wfd phytoplankton indicators of ecological status

DEPARTMENT OF BIOSCIENCE AARHUS UNIVERSITY 22 October 2014 ECOSTAT meeting in London

WFD phytoplankton indicators of ecological status

Jacob Carstensen ECOSTAT, London, 22 October 2014 With contribution from SCOR WG137 and support from by:

DEPARTMENT OF BIOSCIENCE AARHUS UNIVERSITY ECOSTAT meeting in London 22 October 2014

Outline for this presentation ›  Indicators for phytoplankton composition

›  Some theory ›  Recent results from SCOR WG137 and other studies ›  Linking phytoplankton composition to nutrient pressure ›  Conclusion on suitability of indicators for phytoplankton

composition

›  Indicators for phytoplankton blooms ›  Some theory ›  Linking phytoplankton composition to nutrient pressure ›  Conclusions of suitability of bloom indicators

›  Monitoring requirements for chla indicators ›  Responses to comments from the review panel


The coastal zone is a dynamic and productive ecosystem with strong gradients

Carstensen, Cloern & Paerl (in prep.)


Dominant life forms Diatoms, e.g. Skeletonema costatum Dinoflagellates, e.g. Peridiniella catenata

Cyanobacteria, e.g. Planktothrix agardhii Chlorophytes, e.g. Chlamydomonas spp.

DEPARTMENT OF BIOSCIENCE AARHUS UNIVERSITY ECOSTAT meeting in London 22 October 2014 SCOR137: An

unprecedented large data set

85 sites and 30040 samples in total

Area # of stations

Period # of sample

s Chesapeake Bay 21 1984-2009 7198 Danish waters 18 1979-2013 7317 Dutch waters 14 1990-2011 6300 Finnish waters 11 1966-2010 4488 German Wadden Sea 1 1999-2012 1002 Gulf of Riga 4 1976-2012 899 Neuse River Estuary 4 2000-2013 615 Patos Lagoon Estuary 1 1993-2011 225 San Francisco Bay 4 1992-2013 656 Swedish waters 7 1983-2012 1340


Diversity of coastal ecosystems - physics

0 10 20 30 40 50 60 70 80 900

10

20

30

40

Water depth (m)

Frequency

n=85A)

0 0.5 1 1.5 2 2.50

20

40

60

Tidal amplitude (m)

Freq

uency

n=85B)

0 5 10 15 20 25 30 350

5

10

15

20

Salinity

Freq

uency

n=85C)

4 6 8 10 12 14 16 18 20 220

10

20

30

Temperature (°C)

Freq

uency

n=85D)


…. and chemistry

0 1 2 3 4 5 6 7 8 90

10

20

30

Secchi depth (m)

Freq

uency

n=73E)

0 2 4 6 8 10 12 14 160

10

20

30

Chlorophyll a (µg L-‐1)

Freq

uency

n=82F)

0 20 40 60 80 1000

5

10

15

20

25

Total Nitrogen (µmol L-‐1)

Freq

uency

n=79G)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

10

20

30

Total Phosphorus (µmol L-‐1)

Freq

uency

n=79H)


Factors regulating the proportion of chlorophytes

-‐8-‐7-‐6-‐5-‐4-‐3-‐2-‐10

0 5 10 15 20 25 30 35

Logit(prop

. of chlorop

hytes)

Salinity

Raw

Adjusted

5%

1%

0.1%

-‐8-‐7-‐6-‐5-‐4-‐3-‐2-‐10

-‐2 -‐1 0 1 2 3

Logit(prop

. of chlorop

hytes)

Log(Total phosphorus) (µmol L-‐1)

Raw

Adjusted

5%

1%

0.1%



Factors regulating the proportion of cyanobacteria

-‐10

-‐8

-‐6

-‐4

-‐2

0

0 5 10 15 20 25 30 35

Logit(prop

. of cyano

bact.)

Salinity

Raw

Adjusted5%

1%

0.1%

-‐10

-‐8

-‐6

-‐4

-‐2

0

0 20 40 60 80 100

Logit(prop

. of cyano

bact.)

TN:TP molar ratio

Raw

Adjusted5%

1%

0.1%



Factors regulating the proportion of diatoms

-‐3

-‐2

-‐1

0

1

2

3

4

2.5 3 3.5 4 4.5 5 5.5 6

Logit(prop

. of d

iatoms)

Log(Total Nitrogen) (µmol L-‐1)

Raw

Adjusted90%

75%

50%

25%

10%

-‐3

-‐2

-‐1

0

1

2

3

4

5 10 15 20

Logit(prop

. of d

iatoms)

Temperature (°C)

Raw

Adjusted90%

75%

50%

25%

10%


-‐3

-‐2

-‐1

0

1

2

3

4

0 5 10 15 20 25 30 35

Logit(prop

. of d

iatoms)

Salinity

Raw

Adjusted90%

75%

50%

25%

10%


Factors regulating the proportion of dinoflagellates

-‐6

-‐5

-‐4

-‐3

-‐2

-‐1

0

1

0 5 10 15 20 25 30 35

Logit(prop

. dino

flagellatres)

Salinity

Raw

Adjusted

25%

10%5%

1%

-‐6

-‐5

-‐4

-‐3

-‐2

-‐1

0

1

0.5 1 1.5 2 2.5 3 3.5

Logit(prop

. dino

flagellatres)

Stratification pattern

Raw

Adjusted25%

10%5%

1%

Mixed Intermittent StratifiedCarstensen, Cloern & Paerl (in prep.)


