The Arctic Ocean (AO) is changing profoundly, with drastic reductions in both sea-ice area and thickness (Kwok, 2018; Stroeve and Notz, 2018). Since 1979, the summer sea-ice extent has declined by >40%, with first-year ice replacing the once prevalent multiyear ice pack (AMAP, 2017). Sea-ice melt is beginning earlier in the year while freeze-up is delayed; consequently, more solar radiation is reaching the upper ocean now than it has in the past (Nicolaus et al., 2012; Arndt and Nicolaus, 2014; Katlein et al., 2019). Such modifications result in a substantially thinner sea-ice cover that is more prone to deformation and melting, favoring the formation of leads and melt ponds, respectively, and ultimately leading to increases in light transmission to the under-ice environment.
Because snow and sea ice strongly attenuate solar radiation, the growth of phytoplankton at high latitudes has long been thought to begin in the open water along sea-ice edges during the spring, when solar elevation increases and sea-ice melt stabilizes the upper water column (Wassmann et al., 1991; Sakshaug, 2004). However, changing sea-ice dynamics (e.g., extent, age and thickness) are challenging our current understanding of Arctic phytoplankton phenology (Kahru et al., 2010; Ardyna et al., 2014; Renaut et al., 2018), including the recent recognition of large under-ice blooms (UIBs). Here we define a UIB as a phytoplankton bloom that starts and develops beneath a significant concentration of sea ice (generally >50%). These blooms are distinct from sea-ice algae that become detached from the bottom sea ice (Mundy et al., 2014) and from blooms at the sea-ice edge that are advected under the sea ice (Johnsen et al., 2018). UIBs have been documented across the Arctic and are now accepted as prevalent phenological phytoplankton features over much of the Arctic Ocean (e.g., Yager et al., 2001; Fortier et al., 2002; Arrigo et al., 2012, 2014; Mundy et al., 2014; Assmy et al., 2017; Hill et al., 2018; Oziel et al., 2019). However, the main ecological drivers behind the initiation, structure and productivity of UIBs and their spatial distribution and interannual variability remain to be assessed at a pan-Arctic scale.
A complex mosaic of multiple bottom-up (i.e., light, nutrients, stratification and water temperature) and top-down drivers (i.e., zooplankton grazing) in the Arctic is suspected to shape the unique ecological niches of UIBs. The amount of light transmitted through the sea ice, which depends on snow and sea-ice thickness, and on the presence of melt-ponds and leads, appears to be critical for UIB initiation (Arrigo et al., 2012, 2014; Assmy et al., 2017; Oziel et al., 2019). Large differences in UIB phytoplankton assemblages have also been reported in the AO, from massive diatom UIBs along the Chukchi shelf break (Arrigo et al., 2012, 2014; Laney and Sosik, 2014) to Phaeocystis pouchetii UIBs north of Svalbard (Assmy et al., 2017). Such differences in UIB phytoplankton assemblages are not yet fully understood and might have important repercussions on productivity, carbon export, and food web dynamics.
Within this context, and given the ongoing changes in the Arctic icescape, an integrative ecological understanding of UIBs is needed. Here, we investigated the environmental and ecological processes responsible for driving UIB initiation, magnitude, and assemblages in the AO. To achieve an integrated understanding of UIBs, observations from a total of 11 expeditions by seven major international Arctic projects were synthesized for the first time, spanning spring sea-ice-covered and summer sea-ice-free conditions (Figure 1 and Table 1). Based on this large dataset, the goals of this study were to 1) delineate the environmental drivers (in both space and time) governing the occurrence of UIBs, 2) examine the assemblage diversity of UIBs and 3) consider the potential roles of UIBs in Arctic biogeochemical cycles and impacts on both pelagic and benthic Arctic ecosystems.
|CASES||Amundsen 2004||Beaufort Sea||Brugel et al. (2009); Tremblay et al. (2008)|
|CFL||Amundsen 2008||Beaufort Sea||Blais et al. (2017); Mundy et al. (2009)|
|Green Edge||Sea-Ice Camp 2015||Baffin Bay||Oziel et al. (2019); Randelhoff et al. (2019); Massicotte et al. (2020)|
|Sea-Ice Camp 2016|
|ICESCAPE||Healy 2010||Chukchi Sea||Arrigo et al. (2012, 2014)|
|N-ICE||RV Lance 2015||Nansen Basin||Assmy et al. (2017)|
|Resolute||Sea-Ice camp 2010||Canadian Archipelago||Mundy et al. (2014)|
|Sea-Ice camp 2011|
|SUBICE||Healy 2014||Chukchi Sea||Arrigo et al. (2017)|
A comprehensive dataset was compiled from 11 Arctic Ocean expeditions, including cruises and sea-ice camps (Figure 1 and Table 1). This large dataset consists of 756 stations sampled between 2004 and 2016. Compared to other existing biological datasets from the Arctic Ocean (Ardyna et al., 2013; Matrai et al., 2013), this new compilation is unique, as it spans the sea-ice-covered spring and sea-ice-free summer periods and includes a diverse array of optical (e.g., under sea-ice daily vertical light profiles), hydrological (i.e., physical properties), biogeochemical (nutrient inventories) and biological (phytoplankton pigment signatures, abundance, biovolume and taxonomic composition) data.
