Under-ice mesocosms reveal the primacy of light but the importance of zooplankton in winter phytoplankton dynamics

Factors that regulate planktonic communities under lake ice may be vastly different than the open-water season. However, under-ice food webs in temperate lakes are poorly understood, despite expected changes in light availability, ice cover, and snowfall associated with climate change. We hypothesized that light limitation (bottom-up control) outweighs zooplankton grazing (top-down control) on phytoplankton biovolume and community structure under ice in a north temperate lake. Using in situ under-ice mesocosms, we found that light had stronger effects on phytoplankton abundance than zooplankton, as expected. Specifically, low light limited growth of diatoms, cryptophytes, chrysophytes, and chlorophytes. Zooplankton, however, also significantly affected phytoplankton by decreasing diatoms and cryptophytes, in contrast to the common assumption that zooplankton grazing has negligible effects under ice. Ammonia and soluble reactive phosphorus decreased in high light treatments presumably through uptake by phytoplankton, whereas ammonia and soluble reactive phosphorus increased in high zooplankton treatments, likely through excretion. In situ experimental studies are commonly applied to understand food web dynamics in open-water conditions, but are extremely rare under ice. Our results suggest that changes in the light environment under ice have significant, rapid effects on phytoplankton growth and community structure and that zooplankton may play a more active role in winter food webs than previously thought. Changes in snow and ice dynamics associated with climate change may alter the light environment in ice-covered systems and significantly influence community structure.


