Plankton community structure south and west of South Georgia (Southern Ocean): Links with production and physical forcing

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Deep-Sea Research I 54 (2007) 1871–1889 www.elsevier.com/locate/dsri

Plankton community structure south and west of South Georgia (Southern Ocean): Links with production and physical forcing Peter Ward, Mick Whitehouse, Rachael Shreeve, Sally Thorpe, Angus Atkinson, Rebecca Korb, David Pond, Emma Young British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road, Cambridge CB3 0ET, UK Received 13 October 2006; received in revised form 24 August 2007; accepted 27 August 2007 Available online 11 September 2007

Abstract During late December 2004 and early January 2005 the plankton community to the south and west of South Georgia was investigated. Satellite imagery had shown the surface expression of a bloom over the southern shelf 1 month prior to the cruise, although by the time of sampling a well-defined sub-surface chl-a maximum was evident at 26 of the 57 stations located mainly at the western end of the southern shelf (and the bloom was declining). Nonetheless, integrated chl-a was still greater over the shelf than elsewhere (18–362 mg m2). Macronutrient distributions essentially mirrored the distribution of chl-a biomass, with depletion greatest in the on-shelf waters at the western end of South Georgia, where the most intense surface bloom had occurred during the preceding November. Nearest neighbour clustering of microplankton and mesozooplankton data revealed the presence of two major station groups within each analysis with broadly congruent distributions. Within the microplankton analysis a southern and western shelf grouping of 18 stations was dominated by Corethron spp., Eucampia antarctica and Thalassiothrix spp. This group corresponded spatially to a shelf zooplankton grouping (12 of the 18 stations in both groups in common) in which mesozooplankton abundance was greatest. Here small copepods such as Oithona spp. and the neritic clausocalaniid Drepanopus forcipatus dominated, along with the thecate pteropod Limacina helicina, appendicularians and calanoid copepod naupliar stages. Acoustic doppler current profiler (ADCP) measurements indicated that water flow over the shelf was low and variable (o15 cm s1). In contrast the largest station groups in both ordinations were distributed along the southern shelf-break and further off-shelf in water flowing rapidly (up to 55 cm s1) to the southeast. Nitzschia spp., Pseudonitzschia spp., and Fragilariopsis kerguelensis were abundant here, and the zooplankton, in addition to Oithona spp., was characterized by Metridia spp., Ctenocalanus spp., Oncaea spp., and the polychaete Pelagobia longicirrata. A third group of 13 stations disclosed by the mesoplankton ordination was confined to the north and west and generally comprised outer shelf stations in deeper waters. Here zooplankton abundance was less than in the adjacent major station groupings, although Calanus simillimus was considerably more abundant than in other groups. Relationships of both micro- and zooplankton ordinations with environmental variables were modest (Spearman rank correlation, rw ¼ 0.49–0.59), albeit complex, with interactions likely to have occurred over different timescales. High levels of ammonium over the shelf, probably resulting from microbial breakdown and zooplankton excretion, contributed most to explaining both ordinations, along with the Si(OH)4:NO3 deficit ratio, a measure of past nutrient use. Model output from Ocean Circulation and Climate Advanced Modelling (OCCAM) supported ADCP-derived flow measurements. Specifically, release of particles along a transect to the southwest suggested there was an extended residence time (in excess of 3 months) over the southern shelf and a slow but significant northwards transport into the Georgia Basin. The spatial extent of the shelf and the current Corresponding author. Tel.: +44 1223 221564.

E-mail address: [email protected] (P. Ward). 0967-0637/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.dsr.2007.08.008

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speed and direction implied that in situ production was locally important and had the potential to contribute significantly to downstream ecosystems. r 2007 Elsevier Ltd. All rights reserved. Keywords: Antarctica; Southern Ocean; South Georgia; Marine plankton communities; Physical oceanography; Island shelf; Production; Phytoplankton blooms

1. Introduction The causes of high biological productivity in the world’s ocean are extremely diverse, although most productive regions have strong links with complex physical environments. Examples include coastal upwelling regions, where key zooplankton species have life cycles that interact with circulatory systems to maintain them over productive continental shelves (e.g., Hutchings et al., 1995; Peterson, 1998; Durbin et al., 2000); frontal regions, where vertical turbulence can stimulate production by replenishing nutrients in the near-surface layer (Roman et al., 2002); and regions in the Arabian Sea where responses to seasonal atmospheric forcing increase production following the southwest monsoon season (Smith, 1995). In polar regions production is highly seasonal and may be linked to the stabilization of surface waters following sea-ice retreat or to the development of under-ice algal blooms (Smith and Sakshaug, 1990). In the Southern Ocean, productive regimes occur around oceanic islands such as South Georgia (Atkinson et al., 2001; Korb et al., 2004), Prince Edward Islands (Pakhomov and Froneman, 2000; Hunt et al., 2001), and the Kerguelen Islands (Blain et al., 2001). In these regions dense phytoplankton blooms form over the island shelves and in the surrounding seas and may be advected many hundreds of kilometres downstream. Advection is also a dominant factor influencing the dynamics of zooplankton populations through determining residence times in different food regimes (Huntley and Niiler, 1995; Zhou et al., 2006). Within these island ecosystems, knowledge of the relative balance between locally derived production and the losses and inputs due to advection is key to understanding how energy is transferred through the food web (Atkinson et al., 2001; Murphy et al., 2004). South Georgia lies in the path of the Antarctic Circumpolar Current (ACC) between two major fronts: the Antarctic Polar Front (APF) to the north and the Southern ACC Front (SACCF) to the south. The ACC diverges to the southwest of the