Light measurements were compiled only for datasets collected during sea-ice camps (considered as pseudo-eulerian expeditions). Vertical profiles of downwelling irradiance and upwelling radiance were collected using profiling radiometers (Mundy et al., 2014; Oziel et al., 2019). Surface albedo at the Green Edge and Resolute sea-ice camps was measured with a custom-built radiometer (Solalb, developed at Institut des Géosciences de l’Environnement) and a shortwave surface albedo sensor (Kipp & Zonen™ CNR1) (Mundy et al., 2014), respectively. Satellite-derived sea-ice concentrations were retrieved from SSMIS at 25-km resolution available from the National Snow and Ice Data Center (NSIDC, https://nsidc.org; Cavalieri et al., 1996). Satellite-derived sea-ice ages were derived from remotely sensed sea-ice motion and sea-ice extent (NSIDC, https://nsidc.org; Tschudi et al., 2019).
At each station, a conductivity-temperature-depth (CTD) profiler, together with various sensors, was deployed to measure water temperature and salinity, as well as ancillary variables such as dissolved oxygen, nitrate (NO3–), photosynthetically active radiation (PAR), and phytoplankton fluorescence. Concentrations of NO3– plus nitrite (NO2–), phosphate (PO43–) and silicate (Si(OH)4) were also determined at multiple depths for all expeditions. Please refer to the references listed in Table 1 for further details on the methods used (i.e., instruments, calibrations and protocols) for each expedition. It should be noted that only nutrient data were available for the CASES expedition. The winter nutrient concentrations were defined as the maximum values in the first 100 m over the duration of each expedition. The maximum nutrient (NO3–, Si(OH)4, PO43–) drawdowns were then determined as the difference between the winter concentration and the lowest measured concentration in the upper 100 m over the duration of the expedition. Following Newton et al. (2013) and Randelhoff et al. (2019), the “Arctic N-P relationship” (ANP) was calculated to delineate the proportion of Atlantic- versus Pacific-derived waters. Essentially, ANP = 0 means the NO3–PO43– pair falls on the regression line for Atlantic Water, whereas for ANP = 1, the pair falls on the regression line for Pacific-derived water.
Phytoplankton chlorophyll a (chl a) and accessory pigments were measured at multiple depths for all expeditions except the sea-ice camp in Resolute (consisting of a total of 2028 samples), using high-performance liquid chromatography (HPLC). The methods used for Green Edge and SUBICE, including information about the standards, calibration, detection limits and quantification procedures, are described in detail in Coupel et al. (2015). For the ICESCAPE, CFL and Norwegian young sea ICE (N-ICE) expeditions, full details on the methods are provided in Arrigo et al. (2014), Alou-Font et al. (2016), and Kauko et al. (2019), respectively. A list of pigments identified and quantified for this study is provided in Table 2. Note that for the sea-ice camp in Resolute, chl a concentrations were measured using a Turner Designs 10-005R fluorometer, as described in Mundy et al. (2014).
|Chlorophyll a||Chl a||All phytoplankton except Prochlorococcus|
|Chlorophyll b||Chl b||Chlorophytes, prasinophytes, euglenophytes|
|Chlorophyll c1 + c2||Chl c1 + c2||Diatoms, prymnesiophytes, dinoflagellates, cryptophytes, chrysophytes, and raphidophytes|
|Chlorophyll c3||Chl c3||Prymnesiophytes, chrysophytes and dinoflagellates type 2 (lacking peridinin)|
|19’-butanoyloxyfucoxanthin||But-fuco||Prymnesiophytes, chrysophytes, and dinoflagellates type 2 (lacking peridinin)|
|Fucoxanthin||Fuco||Diatoms, prymnesiophytes, chrysophytes, pelagophytes, and dinoflagellates type 2 (lacking peridinin)|
|19’-hexanoyloxyfucoxanthin||Hex-fuco||Major in prymnesiophytes and dinoflagellates type 2 (lacking peridinin)|
|Peridinin||Peri||Dinoflagellates type 1|
|Prasinoxanthin||Pras||Prasinophytes type 3 (A, B)|
CHEMTAX software (Mackey et al., 1996) was utilized to determine the relative abundance of distinct algal groups relative to total chl a from in situ pigment measurements. The CHEMTAX software is based on a factorization program that uses “best guess” ratios of accessory pigments to chl a for each phytoplankton taxon. These ratios are based on marker pigment concentrations of algal groups that are known to be present in the Arctic Ocean (Vidussi et al., 2004; Coupel et al., 2015). The program uses a steepest descent algorithm to obtain the best fit to the data based on assumed ratios of pigment to chl a (for more detail, see Mackey et al., 1996). As CHEMTAX is sensitive to the a priori groupings of the initial ratio matrix (Latasa, 2007), the later version (v1.95) was used to obtain more stable output matrices (Wright et al., 2009). In this CHEMTAX version, the initial matrices are optimized by generating 60 further pigment ratio tables using a random function (RAND in Microsoft Excel) as described in Wright et al. (2009). The results of the six best output matrices (with the smallest residuals, equivalent to 10% of all matrices) were used to calculate the averages of the relative abundance estimates and final pigment ratios.
