1. Angel Borja Coordinator of the Group
NEA-GIG Macroinvertebrates group
(Intercalibration Validation Meeting,
Ispra, March 2012)
Intercalibration of transitional water macroinvertebrates within the NEA-GIG
Angel Borja
Coordinator of the Group

2. Germán Rodríguez (Spain) Joxe Mikel Garmendia (Spain)
Introduction
Steps in IC
Types
Data
Intercalibration
Angel Borja (Spain)
Iñigo Muxika (Spain)
Germán Rodríguez (Spain)
Joxe Mikel Garmendia (Spain)
Maria Dulce Subida (Spain)
Pilar Drake (Spain)
Araceli Puente (Spain)
Graham Phillips (UK)
Mats Blomqvist (Sweden)
Willem van Loon (Netherlands)
Jan Witt (Germany)
Karin Heyer (Germany)
Gert van Hoey (Belgium)
Joao Neto (Portugal)
Joao-Carlos Marques (Portugal)
Heliana Teixeira (Portugal)
Assistance from JRC:
Wendy Bonne
Alexander Ventura
Sebastian Birk
Nigel Wilby

3. Introduction
Steps in IC
Types
Data
Intercalibration
Second Phase of the intercalibration ( ) All countries, having transitional waters, are represented in the working group
Active members
Apologies
Non-participant
Absent, since 2010

4. Introduction
Steps in IC
Types
Data
Intercalibration
Meetings organized: June 2009, Lisbon (PT) February 2010, San Sebastian (ES) September 2010, Nantes (FR) November 2010, WebEx meeting January 2011, WebEx meeting March 2011, WebEx meeting April, 2011, WebEx meeting May 2011, Hamburg (DE) June 2011, WebEx meeting July 2011, WebEx meeting
Physical meetings

5. Introduction
Steps in IC
Types
Data
Intercalibration
The following steps for intercalibration were agreed upon by this group:
(i) to establish common water body types across Europe;
(ii) to compile a common dataset;
(iii) to harmonise the taxonomy of the dataset;
(iv) to collate human pressures from each estuary;
(v) to set reference conditions for each type;
(vi) to calculate Ecological Quality Ratios for each of the methods proposed for IC;
(vii) to interpret the response of these methods to different anthropogenic pressures;
(viii) to determine boundaries for each of the 5 quality classes (from bad to high status), using all the selected methods; and
(ix) final agreement in the assessment and intercalibration.

6. Initial National Types collated
Introduction
Steps in IC
Types
Data
Intercalibration
Initial National Types collated

7. Common NEA Types proposed
Introduction
Steps in IC
Types
Data
Intercalibration
Common NEA Types proposed
Data obtained for types C to F
Types C and D merged for IC

8. Introduction
Steps in IC
Types
Data
Intercalibration
Samples available
From 59 estuaries under different pressure conditions (mostly multi-pressure)
Type
Samples
Spp records
Countries
Pressures
D-C
6795
50078
NL, BE, FR, DE, UK, ES, PT
2539
All (-FR) E
638
7257
FR, DE, UK, ES
335 F
1904
16922
FR, SE, PT, BE, FR, UK, SP
1574

9. Harmonisation using UK lists, ERMS and WoRMS
Introduction
Steps in IC
Types
Data
Intercalibration
Samples available
Taxonomy
Harmonisation using UK lists, ERMS and WoRMS
Oligohaline taxa to be checked against register of freshwater list (
Harmonised taxon list includes Benthic Quality Index (BQI) sensitivity scores and AMBI ecological group for each species
A total of 1939 harmonised taxa for NEA estuaries
Single metrics calculated by sample and sieve size. Then harmonised to a unique sample size (0.1 m2 in subtidal, 0.01 m2 in intertidal). The harmonisation includes also the sieve size (0.5 mm and 1 mm)

10. Methods proposed & pressures
Introduction
Steps in IC
Types
Data
Intercalibration
Samples available
Taxonomy
Methods proposed & pressures

11. Methods proposed & pressures
Introduction
Steps in IC
Types
Data
Intercalibration
Samples available
Taxonomy
Methods proposed & pressures
Pressures not available for all samples and/or water bodies
Same pressure value for different samples and years, within the same water body

12. Methods proposed & pressures
Introduction
Steps in IC
Types
Data
Intercalibration
Samples available
Taxonomy
Methods proposed & pressures

13. M-AMBI (ES) Samples available Taxonomy Methods proposed & pressures
Introduction
Steps in IC
Types
Data
Intercalibration
Samples available
M-AMBI (ES)
Taxonomy
Methods proposed & pressures

14. QSB (ES) Samples available Taxonomy Methods proposed & pressures
Introduction
Steps in IC
Types
Data
Intercalibration
Samples available
QSB (ES)
Taxonomy
Methods proposed & pressures
Figure. Spearman correlation between the QSB index and the index of global pressure (Puente et al., 2010).