island, flows northwards across the Scotia Ridge prior to resuming its generally eastwards course (Orsi et al., 1995). Phytoplankton blooms at South Georgia generally originate locally with relatively little growth initiated upstream of the island shelf (Atkinson et al., 2001; Korb and Whitehouse, 2004; Korb et al., 2004). However, downstream of the island (north), intense blooms occur frequently and regularly extend to the APF and beyond (Korb et al., 2004). Drifter releases and shipboard measurements have provided supporting evidence that productive waters move from the northwest region of South Georgia’s shelf/shelf-break, cyclonically around the periphery of the Georgia Basin, and then towards the APF and are advected eastwards (Meredith et al., 2003; Korb and Whitehouse, 2004). The northwest shelf is thought to provide a benthic source of iron, which stimulates production in the generally high nutrient low chlorophyll (HNLC) conditions typical of the Southern Ocean generally (Tre´guer and Jacques, 1992; Boyd, 2002). Strong links have been established between mesozooplankton and differing phytoplankton regimes found in the region, with local production supporting high zooplankton standing stocks, particularly to the north of the island (Ward et al., 2002, 2005). Much of what is known about the waters immediately south of the island dates from Discovery investigations (Hardy and Gunther, 1935; Hart, 1942), and with the exception of surveys undertaken in the early 1980s (Atkinson and Peck, 1990; Atkinson et al., 1990; Whitehouse et al., 1996a), few comprehensive sampling programmes have been carried out on this side of the island in recent times. However, satellite imagery has highlighted the scale of phytoplankton blooms on the island’s southern shelf and a review of SeaWiFS data over 8 summer seasons indicated that the southern shelf was the most consistent centre of high chlorophyll biomass around South Georgia, with elevated levels evident in 29 out of 46 spring and summer months investigated (British Antarctic Survey unpublished data). Indeed, there have been at least two seasons in the past 10 years (1996/1997

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and 2000/2001) when the vast majority of the island’s primary production was located on the southern shelf and appeared to extend into the Georgia Basin and north to the APF (Korb et al., 2004). It was also observed that blooms over the southern shelf extended to the west rather than eastwards, which is the prevailing offshelf current direction. It is likely that the southern shelf also provides a benthic source of iron to promote primary production, but to date we have virtually no direct measurements relating to water flow in this region, few data on zooplankton, and no direct estimates of primary production rates. To better understand production processes at South Georgia a cruise was undertaken in late December 2004–early January 2005 to survey the island’s southern shelf. In this paper we report on the distribution of plankton communities during the cruise and examine links with primary producers and physical oceanography. Also, given the potential for water retention over the shelf (Meredith et al., 2005, Whitehouse et al., in press), we wished to assess the extent to which zooplankton might be retained within the region, and also determine pathways from the southern shelf into the Georgia Basin. 2. Methods 2.1. Plankton sampling and analysis Between December 26, 2004 and January 12, 2005 oceanographic sampling was carried out along a

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series of nine major transects (T1–T9) around the island of South Georgia (Fig. 1). The transects were orientated at right angles to the shelf-break and had between 5 and 8 stations spaced at 20-km intervals located along each one. Two additional stations, along T10, were sampled as part of a concurrent survey which also provided additional sea surface temperature (SST) and acoustic doppler current profiler (ADCP) data. In all, 57 stations were sampled. Stations were numbered 1; 2 . . . n commencing from the inshore end of each transect. At each station, vertical profiles of temperature and salinity were measured with a SeaBird 911+ CTD. The surface mixed layer (SML) depth was taken to be the mid-way point within the maximum density change (D kg m3 10 m1; Whitehouse et al., in press). A 12-position carousel water sampler with 10-l Niskin bottles attached to the CTD was used to collect water samples from 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180 and 200 m (or to within 10 m of the seabed in water o200 m deep). A sub-sample was filtered through a mixed ester membrane (pore size 0.45 mm, Whatman), and the filtrate was analysed colorimetrically for dissolved silicate (Si[OH]4-Si), nitrate (NO3-N), ammonium (NH4-N) and phosphate (PO4-P) with a segmentedflow analyser (Technicon, Whitehouse, 1997). The samples collected between 0 and 160 m were analysed for chlorophyll a (chl-a). Samples were filtered through GF/F filters (particle retention 0.7 mm), the filtered samples were extracted with

Fig. 1. Left panel: The locations of South Georgia (SG), Shag Rocks (SR) and the Georgia Basin (GB) along with the mean positions of the Antarctic Polar Front (APF; Moore et al., 1999), Southern Antarctic Circumpolar Current Front (SACCF; Thorpe et al., 2002) and the Southern Boundary of the Antarctic Circumpolar Current (SB; Orsi et al., 1995). The North Scotia Ridge extends from South Georgia westwards to the Burdwood Bank (BB). Right panel: the study area showing transect (T1–T10) locations during cruise JR116 with stations numbered from South Georgia outwards and alternate stations labelled. Note that only two stations were occupied along transect 10, both of which are labelled. The pale and dark grey shading in both maps are delineated by the 2000 and 500 m isobaths.

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10 ml 90% acetone in the dark for 24 h, and fluorescence was measured before and after acidification with 1.2 M HCl (Turner TD-700, Parsons et al., 1984). These discrete water bottle chl-a measurements were used to calibrate an Aquatracka Mk III fluorometer mounted on the CTD frame. Size fractionation of chl-a was performed on 20-m samples at each station. SST was monitored via the ship’s non-toxic seawater supply located 7 m below the sea surface. Absolute velocity of the upper ocean was measured along transects with a vessel-mounted ADCP (153.6 kHz; RD Instruments) (see Whitehouse et al. (in press) for further details). Nutrient deficits were estimated as the difference between concentrations in the winter water layer (an indication of prebloom concentrations) and those in the water column above it (Jennings et al., 1984; Whitehouse et al., in press). The winter water layer in Antarctic surface water is characterized by a well-defined potential temperature minimum (ymin), located between 70 and 140 m during the present study. These deficits were used as a proxy for the relative amount of primary production that had occurred since the end of winter. Microplankton species composition representative of the SML was determined at each station from water samples collected at 20 m and preserved in 1% acid lugols solution. Microplankton were enumerated by 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 were examined on either 2 or 3 perpendicular transects across the whole slide at  100 magnification. The categories were chosen on the basis of their dominance of the 412-mm microplankton, and their ease of identification. Species counted ranged in size from 1–200 mm. Paired bongo nets (mouth diameter 62 cm, mesh size 200 mm) were deployed to 200 m or near bottom if shallower. The contents of one of the nets were preserved in 10% v:v formalin and set aside for later analysis. The contents of the other were sorted for stages CIV and CV Calanoides acutus, and 30 of each from each station were frozen immediately at 80 1C and subsequently dried at 60 1C onboard ship within 1 week of collection. They were then transferred in a sealed container to the UK, where they were again dried at 60 1C to constant weight. Dry mass of batches of CIV (3  10

