1. Angel Borja Coordinator of the Group
NEA-GIG Macroinvertebrates group
(ECOSTAT, Brussels, October 2011)
Intercalibration of transitional water macroinvertebrates within the NEA-GIG
Angel Borja
Coordinator of the Group

2. 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

3. 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.

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

5. 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

6. From 59 estuaries under different pressure conditions
Introduction
Steps in IC
Types
Data
Intercalibration
Samples available
From 59 estuaries under different pressure conditions

7. 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)

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

9. 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

10. Excluding pressure data Including pressure data
Introduction
Steps in IC
Types
Data
Intercalibration
Samples available
Taxonomy
Methods proposed & pressures
Response of benthos to pressures:
Multivariate analysis between pressures and benthic abundance (BioEnv) to test the effect of:
i) Environmental data alone (salinity, grain size, coordinates)
Ii) Environmental data + NEAGIG pressure data
Results (Spearman correlation):
Benthos dominated by natural conditions
Weak correlation to pressure data (<0.1 increase)
Complex multi-pressure systems (pressures difficult to quantify)
Problematic for setting reliable benchmarks on pressure data
Data set
Excluding pressure data
Including pressure data
Subtidal 0.5mm
0.507
0.598
Subtidal 1mm
0.447
0.546

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

12. 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.

13. Case 1: option 3 Not common level of benchmarks for all countries
Introduction
Steps in IC
Types
Data
Intercalibration
Case 1: option 3
Not common level of benchmarks for all countries
Inssufficient number of benchmark for all countries
High number of samples without EQR for several methods

14. Hence, we decided to use another approach
Introduction
Steps in IC
Types
Data
Intercalibration
Case 1: option 3
Some conclusions:
Tasbem has to be excluded since the slope was (impossible to reduce the bias)
AeTV was excluded since the correlation was negative with the two methods for which there is coincidence (M-AMBI, BEQI)
The IC results were not satisfactory, because of the absence of good benchmark sites, benchmark sites for all countries and low number of sanples with EQR for all methods
Hence, we decided to use another approach

15. Case 2 First phase approach
Introduction
Steps in IC
Types
Data
Intercalibration
Case 1: option 3
Case 2 First phase approach

16. Case 2 First phase approach
Introduction
Steps in IC
Types
Data
Intercalibration
Case 1: option 3
Case 2 First phase approach

17. Case 2 First phase approach
Introduction
Steps in IC
Types
Data
Intercalibration
Case 1: option 3
Case 2 First phase approach
TYPE D
AETV excluded (negative correlation)
M-AMBI and IQI to be adjusted
TYPE E
QSB to be adjusted
TYPE F
BQI excluded (low correlations with other methods)
M-AMBI to be adjusted

18. Case 2 First phase approach
Introduction
Steps in IC
Types
Data
Intercalibration
Case 1: option 3
Case 2 First phase approach
TYPE D
TYPE E
TYPE F

19. Case 2 First phase approach
Introduction
Steps in IC
Types
Data
Intercalibration
Case 1: option 3
Case 2 First phase approach
TYPE D: sample level
TYPE D: WB level

20. Some conclusions: Case 1: option 3 Case 2 First phase approach
Introduction
Steps in IC
Types
Data
Intercalibration
Case 1: option 3
Case 2 First phase approach
Some conclusions:
Some pairwise analyses have problems, probably due to the different approach in deriving these methods, together with the high variability of the transitional waters
At sample level we obtained more consistent results than at WB level, probably because of the number of samples (>2500 and 100, respectively)
After testing IQI with M-AMBI reference conditions, kappa values increased significantly
There is an increased variability resulting from different approaches to setting reference conditions by each MS and the effect on the final correlations (we expected increased correlation if same reference condition methods used).
Regarding the pressure-response analysis, the approach used does not rely on the highly variable pressure data.
Test if the boundaries are correct in terms of pressures and if they are dependent or not on habitats.
IC also at the water body level, improving the integration system for each method and using more data.
Solve the differences in the boundaries for some methods when IC at sample or water body level
In general, we consider the results satisfactory, especially taking into account the many problems faced

21. Thanks for your attention!
Angel Borja