- Email: [email protected]
Nearshore-offshore trends in Lake Superior phytoplankton
Journal of Great Lakes Research xxx (xxxx) xxx
Contents lists available at ScienceDirect
Journal of Great Lakes Research journal homepage: www.elsevier.com/locate/ijglr
Nearshore-offshore trends in Lake Superior phytoplankton Katya E. Kovalenko a,⇑, Euan D. Reavie a, Andrew J. Bramburger a, Anne Cotter b, Michael E. Sierszen b a
Natural Resources Research Institute, University of Minnesota, Duluth, USA U.S. Environmental Protection Agency, Office of Research and Development, National Health and Ecological Effects Research Laboratory, Mid-Continent Ecology Division, Duluth, MN, USA b
a r t i c l e
i n f o
Article history: Received 11 December 2018 Accepted 6 September 2019 Available online xxxx Communicated by R. Michael McKay
Keywords: Littoral-pelagic gradient Algal community Deep chlorophyll maximum Shannon diversity
a b s t r a c t Changes in phytoplankton community composition and structure can have broad-scale ecosystem effects; however, drivers of species diversity in planktonic systems are not well understood. In lakes, a common but not thoroughly tested assumption is that shallow, nearshore waters are much more diverse and productive, and contribute considerably more material and energy to pelagic food webs than deeper waters farther offshore. Lake Superior is a large, cold, oligotrophic freshwater system which can provide insight into community organization under oligotrophic conditions. We used epilimnion and deep chlorophyll layer phytoplankton data from a lake-wide sampling program conducted in 2011 and 2016 to test whether assemblage composition, total algal biovolume, cell concentrations, diversity, and richness vary with depth. Although lake depth was an important factor in structuring assemblage composition, there were no clear nearshore-offshore gradients in cell density or biovolume despite the exposure of nearshore areas to higher concentrations of watershed-derived nutrients. Shannon diversity increased slightly with increasing depth, whereas richness was uncorrelated. Understanding of the nearshoreoffshore patterns in phytoplankton community characteristics in the Great Lakes has implications for designing monitoring strategies and for considering how further changes in climate and nutrient deposition would affect the base of the food web. Ó 2019 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.
Introduction Phytoplankton community composition and structure are important drivers of aquatic ecosystem function (e.g. Ptacnik et al., 2008); however, controls on species diversity in planktonic systems are not well understood (Litchman et al., 2010). Improved understanding of the factors regulating phytoplankton assemblage composition are essential for our ability to predict food web responses to stressors including increased nutrient loading, changes in ice cover, surface temperature, and thermocline depth (Sterner et al., 2007; Austin and Colman, 2008). In particular, nearshore-offshore patterns in phytoplankton assemblage composition, diversity, density, and cell size have implications for the effects of further changes in climate and nutrients on lower trophic levels in large lake food webs. A common but not thoroughly tested assumption is that in nutrient-limited systems, nearshore waters affected by tributary nutrient loading would be much more diverse and productive, and contribute considerably more to pelagic food webs than waters farther offshore (Bocaniov and Smith, 2009). This
in turn would affect the spatial distribution of primary producers and food availability for higher trophic levels. In the Laurentian Great Lakes, several mechanisms can physically and/or ecologically isolate nearshore communities from nearby offshore regions. For example, the development of thermal bars in the nearshore during vernal warming can inhibit delivery of allochthonous and nearshore nutrients to offshore regions prior to the onset of broader stratification (Mortimer, 1974; Stoermer, 1978; Rao and Schwab, 2007). Often, this can lead to pronounced dissimilarities in composition and density between phytoplankton communities and water quality parameters nearshore and offshore of the thermal bar (e.g. Stoermer, 1978; Likhoshway et al., 1996; Auer and Gatzke, 2004). Wind-driven turbulence is more pronounced in shallower areas, also contributing to a nearshoreoffshore gradient in water quality and distribution of species with different buoyancy and mobility. In lakes with high densities of invasive Dreissena mussels, increased filter feeding rates can severely deplete phytoplankton density in nearshore regions (Hecky et al., 2004) and have been implicated as a driver of the decline of spring phytoplankton blooms (Bocaniov et al., 2013). These mechanisms can influence lake-wide phytoplankton dynam-
⇑ Corresponding author. E-mail address: [email protected] (K.E. Kovalenko). https://doi.org/10.1016/j.jglr.2019.09.016 0380-1330/Ó 2019 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.
Please cite this article as: K. E. Kovalenko, E. D. Reavie, A. J. Bramburger et al., Nearshore-offshore trends in Lake Superior phytoplankton, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.09.016
2
K.E. Kovalenko et al. / Journal of Great Lakes Research xxx (xxxx) xxx
ics, and may have lasting implications for spatial structuring during the entire ice-free season. Lake Superior is a large, cold, oligotrophic freshwater system that exhibits a relatively long spring isothermal period, and can provide insight into community organization under extreme conditions (Sterner, 2011). Lake Superior is among the most rapidly warming lakes on Earth (Austin and Colman, 2007), and its phytoplankton assemblages have changed over the past several decades (Reavie and Allinger, 2011; Chraïbi Shaw et al., 2014). Over the past hundred years, there was a significant decline in both assemblagelevel and taxon-specific cell sizes likely related to increasing temperatures and stratification intensity (Bramburger et al., 2017); however, these changes have not been conclusively related to an underlying mechanism. Considering the relative importance of nearshore nutrient inputs (e.g., Han and Allan, 2012 for Lake Michigan) and overall nutrient imbalance in Lake Superior, we might expect non-negligible nearshore-offshore gradients in phytoplankton assemblages. Although previous studies examined the influences of pelagic water quality and seasonal variation (Barbiero and Tuchman, 2001) as well as historical trends in phytoplankton assemblage composition in Lake Superior (Reavie and Allinger, 2011; Chraïbi Shaw et al., 2014; Cai and Reavie, 2018), nearshore to offshore trends in diversity and species composition have not been described across the entire lake. Munawar and Munawar (2000), using data collected during 1973 and 1983, noted spatial variation in algal biomass throughout Lake Superior, but no clear nearshore-offshore gradient was noted. Although Cai and Reavie (2018) did not perform an analysis of depth-related variations in phytoplankton, cluster analysis of assemblages indicated separation of nearshore-pelagic and deep-pelagic samples, though such clustering did not occur for water quality or overall algal biovolume. Aside from theoretical interest, nearshore-offshore gradients in phytoplankton community characteristics are relevant to predicting the effects of present-day anthropogenic change on ecosystem services provided by Lake Superior. For example, thermal limits of pelagic fish habitat are already being affected by changing ice cover and temperature (Cline et al., 2013), and population stability of many valuable pelagic fish will depend on food availability, which is closely tied to phytoplankton assemblage dynamics. We used phytoplankton data from the 2011 and 2016 Cooperative Science and Monitoring Initiative (CSMI) to test whether a) phytoplankton assemblage composition and distribution of dominant taxa are related to depth, and b) total algal biovolume, cell concentrations, diversity and richness are different across depth zones and related to water quality. We hypothesized greater algal counts and biomass in the nearshore as well as spatially unique assemblages in the nearshore and offshore. These results will help us better define the spatial variation in Lake Superior’s primary producers, which will increase our understanding of nearshorepelagic coupling essential for functioning of large lake food webs (Sierszen et al., 2014).
Materials and methods The largest Great Lake in North America and the third largest freshwater lake by volume, Lake Superior is an unproductive system with a short growing season, and is considered a limnological end-member based on high nitrate to total phosphorus ratios (Sterner, 2011). The lake is dimictic, with a pronounced seiche and mean annual temperature of 7 °C. To quantify phytoplankton assemblage structure across the nearshore-offshore gradient, phytoplankton and water quality samples were collected in a stratified-random sampling design across 56 stations in Lake Superior. Sampling was done as part of
the CSMI collaboration, from the U.S. Environmental Protection Agency’s research vessel, the R/V Lake Guardian (draft 3.4 m). Depth categories used in the stratified-random sampling design to select sampling sites were based on a combination of information from research revealing landscape effects on nearshore waters (e.g., Yurista et al., 2016), depth-related shifts in fish assemblages (e.g., Bronte et al., 2003) and macroinvertebrates (Evans et al., 1990, Johannsson, 1995) and changes in the dominant processes supporting Great Lakes food webs with depth (Sierszen et al., 2006, 2014). Sampling was stratified by depth zones, with zone 1 (nearshore) extending from 5 m to 30 m, zone 2 from 30 to 100 m, zone 3 from 100 to 200 m and zone 4 greater than 200 m. At each site, both integrated epilimnetic samples and samples from the deep chlorophyll layer (DCL) when present were collected in 2011; whereas, only epilimnetic samples were collected in 2016. All sites (Fig. 1) were sampled September, 6–16 in 2011 and August, 29 – September, 15 in 2016 (within 10–17 days from each other). Phytoplankton samples were collected at specific depths (or the epilimnetic waters composited) and 1L of unfiltered sample was placed in a dark HDPE bottle, preserved with 7 ml Lugol’s solution, and refrigerated until further processing. After allowing the sample to concentrate by settling, a sub-sample was prepared for softbodied algal and diatom identification, by mounting a known volume of sample in 2-hydroxypropyl-methacrylate (HPMA) onto a glass slide. A complete description of this procedure is available from PhycoTech, Inc. (www.phycotech.com). In samples that were dominated by algae less than 10–20 mm in GALD (greatest axial or linear dimension), cells were counted at a minimum of 400 natural units and 15 fields at 400x. Taxa that were above 20–30 mm in GALD were counted at 200 for a minimum of 25 fields. The counts were spread evenly over 3 slides for each sample. To check for large taxa (>200 mm) the entire slide was scanned at 100x. If the sample was dominated by diatoms, a minimum of 15 fields were counted at 1000x. Diatoms were identified along with the soft-bodied algae to the lowest level practical. A lower resolution of taxonomy was done by digesting another portion of the concentrated sample in nitric acid and preparing acid-cleaned mount in Naphrax. Diatom taxa were then identified at 1000 under oil immersion. The GALD for each taxon was measured on up to 30 natural units. Other measurements included length, width and depth of different aspects of the colony or cells. Tally data were managed using ASA software, which is a proprietary product of PhycoTech, Inc. The mean and total biovolume for each taxon within a sample was calculated by the ASA software, which approximates geometric shape of each taxon using the separate length, width and depth measurements. Biovolume was converted to biomass with an assumed density of 1 mg/mm3 (Willén, 1959). Water was collected at depths determined by CTD cast water column profile and processed based on sample type. Unfiltered sample water for total nitrogen (TN) and total phosphorus (TP) were added to HCL acid-washed HDPE bottles and frozen. Sample water for filtered constituents soluble reactive phosphorus (SRP)/ NOx/NH+4, cations, anions, SiO2, and dissolved organic carbon (DOC) was filtered through a 0.45-um hydrophilic nylon filter and aliquoted into respective containers: HCL acid-washed HDPE, HNO3-washed HDPE, deionized water-soaked HDPE, and muffled amber glass vial. SRP/NOx/NH+4 sample was frozen, the anion and SiO2 sample was refrigerated, cations refrigerated after HNO3 acid preservation, and DOC refrigerated after H3PO4 acid preservation. Total suspended and volatile solids (TSS/VSS) were collected by filtering a water aliquot through a pre-washed, pre-weighed GF/C filter (WhatmanÒ), placed into individual Petri dishes and frozen until further analysis. TN, NOx, NH+4, and TP were measured on a Lachat 8000 flowinjection analyzer (APHA, 1998; Lachat QuikChem methods, Lachat
Please cite this article as: K. E. Kovalenko, E. D. Reavie, A. J. Bramburger et al., Nearshore-offshore trends in Lake Superior phytoplankton, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.09.016
K.E. Kovalenko et al. / Journal of Great Lakes Research xxx (xxxx) xxx
3
Fig. 1. Map of sampling sites in Lake Superior. Inshore refers to zones 1 and 2 (5–100 m deep).