ind.) and CV (6  5 ind.) was determined with a Mettler MT5 balance to an accuracy of 71 mg. Whole samples were then analysed for C, H, and N with a Fisons EA 1108 elemental analyser with acetanilide as a standard. In the UK the formalinpreserved 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. Between 500 and1000 individuals were counted from each sample. Seawater samples (between 1.2 and 3.6 l) collected at 20 m for total fatty acid (TFA) and particulate organic carbon (POC) analyses were filtered onto pre-ashed GF/F filters. Those for TFA analysis were placed in chloroform:methanol (2:1 v/v), and both were then stored 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 ml1 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-characterized marine fish oil. In the UK, samples for POC analysis were acidified under an atmosphere of fuming hydrochloric acid for 24 h and then dried in a vacuum desiccator for 24 h. Elemental C and N were determined in three replicate sub-samples as for the copepod samples. 2.2. Data analysis Microplankton cell counts and mesozooplankton data were initially analysed independently with the statistical package PRIMER 5 (Primer-E Ltd.). Prior to running cluster analysis the large calanoid copepod species were aggregated into early (CI–CIII) and late (CIV–CVI) copepodite stages to ensure that growth and stage progression over the course of the cruise did not unduly influence the grouping of stations. Standardized data in the form of phytoplankton cell counts (ind. 50 ml1) and mesozooplankton abundance (ind. m2, 0–200 m) were then double

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root transformed and subjected to q-type cluster analysis based on the Bray–Curtis similarity and group average linkage classification (Field et al., 1982). Non-metric multi-dimensional scaling (NMDS) was also performed to allow relationships between groups to be assessed. Its purpose is to represent the samples as points in low-dimensional space (2-d) such that the relative distances apart of all the points are in the same rank order as the relative dissimilarities of the samples (as calculated by Bray–Curtis coefficients). The SIMPER (similarity percentages) 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). We also used BIO-ENV, a routine that calculates a measure of agreement between, two (dis)similarity matrices, on both microplankton cell count and mesozooplankton data matrices and another containing a suite of environmental variables measured at each station (see Tables 3 and 4 and Section 3 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). The relationship between C mass, zooplankton abundance and the abundance of copepod eggs attributable to Rhincalanus gigas and Calanus simillimus in the samples collected at each station and a suite of potential predictor variables was examined using best sub-sets regression (see Table 5 and Results Section 3.5 for full details). Response and predictor variables were log transformed where necessary to linearize the relationship, to stabilize variability and to reduce skewness. The Akaike Information Criterion with small-sample adjustment (AICc) was used for model selection (Burnham and Anderson, 2002). Spatial autocorrelation was examined using the variogram of the standardized residuals from the fitted model (Cressie, 1993). Further details of the above can be found in Ward et al. (2006). Analyses were implemented using the statistical software package MINITAB v.13 (Pennsylvania State University).

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2.3. Particle transport along T4 Output from the Ocean Circulation and Climate Advanced Modelling Project (OCCAM) model was also used to investigate the transport of particles from transect T4, which was located close to where flow diverged to pass either side of the island. OCCAM is a z-level primitive equation model of the Bryan-Cox-Semtner type, described by Webb et al.  (1998). The horizontal resolution of the model is 14 . Full details may be found at http://www.noc.soton. ac.uk/JRD/OCCAM. Predicted 5-day mean velocities from OCCAM provided the input data for this particle-tracking model. Particles were advected horizontally using the OCCAM velocities, with an additional random walk to simulate horizontal particle dispersal. Particles were allowed to move randomly in the vertical between the depths of 5 m and 100 m. The initial positions of the 500 particles were randomly distributed in an ellipse centred on T4 (39.21W, 54.41S), with ellipse major and minor axes of 50 and 10 km and an orientation of 301. All particles were released at a depth of 50 m with a start date of November 1, 2000 (most recent data available). 3. Results 3.1. Physical oceanography Temperature varied over the region with SST warmest (43.5 1C) in the off-shelf region to the north and coldest (1.5 1C) in southern off-shelf waters (Fig. 2a). Fig. 2b illustrates ADCP data averaged from 15–200 m (or to near bottom where water depth o200 m). Rapid flow (up to 55 cm s1) was observed to the southeast at the off-shelf stations on transects T5–T9 contrasting with reduced and variable flow (o15 cm s1) over the shelf. Of particular note was the divergence of flow between transects T4 and T5, a region where upwelling is thought to occur. As a consequence of this divergence, flow at the offshore ends of T3 and T4 was north to northwestwards along South Georgia’s southwestern shelf-break and across the Scotia Ridge. To the north of the island flow was generally westwards and of a lower velocity than to the south. A winter-water layer was present along all transects (Whitehouse et al., in press), and the influence of the SACCF was detected at off-shelf stations along transects T6–T9 close to its historical mean position (see Thorpe et al., 2002).

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Fig. 2. (A) Sea surface temperature (1C) along the transects sampled during cruise JR116. T1–T10 are labelled. Additional transects along which temperature measurements were made but no stations were worked are included. (B) ADCP vectors (flow direction away from transect lines) averaged over the top 200 m or to near bottom where water depth is o200 m. T1–T10 are labelled. Additional transects along which ADCP measurements were made but no stations were worked are included.

3.2. Chlorophyll-a and nutrients The spatial distribution of chl-a and nutrients during the cruise has been described in detail by Whitehouse et al. (in press). In summary, SeaWiFS imagery indicated high (45 mg m3) near-surface chl-a concentrations over the South Georgia shelf in November 2004, two months before the cruise. These had declined over the southern shelf by the time of our survey in January, whereas on the northern shelf the bloom extended into the Georgia Basin before declining in March. Two major bloom areas were located during the cruise. The first was to the north of the island in the vicinity of transects T1 and T2 where a bloom was taking place within a shallow mixed layer and surface chl-a concentrations were as high as 8 mg m3 (Fig. 3). The more extensive bloom over the southern shelf, initially observed in November, was a largely stationary event as evidenced by the persistence of silicatedepleted water over the shelf a month later during the cruise (see Fig. 2 in Whitehouse et al., in press and Fig. 3 in this paper). At 26 of the 57 stations sampled, located mainly at the western end of the southern shelf, a well-defined sub-surface chlorophyll maximum (SCM) was evident (Whitehouse et al., in press). Here, integrated chl-a was higher than at stations with near-surface maxima, with values ranging from 18–362 mg m2. Macronutrient distributions essentially mirrored the distribution of chl-a biomass: high offshelf and low onshelf. Silicate depletion was high throughout the surveyed area

