Understanding links between water-quality variables and nitrate concentration in freshwater streams using high frequency sensor data.

Real-time monitoring using in-situ sensors is becoming a common approach for measuring water-quality within watersheds. High-frequency measurements produce big datasets that present opportunities to conduct new analyses for improved understanding of water-quality dynamics and more effective management of rivers and streams. Of primary importance is enhancing knowledge of the relationships between nitrate, one of the most reactive forms of inorganic nitrogen in the aquatic environment, and other water-quality variables. We analysed high-frequency water-quality data from in-situ sensors deployed in three sites from different watersheds and climate zones within the National Ecological Observatory Network, USA. We used generalised additive mixed models to explain the nonlinear relationships at each site between nitrate concentration and conductivity, turbidity, dissolved oxygen, water temperature, and elevation. Temporal auto-correlation was modelled with an auto-regressive-moving-average (ARIMA) model and we examined the relative importance of the explanatory variables. Total deviance explained by the models was high for all sites (99%). Although variable importance and the smooth regression parameters differed among sites, the models explaining the most variation in nitrate contained the same explanatory variables. This study demonstrates that building a model for nitrate using the same set of explanatory water-quality variables is achievable, even for sites with vastly different environmental and climatic characteristics. Applying such models will assist managers to select cost-effective water-quality variables to monitor when the goals are to gain a spatial and temporal in-depth understanding of nitrate dynamics and adapt management plans accordingly.

Cross-continental importance of CH4 emissions from dry inland-waters.

Despite substantial advances in quantifying greenhouse gas (GHG) emissions from dry inland waters, existing estimates mainly consist of carbon dioxide (CO2) emissions. However, methane (CH4) may also be relevant due to its higher Global Warming Potential (GWP). We report CH4 emissions from dry inland water sediments to i) provide a cross-continental estimate of such emissions for different types of aquatic systems (i.e., lakes, ponds, reservoirs, and streams) and climate zones (i.e., tropical, continental, and temperate); and ii) determine the environmental factors that control these emissions. CH4 emissions from dry inland waters were consistently higher than emissions observed in adjacent uphill soils, across climate zones and in all aquatic systems except for streams. However, the CH4 contribution (normalized to CO2 equivalents; CO2-eq) to the total GHG emissions of dry inland waters was similar for all types of aquatic systems and varied from 10 to 21%. Although we discuss multiple controlling factors, dry inland water CH4 emissions were most strongly related to sediment organic matter content and moisture. Summing CO2 and CH4 emissions revealed a cross-continental average emission of 9.6 ± 17.4 g CO2-eq m-2 d-1 from dry inland waters. We argue that increasing droughts likely expand the worldwide surface area of atmosphere-exposed aquatic sediments, thereby increasing global dry inland water CH4 emissions. Hence, CH4 cannot be ignored if we want to fully understand the carbon (C) cycle of dry sediments.

Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters.

In situ sensors that collect high-frequency data are used increasingly to monitor aquatic environments. These sensors are prone to technical errors, resulting in unrecorded observations and/or anomalous values that are subsequently removed and create gaps in time series data. We present a framework based on generalized additive and auto-regressive models to recover these missing data. To mimic sporadically missing (i) single observations and (ii) periods of contiguous observations, we randomly removed (i) point data and (ii) day- and week-long sequences of data from a two-year time series of nitrate concentration data collected from Arikaree River, USA, where synoptically collected water temperature, turbidity, conductance, elevation, and dissolved oxygen data were available. In 72% of cases with missing point data, predicted values were within the sensor precision interval of the original value, although predictive ability declined when sequences of missing data occurred. Precision also depended on the availability of other water quality covariates. When covariates were available, even a sudden, event-based peak in nitrate concentration was reconstructed well. By providing a promising method for accurate prediction of missing data, the utility and confidence in summary statistics and statistical trends will increase, thereby assisting the effective monitoring and management of fresh waters and other at-risk ecosystems.

Increasing anthropogenic salinisation leads to declines in community diversity, functional diversity and trophic links in mountain streams.