Instruments, Loveland, CO, USA). Unfiltered subsamples were digested by persulfate digestion for TP and TN (APHA, 1998). Total phosphorus was determined by the molybdate-ascorbic acid method (APHA, 1998; Lachat QuikChem method 10-115-01-1-B). TN and dissolved NOx (0.45-lm membrane filtered) were analyzed by the cadmium reduction method (APHA, 1998; Lachat QuikChem method 10-107-04-1-O). NH+4 was determined on filtered (0.45-lm pore membrane) samples on the Lachat analyzer with the salicylate method (Lachat QuikChem method 10-107-06-2-C). The cations sodium, potassium, calcium, and magnesium were analyzed by Atomic Absorption Spectrophotometry, Standard Methods 3111B (Varian SpectrAA 240FS). Anions were analyzed by ion chromatography, U.S. EPA 300.1 (Dionex DX-600). TSS/VSS were analyzed by gravimetric analysis (APHA, 1998; method 2130B). Raw epilimnetic water quality data are presented in Electronic Supplementary Material (ESM) Table S1. Statistical analyses Simple linear regressions were used to examine relationships among continuous variables total algal biovolume, cell concentrations, biomass and depth. Biovolume and biomass data were logtransformed to satisfy parametric assumptions. To determine which water quality variables were the strongest predictors of algal assemblage attributes (total algal biovolume, cell concentrations, Shannon diversity and richness), we used a Random Forests (RF) machine learning approach (Breiman, 2001). RF is an ensemble learning method which operates by constructing large numbers of small classification trees, results of which are then tallied across the entire forest. An unbiased estimate of error is obtained at each step internally by using a different bootstrap resample from the original data. Approximately 33% of observations are used to test each run’s performance as the out-of-bag error (OOB). RF does not give the direction of the effect because the algorithm averages many small trees; thereby, it can account for variable importance by integrating cases where effects might be the opposite, which
allows increased flexibility in modeling when the importance of one factor is dependent on another. Nonmetric multidimensional scaling (NMDS) was used to visualize overlap in assemblage composition across depth zones. Due to a more limited sample size in DCL (34 stations, not resampled in 2016), several analyses were performed only on epilimnetic data. Analyses were performed in R (version 3.3.2; R Core Development Team 2016) using vegan (Oksanen et al., 2016) and randomForest (Liaw and Wiener, 2002) packages. Results Community composition and the nearshore-offshore gradient Overall, epilimnetic phytoplankton biovolume in both years and in all depth zones was dominated by Bacillariophyta, followed by Chrysophyta, Cyanophyta and Chlorophyta (Fig. 2). Individual taxa responsible for 75% of the total biovolume across all samples (or >2.0% individually) included diatoms Urosolenia longiseta, Tabellaria flocculosa, centric diatoms Lindavia comensis and Lindavia bodanica (Lindavia have undergone reclassification as Pantocsekiella; Alexson et al., 2018b), chrysophyte Dinobryon sp.1 (a taxon with similarities to Dinobryon sociale and Dinobryon cylindricum) and cyanophyte Aphanothece nidulans. In addition to these taxa, several other species were co-dominant in 2016 including Chrysosphaerella longispina, Fragilaria crotonensis, Chrysochromulina parva, Cryptomonas erosa, Dolichospermum lemmermannii, and Cryptomonas rostratiformis, and overall, algal assemblage had greater evenness than in 2011. Distribution of these dominant taxa was depth zone-dependent (ESM Fig. S1, see ESM Table S2 for raw data), although overall assemblage composition summarized using NMDS did not differ consistently with depth zone (ESM Fig. S2). In terms of cell concentrations, 93% of cells in both years were colonial cyanophytes Aphanocapsa delicatissima, A. nidulans, Rhabdoderma lineare, and the single-celled chrysophyte Chromulina, cyanophytes Synechococcus and unidentified cyanophytes.
Please cite this article as: K. E. Kovalenko, E. D. Reavie, A. J. Bramburger et al., Nearshore-offshore trends in Lake Superior phytoplankton, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.09.016
4
K.E. Kovalenko et al. / Journal of Great Lakes Research xxx (xxxx) xxx
Fig. 2. Biovolume of major epilimnetic phytoplankton divisions by depth zone. Lighter gray indicates shallower zones. Depth zones: zone 1 (nearshore) from 5 m to 30 m, zone 2 from 30 to 100 m, zone 3 from 100 to 200 m and zone 4 greater than 200 m. Data from both years were merged.
For epilimnetic samples, total algal biovolume and cell concentrations were not significantly related to depth (P = 0.67, 0.70; Fig. 3). For biovolume and biomass, year-by-zone interaction or independent effects of year and zone were not significant (twoway ANOVAs P = 0.32–0.94), whereas cell concentrations varied between years (P < 0.0001, zone-by-year interaction and zone effect P = 0.30–0.60). Sites with the greatest cell concentrations were observed in Zone 2 of the eastern part of the lake, including a site off Michipicoten Island which had some of the highest cell concentrations in both years of CSMI sampling. The DCL, sampled only in 2011, likewise had no depth-biovolume or depth-cell concentrations relationships; biovolume was 50% greater in DCL samples than in epilimnetic samples (ESM Fig. S3a, P = 0.004), while the opposite trend was true for cell concentrations (ESM Fig. S3b, P = 0.0003). Overall, few of the water quality variables varied consistently with depth: silica (Spearman r = 0.38, P = 0.004), SRP (r = 0.23, P = 0.017, DOC (r = 0.29, P = 0.0002), and TSS (r = 0.35, P = 0.0002) declined in deeper offshore sampling stations. Total epilimnetic cell concentrations had the strongest relationship with chloride and sulfate, with all water quality and habitat variables explaining 34% of variation in the RF model (ESM Fig. S4a). Total epilimnetic algal biovolume and biomass could not be successfully predicted with all available water quality variables. Diversity and richness Across 108 epilimnetic samples, richness varied from 17 to 42 species and Shannon diversity ranged from 0.66 to 2.33. Epilimnetic phytoplankton Shannon diversity tended to weakly increase with depth, when depth was used as a continuous variable in linear regression (P = 0.09; Fig. 4a). Results of this comparison were not significant for richness (P = 0.42), which ranged from 17 to 42 spe-
cies per sample. When depth was grouped by zone, there was a trend towards decreasing diversity in the nearshore zone, however, it was not significant (F = 2.462, P = 0.073, zone 1 vs. 4, P = 0.090). Richness was not significantly related to depth zone (F = 1.509, P = 0.224 Fig. 4b). Highest diversity was observed at 3 deep offshore sites (zone 4) in the middle of the basin and one offshore (zone 3) site in the western part of the basin. Richness and diversity in the epilimnion were only weakly related to some of the water quality variables. RF models with all water quality variables and depth explained 25% of variation in diversity, driven primarily by chloride, sulfate, and year, whereas for richness 12% of variation was explained mostly by sulfate, calcium, SRP and chloride (SI Fig. S4b,c). Richness and diversity in DCL samples was not closely related to those metrics in epilimnetic samples (P > 0.05). DCL richness increased weakly with increasing depth (r2 = 0.12, P = 0.04), whereas DCL diversity tended to decline, but the relationship was not significant (P = 0.08). Discussion Nearshore-offshore gradient Although lake depth was an important factor in structuring assemblage composition, there were no clear nearshore-offshore gradients in cell density or biovolume, despite the proximity of nearshore areas to much greater concentrations of allochthonous organic and inorganic nutrients from the watershed. Paleolimnological studies indicated that pelagic primary production in Lake Superior during the mid-20th century increased as a result of higher nutrient flux (Chraïbi Shaw et al., 2014), a likely result of inputs from highly productive sources such as the St. Louis River Estuary (Alexson et al., 2018a). Our findings mean that, at least
Please cite this article as: K. E. Kovalenko, E. D. Reavie, A. J. Bramburger et al., Nearshore-offshore trends in Lake Superior phytoplankton, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.09.016
K.E. Kovalenko et al. / Journal of Great Lakes Research xxx (xxxx) xxx
5
Fig. 4. Lake Superior epilimnetic phytoplankton Shannon diversity and species richness as a function of sample station depth (serving as a proxy for nearshoreoffshore gradient). Data from the two years of sampling are light- (2016) and darkshaded (2011).
Fig. 3. Epilimnetic phytoplankton biovolume (top), cell concentrations (middle) and biomass (bottom) as a function of sample station depth (serving as a proxy for nearshore-offshore gradient). Regression line is fitted for illustrative purposes only; none of the parameters were significantly related to depth; grey area indicates standard error. Data from the two years of sampling are light- (2016) and darkshaded (2011).
in the season of peak production examined in this study, offshore areas may not be as depauperate and low in standing stock as would be expected due to greater distance from watershed nutrient inputs (and nearshore areas were neither more diverse nor had greater algal biovolume or cell concentrations). Similarly, there were no consistent nearshore-offshore gradients in bacterial activity and phytoplankton biomass at a site in Lake Superior (Auer and Powell, 2004) and phytoplankton and zooplankton biomass did not vary among lake zones in Lake Michigan (Carrick et al., 2001). However, other studies observed greater chlorophyll and bacterial concentrations (Munawar and Munawar, 1978; Rao, 1978, Fahnenstiel et al., 2016) and greater respiration rates (Urban et al., 2004) in nearshore than offshore Lake Superior zones.
