Ozone depletion and climate change are causing the Southern Annular Mode (SAM) to become increasingly positive, driving stronger winds southward in the Southern Ocean (SO), with likely effects on phytoplankton habitat due to possible changes in ocean mixing, nutrient upwelling, and sea ice characteristics. This study examined the effect of the SAM and 12 other environmental variables on the abundance of siliceous and calcareous phytoplankton in the seasonal ice zone (SIZ) of the SO. A total of 52 surface-water samples were collected during repeat resupply voyages between Hobart, Australia, and Dumont d'Urville, Antarctica, centred around longitude 142
Phytoplankton are the primary producers that feed almost all life in the oceans. In the Southern Ocean (SO), defined as the southern portions of the Atlantic Ocean, Indian Ocean, and Pacific Ocean south of 60
Global standing stocks of phytoplankton are estimated to have been declining by as much as 1 % per year, a decline largely attributed to rising surface ocean temperature
The Antarctic SIZ is one of the most productive parts of the SO
In winter, phytoplankton growth is limited by light availability and temperature. In spring and summer, phytoplankton can proliferate in the high-light, high-nutrient waters that trail the southward retreat of sea ice (Fig.
The Southern Annular Mode (SAM), which is also variously also called the High-Latitude Mode and the Antarctic Oscillation, is well-represented by two alternative definitions: (a) the normalised zonal mean sea-level pressure at 40
There was a trend toward more positive SAM from 1979 to 2017 of 0.011 index points per year
A more positive SAM indicates the occurrence of a strengthening circumpolar vortex
By modulating upwelling, ocean mixed depth, air temperature, and sea ice characteristics and duration, it is likely that a more positive SAM will affect the composition and abundance of phytoplankton in the SIZ of the SO.
Based on the predicted and observed positive relationships between the SAM and phytoplankton standing stocks and productivity in the SIZ of the SO, we hypothesised that changes in the SAM could also elicit changes in the composition of the phytoplankton community. To test this hypothesis, we conducted a scanning electron microscopic survey of hard-shelled phytoplankton in surface waters of the Antarctic SIZ using samples collected between October and February each spring–summer over 11 consecutive years (2002–2003 to 2012–2013). We then related the composition of these communities to environmental variables including the SAM.
Example of phytoplankton identification on a single SEM image, representing 0.0348 mL of seawater. Overlying letters are taxa codes for individual phytoplankton taxa considered in the analysis (listed in Table
A total of 52 surface-water samples were collected from the seasonal ice zone (SIZ) of the Southern Ocean (SO) across 11 consecutive austral spring–summers from 2002–2003 to 2012–2013. The samples were collected aboard the French re-supply vessel MV L'Astrolabe during resupply voyages between Hobart, Australia, and Dumont d'Urville, Antarctica, between 20 October and 28 February. Most samples were collected from ice-free water, although some were collected south of the receding ice edge (Fig.
The sampled area was in the Indian sector of the SO, spanning 270 km of latitude between 62 and 64.5
Samples were obtained from the clean seawater line of the re-supply vessel from around 3 m depth. Each sample represented 250 mL of seawater filtered through a 25 mm diameter polycarbonate-membrane filter with 0.8
The composition of the phytoplankton community of each sample was determined from
A total of 48 phytoplankton taxa were identified, many to species level. Because the diatoms
Phytoplankton abundance data were converted to relative abundance by dividing each value by the total abundance of the 22 taxa groups in the sample. This was to alleviate any variation among samples resulting from dilution, a phenomenon whereby the abundance of cells in surface waters can be reduced in a matter of hours by an abrupt increase in wind speed and associated increase in the mixed layer depth
Variance in phytoplankton community composition explained by the SAM, versus timing and length of the averaged range of daily SAM values. Response surfaces relate the fraction of total variance in phytoplankton community composition attributable to the SAM, versus the number of days in the range of the averaged daily SAM (vertical axis) and the timing of the centre of the range of the averaged daily SAM (horizontal axis). The horizontal axis is expressed as
Phytoplankton abundances were related to a range of environmental covariates available at the time of sampling. These included the SAM, sea surface temperature (SST), salinity (
We obtained daily estimates of the SAM from the US NWS Climate Prediction Center
Three statistical analyses were undertaken to explore the hypothesis: (i) constrained analysis of principal coordinates (CAP,
For CAP and cluster analysis, relative abundance data were square-root-transformed to reduce possible dominance of the analysis by a few abundant taxa. The Bray–Curtis dissimilarity index
Variance in the community composition of 22 phytoplankton taxa groups attributable to constraining environmental covariables in the CAP analysis.