and reasonably high for nitrate with the exception of off-shelf stations to the west and south (Fig. 3). Depletion was greatest in the on-shelf waters at the western end of South Georgia along transects 2–5, where the most intense surface bloom had occurred during the preceding November. The Si(OH)4:NO3 deficit ratio varied from 2 to 6 with the highest ratios in off-shelf waters to the south. Ammonium concentrations were highest in on-shelf waters. 3.3. Microplankton Nearest neighbour clustering of the microplankton cell count data identified two major groupings, one smaller group, and two outlying stations (Fig. 4a and b). The smallest, Group 1 (Gp1), comprised four stations lying along T1 (Fig. 4c) and was characterized by high abundances of the diatoms Eucampia antarctica, Thallasionema spp., Fragilariopsis spp. and Thallasiosira spp. as well as dinoflagellates (10–50 mm) (Table 1). With the exception of a single station at the offshelf end of T2, the second group (Gp2, 18 stations) was geographically coherent, comprising many of the on-shelf stations over the southern and western shelf (Fig. 4c). Dominant species included Corethron spp., Eucampia antarctica and Thallasiothrix spp. Group 3 (Gp3, 29 stations) was the largest grouping present with the highest average withingroup similarity 80%. Mean abundance was highest in nine of the 18 taxonomic categories (11 if Gp4 and Gp5 are excluded) enumerated in this

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Fig. 3. The distribution of surface chl-a concentrations (mg m3), integrated chl-a concentrations (mg m2), silicic acid and nitrate deficits (mol m2), silicic acid:nitrate deficit ratios and ammonium concentrations (mmol m2). Bathymetry is shaded as in Fig. 1.

analysis. Of the diatoms, Nitzschia spp., Pseudonitschia spp., Fragilariopsis kerguelensis, Thallasiothrix spp. and Chaetoceros spp. were all abundant as were dinoflagellates (10–50 mm) (Table 1). The two outliers, stations 8.6 and 9.7, although in close proximity and closest to the SACCF, were not closely related. Station 8.6 had moderately high abundances of F. kerguelensis and Thalassiothrix spp., whereas 9.7 was characterized by

Corethron spp., Nitzschia spp. and Pseudonitzschia spp. (Table 1). 3.4. Mesozooplankton Copepods accounted for X90% of total mesozooplankton abundance across the survey area, with small copepods (p1.5 mm TL) accounting for 80% of this total. Nearest neighbour clustering

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of the mesozooplankton data identified three major groups (Gps 2–4) and one comprising just two stations (Gp1) located at the inshore end of T1 (Figs. 5a–c). Station similarity within groups was almost exclusively X80% across all groups, and with the exception of Gp1, dissimilarity between groups was 20–24%. The major zooplankton contributors to within-group similarity and between-group dissimilarity are shown in Table 2. The distribution of 15 of the mainly copepod taxa that, in addition to being abundant, illustrate the contrasting distribution patterns described below are presented in Fig. 6. Group 1 was most dissimilar to the other groups and was characterized by extremely low abundances of virtually all taxa relative to other groups. Group 2 (Gp2, 18 stations) was distributed mainly over the inner southern shelf along T3–T9 (Fig. 5c). Within this grouping, 11 of the 25 taxonomic groups that contributed X2% of within-group similarity or between-group dissimilarity were most abundant, including the neritic Drepanopus forcipatus, appendicularians, and the thecate pteropod Limacina helicina. Calanoid nauplii (including R. gigas nauplii detailed seperately) were also considerably more abundant within this grouping, as were eggs of R. gigas and C. simillimus (up to 45 times greater than other groups in the case of R. gigas, Fig. 7). Additionally both CIV and CV C. acutus had greater average carbon masses within mesozooplankton Gp 2 compared to other groups, which in the case of stage CV was almost double that of other groups (Fig. 7). Group 3 (Gp3, 13 stations) was confined to the north and west (T1-4) and generally comprised the outer shelf stations in deeper waters. Here zooplankton abundance was less than in adjacent major station groupings, although C. simillimus, particularly juvenile stages, was considerably more abundant than in other groups. Carbon mass of both stages of C. acutus was lower than in Gp2 but comparable to Gp4 (Fig. 7). Fig. 4. Microplankton groups. (A) Dendrogram resulting from clustering performed on the Bray–Curtis similarity matrix created from the double root transformed microplankton cell count data. The numbers displayed down the side of the dendrogram identify stations indicated in Fig. 1, e.g., 5.2 refers to the second station out from the coast along transect 5. Station groups referred to in the text are identified and correspond to the symbols used in the NMDS plot (B) and the geographical distribution plot (4C). n ¼ Gp1, & ¼ Gp2, . ¼ Gp3,  ¼ Gp4, J ¼ Gp5, X ¼ no sample.

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Table 1 Average cell count abundance (ind. 50 ml1), with respect to microplankton station groups, of the species and taxa that SIMPER analysis indicated contributed X2% to within-group similarity or between-group dissimilarity Station locations

Eucampia antarctica Dinoflagellates 10–50 mm Thalassionema/Fragilariopsis spp. Chaetoceros spp. Thalassiosira spp. Ciliates 10–50 mm Corethron spp. Nitzschia/Pseudonitzschia spp. Fragilariopsis kerguelensis. Thalassiothrix spp. Other diatoms Rhizosolenia/Proboscia spp. Dinoflagellates 450 mm Dactylisolen spp. Odontella spp. Ciliates 450 mm Phaeocystis (no of colonies)

Group 1 (n ¼ 4) T1 only

Group 2 (n ¼ 18) T2–T9 predominantly inner and outer shelf

Group 3 (n ¼ 29) T2–T9 predominantly outer shelf and offshelf water

Group 4 (n ¼ 1) Outlier T8

Group 5 (n ¼ 1) Outlier T9

4112 1375 1020 337 961 73 73 55 68 23 5 5 18 0 27 0 0

1437 524 222 458 93 40 906 216 261 567 45 16 38 2 26 7 0

903 1394 630 1917 143 3 538 2037 3092 1113 214 74 45 21 39 3 2

126 583 55 73 73 18 0 0 1548 455 0 18 55 0 0 55 0

78 437 129 0 79 31 251 517 361 175 180 110 0 0 16 0 0

Group 4 (Gp4) was the largest, comprising 24 stations located mainly over the outer parts of the shelf, shelf-break, and oceanic waters to the south of the island. The taxa most abundant within this group included Ctenocalanus spp., Metridia spp., Microcalanus pygmaeus and Pelagobia longicirrata. 3.5. Environment and relationship to microplankton and mesozooplankton station groups The BIO-ENV analyses (Table 3) indicated a modest goodness of fit between environmental variables and the microplankton cell count data matrix. Initially, the largest single contributor to p was ammonium concentration (p ¼ 0.39, Table 3), but in successive iterations, other variables, such as Si(OH)4:NO3 deficit ratio, mesozooplankton abundance, and average temperature, combined with ammonium to produce a better fit. Overall, two 4-variable models gave the best fit (p ¼ 0.496); the first included the above variables, and the second substituted NO3 deficit for average temperature. There was a better goodness of fit with the mesozooplankton data matrix. Again ammonium concentration was initially the largest single contributor (p ¼ 0.42, Table 3), but the successive

