http://ecowin.org/aulas/mega/pce Primary production J. Gomes - - PowerPoint PPT Presentation

Primary production Coastal and Estuarine Processes http://ecowin.org/aulas/mega/pce

• J. Gomes Ferreira

http://ecowin.org/ Universidade Nova de Lisboa

Primary production and how to model it

• Types of producers and production rates
• Measurement of primary production
• Mechanisms and models – PI curves and blooms
• Models of nutrient limitation, succession and biodiversity
• Budgets and climate change
• Synthesis

Topics

CO2 + 2H2A CH2O + 2A + H2O

Light Pigments

Types of primary producers

Phytoplankton and microphytobenthos: microscopic, high P/B ratio (>50) Others: macroscopic, low P/B ratio, shallow waters or intertidal

Pelagic and benthic, microscopic and macroscopic

Producer Nutrient source Examples Phytoplankton Water column Diatoms/dinoflagellates Microphytobenthos Water column, sediment pore water Penate diatoms Macroalgae (seaweeds) Water column Fucus, Laminaria, Ulva Saltmarsh plants Sediment Spartina Seagrasses (SAV) Sediment and water Zostera, Posidonia

Ecosystem-scale relevance

Data fromSEAWIFS, Summer in the northern hemisphere (1998-2001)

Chlorophyll a (mg m-3)

Phytoplankton primary prod. 200-360 X 1014 gC y-1 (98.9%).

Global distribution of chlorophyll from satellite data

Phytoplankton Some examples

Diatoms Dinoflagellates Coccoliths

Phytoplankton - diatoms

• Chavez et al., 1991 - Limnol. & Oceanog. 36, p. 1816-33

5m

Nitzchia bicapitata

SeaWifs images of cocollith blooms

Cornwall, U.K. Tasmania

Management relevance Noctiluca bloom – California, U.S.A.

Courtesy P.J.S. Franks, WHOI

Cyanobacteria bloom – Potomac estuary Nitzchia bicapitata

This dense bloom of cyanobacteria (blue-green algae) occurred in the Potomac River estuary downstream of Washington, D.C. Photo courtesy of

• W. Bennett USGS.

Management relevance – macroalgal bloom, Florida

Nitzchia bicapitata

In Florida Bay, this seaweed bloom smothered seagrasses, leading to disappearance

• f SAV. Brian Lapointe, Harbor Branch Oceanographic Institute.

Management relevance

These macroalgal blooms have occurred annually for the last five years

Ulva prolifera in Jiaozhou Bay, NE China, 2008

Chlorophyll a (mg m-3)

Management relevance

Advection of potential HAB towards the coast from an offshore front

Multi-sensor discrimination of harmful algal blooms, P. I. Miller, J. D. Shutler, G. F. Moore and S. B. Groom, Remote Sensing and Photogrammetry Society annual conference RSPSoc 2004, 7-10 September 2004, Dundee U.K. PML Remote Sensing Group

Courtesy Plymouth Marine Laboratory, UK

http://pml.ac.uk/

Kelp (Laminaria japonica) in Sanggou Bay, China

Kelp cultivation yields eighty-five thousand tons per year in this 140 km2 bay.

Mass balance for Droop-Solidoro nutrient uptake

Illustration for Ulva lactuca

Modified cell-quota model shows lower nutrient uptake.

Productivity of different ecosystems (kg C m-2 y-1)

Marine producers

Corals Laminaria Saltmarsh Posidonia Mangrove Microphytobenthos Coastal phytoplankton Open ocean phytoplankton

Freshwater producers

Macrophytes Phytoplankton (eutrophic) Phytoplankton (oligotrophic)

Producers on land

Tropical forest Temperate forest Pastures Prairies Desert, tundra

1 2 3 4

Productivity, mean biomass, turnover, and chlorophyll in different ecosystems

Productivity per unit area is much higher inshore, but the open ocean is much more vast.

Area (106 km2) Net production (g C m-2 y-1) Biomass (kg C m-2) Turnover (P/B, y-1) Chlorophyll (g m-2)

Open ocean 332 125 0.003 42 0.03 Upwelling 0.4 500 0.02 25 0.3 Shelf 27 300 0.001 300 0.2 Macroalgae/reefs 0.6 2500 2 1.3 2 Estuaries 1.4 1500 1 1.5 1 Total marine 361 155 0.01 0.05 Terrestrial ecosystems 145 737 12 0.061 1.54 Marshes 2 3000 15 0.2 3 Lakes and rivers 2 400 0.02 20 0.2 Total continental 149 782 12.2 0.064 1.5

Whittaker & Likens, 1975. The Biosphere and Man. Primary productivity of the biosphere. Springer-Verlag.

