Seasonal Drivers of Nutrient Concentration in Lake Ecosystems: Evaluating Climate and Ice Phenology Characteristics in a Shallow Eutrophic Lake

doi:10.21203/rs.3.rs-2993097/v1

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Seasonal Drivers of Nutrient Concentration in Lake Ecosystems: Evaluating Climate and Ice Phenology Characteristics in a Shallow Eutrophic Lake

https://doi.org/10.21203/rs.3.rs-2993097/v1

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The climate is shifting across the globe and understanding seasonal drivers of nutrient concentration in lake ecosystems is important to further our understanding of drivers of biotic change. Additionally, the degree to which winter dynamics in lake ecosystems respond to climate and ecosystem level factors and their role in influencing nutrient cycling processes is unclear but important for understanding and managing potential changes. The overall objective of this study was to evaluate seasonal water column nutrient concentrations, climatic conditions, and ice phenology characteristics in a small shallow eutrophic lake. Water column nutrients were sampled from October 2009 to September 2020 at two locations at Petit-lac-Saint-François (Lake Inlet and Lake Outlet) in south-central Quebec, Canada. During this sampling period, ice phenology characteristics and phytoplankton samples were also collected. The degree of correlation with nutrient concentrations varied between climate indices across different time scales and monitoring locations. The timing of lake ice varied during the study period ranging from 102 to 166 days of ice coverage with the duration of ice coverage being significantly correlated with dissolved inorganic nutrient accumulation during the ice-on period. Moreover, phytoplankton composition varied between ice-on/off periods and species richness significantly declined. Studies that evaluate winter limnological characteristics are limited but important to understand the seasonal dynamics of nutrient cycling in these systems, especially given how the climate is shifting. Moreover, the climate is a significant driver of lake biogeochemical with respect to both inputs and internal processes.

ice phenology

temperate lakes

climate dynamics

nutrient cycling

nitrogen

phosphorus

Several studies have documented the reduced duration and coverage of ice on lakes across the world (Hampton et al. 2017; Filazzola et al. 2020; Dugan 2021; Cavaliere et al. 2021). A growing need is to understand both the biogeochemical processes under the ice and also the consequences of reduced ice coverage on in-lake processes. Ice is a physical barrier that limits energy inputs into lakes by dampening wind energy. Ice (and snow) can limit light penetration, while ice can also limit the exchange of gases such as oxygen, and contribute to inverse thermal stratification (Dugan 2021; Cavaliere et al. 2021). As the seasons progress to spring and the ice melts, the transition from ice-covered to open water can happen abruptly resulting in rapid physicochemical changes to the aquatic environment. Once the lake has transitioned to an open-water environment, wind energy often facilitates lake turnover, mixing waters at depth and delivering nutrients to the surface (Goldman et al. 1996; Judd et al. 2005), although the timing, and importance of these different drivers of change will vary widely, including with lake depth. The importance of the ice/no-ice dynamic is captured in the Lake Ice Continuum Concept that explains the processes that are mediated by a continuum of energy inputs (Cavaliere et al. 2021).

The timing of the transition to open water conditions has major ecological implications. Therefore factors that contribute to the change in ice coverage, duration, timing, and thickness (also called ice phenology) are critically important to understanding ecological processes in lake ecosystems. Generally, studies examining lake ice phenology trends vary from individual lakes to global scales revealing patterns of later freeze, earlier breakup, or shorter duration of ice cover (Magnuson et al. 2000; Benson et al. 2012; Hébert et al. 2021; Oleksy and Richardson submitted). On the whole, a warming global climate corresponds with earlier ice-off dates (Schmidt et al. 2019), however, changes in both ice duration and winter severity, which can influence ice thickness, snow cover, and hydrological inputs through winter are also expected to impact lake ecosystems.

Generally, aquatic communities are strongly influenced by thermal stratification, currents and wind-driven transport, sediment resuspension, light penetration, and nutrient concentrations, especially in the nearshore environment in lake ecosystems (Ozersky et al. 2021). As discussed above, ice can be a physical barrier, affecting factors that influence phytoplankton, the base of the aquatic food web. Climate can have a strong impact on these factors ultimately influencing physiology directly and indirectly through changes to the aquatic environment (i.e. stratification, nutrients, light availability, etc.) and heterotroph grazing. (Winder and Sommer 2012).