CAP was applied to the Bray–Curtis resemblance matrix to partition total variance in community composition into unconstrained and constrained components, with the latter representing the variation due to the environmental covariates. CAP is an example of a constrained ordination method in which the typical sample–species matrix of abundances (as used in redundancy analysis) is replaced with a symmetric matrix of pairwise sample similarities. The advantage of this distance-based approach to redundancy analysis is that any ecologically relevant distance measure may be used; here we use the Bray–Curtis metric because it discounts joint absences between samples when determining similarity. A forward selection strategy was used to choose the optimum model containing the minimum subset of constraints required to explain the most variation in phytoplankton community structure
Hierarchical agglomerative clustering based on average linkage was performed on the Bray–Curtis resemblance matrix. Significant differences among sample clusters were determined according to the similarity profile (SIMPROF) permutation method of
Pair-wise correlation analyses were performed using Pearson's correlation coefficient
Continued.
Response surfaces were used to display the variance explained from individual CAP analyses according to the number of days averaged, and the mid-point (or lagged mid-point) of the range of days averaged, for each aggregated SAM index. These allowed identification of maxima in correlation between the SAM and phytoplankton community structure. Response surfaces were derived by evaluating separate CAP analyses for each combination of (i) the temporal positioning of the daily-SAM averaging range and (ii) the length of the daily-SAM averaging range. In constructing the response surfaces, the range of the averaged daily SAM was centred on (i) each calendar day individually (1 January–31 December) through the year associated with each sample, and alternatively (ii) relative to the time of sampling and lagged from 1 to 365 d prior to each sample collection date, in 1 d increments. The length of the SAM averaging range was varied in 2 d increments from zero to plus and minus 182 d from the centre of the range. Similar response surfaces were constructed relating the correlation between the averaged daily SAM and (i) total chlorophyll and (ii) [
Identified taxa groups: taxa, taxa code, cells counted, cells measured, average individual cell volume, abundance (average, minimum, and maximum), average relative abundance, average total volume, average relative volume, and percentage of samples in which each taxa group was identified.
Data management and manipulation, summary statistics, correlation analyses, and scatter plots were undertaken in Microsoft Excel (2016) and R
Maxima of SAM influence on phytoplankton community composition. SAM
Scatter-plots:
CAP analysis and pairwise correlation analysis both indicated the presence of a relationship between the SAM and phytoplankton community composition. Clustering analysis showed there to be sufficient and systematic variation in phytoplankton community composition between samples that samples could be grouped.
Empirical identification of the time between variation in the SAM and the manifestation of this variation in the phytoplankton community structure revealed three maxima in phytoplankton community composition explained by the SAM. The first of the maxima was an autumn seasonal SAM index (SAM
The optimum CAP model contained four covariates that explained the variance in phytoplankton community composition among samples (Table
In total, 15 of the 22 taxa groups showed significant pairwise correlations (
SAM
In the optimum multi-covariate CAP model,
A total of 10 taxa groups showed significant correlation (
Following cluster analysis, similarity profile (SIMPROF) permutation analysis identified seven significantly different groups (
Other considered environmental covariates that did not significantly influence community composition were the time of the day that a sample was collected and the minimum latitude reached by sea ice cover in the previous winter (Supplement Table S1).
These analyses were also undertaken using phytoplankton absolute abundances rather than with relative abundances as reported above. The analysis of absolute abundance showed similar temporal peaks in variance explained (Supplement Fig. S4), although it explained less variance (SAM
Two indicators of the influence of the SAM on phytoplankton productivity were obtained: (i) the influence of the SAM on satellite-derived total chlorophyll and (ii) the influence of the SAM on macronutrient concentrations, indicating nutrient drawdown associated with productivity. Using the times and locations of the 52 samples over the 11 years of our study, satellite-derived total chlorophyll showed positive correlation with all SAM indices:
The observed concentrations of the macronutrients
Abundance of individual taxa groups averaged 133 cells per millilitre and ranged to a maximum of 8796 cells per mL (Table
Our results show that the SAM shows a relationship with the community composition of phytoplankton in the seasonal ice zone (SIZ) of the Southern Ocean (SO). This conclusion was supported by a combination of three analyses. (i) Permutation-based analyses of cluster structure demonstrated that the 52 samples were separable into seven statistically different groups on the basis of community abundance composition of the 22 taxa groups (Fig.
The derived SAM index with greatest influence on phytoplankton community composition, SAM
Extending the spring–summer productive season by delaying the autumn consolidation of sea ice may result in more prolonged declines in relative abundance of taxa that are more prolific earlier in the spring–summer and may thus reduce the population from which the following post-winter bloom is initiated. Of the eight taxa groups showing statistically higher relative abundance earlier in the spring–summer (
Two other derived SAM indices were found to influence phytoplankton: SAM
We expected the SAM prior to sampling (SAM
Nothing has been previously reported with respect to the climatic preferences of the majority of taxa identified in this study, and only 10 of the 22 taxa groups considered in our research had data records in the Ocean Biogeographic Information System (OBIS, 2020). Some of the observed taxa have been reported to show various relationships with environmental factors, including sea-surface temperature, time through the season, and latitude, but often at the taxonomic level of genera rather than at a species level
A third of analysed taxa, comprising 7 taxa and 23 % of all counted cells, showed no detectable relationship with the SAM. This could be due to large errors associated with low counts of rarer taxa, because unaccounted variation was masking any relationship, or because the taxa were insensitive to the SAM. There is less chance of detecting relationships between taxa and environment variables when fewer individuals are counted; however, some less represented taxa did show relationships with SAM indices (e.g.
