Journal of Marine Systems 63 (2006) 35 – 48
                                                       www.elsevier.com/locate/jmarsys




  Long-term patterns of rocky bottom macrobenthic community
structure in an Arctic fjord (Kongsfjorden, Svalbard) in relation to
          climate variability (1980–2003)
            Frank Beuchel a,⁎, Bjørn Gulliksen a,b , Michael L. Carroll c
              a
                Norwegian College of Fishery Science, University of Tromsø, N-9037 Tromsø, Norway
                    b
                     University Centre of Svalbard, N-9171 Longyearbyen, Norway
                  c
                   Akvaplan-niva, Polar Environmental Centre, N-9296 Tromsø, Norway

             Received 22 December 2005; received in revised form 20 April 2006; accepted 2 May 2006
                           Available online 27 June 2006



Abstract

  We investigated temporal variations in marine macrobenthic community structure from 1980 to 2003 in high-arctic
Kongsfjorden (Svalbard) based on analysis of annual photographs at a permanent rocky bottom station. Abundance and area
covered by macrobenthic organisms were estimated based on image analysis of high-resolution photographs, and community
summary parameters were calculated as the basis for examination of interannual patterns. Interannual variability in abundance and
species diversity were related to climate variability. 45% of the variability of the benthic community in Kongsfjorden could be
attributed to environmental factors linked to the North Atlantic Oscillation Index (NAOI) and its local manifestations. The
temperature of the West Spitzbergen Current (WSC) was a link between the NAOI and the benthic community. Biodiversity was
negatively correlated to the NAOI. Severe changes in the benthic community were observed between 1994 and 1996 coinciding
with a shift of the NAOI from a positive to a negative mode. The increase in biodiversity during this period was accompanied by a
decline of actinarians and the appearance of dense carpets of brown algae.
© 2006 Elsevier B.V. All rights reserved.

Keywords: Svalbard; Benthos; Image processing; Climate variability; North Atlantic Oscillation; Arctic climate regime; Redundancy analysis




1. Introduction                              individually or collectively as a community, to integrate
                                      environmental influences over long time scales (Under-
  The marine macrozoobenthos is commonly regarded            wood, 1996). Thus, the study of patterns of variation in
as a good indicator for long-term ecosystem changes            benthic communities in relation to environmental
(Kröncke, 1995). Most of the fauna are sessile or have           variables can provide insight into the biophysical
little motility as adults compared to plankton and many          linkages and mechanisms by which communities are
taxa have life spans of years to decades. These              maintained.
characteristics provide the potential for benthic fauna,           Shallow marine rocky bottom habitats (including
                                      the intertidal) are well-suited to directly examine the
⁎ Corresponding author. Tel.: +47 77646723; fax: +47 77646020.      dynamic relations between organisms and their phys-
  E-mail address: frankb@nfh.uit.no (F. Beuchel).             ical and biological environment because of their
0924-7963/$ - see front matter © 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.jmarsys.2006.05.002
36                F. Beuchel et al. / Journal of Marine Systems 63 (2006) 35–48