Station-based sampling has performed well in identifying depth patterns in other organisms including Diporeia (Evans et al., 1990; Lozano et al., 2001), Mysis (Johannsson, 1995; Pothoven et al., 2004), fish assemblages (e.g., Bronte et al., 2003; Gamble et al., 2011a,b), and processes that fuel food webs in large lakes (Sierszen et al., 2006, 2014). Nearshore-offshore patterns may be less defined for phyto-, zoo- and bacterioplankton due to their greater susceptibility to turbulent mixing and high assemblage turnover. Alternatively, high variability among stations could be limiting our ability to detect trends. Previous studies noted greater variability in nearshore algal abundance (Yurista et al., 2009); however, this trend was not obvious in our data. It is also possible that the greatest nearshore-offshore gradient in phytoplankton biovolume and cell concentrations occurs at more shallow depths and closer to the shore than was sampled by the current monitoring program. Median nearshore depth, determined based on satellite-derived chlorophyll estimates, was less than 20 m in large parts of Lake Michigan, and it was seasonally and spatially variable (Warren et al., 2018). In other studies, the nearshore was defined as the deepest point of intersection of thermocline with the lake bed in late summer and early fall which in Lake Superior corresponded to the 10-m contour (Edsall and Charlton, 1996 cf Warren et al., 2018). In our study, the closest nearshore (zone 1) included areas from 6 to 29 m deep, likely limited by sampling from a high-draft vessel and Lake Superior extreme bathymetry, and it was also more limited in spatial coverage than other zones (6 samples vs. 13–21 samples for each of the other three zones). More extensive sampling within this zone and closer to the shore could provide additional insight; however, if the nearshore ‘cutoff’ is even closer to the shore, it further limits its overall contribu-
Please cite this article as: K. E. Kovalenko, E. D. Reavie, A. J. Bramburger et al., Nearshore-offshore trends in Lake Superior phytoplankton, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.09.016
6
K.E. Kovalenko et al. / Journal of Great Lakes Research xxx (xxxx) xxx
tion to energy flow based on the total surface area (Yurista et al., 2009). Lower offshore epilimnetic biovolume may be offset by elevated biovolume in the DCL. Since early characterization of the Lake Superior DCL (Barbiero and Tuchman, 2004), several new findings have improved our understanding of this layer. Deep chlorophyll maximum is dependent upon the presence of thermal stratification and nutricline (White and Matsumoto, 2012). DCL biovolume is largely composed of sunken cells from the spring epilimnetic assemblage (Bramburger and Reavie, 2016). Further, low cell concentrations in the DCL, as compared to the epilimnion, coupled with increased DCL biovolume confirm that large-celled individuals occupy deeper layers. Other factors may explain peaks of algal biovolume including gyre convergence zones (Kerfoot et al., 2008) and up-tilting of the thermocline associated with the thermally driven coastal current (Zhou et al., 2001). Likewise, biomass peaks for zooplankton corresponded with thermocline regions rather than proximity to nearshore regions (Yurista et al., 2009). Lake Superior is characterized by stoichiometric imbalance with high nitrate and often nearly undetectable TP, and phytoplankton limitation results primarily from phosphorus and several micronutrients; however, TP was not one of the major predictors of total biovolume and cell concentrations. Similarly, Sterner et al. (2004) observed weak linkages between algal biomass and P. Both water quality and algal assemblage composition are snapshots in the state of the system, and algal responses usually lag behind water quality changes and are mediated by zooplankton grazing pressure. Nevertheless, it is interesting to see how smaller-scale, more predictive studies of algal dynamics as a function of nutrient concentrations are played out at this large scale. Diversity trends Stronger diversity than richness responses to depth (positive) and nutrients (negative) indicate that changes in diversity were due to increasing evenness rather than increasing number of taxa with depth. Due to greater variability in physical characteristics in shallower waters we expected higher diversity in nearshore samples, which has been observed in prior studies (e.g., Schweizer, 1997, Lake Constance). Globally, marine algal diversity has a positive relationship with productivity in low-nutrient systems (Vallina et al., 2014), and freshwater diversity may be determined by nutrient limitations (Interlandi and Kilham, 2001). Although phytoplankton assemblages are highly dynamic, in this study, patterns in diversity are less likely to be driven by temporal sampling effort distribution, because all sites were sampled within 2 weeks from each other.
ered in the present study, seasonal resampling in the earlier study), or it could be a true biological phenomenon, reflecting possible community shifts from three decades ago. Increasing peak biomass of Cyclotella sensu lato was related to earlier thermal stratification in other systems (Finkel et al., 2008; Thackeray et al., 2008). Urosolenia longiseta, which was the top volumetrically dominant species representing nearly 32% of the total biovolume in the current study, has not been prevalent in the fossil record. This may be due to its poor preservation of their fragile cell walls in the sedimentary record or alternatively could represent a true recent increase in dominance. Urosolenia is very lightly silicified and able to stay within the water column with minimal turbulence, much like other small, centric diatoms (Winder et al., 2009). Some of the previously observed differences between nearshore and offshore areas could be due to timing of the cruise, e.g. Schelske and Roth (1973) reported that in July, the offshore areas have not yet emerged from limnological spring, unlike the nearshore Whitefish Bay sites examined in that study. However, Cai and Reavie (2018) noted assemblage differences between nearshore and offshore assemblages, some of which were due to cryptophyte Cryptomonas pyrenoidifera which was most abundant in spring. Yet, the abundance of cyclotelloids suggests summer assemblages were established at the time of our sampling (Reavie et al., 2014a). In summary, we detected variability in algal assemblage composition and differences in dominant species across the nearshore-offshore gradient of Lake Superior, but no consistent patterns in total biovolume or cell concentrations. Phytoplankton assemblage diversity increased with depth due to greater evenness but not richness. Although watersheds are considered the major source of nutrients supporting phytoplankton communities, Lake Superior’s nearshore-offshore gradient in algal standing stock is not as pronounced as expected. This has implications for management relevant to food webs because the role of phytoplankton in the cycling of essential nutrients may be less than anticipated in nearshore environments where it is also assumed that populations of zooplankton and fish are higher (Heath et al., 2003; Johnson et al., 2004). It is possible that picoplankton plays an important role in nearshore-offshore cycling of nutrients but unfortunately, despite its known high biomass in Lake Superior (Fahnenstiel et al., 1986), our understanding of bacterial spatial distributions is poor. Detailed studies of nearshore-offshore gradients in productivity and seasonal dynamics are necessary to understand the importance of these patterns for the rest of the food web. Lake Superior is experiencing a slow increase in primary productivity (O’Beirne et al., 2017) and it is essential to determine whether productivity varies across the basin to ensure adequate representation of these regions in monitoring programs and to understand the implications for upper trophic levels.
Overall assemblage structure Bacillariophyta were the dominant division by biovolume in this study, bolstering the findings of Reavie et al. (2014b) and the more recent study which found diatoms to be the most abundant chloroplast OTUs (operational taxonomic units) among eukaryotic phytoplankton in the upper Great Lakes (Rozmarynowycz et al., 2019). Some of the dominant species in the epilimnion overlapped those reported as dominant by Bramburger and Reavie (2016) during the summer, including Lindavia bodanica and Tabellaria flocculosa. Genus Tabellaria was likewise one of the dominant taxa reported by Schelske and Roth (1973), although the total number of genera observed in that study was 28 vs. 80 reported here. Munawar and Munawar (1982) reported different biomassdominant divisions and species from the same lake. In that study, diatoms were mostly absent and the dominant algae were phytoflagellates. This could be due to different spatial extent of the two studies, consideration of the seasonal effect (not consid-
Acknowledgements We thank two anonymous reviewers for their comments and suggestions. Sampling and taxonomic analyses were supported financially by the U.S. Environmental Protection Agency through the Great Lakes National Program Office. This study was partly supported by a grant to University of Minnesota Duluth from the U.S. EPA under Cooperative Agreement GL-00E23101-2. Although the research described herein has been funded by the U.S. EPA, it has not been subjected to Agency review and may not reflect the views of the Agency. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jglr.2019.09.016.
Please cite this article as: K. E. Kovalenko, E. D. Reavie, A. J. Bramburger et al., Nearshore-offshore trends in Lake Superior phytoplankton, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.09.016
K.E. Kovalenko et al. / Journal of Great Lakes Research xxx (xxxx) xxx
References Alexson, E.E., Reavie, E.D., Axler, R.P., Yemets, S.V., Krasutsky, P.A., Edlund, M.B., Pillsbury, R.W., Desotelle, D., 2018a. Paleolimnology of a freshwater estuary to inform Area of Concern nutrient delisting efforts. J. Paleolimnol. 59, 373–395. Alexson, E.E., Wellard Kelly, H.A., Estepp, L.R., Reavie, E.D., 2018b. Morphological variation around the Pantocsekiella comensis complex in the Laurentian Great Lakes. Diatom Res. 33, 321–337. Auer, M.T., Gatzke, T.L., 2004. The spring runoff event, thermal bar formation, and cross margin transport in Lake Superior. J. Great Lakes Res. 30, 64–81. Auer, M.T., Powell, K.D., 2004. Heterotrophic bacterioplankton dynamics at a site off the southern shore of Lake Superior. J. Great Lakes Res. 30, 214–229. Austin, J., Colman, S., 2008. A century of temperature variability in Lake Superior. Limnol. Oceanogr. 53, 2724–2730. Barbiero, R.P., Tuchman, M.L., 2001. Results from the US EPA’s biological open water surveillance program of the Laurentian Great Lakes: I. Introduction and phytoplankton results. J. Great Lakes Res. 27, 134–154. Barbiero, R.P., Tuchman, M.L., 2004. The deep chlorophyll maximum in Lake Superior. J. Great Lakes Res. 30, 256–268. Bocaniov, S.A., Smith, R.E.H., 2009. Plankton metabolic balance at the margins of very large lakes: temporal variability and evidence for dominance of autochthonous processes. Freshw. Biol. 54, 345–362. Bocaniov, S.A., Barton, D.R., Schiff, S.L., Smith, R.E.H., 2013. Impact of tributary DOM and nutrient inputs on the nearshore ecology of a large, oligotrophic lake (Georgian Bay, Lake Huron, Canada). Aquat. Sci. 75, 321–332. Bramburger, A.J., Reavie, E.D., 2016. A comparison of phytoplankton communities of the deep chlorophyll layers and epilimnia of the Laurentian Great Lakes. J. Great Lakes Res. 42, 1016–1025. Bramburger, A.J., Reavie, E.D., Sgro, G.V., Estepp, L.R., Chraïbi, V.L., Pillsbury, R.W., 2017. Decreases in diatom cell size during the 20th century in the Laurentian Great Lakes: a response to warming waters?. J. Plankton Res. 39, 199–210. Breiman, L., 2001. Random forests. Machine Learn. 45, 5–32. Bronte, C.R., Ebener, M.P., Schreiner, D.R., DeVault, D.S., Petzold, M.M., Jensen, D.A., et al., 2003. Fish community change in Lake Superior, 1970–2000. Can. J. Fish. Aquat. Sci. 60, 1552–1574. Cai, M., Reavie, E.D., 2018. Pelagic zonation of water quality and phytoplankton in the Great Lakes. Limnology 19, 127–140. Carrick, H., Barbiero, R.P., Tuchman, M., 2001. Variation in Lake Michigan plankton: temporal, spatial, and historical trends. J. Great Lakes Res. 27, 467–485. Chraïbi Shaw, V.L., Kireta, A.R., Reavie, E.D., Cai, M., Brown, T.N., 2014. A paleolimnological assessment of human impacts on Lake Superior. J. Great Lakes Res. 40, 886–897. Cline, T.J., Bennington, V., Kitchel, J.F., 2013. Climate change expands the spatial extent and duration of preferred thermal habitat for Lake Superior fishes. PLoS One 8, (4). https://doi.org/10.1371/journal.pone.0062279 e62279. Edsall, T.A., Charlton, M.N., 1996. Nearshore waters of the Great Lakes. State of the Lakes Ecosystem Conference 1996, Background Paper, 162 pp. Evans, M.S., Quigley, M.A., Wojcik, J.A., 1990. Comparative ecology of Pontoporeia hoyi populations in southern Lake Michigan: the profundal region versus the slope and shelf regions. J. Great Lakes Res. 16, 27–40. Fahnenstiel, G.L., Sicko-Goad, L., Scavia, D., Stoermer, E.F., 1986. Importance of picoplankton in Lake Superior. Can. J. Fish. Aquat. Sci. 43, 235–240. Fahnenstiel, G.L., Sayers, M.J., Shuchman, R.A., Yousef, F., Pothoven, S.A., 2016. Lakewide phytoplankton production and abundance in the Upper Great Lakes: 2010–2013. J. Great Lakes Res. 42, 619–629. Finkel, Z.V., Vaillancourt, C.J., Irwin, A.J., Reavie, E.D., Smol, J.P., 2008. Environmental control of diatom community size structure varies across aquatic ecosystems. Proc. R. Soc. B. https://doi.org/10.1098/rspb.2008.1610. Gamble, A.E., Hrabik, T.R., Stockwell, J.D., Yule, D.L., 2011a. Trophic connections in Lake Superior Part I: the offshore fish community. J. Great Lakes Res. 37, 541– 549. Gamble, A.E., Hrabik, T.R., Yule, D.L., Stockwell, J.D., 2011b. Trophic connections in Lake Superior Part II: the nearshore fish community. J. Great Lakes Res. 37, 550– 560. Han, H., Allan, J.D., 2012. Uneven rise in N inputs to the Lake Michigan Basin over the 20th century corresponds to agricultural and societal transitions. Biogeochemistry 109, 175–187. Heath, R.T., Hwang, S.J., Munawar, M., 2003. A hypothesis for the assessment of the importance of microbial food web linkages in nearshore and offshore habitats of the Laurentian Great Lakes. Aquat. Ecosyst. Health Manage. 6, 231–239. Hecky, R.E., Smith, R.E.H., Barton, D.R., Guildford, S.J., Taylor, W.D., Charlton, M.N., Howell, T., 2004. The nearshore phosphorus shunt: a consequence of ecosystem engineering by dreissenids in the Laurentian Great Lakes. Can. J. Fish. Aquat. Sci. 61 (7), 1285–1293. Interlandi, S.J., Kilham, S.S., 2001. Limiting resources and the regulation of diversity in phytoplankton communities. Ecol. 82 (5), 1270–1282. Johnson, T.B., Hoff, M.H., Trebitz, A.S., Bronte, C.R., Corry, T.D., Kitchell, J.F., Lozano, S. J., Mason, D.M., Scharold, J.V., Schram, S.T., Schreiner, D.R., 2004. Spatial patterns in assemblage structures of pelagic forage fish and zooplankton in western Lake Superior. J. Great Lakes Res. 30, 395–406. Johannsson, O.E., 1995. Response of Mysis relicta population dynamics and productivity to spatial and seasonal gradients in Lake Ontario. Can. J. Fish. Aquat. Sci. 52, 1509–1522.
