1. ARROW: system for the evaluation of the status of waters in the Czech Republic Jiří Jarkovský 1) Institute of Biostatistics and Analyses, Masaryk University, Czech Republic 2) Research Center for the Environmental Chemistry and Ecotoxicology, Masaryk University, Czech Republic The ARROW project

2. Project aims Good status of surface waters is demanded by WFD EU Identification of good status We need system for the evaluation of state of surface waters Utilization in water management and decision support +

3. DATA

4. Components of state evaluation Ecological state Macrophytes Fytobenthos Benthic macroinvertebrates Fishes Supporting parameters Hydromorphology Chemical and physics parameters One out all out Multimetric evaluation Norms Overall evaluation of state Complex statistical methodology 5 levels 2 levels Specific pollutants Selected chemical parameters

5. What data we need? Structure and composition of biological communities for all biological compounds –Properties of taxa – species traits Influential abiotic factors –Natural parameters (altitude etc.) –Stressors Chemical contamination Data for reference and contaminated sites

6. General methodology of state evaluation Comparison with reference dataset Definition of reference conditions Statistical analysis Expert evaluation Levels of state Reference state Reference conditions define very good environmental state. Complex multivariate data. Possible range of state Evaluated locality Distance from reference condition

7. Biomonitoring network Reference network (green) and its extension in 2006 (red)

8. Sampling network and WFD typology I B system of WFD EU: 35 river types Evaluation of reference conditions within river types

9. Sampling network and WFD typology II Aggregation of river types for some types of analyses Parameters no influenced by region, stability of statistical estimates

10. DATA ANALYSIS

11. Data analysis in implementation of WFD The objective evaluation of ecological state is impossible without correct data analysis Data analysis is included in several steps of the project: –Searching for sufficient level of taxonomic determination that can be accepted in biomonitoring networks –Preliminary analysis of relationship of biological communities and their environment – definition of model for evaluation –Robust methodology of evaluation of ecological state for routine biomonitoring –Reporting of results of evaluation of ecological state

12. Statistical analysis in evaluation of ecological state Biological community Biotic indices Simple computation, taxa individuality is lost Comparison with reference conditions Reference conditions from data analysis or expert judgment = X Multivariate modeling of reference biological community Comparison of expected reference community and measured community Complex analysis, nevertheless data and methodology demanding Comparison with community defined by experts Comparison of expected reference community and measured community Supporting parameters Multivariate similarity with reference conditions Ref.

13. Predictive modeling of macrozoobenthos communities Standard data of monitoring (unknown sites) Reference data Classification into reference groups according to natural heterogeneity Definition of reference categories according to biological communities Difference between observed and expected ecological state Reference model: step I Environmental parameters Community composition Description of reference categories = reference model Environmental parameters Communities Reference model: step II I. II.III. IV. V.VI. Partial evaluation A Partial evaluation B Biological community Final evaluation of state VII. Final result is a single metric with straightforward interpretation based on different communities, possibly also on environmental evaluation Natural heterogeneity Pollution I.II.III.IV.V.VI.VII. Independent problems to solve

14. Definition of reference categories defining homogenous groups within the reference database according to composition of biological communities statistical method: Hierarchical agglomerative clustering on distance matrix (Gower distance metric) of biological communities followed by algorithm for definition of optimal number of clusters and expert opinion will be used for definition of reference groups. Reference data Reference model consists of several homogeneous categories according to their community composition Statistical analysis Results of the analysis have to be confirmed by expert knowledge (valuable reference groups) at least 10 – 15 sites in each reference group

15. Searching for optimal number of clusters Expert separation into 21 clusters „Silhouette“ of given number of clusters Number of clusters Small number of clusters easy to separate No significant differences No discrepancy in expert and statistical solution

16. Reference model Reference categories are described by environmental parameters which are minimally influenced by human activity = natural heterogeneity These parameters are used for classification of unknown sites (standard monitoring) into reference groups according to theory that sites with similar environmental conditions should have similar biological communities Unknown site –Classified to reference category according to natural heterogeneity Reference categories –Defined according to communities’ composition –Description of their natural heterogeneity (forming communities) ? ? ? ? –Natural conditions should be defined for biological communities, environmental metrics and chemical pollutants

