O2 dynamics in artificial and natural sea-ice: influence by algae, bacteria and
physical processes
Morten Kristensen and Dorte H. Søgaard
Greenland Institute of Natural Resources
Box 570
3900 Nuuk
Greenland
University of Copenhagen
Marine Biological Laboratory
Strandpromenaden 5
3000 Helsingør
Denmark
November 2008
1
Preface
The present master‟s thesis is funded by the Danish Energy Agency as part of the climate support
programme to the Arctic and is a contribution to the Zackenberg Basic and Nuuk Basic
programmes in Greenland. Furthermore we want to thank for the irradiance data provided by Asiaq
(Greenland Survey). All field work and laboratory work is the result of a close co-operation between
the authors and the thesis is the result of an independent effort undertaken by the authors. We
thank Greenland institute of Natural Resources for support and accommodation. Especially, we
appreciate the time and effort of Professor Søren Rysgaard from Greenland institute of Natural
Resources as well as his encouragement and friendship.
We are grateful for the assistance and help from Professor Søren Rysgaard, Professor Ronnie N
Glud of the Scottish Association for Marine Science, Post doc. Karen Marie Hilligsøe from the
University of Aarhus and Anna Haxen from Greenland institute of Natural Resources. For help with
the statistical analysis we would like to bring a special thanks to Michael R. Schrøder (cand. polit.)
and Rasmus Hedeholm (Phd-student). We give prominence to Professor Søren Rysgaard,
Professor Ronnie N. Glud and Associated Professor Per J. Hansen for fruitful critique. We also
want to thank colleagues and friends from Greenland institute of Natural Resources: Thomas J.
Pedersen, Kristine Arendt, Ditte Marie Mikkelsen and Paul Batty for rewarding comments. Of great
importance was the love and support from family and friends, especially Karen Marie Hilligsøe and
Michael R. Schrøder.
The present master‟s thesis is comprised of an English and Danish resume, a general introduction,
three chapters representing individual research papers and an appendix. The first chapter is
written by Dorte Haubjerg Søgaard, the second chapter is written by Morten Kristensen and the
third chapter is written by Dorte Haubjerg Søgaard.
2
Resume
In chapter 1, the overall objective was to describe the dynamics of autotrophic and heterotrophic
activity in first-year sea-ice in Marlene Bight, SW Greenland. In this study, we present a
comparison of the net activities i.e. autotrophic and heterotrophic activities measured in the
laboratory and the in situ dynamics of O2 concentration in sea-ice samples kept in gastight plastic
liners throughout the sea-ice season. The results from the laboratory measurements indicated that
the net autotrophic activity was dominant for most of the study period. However, a predominantly
heterotrophy-dominated stage late in March occurred prior to the sea-ice algal bloom. In the
present study the bacteria production was 70 % of the co-occurring primary production during the
sea-ice season. The net autotrophic response of the sea-ice community as resolved in the
laboratory was also observed in the gastight plastic liners kept in situ. Thus, the laboratory based
measurement of primary and bacteria production generally supported the results found in the in
situ measurements.The study shows that light availability was the major factor regulating sea-ice
algae activity. Before April, snow cover inhibited any significant sea-ice primary production (<0.5
mg C m-2 d-1) in Marlene Bight, but as the snow cover decreased, light availability increased and
the sea-ice algae biomass began to flourish resulting in high primary production measurements
(9.54 mg C m-2 d-1). Maximum bacteria production of 3.18 mg C m-2 d-1 occurred late in the sea-ice
season and coincided with maximum primary production. A successional sequence of the
autotrophic and heterotrophic activity was observed, beginning with a low productive winter stage
followed by a predominantly heterotrophy-dominated stage late in March and finally a sea-ice
algae bloom developed until the onset of sea-ice melt. A crucial assumption behind our approach
is that all sea-ice cores are representative of the general condition of the sea-ice. A small scale test
revealed that the horizontal variability was significantly smaller than the temporal variability.
Consequently, temporal patterns of the measured parameters of the intact sea-ice cores were not
compromised by spatial variability. To extend the evaluation of spatial variability a large scale
investigation of the horizontal patchiness was conducted. The sea-ice algal biomass i.e. Chl a
followed sea-ice temperatures with an average radius of 20.5 to 50.5 m, suggesting that snow and
ice thickness were the main factors influencing the light condition in the sea-ice and thus the
phototrophic biomass. This test further supports that light availability was the primary factor
regulating sea-ice algae activity in the present study.
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Chapter 2 presents an in vitro study of the importance of biotic and non-biotic processes for the
flux of solutes out of sea-ice, with particular focus on the flux of oxygen. The oxygen flux from biotic
processes was investigated and quantified in steady state ice whereas the non-biotic oxygen flux
was determined in both growing ice and steady state ice. The chapter starts with a review of the
available methods presently used for determination of biotic production in sea-ice, as well as a
discussion of up- and downsides of these. Thereafter, the experimental studies are presented. The
experiments were conducted in a gastight flow through chamber where ice could be formed. By
adjusting flow rate, temperature and stirring rate the ice thickness, ice shape and ice growth rate
could be regulated. The non-biotic fluxes of salt and oxygen from a growing sea-ice (ice 1) were
determined by using oxygen and conductivity sensors in the chamber setup for mass budget
calculations (note that these fluxes are solely products of physical/chemical freezing processes).
By a somewhat new technique for measuring on ice, planar optodes, biotic respiration and
production in an undisturbed artificial sea-ice (ice 2) was found and determined. This ice (ice 2)
had been inoculated with algae isolated from in situ sea-ice. Ice 2 was at the end of the experiment
melted and the 14C-method was applied on the bulk water, for further comparison to the production
rates found by planar optodes. The two different methods showed great consistence between data,
although they were applied on different media; the planar optodes on ice and the 14C-method on
bulk water. The study clearly points out, that the non-biotic oxygen flux from growing ice has the
potential of surpassing the biotic oxygen flux by far. In this study the ratio between the non-biotic
(ice 1) and the biotic oxygen flux (ice 2) was approximately 230. By comparing the non-biotic
oxygen flux to the biotic oxygen fluxes found in the literature it is clear that the non-biotic oxygen
flux (this study) was considerably higher than any biotic production commonly found in situ.
In chapter 3 we investigate the growth response of three algal species from Arctic sea-ice
Fragilariopsis nana, Fragilariopsis sp. and Chlamydomonas sp, when subjected to different
salinities (salinity 5 to 150) and different pH (pH 8.0 to 10.0), a range covering the natural variation
of salinity and pH in sea-ice brine. The growth experiments were conducted under controlled
laboratory conditions. Salinity did have a pronounced effect on the growth rates for all three sea-ice
algae species. The two pennate diatoms Fragilariopsis nana and Fragilariopsis sp. were able to
grow at all salinities; however, growth was impeded above a salinity of 75. The growth rate
declined almost linearly with increasing salinity from its maximum of 0.23 and 0.63 d -1 at a salinity
of 5. The ability of the two diatoms to tolerate low salinities can be an advantage in the bottom seaice cores and during spring and summer thaw, where sea-ice salinity decreases. The growth rate
of the green flagellate Chlamydomonas sp. exhibited a maximum of 0.67 d-1 at a salinity of 50, but
4
growth ceased at salinities above 100. The green flagellate may have a competitive advantage
during winter month when salinity is high in sea-ice brine. Furthermore, the halotolerant green
flagellate may have an advantage in the upper sea-ice section, where the salinity and light
conditions usually are high. The growth rate of Fragilariopsis nana exhibited a maximum of 0.24 d -1
at pH 8.0 and growth was imbeded above pH 9.0. Fragilariopsis sp. was able to grow at pH 8.0 to
10.0, however the maximum growth rate of 0.50 d-1 was observed at pH 8.5. Chlamydomonas sp.
was able to grow at all tested pH levels (8.0. 8.5, 9.0, 9.5 and 10.0), with the maximum growth rate
of 0.51 d-1 observed at pH 8.0. The ability to tolerate elevated pH levels may be important during
spring and summer thaw and may be an important factor driving sea-ice algal species succession
in sea-ice.
5
Resume
I kapitel 1, var det overordnede formål at beskrive dynamikken af den autotrofiske og heterotrofiske
aktivitet i første års havis i Marlene Bugt, SW Grønland. Vi målte primær- og bakterieproduktion i
laboratorie baserede flaske inkuberinger og målingerne blev sammenlignet med in situ dynamikken
af O2 målt i gastætte plastik poser placeret i havisen (net autotrofisk eller heterotrofisk aktivitet).
Målingerne fra laboratoriet viste, at den netto autotrofiske aktivitet var dominerende igennem det
meste af issæsonen. Men en overvejende heterotrofisk-domineret fase var observeret sent i marts
forud for havis algeopblomstringen. I denne undersøgelse var bakterieproduktionen 70 % af
primærproduktionen i havissæsonen. Den netto autotrofe dominans målt i de laboratorie baserede
flaske inkuberinger blev også observeret i in situ målingerne. Så de laboratorie baserede målinger
af primær- og bakterieproduktionen støttede resultaterne fundet i in situ forsøget. Dette studie
viste, at lystilgængeligheden var den væsentligste faktor, der regulerede havis alge aktiviteten. Før
april, hæmmede det tykke snedække enhver betydelig havis primærproduktion (<0.5 mg C m-2d-1) i
Marlene Bugt, men som snedækket forsvandt, blev lystilgængeligheden højere, og havis
algebiomassen begyndte at blomstre, hvilket resulterede i høje målinger af primærproduktionen
(9.54 mg C m-2 d-1). Den maksimale bakterieproduktion på 3.18 mg C m-2d-1 blev observeret sent i
havissæson, og var sammenfaldende med den maksimale primærproduktion. En successional
sekvens af den autotrofe og heterotrofe aktivitet blev observeret, begyndende med en lav produktiv
vinterfase efterfulgt af en overvejende heterotrof-domineret fase sent i marts og endelig en
opblomstring af havis algerne indtil havisen smeltede. En afgørende forudsætning bag vores
forsøg er, at alle havis kerner er repræsentative for den gennemsnitlige status i havisen i Marlene
bugt. En test viste, at den horisontale variation var bemærkelsesværdigt mindre end den
tidsmæssige variation. Derfor argumenterer vi for, at de observerede tidsmæssige mønstre af de
målte parametre i havis kernerne i Marlene Bugt ikke var kompromitteret af rumlig variation. For at
udvide evalueringen af rumlig variation blev en større undersøgelse gennemført. Havis
algebiomassen (Chl a) fulgte havis temperatur med en gennemsnitlig patch radius på 20.5 til 50.5
m, hvilket tyder på, at sne- og is tykkelse var de vigtigste faktorer, der påvirkede lys betingelserne i
havis og dermed den fototrofe biomasse. Denne test understreger at lystilgængelighed var den
væsentligste faktor der regulerede havis algeproduktionen i dette studie.
I kapitel 2 beskrives et in vitro studie ud i biotiske og abiotiske processers betydning for flux af
opløste stoffer, specielt ilt, ud af havis. Endvidere kvantificeres disse fluxe. Kapitlet starter med en
gennemgang af de i øjeblikket tilgængelige gængse metoder, der bruges til at måle havisalgers
6
produktion og fysiologi, samt en diskussion af metodernes fordele og ulemper. Herefter beskrives
de eksperimentelle studier. Eksperimenterne er udført i et gastæt ”flow through” kammer, hvori is
kunne dannes, endvidere kunne istilvækst, istykkelse og formen af isen reguleres ved at ændre på
temperatur, omrøring og flow rate gennem kammeret. Ved hjælp af ilt- og konduktivitetssensorer
fastgjort i kammeret, samt massebalanceberegninger, blev den abiotiske flux af salt og ilt fra en
voksende is (is 1) bestemt (notér at denne flux udelukkende stammer fra fysiske/kemiske
fryseprocesser). Med en forholdsvis ny teknik til anvendelser på is; planar optoder, blev der fundet
og målt på en biotisk respiration og produktion i en uforstyrret in vitro kunstigt fremstillet havis (is
2). Denne is var inokuleret med alger, der oprindeligt var isoleret fra rigtig havis. Is 2 blev ved
eksperimentets afslutning smeltet, og radioaktivt mærket
14
C blev tilsat det smeltede vand og
inokuleret (14C-metoden) for videre sammenligning med produktionen fundet fra planar optoder. De
to metoder viste en stor konsistens mellem data, selvom de målte på to forskellige medier; den ene
(planar optode) på is og en anden (14C-metoden) på vand. I studiet blev det slået fast, at den
samlede abiotiske flux af ilt fra voksende havis (is 1) var ca. 230 gange højere end den maksimalt
mulige biotisk forårsagede iltflux fra is 2. Ved litteratursammenligninger er det endvidere
tydeliggjort, at den abiotiske iltflux (fundet i dette studie) under isens vækst, var mange gange
større end hvad der normalt kan tilskrives biotisk iltflux.
I kapitel 3 undersøgte vi væksten af tre alge arter fra arktiske havis Fragilariopsis nana,
Fragilariopsis sp. og Chlamydomonas sp, når de udsættes for forskellige saliniteter (salinitet fra 5 150) og forskellige pH niveauer (pH 8.0 til 10.0), disse saliniter og pH niveauer spænder over den
naturlige variation af salinitet og pH i havis brine. Vækstforsøgene blev udført under kontrollerede
laboratorieforhold. De forskellige saliniteter havde en udtalt effekt på vækstraterne for alle tre havis
alge arter. De to pennate kiselalger Fragilariopsis nana og Fragilariopsis sp. var i stand til at vokse
ved alle salinitets niveauerne, men ved saliniter over 75 blev væksten hæmmet. Vækstraten faldt
næsten lineært med stigende saliniteter fra sit maksimum på 0.23 d-1 og 0.63 d-1 ved en salinitet på
5. Evne til at tolerere lave saliniteter kan være en fordel i bunden af havisen og i løbet af forår og
sommer, hvor saliniteten falder i havisen. Chlamydomonas sp. havde højest vækstrate (0.67 d-1)
ved en salinitet på 50, hvilket tyder på, at Chlamydomonas sp. er mere halo tolerant end de to
kiselalger, men Chlamydomonas sp kunne ikke gro når saliniteten var over 100. Den grønne
flagellate kan have en konkurrencemæssig fordel i løbet af vinter månederne, hvor saliniteten er
høj i havis brinen. Desuden kan Chlamydomonas sp. have en fordel i toppen af havisen, hvor
saliniteten som regel også er høj. Vækstraten for Fragilariopsis nana var højest ved pH 8.0
(0.24 d-1), men vækstraten blev hæmmet ved pH niveauer over 9.0. Fragilariopsis sp. var i stand til
7
at vokse ved pH 8.0 til 10.0, men den maksimale vækstrate på 0.50 d-1 blev observeret ved pH 8.5.
Chlamydomonas sp. var i stand til at vokse ved alle de testede pH niveauer (pH 8.0, 8.5, 9.0, 9.5
and 10.0), hvor den maksimale vækstrate på 0.51 d -1 blev observeret ved pH 8.0. Evnen til at
tolerere forhøjede pH-niveauer kan være vigtig i løbet af foråret og sommeren, hvor høj
primærproduktion kan resulterer i høje pH niveauer i havisen.
8
Contents
General introduction ................................................................................................................................10
Arctic and Antarctic sea-ice .................................................................................................................10
Incorporation of organisms into sea-ice ...............................................................................................11
Diversity of sea-ice microbes ...............................................................................................................13
Regulation of sea-ice algal and bacterial communities .........................................................................14
Adaptation to extreme conditions in sea-ice .........................................................................................15
Photosynthetic acclimatization.............................................................................................................18
Measuring primary production and algal biomass in sea-ice.................................................................18
Measuring bacteria production and bacterial biomass in sea-ice ..........................................................22
Succession .........................................................................................................................................23
References .........................................................................................................................................24
Chapter 1 Dynamics of autotrophic and heterotrophic activity in Arctic first-year sea-ice: Case study from
Marlene Bight, SW Greenland. ................................................................................................................31
1.0 Abstract .........................................................................................................................................32
1.1 Introduction ...................................................................................................................................33
1.2 Material and methods ....................................................................................................................34
1.3 Results ..........................................................................................................................................40
1.4 Discussion .....................................................................................................................................48
1.5 Conclusion ....................................................................................................................................55
1.6 References....................................................................................................................................56
Chapter 2 Planar optodes on sea-ice dynamics: a new in vitro approach for determination of autotrophic
production within brine channels of undisturbed sea ice...........................................................................61
2.1 Abstract .........................................................................................................................................62
2.2 Introduction ...................................................................................................................................62
2.3 Materials and methods ..................................................................................................................66
2.4 Results ..........................................................................................................................................77
2.5 Discussion .....................................................................................................................................89
2.6 Conclusion ....................................................................................................................................94
2.7 Perspectives..................................................................................................................................95
2.8 Future improvements to the setup..................................................................................................96
2.9 References....................................................................................................................................97
Chapter 3 Effect of pH and salinity on growth and survival of two Arctic sea-ice diatoms, Fragilariopsis sp.
and Fragilariopsis nana, and the Arctic sea-ice alga, Chlamydomonas sp..............................................101
3.0 Abstract .......................................................................................................................................102
3.1 Introduction .................................................................................................................................103
3.2 Material and methods ..................................................................................................................105
3.3 Results ........................................................................................................................................107
3.4 Discussion ...................................................................................................................................112
3.5 References..................................................................................................................................116
Generel conclusion ...............................................................................................................................120
Appendix...............................................................................................................................................122
Appendix I .........................................................................................................................................122
Appendix II ........................................................................................................................................124
Appendix III .......................................................................................................................................125
Appendix IV ......................................................................................................................................126
Appendix V .......................................................................................................................................127
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General introduction
Arctic and Antarctic sea-ice
The Earth is generally cold and most ecosystems are exposed to temperatures that are
permanently below 5ºC (Feller & Gerday 2003). This is mainly due to the fact that ~70 % of the
Earth surface is covered by oceans that have a mean temperature of 4-5ºC. The presence of seaice is a defining structural feature of polar marine ecosystems. In winter, sea-ice covers 13 % of
the Earth‟s surface, and is clearly one of the largest biomes (Parkinson & Garrison 1993). The
geographical surroundings of the Arctic and Antarctic display some differences. The Antarctic
continent is surrounded by the Southern ocean whereas; the Arctic area is surrounded by the land
masses of Eurasia, North America, Greenland and several small islands. Similar characteristics of
the two regions are highly fluctuating seasonal air temperature, long-term sea-icecover, strong
seasonal variations in irradiance and the incorporation of organisms in the ice (Dieckmann &
Hellmer 2003). The largest expanse of sea-ice occurs in the Southern Ocean during winter with ~
20 x 106 km2 (Thomas & Dieckmann 2002), compared to the Arctic sea-ice covers approximately
14 x 106 km2 (Johannessen et al. 2004). The majority of the Arctic sea-ice is multi-year ice while
the Antarctic sea-ice is dominated by first-year sea-ice. The Antarctic regions are on average more
productive than its northern counterpart, due to the presence of platelet ice, a habitat exclusive to
Antarctic regions (Arrigo 2003). The biology associated with the sea-ice has some similarities,
some species are bipolar; however, many notable differences exist when comparing Arctic and
Antarctic ecology. The polar bear (Ursus maritimus) and humans are top predators on the Arctic
sea-ice; an equivalent is the leopard seal (Hydrurga leptonyx) in the Antarctic. Numerous species
of seals and seabirds are associated with sea-ice in both Polar Regions. However, flightless birds
(penguins) are only found in the Antarctic. A notable difference between the Arctic and Antarctic is
found in crustaceans. Krill (primarily E. superba) is the key crustacean found in Antarctic in terms
of biomass associated with sea-ice while amphipods are the most abundant found crustacean in
the Arctic (Dieckmann & Hellmer 2003). Smaller metazoan and protazoans (e.g. ciliates,
nematodes) have also been found in sea-ice in both polar-regions. Diatoms, commonly found living
in brine channels in sea-ice, are found in both hemispheres. Over 200 diatom species associated
with Arctic sea-ice have been identified and some of the diatoms are found to be bipolar such as
Fragilariopsis cylindrus and F. curta (Hsiao 1983, Horner 1985).
10
The sea-ice in the Polar Regions influence the world‟s climate and ice formation affect marine
plants and animals ranging from microorganisms to whales. However, the entire earth is currently
experiencing a warming trend which may result in decreasing sea-ice extent (Dieckmann &
Hellmer 2003). The main reason for recent rapid temperature rise on Earth, according to IPCC
(IPCC rapport 2007) is increasing concentrations of greenhouse gases in the atmosphere. In the
Arctic, sea-ice reduction has been greater in summer than in winter, resulting in a 4 to 9 %
reduction in area of multi-year ice cover, per decade (Cosmiso 2003). A reduction in sea-ice cover
also has a direct effect on temperature itself as a reduction in sea-ice cover reduces the Earth´s
albedo (Kerr 1999) causing increased energy absorption by the open Arctic Ocean. The shift in the
Arctic from multi-year sea-ice to annual first-year sea-ice may have a profound influence on the
biology associated with the sea-ice.
Incorporation of organisms into sea-ice
Arctic first-year sea-ice cover start forming in autumn when seawater temperatures reach -1.8°C
(Eicken 2003). Initially, sea-ice thickness increases rapidly, however the rate of increase gradually
decreases in January- March (Glud et al. 2007). First-year sea-ice may be associated with land
(i.e. sea-ice on the continental shelves) and unlike drifting pack ice, land-fast sea-ice remains fixed
in place (Carmack & Macdonald 2002).
Unlike freshwater ice, the frozen seawater forms a semisolid matrix which is permeated by a
network of channels and pores. When sea-ice is formed high salinity water also known as “brine” is
being rejected to small enclosed compartments in the newly formed ice. Due to gravity the brine in
these compartments slowly breaks the crystalline structure of underlying ice and melts downwards
through the ice leaving behind a channel like structure, a “brine channel”. The channels and pores
vary in size from a few micrometers to millimeters. Brine channels compose from 1- 30 % of total
sea-ice volume (Weeks & Ackley 1986), where size of channels is temperature and salinity
dependant. Within these brine channels other dissolved substances e.g. dissolved inorganic
carbon (DIC), nutrients, calcium and gases (e.g. oxygen) are trapped in the brine channels. The
brine slowly melting down through the ice ultimately reaches the sea-ice/water interface, and
evacuates the ice as small sinking brine packages. Thus loss of brine lower the total concentration
of dissolved substances in the sea-ice during ice melt , i.e. when measuring concentrations of
dissolved substances in water from melted sea-ice the term “bulk concentration” of the ice is used.
Brine channels still contain high brine concentrations, but the overall bulk concentration of
dissolved substances in sea-ice is continuously decreasing. It is within these labyrinths of brine,
11
channels and pores that sea-ice associated microorganisms live. Inorganic sediments and
microorganisms may be incorporated and accumulated at higher concentrations in the sea-ice
brine than the underlying sea water (Reimnitz et al. 1992, Gradinger & Ikävalko 1998).The
incorporation of microorganisms from the water column into sea-ice occurs due to enclosure of
active concentration, lifting of benthic material and/or by colonization. When sea-ice consolidates
the enclosure of water may occur from frazil ice crystals, which may be essential seeding growing
sea-ice (Lizotte 2003). One mode of active concentration mechanisms, also called ice scavenging
mechanism, is adherence of cells to sea-ice crystals moving through the sea water column.
Another form occurs when frazil ice collects to form a grease ice layer at the surface of the ocean.
The grease ice layer acts as a filter collecting particles, when wave action causes pumping of
water through the ice layer (Gradinger & Ikävalko 1998). Both mechanisms are selective for large
particles and are nonselective for smaller sediment particles or species (Lizotte 2003). This implies
that cell attributes, e.g. cell surface properties, are not involved in the enrichment of
microorganisms, while both mechanisms follow principles of particle aggregation (encounter rates).
Another mechanism for incorporation is benthic material attached to “anchor ice” which is sea-ice
lifted from the seabed to the surface (Reimnitz et al. 1992). This mechanism occurs only in the
shallowest and coldest regions of polar seas.
After sea-ice formation, algae from the top-surface of sea-ice can move down the ice column in the
brine channels due to brine channel drainage. Furthermore, horizontal flow across sea-ice surface
can produce a vertical pulsing which flush brine channels with sea water. The size of the organism
might be important in retention during these flushing events, and Gradinger & Ikävalko (1998)
showed that photo- and heterotrophic flagellates larger than 10 µm in size exhibited a higher
enrichment index than smaller cells in sea-ice. However, under low temperatures the decreasing
brine volume limits growth and survival of large microalgae, especially those possessing spines
(e.g. Chaetoceros species) (Kirst 1989). High concentrations of marine bacteria occurs in sea-ice,
not as a result of scavenging mechanism, except for the largest cells (~ 1 µm3), as they are too
small for active transport in sea-ice (Grossmann & Gleitz 1993, Gradinger & Ikävalko 1998).
Grossmann & Dieckmann (1994) proposed that bacteria may attach to the outer surface of the
algae, i.e. epiphytic attachment, or to particles in the water column which transport them into the
sea-ice.
12
Diversity of sea-ice microbes
Organisms incorporated into sea-ice face physiochemical challenges when the environment
changes from a pelagic to a semi-benthic habitat (Gradinger & Ikävalko 1998). After incorporation
some organisms adapt to the new environment and develop so-called sympagic communities,
comprising viruses (Wells & Deming 2006), bacteria (Junge et al. 2004), heterotrophic e.g.
flagellates and ciliates (Horner 1985) and autotrophic e.g. diatoms (Ikävalko & Gradinger 1997)
protists. The most dominant autotrophs in sea-ice are photosynthetic protists, including diatoms,
dinoflagellates, prymnesiophytes, prasinophytes, chrysophytes, cryptophytes, chlrophytes,
euglenophytes and Mesodinium rubrum (a photosynthetic ciliate) (Lizotte 2003). Blooms are
dominated by diatoms, particularly pennate with nearly 200 species of diatoms recorded in Arctic
sea-ice (Horner 1985). Heterotrophic species of dinoflagellates are dominant grazers of
photosynthetic sea-ice algae. In addition, heterotrophic protists such as ciliates, kinetoplastids,
choanoflagellates, amoebae, heliozoans, foraminiferans and protists are also found in sea-ice.
