02

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Deep-Sea Research I 52 (2005) 421–441 www.elsevier.com/locate/dsr

Phyto- and zooplankton community structure and production around South Georgia (Southern Ocean) during Summer 2001/02 Peter Ward, Rachael Shreeve, Mick Whitehouse, Beki Korb, Angus Atkinson, Mike Meredith, David Pond, Jon Watkins, Cathy Goss, Nathan Cunningham British Antarctic Survey, Natural Environment Research Council (NERC), High Cross Site, Madingley Road, Cambridge CB3 OET, UK Received 28 January 2004; received in revised form 23 August 2004; accepted 26 October 2004 Available online 1 January 2005

Abstract During Austral summer 2001/02 the spatial distribution of phytoplankton and zooplankton communities and associated production processes were investigated in waters to the north of the island of South Georgia. Nearest neighbour cluster analysis of phytoplankton and zooplankton data sets indicated the presence of 3 major station groupings within each analysis that collectively had great geographic integrity. Thus a shelf station group was characterised by low phytoplankton biomass and primary production rates (median values 69 mg chl a m
2; 418 mg C m
2 d
1, respectively) and a dominance of nano- and pico-phytoplankton, on the one hand, and low overall mesozooplankton levels (median 50,135 ind. m
2), which was also coincident with elevated krill biomass, on the other. At the western end of the island high mesozooplankton abundance (median 4250,000 ind. m
2) in oceanic waters was co-incident with a phytoplankton bloom that originated over the North West Georgia Rise (NWGR). These ‘bloom’ stations were characterised by high phytoplankton biomass and primary production rates (median 218 mg m
2 and 916 mg C m
2 d
1, respectively) and were dominated by microphytoplankton. This elevated production was associated with an anticyclonic eddy which was considered to have been initiated where the Southern Antarctic Circumpolar Current Front (SACCF) interacted with the bathymetry of the NWGR. SeaWiFS and drifter buoy data indicated that this production was subsequently entrained and transported cyclonically around the Georgia Basin downstream from the island. In contrast, at the eastern end of the island, in waters south of the SACCF, phytoplankton biomass and primary production rates were lower (median 40 mg m
2 and 214 mg C m
2 d
1, respectively) although median zooplankton abundance was intermediate (157,000 ind. m
2). Of the environmental variables measured in this study the proportion of microphytoplankton within samples, rather than for example, phytoplankton biomass, explained the greatest proportion of the variance associated with the carbon mass of individual copepod species stages, egg production rates and total zooplankton abundance. We conclude that stations in the western and eastern oceanic regions were dominated by bottom up controls, with physical forcing and adequate nutrient availability leading to bloom conditions of large diatoms in the west, whereas in the east, Corresponding author. Tel.: +44 1223 221564.

E-mail address: [email protected] (P. Ward). 0967-0637/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.dsr.2004.10.003

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phytoplankton growth was lower (possibly due to micronutrient limitation) and zooplankton abundance was lower. Over the shelf we suggest that a higher average biomass of krill (80 g wet mass m
2) compared to offshelf (43 g wet mass m
2) may have exerted top down control and grazed out chl a and thus have been responsible for low mesozooplankton abundance. r 2004 Elsevier Ltd. All rights reserved. Keywords: Southern Ocean; Phytoplankton; Zooplankton; Community structure; Production; South Georgia

1. Introduction Analyses of ocean colour satellite time-series data have provided an overview of large-scale chlorophyll a (chl a) distribution in the Southern Ocean and its relationships with physical factors (Moore and Abbott, 2000; Constable et al., 2003). Phytoplankton blooms (chlorophyll values exceeding 1.0 mg m
3) are frequently associated with coastal and shelf waters (o500 m deep) of the continent and oceanic islands where a significant proportion of primary production takes place. Such locations, although representing only 2.5% of the Southern Ocean by area, account for 9% of total primary production (Moore and Abbott, 2000). These ‘hotspots’ are very often sites of heightened biological activity, which despite their small area, have important consequences for nutrient enrichment and seeding of blooms downstream, as well as in some cases being the focus of commercial fisheries. Examples include the southern Weddell and Ross Seas and the oceanic islands such as Kerguelen, Marion Island, South Sandwich Islands and South Georgia (see Nelson et al., 1989; Perissinotto et al., 1992; Atkinson et al., 2001). The latter located in the NE Scotia Sea, has long been recognised as a productive marine ecosystem. The island was a centre for the whaling industry in the early part of the 20th century and presently supports commercial fisheries for fin-fish and krill as well as abundant higher predators. Satellite images taken in spring through autumn often show extensive phytoplankton blooms occurring in the region which are frequently advected downstream considerable distances towards and along the Polar Front (Atkinson et al., 2001; Korb et al., 2004). Zooplankton biomass can be similarly enhanced during austral summer particularly towards the western end of the island

(Ward et al., 1995) and copepod growth rates are also elevated in this region (Shreeve et al., 2002). Considerable regional heterogeneity in plankton distributions has been previously noted and the existence of a ‘shelf community’ repeatedly demonstrated (e.g. see Atkinson and Peck, 1990; Ward et al., 2002). Seasonal changes in zooplankton abundance between shelf and ocean have been linked to seasonal migratory patterns (Atkinson and Peck, 1990) and krill distribution around the island has recently been linked to differences in temperature and bathymetry (Trathan et al., 2003). The impact of oceanic fronts lying close to the island on zooplankton production and flux has also been investigated, highlighting the role of advection in bringing zooplankton into the region (Murphy et al., 2004; Ward et al., 2002). However, despite these and many other studies, major questions about the relationships between primary and secondary producers remain unanswered, such as the extent to which phytoplankton blooms are controlled from the top down versus the bottom up and their advective fate and benefit to downstream consumers. Seeking pattern in repeated observation is often a necessary prerequisite to understanding ecosystem structure and function. In a 4-year study Shreeve et al. (2002) found variability in copepod age structure, abundance and body mass at South Georgia to be significantly and negatively related to the abundance of silicic acid in the water column and hence to past production levels. In a comparable study undertaken at Palmer Station, Antarctic Peninsula, over 4 summer seasons, krill growth was found to be related to both food quality and quantity with highest growth associated with periods of peak phytoplankton biomass, particularly when diatoms dominated (used in this context as a relative measure of food quality—see Ross et al., 2000).

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There is thus evidence from 2 dominant grazer groups, copepods and krill, that both individual and population growth rates are strongly influenced by food quantity and quality but the extent to which either group can exert top down control on phytoplankton biomass is unclear. Based on in vivo experiments, Grane´li et al. (1993) concluded that ‘natural’ concentrations of copepods allow blooms to develop, but higher concentrations, whilst impacting phytoplankton biomass to some extent could have substantial influence on species composition. In contrast krill rapidly reduced phytoplankton biomass and upon their removal communities dominated by flagellates developed. In situ observations indicated greater efficiency for krill feeding on large diatoms (Meyer and ElSayed, 1983; Quetin and Ross, 1985), and a dominance of flagellates in oceanic areas where krill were abundant (Kopczynska, 1992). However Shreeve et al. (2002) concluded that at South Georgia, variation of phytoplankton biomass appears to be dependent on temperature rather than the grazing pressure exerted by copepods or krill. Previous analysis of SeaWiFS images has shown that despite great temporal and spatial heterogeneity in the patterns of primary production, the oceanic region at the western downstream end of the island is often more productive than elsewhere (Korb et al., 2004). Zooplankton data from 4 summer surveys have also demonstrated a similar trend for copepods (Shreeve et al., 2002). The build-up of zooplankton biomass in this highly advective area is somewhat counter-intuitive and accordingly we wished to examine patterns of zooplankton distribution and its relationships to water masses and food supply to better understand production and flux.