17. Description of final classification Example of abiotic data description

18. Multimetric evaluation of ecological state 100% 0% Metric 1 Metric 2 Metric 3 Different metrics - > indication of stressors Ref. Position of evaluated locality towards reference conditions? Stressor identificationAggregation of metrics into final multimetrics Reporting Levels of state Multimetric evaluation of ecological state combine several views on biological community and influencing stressors.

19. Selection of indices for final multimetric Identification of suitable indices –Metrics which distinguish between reference and standard localities –Relationship to abiotic stresors Aggregation of indices –Similar metrics aggregated into modules with relationship to specific stresor Selection of candidate indices –Indices decribing different aspects (species composition, diversity, saprobity) –Indices which are not correlated –Indices without outliers, etc. Altogether 19 indices selected for final multimetric –Indices with significant difference between reference and non-reference state –or important indices from expert point of view (number of taxa, etc.)

20. Final multimetric Number of individuals Q statistic stochastic EPT – number of taxa of Ephemeroptera, Plecoptera, Trichoptera P – number of taxa of Plecoptera Makrozoobentos Final multimetric Makrozoobentos Final multimetric A. Saprobity and trophism Saprobic index RETI B. Diversity C. Habitat degradation D. Predictive model of community Expected community predicted - similarity index with expected community Zonation zonation hypocrenal (WSES) zonation epirhithral (WSES) zonation epipotamal (WSES) zonation metapotamal (WAES) zonation hypopotamal (WAES) zonation litoral (WAES) zonation profundal (WAES) Microhabitat preference microhab. psammal (WSES) microhab. pelal (WAES) microhab. lithal (WAES) Feeding preference feeding type: grazer and scrapers (WAES) feeding type: active filter (WAES)

21. Ecological state evaluation and its components Ecological state Macrophytes Fytobenthos Macrozoobentos Fishes Supporting characteristics Hydromorphology Chemical and physics Diversity Saprobity and trophism Habitat degradation Predictive model of community Partial indices Migration Reproduction Tolerance Oxygen consumption 10 hydromorphology parameters Biotic index Expert community Biotic index (saprobity) Expert community Biotic index (mineralisation) N, P Conductivity pH Specific pollutants Comparison with norm

22. Reference conditions Reference conditions of indices and abiotic parameters is based on percentile method Ecological state Macrophytes Fytobenthos Macrozoobenthos Fishes Supporting parameters Indices Reference conditions Community composition Indices Community composition Indices Community composition Expert opinion + data analysisData analysis

23. Macrozoobenthos: data analysis and expert opinion Expert opinion on reference conditions Data analysis of reference conditions

24. Fishes: data analysis and expert opinion Expert opinion on reference conditions Data analysis of reference conditions

25. Overall evaluation of state Chemical state 2 levels Weighted computation of ecological state from its components 3. Macrophytes 4. Fytobenthos 1. Macrozoobenthos 2. Fishes Overall score: 5. Supporting parameters max ( 1. 2. 3. 4. 5. ) Levels of state One out all out Overall evaluation of state Norms

26. Implementation Central ARROW Database ARROW Client Monitoring Scientific ARROW Routine analysis Scientific analysis, preparation of templates for routine work ARROW Client ARROW Client ARROW Client ARROW Client Monitoring PHP JavaScript Oracle PHP JavaScript Java

27. Web based system The Arrow system have web-based interface for both data input and results reporting

28. CONCLUSIONS

29. Conclusion  The presented methodology is universal for any type of date (i.e. any biological communities) and respects the problems of data distribution and variability as well as WFD EU demands. Multimetric approach which combine both site and type specific approach is used for the final evaluation of ecological state  The software implementation use standardized tools and have central management of rights, data and reporting Complex system for evaluation of ecological state of surface waters with standardized procedure of data processing.