1. The use of pressure response relationships between nutrients and biological quality elements as a method for establishing nutrient supporting element boundary values for the Water Framework Directive Coastal and transitional waters
Heliana Teixeira, Fuensanta Salas
Meeting on the Best practice guide on nutrients
9-10 Berlin, 2016

2. Main issues encountered Toolkit analyses tested (examples)
Provide examples for the Guidance  on nutrient standards for the WFD – helping the establishment of nutrient concentrations to support good ecological status.
Test the Guidance protocol and its suggested approaches in the coastal (CW) and transitional (TRW) waters data;
Contribute to adjust the protocol and its recommendations accounting for the specificities of these water categories.
Type of data available
Main issues encountered
Toolkit analyses tested (examples)
Next steps - discussion

3. (MS original nutrient units)1
Type of data available
Transitional waters
Estuaries //
Lagoons
Common Type
Supp. Env data
Season
Nutrient parameters (units)
Chla
EQR
IC boundaries
Dataset
(MS original nutrient units)
Yes/No;
parameter
DIN
NH4
NO3
NO2 TN TP
OrthoP Si
Countries
(n obs*)
TRWBALBT1
(mg L-1) No Su
µg L-1
Yes: LT (27); PL (29)
TRWMEDpolyCL
(µg L-1) --
EQR (MPI)
Yes IT/GR (17)
(µg L-1)
EQR_Phyto
Yes: FR (15)
TRWNEA11
(µM) Wi
EQR_Chla
no boundaries (232)
using data from the WFD intercalibration  (IC) exercise
nutrients used for statistical analysis: TN TP DIN

4. 1 Coastal waters Type of data available Common Type Supp. Env data
Season
Nutrient parameters (units)
Chla
EQR
IC boundaries
Dataset
(MS original nutrient units)
Yes/No;
parameter
DIN
NH4
NO3
NO2 TN TP
OrthoP Si
Countries
(n obs*)
CWBALBC4
(µmol L-1) No Su
µmol L-1
µg L-1
EQR_Chla
Yes: LV (92); EE (44)
CWBALBC5
(mg L-1)
Yes: LT (65); LV (104)
CWMEDI
shore;T;Sal;pH;Turb;O2D
All
Yes: IT (88)
CWMEDIIAdriatic
Yes: IT (336)
CWMEDIIThyrrenian
Yes: IT (245)
CWMEDIIIE --
Yes: GR/CY (99)
CWNEA1-26A
(µM)
Turb;flush Wi
Log
Yes: FR (45); RoI (45); UK(13); ESWCC (8);
NO/ESEC/ESGC (23)
CWNEA1-26B
TidalRangTurb;flush
Yes: FR/NL (7);
UKs (41); UKn (16);
BE (8)
CWNEA1-26C
Yes: DK(5);DE(3)
CWNEA1-26E
Yes: PTsUpW/ES (27); PTUpW (9)
CWNEA3-4
no boundaries (14)

5. 1 EQR Boundaries Phytoplankton Type of data available normalising data
Common Type
Country
Dataset
H/G
G/M
M/P
P/B
TRWBALBT1 LT
ds1
0.83
0.57
0.39
0.29 PL
ds2
0.77
0.61
0.5
0.4
TRWMEDpolyCL
IT/GR
ds3
0.78
0.51 -- FR
ds4
0.71
TRWNEA11
no boundaries
CWBALBC4 LV
ds5
0.82
0.67
0.33
0.23 EE
ds6
CWBALBC5
ds7
0.65
0.2
ds8
0.87
0.6
0.28
0.21
CWMEDI IT
ds9
0.85
0.62
CWMEDIIAdriatic
ds10
0.81
CWMEDIIThyrrenian
ds11
0.84
CWMEDIIIE
GR/CY
ds12
0.66
0.37
CWNEA1-26A
ds13
RoI
ds14
0.60 UK
ds15
0.8
ESWCC
ds16
0.44
NO/ESEC/ ESGC
ds17
CWNEA1-26B
FR/NL
ds18
UKsouth
ds19
0.63
Uknorth
ds20 BE
ds21
CWNEA1-26C DK
ds22
0.7 DE
ds23
CWNEA1-26E
PTsUpW/ES
ds24
PTUpW
ds25
0.56
CWNEA3-4
CW BLACK SEA
no data available
normalising data

6. 2 Main issues encountered
low number of observations (normalising data)
overlap of EQS categories (scattered data)
insufficient coverage of the gradient of disturbance
EQR range >>>1
outliers // range modelled
interaction between explanatory variables: nutrients (& other)
effects of other factors e.g. pressures (wedge shape data)
Toolkit Statistical Analyses:
Linear Regression Type II
Categorical analysis (4 approaches )
Multivariate Linear Regression
Quantile Regression – ongoing not in the toolkit yet

7. 2 Main issues encountered low number of observations (normalise data?)
Denmark and Germany
CW NEA 1-26C DK DE n 5 3
G/M
0.4
0.6
max EQR
0.275
0.54
n = 8
different EQR boundaries
insufficient gradient coverage

8. 2 Main issues encountered overlap of EQS categories (scattered data)
Latvia ds5
CW BAL BC4
Estonia ds6 CW BAL BC4
Poland ds2 TRW BAL BT1

9. 2 Main issues encountered
insufficient coverage of the gradient of disturbance
Italy and Greece ds3
TRW MED polyCL
France ds4
TRW MED polyCL
In particular if it is in the G/M boundary (good/not good)

10. 2 Main issues encountered EQR range >>>1
Latvia ds5 CW BAL BC4
EQR boundaries set within [0-1] range.
Able to capture response signal?
Italy ds9
CW MED I

11. 2 Main issues encountered outliers // range modelled
Estonia ds6 CW BAL BC4

12. 2 Main issues encountered
interaction between explanatory variables: nutrients (& other)
Italy ds10 CW MED II Adriatic

13. 2 Main issues encountered
effects of other factors e.g. pressures (wedge shape data)
Latvia ds7 CW BAL BC5

14. Summary of toolkit analyses preliminary results3
Toolkit analyses tested
Summary of toolkit analyses preliminary results

15. Poland ds2 TRW BAL BT1 TN TP

16. Poland ds2 TRW BAL BT1
Great EQR/EQS classes overlap across nutrient ranges
Maybe look for interaction

17. 3
Toolkit analyses tested

18. 3
Toolkit analyses tested

19. 3 Toolkit analyses tested Latvia ds2 TRW BAL BT1 TN TP
TP range G/M th th H/G th th

20. 3
Toolkit analyses tested
Italy ds10 CW MED II Adriatic

21. 3 Toolkit analyses tested TN TP Italy ds10 CW MED II Adriatic
EQR ~ TN+TP

22. 3 Toolkit analyses tested TN LQR 90th Italy ds10 CW MED II Adriatic
EQR range >>>1 (G/M = 0.6): effect on nutrient boundaries derived? Try in a dataset EQR [0-1].
Outliers
TN QRrqss
90th
80th
70th
50th
20th
90th

23. 4 Thank you! Complete analyses for the remaining CW datasets
Next steps
Complete analyses for the remaining CW datasets
Consider all other nutrients available?
(and for TRW NEA11 if IC EQR boundaries are finalised)
Compare results obtained with nutrient boundaries available in each region (MS, RSCs)
Further test statistical approaches such as Mv and QR
Discuss with MS the preliminary results obtained
Thank you!