15. TasBeM (ES) Samples available Taxonomy Methods proposed & pressures
Introduction
Steps in IC
Types
Data
Intercalibration
Samples available
TasBeM (ES)
Taxonomy
Methods proposed & pressures

16. M-AMBI (DE) AeTV (DE) Samples available Taxonomy
Introduction
Steps in IC
Types
Data
Intercalibration
Samples available
M-AMBI (DE)
Taxonomy
Methods proposed & pressures
AeTV (DE)

17. BAT (PT) Samples available Taxonomy Methods proposed & pressures
Introduction
Steps in IC
Types
Data
Intercalibration
Samples available
BAT (PT)
Taxonomy
Methods proposed & pressures

18. BEQI-2 (NL) Samples available Taxonomy Methods proposed & pressures
Introduction
Steps in IC
Types
Data
Intercalibration
Samples available
BEQI-2 (NL)
Taxonomy
Methods proposed & pressures

19. IQI (UK & RoI) Samples available Taxonomy Methods proposed & pressures
Introduction
Steps in IC
Types
Data
Intercalibration
Samples available
IQI (UK & RoI)
Taxonomy
Methods proposed & pressures

20. BQI (SE) Samples available Taxonomy Methods proposed & pressures
Introduction
Steps in IC
Types
Data
Intercalibration
Samples available
BQI (SE)
Taxonomy
Methods proposed & pressures

21. BEQI (BE) Samples available Taxonomy Methods proposed & pressures
Introduction
Steps in IC
Types
Data
Intercalibration
Samples available
BEQI (BE)
Taxonomy
Methods proposed & pressures

22. Methods proposed & pressures
Introduction
Steps in IC
Types
Data
Intercalibration
Samples available
Taxonomy
Methods proposed & pressures

23. Partial reference sites,
Introduction
Steps in IC
Types
Data
Intercalibration
Samples available
Taxonomy
Methods proposed & pressures
Reference conditions
Partial reference sites,
Expert judgment (to define ‘virtual’ reference conditions)
95th percentile (referred to habitats: salinity, sediment type, intertidal/subtidal)
UK: metric reference condition values adapted in response to environmental gradients (salinity and grain size)
Reference conditions were derived for most of the ecotopes and all types for M-AMBI, BAT and TaSBeM, the remainder of methods provided RC for part of the dataset (one type or some ecotopes within types)

24. Previous studies Option 2: common metric
Introduction
Steps in IC
Types
Data
Intercalibration
Previous studies
Option 2: common metric
The BEQI-2 (NL) was chosen as a common metric for the Option 2 approach based on the correlation with the other national methods.
It takes the same indicators as used in the M-AMBI, but combines them with an univariate calibration (just as the IQI, DKI and NKI with a linear combination of univariately calibrated indicator EQR-values) and not in a multivariate way as in the M-AMBI and BAT. So this makes the BEQI2 quite appropriate as common metric “in between these two aspects of the methods“.
Type D:

25. Option 2: without benchmark standardization
Introduction
Steps in IC
Types
Data
Intercalibration
Previous studies
Option 2: without benchmark standardization
- almost 1.5 class difference in G/M boundary for UK
- more than 2 classes difference in G/M boundary for ES-Andalusia D NL PT SP UK
Max
1.140
1.110
1.145
1.000
1.117
H/G
0.850
0.800
0.790
0.750
G/M
0.700
0.600
0.580
0.660
0.640
Not stringent enough!

26. UK EQRs appeared to be considerably lower
Introduction
Steps in IC
Types
Data
Intercalibration
Previous studies
Option 2: without benchmark standardization
MS’s EQR calculated on the benchmark sites using the BEQI-2 showed specific country effects
UK EQRs appeared to be considerably lower

27. Option 2: without benchmark standardization
Introduction
Steps in IC
Types
Data
Intercalibration
Previous studies
Option 2: without benchmark standardization
General Linear Models (GLMs) were used to calculate offsets between MS’s EQRs:

28. Option 2: without benchmark standardization
Introduction
Steps in IC
Types
Data
Intercalibration
Previous studies
Option 2: without benchmark standardization
The offsets calculated using GLMs are introduced into the calculation sheet (offset manual) and the common metric scale is corrected for each country effect, so that this systematic difference is not taken into account for boundary bias evaluation.
Between the UK / ES-Andalusia there is almost 0.5 class difference as country effect that is being corrected for.
For ES-Andalusia there is an additional problem: the good and especially the high status class does not correspond with the same class on the common metric scale, but with lower classes, which causes the slope not to be steep enough.

29. Option 2: with benchmark standardization
Introduction
Steps in IC
Types
Data
Intercalibration
Previous studies
Option 2: with benchmark standardization
- UK is more than 0.5 class too relaxed (already better than without benchmark standardization)
- ES-Andalusia is less than 0.25 classes too relaxed

30. Option 2: with benchmark standardization
Introduction
Steps in IC
Types
Data
Intercalibration
Previous studies
Option 2: with benchmark standardization
- after boundary adjustment G/M = 0.73 and H/G = 0.88 for UK
- after boundary adjustment G/M = 0.68 and H/G unadjustable
(due to too soft slope with common metric)
Example with benchmark standardization using original data (we still need to get national EQRs assessed with national reference) from DE – ES – PT – NL:

31. Introduction
Steps in IC
Types
Data
Intercalibration
Some conclusions:
Many problems with pressure data, probably due to the lack of information at station level and for each sampling period
For intercalibration, Option 2 seems to be the most adequate
There are many problems trying to adjust some methods
However, the approach explored in past 3 months, seems to be promising to get an intercalibration for some of the methods
We need EQRs calculated by each MS using their national reference conditions
We need to apply this approach to the rest of types and get the approbation of the Member States
A consideration: it is interesting to note that some BQEs have successful IC with very few data. In our case, may be we have an excess of data, producing a lot of noise

32. Introduction
Steps in IC
Types
Data
Intercalibration
Final conclusion:
I’m very disappointed by the lack of clear results from TW
But, I’m even more disappointed with the decision of not taking into account the results from the first phase

33. Thanks for your attention!
Angel Borja