Unravelling large-scale patterns and drivers of biodiversity in dry rivers.

More than half of the world's rivers dry up periodically, but our understanding of the biological communities in dry riverbeds remains limited. Specifically, the roles of dispersal, environmental filtering and biotic interactions in driving biodiversity in dry rivers are poorly understood. Here, we conduct a large-scale coordinated survey of patterns and drivers of biodiversity in dry riverbeds.

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.

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.

Cross-continental importance of CH 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 (CO) emissions. However, methane (CH) may also be relevant due to its higher Global Warming Potential (GWP). We report CH emissions from dry inland water sediments to i) provide a cross-continental estimate of such emissions for different types of aquatic systems (i.

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.

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.

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.

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.

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.

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.

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.

Simulating rewetting events in intermittent rivers and ephemeral streams: A global analysis of leached nutrients and organic matter.

Climate change and human pressures are changing the global distribution and the extent of intermittent rivers and ephemeral streams (IRES), which comprise half of the global river network area. IRES are characterized by periods of flow cessation, during which channel substrates accumulate and undergo physico-chemical changes (preconditioning), and periods of flow resumption, when these substrates are rewetted and release pulses of dissolved nutrients and organic matter (OM). However, there are no estimates of the amounts and quality of leached substances, nor is there information on the underlying environmental constraints operating at the global scale.

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.

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.

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.

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.