Considering interannual variations - spring

Jurgensone et al. (2011) Estuaries and Coasts


Considering interannual variations - summer

Jurgensone et al. (2011) Estuaries and Coasts


What is controlling the phytoplankton composition? ›  A general biomass increase is the most

prominent response to nutrient enrichment 1.  Phenology and physical factors (salinity,

temperature and stratification) are most important

2.  Changing nutrient ratios can lead to shifts between diatoms, cyanobacteria and other species

3.  Diatoms may proliferate increasingly with nutrient enrichment


Is an increase in the proportion of diatoms necessarily bad quality?


Can we develop operational indicators? ›  76 taxa investigated ›  ~50% responded to

changing nutrient level ›  4 taxa changed their

probability of presence by >30%

›  1 taxon allowed for distinguishing a 20% change in nutrients (N=50) Carstensen & Heiskanen (2007)

Marine Ecology Progress Series


Conclusion on indicators of phytoplankton community ›  The structure of the phytoplankton community

is extremely variable and mostly governed by physical processes rather than changing nutrient concentrations

›  There is evidence that the phytoplankton community changes with nutrient concentrations, but these changes occur with large orders of magnitude

›  Operational indicators that can discriminate between realistic pressure levels are not available at present …… and may not be possible at all!


Phytoplankton bloom indicators

Carstensen, Henriksen & Heiskanen (2007) Limnology & Oceanography


There are many sites in the Baltic Sea where bloom frequency can be calculated

0

200

400

600

800

1000

1200

1400

0 30 60 90 120 150 180 210 240 270 300 330 360

Carbon

biomass (mg m

-‐3)

Julian day

Blooms

Non-‐blooms

B Baltic SeaTvärminne0

50

100

150

200

250

0 30 60 90 120 150 180 210 240 270 300 330 360

Carbon

biomass (mg m

-‐3)

Julian day

Blooms

Non-‐bloomsAGulf of BothniaQuark

Carstensen, Cloern & Klais (Subm.) Estuarine Coastal Shelf Science

There is no specific requirement on the sampling frequency, but monthly sampling would probably be a good idea.


0%

10%

20%

30%

40%

0.1 1 10

Bloo

m freq

uency

Total Phosphorus (µM)

BS CB DSE GBNRE RMS SFB WS

H p(linear)=0.0094p(spline)=1.0000

0%

10%

20%

30%

40%

0.01 0.1 1 10

Bloo

m freq

uency

Tidal amplitude (m)


F p(linear)=0.0229p(spline)=1.0000

Factors governing the bloom frequency

0%

10%

20%

30%

40%

Bloo

m freq

uency

Mixed Intermittent Stratified

BSCBDSEGBNRERMSSFBWS

D F2,83=2.48; p=0.0897

0%

10%

20%

30%

40%

10 100

Bloo

m freq

uency

Total Nitrogen (µM)


G p(linear)=0.0163p(spline)=1.0000

Carstensen, Cloern & Klais (Subm.) Estuarine Coastal Shelf Science


Conclusion on indicators of phytoplankton blooms ›  The likelihood of phytoplankton blooms

increases with nutrient enrichment, but the triggering mechanism is physical – this adds to variability

›  Bloom indicators can be calculated for many monitoring sites in the Baltic Sea, especially because time series are already long in many places

›  Operational bloom indicators may potentially be developed, but the sensitivity of these in a realistic nutrient management scenario may be too coarse!


Uncertainty aspects of phytoplankton indicators

Chla G-M = 2.0 µg/l Chla True = 1.6 µg/l


Responses to review panel ›  Q1: Frequency of monitoring data for bloom

indicator (Carstensen et al. 2007) ›  The indicator can be calculated for many different

monitoring sites around the Baltic and it is generally applicable

›  Q1: Bloom indicators from EUTRO-OPER based on Alg@line data ›  Data cover the open Baltic Sea and indicators are

developed for the open Baltic Sea ›  Q1: Bloom indicators based on remote

sensing ›  Remote sensing data in the coastal zone is

sensitive to shallowness, resuspended particles, CDOM etc.

›  Indicators developed apply to the open Baltic Sea


Responses to review panel ›  Q3: Causal linkages between eutrophication

and phytoplankton blooms/composition ›  Yes, these causal linkages exist, but they are not

operational on a realistic eutrophication range ›  Q3: Use of composite indicators

›  Only useful when there are clear synergies in the pressure-response relationships

›  Transparency of indicators is important, particularly for the intercalibration

›  The suggested indicators have not been documented to be operational on a realistic eutrophication range

›  Q3: Connection to HELCOM ›  There are links between CORESETII and GIG

experts


Responses to review panel ›  Q3: Use of the large phytoplankton data set

from the Baltic Sea ›  These data have been analyses in many research

publications, but there is a gap between research ideas and operational tools needed for WFD implementation

›  The GIG phytoplankton group has been ambitious ›  AQ1: Carvalho et al. (2013) recommended at

least 3 samples per year in at least 3 years ›  This is too simple! As shown, this depends on the

required sensitivity of the indicator as well as confidence and power levels.


Responses to review panel ›  AQ2: Believe that a multimetric indicator will

reduce uncertainty ›  Both yes and no. However, this has never been

documented – just postulated. ›  AQ3: Use of novel technologies

›  Agree. Novel techniques do offer new possibilities, but the techniques need to mature and their reliability needs to be documented before operational indicators can be developed on such data

›  There are no large data sets yet on many of the novel techniques that could potentially be used, e.g. pigment analyses, FlowCAMs, etc.

”For an operational WFD implementation, it is important that we use well-proven and sensitive phytoplankton indicators”

wfd phytoplankton indicators of ecological status

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