7
Kerfoot, W.C., Budd, J.W., Green, S.A., Cotner, J.B., Biddanda, B.A., Schwab, D.J., Vanderploeg, H.A., 2008. Doughnut in the desert: late-winter production pulse in southern Lake Michigan. Limnol. Oceanogr. 53, 589–604. Liaw, A., Wiener, M., 2002. Classification and regression by randomForest. R News 2 (3), 18–22. Likhoshway, Y.V., Kuzmina, A.Y., Potyemkina, T.G., Potyemkin, V.L., Shimaraev, M.N., 1996. The distribution of diatoms near a thermal bar in Lake Baikal. J. Great Lakes Res. 22, 5–14. Litchman, E., de Tezanos Pinto, P., Klausmeier, C.A., Thomas, M.K., Yoshiyama, K., 2010. Linking traits to species diversity and community structure in phytoplankton. In: Naselli-Flores, L., Rossetti, G. (Eds.), Fifty years after the ‘‘Homage to Santa Rosalia”: Old and new paradigms on biodiversity in aquatic ecosystems. Developments in Hydrobiology 213. Springer, Dordrecht. Lozano, S.J., Scharold, J.V., Nalepa, T.F., 2001. Recent declines in benthic macroinvertebrate densities in Lake Ontario. Can. J. Fish. Aquat. Sci. 58, 518– 529. Mortimer, C.H., 1974. Lake hydrodynamics. Int. Vereinig. Theoret. Angew. Limnol.: Mitteilungen 20 (1), 124–197. https://doi.org/10.1080/ 05384680.1974.11923886. Munawar, M., Munawar, I.F., 1978. Phytoplankton of Lake Superior 1973. J. Great Lakes Res. 4 (3–4), 415–442. Munawar, M., Munawar, I.F., 1982. Phycological studies in lakes Ontario, Erie, Huron, and Auperior. Can. J. Bot. 60, 1837–1858. Munawar, M., Munawar, I.F., 2000. Phytoplankton dynamics in the North American Great Lakes: Lakes Michigan, Superior, and Huron. Vol. II. Ecovision World Monograph Series. O’Beirne, M.D., Werne, J.P., Hecky, R.E., Johnson, T.C., Katsev, S., Reavie, E.D., 2017. Anthropogenic climate change has altered primary productivity in Lake Superior. Nat. Commun. 8, 15713. Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R., O’Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H., Szoecs, E., Wagner, H., 2016. vegan: community ecology package. R package version 2.4-1. https:// CRAN.R-project.org/package=vegan. Pothoven, S.A., Fahnenstiel, G.L., Vanderploeg, H.A., 2004. Spatial distribution, biomass and population dynamics of Mysis relicta in Lake Michigan. Hydrobiologia 522, 291–299. Ptacnik, R., Solimini, A.G., Andersen, T., Tamminen, T., Brettum, P., Lepistö, L., Willén, E., Rekolainen, S., 2008. Diversity predicts stability and resource use efficiency in natural phytoplankton communities. Proc. Natl. Acad. Sci. U.S.A. 105, 5134– 5138. Rao, S.S., 1978. Surface distributions of aerobic heterotrophs and their relationships to temperature and nutrients in Lake Superior during 1973. J. Great Lakes Res. 4, 408–414. Rao, Y.R., Schwab, D.J., 2007. Transport and mixing between the coastal and offshore waters in the Great Lakes: a review. J. Great Lakes Res. 33, 202–218. Reavie, E.D., Allinger, L.E., 2011. What have diatoms revealed about the ecological history of Lake Superior?. Aquat. Ecosyst. Health Manag. 14, 396–402. Reavie, E.D., Heathcote, A.J., Shaw Chraïbi, V.L., 2014a. Laurentian Great Lakes phytoplankton and their water quality characteristics, including a diatombased model for paleoreconstruction of phosphorus. PLoS One 9, (8) e104705. Reavie, E.D., Barbiero, R.P., Allinger, L.E., Warren, G.J., 2014b. Phytoplankton trends in the Great Lakes, 2001–2011. J. Great Lakes Res. 40, 618–639. Rozmarynowycz, M.J., Beall, B.F.N., Bullerjahn, G.S., Small, G.E., Sterner, R.W., Brovoldb, S.S., D’souza, N.A., Watson, S.B., McKay, R.M.L., 2019. Transitions in microbial communities along a 1600 km freshwater trophic gradient. J. Great Lakes Res. 45, 263–276. Schelske, C.L., Roth, J.C., 1973. Limnological Survey of Lakes Michigan, Superior, Huron and Erie. Publication No. 17. University of Michigan, Institute of Science and Technology, Great Lakes Research Division, Ann Arbor, Michigan. Schweizer, A., 1997. From littoral to pelagial: comparing the distribution of phytoplankton and ciliated protozoa along a transect. J. Plankton Res. 19, 829– 848. Sierszen, M.E., Peterson, G.S., Scharold, J.V., 2006. Depth-specific patterns in benthic–planktonic food web relationships in Lake Superior. Can. J. Fish. Aquat. Sci. 63, 1496–1503. Sierszen, M.E., Hrabik, T.R., Stockwell, J.D., Cotter, A.M., Hoffman, J.C., Yule, D.L., 2014. Depth gradients in food-web processes linking habitats in large lakes: Lake Superior as an exemplar ecosystem. Freshw. Biol. 59, 2122–2136. Sterner, R.W., Anagnostou, E., Brovold, S., Bullerjahn, G.S., Finlay, J.C., Kumar, S., McKay, R.M.L., Sherrell, R.M., 2007. Increasing stoichiometric imbalance in North America’s largest lake: Nitrification in Lake Superior. Geophys. Res. Lett. 34. https://doi.org/10.1029/2006GL028861. Sterner, R.W., 2011. C:N: P stoichiometry in Lake Superior: freshwater sea as end member. Inland Waters 1, 29–46. Sterner, R.W., Smutka, T.M., McKay, R.M.L., Xiaoming, Q., Brown, E.T., Sherrell, R.M., 2004. Phosphorus and trace metal limitation of algae and bacteria in Lake Superior. Limnol. Oceanogr. 49, 495–507. Stoermer, E.F., 1978. Phytoplankton assemblages as indicators of water quality in the Laurentian Great Lakes. Trans. Am. Microsc. Soc. 97, 2–16. Thackeray, S.J., Jones, I.D., Maberly, S.C., 2008. Long-term change in the phenology of spring phytoplankton: species-specific responses to nutrient enrichment and climatic change. J. Ecol. 3, 523–535. Urban, N.R., Apul, D.S., Auer, M.T., 2004. Community respiration rates in Lake Superior. J. Great Lakes Res. 30, 230–244.
Please cite this article as: K. E. Kovalenko, E. D. Reavie, A. J. Bramburger et al., Nearshore-offshore trends in Lake Superior phytoplankton, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.09.016
8
K.E. Kovalenko et al. / Journal of Great Lakes Research xxx (xxxx) xxx
Vallina, S.M., Follows, M.J., Dutkiewicz, S., Montoya, J.M., Cermeno, P., Loreau, M., 2014. Global relationship between phytoplankton diversity and productivity in the ocean. Nat. Commun. 5, 4299. https://doi.org/10.1038/ncomms5299. Warren, G.J., Lesht, B.M., Barbiero, R.P., 2018. Estimation of the width of the nearshore zone in Lake Michigan using eleven years of MODIS satellite imagery. J. Great Lakes Res. 44, 563–572. Willén, T., 1959. The phytoplankton of Görväln, a bay of Lake Mälaren. Oikos 10, 241–274. Winder, M., Reuter, J.E., Schladow, S.G., 2009. Lake warming favours small-sized planktonic diatom species. Proc. R. Soc. Lond. [Biol.] 276, 427–435.
White, B., Matsumoto, K., 2012. Causal mechanisms of the deep chlorophyll maximum in Lake Superior: a numerical modeling investigation. J. Great Lakes Res. 38, 504–513. Yurista, P.M., Kelly, J.R., Miller, S.E., 2009. Lake Superior zooplankton biomass: Alternate estimates from a probability-based net survey and spatially extensive LOPC surveys. J. Great Lakes Res. 35, 337–346. Yurista, P.M., Kelly, J.R., Scharold, J.V., 2016. Great Lakes nearshore–offshore: Distinct water quality regions. J. Great Lakes Res. 42, 375–385. Zhou, M., Zhu, Y., Putnam, S., Peterson, J., 2001. Mesoscale variability of physical and biological fields in southeastern Lake Superior. Limnol. Oceanogr. 46, 679–688.