Anthropogenic salinisation is becoming an increasing global issue for freshwater ecosystems, leading to serious biodiversity loss and ecosystem degradation. While the effect of anthropogenic salinisation on freshwater ecosystems has been intensively studied in recent years, most studies focus on salinisation effects on the individual or single groups of organisms without considering the effect on the ecosystem levels, such as diversity and trophic links. Therefore, we conducted a long-term field survey from May 2009 to August 2016 at 405 sites in northeast China to investigate the effect of a gradient of salinisation on community diversity, functional diversity and trophic links in mountain streams. Samples of water chemistry, periphyton, macroinvertebrates and fish were collected. Our results showed that as anthropogenic salinisation increased, Ca2+, Mg2+, HCO3- and SO42- exhibited significant increases (p < 0.05). These increased ions caused decreases in taxonomic evenness and biotic integrity, but an increase in the beta diversity for periphyton and macroinvertebrates, and a slight increase in the evenness of fish. The increased salinisation resulted in the extirpation of salt-sensitive taxa and declines in macroinvertebrate functional richness and functional redundancy, which consequently led to simplified trophic links. Our results implied that if salt-tolerant taxa in high salinisation sites were not functionally redundant with less tolerant taxa, alterations of their functional composition probably decrease the stability of ecosystem functions. Overall, our study suggests that the ongoing anthropogenic salinisation is posing serious threats to biodiversity and trophic links in river ecosystems, and should be considered in future river restoration and biodiversity conservation.

SSNdesign-An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks.

Streams and rivers are biodiverse and provide valuable ecosystem services. Maintaining these ecosystems is an important task, so organisations often monitor the status and trends in stream condition and biodiversity using field sampling and, more recently, autonomous in-situ sensors. However, data collection is often costly, so effective and efficient survey designs are crucial to maximise information while minimising costs. Geostatistics and optimal and adaptive design theory can be used to optimise the placement of sampling sites in freshwater studies and aquatic monitoring programs. Geostatistical modelling and experimental design on stream networks pose statistical challenges due to the branching structure of the network, flow connectivity and directionality, and differences in flow volume. Geostatistical models for stream network data and their unique features already exist. Some basic theory for experimental design in stream environments has also previously been described. However, open source software that makes these design methods available for aquatic scientists does not yet exist. To address this need, we present SSNdesign, an R package for solving optimal and adaptive design problems on stream networks that integrates with existing open-source software. We demonstrate the mathematical foundations of our approach, and illustrate the functionality of SSNdesign using two case studies involving real data from Queensland, Australia. In both case studies we demonstrate that the optimal or adaptive designs outperform random and spatially balanced survey designs implemented in existing open-source software packages. The SSNdesign package has the potential to boost the efficiency of freshwater monitoring efforts and provide much-needed information for freshwater conservation and management.

Detecting Technical Anomalies in High-Frequency Water-Quality Data Using Artificial Neural Networks.

Anomaly detection (AD) in high-volume environmental data requires one to tackle a series of challenges associated with the typical low frequency of anomalous events, the broad-range of possible anomaly types, and local nonstationary environmental conditions, suggesting the need for flexible statistical methods that are able to cope with unbalanced high-volume data problems. Here, we aimed to detect anomalies caused by technical errors in water-quality (turbidity and conductivity) data collected by automated in situ sensors deployed in contrasting riverine and estuarine environments. We first applied a range of artificial neural networks that differed in both learning method and hyperparameter values, then calibrated models using a Bayesian multiobjective optimization procedure, and selected and evaluated the "best" model for each water-quality variable, environment, and anomaly type. We found that semi-supervised classification was better able to detect sudden spikes, sudden shifts, and small sudden spikes, whereas supervised classification had higher accuracy for predicting long-term anomalies associated with drifts and periods of otherwise unexplained high variability.

Rice-shrimp ecosystems in the Mekong Delta: Linking water quality, shrimp and their natural food sources.