In addition to global climate trends several large-scale atmospheric and oceanic oscillations or teleconnections, are known to drive temperature anomalies on time scales ranging from weeks to decades (Shabbar et al. 1997; Bonsal et al. 2006; Schmidt et al. 2019). Climate indices such as the North Atlantic Oscillation (NAO) and Southern Oscillation Index (SOI) are associated with shifts in the strength and position of the jetstream with positive values corresponding to cool temperature anomalies in eastern Canada and warm temperatures in northern Europe, and function in a low-frequency mode on the order of weeks to months (Shabbar et al. 1997; Hurrell et al. 2003). Meanwhile, the El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) represent variability in sea surface temperature in the North Pacific and North Atlantic oceans. The PDO and AMO represent climatic conditions on the order of years to decades, while ENSO accounts for climatic conditions on the order of years (Trenberth and Caron 2000). These climate indices (and others) account for climate variability across space and time with indices such as NAO and SOI operating in the relative short-term, while ENSO, PDO, and AMO are relatively long-term metrics.

The global and local climate is a significant driver of ice phenology and water quality conditions (Dröscher et al. 2009; Brown and Duguay 2010; Sullivan et al. 2021) however few studies have evaluated ice phenology and water quality together, especially in a shallow eutrophic lake. Therefore, the primary objectives of this study was to evaluate total and dissolved phosphorus (P) and nitrogen (N) relative to global climate metrics and examine changes in nutrient concentrations during winter in a seasonally ice-covered lake focusing on the changes to inorganic nutrients relative to the lake ice continuum. We hypothesize the water column nutrient concentrations will be correlated with AMO and PDO climate metrics at different time scales due to a combination of in-situ processes and the relative temporal scale captured in each climate metric (Bonsal et al. 2006; Free et al. 2022). Additionally, we hypothesize that due to the constraints that lake ice places on energy and nutrient fluxes, concentrations of total and dissolved inorganic nutrients will be greater in the winter/ice-on period and accumulation rates of nutrients under the ice will be sensitive to the duration of ice cover.

Study Site

Petit-lac-Saint-François (PLSF) is located in south-central Quebec, Canada (45.538 N, -72.038 W decimal degrees, 207 m above mean sea level) (Fig. 1) with a catchment area of 18.5 km². It is situated within the larger St. Francis River catchment. The lake is a shallow, hypereutrophic, polymictic lake with an area of 0.83 km², a watershed-to-lake area ratio of 24.6 and a catchment composed predominately of agricultural and forestry land uses (~ 79% cira 2019; Ministère de l’Environnement, Quebec 2023). The agricultural area is dominated by dairy farms employing conventional methods for the cultivation of annual crops, such as maize (Zea mays) and soybean (Glycine max). The predominant soils in this watershed are part of the Brompton stony loam series, a glacial till (Lamontagne and Nolin 1997).

Field Measurement and Laboratory Protocols

Surface (epilimnetic) water samples were collected from the Lake Inlet and Lake Outlet between October 2009 and September 2020 (Fig. 1). Sample collection, transport, and storage were conducted according to procedures outlined by Chorus and Welker (Chorus and Welker 2021). Quality control measures, including field, transport, and laboratory blanks, consisting of HPLC grade water and preservative where required, as well as split samples for interlaboratory comparisons, were included quarterly in the sampling program along with regular samples. Field measurements were made for dissolved oxygen, water temperature, specific conductance, and pH.

Water samples were returned to the laboratory for the determination of total nitrogen (TN), nitrate-nitrite (NOx), ammonium (NH₄⁺), total phosphorus (TP), and soluble reactive phosphorus (SRP). Dissolved fractions were filtered using 0.45 µm filter. Nitrogen fraction analysis was performed using a Lachat Quikchem 8500 flow injection analysis system (Hach Company, London, ON, Canada), USEPA method 351.2 was used to quantify TN and NOx while USEPA method 350.1 was used to quantify NH₄⁺ (US EPA 1993a, b). Phosphorus fraction analysis (TP and SRP) was analyzed using an Astoria-Pacific Astoria 2 analyzer (Astoria-Pacific, Clackamas, OR, USA) according to USEPA method 365.3 (US EPA 1993c). For purposes of this analysis, data reported less than the method detection limit (MDL) were assigned a value of one-half the MDL value unless otherwise noted. Dissolved inorganic N concentrations were estimated as the sum of NOx and NH₄⁺. Limnological data used in this study are available via Husk et al (2022).

Ice-on and ice-off observations were recorded between 2010 and 2020. During weekly sampling visits, due to the relatively small size of the lake, visual observations were made from the shoreline. The ice-on date was determined as the date when ice coverage achieved > 95%, conversely, the ice-off date was determined when ice coverage achieved < 95% coverage.

