Broad-scale predictability of carbohydrates and exopolymers in Antarctic and Arctic sea ice

Abstract

Sea ice can contain high concentrations of dissolved organic carbon (DOC), much of which is carbohydrate-rich extracellular polymeric substances (EPS) produced by microalgae and bacteria inhabiting the ice. Here we report the concentrations of dissolved carbohydrates (dCHO) and dissolved EPS (dEPS) in relation to algal standing stock [estimated by chlorophyll (Chl) a concentrations] in sea ice from six locations in the Southern and Arctic Oceans. Concentrations varied substantially within and between sampling sites, reflecting local ice conditions and biological content. However, combining all data revealed robust statistical relationships between dCHO concentrations and the concentrations of different dEPS fractions, Chl a, and DOC. These relationships were true for whole ice cores, bottom ice (biomass rich) sections, and colder surface ice. The distribution of dEPS was strongly correlated to algal biomass, with the highest concentrations of both dEPS and non-EPS carbohydrates in the bottom horizons of the ice. Complex EPS was more prevalent in colder surface sea ice horizons. Predictive models (validated against independent data) were derived to enable the estimation of dCHO concentrations from data on ice thickness, salinity, and vertical position in core. When Chl a data were included a higher level of prediction was obtained. The consistent patterns reflected in these relationships provide a strong basis for including estimates of regional and seasonal carbohydrate and dEPS carbon budgets in coupled physical-biogeochemical models, across different types of sea ice from both polar regions.

Keywords: algae; biogeochemistry; global relationships; microbial.

Fig. 1.

Representation of the molecular-size spectrum from large polysaccharides to low molecular-weight components of the total DOC pool (<0.2 µm) in melted sea ice, and partitioning of DOC into dCHO, dUA (by dialysis >8 kDa), dEPS, complex dEPS, and non-EPS carbohydrate fractions (by alcohol precipitation). Dotted boxes indicate a subcomponent of the main category.

Fig. 2.

Concentrations of (A) dCHO, (B) dUA, (C) dEPS, and (D) dCHO_non-EPS, (E) Chl a, and (F) DOC in melted sea ice cores from four Antarctic (ISPOL, WWOS, SIPEX, PRYDZ) and two Arctic (RES10, RES11) datasets obtained between 2004 and 2011. Box plots [median, 25th and 75th (box), 10th and 90th (error bars) percentiles, and outliers] and mean value (dotted line). Significant differences between mean values (nested GLM, P < 0.001) indicated by different letter codes (a, b, c; no significant differences between data with same letter code).

Fig. 3.

Relationship between concentrations (µmol C L⁻¹) of dCHO and (A) dUA, (B) dEPS, (C) the percent contribution of non-EPS carbohydrates to overall dCHO (%dCHO_non-EPS), (D) the percent contribution of complex EPS (%dEPS_complex), and (E) Chl a, in melted ice cores from four Antarctic (ISPOL, WWOS, SIPEX, PRYDZ) and two Arctic (RES10, RES11) datasets obtained between 2004 and 2011. Concentration data are log (n + 1) -transformed and percent data arcsin-transformed. Best-fit linear regression line (solid), error (dotted), and 95% confidence limits of regression (red lines) are shown for each relationship; regression details are in Table S4.

Fig. 4.

Ice core carbohydrate concentrations predicted from models based on (A) ice core length, relative position in core and core salinity (physical), and (B) physical (bulk salinity, core length) and Chl a data, derived from a combined dataset of Antarctic and Arctic sea ice cores, compared with the measured concentrations (log n + 1) of dCHO in the source ice core samples, and in two independent validation datasets (12, 35). Best-fit linear regression (solid), error (dots) and 95% confidence limits of regression (dashed) lines shown, and regression details are in Table S6.