accessibility, well-described species compositions and       order to determine how these indices of atmospheric
interactions. Further, gradients in environmental para-      forcing are related to the variations in the benthic
meters such as temperature, nutrient concentration, and      community.
turbidity tend to be amplified in these shallow habitats        Alterations of the atmospheric circulation may result
compared to deeper water because of the proximity to        in changes of the trophic system on an ocean-wide scale
the surface ocean and its atmospheric drivers (Paine,       (Arntz and Fahrbach, 1991; Stenseth et al., 2002) with
1966; e.g. Connell, 1972; Underwood, 1996 and           major impacts on the distribution on phyto- and
references therein). Therefore, the detection of long-       zooplankton. Benthic communities may have a delayed
term changes in shallow benthic communities can lead        response due to time lags related to the propagation of
to inferences about the physical forcing mechanisms        external forcing through the production and settlement
which drive these ecological patterns (Barry et al.,        of organic matter from the plankton to the benthos and
1995; Kröncke, 1995; Kröncke et al., 1998, 2001;          to changes of reproductive rates of the benthos (Gray
Sagarin et al., 1999).                       and Christie, 1983). Andersin et al. (1978) document
  The North Atlantic Oscillation Index (NAOI)           abundance cycles of 6–7 years for the amphipod
(Hurrell, 1995) is the dominant signal of the interannual     Pontoporeia affinis in the Bothnian Sea, while Svane
variability in the atmospheric circulation across the       and Lundälv (1982) identify a 10–11 year cycle for the
North Atlantic with a cyclical component of ≈ 7.9 years.      ascidian Pyura tessulata in the Skagerak. Several
The NAOI is usually measured as the mean deviation         studies provide evidence of relationships between
from the average atmospheric sea level pressure (SLP)       atmospheric circulation indices and benthic populations.
between Iceland and the Azores, but variations of the       The NAOI was used to explain community variations in
index using Lisbon or Gibraltar as southern stations are      the North Sea and the Skagerak (Carpentier et al., 1997;
also common. It influences the temperature and the         Tunberg and Nelson, 1998; Hagberg and Tunberg,
current regime of the entire North Atlantic (Tunberg and      2000; Nordberg et al., 2000; Kröncke et al., 2001;
Nelson, 1998). The northernmost extension of the North       Schroeder, 2005). The ACRI was linked to variations in
Atlantic Current is the West Spitsbergen Current          bivalve growth in a high-arctic fjord from Northeast
(WSC), which has a major influence on the hydro-          Svalbard (Ambrose et al., in press).
graphical regime in western Svalbard fjords, including         In the high-arctic, few studies have investigated
Kongsfjorden (79°N). Strong interannual fluctuations in      interannual variations in composition of arctic macro-
WSC temperature were revealed in a time series of         benthic communities on rocky bottom habitats. The
subsurface hydrographical data from 1970 to 1997,         relationship between climate indices, local environmen-
which were related to variations in large-scale atmo-       tal parameters and changes in benthic communities is
spheric patterns, as indicated by the NAOI (Saloranta       poorly understood compared to pelagic ecosystem
and Haugan, 2001).                         components (phytoplankton and zooplankton) (Beau-
  Two alternating (cyclonic and anticyclonic) wind-        grand et al., 2002; Drinkwater et al., 2002). In this study,
driven circulation regimes have been described for the       we examine interannual patterns of benthic community
Arctic Ocean, with each regime persisting for 5–7 years      structure over a 23-year period spanning positive and
(Proshutinsky and Johnson, 1997; Mysak and Venegas,        negative modes of major oscillatory climate indices and
1998). These regimes are associated with different         investigate how climate indices and their related
patterns in Arctic SLP (Johnson et al., 1999) and sea ice     environmental factors explain the observed variations
concentration (Mysak and Venegas, 1998), while the         in a benthic community in Kongsfjorden.
shifts from one regime to the other are forced by changes
in the location and intensity of the Icelandic low and       2. Materials and methods
Siberian high pressure systems (Proshutinsky and
Johnson, 1997; Mysak and Venegas, 1998). The            2.1. Study area
resulting Arctic Climate Regime Index (ACRI) may
help explain cyclical variations in the Arctic Ocean's        The study area is situated near Kvadehuken at the
temperature and salinity structure and thus changes in       outer part of Kongsfjorden on the northwest corner of
biological systems. The ACRI should reflect more          the Svalbard archipelago (Fig. 1). Our permanent station
specifically the shifting atmospheric pattern in the        (78° 58.6′N, 11° 30.1′E) is located at 15 m depth on
Arctic than the Arctic Oscillation Index (AOI), which       horizontal bottom, about 300 m from shore. The bottom
covers a broader range of the Northern Hemisphere. We       surface at the station is characterised by bedrock, with
therefore use the ACRI and compare it to the NAOI in        smaller and larger pebbles.
                   F. Beuchel et al. / Journal of Marine Systems 63 (2006) 35–48                   37




   Fig. 1. Map with location of the permanent monitoring station at Kvadehuken (position: 78° 58.6′N, 11° 30.1′E) in Kongsfjorden.


  Kongsfjorden is an arctic glacial fjord that is            2.2. Sampling design
influenced both by Atlantic and Svalbard-Coastal
water masses. There is a particular interest in               Data for the present study were obtained from
Kongsfjorden as a suitable research site for exploring          underwater photographs based on a non-destructive
the impacts of possible climate changes, with altera-          sampling technique (Lundälv, 1971; Torlegard and
tions in Atlantic water influx being linked to climate          Lundälv, 1974). The station at Kvadehuken was
variability (Hop et al., 2002; Cottier et al., 2005). The        established in 1980 as an array of ten adjacent
WSC, the local conduit of Atlantic Water west of             quadrates (0.25 m 2 ) with the initial purpose of
Svalbard, has been considered as the major pathway            studying succession and colonisation of new hard
both for heat and water volume export to the Arctic           bottom areas in an arctic fjord (Beuchel and Gulliksen,
Ocean (Aagaard and Greisman, 1975; Gammelsrød and            unpublished data). In the present study, we focus on
Rudels, 1983; Aagaard et al., 1987). The Atlantic water         the patterns of variation in the unmanipulated control
(S > 35 ppt; t > 3 °C) inflow into Kongsfjorden varies          group of five quadrates, and their possible links to
significantly from year to year as a consequence of the         large-scale climate cycles.
regional atmospheric circulation (Svendsen et al.,              Underwater photographs were recorded every year in
2002).                                  August from 1980 to 2003, using a Hasselblad Super
  More than 450 taxa have been recorded on hard             Wide Camera (SWC), with a Biogon 38 mm lens
bottom substrata in Kongsfjorden, with a mixture of           (including a correction lens) in a Hasselblad underwater
boreal and Arctic flora and fauna (Hop et al., 2002).          casing fitted with a Zeiss corrective glass port. The
The abundance and biomass of individual species as            camera system (including an electronic flash) was
well as the community structure varies with depth and          mounted on a rig with a 50 cm × 50 cm metal frame
substratum (Wlodarska-Kowalczuk et al., 1998;              enclosing the photographed area of seabed (see also
Jørgensen and Gulliksen, 2001; Wlodarska-Kowalczuk            Lundälv, 1971; Torlegard and Lundälv, 1974). The
and Pearson, 2004). The distribution of species and           photographs were scanned in high resolution (about
populations is strongly structured along fjord-associat-         3 Mega-pixel), and digital image analysis was carried
ed gradients such as salinity, turbidity and bottom type/        out using Adobe Photoshop® and the measurement
sedimentation patterns as well as by interannual             toolkit Fovea Pro® (Russ, 2001, 2002). Organisms were
variations in physical factors (Weslawski and Adamski,          first grouped into solitary and colonial species. A semi-
1987).                                  automated image analysis technique was developed,
38                F. Beuchel et al. / Journal of Marine Systems 63 (2006) 35–48