Phytoplankton were identified to species monthly from composite samples collected at the Lake Outlet location consistent with American Public Health Association (2017). Phytoplankton biovolume was measured using a microscope, cell dimensions of up to 20 individuals cells/species were measured and the average measurements were applied to standard geometric formulae that best fit the shape of the cell consistent with Wetzel and Likens (2000).

Regional Climate Variables

Winter air temperature records were retrieved from Environment and Climate Change Canada (https://climate.weather.gc.ca) between water years (WY) 1979 and 2021 (October 1, 1978 - September 30, 2021) for weather stations Bonsecours and Bromptonville (Site ID 5322 and 5327, respectively) using the “weathercan” R-package (LaZerte and Albers 2018). These monitoring locations were the closest in proximity to the PLSF watershed with a long-term climate times series. Annual mean winter temperatures (December – February) and cumulative freezing degree days (FDD) were calculated from spatially average daily data. FDD provides a metric of the duration and intensity of cold temperatures and is related to the likelihood of water to freeze (Boyd 1976). Cumulative FDD was calculated as the sum of the spatially averaged minimum daily degrees (in °C) below freezing between December to February during a given water year (October to September) consistent with (Jankowski et al. 2021).

While several climate indices were considered for this study, the NAO and PDO were selected to evaluate potential drivers in nutrients relative to short and long-term climate patterns, respectively. The NAO climate index is based on the difference in surface sea-level pressure between two regions in the north Atlantic (Azores and Subpolar regions), reflects the inter-annual variability in the atmosphere, and is a key factor in the cool-season climate variability (Enfield et al. 2001; Chartrand and Pausata 2020). Meanwhile, the PDO is a climate index of large-scale climate variability and is often described as a long-lived El Niño-like (warming conditions) pattern of Pacific climate variability (Zhang et al. 1997). Monthly AMO and PDO index datasets were retrieved from the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (Table S1).

Data Analysis

To evaluate long-term trends in winter mean air temperature and cumulative freezing degree days (FDD), the Mann-Kendall trend test was used. We calculated cumulative FDD as the sum of the spatially-averaged minimum daily degrees (in °C) below freezing between December to February during a given water year (October to September). Meanwhile, winter mean air temperature was estimated as the mean spatially-averaged minimum daily air temperature between December to February.

To evaluate the potential influence of climate on nutrient concentrations, monthly mean TN, DIN, TP, and SRP were calculated from weekly sampling events for Lake Inlet and Lake Outlet and paired with monthly AMO and PDO index values. Lagged cross-correlation analysis using spearman’s rank correlation was applied to the combined dataset consistent with the “ccf” function in the base statistics R package. Since the objective of this study is to evaluate the potential driver of climate on nutrient concentrations climate index values were lagged before (i.e. negative lags) monthly nutrient concentration estimates for the entire period of record.

Annual seasonal mean total and dissolved nutrient concentrations for Lake Inlet and Lake Outlet monitoring locations were compared between ice and no-ice seasons using the Kruskal-Wallis rank sum test. Ice and no-ice seasons were delineated based on historic ice-on/off periods (Fig. 2). Broadly, ice and no-ice seasons are delineated as December to April and May to November, respectively.

Total and dissolved nutrients were compared to days since ice-on with Spearman’s rank sum correlation over the entire study. However, the preliminary analysis indicated that the rate of change in nutrient concentrations at the Lake Outlet monitoring location varied between years during periods of ice cover (Fig S1). Therefore, the annual trend in nutrient concentration rate of change during ice cover period was evaluated using Kendall trend analysis. Annual nutrient concentration rate of change (i.e. slope) during the ice cover period was determined using a median-based linear model (‘mblm’ R-package; Komsta 2013) for each year.

Phytoplankton biovolume were qualitatively evaluated for inter and intra-annual variability. Phytoplankton species richness values were calculated using the “specnumber” function in the “vegan” R-package (Oksanen and Guillaume 2018) using the relative proportion of cell concentration at the species level. Overall (all-data) and winter-only phytoplankton richness trends were evaluated using the Mann-Kendall trend test.

All plots were generated in base R and tables were formatted using ‘‘flextable’’ R-package (Gohel 2021). All statistical operations were performed using R (Ver 4.0.4, R Foundation for Statistical Computing, Vienna, Austria). Unless otherwise stated, all statistical operations were performed using the base R library. The critical level of significance was set at a = 0.05.

