Ice cover extent drives phytoplankton and bacterial community structure in a large north-temperate lake: implications for a warming climate
B. F. N. Beall
Department of Biological Sciences, Bowling Green State University, Bowling Green, OH, 43403 USA
Search for more papers by this authorM. R. Twiss
Department of Biology, Clarkson University, Potsdam, NY, USA
Search for more papers by this authorD. E. Smith
Department of Biology, Clarkson University, Potsdam, NY, USA
Search for more papers by this authorB. O. Oyserman
Department of Biological Sciences, Bowling Green State University, Bowling Green, OH, 43403 USA
Search for more papers by this authorM. J. Rozmarynowycz
Department of Biological Sciences, Bowling Green State University, Bowling Green, OH, 43403 USA
Search for more papers by this authorC. E. Binding
Water Science & Technology Directorate, Environment Canada, Burlington, ON, Canada
Search for more papers by this authorR. A. Bourbonniere
Water Science & Technology Directorate, Environment Canada, Burlington, ON, Canada
Search for more papers by this authorG. S. Bullerjahn
Department of Biological Sciences, Bowling Green State University, Bowling Green, OH, 43403 USA
Search for more papers by this authorM. E. Palmer
Sport Fish and Biomonitoring Unit, Ontario Ministry of the Environment and Climate Change, Toronto, ON, Canada
Search for more papers by this authorE. D. Reavie
Center for Water and the Environment, Natural Resources Research Institute, University of Minnesota Duluth, Duluth, MN, USA
Search for more papers by this authorLCDR M. K. Waters
USCGC Neah Bay (WTGB 105), Cleveland, OH, USA
Search for more papers by this authorLCDR W. C. Woityra
USCGC Neah Bay (WTGB 105), Cleveland, OH, USA
Search for more papers by this authorCorresponding Author
R. M. L. McKay
Department of Biological Sciences, Bowling Green State University, Bowling Green, OH, 43403 USA
For correspondence. E-mail [email protected]; Tel. +1 419 372 6873; Fax +1 419 372 2024.Search for more papers by this authorB. F. N. Beall
Department of Biological Sciences, Bowling Green State University, Bowling Green, OH, 43403 USA
Search for more papers by this authorM. R. Twiss
Department of Biology, Clarkson University, Potsdam, NY, USA
Search for more papers by this authorD. E. Smith
Department of Biology, Clarkson University, Potsdam, NY, USA
Search for more papers by this authorB. O. Oyserman
Department of Biological Sciences, Bowling Green State University, Bowling Green, OH, 43403 USA
Search for more papers by this authorM. J. Rozmarynowycz
Department of Biological Sciences, Bowling Green State University, Bowling Green, OH, 43403 USA
Search for more papers by this authorC. E. Binding
Water Science & Technology Directorate, Environment Canada, Burlington, ON, Canada
Search for more papers by this authorR. A. Bourbonniere
Water Science & Technology Directorate, Environment Canada, Burlington, ON, Canada
Search for more papers by this authorG. S. Bullerjahn
Department of Biological Sciences, Bowling Green State University, Bowling Green, OH, 43403 USA
Search for more papers by this authorM. E. Palmer
Sport Fish and Biomonitoring Unit, Ontario Ministry of the Environment and Climate Change, Toronto, ON, Canada
Search for more papers by this authorE. D. Reavie
Center for Water and the Environment, Natural Resources Research Institute, University of Minnesota Duluth, Duluth, MN, USA
Search for more papers by this authorLCDR M. K. Waters
USCGC Neah Bay (WTGB 105), Cleveland, OH, USA
Search for more papers by this authorLCDR W. C. Woityra
USCGC Neah Bay (WTGB 105), Cleveland, OH, USA
Search for more papers by this authorCorresponding Author
R. M. L. McKay
Department of Biological Sciences, Bowling Green State University, Bowling Green, OH, 43403 USA
For correspondence. E-mail [email protected]; Tel. +1 419 372 6873; Fax +1 419 372 2024.Search for more papers by this authorSummary