Introduction
The relatively recent and rapid changes in winter conditions in temperate zones have led to declining ice cover in temperate lakes (Sharma et al. 2019)  Light is widely accepted as the major driver of phytoplankton production under ice because it limits photosynthetic activity and can be highly variable depending on winter conditions (Salonen et al. 2009;Hampton et al. 2015), including day length, ice thickness, ice clarity, and especially snow cover (Bolsenga and Vanderploeg 1992). Physical factors such as light and temperature are considered the main drivers of winter phytoplankton biovolume in the Plankton Ecology Group (PEG) Model that describes planktonic community succession in temperate lakes (Sommer et al. 1986(Sommer et al. , 2012. Predictably, total phytoplankton production is highest when light transmission is highest during winter (Maeda and Ichimura 1973).
Additionally, community structure may be highly sensitive to changes in light levels because different taxa may be better equipped to deal with different light conditions during winter. For example, diatoms have been found at bloom densities in conditions with clear ice and minimal snowpack (Katz et al. 2015). Some phytoplankton taxa with adaptations that allow them to succeed during light-limited conditions, such as mixotrophic or mobile flagellated taxa, are often found in high proportions under ice (Özkundakci et al. 2016). To this end, we may expect a higher proportion of known mixotrophic taxa, such as chrysophytes (Sanders et al. 1990), when light is limited, and higher total phytoplankton biovolume with high light transmission.
Zooplankton can control phytoplankton biovolume and community structure during the open water season through grazing, including selective feeding on specific phytoplankton groups (Bergquist et al. 1985). However, less is known about top-down effects of zooplankton under ice, which makes interpretation and prediction of food web interactions under ice and entering the spring phytoplankton bloom difficult (Sommer et al. 2012 suggesting that winter zooplankton communities that are actively feeding may influence underice phytoplankton community structure and biovolume. Changes in nutrient concentrations under ice may be closely linked to changes in phytoplankton and zooplankton communities. Nutrients are generally not expected to limit phytoplankton growth during winter, especially in eutrophic systems (Sommer et al. 2012).
However, we may still expect a signal from phytoplankton communities in nutrient concentrations. Higher phytoplankton growth generally corresponds with reductions in forms of nitrogen and phosphorus that can be assimilated quickly, such as soluble reactive phosphorus (SRP), ammonia, and sometimes nitrate (Glibert et al. 2016). Manipulation of zooplankton biomass may also alter nutrient levels through excretion (Oliver et al. 2015). Other sources of nutrient inputs that are important in the open-water season, including phosphorus release from sediment (Penn et al. 2000), may also be a significant source of phosphorus under ice if oxygen is limited (Joung et al. 2017). We expected that nutrient concentrations would respond to changes in plankton communities but not to the extent that they affected phytoplankton growth (Sommer et al. 2012).
In this study, we used an in situ under ice carboy experiment to test the relative importance of zooplankton grazing versus light limitation on winter phytoplankton biovolume, community structure, and nutrient concentrations in a north temperate lake. We hypothesized that both low light and high zooplankton grazing would decrease phytoplankton biovolume, but that light would be quantitatively more important than zooplankton to drive total phytoplankton biovolume and have a larger impact on phytoplankton community structure under ice. Our hypothesis follows the PEG Model (Sommer et al. 1986) and its recent update (Sommer et al.  approximately 0.5 m below the ice by a steel cable harness connected to a PVC anchor in the shape of an "X." PVC was placed on loose wood blocks to prevent it from freezing into the ice. B.) Greenhouse shade cloth covers blocked 85% of incoming light to simulate snow cover (photo credit: Hannah Lachance). C.) We crossed two light levels (unshaded and shaded) with three zooplankton levels with four replicates per treatment (see Results for zooplankton abundances).
We tested four replicates for each of six treatments: (1) low zooplankton/unshaded, (2) low zooplankton /shaded, (3) medium zooplankton/unshaded, (4) medium zooplankton/shaded, (5) high zooplankton/unshaded, and (6) high zooplankton/shaded ( Fig. 1B/C). We mixed ambient water for all treatments in a 208-L plastic barrel. The barrel was filled by raising and lowering the intake of a hand pump throughout the top 4.0 m of the water column and filtering through a 350-µm sieve to remove large zooplankton. Pilot work indicated 350-µm was the best mesh size to remove large grazing zooplankton but kept most colonial phytoplankton. The sieved ambient lake water in the barrel was mixed constantly as each group of six 22.7-L carboys (i.e., one replicate for each treatment) was filled.
We then added zooplankton treatments directly to carboys. Two of the six carboys had no zooplankton added, two had ambient zooplankton added, and two had 10X natural abundance of large (i.e., sieved) zooplankton added. Zooplankton for the treatments were collected with a 13cm diameter, 64-µm Wisconsin net through the upper 4.0 m of the water column and then retained on a 350-µm sieve. Desired densities were achieved for the ambient zooplankton treatment by using a plankton splitter and adding half of the sieved net haul to each of the two ambient abundance treatments because half of the volume strained for a 4-m tow was approximately equal to the carboy volume. The high zooplankton treatments had sieved zooplankton from five zooplankton net tows added to each carboy. Finally, we covered one carboy from each zooplankton treatment with greenhouse shade cloth that blocked 85% of incoming light to simulate the light-limiting effects of snow cover. The set-up process was repeated twice on 25 January and twice on 26 January for a total of 4 replicates per treatment.
All setup processes, including filtering zooplankton, were performed in the field at the time of deployment. We affixed a MK-9 light and temperature sensor (Wildlife Computers Inc., Redmond, WA, USA) to the outside of one carboy without shade cloth and between another carboy and its shade cloth on 25 January. We also added a HOBO temperature sensor (Onset Computer Corporation, Bourne, MA, USA) to the inside of one shaded and one unshaded carboy on 26 January. Light values from MK-9 sensors were converted from relative units to µE·m -2 ·s -1 (Kotwicki et al. 2009). We did not leave any head space in the carboys to simulate the sealed conditions of an ice-covered lake. The ice over the carboys re-froze within one day of deployment.
Carboys were extracted from the lake 14 days after deployment. Although most summer carboy experiments are much shorter in duration (e.g., Griniene et al. 2016), we expected that slow phytoplankton growth rates at low temperatures (Cloern 1977) would necessitate a longer incubation time. Each carboy was inverted several times to homogenize contents before opening.
We sampled phytoplankton, nutrients, and zooplankton from each carboy. We collected three phytoplankton samples by filling 125-mL bottles with water and its associated phytoplankton community, which was later measured out to 100 mL and preserved in Lugol's solution. One 500-mL water sample was taken per carboy, and split into portions for different nutrient analyses once back at the lab as follows. A 100-mL sample was preserved with three drops of sulfuric acid to achieve a pH of 2.0 for later analysis of total phosphorus (TP), total nitrogen (TN), and total organic carbon (TOC). Two 45-mL samples were filtered through 0.45-µm syringe filters and frozen: one for soluble reactive phosphorus (SRP), and one for ammonia and nitrate + nitrite (NO x ) quantification. We strained the remaining 21.84 L of water through a 20-µm Wisconsin net to sample crustacean zooplankton and rotifers. All zooplankton and rotifers were anesthetized with Alka Seltzer before preservation with 70% ethanol.
We identified phytoplankton to genus and counted full fields of view at 400x until reaching at least 300 natural units (cells for single-celled species or colonies for colonial species). We measured ten natural units per genus for each sample to calculate biovolume using Spot Basic software (Spot Imaging, Sterling Heights, MI). Dimensions measured were dependent on phytoplankton taxa present, e.g., we measured diameter for spherical cells and diameter and length for ellipsoid cells (Hillebrand et al. 1999). Within colonies, we measured ten individual cells per colony when possible. If fewer than ten cells were present or clearly visible, we measured all cells. We used those median measurements to calculate taxon-specific biovolume for each sample (Hillebrand et al. 1999), then taxon-specific biovolume was multiplied by cell abundance to estimate total biovolume per sample for each taxon. We only processed one out of the three phytoplankton samples collected per carboy because replicates within each carboy were very similar and would not have added to statistical power due to pseudoreplication. Analysis of three pairs of phytoplankton samples from the same carboys showed an average of 4.2% difference in cell counts for each genus. We measured nutrient concentrations primarily to ensure that we did not artificially limit nutrients in our study. Nutrient samples were either stored frozen (SRP, ammonia, and NO x ) or acidified and refrigerated (TN, TOC, and TP) until analysis. We measured TN and TOC on a TOC-L total organic carbon analyzer with a TNM-L TN measuring unit (Shimadzu Corporation, Kyoto, Japan). We analyzed TP and SRP using the molybdenum colorimetry method (USEPA 1993) with ascorbic acid modification and a persulfate digestion for TP on a Shimadzu UV-VIS