where solitary organisms were counted and cover of         the various environmental parameters. These para-
colonial taxa was measured as percentage of the whole       meters were species density (S), total organism
frame area. The minimum size of organisms observed in       abundance (N), species richness (d), Pielou's evenness
the pictures was about 1–3 mm, depending on their         index (J′) and Shannon–Wiener diversity index (H′)
contrast to the substrate, colour and how cryptic they       (Table 1). Correlations between community data and
are.                                environmental factors were computed using Pearson's
                                  Correlation Coefficient (σ (Sokal and Rohlf, 1995).
2.3. Environmental factors                     Only environmental factors that showed significant
                                  correlations (95% significance level) were considered.
  Mean sea temperature (depth-integrated over 100–        Correlations were computed with contemporaneous
300 m depth) and salinity data (1980–1997) were          year-on-year data and with time lags up to 2 years (i.e.,
derived from a long-term data set collected for the        species data were correlated to environmental data
northern extension of the WSC by Saloranta and           from the two preceding years) for all environmental
Haugan (2001). From this study, we used data from         variables and indices.
station “E1” (78.90–79.55°N, 7.5–8.8°E), which was          Multi-dimensional scaling (MDS) analysis (Kruskal,
closest to our sampling location at Kvadehuken (Fig.        1964) was carried out based on the Bray–Curtis
2a). This data set was extended in time to 2003 with data     similarity matrix for species abundance data (Bray and
from Schauer et al. (2004) and Tverberg (pers. comm.).       Curtis, 1957) and a normalised Euclidian distance
All these data were from the autumn season (August–        matrix for environmental factors (Legendre and
September), which represents the time of the year with       Legendre, 1998). All correlations, summary parameters
the highest temperature in the upper layer of the WSC       and MDS-analysis were calculated using the statistical
(Morison, 1991).                          package PRIMER (Clarke, 1993; Clarke and Warwick,
  Meteorological data for Kongsfjorden were obtained       2001).
from the Norwegian Meteorological Institute (http://         In order to separate effects related to different
eklima.met.no) for the weather station in Ny-Ålesund        environmental predictors, direct ordination was applied
(about 11 km from the monitoring station). We used         using the program package CANOCO 4.5 (Ter Braak
monthly means of air temperature (Temperature Air),        and Smilauer, 2002). All data were log10-transformed in
precipitation, average wind speed (Wind), maximum         order to reduce the influence of common taxa. Further,
average wind speed (Wind Max) and extreme wind           the data were transformed (centred and standardized by
speed (Wind Extreme). From these data, annual average       species), to harmonise different scales (i.e. percentage
and seasonal values for winter (January–March = W)         cover and abundance) (Leps and Smilauer, 2003).
and summer (June–August = S) were calculated.           Linear ordination (Redundancy Analysis = RDA) was
  Data for the NAOI (Hurrell, 1995) were obtained        applied based on the length of the main gradient. A
from the Climate Analysis Section of the National         forward selection of environmental factors was
Centre for Atmospheric Research (NCAR), Boulder,
USA (http://www.cgd.ucar.edu/cas/jhurrell/indices.
html). The biological data of our study were sampled        Table 1
at the end of August each year. Therefore we calculated      Description of summary parameters of the benthic community used in
a modified annual mean of the NAOI from the available       this study
monthly NAOI data, encompassing the 12-month period        Measure       Description       Calculation
immediately preceding the sampling date (i.e. Septem-       Species abundance  Number of species
ber–August). For the Arctic Ocean SLP anomalies (the         (S)        per unit area
Arctic Climate Regime Index = ACRI), data from           Organism      Number of organisms
                                   abundance (N)   per unit area
Proshutinsky and Johnson (1997) were used. For both
                                  Species richness  Number of species    d ¼ ðS À 1Þ=loge N
climate indices, a 3-year mean as well as a winter index       (d) (Margalef)  relative to the number
(December–March) was also calculated (NAOI 3 y,                    of organisms
                                                             P
ACRI 3 y, NAOI (W), ACRI (W)).                   Diversity (H′)   Combines aspects of   H V¼ À    i  pi log2 ðpi Þ
                                   (Shannon–     species richness and     Ni
                                                        pi ¼
                                   Wiener index)   evenness           S
2.4. Statistical analysis

  Summary parameters of the benthic community were        Pielou's evenness  Summarizes dominance J V¼ H V=loge S
                                   (J′)       structure of community
calculated in order to relate changes in the community to
                     F. Beuchel et al. / Journal of Marine Systems 63 (2006) 35–48               39