2. Methods Sampling took place during January and early February 2002 at 59 stations off the north coast of the island of South Georgia (Fig. 1). Twenty five of the stations were located within a mesoscale rectangular box (WMB) located at the western end of the island and orientated so that the inner

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Fig. 1. Study area showing station locations (black circles) in relation to South Georgia. Illustrated are the Western Mesoscale Box (WMB), North West Georgia Rise (NWGR), the Southern Antarctic Circumpolar Current Front (SACCF) and the Western and Eastern Long Transects (WLT and ELT). Depth contours represented by half tones are 500 and 2500 m. Inset shows wider geographical position.

transect lay over the shelf, the middle one along the shelf break (1000 m) and the outer one in oceanic water. Additional sampling was undertaken along two transects oriented along satellite altimeter tracks (ERS 635 and TPO59) which we have designated western (WLT) and eastern long transects (ELT), respectively. 2.1. Physics, nutrients and phytoplankton Twenty drifters (drogued at 20 and 50 m) with Global Positioning System receivers were deployed during the cruise to monitor circulation and current velocities. Twelve were deployed within the WMB at the western end of the island and a further 6 along the ELT. The remaining two were deployed over the western end of the southern shelf break. Positions subsequent to deployment were reported via the Argos satellite system. Full details are given in Meredith et al. (2003). Water for analysis of chl a and phaeopigments, size fractionated chl a and nutrients was obtained from standard depths (approximately 20, 40, 60, 80, 100, 125, 150 and 200 m, and a further four evenly spaced depths sampled between 200 m and the bottom of the cast) at each of the 59 stations with a Seabird 911+CTD and carousel sampler

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equipped with twelve 10 l Niskin bottles (See Korb and Whitehouse, 2004 for details). Additional samples were obtained from the ship’s non-toxic seawater supply located 6–7 m below the sea surface as the CTD was surfacing. Chlorophyll a was measured with an Aqua Tracka fluorometer (Chelsea Instruments) attached to the CTD frame. Fluorescence was binned every 2 m and calibrated against discrete samples of chl a taken at CTD stations. Size fractionated chl a was measured on water samples from a depth of 20 m by passage through a series of 47 mm polycarbonate filters (12, 2 and 0.2 mm). Thus pico, nano and microphytoplankton were represented by the 0.2–2, 2–12 and 412 mm size fractions. Primary production rates were measured at 23 stations during the cruise by the JGOFS 14C protocol in conjunction with a simulated in situ incubator mounted on deck (Korb and Whitehouse, 2004). Additional estimates for the remaining stations were made with the depth-integrated model from Behrenfeld and Falkowski (1997). Macro-nutrient concentrations were determined with a Technicon segmented flow analyser (Whitehouse, 1997). Nutrient depletions (deficits) were obtained by extracting near surface (0–50 m) values from approximately pycnocline values which were similar to pre-bloom and winter values recorded for the South Georgia region (see Korb and Whitehouse, 2004). Thus silicate deficits are used in this paper as an indicator of past production levels and NO3-N:PO4-P depletion ratios as an indicator of different patterns of nutrient use (see Korb and Whitehouse (2004) and discussion in this paper for a fuller account). Density profiles were assessed to establish the upper mixed layer depth (UML), which was defined as a X0.05 kg m
3 density change within 10 m. The depth of the euphotic zone was defined as that of the 1% incident light level. Profiles of downwelling PAR were obtained from a sensor mounted on the frame of the CTD (Biospherical Instruments Inc., model QCD905L) or from an irradiance sensor attached to an undulating oceanographic recorder (Chelsea Instruments Nv Shuttle Mark II). The scalar attenuation coefficient (K0) of the water column was calculated by linear regression of a log-transformed irradiance

versus depth profile after Kirk (1994). Due to technical problems, it was not possible to obtain light profiles at all stations. Instead, K0 was derived from the relationship between K0 and integrated chl a concentration over 100 m (Chlint) throughout the north-western survey areas (K 0 ¼ 0:0004½Chlint  þ 0:0992; R2 ¼ 0:81; n ¼ 17). 2.2. Fatty acid and POC analyses Seawater samples (between 1.2 and 3.6 1) for particulate and fatty acid analysis were filtered onto pre-ashed GF/F filters and stored in chloroform:methanol (2:1 v/v) at
80 1C until analysis. After the addition of an internal fatty acid standard (21:0) lipids were extracted according to Folch et al. (1957). Fatty acid methyl esters were prepared in methanol containing 1% sulphuric acid and transmethylated at 50 1C for 16 h (Christie, 1982). After purification by thin-layer chromatography, fatty acid methyl esters were dissolved in hexane at a concentration of 1 mg ml
1 and analysed on a Carlo Erba Trace 2000 gas chromatograph equipped with a ZBWAX fused silica capilliary column (30 m  0.32 mm). Hydrogen was used as the carrier gas and fatty acids were identified by comparison with a well characterised marine fish oil. Similar quantities of seawater for POC analysis were also filtered onto pre-ashed filters and stored at
80 1C. In UK samples were acidified under an atmosphere of fuming hydrochloric acid for 24 h, and then dried in a vacuum desiccator for 24 h. Elemental carbon and nitrogen were determined in 3 replicate subsamples with a Fisons EA 1108 elemental analyser with acetanilide as a standard. 2.3. Phytoplankton cell counts Species composition representative of the surface mixed layer (SML) was determined at each station from water samples collected at 20 m and preserved in 1% acid lugols solution. Microplankton were enumerated by use of the Utermo¨hl (1958) technique. Sample solutions were left to settle in 50 ml chambers for at least 24 h before analysis of selected microplankton taxa by inverted microscopy. Sixteen selected categories

