At the beginning of November in S1, Ryder Bay was completely covered by fast ice which rapidly declined to 10% by mid-December (Fig. 2a). For the remainder of S1, brash ice was the main ice type with gradual declining cover (from 80 to 10%) until mid-January after which it remained relatively low but with considerable variability. At the beginning of S2, grease ice was the dominated ice type (up to 100% cover), but ice cover rapidly changed to pack ice and declined in extent to 10% by 17th November. Following this, brash ice took over and cover increased to 100% (Fig. 2b). A short period of pack ice cover was observed in mid-December, returning into brash ice as ice cover rapidly declined during the second half of December. For the remainder of the season, brash ice remained generally low but variable (10–80%).
Stratification increased steadily at the beginning of December in both seasons (Fig. 2c) accompanied by a shallowing of the MLD (Fig. 2d). Stratification peaked at the end of December (1544 in S1 and 1264 J m−2 in S2) and then declined during the first half of January in both seasons. In S2, stratification increased again and remained high until 10th February. This difference in stratification between the two seasons was apparent in MLD which was deeper in S1 at 16 ± 8 m (n = 3) compared to 2 ± 1 m (n = 3) during the same period of S2. Stratification levels were weak for both seasons in March and April, and MLD were relatively high ranging from 20 to 40 m.
In both seasons, seawater temperature steadily increased from − 1.7 °C in November to 0 °C during early January (Fig. 3a). Temperature during S1 peaked at 1.9 °C on 16th January and remained around 1.2 °C for 1 month before gradually declining to below 0 °C in April. In S2, seawater temperature peaked later in the season (1.4 °C on 5th February) and declined to < 0 °C in early February, followed by a gradual decrease to around − 1.1 °C in April. Compared to S1, average temperature during S2 was ~ 2.5-fold lower (0.36 ± 1 vs. − 0.45 ± 0.6 °C, n = 39 and 50, respectively), with the strongest difference during bloom period B2 (1.09 ± 0.62 vs. 0.09 ± 0.63 °C, n = 13 and 16, respectively) and B3 (half February to April; 0.64 ± 0.49 vs. − 0.55 ± 0.40 °C, n = 15 and 11, respectively). Salinity gradually declined until January during S1 (Fig. 3b), whereas in S2, a rapid drop was observed during January, and values remained low for 3 weeks before stabilising at ~ 33.0 psu by March, slightly lower than the salinity of 33.2 psu in S1 over this period. PAR levels were the highest at the start of both seasons (Fig. 3c), declining thereafter to potentially growth-limiting levels (as indicated by Zeu/15 m; Fig. 3d) and reaching values as low as 0.3–0.4 µmol quanta m−2 s−1 by the second half of December. During the B2 bloom period of S2, PAR was generally low (17 ± 23 µmol quanta m−2 s−1, n = 15) and had a higher potential for light limitation compared to the same period in S1. Conversely, during the bloom period B3 of S1, PAR levels (< 3 µmol quanta m−2 s−1) were much lower compared to S2.
Phosphate and nitrate in S1 were drawn down from concentrations of 1.7 and 28 µM on 8th Dec to 0.17 and 1.3 µM, respectively, at the start of January, before rising again until mid-February (Online Resource 3). In S2, the initial drawdowns of phosphate and nitrate were more gradual and variable than S1, lasting until the beginning of February and reaching 0.04 and 0.03 µM, respectively. A second large drawdown of phosphate was observed during S1, declining to 0.06 µM on 12th March. Ammonium concentrations were initially low in both seasons (~ 0.35 µM) and increased to 5 µM on 5th February in S1 and 2.6 µM on 17th February in S2 (Online Resource 3). Silicate concentrations remained replete over the observation period of both seasons (Online Resource 3), as were concentrations of dissolved iron (DFe, Bown et al. 2017).
Chlorophyll-a and taxonomic groups
The mean Chl-a concentration in S1 (4.3 ± 4.4 µg L−1, n = 51) was 1.6-fold higher than in S2 (2.7 ± 2.2 µg L−1, n = 50, Fig. 4). This was due to the S1 phytoplankton blooms (i.e. peaks in Chl-a) during bloom periods B1 and B3 (12.3 and 16.1 µg Chl-a L−1, respectively). In contrast, the B2 period in S2 displayed a higher mean Chl-a concentration than that in S1, i.e. 4.6 ± 1.7 and 2.0 ± 1.5 µg L−1, n = 17 and 16, respectively. Diatoms (Bacillariophyceae) often dominated total Chl-a both during S1 and S2 (Fig. 4c, d), but at the start of S2 (NB period), Prymnesiophyceae and Prasinophyceae both dominated, and during B2 in S1, Cryptophyceae contributed most to total Chl-a concentration. Furthermore, at the start of B3 (second half of February) for both S1 and S2 the Cryptophyceae and Prymnesiophyceae co-dominated, while towards the end of B3 in S2, Prasinophyceae were most important. Contributions to Chl-a by Chlorophyceae and Dinophyceae were marginal over both seasons of this study.
