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SNIFFER WFD72C Final Report on River Invertebrate Classification Tool

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SNIFFER WFD72C Final Report on River Invertebrate Classification Tool Final Report Project WFD72C River Invertebrate Classification Tool June/2008 © SNIFFER 2008 All rights reserved. No part of this document may be reproduced, stored in a retrieval system or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise without the prior permission of SNIFFER. The views expressed in this document are not necessarily those of SNIFFER. Its members, servants or agents accept no liability whatsoever for any loss or damage arising from the interpretation or use of the information, or reliance upon views contained herein. Dissemination status Unrestricted Project funders SNIFFER Whilst this document is considered to represent the best available scientific information and expert opinion available at the stage of completion of the report, it does not necessarily represent the final or policy positions of the project funders. Research contractor This document was produced by: John Davy-Bowker*, Ralph Clarke†, Tracy Corbin*, Helen Vincent*, James Pretty*, Adrianna Hawczak*, John Blackburn, John Murphy* and Iwan Jones* *Centre for Ecology & Hydrology, c/o Freshwater Biological Association, River Laboratory, East Stoke, Wareham, Dorset, BH20 6BB, United Kingdom †Bournemouth University, Christchurch House, Talbot Campus, Poole, Dorset, BH1 5BB, United Kingdom SNIFFER’s project manager SNIFFER’s project manager for this contract is: Alan Croal, Scottish Environment Protection Agency SNIFFER’s project steering group members are: Alan Croal, Scottish Environment Protection Agency Robin Guthrie, Scottish Environment Protection Agency Peter Hale, Environment & Heritage Service Imelda O’Neill, Environment & Heritage Service John Murray-Bligh, Environment Agency Nathan Critchlow-Watton, Scottish Environment Protection Agency Anthony Kyriakides, Scottish Environment Protection Agency SNIFFER First Floor, Greenside House 25 Greenside Place EDINBURGH EH1 3AA Scotland, UK Company No: SC149513 Scottish Charity: SCO22375 www.sniffer.org.uk EXECUTIVE SUMMARY WFD72C: River Invertebrate Classification Tool (June, 2008) Project funders/partners: SNIFFER 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 compatibility across the revised Maitland, 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, 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 free of charge to all interested users Key words: RIVPACS IV, River Invertebrate Classification Tool, Water Framework Directive SNIFFER WFD72C: River Invertebrate Classification Tool June, 2008 TABLE OF CONTENTS EXECUTIVE SUMMARY 1. INTRODUCTION 1 2. WORK ELEMENT SUMMARIES 5 3. WORK ELEMENT REPORTS 17 WE 1.3-1.7 Generic algorithms for discriminant functions 17 WE 2.1 Refinement of reference sites for model development 61 WE 2.2 Compilation of data for errors and compare 67 WE 2.3 Compilation of data for estimating abundances in the RIVPACS dataset 81 WE 3.1 Review and development of the taxonomic framework 87 WE 3.2 Generation of family level abundance data for the RIVPACS dataset 91 WE 4.1 Allocation of reference sites to geographical models and end groups 95 WE 4.3 Confirmed variables for taxonomic prediction 107 WE 4.4 Confirmed variables for predicting indices 109 WE 4.5 Algorithms to adjust indices to standard WFD reference state 113 WE 5.0 & 6.0 Algorithms and variables for errors and compare modules 121 5. ACKNOWLEDGEMENTS 161 6. APPENDICIES 163 References are given either within or at the end of each chapter List of Tables Table 1 Seven super-group level of classification of the 43 end groups of the 685 GB reference sites. 12 Table 2 Estimates of adjustment parameters (A1 – A5) for the effects of assessment score (1 (top-high), 2 (mid-high), 3 (high/good), 4 (mid-good) 5 (good/moderate)) in model (M4) for each biotic index based on using every possible combination of single and multiple season samples for the 793 UK-wide reference sites (AWIC and LIFE based on all single season samples only) 13 Table 3 The 60 reference sites identified for potential removal from the RIVPACS reference site dataset together with the final decision on which sites should actually be removed. Those to be retained (n=18) and those to be removed (n=42) are identified in the column ‘Final list’ 63 SNIFFER WFD72C: River Invertebrate Classification Tool June, 2008 Table 4 The 42 reference sites removed (out of the original 835 reference sites) and now no longer available for model construction in this project 66 Table 5 Example of the compiled data for the estimation of nominal abundances to be applied to the RIVPACS samples (126,221 rows in full GB-wide dataset) 85 Table 6 Seven super-group level of classification of the 43 end groups of the 685 reference sites 97 Table 7 Estimates of adjustment parameters (a1 – a5) for the effects of assessment score (1- 5) in model (M4) for each biotic index based on using every possible combination of single and multiple season samples for the 793 UK-wide reference sites (AWIC and LIFE based on all single season samples only); p = model test probability value for effect of score 116 Table 8 Estimates of adjustment parameters (A1 – A5) for the effects of assessment score (1-5) in model (M4) for each biotic index based on using every possible combination of single and multiple season samples for the 793 UK-wide reference sites (AWIC and LIFE based on all single season samples only); p = model test probability value for effect of score. (Note: Ai = 10 to the power ai , where ai is as in Table 1) 117 Table 9 Number of Reference sites with each Assessment Score (1-5) in each End-Group 119 Table 10 Components of variability which can be estimated, or for which there is information, within each dataset (indicated by ticks) 131 Table 11 Taylor’s power law regressions of log replicate variance again log replicate mean for the single season samples for the (a) 16 BAMS sites and (b) BAMS + Tay datasets combined; b = regression slope, SE(b) = standard error of b, r2 = % variation explained) 134 Table 12 12 Quadratic regression estimates of Replicate SD of single season sample AWIC values for replicate mean or observed single sample values of AWIC 139 Table 13 Estimate of sampling standard deviation (SD) of observed LIFE for sites where NLIFE LIFE-scoring families are present in a sample (estimates based on equation (6.2)) 142 Table 14 Pearson correlations between the biotic indices (transformed where appropriate) based on single season samples for (a) raw values for all three datasets combined and (b) residual variation in index values among replicate samples after allowing for all site and season combination differences for the BAMS dataset.
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