Between 1979 and 2021, winter mean air temperature ranged from − 18.3°C to -8.6°C with a significantly increasing trend (τ = 0.37, ρ < 0.01) at a rate of 0.10°C yr^− 1 (Fig. 3). Consequently, cumulative FDD ranged from − 1653°C to -723°C with a significantly decreasing trend (τ = -0.44, ρ < 0.01) at a rate of 11°C yr^− 1 (Fig. 3). These trends show a long-term change in climatic conditions in the region. However, during the period of water column sampling (WY2010–2020), there was no short-term trend in winter mean air temperature or cumulative FDD (τ = -0.05, ρ = 0.88 and τ = -0.05, ρ = 0.88, respectively). This short-term lack of trend could be the result of extreme variability during this period with winter mean air temperatures ranging from − 15.2°C to -8.6°C and cumulative FDD from − 1275°C to -723°C. This variability in climate is also reflected in the ice-on and ice-off dates observed at PLSF. Generally, during the period of this study, the period of ice cover moved to the upper left of Fig. 2, suggesting that periods of ice cover are getting shorter. This change in the lake ice continuum could have effects on in-situ processes such as nutrient accumulation and cycling.

Lake Inlet and Lake Outlet water quality responded differently to the various climate indices or teleconnections with varying degrees of correlation and time lags (Fig. 4, Fig S2, Table S2, and S3). Correlation coefficients range from − 0.43 to 0.41 for the NAO and PDO climate indices and monthly mean water quality parameters (Table S2 and S3). At the Lake Inlet, monthly mean TN concentrations were negatively correlated with NAO at a lag of five months and positively correlated at a lag of 10 months (Fig. 4 and Table S2). Monthly mean DIN concentrations were positively correlated with NAO values at no monthly lag, however, the highest correlation coefficient occurred at a lag of 10 months (r-value = 0.35; ρ-value < 0.01; Fig. 4 and Table S2) at the Lake Inlet monitoring location. No lagged crossed correlation in monthly mean TP concentrations with NAO was detected but several lagged correlations (six and 16-month lags) were detected for monthly mean SRP concentrations with NAO at the Lake Inlet (Fig. 4 and Table S2). Lagged cross correlations with monthly mean nutrient concentrations and PDO index were also detected at the Lake Inlet monitoring stations with TN and DIN having several significant negative correlations although the temporal scale was different between the two fractions (Fig. 4 and Table S3). Similar to NAO no lagged cross-correlations were detected for TP and PDO but two periods of cross-correlation were detected for SRP and PDO at the Lake Inlet monitoring location (2–3 months and 15–17 months; Fig. 4 and Table S3).

At the Lake Outlet monitoring location, lagged cross-correlations appear to be cyclical for monthly mean TN and TP relative to NAO with negative correlations detected before the six-month lag and between 12 and 15 months while positive correlations were detected between six and 12 months and after 16 months (Fig. 4 and Table S2). Similar to the total fractions, dissolved inorganic fractions of N and P shared a similar pattern of correlation with a cyclic nature to the degree of correlation and direction at the Lake Outlet location (Fig. 4 and Table S2). Similar to NAO, lagged cross-correlations of monthly mean nutrient concentrations and PDO was detected for TN, DIN, and TP at the Lake Outlet monitoring location with a cyclic pattern in cross-correlations (Fig. 4 and Table S3). Additionally, similar to monthly mean TN and TP correlations with NAO, cross-correlations with PDO resulted in similar patterns in TN and TP with significant cross-correlations occurring between nine and 13 months (Fig. 4 and Table S3). Lagged cross-correlations in monthly mean DIN concentration and PDO at the Lake Outlet monitoring location occurred much later than the total fractions with significant cross-correlations occurring after 15 months (Fig. 4 and Table S3).

Between the Lake Inlet and Lake Outlet monitoring locations, some notable differences in annual seasonal mean total and dissolved nutrient concentrations were observed during the study period. At the Lake Inlet location, SRP concentrations were significantly lower in the ice-on season, while NO_x was significantly greater in the ice-on season relative to the no-ice season (Table 1 and Fig S5). Meanwhile, at the Lake Outlet location, TP and TN concentrations were significantly lower in the ice-on season relative to the no-ice season, while DIN and NOx were significantly greater in the ice-on season relative to the no-ice season (Table 1 and Fig S5).