generated prior to the analysis in order to identify the          P < 0.001 (Table 2, Fig. 2b). The 1-year lag of the
most significant combination of environmental factors            NAOI also showed a strong correlation (σ = − 0.67;
that best explains the variations in species data.             P < 0.001). Lower, but still significant is the correlation
Significance tests were performed using Monte-Carlo             of the ACRI (3-year mean) (σ = − 0.51; P < 0.05) and the
restricted permutations adjusted for temporal auto-             2-year lag of the NAOI (3-year mean) (σ = − 0.52;
correlation (Leps and Smilauer, 2003).                   P < 0.01). Generally, the 3-year means of atmospheric
                                      indices yielded higher correlation factors than the
3. Results                                 annual values (Heyen and Dippner, 1998).
                                        Total organism abundance (N) was positively
3.1. Community parameters                          correlated to all wind parameters and temperature of
                                      the WSC with a 2-year lag, with average annual wind
  Significant correlations between environmental fac-           speed showing the greatest correspondence (σ = 0.59;
tors and the benthic communities were detected for             P < 0.01) and the ACRI (3-year mean) (σ = 0.54;
summary parameters of the community as well as for             P < 0.01). Both species number (S) and species richness
individual taxa. Within the summary parameters,               (d) showed positive correlation to winter precipitation
Pielou's evenness index (J′) and Shannon–Wiener               with 1-year lag (σ = 0.49 and 0.47, respectively;
diversity index (H′) yielded quite similar results, and a          P < 0.05) and extreme summer winds (σ = 0.59 and
high inter-correlation (σ > 0.9) between these two             0.54 respectively; P < 0.01) (Table 2).
factors was detected. Because this indicates that the
interannual differences are related to abundances rather          3.2. Individual taxa
than number of species, we therefore exclude Pielou's
evenness index from further analysis.                     In total, 23 species and taxa were detected from the
  We found significant correlations between commu-             photographs and included in the analysis. Some species
nity parameters and both climate indices and local             and taxa showed strong correlations to either one, or in
environmental variables (Table 2). The most conspicu-            other cases a group of environmental factors or
ous among these is a negative correlation between the            atmospheric indices. The actinarians Urticina eques
NAOI and the Shannon–Wiener species diversity (H′),             and Hormathia nodosa were present in high abundances
indicating that the temporal variation of the NAOI (3-           during the entire observation period (Beuchel and
year mean) is strongly tracked by H′ (σ = − 0.73;              Gulliksen, unpublished data). The abundances of these
                                      species showed significant relationships to both atmo-
                                      spheric pressure indices. They are positively correlated
Table 2                                   to the 1-year lag of the NAOI 3 y-mean (σ = 0.66;
Significant correlations between environmental factors and summary
parameters of the benthic community in Kongsfjorden
                                      P < 0.001), closely followed by the NAOI 3 y (σ = 0.64;
                                      P < 0.01) and ACRI 3 y (σ = 0.63; P < 0.01) (Table 3).
Summary parameter     Environmental parameter      σ
                                        Gastropods are significantly correlated to tempera-
H′             NAOI 3 y             − 0.73⁎⁎⁎   ture (current year and 1-year lag), average and
              NAOI 3 y (lag-1)         − 0.67⁎⁎⁎
                                      maximum wind speed (2-year lag). Correlations of
              NAOI               − 0.56⁎⁎
              NAOI (lag-1)           − 0.56⁎⁎    winds with 2-year lag were also detected for all
              NAOI 3 y (lag-2)         − 0.52⁎⁎    decapods combined, and Pagurus sp. and Natantia as
              ACRI 3 y             − 0.51⁎    single taxa.
N             Wind (2)              0.59⁎⁎     Bivalves (and Hiatella arctica separately), ascidians
              Wind maximum (lag-2)        0.56⁎⁎
                                      and brown algae (Phaeophycea) were all negatively
              ACRI 3 y              0.54⁎⁎
              Wind extreme (lag-2)        0.46⁎    correlated to the NAOI with different time lags. In
              Temperature WSC (lag-2)       0.46⁎    addition, bivalves were correlated positively to annual
S             Wind extreme S           0.59⁎⁎    and summer precipitation and temperature of the
              Precipitation W (lag-1)       0.49⁎    WSC, and negatively to extreme winds (with 1-year
d             Wind extreme S           0.54⁎⁎
                                      lag) (Table 3).
              Precipitation W (lag-1)       0.47⁎
Summary parameters: H′=diversity, N = organism abundance, S =        3.3. Inter-correlations of environmental parameters
species abundance, d = species richness; 3 y = 3-year mean; W =
winter, S = summer, WSC = West Spitzbergen current; σ:
Pearson's correlation coefficient with significance level (⁎ = P < 0.05,    The atmospheric indices and environmental factors
⁎⁎ = P < 0.01, ⁎⁎⁎ = P < 0.001).                      used in this study also show inter-correlations (Table
40                   F. Beuchel et al. / Journal of Marine Systems 63 (2006) 35–48




Fig. 2. a) Mean autumn (August–September) temperature of the WSC at about 79°N between 100 and 300 m depth. Data from station E1 (close to
Kvadehuken) from Saloranta and Haugan (2001), Schauer et al. (2004) and Tverberg (pers. comm.). b) Correlation between the NAO Index (3-year
mean calculated from September–August) and Shannon–Wiener diversity index (H′). The secondary y-axis scale (species diversity) is inverted.