Heterotrophic uptake by autotrophs, i.e. mixotrophy, appears to be minor, however uptake of
sugars and amino acids have been demonstrated for sea-ice diatoms. Facultative heterotrophy, i.e.
mixotrophy, and energy storage could be an important strategy for overwintering survival in sea-ice
(Syvertsen 1991, Lizotte 2003, Zhang 2003).
Bacteria are the most abundant heterotrophs in sea-ice (Kaartokallio 2004, Mock & Thomas 2005),
and bacterial heterotrophy includes direct consumption of dissolved substrates and extracellular
decomposition of dissolved and particulate matter followed by uptake. The diversity of prokaryotic
organisms, archea and eubacteria represent more than 20 genera, where most of the isolated
organisms are from the γ-Proteobacter and Flexibacter-Bacteroides-Cytophaga groups (Lizotte
2003, Kaartokallio et al. 2008). These bacteria species are psychrophillic and aerobic
chemoheterotrophs, which can degrade a broad spectrum of substrates. Anoxic micro-zones also
occur in sea-ice where fermenting bacteria and anoxygenic phototrophic purple sulphur bacteria
have been found (Petri & Imhoff 2001). In the Baltic Sea (Kaartokallio 2001) and in Arctic sea-ice
(Rysgaard & Glud 2004) active denitrificans have been found. Sea-ice bacteria may be parasitic on
eukaryotes or other bacteria or epiphytic on algae, particularly to diatoms. The fraction of attached
sea-ice bacteria to particles, surfaces (e.g. sediment grains, detritus, ice crystal boundaries) or
algae is 50 % of the population (Junge et al. 2004), which is higher than the 10-15 % reported in
marine waters (Fenchel et al. 1998).
High microbial abundance is found in the ice-seawater interface and surface ponds and low
abundances are found in new sea-ice or the upper ice column. Abundances and biomasses of sea-
13
ice algae vary between < 0.1· 106 to 2800 · 106 cells l-1 and 2 to 1200000 µg C l-1. For sea-ice
bacteria the number range from 105 to 20000 · 106 cells l-1 and < 1 to 2400 µg C l-1. Heterotrophic
protists < 104 to 250 · 106 cells l-1 and 1 to 1800 µg C l-1 and autotrophic biomass varies between <
0.1 to 60000 µg chlorophyll a l-1 (Lizotte 2003, Arrigo 2003).
Regulation of sea-ice algal and bacterial communities
Regulation of growth and production of sea-ice algae and bacteria can be divided into three
categories:
The first category is the physical sea-ice properties, where the primary factor limiting sea-ice algae
productivity is light availability (e.g. Cota & Smith 1991). Transmitted irradiance is largely
dependent on the overlying snow cover (Mundy et al. 2005). Increased light conditions enhance
sea-ice algae productivity, which in turn enhance dissolved organic matter (DOM) exudation
(Kaartokallio 2004). DOM is assumed to be the primary substrate for sea-ice bacteria and thus light
conditions may indirectly influence the bacteria productivity. Other physical properties affecting
sea-ice algae and bacteria are temperature, brine salinity and sea-ice brine volume (affecting the
space available for colonization and brine movement). Biological activity is also affected by
changes in pH and oxygen levels (Thomas & Papadimitriou 2003).
The second category is the availability of nutrients and substrate, which are defined by multiple
factors in the sea-ice internal environment: sea-ice growth processes, physical interactions
between sea-ice and the water column during winter and biological factors such as uptake and
regeneration of nutrients, production and consumption of DOM. Sea-ice algae require relatively
large amount of nutrients (C,N,P) and sympagic diatoms need, in addition to other nutrients,
silicate for their frustules. The main forms available are inorganic forms of carbon dioxide, nitrate,
phosphate and silicic acid. DOM accumulates if released from the algal cells via excretion, cell
lysis or by grazing. Bacteria, fungi and protists can also utilize particulate organic matter (POM)
and bacteria convert POM into DOM, as well as converting DOM into more usable forms (e.g.
amino acids) by exudation of extracellular enzymes. Waste products of degradation are fully
remineralized compounds (e.g. carbon dioxide, nitrate, and phosphate) and slowly remineralized
complex organic molecules (e.g. biogenic silicate). Remineralization of Si in sea-ice is unknown
and evidence suggests that Si is the limiting factor for diatom growth (e.g. Gosselin et al. 1990,
Kaartokallio 2004).
The third category is the sea-ice food web interactions. Sea-ice is dominated by microbes and
microbial processes and the small spaces and extreme physical and chemical conditions exclude
14
large marine organisms (Krembs et al. 2000). Reduced grazing pressure on sea-ice algae and
bacteria due to restricted access of grazers to the brine channels may result in high buildup of
bacterial and algal biomasses in sea-ice (Brierley &Thomas 2002). The understanding of sea-ice
microbial food webs is limited by the lack of methods for evaluating process rate beyond
photosynthetic production and bacterial production. However, the abundance of well adapted
heterotrophic protists indicates the existence of functional microbial food webs in sea-ice and
effective protozoan grazing (mainly flagellates and ciliates) on bacteria (Sime-Ngando et al. 1997).
During spring and summer sea-ice temperature increases, resulting in increased brine volume and
allowing metazoan grazers to reach previously isolated brine channels within the sea-ice matrix.
Adaptation to extreme conditions in sea-ice
The ability of sea-ice algae and bacteria to survive and grow under extreme conditions depends on
their physiological acclimation and tolerance. Biological activity is affected by changes in pH and
oxygen level. The interior of sea-ice with high primary production and accumulation of large algal
biomass is characterized by considerable reductions in DIC and highly alkaline pH, with values up
to 10 (Gleitz et al. 1995, Thomas et al. 2001b). Sea-ice with high primary production and
accumulation of large algal biomass shift carbonates producing high alkaline environments. Effects
of high pH on sea-ice algae are not well established, however, previous studies indicate that high
extracellular pH may cause gross alterations in membrane transport processes and metabolic
functions involved in internal pH regulation (Raven 1980) or cause alterations of cellular content of
amino acids and their composition, which might affect cellular growth (e.g. Taraldsvik & Myklestad
2000). Changes of pH influence the inter-speciation of inorganic carbon (CO2 (aq), HCO3-, CO32-).
At pH 8 in sea water (DIC ~2mM in marine waters), ca. 1 % of DIC is present as CO 2, while at pH 9
only 0.1 % of DIC is present in this form (Hinga 2002). Potentially, the limitation in the supply of
CO2 due to elevated pH may restrict photosynthesis and growth of phytoplankton (Hansen 2002).
However, some phytoplankton species have active transport systems by which they utilize HCO3in order to avoid DIC limitation at elevated pH, thus elevated pH may favor species which can
utilize HCO3- as an inorganic carbon source (e.g. Korb et al. 1997, Huertas et al. 2000, Hansen
2002). Bacteria living in extreme alkaline environments require several adaptations to prevent
alkalization of their cytoplasma, which would affect intracellular functions such as DNA replication
and protein synthesis (Schmidt 2005).
Adaptations to alkaline environments can be active or passive. Active adaptation is an energyrequiring process which catalyse an inward proton transport this process is powered by ATP, the
15
most important mechanism is by the Na +/H+-antiporters (membrane-associated proteins). Passive
adaptation is e.g. when the anionic polymer layer of the cell fixates anions to the polymer chains to
the polymer chains resulting in a binding of positive ions to the aqueous phase of the layer. When
equilibrium is reached a barrier is formed which prevents hydroxide ions (OH -) from entering the
cell (Tsujii 2002). The composition of fatty acids in the cell membrane is another passive
adaptation to high pH, where a higher amount of unsaturated fatty acids are found in the cell
membrane at elevated pH (Dunkley et al. 1991, Hicks & Krulwich 1995, Ma et al. 2004, Banciu et
al. 2005). Unsaturated fatty acids are less flexible due to double bonds, which may influence the
membrane permeability or function as proton conductors, and thereby give the bacteria an
advantage in high pH environment. A further essential adaptation in bacteria capable of living in
high alkali environments is an effective respiration that provides energy, not only to ordinary
cellular functions but also to pH homeostasis (Hicks & Krulwick 1995, Krulwich et al. 1998).
Photosynthetic activity may lead to elevated concentrations of dissolved oxygen and because of
the increased solubility of oxygen in sea water at low temperatures; these organisms are likely to
experience some of the most extreme high oxygen concentrations observed on the planet (McMinn
et al. 2005). The high concentration may lead to an increased production of oxygen radicals and
peroxides, which are toxic to all living organisms.
Sea-ice environments are dominated by psychrophillic algae and bacteria. Most microorganisms
isolated from cold environments are either psychrotolerant or psychrophilic. Psychrotolerant
organisms often grow well at temperatures close to 0ºC, but they may have an optimal growth
temperature at 20ºC (Cameotra & Makkar 1998). Psychrophilic organisms have optimal growth
rates at temperatures below 15ºC and they are unable to grow at temperatures above of 20ºC
(Cameotra & Makkar 1998). Adaptation to low temperatures in all cellular components is of great
importance for algae and bacteria living in cold environments. One reproductive strategy of some
algae and protozoans is the formation of robust stress resistant cysts, which can lie dormant until
suitable conditions for growth are present. The cysts probably form as a response to the
temperature fall in autumn when sea-ice forms (Stoecker et al. 1998). Another important
adaptation by sea-ice bacteria and algae to withstand the cold environment is the membrane
fluidity (Gounot & Russell 1999). Maintenance of proper lipid membrane fluidity at low
temperatures can be achieved by incorporating shorter and branched fatty-acid chains into the lipid
membrane, which decrease the packing density and thereby increase the membrane fluidity
(Suutari & Laakso 1994, Nedwell 1999). Most biological growth rates and specific enzyme reaction
rates show Q10 (i.e. the metabolic change rate for every 10°C change in temperature) values of 2
(Stapleford & Smith 1996). However, Q 10 values above 10, for carbon fixation, nitrate and
16
ammonium uptake, have been reported in sea-ice diatoms at temperatures near zero (Priscu et al.
1989), which suggest small changes in temperature can have a large impact on physiological rates
of sea-ice algae. Sea-ice bacteria show higher production of extracellular enzymes at near zero
temperature, which imply that some sea-ice bacteria is psychrophillic (Feller & Gerday 2003). Polar
organisms generally increase production of cold-adapted enzymes with increased catalytic
efficiency. The increased catalytic efficiency can be reached by an increase of the specific activity
or by an improvement in the substrate affinity due to an increased flexible structure of the enzyme
(Schmidt 2005). The flexible structure of the cold-adapted enzymes allows activity to increase an
order of magnitude than that of their mesophilic homologous at low temperatures (Feller & Gerday
2003). However, a subsequent disadvantage is increased thermolability, which results in
inactivation of the cold-adapted enzymes at much lower temperatures than in their mesophilic and
thermophilic homologous. An increasing interest in the properties of cold-adapted enzymes
produced by polar bacteria has developed in recent years; many enzymes found in cold regions
have wide biotechnological applications and can be useful for industrial applications, e.g. domestic
processes in the food industry, in bioremediation of high polluted areas and added to detergents to
hydrolyse macromolecular strains at low temperatures (Feller & Gerday 2003).
Sea-ice microbes must cope with severe physicochemical stress factors caused by natural
variations of sea-ice salinity. During sea-ice formation, consolidation and melt, brine salinity
fluctuates along with ice temperature (Eicken 2003). Salinity variations are suggested to be a major
selective factor shaping algal and bacterial communities (Eicken 2003, Mikkelsen & Witkowski in
press). Sea-ice algae are likely to exhibit increased halo tolerance and some studies show that
sea-ice algae remain physiologically active at salinities above 100 (Stoecker et al.1997). Specific
adaptations of sea-ice algae to high salinities include a regulation of their organic osmolyte
concentration (e.g. proline, simple sugars and polyols), their membrane ion transport and several
morphological features which decrease their surface to volume ratio. As sea-ice algae show
adaptations to changing salinities as does bacteria. Bacterial adaptation to high salinities includes
changes in membrane fatty acid composition, production of salt tolerant enzymes and
concentration change of organic osmolytes (Thomas & Dieckmann 2002).
17
Photosynthetic acclimatization
Some studies have shown autotrophic species of sea-ice algae to be possibly mixotrophic and able
to switch from autotrophy to heterotrophy as their immediate energy source, when light is not
available. They are typically found in high arctic areas and their switch to heterotrophy is an
adaptation to the highly scarce light conditions of the arctic winter (e.g. Palmisano & Garrison
1993, Lizotte 2003). However in general, sea-ice algae are most efficient in exploiting low levels of
irradiance for photosynthesis compared to typical phytoplankton. Attenuation of irradiance by snow
and sea-ice is probably the major limiting factor for algal growth and sea-ice algae have to be
physiological geared for life at low light intensities (Mundy et al. 2005). Some sea-ice algae are
able to live and grow successfully at light levels near 0.1 % of the total surface irradiance. Such
photoacclimation requires a high adaptation seen as increase of the accessory photosynthetic
pigments (e.g. fucoxanthin and chlorophyll c for diatoms) which are highly effective at absorbing
the wavelengths of light penetrating the snow and sea-ice, especially green light. The
concentration of main photosynthetic pigment, chlorophyll a (Chl a) is low, as Chl a is less efficient
for absorbing green light (Arrigo 2003). Furthermore, under low light intensities the concentration of
accessory pigments that serve a photoprotective role (e.g. β- carotene) is decreased and this may
lead to photosystem damage when light intensity increases.
Another adaptation observed in sea-ice diatoms to low light is their increased ability to allocate
carbon to glycolipids, which is a component of the thylakoid membranes in their chloroplasts,
leading to an increased number of photosynthetic units per cell and a raise in concentration of the
components that are involved in the electron transport chain (Arrigo 2003).
Measuring primary production and algal biomass in sea-ice
In vitro labeled 14C incubations
Photosynthesis versus irradiance (PE) is a widely used method for estimating primary production in
sea-ice (Arrigo 2003). Sea-ice cores collected in field are brought back to the laboratory and
melted, then labeled 14C is added the bulk water to measure 14C-uptake as a function of light
intensity. Maximum photosynthetic rate (Pm) and photosynthetic efficiency (α) can then be
calculated. Estimates of primary production at different depths can be calculated when the PEcurve is found and combined with field measurements of light. A bias, when estimating primary
productivity in sea-ice, using the 14C approach, is that the spectral composition and intensity of the
18
light source used for the in vitro incubations and the spectral composition and intensity of in situ
solar light are to a certain degree different of each other (Arrigo 2003). Photosynthetic pigments
are much more efficient at absorbing blue light than red light, and incubation light sources are
usually weighted towards the red end of the spectrum, while blue and green wavelength are
dominating in situ. Applying a spectral filter to the incubation light source may compensate for this
effect, however this correction does not account for the spectral light distribution change with seaice depth. Another bias is that the sea-ice cores are melted to separate the organisms from the
sea-ice cores. The organisms will experience a shift in salinity from hypersaline (>34) or marine
(~34) to brackish (<10), which may induce osmotic shock that cause cells to lyse (Lizotte 2003).
In situ labeled 14C incubations
An in situ incubation technique has been developed to measure primary production in a wide
variety of habitats (Mock & Gradinger 1999). The sea-ice cores is sectioned into appropriate
segments and placed into individual petri dishes for inoculation with
14
C bicarbonate. The petri
diches are then stacked, and placed in an acrylic-glass barrel, and returned to their original
position in the sea-ice, where they are incubated. Fine scale (1 cm) resolution of primary
production is provided by this method without destroying the sea-ice microenvironment and thus
ambient light field. The only drawback is that nutrient supply from the underlying sea water is
hampered. However, under conditions where nutrient concentrations are not limited this technique
is considered superior to most other methods (Arrigo 2003).
Oxygen microelectrodes
Direct measurements of the in situ primary production are the ideal approach. Oxygen
microelectrodes is a new approach which has been used to measure photosynthetic rates of
Antarctic fast ice algae in nearly intact cores (McMinn & Ashworth 1998). Rysgaard et al. (2001)
measured the net photosynthesis and aerobic respiration in the lowermost layer of intact Arctic
sea-ice using the oxygen microelectrodes. Using this method measurement of the O 2 distribution
across the water-ice interface could be done at in situ ambient flow and light conditions.
However, there are a some drawbacks to this method: sea-ice algae are distributed
heterogeneously and the O2 profile generated by this method average over this variability, the
actual O2 flux is altered when inserting the sampling apparatus through the diffusive boundary
layer, however, this effect can be minimized where O 2 gradients are small and by avoiding a flow
19
of fluid around the tip of the O2 probe (Arrigo 2003). McMinn & Achworth (1998) found that
estimates of photosynthetic carbon fixation using microelectrodes were low compared to
production rates found in Antarctic sea-ice by other methods, with hourly production rates of 0-1.78
mg C m-2 h-1. This method is less invasive than many other methods, which have led to speculation
that previous estimates of sea-ice algae production may be overestimated. However, the in situ
14
C petri dich technique described above showed that primary production was much higher than
previously reported and it suggests that production rates may be underestimated.
Rysgaard & Glud (2004) introduced a close to in situ method to measure the net aerobic activity of
the sea-ice communities. The concept behind this method is to enclose sea-ice cores in gas-tight
plastic bags (Hansen et al. 2000). The cores are then placed back in the sea-ice holes, and
afterwards sampling occurs on a day to week basis to determine the O 2 development of the seaice within the bags over time. The net aerobic activity (net production or consumption of O2) of the
enclosed sea-ice community is in this way followed under conditions close to in situ. This method
can, when evaluating oxygen development reveal if there is a net heterotrophic or autotrophic
response of the sea-ice community during the time of investigation.
Oxygen sensitive planar optodes
Another technique, planar optodes, was recently used for qualitative assessments of oxygen
transport associated with freezing and thawing processes of sea-ice (Rysgaard et al. 2008). As
planar optodes has the advantage of measuring oxygen dynamics directly in ice without destroying
or manipulating it and has a high resolution, planar optodes would most likely allow a better and
more correct estimate of primary production from oxygen exchange rates. The approach is based
on the principle of dynamic quenching of an oxygen sensitive fluorophore. The sensing agent
(ruthenium (III)-Tris-4.7-diphenyl-1.10-phena-throline) can be immobilized in thin sheets (planar
optodes) allowing 2-dimensional determination of oxygen dynamics of an ice in the immediate
proximity of the sheets. Images of the oxygen sensitive fluorescent light emitted are acquired with
a CCD camera, which allow quantification of oxygen saturation in front of the planar optode film. A
PI-relation from sea-ice algae inoculated in an artificial ice can be found by the planar optode
technique and compared to a PI-relation found by the 14C-method on the bulk water from the same
ice.
20
Pulse amplitude modulated fluorometry (PAM)
Pulse amplitude modulated fluorometry (PAM) is also used for sea-ice algae PI estimations; it is
based on sample illumination and measurements of cohering chlorophyll fluorescence. A
disadvantage of this technique (in sea-ice regime) is its requirement of a relatively large amount of
chlorophyll (Rysgaard et al. 2001). Another disadvantage of this method is that measurements, for
optical reasons, only include the lower most 1 cm of the sea-ice, and therefore does not take into
account the sea-ice algal communities further inside the sea-ice matrix.
Chl a and HPLC pigment analysis
The most common method used for biomass estimation is algal pigment concentration. Chl a is
present in almost all photosynthetic organisms in sea-ice. However, the ratio of Chl a to carbon
may vary amongst species and in response to changes in light (Lizotte 2003). Furthermore,
photosynthetic organisms contain many different photosynthetic pigments and other pigments may
interfere by fluoresce and absorb light. To overcome the interference problem a method which
separate pigment by high-performance liquid chromatography (HPLC) followed by fluorescence
and/or absorbance detections is being used. HPLC pigment analysis provides information which
can be used to estimate the contributions of major algal groups to the total Chl a biomass (Lizotte
et al. 1998)
Productivity and Chl a measurements of sea-ice
The highest primary production measured is in platelet ice in the Antarctic of 2-1249 mg C m-2 d-1
(Grossi et al. 1987) and in the Arctic pack ice of 0.5-310 mg C m-2 d-1 (Gosselin et al. 1997). The
highest algal biomass reported is also in Antarctic sea-ice where Chl a accumulation of 400 mg Chl
a m-2 was observed in pack ice (e.g. Hempel 1989, Riaux Gobin et al. 2000). In Antarctic platelet
sea-ice Chl a accumulations of ~1000 mg Chl a m-2 was reported (Bunt & Lee 1970, Arrigo et al.
1993a). The highest algal biomass reported for Arctic sea-ice was 300 mg Chl a m-2 in sea-ice of
Resolute Passage (Welch et al. 1989). A marked variation in primary production between different
areas is recorded with the sea-ice algal contribution to total annual primary production in the Arctic
ranges from <1 % (Haecky & Andersson 1999, Rysgaard et al. 2001) to 57-66 % (Horner &
Schrader 1982, Gosselin et al. 1997).
21
Measuring bacteria production and bacterial biomass in sea-ice
The importance of heterotrophic bacteria in sea-ice has in most studies been based on simple
enumeration (cell counts), quantification of prokaryotic diversity (e.g. fluorescent stains specific to
nucleic acids, flow cytometry especially with species or group-specific fluorescent probes), on
culture work or chemical analyses (e.g. Lizotte 2003, Grossart & Simon 2007, Junge et al. 2002).
New methods to observe the in situ spatial organization of microbial communities have been
developed in enclosed sea-ice (Deming 2002, Junge et al. 2002). Determination of biomass of
different bacteria by molecular methods has recently been developed e.g. 16S ribosomal RNA
(rRNA) gene sequences, fluorescent in situ hybridization (FISH) (Deming 2002, Junge et al. 2002).
The same experimental difficulties faced by scientists when quantifying in situ photosynthetic
activity are observed when quantifying the in situ microbial activity of sea-ice. However, a close to
in situ investigation of the net aerobic activity was reported in Arctic sea-ice by enclosing sea-ice
cores in gastight plastic bags (Rysgaard & Glud 2004, Glud et al. 2007, Rysgaard et al. 2008).
They reported that the sea-ice cores turned anoxic within a week indicating a high heterotrophic
activity in sea-ice.
The most widely used method to determine bacteria production is by measuring incorporation
[3H]thymidine into DNA and using a conversion factor to relate DNA production to production of
bacterial cells (Fuhrman & Azam 1982). To estimate the bacteria production a suitable conversion
factor should be used. However, a universal conversion factor does not exist. The conversion
factor can be derived, theoretically from assumptions of the extent of isotope dilution, thymine
content of bacterial DNA and the amount of DNA per cell (Fuhrmann & Azam 1982), or empirically,
comparing incorporation rates with increases in bacterial numbers (e.g. Kirchman et al. 1982). A
variety of approaches exists to derive the conversion factor (Ducklow et al. 1992, Kirchman &
Ducklow 1993). Another bias is non-specific incorporation of thymidine into other macromolecular
compounds than DNA (Hollibaugh 1988) and eukaryotic uptake of [ 3H]thymidine.
Another method to measure protein synthesis and bacterial biomass production is by leucine
incorporation into protein (hot-TCA-insoluble material) (Kirchman et al. 1985). The increase in
leucine incorporation agrees with increases in cell number and protein content (Kirchman et al.
1986) and biomass production can be calculated from rates of protein synthesis while protein
comprise a large fraction of bacterial biomass (ca. 60 %). The changes in leucine incorporation are
not due to changes in the leucine/protein ration, while leucine comprises a rather constant fraction
of bacterial protein (Kirchman et al 1985, Simon & Azam 1989). The biomass production may be
overestimated when bacterial growth rates are low, while microbial cells can synthesize and
22
degrade some proteins. Radiolabel (leucine) would be incorporated into new proteins while little
radioactivity would be lost as old proteins are degraded (Kirchman et al. 1986). However, rates of
leucine incorporation could provide an independent check of the thymidine method.
In Arctic and Antarctic sea-ice the bacterial abundance range between 105 to 20000·106 cells l-1
(Lizotte 2003). The bacterial abundance in Arctic sea-ice was reported by Maranger et al. (1994) to
range between 6.1 · 109 bacteria m-2 and 4.15 · 1011 bacteria m-2 which are similar to the bacterial
abundance reported by Smith et al. (1989). The highest bacteria production measured is in
Antarctic pack ice of 4-1100 mg C m-3 d-1 (Kottmeier & Sullivan 1990).
Succession
During sea-ice formation the ice is colonized with pioneer communities i.e. only a subset of the
organisms found in the underlying sea water, which are typically characterized by low productivity
in the winter stage. The most common pioneer species is pennate diatoms but bacteria may play a
critical role in a pioneer community (Lizotte 2003). Rózánska et al. (2008) showed that there was a
selective incorporation of larger cells, mainly pennate diatoms in newly formed sea-ice. Later in the
sea-ice season, blooming of sea-ice algae may occur followed by a heterotrophy-dominated stage
at the end of the sea-ice season (Kottmeier et al. 1987, Grossmann & Gleitz 1993, Kaartokallio
2004). However, some studies have indicated a positive correlation between the phototrophic and
the heterotrophic biomass in some sea-ice habitats (Gosink et al. 1993). Other studies have
reported that sea-ice during winter has bacterial production that equals or surpass the algal
production and heterotrophic biomass i.e. bacteria plus protists, hence exceeding the autotrophic
biomass (Lizotte 2003).
First-year sea-ice can also have a type of primary succession, which begins with a pioneer stage
where small size species with high growth rates are dominating. During the sea-ice season the late
successional species are larger in size, grow more slowly and have more specific requirements,
e.g. food sources (Lizotte 2003). The primary succession ends due to loss of the habitat at ice
melt.
In sea-ice the community structure change over time, this is a process known as succession. Few
long term studies have been made for sea-ice communities. However, a previous study by Lizotte
& Arrigo (1998) shows that algal diversity decreases with age of sea-ice, and diatoms became
dominating. This observation is supported by another study by Mikkelsen et al. (2008); they
observed a diverse winter sea-ice algal community with gymnodinoid, dinoflagellates, cryptophytes
(particularly Rhodomonas), prasinophytes and other small flagellates. Only two species of diatoms
23
were observed in winter, a small centric (Chaetoceros simplex) and a naviculoid pennate diatom.