The choices of the initial pigment ratio and the algal groups (Table 2) were made according to previous chemotaxonomic studies conducted in the Arctic Ocean (Vidussi et al., 2004; Coupel et al., 2015), the Labrador Sea (Fragoso et al., 2017), and the global ocean (Higgins et al., 2011). The algal groups were clustered into nine chemotaxonomic classes (see Table 3). The pigment ratios for dinoflagellates and cryptophytes were selected from Vidussi et al. (2004), diatoms and chryso-pelagophytes from Coupel et al. (2015), the haptophyte Phaeocystis from Antajan et al. (2004), and the others from Higgins et al. (2011).
|Classa||Chl b||Chl c3||Fuco||Peri||Allo||But-fuco||Hex-fuco||Chl c1+2||Neo||Pras||Lut|
|Initial ratio matrixb|
|Final ratio matrixc|
Imaging FlowCytobots (IFCBs) were used in parallel with CHEMTAX for the identification and enumeration of nano- and microphytoplankton (see Olson and Sosik, 2007, and Laney and Sosik, 2014, for the specifications of these instruments) in several expeditions (ICESCAPE, SUBICE and Green Edge). The targeted size range was 1–150 μm, although the image resolution of ~3.4 pixels μm–1 limited the identification of <10 μm cells to broad functional groups. The same method was used for the Green Edge, ICESCAPE and SUBICE expeditions. Briefly, image processing followed the protocol described by Sosik and Olson (2007) with a custom MATLAB (2013b) code (https://github.com/hsosik/ifcb-analysis). The classification step and the machine-learning algorithm of the Ecotaxa application were used (Picheral et al., 2017). After automatic identification, each object was either validated visually or corrected. Within these image libraries, only living objects were kept for further analyses (images with a bad focus, air bubbles, or detritus were removed). Finally, the biovolume of each IFCB-imaged cell was computed according to Moberg and Sosik (2012).
The Kruskal-Wallis H test was used to test for significant differences in nutrient concentrations and ratios between the different expeditions. A correlation matrix (based on Spearman’s ρ) was also produced between the major algal groups and the environmental drivers and visualized as a heatmap. All statistical analyses were performed using the software package R version 3.6.
All of the sea ice sampled in this study was first year sea ice. However, sea-ice conditions varied with each expedition and from station to station in terms of type, thickness, and optical properties. Sea-ice concentrations derived from satellite-derived observations (Figures 2 and 3) show that for the SUBICE and N-ICE expeditions, more than 90% of the stations were sea-ice-covered (>50% sea-ice concentration; Figure 2C, D). However, only 56, 44, 37, 22, and 12% of the stations were sea-ice-covered for CASES, CFL, GE Amundsen and ICESCAPE 1 and 2 expeditions, respectively (Figure 2A, B and E, F). Sampling from an ice camp requires consolidated sea ice (~100% sea-ice concentration) and usually stops just before ice breakup (for obvious safety concerns). However, fixed sampling allowed for time-series of snow and sea-ice properties (albedo, thickness, etc.) in much more detail than using “destructive” icebreakers. All sea-ice camps (Resolute 2010 and Green Edge 2015, 2016) were conducted on landfast sea ice (Figure 3). Before the melting periods, sea-ice thickness was similar for all ice camp expeditions, ranging from 1.0 to 1.4 m (Figure 4D, E, F). However, snow depth on top of the sea ice varied greatly. For instance, the average snow depth before melt was ~14, 40 and 30 cm (Figure 4D, E, F), respectively, for the three ice camps. This large variability in snow cover had little impact on surface albedo (~0.9 before melt for all ice camps; Figure 4A, B, C) but largely controlled under-ice light transmittance (0.13, 0.06, 0.11 mol photons m–2 d–1 at 2 m under the ice bottom, respectively for the three ice camps Figure 4G, H). Sea-ice algae also contributed to the reduction of under-ice PAR, albeit to a lesser degree than snow cover.
In terms of water masses, both Atlantic- and Pacific-derived waters were included in our dataset (Figure 5), from the western AO (i.e., Chukchi and Beaufort Seas) where water masses carried a strong Pacific signature (indicated in blue; Figure 5B) to the eastern AO (i.e., north of Svalbard) influenced by Atlantic-derived waters (shown in red; Figure 5A). In Baffin Bay, a clear gradient exists between Pacific waters to the west and Atlantic waters to the east (Figure 5A; see also Randelhoff et al., 2019). Finally, the sea-ice camps were dominated by Arctic waters of Pacific origin. Main water masses were identified based on their hydrographic and nutrient (ANP) signatures. Atlantic waters were characterized by low (~0) ANP values and are warm and saline (conservative temperature Tc > 0°C, and absolute salinity SA > 34.0 g kg–1; Figure 5C). Winter Atlantic waters and Atlantic cold halocline waters are saline waters from the Atlantic which have cooled down during the previous winter (Tc < 0°C, and SA > 33.0 g kg–1; Figure 5C). They are generally located below the Atlantic or Arctic waters (less buoyant) and have different salinity in the Atlantic sector (N-ICE, SA ~ 34.5 g kg–1) compared to Baffin Bay (GE Amundsen, SA ~33.5 g kg–1; Figure 5C). The Arctic waters are fresher and colder than the Atlantic waters (Tc < 0°C, and SA < 33.0 g kg–1) and have high (~1) ANP values (Figure 5C). The remaining points in the diagram are associated with fresh and warm surface melt waters. Their characteristics are highly scattered, but could be defined with Tc > 0°C and SA < 33.0 g kg–1 (Figure 5C).
