River Invertebrate Classification Tool

Background to research The Regulatory Agencies in the UK (the Environment Agency; Scottish Environment Protection Agency; and the Environment & Heritage Service) currently use RIVPACS III+ software to classify the ecological quality of rivers. However, because RIVPACS III+ pre-dates the WFD, there has been a requirement to ensure that the RIVPACS reference sites are fully WFD compliant, to add new biotic indices to the RIVPACS models, and to improve the robustness of the RIVPACS software to fully meet the needs of the Agencies in their delivery of WFD monitoring. These issues have been addressed in this project and have led to the development of new RIVPACS IV predictive models that will be programmed into a new River Invertebrate Classification Tool being built by SEPA. This new system will be based on a modern software programming language, be compatible with the agencies’ computer systems and include the ability to predict new biological indices, produce biological status assessments based on these new indices and be able to estimate the errors involved in using these new indices. Because access to the new system will be essential for the UK Agencies to be able to implementation the WFD, the new tool will be readily and freely available to anyone who might seek to use it. Objectives of research • The overall objective of the project was to produce a new set of RIVPACS predictive models for use within a new River Invertebrate Classification Tool that will be used to classify the ecological status of rivers for Water Framework Directive compliance monitoring • The new RIVPACS models constructed with this project required considerably enhanced functionality compared to RIVPACS III+ to properly address the monitoring requirements of the UK Agencies in their implementation of the Water Framework Directive. Key findings and recommendations This project has produced new RIVPACS IV models with considerably enhanced functionality compared to RIVPACS III+. These models incorporate: • A full revision of the taxonomic framework of RIVPACS to bring the taxonomy up-to-date and enable compatiability across the revised Miatland, Furse code and National Biodiversity Network taxon coding systems used across the UK Agencies and beyond • Predictions that fully satisfy the WFD definition of ‘reference condition’ by adjusting predictions for certain stream types and by removal of sites that were not in reference condition when sampled • Allocation of actual abundance values to family level records in the RIVPACS reference data set. Lack of actual abundance data, especially at family level, has affected all versions of RIVPACS and has constrained the types of biotic indices that RIVPACS can predict • Extension to the suite of biotic indices so that the new system can predict a wider range of reference state “expected” index values. This enables full WFD quality reporting capabilities as well as providing the system with the general functionality to predict a much wider range of indices e.g. intercalibration indices (e.g. ICMi), stress-specific indices, and ecological and functional trait indices • Extension of the uncertainty/errors module to estimate and assess uncertainty in (i) assignment to status class and (ii) comparison of samples for temporal change in quality and status. This needs to be done for a wider range of biotic indices (including those incorporating abundance data) These new RIVPACS IV models can be used by the UK Agencies across Great Britain and Northern Ireland in their WFD compliance monitoring. All of the algorithms, variables and data necessary to build these models have been provided to SEPA for programming into a new River Invertebrate Classification Tool that will be disseminated made free of charge to all interested user

Understanding the controls on deposited fine sediment in the streams of agricultural catchments

Excessive sediment pressure on aquatic habitats is of global concern. A unique dataset, comprising instantaneous measurements of deposited fine sediment in 230 agricultural streams across England and Wales, was analysed in relation to 20 potential explanatory catchment and channel variables. The most effective explanatory variable for the amount of deposited sediment was found to be stream power, calculated for bankfull flow and used to index the capacity of the stream to transport sediment. Both stream power and velocity category were highly significant (p<<0.001), explaining some 57% variation in total fine sediment mass. Modelled sediment pressure, predominantly from agriculture, was marginally significant (p<0.05) and explained a further 1% variation. The relationship was slightly stronger for erosional zones, providing 62% explanation overall. In the case of the deposited surface drape, stream power was again found to be the most effective explanatory variable (p<0.001) but velocity category, baseflow index and modelled sediment pressure were all significant (p<0.01); each provided an additional 2% explanation to an overall 50%. It is suggested that, in general, the study sites were transport-limited and the majority of stream beds were saturated by fine sediment. For sites below saturation, the upper envelope of measured fine sediment mass increased with modelled sediment pressure. The practical implications of these findings are that (i) targets for fine sediment loads need to take into account the ability of streams to transport/retain fine sediment, and (ii) where agricultural mitigation measures are implemented to reduce delivery of sediment, river management to mobilise/remove fines may also be needed in order to effect an improvement in ecological status in cases where streams are already saturated with fines and unlikely to self-cleanse