4). The factors NAOI (annual), NAOI (Winter) and             (marked light grey), and years 1990–1994 clustered in
WSC-temperature are most significant in explaining            another group (marked dark grey) nearby. From 1995
the variability of the benthic community (redundancy           until 2003, both the benthic community and the
analysis, Fig. 4). In addition, these factors also show         environmental factors undergo major changes. Howev-
the highest and most relevant correlations to other           er, small differences can also be detected between the
factors. The atmospheric pressure indices (NAOI and           biological and environmental plots. In the plot of
ACRI) are inter-correlated (σ = 0.52, P < 0.01) to some         environmental factors (Fig. 3b), the 1980s are more
extent, and both are positively correlated to the            separated, while the years 1995–1998 and 1999–2001
temperature of the WSC (σ = 0.40 and 0.45, respec-            are more tightly clustered. In the abundance data plot
tively; P < 0.05. A very high inverse correlation            (Fig. 3a), the year 1987 does not fit into the compact
coefficient (σ = 0.64, P < 0.01) exists between NAOI           group of the 1980s. In the same plot, the group 1990–
and precipitation during the winter month indicating           1994 is less separated from the 1980s compared to the
that the precipitation is higher in years with low NAOI         environmental plot (Fig. 3b).
(Table 4).                                  Redundancy analysis (Fig. 4, Table 5) reveals that the
                                     first two axes account for 22.6% of the total variance of
3.4. Multivariate analysis                        species abundance. The forward selection of environ-
                                     mental factors with Monte-Carlo permutation tests (999
  An MDS-plot based on benthos abundance data (Fig.           permutations) reveals that time, temperature of the
3a) shows a similar pattern as that based on the three          WSC, NAOI and NAOI-winter index (NAOI-W) are the
most important environmental factors identified from           significant factors that contribute to the observed
redundancy analysis (NAOI, temperature and time)             variability in abundance. The four environmental
(Fig. 3b). Both plots show the 1980s tightly clustered          variables together explained 32% of the total variance
                    F. Beuchel et al. / Journal of Marine Systems 63 (2006) 35–48                   41


Table 3                                  scales (Gray and Christie, 1983). The challenge for
Significant correlations between environmental factors and abundance   long-term field experiments is to encompass a time span
or cover of selected species or taxa of the benthic community in
Kongsfjorden
                                     that is long enough to detect and extract such periodic
                                     components in community patterns. Data series cover-
Species/taxon       Environmental         σ
              parameters
                                     ing shorter time periods may be misinterpreted by using
                                     linear regression models instead of polynomials (i.e.
Urticina eques,      NAOI 3 y (lag-1)          0.66⁎⁎⁎
                                     Josefson, 1990), thereby losing the cyclical components.
 Hormathia nodosa
              NAOI 3 y             0.64⁎⁎    We have found such cyclical components in our cover
              ACRI 3 y             0.63⁎⁎    and abundance data in Kongsfjorden. The analyses
              NAOI               0.62⁎⁎    indicate that large-scale atmospheric processes and their
              NAOI 3 y (lag-2)         0.50⁎     mediators (local environmental factors) are important
              ACRI 3 y (lag-1)         0.50⁎
                                     drivers of biological changes in the macrobenthic
              NAOI (lag-1)           0.47⁎
              ACRI               0.46⁎     community.
Gastropoda (combined)   Temperature WSC 3 y       0.51⁎      The most profound correlation in our data is the
              Temperature WSC (lag-1)     0.47⁎     tracking of the NAOI by species diversity (Table 2, Fig.
              Wind (lag-2)           0.44⁎     2b). The relationship is maintained during most of the
              Temperature WSC         0.44⁎
                                     23-year monitoring period, with divergence only during
              Wind max (lag-2)         0.41⁎
Bivalvia (combined)    Precipitation S         0.52⁎⁎    two short intervals (1980–83, 1986–87). Most conspic-
              Temperature WSC         0.49⁎     uous is the coincidence in the period 1993–1996, with a
              Precipitation          0.44     rapid shift in the NAOI-regime from positive to negative
              NAOI 3 y            − 0.43⁎     mode mirrored by a strong increase in diversity (Fig.
              Wind extreme (lag-1)      − 0.43⁎
                                     2b). With this striking congruence between the NAOI as
Hiatella arctica      NAOI 3 y (lag-1)        − 0.45⁎
              NAOI (lag-2)          − 0.42⁎     a large-scale climate driver and benthic community
Ascidia (combined)     NAOI              − 0.40⁎     diversity, we can examine the local forces linking the
              NAOI 3 y            − 0.50⁎     two.
Decapoda (combined)    Wind max (lag-2)         0.46⁎      Gray (1997) stated that temperature rise in coastal
              Wind extreme (lag-2)       0.40⁎
                                     waters might be a possible threat to marine biodiversity.
              Wind (lag-2)           0.41⁎
Natantia          Wind max (lag-2)         0.42⁎     One of the possible consequences of climate warming
Pagurus sp.        Wind extreme (lag-2)       0.45⁎     for Arctic fjord ecosystems will be a decline of benthic
              Wind               0.43⁎     biodiversity, due to an increase of mineral sedimentation
Phaeophycea        NAOI 3 y            − 0.41⁎     from melt waters (Wlodarska-Kowalczuk and
σ: Pearson's correlation coefficient with significance level (⁎ =     Weslawski, 2001). Although we have no continuous
P < 0.05, ⁎⁎ = P < 0.01,⁎⁎⁎ = P < 0.001); 3 y = 3-year mean; W =     data about melt water run-off and sedimentation in
winter, S = summer.
                                     Kongsfjorden, our precipitation data indicates a rela-
                                     tively strong negative correlation (σ = − 0.64, P < 0.01)
in species abundance. All other tested factors were not          between NAOI and precipitation during winter months
significant, and therefore not included in the analysis.
  Redundancy analysis visualises correlations of             Table 4
species and taxa to environmental factors and time            Correlations between selected environmental factors
(Fig. 4). Strong positive correlations with axis “time”
                                     Environmental parameters                  σ
indicate that those taxa undergo major increases in
                                     NAOI–ACRI                         0.52⁎⁎
abundance during the observation period. This includes
                                     NAOI–temperature WSC                    0.40⁎
brown algae (Phaeophycea), Balanus sp. and the sea            NAOI–wind                         0.37n.s.
urchin Strongylocentrotus droebachiensis. Bivalves and          NAOI–wind extreme                     0.54⁎⁎
the actinarians U. eques/H. nodosa show a strong             NAOI W–precipitation W                   − 0.64⁎⁎
positive correlation to temperature and NAOI,               Temperature WSC–ACRI                    0.45⁎
                                     Temperature WSC–salinity                  0.58⁎⁎
respectively.
                                     Temperature WSC–temperature air              0.53⁎⁎
                                     Temperature WSC–wind                    0.49⁎
4. Discussion                               Temperature WSC–wind max                  0.56⁎⁎
                                     σ: Pearson's correlation coefficient with significance level (⁎ =
  It is well known that many marine benthic popula-           P < 0.05, ⁎⁎ = P < 0.01, ⁎⁎⁎P = < 0.001, n.s.= non significant); W =
tions exhibit periodic variations at different temporal          winter, WSC = West Spitzbergen Current.
42                   F. Beuchel et al. / Journal of Marine Systems 63 (2006) 35–48