Winter nutrient concentrations were higher in Pacific-influenced coastal waters of the Western Arctic Ocean than in the sea-ice camps in the Canadian Arctic Archipelago, Arctic outflow waters of Baffin Bay, and inflowing Atlantic-influenced waters north of Svalbard (Figure 6A, B, C). In Pacific-influenced waters of the Western Arctic Ocean (e.g., SUBICE, ICESCAPE 2010 and 2011, CASES and CFL), winter water concentrations of NO3–, Si(OH)4, and PO43– had ranges of 14.4–20.2, 31.4–64.5, and 1.64–2.5 μmol L1, respectively (Figure 6A, B, C). In the sea-ice camps dominated by Arctic waters of Pacific origins (e.g., Resolute 2010 and 2011, GE SIC 2015 and 2016), winter water ranges of NO3–, Si(OH)4, and PO43– concentrations were 6.35–10.5, 8.32–17.0, and 0.9–1.4 μmol L1, respectively (Figure 6A, B, C). Finally, in Arctic outflow waters of Baffin Bay and inflowing Atlantic-influenced waters (i.e., GE Amundsen and N-ICE), the ranges of winter water NO3–, Si(OH)4, and PO43– concentrations were 12.2–12.9, 3.42–12.8, and 0.9–1.21 μmol L1, respectively (i.e., GE Amundsen and N-ICE; Figure 6A, B, C).
By spring, nutrient drawdown was generally highest in the western Arctic (Chukchi Sea), with the two ICESCAPE expeditions having the greatest drawdown of Si(OH)4 (47.0 and 59.0 μmol L1) and PO43– (~2.1 μmol L1) (Figure 7A, B, C). The lowest Si(OH)4 drawdown (3.0–4.0 μmol L1) was seen in the Resolute 2010, GE SIC 2015 and 2016, and N-ICE expeditions. Similarly, PO43– drawdown (0.2–0.4 μmol L1) was lowest in the Resolute 2010 and both GE SIC studies. Drawdown of NO3– was also high (14.4 and 20.2 μmol L1) at the ICESCAPE sites but similar to drawdown during the CFL expedition. NO3– drawdown was slightly less (11.7 and 12.9 μmol L1) in the N-ICE and GE Amundsen studies, and much less (2.5–5.0 μmol L1) in the Resolute and GE SIC studies. Although the SUBICE and ICESCAPE studies were both in the Chukchi Sea, SUBICE took place earlier in the season (Figure 1) when the bloom did not deplete nutrient inventories, particularly PO43– and Si(OH)4, to the same extent.
Based on our pan-Arctic compilation, a diverse array of phytoplankton blooms, reported in contrasting sea-ice conditions, varied considerably in terms of magnitude and structure (Figures 2, 3, 8 and 9). The maximum chl a concentrations of the UIBs ranged from massive diatom blooms in the Chukchi Sea (e.g., 30.0 mg chl a m–3; Figures 2A, B and 8A, B) to modest UIBs north of Svalbard (e.g., 3.41 mg chl a m–3; Figures 2D and 8J). In some cases, the maximum values of chl a concentration associated with the UIBs were similar to those in adjacent sea-ice-free conditions (e.g., 32.8 mg chl a m–3 in the Chukchi Sea; Figures 2A, B and 8A, B). The bloom magnitude decreased from the western to the eastern Arctic Ocean (i.e., 30.0 mg chl a m–3 in the Chukchi Sea, 17.2 mg chl a m–3 in the Beaufort Sea/Amundsen Gulf, 15.4 mg chl a m–3 in the Canadian Arctic Archipelago, and 12.8 mg chl a m–3 in Baffin Bay; Figures 2, 3 and 8), with some exceptions to this general trend. First, the maximum chl a concentration (mg m–3) and depth-integrated chl a (mg m–2) over the first 100 meters are highly correlated (r2 = 0.71, p < 0.0001; Figure 8), though differences between the two can be large when subsurface chl a maxima (SCMs) are present in open water. Second, the higher bloom magnitude observed in the Beaufort Sea/Amundsen Gulf (CFL cruise; Figures 2F and 8D) was associated with a localized ice-edge upwelling event (Mundy et al., 2009). These observations are not common for the Beaufort Sea, which is classified more typically as an oligotrophic system with N-depleted stratified surface waters (Tremblay et al. 2008; Brugel et al. 2009; Ardyna et al. 2017).