The sea-ice algal spring community was dominated by a bottom bloom of centric diatoms, and in
summer a diatom dominating community was reported with species of pennate diatoms (e.g.
Navicula, Nitzschia).
The main heterotrophic organisms in sea-ice are the heterotrophic bacteria (Mock et al. 1997),
diverse heterotrophic flagellates (e.g. Haecky & Andersson 1999), ciliates and metazoans (e.g.
rotifers).
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30
Chapter 1
Dynamics of autotrophic and heterotrophic activity in Arctic firstyear sea-ice: Case study from Marlene Bight, SW Greenland.
Dorte H. Søgaard* and Morten Kristensen
Greenland Institute of Natural Resources
Box 570
3900 Nuuk
Greenland
University of Copenhagen
Marine Biological Laboratory
Strandpromenaden 5
3000 Helsingør
Denmark
*Corresponding author: [email protected]
31
1.0 Abstract
Autotrophic and heterotrophic activity was investigated in the sea-ice of Marlene Bight (64°082´N
51°42´W), SW Greenland. Primary and bacterial production was measured in melted sea-ice
sections from the study area. This study provides a comparison of the net activities measured in
the laboratory and the in situ dynamics of O2 concentration of sea-ice samples kept in gastight
plastic liners throughout the sea-ice season. Supporting parameters: temperature, salinity, light
availability, nutrients and chlorophyll a (Chl a) concentrations of the sea-ice were measured at the
study site at regular intervals.
Sea-ice covered the study area from February 1 st to April 14th year 2008. Sampled sea-ice
thickness varied from 39 - 58 cm and was in the sampling period covered by 0 - 28 cm of snow.
Towards the end of the sea-ice period snow turned into slush-ice covering the sea-ice.
Early in the season, irradiance in the lower most sea-ice section was low (0.03-0.15 µmol photons
m-2 s-1) as were Chl a levels (<0.08 mg Chl a m-2) and primary productivity (<0.5 mg C m-2 d-1).
Light availability increased with increasing day length towards the end of the season, reaching a
maximum downwelling irradiance of 76 µmol photons m-2 s-1 in the upper most sea-ice section and
4 µmol photons m-2 s-1 below the sea-ice on April 4th. Increasing light stimulated Chl a and primary
production, reaching 0.50 mg Chl a m-2 and 9.54 mg C m-2 d-1, respectively. Throughout the
season the sea-ice algae community expressed adaptation to the in situ light conditions. The initial
photoadaptation index, Ek was 13 µmol photons m-2 s-1 and gradually increased to 37 µmol
photons m-2 s-1at the end of the season. Heterotrophic activity as inferred from bacteria production
increased during the sea-ice season from 0.09 mg C m-2 d-1 in February to 3.18 mg C m-2 d-1 on
April 4th. A successional sequence of the autotrophic and heterotrophic activity was observed,
beginning with a low productive winter stage followed by a predominantly heterotrophy-dominated
stage late in March and finally a blooming of the sea-ice algae until the onset of sea-ice melt. The
thymidine based bacterial production was 70 % of the co-occurring primary production during the
sea-ice season.
The net autotrophic response of the sea-ice community as resolved in the laboratory based bottle
incubations was also observed in the gastight plastic liners kept in situ. Net microbial activity
reflected a gradual increase of 0.6-6.4 µmol O2 l-1 sea-ice d-1 (0.50-5.10 µmol C l-1 sea-ice d-1)
during most of the season. The net oxygen formation was highest in the bottom sea-ice reaching
values of 1.2-6.4 µmol O2 l-1 sea-ice d-1 (1.0-5.10 µmol C l-1 sea-ice d-1).
A multiscale sampling analysis showed that sea-ice algal biomass (Chl a) was distributed in
patches with an average radius of ~20 to 50 m. Patch size (Chl a) followed sea-ice temperature,
32
suggesting that snow and ice thickness were the main factors influencing the light condition in the
sea-ice and thus the phototrophic biomass. A small scale test revealed that the horizontal
variability was remarkably smaller than the temporal variability, indicating that the observed
temporal patterns of the measured parameters of the intact sea-ice cores in Marlene Bight was not
compromised by spatial variability.
1.1 Introduction
The presence of sea-ice is a defining feature of most polar marine ecosystems. The Polar Regions
account for approximately 35 x 10 6 km2, or 13 % of the world surface area (Parkinson & Garrison
1993). Hence the sea-icecover is larger than most terrestrial biomes (e.g. grassland, tundra, or
deserts) (Lizotte 2001). Polar sea-ice plays a major role in the energy exchange between the
ocean and the atmosphere and acts as a sink for atmospheric CO 2 (Takashi et al. 2002, Rysgaard
et al. 2007). Given the tendency towards decreasing sea-ice in the Arctic, increasing knowledge of
the biogeochemical processes involved in sea-ice organic carbon cycling is essential in order to
better understand possible change to the Arctic Ocean carbon cycle.
The sea-ice provides a low-temperature habitat for diverse communities of microorganisms
including viruses, bacteria, heterotrophic- (e.g. flagellates and ciliates) and autotrophic protists
(e.g. diatoms) (Kaartokallio et al. 2007). During sea-ice formation, inorganic solutes and solids and
microorganisms can be incorporated, accumulating at concentrations higher than in the underlying
sea-water (Reimnitz et al. 1992, Gradinger & Ikävalko 1998). The organisms which are
incorporated into sea-ice are challenged when their environment changes from a pelagic to a semibenthic habitat (Gradinger & Ikävalko 1998). This comprises changes in, for example, space
availability, light intensity, salinity, nutrient concentration, temperature, pH, DIC (dissolved
inorganic carbon) and oxygen content. Especially light availability within the sea-ice has a major
influence on the sea-ice algal biomass and production (e.g. Kühl et al. 2001, Cota & Horne 1989).
Transmitted irradiance is largely dependent on the highly variable depth history of the overlying
snow cover (Mundy et al. 2005). Sea-ice algae are an important component of sympagic
communities and have been extensively studied in Arctic sea-ice (e.g. Gosselin et al. 1997, Arrigo
2003, Glud et al. 2007, Mikkelsen et al. 2008). Dominant characteristics of communities in sea-ice
include biomass accumulation of micro-algae, which generally follows seasonal variations in solar
radiation and a patchy distribution in relation to variable snow cover (Rysgaard et al. 2001, Mundy
et al. 2005). Other components, which may be important contributors to sea-ice organic carbon
cycling, may include dissolved organic carbon (DOC), exopolymeric substances (EPS) (Krebs et
33
al. 2002, Meiners et al. 2003, Riedel et al. 2007) and bacteria (Smith & Clement 1990, Kaartokallio
2004). The sea-ice algae represent an important food source for metazoan grazers, and
photosynthetic products or entrained organic material can lead to elevated bacteria abundance and
production within the sea-ice (e.g. Meiners et al. 2003, Riedel et al. 2007). Previous studies have
shown that heterotrophic bacteria are active and abundant components of Arctic sea-ice (Smith &
Clement 1990, Kaartokallio 2004). However, measurements of vertical and temporal distribution of
bacteria in sea-ice are lacking despite the fact that heterotrophic bacteria have a dual role in
nutrient cycling, particular in nitrogen (Lizotte 2003, Kaartokallio 2004), as they regenerate and
directly utilize inorganic nutrients (Cota et al. 1996).
Sea-ice represents a partially interconnected network of brine-filled channels comprising 1 to 30 %
of the sea-ice volume (e.g. Weeks & Ackley 1986). The degree of interconnection of the brine
enclosures is generally enhanced with increasing temperature and potential biomass accumulation
of sea-ice algae therefore increases towards the polar spring. The significance of heterotrophic
processes typically increases during late-bloom and post-bloom situations close to the spring thaw
(Kaartokallio 2004). Consequently, the sea-ice matrix is highly heterogeneous, dynamic and
quantification of the in situ bacteria and algal productivity represent a true challenge to many
scientists.
In order to understand and quantify Arctic carbon cycling, it is important to understand the sea-ice
related activity. The overall objective of this investigation was to describe the dynamic of
autotrophic and heterotrophic activity in first-year sea-ice in Marlene Bight, SW Greenland. We
measured autotrophic and heterotrophic activity under close to in situ conditions in gastight plastic
liners and the measurements were compared to the primary and bacteria production as measured
in laboratory based bottle incubations.
1.2 Material and methods
Study site and sampling
Sea-ice measurements were conducted on first-year land-fast sea-ice in Marlene Bight in the
vicinity of Nuuk, West Greenland (64°082´N 51°42´W) (Fig. 1). Sampling was conducted
continuously, at 1- to 2-week intervals from February 15th to April 11th 2008.
34
Sea-ice was established on February
1st and broke up on April 14th in
Marlene Bight. Sampled sea-ice
thickness varied between 39 cm to 58
cm, and the snow cover ranged
between 0-28 cm. The net aerobic
activity of an enclosed sea-ice
community was followed in situ by
determining the O2 dynamics in ice
cores sealed in gastight plastic liners
and placed under natural sea-ice
cover. These measurements were supplemented with parallel measurements, i.e. the basic
measurements, of temperature, salinity, irradiance attenuation in snow and ice, nutrients,
chlorophyll a (Chl a) concentrations, sea-ice algal productivity and bacteria productivity during each
sampling.
In situ incubations
At the station, ten sea-ice cores were collected using a MARK II coring system (Kovacs
Enterprises Ltd, NH, USA). Cores were cut into two equally long (~22 cm) sections i.e. top and
bottom, which were brought back to the laboratory in plastic bags, within 1 h of sampling collection.
In a laboratory cold room (3±1°C), sea-ice sections were placed in a gas-tight laminated NEN/PE
plastic bag (Hansen et al., 2000) fitted with a gas-tight Tygon tube and valve for sampling. We
tested the NEN/PE plastic bags at three different temperatures (20°C, 3°C, -20°C) in a previous
study. The test showed that the plastic bags were made of an extremely gas-tight laminated plastic
material, which is suitable for incubation of sea-ice. The fluxes of O2 through the plastic film were
insignificant; however the study showed that when freezing sea water at -20°C, a significant
fraction of O2 is present in gas bubbles (Appendix I).
Artificial seawater (with salinity of 33) with a known O2 concentration was added (10-30 ml)
(Grasshoff et al. 1983). The bags were closed and excess air quickly extracted trough the valve.
Two bags, i.e. top and bottom, were melted over night in the dark (5°C). The gas bubbles released
from the melting sea-ice were subsequently transferred to Exetainers (12 ml Exetainer ®, Labco
High Wycombe, UK) containing 20 µl HgCl2. The gas bubbles were analyzed for gaseous O2 and
CO2 by mass spectrometry (Sercon hydra 20-20 isotope radio mass spectrometer). The melt water
35
was similarly transferred to Exetainers for O 2 and dissolved inorganic carbon (DIC) measurements.
Dissolved O2 in the melt water was measured by Winkler titration (Grasshoff et al. 1983) and DIC
was measured in the sea-ice melt water using a CO2 analyzer (CM5012 CO2 Coulometer). The
remaining sea-ice melt water from the two bags was filtered on Whatmann GF/F filters 25 mm for
Chl a analysis. The filters were extracted for 18 h in 96 % ethanol (Jespersen & Christoffersen
1987) and analyzed fluorometrically (Turner TD-700 fluorometer, Turner Designs, California, USA)
before and after addition of 200 µl of a 1 M hydrochloric acid solution. The fluorometer was
calibrated against a pure Chl a standard (Turner Designs).
The remaining nine sea-ice cores in NEN/PE plastic bags were transferred to the drilled holes at
the sea-ice sampling site within 2 hours, and the in situ snow cover above the cores was gently reestablished. The sea-ice cores, i.e. top and bottom, were sampled at 1- or 2-week intervals to
determine the total O2 bulk concentration of the sea-ice.
O2 bulk concentration in the sea-ice (Ci) was calculated as:
Ci
CmMm CaMa
Mi
(1.1)
where Cm is O2 concentration in the melt water (gas bubble + sea-ice melt water), Mm, weight of the
melt water, Ca, O2 concentration in the artificial sea water, Ma is the weight of the artificial sea
water, and Mi is the weight of the sea-ice (Rysgaard et al. 2008). The photosynthetic quotient was
assumed to be 1.25 (Geider 1997).
Sampling, temperature and irradiance
On each sampling occasion, a parallel set of triplicate ice cores
were collected using a MARK II coring system (Kovacs Enterprises
Ltd, NH, USA). Downwelling irradiance was measured directly
above the snow (LiCor 1400, Li-Cor, NE, USA). In addition, air
temperature was measured 2 meters above the snow (Testo
Thermometer, Germany) and snow and sea-ice thickness were
measured using a measuring stick. The three ice cores were cut
into sections using a handsaw (Fig. 2) and vertical temperature
36
profiles were measured in each section using a thermometer (Testo thermometer, Germany).
Available downwelling irradiance below the snow and sea-ice was measured with a LiCor 1400 (LiCor, NE. USA) before the sea-ice section was placed in plastic containers in a dark thermobox and
brought back to the laboratory. All ice samples were melted for nutrients, salinity, Chl a, primary
production and bacteria production analysis at 3±1°C in darkness. The melting process lasted 48 h
and the plastic containers were placed on a shaking-table.
Chl a, nutrients and salinity
The sea-ice melt water was analyzed for Chl a as described above. The remaining sea-ice melt
water was filtered on Whatmann GF/F filters 25-mm and frozen (-18°C) until analysis of phosphate
(PO43-), nitrate and nitrite (NOx), silicate (SiO2) and ammonium (NH4+). NOx concentrations were
measured by vanadium chloride reduction (Braman & Hendrix 1989). Concentrations of SiO2, PO43and NH4+ were determined by spectrophotometric analysis (Strickland & Parsons 1972, Grasshoff
et al. 1983, Koroleff 1983). Triplicate measurements were performed on each sample showing
standard deviation less than ±0.07 µM (PO43-), ±0.5 µM (NOx), ±0.04 µM (SiO2) and ±0.29 µM
(NH4+), respectively (n=270). Conductivity of the melted sea-ice sections was measured using a
conductivity cell (Thermo Orion 3-star with an Orion 013610MD conductivity cell, UK) and
converted to bulk salinity (Grasshoff et al. 1983). Sea-ice brine salinity was calculated as a function
of temperature (Cox & Weeks 1983) and the brine volume as a function of bulk salinity, density and
temperature. Brine volume was calculated according to Leppäranta & Manninen (1988) for
temperatures > -2°C and according to Cox & Weeks (1983) for temperatures < -2°C.
Primary production
Primary production was determined in melted sea-ice water at three light intensities 42, 21 and 9
µmol photon m-2 s-1 and one dark using the 14C incubation technique (Steeman-Nielsen 1952).
Primary production was measured in all sea-ice sections (Fig. 2). Winkler bottles (120 ml) were
incubated on a vertically rotating plankton wheel at 1 rpm for 5 h at 3±1°C (at three light intensities
and one dark). Incubation was terminated by addition 200 µl 5 % ZnCl2 and was subsequently
filtered onto Whatmann GF/F filters 25-mm. The filters were added with 200 µl hydrochloric acid
(concentration 1M) in scintillation vials, to remove inorganic carbon, and was extracted in
scintillations liquid (PerkinElmer Ultima Gold) for 22 h and counted using a PerkinElmer TricCarb
2800 TR liquid scintillation analyzer (PerkinElmer, USA). DIC concentration in the sea-ice melt
37
water was measured with a CO2 coulometer as described by Rysgaard & Glud (2004). After liquid
scintillations counting the counts are converted to potential primary production.
Potential primary production (PPi) in sea-ice melt water was calculated as:
PP(µmol
C l-1h-1 )
i
DPMactivity DIC sea
Fdiscr Fcor
ice
(1.2)
DMPadded Tinc
Where DMPactivity is the 14C assimilated carbon corrected for assimilated carbon in dark (the
disintrations per minute on filter). DICsea-ice is dissolved inorganic carbon in the sea-ice melt water
(~450 µmol l-1). Fdiscr is the discrimination factor for the differences in 12C – CO2 and 14C-CO2
(1.05), Fcor is the correction factor for the
is the specific activity of the
14
C which is released and re-assimilated (1.06). DPMadded
14
C labelled medium (DPM ml-1) in which cells were labelled, Tinc is the
incubation time.
The dark-incubated bottles were used to correct for unspecific labeling (dark bottles rates was
subtracted from the light bottle rates to correct for non-photoautotrophic carbon fixation and
absorption). The potential primary production measured in the laboratory at different sea-ice
depths was plottet against the three laboratory light intensities 42, 21 and 9 µmol photon m-2 s-1
and fitted to a PE curve (Platt et al. 1980) to yield the photosynthetic parameters P m, α and Ek.
Curve fitting: Primary production (under no photoinhibition) versus laboratory light irradiance:
P(µmol C l-1h-1)
Pm 1 exp(
EPAR
)
Pm
(1.3)
Where Pm is the maximal photosynthesis rate at light saturation, α is the initial slope of the light
curve, EPAR is the laboratory irradiance. The photoadaptation index, E k was calculated as Pm/α.
In situ primary production
Downwelling irradiance at ground level was measured by Asiaq (Greenland Survey) every tenth
minute and hourly averages were calculated. Hourly downwelling irradiance was converted into
hourly PAR after calibration (R2=0.99, P<0.001, n=133) with a LiCor 1400 (Li-Cor, NE, USA). The
in situ hourly PAR irradiance was calculated at different depths (Fig. 2), depending on the sea-ice
and the snow thickness, using the attenuation coefficients measured during the sea-ice season.
38
In situ primary production was calculated (each hour) at different sea-ice depths from equation
(2.3) using hourly in situ PAR irradiance. Total daily in situ primary production was calculated as
the sum of hourly in situ primary production for each depth.
Bacterial production
Bacteria production was determined by measuring incorporation of [ 3H]thymidine into DNA and
using a conversion factor to relate DNA production to production of bacterial cells. Bacteria DNA
production was determined in the melt water from all sea-ice sections (Fig. 2). Triplicate
subsamples (Vfilt=0.010 l) were incubated at 3±1°C with 10 nM (10.1 Ci mmol-1) of labeled
[3H]thymidine. TCA-killed (trichloracetic acid) controls were made to measure the abiotic
adsorption. At the end of the incubation period (tinc=6 h), 1 ml of 50 % cold-TCA was added to all
the subsamples (Fuhrman & Azam 1982). [3H]thymidine subsamples were stored at 3±1°C until
filtration in scintillations vials. Subsamples were filtrated through mixed Cellulose Ester filters 25mm. Scintillations vials were rinsed with 5 ml of 5 % cold TCA. Subsequently, filters were rinsed 7
times with 1 ml of cold 5 % TCA and then counted using a PerkinElmer TricCarb 2800 TR liquid
scintillation analyzer (PerkinElmer, USA). We assumed that all bacteria assimilate exogenous
thymidine and that eukaryotes do not. Furthermore, to extrapolate the bacteria production to in situ
production we assumed that the respiration was light independent (multiply by 24).
Bacteria production (BPi) was calculated as:
BP(µmol C l-1h-1)
DPMsample Ncells Nc
(1.4)
SA Tinc Vfilt Mc
Where DPMsample is calculated as:
DPMsample
DPMsample(raw )
DPMblind
(1.5)
Ncells= 2.09·1018 cells/mol 3H is the conversion factor (according to Smith & Clement 1990), and Nc
= 5.7·10-8 ugC/cell is calculated as:
Nc ( gC / cell)
Cellsize ( m3 ) Cfactor (pgC / m3 )
1000000
(1.6)
39
Where, Cellsize is the average bacteria cell size (0.473 µm3). Cfactor is the factor to convert cell
volume to carbon units (0.12 pgC/µm3) according to Smith & Clement (1990). Mc is the molecular
mass of carbon (12.01g/mol) and the thymidine solution used had a specific activity (SA) of
2.24·1016 dpm/mole.
Heterogeneity
February 23th, 10 sea-ice cores were collected along a 10 m long transect to investigate the
horizontal, vertical and temporal variability of the biotic parameters (Chl a, primary production and
bacteria production) and the abiotic parameters (sea-ice temperature, brine salinity and volume) in
the sea-ice. The 10 sea-ice cores were collected close to the in situ incubations at 1 m intervals.
The cores were cut into two sections, i.e. top and bottom, and brought back to the laboratory in a
dark thermobox for further analysis as described above.
To extent the evaluation of the spatial variability a large scale investigation of the horizontal
variability was conducted on February 29th. Fifty-two sea-ice cores were collected along a 367 m
long transect to investigate the heterogeneity of Chl a, sea-ice temperature, brine salinity and brine
volume at the sea-ice water interface (section 1, Fig. 2). The snow and sea-ice thickness was
measured as described above. Four sea-ice cores were sampled at 20 cm intervals at position 1 m
(Fig. 1). The first core was cut vertically in two pieces at every sampling position. Sea-ice cores
were collected at a distance of 1, 3, 5, 7, 9, 20, 31, 42, 53, 64, 165, 266 and ~ 367 m. All the icecores were brought back to the laboratory in a dark thermobox for determination of brine salinity
and volume and Chl a concentration as described above.
Spatial autocorrelation (Legendre & Legendre 1998) was used to analyze the two-dimensional
distribution of the variables: Chl a, sea-ice temperature, brine salinity, brine volume, snow
thickness and sea-ice thickness. The autocorrelation was estimated by Moran‟s I (Moran 1950,
Legendre & Legendre 1998). This coefficient was calculated for each of the following intervals
along the transect (classes of distance): 0-0.50 m, 0.50-1.5 m, 1.5-2.5 m, 2.5-5.5 m, 5.5-10.5 m,
10.5-20.5 m, 20.5-50.5 m, 50.5-100.5 m and >100.5 m.
1.3 Results
Abiotic and chemical parameters
The air temperature varied from -14.9°C to +7°C during the sampling period. The snow-cover
temperature varied from -13°C to 0°C and the sea-ice temperature varied from -5°C to 0°C (Fig.
40
3A). In February, temperature within the sea-ice was lowest in the uppermost section of the cores
and highest in the bottom section and from late February the temperature within the sea-ice
increased until the spring thaw. Brine salinity was calculated as a function of the bulk salinity and
sea-ice temperature (Fig. 3B). The brine salinity decreased over time with a maximum brine salinity
of 80 in early March and minimum salinity of <10 from March 25th (Fig. 3B). Brine salinity varied
vertically within sea-ice cores during winter, with lower values encountered in the bottom sea-ice
from February 15th until late March. From March 25th to April 4th, brine salinity was lowest in the
uppermost part of the sea-ice. The brine volume increased throughout the sea-ice season, with a
maximum in late March at the uppermost section of the sea-ice (Fig. 3C).
Nutrient concentrations (silicate, phosphate and NO x) decreased over time (Fig. 4A-C).
Furthermore, during the sea-ice season, no large vertical variation in nutrient concentrations was
observed within the sea-ice profile.
Ammonium increased throughout the winter period, reaching a maximum of 23.5 µM at the end of
the sea-ice season (Fig. 4D).
41
NH4:NOx ratio was higher than 1 during the entire sea-ice season and increased towards the end
of the study period, which emphasize that ammonium was the dominant form of dissolved
inorganic nitrogen (DIN) (Fig. 5). Ammonium concentrations in the underlying sea water were also
high (13 µM) (data not shown).
42
Autotrophic activity
The downwelling irradiance was corrected for the relative attenuation coefficient of snow and seaice cover. Early in the season, irradiance in the lower most sea-ice section was low (0.03-0.15
µmol photons m-2 s-1, 0.1 % of the downwelling irradiance) (Fig. 6A). Light availability increased
with increasing day length towards the end of the season, reaching a maximum downwelling
irradiance of 76 µmol photons m-2 s-1 (26 % of the downwelling irradiance) in the upper most seaice section and 4 µmol photons m-2 s-1 (1.5 % of the downwelling irradiance) below the sea-ice on
April 4th. The high downwelling irradiance correlated with the disappearing snow cover observed in
this period.
Sea-ice profiles of the algal biomass expressed as Chl a showed that the biomass was highest in
the lower 15 cm of the sea-ice cores with maximum values of 2.80 µg l-1(Fig. 6B). The algal
biomass in the lower 15 cm of the sea-ice cores reached a maximum on March 25th, but then
quickly decreased, remaining constant for the rest of the sea-ice season. In addition the algal
biomass was high in the mid-section of the sea-ice profile on March 19th and March 25th. The algal
biomass in the upper-section decreased from March 25 th to April 4th, when the downwelling
irradiance increased (Fig. 6A). Sea-ice primary production increased throughout the winter from
0.07 mg C m-2 d-1 on February 15th to 9.54 mg C m-2 d-1 on April 4th (Fig. 6C). The highest primary
production was encountered in the middle-part (15-25 cm) of the sea-ice profile on April 4th.
43
From February until mid-March irradiance
remained low which resulted in a low maximal
photosynthesis (Pmax) ranging between 0.42 and
1.06 µmol C l-1 d-1 in the bottom section of the
sea-ice cores (Table 1).
The sea-ice algae showed adaptation to the low
irradiance with a photoadaptation index (E k) in
February of 15.31 µmol photons m-2 s-1 and
13.00 µmol photons m-2 s-1 in the bottom and top sections of the sea-ice cores, respectively (Table
1). The sea-ice algae were exposed to higher irradiance in the top sections cores and the
measurements exhibited a higher photoadaptation index (E k=36.63 µmol photons m-2 s-1) at the
end of the sea-ice season. The downwelling irradiance decreased in the bottom sections on March
25th, and the sea-ice algae showed adaptation to lower irradiance with E k of 3.93 µmol photons m-2
s-1. Furthermore, the maximal photosynthesis (Pmax) increased during the sea-ice season from 0.27
to 2.27 µmol C l-1d-1 and from 0.42 to 3.69 µmol C l-1d-1, respectively in the top and bottom sections
of the sea-ice cores.
