Stable isotopes of nitrogen (N) were analyzed in modern sediments of mountain lakes, dissolved organic matter (DOM), and sediment cores spanning the past 12,000 yr to test the hypothesis that spatial and temporal (100-1000 yr) variation in the N content of mountain lakes is regulated by influx of allochthonous DOM. Analysis of spatial patterns in an elevation gradient of 75 mountain lakes revealed that most N was associated with DOM rather than inorganic N, particularly in subalpine lakes (, 1700 m above sea level). Similarly, analysis of N isotope ratios (d 15 N) from 22 lakes showed that whole sediments of subalpine sites were significantly more depleted (0.74% 6 1.58%) than were those of alpine lakes above 2200 m (3.04% 6 1.21%), consistent with the depleted d 15 N of isolated DOM (, 1.3%). Sedimentary d 15 N values of Crowfoot Lake, presently near tree line, also varied greatly during the past 12,000 yr, with enriched values (, 4%) during the alpine phases of the lake's history and depleted values (, 1%) during the intervening subalpine phase (ca. 10,050-4160 14 C yr before present) when DOM was abundant. In contrast, sedimentary d 15 N values remained constant (, 2.5%) at Snowflake Lake, an alpine reference site that never experienced a DOM-rich subalpine phase. These analyses suggest that climate regulates N influx and lake biogeochemistry by changing the subsidies of terrestrial DOM, and warn that future climate change may initially reduce N influx on a decadal scale by reducing hydrologic transfer before increasing N subsidies on a centennial scale by increasing terrestrial production of DOM.

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1. Benthic chironomid larvae and the amphipod Gammarus lacustris have been observed in the pelagic habitats of many mountain lakes. The main goal of this study was to determine if chironomid larvae and gammarids potentially affect predator-prey and nutrient dynamics in pelagic food webs of mountain lakes. 2. Eighty-six mountain lakes were surveyed in Alberta and eastern British Columbia during the years 1965-1984, 1991-2004 and 2005-2007. Pelagic chironomid larvae were found in 86% of these lakes, and pelagic gammarids were found in 29% of lakes. Densities of pelagic chironomid larvae were 92% lower in lakes with pelagic gammarids and 76% lower in lakes with trout (P < 0.05). Intraguild predation of trout on gammarids appeared to reduce predation pressure on chironomid larvae. Gammarids consumed in vitro about 1 chironomid per gammarid per day or about 20% of their body mass in chironomid biomass per day. 3. Concentrations of total dissolved P and N, particulate C, and chlorophyll-a increased with increasing densities of pelagic gammarids and chironomid larvae in situ (R 2 = 0.14 ± 0.19 SD, P < 0.1) and in vitro (P < 0.001). 4. Our findings suggest that gammarids and chironomid larvae are linked as predators and prey in pelagic food webs, possibly stimulating phytoplankton abundance via nutrient release.

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High-frequency acquisition of nutrient concentrations in rivers is needed to generate nutrient loading estimates commensurate with flow and discharge data. Although the combination of field sampling and laboratory analysis is the standard approach to riverine water quality analysis, this strategy is expensive and can miss important storm-related events. Ultraviolet-visual (UV-Vis) spectroscopy is widely used in drinking water and wastewater systems for high-frequency concentration estimates. However, surface waters present a unique challenge as co-occurring constituents in environmental samples cause spectral interference at the wavelengths used to measure concentrations of dissolved nutrients. Partial least squares regression (PLSR), Lasso regression (Lasso), and stepwise multivariate linear regression (Stepwise) models can be effective predictors of nitrate concentrations using UV-Vis absorbance and are used in many available in-situ nitrate sensors; however, the proliferation of user-friendly open-source machine learning (ML) algorithms offers an opportunity to use sophisticated bigdata techniques to predict nutrient concentrations in surface waters. We collected samples from four rivers across southern Ontario with a variety of nitrate concentrations, flow regimes, and interfering co-contaminants. We demonstrated that ML applications of random forest and gradient boosting models significantly outperformed PLSR, Lasso, and Stepwise methodologies to estimate nitrate concentrations in complex environmental samples via UV-Vis absorbance. Importantly, ML applications outcompete current models at low concentrations. This new predictive methodology provides regulators and stakeholders an opportunity to establish low cost, continuous monitoring environmental programs using UV-Vis approaches.