Fig. 3. a) MDS-plot of community change from 1980 to 2003 at Kongsfjorden, based on Bray–Curtis similarities. Lines connect consecutive years.
Bold lines indicate major shifts in the community. b) MDS-plot of three main environmental factors NAOI, temperature and time, based on Euclidian
distance.



(Table 4). Rogers (1984) found that during periods of            a significant increase in the cover of dense brown algae
low NAOI values, winter precipitation in Scandinavia is           carpets on the bottom (mainly Desmarestia sp.) in years
higher than average, which coincides well with our             with low NAOI (Table 3). Especially between 1994 and
findings for northwest Svalbard. Increased precipitation          1996, when the NAOI shifted from a positive to a
in winter leads to higher melt water run-off, which in           negative mode, these brown algae increased dramati-
turn may increase the nutrient input into the fjord.            cally from < 5% to 80% (Fig. 2b). Dominance by the
Increased nutrient concentration in coastal waters             brown algae with a negative NAOI was accompanied by
increases surface primary production and hence the             an increase in diversity. This perhaps counterintuitive
food supply to benthic populations (Josefson, 1990). We           result is primarily due to an associated reduction in the
have found no direct significant correlations between            abundance of sea anemones, which previously had
precipitation and biodiversity, but there are positive           dominated (> 50% of relative abundance) the commu-
relationships between number of species (S) and species           nity before 1994. Along with brown algae, several other
richness (d) to precipitation with a 1-year lag (Table 2),         faunal taxa increased in abundance in the absence of sea
which suggests that there are indirect effects of              anemones, leading to the observed higher biodiversity
precipitation to the benthic communities. We observed            after 1994 (Beuchel and Gulliksen, unpublished data).
                    F. Beuchel et al. / Journal of Marine Systems 63 (2006) 35–48                  43


                                     cooling (1984–1987 and 1992–1996) (Saloranta and
                                     Haugan, 2001). The last warming period 1987–1992
                                     (Fig. 2a) was the most extensive warm anomaly in this
                                     study and agrees with other evidence of Atlantic Water
                                     warming in the 1990s (Quadfasel et al., 1993; Carmack
                                     et al., 1995; Swift et al., 1997; Grotefendt et al., 1998;
                                     Melling, 1998; Morison et al., 1998; Steele and Boyd,
                                     1998; Zhang et al., 1998). After 1997, the temperature
                                     of the WSC increases again (Fig. 2a). Schauer et al.
                                     (2004) showed a very strong increase in heat transport in
                                     the WSC from 28 to 46 TW in the period 1997–2000,
                                     concluding that half of the heat flux increase was due to
                                     a higher temperature while the other half was due to a
                                     stronger flow. Other studies (Grotefendt et al., 1998;
Fig. 4. Redundancy Analysis (RDA) (axes 1 and 2) of selected species
and taxa of benthic fauna and their relation to significant environ-
                                     Blindheim et al., 2000) suggest an even stronger
mental variables and atmospheric indices.                 temperature rise in the WSC in the 1980s and 1990s,
                                     respectively.
                                       Our permanent monitoring station is located on the
The pattern of increased algal cover could also be an           southern entrance of Kongsfjorden. Here the Coriolis
indication of increased nutrient supply to the benthos          effect generates an overall current pattern with a net
during years with low NAOI.                        inflow of Transformed Atlantic Water (TAW), originat-
  Higher diversity in the presence of dense stands of          ing from the warm WSC (Basedow et al., 2004). Events
brown algae, compared to periods with high abundance           of warm Atlantic Water intrusion into Kongsfjorden
of sea anemones suggests a competitive exclusion by the          occur frequently, especially during mid-summer, giving
latter. Anemones can exclude other fauna by its physical         rise to intense seasonality. More TAW enters the fjord in
occupation of space and through its feeding activities,          years were the WSC is warmer and stronger, so the fjord
which through efficient particle capture of sedimenting          adopts a ‘cold’ or ‘warm’ mode according to the degree
material may leave little food for other fauna. The            of Atlantic water influence (Cottier et al., 2005).
extensive cover of brown algae, though occupying space            It is unclear from the literature how warm vs. cold
on the substratum, does not inhibit, and may in fact           periods will affect community faunal patterns. Higher
enhance (through degradation of thalli and dampening of          numbers of species and biodiversity during years with
near-bottom currents), particulate food material avail-          increased sea temperature are reported from a study of
ability to the faunal community. The higher biodiversity         benthic communities at two localities around Tromsø,
we observed in the macrobenthic community after 1994,           northern Norway (Bahr and Gulliksen, 2001). In
associated with dense brown algae but few sea anemones          contrast, Coyle et al. (in press) found that increased
(Fig. 2b) reflects this community structuring influence.         benthic biomass in the Alaskan Bering Sea occurred
  There was a correlation between the sea temperature
of the WSC in August–September and the NAOI                Table 5
(Saloranta and Haugan, 2001) (Fig. 2a,b; Table 4). Some          Summary of RDA of the macrobenthic community in Kongsfjorden
oceanographic models predict a warming and broaden-
                                     Axes          1    2    3    4     Total
ing of the Atlantic layer in the Arctic Ocean, with a                                        variance
consequence being considerable retreat of arctic sea ice
                                     Eigenvalues:      0.148 0.078 0.063  0.034 1.000
(Washington and Meehl, 1996; Zhang et al., 1998) in the          Species–environment
near future. Recent observations show significant              correlations:     0.873 0.783 0.684  0.677
warming in the Arctic Ocean in 1990–1995 when               Cumulative %
compared to Russian climatology data of the 1940s and            variance of
                                      species data:    14.8  22.6 28.8  32.3
1970s (Grotefendt et al., 1998; ACIA, 2004) and a
                                     Cumulative %
reduction in both extent (Johannessen et al., 1999) and           variance of species–
thickness (Rothrock et al., 1999) of arctic sea ice. A time         environment
series with data of subsurface temperature from 1970 to           relation:      45.7  70.0 89.4 100.0
1997 west of Kongsfjorden showed that there were two           Results are based on abundance of solitary organisms (ind./m2) and
periods of warming (1978–1984 and 1987–1992) and             percentage cover of colonial species.
44                F. Beuchel et al. / Journal of Marine Systems 63 (2006) 35–48