Results
Light and temperature behaved as expected throughout the experiments. Light was reduced by greenhouse shade cloth (Supporting Information Fig. S1). Temperature remained consistent between light and shaded treatments, although internal carboy temperatures were slightly higher than external water temperatures. However, the difference between internal and external temperature (<0.3°C) was small compared to the overall increase in water temperature over the course of the experiment (Fig. S1).
Zooplankton abundance and biomass were significantly different between treatments (ANOVA; F 1,22 = 225.1, p < 0.0001), but differences were not as great as intended (Fig. 2). Our low zooplankton treatments averaged (± SD) 64.5 ± 14.21 µg dry/L, medium zooplankton treatments averaged 99.8 ± 5.36 µg dry/L, and high zooplankton treatments averaged 338.6 ± 56.93 µg dry/L. That is, our intended "10x" zooplankton level had 3.4x the zooplankton biomass as our intended "1x" treatment. Consequently, we refer to zooplankton levels as low, medium, and high rather than 0x, 1x, and 10x. Zooplankton biomass was dominated by Diacyclops thomasi and zooplankton abundance was dominated by both D. thomasi and copepod nauplii ( Fig. S2). Crustacean zooplankton body size followed a bimodal distribution with a smaller peak that represented copepod nauplii and Chydorus spp. and the larger peak represented adult zooplankton (Fig. S3). The smaller zooplankton were depleted in treatments with higher densities of large-bodied zooplankton (Fig. S3). Differences in phytoplankton community composition among treatments was primarily driven by light, although some groups were also significantly affected by zooplankton levels.
The biovolume of total phytoplankton and chlorophytes, and abundance and biovolume of diatoms, cryptomonads, and chrysophytes significantly increased with light (    Non-metric multidimensional scaling resulted in a clear distinction in phytoplankton 1 communities between shaded and unshaded treatments for all axes, but little effect from 2 zooplankton (Fig. 5). Treatments at the beginning of experiments were more similar to shaded 3 treatments than unshaded treatments. Our final ordination had three axes to reduce stress from 4 0.22 with two axes to 0.15 with three axes (Fig. 5). Phytoplankton genera that drove separations 5 along nMDS axes were mostly rare species that were only found in some treatments such as 6 Crucigenia, Diatoma, Aphanocapsa, and Dictyosphaerium for the first axis and Oocystis, 7 Stephanodiscus, Selenastrum, and Gonium for the second axis (Fig. S4). More common genera 8 such as Chrysochromulina and Woronichinia were found across treatments, so contributed little 9 to separations in nMDS axes (Fig. S4). PerMANOVA indicated a significant effect of light 1 0 treatment (pseudoF 1,20 = 12.3; p=0.001) but no effect of zooplankton treatment (pseudoF 2,20 = 1 1 1.36; p=0.184) on overall phytoplankton community composition. Rotifer abundance was not dependent on experimental conditions, but rotifer biomass their small size (Fig. S5). Other rotifers found in mesocosms were Keratella hiemalis, 2 3 Brachionus angularis, Asplanchna spp., and Filinia spp.