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were examined on either 2 or 3 perpendicular transects across the whole slide on  100 magnification. The categories were chosen on the basis of their dominance of the 412 mm microplankton, and their ease in identification. Species counted ranged in size from 1–200 mm. 2.4. Mesozooplankton At each station a motion compensated bongo net (mouth diameter opening 61 cm, net mesh 200 mm) with two solid cod-ends was deployed from 0 to 200-0 m. Upon recovery the contents of one cod-end were immediately diluted with surface seawater at ambient temperature for sorting of live material. To investigate production processes and understand how they varied spatially we required species and stages that we were confident would be present over the entire survey area. Therefore Rhincalanus gigas females were chosen for egg production experiments and stages CIV and CV Calanoides acutus for determination of carbon mass. Female R. gigas were sorted and incubated in groups of 10 for 24 h to determine egg production rates (EPR) (see Ward and Shreeve (1995) for further details). Females were then removed, rinsed briefly in ammonium formale and placed in pre-weighed ultra light-weight tin foil capsules. Additionally, 30 CIV and CV C. acutus from each station were frozen for C mass determination. Samples were frozen at
80 1C and subsequently dried at 60 1C onboard ship within 1 week of collection. They were then transferred to the UK in a sealed container where they were again dried at 60 1C to constant weight. Dry mass was measured on a Mettler MT5 balance to an accuracy of 71 mg. Whole samples were then analysed for carbon and nitrogen as for POC samples above. The contents of the second cod-end were preserved in 10% (v:v) formalin in seawater for community analysis in the UK. In the UK the formalin preserved samples were divided into appropriate aliquots with a Folsom plankton splitter and examined under a binocular microscope. Zooplankton were identified to species and stage or higher taxonomic categories and enumerated. An average number of between 1000 and

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1500 individual plankters were counted in each sample. 2.5. Data analysis Phytoplankton cell count and mesozooplankton data were initially analysed with the statistical package PRIMER 5 (Primer-E Ltd). Standardised data in the form of phytoplankton cell counts (50 ind. ml
1) and mesozooplankton abundance (ind. m
2) were double root transformed and subjected to q type cluster analysis to group stations based on the Bray–Curtis similarity and complete linkage classification (Field et al., 1982). Non-metric multidimensional scaling (MDS) was also performed to configure the data in twodimensional space allowing relationships between groups to be assessed. The similarity percentages (SIMPER) routine was also performed on both data sets. SIMPER examines how much each species/taxon contributes to the average sample similarity within, and dissimilarity between, groups (Clarke and Warwick, 2001). BIO-ENV, a routine which calculates a measure of agreement between two (dis)similarity matrices, was used on the mesozooplankton data matrix and another containing information on environmental variables measured at each station (see Tables 4 and 7 in results section for details of variables included). Within the analysis, rank correlation (r) of the matching elements is carried out with combinations of the environmental variables being considered at steadily increasing levels of complexity. In this way an optimal subset of environmental variables that ‘best explains’ the biotic structure is identified. A value of r ¼ 0 would imply an absence of any match between the two patterns but typically values of r will be positive with a value of +1 being a perfect match (Clarke and Ainsworth, 1993). Best subsets regression analysis was performed within the statistical software package MINTAB v.13 (Pennsylvania State University) using the carbon mass and EPR data collected at each station as response variables and a suite of nine potential predictor variables (see table results section for full details). Best subsets regression analysis successively examines models containing

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1, 2, 3, etc. explanatory variables and in each case selects the one with the largest value of R2. Mallows’ Cp statistic is also calculated: Cp ¼

Residual SSp
ðn
2pÞ: Residual MSm

If the model with p parameters is the true model then Cp has an expected value close to p and is accordingly selected. 2.6. Acoustics Acoustic data were collected at 10 knots between stations and along transects oriented at right angles to the shelf within the mesoscale western box with a Simrad EK500 scientific echo sounder and hull-mounted 38, 120 and 200 kHz transducers with a ping rate set at 2.5 s. The acoustic data were logged with SonarData Echolog-EK software, and post-processing was carried out with SonarData Echoview software. The echosounder was calibrated at Stromness Harbour, South Georgia, on January 17th by the standard target technique (Foote et al., 1987). 2.7. Identification of krill Acoustic backscatter that could be attributed to Antarctic krill (Euphausia superba) was delineated from other sources by the relative values of signal return between 120 and 38 kHz by calculating the dB difference, where DSv (Difference in decibels) ¼ Sv120 (Scattering volume dB at 120 kHz)
Sv38 (Scattering volume, dB at 38 kHz). A range of DSv has been attributed to krill. Previous studies have used 2–12 or 2–16 dB. In this study, fishing of acoustic targets had shown that small krill were prevalent in the transect area (mean total length 32 mm), and that swarms of these krill had a considerable proportion (16%) of the backscatter with a DSv in the 12–14 dB range, and lesser amount in the 14–16 dB range (4%). Acoustic scatterers smaller than krill tend to have a DSv greater than 12 dB (Madureira et al., 1993). Therefore 2–16 DSv was chosen so that most of the biomass due to krill would be included, whilst minimizing the inclusion of other targets.

Target strength (TS) in dB kg
1 was calculated from TS per individual in dB (Foote and Stanton, 2000), using the TS to length and length to weight relationships given in Brierley and Watkins (1996). Biomass was estimated from the nautical area backscattering coefficient (NASC) values integrated over cells where the DSv satisfied the criteria for attributing echoes to krill, and divided by the TS.

3. Results 3.1. Summary of physical and chemical oceanography Detailed descriptions of the physical oceanography during the cruise can be found in Meredith et al. (2003) and Korb and Whitehouse (2004) but here we summarise the main features relevant to the work carried out in this study. Along the western transect temperatures varied from 2 1C at the southern end to 46 1C at the north. The westward flowing Southern Antarctic Circumpolar Current Front (SACCF) was located on the northern flank of the North-West Georgia Rise (NWGR) while its eastwards flowing retroflection was located further north (Fig. 1). Along the eastern transect the temperature range was less (2.3–3.8 1C) and the westward flowing jet of the SACCF was located to the north of the shelf break but its eastwards retroflection was not detected along the transect. Within the WMB near-surface waters were fairly uniform (3–3.5 1C) but salinities were reduced along the central and southern transect lines. Conditions within the box were complex largely as a result of water crossing from the southern island shelf becoming entrained and circulating around the Georgia Basin and also because the box was located downstream of an anticyclonic eddy created by the interaction of the SACCF with the bathymetry of the NWGR. Mixed layer depth varied across the survey area with the average for the WLT (33 m) some 1.7 times shallower than in the ELT (55 m). In the WMB, the offshelf average (37 m) was deeper than over the shelf (21 m). Euphotic depth followed a similar pattern being deepest along the ELT and

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shallower towards the west where phytoplankton biomass was highest (see Korb and Whitehouse, 2004). Nutrient concentrations within the 0–50 m depth range varied considerably between stations with values generally being high in the east in waters originating from south of the SACCF and lower towards the west in waters lying between the Polar Front and the SACCF. Substantial depletions were observed in the west, which after allowing for differences in winter levels between water masses (see Whitehouse et al., 1996), were greatest at oceanic stations comprising phytoplankton cell count station group 2 (see below Phytoplankton community structure and composition) and were closely linked to areas of greatest phytoplankton growth. Subsurface (50–100 m) ammonium values were uniformly high but again particularly high within phytoplankton station group 2. 3.2. Drifter deployments The circulation patterns determined from the drifter tracks have been described by Meredith et al. (2003). In the context of this study it is important to note the westwards track of drifters released over the northern shelf and within the SACCF at the eastern end of the island compared to the NE drift seen when drifters were released offshore along the eastern transect (Fig. 2). The shelf releases were transported to the north of the NWGR before either being advected around the periphery of the Georgia Basin or being retroflected eastwards. Drifters released within the WMB generally passed rapidly and cyclonically around the edge of the Georgia Basin, particularly those released in deep water. Those released on the shelf in the core box at the western end of the island showed low initial speed but after varying periods all moved northwards off the shelf and entered the main cyclonic flow. Velocities were variable, although generally high (up to 50 cm s
1) associated with the north-eastern flank of the South Georgia shelf slope and up the northeast side of the NWGR. High velocities were also seen around the periphery of the western Georgia Basin (20–40 cm s
1) and along the northern part of the basin near the Polar Front (30–40 cm s
1). There