Measurement of primary production in marine and freshwater systems

Different methods are used for different producers. Upscaling may be done using models, including GIS, remote sensing, and dynamic simulation. Producer Indicator Method Units Phytoplankton & Biomass Chlorophyll a (filtered sample) g L-1 microphytobenthos Production

14C, O2 (incubation)

d-1 Seaweeds Biomass Cropping g DW m-2 Seagrasses Production O2 (incubation), cropping g C m-2 d-1 Saltmarsh Biomass Cropping g DW m-2 Production O2 (incubation), cropping g C m-2 d-1

Saltmarsh production estimated by cropping, NDVI, and bathymetry

NDVI = (Near_Infrared - Red) / (Near_Infrared + Red) Near_Infrared and Red are two satellite image bands. NDVI ranges between -1 and 1. Pigments absorb lots of energy in R, but barely any in NIR. Other objects absorb both spectra identically.

The PI curve – relationship between photosynthesis (P) and light energy (I)

Some producers display photosaturation, others display photoinhibition.

Iopt Pmax Ic Ik R PPB PPL dP dI Light energy (E m-2 s-1) Production (mg C m-2 d-1)

http://insightmaker.com/insight/6497

Phytoplankton blooms and vertical mixing

Without physics, there is no bloom.

Integrated production(GPP) abcd Integrated respiration aefd Conditions for blooming abcd > aefd

Production and respiration

Phytoplankton production (m-3 day-1) Limit of mixed layer

Depth (z)

Compensation depth Phytoplankton respiration (m-3 day-1)

a c e d f b

NPP=0 Sverdrup, H.U., 1953. On conditions for the vernal blooming of phytoplankton. J. Cons. Perm. Int. Exp. Mer, 18: 287-295

http://insightmaker.com/insight/6503

Phytoplankton blooms and tidal mixing in estuaries

Without physics, there is no bloom.

Ketchum (1954) Relation between circulation and planktonic populations in estuaries. Ecology 35: 191-200.

Phytoplankton growth: P0 = initial population, Pt = population at time t Freshwater inflow Q (m3s-1) Tidal exchange with the ocean

Pt = P0 ekt

Phytoplankton flushing: P0 = initial population, Pm = population after m tidal cycles, r = exchange ratio (proportion of estuary water which does not return each tidal cycle)

Pm = P0 (1-r)m

http://insightmaker.com/insight/6531

Phytoplankton blooms and tidal mixing in estuaries

For phytoplankton to exist and potentially bloom in an estuary, growth must balance flushing, i.e. k ≥ -ln(1-r)

Ketchum (1954) Relation between circulation and planktonic populations in estuaries. Ecology 35: 191-200.

Combining the two equations (and expressing t in terms of m):

Pt = P0 ekt Pm = P0 (1-r)m

Growth Flushing

Pm = P0 emk(1-r)m For a steady-state population , Pm = P0 :

k = -ln(1-r)

 

mk m

e r   1 1

Phytoplankton blooms and tidal mixing in estuaries

Ferreira et al., 2005. Ecological Modelling, 187(4) 513-523.

Lower growth rate required for systems with longer water residence time.

Exchange ratio (r) Multiplication of population each tidal cycle Required coefficient of reproduction 0.5 1.0 2 5 10 20 1.0 2.0 3.0 Moriches Bay Raritan Bay Raritan River Alberni Inlet Barnstable Harbour Population will increase Population will decrease

Biodiversity of phytoplankton in estuaries

Pmax Number of species Fast growing Slow growing

Ferreira, J.G., Wolff, W.J., Simas, T.C., Bricker, S.B., 2005. Does biodiversity of estuarine phytoplankton depend on hydrology? Ecological Modelling, 187(4) 513-523.

Distribution of phytoplankton production across different species may follow a gaussian function.

Number of phytoplankton species as a function of water residence time

r = 0.93 p < 0.01 50 100 150 200 250 300 350 400 450 500 5 10 15 20 25 Number of phytoplankton species Tejo Mondego Sado Minho

• R. Aveiro

Guadiana y = 14.79x + 122.6 r = 0.93 p < 0.01 5 10 15 20 25 Water residence time (days) Number of phytoplankton species Tejo Sado Minho

• R. Aveiro

Guadiana

Species data: 1929-1998 Ferreira et al., 2005. Ecological Modelling, 187(4) 513-523.