Bacteria production
Sea-ice bacteria production showed a single peak (0.36 µmol C l-1 d-1) on March 10th in the upper
10 cm section of the sea-ice profile (Fig. 7A). Production then decreased until onset of sea-ice melt
on April 4th, where a peak in the production (1.14 µmol C l-1 d-1) at the center section of the sea-ice
profile occurred (Fig. 7A).
A successional sequence of the autotrophic and heterotrophic activity, as interfered from bacteria
and primary production, was observed, beginning with a low productive winter stage followed by a
predominantly heterotrophy-dominated stage late in March and finally a blooming of the sea-ice
algae until the onset of sea-ice melt (Fig. 7B). The thymidine based bacterial production was 70 %
of the co-occurring primary production during the sea-ice season.
44
Comparisons of methods to
estimate the thymidine based
bacterial production using various
methods are shown in Table 2. The
average bacteria production was in
the range 0.10 to 0.51 µmol C l-1 d-1
depending on the conversion factors
used (Table 2). The sea-ice was
autotrophic-dominated or
heterotrophic-dominated depending
on which conversion factor we used.
In this present study the bacteria
production was calculated according
to Smith & Clement (1990).
45
In situ measurements of net aerobic activity
The net aerobic activity of the enclosed sea-ice community was followed under in situ conditions.
The sea-ice cores remained aerobic during the entire experimental period due to constant net O2
production rate of 0.60-0.80 µmol O2 l-1 d-1 (0.50-0.60 µmol C l-1 d-1) at the top core sections (Fig.
8A) and 1.20-6.40 µmol O2 l-1 d-1 (1.0-5.10 µmol C l-1 d-1) at the bottom core sections (Fig. 8B). This
indicates a “strong” net autotrophic dominance in the bottom cores section of the sea-ice and a
“weak” net autotrophic dominance in the top cores section of the sea-ice. A t-test shows that the
slope on the regression line is positive and significant (p<0.01) in the bottom cores, whereas the
slope is positive but insignificant (p>0.05) in the top cores. Given the concurrent bacteria
production measured in parallel incubation, corresponding to 0.39 µmol O2 l-1d-1 (Fig. 7A), the
gross autotrophic activity amounted to 1.00-1.20 µmol O2 l-1 (0.80-1.00 µmol C l-1 d-1) at the top
cores sections and 1.60- 6.80 µmol O2 l-1 (1.30-5.40 µmol C l-1 d-1) at the bottom cores sections.
A slightly higher algal biomass was observed in the bottom core sections of the sea-ice with
maximum values of 4.80-10.60 µg Chl a l-1 (Fig. 9B). In addition, the maximum algal biomass in the
top cores sections of the sea-ice was 7.00 µg Chl a l-1 (Fig. 9A).
46
Heterogeneity
On February 23th abiotic and biotic
parameters such as, bulk salinity,
brine salinity, brine volume,
temperature, Chl a, primary
production and bacteria production
were measured in top and bottom
section of the sea-ice cores (10
cores) along a 10 m transect
(Table 3 & 4). The measurements
were performed to describe the
vertical and horizontal variation of
these variables on a small scale.
There was a significant effect of depth on Chl a levels (two-way ANOVA, F1,19=7.452, p=0.023)
with the bottom level being higher than the top level. The primary production rates were highest at
the bottom and lowest in the top (two-way ANOVA, F9,19=119.180, p<0,0005), while the bacteria
production was higher at the top than at the bottom (two-way ANOVA, F1,19=6.521, p<0.031) (Table
4). There was also an effect of depth on all the abiotic parameter. Across all biotic parameters
there was a small horizontal variation (two-way ANOVA, F9,19=0.142, p≥0.142). For all the biotic
and abiotic parameters there was a significant temporal variability (two-way ANOVA, F6,4950=37.157,
p<0.0005) and vertical variability (two-way ANOVA, F5,49-50=15.215, p<0.0005) but
there was also in all cases a significant interaction (two-way ANOVA, F=4.145, p≤0.002).
To extent the evaluation of the spatial variability a large scale investigation was conducted on
February 29th. Algal biomass (Chl a), bulk salinity and temperature in the lowest section (5 cm) of
the sea-ice, snow and sea-ice thickness were measured along a 367 m transect to describe the
horizontal 2-dimensional distribution of these variables. Moran´s I was used to estimate the spatial
autocorrelation within the dataset (Moran 1950, Legendre & Legendre 1998, Rysgaard et al. 2001).
Positive or negative values indicate positive or negative autocorrelation, respectively (Fig. 10). The
change from positive to negative values of Moran´s I for Chl a occurred from the 20.5 m class of
distance to the 50.5 m class of distance. This indicates that the average radius of the sea-ice algae
biomass patches was probably between 20.5 and 50.5 m, where correlation changes from positive
to negative in the correlogram is observed. The change from positive to negative values of Moran´s
I for sea-ice temperatures and bulk salinity occurred from the 20.5 m class of distance to the 50.5
47
m class of distance. In contrast the average radius of sea-ice and snow thickness was >100.5 m
where the first change from positive to negative values of Moran´s I occurred.
1.4 Discussion
This study provides a comparison of primary and bacteria production measured in laboratory
based bottle incubations and the in situ dynamics of O2 concentration of sea-ice samples kept in
gastight plastic liners throughout the sea-ice season. The laboratory measurement of primary
production and bacteria production supported the measurements of O2 in the gas tight liners,
where a net autotrophic response of the sea-ice community was observed.
Laboratory measurements
Primary production in sea-ice
In this study, downwelling irradiance was reduced to less than 12 % (2.2-11.7 %) of the surface
irradiance in the upper sea-ice section and less than 2 % (0.10-1.5 %) at the sea-ice water
interface from February until April. Light availability increased with increasing day length towards
the end of the season, reaching a maximum downwelling irradiance of 76 µmol photons m-2 s-1 (26
% of the downwelling irradiance) in the upper most sea-ice section and 4 µmol photons m-2 s-1 (1.5
% of the downwelling irradiance) below the sea-ice on April 4th. Sea-ice algae are known to be
shade adapted and are able to grow at light intensities below 10 µmol photon m-2s-1 (Gradinger &
Ikävalko 1998). This acclimation is primarily achieved by increasing cell pigment concentrations
48
and the ratio of accessory pigments to Chl a, thus enhancing light-harvesting capability in response
to decreased irradiance (Robinson et al. 1995).
The data presented here agree with results presented elsewhere, with low Ek values compared to
typical phytoplankton Ek values of ~150 µmol photon m-2s-1 (Arrigo 2003), thus indicates an
adaption of the sea-ice algae to maintain photosynthesis production. Despite acclimation sea-ice
algae growth is often light limited, which was probably also the case in the present study, since a
maximum sea-ice algae biomass of only 2.8 µg l-1 was observed at the bottom of sea-ice late in
March. However, the sea-ice algal biomass reported in present study is similar to previously
reported values from the same region (Mikkelsen et al. 2008). The maximum sea-ice algae
biomass did not coincide with the maximum primary production. An explanation of the low primary
production late in March could be that the downwelling irradiance was reduced to less than 6 % (35.50 %) of the surface irradiance in the upper sea-ice section and less than 0.1 % (0.02-0.99 %) at
the sea-ice water interface, which is likely to result in low primary production. Consistent with this
notion, Chl a-specific photosynthetic activity in late March was 0.84 mg C mg -1 Chl a d-1 ~40 times
lower than the Chl a-specific photosynthetic activity of 35 mg C mg-1 Chl a d-1 on April 4th.
The microalgae living near the ice-snow interface were exposed to relatively high light intensities
on April 4th and an associated decline in Chl a concentration from 0.95 to 0.35 µg l-1 was observed.
The relatively low Chl a biomass observed on April 4th may reflect reduced chlorophyll per cell as
an adaptation to increasing light levels rather than fewer cells (McMinn et al. 2000). At the bottom
of the sea-ice core decline in Chl a from 2.80 µg l-1 to 1.28 µg l-1 was observed. Ice-warming
towards sea-ice melt resulted in an increase in brine-channel diameter, which may allow
metazoans to reach otherwise isolated channels inside the sea-ice matrix (Krembs et al. 2000).
Increasing metazoan grazing could to some extent explain the decline in Chl a concentration in the
lower 5-10 cm. However, despite a decrease in Chl a, primary production showed a peak of 9.54
mg C m-2d-1 on April 4th (Fig. 6C).
Annual primary production was in this study found to be 0.10 g C m-2, which is lower than the value
(0.78 g C m-2) measured in the same region in a previous study by Mikkelsen et al. (2008). Within
the sea-ice abiotic factors such as temperature, salinity and inorganic nutrient limitations can all
contribute to reduce rates of primary production. In our study these variables did not diverge
considerably from what was found by Mikkelsen et al. (2008). In contract, attenuation of the
irradiance by the sea-ice and snow seemed to be the major factor limiting algal growth, which also
has been the case in other studies (e.g. Perovich 1990, Mundy 2005, Glud et al. 2007). In this
study, the average light attenuation coefficient of snow and sea-ice was 24.30 m-1 (±6.35 m-1) and
2.52 m-1(±0.49 m-1), respectively, which was higher compared to the light attenuation coefficient of
49
snow 4.8 m-1 (Glud et al. 2007) and sea-ice of 0.9 m-1 (Roberts et al. 2002) used by Mikkelsen et
al. (2008). The light attenuation coefficient of snow may vary from 4 - 40 m-1 depending on the
water content in the snow cover (Perovich 1996) whereas that of sea-ice depends on the
temperature, brine volume, amount of air bubbles and particulate material enclosed in the sea-ice
matrix (Glud et al. 2007). The use of a lower or higher attenuation coefficient may result in an
overestimation or underestimation of the primary production in the sea-ice.
Generally, primary production of sea-ice algae is assessed by the 14C method (Steemann Nielsen,
1952). An associated problem with this technique is that melting or crushing of sea-ice is required
to separate the organisms from the surrounding sea-ice, thus altering the complex sea-ice
microenvironment. The photosynthetic performance may be considerably affected, when the seaice environment (i.e. irradiance, substrate, salinity, temperatures, spectral composition) is altered.
Secondly, melting of the sea-ice may influence algal abundance and species diversity (Gradinger
1999). This bias can be reduced by melting sea-ice samples in large volumes of pre-filtered sea
water (Ikävalko 1998, Ryan et al. 2004). In this study the samples were melted slowly in darkness
at 3±1°C according to Mikkelsen & Witkovsky (in Press). Previous studies have shown that salinity
induced stress to sea-ice algae was light dependent, such that incubated samples only suffered
photosynthetic damage when irradiance was applied in parallel (Ralph et al. 2007). Here we argue
that the approach used in present study may have limit photosynthetic damage and osmotic stress
during melting.
A problem when melting sea-ice samples in darkness may be that sea-ice algae could remain
inactive during a subsequent
14
C incubation period in light. However, it has been shown that sea-
ice algae adapt to increasing light intensity within hours (Smith & Sakshaug 1990). In addition
Peter & Thomas (1996b) showed that some algae cells are thus able to sustain an active
photosynthetic apparatus for months in darkness, which allows carbon assimilation immediately
after reintroduction to light.
Another problem with the
14
C method is that little information in the literature is available on the
DIC concentration used for quantifying primary production in sea-ice. However, one should be
cautious using table values for sea water (2 mM) as a standard concentration, where this is ~4
times higher, compared to actual DIC concentration measured in the sea-ice during the present
study. This will consequently affect primary production proportionally.
Bacteria production in the sea-ice
The maximum bacteria production of 3.18 mg C m-2 d-1 was reported on April 4th. Compared to
bacteria production of ~3.0 mg C m-2 d-1 measured in high Arctic sea-ice using the same
50
conversion factors (Smith & Clement 1990), the maximum bacteria production measured in this
study was slightly higher.
In the present study the sea-ice was net autotrophic-dominated, however, a predominantly
heterotrophy-dominated stage late in March occurred prior to the sea-ice algal bloom. The
sequential succession of the microbial community found in the first-year sea-ice in Marlene Bight
was different compared to studies conducted by e.g. Grossmann & Gleitz 1993, Günther &
Dieckmann 1999 and Kaartokallio 2004 as it was observed, in opposition to their studies, that the
heterotrophic activity was predominant before the sea-ice algae bloom. This may be due to very
low light availability late in March resulting in a predominantly heterotrophy-dominated stage, but
on April 4th the light availability increased, which resulted in an sea-ice algal bloom.
In both the Antarctic (Grossi et al. 1984, Kottmeier et al. 1987) and the Arctic (Smith et al. 1988a)
the net bacterial production in sea-ice is only a few percent of co-occurring algal production. In the
present study our bacteria production estimates were 70 % of the co-occurring primary production
during the sea-ice season. One explanation for the much higher ratio of bacterial to algal
production compared to previous measurements in either Arctic or Antarctic sea-ice communities
may be due to the very low primary production measured in present study. A further explanation
for the higher ratio of bacterial to algal production may be due to the laboratory procedure. The
sea-ice cores were melted in darkness at 3±1°C. Studies by Hancke & Glud (2004) have shown
that increasing temperature stimulates heterotrophic activity more than photosynthesis. Thus,
because the sea-ice community was exposed to temperatures higher than in situ temperatures
during melting it could have contributed to the higher bacterial production observed. When the
stronger heterotrophic temperature response is taken into account the ratio of bacterial to algal
production is decreased to 30 %.
Previous studies have indicated a positive correlation between phototrophic and heterotrophic
activity in some sea-ice habitats (Gosink et al. 1993). In present study the maximum bacteria
production was observed in the mid section of the sea-ice cores, where also the maximum primary
production was observed. This may suggest a close metabolic coupling between the two
communities (Gradinger et al. 1992, Thomas et al. 1995). As shown by Robinson et al. (1998) in
Antarctica, by Gosselin et al. (1990) in Canadian Arctic and by Haecky & Andersson (1998) in the
Baltic Sea, sea-ice algal growth is sequentially light-or nutrient-limited along with the winter
progress. While light increases primary production, thus enhancing the exudation of dissolved
organic matter (DOM) from algae the light conditions may possible indirectly affect the bacterial
productivity, where DOM is assumed to be the primary substrate in the sea-ice bacteria
metabolism (Kaartokallio 2004). However, our heterogeneity study performed on February 23 th
51
showed no vertical correlation between bacteria and primary production in the sea-ice, which may
indicate that an alternative carbon sources may also be important during winter for bacteria
production.
The maximum bacteria production in the sea-ice cores correlated with an increased ammonium
concentration from 20 µM to 23 µM. Apart from ammonium a strong tendency towards decreasing
nutrient concentrations over time was observed (Fig. 4A-C). Enrichment of ammonium and
declining concentrations of phosphate, silicate and NOx in this study are consistent with the results
from Gradinger & Ikävalko (1998). These investigators suggested that the enrichment of dissolved
inorganic nitrogen (DIN) was a consequence of heterotrophic regeneration within the sea-ice or
advective exchange with the DIN enriched underlying sea water. At the beginning of the sea-ice
season the upper part of the sea-ice was found to contain high concentrations of nitrogenous
nutrients, which presumably were of atmospheric origin (Kaartokallio 2004). However, the NOx
concentration was low towards the end of the season and ammonium was observed to be the
dominant form of DIN (Fig. 5). The accumulated ammonium observed in the present study could
be explained by reduced metabolic activity of autotrophic cells during the winter (Zhang et al. 1998)
together with a continued heterotrophic regeneration and reduced oxic conditions that inhibit
potential nitrification. This may also further support the onset of the spring sea-ice algal bloom
(Conover et al. 1999). Since there is no direct evidence, we suggest that future studies should
include detailed surveys on the evolution of nutrient status in the forming sea-ice with a special
focus on DIN.
The uptake and incorporation of [ 3H]thymidine have been used as a measure of (heterotrophic)
bacterial DNA synthesis (Fuhrman & Azam 1982). This method assumes that all bacteria
assimilate exogenous thymidine and that eukaryotes do not. Uptake of [ 3H]thymidine by eukaryotic
microalgae have been clearly demonstrated (Rivkin 1986). However, Fuhrman & Azam (1980)
have shown that at low concentration and at relatively short incubation period the uptake of
[3H]thymidine by eukaryotes should be negligible.
The [3H]thymidine method is a widely used index of bacteria production, requiring a conversion
factor to relate DNA production to production of bacteria cells. In the present study, we
extrapolated incorporation data to bacteria production by using the assumption discussed by Smith
& Clement (1990). The authors estimated the conversion factor for high Arctic sea-ice bacteria,
and they found that the factor was 2.09 ·10 18 cells mol-1 in the early sea-ice season and 0.47·1018
cells mol-1 in the late sea-ice season. The conversion factor from the early season fell within the
commonly reported range of 1 to 4·10 18 cells mol-1 (e.g. Riemann et al. 1987, Fuhrmann & Azam
1982) and Smith & Clement (1990) suggested that this factor was more appropriate, than the lower
52
conversion factor from the late season. In this study we used the conversion factor (2.09 ·10 18 cells
mol-1) from the early sea-ice season found by Smith & Clement (1990).
The choice of conversion factor is intensively debated in the literature. The conversion factor is
often derived, theoretically from assumptions of the extent of isotope dilution, thymidine content of
bacterial DNA and the amount of DNA per cell (Fuhrmann & Azam 1982), or by empirically
comparing incorporation rates with increase in bacterial numbers (e.g. Kirchman et al. 1982, Smith
& Clement 1990). Since the conversion factor may vary (e.g. Riemann et al. 1987), it is unclear
whether changes in the calculated bacteria production rate are real or are due to variations in the
factor. The conversion factor should ideally be determined experimentally for each environment or
season sampled. To determine a conversion factor, an independent measurement of bacterial
production or growth rate should be made, or the relationship between thymidine incorporation and
bacteria production must be determined. However, experiments are time-consuming and
problematic. In this study, we calculated the average bacteria production with different
assumptions discussed in the literature. The average bacteria production ranging between 0.10 to
0.51 µmol C l-1d-1 depending on the assumptions used (Table 2). In our study, bacterial production
was compared to primary production (Fig. 7B). The sea-ice was autotrophic-dominated or
heterotrophic-dominated depending on which conversion factor we used, although mostly
autotrophic-dominated. The fivefold variation observed in the average bacteria production
emphasizes the significance of using the appropriate conversion factor.
In situ measurements of net aerobic activity
The net aerobic activity of an enclosed sea-ice community was followed in the field by determining
O2 dynamics in sea-ice cores sealed in gastight plastic liners and placed in pre-drilled holes. These
sea-ice cores were sampled continuously at 1-2 weeks intervals to determine total O 2
concentration of sea-ice. The net aerobic activity of the enclosed sea-community was in this way
followed under conditions close to in situ. The net autotrophic response of the sea-ice community
as resolved in the laboratory was also observed in the gastight plastic liners kept in situ, where the
sea-ice algal primary production was dominant throughout the sea-ice season, resulting in net
oxygen formation in the sea-ice of 0.60-6.40 µM O2 d-1 (Fig. 8). Given the concurrent bacteria
production measured in parallel incubation, corresponding to 0.39 µmol O2 l-1 sea-ice d-1 (Fig. 7A &
Table 2), the gross autotrophic activity amounted to 1.00-1.20 µmol O2 l-1 (0.80-1.00 µmol C l-1 d-1)
at the top cores sections and 1.60- 6.80 µmol O2 l-1 (1.30-5.40 µmol C l-1 d-1) at the bottom cores
sections. The gross autotrophic activity of 0.80-5.40 µmol C l-1 d-1reported in situ was slightly
higher than the autotrophic response reported in the laboratory based measurements.
53
It is generally accepted that in order to get realistic estimates of the in situ productivity the sea-ice
microenvironment (i.e. temperature, salinity, nutrient concentration) should be maintained (e.g.
McMinn & Ashworth 1998). The in situ measurements shows that the sea-ice during the entire seaice season was net-autotrophic, which correspond to the results found in previous studies
conducted on sea-ice (Smith & Clement 1990, Kaartokallio et al. 2007).
Sea-ice related processes interact with the underlying sea water via ice melting/freezing, advection
and grazing. Whole-core incubations of intact sea-ice cores in the NEN/PE plastic bags have the
advantage to minimize physical disturbance of the sea-ice microenvironment. However, brine
leakage is impeded when using this method. Rysgaard et al. (2008) have shown that O 2 depletion
occurs in association with the sea-ice spring thaw and Glud et al. (2002) have shown that O 2
percolates out of the sea-ice matrix due to the density gradient induced brine leakage. The brine
leakage is most significant near the ice-water interface, which may explain the higher O2
production observed in bottom cores in this study compared to the net autotrophic response
observed in the laboratory based measurements during the sea-ice season (Fig. 8). Advection
exchange of e.g. O2, nutrients, salt, bacteria and algae with the underlying sea water is hampered
by the method, which makes this method a “close” to in situ measurements.
Heterogeneity
A crucial assumption behind our in situ approach is that all sea-ice cores are representative of the
average condition in the sea-ice. However, it cannot be fully excluded that micro-or meso scale
variability could compromise this assumption.
According to tests conducted on February 23th the horizontal variability, i.e. biotic parameters,
along the 10 m transect (which corresponds to the sampling plot size where samples were
obtained) was markedly smaller than the temporal variability (Table 4). Furthermore a significant
vertical variability in the biotic parameters was observed; Chl a levels at the bottom level of the
sea-ice cores was higher (0.3400 µg l-1) than at the top level (0.1548 µg l-1), the primary production
rates were highest at the bottom level (0.03 µmol C l-1d-1) and lowest at the top level (0.02 µmol C l1 -1
d ) and the bacteria production was higher at the top level (0.031 µmol C l-1d-1) than at the bottom
level (0.015 µmol C l-1d-1). Consequently, we argue, that the observed temporal patterns of the
measured parameters of the intact sea-ice cores are not compromised by spatial variability.
However, the spatial test was performed at the beginning of the sea-ice season, where the light
availability was very low, which resulted in Chl a level and primary production close to zero.
Previous studies have shown that spatial variability of phototrophic biomass is controlled by light
availability at the underside of the ice during early stages of snow melt (Glud et al. 2007).
54
Potentially spatial variability would become more important in the later stages of the sea-ice
season where communities may have become established.
To extend the evaluation of spatial variability a large scale investigation of the horizontal
patchiness of Chl a, sea-ice temperature, brine salinity and brine volume and snow and sea-ice
thickness was conducted along a 367 m transect on February 29th. If knowledge of horizontal
variations is unknown evaluation of data obtained through point sampling is impeded (Eicken et al.
1991). The sea-ice algal biomass (Chl a) followed sea-ice temperature with an average radius of
20.5 to 50.5 m, suggesting that snow and sea-ice thickness were the main factors influencing the
light condition in the sea-ice and thus the phototrophic biomass. Horizontal distribution of snow
cover is controlled by snowfall and is affected by storm events (e.g. Mundy et al. 2005) thus the
snow cover present at the time of sampling does not necessarily reflect later stages of the
developed sea-ice. Our study indicates that snow cover and thus light availability was the
controlling factor influencing the algal biomass, which further supports observations by Rysgaard et
al. (2001).
1.5 Conclusion
In this study, we present a comparison of the net activities i.e. autotrophic and heterotrophic
activities measured in the laboratory and in situ dynamics of O2 concentration in sea-ice samples
kept in gastight plastic liners throughout the sea-ice season. The results from the activities
measured in the laboratory show that the net autotrophic activity was dominant for most of the seaice season. However, a predominantly heterotrophy-dominated stage late in March occurred prior
to the sea-ice algal bloom. The net autotrophic response of the sea-ice community as resolved in
the laboratory was also observed in the gastight plastic liners kept in situ. Thus the laboratory
based measurement of primary and bacteria production generally supported the results found in
the in situ measurements.
Furthermore, light availability was the major factor regulating sea-ice algae activity. Before April,
snow cover inhibited any significant sea-ice primary production (<0.5 mg C m-2 d-1) in Marlene
Bight, but as the snow cover decreased, light availability increased and the sea-ice algae biomass
began to flourish resulting in high primary productivity measurements (9.54 mg C m-2 d-1).
Maximum bacteria production of 3.18 mg C m-2 d-1 occurred late in the sea-ice season and
coincided with the maximum primary production. A successional sequence of autotrophic and
heterotrophic activity was observed, beginning with a low productive winter stage followed by a
55
predominantly heterotrophy-dominated stage in late March and finally a sea-ice algae bloom
developed until the onset of sea-ice melt.
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60
Chapter 2
Planar optodes on sea-ice dynamics: a new in vitro approach for
determination of autotrophic production within brine channels of
undisturbed sea ice.
Morten Kristensen* and Dorte H. Søgaard
Greenland Institute of Natural Resources
Box 570
3900 Nuuk
Greenland
University of Copenhagen
Marine Biological Laboratory
Strandpromenaden 5
3000 Helsingør
Denmark
*Corresponding author: [email protected]
61
2.1 Abstract
An experimental chamber for studying biotic and non-biotic gas dynamics of sea-ice in the
laboratory was constructed and optimized. Sea-ice shape and sea-ice thickness could be regulated
by adjusting the water flow below the ice and temperature gradients of the set up. Oxygen
dynamics as induced by formation and melting of sea-ice was studied by combined use of planar
optodes and mass balance approaches. Ice thickness varied from 15-51 mm depending on flow
rate, stirring level, and temperature of the cooling liquid. The average oxygen release (during the
first 4500 minutes of an experiment until steady state was reached) measured from a growing seaice was 0.095 nmol O2 *cm-2 *s-1. The maximum ice growth rate in this setup was 0.25 mm*min -1
and this growth rate was also the highest measured in any of the chambers.
Sea-ice was inoculated with a mixed culture of sea-ice algae (Fragilariopsis nana,
Chlamydomonas sp., Fragilariopsis sp., Melosira arctica and Pyramimonas quadrifolia.). In quasi
steady state the oxygen distribution in the biologically active sea-ice reflected patches of net
oxygen production when exposed to light and patches of oxygen consumption in darkness,
measured by planar optodes. The distribution of activity was, however, highly heterogeneous with
inactive patches being non sensitive towards shifts in ambient irradiance as opposed to other
patches showing a clear light associated oxygen dynamics. The same ice was subsequently
melted and the 14C-metod was applied, there were a great consistency between data from the two
different methods even though they were applied on two different media, ice and water. Based on
a series of measurements the quantitative importance of biotic versus non-biotic factors for seaiceoxygen dynamics is discussed. The non-biotic oxygen response in an ice that was not in a
steady state, was found to exceed the oxygen response found from biotic activity in a similar ice by
far.