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Fig. 2. Trajectories of drogued drifters released during cruise JR70 in January 2002 up to March 31 2002. Taken from Meredith et al. (2003). Red trajectories are those drifters that were influenced by the circulation patterns over the NWGR, otherwise coloured blue. Also shown are 2 min averaged ADCP vectors from 22 m collected on passage along the WLT on January 6 2002. Note anticyclonic circulation over the NWGR. Scale bar relates to ADCP data.

was no evidence of a return flow within the Georgia Basin with all drifters, once they had traversed the western edge, passing away to the east. 3.3. Phytoplankton community structure and composition The location of the sampling stations in relation to phytoplankton distribution across the survey area is shown in a composite SeaWiFS image for January 2002, the month in which the cruise took place (Fig. 3). Highest phytoplankton biomass was located to the NW of the island near to the North West Georgia Rise (NWGR) where chl a concentrations of up to 10 mg l
1 were measured. The interactions of phytoplankton with the flow fields as revealed by the drifters (Fig. 2) can be seen clearly as water is moved in a cyclonic gyre around the Georgia Basin. The dendrogram resulting from nearest neighbour clustering of the phytoplankton cell count data and an accompanying MDS plot are presented in Fig. 4. Four groupings of stations were

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-50 January 2002

Latitude °S

-52

-54

-56 -42

-38

-34

Longitude °W Chlorophyll a (mg m-3) 0

1

2

3

4

5

6

7

8

9 >10

Fig. 3. Composite SeaWiFS image for January 2002 with station sampling positions overlain. We are grateful to the SeaWiFS Project and the Goddard Earth Sciences Data and Information Services for the production and distribution of SeaWiFS data respectively.

disclosed which exhibited great geographic integrity (Fig. 5). The 13 stations in group 1 were located mainly over the shelf and shelf break although some were present towards the offshore end of the western transect. Group 2 (n ¼ 26) comprised stations located in the western oceanic and deeper shelf areas of the mesoscale box and along the western transect, whereas group 3 stations (n ¼ 17) were predominant along the eastern transect with a few also encountered along the western transect. Group 4 (n ¼ 3) comprised 2 stations at the south-eastern corner of the mesoscale box and a further one at the shelf break along the western transect (Fig. 5). Of the species/taxa in the phytoplankton cell count similarity matrix, small dinoflagellates contributed most to groups 1 (34%) and 4 (89%) whereas Thallasiosira spp., Nitzschia spp. and small dinoflagellates collectively contributed 50% of within group similarity to group 2. Small

dinoflagellates, Nitzschia spp., Fragilariopsis spp. and Chaetoceros spp. contributed 73% of within group similarity to stations belonging to group 3. Overall, cell counts were dominated by diatoms, which, across all groups, were 5 times more abundant than the next contributor, dinoflagellates. Highest diatom counts were found in group 2, some seven times higher than group 3, with dinoflagellates dominating groups 1 and 4 (Table 1). There were clear differences between groups with respect to average integrated chl a concentrations, POC and primary production rates; all some 1.5–4 times higher in phytoplankton group 2 than in other groups (Table 2). Group 2 was dominated by microphytoplankton (412 mm) whereas group 1 was dominated by nano- and pico plankton. Group 3 was somewhat intermediate in character with an approximately equal split of micro (46%) and nano- and picophytoplankton (54%) (Table 3). The

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20

Similarity

40

60

80

100 Group 4

Group 1

Group 3

Group 2

Stress 0.13

1

2

3

4

Fig. 4. Results of the clustering performed on the Bray–Curtis similarity matrix created from the double root transformed phytoplankton cell count data (upper panel). The four station groups referred to in the text are identified here and correspond to the symbols used in the MDS plot (lower panel).

environmental variables in best agreement with the multivariate pattern of the phytoplankton cell count similarity matrix were determined through the application of the BIO-ENV analysis. Model outputs are presented in Table 4. A five variable model which comprised UML, euphotic depth, phaeopigment, krill biomass and silicate deficit provided the strongest rank correlation value of r ¼ 0:47: All models in the table included UML

and krill biomass as explanatory variables and all but one included phaeopigment. Silicate deficit, a proxy for past diatom growth and euphotic depth were other consistent contributors. Thus there appeared to be combination of bottom up and top down physical and biological controls on phytoplankton growth. Systematic removal and addition of variables was undertaken to maximise the fit. A single variable model using UML alone

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gave a rank correlation of r ¼ 0:280 and the addition of krill biomass improved this to r ¼ 0:36: Addition of phaeopigment further improved the fit (r ¼ 0:439) and euphotic depth, and silicate deficit increased r to 0.47. 3.4. Mesozooplankton community structure and distribution The dendrogram resulting from nearest neighbour clustering of the mesozooplankton data and

-52

group 1 group 2 group 3 group 4

WLT

Latitude °S

NWGR -53

SACCF

WMB

-54

-55 -40

ELT

-38 -36 Longitude °W

-34

Fig. 5. Spatial distribution of station groups resulting from the cluster analysis of the plankton cell count data (see text for details).

an accompanying MDS plot are presented in Fig. 6. Five station groups were identified, the 3 largest collectively containing 54 of the 59 stations (Fig. 6). Group 1 stations were primarily restricted to the shelf and shelf-break whereas group 2 occupied the western oceanic regions including the greater part of the western transect and group 3 the eastern oceanic region of the eastern transect and a few oceanic stations along the western transect. The remaining groups, 4 and 5, were largely variants of group 1 (Fig. 7). Mesozooplankton abundance was higher within group 2 than in other station groups with copepods the dominant zooplankton group across all groups accounting for between 80% and 97% by number (Table 5). Eight of the 13 taxa achieved highest abundance within this group and across all groups Oithona spp., Ctenocalanus spp., Metridia spp., Drepanopus forcipatus and copepod nauplii generally contributed most to within group similarity (Table 6). The same species with the addition of Limacina spp. and Appendicularians contributed most to between group dissimilarity. Although overall abundance was significantly highest within group 2 (Table 6) the major difference between group 2 and group 3 (the next most abundant) was principally accounted for by

Table 1 Average cell count abundance (50 ind. ml
1) within phytoplankton station groupings of the species and taxa which the SIMPER analysis indicated contributed most to within group similarity and between group dissimilarity. Highest value for each species/taxon emboldened Species/taxon

Small dinoflagellates Thallasiosira sp. 1 Nitzschia spp. Chaetoceros spp. Fragilariopsis spp. Thallasionema sp. Eucampia sp. Thallasiosira sp. 2 Corethron sp. Small ciliates Rhizosolenia sp.

