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River impoundment constitutes one of the most important anthropogenic impacts on the World’s rivers. An increasing number of studies have tried to quantify the effects of river impoundment on riverine ecosystems over the past two decades, often focusing on the effects of individual large reservoirs. This study is one of the first to use a large-scale, multi-year diatom dataset from a routine biomonitoring network to analyse sample sites downstream of a large number of water supply reservoirs (n = 77) and to compare them with paired unregulated control sites. We analysed benthic diatom assemblage structure and a set of derived indices, including ecological guilds, in tandem with multiple spatio-temporal variables to disclose patterns of ecological responses to reservoirs beyond the site-specific scale. Diatom assemblage structure at sites downstream of water supply reservoirs was significantly different to control sites, with the effect being most evident at the regional scale. We found that regional influences were important drivers of differences in assemblage structure at the national scale, although this effect was weaker at downstream sites, indicating the homogenising effect of river impoundment on diatom assemblages. Sites downstream of reservoirs typically exhibited a higher taxonomic richness, with the strongest increases found within the motile guild. In addition, Trophic Diatom Index (TDI) values were typically higher at downstream sites. Water quality gradients appeared to be an important driver of diatom assemblages, but the influence of other abiotic factors could not be ruled out and should be investigated further. Our results demonstrate the value of diatom assemblage data from national-scale biomonitoring networks to detect the effects of water supply reservoirs on instream communities at large spatial scales. This information may assist water resource managers with the future implementation of mitigation measures such as setting environmental flow t

Stream water temperature is a key control of many river processes (e.g. ecology, biogeochemistry, hydraulics) and services (e.g. power plant cooling, recreational use). Consequently, the effect of climate change and variability on stream temperature is a major scientific and practical concern. This paper aims (1) to improve the understanding of large-scale spatial and temporal variability in climate–water temperature associations, and (2) to assess explicitly the influence of basin properties as modifiers of these relationships. A dataset was assembled including six distinct modelled climatic variables (air temperature, downward short-wave and long-wave radiation, wind speed, specific humidity, and precipitation) and observed stream temperatures for the period 1984–2007 at 35 sites located on 21 rivers within 16 basins (Great Britain geographical extent); the study focuses on broad spatio-temporal patterns, and hence was based on 3-month-averaged data (i.e. seasonal). A wide range of basin properties was derived. Five models were fitted (all seasons, winter, spring, summer, and autumn). Both site and national spatial scales were investigated at once by using multi-level modelling with linear multiple regressions. Model selection used multi-model inference, which provides more robust models, based on sets of good models, rather than a single best model. Broad climate–water temperature associations common to all sites were obtained from the analysis of the fixed coefficients, while site-specific responses, i.e. random coefficients, were assessed against basin properties with analysis of variance (ANOVA). All six climate predictors investigated play a role as a control of water temperature. Air temperature and short-wave radiation are important for all models/seasons, while the other predictors are important for some models/seasons only. The form and strength of the climate–stream temperature association vary depending on season and on water temperature. The dominating

Changes in water temperature can have important consequences for aquatic ecosystems, with some species being sensitive even to small shifts in temperature during some or all of their lifecycle. While many studies report increasing regional and global air temperatures, evidence of changes in river water temperature has thus far been site-specific and often from sites heavily influenced by human activities that themselves could lead to warming. Here we present a tiered assessment of changing river water temperature, covering England and Wales with data from 2,773 locations. We use novel statistical approaches to detect trends in irregularly sampled spot measurements taken between 1990 and 2006. During this 17 year period, on average, mean water temperature increased by 0.03 °C per year (± 0.002 °C) and positive changes in water temperature were observed at 2,385 (86%) sites. Examination of catchments, where there has been limited human influence on hydrological response, shows that changes in river flow have had little influence on these water temperature trends. In the absence of other systematic influences on water temperature, it is inferred that anthropogenically-driven climate change is driving some of this trend in water temperature.

Lake phytoplankton are adopted world-wide as a sensitive indicator of water quality. European environmental legislation, the EU Water Framework Directive (WFD), formalises this, requiring the use of phytoplankton to assess the ecological status of lakes and coastal waters. Here we provide a rigorous assessment of a number of proposed phytoplankton metrics for assessing the ecological quality of European lakes, specifically in response to nutrient enrichment, or eutrophication, the most widespread pressure affecting lakes. To be useful indicators, metrics must have a small measurement error relative to the eutrophication signal we want them to represent among lakes of different nutrient status. An understanding of variability in metric scores among different locations around a lake, or due to sampling and analytical variability can also identify how best this measurement error is minimised. To quantify metric variability, we analyse data from a multi-scale field campaign of 32 European lakes, resolving the extent to which seven phytoplankton metrics (including chlorophyll a, the most widely used metric of lake quality) vary among lakes, among sampling locations within a lake and through sample replication and processing. We also relate these metrics to environmental variables, including total phosphorus concentration as an indicator of eutrophication. For all seven metrics, 65–96% of the variance in metric scores was among lakes, much higher than variability occurring due to sampling/sample processing. Using multi-model inference, there was strong support for relationships between among-lake variation in three metrics and differences in total phosphorus concentrations. Three of the metrics were also related to mean lake depth. Variability among locations within a lake was minimal (<4%), with sub-samples and analysts accounting for much of the within-lake metric variance. This indicates that a single sampling location is representative and suggests that sub-sample repli