Aquatic ecosystems are used for extensive rice-shrimp culture where the available water alternates seasonally between fresh and saline. Poor water quality has been implicated as a risk factor for shrimp survival; however, links between shrimp, water quality and their main food source, the natural aquatic biota inhabiting these ponds, are less well understood. We examined the aquatic biota and water quality of three ponds over an entire year in the Mekong Delta, Vietnam, where the growing season for the marine shrimp Penaeus monodon has been extended into the wet season, when waters freshen. The survival (30-41%) and total areal biomass (350-531 kg ha-1) of shrimp was constrained by poor water quality, with water temperatures, salinity and dissolved oxygen concentrations falling outside known optimal ranges for several weeks. Declines in dissolved oxygen concentration were matched by declines in both shrimp growth rates and lipid content, the latter being indicative of nutritional condition. Furthermore, as the dry season transitioned into the wet, shifts in the taxonomic composition of phytoplankton and zooplankton were accompanied by declines in the biomass of benthic algae, an important basal food source in these systems. Densities of the benthic invertebrates directly consumed by shrimp also varied substantially throughout the year. Overall, our findings suggest that the survival, condition and growth of shrimp in extensive rice-shrimp ecosystems will be constrained when poor water quality and alternating high and low salinity negatively affect the physiology, growth and composition of the natural aquatic biota. Changes in management practices, such as restricting shrimp inhabiting ponds to the dry season, may help to address these issues and improve the sustainable productivity and overall condition of these important aquatic ecosystems.

Using virtual reality and thermal imagery to improve statistical modelling of vulnerable and protected species.

Biodiversity loss and sparse observational data mean that critical conservation decisions may be based on little to no information. Emerging technologies, such as airborne thermal imaging and virtual reality, may facilitate species monitoring and improve predictions of species distribution. Here we combined these two technologies to predict the distribution of koalas, specialized arboreal foliovores facing population declines in many parts of eastern Australia. For a study area in southeast Australia, we complemented ground-survey records with presence and absence observations from thermal-imagery obtained using Remotely-Piloted Aircraft Systems. These field observations were further complemented with information elicited from koala experts, who were immersed in 360-degree images of the study area. The experts were asked to state the probability of habitat suitability and koala presence at the sites they viewed and to assign each probability a confidence rating. We fit logistic regression models to the ground survey data and the ground plus thermal-imagery survey data and a Beta regression model to the expert elicitation data. We then combined parameter estimates from the expert-elicitation model with those from each of the survey models to predict koala presence and absence in the study area. The model that combined the ground, thermal-imagery and expert-elicitation data substantially reduced the uncertainty around parameter estimates and increased the accuracy of classifications (koala presence vs absence), relative to the model based on ground-survey data alone. Our findings suggest that data elicited from experts using virtual reality technology can be combined with data from other emerging technologies, such as airborne thermal-imagery, using traditional statistical models, to increase the information available for species distribution modelling and the conservation of vulnerable and protected species.

Predicting sediment and nutrient concentrations from high-frequency water-quality data.

Water-quality monitoring in rivers often focuses on the concentrations of sediments and nutrients, constituents that can smother biota and cause eutrophication. However, the physical and economic constraints of manual sampling prohibit data collection at the frequency required to adequately capture the variation in concentrations through time. Here, we developed models to predict total suspended solids (TSS) and oxidized nitrogen (NOx) concentrations based on high-frequency time series of turbidity, conductivity and river level data from in situ sensors in rivers flowing into the Great Barrier Reef lagoon. We fit generalized-linear mixed-effects models with continuous first-order autoregressive correlation structures to water-quality data collected by manual sampling at two freshwater sites and one estuarine site and used the fitted models to predict TSS and NOx from the in situ sensor data. These models described the temporal autocorrelation in the data and handled observations collected at irregular frequencies, characteristics typical of water-quality monitoring data. Turbidity proved a useful and generalizable surrogate of TSS, with high predictive ability in the estuarine and fresh water sites. Turbidity, conductivity and river level served as combined surrogates of NOx. However, the relationship between NOx and the covariates was more complex than that between TSS and turbidity, and consequently the ability to predict NOx was lower and less generalizable across sites than for TSS. Furthermore, prediction intervals tended to increase during events, for both TSS and NOx models, highlighting the need to include measures of uncertainty routinely in water-quality reporting. Our study also highlights that surrogate-based models used to predict sediments and nutrients need to better incorporate temporal components if variance estimates are to be unbiased and model inference meaningful. The transferability of models across sites, and potentially regions, will become increasingly important as organizations move to automated sensing for water-quality monitoring throughout catchments.

