1. FITTING THE ITALIAN METHOD FOR EVALUATING LAKE ECOLOGICAL QUALITY FROM BENTHIC DIATOMS (EPI-L) IN THE “PHYTOBENTHOS CROSS-GIG” INTERCALIBRATION EXERCISE
Aldo Marchetto

2. When the phytobenthos cross-GIG IC was carried out, Italy had not a national method ready. A national method (EPI-L) was developed in JRC kindly provided the draft of the “Instruction manual to fit new or revised national classifications to the completed IC exercise”. The whole procedure was successfully performed following this guidance.

3. Outline of this presentation 1
Outline of this presentation 1. (short) description of the EPI-L method 2. WFD compliance checking 3. IC feasibility checking 4. description of the IC procedure 5. short comment 6. conclusions

4. 1. (short) description of the method data set: selected taxa (out of a total of 475) with abundance > 1% in 3 lakes and > 3% in 1 lake 119 samples collected on 80 lakes, mainly on submerged stones (some samples on Phragmites stems or submersed macrophytes)

5. 80 lakes: 10 volcanic 8 reservoirs 11 Mediterranean 3 large deep 16 morainic 31 mountain

6. For each selected species, trophic value (p) and indicator value (v) were calculated from percent abundance (a) using weighted averaging of total phosphorus concentration EPI-L is then calculated for each sample as selected species should account for at least 70% of total count

7. EPI-L was calibrated for two lake types: 1) mean depth larger than 15 m 2) mean depth smaller than 15 m Reference condition were taken from 4 deep and 12 shallow lakes with negligible human pressure Good/Moderate boundary was obtained using multivariate regression trees: other boundaries by equal class width
Deep lakes
EQR = 0.60

8. 2. WFD compliance checking
Ecological status is classified by one of five classes (high, good, moderate, poor and bad). YES High good and moderate ecological status are set in line with the WFD normative definition. YES All relevant parameter of the BQE are covered and a combination rule too combine parameter assessment into BQE is defined. NO because this IC only covered phyto-benthos and not he full “aquatic flora” BQE

9. 2. WFD compliance checking
Assessment is adapted to intercalibration common types that are defined in line with the topological requirements of the WFD Annex II. YES The waterbody is assessed against type-specific near-natural reference conditions. YES Assessment results are expressed in EQR. YES Sampling procedure allows for representative information about water body quality in space and time. YES: multiple samples taken in the same lakes showed good repeatability, also comparing samples taken on stones and on Praghmites stems or other macrophytes

10. 2. WFD compliance checking
All data relevant for assessing biological parameters specified in the WFD normative definition are covered by the sampling procedure. YES in the national method phytobenthos is combined with macrophytes but in the IC only phytobenthos was considered Selected taxonomic level achieves adequate confidence and precision in classification. YES: taxonomic level requested is species level

11. 3. IC feasibility checking
1. Typology: national typology differs from cross-GIG broad types, but all lakes can be classified in the cross-GIG types. OK 2. Pressure addressed. In the cross-GIG IC pressure considered was eutrophication. EPI-L is also calibrated against a trophic gradient. OK

12. 4. description of the IC procedure (strictly following the manual)
Cross-GIG IC was performed on 3 lake types: 1) low alkalinity lakes (no case in Italy) 2) medium alkalinity lakes 3) high alkalinity lakes We performed the IC procedure for (2) and (3) exactly in the same way. Here I will show only (2)

13. 4. description of the IC procedure (strictly following the manual)
Medium alkalinity lakes: Cross-GIG IC was performed using option 2 and continuous benchmarking. IC procedure following the manual: 1. Calculate the value of the common metric (CM_obs) for sites in the national dataset.

14. 4. description of the IC procedure
2. Using the global relationship between the common metric and pressure established in the completed exercise, calculate the expected values of the common metric (CM_pred) for the joining method’s national dataset from its associated pressure data. 3. Calculate the mean residual between (CM_pred - CM_obs) and then create CM_bm = CM_obs + residual. Mean residual resulted 0.116

15. 4. description of the IC procedure
4. Use least squares regression to establish the relationship between CM_bm (y) and the joining national EQR (x).

16. 4. description of the IC procedure
5. Predict the position of the national class boundaries (MP, GM, HG and ref) on the CM_bm scale.

17. 4. description of the IC procedure
6. Apply the comparability criteria: National boundaries were stricter than the common view by about 90% class width. As a consequence, national boundaries were moved.

18. 4. description of the IC procedure
The new national boundaries are still stricter than the common view, but we decided not to move them further.

19. Cross-GIG medium alkalinity lakes
5. short comment
Cross-GIG medium alkalinity lakes
common
view H G M P

20. New Italian dataset in red
5. short comment
New Italian dataset in red
common
view H G M P

21. The Italian dataset cover a part of the TP gradient
corresponding to high and
good quality in the
cross-GIG data set.
This difference in TP
may be related to:
a difference in hydrological features (deeper lakes with shorter residence time),
the fact that the protection of lake water quality from eutrophication was introduced in the Italian law in the 1980’s, resulting in strong reduction of lake trophy in the whole country
common
view H G M P

22. The Italian dataset cover a part of the TP gradient
corresponding to high and
good quality in the
cross-GIG data set.
This difference in TP
may be related to:
a difference in hydrological features (deeper lakes with shorter residence time),
the fact that the protection of lake water quality from eutrophication was introduced in the Italian law in the 1980’s, resulting in strong reduction of lake trophy in the whole country
common
view H G M P
Lago Maggiore

23. multivariate regression tree (EQR = 0.6) has to be ascribed to a
The main difference
in diatoms community
detected using the
multivariate regression
tree (EQR = 0.6) has
to be ascribed to a
High/Good boundary
and not to a Good/Moderate boundary.
Less-than-good quality was virtually absent in the national data set.
common
view H G M P

24. Conclusions: The procedure was successfully performed following the draft manual for both medium and high alkalinity lakes. National boundaries were revised. At least for option 2, the procedure described in the manual is simple and can be performed at any time needing only MS data.

25. Aldo Marchetto a.marchetto@ise.cnr.it