It is critical that the impacts of environmental stressors on natural systems are detected, monitored and assessed accurately in order to legislate effectively and to protect and restore ecosystems. Biomonitoring underpins much of modern resource management, especially in fresh waters, and has received significant sums of money and research effort during its development. Despite this, the incorporation of science has not been effective and the management tools developed are sometimes inappropriate and poorly designed. Much biomonitoring has developed largely in isolation from general ecological theory, despite the fact that many of its fundamental principles ultimately stem from basic concepts, such as niche theory, the habitat template and the r–K continuum. Consequently, biomonitoring has not kept pace with scientific advances, which has compromised its ability to deal with emerging environmental stressors such as climate change and habitat degradation. A reconnection with its ecological roots and the incorporation of robust statistical frameworks are key to progress and meeting future challenges. The vast amount of information already collected represents a potentially valuable, and largely untapped, resource that could be used more effectively in protecting ecosystems and in advancing general ecology. Biomonitoring programmes have often accumulated valuable long-term data series, which could be useful outside the scope of the original aims. However, it is timely to assess critically existing biomonitoring approaches to help ensure future programmes operate within a sound scientific framework and cost-effectively. Investing a small proportion of available budgets to review effectiveness would pay considerable dividends. Increasing activity has been stimulated by new legislation that carries the threat of penalties for non-compliance with environmental targets, as is proposed, for example, in the EU's Water Framework Directive. If biomonitoring produces poor-quali

The linkage of trait responses to stressor gradients has potential to expand biomonitoring approaches beyond traditional taxonomically based assessments that identify ecological effect to provide a causal diagnosis. Traits-based information may have several advantages over taxonomically based methods. These include providing mechanistic linkages of biotic responses to environmental conditions, consistent descriptors or metrics across broad spatial scales, more seasonal stability compared with taxonomic measures, and seamless integration of traits-based analysis into assessment programs. Atraits-based biomonitoring approach does not require a new biomonitoring framework, because contemporary biomonitoring programs gather the basic site-by-species composition matrices required to link community data to the traits database. Impediments to the adoption of traits-based biomonitoring relate to the availability, consistency, and applicability of existing trait data. For example, traits generalizations among taxa across biogeographical regions are rare, and no consensus exists relative to the required taxonomic resolution and methodology for traits assessment. Similarly,we must determine if traits form suites that are related to particular stressor effects, and whether significant variation of traits occurs among allopatric populations. Finally, to realize the potentialof traits-based approaches in biomonitoring, a concerted effort to standardize terminology is required, along with the establishment of protocols to ease the sharing and merging of broad, geographical trait information.

1. There is a need to relate changing river flows to ecological response, particularly using methods which do not require extensive new data for water bodies without historical data. This paper describes how local-scale habitat features and changing discharge appear to influence a macroinvertebrate-based biotic index. 2. The study used 87 time-series of river biomonitoring data from upland, wadeable streams with quasi-natural flow regimes across England and Wales. Twenty-seven of the sites were matched to a nearby flow gauging station, and historical, natural flows using a generalized rainfall-runoff model were synthesized for 60 sites. All sites were matched to a River Habitat Survey (RHS) within 1 km. 3. The data were analysed using multilevel linear regression, combining sample- and site-level characteristics as predictors. Common responses were assessed across sites, using the biotic index LIFE (Lotic Invertebrate index for Flow Evaluation), an average of abundance-weighted Flow Groups which indicate the relative preference among taxa for higher velocities with gravel/cobble substrates or slow velocities with finer substrates. The aim was to understand the influence of habitat on macroinvertebrate response to antecedent high and low flow magnitude. 4. There was a positive relationship between LIFE score calculated from spring and autumn samples and antecedent high (Q10) and low flows (Q95). The relationship between summer Q10 and autumn LIFE score was steeper than the relationship between winter Q10 and spring LIFE score. Bed and bank resectioning reduced overall LIFE and increased the steepness of the response of LIFE to low (Q95) flow. 6. The models derived may be used to guide environmental flow allocations and to quantify the relative influence of flow and physical habitat change on macroinvertebrate responses. The interaction between resectioning and low flow has particular implications for the conservation of macroinvertebrate taxa with requirements for fa

1. Invertebrate drift is commonly investigated in streams, with the majority of studies focussed on temporal (typically diel) variation. In comparison, few studies have investigated spatial variation in drift and there is little consensus among them. We tested the hypothesis that spatial variation in invertebrate drift is as important as temporal variation. 2. The density of drifting invertebrates in a chalk stream was sampled using an array of nets arranged to determine vertical, lateral and longitudinal variation. Samples were collected at dawn, during the day, at dusk and by night, on four separate monthly occasions. Insecta and Crustacea were analysed separately to identify the effect of differing life history strategies. The density of drifting debris was also recorded, to act as a null model. 3. Time of day and vertical position together explained the majority of the variance in invertebrate drift (79% for Insecta and 97% for Crustacea), with drift densities higher at dusk and night, and nearer the stream bed. Independently, time of day (38%, Insecta; 52%, Crustacea) and vertical position (41%, Insecta; 45%, Crustacea) explained a similar amount of the observed variance. Month explained some of the variance in insect drift (9%) but none for Crustacea. 4. Variation in the density of drifting debris showed little in common with invertebrate drift. There was little variation associated with time of day and only 27% of the observed variation in debris could be explained by the factors investigated here, with month explaining the largest proportion (20%). We suggest the difference in drifting debris and invertebrates provides further evidence for a strong behavioural component in invertebrate drift. 5. Spatial variation in invertebrate drift can be of the same order of magnitude as the much-described diel temporal variation. The extent of this spatial variation poses problems when attempting to quantify invertebrate drift and we recommend that spatial replication s