during periods with colder temperatures and attributed       Atlantic sector, but the interannual variability was
this pattern to elevated carbon flux to the benthos due to     higher in Arctic (26%) compared to Atlantic waters
inhibition of pelagic grazers and exclusion of benthic       (7%). The increase of primary production in warm years
infaunal predators by cold bottom water masses. The         is > 30% compared to cold years (Wassmann et al.,
results of our study show a relationship between the total     2006b). In the Arctic sector of the Barents Sea, the
number of organisms (N, Table 2) and temperature of the       carbon flux is mostly controlled by sea ice extent and
WSC, but give no clear indication how the temperature        thickness (limits production), while the frequency of
of the WSC affects the benthic diversity. In the period       low pressure events and wind are the driving forces in
when the WSC was warmest, biodiversity was low           the Atlantic sector (Wassmann et al., in press;
before increasing after the cooling event of 1992–1996.       Wassmann et al., 2006b).
As the WSC temperature rose again at the end of the
1990s, no marked effect was observed as species           4.1. Air temperature and precipitation
diversity remained high. A possible reason could be
that most of the species observed in Arctic waters thrive        Winter air temperatures and precipitation in Scandi-
at a relatively wide range of temperatures, as has been       navia are significantly correlated to the NAOI (Loon and
shown for key species on Spitzbergen (Weslawski,          Rogers, 1978). There are strong indications that
1993). We expected that boreal species favoured by         temperate macrobenthic communities may be severely
Atlantic water species may gain advantage over Arctic-       affected by extremes in winter sea surface temperatures
related species when sea temperature increases, but this      (Kröncke et al., 1997, 1998; Zeiss and Kröncke, 1997).
effect was not observed in our data because the           We have not found explicit correlations between the air
monitoring station is situated at the entrance of the        temperature data and the NAOI for Ny-Ålesund. But,
fjord which is inhabited predominantly by Atlantic         we found an interesting strong inverse correlation
species. Only some species, especially filter feeding        between winter NAOI and winter precipitation (Table
bivalves and gastropod grazers (Table 3), seem to          4). This seems to be different from the positive relation
respond to changes in the water temperature regime. Sea       found for Northwest Europe and Scandinavia (Marshall
temperature is regarded to have a major effect on the        et al., 2001) but similar to the pattern described for
phytoplankton production (Edwards et al., 2001), thus        central and Southwest Europe (Hurrell and VanLoon,
an increased sinking of particulate detritus to the bottom     1997). According to our data, higher precipitation in
may favour these groups of species. Huston (1994) and        winter (in years with low NAOI) would increase the
Pielou (1975) hypothesise a correlation between           meltwater run-off later in the season and thus the
productivity and biodiversity. We have shown a           freshwater and nutrient inputs into the fjord.
correlation between the NAOI and biodiversity, and           Wind is an important factor for the stability of the
there is a known (weak) correlation between NAOI and        water column. We found lower average wind speeds and
temperature of the WSC, but lacking long-term data         less extreme wind speeds in years with low NAOI
about primary production in Kongsfjorden, the exis-         (Table 4). Combined with increased freshwater input
tence of a direct link from primary production to benthic      early in the season (due to the increased winter
biodiversity in our study remains speculative. Consid-       precipitation), we can expect a more stratified water
ering the relatively shallow location of the monitoring       column in years with low NAOI. While more nutrients
station, surface-related mixing processes, such as wind       may reach the seafloor and the benthos early in the
action and wave turbulence, as well as seasonal           season, this pattern might be reversed later in summer
variations of the freshened surface layer, may also         due to the more efficient retention of organic carbon in
alter the hydrographical conditions. The monitoring         the upper water column because of the increased
station is in the transition zone controlled by both        stratification. This may also affect the light penetration
surface and deeper layer processes (Tverberg, pers.         through the water column, potentially explaining the
comm.), thus most likely a mixture of these factors         appearance of dense brown algal carpets (mainly
influence the hydrographical conditions.              Desmarestia sp.) and the increased biodiversity after
  Lindahl et al. (1998) found that primary production at     1994 when the NAOI switched from a strong positive
a monitoring site at the mouth of Gullmarksfjorden         into a negative mode (Fig. 2b).
(Southern Sweden) was positively related to the NAOI          Our data indicate a fundamental change in the
because of stronger winds during high NAOI-mode.          benthic community after 1994 (Beuchel and Gulliksen,
Annual total primary production in the Arctic sector of       unpublished data). This period precisely coincides with
the Barents Sea is generally low compared to the          a major shift in the NAOI-regime from a positive to a
                 F. Beuchel et al. / Journal of Marine Systems 63 (2006) 35–48              45