Table 1

– Summary statistics and Kruskal Wallis statistics of annual seasonal mean total and dissolved nutrient concentrations for PLSF Lake Inlet and Lake Outlet monitoring locations between no-ice (May – Nov) and ice-on (Dec – April) seasons.
		No Ice		Ice On		Kruskal-Wallis Statistic
Site	Parameter	Mean	SE	Mean	SE	χ²	ρ-value
Lake Inlet	TP (µg L⁻¹)	42.9	3.0	53.8	13.5	0.06	0.81
	SRP (µg L⁻¹)	13.4	0.7	10.3	0.9	5.74	< 0.05
	TN (mg L⁻¹)	1.05	0.12	1.18	0.06	1.13	0.29
	DIN (mg L⁻¹)	0.55	0.06	0.72	0.06	2.48	0.12
	NOₓ (mg L⁻¹)	0.46	0.07	0.67	0.07	4.51	< 0.05
	NH₄ (µg L⁻¹)	34.7	5.0	43.9	6.7	0.71	0.40
Lake Outlet	TP (µg L⁻¹)	206.8	17.4	71.7	6.0	15.00	< 0.01
	SRP (µg L⁻¹)	21.2	4.1	20.4	2.5	0.02	0.89
	TN (mg L⁻¹)	2.04	0.15	1.12	0.07	12.91	< 0.01
	DIN (mg L⁻¹)	0.18	0.01	0.50	0.04	11.29	< 0.01
	NOₓ (mg L⁻¹)	0.06	0.01	0.39	0.03	13.50	< 0.01
	NH₄ (µg L⁻¹)	103.5	21.8	101.4	24.0	0.01	0.92

Overall, TP, TN, and DIN concentrations were negatively correlated with days since ice-on (TP: r = -0.25; ρ < 0.01; TN: r = -0.63; ρ < 0.01 and; DIN r = -0.21; ρ < 0.05). Meanwhile, SRP concentrations were not correlated with days since ice-on (r = -0.07; ρ = 0.36) partly driven by low concentrations and/or samples at or near the MDL (Fig S1). The daily rate of change (i.e. slope) or nutrient accumulation rates during the ice-on period varied between the different parameters (Fig. 5 and Table 2) where TP (-0.56–0.49 µg L^− 1 d^− 1), SRP (-0.41–0.29 µg L^− 1 d^− 1) and DIN (-8.03–1.31 µg L^− 1 d^− 1) range from loss (negative slopes) to accumulation (positive slopes) over the course of the study. Meanwhile, the slope values of TN were consistently negative during the study, ranging from − 8.07 to -1.10 µg L^− 1 d^− 1 during the ice-on period (Fig. 5 and Table 2). The annual rate of change was significantly correlated with total days of ice-on for DIN (r = 0.90; ρ < 0.01; Fig. 5). However, due to variability in slope values, TP (r = 0.05; ρ = 0.89), SRP (r = -0.05; ρ = 0.88), and TN (r = 0.37; ρ = 0.32) were not correlated with total days since ice-on (Fig. 5). While the data is somewhat limited, the change in slope over time relative to total days since ice-on suggests that processes that govern DIN during the lake ice continuum are more sensitive than factors or processes that relate to the other nutrient fractions.

Table 2

– Summary statistic of nutrient accumulation rates for total phosphorus (TP), soluble reactive phosphorus (SRP), total nitrogen (TN), and dissolved inorganic nitrogen (DIN) during ice-on periods at the PLSF Lake Outlet monitoring point.
Parameter	Minimum	Mean	Maximum	Standard Deviation
TP (µg L⁻¹ d¹)	-0.54	-0.14	0.49	0.27
SRP (µg L⁻¹ d¹)	-0.41	-0.07	0.29	0.18
TN (µg L⁻¹ d¹)	-8.07	-4.28	-1.10	2.54
DIN (µg L⁻¹ d¹)	-8.04	-2.80	1.31	3.69

Phytoplankton biovolume was determined relatively consistently starting in June 2012 and concluding in September 2020. Phytoplankton biovolume ranged from 0.01 to 472 mm³ L^− 1, with generally lower biovolume values during ice-on period (Fig. 6). Average relative biovolume between ice-on and ice-off periods varied with the class Cryptophyceae making up the majority of the biovolume during the ice-on period and Cyanophyceae dominating ice-off periods (Fig S6) During the period of record, phytoplankton species richness significantly declined (τ=-0.48; ρ-value < 0.01) however, species richness during the winter period did not significantly decline (τ=-0.35; ρ-value = 0.10).