A framework for automated anomaly detection in high frequency water-quality data from in situ sensors.

Monitoring the water quality of rivers is increasingly conducted using automated in situ sensors, enabling timelier identification of unexpected values or trends. However, the data are confounded by anomalies caused by technical issues, for which the volume and velocity of data preclude manual detection. We present a framework for automated anomaly detection in high-frequency water-quality data from in situ sensors, using turbidity, conductivity and river level data collected from rivers flowing into the Great Barrier Reef. After identifying end-user needs and defining anomalies, we ranked anomaly importance and selected suitable detection methods. High priority anomalies included sudden isolated spikes and level shifts, most of which were classified correctly by regression-based methods such as autoregressive integrated moving average models. However, incorporation of multiple water-quality variables as covariates reduced performance due to complex relationships among variables. Classifications of drift and periods of anomalously low or high variability were more often correct when we applied mitigation, which replaces anomalous measurements with forecasts for further forecasting, but this inflated false positive rates. Feature-based methods also performed well on high priority anomalies and were similarly less proficient at detecting lower priority anomalies, resulting in high false negative rates. Unlike regression-based methods, however, all feature-based methods produced low false positive rates and have the benefit of not requiring training or optimization. Rule-based methods successfully detected a subset of lower priority anomalies, specifically impossible values and missing observations. We therefore suggest that a combination of methods will provide optimal performance in terms of correct anomaly detection, whilst minimizing false detection rates. Furthermore, our framework emphasizes the importance of communication between end-users and anomaly detection developers for optimal outcomes with respect to both detection performance and end-user application. To this end, our framework has high transferability to other types of high frequency time-series data and anomaly detection applications.

Flow intermittence and ecosystem services in rivers of the Anthropocene.

Intermittent rivers and ephemeral streams (IRES) are watercourses that cease flow at some point in time and space. Arguably Earth's most widespread type of flowing water, IRES are expanding where Anthropocenic climates grow drier and human demands for water escalate.However, IRES have attracted far less research than perennial rivers and are undervalued by society, jeopardizing their restoration or protection. Provision of ecosystem services by IRES is especially poorly understood, hindering their integration into management plans in most countries.We conceptualize how flow intermittence governs ecosystem service provision and transfers at local and river-basin scales during flowing, non-flowing and dry phases. Even when dry or not flowing, IRES perform multiple ecosystem services that complement those of nearby perennial rivers.Synthesis and applications. Conceptualizing how flow intermittence in rivers and streams governs ecosystem services has applied a socio-ecological perspective for validating the ecosystem services of intermittent rivers and ephemeral streams. This can be applied at all flow phases and in assessing impacts of altered flow intermittence on rivers and their ecosystem services in the Anthropocene.

IRBAS: An online database to collate, analyze, and synthesize data on the biodiversity and ecology of intermittent rivers worldwide.