2.2 Introduction
Seasonal sea-ice is an important component of the marine ecosystem which may cover an area up
to about 7 % of the earth entire surface area. Around Antarctica the seasonal sea-icecover
changes by about 80 % whereas the seasonal changes in the Arctic Ocean are less pronounced
because a significantly higher fraction of sea-iceis multiyear ice (Gloersen and Campbell, 1991),
i.e., more than 1 year old.
62
Sea-iceformation and composition
When sea-iceforms, ice crystals accumulate at the sea surface and consolidate to a loosely
meshed matrix. As the initial centimetre-thick ice layer thickens (when ice crystals coalesce)
hypersaline brine is produced, in which the dissolved constituents of the seawater are
concentrated (Gleitz et al. 1995, Cox & Weeks 1983). Expelled brine drains into milli- to
micrometer sized pockets and channels that pervade the ice matrix (Lake and Lewis, 1970;
Niedrauer and Martin, 1979).
The chemical composition of the brine is controlled by sea-icetemperature, which in turn
determines the ionic strength of the expelled liquid. A large fraction of the brine is ejected from the
growing ice sheet (Scherbina et al. 2003, Rysgaard et al. 2007), which as a consequence lowers
the overall bulk concentration. Upon sea-iceformation, the chemical composition of the enclosed
brine is determined by the composition of the seawater from which it is derived (Weeks and Ackley,
1986). As brine temperature decreases, its salinity increases, affecting the equilibrium between the
solid ice phase, the precipitated salts and the liquid brine solution (Weiss R.F. 1970, Cox & Weeks
1983). Typically, brine channels compose from 1- 30 % of total sea-ice volume (Weeks & Ackley,
1986, Krembs & Engel, 2001).
The highly complex network of brine channels represents a favorable niche for a great diversity of
halotolerant and cryophilic microorganisms (Gosselin et al. 1997, Morgan-Kiss et al. 2006, McMinn
et al. 2000, 2005, 2007).
When investigating the biotic community living within the sea-ice brine matrix, the restricted space
available, strongly limits the number of methods which can be used for exploring the environment
and up until now only few in situ techniques that do not require a complete disruption of the ice are
available. The ideal approach would be to measure primary production in situ, without destroying
any of the sea-ice algae or their habitat. Although new methods are coming closer to this ideal it
has not yet been obtained.
The following is a presentation of some of the strengths and limitations of the methods which are
being used for measurements of autotrophic activity and especially primary production in sea ice:
63
Methods for measurements of biotic processes
Pulse amplitude modulated fluorometry (PAM)
Pulse amplitude modulated fluorometry (PAM) is a completely noninvasive technique used for
evaluations of sea-icealgae physiology as well as indication of the existence of a phototrophic
biomass. It is based on sample illumination and measurements of cohering chlorophyll
fluorescence (Rysgaard et al. 2001, Glud et al. 2002, Consalvey et al. 2005). A disadvantage of
this technique (in the sea-iceregime) is that a PAM cannot perform deep measurements within ice
(although a signal can be obtained a couple of centimeters from the surface within the ice) and are
mainly used for physiology measurements of surface adhering phototrophs. Furthermore,
measurements are regrettably not directly convertible into production rates (Consalvey et al. 2005).
Primary production estimation based on phototrophic uptake of radioactive labeled
Primary production is often seen measured by the
14
C
14
C-method (Horner & Schrader 1982, Arrigo et
al. 2003, Rysgaard et al. 2001, Glud et al. 2002, Mikkelsen et al. 2008). This approach is based on
the autotrophic uptake of carbon where a known amount of radioactively labeled carbon ( 14C
bicarbonate) has been added to the sample. The
14
C uptake by the autotrophs during a time period
is then measured by liquid scintillation counting and the potential carbon uptake can be calculated
(see Materials and methods, Primary production – 14C-method).
The 14C -method has been well adopted by researchers worldwide for measuring primary
production from autotrophy, and it is easy to apply. However, in the sea-iceregime, the
14
C-
method applied on bulk water from melted sea ice, may not be the best method for measuring
primary production as melting of the sea-icecompletely changes the internal environment of the ice
and practically all compounds from within the brine channel network are diluted into a fraction of
their original concentration, which may lead to a loss of control in the microbial community (it has
been shown by Garrison & Buck 1986 and Ralph et al. 2007 that melting out can induce osmotic
stress in sea-icealgae). The principles of the 14C -method are originally described in SteemanNielsen 1952. Another approach of using the
sea ice, is by the addition of
14
C -method for primary production determination in
14
C to whole samples (ice cores) or crunched sea-ice, and
14
subsequently measure the C uptake as described earlier. But due to slow diffusion rates in seaiceand the long diffusion path through the brine channels, the
14
C added would most likely not
reach the interior parts of the ice during the incubation time. If it did, the concentration differences
of
14
C throughout the ice would still be too large for the method to work, as it is based on an even
64
distribution between the dissolved inorganic carbon (DIC) originally present in the media and the
added 14C.
Another in situ incubation technique using the
14
C -method has been developed to measure
primary production in a wide variety of habitats (Mock & Gradinger, 1999). Sea-icecores are
sectioned into appropriate segments and placed into individual petri dishes for inoculation with 14C.
The petri dishes are then stacked, and placed in an acrylic-glass barrel, and returned to their
original position in the sea-ice, where they are incubated. Fine scale (1 cm) resolution of primary
production is provided by this method without destroying the sea-ice microenvironment including
ambient light field (Arrigo, 2003). This is a truly in situ measurement of sea-ice primary production.
The only drawback is that nutrient supply from the underlying sea water is hampered. However,
under conditions where nutrient concentrations are not limited this technique is superior to most
other methods.
Primary production estimation based on oxygen exchange rates - Oxygen microelectrodes
Oxygen microelectrodes are excellent tools for evaluating oxygen production or consumption of
adhering microorganisms on a steady state sea-ice(McMinn & Ashworth, 1998, McMinn et al.
2000, Trenerry et al. 2002, Glud et al. 2002). The approach does not infer outer stimuli to the
environment and is probably one of the less invasive techniques for measuring on sea-icebiotics.
The downsides of the approach are that the thin tip of oxygen microelectrodes easily fractures if
forced into ice, furthermore microelectrodes (as opposed to micro-optodes) consume oxygen
(Mock et al. 2002), hence measurements of closed compartments are not possible as
measurements from the interior parts of ice are possible. Oxygen microelectrodes are limited to the
measurement of oxygen outside ice only, the biologically active ice communities living within ice
are subsequently easily avoiding detection as their oxygen diffusion paths simply are too long to be
detected beneath the ice (Arrigo 2003). Direct measurements within or in close proximity of brine
channels may also be biased as a result of local freezing and melting processes around the
microelectrode tip if it were to come in direct contact with the ice (Glud et al. 2002). Furthermore,
fluctuations of oxygen concentrations associated with non-biotic freezing and thawing processes
may also bias measurements (Glud et al. 2002, Rysgaard 2008, this study) and measurements
have to be made on ice that is in steady state.
65
Oxygen micro-optodes
In Mock et al. 2002 another approach of investigating oxygen dynamics within undisturbed seaiceis described. In their study micro-optodes were frozen into an ice at different depths during its
formation, the experiment was formed in a 4 m3 large tank filled with artificial sea-water and
inoculated with sea-icealgae. Their study revealed a strong heterogeneity in oxygen concentrations
caused by bubble formations and aggregates of either oxygen producing or oxygen consuming
organisms. The study confirmed that micro-optodes could be fine tools for investigating oxygen
dynamics within brine channels of sea ice, both for deployment in vitro and with further
development also in situ (Mock et al. 2002). However as with microelectrodes the ice on which
measurements are done has to be in a steady state in order to avoid non-biotic oxygen
fluctuations, furthermore a great number of micro-optodes are required in order to get a good
resolution of a large ice volume. But when this is said, it certainly looks like a promising method for
future pioneering work on the much understudied biotic processes from deep within sea ice, and if
it were to be transferred to in situ, it would certainly also be one of the least invasive methods
available.
Planar optodes
This study focuses on a somewhat new technique in the sea-ice regime, planar optodes (Glud et
al. 1996, Holst & Grunwald 2001) that may lead to an increased understanding of both biotic and
non-biotic processes in sea ice, by its high resolution and as with micro-optodes by being
applicable directly on an ice without inferring any external disturbances. Recently it was used for
qualitative assessments of oxygen transport associated with freezing and thawing processes of
sea-ice (Glud et al. 2002, Rysgaard et al. 2008). Unfortunately the equipment available at present
is not yet ready for deployment in situ.
2.3 Materials and methods
Setup and initial trial runs
Two kinds of setups were developed where ice was formed within a small transparent chamber. A
series of transparent chambers (14cm x10cm x 3cm), which could be equipped with planar
optodes were established for each setup. Setup 1 as shown on figure 1 was used for
66
measurements of non-biotic and biotic oxygen release from the ice and oxygen distribution within
the ice. This type of chamber had a planar optode foil attached to the side. In the second type of
setup (setup 2 not shown), the chambers were not equipped with planar optodes. This setup was
mainly used for non-biotic quantification of oxygen and salinity fluxes from the ice, by
measurements on the water phase. In setup 2 sensors for conductivity and oxygen measurements
were inserted to drilled holes in the sides of the chamber. Both sensors were placed in the middle
and in the sides of the chamber 6-7 cm. below the stainless steel tube, in order to measure
conductivity and oxygen saturation from the water phase beneath the ice. Salinity was calculated
based on conductivity measured by an Orion 3 star conductivity benchtop (Thermo electron
corporation) and oxygen was measured by an optical fiber (Presense).
Figure 1: Setup 1. Flow through systems were built: A cubic tube used for cooling (1) made of
stainless steel was attached with silicone on top of the chamber (2) measuring h 12 cm. d 3 cm. w
10 cm. volume 360 ml. In each side a tygon tube was attached, one leading sea-water from a 15 L
water reservoir (3) into our chamber (inflow (4)) and one leading it back to the reservoir (outflow
(5)). The flow rate was controlled by a peristaltic tube pump (6). The chambers were cooled by
67
flushing cooling liquid from a Thermo cooling bath (8) through stainless steel tubes on top of the
chambers. The cooling bath was also used for cooling down a water bath (7) wherein the 15 L
water reservoir was placed, this water bath was cooled by running cooling fluid through a stainless
steel spiral (9) placed in the water bath. A fiber lamp (10) with adjustable light intensity was used
as light source. A halogen lamp (a) covered with an excitation filter (b) emitted light in short
flashes. Inside the chamber a planar optode film was attached (c) (the planar optode is seen from
the side - turned 90 ). The planar optode film quenches the received light and emits it to a CCD
camera (e) with a 612 nm emission filter (d).
Measurements of non-biotic dynamics
Temperature 1.5 cm below the cooling skin and 2.5 cm from middle chamber side of was
measured by manual reading of a Testo thermometer, its sensor was frozen into the ice.
Measurement of ice thickness was made manually with a ruler. Salinity was calculated from
conductivity. As conductivity increases with declining temperatures a temperature calibration (-1 C
to + 10 C) was made. The temperature measurements from the conductivity sensor were
subsequently used for temperature correction on all measurements of conductivity during the
experiment.
Oxygen concentrations in the water phase were measured in [% saturation] and recalculated to
concentrations in [µmol*L-1] and corrected with respect to temperature and salinity as described in
Weiss R. F. 1970.
Estimation and calculation of temperature, bulk and brine salinity, in the area of ice under
investigation.
For brine salinity calculation, the bulk salinity and temperature of the ice have to be known (Weiss
R.F. 1970). The temperature, in the area under investigation, was estimated based on the
assumption that temperature increased linearly downward through the ice. Subsequently, as the
temperature of the stainless steel tube and ice-water interphase was known, a temperature
gradient could be determined, and as a function of distance between the stainless steel tube and
the ice-water inter phase, the temperature of the area under investigation could be calculated.
Average temperature in the area under study was approximately -2 C.
68
For determination of bulk salinity an amount of ice was melted in the end of the experiment and the
salinity of the bulk water was determined by conductivity. Subsequently, brine salinity was
determined as in Cox 1983 from bulk salinity and temperature.
Planar optodes
The measuring principle of the oxygen optode is based on oxygen´s ability to act as a dynamic
fluorescence quencher that decreases the fluorescence quantum yield of an immobilized
fluorophore. The fluorophore receives blue light with a defined short wavelength and emits red
luminescence light. The luminescence light emitted decreases in a predictable way (non-linear)
with increasing oxygen concentration. In ideal systems a calibration curve for fluorescent oxygen
sensors can be described by the Stern-Volmer equation:
0
(2.1)
1 Ksv[O2 ]
Where τ0 and τ are the fluorescence lifetimes in the absence and presence of oxygen respectively,
Ksv is the bimolecular quenching coefficient and [O 2] is the oxygen concentration in vol. %. The
simple Stern-Volmer equation is only valid in ideal systems, however it has been shown that a
slightly modified Stern-Volmer equation adequately describes the response of most optodes
(Klimant et al. 1995)
0
Where
(1
)
1
1 Ksv[O2 ]
is the non-quenchable fraction of fluorescence,
constant of 0.2 (Glud et al. 1996).
(2.2)
was set at the empirically derived image
is the planar optode lifetime in the absence of oxygen. For
calculation of the oxygen concentration in vol. % equation 2.2 was rearranged to:
O2sat
0
Ksv(
0
(2.3)
)
69
Calibrating the planar optode:
The fluorescent lifetime of ruthenium (II)-Tris-4,7-diphenyl-1,10-phena-throline is temperature
sensitive (Klimant et al. 1997). Therefore the temperature response of the planar optodes (PO)
used in the present study had to be characterized. Ideally the calibration of the PO sensors should
be based on two calibration points obtained at the same temperature as the sensors are applied.
To determine the temperature response of the two calibration constants
0
and Ksv, a series of PO
images at 0 and 100 % air saturations were obtained in a brine solution (approximately 140 PSU)
kept at air saturation or at anoxia in the temperature range of 0.5 C to 10.4 C. (figure 2 and figure
3). From these images the temperature characteristics of
and Ksv were calculated.
(The temperature calibration was made by running cooling fluid through a stainless steel spiral that
was submerged into an open chamber, the temperature of the cooling fluid could be adjusted. A
100 % saturation (
100)
curve was made by bubbling atmospheric air through a brine solution and
measuring lifetimes at different temperatures, in this case 0.5 C to 10.4 C. Subsequently, a 0 %
saturation ( 0) curve, where sodium dithionite was added to the brine solution, was made in the
same temperature interval, the reason for using a brine solution was also to avoid freezing.
On the basis of the temperature in the specific area of the ice, curves of
0
and
100
were
extrapolated to this temperature and the values inserted in equation 2.3 which was rearranged to:
Ksv
0
(O2sat)(
0
(2.4)
)
in order to calculate Ksv of the specific area and temperature. The temperature calibration in the
range from 0.5 C to 10.4 C, showed a linear increase in Ksv of 1.76 % and a decrease in
0
of 0.44
% per C increase. The increase in Ksv per C, is a bit higher than values seen in Glud et al. 1996,
and Rysgaard et al. 2008, where the increase was found to be 0.5% and 0.4% respectively and
in Rysgaard et al. 2008 declined 0.3 % per C increase. However, Klimant et al.1999 used the
same ruthenium (II)-Tris-4,7-diphenyl-1,10-phena-throline complex in a oxygen micro optode
where Ksv was found to increase 1.4 % per C and
70
0
decrease of 0.6 % per C increase.
0
Calibration curve - Anoxic
-6
x 10
Lifetime [ ]
Figure 2 shows the lifetime vs.
temperature relationship of a 100 %
and 0 % atmospheric air saturated
brine solution. Dots represent the
100 % saturation curve, and +
represent the 0 % saturation curve
Anoxic
100% air sat.
6
5.5
5
4.5
0
2
4
6
8
10
12
Temp [ C]
Calibration - Ksv
-3
3.4
x 10
Figure 3 shows the bimolecular
quenching coefficient Ksv vs.
temperature relationship calculated
from a 100 % and 0 % atmospheric
air saturated brine solution
y = 5.2e-005*x + 0.0027
3.3
3.2
[Ksv]
3.1
3
2.9
2.8
2.7
data 1
linear
0
2
4
6
8
10
12
Temp [ C]
In this case it means that a temperature 1 C higher than the calibration temperature would cause
oxygen air saturation to be overestimated by 4.48 %. This is a relatively high value compared to
previous studies (see references above). This could potentially be due to extensive photo
degradation and bleaching of the sensor used in the current study as it was exposed to continuous
illumination for 4 weeks facilitating the establishment of the microalgae community.
71
Testing for bleaching
Photo bleaching of the planar optode was tested by exposing planar optode films to a light intensity
of approximately 490 µmol photons·m-2·s-1 for 24 hours, a fiber lamp was used as light source.
Planar optodes were attached to the inner surface of two small chambers, both chambers were
filled with artificial seawater. One chamber was made anoxic by addition of sodium dithionite, the
other was kept 100 % saturated by bubbling atmospheric air through it. It was found that the
lifetime ( ) of the area in front of the fiber lamp had increased by 5.9 % when measuring on the air
saturated seawater (
100).
In contrast the lifetime only increased by 1.2 % in the area illuminated,
when measuring on the anoxic seawater (see figure 4). However in both cases increases when
the planar optodes is exposed to light.
The percentage of increase of τ 0 and τ 100 were not the same for the two areas of the planar
optode, even though the temperature and light intensity on the surface were the same. If lifetime
versus temperature fits were made based on lifetime measurements from these films, the photo
bleaching would cause the slopes to differ more from each other than slopes of τ 0 and τ 100 from a
film that had not experienced any bleaching, hence bleaching may be the cause of why the high
percentage of increase in Ksv per C is seen.
Area Lifetimes 100% sat. photodamaged
Area
Lifetimes 100 % sat. norm.
1
4.304E-06
7
4.017E-06
2
4.196E-06
8
4.019E-06
3
4.273E-06
9
4.021E-06
Area Lifetimes 0% sat. photodamaged
Area
Lifetimes 0% sat. norm.
4
4.810E-06
10
4.734E-06
5
4.819E-06
11
4.737E-06
6
4.797E-06
12
4.740E-06
Table 1. Lifetime values (τ) at different locations on a planar optode (see figure 4) exposed to
light (490 µmol photons·m-2·s-1) for 24 hours. Values from „photodamaged‟ is taken from areas
within the circular area that was in front of the fiber lamp, „norm‟. values are from the lower half
of the planar optode that was not directly illuminated.
72
Figure 4 shows bleaching of the planar optode films,
when they were exposed to high light intensity originating
from a fiber lamp. Arrows point to the center of the area
illuminated by the fiber lamp. The chamber on the left
image was filled with anoxic brine, wheras on the right
image it was filled with air saturated brine. The photo
damaged area has increased values of τ, leading to
another calibration curve as compared to the rest of the
PO-film.
Oxygen concentration from oxygen saturation
The oxygen concentration in the brine was calculated by multiplying the calibrated oxygen
saturation levels found, by planar optodes (equation 2.3), by the oxygen concentration of a 100 %
saturated brine. The oxygen concentration of the 100 % air saturated brine solution was calculated
in accordance with Weiss R.F. 1970 on the basis of bulk salinities and temperatures in the given
areas of the experiment (found as described in the previous section, Estimation and calculation of
temperature, bulk and brine salinity, in the area of ice under investigation.)
Inoculating algae
Sediment (silt) was autoclaved at 125 C for two hours to avoid bacterial contamination. Two grams
of sediment was transferred to 15 ml (double concentration strength) L-medium (Guillard &
Hargraves, 1993) and shaken. This mixture of sediment and L-medium was transferred to a
chamber and frozen in on top of the stainless steel tube, the chamber was turned upside down.
After the mixture was frozen, 50 ml of a mixed algae culture with five different algae species
(Fragilariopsis nana, Chlamydomonas sp., Fragilariopsis sp, Melosira arctica and Pyramimonas
quadrifolia.) and a concentration containing approximately 500.000 cells/ml was transferred to the
chamber and frozen in on top of the sediment/L-medium mixture. The chamber was then filled with
-1.5 C GFF-filtered seawater trough the flow through system, air was removed by a vent in the
bottom of the chamber. After filling the chamber with sea water it was turned „the right way‟ so ice
growth would continue from the underside of the ice. One of the reasons why this approach of
freezing, sediment, nutrients and the algae culture in as the uppermost layer (respectively) of the
ice, was to avoid algal settlement on the planar optode film around (which were to be) the ice-water
interface, as it was presumed that this was the area where algal growth was possible. Another
reason why this approach was used was to get a vertical downward transport of the nutrients within
73
the brine cannels, after the chamber was turned „the right way‟. The reason why sediment was
added was to mimic natural sea-icein which a certain amount of aioli transported particles resides.
Initially a couple of chambers without sediment were build, but no algal growth was detected there,
when on the other hand sediment was added to the chamber algae patches began to show after
two-three weeks of inoculation.
Identification of Hotspots
The determination of areas with biotic activity can be made by calculating (CALMOLLI software)
the difference of
∆
in photos taken at different times during a dark period, higher values of
∆ means more oxygen has been removed. After a long dark series, where oxygen saturation has
been found to drop considerably compared to the initial saturation, light is switched on again.
Subsequently a oxygen build up initiates, in certain areas the oxygen production is much higher
than in others indicating that algae are not homogenously distributed but instead concentrated in
small enclaves. The same approach of calculating ∆ is used for evaluating oxygen increase in
light series and for evaluating oxygen decrease in dark series (Figure 5).
τ
∆τ
Figure 5. Planar optode photos (1 Black and white photo of the ice under investigation, 2, 3, 4 show
lifetime images after light was switched off for 1, 7, 15 hours respectively). The left series shows
development of τ in a long dark period, from a chamber inoculated with a mixed algae culture. The
right series shows the same images subtracted by a lifetime image taken immediately after light was
turned off. Decreasing oxygen saturation levels are seen as higher values of ∆τ as time passes.
74
,
vs. time
-6
6.2
x 10
6
Figure 6. Development of
lifetime (τ) in the three areas
depicted in figure 5 after
light was turned off. Note
that an increase in τ is
equivalent to a decrease in
oxygen concentration.
Lifetime [ ]
5.8
5.6
5.4
5.2
A1
A2
A3
5
4.8
0
2
4
6
8
10
12
14
16
Time [h]
Light-dark shift technique
When primary production appears in a narrow space like a brine channel, the rate of the oxygen
build up, is well described by a logarithmic function. The oxygen build up is made when the amount
of oxygen from primary production is higher than the sources of removal, diffusion (D) and
respiration (R). After a period of constant primary production steady state is reached and PP =
D+R. When this steady state is reached all three parameters can be estimated by removing their
contribution to the equation, just not at the same time, two have to be known, the last is then easily
deducted. While diffusion and respiration rates are hard to separate from within an ice, and their
pathway difficult to cut off without disturbing the environment, oxygen production from primary
production is not. When turning off light primary production ceases (PP = 0), but oxygen is still
removed by respiration and diffusion, and in a short period after the oxygen contribution from
primary production has ceased, the decrease in oxygen concentration due to respiration and
diffusion approximately equals the oxygen production from primary production. Consequently, by
measuring the oxygen decrease immediately after light is turned off, oxygen production from
primary production, before light was turned off, can be estimated. This technique known as the
light-dark shift technique is described in further detail in Revsbech & Jørgensen 1983 and Glud et
al. 1992.
75
Measurements of oxygen distribution and gross photosynthesis by planar optodes
Light intensity was increased from 0 to 293.5 µmol photons·m-2·s-1 in seven steps. Photo series for
photosynthesis estimations with duration of 270-300 sec. were taken at all eight light intensities by
switching off light and immediately after taking a photo series. Oxygen saturation in front of the
planar optode was calculated by inserting lifetimes (τ) (found in the areas of interest (AOI) and in
the underlying water) into equation (2.3). As the solubility of oxygen changes with salinity and
temperature, a recalculation of the oxygen saturation into concentration [µmol*L -1] was made (as
previously described).
Primary production 14C-method
Primary production was determined at five light intensities 0, 9.3, 21.3, 78.3 and 271.4 µmol
photons m-2 s-1 using the 14C incubation technique (Steeman-Nielsen 1952) in the bulk water from
artificial sea-iceformed within the setup after the chamber studies were completed. The bulk water
was transferred to Winkler bottles (120 ml) and incubated on a rotating plankton wheel at 1 rpm for
5 h at 3±1°C. The incubation was terminated by addition of 200 µl 5 % ZnCl 2 and the water were
subsequently filtered onto Whatmann GF/F filters 25-mm in diameter. The filters were added with
200 µl hydrochloric acid (concentration 1M) in scintillation vials, to remove inorganic carbon, and
was extracted in scintillations liquid (PerkinElmer Ultima Gold) for 22 h and counted using a
PerkinElmer TricCarb 2800 TR liquid scintillation analyzer (PerkinElmer, USA). DIC (dissolved
inorganic carbon) concentration in the sea-ice melt water was measured with a CO 2 coulometer as
described by Rysgaard & Glud (2004).
Primary production (PPi) in the bulk water was calculated as:
PP(µmol
C l-1h-1 )
i
DPMactivity DIC sea
ice
Fdiscr Fcor
(2.5)
DMPadded Tinc
Where DMPactivity is the 14C assimilated carbon corrected for assimilated carbon in dark (the
disintrations per minute on filter). DICsea-ice is dissolved inorganic carbon in the sea-ice melt water
(~450 µmol l-1). Fdiscr is the discrimination factor for the differences in
12
C-CO2 and 14C-CO2 (1.05),
Fcor is the correction factor for the 14C which is released and re-assimilated (1.06). DPMadded is the
specific activity of the
14
C labelled medium (DPM ml-1) with which cells were labelled, Tinc is the
incubation time. The dark-incubated bottles were used to correct for unspecific labeling.