Climate shifts and the occurrence of extreme meteorological events are accelerating, resulting in climate change-driven alterations to seasonal drivers of aquatic systems, especially in northern and temperate regions (Filazzola et al. 2020). These shifts in climate have resulted in the reduction of lake ice coverage and duration thereby altering the lake ice continuum (Benson et al. 2012; Dugan 2021; Cavaliere et al. 2021; Oleksy and Richardson submitted). In the region where Petit-lac-Saint-François has experienced long-term increases in average winter air temperatures and subsequently, a decrease in FDD over the past several decades potentially marking a shift in climatic conditions (Fig. 3). Concurrent with these changes in winter climatic conditions, the ice cover on Petit-lac-Saint-François has become more dynamic (Fig. 2). Duration in ice cover is driven by the interplay between global climate change and teleconnections that influence local weather drivers and therefore lake ice dynamics (Oleksy and Richardson submitted).

Winter climate teleconnections

Climate processes like the ENSO, and climate trends can drive interannual variation in water budgets in catchments (Maity and Nagesh Kumar 2009). Climate variability can be a significant driver of lake ecosystems ranging from changes in water quality, thermal characteristics, internal processes, and succession of biotic communities (Schindler and Donahue 2006; Bonsal et al. 2006; Vogt et al. 2018). In North America, this climatic variability is caused by the interaction of three major climate systems and air masses (Arctic, Pacific, and Gulf of Mexico) that influence precipitation and seasonal temperature patterns (Shabbar et al. 1997; St. Jacques et al. 2010). Generally, PDO and ENSO influence the influx of Pacific precipitation and thereby run-off patterns, with ENSO having periods of years and PDO decades. Meanwhile, NAO regulates the annual cyclonic activity and movement of the Arctic air mass affecting the timing of ice melt, lake clarity, and in-situ nutrient cycling, thereby affecting aquatic biota production, composition, and abundance (McGowan et al. 2005b, a; Vogt et al. 2018). A positive NAO index is associated with cool/dry temperature anomalies in eastern Canada on the order of weeks (Hurrell et al. 2003).

How lake limnological characteristics vary with climate systems/climate indices depends on the duration of climate cycles, geographic location, its contribution to climatic variability, in-situ processes, and synergistic interactions of long-term and short-term teleconnections relative to shifting global climate forcing factors. Therefore, there is some variability in how limnological characteristics relate to the various climate cycles. However, there are some consistencies in how total and dissolved nutrients relate to the different climate indices observed at PLSF (Fig. 4). Generally, nutrient concentrations and climate indices are correlated at different time steps (i.e. lag) potentially indicating that climate either directly or indirectly affects nutrient concentrations depending on the timing of climate shifts that also interact with other processes to drive nutrient concentrations. Generally, TN, DIN, and SRP are positively correlated with AMO at the Lake Inlet potentially, albeit at different time scales (Fig. 4) suggesting that warmer and wetter climatic conditions are driving local water quality within the watershed. These climatic drivers of nutrient concentrations are also apparent for in-lake processes with short-term strong cyclic patterns of lagged correlations between AMO and nutrient concentrations observed at the Lake Outlet location (Fig. 4) but also within the lake. However, nutrient concentrations relative to longer-term climate indices such as PDO (Fig. 4) result in slightly different patterns but what is consistent is that these correlations occur at a lagged state, typically much longer and smoothed relative to the shorter-term AMO correlations.

Shifts in climate and changes to climate cycles can be major drivers of nutrient transport and forcing factors in aquatic systems as observed in this study and others (McGowan et al. 2005b; Bonsal et al. 2006; Maity and Nagesh Kumar 2009; Vogt et al. 2018). Furthermore, nutrient concentrations and factors that influence them can have a significant impact on within-season conditions, but also on the following seasons and years. This change in seasonal conditions and available nutrients can cascade to change the timing of the seasonal occurrence of various species, altering the formerly synchronized interactions between species, life cycles, and their environment (Peeters et al. 2007).

Lake Ice Continuum Biogeochemistry: N &P Dynamics

Ice cover substantially changes the physicochemical environment, benthic foodwebs, biotic assemblages, and therefore biogeochemical processes (McGowan et al. 2005a; Bertilsson et al. 2013) in lake ecosystems important to cycling of N (Powers et al. 2017)d (Nicholls 1998; Kleeberg et al. 2013; Schroth et al. 2015). The transformations of N species, more specifically the oxidation of ammonium to nitrate and nitrite facilitated by microbial processes (N mineralization and nitrification) is a predominate N cycling pathway across different ecosystems (Wetzel 2001; Bernhardt et al. 2002; Reddy and DeLaune 2008; Schlesinger and Bernhardt 2013). In lake ecosystems that experience cold/freezing conditions, nitrification can be a major seasonal influence on N speciation providing an ammonium sink and nitrate source leading to the accumulation of nitrate during ice-on periods (Voytek et al. 1999; Pettersson et al. 2003; Powers et al. 2017; Kincaid et al. 2022).

