1. Böhmer, J. Birk, S., Schöll, F. Intercalibration of large river assessment methods

2. Specific challenges of large river classification Typology Large rivers are individual and highly complex ecosystems. Reference Conditions Near-natural conditions are no longer existing. Historical data are scarce and often imprecise. Neobiota have restructured original communities. Sampling Difficult and time-consuming (compared to wadeable streams) Lack of standardised protocol for representative sampling Data Data availability low for very large rivers in some countries

3. Data availability Sufficient for benthic fauna and diatoms Too low for Phytoplankton, macrophytes and fish Intercalibration of diatoms and benthic fauna Almost 500 sampling sites 48 large rivers 18 countries

4. Data harmonisation Diatoms –Latest version of expert taxa harmonisation list (supplied by M.Kelly; originated in IC phase one for streams) was applied, but covered only half of the taxa –Wiser synonym list for remaining taxa Benthic fauna –Original taxonomic level was kept

5. Phytobenthos-Diatoms (12 methods)

6. Benthic fauna (12 methods + one?) One additional method was submitted last week (new method HU + new data) - will be included, if possible without redoing the the previous analysis, and if compliance checking will be successfull

7. Compliance check Diatoms –All methods compliant Benthic fauna –For ES and one AT river type the benthic fauna method is not fully compliant (not covering al WFD criteria).  AT type will be included in boundary comparison but not in the averaging of class boundaries used to harmonise the national classifications  ES BMWP method already accepted in MED-GIG, because of high correlation with multimetric indices –HU to be checked –All others ok

8. Feasibility types Type analysis by species occurence  4 types for diatoms (Western, Central, Nordic high alkalinity, Nordic medium-high alkalinity) and 4 types for benthic fauna (warm major, warm minor, cold major, cold minor) Are these types relevant? Only if they influence the assessment or the common metrics  metric analysis  two types left for diatoms (low-alkalinity and medium- to high-alkalinity, for standardisation and boundary comparison)  two types left for benthic fauna (major and minor, only for standardisation, no type differentiation for boundary comparison)

9. Example: type difference in diatom dose response curve Logarithmic regression curve  x-axis logarithmic for a linear regression

10. IC-option Option 3 (application of all national methods to the data of all countries) not possible (too large methodological differences in BF, national typologies could not be applied and additionally not enough time to solve all technical problems  option 2 (common metrics)

11. Pressure variables Diatoms –PO4, –TP and –a statistical chemistry gradient based on 5 chemistry parameters  correlations with diatom metrics very similar  PO4 selected for benchmarking, because of greatest data availability Benthic fauna –Combination of several hydromorphological parameters and PO4 –Statistical gradient of all pressure parameters  Manual combination preferred because of slightly better correlations and because it is more transparent

12. Benchmarking of metrics Only very few reference data Impossible to find a common window of pressure for alternative benchmarking.  continuous benchmarking applied

13. Common metric analysis General –Differences in dose response curves for regions and countries, as well as for all typological parameters mostly vanished after standardisation –Metrics were selected primarily on the strength of the correlations with the national methods and with the pressure parameters Correlations very weak for some countries, because of the short gradients covered, And often not significant because of the low number of samples Diatoms –Only two major metrics available (with regional variants): RT and IPS –Tested separately and in combination (as in IC phase one for small streams: (‚DICM‘) –IPS better correlated with national assessments than RT, but RT better correlated with pressures –DICM is as strongly correlated with pressures as RT and even stronger correlated with national assessments than IPS –DICM is best suited as common metric. Benthic fauna –>200 metrics tested as candidates for a multimetric index; 9 Candidate metrics selected based on correlations; >20 multimetric index combinations tested, Decision for final index until the end of the month.

14. Boundary Comparisons Boundary translation into common metric and back is directly possible for countries with a sufficient number of sites For the others the regression curves are not reliable  must be combined, but is that possible?

15. Necessity to use country specific regressions or EQR-standardisation Example with some countries with good correlations and sufficient data (except the country in red) Good/moderate boundary for two countries which seem to be very different in EQR-Common Metric relationship. However boundaries turn out to be very similar  Conclusion: Boundary translation has to be performed on separate national regression curves, unless it can be made sure that all EQR values mean the same in terms of the common metric  Decision to use individual regression curves for countries with sufficient data, and to standardise the EQRs of countries versus the common metric, Then they may be combined to a common regression curve for boundary translation

16. Timetable Decision on BF common metrics: 31 October Final results: 14 November draft final report: 30 November 2011 final report: 31 December 2011