The maximum chl a concentration measured during each bloom was related to maximum macronutrient drawdown (Figure 7A, B, C), but, surprisingly, NO3– drawdown was not a good predictor of this measure of bloom magnitude (r2 = 0.34, p = 0.08). Instead, Si(OH)4 (r2 = 0.70, p < 0.01) and PO43– (r2 = 0.74, p < 0.01) drawdown were both significantly positively correlated to the measured maximum chl a concentration. Similarly, both winter water Si(OH)4 (r2 = 0.52, p < 0.05) and PO43– (r2 = 0.70, p < 0.01) concentrations were significantly correlated to maximum measured chl a concentrations, while winter water NO3– (r2 = 0.36, p = 0.07) concentration was not (Figure 7D, E, F). The ratio Si(OH)4:NO3– of the winter water across the different sites was also a significant predictor of the maximum measured chl a (r2 = 0.53, p < 0.05; Figure 7G). The highest chl a concentrations measured across all sites were in regions that had the highest concentrations of winter water Si(OH)4. In fact, the four campaigns that had Si(OH)4 concentrations >30 μmol L–1 (SUBICE, ICESCAPE 2010 and 2011, and CFL) averaged 2.4x higher chl a concentrations than the other sites. Si(OH)4 was 4.4x higher at these four sites while NO3– and PO43– were only 1.9x higher. Predictably, the blooms at the sites with the greatest Si(OH)4 concentrations were dominated fractionally to the greatest extent by diatoms (see below).
UIBs are typically dominated by diatoms, as both CHEMTAX and the IFCB images reveal (Figures 8 and 9). Several genera of the dominant diatoms in UIBs have been identified, including Chaetoceros, Thalassiosira, and Fragilariopsis in the Chukchi Sea during ICESCAPE (where diatoms summed to 75% and 96% of the biovolume in 2010 and 2011, respectively; Figure 9) and SUBICE (where they summed 98% of the biovolume; Figure 9). In the eastern Arctic Ocean, the decrease in the UIB magnitude was associated with a diminishing contribution by diatoms and an increase in Phaeocystis (Figure 8). High abundance of Phaeocystis pouchetii was observed in Baffin Bay during the GE Amundsen cruise (up to 65% of the biovolume in open waters; Figure 9) and north of Svalbard in sea-ice-covered waters during N-ICE (Figure 8J). During the Green Edge sea-ice camps, diatoms remained dominant during the UIB periods, again with Chaetoceros and Thalassiosira as the most abundant genera (from 97% to 86% of the biovolume in 2015 and 2016, respectively). Based on CHEMTAX, a modest abundance of prasinophyte type 3 was observed during three expeditions (i.e., CFL, Green Edge sea-ice camp 2016, and GE Amundsen cruise; Figure 8D, H, I), which was likely the endemic Arctic prasinophyte Micromonas polaris (previously called Micromonas pusilla; Simon et al., 2017; Joli et al., 2018).
After considering numerous environmental factors (listed in Figure 10), nutrient concentrations were shown to be the critical drivers explaining variability in phytoplankton bloom magnitude and assemblage composition (Figures 7 and 10). Based on CHEMTAX, only the relative abundance of diatoms correlated positively with nutrient concentrations (Figure 10). All of the remaining phytoplankton groups correlated negatively with Si(OH)4 and PO43– and most also correlated negatively with NO3– (with the exception of dinoflagellates, prasinophytes type 2 and Phaeocystis). When these negative correlations were strongly significant (as for chlorophytes, haptophytes type 7, prasinophytes type 3), nutrient concentrations were very low and/or sampling was conducted during post-bloom periods.
The molar ratio NO3–:Si(OH)4 correlated positively with the relative abundance of Phaeocystis and thus negatively with the relative abundance ratio of diatoms to Phaeocystis, suggesting that Si(OH)4 availability plays a critical role in controlling the relative abundance of the two main bloom-forming phytoplankton groups. NO3–:Si(OH)4 molar ratios of <1 tended to favor diatom-dominated UIBs, while Phaeocystis seems to play a co-dominant role in Atlantic-influenced waters with NO3:Si(OH)4 molar ratios >1, as observed to some extent in Baffin Bay and north of Svalbard (Figure 6D). Finally, the NO3–:PO43– molar ratio was low across all the expeditions, with values <7.5 for all studies except N-ICE, which had a NO3–:PO43– of 11.6 (Figure 6F).
A strong positive correlation was observed for Phaeocystis and sea-ice concentration (Figure 10), indicating that factors other than nutrient concentrations and ratios could play a critical role in shaping UIB composition. The most likely factor, modulated by sea-ice concentration (including algal blooms in the ice) and snow cover, is the under-ice light field.