Group 1

Group 2 abundance

(n ¼ 13)

(n ¼ 26)

Group 4

Group 3 ind. 50 ml
1 (n ¼ 17)

(n ¼ 3)

Total abundance across all groups

1119

687

615

1972

4393

172 81 5 81 576 0 0 9 71 1

3398 1595 1189 567 18 346 191 141 9 7

185 649 205 172 6 0 5 11 44 13

14 0 3 0 3 0 0 0 0 0

3769 2325 1402 820 603 346 196 161 124 21

Species have been arranged in order of total abundance across all groups with respect to the major taxonomic groupings copepods and others. Highest value for each species/taxon emboldened.

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Table 2 Median values (upper and lower quartile values) for Chl a (mg m
2, 0–100 m), POC (mg m
2, 0–50 m), Carbon fixation (mg m
2 d
1, Euphotic zone), NO3-N:P-PO4 depletion ratio (0–50 m), particulate C:N (20 m) and total fatty acids (mg l
1, 20 m) with respect to the phytoplankton station groups

Chl a (mg m
2 0–100 m) POC (mg m
2 0–50 m) Carbon fixation (mg m
2 d
1 Euphotic zone) NO3-N:P-PO4 depletion ratio (0–50 m) C:N Total fatty acids (mg l
1)

Group 1 (n ¼ 13)

Group 2 (n ¼ 26)

Group 3 (n ¼ 17)

Group 4 (n ¼ 3)

69 (50–114) 233 (164–286) 418 (143–564) 9.92 (6.7–10.9) 5.31 (4.52–5.50) 71.6 (47.1–75.4)

218 (115–333) 352 (263–434) 916 (599–1100) 9.81 (9.3–10.9) 5.34 (5.20–5.86) 93.2 (63.3–121.1)

40 (27–58) 163 (131–206) 214 (90–166) 4.87 (2.9–8.1) 5.74 (5.16–6.31) 41.3 (36.4–46.4)

71 (62–123) 189 (151–486) 639 (336–998) 6.27 (6.27–10.1) 5.28 (4.29–6.01) 26.2 (26.2–152.9)

Table 3 Mean values of percentage size fractionated chl a biomass from the 20 m water bottle with respect to phytoplankton station groups (upper and lower quartile values)

% Microphytoplankton (412 mm) % Nanophytoplankton (2–12 mm) % Picophytoplankton (o2 mm)

Group 1

Group 2

Group 3

Group 4

16% (8–21) 41% (26–59) 43% (26–60)

74% (65–91) 16% (5–18) 10% (4–14)

46% (34–60) 25% (17–320) 29% (20–35)

5% (3–9) 62% (32–81) 33% (16–58)

Table 4 Results of BIO-ENV analysis K

Spearmann rank correlation (rw)

5

0.470

UML

4

0.455

4

Environmental variables

UML

Krill Biomass Krill biomass

Phaeopigment Phaeopigment

0.445

UML

Krill biomass

Euphotic depth

5

0.444

UML

Krill Biomass

Phaeopigment

4

0.441

UML

Krill biomass

Phaeopigment

3

0.439

UML

Krill biomass

Phaeopigment

Euphotic depth Silicate deficit Silicate deficit NO3

Silicate deficit

Silicate deficit

Euphotic depth

Combinations of K variables giving the largest rank correlations (rw) between the phytoplankton cell count and environmental similarity matrices. First six models listed, best overall fit in bold. Environmental variables included were euphotic depth (m), mixed layer depth (UML, m), nitrate (NO3, average 0–50 m) phosphate (PO4, average 0–50 m), silicate (Si, average 0–50 m), phaeopigment, (mg m
2 0–100 m), large copepods (ind. m
2 0–200 m), small copepods (ind. m
2 0–200 m), krill biomass (g m
2 0–250 m), nitrate deficit (average depletion 0–50 m), silicate deficit (average depletion 0–50 m), sea surface temperature (SST 1C), chlorophyll a biomass (mg m
2 0–100 m).

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432

60

Similarity

70 80 90 100 5 Group 1

Group 2

Group 3

Group 4

Stress 0.1

1

2

3

4

5

Fig. 6. Results of the clustering performed on the Bray–Curtis similarity matrix created from the double root transformed mesozooplankton data (upper panel). The four station groups referred to in the text are identified here and correspond to the symbols used in the MDS plot (lower panel).

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WLT

Latitude °S

NWGR

-53

WMB

group 1 group 2 group 3 group 4 group 5

SACCF

-54

-55 -40

ELT

-38

-36

-34

Longitude °W Fig. 7. Spatial distribution of station groups resulting from the cluster analysis of the mesozooplankton data (see text for details).

an increased abundance of large calanoids, notably Calanus simillimus and Calanoides acutus in the former group along with appendicularians and the thecate pteropod Limacina helicina. Many of the smaller species were either broadly similar in abundance across the two groups (e.g. Oithona spp. and Metridia spp.), or were more abundant in group 3 (e.g. Ctenocalanus spp.). Copepod nauplii (predominantly those of cyclopoids) were also slightly more abundant within group 3 than group 2. In the BIO-ENV analysis, variables best describing the overall zooplankton similarity matrix reflected the size distribution of the phytoplankton rather than absolute measures of biomass, with % nanoplankton, % picoplankton

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433

Table 5 Median abundance (ind. m
2, 0–200 m) plus upper and lower quartile values) of mesozooplankton, large and small copepods with respect to mesozooplankton station group Group 1 (n ¼ 12) Mesozooplankton (ind. m
2, 0–200 m)

50,135 (39,206–63,391) Large copepods (ind. m
2, 0–200 m) 2456 (1281–3900) Small copepods (ind. m
2, 0–200 m) 47,182 (36,326–58,814) Copepods as a % of total Zooplankton nos. 97%

Group 2 (n ¼ 25)

Group 3 (n ¼ 17)

Group 4 (n ¼ 4)

Group 5 (n ¼ 1)

276,129 (219,604–433,117) 30,214 (16,727–40,751) 191,795 (146,200–270,673) 80%

157,530 (118,199–256,821) 3367 (2723–6951) 142,067 (107,269–243,000) 93%

90,356 (39,299–132,998) 7723 (2363–19,814) 76,683 (33,349–110,060) 93%

99,335 1745 94,757 97%

Copepod abundance as a percentage of total zooplankton numbers also shown.

Table 6 Average zooplankton abundance (ind. m
2, 0–200 m) with respect to zooplankton station group of the species and taxa which SIMPER analysis indicated contributed most to within group similarity and between group dissimilarity Species/taxon

Group 1 (n ¼ 12)

Group 2 (n ¼ 25)

Group 3 (n ¼ 17)

Group 4 (n ¼ 4)

Group 5 (n ¼ 1)

Total abundance across all groups

Oithona spp. Ctenocalanus spp. Drepanopus forcipatus Copepod nauplii Metridia spp. Calanus simillimus Calanoides acutus Rhincalanus gigas nauplii Oncaea spp. Microcalanus pygmaeus Limacina helicina Appendicularians Thysanoessa spp.