Mid-winter limnological surveys of Lake Erie captured extremes in ice extent ranging from expansive ice cover in 2010 and 2011 to nearly ice-free waters in 2012. Consistent with a warming climate, ice cover on the Great Lakes is in decline, thus the ice-free condition encountered may foreshadow the lakes future winter state. Here, we show that pronounced changes in annual ice cover are accompanied by equally important shifts in phytoplankton and bacterial community structure. Expansive ice cover supported phytoplankton blooms of filamentous diatoms. By comparison, ice free conditions promoted the growth of smaller sized cells that attained lower total biomass. We propose that isothermal mixing and elevated turbidity in the absence of ice cover resulted in light limitation of the phytoplankton during winter. Additional insights into microbial community dynamics were gleaned from short 16S rRNA tag (Itag) Illumina sequencing. UniFrac analysis of Itag sequences showed clear separation of microbial communities related to presence or absence of ice cover. Whereas the ecological implications of the changing bacterial community are unclear at this time, it is likely that the observed shift from a phytoplankton community dominated by filamentous diatoms to smaller cells will have far reaching ecosystem effects including food web disruptions.
Supporting Information
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| emi12819-sup-0001-si.pdf2 MB |
Fig. S1. Mid-winter limnological surveys captured extremes of ice cover on Lake Erie. A. Moderate resolution imaging spectroradiometer image captured on 3 February, 2011 showing expansive ice cover. B. Moderate resolution imaging spectroradiometer image captured 15 February showing the mainly ice-free condition of Lake Erie during winter 2012. Sampling locations are shown for surveys onboard CCGS Griffon (mid-February) and USCGC Neah Bay (January–March). Moderate resolution imaging spectroradiometer images obtained from Great Lakes CoastWatch Program, NOAA-Great Lakes Environmental Research Lab (http://coastwatch.glerl.noaa.gov/). Fig. S2. Central basin phytoplankton chl a biomass reported from US EPA spring monitoring surveys. Aulacoseira islandica and Stephanodiscus spp. that emerge as dominant phytoplankton during winter persist into the early spring in Lake Erie and are routinely documented as abundant flora during the EPA's annual spring (April) survey of the lake (Barbiero and Tuchman, 2001; Reavie et al., 2014). Notably, the major decline in chl a biomass between high and low ice years documented during our February surveys persisted into early April based on comparison of 2011-2012 EPA monitoring results (two-tailed t-test, DF = 18, P < 0.0001). Likewise, review of historical EPA data obtained from the online Great Lakes Environmental Database (accessed from http://www.epa.gov/cdx/) showed a similar large (> 80%) decline in chl a biomass comparing early April surveys following years of high ice in 2001 and 2003 with the extreme low ice winter of 2002 (two-tailed t-test, DF = 18, P < 0.0005). Data are presented as box and whisker plots showing median extractive chl a concentration. Mean values for chl a are shown with a dashed line. Vertical boxes around each median show the upper and lower quartiles, whereas whiskers extend from the 5th to 95th percentile. Fig. S3. The phytoplankton community structure was examined using a flow cytometric size-based analysis (Marie et al., 2005). The approach measured cells in size ranging from approximately 0.8 μm to ≤ 50 μm in length, excluding large cells and cells in chains which dominated the Lake Erie community during winter 2011. Cells were classified into two phytoplankton size groups: A, C. ∼6 to 30 μm diameter and B, D. ∼2 to 6 μm diameter, and by the presence of orange fluorescent phycoerythrin (C, D). Samples were fixed in buffered formaldehyde (final concentration 1% v/v) and flash-frozen in liquid nitrogen. Samples were kept in liquid nitrogen or at −80 °C until thawing and immediate analysis. Flow cytometry samples were analysed on a FACSCalibur flow cytometer (Becton–Dickinson, San Jose CA, USA.). All data acquisitions were done with logarithmic signal amplification. Cytometer sample flow rates were calibrated using bead stocks of known concentration (Calibrite beads, Becton-Dickinson) and particle sizes were