Environmental drivers of phytoplankton community dynamics in an agriculturally-influenced tributary in the lower Great Lakes.

Phytoplankton community composition in tributaries differs from that in their receiving waters, due to light limitation from suspended particles and other factors such as nutrient availability and temperature. This study was designed to manipulate light levels in early, mid, and late summer to determine the combined effects of light attenuation and naturally varying nutrient availability on phytoplankton community composition in an agriculturally-influenced tributary of the lower Great Lakes. In all trials, in situ microcosm experiments show that phytoplankton abundance increased under three light attenuation treatments (60 %, 75 %, and 85 % attenuation) relative to time-zero, but higher light attenuation reduced total phytoplankton abundance relative to controls. Highest phytoplankton diversity in terms of richness and evenness occurred in September (late summer), and across all three trials was lowest under the highest light attenuation treatments (85 %). Phytoplankton community composition followed a normal seasonal shift from diatoms dominating in June (early summer), followed by cyanobacteria dominating in mid to late summer. In general, lower light levels (especially 85 % attenuation) corresponded with an increased dominance of cyanobacteria. These findings support the hypothesis that phytoplankton abundance and diversity vary with light and nutrient availability and that light attenuation promotes the shift from buoyant cyanobacteria to other taxa more tolerant of low light levels.

Recent water-level fluctuations, future trends and their eco-environmental impacts on Lake Qinghai.

The water level of Lake Qinghai, the largest lake on the Qinghai-Tibetan Plateau, has increased continuously, at an average speed of 0.21 m per year since 2005, causing a rapid expansion of the lake area. We investigated the hydrological processes of Lake Qinghai and the surrounding watershed that have influenced water level and lake area from 1956 to 2019. Relationships among water level, climate change and human activities were also assessed. Water level and lake area were positively correlated with precipitation and runoff into the lake, and negatively correlated with evaporation. Climate change factors including precipitation and runoff were the primary causes of lake level change, whereas human activities, including variation in a human footprint index, land use, and grassland irrigation, were secondary factors. A time series model forecasted that from 2020 to 2050 water levels will increase further by 2.45 m. Although this increase in water level may have some benefits, such as reduced local desertification, the expansion of lake area will continue to flood low beaches, pasture lands, near shore infrastructure and roads, and impact tourism locations. However, continued water level rise may also have negative ecological effects, such as reduce habitat of seasonal birds and reduced water quality due to erosion and sediment resuspension in shallow nearshore lake areas. Local stakeholders, government authorities, and scientists should give greater attention to anticipated changes in water level, and further ecological studies and infrastructure adaptation measures should be implemented.

Effects of detection limits on spatial modeling of water quality in lakes.

Identifying sources and fate of nutrients and pollutants in lake waters is often difficult when key analytes (e.g., dissolved phosphate) are frequently below analytical detection limits (non-detects). One way of dealing with this problem in water quality data is to replace non-detects with "fill-in" values using imputation methods (IMs). While their performance for estimating descriptive statistics (e.g., mean and variance) has been evaluated comprehensively for many environmental variables, whether IMs can reconstruct spatial patterns using long-term water quality data with non-detects under different magnitudes of spatial variation remains under-studied. We developed an integrative framework, combining numerical simulations with univariate and multivariate approaches, to compare performance of nine IMs in recovering spatial patterns of water quality data with different degrees of spatial heterogeneity. We applied this framework to a 12-year water quality dataset sampled from the nearshore region of Lake Ontario near Pickering and Ajax to show the usefulness of IMs in estimating water quality spatial variation. Firstly, in the simplest modeling scenario, we found that most IMs reproduced spatial patterns of univariate data well with ≤30 % non-detects in the dataset. Secondly, when spatial patterns were heterogeneous (e.g., when weak water mixing in nearshore regions limited nutrient transport from input sources to offshore regions), most IMs also performed well by recovering spatial variation in multivariate data with ≤80 % non-detects. Thirdly, when spatial distributions were homogeneous (e.g., when strong water mixing increased transport of nutrients from input sources to other lake areas), only weighted quantile sum regression (WQSR) performed well in reconstructing spatial multivariate data trends with ≤10 % non-detects. Our study highlighted that IMs (especially WQSR) are useful for reconstructing spatial trends of water quality in large lakes. However, potential interactions between spatial heterogeneity and non-detect frequency must be considered when selecting an appropriate IM procedure to accurately model spatial patterns in water quality.