35,750 6709 693 312 4146 598 1244 182 255 369 150 586 434

105,190 30,842 5530 26,975 28,710 20,524 6155 8510 4203 1679 44,602 25,416 262

89,393 52,971 0 30,368 20,518 840 2739 515 2769 2086 8178 5048 293

25,807 10,023 25,369 5404 3763 4754 4058 2196 506 0 1607 1765 45

20,199 2627 69,630 0 1313 195 872 0 492 492 164 2299 10

276,339 103,172 101,222 63,059 58,450 26,911 15,068 11,403 8225 4626 54,701 35,114 1044

Species have been arranged in order of total abundance across all groups with respect to the major taxonomic groupings copepods and others. Highest value for each species/taxon emboldened.

along with UML and krill biomass appearing in all the models (Table 7). The single most important variable was % microphytoplankton, which alone accounted for a rank correlation coefficient (r) of 0.49; however, a two parameter model using % nano- and % picophytoplankton gave a somewhat better fit (r ¼ 0:59). The inclusion of krill biomass, UML, % microphytoplankton, and silicate marginally improved the fit to r ¼ 0:609:

3.5. Krill distribution The distribution of krill biomass within the survey region is shown in Fig. 8. Combined average biomass values within phytoplankton station groups 1 and 4 during the period that the stations were sampled were 43 g m
2 and in groups 2 and 3 14 g m
2. The boundary of the western box and transects within it were surveyed on four separate occasions throughout the cruise

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434 Table 7 Results of BIO-ENV analysis K

Spearmann rank correlation (rw)

6

0.609

% Nanoplankton

% Picoplankton

UML

6

0.604

% Nanoplankton

% Picoplankton

UML

6

0.600

% Nanoplankton

% Picoplankton

UML

5

0.597

% Nanoplankton

% Picoplankton

UML

5

0.596

% Nanoplankton

% Picoplankton

UML

6

0.592

% Nanoplankton

% Picoplankton

UML

Environmental variables Krill Biomass Krill Biomass Krill Biomass Krill Biomass Krill Biomass Krill Biomass

% Microplankton

Silicate

% Microplankton

SST

% Microplankton

N:P

Silicate % Microplankton % Microplankton

NO3

Combinations of K variables giving the largest rank correlations (rw) between the mesozooplankton and environmental similarity matrices. First six models listed, best overall fit emboldened. Environmental variables included were euphotic depth (m), mixed layer depth (UML, m), nitrate (NO3, average 0–50 m) phosphate (PO4, average 0–50 m), silicate (Si, average 0–50 m), nitrate:phosphate (N:P average 0–50 m), phaeopigment, (mg m
2 0–100 m), krill biomass (g m
2 0–250 m), % nanoplankton (20 m), % picoplankton (20 m), % microplankton (20 m), nitrate deficit (average depletion 0–50 m), silicate deficit (average depletion 0–50 m), sea surface temperature (SST 1C), chlorophyll a biomass (mg m
2 0–100 m).

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80 g m
2 inshore of the 500 m depth contour and 43 g m
2 offshore.

WLT

Latitude °S

NWGR

-53

WMB

-54

-55 -40

3.6. Mesozooplankton production processes

SACCF

ELT

-38 -36 Longitude °W

-34

Fig. 8. Average krill biomass observed between stations. Circles scaled from 0 to 438 g wet mass m
2, squares scaled from 0 to 110 g wet mass m
2. Data integrated from 0 to 250 m.

and each time highest krill biomass (up to 8 kg wet mass m
2) was consistently located in the southeastern corner over the shelf, in the region occupied by mesozooplankton station groups 1 and 5 and phytoplankton groups 1 and 4. Average krill biomass determined during two detailed surveys subsequent to the station sampling was

Carbon mass of the species stages of Calanoides acutus and Rhincalanus gigas and EPR of R. gigas with respect to zooplankton station group are presented in Table 8. The highest carbon mass values examined and highest average egg production rates (up to 10 eggs ~ d
1) generally occurred within mesozooplankton station group 2 and were some 2–3 times higher than in other groups. Best subsets regression analysis, using 9 potential predictors of carbon mass, EPR and total abundance (Table 9) was used to try and identify the main cause of the differences between groups. For carbon mass of both stages of C. acutus a single variable model using the percentage of microphytoplankton recorded at 20 m depth at each station accounted for 50% of the variability in C mass which could be increased to around 466% by including the NO3-N:PO4-P depletion

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435

Table 8 Median carbon mass (mg) of Calanoides acutus copepodite stages CIV and CV and Rhincalanus gigas females and median R, gigas egg production rates (EPR) with respect to mesozooplankton station groups

C. acutus CIV

Group 1 (n ¼ 12)

Group 2 (n ¼ 25)

Group 3 (n ¼ 17)

Group 4 (n ¼ 4)

Group 5 (n ¼ 1)

24 (18–29) (n ¼ 12)

74 (55–108) (n ¼ 21)

30 (21–35) (n ¼ 15)

44 (33–54) (n ¼ 3)

ND

110 (61–156) (n ¼ 12)

274 (190–412) (n ¼ 22)

83 (63–109) (n ¼ 16)

344 (248–370) (n ¼ 3)

ND

1000 (782–1166) (n ¼ 10)

1821 (1446–2030) (n ¼ 22)

896 (692–1098) (n ¼ l5)

1150 (1013–1334) (n ¼ 4)

897 (n ¼ 1)

0 (0–1.7) (n ¼ 10)

9.9 (2.2–14.8) (n ¼ 24)

1.7 (0–9.6) (n ¼ 15)

5.5 (0.8–11.2) (n ¼ 4)

5.6 (n ¼ 1)

C. acutus CV

R. gigas ~

EPR R. gigas (eggs ~ d
1)

ND ¼ not determined (lower and upper quartile ranges). n ¼ number of stations within station groups for which data are available out of the possible total (second row).

Table 9 Best subsets regression analysis using carbon mass of named species stages, EPR of Rhincalanus gigas and total zooplankton abundance as response variables Response variable

Best fit

Predictors

R2 (mallows Cp)

C. acutus CIV C mass (mg)

%micro

NO3-N:PO4-P depletion ratio

TFA

—

0.70 (3.2)

C. acutus CV C mass (mg)

%micro

NO3N:PO4-P depletion ratio

TFA

Si def

0.70 (3.8)

R. gigas ~ C mass (mg)

%micro

TFA

NO3N:PO4-P depletion ratio

C:N

0.63 (4.2)

R. gigas (EPR)

%micro

NO3-N:PO4-P depletion ratio

Total Zooplankton abundance

%micro

POC

0.18 (2.2) Si def

C:N

0.52 (2.5)

Best fit predictors shown were chosen from the following nine possible: %microphytoplankton (%micro, 20 m), % nanophytoplankton (%nano, 20 m), NO3-N:P-PO4 depletion ratio (average 0–50 m), Chl a (mg m
2, 0–100 m), POC (20 m), Particulate C:N (C:N, 20 m), Sea surface temperature (1C), silicate deficit (Si def average 0–50 m) and Total fatty acids (TFA, mg l
1, 20 m). Best fit predictors reading from left to right and overall R2 (Mallows Cp see text) column on extreme right.