calibrated using beads of known diameter (Flow Cytometry Size Bead Kit, Invitrogen, Life Technologies, Grand Island, NY, USA). Eukaryotic phytoplankton were distinguished by size and red fluorescence (instrument settings: Forward Scatter = E- 01, and FL3 = 350). Cell abundances were calculated from acquisition duration, the number of events, and instrument flow rate. Results obtained by flow cytometry showed that cell abundance in the combined fraction containing large nanophytoplankton (6–30 μm) and smaller microphytoplankton (20–30 μm) (A) declined more than 3-fold during the low ice winter 2012. A similar decline was observed among phycoerythrin (PE)-rich taxa from the same size class (C). In contrast, small nanophytoplankton (2–6 μm; B) increased by 1.3-fold, whereas PE-rich small nanophytoplankton did not vary between years (D). Fig. S4. Rarefaction curves of observed species (97% OTUs) from 16S amplicons for (A) Bacteria and (B) chloroplast sequences. Use of rarefied samples of 10 000 random reads per site was based on the lowest return of bacterial reads. C. Breakdown of Bacteria and chloroplast sequences recovered from each site. A central finding from our winter surveys was that in 50% of the samples, chloroplast reads representing phytoplankton occurred in comparable (± 1%) or higher numbers than Bacteria. The bacterial 16S rRNA Itag sequences returned 3929 OTU's affiliated with 362 unique bacterial orders within 35 phyla. Of the phyla represented, 19 were established lineages, whereas the remaining 16 were Greengenes-defined candidate phyla (McDonald et al., 2012). Likewise, only 99 of the bacterial orders were established lineages with the remainder representing candidate orders. While the high number of unique phyla and orders are suggestive of a diverse bacterial community in Lake Erie, even during winter, 75% of the bacterial sequences from water samples where affiliated with only 8 bacterial orders belonging to four phyla. These orders included Actinomycetales (Actinobacteria), Burkholderiales, Methylophilales and Rickettsiales (Proteobacteria), Chthoniobacterales (Verrucomicrobia), and Flavobacteriales, Saprospirales and Sphingobacteriales (Bacteriodetes). Three orders (Actinomycetales, Burkholderiales and Rickettsiales) consistently contributed > 30% of total bacterial reads, consistent with their dominance reported from a recent pyrosequencing survey of Western Lake Erie and Sandusky Bay during summer (Mou et al., 2013). However, seasonal similarities ended there with different orders comprising the remaining dominant taxa by season. Fig. S5. Seasonal and depth-resolved abundance of bacterial heterotrophs in Lake Erie for 2011 and 2012. Samples were divided into summer (July/August 2010 and 2011) and winter (February 2010, 2011, and 2012) and divided by depth into epilimnetic (E) and hypolimnetic (H) samples based on summer thermal stratification profiles. Observations were pooled across years; however, summer samples were not available from 2012. Samples were fixed in buffered formaldehyde (final concentration 1% v/v) and flash-frozen in liquid nitrogen. Samples were kept in liquid nitrogen or at −80°C until thawing and immediate analysis. Flow cytometry samples were analysed on a FACSCalibur flow cytometer (Becton-Dickinson, San Jose CA, USA.). All data acquisitions were done with logarithmic signal amplification. Cytometer sample flow rates were calibrated using bead stocks of known concentration (Calibrite beads, Becton-Dickinson). Bacterial heterotrophs were identified by their size using the side scatter channel (SSC) and SYBR Green fluorescence (FL1). SYBR Green I (Invitrogen, Life Technologies, Grand Island, NY, USA) is cell-permeable and binds to double-stranded DNA. Fixed samples were thawed and then incubated at room temperature in the dark for 15 minutes with SYBR Green I (Marie et al., 1997). Cell abundances were calculated from acquisition duration, the number of events, and instrument flow rate. The abundance of bacterial heterotrophs was generally greater, but more variable, in summer than in winter in Lake Erie. No evidence was observed for