Please cite this article as: K. E. Kovalenko, E. D. Reavie, A. J. Bramburger et al., Nearshore-offshore trends in Lake Superior phytoplankton, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.09.016
Contents lists available at ScienceDirect
Journal of Great Lakes Research journal homepage: www.elsevier.com/locate/ijglr
Nearshore-offshore trends in Lake Superior phytoplankton Katya E. Kovalenko a,⇑, Euan D. Reavie a, Andrew J. Bramburger a, Anne Cotter b, Michael E. Sierszen b a
Natural Resources Research Institute, University of Minnesota, Duluth, USA U.S. Environmental Protection Agency, Office of Research and Development, National Health and Ecological Effects Research Laboratory, Mid-Continent Ecology Division, Duluth, MN, USA b
a r t i c l e
i n f o
Article history: Received 11 December 2018 Accepted 6 September 2019 Available online xxxx Communicated by R. Michael McKay
Keywords: Littoral-pelagic gradient Algal community Deep chlorophyll maximum Shannon diversity
a b s t r a c t Changes in phytoplankton community composition and structure can have broad-scale ecosystem effects; however, drivers of species diversity in planktonic systems are not well understood. In lakes, a common but not thoroughly tested assumption is that shallow, nearshore waters are much more diverse and productive, and contribute considerably more material and energy to pelagic food webs than deeper waters farther offshore. Lake Superior is a large, cold, oligotrophic freshwater system which can provide insight into community organization under oligotrophic conditions. We used epilimnion and deep chlorophyll layer phytoplankton data from a lake-wide sampling program conducted in 2011 and 2016 to test whether assemblage composition, total algal biovolume, cell concentrations, diversity, and richness vary with depth. Although lake depth was an important factor in structuring assemblage composition, there were no clear nearshore-offshore gradients in cell density or biovolume despite the exposure of nearshore areas to higher concentrations of watershed-derived nutrients. Shannon diversity increased slightly with increasing depth, whereas richness was uncorrelated. Understanding of the nearshoreoffshore patterns in phytoplankton community characteristics in the Great Lakes has implications for designing monitoring strategies and for considering how further changes in climate and nutrient deposition would affect the base of the food web. Ó 2019 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.
Introduction Phytoplankton community composition and structure are important drivers of aquatic ecosystem function (e.g. Ptacnik et al., 2008); however, controls on species diversity in planktonic systems are not well understood (Litchman et al., 2010). Improved understanding of the factors regulating phytoplankton assemblage composition are essential for our ability to predict food web responses to stressors including increased nutrient loading, changes in ice cover, surface temperature, and thermocline depth (Sterner et al., 2007; Austin and Colman, 2008). In particular, nearshore-offshore patterns in phytoplankton assemblage composition, diversity, density, and cell size have implications for the effects of further changes in climate and nutrients on lower trophic levels in large lake food webs. A common but not thoroughly tested assumption is that in nutrient-limited systems, nearshore waters affected by tributary nutrient loading would be much more diverse and productive, and contribute considerably more to pelagic food webs than waters farther offshore (Bocaniov and Smith, 2009). This
in turn would affect the spatial distribution of primary producers and food availability for higher trophic levels. In the Laurentian Great Lakes, several mechanisms can physically and/or ecologically isolate nearshore communities from nearby offshore regions. For example, the development of thermal bars in the nearshore during vernal warming can inhibit delivery of allochthonous and nearshore nutrients to offshore regions prior to the onset of broader stratification (Mortimer, 1974; Stoermer, 1978; Rao and Schwab, 2007). Often, this can lead to pronounced dissimilarities in composition and density between phytoplankton communities and water quality parameters nearshore and offshore of the thermal bar (e.g. Stoermer, 1978; Likhoshway et al., 1996; Auer and Gatzke, 2004). Wind-driven turbulence is more pronounced in shallower areas, also contributing to a nearshoreoffshore gradient in water quality and distribution of species with different buoyancy and mobility. In lakes with high densities of invasive Dreissena mussels, increased filter feeding rates can severely deplete phytoplankton density in nearshore regions (Hecky et al., 2004) and have been implicated as a driver of the decline of spring phytoplankton blooms (Bocaniov et al., 2013). These mechanisms can influence lake-wide phytoplankton dynam-
⇑ Corresponding author. E-mail address: [email protected] (K.E. Kovalenko). https://doi.org/10.1016/j.jglr.2019.09.016 0380-1330/Ó 2019 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.
Please cite this article as: K. E. Kovalenko, E. D. Reavie, A. J. Bramburger et al., Nearshore-offshore trends in Lake Superior phytoplankton, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.09.016
2
K.E. Kovalenko et al. / Journal of Great Lakes Research xxx (xxxx) xxx
ics, and may have lasting implications for spatial structuring during the entire ice-free season. Lake Superior is a large, cold, oligotrophic freshwater system that exhibits a relatively long spring isothermal period, and can provide insight into community organization under extreme conditions (Sterner, 2011). Lake Superior is among the most rapidly warming lakes on Earth (Austin and Colman, 2007), and its phytoplankton assemblages have changed over the past several decades (Reavie and Allinger, 2011; Chraïbi Shaw et al., 2014). Over the past hundred years, there was a significant decline in both assemblagelevel and taxon-specific cell sizes likely related to increasing temperatures and stratification intensity (Bramburger et al., 2017); however, these changes have not been conclusively related to an underlying mechanism. Considering the relative importance of nearshore nutrient inputs (e.g., Han and Allan, 2012 for Lake Michigan) and overall nutrient imbalance in Lake Superior, we might expect non-negligible nearshore-offshore gradients in phytoplankton assemblages. Although previous studies examined the influences of pelagic water quality and seasonal variation (Barbiero and Tuchman, 2001) as well as historical trends in phytoplankton assemblage composition in Lake Superior (Reavie and Allinger, 2011; Chraïbi Shaw et al., 2014; Cai and Reavie, 2018), nearshore to offshore trends in diversity and species composition have not been described across the entire lake. Munawar and Munawar (2000), using data collected during 1973 and 1983, noted spatial variation in algal biomass throughout Lake Superior, but no clear nearshore-offshore gradient was noted. Although Cai and Reavie (2018) did not perform an analysis of depth-related variations in phytoplankton, cluster analysis of assemblages indicated separation of nearshore-pelagic and deep-pelagic samples, though such clustering did not occur for water quality or overall algal biovolume. Aside from theoretical interest, nearshore-offshore gradients in phytoplankton community characteristics are relevant to predicting the effects of present-day anthropogenic change on ecosystem services provided by Lake Superior. For example, thermal limits of pelagic fish habitat are already being affected by changing ice cover and temperature (Cline et al., 2013), and population stability of many valuable pelagic fish will depend on food availability, which is closely tied to phytoplankton assemblage dynamics. We used phytoplankton data from the 2011 and 2016 Cooperative Science and Monitoring Initiative (CSMI) to test whether a) phytoplankton assemblage composition and distribution of dominant taxa are related to depth, and b) total algal biovolume, cell concentrations, diversity and richness are different across depth zones and related to water quality. We hypothesized greater algal counts and biomass in the nearshore as well as spatially unique assemblages in the nearshore and offshore. These results will help us better define the spatial variation in Lake Superior’s primary producers, which will increase our understanding of nearshorepelagic coupling essential for functioning of large lake food webs (Sierszen et al., 2014).
Materials and methods The largest Great Lake in North America and the third largest freshwater lake by volume, Lake Superior is an unproductive system with a short growing season, and is considered a limnological end-member based on high nitrate to total phosphorus ratios (Sterner, 2011). The lake is dimictic, with a pronounced seiche and mean annual temperature of 7 °C. To quantify phytoplankton assemblage structure across the nearshore-offshore gradient, phytoplankton and water quality samples were collected in a stratified-random sampling design across 56 stations in Lake Superior. Sampling was done as part of
the CSMI collaboration, from the U.S. Environmental Protection Agency’s research vessel, the R/V Lake Guardian (draft 3.4 m). Depth categories used in the stratified-random sampling design to select sampling sites were based on a combination of information from research revealing landscape effects on nearshore waters (e.g., Yurista et al., 2016), depth-related shifts in fish assemblages (e.g., Bronte et al., 2003) and macroinvertebrates (Evans et al., 1990, Johannsson, 1995) and changes in the dominant processes supporting Great Lakes food webs with depth (Sierszen et al., 2006, 2014). Sampling was stratified by depth zones, with zone 1 (nearshore) extending from 5 m to 30 m, zone 2 from 30 to 100 m, zone 3 from 100 to 200 m and zone 4 greater than 200 m. At each site, both integrated epilimnetic samples and samples from the deep chlorophyll layer (DCL) when present were collected in 2011; whereas, only epilimnetic samples were collected in 2016. All sites (Fig. 1) were sampled September, 6–16 in 2011 and August, 29 – September, 15 in 2016 (within 10–17 days from each other). Phytoplankton samples were collected at specific depths (or the epilimnetic waters composited) and 1L of unfiltered sample was placed in a dark HDPE bottle, preserved with 7 ml Lugol’s solution, and refrigerated until further processing. After allowing the sample to concentrate by settling, a sub-sample was prepared for softbodied algal and diatom identification, by mounting a known volume of sample in 2-hydroxypropyl-methacrylate (HPMA) onto a glass slide. A complete description of this procedure is available from PhycoTech, Inc. (www.phycotech.com). In samples that were dominated by algae less than 10–20 mm in GALD (greatest axial or linear dimension), cells were counted at a minimum of 400 natural units and 15 fields at 400x. Taxa that were above 20–30 mm in GALD were counted at 200 for a minimum of 25 fields. The counts were spread evenly over 3 slides for each sample. To check for large taxa (>200 mm) the entire slide was scanned at 100x. If the sample was dominated by diatoms, a minimum of 15 fields were counted at 1000x. Diatoms were identified along with the soft-bodied algae to the lowest level practical. A lower resolution of taxonomy was done by digesting another portion of the concentrated sample in nitric acid and preparing acid-cleaned mount in Naphrax. Diatom taxa were then identified at 1000 under oil immersion. The GALD for each taxon was measured on up to 30 natural units. Other measurements included length, width and depth of different aspects of the colony or cells. Tally data were managed using ASA software, which is a proprietary product of PhycoTech, Inc. The mean and total biovolume for each taxon within a sample was calculated by the ASA software, which approximates geometric shape of each taxon using the separate length, width and depth measurements. Biovolume was converted to biomass with an assumed density of 1 mg/mm3 (Willén, 1959). Water was collected at depths determined by CTD cast water column profile and processed based on sample type. Unfiltered sample water for total nitrogen (TN) and total phosphorus (TP) were added to HCL acid-washed HDPE bottles and frozen. Sample water for filtered constituents soluble reactive phosphorus (SRP)/ NOx/NH+4, cations, anions, SiO2, and dissolved organic carbon (DOC) was filtered through a 0.45-um hydrophilic nylon filter and aliquoted into respective containers: HCL acid-washed HDPE, HNO3-washed HDPE, deionized water-soaked HDPE, and muffled amber glass vial. SRP/NOx/NH+4 sample was frozen, the anion and SiO2 sample was refrigerated, cations refrigerated after HNO3 acid preservation, and DOC refrigerated after H3PO4 acid preservation. Total suspended and volatile solids (TSS/VSS) were collected by filtering a water aliquot through a pre-washed, pre-weighed GF/C filter (WhatmanÒ), placed into individual Petri dishes and frozen until further analysis. TN, NOx, NH+4, and TP were measured on a Lachat 8000 flowinjection analyzer (APHA, 1998; Lachat QuikChem methods, Lachat
Please cite this article as: K. E. Kovalenko, E. D. Reavie, A. J. Bramburger et al., Nearshore-offshore trends in Lake Superior phytoplankton, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.09.016
K.E. Kovalenko et al. / Journal of Great Lakes Research xxx (xxxx) xxx
3
Fig. 1. Map of sampling sites in Lake Superior. Inshore refers to zones 1 and 2 (5–100 m deep).
