Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression.

Stream nutrient concentrations exhibit marked temporal variation due to hydrology and other factors such as the seasonality of biological processes. Many water quality monitoring programs sample too infrequently (i.e., weekly or monthly) to fully characterize lotic nutrient conditions and to accurately estimate nutrient loadings. A popular solution to this problem is the surrogate-regression approach, a method by which nutrient concentrations are estimated from related parameters (e.g., conductivity or turbidity) that can easily be measured in situ at high frequency using sensors. However, stream water quality data often exhibit skewed distributions, nonlinear relationships, and multicollinearity, all of which can be problematic for linear-regression models. Here, we use a flexible and robust machine learning technique, Random Forests Regression (RFR), to estimate stream nitrogen (N) and phosphorus (P) concentrations from sensor data within a forested, mountainous drainage area in upstate New York. When compared to actual nutrient data from samples tested in the laboratory, this approach explained much of the variation in nitrate (89%), total N (85%), particulate P (76%), and total P (74%). The models were less accurate for total soluble P (47%) and soluble reactive P (32%), though concentrations of these latter parameters were in a relatively low range. Although soil moisture and fluorescent dissolved organic matter are not commonly used as surrogates in nutrient-regression models, they were important predictors in this study. We conclude that RFR shows great promise as a tool for modeling instantaneous stream nutrient concentrations from high-frequency sensor data, and encourage others to evaluate this approach for supplementing traditional (laboratory-determined) nutrient datasets.

Long-term dataset on aquatic responses to concurrent climate change and recovery from acidification.

Concurrent regional and global environmental changes are affecting freshwater ecosystems. Decadal-scale data on lake ecosystems that can describe processes affected by these changes are important as multiple stressors often interact to alter the trajectory of key ecological phenomena in complex ways. Due to the practical challenges associated with long-term data collections, the majority of existing long-term data sets focus on only a small number of lakes or few response variables. Here we present physical, chemical, and biological data from 28 lakes in the Adirondack Mountains of northern New York State. These data span the period from 1994-2012 and harmonize multiple open and as-yet unpublished data sources. The dataset creation is reproducible and transparent; R code and all original files used to create the dataset are provided in an appendix. This dataset will be useful for examining ecological change in lakes undergoing multiple stressors.

Aluminum toxicity risk reduction as a result of reduced acid deposition in Adirondack lakes and ponds.

In 1990, the US Congress amended the Clean Air Act (CAA) to reduce regional-scale ecosystem degradation from SO x and NO x emissions which have been responsible for acid deposition in regions such as the Adirondack Mountains of New York State. An ecosystem assessment project was conducted from 1994 to 2012 by the Darrin Fresh Water Institute to determine the effect of these emission reduction policies on aquatic systems. The project investigated water chemistry and biota in 30 Adirondack lakes and ponded waters. Although regulatory changes made in response to the 1990 CAA amendments resulted in a reduction of acid deposition within the Adirondacks, the ecosystem response to these reductions is complicated. A statistical analysis of SO4, pH, Al, and DOC data collected during this project demonstrates positive change in response to decreased deposition. The changes in water chemistry also have lowered the risk of Al toxicity to brook trout (Salvelinus fontinalis [Mitchill]), which allowed the re-introduction of this species to Brooktrout Lake from which it had been extirpated. However, pH and labile aluminum (Alim) fluctuate and are not strongly correlated to changes in acid deposition. As such, toxicity to S. fontinalis also is cyclic and provides rationale for the difficulties inherent in re-establishing resident populations in impacted aquatic environments. Overall, aquatic ecosystems of the Adirondacks show a positive response to reduced deposition driven by changes in environmental policy, but the response is more complex and indicates an ecosystem-wide interaction between aquatic and watershed components of the ecosystem.

Brooktrout Lake case study: biotic recovery from acid deposition 20 years after the 1990 Clean Air Act Amendments.

The Adirondack Mountain region is an extensive geographic area (26,305 km(2)) in upstate New York where acid deposition has negatively affected water resources for decades and caused the extirpation of local fish populations. The water quality decline and loss of an established brook trout (Salvelinus fontinalis [Mitchill]) population in Brooktrout Lake were reconstructed from historical information dating back to the late 1880s. Water quality and biotic recovery were documented in Brooktrout Lake in response to reductions of S deposition during the 1980s, 1990s, and 2000s and provided a unique scientific opportunity to re-introduce fish in 2005 and examine their critical role in the recovery of food webs affected by acid deposition. Using C and N isotope analysis of fish collagen and state hatchery feed as well as Bayesian assignment tests of microsatellite genotypes, we document in situ brook trout reproduction, which is the initial phase in the restoration of a preacidification food web structure in Brooktrout Lake. Combined with sulfur dioxide emissions reductions promulgated by the 1990 Clean Air Act Amendments, our results suggest that other acid-affected Adirondack waters could benefit from careful fish re-introduction protocols to initiate the ecosystem reconstruction of important components of food web dimensionality and functionality.