Impacts of invasive mussels on a large lake: Direct evidence from in situ control-volume experiments.

Invasive dreissenid mussels have reengineered many freshwater ecosystems in North America and Europe. However, few studies have directly linked their filter feeding activity with ecological effects except in laboratory tests or small-scale field enclosures. We investigated in situ grazing on lake seston by dreissenid mussels (mainly quagga mussel Dreissena rostriformis bugensis) using a 'control volume' approach in the nearshore of eastern Lake Erie in 2016. Flow conditions were measured using an acoustic Doppler current profiler, surrounded by three vertical sampling stations that were arranged in a triangular configuration to collect time-integrated water samples from five different depths. Seston variables, including chlorophyll a, phaeopigment, particulate organic carbon and nitrogen, and particulate phosphorus, along with stoichiometric ratios and water flow over mussel colonies, were considered when estimating grazing rates. We observed suboptimal flow velocity for mussel grazing, i.e., 0.028 m s-1 at 0.1 m above bottom (mab), and resuspension was deemed minimal. Water temperature (mean: 25.1 °C) and an unstratified water column were optimal for grazing. Concentration of seston was low (mean: 0.2 mg L-1 particulate organic carbon) and decreased from surface to lakebed where noticeable depletion was observed. Grazing rates calculated at discrete depths varied substantially among trials, with maximum rates occurring at 0.25 or 0.5 mab. Positive grazing rates were restricted to 0.5 mab and below, defining an effective grazing zone (0.1-0.5 mab) in which the flow velocity, seston concentration, and water depth were consistently and positively correlated with grazing rates of different lake seston variables. Horizontal changes in stoichiometric ratios of seston were strongly associated with grazing rates, revealing higher uptake of particulate phosphorus than nitrogen and carbon. Our study supports the nearshore phosphorus shunt hypothesis, which posits that dreissenid mussels retain phosphorus on the lake bottom and contribute to a wide range of ecological effects on freshwater ecosystems.

A statistical framework for testing impacts of multiple drivers of surface water quality in nearshore regions of large lakes.

In nearshore regions of large freshwater ecosystems, complex biophysical processes across large geographic regions, combined with the common logistical challenges of data collection by multiple research agencies and shifting monitoring survey designs over time, present challenges for detecting and managing the influence of multiple sources of nutrients and pollution. We present a statistical framework using linear mixed models (LMMs) to test impact of multiple drivers on nearshore water quality of large lakes. Under this framework, we analyzed a 12-year dataset of water quality variables that were measured from a nearshore region along the Canadian shoreline of Lake Ontario (~86 km2), near Pickering and Ajax. Spatial interpolation showed that almost all water quality parameters decreased in magnitude from the shoreline to the offshore. Two exceptions to this nearshore-offshore gradient occurred in a region that extended ~8 km southwest from the outfall of the Duffin Creek Water Pollution Control Plant (DCWPCP) where ammonia + ammonium (NH3+4) increased and pH decreased slightly. Other LMMs combined explanatory factors into major groups (geographic proximities to shoreline tributary mouths and stormwater drains [inflows], tributary discharges, discharge or loading from the DCWPCP, and climate factors). These models showed that geographic proximity to shoreline inflows and/or tributary discharges were the most important drivers for most water quality parameters including concentrations of phosphorus, a key variable for regulating proliferation of harmful algae blooms and nuisance benthic algae in the Great Lakes. Air temperature was correlated with decreased phosphorus concentrations and nitrate + nitrite, whereas total precipitation and snow were correlated with increased concentrations of most nutrients except NH3+4, which was negatively correlated with duration of lake ice cover in winter. Our framework highlights how influence of individual nutrient sources can be distinguished from climate factors within a dominant nearshore-offshore gradient in water quality within nearshore regions of large lakes.