Instruments, Loveland, CO, USA). Unfiltered subsamples were digested by persulfate digestion for TP and TN (APHA, 1998). Total phosphorus was determined by the molybdate-ascorbic acid method (APHA, 1998; Lachat QuikChem method 10-115-01-1-B). TN and dissolved NOx (0.45-lm membrane filtered) were analyzed by the cadmium reduction method (APHA, 1998; Lachat QuikChem method 10-107-04-1-O). NH+4 was determined on filtered (0.45-lm pore membrane) samples on the Lachat analyzer with the salicylate method (Lachat QuikChem method 10-107-06-2-C). The cations sodium, potassium, calcium, and magnesium were analyzed by Atomic Absorption Spectrophotometry, Standard Methods 3111B (Varian SpectrAA 240FS). Anions were analyzed by ion chromatography, U.S. EPA 300.1 (Dionex DX-600). TSS/VSS were analyzed by gravimetric analysis (APHA, 1998; method 2130B). Raw epilimnetic water quality data are presented in Electronic Supplementary Material (ESM) Table S1. Statistical analyses Simple linear regressions were used to examine relationships among continuous variables total algal biovolume, cell concentrations, biomass and depth. Biovolume and biomass data were logtransformed to satisfy parametric assumptions. To determine which water quality variables were the strongest predictors of algal assemblage attributes (total algal biovolume, cell concentrations, Shannon diversity and richness), we used a Random Forests (RF) machine learning approach (Breiman, 2001). RF is an ensemble learning method which operates by constructing large numbers of small classification trees, results of which are then tallied across the entire forest. An unbiased estimate of error is obtained at each step internally by using a different bootstrap resample from the original data. Approximately 33% of observations are used to test each run’s performance as the out-of-bag error (OOB). RF does not give the direction of the effect because the algorithm averages many small trees; thereby, it can account for variable importance by integrating cases where effects might be the opposite, which
allows increased flexibility in modeling when the importance of one factor is dependent on another. Nonmetric multidimensional scaling (NMDS) was used to visualize overlap in assemblage composition across depth zones. Due to a more limited sample size in DCL (34 stations, not resampled in 2016), several analyses were performed only on epilimnetic data. Analyses were performed in R (version 3.3.2; R Core Development Team 2016) using vegan (Oksanen et al., 2016) and randomForest (Liaw and Wiener, 2002) packages. Results Community composition and the nearshore-offshore gradient Overall, epilimnetic phytoplankton biovolume in both years and in all depth zones was dominated by Bacillariophyta, followed by Chrysophyta, Cyanophyta and Chlorophyta (Fig. 2). Individual taxa responsible for 75% of the total biovolume across all samples (or >2.0% individually) included diatoms Urosolenia longiseta, Tabellaria flocculosa, centric diatoms Lindavia comensis and Lindavia bodanica (Lindavia have undergone reclassification as Pantocsekiella; Alexson et al., 2018b), chrysophyte Dinobryon sp.1 (a taxon with similarities to Dinobryon sociale and Dinobryon cylindricum) and cyanophyte Aphanothece nidulans. In addition to these taxa, several other species were co-dominant in 2016 including Chrysosphaerella longispina, Fragilaria crotonensis, Chrysochromulina parva, Cryptomonas erosa, Dolichospermum lemmermannii, and Cryptomonas rostratiformis, and overall, algal assemblage had greater evenness than in 2011. Distribution of these dominant taxa was depth zone-dependent (ESM Fig. S1, see ESM Table S2 for raw data), although overall assemblage composition summarized using NMDS did not differ consistently with depth zone (ESM Fig. S2). In terms of cell concentrations, 93% of cells in both years were colonial cyanophytes Aphanocapsa delicatissima, A. nidulans, Rhabdoderma lineare, and the single-celled chrysophyte Chromulina, cyanophytes Synechococcus and unidentified cyanophytes.
Please cite this article as: K. E. Kovalenko, E. D. Reavie, A. J. Bramburger et al., Nearshore-offshore trends in Lake Superior phytoplankton, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.09.016
4
K.E. Kovalenko et al. / Journal of Great Lakes Research xxx (xxxx) xxx
Fig. 2. Biovolume of major epilimnetic phytoplankton divisions by depth zone. Lighter gray indicates shallower zones. Depth zones: zone 1 (nearshore) from 5 m to 30 m, zone 2 from 30 to 100 m, zone 3 from 100 to 200 m and zone 4 greater than 200 m. Data from both years were merged.
For epilimnetic samples, total algal biovolume and cell concentrations were not significantly related to depth (P = 0.67, 0.70; Fig. 3). For biovolume and biomass, year-by-zone interaction or independent effects of year and zone were not significant (twoway ANOVAs P = 0.32–0.94), whereas cell concentrations varied between years (P < 0.0001, zone-by-year interaction and zone effect P = 0.30–0.60). Sites with the greatest cell concentrations were observed in Zone 2 of the eastern part of the lake, including a site off Michipicoten Island which had some of the highest cell concentrations in both years of CSMI sampling. The DCL, sampled only in 2011, likewise had no depth-biovolume or depth-cell concentrations relationships; biovolume was 50% greater in DCL samples than in epilimnetic samples (ESM Fig. S3a, P = 0.004), while the opposite trend was true for cell concentrations (ESM Fig. S3b, P = 0.0003). Overall, few of the water quality variables varied consistently with depth: silica (Spearman r = 0.38, P = 0.004), SRP (r = 0.23, P = 0.017, DOC (r = 0.29, P = 0.0002), and TSS (r = 0.35, P = 0.0002) declined in deeper offshore sampling stations. Total epilimnetic cell concentrations had the strongest relationship with chloride and sulfate, with all water quality and habitat variables explaining 34% of variation in the RF model (ESM Fig. S4a). Total epilimnetic algal biovolume and biomass could not be successfully predicted with all available water quality variables. Diversity and richness Across 108 epilimnetic samples, richness varied from 17 to 42 species and Shannon diversity ranged from 0.66 to 2.33. Epilimnetic phytoplankton Shannon diversity tended to weakly increase with depth, when depth was used as a continuous variable in linear regression (P = 0.09; Fig. 4a). Results of this comparison were not significant for richness (P = 0.42), which ranged from 17 to 42 spe-
cies per sample. When depth was grouped by zone, there was a trend towards decreasing diversity in the nearshore zone, however, it was not significant (F = 2.462, P = 0.073, zone 1 vs. 4, P = 0.090). Richness was not significantly related to depth zone (F = 1.509, P = 0.224 Fig. 4b). Highest diversity was observed at 3 deep offshore sites (zone 4) in the middle of the basin and one offshore (zone 3) site in the western part of the basin. Richness and diversity in the epilimnion were only weakly related to some of the water quality variables. RF models with all water quality variables and depth explained 25% of variation in diversity, driven primarily by chloride, sulfate, and year, whereas for richness 12% of variation was explained mostly by sulfate, calcium, SRP and chloride (SI Fig. S4b,c). Richness and diversity in DCL samples was not closely related to those metrics in epilimnetic samples (P > 0.05). DCL richness increased weakly with increasing depth (r2 = 0.12, P = 0.04), whereas DCL diversity tended to decline, but the relationship was not significant (P = 0.08). Discussion Nearshore-offshore gradient Although lake depth was an important factor in structuring assemblage composition, there were no clear nearshore-offshore gradients in cell density or biovolume, despite the proximity of nearshore areas to much greater concentrations of allochthonous organic and inorganic nutrients from the watershed. Paleolimnological studies indicated that pelagic primary production in Lake Superior during the mid-20th century increased as a result of higher nutrient flux (Chraïbi Shaw et al., 2014), a likely result of inputs from highly productive sources such as the St. Louis River Estuary (Alexson et al., 2018a). Our findings mean that, at least
Please cite this article as: K. E. Kovalenko, E. D. Reavie, A. J. Bramburger et al., Nearshore-offshore trends in Lake Superior phytoplankton, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.09.016
K.E. Kovalenko et al. / Journal of Great Lakes Research xxx (xxxx) xxx
5
Fig. 4. Lake Superior epilimnetic phytoplankton Shannon diversity and species richness as a function of sample station depth (serving as a proxy for nearshoreoffshore gradient). Data from the two years of sampling are light- (2016) and darkshaded (2011).
Fig. 3. Epilimnetic phytoplankton biovolume (top), cell concentrations (middle) and biomass (bottom) as a function of sample station depth (serving as a proxy for nearshore-offshore gradient). Regression line is fitted for illustrative purposes only; none of the parameters were significantly related to depth; grey area indicates standard error. Data from the two years of sampling are light- (2016) and darkshaded (2011).
in the season of peak production examined in this study, offshore areas may not be as depauperate and low in standing stock as would be expected due to greater distance from watershed nutrient inputs (and nearshore areas were neither more diverse nor had greater algal biovolume or cell concentrations). Similarly, there were no consistent nearshore-offshore gradients in bacterial activity and phytoplankton biomass at a site in Lake Superior (Auer and Powell, 2004) and phytoplankton and zooplankton biomass did not vary among lake zones in Lake Michigan (Carrick et al., 2001). However, other studies observed greater chlorophyll and bacterial concentrations (Munawar and Munawar, 1978; Rao, 1978, Fahnenstiel et al., 2016) and greater respiration rates (Urban et al., 2004) in nearshore than offshore Lake Superior zones.