Consistent with prior studies (Powers et al. 2017; Kincaid et al. 2022), DIN accumulation, of which nitrate is a component, was strongly related to the number of days since ice-on and the severity of the winter period (Fig. 5). Powers et al (2017) reported nitrate accumulation rates ranging from 0.15 to 2.70 µg N L^− 1 d^− 1, indicating a consistent net accumulation of nitrate during the ice-on period. Moreover, Kincaid et al (2022) reported reduced rates of -11.2 to -0.42 µg N L^− 1 d^− 1 (Kincaid et al. 2022). Based on these two studies it appears that accumulation rates may be depth-dependent and could be influenced by the occurrence of stratification and/or turn-over (Dugan 2021). In PLSF, near-surface DIN accumulation rates range from − 8.04 to 1.31 µg N L^− 1 d^− 1 dependent on days since ice-on (Fig. 5) and initial concentration (Fig S4). However, contrary to Powers et al (2017) and Kincaid et al (2022), shorter ice-on periods in PLSF correspond with DIN consumption (negative accumulation rates) rather than accumulation (Fig. 6). Since NO_x, more specifically nitrate, makes up a predominant fraction of DIN, the “consumption” could be the result of the lack of nitrification, or slower rates of nitrification, or coupled nitrification-denitrification. For PLSF, NOx accumulation rates ranged from − 6.53 to 2.62 µg N L^− 1 d^− 1 (Table S4), consistent with DIN accumulation rates. Based on the limited data, it would appear that an ice-on period longer than 155 days would result in positive DIN accumulation rates. However, due to ice-on periods getting shorter with climate change, this possibility would appear to be weakening (Fig. 2).

Our current understanding of under-ice P dynamics in lake ecosystems is primarily limited to resuspension/settling, flux from lake sediments, and potential flux of P from groundwater (i.e. seepage) (Nicholls 1998; Kleeberg et al. 2013; Kidmose et al. 2013; Schroth et al. 2015). Broadly speaking, P cycling is much more simplistic than N cycling, with P existing in two basic forms dissolved and particulate, and available as organic and inorganic fractions (Reddy and DeLaune 2008). Inorganic fractions of P can be associated with or loosely bound to minerals such as aluminum, iron, manganese, calcium, or magnesium and be pH dependent (Reddy and DeLaune 2008; Schroth et al. 2015). Mineralization of organic P is regulated by several biotic and abiotic factors including microbial activity, oxidation-reduction conditions, pH, and temperature to name a few, all of which vary along the lake ice continuum (Reddy and DeLaune 2008; Cavaliere et al. 2021). In this study, near-surface TP and SRP accumulation rates were not correlated with days since ice-on (Fig. 5). Furthermore, TP and SRP accumulations were generally negative, with a few exceptions, suggesting consumption or settling of P during the ice-on period. Moreover, PLSF is a hyper-eutrophic lake (Husk et al., In Prep) with relatively high sediment P concentrations (Personal communications, Barry Husk), therefore it would be expected that during exclusion of mixing due to ice cover, hypoxic conditions would develop (Dugan 2021; Cavaliere et al. 2021) facilitating P release from lake sediments to the water column causing an accumulation of P during the ice-on period as observed in other studies (Smith et al. 2011; Schroth et al. 2015). It is possible that sediment iron concentrations and redox conditions are such that P tends to be sequestered in this lake, ultimately retarding the flux of P to the water column. Conversely, P may be fluxing to the water column but since PLSF is a predominately P-growth limited system, it is likely that biotic uptake, while limited is occurring, thereby modulating P accumulation rates during the ice-on period.

Under ice biota

As identified by the lake ice continuum concept, factors that regulate planktonic communities under lake ice may differ dramatically from open water seasons. As expected during ice-on periods, light availability, metabolic activity due to low ambient temperatures and mixing/stratification can be reduced (Levasseur et al. 1984; Winder and Sommer 2012). Interactions between climate and plankton communities are complex due to confounding factors such as resource availability, density dependant community dynamics, and predation effects. However, despite these complexities, some climate-related responses have been noted, mostly concerning seasonality, species composition, and population size structure (Richardson 2008; Adrian et al. 2009; Winder and Sommer 2012; Hrycik and Stockwell 2021). As observed in PLSF during this study, despite some data limitations, variance in phytoplankton biovolume has increased over time concurrently with shifts in ice coverage and compositional variability (Figs. 3, 6, and S6).