significant differences in the abundance of heterotrophic bacteria related to the extent of ice cover. Numbers shown below the axis refer to the number of discrete samples analysed. Fig. S6. Phylogenetic clustering of winter samples by principle coordinates analysis (PCoA) of unweighted UniFrac distances for bacterial (A) and chloroplast (B) communities. Fig. S7. Maximum-likelihood tree of OTUs identified by random forest analysis as important features for distinguishing between a high-ice year (2010) and a low ice year (2012), along with associated blast hits. Percent change reflects changes in abundance from high ice to low ice conditions. Fig. S8. Linear discriminant analysis effect size cladogram comparing the taxa of high ice winter 2010 communities with those of low ice winter 2012. Significantly discriminant nodes are coloured by year with the highest mean abundance, and branches are shaded by highest ranking taxon. Fig. S9. Maximum-likelihood trees showing phylogenetic placement of dominant OTUs from Lake Erie winter samples within (A) Alphaproteobacteria and (B) Actinobacteria. Trees were generated with bootstrap values based on 1000 replications. Alignments and trees for determining the phylogenetic placement of dominant OTUs were done with mega 5.2.2 (Tamura et al., 2011) and based on sequences from Van den Wyngaert et al., (2011) and Warnecke and colleagues (2004); respectively. A. A single OTU dominated the Alphaproteobacteria accounting for 66% of the reads for this class and 18% of reads of all Proteobacteria. B. Dominant OTUs of Actinobacteria cluster with the acI and acIV lineages which dominate freshwater environments. Fig. S10. Dissolved oxygen concentrations reported from US EPA spring and summer monitoring surveys of 10 central basin stations. From each station, data from the ‘bottom minus 2 m’ depth was recorded and plotted as box and whisker plots showing median dissolved oxygen concentration. Vertical boxes around each median show the upper and lower quartiles whereas whiskers extend from the 5th to 95th percentile. Use of the ‘bottom minus 2 m’ depth ensured that the hypolimnion was represented during the August surveys. Regardless of Lake Erie ice expanse between 2011 and 2013, hypoxia developed in the central basin hypolimnion by the August survey date. Likewise, review of historical EPA data obtained from the online Great Lakes Environmental Database (GLENDA; accessed from http://www.epa.gov/cdx/) showed that hypoxia developed following the low ice winter of 2002. Table S1. In 2012, light extinction coefficients in water not covered directly by ice were measured using a free-falling hyperspectral Optical Profiler (Satlantic, Halifax, NS, Canada). The light extinction coefficient (KdPAR) was calculated from spectral down-welling irradiance measurements integrated over PAR. The mean daily scalar irradiance just beneath the surface was estimated for PAR using radiative transfer model HydroLight (v 5.2; Sequoia Scientific, Bellevue, WA, USA) which in combination with measured vertical attenuation coefficients allowed estimates of the mean water column irradiance using the formula I = [E0 (1 − eKT)] [KT]−1 (Riley 1957), where E0 is the mean solar flux at the surface of the lake integrated over 24 h, K is the vertical light extinction coefficient, and T is the depth of mixing. To measure light penetration through ice in 2011, a LI-192 Underwater Quantum Sensor (LI-COR, Lincoln, NE, USA) was lowered through holes augered through the ice in order to approximate in situ measures of light attenuation. Light extinction coefficients measured in February 2012 ranged widely from 0.8–5.6 m−1 with a 12 station average of 2.05 m−1. Only stations where mean water column irradiance (Iwc) could be calculated are shown below. The mean KdPAR measured during the 2012 survey was > 2-fold higher than the average light extinction coefficient measured during surveys from 2008-2010 (Twiss et al., 2012) when the lake was predominantly ice-covered (two-tailed unpaired t-test, t = 2.89, DF = 24, P < 0.01). Further, light extinction coefficients reported in Twiss and colleagues (2012) may