Station-based sampling has performed well in identifying depth patterns in other organisms including Diporeia (Evans et al., 1990; Lozano et al., 2001), Mysis (Johannsson, 1995; Pothoven et al., 2004), fish assemblages (e.g., Bronte et al., 2003; Gamble et al., 2011a,b), and processes that fuel food webs in large lakes (Sierszen et al., 2006, 2014). Nearshore-offshore patterns may be less defined for phyto-, zoo- and bacterioplankton due to their greater susceptibility to turbulent mixing and high assemblage turnover. Alternatively, high variability among stations could be limiting our ability to detect trends. Previous studies noted greater variability in nearshore algal abundance (Yurista et al., 2009); however, this trend was not obvious in our data. It is also possible that the greatest nearshore-offshore gradient in phytoplankton biovolume and cell concentrations occurs at more shallow depths and closer to the shore than was sampled by the current monitoring program. Median nearshore depth, determined based on satellite-derived chlorophyll estimates, was less than 20 m in large parts of Lake Michigan, and it was seasonally and spatially variable (Warren et al., 2018). In other studies, the nearshore was defined as the deepest point of intersection of thermocline with the lake bed in late summer and early fall which in Lake Superior corresponded to the 10-m contour (Edsall and Charlton, 1996 cf Warren et al., 2018). In our study, the closest nearshore (zone 1) included areas from 6 to 29 m deep, likely limited by sampling from a high-draft vessel and Lake Superior extreme bathymetry, and it was also more limited in spatial coverage than other zones (6 samples vs. 13–21 samples for each of the other three zones). More extensive sampling within this zone and closer to the shore could provide additional insight; however, if the nearshore ‘cutoff’ is even closer to the shore, it further limits its overall contribu-
Please cite this article as: K. E. Kovalenko, E. D. Reavie, A. J. Bramburger et al., Nearshore-offshore trends in Lake Superior phytoplankton, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.09.016
6
K.E. Kovalenko et al. / Journal of Great Lakes Research xxx (xxxx) xxx
tion to energy flow based on the total surface area (Yurista et al., 2009). Lower offshore epilimnetic biovolume may be offset by elevated biovolume in the DCL. Since early characterization of the Lake Superior DCL (Barbiero and Tuchman, 2004), several new findings have improved our understanding of this layer. Deep chlorophyll maximum is dependent upon the presence of thermal stratification and nutricline (White and Matsumoto, 2012). DCL biovolume is largely composed of sunken cells from the spring epilimnetic assemblage (Bramburger and Reavie, 2016). Further, low cell concentrations in the DCL, as compared to the epilimnion, coupled with increased DCL biovolume confirm that large-celled individuals occupy deeper layers. Other factors may explain peaks of algal biovolume including gyre convergence zones (Kerfoot et al., 2008) and up-tilting of the thermocline associated with the thermally driven coastal current (Zhou et al., 2001). Likewise, biomass peaks for zooplankton corresponded with thermocline regions rather than proximity to nearshore regions (Yurista et al., 2009). Lake Superior is characterized by stoichiometric imbalance with high nitrate and often nearly undetectable TP, and phytoplankton limitation results primarily from phosphorus and several micronutrients; however, TP was not one of the major predictors of total biovolume and cell concentrations. Similarly, Sterner et al. (2004) observed weak linkages between algal biomass and P. Both water quality and algal assemblage composition are snapshots in the state of the system, and algal responses usually lag behind water quality changes and are mediated by zooplankton grazing pressure. Nevertheless, it is interesting to see how smaller-scale, more predictive studies of algal dynamics as a function of nutrient concentrations are played out at this large scale. Diversity trends Stronger diversity than richness responses to depth (positive) and nutrients (negative) indicate that changes in diversity were due to increasing evenness rather than increasing number of taxa with depth. Due to greater variability in physical characteristics in shallower waters we expected higher diversity in nearshore samples, which has been observed in prior studies (e.g., Schweizer, 1997, Lake Constance). Globally, marine algal diversity has a positive relationship with productivity in low-nutrient systems (Vallina et al., 2014), and freshwater diversity may be determined by nutrient limitations (Interlandi and Kilham, 2001). Although phytoplankton assemblages are highly dynamic, in this study, patterns in diversity are less likely to be driven by temporal sampling effort distribution, because all sites were sampled within 2 weeks from each other.
ered in the present study, seasonal resampling in the earlier study), or it could be a true biological phenomenon, reflecting possible community shifts from three decades ago. Increasing peak biomass of Cyclotella sensu lato was related to earlier thermal stratification in other systems (Finkel et al., 2008; Thackeray et al., 2008). Urosolenia longiseta, which was the top volumetrically dominant species representing nearly 32% of the total biovolume in the current study, has not been prevalent in the fossil record. This may be due to its poor preservation of their fragile cell walls in the sedimentary record or alternatively could represent a true recent increase in dominance. Urosolenia is very lightly silicified and able to stay within the water column with minimal turbulence, much like other small, centric diatoms (Winder et al., 2009). Some of the previously observed differences between nearshore and offshore areas could be due to timing of the cruise, e.g. Schelske and Roth (1973) reported that in July, the offshore areas have not yet emerged from limnological spring, unlike the nearshore Whitefish Bay sites examined in that study. However, Cai and Reavie (2018) noted assemblage differences between nearshore and offshore assemblages, some of which were due to cryptophyte Cryptomonas pyrenoidifera which was most abundant in spring. Yet, the abundance of cyclotelloids suggests summer assemblages were established at the time of our sampling (Reavie et al., 2014a). In summary, we detected variability in algal assemblage composition and differences in dominant species across the nearshore-offshore gradient of Lake Superior, but no consistent patterns in total biovolume or cell concentrations. Phytoplankton assemblage diversity increased with depth due to greater evenness but not richness. Although watersheds are considered the major source of nutrients supporting phytoplankton communities, Lake Superior’s nearshore-offshore gradient in algal standing stock is not as pronounced as expected. This has implications for management relevant to food webs because the role of phytoplankton in the cycling of essential nutrients may be less than anticipated in nearshore environments where it is also assumed that populations of zooplankton and fish are higher (Heath et al., 2003; Johnson et al., 2004). It is possible that picoplankton plays an important role in nearshore-offshore cycling of nutrients but unfortunately, despite its known high biomass in Lake Superior (Fahnenstiel et al., 1986), our understanding of bacterial spatial distributions is poor. Detailed studies of nearshore-offshore gradients in productivity and seasonal dynamics are necessary to understand the importance of these patterns for the rest of the food web. Lake Superior is experiencing a slow increase in primary productivity (O’Beirne et al., 2017) and it is essential to determine whether productivity varies across the basin to ensure adequate representation of these regions in monitoring programs and to understand the implications for upper trophic levels.
Overall assemblage structure Bacillariophyta were the dominant division by biovolume in this study, bolstering the findings of Reavie et al. (2014b) and the more recent study which found diatoms to be the most abundant chloroplast OTUs (operational taxonomic units) among eukaryotic phytoplankton in the upper Great Lakes (Rozmarynowycz et al., 2019). Some of the dominant species in the epilimnion overlapped those reported as dominant by Bramburger and Reavie (2016) during the summer, including Lindavia bodanica and Tabellaria flocculosa. Genus Tabellaria was likewise one of the dominant taxa reported by Schelske and Roth (1973), although the total number of genera observed in that study was 28 vs. 80 reported here. Munawar and Munawar (1982) reported different biomassdominant divisions and species from the same lake. In that study, diatoms were mostly absent and the dominant algae were phytoflagellates. This could be due to different spatial extent of the two studies, consideration of the seasonal effect (not consid-
Acknowledgements We thank two anonymous reviewers for their comments and suggestions. Sampling and taxonomic analyses were supported financially by the U.S. Environmental Protection Agency through the Great Lakes National Program Office. This study was partly supported by a grant to University of Minnesota Duluth from the U.S. EPA under Cooperative Agreement GL-00E23101-2. Although the research described herein has been funded by the U.S. EPA, it has not been subjected to Agency review and may not reflect the views of the Agency. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jglr.2019.09.016.
Please cite this article as: K. E. Kovalenko, E. D. Reavie, A. J. Bramburger et al., Nearshore-offshore trends in Lake Superior phytoplankton, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.09.016
K.E. Kovalenko et al. / Journal of Great Lakes Research xxx (xxxx) xxx
References Alexson, E.E., Reavie, E.D., Axler, R.P., Yemets, S.V., Krasutsky, P.A., Edlund, M.B., Pillsbury, R.W., Desotelle, D., 2018a. Paleolimnology of a freshwater estuary to inform Area of Concern nutrient delisting efforts. J. Paleolimnol. 59, 373–395. Alexson, E.E., Wellard Kelly, H.A., Estepp, L.R., Reavie, E.D., 2018b. Morphological variation around the Pantocsekiella comensis complex in the Laurentian Great Lakes. Diatom Res. 33, 321–337. Auer, M.T., Gatzke, T.L., 2004. The spring runoff event, thermal bar formation, and cross margin transport in Lake Superior. J. Great Lakes Res. 30, 64–81. Auer, M.T., Powell, K.D., 2004. Heterotrophic bacterioplankton dynamics at a site off the southern shore of Lake Superior. J. Great Lakes Res. 30, 214–229. Austin, J., Colman, S., 2008. A century of temperature variability in Lake Superior. Limnol. Oceanogr. 53, 2724–2730. Barbiero, R.P., Tuchman, M.L., 2001. Results from the US EPA’s biological open water surveillance program of the Laurentian Great Lakes: I. Introduction and phytoplankton results. J. Great Lakes Res. 27, 134–154. Barbiero, R.P., Tuchman, M.L., 2004. The deep chlorophyll maximum in Lake Superior. J. Great Lakes Res. 30, 256–268. Bocaniov, S.A., Smith, R.E.H., 2009. Plankton metabolic balance at the margins of very large lakes: temporal variability and evidence for dominance of autochthonous processes. Freshw. Biol. 54, 345–362. Bocaniov, S.A., Barton, D.R., Schiff, S.L., Smith, R.E.H., 2013. Impact of tributary DOM and nutrient inputs on the nearshore ecology of a large, oligotrophic lake (Georgian Bay, Lake Huron, Canada). Aquat. Sci. 75, 321–332. Bramburger, A.J., Reavie, E.D., 2016. A comparison of phytoplankton communities of the deep chlorophyll layers and epilimnia of the Laurentian Great Lakes. J. Great Lakes Res. 42, 1016–1025. Bramburger, A.J., Reavie, E.D., Sgro, G.V., Estepp, L.R., Chraïbi, V.L., Pillsbury, R.W., 2017. Decreases in diatom cell size during the 20th century in the Laurentian Great Lakes: a response to warming waters?. J. Plankton Res. 39, 199–210. Breiman, L., 2001. Random forests. Machine Learn. 45, 5–32. Bronte, C.R., Ebener, M.P., Schreiner, D.R., DeVault, D.S., Petzold, M.M., Jensen, D.A., et al., 2003. Fish community change in Lake Superior, 1970–2000. Can. J. Fish. Aquat. Sci. 60, 1552–1574. Cai, M., Reavie, E.D., 2018. Pelagic zonation of water quality and phytoplankton in the Great Lakes. Limnology 19, 127–140. Carrick, H., Barbiero, R.P., Tuchman, M., 2001. Variation in Lake Michigan plankton: temporal, spatial, and historical trends. J. Great Lakes Res. 27, 467–485. Chraïbi Shaw, V.L., Kireta, A.R., Reavie, E.D., Cai, M., Brown, T.N., 2014. A paleolimnological assessment of human impacts on Lake Superior. J. Great Lakes Res. 40, 886–897. Cline, T.J., Bennington, V., Kitchel, J.F., 2013. Climate change expands the spatial extent and duration of preferred thermal habitat for Lake Superior fishes. PLoS One 8, (4). https://doi.org/10.1371/journal.pone.0062279 e62279. Edsall, T.A., Charlton, M.N., 1996. Nearshore waters of the Great Lakes. State of the Lakes Ecosystem Conference 1996, Background Paper, 162 pp. Evans, M.S., Quigley, M.A., Wojcik, J.A., 1990. Comparative ecology of Pontoporeia hoyi populations in southern Lake Michigan: the profundal region versus the slope and shelf regions. J. Great Lakes Res. 16, 27–40. Fahnenstiel, G.L., Sicko-Goad, L., Scavia, D., Stoermer, E.F., 1986. Importance of picoplankton in Lake Superior. Can. J. Fish. Aquat. Sci. 43, 235–240. Fahnenstiel, G.L., Sayers, M.J., Shuchman, R.A., Yousef, F., Pothoven, S.A., 2016. Lakewide phytoplankton production and abundance in the Upper Great Lakes: 2010–2013. J. Great Lakes Res. 42, 619–629. Finkel, Z.V., Vaillancourt, C.J., Irwin, A.J., Reavie, E.D., Smol, J.P., 2008. Environmental control of diatom community size structure varies across aquatic ecosystems. Proc. R. Soc. B. https://doi.org/10.1098/rspb.2008.1610. Gamble, A.E., Hrabik, T.R., Stockwell, J.D., Yule, D.L., 2011a. Trophic connections in Lake Superior Part I: the offshore fish community. J. Great Lakes Res. 37, 541– 549. Gamble, A.E., Hrabik, T.R., Yule, D.L., Stockwell, J.D., 2011b. Trophic connections in Lake Superior Part II: the nearshore fish community. J. Great Lakes Res. 37, 550– 560. Han, H., Allan, J.D., 2012. Uneven rise in N inputs to the Lake Michigan Basin over the 20th century corresponds to agricultural and societal transitions. Biogeochemistry 109, 175–187. Heath, R.T., Hwang, S.J., Munawar, M., 2003. A hypothesis for the assessment of the importance of microbial food web linkages in nearshore and offshore habitats of the Laurentian Great Lakes. Aquat. Ecosyst. Health Manage. 6, 231–239. Hecky, R.E., Smith, R.E.H., Barton, D.R., Guildford, S.J., Taylor, W.D., Charlton, M.N., Howell, T., 2004. The nearshore phosphorus shunt: a consequence of ecosystem engineering by dreissenids in the Laurentian Great Lakes. Can. J. Fish. Aquat. Sci. 61 (7), 1285–1293. Interlandi, S.J., Kilham, S.S., 2001. Limiting resources and the regulation of diversity in phytoplankton communities. Ecol. 82 (5), 1270–1282. Johnson, T.B., Hoff, M.H., Trebitz, A.S., Bronte, C.R., Corry, T.D., Kitchell, J.F., Lozano, S. J., Mason, D.M., Scharold, J.V., Schram, S.T., Schreiner, D.R., 2004. Spatial patterns in assemblage structures of pelagic forage fish and zooplankton in western Lake Superior. J. Great Lakes Res. 30, 395–406. Johannsson, O.E., 1995. Response of Mysis relicta population dynamics and productivity to spatial and seasonal gradients in Lake Ontario. Can. J. Fish. Aquat. Sci. 52, 1509–1522.