76
2.4 Results
Ice formation
Setup 2 was used for determination of sea-iceshape, growth rate and water column mixing.
When conducting these experiments ice thickness varied when adjustments were made to the
setups. Testing showed that the optimal settings for forming a thick ice were when filtered
seawater was run through the chamber with a flowrate of 4.5 ml/min, as well as a low stirring and a
temperature of -19 to -20 C in the cooling fluid that runs through the stainless steel tube on top of
the chamber. These settings were maintained for most of the experiments where non-biotic
parameters were investigated. During the experiments sea-ice thickness was only adjusted via
shear as induced by stirring of the rotating magnet at the chamber bottom (Fig 1). A series of
chambers where flow rate, temperature in cooling liquid and stirring level were altered are
presented below. The following data originate from an experiment investigating dynamics of the
non-biotic parameters. In this experiment flow rate, temperature and stirring rate were kept
constant. Finding the optimal stirring rate was a challenge, a total mixing of the underlying water
column by applying a rapid stirring rate is of high priority. If a pychnocline is present somewhere in
the water phase the measured oxygen concentration and conductivity would either be slightly
increased or decreased (depending on the sensors location in regard to the pychnocline)
compared to the average value of the entire fluid in the chamber (this could cause mass budget
calculations of the chambers to get biased). A low stirring rate would on the other hand result in the
formation of a homogenous ice, much easier to work with. These two factors were opposing as an
increased stirring rate caused the ice bottom to bend upward in the middle of the chamber. When
applying a rapid stirring rate for a long time, alterations by ice retraction and reformation by
refreezing also made the ice difficult to work with, as salt and oxygen concentrations continuously
oscillated even after several days of experiment. Consequently stirring rate was lowered with the
risk of incomplete water column mixing.
77
Ice thickness versus stirring and temperature
60
-9 C, 4.5 ml/min, no stirring
-10 C, 8.5 ml/min, medium stirring
-19 C, 4.5 ml/min, no stirring
50
-20 C, 8.5 ml/min, medium stirring
-20 C, 8.5 ml/min, high stirring
Fig. 7 shows ice thickness
versus time for various
stirring levels, flow rates and
temperatures of the cooling
liquid. Triangular shapes
represent one of the final
runs that took place over a 6
days period, the maximum
ice thickness during this run
was 48 mm.
Ice Thickness [mm.]
40
30
20
10
0
0
100
200
300
400
Temperature C -
Ice thicknes
cooling liquid
mm.
500
Time [min.]
-16
35
-16
35
-16
32
-16
32
-8
21
600
700
800
900
1000
Table 2 shows ice thickness and cohering
temperature in five runs where no flow and no
stirring was applied, note the 3 mm difference
between the four runs at -16 C, even though
isolation was kept as similar as possible minor
differences nevertheless occurred. The changes
may also be a consequence of different room
temperatures as the five setups were not run at
the same time, room temperature was not logged.
All setups have been running > 8 days.
When repetitive runs were made it was unlikely to get the exact same, but the final ice thickness is
in similar runs were within 10% of each other. A series of chambers with no stirring and flow
through was also made (table 2). Variations in ice thickness (+ - 0.3 cm.) occurred in these as well,
even though the setup and temperature of the cooling liquid was the same, the chronology of the
runs varied and therefore ice thickness could have differed as a result of external temperature
changes and small differences in isolation.
78
The testing determined when a steady state regarding ice thickness was reached. Furthermore, it
was concluded that ice thickness and shape was very sensitive to changes in stirring rate, flow rate
and temperature of the cooling liquid. It was not possible to obtain both a high degree of mixing in
the water and a homogenously shaped ice.
Non-biotic ice – Ice 1
In addition to the determination of the inferred changes‟ effect on ice shape, growth rate and water
column mixing, setup 2 was also used for determination and quantification of solute ejections
(oxygen, salt) from the ice by non-biotic freezing processes. The non-biotic freezing processes
were investigated in the water phase by oxygen-, conductivity- and thermo sensors, in addition a
thermo sensor was frozen into the ice 1.5 cm below the stainless steel tube. Temperature was
regulated in order to get maximal ice thickness, a low stirring rate was used for the same reason.
Maximum ice thickness during the experiment was 48 mm and the minimum temperature
measured in the ice was – 10.2 C. After 3000 minutes from the start of the experiment both curves
plateou and a somewhat steady state with regard to ice thickness and ice temperature was
reached (Figure 8).
Ice thickness and ice temperature vs. time
5
0
-2
4
3.5
-4
3
2.5
-6
2
-8
1.5
1
-10
Ice Temperature [ C] - marker ---
Ice Thickness [cm.] - marker o
4.5
Figure 8 shows ice thickness
and temperature of the ice 1.5
cm below the stainless steel
tube plotted against time of the
experiment. Circles represent
the temperature 1.5 cm below
the cooling skin within the ice,
the line represents ice
thickness in cm.
0.5
0
0
1000
2000
3000
4000
5000
6000
7000
8000
-12
9000
Time [min.]
In the first 4000 minutes oxygen is rejected with salt, from around 4000 minutes the two curves
separate and a second salt peak stretching for 3000 minutes is seen (figure 9). The oxygen
79
concentration is dropping in the water phase. This most likely occurs as a consequence of bubble
formation (as often seen in natural sea-ice).
Oxygen concentration and salinity vs. time
32
390
31.9
385
380
31.7
375
31.6
370
-1
31.5
365
31.4
360
31.3
355
31.2
350
31.1
31
O2 [ mol*L ] - marker ---
Salinity [PSU] - marker *
31.8
Figure 9 shows salinity in
PSU (dots) and oxygen
concentration in µmol/L
(line) plotted against
time. After 3000 minutes
the two curves diverge.
Oxygen concentration in
the water phase
decreases, this may be a
result of bubble
formation.
0
1000
2000
3000
4000
5000
6000
7000
8000
345
9000
Time [min.]
These bubble formations occur as the brine becomes saturated with oxygen. The cold brine holds
high oxygen concentrations when saturated, but when it leaves the ice and is mixed with the
warmer water under the ice, the amount of oxygen in the released brine makes the brine
supersaturated, and oxygen begins to step out into a gaseous phase, causing bubbles to appear.
80
Temperature of water phase
3
Figure 10. Temperature of
water phase measured in
the middle of the chamber.
Average temperature in the
water phase is 1.43 C.
Water temperature [ C]
2.5
2
1.5
1
0.5
0
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
time [min.]
In figure 9 oxygen concentrations measured in the water phase is shown, figure 10 shows the
temperature of the water phase during the experiment, and in table 3 oxygen concentrations are
given for 100 % air saturated seawater with a salinity of 31.5 (approximately the salinity during this
experiment), at temperatures ranging from -1.7 to 2 C (the temperature of the water in this
experiment is within this range). It can be seen that the concentration of oxygen measured in the
water phase (figure 9), exceeds the oxygen concentration of 100 % air saturated water (table 3) .
Bubbles were observed under the ice, the formation of these might very well happen as a result of
some of the excess oxygen (the amount that causes oxygen oversaturation in the water) from the
hyperoxic water, steps into a gaseous phase.
81
Sea water with a salinity of 31.5 PSU
Temperature [ C]
O2 concentration [µM] if 100 % O2 sat.
-1.7
385
-1
377.7
-0.5
372.6
0
367.6
0.5
362.8
1
358
2
348.8
Table 3 shows the oxygen
concentration in seawater with a
salinity of 31.5 for temperatures
ranging from -1.7 to 2 C if the
water is 100 % air saturated.
It is seen from this ice that non-biotic solute ejections (especially salt) continues even after a
steady state regarding ice thickness and internal temperature has been reached. Furthermore,
ejected brine contains a large amount of oxygen during freezing, and brine ejections may be the
cause why the underlying water gets oversaturated with oxygen, subsequently resulting in bubble
formations.
Determination of oxygen production from biotics – Ice 2
Setup 1 which had a planar optode film attached to its side, was used for identification of biotic
processes within an ice, and for determination of biotic oxygen production and consumption rates
under changing light regimes.
The ice inoculated with algae was investigated for „hotspots‟ as described in materials and
methods. From the dark series ∆ τ is calculated by subtracting the last image in the series by the
first image taken right after light was turned off. The differences in oxygen saturation levels
between T420 and T0 in the areas of interest (AOI) could originate from biotic respiration or from
non-biotic processes, the differences in oxygen saturation levels could also be a product of a biotic
production further inside the ice, away from the planar optodes, where the pathway through the
brine channels from a photosynthetic active enclave of algae, had been blocked over the time of
the photo series by freezing and hence appear to be the result of a non-biotic dynamics in the ice.
The ice has been in a quasi-steady state for almost three weeks, so the latter is unlikely to be the
case but both explanations support the fact that ice actually is an extremely dynamic environment
and that the distribution of algae likewise is extremely heterogeneous when working on small areas
in ice. Nevertheless, it is not possible from a single dark series to determine if the oxygen decrease
is a result of respiration by biotic processes alone or if the decrease is a result of non-biotic
82
freezing and thawing processes or even a combination of the two, therefore a light series was
made. If a decrease in oxygen saturation is seen during a dark series followed by an increase
during a light series, surely it is most likely that an area with primary producers has been found (as
was the case in this study. Data is presented below in figure 11, 12, 13).
Figure 11. Right image represents
subtracted lifetimes of two pictures (T0-T420)
from a dark series that stretch for 420
minutes, image T0 is taken immediately after
light was switched off and T420 after 420
minutes of dark. AOI are areas of interests
where lifetimes (τ) differ in the period of the
dark series. Left image is a black and white
photo taken at time T0, there was no visual
difference between the black and white
photos taken at time T0 and T420.
AOI 6 in figure 11 is used as an internal reference to process the planar optode data obtained in
the short (300 sec.) photo series that was made for determination of oxygen production by the light
dark shift approach. No large amount of microorganisms is assumed to be present in the water
phase, hence no significant fluctuations in oxygen saturation caused by biotic production are
assumed to take place there, and external noise can then be removed.
After the 420 minutes dark series, light was turned on and another series was taken. In this series
taken in light it was possible to identify an area (AOI 4) where oxygen saturation levels increased
as a result of primary production. AOI 4 was interesting, the other areas did not show any
substantial decrease in τ when exposed to light, but the increase in oxygen content seen by the
decline in τ in AOI 4 is a good sign of primary production (Figure 13).
83
-6
4.95
x 10
Lifetime [ ] vs. time in dark
Figure 12 shows the
increase in lifetime (τ)
during a 420 minutes dark
series.
4.9
Lifetime [ ]
4.85
4.8
4.75
4.7
4.65
4.6
AOI 4
Entire ice
AOI 5
4.55
4.5
0
50
100
150
200
250
300
350
400
450
Time [min.]
-6
4.95
x 10
Lifetime [ ] vs. time in light
Figure 13 shows lifetime (τ) vs time after
light (185 µM photons*m-2*s-1) has been
turned on. AOI 4 is the only area where τ
has a notable decline, during the 210
minutes of light. The decline is
equivalent to an increase in oxygen
saturation level, the increase in oxygen
saturation level is a result of primary
production. “Entire ice” shows τ of the
entire ice during the light series. The
temperature in this part of the ice was
estimated to be approximately -2 C.
4.9
Lifetime [ ]
4.85
4.8
4.75
4.7
4.65
4.6
AOI 4
Entire ice
AOI 5
4.55
4.5
0
50
100
150
200
250
Time [min.]
Subsequently a PI-relation of AIO 4 was made. The PI-relations of three areas of interest (AOI 4,
AOI 5, Entire ice) are shown in figure 14. AOI 4 (4.78 mm 2) was the only area where a clear
response to the changing light intensities was found. The large decrease seen in AOI 5 around 175
µM photons/(m2*s) is (due to its strength) most likely a consequence of non-biotic processes, prior
to this event the small fluctuations may also show a small amount of other biotic dynamics (than
those identified in AOI 4) of the ice.
84
Oxygen Production - by planar optodes
100
80
2
Oxygen prod. [ mol(O )/(L*h)]
Figure 14. Oxygen
production in the areas
investigated as a function of
light intensity.
AOI 4
AOI 5
Entire ice
60
40
20
0
-20
-40
-60
-80
0
50
100
150
200
250
300
2
Light intensity [ mol/(m *s)]
Figure 15 shows the PI-relation of the entire surface of the ice (explanation follows), as measured
by planar optodes (black lines) and by the
14
C -method in the end of the experiment (red lines).
The PI-relation of the entire ice surface is found according to the following formula:
PPent.ice
A AOI4
PPAOI4
A ent.ice
(2.6)
where AAOI 4 is the area of AOI 4, Aent. ice is the area of the entire visible
ice (figure 11) and PPAOI4 the PI-relation of AOI 4 (AOI 4 made up approximately 0.4% of the entire
visible ice). Since no other detectable biological oxygen production could be measured in the rest
of the ice AOI 4 may be representative of the entire ice, given that the distribution of
microorganisms in 2 dimensions is the same as that in 3 dimensions which is the basic assumption
when working with planar optodes. However, if there was heterogeneity in the third dimension that
was not equal to heterogeneity in 2 dimensions, data would be biased. The
14
C -method was
subsequently applied on the bulk water from the ice, which was melted for the same reason, in the
end of the experiment. Figure 15 also shows the PI-relation from the
14
C-method and when
compared to the production rates of the entire ice made by planar optodes the 14C-method gives
values less than 40 % higher (estimate based on three points with positive production) than the
planar optode, the estimate is made based on photosynthesis stoicheometry, where ideally carbon
uptake approximately equals oxygen production. Pmax (maximum net production) values might
85
seem to differ when comparing the two methods. This is not surprising with respect to P max as
water tends to be warmer than ice and the algae by this reason increase their productivity.
0.5
Figure 15. Black curve shows total
0.4
oxygen production of the entire ice.
0.3
Oxygen saturation was measured
by planar optodes and
0.2
concentrations calculated as
0.1
described in Measurements of non-
2
Oxygen prod. [ mol(O )/(L*h)], PP [ mol(14C)/(L*h)]
Oxygen and Primary production (PP) vs Light intensity
biotic dynamics. Red curve shows
0
primary production, as measured by
-0.1
the 14C-method which was applied
-0.2
Oxygen production
Primary production
0
50
100
150
200
250
on bulk water from the ice in the
300
end of the experiment.
2
Light intensity [ mol/(m *s)]
In conclusion, by planar optodes it was in this ice possible to identify an area within the ice where a
substantial amount of biotic production (photosynthesis) occurred. Oxygen production rates from
this site was investigated under different irradiences (a PI-relation was found). Subsequently, the
ice was melted and the 14C -method was applied on the bulk water. The bulk water was incubated
under different irradiences for comparison to the oxygen production rates found by planar optodes.
The two different methods revealed a high degree of consistency between the data sets, even
though the 14C -method was applied on bulk water from the ice, and the planar optode directly on
the ice.
Identification of biotic production – Ice 3
The same approach, as in ice 2, was used in ice 3 for identification of areas with biotic oxygen
production and consumption (Ice 3 is depicted in figure 5. The areas A 3* and A 3** were both
found from within A3 ). Ice 3 was inoculated with algae and frozen the same way as ice 2.
A PI- relation as the one just presented, could not be detected in the other 4 chambers even
though they were made in the same way. However a decrease in oxygen concentration could be
determined in all of these 4 chambers, when light was turned off in a longer period (> 30 minutes).
A rebuild in oxygen saturation level could also be detected (Figure 16) in at least three of these
chambers when illuminated for a longer period of time (> 30minutes). Figure 16 shows oxygen
86
saturation in ice 3, where light intensity was increased every 45 minutes (a photo series was taken
at all light intensities right before light intensity was increased to the next level), at light intensities
above 250 µM photons/(m2*s) the saturation level plateaus.
Figure 17 shows ∆ O2 saturation as a function of light where ∆ O 2 saturation is calculated as:
O2sat
O2satIi
1
O2satIi
O2satIi
1
where O2satIi O2 satIi is oxygen saturation at light intensity Ii and
is oxygen saturation at the preceding light intensity Ii-1. These data cannot be used for
oxygen production quantification as no respond in oxygen saturation could be determined by the
short dark series (described previous in Measurements of oxygen distribution and gross
photosynthesis by planar optode). However, it is seen that the increase in oxygen saturation starts
to decline at light intensities above 250 µM photons/(m 2*s), which indicates that above this
irradiance the algae start to get photoinhibited. At the end of the experiment the 14C -method was
applied to the bulk water (Figure 18), primary production could be detected under different
irradiances although the shape of the PI-relation did not fit well with the standard shape of a PI
relation.
Oxygen saturation vs. light intensity - by planar optodes
Oxygen saturation vs. light intensity - by planar optodes
110
3
A 3*
A 3**
2.5
Oxygen saturation [%]
Oxygen saturation [%]
108
106
104
102
100
2
1.5
1
0.5
98
A 3*
A 3**
96
0
0
50
100
150
200
250
300
350
400
450
0
50
100
150
200
250
300
350
400
450
2
2
Light intensity [ mol/(m *s)]
Light intensity [ mol/(m *s)]
Figure 17 shows ∆ O2 saturation from figure
16 as a function of light intensity. ∆ O 2
saturation was calculated as:
O2sat O2satIi O2satIi 1 . O2satIi is the
Figure 16. Oxygen saturation versus
light intensity. Light intensity was
increased every 45 minutes and a
photo series was taken (for oxygen
saturation estimation) at all light
intensities right before the light intensity
was increased to the next level
oxygen saturation at light intensity and
O2satIi 1 is the oxygen saturation at the
preceding light intensity Ii-1.
87
Primary Production vs Light intensity (PI)
Primary production [ mol C*L-1*h-1]
0.25
Figure 18 shows primary
production, the 14C-method
was applied on bulk water
from the ice in the end of
the experiment.
0.2
0.15
0.1
0.05
0
0
50
100
150
200
250
300
2
Light intensity [ mol/(m *s)]
In conclusion no immediate oxygen production rate was found in ice 3, due to a long response time
for oxygen saturation levels to increase and decrease. Nevertheless, the planar optode data
strongly point out that photosynthesis occurred in the ice, and that a decrease in oxygen
production occurred at irradiances above 250 µM photons/(m 2*s). In addition primary production
was also detected by the
14
C -method when bulk water was incubated under different irradiances.
Non-biotic (ice 1) vs. biotic strength (ice 2) of oxygen exchange rates
The experiment shows that when seawater freezes considerable amounts of oxygen are
evacuated from the initial water. The oxygen flux during the freezing process (O2FluxPhysics ) could
be as high as in the setup without algae could be as high as 232 times higher than the highest
oxygen flux found within the ice in the setup with algae (O2FluxBiolog y ) as measured by planar
optodes, based on the following calculation:
R
2
O2FluxPhysics
0.095nmolO2 cm
O2FluxBiolog y
4.1 10 4 nmolO2 cm
s
2
1
s
1
(2.7)
R= 232
The O2Flux is calculated as:
O2FluxPhysics
[O2 ]Outflow [O2 ]Inflow
Aice
88
F
(2.8)
Where Aice is the area of the bottom of ice in [cm2], and F is the flowrate in [l/s]. The flux from
biology O2FluxBiolog y is calculated as:
O2FluxBiolog y
PPmax
VIce
AIce
(2.9)
Where Vice is the ice volume and PPmax is the maximum average oxygen production rate from the
ice (figure 15) measured at a light intensity of 180 µmol photons*m-2*s-1 in the setup.
In summary, when comparing oxygen flux from biotic and non-biotic processes it is seen that the
strength of the flux from non-biotic processes surpasses the oxygen flux from biotic processes by
far, this is exactly the reason why precautions are necessary when biotic production wish to be
evaluated and quantified based on oxygen exchange rates (Glud et al. 2002), luckily the strength
of the non-biotic oxygen flux tends to decrease as time passes and ice stabilizes, but nevertheless
even small increases or decreases in ice thickness can quickly make any attempts on biotic
oxygen production quantification impossible, with gray hair as the sole thing acquirable for the
effort (comment author).
2.5 Discussion
This study conducted with planar optodes as well as oxygen, conductivity, and thermo sensors,
was made mainly as an investigation of the applicability of planar optodes to determine sea-ice
algal oxygen production and biotic oxygen consumption in an undisturbed in vitro ice. As the
setups, especially the inoculation of algae, were highly artificial they do not reflect nature, but
elements from this study help giving an understanding of ice as a highly dynamical environment
and of the importance of working on an ice that is in steady state, when evaluating biotic activities
from oxygen measurements.
Setup
The setups used in this study gave the opportunity to simultaneously measure oxygen content and
temperature within the ice and in the water phase as well as the salinity of the water. In addition
sampling of other solutes could be made from both the in- and outflow of the chamber. The
pioneering work of building the setups where biotic oxygen production within a brine channel could
be determined in a closed environment, gave rise to various perspectives on the possibilities of
89
chamber experiments. The experiments may not be identical to nature, but in contradiction to
nature they are much easier to maintain in, or take to, a state that may reflect a certain dynamics of
nature. The setup contains possibilities of experiments that can be used for improvement of other
methods, and experiments regarding quantification of brine solutes and their fate in nature, which
surely is an area far underexplored.
In this study the non-biotic processes were investigated, and the time before a steady state
regarding oxygen ejection was reached, was determined. The knowledge of when a steady state
has been reached is important when measuring biotic production, as any oxygen contribution from
the freezing process of the ice will result in inaccurate production estimates. Ice thickness in this
study was 4.8 cm and a steady state regarding oxygen ejection was reached >3000 minutes after
experiment start. As the study was conducted in vitro, the ice could be maintained in a steady state
for as long as required (completely steady states in ice in situ, are on the other hand rare, as
freezing or thawing processes quickly become reality following a change of weather). The biotic
oxygen production rates from within an ice presented in the Results chapter are therefore almost
certainly not biased on any non-biotic dynamics.
Non-biotic oxygen flux can surpass oxygen flux from biotic production by far
This study and others (Rysgaard et al. 2008, 2007, Glud et al. 2002, Gleitz et al.1995) show that
concentrated amounts of dissolved constituents of the seawater from where ice originates are
ejected by brine ejection, when the ice is growing. The ejection or flux of solutes (e.g. oxygen) from
an ice in a non-steady state tends to be strong compared to the flux from biotic processes. As a
consequence of the high flux from non-biotic processes, it is most likely impossible to obtain valid
in situ data of biotic production in an ice that is in a non-steady state, if these are based on oxygen
measurements.
The maximum non-biotic oxygen flux found in this study was 0.095 nmol O 2*s-1*cm-2 during
freezing, as a comparison the maximum biotic oxygen flux out of the ice, if O 2 production = O2 flux,
was 4.1*10-4 nmol O2*s-1*cm-2, approximately ~ 232 times lower, furthermore it is reasonable to
assume that the oxygen flux (by biotic production) out of the ice, is even smaller due to
multidirectional oxygen diffusion and respiration.
In situ studies have reported fluxes from biotic production in the same size range as the flux from
the 4.78 mm2 large area (AOI 4) in ice 2 (if the vertical cross section of the entire ice had a
production as high as AOI 4, the flux out of the bottom of the entire ice could be as high as 0.1025
nmol O2*s-1*cm-2) even though this setup was highly artificial and the ice thickness only a fraction
90
of the thickness of the in situ studies (note that in situ studies may be even more affected by nonbiotic processes, as the ice on which the studies are conducted, tends to be much thicker than in
this experiment, as a consequence the time before the ice would be in a steady state is
presumably much longer). McMinn et al. 2000 reported biotic oxygen production rates in an 90 cm.
thick Antarctic fast ice (measured by oxygen microelectrodes beneath the ice) between 0.0084 and
0.044 nmol O2*s-1*cm-2 (these values are between 11.3 and 2.2 times lower than the non-biotic
oxygen flux found in this study, respectively) the study was made during Antarctic summer and the
production rates are considered to be very high according to the authors and compared to other
studies (Trenerry et al. 2002, Rysgaard 2001, Kühl et al. 2001), still the biotic oxygen fluxes found
in their study are considerably lower than the non-biotic oxygen flux found from a growing ice in
this study. The biotic oxygen production found in McMinn et al. 2000, even though high, clearly
supports the necessity of only measuring on sea-icein steady state, when this is said it is also
appropriate to point out that McMinn et al. 2000 in fact succeeded in measuring biotic oxygen
production from beneath in situ sea ice, as they were able to present excellent PI-relations (see
also Mcminn et al. 2007, Trenerry et al. 2002). However, it is important to point out that their
measurements were based on point measurements and the production rates they present may not
be representative for a large fraction of ice. As a comparison if a microelectrode was to measure
on a brine channel leading directly from AOI 4 in this study (assumed that all the oxygen
production gets transported out of the ice through this brine channel) the flux could be as high as
0.1025 nmol O2*s-1*cm-2, if that were to be the only measurement taken from the ice and this
measurement was assumed to be representative for the entire ice, the resulting flux would be
approximately 250 times to high, as the area from where the flux originated, in fact only made up
around 0.4 % of the entire ice.
Setups for measuring on biotic production
Although the experiments conducted in this study as said were highly artificial and especially the
inoculation of algae does not reflect nature the areas where biotic activity was found had
similarities to studies from in situ. In situ algae patches with high production and abundance are
often found in the bottom parts of sea-ice(Trenerry et al. 2002) likely as a consequence of a less
harsh environment. A reason for this could be that brine channels in the bottom part of sea-ice are
wide as a consequence of high temperature, allowing settlement of larger algae. Furthermore,
brine salinity is low compared to the upper parts of ice (it has been shown by Palmisano et al.
1987, Grant and Horner 1976 and Vargo et al. 1986 that increasing salinities cause decreasing
91
photosynthetic activity). Despite the high algae cell concentration frozen in under a nutrient
medium, the only areas where photosynthesis was detected were in the bottom parts of the ice
where temperatures were ~-2 C. The rest of the cells probably died as the
14
C method did not give
significant elevated production values compared to the production values found in the planar
optode study, those minor elevations that are seen could be a consequence of the temperature
increase when the ice was melted in the end of the experiment so the
14
C could be added to the
water, as a temperature increase can lead to increased biotic activity.