Key questions dominating contemporary ecological research and management concern interactions between biodiversity, ecosystem processes, and ecosystem services provision in the face of global change. This is particularly salient for freshwater biodiversity and in the context of river drying and flow-regime change. Rivers that stop flowing and dry, herein intermittent rivers, are globally prevalent and dynamic ecosystems on which the body of research is expanding rapidly, consistent with the era of big data. However, the data encapsulated by this work remain largely fragmented, limiting our ability to answer the key questions beyond a case-by-case basis. To this end, the Intermittent River Biodiversity Analysis and Synthesis (IRBAS; http://irbas.cesab.org) project has collated, analyzed, and synthesized data from across the world on the biodiversity and environmental characteristics of intermittent rivers. The IRBAS database integrates and provides free access to these data, contributing to the growing, and global, knowledge base on these ubiquitous and important river systems, for both theoretical and applied advancement. The IRBAS database currently houses over 2000 data samples collected from six countries across three continents, primarily describing aquatic invertebrate taxa inhabiting intermittent rivers during flowing hydrological phases. As such, there is room to expand the biogeographic and taxonomic coverage, for example, through addition of data collected during nonflowing and dry hydrological phases. We encourage contributions and provide guidance on how to contribute and access data. Ultimately, the IRBAS database serves as a portal, storage, standardization, and discovery tool, enabling collation, synthesis, and analysis of data to elucidate patterns in river biodiversity and guide management. Contribution creates high visibility for datasets, facilitating collaboration. The IRBAS database will grow in content as the study of intermittent rivers continues and data retrieval will allow for networking, meta-analyses, and testing of generalizations across multiple systems, regions, and taxa.

Understanding multiple ecological responses to anthropogenic disturbance: rivers and potential flow regime change.

Human-induced alteration of the natural flow regime is a major threat to freshwater ecosystems and biodiversity. The effects of hydrological alteration on the structural and functional attributes of riverine communities are expected to be multiple and complex, and they may not be described easily by a single model. Based on existing knowledge of key hydrological and ecological attributes, we explored potential effects of a flow-regulation scenario on macroinvertebrate assemblage composition and diversity in two river systems in Australia's relatively undeveloped wet-dry tropics. We used a single Bayesian belief network (BBN) to model potential changes in multiple assemblage attributes within each river type during dry and wet seasons given two flow scenarios: the current, near-natural flow condition, and flow regulation. We then used multidimensional scaling (MDS) ordination to visually summarize and compare the most probable attributes of assemblages and their environment under the different scenarios. The flow-regulation scenario provided less certainty in the ecological responses of one river type during the dry season, which reduced the ability to make predictions from the BBN outputs directly. However, visualizing the BBN results in an ordination highlighted similarities and differences between the scenarios that may have been otherwise difficult to ascertain. In particular, the MDS showed that flow regulation would reduce the seasonal differentiation in hydrology and assemblage characteristics that is expected under the current low level of development. Our approach may have wider application in understanding ecosystem responses to different river management practices and should be transferred easily to other ecosystems or biotic assemblages to provide researchers, managers, and decision makers an enhanced understanding of ecological responses to potential anthropogenic disturbance.

Predicting the vulnerability of reservoirs to poor water quality and cyanobacterial blooms.

Cyanobacterial blooms in drinking water reservoirs present a major ecosystem functioning and human health issue. The ability to predict reservoir vulnerability to these blooms would provide information critical for decision making, hazard prevention and management. We developed a new, comparative index of vulnerability based on simple measures of reservoir and catchment characteristics, rather than water quality data, which were instead used to test the index's effectiveness. Testing was based on water quality data collected over a number of seasons and years from 15 drinking water reservoirs in subtropical, southeast Queensland. The index correlated significantly and strongly with algal cell densities, including potentially toxic cyanobacteria, as well as with the proportions of cyanobacteria in summer months. The index also performed better than each of the measures of reservoir and catchment characteristics alone, and as such, was able to encapsulate the physical characteristics of subtropical reservoirs, and their catchments, into an effective indicator of the vulnerability to summer blooms. This was further demonstrated by calculating the index for a new reservoir to be built within the study region. Under planned dimensions and land use, a comparatively high level of vulnerability was reached within a few years. However, the index score and the number of years taken to reach a similar level of vulnerability could be reduced simply by decreasing the percentage of grazing land cover via revegetation within the catchment. With climate change, continued river impoundment and the growing demand for potable water, our index has potential decision making benefits when planning future reservoirs to reduce their vulnerability to cyanobacterial blooms.