Greater phytoplankton diversity with longer water residence time.

Water residence time and number of species

Ferreira et al., 2005. Ecological Modelling, 187(4) 513-523.

Greater phytoplankton diversity with longer water residence time.

Residence time (days) Number of species

Mondego Minho Tejo Ria de Aveiro Sado

Nº species = 14.012Tr + 137.78 r = 0.93 (p< 0.025)

50 100 150 200 250 300 350 400 450 5 10 15 20 25

Species data: 1929-1998

Simulation of growth for three hypothetical phytoplankton species

5 10 15 20 25 30 35 40 25 50 75 100 125 150 175 200 Species A (P

max = 5)

mgC m

• 3

mgC m

• 3

Species B (P

max = 3)

Species C (P

max = 1)

A

• River flow Q = 3 m

3 s

• 1

20 40 60 80 100 120 140 150 152 154 156 158 160 300 600 900 1200 1500 1800 2100 Species A (P

max

Julian day Species B (P

max = 3)

Species C (P

max = 1)

B

• River flow Q = 1.5 m

3 s

• 1

5 10 15 20 25 30 35 40 25 50 75 100 125 150 175 200 Species A (P

max = 5)

mgC m

• 3

mgC m

• 3

Species B (P

max = 3)

A

• River flow Q = 3 m

3 s

• 1

20 40 60 80 100 120 140 150 152 154 156 158 160 300 600 900 1200 1500 1800 2100 = 5) Julian day B

• River flow Q = 1.5 m

3 s

• 1
• Species B is slower growing,

cannot compete at higher river flows;

• If residence time increases, e.g.

through an impoundment, both species grow.

(species A on right axis) No nutrient limitation

Simulation of nutrient limited growth for three hypothetical phytoplankton species

5 10 15 20 25 30 35 40 45 50 20 40 60 80 100 120 140 150 155 160 165 Species A (P

max = 5, high k s )

mgC m

• 3

A

• River flow Q = 3 m

3 s

• 1

Julian day B

• River flow Q = 2 m

3 s

• 1

Species B (P

max = 3, low k s )

Species C (P

max = 1, low k s )

= 5, high k

s )

Species B (P

max = 3, low k s )

Species C (P

max = 1, low k s )

5 10 15 20 25 30 35 40 45 50 20 40 60 80 100 120 140 150 155 160 165 Species A (P

max = 5, high k s )

mgC m

• 3

A

• River flow Q = 3 m

3 s

• 1

Julian day B

• River flow Q = 2 m

3 s

• 1

Species B (P

max = 3, low k s )

Species C (P

max = 1, low k s )

Species A (P

max

Species B (P

max = 3, low k s )

Species C (P

max = 1, low k s )

• Species B is slower growing,

cannot compete at higher river flows;

• If residence time increases, B

can succeed A as nutrients decrease, due to its lower ks

Nutrient limitation

CZCS derived sea-surface pigments Mediterranean Sea

Since the construction of the Aswan dam, the eastern Mediterranean has become increasingly oligotrophic. 5oW 0o 5oE 10oE 15oE 20oE 25oE 30oE 35oE 45oN 40oN 35oN 30oN 5oW 0o 5oE 10oE 15oE 20oE 25oE 30oE 35oE 45oN 40oN 35oN 30oN

0.01 0.03 0.05 0.10 0.20 0.30 0.50 1.00 3.00

http://www.obs-vlfr.fr/

Chlorophyll a in the Tagus Estuary Surface values along a longitudinal section

In the early 1980s very high values occurred in spring.

10 20 30 40 50 60 70 80 90 50 100 150 200 250 300 350 400 Julian day Chlorophyll a (mg L-1) Hypereutrophic High Medium Low Data from BarcaWin2000 - Stations #1.0, #2.0, #3.9, #4.0, #5.0 and #8.0 – 385 values

Chlorophyll a trends in the Tagus Estuary

There appears to be a clear reduction in chlorophyll a concentrations over a period of 15 years.