addition of %microphytoplankton 420 mm, Si(OH)4: NO3 deficit ratio, silicate deficit, surface chl-a, SST, and SML provided a best-fit model (p ¼ 0.59). Mean values of the variables identified by the BIO-ENV analysis with respect to microplankton and mesozooplankton station groups are given in Table 4. Best subsets regression analyses were used to determine how well environmental variables explained variation in carbon mass of C. acutus stages, mesozooplankton abundance, and the abundance of R. gigas and C. simillimus eggs in the water column (see Fig. 7). In the case of C. acutus carbon mass, the models indicated by the AICc (see Section 2.3) suggested key variables were temperature, nutrient concentrations, and TFA concentrations as well as size properties of the phytoplankton community (Table 5). The fit was considerably stronger for stage CV C. acutus than for stage CIV. Data suggest complex relationships rather than simple correlations between plankton and the environment at the time of sampling. This was further suggested by the identification of Si(OH)4 deficit and NO3 as best-fit variables when zooplankton abundance was the response variable. Silicate deficit has been identified previously as relating strongly to zooplankton

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abundance (Shreeve et al., 2002), whereas egg numbers related more to existing levels of chl-a than to nutrient deficits. 3.6. Transport from transect T4 Following particle release along transect T4, we ran our tracking model for 3 months, producing the distribution illustrated in Fig. 8. This indicates a wide dispersal of particles with the majority transported cyclonically around South Georgia to the north and northeast. To the north of the island, although a high proportion of particles were retained on the shelf, the majority had been transported into the Georgia Basin and further downstream. However, of the 28% of total particles that were transported southeastwards from transect T4, most were retained on the southern shelf. 4. Discussion Sampling took place in late December/early January during the summer period, when growth and production proceed quickly. This has the potential to alias the spatial separation of station groups because of rapid changes in stage composition of zooplankton, particularly copepods, which may occur during the cruise. Aggregation of copepod counts within species, or through grouping of early and late copepodite stages prior to analysis, as carried out here, will reduce such effects. In addition, all stations were sampled within 14 days with the exception of 1.1 and 1.2, which were sampled 3 days later. Although these last two stations form a group separate from the others in the zooplankton ordination, this is unlikely to be due to temporal aliasing, as both were characterized by low abundance rather than more fundamental changes in species composition. Overall the stations were sampled from east to west, whereas the major groupings of stations tended to be north to south. The spatial and temporal scale of this study (10 s of kms and days to weeks) places it within the Fig. 5. Mesozooplankton groups. (A) Dendrogram resulting from clustering performed on the Bray–Curtis similarity matrix created from the double root transformed mesozooplankton data. The numbers displayed down the side of the dendrogram identify stations indicated in Fig. 1, e.g., 5.2 refers to the second station out from the coast along transect 5. Station groups referred to in the text are identified and correspond to the symbols used in the NMDS plot (B) and the geographical distribution plot (C). n ¼ Gp1, & ¼ Gp2, . ¼ Gp3, ’ ¼ Gp4.

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Table 2 Average zooplankton abundance (ind.  103 m2, 0–200 m), with respect to zooplankton station group, of the species and taxa that SIMPER analysis indicated contributed X2% to within-group similarity or between-group dissimilarity Station locations

Group 1 (n ¼ 2) T1 inshore

Group 2 (n ¼ 18) T3–T9 predominantly inner shelf

Group 3 (n ¼ 13) T1–T4 predominantly in deeper water

Group 4 (n ¼ 24) T2–T9 predominantly outer shelf and offshelf

Calanus simillimus (CIV–CVI) Calanus simillimus (CI–CIII) Calanus propinquus (CIV–CVI) Calanus propinquus (CI–CIII) Calanoides.acutus (CIV–CVI) Calanoides acutus (CI–CIII) Rhincalanus gigas (CIV–CVI) Rhincalanus gigas (CI–CIII) Rhincalanus gigas nauplii Microcalanus pygmaeus Clausocalanus laticeps Ctenocalanus spp. Drepanopus forcipatus Euchaeta spp. Scolecithricella minor Metridia spp. Calanoid nauplii Oithona spp. Oncaea spp. Cyclopoid nauplii Thysanoesaa spp. Thysanoessa spp. calyptopes Euphausia frigida furcilia Euphausia frigida calyptopes Pelagobia longicirrata Limacina helicina Appendicularians Ostracoda Chaetognatha

0.6 2.9 0.001 0 1.4 0.5 0.8 0.3 0.6 0 0.1 10.6 3.1 0 0 2.9 2.1 37.2 0.1 0.05 0.3 0 0.05 0 0 0.3 0.5 0 0.1

1.5 7.4 0.10 0.1 6.7 4.1 1.6 2.7 21.1 0.9 0.2 41.6 177.3 0.2 0.9 43.1 184.6 300.2 29.1 61.7 0.4 0.2 0.04 0.03 18.3 56.7 78.3 0.1 0.4

2.2 13.1 0.2 0.4 6.4 2.4 1.2 3.0 5.3 0.8 0.4 68.9 3.9 0.2 0.4 35.2 26.5 256.4 4.1 5.7 0.8 0.8 0.1 0.2 3.7 30.3 15.2 0.4 0.8

0.4 2.6 0.25 0.1 6.4 2.1 0.6 6.3 5.7 3.5 0.6 70.9 2.4 0.1 0.5 72.8 81.7 267.0 32.3 50.8 1.5 1.0 0.2 0.3 33.4 40.0 44.1 0.3 1.0

mesoscale. As such, discrimination of station groupings was based on changes in abundance of a broadly common taxonomic list, rather than more fundamental changes in species composition (Mackas and Sefton, 1982, Marin, 1987, Pakhomov et al., 2000). This is reflected in the similarity levels between groups across both ordinations, which were generally around 75–80%. Nonetheless, the differences observed were robust. For example, within zooplankton Gp2, the removal of the abundant D. forcipatus from the species/station matrix did not alter any stations’ affiliation to this group. 4.1. Physical setting Recent advances in our understanding of the oceanography of the Scotia Sea have done much to set South Georgia into a wider physical context.