7
Kerfoot, W.C., Budd, J.W., Green, S.A., Cotner, J.B., Biddanda, B.A., Schwab, D.J., Vanderploeg, H.A., 2008. Doughnut in the desert: late-winter production pulse in southern Lake Michigan. Limnol. Oceanogr. 53, 589–604. Liaw, A., Wiener, M., 2002. Classification and regression by randomForest. R News 2 (3), 18–22. Likhoshway, Y.V., Kuzmina, A.Y., Potyemkina, T.G., Potyemkin, V.L., Shimaraev, M.N., 1996. The distribution of diatoms near a thermal bar in Lake Baikal. J. Great Lakes Res. 22, 5–14. Litchman, E., de Tezanos Pinto, P., Klausmeier, C.A., Thomas, M.K., Yoshiyama, K., 2010. Linking traits to species diversity and community structure in phytoplankton. In: Naselli-Flores, L., Rossetti, G. (Eds.), Fifty years after the ‘‘Homage to Santa Rosalia”: Old and new paradigms on biodiversity in aquatic ecosystems. Developments in Hydrobiology 213. Springer, Dordrecht. Lozano, S.J., Scharold, J.V., Nalepa, T.F., 2001. Recent declines in benthic macroinvertebrate densities in Lake Ontario. Can. J. Fish. Aquat. Sci. 58, 518– 529. Mortimer, C.H., 1974. Lake hydrodynamics. Int. Vereinig. Theoret. Angew. Limnol.: Mitteilungen 20 (1), 124–197. https://doi.org/10.1080/ 05384680.1974.11923886. Munawar, M., Munawar, I.F., 1978. Phytoplankton of Lake Superior 1973. J. Great Lakes Res. 4 (3–4), 415–442. Munawar, M., Munawar, I.F., 1982. Phycological studies in lakes Ontario, Erie, Huron, and Auperior. Can. J. Bot. 60, 1837–1858. Munawar, M., Munawar, I.F., 2000. Phytoplankton dynamics in the North American Great Lakes: Lakes Michigan, Superior, and Huron. Vol. II. Ecovision World Monograph Series. O’Beirne, M.D., Werne, J.P., Hecky, R.E., Johnson, T.C., Katsev, S., Reavie, E.D., 2017. Anthropogenic climate change has altered primary productivity in Lake Superior. Nat. Commun. 8, 15713. Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R., O’Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H., Szoecs, E., Wagner, H., 2016. vegan: community ecology package. R package version 2.4-1. https:// CRAN.R-project.org/package=vegan. Pothoven, S.A., Fahnenstiel, G.L., Vanderploeg, H.A., 2004. Spatial distribution, biomass and population dynamics of Mysis relicta in Lake Michigan. Hydrobiologia 522, 291–299. Ptacnik, R., Solimini, A.G., Andersen, T., Tamminen, T., Brettum, P., Lepistö, L., Willén, E., Rekolainen, S., 2008. Diversity predicts stability and resource use efficiency in natural phytoplankton communities. Proc. Natl. Acad. Sci. U.S.A. 105, 5134– 5138. Rao, S.S., 1978. Surface distributions of aerobic heterotrophs and their relationships to temperature and nutrients in Lake Superior during 1973. J. Great Lakes Res. 4, 408–414. Rao, Y.R., Schwab, D.J., 2007. Transport and mixing between the coastal and offshore waters in the Great Lakes: a review. J. Great Lakes Res. 33, 202–218. Reavie, E.D., Allinger, L.E., 2011. What have diatoms revealed about the ecological history of Lake Superior?. Aquat. Ecosyst. Health Manag. 14, 396–402. Reavie, E.D., Heathcote, A.J., Shaw Chraïbi, V.L., 2014a. Laurentian Great Lakes phytoplankton and their water quality characteristics, including a diatombased model for paleoreconstruction of phosphorus. PLoS One 9, (8) e104705. Reavie, E.D., Barbiero, R.P., Allinger, L.E., Warren, G.J., 2014b. Phytoplankton trends in the Great Lakes, 2001–2011. J. Great Lakes Res. 40, 618–639. Rozmarynowycz, M.J., Beall, B.F.N., Bullerjahn, G.S., Small, G.E., Sterner, R.W., Brovoldb, S.S., D’souza, N.A., Watson, S.B., McKay, R.M.L., 2019. Transitions in microbial communities along a 1600 km freshwater trophic gradient. J. Great Lakes Res. 45, 263–276. Schelske, C.L., Roth, J.C., 1973. Limnological Survey of Lakes Michigan, Superior, Huron and Erie. Publication No. 17. University of Michigan, Institute of Science and Technology, Great Lakes Research Division, Ann Arbor, Michigan. Schweizer, A., 1997. From littoral to pelagial: comparing the distribution of phytoplankton and ciliated protozoa along a transect. J. Plankton Res. 19, 829– 848. Sierszen, M.E., Peterson, G.S., Scharold, J.V., 2006. Depth-specific patterns in benthic–planktonic food web relationships in Lake Superior. Can. J. Fish. Aquat. Sci. 63, 1496–1503. Sierszen, M.E., Hrabik, T.R., Stockwell, J.D., Cotter, A.M., Hoffman, J.C., Yule, D.L., 2014. Depth gradients in food-web processes linking habitats in large lakes: Lake Superior as an exemplar ecosystem. Freshw. Biol. 59, 2122–2136. Sterner, R.W., Anagnostou, E., Brovold, S., Bullerjahn, G.S., Finlay, J.C., Kumar, S., McKay, R.M.L., Sherrell, R.M., 2007. Increasing stoichiometric imbalance in North America’s largest lake: Nitrification in Lake Superior. Geophys. Res. Lett. 34. https://doi.org/10.1029/2006GL028861. Sterner, R.W., 2011. C:N: P stoichiometry in Lake Superior: freshwater sea as end member. Inland Waters 1, 29–46. Sterner, R.W., Smutka, T.M., McKay, R.M.L., Xiaoming, Q., Brown, E.T., Sherrell, R.M., 2004. Phosphorus and trace metal limitation of algae and bacteria in Lake Superior. Limnol. Oceanogr. 49, 495–507. Stoermer, E.F., 1978. Phytoplankton assemblages as indicators of water quality in the Laurentian Great Lakes. Trans. Am. Microsc. Soc. 97, 2–16. Thackeray, S.J., Jones, I.D., Maberly, S.C., 2008. Long-term change in the phenology of spring phytoplankton: species-specific responses to nutrient enrichment and climatic change. J. Ecol. 3, 523–535. Urban, N.R., Apul, D.S., Auer, M.T., 2004. Community respiration rates in Lake Superior. J. Great Lakes Res. 30, 230–244.
Please cite this article as: K. E. Kovalenko, E. D. Reavie, A. J. Bramburger et al., Nearshore-offshore trends in Lake Superior phytoplankton, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.09.016
8
K.E. Kovalenko et al. / Journal of Great Lakes Research xxx (xxxx) xxx
Vallina, S.M., Follows, M.J., Dutkiewicz, S., Montoya, J.M., Cermeno, P., Loreau, M., 2014. Global relationship between phytoplankton diversity and productivity in the ocean. Nat. Commun. 5, 4299. https://doi.org/10.1038/ncomms5299. Warren, G.J., Lesht, B.M., Barbiero, R.P., 2018. Estimation of the width of the nearshore zone in Lake Michigan using eleven years of MODIS satellite imagery. J. Great Lakes Res. 44, 563–572. Willén, T., 1959. The phytoplankton of Görväln, a bay of Lake Mälaren. Oikos 10, 241–274. Winder, M., Reuter, J.E., Schladow, S.G., 2009. Lake warming favours small-sized planktonic diatom species. Proc. R. Soc. Lond. [Biol.] 276, 427–435.
White, B., Matsumoto, K., 2012. Causal mechanisms of the deep chlorophyll maximum in Lake Superior: a numerical modeling investigation. J. Great Lakes Res. 38, 504–513. Yurista, P.M., Kelly, J.R., Miller, S.E., 2009. Lake Superior zooplankton biomass: Alternate estimates from a probability-based net survey and spatially extensive LOPC surveys. J. Great Lakes Res. 35, 337–346. Yurista, P.M., Kelly, J.R., Scharold, J.V., 2016. Great Lakes nearshore–offshore: Distinct water quality regions. J. Great Lakes Res. 42, 375–385. Zhou, M., Zhu, Y., Putnam, S., Peterson, J., 2001. Mesoscale variability of physical and biological fields in southeastern Lake Superior. Limnol. Oceanogr. 46, 679–688.
Please cite this article as: K. E. Kovalenko, E. D. Reavie, A. J. Bramburger et al., Nearshore-offshore trends in Lake Superior phytoplankton, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.09.016