Seawater zone (1980-1999)

10 20 30 40 50 60 70 80 90

1/01/80 27/09/82 23/06/85 19/03/88 14/12/90 9/09/93 5/06/96 2/03/99

Seawater zone (1980-1999)

10 20 30 40 50 60 70 80 90

1/01/80 27/09/82 23/06/85 19/03/88 14/12/90 9/09/93 5/06/96 2/03/99

Mixing zone (1980-1999)

10 20 30 40 50 60 70 80 90 1/01/80 27/09/82 23/06/85 19/03/88 14/12/90 9/09/93 5/06/96 2/03/99

Mixing zone (1980-1999)

10 20 30 40 50 60 70 80 90 1/01/80 27/09/82 23/06/85 19/03/88 14/12/90 9/09/93 5/06/96 2/03/99

Tidal freshwater zone (1980-1998)

10 20 30 40 50 60 70 80 90

1/01/80 27/09/82 23/06/85 19/03/88 14/12/90 9/09/93 5/06/96 2/03/99

Tidal freshwater zone (1980-1998)

10 20 30 40 50 60 70 80 90

1/01/80 27/09/82 23/06/85 19/03/88 14/12/90 9/09/93 5/06/96 2/03/99

GIS – Chlorophyll a

Composite annual mean

Elevated concentrations appear upstream, due to the pattern of nutrient loading. g l-1 chl a

10 20 km

GIS mean chlorophyll a. Winter, summer, and global

High summer values upstream reflect the loading from the rivers.

<2 2-3 3-5 5-7 7-8 8-10 10-12 12-15 15-20 >20

10 20 km

g l-1 chl a

Data from 1980- 1983, Tagus estuary, Portugal

GIS – Comparison between January and April chlorophyll a

Clear evidence of a spring bloom.

January April

<2 2-3 3-5 5-7 7-8 8-10 10-12 12-15 15-20 >20 g l-1 chl a

10 20 km

Data from 1980-1983, Tagus estuary, Portugal

GIS – chlorophyll a surface-bottom

Water column is well-mixed, so there is no significant difference between bottom and surface chlorophyll .

10 20 km

g l-1 chl a

• 1.35- 0

0-0.5 0.5-1 1-2 2-3.5

The Guadiana Estuary

Surce: Ferreira et al., 2003.

Guadiana estuary - salinity profile

Source: Ferreira et al., 2003.

Guadiana estuary – chlorophyll a

Source: Ferreira et al., 2003.

Interannual variation in chlorophyll a over a 4 year period

Why do the last two years show such a marked decrease?

10 20 30 40 50 60 70 80 365 730 1095 1460

Days Chlorophyll a (ug l-1)

Station #2.0, surface samples

Human impact on San Francisco Bay, U.S.A.

http://ian.umces.edu/neea/ Agricultural and urban nutrient loading in northern California

Suisun San Pablo South Bay

Chlorophyll a in S. Francisco Bay (South Bay)

1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 5 10 15 20 25 30 5 10 15 20 25 30 35 40 45

1977 1978 1979 1980

10 20 30 40 50 60

1982

2 4 6 8 10 12 14 16 18

1984

5 10 15 20 25 30 35 40

1985

2 4 6 8 10 12 14

1987

10 20 30 40 50 60

1989

5 10 15 20 25 30 35 40 100 200 300 400

1996

20 40 60 80 100 120 100 200 300 400

1998 1970’s 1980’s 1990’s

5 10 15 20 25 100 200 300 400

1999 Max: 6.9 Max: 6.9 Max: 27.7 Max: 38.1 Max: 50.6 Max: 16.2 Max: 36.6 Max: 34.2 Max: 12.3 Max: 21.3? Max: 53.2 Max: 113.3 # 30 - Redwood Creek, 37o33.3’N, 122o11.4’W, z= 12.8 m

Chlorophyll a trends in San Francisco Bay (South Bay) – annual maximum in g chl a L-1

Pick a few years at random, chlorophyll is increasing.

20 40 60 80 100 120 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

1970’s 1980’s 1990’s

Upward trend

Chlorophyll a trends in San Francisco Bay (South Bay) – annual maximum in g chl a L-1

Pick a few years at random, chlorophyll is decreasing.

20 40 60 80 100 120 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

1970’s 1980’s 1990’s

Downward trend

Chlorophyll a maximum in San Francisco Bay (South Bay, g chl a L-1) as a function of number of samples

The more you sample, the higher the chlorophyll.