Size-fractionated Chl-a shows that initially during the NB period when total Chl-a concentrations were still low (Fig. 4), the phytoplankton community was mostly < 20 µm (Online Resource 4). Temporal Chl-a size fraction dynamics in S1 revealed a shift from larger-sized (> 20 µm) phytoplankton during the first bloom period B1 to smaller-sized phytoplankton during the remainder of the season (up to 89% of total Chl-a in B3; Online Resource 4a). In contrast, relatively smaller (< 20 µm) cells contributed most to phytoplankton biomass during the initial increase during B1 in S2 and larger cells dominating during B2 (Online Resource 4b). Taxonomic analysis of fractionated Chl-a revealed that diatoms and Cryptophyceae were present in all the three size fractions. Prymnesiophyceae were present largely in the < 5 µm fraction but were also observed in the > 20 µm fraction, during March (B3) in S1 and late January to beginning of February in S2, presumably indicative for P. antarctica colonies (confirmed by light microscopy, data not shown).
Canonical correspondence analysis (CCA) was used to investigate the relationship between taxonomic composition (in Chl-a concentration) and the environmental variables with both S1 and S2 combined. The eigenvalues (obtained from the model output; Online Resource 5) indicated that the main environmental variables contributing to the formation of the axes explained 35% of total variation in the dataset (23 and 11% for first and second axes, respectively). These variables were temperature (p < 0.001, n = 101), Zeu/15 m (p < 0.001, n = 101), salinity (p = 0.058, n = 101), phosphate (p = 0.005, n = 101) and nitrate (p = 0.039, n = 101, Fig. 5a). Overall, higher concentrations of Cryptophyceae were associated with high temperature (> 1 °C) and Prymnesiophyceae with relatively high Zeu/15 m and low salinity. More specifically, the B2 data points in S1 clustered with high temperature and Cryptophyceae Chl-a, which were both highest during this period.
Phytoplankton abundance (and taxonomy)
The 10 phytoplankton clusters distinguished by FCM (Fig. 6) could be partially characterized (taxonomically). Temporal dynamics of Phyto III matched well with the dynamics of Prymnesiophyceae and light microscopy confirmed co-occurrence of P. antarctica single cells at times of high Phyto III abundances. Phyto III cell size (range 2–5 µm in diameter) corresponds to that of solitary P. antarctica cells (between 2 and 6 µm, Schoemann et al. 2005; Lee et al. 2016). Phyto IV dynamics compared well to Cryptophyceae Chl-a (< 20 µm, Online Resource 6) and was identified as a cryptophyte group based on high orange (phycoerythrin) autofluorescence (Li and Dickie 2001) in combination with microscopic identification during times of high abundance. Phyto V-X are most likely diatoms, based on the good comparison of relative carbon contribution of individual phytoplankton groups with CHEMTAX analysis of specific Chl-a size fractions (at times when large group-specific carbon contributions were observed). The picoeukaryotes (Phyto I and II) could not be classified due to their small cell size and limited contribution to FCM-C.
Similar to the average Chl-a concentration, mean phytoplankton abundance in S1 was higher than that in S2 (5.5 ± 3.0 vs. 3.7 ± 2.2 × 103 cells mL−1, n = 40 and 51, respectively, Online Resource 7). Overall, the contribution of nano-sized phytoplankton (Phyto IV–X) was 1.5 times higher in S1 compared to S2 (53 ± 24 and 35 ± 16%, n = 40 and 51, respectively), with Phyto IV, VI and IX dominating in S1 (13 ± 17, 24 ± 16 and 9 ± 16%, n = 40, respectively) and Phyto V in S2 (17 ± 13%, n = 51). All 10 phytoplankton clusters distinguished by FCM showed strong temporal dynamics with distinct differences between seasons (Fig. 6). At the start of the season during the NB period, the picoeukaryotic phytoplankton Phyto I and II were most abundant in S1 (45 ± 4% and 22 ± 3%, n = 3; Fig. 6a–c) and equally abundant to Phaeocystis Phyto III in S2 (25 ± 20, 26 ± 13, and 25 ± 14%, respectively, n = 5; Online Resource 8). Phyto I–III persisted during B1 in S1 (24 ± 19, 31 ± 11 and 15 ± 8%, respectively, n = 9), while diatom Phyto V bloomed during this period in S2 (30 ± 10%, n = 15; Fig. 6e). Phyto IX (diatom) became also abundant during B1 of both seasons, contributing up to 18% in S1 and 45% in S2 (Fig. 6i and Online Resource 8). The B2 period of S1 was dominated by cryptophytes (Phyto IV 26 ± 21%, n = 16; Fig. 6d and Online Resource 7) and diatom Phyto VI (26 ± 14%, n = 16; Fig. 6f), whereas Phaeocystis Phyto III and diatom Phyto V had the largest share in S2 (51 ± 14% and 16 ± 7%, respectively, n = 17; Fig. 6c, e). During the initial stage of the large phytoplankton bloom in B3 of S1 (Fig. 4a), P. antarctica and diatom group Phyto VI were most abundant (contributing up to 23 and 56% of total abundances, respectively; Fig. 6c, f, Online Resource 8). The larger diatom group Phyto IX (11.5 ± 3 µm, n = 15) succeeded, making up to 64% of total abundance (Fig. 6i). Unlike S1, total phytoplankton abundances during B3 in S2 were relatively low (mean 5.1 ± 3.4 and 1.9 ± 1.2 × 103 mL−1, n = 40 and 51, respectively) and mostly dominated by Phyto III (44 ± 24%, n = 51).