While investigations at the small to mesoscale have demonstrated the importance of local influences such as increased precipitation and island runoff in modifying shelf waters, the retention of water over the island’s shelf and the occurrence of shelf-break fronts (Brandon et al., 2000; Meredith et al., 2005), others have shown how remote influences can also potentially affect the ecosystem (e.g. Ward et al., 2002; Murphy et al., 2004; Thorpe et al., 2004). For instance the SACCF may introduce production to the region, potentially from as far afield as the icecovered regions further south (Ward et al., 2002; Murphy et al., 2004). In the present study none of the transects crossed the SACCF. Although there were indications of SACCF influence at the southernmost stations of T6–T9 (Whitehouse et al., in press), the water at most of the stations to the south of the island had originated from between the APF

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Fig. 6. Distribution and abundance of the principal zooplankton taxa found in this study. Symbols are scaled from 0 to maximum values indicated in each panel (ind.  103 m2, 0–200 m).

and the SACCF (Fig. 1). In the present study we confine our observations to local environmental influences. 4.2. Environmental links The geographical distribution of station groupings within both ordinations was superficially similar insofar as the majority of stations fell into inner and outer shelf groupings, particularly along T3–T9. Such correspondence between microplankton and zooplankton ordinations has been observed previously in a study carried out on the north side of the island, where shelf and oceanic communities were defined and the large-scale chl-a distribution showed close links to the physical environment (Ward et al., 2005). In the present study, boundaries between the respective microplankton and zooplankton groupings meandered along shelf and shelf-break regions and were broadly coincident with the changes in current speed apparent between the more rapidly moving offshore waters and the variable and sluggish flows over the shelf (Figs. 2, 4 and 5). This

was particularly the case for the inshore microplankton Gp2 which extended further along T8 and T9 in low-flow areas than did the zooplankton Gp2, which was largely restricted to the inner shelf where D. forcipatus was particularly abundant. Within the low-flow shelf region the bloom that had developed prior to the survey was declining when sampled during late December and early January. Surface chl-a concentrations were lower than suggested by satellite imagery from a month earlier, although substantial subsurface concentrations of chl-a (up to 5 mg m3) were still present, particularly over the western part of the southern shelf. Nutrient levels were also depleted, particularly silicate, over the western part of the southern shelf, where the intense surface bloom had been located the previous November. Zooplankton abundance within zooplankton Gp2 was markedly higher than in the other groups. In addition to D. forcipatus and other small copepods (Table 2), concentrations of R. gigas and C. simillimus eggs (Fig. 7) and calanoid nauplii including those of R. gigas (Fig. 6) were also more abundant, suggesting strongly that zooplankton populations had responded to the increased in situ

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Fig. 7. Total zooplankton abundance (ind. m2, 0–200 m), carbon mass (mg) of stages CIV and CV Calanoides acutus, and net catches of Rhincalanus gigas and Calanus simillimus eggs (ind.  103 m2, 0–200 m) at stations occupied during cruise JR116. Symbols are scaled from minimum to maximum values indicated in each panel.

production. Further evidence for this comes from the greatly increased C mass of C. acutus stages, particularly for CV, over the shelf (Fig. 7). The elevated numbers of nauplii and the neritic D. forcipatus within Gp2, relative to other groups, also supports the idea that there was little water exchange between shelf and oceanic regions in the period leading up to the survey. Species previously found to be more typical of oceanic regions rather

than shelf and shelf-break areas, such as Ctenocalanus spp., Metridia spp., and Microcalanus pygmaeus (see Ward et al., 2005) were more abundant in the offshelf zooplankton Gp4 in the waters flowing rapidly southeastwards. The underlying premise of the BIO-ENV analysis is that the suite of environmental variables responsible for structuring the biotic community should group stations in the same way with the omission of

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Table 3 Results of BIO-ENV analysis: combinations of K variables giving largest Spearman rank correlations (rw) between microplankton cell count data, mesozooplankton data and environmental similarity matrices K

rw

Environmental variables

Microplankton data 1 0.39 2 0.44 3 0.48 4 0.50 4 0.50

NH4 Si(OH)4:NO3 deficit ratio, NH4 Zooplankton abundance, Si(OH)4:NO3 deficit ratio, NH4 Average Temperature. 0–50 m, Zooplankton abundance, Si(OH)4:NO3 deficit ratio, NH4 NO3 deficit, Zooplankton abundance, Si(OH)4:NO3 deficit ratio, NH4

Mesozooplankton data 1 0.42 2 0.49 3 0.51 4 0.54 5 0.56 6 0.58 7 0.59

NH4 % microplankton (420 mm), NH4 Si(OH)4:NO3 deficit ratio, % microplankton (420 mm), NH4 Silicate deficit, Si(OH)4:NO3 deficit ratio, % microplankton (420 mm), NH4 Surface chl a, Silicate deficit, Si(OH)4:NO3 deficit ratio, % microplankton (420 mm), NH4 SST, Surface chl a, Silicate deficit, Si(OH)4:NO3 deficit ratio, % microplankton (420 mm), NH4 SML, SST, Surface chl a, Si(OH)4 deficit, Si(OH)4:NO3 deficit ratio, % microplankton (420 lm), NH4

Models of increasing complexity listed, best overall fit in bold. Environmental variables included in the microplankton analysis Surface Mixed Layer (SML) depth (m), average temperature 0–50 m (1C), Si(OH)4 deficit (mol m2), NO3 deficit (mmol m2), Si(OH)4:NO3 deficit ratio, NH4 (mmol m2) and zooplankton abundance. After various permutations sea surface temperature (SST 1C) was substituted for average temperature in the mesozooplankton analysis and additional variables were included as follows surface chl a (mg m3), integrated chl a (mg m2, 0–100 m), total fatty acids, % microplankton (420 mm), % nannoplankton (2–20 mm), particulate carbon (mg l1) and particulate nitrogen (mg l1). Table 4 Mean values (7standard deviation) of model variables contributing most to best fit between environmental data and microplankton and mesozooplankton data matrices in the BIO-ENV analysis (see Table)

Microplankton station groups NH4 (mmol m2) NO3 deficit (mmol m2) Si (OH)4:NO3 deficit ratio Zooplankton abundance  103 m2 Average temperature (1C 0–50 m)

