Using a , 1000 lake data set that spans the entire continental United States, we applied empirical modeling approaches to quantify the relative strength of nutrients and water temperature as predictors of cyanobacterial biomass (CBB). Given that cyanobacteria possess numerous traits providing competitive advantage under warmer conditions, we hypothesized that water temperature, in addition to nutrients, is a significant predictor of CBB. Total nitrogen (TN), water temperature, and total phosphorus were all significant predictors of CBB, with TN explaining the most variance. Using multiple linear regression analysis, we found that TN and water temperature provided the best model and explained 25% of the variance in CBB. However, when the data set was divided according to basin type, these same variables explained a higher amount of the variation in deep natural lakes (33%, n 5 253), whereas the least amount of variation was explained by these variables in shallow reservoirs (12%, n 5 307). Competing path models on the full data set using the best variables selected by multiple linear regression show that nitrogen and temperature are indirectly linked to cyanobacteria by association with total algal biomass, which likely reflects changes in light climate and other secondary factors. Our models also indicated that temperature was linked to cyanobacteria by a direct pathway. Under a scenario of atmospheric CO 2 doubling from 1990 levels (resulting in an estimated 3.3uC increase of the maximum lake surface water), we predict on average a doubling of CBB.

Aim Scientists, governments and non‐governmental organizations are increasingly moving towards the collection of large, open‐access data. In aquatic sciences, this effort is expanding the scope of questions and analyses that can be performed to further our knowledge of the global drivers of water quality. Cyanotoxin concentration is one variable that has received considerable attention, and although strong local‐scale models have been described in the literature, modelling cyanotoxin concentrations across broader spatial scales has been more difficult. Commonly used statistical frameworks have not fully captured the complex response of toxic algal blooms to global change, limiting our ability to predict and mitigate the impairment of freshwaters by toxic algae. Here, we advance our understanding of emergent drivers of cyanotoxins across a structured landscape by applying a hierarchical “hurdle” model. Location Lakes and reservoirs in the conterminous United States [n = 1127]. Methods We studied cyanobacteria and their toxins [microcystins] during the 2007 summer period. We applied a hierarchical zero‐altered model to test the importance of multi‐scale interactions among environmental features in driving microcystin concentrations above the limit of detection. We then used boosted regression trees [BRTs] to identify environmental thresholds associated with severe impairment by microcystins. Results Accounting for numerous non‐detections, spatial heterogeneity and cross‐scale interactions substantially improved continental‐scale predictions of bloom toxicity. Our model accounted for 55% of the variance in the probability of detecting microcystins across the United States, and 26% of the variability in microcystin concentrations once detected. BRTs further showed that although both local and regional drivers were associated with microcystin concentrations at low to intermediate provisional guidelines, only local drivers came into play when predicting higher limits. Main conclusions Identifying the interaction between local and regional processes is key to understanding the heterogeneous responses of microcystins to environmental change. Our framework could increase the effectiveness of continental‐scale analyses for many different water variables.

The annual hydrographs of British Columbian rivers are characterized by glacial, nival, pluvial or ‘hybrid’ sources of runoff. Climate change scenarios for the 2050s indicate that snow water equivalent could diminish by 50% to 80% in low‐elevation basins of the south coastal region of British Columbia. This could trigger a shift from a hybrid to a pluvial regime for many creeks originating in the coastal mountains and could negatively affect summer low flows. However, the connection between recharge occurring in the headwaters during snowmelt and late‐summer water yield remains unclear. A mountainous creek (Stephen's Creek) was monitored in a nested design from September 2008 to November 2009. A two‐component isotopic hydrograph separation method was developed by adapting the runoff‐corrected model to a semidistributed environment to account for both the spatial and the temporal variability of the isotopic release from the snowpack in the headwater catchment. Results show that snowmelt composed most of the streamflow both at the headwater site (66% ± 19%) and at the mouth (62% ± 23%) during the peak of the freshet, and its contribution to streamflow was significantly different in July (34% ± 11% at the headwater vs 7% ± 4% at the mouth). Streamflow recession analysis suggests that a snowmelt‐recharged headwater catchment can support a steadier summer base flow compared to a much larger but rainfed‐dominated watershed. This study concluded that the large input of meltwater during the spring was sufficient to ‘overturn’ shallow subsurface reservoirs and to recharge deeper flow paths at a rate that cannot be matched by rainfed‐dominated systems. Copyright © 2012 John Wiley & Sons, Ltd.