The controlling factors of phytoplankton under ice could be bottom-up controls such as changes in light limitation and/or nutrient supplies or top-down controls such as zooplankton grazing. Using an in-situ under-ice mesocosm, Hrycik and Stockwell (2021) evaluated top-down and bottom-up controls of phytoplankton succession during ice-on periods. Hrycik and Stockwell (2021) determined that bottom-up controls (i.e. light) were an important factor in regulating phytoplankton biovolume and community composition more so than top-down controls. Although zooplankton did affect particular taxonomic groups, more specifically, diatoms and cryptophytes contrasted with the commonly accepted assumption that zooplankton grazing has negligible effects under ice-on conditions. Moreover, they also demonstrated that variation in light can also lead to changes in nutrient cycling via the uptake of phytoplankton and also waste excretion by zooplankton. Therefore, changes in snow and ice dynamics relative to climate change can influence basal food web dynamics by altering the light environment in ice-covered systems, significantly influencing the community structure, nutrient cycling, and intra- and inter-annual variation in plankton biomass (Hrycik and Stockwell 2021; Hrycik et al. 2022). Despite relatively limited data, these dynamics are apparent in PLSF with changes in phytoplankton biovolume, high variability in zooplankton biomass during ice-on periods, and shifts in nutrient accumulation rates (Figs. 5 and S7). Something less explored is the role of protozoa in nutrient cycling in lake ecosystems, a prior study (Bloem et al. 1989) suggests that protozoa can influence nutrient cycling. Unfortunately, due to limited data, it is currently unknown how or if protozoa affect nutrient cycling and/or interact with the foodweb structure at PLSF.

The climate is a significant driving force in lake biogeochemical cycling, physical forcing factors, and biotic composition. Lake ice phenology is largely influenced by the regional and global climate as indicated by the various atmospheric forcing indices such as the PDO, MEI, etc (Bonsal et al. 2006; Brown and Duguay 2010). For PLSF, while the data is limited, the duration of ice cover is getting shorter. Lake ice phenology has a significant influence on in-lake biogeochemical cycling and with the changes to ice phenology, nutrient cycling, and storage are expected to continue to change, as observed at the PLSF Lake Outlet long-term monitoring location. In addition to the climate effect of in-situ lake processes, the climate also contributes to upstream/watershed processes potentially affecting the transport of nutrients in the form of magnitude and timing of loading events to downstream systems as observed in changes at the PLSF Lake Inlet monitoring location. Overall, a limited number of studies are conducted during winter (Powers and Hampton 2016), numerous studies have studied ice phenology and the lake ice continuum concept (Schmidt et al. 2019; Sharma et al. 2019; Dugan 2021; Cavaliere et al. 2021; Hébert et al. 2021; Oleksy and Richardson submitted) but a more integrated understanding of the interaction between biogeochemical cycling, lake ice phenology and changing climate is needed to evolve our understanding of the consequences of a changing climate and ecosystem level drivers of nutrient cycling. Such understanding is precious to study the impact that climate change will have on lakes that freeze over winter.

Acknowledgments

This study was conducted within the framework of the Algal Blooms, Treatment, Risk Assessment, Prediction and Prevention through Genomics (ATRAPP) project, with financial support from Genome Quebec and Genome Canada (grant #10512) The authors would also like to thank Debra Nieuwenhuis for algae, zooplankton, and protozoa identification, and anonymous peer reviewer(s) and editor(s) for their efforts and constructive review of this manuscript. This research was conducted on lands and waters that have been cared for by innumerable generations of original peoples of the past, whose memory we honor.

Funding: This work was supported by Genome Quebec and Genome Canada (grant #10512).

Ethics approval and consent to participate: Not Applicable.

Consent for publication: Not Applicable.

Competing Interest: The authors have no relevant financial or non-financial interests to disclose.

Conflict of Interest: The authors declare no conflict of interest.

Author Contributions: PJ conducted data analysis and wrote the manuscript. BH collected samples and contributed to the writing of the manuscript. HMB, DFS, NT, and SS contributed to the writing and editing of the manuscript.

Data Availablity: Limnological data and metadata are available in the Environmental Data Initiative data repository (https://doi.org/10.6073/pasta/301c9b21635ee75b8601afce3953a0ea). Other data are available upon request.

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Seasonal Drivers of Nutrient Concentration in Lake Ecosystems: Evaluating Climate and Ice Phenology Characteristics in a Shallow Eutrophic Lake

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