Multiple factors regulate filtration by invasive mussels: Implications for whole-lake ecosystems.

The quagga mussel (Dreissena rostriformis bugensis) is a filter-feeding invasive species that has re-engineered many freshwater ecosystems worldwide. High clearance rates (CRs) and dense populations underpin their ecological impacts. CRs, however, are highly variable, as are environmental factors that regulate them. Despite their widespread distribution in Europe and North America, knowledge of how multiple environmental factors regulate CRs of quagga mussels remains limited. We investigated quagga mussel CRs under varying conditions including water temperature, food availability, habitat depth, flow velocity, and duration of incubation in chambers with both static and flowing water. We found that CR was positively related to water temperature and initial food concentration in static chambers. When coupled with limited food concentration, cold water (7.5 °C), due to a deep-water upwelling event, produced very low CR (~ 10× lower) compared to warmer water (12-24 °C) (0.47 vs. 3.12-5.84 L g-1 DW h-1). Mussels from deeper water (20 m) had CRs that were ~ 3.5× higher than from shallower depths (2-10 m) and CRs were inversely affected by total mussel dry weight. Flow rates from 1 to 22 cm s-1 generated a unimodal pattern of CR with an optimal flow velocity of 6-12 cm s-1 (~ 2× higher than suboptimal CRs). Enhanced flow velocity (22 cm s-1), reflective of storm conditions in shallow waters, significantly increased the closing/reopening activity of mussel valves relative to lower velocities (1-12 cm s-1). Incubation time had a strong negative effect (~ 2-4× reduction) on CRs likely reflecting refiltration in static chambers versus food saturation of mussels in flowing chambers, respectively. Our findings highlight how multiple factors can influence quagga mussel CRs by factors of 2-10. Given widespread habitat heterogeneity in large aquatic ecosystems, whole-lake estimates of mussel impacts should include multiple regulatory factors that affect mussel filtration.

Impacts of industrial atmospheric emissions on watershed export of dissolved ions in coastal streams: a Bayesian modeling approach.

Anthropogenic atmospheric emission and subsequent deposition of sulfur (S) has been linked to disrupted watershed biogeochemical processes through soil and surface water acidification. We investigated watershed-scale impacts of acidic deposition on tributary concentrations and watershed exports of major nutrients and ions for the Kitimat River Watershed, British Columbia. Since the 1950s, the Kitimat watershed had an aluminum smelting facility with substantial emissions at the river estuary. Emissions load the airshed overlying the watershed and potentially impact western tributaries leaving eastern tributaries available as reference. We assessed concentrations and export of key compounds in three reference and six potentially impacted tributaries and watersheds in 2015 and 2016. Sulfate (SO4), fluoride (F), nitrate (NO3), and chloride (Cl) were significantly higher in impacted tributaries. F concentrations exceeded the Canadian Council of Ministers of the Environment guideline for aquatic life in 83% of samples collected from impacted streams. Watershed export and associated uncertainty were determined by bootstrapped flow-stratified Beale's unbiased estimator. Impact of emissions on watershed export was modeled in a Bayesian approach to include variance in the export estimate to inform the uncertainty of model parameters. Export of SO4 and Ca increased significantly within 16 km and 8 km, respectively, toward the smelter emissions. The corresponding impacted area for SO4 and Ca was approximately 100 km2 and 45 km2, respectively. SO4 export is likely due to direct impacts of S deposition, with excess S being flushed from the watersheds. Ca export patterns likely result from indirect impacts of S deposition on soil chemistry and flushing of Ca. These impacts may contribute to effects within tributaries on benthic stream communities and regionally important juvenile Pacific salmon.