At ~-2 C the size of the brine volume ranges between 13 % and 26 % (at bulk salinities of 5 and
10, respectively) and containing a brine salinity of approximately 37 (Cox 1983, Leppäranta &
Manninen 1988). As the experimental setup induced a strong vertical thermo gradient in the ice
ranging from -8° C to -1.8° C over a distance of only 4.8 cm, the area which had optimum
conditions for sea-icealgal growth was very small, hence the chance of settlement was not good,
for the same reason the algae culture used for inoculation had a very high cell concentration
compared to what could be expected to be found in situ. Another study (Glud et al. 2002) also
succeeded in growing algae in in vitro ice, however their technique of transferring filters containing
algae and subsequently freezing them, resulted in a high patchiness. The similarities between the
setup in that study and the present study are few. The purpose of the present study was to quantify
biotic production and biotic distribution within undisturbed ice using planar optodes, and therefore a
large verticall surface was needed. The other study measured algae physiology by a diving-PAM
(Glud et al. 2002) directly by touching the algae communities with the fiber head , and they needed
a large horizontal surface. In addition to physiology measurements they also gave biotic oxygen
production estimates and non-biotic oxygen dynamics evaluations associated with the freezing and
melting process, based on microelectrode measurements (taken beneath the ice) in their setup.
The approach of inoculating algae in artificial se ice by filtering sea-icebulk water to obtain a high
biomass on filters which subsequently are frozen, could be a better approach as the phototrophic
productivity in this study was extremely low, which could be seen by the low potential oxygen flux
from autotrophic activity from the entire ice (it was around 107 times lower than the highest actual
flux found in McMinn et al. 2000)
Planar optodes on sea-icebiotics
The oxygen production rates found by planar optodes and the primary production rates found by
the 14C -method are within close proximity of each other (<40 % based on three points on the curve
from the 14C -method and three points on the curve from the planar optode, figure 15) if the ratio
92
Cuptake : O2 production are considered to be 1 (photosynthesis stoicheometry – not exactly 1, but close).
As the production rates, even though measured by two separate methods, are in so close vicinity
to each other, the area where production was measured (AOI 4 in ice 2) by planar optodes might
very well have been the sole area where biotic production occurred in the entire ice.
All in all, a total of 5 chambers with planar optodes attached to their transparent sides were made
in approximately the same way, but only one chamber had an ice with an area which showed a
strong response to changing light intensities, in the 4 other chambers it was possible to detect a
decrease in the oxygen saturation when light was switched off for a longer period > 30 minutes. A
rebuild in oxygen saturation level could also be detected (Figure 16) in at least three chambers
when the ice in the chambers were illuminated for a longer period of time (the time before a clear
increase in saturation level was detected in the 3 other setups varied between 30 min and 180
min). One of the reasons why only one ice had an area with a clear biotic production large enough
for quantication could be that the distance from areas with autotrophic activity in the other ices and
the planar optode film was too large as oxygen diffusion happens slowly in ice. When investigating
brine channels it is seen that most are vertically orientated, hence horizontal diffusion is hard to
obtain. As a consequence of the planar optodes‟ applicability on measuring oxygen exchange rates
in two dimensions only, areas with autotrophic activity that are not in direct contact with the planar
optode are likely not to show any response at all.
High heterogeneity on a small scale and narrow internal environments – a methodological
challenge
Planar optodes certainly provide us with a unique tool to increase our understanding of the
microscale environment of ice. Microelectrodes can also be used for this purpose, but their major
limitation, and the reason why they are surpassed by planar optodes, is that microelectrodes
cannot be forced into the interior of ice without being destroyed, a well defined understanding of
the micro scale environment of the interior of ice may come from planar optodes.
Many studies have shown a significant degree of homogeneity in sea-icethat range for distances of
several meters, the degree of homogeneity is determined by heterogeneity tests e.g. a Morans I
test (described in Legandre et al. 1989, 1993, Rysgaard et al. 2001). However, when evaluating
distances of cm in sea-ice, the heterogeneous distribution of microalgae presents a real challenge
to oxygen microelectrodes studies, as exact deployment of the oxygen microelectrode sensor tip
just beneath a brine channel with biotic activity is of high priority, and moreover the deployment
calls for great precaution (Glud et al. 2002). As mentioned before the ice also has to be in a steady
93
state, as non-biotic processes quickly can result in oxygen fluxes much stronger than those from
biotic processes. If the ice is in a steady state, surely, oxygen microelectrodes have many
applications regarding the oxygen dynamics of both non-biotic and biotic processes in association
with sea ice, but as so much other splendid equipment they are confined to measure only on the
outside of sea-iceor on bulk water, and do not really give a clue of the processes of the interior
parts of the sea ice. Up until now, no previous study has been able to quantify oxygen production
from within the brine channels of undisturbed sea ice. Planar optodes (used in this study) represent
a unique tool for investigating the biotic life of the interior of sea ice, and may have the possibility to
specialize some of the present methods for measuring on biotic life. As an example the
14
C-method
is a method that by comparison with planar optode data probably can be improved. A natural
improvement for work on sea-ice would be to introduce a new set of constants, where the effect of
temperature changes, between ice and the melted ice (the bulk water), on biotic cells production
was taken into account. Also the osmotic consequences in cells of the dilution of solutes
associated with thawing (Garrison & Buck 1986) could probably be more specialized by multiple in
vitro comparisons between productions rates measured by planar optodes in ice and the
commonly used 14C -method applied on the bulk water of the ice at the end of the experiment. One
of the planar optodes‟ limitations is their ability of only measuring in two dimensions, which has the
consequence that areas with biotic activity not in direct contact with the planar optode are likely to
evade detection. To eliminate this bias either repetitive runs can be made or the planar optode can
be applied to a large surface area.
2.6 Conclusion
A controlled ice can be formed using the setup type developed in the study. Experiments showed
that it could take days before a steady state regarding ice thickness was reached in this setup.
During the freezing process of air saturated water a large amount of oxygen and salt was released
from the growing ice and even after a steady state regarding ice thickness was reached solute
ejections would continue, but at decreasing rates. Based on these observations the importance of
being in a complete steady state, when measuring on biotic oxygen production in or from ice, must
be considered fundamental for acquiring valid data, as this study also shows that the non-biotic
oxygen flux, from growing ice, easily surpasses biotic oxygen production by far.
In order to investigate the biotic oxygen production, inoculation of live sea-icealgae in ice formed
within the setup was achieved with success. By planar optodes it was possible to measure biotic
oxygen production and consumption under variable light intensities from within the ice, the
94
commonly used 14C -method was applied on bulk water from the same ice as the planar optodes,
after measurements with planar optodes were completed. The two different methods revealed a
high degree of consistency between data, even though the
14
C -method was applied on bulk water
from the ice under study, and the planar optode directly on the ice under study. In conclusion,
planar optodes used in this study showed to be very applicable for investigating biotic oxygen
dynamics within ice in in vitro experiments.
2.7 Perspectives
Non-biotic solute and gas transport in sea-ice constitutes an area that has just recently been given
attention (Gleitz et al. 1995, Rysgaard et al. 2007, 2008, Semiletov et al. 2007). In vitro chamber
experiments may lead to increased understanding of the in situ processes concerning gas and
solute transport in sea ice. The possibilities of measuring on ice dynamics with controlled setups
are many, in the setup developed during this study oxygen, salt and temperature were of primary
concern, however as the setup was built as a flow through system sampling for DIC, alkalinity and
nutrients etc. of ice was also possible. Surely, the strictly physical processes of sea-iceregarding
solute and gaseous flux, is an area that requires much further study, as gaseous flux in seaicemight have a high significance in the global carbon and oxygen cycle. Planar optodes present
an in vitro technique that is highly applicable for investigating non-biotic and biotic induced oxygen
dynamics within the microenvironments of brine channels in sea ice, planar optodes are not just
tools for quantification but also tools that can be used to increase the understanding of oxygen
dynamics in sea-iceby their unique high quality resolution and 2D-imaging. In addition planar
optodes that measure pH also exist and could be applied the same way as the oxygen sensitive
planar optodes, pH sensitive planar optodes may (as oxygen sensitive planar optodes do on the
oxygen dynamics in sea ice) give a unique insight into the pH dynamics of sea-iceand how this is
affected by non-biotic and biotic processes. Furthermore, planar optodes might possibly be the tool
that can create a new set of constants to the common and easily used
14
C -method, for future work
on sea-ice. In a large perspective more exact production estimates from polar areas would improve
models of the oxygen and carbon cycle, subsequently leading to a better understanding of the
global carbon cycle and thereby potentially to more exact predictions concerning global climate
change.
95
2.8 Future improvements to the setup
Salinity: This experiment was conducted with a Orion 3 star conductivity benchtop (Thermo
electron corporation) which measures conductivity, the instrument was placed in the middle of the
chamber. The instrument could only measure differences in conductivity of 0.1 mS cm-1 hence the
uncertainty was too large in order to make a precise quantitative estimation of salt flux from an ice
volume the size as the one in this experiment. Another bias in the setup was the location of
conductivity measurement which also was of importance, when stirring is insufficient to mix the
water column a proper measurement close to the ice would be biased by a density gradient
forming in the middle of the chamber, hence our results may be biased by this, in future
experiments conductivity is recommended to be measured outside the chamber directly in the
outflow water. Evaporation from the reservoir would also increase inflow salinity hence conductivity
ought to be measured in inflow water as well if calculating a mass budget for salt.
Oxygen – Presense sensor: oxygen concentration was measured within the chamber with a optical
fiber (Presense). Mass budget calculations turned out to be overestimated, as precipitation from
the ice was larger than the maximum oxygen content of the corresponding amount of water.
Bubble formation under the ice may explain this, as bubbles could have kept the upper part of the
water column in a saturated state of equilibrium with an air bubble of high oxygen content. Another
and more plausible reason why the mass budget calculation turned out to be too high is
uncertainties regarding the oxygen concentration of the inflow water; the oxygen content in the
water phase inside the chamber was measured by the optical fibre every tenth minute but the
oxygen concentration of the inflow water was determined from samples, taken in average four
times a day, by winkler titration. A small error offset between the two concentrations of in- and
outflow, would due to the long time span of the experiment, result in large total over- or
underestimation of the total oxygen flux from the entire ice. If the oxygen concentration of inflow
water were determined to be 1µmol lower than the actual concentration, it would cause an
immediate flux increase of 4.7*10-3 nmol O2*s-1*cm-2, but integrated over the entire time of the
experiment, the error offset in concentration difference between in- and outflow of 1 µmol, will
cause mass budget calculation to be overestimated by 2.37 µmol*cm -2 or approximately 71.06
µmol if calculated for the entire ice on the chamber.
In order to avoid bubble creation in the chamber inflow water should be under saturated regarding
oxygen. This could be done by heating the reservoir to a higher temperature than that within the
chamber and subsequently cool down the water that moves towards inflow down immediately
96
before it enters the chamber. As with salt, oxygen should be measured directly in inflow and
outflow at the same time if a precise mass budget is to be made.
Oxygen sensitive planar optodes: In view of the fact that planar optodes are sensitive to
temperature a calibration curve has to be made on each foil in the end of the experiment.
Temperature and salinity of the respective areas where oxygen are to be measured have got to be
14
determined in order to calculate oxygen concentrations. If the
C-method is to be improved,
identification of the oxygen response origin is crucial as oxygen exchange rates by physical
processes have been shown to be several orders higher than that of microorganisms. Physical
processes might still be working in a steady state ice and the only way to be sure of the origin of
oxygen is to make several PI-relationships on specific areas. Production rates by planar optodes
can then be compared to production rates measured by the
exact same ice and adding
14
C-method, obtained by melting the
14
C on bulk water. It is important to measure all sites of biological
production with planar optodes in order to scale production to the entire ice surface. As planar
optodes only measure in 2-D it is assumed that the dynamics caused by microorganisms in 2-D is
representative in 3-D, but repetitive experiments have to be made in order to minimize bias due to
the inevitable differences in spatial distribution of microorganisms through an ice.
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doi:10.1029/2006JC003572.
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Trenerry L J, McMinn A, Ryan K G. (2002). In situ microelectrode measurements of bottom-ice
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100
Chapter 3
Effect of pH and salinity on growth and survival of two Arctic sea-ice
diatoms, Fragilariopsis sp. and Fragilariopsis nana, and the Arctic
sea-ice alga, Chlamydomonas sp.
Dorte H. Søgaard* and Morten Kristensen
Greenland Institute of Natural Resources
Box 570
3900 Nuuk
Greenland
University of Copenhagen
Marine Biological Laboratory
Strandpromenaden 5
3000 Helsingør
Denmark
*Corresponding author: [email protected]
101
3.0 Abstract
The effect of elevated salinity and pH on growth and survival of three Arctic algal species
(Fragilariopsis nana, Fragilariopsis sp .and Chlamydomonas sp.) was investigated in June 2008
using laboratory cultures from Arctic sea-ice. Experiments were conducted at a range of values
covering the natural variation of salinity and pH in sea-ice brine. This study indicates that sea-ice
algae have different tolerances to elevated pH and salinities.
Salinity did have a pronounced effect on the growth rates for all three sea-ice algae species. The
two pennate diatoms Fragilariopsis nana and Fragilariopsis sp. were able to grow at all salinities;
however, growth was impeded above a salinity of 75. The growth rate declined almost linearly with
increasing salinity from its maximum of 0.23 and 0.63 d -1 at a salinity of 5. The ability of the two
diatoms to tolerate low salinities can be an advantage in the bottom sea-ice cores and during
spring and summer thaw, where sea-ice salinity decreases. The growth rate of the green flagellate
Chlamydomonas sp. exhibited a maximum of 0.67 d-1 at a salinity of 50, but growth ceased at
salinities above 100. The green flagellate may have a competitive advantage during winter month
when salinity is high in sea-ice brine. Furthermore, the halotolerant green flagellate may have an
advantage in the upper sea-ice section, where the salinity and light conditions usually is high.
The growth rate of Fragilariopsis nana exhibited a maximum of 0.24 d-1 at pH 8.0 and growth was
impeded above pH 9.0. Fragilariopsis sp. was able to grow at pH 8.0 to 10.0, however the
maximum growth rate of 0.50 d-1 was observed at pH 8.5. Chlamydomonas sp. was able to grow at
all tested pH levels (8.0. 8.5, 9.0, 9.5 and 10.0), with the maximum growth rate of 0.51 d -1 observed
at pH 8.0. The ability to tolerate elevated pH levels may be important during spring and summer
thaw and may be an important factor driving sea-ice algal species succession in sea-ice.
102
3.1 Introduction
Sea-ice plays an important role in structuring polar marine ecosystems. Sea-ice determines the
physical properties of the underlying sea water column (Weeks & Ackley 1982) and is in contrast to
freshwater ice, permeated with pores and brine channels which is host to a unique community
dominated by microorganisms. The sea-ice often contains an algal biomass many times greater
than the underlying sea water column (Garrison et al. 1983), which can contribute up to 30 % of
the annual primary production of seasonal sea-ice covered areas (Arrigo et al. 1997).
The sea-ice produces highly variable microenvironments with steep vertical gradients in
temperature, light intensities, salinity and nutrients concentration within the columns (Kaartokallio
et al. 2005). The total brine volume of sea-ice typically ranges between 1 to 30 % depending on
salinity, temperature and ionic composition of the brine fluid (Weeks & Ackley 1986). When the
temperature decreases the thermodynamic phase equilibrium drives the sea-ice towards a lower
brine space volume and towards increasing brine salinities (Glud et al. 2007). Brine temperatures
and brine salinity are coupled within the sea-ice and usually the brine temperature varies between
-1.9 to -6.7°C and brine salinity range from 34 to 108 (Gleitz et al. 1995). However, when sea-ice is
exposed to temperatures below -20°C, the brine salinity can be well above 200 (Cox & Weeks
1983). In the summer when sea-ice begins to melt, the salinity of the brine in the sea-ice can be as
low as one-third of normal sea water (salinity of <10) (Ryan et al. 2004). Only a few studies
investigation includes effect of salinity stress on growth of sea-ice algae. However, sea-ice algae
have been observed to have optimal photosynthetic activity at higher brine salinities during cold
sea-ice season, or at lower salinity during ice melt (Thomas & Papadimitriou 2003). Previous
studies with cultures of Amphiprora kufferathii, Nitzschia and Thalassiosira antarctica isolated from
ice cores from Weddell Sea observed growth at salinities up to 90 at a temperature of -5.5°C and
the sea-ice diatoms survived for 20 days at salinities up to 145 (Thiel et al. 1996). However, other
studies have shown that the growth of different sea-ice diatoms from Weddell Sea ceased at
salinities above 50 (Grant & Horner 1976). The differences in tolerance between sea-ice algae
species have been ascribed to osmotic acclimation and species-specific differences and changes
in sea-ice salinity may influence microbial succession. Those sea-ice algae species that are
capable to cope with the changes in salinity may have an advantage and become the dominant
alga species in the sea-ice community. At the cellular level, salinity can result in reduced
photosynthesis and damage to enzymes. Salinity acclimation requires energy for both
maintenance of ionic pumps and synthesis of osmolytes e.g. proline and glycerol (Ralph et al.
2005). Furthermore, sea-ice algae may produce exopolymeric substances (EPS) in response to
103
salinity and/or temperature changes (Krembs et al. 2002), which may have an important role in the
protection of algal cells within sea-ice.
Alkaliphilic microorganisms have been isolated from a variety of ecological environments like sea
water and from environments influenced by industrial activities such as cements production, mining
operations, food processing and sewage plants (Grant & Tindall 1980, Maeda & Taga 1980,
Horikoshi 1991, Grant 1992). However, stable natural alkaline environments are very rare and they
are only found in special locations e.g. in soda lakes in the East African Rift Valley, Central Asia,
California and in the Ikka Fjord in SW, Greenland (Buchardt et al. 1997).
In sea water, pH is generally very stable (pH≈8.2), as it is buffered by the carbonate system (Hinga
2002). However, a number of biological and physical processes may influence the pH in marine
surface waters. Photosynthetic depletion of dissolved inorganic carbon (DIC) during
photosynthesis elevates pH, while respiration processes will increase the pool of DIC (and thus
CO2), which will lead to decreased pH (Hansen 2002). Fluctuations of pH in sea waters are
counteracted by the exchange of CO 2 at the air-water transition. However, the physical exchange
of CO2 is greatly dependent on the vertical mixing of the water column. In previous studies
phytoplankton blooms in marine coastal eutrophic areas like lagoons, bays and fjords have been
reported to elevate the pH of the surrounding water, where the highest pH value reported being
around 10 (e.g. Lindholm & Nummelin 1999, Macedo et al. 2001, Hansen 2002). Changes of pH in
marine waters influence the inter-speciation of inorganic carbon (CO2 (aq), HCO3-, CO32-). At pH 8
in sea water (DIC ~2mM), ca. 1 % of DIC is present as CO 2, while at pH 9 only 0.1 % of DIC is
present in this form (Hinga 2002). Potentially, the limitation in the supply of CO2 due to elevated pH
may restrict photosynthesis and growth of phytoplankton (Hansen 2002). However, some
phytoplankton species have active transport systems by which they utilize HCO 3- in order to avoid
DIC limitation at elevated pH, thus elevated pH may favor species which can utilize HCO 3- as an
inorganic carbon source (e.g. Korb et al. 1997, Huertas et al. 2000, Hansen 2002). Especially,
diatoms have been found to utilise HCO 3- by taking up HCO3- actively and converting it into
intracellular CO2 by using extracellular carbonic anhydrase (e.g. Korb et al. 1997, Tortell et al.
1997). Another mechanism has been found where diatom can utilize HCO3- directly for carbon
fixation thought C4-photosynthesis (Tortell et al. 1997, Reinfelder et al. 2000). However, a previous
study has shown that the ability to tolerate high pH is not related to any particulate algae groups,
but is rather species-specific (Hansen 2002).
Studies in sea-ice have shown that where high primary production and accumulation of large algal
biomass have occurred, the sea-ice brine is characterized by considerable reductions in DIC and
highly alkaline pH, with values up to 10 (Gleitz et al. 1995, Thomas et al. 2001b). Photosynthetic
104
activity and growth of microalgae in sea-ice may also be affected by changes in pH (Thomas &
Papadimitriou 2003). The direct effects of high pH on sea-ice microalgae are not well established,
but changes in the chemical environment may influence ionic composition of substrates (Lizotte
2003). Furthermore, like in sea water the changes in pH influence the equilibrium of the carbonate
system and therefore the inter-speciation of DIC (CO2 (aq), HCO3-, CO3-), which may potentially
influence microalgae species succession and distribution (Hansen 2002, Rost et al. 2003).
Alternatively, high extracellular pH may cause gross alterations in the membrane transport
processes and metabolic function involved in internal pH regulation (Raven 1980) or affecting
cellular growth due to change in cellular content of amino acids (e.g. Taraldsvik & Myklestad 2000).
In this present study we used alkalinity adjustment (additions of acid or base) to achieve the
desired pH. This approach gives conditions that approximate the relationship between pH and the
dissolved CO2 species that result from addition or removal of CO 2. Thus, at high pH the observed
pH effect on growth of the sea-ice algae may also be caused by limiting concentrations of free
CO2.
The aim of the study present here was to determine to upper limit for growth with respect to salinity
and pH for three Arctic sea-ice algae species Fragilariopsis nana, Fragilariopsis sp. and
Chlamydomonas sp. in controlled laboratory experiments. The physiological response of sea-ice
algae to salinity and pH stress is of central importance to the understanding of the factors driving
sea-ice algal species succession during the sea-ice season.
3.2 Material and methods
Algae species and experimental conditions
The diatom Fragilariopsis sp (Strain FSIA) and the green algae, Chlamydomonas sp. (strain Chlam
SI50) isolated from the Arctic sea-ice was selected for this study. Furthermore, the diatom
Fragilariopsis nana was provided by Nina Lundholm isolated from the Labrador Sea (The
Scandinavian Culture Collection of Algae and Protozoa, Department of Phycology). The three
algae species are regularly found in sea-ice. The cultures were grown in L1-medium (Guillard &
Hargraves 1993) based on autoclaved seawater with a salinity of 34. The concentration of nutrients
was high to ensure that nutrient limitation would not influence growth performance of the algae.
The stock cultures were maintained at 3±1°C and 50 µE m-2 s-1 following a light:dark cycle of 16:8
h. Illumination was provided by cool fluorescent lamps and irradiance was measured using a LiCor
1400 (Li-Cor, NE, USA).
105
Fragilariopsis nana.is a pennate diatom. The cell length and width of Fragilariopsis nana is
between 8.0-9.4 µm and 1.9-2.0 µm, respectively. Fragilariopsis sp. is a pennate diatom. The cell
length and width of Fragilariopsis sp. is between 12-16 µm and 6-10 µm, respectively.
Chlamydomonas sp.is a unicellular flagellate chlorophyte. Chlamydomonas is isokont, which
means that its two flagella are similar in structure. The cell length and width of Chlamydomonas sp.
is between 8-10 µm and 4-6 µm, respectively.
Calculation of cell volumes were based on measurements on 400 cells, picked in exponential
growth phase. For enumeration of cells, subsamples were fixed in Lugol´s iodine (3.1 % final
concentration). Cells were counted in a Sedgewick-Rafter chamber.
Growth rates (µ) were measured as increase in cell number and were calculated assuming
exponential growth:
ln(Nt 2 / Nt1 )
(t 2
(3.1)
t1 )
Nt2 and Nt1 are number of cells at time t2 and t1. All experiments were carried out in triplicates, and
data from each triplicate were the mean of at 3 growth rates. Dilutions due to subsampling were
adjusted for in the calculations of the growth rates.
Effect of salinity on growth rate of the three sea-ice algae
The growth rates of Fragilariopsis nana, Fragilariopsis sp and Chlamydomonas sp. were measured
at different salinity from 5 to 150 (salinity of 5, 20, 34, 50, 75, 100, 125 and 150). The salinity was
adjusted by addition of Red Sea salt. The pH was kept constant at 8.0. All experiments were
carried out in 62 ml polystylene bottles, which were mounted on a vertically plankton wheel (1 rpm)
with an external cooling system in order to keep the phytoplankton in suspension and to prevent
heating due to the light. Temperature was thereby kept at 3±1º C. All experiments were carried out
at a irradiance of 50 µE m-2 s-1 following a light:dark cycle of 16:8 h. The experiment was initiated
by the inoculation of 1000 cells ml-1 and allowed to run for minimum 18 d and maximum 22 d.
Every second day, pH was measured, and subsamples (1 ml) were taken for enumeration of
phytoplankton cells. After subsampling, the bottles were refilled to capacity with L1-growth medium
(1 ml), the L1 growth medium was adjusted to the different salinities to prevent the salinity in the
experimental bottles to drift. The salinity was measured at the initiation and at the termination of
the study. The salinity drifted ±3 from start to finish. Dissolved inorganic carbon (DIC) was
106
measured at the initiation and at the termination of the study to ensure that the DIC concentration
was sufficient for phytoplankton growth during the experiments.
The first 6-16 d of the experiment were considered an acclimation period; therefore cell counts
from these samplings were not included in the calculations of growth rates. Dilution (of 1ml) is
accounted for in the calculations of algal growth rates.