negative mode (Fig. 2b). The track of this pattern for the     trast the growth pattern of S. groenlandicus from
whole community is revealed in the MDS-plot (Fig 3a),        Rijpfjorden (Northeast Svalbard) was very different
which clearly shows how the distance (therefore           from the Kongsfjorden population and was related to the
variability) from one year to another is greater after       ACRI (Ambrose et al., in press). Rijpfjorden is
1994, superseding the relatively stable period 1980–        considered as a true high-arctic fjord, primarily
1993. The changes are accompanied by the marked           influenced by polar processes and only minimally by
decline of the populations of the actinarians U. eques       Atlantic water. While not definitive, the suggestion of
and H. nodosa (Beuchel and Gulliksen, unpublished          different overriding ocean-atmospheric forcing patterns
data), species that were highly positively correlated to      on benthic communities over small geographical scales
the NAOI (Table 3, Fig. 4). The dense carpets of brown       (< 100 km), manifested through local environmental
algae may limit the food availability for the sea          conditions, is intriguing and warrants further
anemones, which consists mainly of plankton and           investigation.
various invertebrates, even small fish (Jackson and
Hiscock, 2004; Moen and Svensen, 2004). The algae          4.3. Individual taxa
may also begin to cover the anemone or mechanically
interfere with feeding. An energetic cost will result from       Significant relationships of individual species with
efforts to clean off algae and particles, e.g. through       environmental factors were few compared to the large
mucus production and sloughing. Repeated energetic         number of tests performed. This indicates that some of
expenditure may cause loss of condition (Jackson and        the positive correlations are actually “accidental
Hiscock, 2004). U. eques is not known to contain          correlations” (i.e. false positives). In general, an
symbiotic algae, therefore shading as a result of dense       analysis of the correlation of single species abundance
brown algae carpets cannot be regarded as a factor         to environmental factors would imply the assumption of
reducing population size. This shift in community          similar influences on the population size of the species.
structure could probably be linked to an increased         For a reasonable estimate of the development of the
inflow of warmer TAW in this period, since temperature       population size of benthic organisms it is necessary to
and volume of inflow of the WSC increased during the        sample a large area. Single stations may not be very
1990s (Saloranta and Haugan, 2001; Schauer et al.,         representative for the population size. Local densities
2004).                               are most likely influenced by interactions between
                                  species and by local environmental factors (Schroeder,
4.2. NAOI/ACRI                           2005). Therefore, we have discussed only few species
                                  and taxa that show a pattern that can be clearly
  The linkages between large-scale ocean-atmospheric       interpreted. We suggest that these correlations can be
climatic drivers and arctic marine ecosystems are of        seen as the “most probable relationships” for the
particular interest as we try to understand the possible      respective species or taxon. Nevertheless, these rela-
ecosystem consequences of warming in the Arctic           tions are probably valid due to the length of the time
(ACIA, 2004). In general, the Atlantic-based NAOI          series, despite of the limited sampling area. However,
was much more strongly correlated to species diversity       further studies should cover larger areas so that the
and other summary parameters of the community than         effects of environmental changes to single species could
the Arctic-based ACRI (Table 2). The NAOI also           be elucidated in more detail.
explains much more of the observed variations of single
species and taxa than the ACRI (Table 3). Kongsfjorden       5. Conclusions
is an open fjord-system on the west coast of Spitsbergen.
It is strongly influenced by episodic warm water inflows        Kongsfjorden is an open fjord-system on the west
from the WSC as part of the NAOI-influenced current         coast of Spitsbergen and predominantly influenced by
regime of the North Atlantic (Tunberg and Nelson,          the warm WSC current system, which in turn is linked to
1998). The location of the monitoring station at the        the NAOI. A 23-year analysis of the hard-bottom
mouth of Kongsfjorden is dominated by Atlantic-boreal        benthos in Kongsfjorden reveals cyclical variations in
species, reflecting the prevailing influence of mid-        community structure, which could be related to large-
latitude forcing. Ambrose et al. (in press) have also seen     scale atmospheric processes. A comparison of the
a strong correspondence between NAOI and interannual        Atlantic-based NAOI to the Arctic-based ACRI
patterns of growth variations in the Greenland Cockle        revealed that the NAOI was much stronger correlated
(Serripes groenlandicus) from Kongsfjorden. In con-         to species diversity and variations of selected taxa than
46                    F. Beuchel et al. / Journal of Marine Systems 63 (2006) 35–48


the ACRI. The most striking congruence was found                Pontoporeia affinis Lindström (Amphipoda). Finnish Marine
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