Mesozooplankton station groups Si(OH)4:NO3deficit ratio SST (1C) % microplankton 420 mm NH4 (mmol m2) Surface chl a (mg m3) Mixed layer depth (m) Silicate deficit (mmol m2)

Gp1 (n ¼ 4)

Gp 2 (n ¼ 18)

Gp 3 (n ¼ 29)

Gp 4 (n ¼ 1)

Gp 5 (n ¼ 1)

F

p

243 779 2.85 313 3.13

185 861 3.32 898 2.37

127 586 4.21 679 2.56

89 266 6.45 490 1.75

104 468 5.37 691 1.99

11.18 9.93 9.56 9.55 4.39

0.0001 0.0001 0.0001 0.0001 0.018

36.66 52.70 10.09 37.54 12.02 4.40 6.04

0.0001 0.0001 0.0001 0.0001 0.0001 0.017 0.004

(0.32) (120) (0.45) (287) (0.32)

(0.39) (220) (0.74) (302) (0.39)

(0.52) (210) (0.89) (233) (0.52)

Gp 1 (n ¼ 2)

Gp 2 (n ¼ 18)

Gp 3 (n ¼ 13)

Gp 4 (n ¼ 24)

2.59 3.4 91 306 4.73 35 1941

3.06 2.75 91 213 2.0 55 2816

3.5 3.40 64 152 1.32 45 2345

4.7 2.29 82 107 0.8 68 2356

(0.59) (0.45) (5.5) (31) (5) (4) (651)

(0.09) (0.24) (5.7) (61) (0.7) (30) (476)

(0.59) (0.23) (28) (30) (1.4) (14) (610)

(0.81) (0.39) (13.5) (30) (0.4) (20) (357)

One way ANOVA was used to detect significant differences between variables within microplankton Groups 1–3 and mesozooplankton groups 2–4.

key variables causing the correspondence of the two plots to deteriorate. Within both the microplankton and mesozooplankton analyses the complexity of community structure was reflected in the multivariable best-fit models and by the modest corre-

spondence between biotic and environmental data. Not all of the environmental factors available to us can be considered as potential causative agents of community structure despite strong correlations. For example chl-a and various chemical indices of

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Table 5 Models from best subsets regression analysis Regression equations log C mass CIV C: acutus ¼ 2:80ð0:14Þ  0:012ð0:003ÞNO3

R2(%)

p

26.1

o0.01

67.6

o0.01

38.7

o0.01

49.9

o0.01

40.5

o0.01

 0:17ð0:07Þlog10 TFA  0:077ð0:02ÞT log C mass CV C: acutus ¼ 2:57ð0:61Þ þ 0:005ð0:005Þ%chl420 mm þ 0:009ð0:007Þ%chl a 2220 mm  0:037ð0:009ÞNO3  0:24ð0:14Þlog10 TFA  0:12ð0:05ÞT þ 0:33ð0:09Þlog10 chl a pp

Mesozooplankton abundance

¼ 5:72ð7:86Þ þ 0:004ð0:001ÞSiðOHÞ4 deficit þ 0:732ð0:234ÞNO3 þ 5:28ð1:93ÞLog10 chl a Rhincalanus gigas eggsðlog10 nos m2 Þ ¼ 1:17 þ 0:0004ð0:0020ÞSiðOHÞ4 deficit þ 0:68ð0:32Þlog10 chl a þ 1:96ð0:84Þlog10 TFA Calanus simillimus eggsðlog10 nos m2 Þ ¼ 0:87  0:12ð0:04ÞNO3 þ 2:02ð0:58ÞPO4 þ 0:75ð0:30Þlog10 chl a þ 0:44ð0:22ÞT Tests for spatial autocorrelation using statistics of Moran (I) and Geary (c) applied to pairs of locations within 30 km and estimates of b for the autocorrelation function r(d) ¼ exp(d/b) for cases with statistically significant autocorrelation. Mass CIV Calanoides acutus: Mass CV Calanoides acutus: Mesozooplankton abundance: Rhincalanus gigas eggs: Calanus similimus eggs:

I ¼ 0.078, p ¼ 0.51, c ¼ 0.81, p ¼ 0.25 I ¼ 0.035, p ¼ 0.85, c ¼ 1.13, p ¼ 0.45 I ¼ 0.28, p ¼ 0.024, c ¼ 0.57, p ¼ 0.006, b ¼ 20.8 km I ¼ 0.28, p ¼ 0.022, c ¼ 0.71, p ¼ 0.042, b ¼ 28.0 km I ¼ 0.26, p ¼ 0.05, c ¼ 0.65, p ¼ 0.024, b ¼ 13.0 km

Response variables were log10 chl a (mg m2, 0–100 m), log10 POC (mg l1, 20 m), log10 TFA (mg l1, 20 m), % microplankton 420 mm (20 m), % chl-a 2–20 mm (20 m), NO3 average (mmol m3, 0–50 m), PO4 average (mmol m3, 0–50 m), average temperature (T 1C, 0–50 m), Si(OH)4 deficit (mmol m2). Models shown were selected by calculation of Akaike Information Criterion (see methods for explanation). Regression equations include (standard error of the regression coefficient) which have been adjusted for the effect of autocorrelation (see Diggle, 1990). OO ¼ double root transformation.

the phytoplankton, such as TFA and POC, were omitted from the microplankton analysis on the grounds that they probably reflect the properties of the different groups rather than underpin their structure. However, variables such as temperature, zooplankton abundance, and nutrient levels can have a potential impact on microplankton community structure, either by directly influencing microplankton growth rates and biomass, as in the case of temperature and zooplankton grazing, or by reflecting the integrated pattern of past growth and development such as nutrient deficits. The inclusion of ammonium concentration as a factor in both analyses is interesting. Over the southern and

western shelf regions ammonium levels were considerably higher than off-shelf. Whitehouse et al. (in press) attribute this to microbial breakdown of a declining bloom over the southern shelf and to an efficient use of nitrate by diatoms resulting in ammonium-nitrogen remaining underutilized in the west. It is likely that zooplankton excretion will also contribute to the pool. We therefore conclude that the high onshelf abundances of mesozooplankton (Gp 2) compared to offshelf (Gp 3) reflect the elevated chl-a biomass and patterns of nutrient use that have occurred over the shelf. Nutrient deficits provide a unique way of viewing past production levels, particularly given the ability