This is the first study to show a link between variation in the SAM and the composition of phytoplankton communities in the SO, although similar findings have been reported for other major climatic phenomena in other parts of the globe. The climatically similar Northern Hemisphere Annular Mode (NAM) causes increased westerly winds and deeper mixed layers at middle to high northern latitudes in its positive phase
Phytoplankton are the pastures of the oceans and it is plausible that the climate in both autumn and spring influence the phytoplankton community composition of phytoplankton and their ecological progression through the productive spring–summer period in the SIZ. Climate change impacts have now been documented across every type of ecosystem on Earth
A positive SAM has previously been shown to be associated with increased standing stocks and productivity of phytoplankton in the SIZ of the SO
The observed positive relationship between total chlorophyll and all the SAM indices (
The SIZ is a productive region of the SO
The SAM is predicted to become increasingly positive in the future
Statistical analyses indicated that, together, the autumn and spring SAM explained a higher percentage (17.9 %) of the variation in phytoplankton community composition than any variable, mostly due to the autumn SAM (up to 13.3 %). In total this exceeded the variance explained by any other variable, even that attributable to the time of the season that the sample was collected (15.4 %) or other critical physical variables such as temperature, salinity, and latitude. Furthermore, 15 of the 22 phytoplankton taxa identified in this study showed significant correlation with the SAM and there were indications that a more positive SAM was related to increased phytoplankton productivity in the SIZ. While this study was limited in both timespan (11 austral spring–summers) and the overall variance in phytoplankton composition explained by all the constraining variables (37.5 %), it suggests that the phytoplankton of the SIZ are indeed sensitive to changes in the SAM and thus possibly responsive to climate change.
The dataset used in this paper is available at
The supplement related to this article is available online at:
Author contributions. BLG contributed to conceptualisation, data curation, formal analysis, investigation, methodology, software, and supervision, validation, visualisation, writing of the original draft, writing and review, and editing. ATD contributed to conceptualisation, funding acquisition, formal analysis, methodology, project administration, resources, supervision, writing and review, and editing. ADF contributed to formal analysis, methodology, resources, writing and review, and editing. JPM contributed to formal analysis, methodology, software, writing and review, and editing. AM contributed to project administration, supervision, writing and review, and editing. AMcM contributed to funding acquisition, project administration, resources, writing and review, and editing. SWM contributed to conceptualisation, funding acquisition, formal analysis, writing and review, and editing.
The authors declare that they have no conflict of interest.
Sampling on Astrolabe was supported by a French–Australian research collaboration. The Institut Polaire Français Paul-Émile-Victor supported access to the ship and field operations. The biogeochemical data collection was coordinated by Alain Poisson and Nicolas Metzl, Sorbonne Université, and Bronte Tilbrook, CSIRO Oceans and Atmosphere. Steve Rintoul (CSIRO) and Rose Morrow (LEGOS) coordinated the collection of salinity and temperature data. The Antarctic Climate and Ecosystems CRC and the Integrated Marine Observing System are thanked for supporting the operation of sensors, the collection of water samples, and nutrient analyses reported in this study. Alan Poole, Matt Sherlock, John Akl, Kate Berry, Lesley Clementson, Brian Griffiths (CSIRO), Rick van den Enden, Rob Johnson (AAD), and the many dedicated volunteers and ships’ officers and crew are thanked for their important contributions to the field efforts and data management. We thank the University of Tasmania and the Australian Antarctic Division for the space and resources needed to undertake this work. Thanks to Nathaniel Bindoff and Simon Wotherspoon for their consideration of parts of the paper. Thanks are due to the reviewer, Damiano Righetti, for the valuable input he provided, in particular for pointing out ambiguities and small errors and improving the clarity of the paper, and an anonymous reviewer for the structural and theoretical considerations. Total chlorophyll data used in this paper were produced with the Giovanni online data system, developed and maintained by the NASA GES DISC.
This research has been supported by the University of Tasmania (Institute of Marine and Antarctic Studies), by the Australian Government's Cooperative Research Centre program through the Antarctic Climate and Ecosystems CRC, the Australian Antarctic Division (projects 40 and 4107), and by the Australian Research Council's Special Research Initiative for Antarctic Gateway Partnership (project no. SR140300001).
This paper was edited by Julia Uitz and reviewed by Damiano Righetti and one anonymous referee.