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Atrazine contamination is ubiquitous in North American surface waters, but the dependency of the herbicide's distribution on landscape and within-lake processes is currently poorly known. We sought to identify novel predictors of atrazine and to build a coherent framework to model its concentration in waterbodies through the development of binomial-gamma hurdle models and LASSO regression models. We constructed models for over 900 waterbodies in the contiguous United States using data from the 2012 U.S. EPA National Lake Assessment, the 2012 U.S. Department of Agriculture CropScape and the Global HydroLAB HydroLAKES databases. Atrazine was detected in 32% of U.S. waterbodies, with a mean concentration of 0.17 μg L −1 when detected. The two-part hurdle model explained as much as 75% of the variance in atrazine across a spatially and temporally heterogeneous landscape. Three predictors explained 31% of the variability in atrazine detection in U.S. waterbodies, where the proportion of corn + soy cultures in the watershed was the most important variable. Once atrazine was detected, our models explained an additional 29% of the variability in atrazine concentrations, where the estimated areal weight of atrazine application (kg atrazine km 2) in the watershed was the most important predictor. Spatially, water quality variables associated with eutrophication were linked to increased levels of atrazine contamination while cooler water temperatures and natural lakes and landscapes were associated with decreased levels of contamination. Our results suggest that changes in land-use practices may be the most effective way to mitigate atrazine contamination in waterbodies.

Inherent differences between naturally-formed lakes and human-made reservoirs may play an important role in shaping zooplankton community structure. For example, because many reservoirs are created by impounding and managing lotic systems for specific human purposes, zooplankton communities may be affected by factors that are unique to reservoirs, such as shorter water residence times and a reservoir’s management regime, compared to natural lakes. However, the environmental factors that structure zooplankton communities in natural lakes vs. reservoirs may vary at the continental scale and remain largely unknown. We analyzed data from the 2007 U.S. Environmental Protection Agency’s National Lakes Assessment and the U.S. Army Corps of Engineers’ National Inventory of Dams to compare large-bodied crustacean zooplankton communities (defined here as individuals retained by 0.243 mm mesh size) in natural lakes and reservoirs across the continental U.S. using multiple linear regressions and regression tree analyses. We found that large-bodied crustacean zooplankton density was overall higher in natural lakes compared to reservoirs when the effect of latitude was controlled. The difference between waterbody types was driven by calanoid copepods, which were also more likely to be dominant in the >0.243 mm zooplankton community in natural lakes than in reservoirs. Regression tree analyses revealed that water residence time was not a major driver of calanoid copepod density in natural lakes but was one of the most important drivers of calanoid copepod density in reservoirs, which had on average 0.5-year shorter water residence times than natural lakes. Reservoirs managed for purposes that resulted in shorter residence times (e.g., hydroelectric power) had lower zooplankton densities than reservoirs managed for purposes that resulted in longer residence times (e.g., irrigation). Consequently, our results indicate that water residence time may be an important characteristic driving differing large-bodied zooplankton dynamics between reservoirs and natural lakes.

No abstract

<p>Strong measures have been taken since the 1970s to reduce mercury emissions in Canada. However, long-range transport of emissions continues and constitutes a large percentage of the total anthropogenic deposition of mercury in Canada. Natural sources of mercury are also heterogeneously distributed across the Canadian landscape.  As part of the LakePulse network (www.lakepulse.ca), we are quantifying mercury concentration in hundreds of lake sediment cores across 13 Canadian ecozones. Analyses from eastern Canada lakes showed that total mercury is significantly different among ecozones, and many ecozones showed higher total mercury concentrations in contemporary sediments.  Contemporary methyl mercury concentrations also differed among ecozones. Our overarching goals are to map the heterogeneity in mercury concentrations across the country and to identify the most parsimonious set of predictors considering a suite of physico-chemical and land-use variables from lakes and their watersheds set across the temperate to subarctic landscape.</p>