An important and unresolved question about UIBs still remains: are they really new phenological characteristics or have they simply not been recognized historically and integrated into Arctic phytoplankton paradigms? The re-examination of datasets from previous expeditions over the past two decades suggests that UIBs may have already had a circumpolar distribution. High concentrations of phytoplankton biomass under Arctic sea ice (typically with >50% coverage) have been reported in areas as widespread as Resolute Passage and Allen Bay in the Canadian Arctic Archipelago (Michel et al., 1996; Fortier et al., 2002; Duerksen et al., 2014; Galindo et al., 2014, 2015, 2016; Mundy et al., 2014), Baffin Bay (Hussherr et al., 2017; Oziel et al., 2019), the Greenland Sea (Mayot et al., 2018), the Barents Sea (Strass and Nöthig, 1996; Assmy et al., 2017; Pavlov et al., 2017; Kauko et al., 2019), the Laptev Sea (Lalande et al., 2014), the Chukchi Sea (Yager et al., 2001; Arrigo et al., 2012, 2014; Ha et al., 2015; Hill et al., 2018), and the Central Arctic (Laney et al., 2014; Boles et al., 2020). Many locations where UIBs were observed had first year sea ice, which was not the case in the 1980s when multi-year sea-ice was more prevalent (Figure 11). In the Arctic Ocean, the first-year ice comprised 61% of the total sea-ice coverage in March 2018 (i.e., an area of 3.2 × 106 km2), but only 34% in March 1984 (Figure 11; Tschudi et al., 2019). Such a radical transformation of the icescape, with the loss of multi-year sea ice and an overall sea-ice thinning, has certainly contributed to the greater exploration of the Arctic Ocean and the more regular observation of UIBs throughout it. Given this context, a pan-Arctic compilation of comparable data sets on UIBs provides an important opportunity to gain a more comprehensive understanding of the environmental and ecological processes underlying UIB initiation, magnitude, and phytoplankton assemblages.
Sea-ice edge blooms (as observed in Figure 2) are known to begin once the highly reflective sea-ice retreats in spring, as solar elevation increases, and surface waters become stratified by the addition of sea-ice melt water. With a more fragmented and thinner sea-ice environment, however, UIBs are expected to increase throughout the Arctic Ocean (e.g., in the Chukchi Sea, the Canadian High Arctic and north of Svalbard; Figures 2 and 3) due to increasing under-ice light that drives bloom initiation. By combining three distinctive times series (i.e., the Resolute and Green Edge sea-ice camps), we propose here a conceptual representation (potentially generalizable to first-year sea ice AO areas) of the main physical drivers of under-ice light availability and, ultimately, the initiation of UIBs. For this representation, only the expeditions that were considered to be “pseudo-eulerian” and where biomass was produced locally were selected (i.e., the ice camps). Unfortunately, these criteria were not met by the expeditions, such as ICESCAPE 2011, where the massive UIB, given its magnitude and the high dominance by centric diatoms (Figures 2, 8 and 9), certainly started several weeks prior to sampling. Even with these constraints, we were able to delineate similar temporal patterns with four successive stages and three structuring events of UIB dynamics, driven mostly by changing snow, sea-ice conditions, and under-ice light availability (Figure 4):
Stage I: This first stage is defined by winter snow and ice conditions with a relatively constant high albedo (>0.85), low under-ice PAR (<0.1–0.2 mol photons m–2 d–1), and the development of a sea-ice algal bloom.
Stage II: This second stage is triggered by the onset of snow melt, which decreases the albedo (<0.85), increases under-ice PAR (>0.1–0.2 mol photons m–2 d–1), and marks the concomitant termination of the sea-ice algal bloom and the initiation of the UIB. Stage II lasts approximately two weeks.
Stage III: This third stage is characterized by the appearance of melt ponds. Albedo decreases (<0.75), under-ice PAR rapidly increases (~1–2 mol photons m–2 d–1) and the UIB reaches its maximum in terms of depth-integrated biomass. Peak biomass is controlled by the balance between euphotic zone depth and surface nutrient inventories (Oziel et al., 2019). Stage III ends when the sea-ice breaks up.
Stage IV: This last stage consists of the transition to an ice-free ocean (albedo <0.2) and the deepening of phytoplankton biomass to form a subsurface chl a maximum at the depth where the balance between light and nutrient availability is still sufficient for phytoplankton growth. The duration of phase IV depends on the depth of the nitracline (i.e., generally related to the upper halocline) and the nutrient inventories in the halocline down to the base of the euphotic zone (Tremblay et al., 2008; Bergeron et al., 2014).
