentladen ice Site 2: water 

Site 1: sediment-free ice 

Site 1: water 

Site 2: Sediment-laden ice 

Sitwa

 April 02 45.2 1.6 246.8 0.4 37.5 0.1 23.5  Feb 03 7.4 0.0 1.9 0.0 0.7 0.0 0.0  April 03 71.4 0.1 3.3 0.1 16.1 0.0 1.1  May 03 329.3 0.9 8.3 0.9 42.0 0.0 7.0 Chlorophyll/phaeophytin ratio time 

Site 1: sediment-free ice 

Site 1: water 

Site 2: Sedimentladen ice Site 2: water 

Site 1: sediment-free ice 

Site 1: water 

Site 2: Sediment-laden ice 

Sitwa

 April 02 18.3 3.8 5.1 1.7 14.9 0.5 1.0  Feb 03 13.3 1.2 10.3 1.3 1.7 0.2 4.9  April 03 3.7 0.8 6.8 0.7 0.7 0.1 2.8  May 03 6.3 1.7 2.7 1.8 1.3 0.1 0.8 POC (μg/l) time 

Site 1: sediment-free ice 

Site 1: water 

Site 2: Sedimentladen ice Site 2: water 

Site 1: sediment-free ice 

Site 1: water 

Site 2: Sediment-laden ice 

Sitwa

 April 02 7.38 0.15 9.64 0.21 0.00 2.37 0.09 1.81  Feb 03 1.28 0.17 0.98 0.20 0.00 0.62 0.09 0.22  April 03 3.93 0.09 0.83 0.10 0.00 0.59 0.01 0.26  May 03 15.24 0.13 0.57 0.12 0.00 1.95 0.02 0.43 

 Feb 03 -24.9 -23.8 -25.5 -24.9 1.64 0.27 0.44  April 02 -20.4 -25.9 -21.5 -24.5 0.20 3.79 0.11  April 03 -23.8 -26.6 -25.8 -28.3 0.95 0.65 0.19  May 03 -15.5 -24.3 -24.2 -22.5 0.76 2.72 0.15 

time 

Site 1: sediment-free ice 

Site 1: water 

Site 2: Sedimentladen ice Site 2: water 

Site 1: sediment-free ice 

Site 1: water 

Site 2: Sediment-laden ice 

Sitwa

d13C ratio 
 

Table 7: Vertical temperature gradient at site 1 and 2 in 2003. 

Site 1 Site 2 

Depth (cm) Febr Apr May Depth (cm) Feb Apr May 

air -29.7 -14.6 1.5 air -25.4 -14.3 0.8 

snow surface -27.8 0.1 snow surface -24.2 -13.1 -0.1 

snow bottom -25.4 -0.2 snow bottom -20.9 -14.6 -0.4 

5 -25 -13 -0.8 5 -16.8 -12.4 -0.2 

15 -24.5 -11.6 -1.6 15 -14.8 -12.6 -1 

25 -20.7 -11 -1.7 25 -12.4 -12.1 -1.4 

35 -17.1 -9.8 -1.6 35 -10.5 -11.4 -1.8 

45 -14.7 -8.9 -1.5 45 -9.1 -10.3 -1.8 

55 -13.5 -7.9 -1.5 55 -6.7 -10.3 -1.8 

65 -11.3 -7.4 -1.9 65 -4.6 -9.4 -2 

75 -9.5 -6.2 -1.5 75 -3.4 -8.6 -2 

85 -8.4 -5.2 -1.4 85 -7.6 -2 

95 -6.1 -4.5 -1.3 95 -7.2 -2 

110 -5.7 -3.8 -1.1 110 -7.1 -1.8 

115 -4.5 -2.8 -0.8 115 -5.3 -1.7 

125 -0.8 125 -5.2 -1.8 

135 -1.3 135 -4.9 -1.6 

145 -3.6 -1.5 

155 -2.7 -1.5 

165 -1.86 -1.4 

172 -1.4 

60
 

Data Appendix II 

Digital images for the FlowCam Analysis 

61
 

Site 1: February 2003, Sea ice 1 

62
 

63

Site 1: February 2003, Sea ice 2 
 

Site 1: February 2003, Sea ice 3 

64
 

Site 1: February 2003, Sea ice, 4 

65
 

Site 1: February 2003, Water 1 

Site 1: February 2003 Water 2 

66
 

Site 1: February 2003 Water 3 

67
 

Site 1: February 2003, Water 4 

68
 

Site 1: May 2003, Sea ice 1 

69
 

Site 1: May 2003, Sea ice 1 contin. 

70
 

Site 1: May 2003, Sea ice 2 

71
 

72

Site 1: May 2003, Sea ice 3 
 

Site 1: May 2003, Water 1 

73
 

Site 1: May 2003, Water 2 

74
 

Site 1: May 2003, Water 3 

Site 1: May 2003, Water 4 

75
 

Site 2: February 2003, Sea ice 1 

76
 

77

Site 2: February 2003, Sea ice 2 
 

78

Site 2: February 2003, Sea ice 3 
 

79

Site 2: February 2003, Sea ice 4 
 

Site 2: February 2003, Water 1 

Site 2: February 2003, Water 2 

80
 

Site 2: February 2003, Water 3 

Site 2: February 2003, Water 4 

81
 

Site 2: May 2003, Sea ice 1 

82
 

83

Site 2: May 2003, Sea ice 2 
 

84

Site 2: May 2003, Sea ice 3 
 

Site 2: May 2003, Sea ice 4 

85
 

Site 2: Mary 2003, Water 1 

Site 2: Mary 2003, Water 2 

86
 

Site 2: May 2003, Water 3 

Site 2: May 2003, Water 4 

 

87