The CCA of FCM abundances revealed that the models shown in Fig. 5b, c explained 66% of the variation in the dataset of S1 (33% and 25% by first two axes) and 71% of S2 (35 and 22% by first two axes explained). Contributing to the formation of both axes during both seasons were ice type (S1 p < 0.001, n = 40; S2 p = 0.010, n = 51), temperature (S1 p < 0.001, n = 40; S2 p < 0.001, n = 51), PAR (S1 p < 0.001, n = 40; S2 p = 0.008, n = 51) and salinity (S1 p < 0.001, n = 40; S2 p < 0.001, n = 51), followed by phosphate (S1 p < 0.001, n = 40; S2 p < 0.001, n = 51), and also for S1 nitrate (S1 p < 0.001, n = 40) and S2 Zeu/15 m (S2 p < 0.001, n = 51). Phyto I, II, VIII and X in S1 were associated with fast ice, as well as high salinity and low temperature. In S2, ice type (grease ice) was again associated with Phyto I and II, but also diatoms Phyto VI and VIII. Additionally (in S2), these phytoplankton clusters were associated with high salinity, PAR, Zeu/15 m and phosphate. Phyto X in S2 was, together with other diatom clusters, Phyto V, VII and IX, associated with low PAR and low Zeu/15 m conditions (Fig. 5c). Phyto IX in S1 was also correlated with low PAR and low phosphate concentrations, the predominant environmental conditions during B3 in S1 (open triangles in Fig. 5b). Phyto IV (cryptophytes) in S1 was associated with high temperature, PAR and phosphate conditions (as for example found during B2 in S1; open circles Fig. 5b). In S2, these cryptophytes, as well as Phaeocystis (Phyto III), were similarly associated with high temperature and high PAR, but also low salinity and low phosphate (Fig. 5c).
Considering the variation in cell sizes, we converted both phytoplankton abundances and Chl-a to cellular carbon for a more detailed comparison (Fig. 7a, b). As to be expected, the larger-sized phytoplankton clusters made up most of the cellular carbon (FCM-C) during both seasons (Fig. 8a, b), despite occasional low abundances (e.g. diatom Phyto X during March in S2; Fig. 6j). Due to size restrictions of FCM analysis (reliable acquisition is limited to cells around 20 µm in diameter, based on the laser beam width), we focused our comparison of FCM-C with CHEMTAX-C on the ≤ 20 µm size fraction. The < 5 µm and 5–20 µm fractions showed generally good agreement for both seasons (Fig. 7c–f). The few discrepancies were related to FCM population-specific variations in cell diameter over time, whereby the actual diameter of the dominant population, at that specific moment in time, was larger than the mean. A larger diameter (compared to the mean) combined with high abundance resulted in observably lower FCM carbon estimations when using mean cell size compared to the actual cell size at the time.
When taxonomic composition was expressed in Chl-C, 42% of variation was explained by the CCA model shown in Fig. 9a (40 and 1% by first 2 axes). In particular, Cryptophyceae were associated with the main environmental variables contributing to the formation of the two primary axes, i.e. higher temperature (p < 0.001, n = 101), a reduced potential for light limitation (higher Zeu/15 m, p < 0.001, n = 101) and higher phosphate concentrations (p = 0.002, n = 101). The CCA based on FCM-C in S1 showed similar clear clustering of cryptophytes Phyto IV with B2 environmental data points when temperature was highest (Fig. 9b). Besides temperature, Phyto IV also correlated with higher PAR in both S1 and S2 (Fig. 9b, c). The FCM-C-based CCA showed that the prymnesiophytes and Phyto III also related strongly to higher PAR in S1 and high PAR, high Zeu/15 m, and relatively low salinity in S2. The different diatoms did not display similar relationships based on FCM-C. Phyto V, VII, and VIII were all associated with high PAR in S1, and ice type especially in S2 (Phyto VI and VIII with grease and Phyto V and VII with pack ice; Fig. 9b, c). With the FCM-C-based CCA model, 42% of variation in the dataset of S1 was explained by the environmental variables with the first 2 axes explaining 31 and 9%, respectively. During S2, the CCA model explained even more, i.e. 65% of total variation in FCM-C with the first two axes explaining 40 and 16% of variation, respectively.