Predicted Observed 20 40 60 80 100 120 10 20 30 40 50 Samples per year Cchlorophyll a (g l-1)

y=-1.3+1.24x r=0.6 (P>0.99)

Primary production budget for the Tagus estuary (t C y-1)

Benthic production accounts for 38% of total carbon removal. Pelagic producers Benthic producers Phytoplankton*1 41160

• 62% Microphytobenthos*2

4265

• 6%

Seaweeds 13770

• 21%

Saltmarsh vegetation*4 7700

• 11%

Sub-total pelagic 41160

• 62% Sub-total benthic

25735

• 38%

Alvera-Azcárate, A., Ferreira, J.G. & Nunes, J.P., 2002. Modelling eutrophication in mesotidal and macrotidal estuaries - The role of intertidal seaweeds. Est. Coast. Shelf Sci. 57(4), 715-724

Phytoplankton (62%) Seaweeds (21%) Saltmarsh (11%) Microphytobenthos (6%)

*1 – EcoWin2000 ecological model, Ferreira (2000) *2 – Modelling and field measurements, Serôdio & Catarino (2000) *3 – Modelling and field measurements, Alvera-Azcárate et al, (2002) *4 – Modelling and field measurements, Simas et al. (2001)

The relationship between chlorophyll a and nutrients

Tett, P., Gilpin, L., Svendsen, H., Erlandsson, C.P., Larsson, U., Kratzer, S., Fouilland, E., Janzen, C., Lee, J., Grenz, C., Newton, A., Ferreira, J.G., Fernandes, T., Scory, S., 2003. Eutrophication and some European waters

• f restricted exchange. Continental Shelf Research, 23, 1635-1671.

Maximum spring phytoplankton (chl a g L-1) Maximum winter DIN (M)

5 10 15 20 25 30 10 20 30 40 GF KF FC Ria Formosa HF GM 50 60 70 80 Tejo Sado 90 Mondego Mira*1 100 110 120 Guadiana Ria de Aveiro*2

*1 – Chlorophyll determined from graphical data *2 – Nitrate, not DIN

Why is there no relationship?

• Estuaries are not lakes
• Differences in residence time
• Range of turbidity
• Top-down pressure from filter-feeders such as clams
• Limiting factors vary
• Phytoplankton chlorophyll may not be the best, and is

certainly not the only, indicator

• Nevertheless, ‘old’ thinking still defines the OSPAR

COMPP approach to eutrophication assessment

Climate change, primary production, and micronutrients

Global climatology of mean annual wind stress

Wind stress (nt m-2)

0.0 0.1 0.2 0.3

Global seven year mean pigment fields

0.05 0.10 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00 5.00

Pigments (mg m-3)

High nutrient low chlorophyll paradox

0.05 0.10 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00 5.00

Pigments (mg m-3)

Phytoplankton from 40m Fe enrichment incubations Nitschia sp.

Chavez et al., 1991 - Limnol. & Oceanog. 36, p. 1816-33

2m

Effect of iron on P-I curves for Phaeodactylum tricornutum

• Fe

+Fe

• Fe

+Fe I (E m-2 s-1)

Pm (mol O2 cell-1 min-1 X 10-10) Pm

B (mol O2 mol Chl-1 min-1)

1000 2000 3000 4000 3 6 9 10 20 30 40 Chlorophyll-specific P vs. I Cell-specific P vs. I

Greene et al., 1991. Limnol. & Oceanog. 36, 8, 1772-1782

IronEx I - Large-scale patch experiment in 1993

Mixing Fe and SF6 (artificial tracer) in the equatorial Pacific Ocean.

IronEx I was followed by IronEx II in 1995, which showed conclusively that phytoplankton production may be limited by Fe.

Dissolved Fe profiles - Antarctic Polar Front

Dissolved Fe profiles North (red) and South (blue) of the Polar Front during JGOFS experiment in the late 1990’s

North South North South

Phytoplankton growth rates versus initial Fe concentration

Phytoplankton incubation experiments North and South of Polar Front during Survey I (blue) and Survey II (red)

Phytoplankton growth rates versus initial Fe concentration

Phytoplankton incubation experiments North and South of Polar Front during Survey I (blue) and Survey II (red)

Phytoplankton growth rates versus initial Fe concentration

Comparison between North and South results “The pseudo-Michaelis Menten response to added iron in deckboard enrichment experiments differs north of the APFZ relative to south of the APFZ, indicating:

• All dissolved iron concentrations are below half

saturation constants, indicating limiting conditions persist throughout the entire Southern Ocean.

• Waters to the North may be limited by something in

addition to iron (silicate).

• Similar saturation values are consistent with other
• bservations from other oceans.”

http://color.mlml.calstate.edu/www/news/workshp2.htm

High Nutrient Low Chlorophyll Paradox

Synthesis

• Different kinds of primary producers in the sea
• Rates of primary production (and therefore carbon fixation)

are difficult to measure and very difficult to scale

• An excess of primary production in coastal zones is now

common in many parts of the world

• The study of primary production is important for

understanding world food supply, coastal eutrophication, and climate change

http://ecowin.org/aulas/mega/pce

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