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ratio (an indication of phytoplankton’s capacity to assimilate NO3-N see discussion). Addition of total fatty acids for stage CIV and additionally silicate deficit for stage CV gave an overall R2 of 0.70. For R. gigas carbon mass, % microphytoplankton accounted for 45% of variability and could be increased to 64% by the inclusion of TFA, NO3-N:PO4-P depletion ratio and C:N. EPR was best described by a 2 variable model that in addition to percentage microphytoplankton, included TFA although the percentage variability accounted for was low. Large calanoid abundance was best described by a four variable model incorporating % microphytoplankton, POC, silicate deficit and C:N giving an R2 of 0.52.

4. Discussion The similar spatial distribution of the phytoplankton (Fig. 5) and mesozooplankton (Fig. 7) station groups suggests close links between them. Further, the large-scale chlorophyll distribution (Fig. 3) considered in relation to the flow fields (Fig. 2) suggests close links to the physical environment. Meredith et al. (2003) indicated that the surface circulation over the deep ocean close to South Georgia was strongly influenced by underlying bathymetry resulting in the SACCF being diverted away from the island by the presence of the NWGR. During the 2001/02 season the interaction of the front with the NWGR gave rise to an anticyclonic circulation pattern which strongly influenced primary production processes in the region (Korb and Whitehouse, 2004). What is apparent in this study is how well the zooplankton station groupings reflect the largescale patterns of primary production which in turn are influenced by the physics and in particular the position of the SACCF. 4.1. Coupling of plankton communities to physical environment At the large-scale different ocean biomes are characterised by communities which differ fundamentally in terms of taxonomic composition, size and trophic structure (Longhurst, 1998). Within

biomes frontal regions are often identified as boundaries between different communities that may differ more in terms of the relative abundance of its occupants rather than by virtue of distinct differences in taxonomic composition (e.g. Pakhomov et al., 2000; Ward et al., 2003). At South Georgia the SACCF is a full depth front that marks the boundary between more northerly waters that extend up to the Polar Front and those to the south that lie in the seasonally influenced ice zone. During season 2001/02 waters originating from south of the SACCF were significantly less productive in terms of absolute integrated standing stock of chl a and primary production rates than either shelf or oceanic waters to the northern side of the front (Fig. 3, Table 2). Similarly, mesozooplankton abundance was considerably reduced to the south, in line with repeated observations over recent years that have indicated the presence of plankton-rich waters to the west of the island compared to less productive waters to the south and east (see Shreeve et al., 2002). 4.2. Controls on phytoplankton Major controls on phytoplankton production in the Southern Ocean include light, iron, silicic acid supply and grazing (cf. Boyd, 2002). The BIOENV analysis indicated that a five variable model provided the strongest rank correlation with the phytoplankton cell count similarity matrix (r ¼ 0:47). A combination of bottom up and top down controls on phytoplankton growth were suggested but the interrelationships are complicated. Differences in chl a biomass and primary production rates between the areas to the northwest and the northeast of South Georgia (predominantly phytoplankton station groups 1 and 2 versus group 3) have been described by Korb and Whitehouse (2004). They concluded that differences between areas were not directly attributable to light limitation as critical depths never exceeded euphotic depths. Mixed layer depths were generally o50 m at the stations in group 1 but rarely exceeded the euphotic zone depth, implying that deep mixing and light limitation were not responsible for low production although UMLs were

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considerably deeper in the eastern part of the northern shelf. Instead they associated phytoplankton distribution with differences in nutrient availability and use. Greatest NO3-N and PO4-P depletions were evident to the northwest associated with the anticyclonic eddy situated over the NWGR. Furthermore, compared with upstream stations, the area over the NWGR along with the stations downstream of South Georgia had significantly different NO3-N:PO4-P depletion ratios, with NO3-N use far greater downstream of the island even though NH4-N was abundant throughout the survey area. Although there are no dissolved Fe data for the waters around South Georgia, Korb and Whitehouse (2004) proposed that this increased NO3-N use is facilitated by a greater availability of Fe from the island and its shelf (see also Atkinson et al., 2001). Southern Ocean phytoplankton communities are also subject to control from mesozooplankton grazing but this has rarely been demonstrated and may occur only occasionally around South Georgia during summer (see Atkinson et al., 2001). We have no direct measurements that would indicate that grazing was a major factor influencing phytoplankton species/size composition or biomass. Moreover, the existence of extensive phytoplankton blooms around the island that may persist for weeks–months clearly indicates that phytoplankton growth considerably exceeds grazing from all sources for considerable periods during the summer (Korb et al., 2004). In this study mesozooplankton abundance was greater in zooplankton station group 2 waters where phytoplankton production and standing stock was also highest, and so a major impact is thought unlikely. We have no direct measure of microzooplankton biomass and grazing impact but with the exception of Burkill et al. (1995), many studies suggest that protist grazing impact is considerably reduced at the low temperatures found in the Southern Ocean (see Caron et al., 2000). Protist abundance is generally not in quantitative balance with phytoplankton production and increases in response to enhanced diatom biomass. In the relatively phytoplankton poor waters, high macronutrient concentrations in phytoplankton group 1 suggest that little phyto-

437

plankton growth had occurred and therefore microzooplankton numbers may well be low. In group 3 waters, NO3-N:PO4-P depletion ratios suggested that lower phytoplankton biomass and growth rates may be the result of micronutrient limitation rather than grazing activity. Evidence for grazing control of both phytoplankton and mesozooplankton by krill, although largely circumstantial, arises from the distribution of krill, which was strongly associated with mesozooplankton station groups 1 and 4. Stations in phytoplankton groups 1 and 4 differed markedly from those in the other station groups, both in terms of phytoplankton size and species composition (dominated by nano- and pico-phytoplankton and dinoflagellates) and were also characterised by low overall mesozooplankton abundance and high macronutrient concentrations. Krill around South Georgia have previously been reported to have a strong association with the shelf-break regions, occurring in significantly higher abundances between depths of 250 and 1000 m (Trathan et al., 2003). This was certainly the case during this study when they were persistently found over the shelf and shelf-break, both in the western box and further east, compared to offshore regions (Fig. 4). The phytoplankton species assemblage characteristic of station group 1 was consistent with experimental and field observations of size selective grazing by krill that showed substantially altered phytoplankton species composition (Grane´li et al., 1993; Kopczynska, 1992). By feeding more efficiently on large diatoms (Meyer and ElSayed, 1983; Quetin and Ross, 1985; Haberman et al., 2003) krill are reported to cause flagellates to dominate (Grane´li et al., 1993; Kopczynska, 1992). Similar suggestions have recently been made by Garibotti et al. (2003) to explain low diatom standing stock in a study undertaken along the western Antarctic Peninsula. Krill have also been reported to predate copepods in experiments carried out near to South Georgia (Atkinson and Snyder, 1997). Thus there is also the possibility that direct predation was also a factor influencing copepod numbers as well as competition for phytoplankton. In this study the separation of shelf and oceanic regions in both ordinations has clear parallels with