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Fig. 8. Output from particle-tracking model. Particle distribution (expressed as a % of the total, see scale) within the gridded study area 90 days after their release along transect T4 (see Text 2.3 for further details).

of trace metals such as iron to affect silicate and nitrate uptake ratios (Franck et al., 2000). In this study, although the majority of microplankton species were present within all station groupings, the two most abundant, Fragilariopsis kerguelensis and Eucampia antarctica, were significantly and negatively correlated. The former was distributed largely in the region to the south and west (predominantly within microplankton Gp 3 see Table 1), where chl-a biomass was low and high average Si(OH)4:NO3 deficit ratios indicated potential iron stress. Eucampia antarctica, on the other hand, was especially abundant at stations along T1 and T2 in the north and shelf stations along T8 to the south of the island, where Si(OH)4:NO3 deficit ratios were generally low. In contrast to the above species, Corethron spp. was most abundant within microplankton Gp 2 and was significantly related to chl-a standing stock (Whitehouse et al., in press). Clearly the available variables only partly explain structure within the microplankton species matrix, and there are many other unmeasured and perhaps unmeasurable (at least at this scale) factors that could potentially contribute, particularly given the large-scale links between plankton development and climate (e.g. Richardson and Schoeman, 2004; Planque and Taylor, 1998). It should also be borne in mind that the microplankton station matrix was based on samples taken at 20 m depth, which in

previous cruises has been broadly coincident with near-surface chl-a maxima, whereas during this cruise, SCMs occurred at approximately half of the stations. Whether this was a factor influencing microplankton station groupings is unknown, but the SCMs were not confined to any one station grouping. The wider range of environmental variables input into the mesozooplankton BIO-ENV analysis resulted in a best-fit model with a higher Spearman rank correlation value than seen in the corresponding microplankton analysis. This has been the case in previous studies (see Ward et al., 2005), and the inclusion of an index of phytoplankton size (% microphytoplankton 420 mm), silicate deficit, Si(OH)4:NO3 deficit ratio, and surface chl-a in the best fit model clearly reflects trophic linkages. Nutrient depletions reflect plant production since growth commenced and studies around South Georgia have previously demonstrated a strongly inverse relationship between silicate concentration and total copepod abundance (Shreeve et al., 2002). The low Si(OH)4:NO3 deficit ratio over the shelf is in distinct contrast to the higher ratios found off-shelf. As Fe limitation is known to restrict the uptake of silicate by diatoms (Franck et al., 2000), it seems likely that the increased production observed over the shelf takes place in Fe-replete conditions.

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4.3. Connections with the Georgia Basin and the APF The ACC approaches the southern shelf from the southwest and diverges between T4 and T5. The northwestward flow at the off-shelf end of T4 continues between South Georgia and Shag Rocks and out into the Georgia Basin (Fig. 2b). In contrast, water flowed rapidly southeastwards over the offshore ends of T5–T9. Previous satellite imagery has also indicated a connection between the blooms of the southern shelf and the northwest and the Georgia Basin (Korb et al., 2004). The OCCAM particle release simulations along T4 also, in part, support this suggestion (Fig. 8). Therefore, it seems likely that a significant proportion of production can be retained over the inner shelf, with timescales suggesting blooms and secondary producers can develop in situ whereas offshelf, water approaching the shelf tends to be diverted, either through the gap in the North Scotia Ridge between South Georgia and Shag Rocks, or to the southeast along the island’s shelf edge. While microplankton at offshore stations fell into one group, zooplankton split into two associated with the divergence of water masses between transects T4 and T5 (Fig. 2b). The differences between these two zooplankton groups had more to do with changes in dominance of a shared species set, rather than more fundamental changes in taxonomic composition, although average abundances of shared taxa were often considerably lower in Gp3 (Table 2). Reasons for this are unclear, although again may be related to physical structure. An abrupt increase in SST was apparent at the end of T4 and along the lengths of all transects on the north coast relative to the south (Fig. 2a). Conservative nutrient values and ratios below the biologically active layers (4160 m) at the offshore end of transect T4 were typical of waters more usually found closer to the APF (British Antarctic Survey unpublished data). Also a broad temperature maximum (42 1C) in the Upper Circumpolar Deep Water suggested water from nearer the APF (cf. Sievers and Nowlin, 1984). Furthermore, altimetry data (not illustrated) indicated a warmcore anticyclonic eddy located just to the west of station T4.7 that we suggest might have shed from the APF further to the west (see http://argo.colorado. edu/realtime/gsfc_global-real-time_ssh/). Thus the zooplankton, rather than being representative of

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waters to the south and west (as we assume Gp4 to be), may to some extent reflect a faunal displacement by water originating from further west. In earlier studies Atkinson et al. (1990) noted the presence of a warm-water intrusion in approximately the same position as the warmer stations at the offshore end of T4, and Whitehouse et al. (1996b) have also observed warm water eddy-like features moving eastwards in the ACC to the south of Shag Rocks during a survey in 1994. Faunally, then, these stations have a closer taxonomic affinity with stations along the north coast transects, which connect with the Georgia Basin. However, a comparable change in community affiliation between T4 and T5 was not apparent in the microplankton ordination, and it may be that nutrient distribution and uptake characteristics on- and off-shelf, were of greater importance in determining taxonomic composition. 5. Conclusion The results of this study indicated that two major production regimes were present in waters to the south of South Georgia. High current flows offshelf suggested that for secondary producers in particular, advection may play a dominant role in bringing production into the region. In contrast, over the shelf, low flows dominated and an extensive southern shelf community was present that appeared to have developed largely in situ. This conclusion was supported by the extensive nutrient depletions observed over the shelf, and low current speeds, and output from a particle-tracking model, all of which indicated water retention, particularly over the inner shelf. The particle-tracking model indicated shelf retention timescales of X3 months for a significant proportion of particles released along T4. Additionally, gradual transport from parts of the southern shelf to the north of the island suggested that locally derived production may periodically contribute significantly to the high biomass and production often present within the Georgia Basin downstream of the island. Acknowledgements We thank the officers and crew onboard RRS James Clark Ross for ensuring the successful completion of cruise JR116. We acknowledge our colleagues who participated in the cruise planning and the collection and analysis of various data sets

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throughout the cruise. Peter Rothery is thanked for assistance with the autocorrelation analysis. We also thank researchers at the National Oceanography Centre for the supply of OCCAM data and the referees for their critical reading of earlier drafts of this paper. This paper forms a contribution to the BAS ‘Discovery 2010’ Foodwebs project.

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