Coastal and Estuarine Processes http://ecowin.org/aulas/mega/pce Primary production J. Gomes Ferreira http://ecowin.org/ Universidade Nova de Lisboa
Primary production and how to model it Topics • 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 Light CO 2 + 2H 2 A CH 2 O + 2A + H 2 O Pigments
Types of primary producers Pelagic and benthic, microscopic and macroscopic Producer Nutrient source Examples Phytoplankton Water column Diatoms/dinoflagellates Microphytobenthos Water column, sediment Penate diatoms pore water Macroalgae Water column Fucus, Laminaria, Ulva (seaweeds) Saltmarsh plants Sediment Spartina Seagrasses (SAV) Sediment and water Zostera, Posidonia Phytoplankton and microphytobenthos: microscopic, high P/B ratio (>50) Others: macroscopic, low P/B ratio, shallow waters or intertidal
Ecosystem-scale relevance Global distribution of chlorophyll from satellite data (mg m -3 ) Chlorophyll a Data fromSEAWIFS, Summer in the northern hemisphere (1998-2001) Phytoplankton primary prod. 200-360 X 10 14 gC y -1 (98.9%).
Diatoms Phytoplankton Some examples Dinoflagellates Coccoliths
Phytoplankton - diatoms Nitzchia bicapitata 5 m • Chavez et al ., 1991 - Limnol. & Oceanog. 36, p. 1816-33
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 of SAV. Brian Lapointe, Harbor Branch Oceanographic Institute.
Management relevance Ulva prolifera in Jiaozhou Bay, NE China, 2008 (mg m -3 ) Chlorophyll a These macroalgal blooms have occurred annually for the last five years
Management relevance Advection of potential HAB towards the coast from an offshore front PML Remote Sensing Group Courtesy Plymouth Marine Laboratory, UK http://pml.ac.uk/ 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.
Kelp ( Laminaria japonica ) in Sanggou Bay, China Kelp cultivation yields eighty-five thousand tons per year in this 140 km 2 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 ) 0 1 2 4 3 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
Productivity, mean biomass, turnover, and chlorophyll in different ecosystems Area Net production Biomass Turnover Chlorophyll (10 6 km 2 ) (g C m -2 y -1 ) (kg C m -2 ) (P/B, y -1 ) (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 Productivity per unit area is much higher inshore, but the open ocean is much more vast. Whittaker & Likens, 1975. The Biosphere and Man. Primary productivity of the biosphere. Springer-Verlag.
Measurement of primary production in marine and freshwater systems Producer Indicator Method Units g L -1 Phytoplankton & Biomass Chlorophyll a (filtered sample) microphytobenthos Production 14 C, O 2 (incubation) d -1 Seaweeds Biomass Cropping g DW m -2 Seagrasses Production O 2 (incubation), cropping g C m -2 d -1 Saltmarsh Biomass Cropping g DW m -2 g C m -2 d -1 Production O 2 (incubation), cropping Different methods are used for different producers. Upscaling may be done using models, including GIS, remote sensing, and dynamic simulation.
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) http://insightmaker.com/insight/6497 P max Production (mg C m -2 d -1 ) dP dI PPL PPB 0 R I k I c I opt Light energy ( E m -2 s -1 ) Some producers display photosaturation, others display photoinhibition.
Phytoplankton blooms and vertical mixing Production and respiration 0 a e b Integrated production(GPP) NPP=0 Phytoplankton abcd production (m-3 day-1) Depth (z) Integrated Compensation depth respiration aefd Conditions for Phytoplankton blooming respiration (m-3 day-1) abcd > aefd Limit of mixed layer d c f http://insightmaker.com/insight/6503 Without physics, there is no bloom. Sverdrup, H.U., 1953. On conditions for the vernal blooming of phytoplankton. J. Cons. Perm. Int. Exp. Mer, 18: 287-295
Phytoplankton blooms and tidal mixing in estuaries http://insightmaker.com/insight/6531 Phytoplankton growth: P 0 = initial P t = P 0 e kt population, P t = population at time t Freshwater inflow Q (m 3 s -1 ) Tidal exchange with the ocean Phytoplankton flushing: P 0 = initial population, P m = population after m tidal cycles, r = exchange ratio (proportion of estuary water which does not return each tidal cycle) P m = P 0 (1-r) m Without physics, there is no bloom. Ketchum (1954) Relation between circulation and planktonic populations in estuaries. Ecology 35: 191-200.
Phytoplankton blooms and tidal mixing in estuaries Flushing Growth Combining the two equations (and P t = P 0 e kt P m = P 0 (1-r) m expressing t in terms of m): P m = P 0 e mk (1-r) m 1 mk e For a steady-state population , P m = P 0 : m 1 r k = -ln(1-r) 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.
Phytoplankton blooms and tidal mixing in estuaries 20 3.0 Multiplication of population each tidal cycle Required coefficient of reproduction Barnstable 10 Harbour 2.0 Population will increase 5 1.0 Alberni Inlet Population 2 will decrease Raritan Bay Raritan River Moriches Bay 0.5 1.0 0 Exchange ratio (r) Lower growth rate required for systems with longer water residence time. Ferreira et al., 2005. Ecological Modelling, 187(4) 513-523.
Biodiversity of phytoplankton in estuaries Number of species Slow Fast growing growing P max Distribution of phytoplankton production across different species may follow a gaussian function. 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.
Number of phytoplankton species as a function of water residence time 500 y = 14.79x + 122.6 450 Number of phytoplankton species Number of phytoplankton species r = 0.93 r = 0.93 Sado Sado 400 p < 0.01 p < 0.01 Tejo Tejo 350 300 R. Aveiro R. Aveiro 250 Mondego 200 150 Guadiana Guadiana 100 Minho Minho 50 Species data: 1929-1998 0 0 0 5 5 10 10 15 15 20 20 25 25 Water residence time (days) Greater phytoplankton diversity with longer water residence time. Ferreira et al., 2005. Ecological Modelling, 187(4) 513-523.
Water residence time and number of species Species data: 1929-1998 450 Number of species Nº species = 14.012T r + 137.78 Sado 400 r = 0.93 (p< 0.025) 350 Tejo Ria de 300 Aveiro 250 200 Mondego 150 Minho 100 50 0 0 5 10 15 20 25 Residence time (days) Greater phytoplankton diversity with longer water residence time. Ferreira et al., 2005. Ecological Modelling, 187(4) 513-523.
- - 3 3 - - 3 3 mgC m mgC m mgC m mgC m 40 40 200 200 3 s 3 s - - 1 1 A A - River flow Q = 3 m - River flow Q = 3 m 35 35 175 175 Species A Species A Simulation of growth for (P (P max = 5) max = 5) Species B Species B 30 30 150 150 (P (P max = 3) max = 3) three hypothetical 25 25 125 125 phytoplankton species 20 20 100 100 15 15 75 75 (species A on right axis) 10 10 50 50 Species C No nutrient limitation 5 5 (P max = 1) 25 25 0 0 0 3 s 3 s - - 1 1 B B - River flow Q = 1.5 m - River flow Q = 1.5 m 140 140 2100 2100 • Species B is slower growing, Species B 120 120 1800 1800 (P max = 3) cannot compete at higher river 100 100 1500 1500 flows; 80 80 1200 1200 • If residence time increases, e.g. Species A 60 60 900 900 (P = 5) max through an impoundment, both 40 40 600 600 Species C species grow. (P max = 1) 20 20 300 300 0 0 0 0 150 150 152 152 154 154 156 156 158 158 160 160 Julian day Julian day
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