Based on this novel pan-Arctic compilation, two main types of Arctic phytoplankton blooms were identified (occurring under both sea-ice-covered and sea-ice-free conditions; Figure 8), those dominated by diatoms and those dominated by the haptophyte Phaeocystis pouchetii (Figures 8 and 9). Phaeocystis pouchetii blooms have long been observed in the European Arctic sector (Smith et al., 1991; Schoemann et al., 2005) but have only recently been observed in Labrador fjords (Simo-Matchim et al., 2017) and Baffin Bay. Amongst these blooms, only one has been designated as an UIB (Assmy et al., 2017). NO3– is generally the main limiting nutrient in Arctic waters (Harrison and Cota, 1991), driving shifts from diatom- to flagellate-based assemblages as NO3– is depleted (Ardyna et al., 2011). We suspect that high NO3– and low Si(OH)4 waters, i.e., having a NO3:Si(OH)4 molar ratios >1 (as previously suggested for open waters by Brzezinski, 1985; Legendre et al., 1993; Takeda, 1998; and Krause et al., 2018), could also drive assemblage shifts from diatom to Phaeocystis dominance in sea-ice-covered areas. During an expedition to Svalbard fjords and the Barents Sea in May 2016, Krause et al. (2018) demonstrated that depleted Si(OH)4 concentrations during the peak or late phase of the spring bloom drastically reduced Si(OH)4 uptake rates and limited diatom growth. Depending on the severity of the Si(OH)4 limitation, more favorable growth conditions for Phaeocystis compared to diatoms (Nöthig et al., 2015) could result in succession from diatoms to Phaeocystis (i.e., only if Si(OH)4 is exhausted before NO3–). Since the early 1990s, Si(OH)4 concentrations have been declining in the North Atlantic Ocean (Rey, 2012) due to natural multi-decadal variability through a decrease in the maximal depth of winter convection and weakening of the subpolar gyre (Hátún et al., 2017). This low and declining Si(OH)4 inventory sets a lower limit to the build-up of diatom biomass relative to what is possible in the Pacific sector, leaving NO3– for non-diatom phytoplankton (Phaeocystis) to utilize. As a result, more evidence may be found of increasing high NO3– and low Si(OH)4 waters where Phaeocystis accounts for a relatively large share of the seasonal NO3– consumption, for example in Baffin Bay (influenced directly by Atlantic waters in the eastern AO; Figure 4) and in higher latitude waters of the European Arctic sector.
Additionally, our analysis identifies Si(OH)4 and PO43–, rather than NO3–, as good predictors of UIB magnitude (i.e., maximum chl a; Figure 7). Winter water concentrations of both nutrients, as well as their maximum drawdown, were significantly related to the maximum chl a measured at each study site. Likewise, those campaigns that had winter water concentrations of Si(OH)4 > 30 μmol L–1 averaged maximum chl a concentrations 2.4x higher than campaigns with lower Si(OH)4 concentrations. Three of these high Si(OH)4 campaigns (ICESCAPE 2010 and 2011 and SUBICE) recorded diatoms as >75% of the phytoplankton community (no assemblage information available from the fourth campaign, CFL). These three campaigns recorded the lowest NO3–:Si(OH)4 drawdown ratios (mean of 0.26 ± 0.04), as well as low NO3–:PO43– drawdown ratios (mean of 7.05 ± 1.08; Figure 6D,F). As such, blooming diatoms use less NO3– per unit of Si(OH)4 or PO43– used. Given that the C:N between different phytoplankton varies much less than either C:Si or C:P, diatoms can likely grow to greater biomass on the same N inventory than other taxa, and thus both winter water Si(OH)4 and PO43– concentrations, and Si(OH)4:NO3– and NO3–:PO43– drawdown ratios, are good predictors of bloom magnitude. That maximum chl a was predicted best by Si(OH)4 and PO43– is surprising, given that NO3– is generally considered the nutrient that limits Arctic phytoplankton growth (Tremblay and Gagnon, 2009; Ardyna et al., 2011; Mills et al., 2018; Randelhoff et al., 2020). We suspect that, while NO3– is typically the first nutrient depleted during typical Arctic UIBs, the inventory of nutrients (specifically Si(OH)4) allows for the growth of diatoms which can attain higher biomass than other taxa on the same inventory of NO3–.
Despite the observed Si(OH)4 decline in the Atlantic inflow to the Arctic Ocean over the last 25 years, diatoms play a significant role for the phytoplankton spring bloom in the Atlantic sector of the Arctic (Johnsen et al., 2018; Krause et al., 2018), and relative low winter Si(OH)4 concentrations of 4–5 μmol L1 are not limiting at the start of the growth season (Mousing et al., 2018). During N-ICE, Si(OH)4 concentrations remained at winter values of around 4 μmol L1 throughout the UIB, except for a few days in early June when the UIB was dominated by a mixed diatom-Phaeocystis community and Si(OH)4 concentrations were <3 μmol L1 (Assmy et al., 2017). Thus, the low but variable light field under the consolidated, snow-covered ice pack with leads was hypothesized to favor Phaeocystis over diatoms during N-ICE (Assmy et al., 2017), as the former seems to be better adapted to those light conditions (as observed in the Southern Ocean; Arrigo et al., 1999, 2010). Indeed, the typical spring bloom diatoms were present at low abundances during N-ICE but did not increase to considerable biomass, except in the snow-infiltration layers which presumably had higher and more constant light levels (Fernandez-Mendez et al., 2018). High light acclimation of large diatoms dominating an apparent UIB north of Svalbard indicated that the bloom had instead developed in open waters and subsequently advected beneath the snow-covered sea ice (Johnsen et al., 2018), emphasizing the importance of the under-ice light field in determining UIB composition. Diatom-dominated UIBs considered in this study were generally observed during the melt season characterized by extensive melt pond coverage and hence under a more transparent sea-ice cover (i.e., Arrigo et al., 2012, 2014; Mundy et al., 2014; Oziel et al., 2019; see also Figure 4).