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other Southern Ocean island groups. For example the surface waters downstream of the Kerguelen archipelago are also rich in chl a compared with HNLC oceanic waters of the Southern Ocean. A study by Blain et al. (2001) has linked such production patterns to the presence of iron and identified the presence of 3 water types during a summer survey in the region. These consisted of a high iron coastal zone with low algal biomass dominated by small phytoplankton cells in which grazing pressure by a high standing stock of neritic copepods (mainly Drepanopus pectinatus) kept algal biomass low. An intrusion of iron rich Antarctic surface water, in which the light mixing regime was unfavourable to phytoplankton growth and an offshelf iron rich regime in which high algal biomass was observed. Such similarities between South Georgia and Kerguelen are unsurprising. Both experience high wind stress and lie in the permanently open ocean zone of the Antarctic Circumpolar Current which is generally recognised to have growth limiting quantities of micro-nutrients such as iron. Iron measurements have yet to be made at South Georgia, but the high phytoplankton biomass seen at South Georgia in this and other seasons and the long growing season, which in some years appears to extend from October to April, strongly suggest that iron is readily available, at least in the region downstream of the island (Atkinson et al., 2001; Korb and Whitehouse, 2004). 4.3. Controls on mesozooplankton The environmental factors best approximating the zooplankton ordination in the BIO-ENV and best subsets regression analyses were related to phytoplankton size rather than measures of absolute biomass. The importance of microphytoplankton in accounting for a large part of the variance in large calanoid abundance and carbon mass reflects findings by a number of different authors. For example Paffenhofer (1984) and Berggreen et al. (1988) have both found clear positive relationships between grazer size and optimum food size, although others, e.g. Schnack (1985), Turner and Tester (1989), and Turner (1991), have found that copepods that varied 10-

fold in body mass were capable of feeding on the same food items and so were potentially in competition. At South Georgia Atkinson (1994) found that although a wide size range of copepods were capable of ingesting particles across a wide size spectrum, maximum clearance rates of large copepods were on large net diatoms whereas smaller copepod species, cleared small particles more effectively. The presence of relatively high numbers of Oithona spp. and cyclopoid nauplii in zooplankton station group 3 probably reflects this and the fact that cyclopoids have been found to maintain high rates of growth and fecundity in low chlorophyll environments (Sabatini and Kiorboe, 1994; Hirst and Bunker, 2003). Elsewhere seasonal growth of young E. superba was also found to be positively related to diatom chl a (Ross et al., 2000), although overall was best described by a combination of chl a and prymnesiophyte concentrations (the latter was negatively related to growth). The inclusion of NO3-N:PO4-P depletion ratio as a factor linked to carbon mass is interesting. Although never explaining a large proportion of the variance it appeared as a contributory factor in all cases although was not significantly collinearly related with either chl a biomass or any category of phytoplankton size distribution. It was clearly associated with differences in phytoplankton growth characteristics (station group 2 versus station group 3) and as such may reflect some aspect of food quality. Total fatty acid concentrations, often a more reliable indicator of food quality than POC (Pond et al., 1996), were also a factor that helped explain variability in carbon mass and EPR, albeit a relatively low proportion. By selectively removing microphytoplankton krill are disadvantaging large calanoid copepods which evidently require the quantity and quality of food afforded by large diatom blooms to optimise recruitment (see Tables 7 and 8). However, it was not just large calanoid copepods that were reduced relative to other station groups: all categories of copepods occurred in low abundance, leading to the conclusion that direct predation, as demonstrated by Atkinson and Snyder (1997), Atkinson et al. (1999) and Cripps and Atkinson (2000) may have also been an important factor influencing

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zooplankton dynamics. The relationship of high krill biomass and high silicic acid concentration has been seen before around the island and elsewhere in the Southern Ocean (Priddle et al., 1986; Shreeve et al., 2002). By controlling diatom growth krill will ensure that macronutrient levels remain high; thus intense grazing is seen as a mechanism which can explain these observations. 4.4. Advected versus local production Given the lower abundances of many of the mesozooplankton taxa, particularly the large calanoids (Calanus simillimus and Rhincalanus gigas), within zooplankton station group 3 compared to the downstream group 2, questions arise about the origins of these populations and the timescales of development in relation to their exposure to higher production levels. Second, how have they been able to maintain themselves within the survey area given the rapid transit times of drifters? Mean population age of these large calanoids (see Ward et al., 2002) reflected the new summer generation of copepodites within mesozooplankton group 2 with an average population age of stage CII–CIII, which was at least 50 days in advance of the overwintered generation (mean stage 4CV) found on the shelf within group 1. However in the estimated 50 or so days from being spawned we would anticipate that animals within group 2 would have cleared the survey area based on the speeds of the off-shelf drifters (35 cm s
1) (see Meredith et al. (2003)). Korb and Whitehouse (2004) have suggested that the source of the elevated phytoplankton biomass in the Georgia Basin was the anticyclonic circulation set up over the NWGR with the ensuing conditions of stability and elevated temperature giving rise to elevated primary production rates. Sea-surface height data (Whitehouse unpub) indicates that this eddy had become established over the NWGR the previous November but 2–3 months later at the start of our cruise had started to decay. At the time we surveyed the western transect (early February; 3–4 weeks later than the western box) it was not such a pronounced feature and chl a values were uniformly high downstream of the eddy. It seems probable that primary

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production was retained within this feature and phytoplankton gradually released over time although whether zooplankton were also retained within the eddy to the same degree is unknown and neither is it clear whether vertical migratory behaviour could maintain the copepod populations within the area over the timescales proposed. SeaWiFS images taken prior to the cruise also show the development of a bloom over the southern shelf prior to the establishment of the bloom within the gyre. During December this feature extended northwestwards over the survey area and converged with the bloom from the NWGR to the west of the island before being transported around the periphery of the Georgia Basin (see Korb and Whitehouse, 2004, Fig. 8). We cannot therefore discount the southern shelf as a possible source of the new generation of large calanoids, particularly as stations on the westerly edge of the southern most transect of the western box belong to station group 2. Pathways and transit times from the southern shelf are however presently unknown.

Acknowledgements We thank the officers and crew onboard RRS James Clark Ross for their great help in the successful completion of this cruise. Our many colleagues who participated in the collection of various data sets throughout the cruise, and the referees, whose thoughtful reading of the manuscript did much to improve it, are also gratefully acknowledged. References Atkinson, A., 1994. Diets and feeding selectivity among the epipelagic copepod community near South Georgia in summer. Polar Biology 14, 551–560. Atkinson, A., Peck, J.M., 1990. The distribution of zooplankton in relation to the South Georgia shelf in summer and winter. In: Kerry, K.R., Hempel, G. (Eds.), Antarctic Ecosystems. Ecological Change and Conservation. Springer, Berlin, pp. 159–165. Atkinson, A., Snyder, R., 1997. Krill-copepod interactions at South Georgia, Antarctica, I. Omnivory by Euphausia superba. Marine Ecology Progress Series 160, 63–76.

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