Effect of pH on growth rate of the three sea-ice algae
The growth rates of Fragilariopsis sp and Chlamydomonas sp. were measured at different pH
within the range of 8.0 to 10.0 (pH 8.0, 8.5, 9.0, 9.5 and 10.0) and Fragilariopsis nana at pH 8.0,
9.0, 9.5 and 10.0. The salinity was 34 during the experiment period. The pH was adjusted by the
addition of 0.1 M HCL or 0.1 M NaOH. All experiments were carried out in 62 ml polystylene
bottles, which were mounted on a vertically plankton wheel (1 rpm) in order to keep the
phytoplankton in suspension and to prevent heating due to the light. Temperature was kept at
3±1ºC due to an external cooling system. All experiments was carried out at a irradiance of 50 µE
m-2 s-1 following a light:dark cycle of 16:8 h. The experiment was initiated by the inoculation of 1000
cells ml-1 and allowed to run for minimum 14 d. and maximum 22 d. Every second day, pH of the
culture media was measured, and subsamples (1 ml) were taken for enumeration of phytoplankton
cells. After subsampling, the bottles were refilled to capacity with pH adjusted L1-growth medium
(pH 8.0, 8.5, 9.0, 9.5, 10.0) and the bottles were remounted on the plankton wheel. If the pH differ
more than 0.03 from the set point, it was adjusted by addition of small amounts of 0.1 M NaOH or
HCL. pH was measured using a Sentron
®
2001 pH-meter equipped with Red Line electrode,
®
which is an ISFET sensor (Semi-conductor Ion Field Effect Transistor) with dectection limit of
0.01. The pH sensor was calibrated (2 point) using Sentron buffers of pH 7 and 10.
The first 6-14 d of the experiment were considered an acclimation period; therefore cell counts
from these samplings were not included in the calculations of growth rates. Also, the dilution (1ml)
was adjusted for in the calculations of algal growth rates.
3.3 Results
Effect of salinity on growth of the three sea-ice algae
The two sea-ice diatoms (Fragilariopsis nana and Fragilariopsis sp.) showed exponential growth at
salinities of 5, 20, 34, 50 and 75 after a long lag phase of 6-16 d (Fig. 1A-B). At a salinity of 100 a
107
slight increase in cell number was observed for the two diatoms. The green flagellate
Chlamydomonas sp. inoculated at salinities of 5, 20, 34, 50, 75 and 100 showed exponential
growths after a lag phase of 6-16 d (Fig. 1C). The growth of the three sea-ice algae either
remained constant or declined during the investigation period at salinities of 125 and 150 (Fig. 1).
108
Effect of pH on growth of the three sea-ice algae
The pH was adjusted to pH 8.0, 8.5, 9.0, 9.5 and 10.0 respectively, and kept stable by adjustments
at each sampling occasion.
109
The incubation with pH levels between 8.0 and 9.0 treatments a maximum reduction of 0.2 units
occurred between sampling days. In the pH 9.0 to 10.0 treatments a maximum reduction of 0.4
units was observed in the incubations. The experiments was terminated when the pH increased by
more than 0.5 units. The three sea-ice algae species showed exponential growth rate after a lag
phase of 6-18 d at pH 8.0 to 9.0 (Fig. 2). The two diatoms (Fragilariopsis nana and Fragilariopsis
sp.) showed a slight increase in cell number at pH 9.5 and 10.0 (Fig. 2A-B). The green flagellate
Chlamydomonas sp. showed exponential growth from d 10-14 at pH 9.5 and 10.0 and towards the
end of the experiment entered stationary growth phase (Fig 2C).
Effects of salinity and pH on growth rates of the three sea-ice algae
The effect of salinity on the growth rate was very profound in the two diatom species Fragilariopsis
nana and Fragilariopsis sp. (Fig. 3). The two pennate diatoms showed maximum growth at
salinities between 5 and 75. However, the growth rate was highest at a salinity of 5 in the two
species. The growth rate of Fragilariopsis sp. at a salinity of 5 was 3 fold higher than the growth
rate in Fragilariopsis nana at the same salinity. Slightly increases in cell number were recorded for
Fragilariopsis nana and Fragilariopsis sp at salinities above 75.
The growth rate of Chlamydomonas sp. was highest at a salinity of 50. A decline was observed at
both lower salinities and higher salinities. However, the growth rate at salinities of 5 was 2 fold
higher than the maximum growth rate in Fragilariopsis nana at the same salinity. At a salinity of
100, the growth rate of Chlamydomonas sp. had declined by ~50 %, while the growth at salinity of
34 the growth rate had declined by ~30 %. Growth of Chlamydomonas sp. ceased at salinities
above 100.
The effect of pH on the growth rate was also very profound in the three Arctic sea-ice algae
species Fragilariopsis nana, Fragilariopsis sp. and Chlamydomonas sp. (Fig. 3). The growth rate of
Fragilariopsis nana and Chlamydomonas sp. was highest at pH 8.0, while the growth rate of
Fragilariopsis sp was highest at pH 8.5. The growth rate of Fragilariopsis nana declined by 40 % at
pH 9.0. A similar reduction in the growth rate in Chlamydomonas sp. was achieved at pH 9.0. The
growth rate of Fragilariopsis nana was very low at pH above 9.0, while the growth at pH 9.5 in
Fragilariopsis sp was still relatively high.
110
Experimental conditions
A long lag phase compared to other studies was observed in present study at all pH and salinities
(e.g. Lundholm et al. 2004, Ralph et al. 2007). The lag phase is the phase where the algae adapt
themselves to the growth conditions. An explanation for the long lag phase observed in this study
may be that the algae cultures were transferred from one set of growth conditions to another. At
the initiation of the experiments the stock cultures were diluted (from~10 4 cells ml-1 to 102 cells ml-1)
which may induce stress. Furthermore the culture were exposed to elevated salinities and pH,
which results in a wide range of physiological impacts (e.g. cell rupture, reduced photosynthesis,
damage to enzymes), which may result in a long lag phase.
111
The DIC concentration was measured at the initiation and at the termination of the study to ensure
that the DIC concentration was sufficient for phytoplankton growth during the experiments. The
DIC concentration was slightly higher in the salinity experiments due to addition of Red Sea salt
with a maximum DIC concentration of 3.0 mM at a salinity of 150 in the initiation of the experiment.
The DIC concentration in the pH experiments was between 2.0 and 2.5 mM at the initiation of the
experiments. At the termination of the experiments DIC concentrations were reduced by < 600 mM
in all the experiments flasks.
3.4 Discussion
Our aim was to study how salinity and pH affected growth and survival of three sea-ice algae
species: Fragilariopsis nana, Fragilariopsis sp. and Chlamydomonas sp. Consequently, we had
ensured that other factors such as macronutrients, micronutrients and vitamins were not limiting
sea-ice algae growth.
Effects of salinity on growth of ice algae
During cold winter periods sea-ice algae habitats undergo large variations in salinities, where
salinities can exceed a salinity of 100 (Gleitz et al. 1995). In late spring and summer, as the sea-ice
melts, the sea-ice salinity becomes low (salinity of <10) and the sea-ice algae are exposed to
hyposaline conditions (Ralph et al. 2007). Brine salinity also fluctuates vertically within the sea-ice,
with the lowest salinities usually encountered in the bottom sea-ice (Lizotte, 2003). Some
microalgae are considered euryhaline, because they can adapt to varying external salinities
(Hellebust 1985). However, the salinity range over which active growth takes place differs greatly
among species and the physiochemical conditions in the sea-ice will provide a selection pressure
that influences the final community composition (Ryan et al. 2004). Previous studies have indicated
that salinity stress may even exert a greater selective pressure on sea-ice microalgae than nutrient
limitations (Meguro et al. 1967). Salinity has a pronounced effect on plant growth, photosynthetic
efficiency and metabolism (Misra et al. 2001). However, some sea-ice algal species, such as
Fragilariopsis curta and Entomoneis kjellmanii, tolerate a wide range of salinities, while other
species, such as Cylindrotheca clostrium are more sensitive to altered salinity (Ryan et al. 2004).
Furthermore, previous studies have shown that Nitzschia stellate-dominated communities tolerate
higher salinities better than lower salinities, while maximum photosynthetic rate in a
Pleurosigma/Amhiprora/Pinnilaria-dominated community was observed at a salinity of 6-10 (Arrigo
112
& Sullivan 1992). Salinity stress tolerance has been tested with culture of Amphiprora kufferathii,
Nitzschia and Thalassiosira antarctica isolated from sea-ice in the Weddell Sea. Growth was
observed up to a salinity of 90 at a temperature of -5.5°C and the three diatoms survived 20 days
at a salinity of 145 at a temperature of -10.0°C (Thiel et al. 1996). Recent studies have showed
that hypo osmotic conditions are more harmful than high salinities and growth rates are drastically
reduced in salinities below 20 (Kottmeier & Sullivan 1988). However, other studies have shown
that hyper saline treatments produce more photosynthetic stress than hypo saline treatments (Kirst
& Wiencke 1995, Ralph et al. 2007).
In the present study the three sea-ice algae species showed different responses to salinity. The
two pennate diatoms Fragilariopsis nana and Fragilariopsis sp. showed maximum growth at
salinities between 5 and 75, which indicates that these sea-ice species thrive at low salinity and at
salinities far above 34. Grant & Horner (1976) also found little growth by Arctic sea-ice diatoms at
salinity above 60. Previous studies have shown that diatoms and other silicified organisms are only
slightly affected by decreasing salinities, whereas decreasing salinities may result in substantial
losses of ciliates and flagellates (Garrison & Buck 1986). The result in the present study indicates
that when sea-ice salinity becomes low the organisms with rigid cell material e.g. diatoms may be
dominant in the sea-ice. Thus, the diatoms may have a competitive advantage during summer and
spring thaw when the sea-ice salinity becomes low. This hypothesis is supported by Mikkelsen et
al. (2008). They observed that pennate diatoms were dominant late in the sea-ice season, where
low brine salinity was observed in the sea-ice. Furthermore, the two sea-ice diatoms may have an
advantage in the bottom of the sea-ice due to their tolerance to lower salinities, which is support by
a previous study where diatoms are found to be dominant in the bottom layers of the sea-ice
(Gradinger 1999).
Maximum growth rate of the flagellate Chlamydomonas sp. was observed at a salinity of 50 and
growth was still fairly high (50 % of maximum growth rate) at very high salinities (100), which
indicates that this flagellate is more halo tolerant than the two diatoms (Fragilariopsis nana and
Fragilariopsis sp.). Nevertheless, even Chlamydomonas sp. could not grow at a salinity of 125 and
150. In a previous study, Chlamydomonas pulsatilla was observed to grow at very high salinities
(salinity of >200) (Hellebust 1985). The change of salinity affects the water potential of the
microorganisms. To maintain an active metabolism at high salinities, the intracellular conditions
have to be kept at a constant level within a narrow range, such as ionic composition and metabolite
pools. In general, this process of osmotic acclimation results in a regulation of cell volume in
organisms without a rigid cell wall. Usually, the original cell volume is regained by adjusting internal
osmolyte concentration (Bisson & Kirst 1995). Hellebust (1985) suggest that all extremely halo
113
tolerant microalgae may be species without a rigid cell wall due to a more rapid regulation of cell
volume in these species. This may explain why the two diatoms did not show high growth rates
compared to Chlamydomonas sp at salinities above 50. In a previous study, flagellates were
observed to be dominant in the sea-ice in winter and spring, when sea-ice brine salinity was high
(salinity of 70) (Mikkelsen et al. 2008). The green flagellate alga Chlamydomonas sp. may have a
competitive advantage during winter, when the sea-ice brine salinity is high. However, when the
brine salinity exceeds a salinity of 100 Chlamydomonas sp. may be outcompeted by more halo
tolerant sea-ice algae species. In a previous study Chlamydomonas sp. was only associated with
the upper section of the sea-ice (Melnikov et al. 2002), where the sea-ice brine salinity is high. The
halo tolerant green flagellate may be able to grow in the upper sea-ice section, where the salinity
usually is high. Light intensities are higher in the upper section of the sea-ice and the ability to grow
there may be an advantage.
If a multispecies culture experiment was performed the succession of the three sea-ice algae
species may vary according to salinity level. Chlamydomonas sp. would have a competitive
advantage at high salinities (up to a salinity of 100) and Fragilariopsis sp. would outgrow
Chlamydomonas sp. at low salinities (salinity of <34). The maximum growth rate of Fragilariopsis
nana was lower than the maximum growth rate of Chlamydomonas sp at low salinities, which may
indicate that Chlamydomonas sp. would outgrow Fragilariopsis nana at all salinities below 100 in a
multispecies culture experiment.
The effect of pH and the growth of Ice algae
In present study DIC concentrations were available in non-limiting concentrations. We used
alkalinity adjustment (addition of acid or base) to achieve the desired pH in the flasks. This
approach gives conditions that approximate conditions in sea-ice and approximate the relationship
between pH and the dissolved CO 2 species. In present study the change in pH in the flasks
influence the inter-speciation of inorganic carbon (CO2, HCO3-, CO32). Thus, at high pH the
observed effect on growth of the sea-ice algae may also be caused by limiting concentrations of
free CO2 rather than a direct pH effect. To determine whether pH or DIC was the main factor
controlling growth of the three sea-ice algae, we should have tested the growth of the algae at
different pH levels with initial high or low DIC concentrations. Nevertheless, the approach used in
present study approximate the conditions in natural sea-ice, where DIC depletion result in an
increased pH and a decrease of free CO 2. Furthermore, a previous study has shown that carbon
limitations only influence algae growth at very high pH and at very low DIC concentrations (Hansen
et al. 2007).
114
Increase in sea-ice pH is observed where high primary production and accumulation of large algal
standing stocks have occurred (Gleitz et al. 1995, Thomas et al. 2001b). High primary production is
normally observed when light irradiance increasing during spring or summer thaw (e.g. Kühl et al.
2001, Cota & Horne 1989), and the microalgae observed during spring or summer thaw may be
more tolerant to high pH and/or low CO 2 concentrations. Here, we studied the effect of pH on the
growth of three sea-ice algae. However, an indirect pH effect of the change in the chemical
speciation of the DIC pool may also have influenced the growth of the sea-ice algae in present
study.
The knowledge on how sea-ice algae response to high pH is very sparse. However, the effect of
high pH on marine phytoplankton is for some species well established. The most common marine
phytoplankton species, which is found at high pH in nature are the dinoflagellates Heterocapsa
triquetra, Prorocentrum minimum and P. micans, and the diatom Skeletonema costatum (Macedo
et al. 2001, Hansen 2002). Other taxa of marine phytoplankton, which have been found to grow at
pH above 9.0 is some cryptophytes, diatoms, dinoflagellates and prymnesiophyte species
(Pedersen & Hansen 2003 and reference herein).
In the present study Fragilariopsis nana and Chlamydomonas sp. showed maximum growth rate at
pH 8.0. While Fragilariopsis sp. showed maximum growth rate at pH 8.5. Fragilariopsis nana
almost stopped growing at pH above 9.0, which may be a disadvantage during the summer thaw,
where increased primary production, due to increased light conditions, may result in an increase in
pH. Slightly positive growth of Fragilariopsis sp. continued until pH 9.5 where the growth rate
became reduced. Perhaps the two diatoms are unable to utilize HCO 3- as an inorganic carbon
source, which may result in decreased growth at very high pH. However, previous studies have
indicated that especially diatom are able to utilize HCO3- by taking up HCO3- actively and
converting it into intracellular CO 2 by using extracellular carbonic anhydrase (e.g. Korb et al. 1997,
Tortell et al. 1997). Gleitz et al. (1996) proposed that the capacity to efficiently utilize ambient DIC
in sea-ice, may be mediated by a favourable surface to volume ratios as well as active pathways of
inorganic carbon acquisition. They further proposed that this may favour growth of small diatoms in
sea-ice, and may be an important factor driving ice algal species succession during summer
blooms. Gleitz et al. (1996) observed positive growth of four Antarctic diatoms (Fragilariopsis
cylindrus, Thalassiosira antarctica, Porosira pseudodenticulat and Chaetoceros cf. neograci) until
pH 10 and the diatoms survived at pH above 10.0. Compared to present study the Antarctic
diatoms showed tolerance to a higher pH level, which indicate that the ability to tolerate high pH is
not related to any particulate algae groups, but is rather species-specific.
115
Positive growth of Chlamydomonas sp. continued until pH 10.0, where the growth rate became
slightly reduced. Previous studies have indicated that smaller cells have higher pH limits for growth
(Lundholm et al. 2004), which may explain why Chlamydomonas sp. separates from the two other
algae species. The mechanism responsible for the relationship is related to the regulation of
intracellular pH (Lundholm et al. 2004). The maintenance of intracellular pH is presumed to be
controlled by surface-associated ion-exchange and the larger surface/volume of smaller cells size
allows them to better regulate their intracellular pH (Biagini et al. 2001). Chlamydomanas sp.
produced some gel-like substances in the growth experiments at high pH (9.5), which surrounded
the algae colonies (Appendix III). This gel-like substance could be exopolymetric substances
(EPS), which are produced by bacteria and algal cells in marine environments (Riedel et al., 2006),
especially in harsh environments (e.g. cryoprotection of algal cells within first-year sea-ice)
(Krembs et al., 2002). Production of the gel-like substance may protect Chlamydomonas sp. at
high pH and may give the green flagellate a competitive advantage compared to the two diatoms in
the sea-ice. Furthermore, this gel-like substance may explain why Chlamydomonas sp. tolerated
higher pH levels than the two diatoms. The ability to grow at high pH levels may be an advantage
late in the sea-ice season, when pH increases due to high primary production and accumulation of
large algal biomass.
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119
Generel conclusion
The studies presented in the three chapters in this thesis were performed to investigate: 1) the
seasonal in situ variation and dominance of autotrophic and heterotrophic production during a seaice season in Marlene Bight, SW Greenland 2) a new methodological approach for determination
and quantification of biotic production and respiration within undisturbed in vitro formed sea-ice and
3) the response of three species of sea-ice algae to fluctuating salinities and pH.
The results presented in this thesis suggest that sea-ice comprises a highly dynamic series of
habitats. The interaction between physical, chemical and biological properties determines the
succession of the sea-ice microbial organisms, with light availability as the key factor for regulating
the sea-ice algae activity and biomass build-up, in sea-ice of Marlene Bight. However, the variation
in salinity and pH in the brine channel environment is clearly another important factor driving the
seasonal sea-ice algal succession. The three different sea-ice algae species investigated had
optimum growth rate at salinities and pH levels similar to those of pelagic algae, however, the seaice algae investigated showed to be actively dividing between a large salinity and pH span ranging
from salinity 5 to 100 and pH 8.0 to 10.0, respectively. This study supports the general conception
that sea-ice algae remain active in sea-ice even though exposed to large fluctuations of salinity
and pH.
In Marlene Bight the sea-ice was net autotrophy-dominated during the season studied, however, a
predominantly heterotrophy-dominated stage late in March occurred prior to the sea-ice algal
bloom. The sequential succession of the microbial community found in the first-year sea-icein
Marlene Bight was different compared to studies conducted by e.g. Grossmann & Gleitz 1993,
Günther & Dieckmann 1999 and Kaartokallio 2004 as it was observed, in opposition to their
studies, that the heterotrophic activity was predominant before the sea-ice algae bloom. This may
be due to very low light availability late in March resulting in a predominantly heterotrophydominated stage, but on April 4th the light availability increased, which resulted in an algal bloom.
Rysgaard et al. 2007 suggested that besides the well accepted thesis of solubility and biologically
driven carbon pump another strictly physical driven sea-ice carbon pump may exist. It is suggested
that in winter when the surface water freezes dissolved inorganic carbon (DIC) is extruded with salt
through brine ejections, and the brine which contains large amounts of DIC (and other solutes)
then sinks due to its high density (this process is also known as deep water formation, especially
the waters of the eastern cost of Greenland are well known for their deep water formation, the high
density of brine is mainly caused by its relatively large amount of salt).
120
The results presented in chapter 2 support the suggestion of an additional existing pump that has
the ability to remove solutes from sea-ice, and hence the surface waters from where it was frozen.
It was also concluded, that non-biotic oxygen flux from growing sea-ice has the potential to be
several magnitudes higher than oxygen fluxes from biotic production. All in all, the study clearly
shows that large amounts of solutes (in this study oxygen and salt) are being ejected from growing
sea-ice, and as a consequence the second chapter supports the statement, made previously, that
sea-ice can be an extremely dynamic environment and emphasizes the importance of being in a
steady state when estimating biotic processes based on oxygen measurements. The requirements
for further development of methods and equipment that can be used to improve our understanding
of biotic as well as non-biotic processes in sea-ice are finally discussed.
Overall, this thesis casts light over some of the extreme conditions biotic life associated with seaice has got to deal with. The first and third study both show how biotic life in sea-ice responds and
adapts to the extreme dynamics involved with this way of life. The second study points out, how
large fluctuations in the internal environment of sea-ice the biotic life may be presented with, as
well as the requirement for further development of non-invasive methods and equipment for in situ
measurements. In conclusion, sea-ice is certainly one of the most extreme habitats found, both for
its inhabitants but without doubt also for its investigators. Furthermore, a considerable amount of
knowledge concerning biological as well as physical/chemical processes and their consequences
on global climate etc. has yet to be obtained from Arctic areas.
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121
Appendix
Appendix I
Anoxic incubation of sea-ice in gas-tight plastic bags
The permeability of a multi-laminar plastic bag to oxygen (O2) and carbon dioxide (CO2) is
presented. In a previous study a more comprehensive experiment was conducted at higher
temperature with sediments (Hansen et al. 2000). The plastic bags were used for in situ incubation
of sea-ice in chapter 1.
Material and methods
Bags for incubation of sea-ice were made from 180 µm-thick transparent laminated plastic
(NEN/PE 80/100, Danisco Flexible, Denmark). Bags, 60 x 30 cm in size were produced from a
single sheet of plastic by use of an impulse heatsealer (Elwis-Pack, Anderteck international,
Denmark). The plastic bags were fitted with a gas-tight Tygon tube and valve for sampling.
Experiments were performed at three different temperatures 20°C, 3°C and -20°C. The bags were
filled with 2 l of autoclaved artificial seawater containing 100 µl HgCl 2 (salinity of 34) with a known
O2, DIC (dissolved inorganic carbon) concentration and TA (total alkalinity) and the bags were
closed and excess air quickly extracted trough the valve. Triplicate bags were incubated at 20°C,
3°C and -20°C in 7 d. After 7 d of incubation (bags which were incubated at -20°C were melted
over night in the dark (3±1°C)), the gas bubbles released in the bags were then transferred to
Exetainers (12 ml Exetainer ®, Labco High Wycombe, UK). The gas bubbles were analyzed for
gaseous O2 and CO2 by mass spectrometry (Sercon hydra 20-20 isotope radio mass
spectrometer). The melt water was similarly transferred to Exetainers for O 2 and dissolved
inorganic carbon (DIC) measurements. Dissolved O2 in the melt water was measured by Winkler
titration (Grasshoff et al. 1983) and DIC was measured in the sea-ice melt water using a CO2
analyzer (CM5012 CO2 Coulometer), TA was measured in the sea-ice by potentiometric titration
(Haraldsson et al. 1997).
Results and discussion
The flux of O2 in and out of the plastic bags was low in the three experiments. However, dissolved
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gases in freezing seawater can also establish bubbles (Glud et al. 2007). The gas bubble volume
was high in the bags incubated at -20°C, which suggest that a significant fraction of the O 2 was
actually present in the gas bubbles.
Conclusion
The present study showed that the NEN/PE plastic bags are made of an extremely gas-tight
laminated plastic material, which is suitable for incubation of sea-ice. The fluxes of O2 through the
plastic film were insignificant; however the present study showed that when freezing sea water, a
significant fraction of O2 is present in the gas bubbles.
References
Glud RN, Rysgaard S, Kühl M, Hansen JW (2007) The sea-icein Young Sound: Implications for
carbon cycling. Bioscence 58:62-85
Grosshoff K (1983) Determination of oxygen I: Grasshoff, K., Erhardt, M. & Kremling, K. Methods
of seawater analysis. Vol. Verlag Chemie, Weinheim. 61-72
Hansen JW, Thamdrup B, Jørgensen BB (2000) Anoxic incubation of sediment in gas-tight plastic
bags: a method for biogeochemical process studies. Mar Ecol Prog Ser 208:273-282
Haraldsson C, Andersson LG, Hassellöv M, Hult S, Olsson K (1997) Rapid, high-precision
potentiometric titration of alkalinity in ocean and sediment pore waters. Deep-Sea Res Part I.
44:2031-2044
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Appendix II
An experimental chamber for studying biotic and abiotic gas dynamic of sea-ice in the laboratory
was constructed and optimized in chapter 2. Two kinds of setups were developed where ice was
formed within a small transparent chamber (Picture A-B). One type of chamber had a planar
optode foil attached to the side (Picture B), the other type did not (Picture A). The chamber with a
planar optode foil was inoculated with a mixed culture of sea-ice algae (Fragilariopsis nana, ,
Fragilariopsis sp, Chlamydomonas sp., Melosira arctica and Pyramimonas quadrifolia.) and the
sea-ice algae were incorporation within the sea-ice (Picture B). In this way the biotic gas dynamic
within the sea-ice was quantified (Picture C). The chambers without planar optodes foil were
mainly used for non-biotic quantification of oxygen and salinity fluxes from sea ice, by measuring
on the water phase. Picture C show the experimental setup (planar optode).
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Appendix III
Chlamydomanas sp. produced some gel-like substances in the growth experiments at high pH
(9.5), which surrounded the algae colonies. This gel-like substance could be exopolymetric
substances (EPS), which are produced by bacteria and algal cells in marine environments (Riedel
et al. 2006), especially in harsh environments (e.g. cryoprotection of algal cells within first-year
sea-ice) (Krembs et al. 2002).
Reference
Krembs C, Eichen H, Junge K, Deming JW (2002) High concentrations of exopolymeric
substances in Arctic winter sea ice: implications for polar ocean carbon cycle and cryoprotection of
diatoms. Deep-Sea. Res I 49:2163-2181
Riedel A, Michel C, Gosselin M (2006) Seasonal study of sea-ice exopolymeric substances on the
Mackenzie shelf:implications for transport of sea-ice bacteria and algae. Aqua Microbiol Ecol
45:195-206
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Appendix IV
We isolated a new species from the sea-ice. The alga is a coccoid green algae from the genus
Chlorococcum. The sea-ice alga is send to Germany for DNA sequencing. Eventually an article
describing the new sea-ice species will be presented.
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Appendix V
In order to perform our experiments on the Greenland Institute of Natural Resources we
constructed a rotating plankton wheel and luminaries in a room with an external cooling system.
We used the room for growing and storing sea-ice algae cultures. Furthermore, the experimental
setup was used in chapter 1 (primary production measurements) and 3 (growth experiments of
three sea-ice algae).
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