5
SRP and ammonia significantly decreased with light and significantly increased with 2 6 zooplankton. Light explained 73% of the variation in SRP and 72% of the variation in ammonia, 2 7 while zooplankton explained 15% of the variation in SRP and 21% in ammonia (Table 1).

8
Zooplankton also significantly increased TN and explained 20% of the variance between 2 9 treatments ( minimum and maximum of data. ice-out.

2
Light had a stronger effect on phytoplankton community composition than zooplankton, 5 3 supporting the hypothesis that light is the main driver of phytoplankton biovolume under ice.

4
The difference was apparent in both nMDS visualizations and ANOVA analysis of specific treatments. In particular, we observed higher proportion of chrysophytes (mostly Chrysococcus) phytoplankton community structure over relatively short time scales.

8
Light treatments significantly altered ammonia and SRP presumably through increased 6 9 phytoplankton production. NO x decreased in treatments with high phytoplankton levels, but phytoplankton production is highest.

6
Despite discrepancies between expected and actual zooplankton biomass, zooplankton Mesocosms were effective in maintaining physical parameters such as light and 1 0 7 temperature but were less predictable in maintaining zooplankton levels. Water temperatures 1 0 8 increased throughout the course of the experiment similarly between shaded and unshaded 1 0 9 treatments, so temperature was not an influential factor in differences between treatments. The 1 1 0 shade cloth covers maintained differences in light readings between treatments, including during 1 1 1 a significant snowfall the night before we began extracting carboys at the end of the experiment 1 1 2 (February 8). Zooplankton maintained differences between treatments, but at lower magnitudes 1 1 3 than expected. The most likely explanation is that zooplankton experienced mortality as they In this experiment, we demonstrated that light is the main driving factor of phytoplankton techniques to ice-covered ecosystems is an important step in disentangling food web drivers 1 3 7 under ice. was enough to significantly alter phytoplankton community structure, indicating that minor 1 4 5 events such as rain-on-snow events that melt a layer of snow or slightly altered snowfall totals outside the scope of this study, we would expect that shorter ice duration would alter spring phytoplankton communities will likely change rapidly with climate change. Cooperative Agreement 80NSSC18K1394 P00001.