have been overestimated as light profiles were measured after the icebreaker had cleared ice from the sampling ocation. An effort to measure light attenuation using an underwater PAR sensor deployed through holes augered through plate ice having a thickness of > 20 cm in February 2011 reinforced this notion. Whereas the ice was shown to attenuate PAR by ∼60%, the light extinction coefficient measured through the top 10 m of the water column was 0.48 m−1, 45% lower than the mean extinction coefficient measured from 2008–2010. Table S2. Photosynthetic rates were measured by tracing the acid-stable uptake of radiolabelled 14C by photoautotrophs from the dissolved inorganic form. Briefly, in each year of the study, samples were collected from central basin site EC 1326 (41° 44′ 00” N; 81° 41′ 52” W) as USCGC Neah Bay was returning to port. The sampling site is located 10 nm from the homeport of Cleveland, OH. Once the vessel had returned to port, samples were immediately retrieved from the vessel and kept on ice during transport to our lab at BGSU. Samples were generally stored overnight in the dark at 4°C prior to measuring photosynthetic carbon uptake. In darkness, NaH14CO3 ([60 μCi; specific activity: 58 mCi mmol−1] MP Biomedicals, Solon, OH, USA) was added to dark-adapted samples. The cell suspension was distributed as 1 mL aliquots into 7 mL chilled glass scintillation vials that were incubated simultaneously under 24 different light intensities for 2–3 h using a temperature-controlled photosynthetron (CHPT Mfg. Inc., Georgetown, DE, USA) as described previously (McKay et al., 1997). The reaction was terminated by the addition of 50 μL of formaldehyde to each sample. Acid-stable 14C assimilation was measured by liquid scintillation counting following the addition of 4.5 mL of Ecolite (+) cocktail (MP Biomedicals) to each vial. Total activity of the added 14C was determined by adding 20 μL of the sample at t = 0 to scintillation cocktail containing 200 μL of ß-phenylethylamine (Sigma, St. Louis, MO, USA). Background activity was determined at t = 0 by dispensing a sample aliquot directly into formaldehyde prior to adding scintillation cocktail. Photosynthetic rates, normalized to chl a biomass, were used to construct photosynthesis – irradiance curves using a non-linear regression curve fitting function (SigmaPlot 12.5, Systat Software, San Jose, CA) based on the equation of Platt and colleagues (1980). The model returned three parameters: Pmax, the maximum photosynthetic rate at light saturation (g C g Chl a−1 h−1), α, the slope of the curve at low irradiances (g C g Chl a−1 h-1 [μmol quanta−1 m−2), and β, the slope of the curve associated with photoinhibition at high irradiance. From these parameters, we could calculate Ik (μmol quanta m−2 s−1) to estimate the irradiance at which photosynthesis becomes light saturated. Where replicates were measured, values are provided as the mean ± SD. Our results indicated moderate rates of production in winter and early spring similar to rates reported in Fahnenstiel and colleagues (2000) for the spring isothermal period. Calculated values of Ik suggested that photosynthesis saturated at low PAR as might be expected due to low seasonal insolation as well as the inhibitory effects of ice (10 Feb 2010, 3 March 2011) and turbidity (5 April 2012), respectively, on light penetration. Table S3. Summary of Itag sequences, numbers of operational taxonomic units (OTUs; 97% sequence identity) and alpha-diversity estimates for A) Bacteria and B) chloroplasts. For each analysis, an equal number of sequences (10 000) from each community was randomly selected. Values reported as mean ± S.D. The Chao1 species richness estimator showed that diversity was not fully captured in most communities with 52% of all OTUs identified in rarefied samples for Bacteria. Shannon's diversity index which combines species richness and abundance into a measure of evenness did not vary for bacterioplankton communities assayed during high- and low-ice years (two-tailed unpaired t-test, t = 2.21, DF = 8, P = 0.058). |
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