anne lyche solheim, norwegian institute for water research, oslo, norway workshop on ”in situ...

Anne Lyche Solheim, Norwegian Institute for Water Research, Oslo, Norway

Workshop on ”In situ trialing for ecological and chemical studies in support of WFD implementation”, Pau, France, 14.-15. May 2008

Outline: Introduction – WFD Annex V – how to quantify?

What has the REBECCA project given?

Examples of dose-response functions relating ecological and chemical elements

How to use automated methods?

Approaches for establishing links among ecological and chemical status



Introduction – WFD Annex V – how to quantify? Monitoring of chemical elements has dominated

classification systems before WFD Eutrophication/organic pollution: P, N, BOD, COD, O2 Acidification: pH, ANC, Al Toxic substances: metals, POPs

WFD require monitoring of biological elements Phytoplankton, benthic algae, macrophytes, benthic fauna, fish Links between biological and chemical elements needed for river

basin management plans Challenge to develop classification systems with

biological indicators and class boundaries in line with Annex V normative definitions

Challenge to obtain type-specific relationships between chemical and biological indicators for different pressures



What has the REBECCA project given?

REBECCA: Relationships between ecological and chemical status of surface waters

EU FP 6 project: 2004-2007 Constructing relationships based on existing data Lakes, rivers and coastal waters included Eutrophication, acidification, toxic substances and

hydromorphological pressures included Publications:

Lakes results published in Aquatic Ecology (May 2008) Rivers results will be published in Freshwater Biology (autumn 2008) Coastal results published in different marine biology journals Final conference in Oslo in May 2007 All reports and presentations available at www.rbm-toolbox.net



REBECCA dataset for Lakes

Largest Pan-European dataset ever: >5000 lakes (Moe et al. 2008)



Examples of dose-response functions relating ecological and chemical elements

Lakes: Phytoplankton and nutrients Macrophytes and nutrients

Rivers: Benthic invertebrates and organic pollution Benthic algae (diatoms) and nutrients Benthic invertebrates and toxic pollution (heavy metals)

Coastal waters: Phytoplankton and nutrients



Lakes: Phytoplankton and nutrients Phytoplankton taxonomic indicator responses

(Ptacnik et al. 2008, Lyche-Solheim et al. 2008)

1 2 5 10 20 50

0.0

0.1

0.2

0.3

0.4

0.5

L-N1

Chl-a

% b

iovo

l.

Chlorophyll-a µg L-1

% o

f bi

ovol

ume Cyanophytes

(excl. Chroococcales but incl. Microcysta and Woronichinia)

Pennate diatoms

spring/early summer samples

late summer samples

Chrysophytes(excl. Synura and Uroglena)

H/G G/M



Lakes: Macrophytes and nutrients

Macrophyte indicator (Isoetids) responses in low alkalinity lakes (Penning et al. 2008)

2 5 10 20 50 100 200

02

46

81

01

21

40.950.90.750.5

total phosphorus, µg/l

cum

ulat

ive

freq

uenc

yin

dex



Rivers: benthic invertebrates Benthic invertebrates integrated common metric index (ICMi)

response to organic pollution (Buffagni & Owen, R. 2007)



Rivers: Benthic algae and nutrients

Diatom responses (Deflandre-Vlandas et al. submitted)



Rivers: benthic invertebrates and toxic pollution Benthic invertebrates and impact of heavy metals (Cu) in a Norwegian

river exposed to mining waste (Moe et al. submitted)

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12 14 16 18

Cu (ug/L)

msPAF MI chronic

% reduction Ephem. species



Coastal waters: Phytoplankton and nutrients

Bloom frequency and biomass increases with total nitrogen concentrations (Carstensen et al. 2006)

n=80p=0.8934y=probit(2.15+0.0003x)

1

10

100

0 50 100 150TN (µmol L-1)

3702

3708

3711

3723

3727

4273

5790

60

6900017

Blo

om in

tens

ity (µ

g L

-1)

n=92p<0.0001y=probit(-1.68+0.0098x)

0

0.2

0.4

0.6

0.8

0 50 100 150

Blo

om fr

eque

ncy



Coastal waters: Macroalgae and nutrients

Macroalgae response to nitrogen (Krause-Jensen et al.)



How to use automated methods?

Essential to establish good correlations between the automated method parameter and the biological indicators used in the new WFD classification systems

Automated methods that are well correlated to the biological indicators may enhance spatial and temporal resolution of monitoring data, and allow detection of effect of measures

Two examples of automated methods: Monitoring phytoplankton in coastal waters in Norway using

satellite images and sensors on ferries Mapping the riparian corridor as a proxy for ecological status of

rivers



Sensors for marine phytoplankton Sensors on ferries detect algal blooms in the Oslofjord (green line) in contrast to conventional

sampling programmes (red dots in figure) (Sørensen 2008)

0

2

4

6

8

10

12

14

16

18

20

22

01-0

1-06

15-0

1-06

29-0

1-06

12-0

2-06

26-0

2-06

12-0

3-06

26-0

3-06

09-0

4-06

23-0

4-06

07-0

5-06

21-0

5-06

04-0

6-06

18-0

6-06

02-0

7-06

16-0

7-06

30-0

7-06

13-0

8-06

27-0

8-06

10-0

9-06

24-0

9-06

08-1

0-06

22-1

0-06

05-1

1-06

19-1

1-06

03-1

2-06

17-1

2-06

31-1

2-06

Dato

Klor

ofyll-a

(µg/l

)

Klorofyll-a

Tokt i overvåkingsprogrammet

Prøver fra Color Festival



Satellite images for marine phytoplankton Algal biomass in Skagerak during spring bloom 2008

(Sørensen in prep., www.ferrybox.no)



Mapping of the riparian corridor as a proxy for ecological status of rivers

From JRC IES Agri-Env action on Impact assessment and riparian mapping using time series of remote sensing (Ivits-Wasser et al.)

Permanent vegetation fraction derived from remote sensing correlates to the favourable status

Classification of the riparian-zone into favourable and unfavourable status in the Andalusia river network based on field observations

Classification accuracy: 89%

favourable status unfavourable status



Thank you for your attention!

anne lyche solheim, norwegian institute for water research, oslo, norway workshop on ”in situ...

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