支那近代工業の性格

1. Detrimental impacts of excessive fine-grained sediment inputs to streams and rivers are well established. What is less well understood is the susceptibility of different elements of the freshwater biota to such perturbations and how such knowledge of their susceptibility could aid in identifying where excessive fine-grained sediment is impairing ecological condition. 2. Following the collection of biological and sediment data from 179 streams across England and Wales, representative of a range of river types over a gradient of fine sediment loading, objective statistical approaches were applied to establish relationships between the macroinvertebrate assemblage and fine-grained sediment inputs to river channels. 3. Having factored out that portion of the biological variation associated with natural environmental gradients, a model comprising mass of organic sediment in erosional areas of the stream bed [predominantly associated with the first axis of the partial canonical correspondence analysis (pCCA)], and mass of fine-grained sediment in the surface drape of depositional areas and % organic content in erosional areas (associated with the second axis of the pCCA) as explanatory variables best accounted for the residual variation in the macroinvertebrate assemblage. 4. The relative position of taxa along both axes of the pCCA, provided a ranking of taxa in relation to the two gradients of fine-grained sediment and provided the basis for a new empirically derived diagnostic index for fine-grained sediment stress in rivers. Two sub-indices were derived to capture the assemblage responses to both the gradient of organic sediment in erosional areas and the gradient of total fines in depositional areas. The two sub-indices were then combined to derive the new combined fine sediment index (CoFSIsp). 5. The index was tested on an independent test data set (comprising 127 samples from 83 sites) and was found to provide a robust indication of benthic fine-grained sediment conditions (Spearman's rank correlations ρ = −0.519 to −0.703). The strength of correlation with the total fine-grained sediment gradient was always greater than that for other routinely used indices, confirming that CoFSIsp offered additional explanatory power when assessing this stressor of aquatic environments

What do macroinvertebrate indices measure? Stressor‐specific stream macroinvertebrate indices can be confounded by other stressors

Monitoring programmes worldwide use biota to assess the “health” of water bodies. Indices based on biota are used to describe the change in status of sites over time, to identify progress against management targets and to diagnose the causes of biological degradation. A variety of numerical stressor-specific biotic indices have been developed based on the response of biota to differences in stressors among sites. Yet, it is not clear how variation in pressures within sites, over what time period, and in what combination has the greatest impact on different biotic groups. An understanding of how temporal variation in pressures influences biological assessment indices would assist in setting achievable targets and help focus catchment-scale mitigation strategies to ensure that they deliver the desired improvements in biological condition. Hydrochemical data provided by a network of high-frequency (15 or 30 min) automated monitoring stations over 3 years were matched to replicated biological data to understand the influence of spatio-temporal variation in pollution pressures on biological indices. Hydrochemical data were summarised in various ways to reflect central tendency, peaks, troughs and variation over 1–90 days before the collection of each biological sample. An objective model selection procedure was used to determine which hydrochemical determinand, and over what time period, best explained variation in the biological indices. Stressor-specific indices derived from macroinvertebrates which purportedly assess stress from low flows, excess fine sediment, nutrient enrichment, pesticides and organic pollution were significantly inter-correlated and reflected periods of low oxygen concentration, even though only one index (ASPTWHPT, average score per taxon) was designed for this purpose. Changes in community composition resulting from one stressor frequently lead to confounding effects on stressor-specific indices. Variation in ASPTWHPT was best described by dissolved oxygen calculated as Q5 over 10 days, suggesting that low oxygen events had most influence over this period. Longer-term effects were apparent, but were masked by recovery. Macroinvertebrate abundance was best described by Q95 of stream velocity over 60 days, suggesting a slower recovery in numbers than in the community trait reflected by ASPTWHPT. Although use of ASPTWHPT was supported, we recommend that additional independent evidence should be used to corroborate any conclusions regarding the causes of degradation drawn from the other stressor-specific indices. The use of such stressor-specific indices alone risks the mistargeting of management strategies if the putative stressor-index approach is taken to be more reliable than the results herein suggest