As reported previously, phytoplankton biomass and assemblage structure are essential for shaping the biogeography of the Arctic Ocean (i.e., from oligotrophic to eutrophic regions; Tremblay et al., 2009; Ardyna et al., 2011). Diatom-dominated blooms both under the ice and in open water are characterized by high rates of primary production, which support a large fraction of the biomass of Arctic marine ecosystems (Tremblay et al., 2006; Ardyna et al., 2011; Arrigo et al., 2012, 2014). In contrast, shifting to a more Phaeocystis- and flagellated-dominated assemblage could decrease the amount of carbon transferred to higher trophic levels (Saiz et al., 2013; Ardyna et al., 2017). Phaeocystis blooms are generally dominated by the colony (rather than solitary) stage, which is considered unpalatable for most zooplankton grazers (Lancelot, 1995; Wilson et al., 2015; Ray et al., 2016). Although mass sedimentation of the colony stage of Phaeocystis blooms in the Barents Sea has been observed (Wassmann, 1994), the contribution of Phaeocystis to deep vertical organic carbon export is considered small compared to diatoms (Reigstad and Wassmann, 2007), unless associated with downwelling (Lalande et al., 2011), deep vertical mixing (Reigstad and Wassmann, 2007), or mineral ballast (Wollenburg et al., 2018). Phaeocystis tends to be mineralized in the surface water column (Wassmann, 1994). Thus, a shift towards Phaeocystis-dominated blooms in the European Arctic sector could weaken pelagic-benthic coupling and energy transfer to mesopelagic ecosystems.
Compiling and synthesizing this large data set has enabled delineation of the environmental drivers responsible for initiating and shaping the timing, magnitude, and assemblage composition of UIBs. Interestingly, both the bloom magnitude and assemblage structures in a given region were observed to be similar whether the bloom was under sea-ice cover or in open water. This similarity implies that an increasing fraction of Arctic Ocean phytoplankton blooms could become (and potentially have been) invisible to satellite sensors, resulting in underestimates of annual net primary production by an order of magnitude or more (Arrigo et al., 2014). Early bloom formation and occurrence of UIBs have been shown to be tightly related to snow and sea-ice conditions and to the resulting under-ice light availability. In particular, a shift from multi-year to first-year sea ice (with more melt ponds and leads) could partly explain the increasing occurrence of UIBs in the Arctic Ocean (Horvat et al., 2017). Two main types of phytoplankton taxa dominate UIBs depending on the winter NO3–:Si(OH)4 ratio and the under-ice light climate. Shifts from diatom- to Phaeocystis-dominated blooms could occur in Si(OH)4-depleted waters of the Eastern Arctic Ocean. Changes in phytoplankton assemblage composition, timing, and location could have important ramifications for Arctic biogeochemical cycles, and ultimately impact carbon flows to higher trophic levels and to the deep ocean.
M.A. was supported by a European Union’s Horizon 2020 Marie Sklodowska-Curie grant (no. 746748). This work represents a contribution to the Sorbonne Université and Stanford University. The CASES expedition was supported by a Natural Sciences and Engineering Research Council (NSERC) of Canada Network grant with logistical support from the Polar Continental Shelf Program (PCSP) of Natural Resources Canada. The CFL expedition is a contribution to the International Polar Year–Circumpolar Flaw Lead system study (IPY–CFL 2008), supported through grants from the Canadian IPY Federal program office, NSERC and numerous international collaborators. The GreenEdge project is funded by the following French and Canadian programs and agencies: ANR (Contract #111112), ArcticNet, CERC on Remote sensing of Canada’s new Arctic frontier, CNES (project #131425), French Arctic Initiative, Fondation Total, CSA, LEFE and IPEV (project #1164). This project was conducted using the Canadian research icebreaker CCGS Amundsen with the support of the Amundsen Science program funded by the Canada Foundation for Innovation (CFI) Major Science Initiatives (MSI) Fund. The ICESCAPE expedition was funded by the Ocean Biology and Biogeochemistry Program and the Cryosphere Science Program of the National Aeronautic and Space Administration (NNX10AF42G to K. Arrigo, R. Pickart, and J. Swift, NNH10A017I to D. Perovich, NNX10AT67G to W. Balch, NNX10AG36G to N. Bates and J. Mathis, NNX09AE42G and NNX11AF65G to B. G. Mitchell and C. Benitez-Nelson, NNX10AH71G to K. Frey, NNX10AG07G to S. Laney and H. Sosik, and NNX10AG05G to R. Reynolds). The N-ICE expedition was supported by the former Centre for Ice, Climate and Ecosystems (ICE) at the Norwegian Polar Institute and the Ministry of Climate and Environment, Norway. P.A. was funded by the Research Council of Norway (project no. 244646), and the Ministry of Foreign Affairs, Norway (project ID Arctic). The Resolute expedition was supported by PCSP and by NSERC Discovery and Northern Research Supplement Grants to C.J. Mundy and M. Gosselin. The SUBICE expedition was sponsored by the NSF Office of Polar Programs (PLR-1304563 to K. R. Arrigo and PLR-1303617 to R. S. Pickart).
The authors have no competing interests to declare.
M.A. led the design of the study. M.A., M.M., L.O., P.-L. G., L.L., G.v.D. conducted the analysis. M.A., L.O., L.L. made the figures. All authors revised the earlier version of the manuscript, helped in the interpretation and approved the final version for publication.
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