A Modeling Study of Alternative Stable States in the
Florida Bay Ecosystem: Benthic-Pelagic Coupling
Christopher J. Madden1, Amanda McDonald1, Marguerite Koch2, Patricia Glibert3, Cynthia Heil4,
Bernard Cosby5, W. Michael Kemp3
1SFWMD
Everglades Division, 2FL Atl. Univ., 3Univ. MD HPL, 4FL FWCC, 5Univ. VA
ASLO
Nice, FR
January 30, 2009
Florida Bay and the Everglades Watershed:
Hydrologic Model Domains
2
Integration of Watershed Hydrologic Models
Langevin et al. (2004)
3
FATHOM (Flux Accounting and Tidal Hydrology
Ocean Model)
Cosby et al. (2005)
Hunt et al. (2006)
4
Florida Bay SAV Numerical Ecological
Model Calibration Sites
Everglades
National
Park
Taylor
Slough
Florida
Keys
Florida Bay
Nuttle et al. (2005)
N
Madden et al. (2007)
5
Seagrass Community Ecological Model
Madden et al. (2007)
6
Plant Growth Responses
Thalassia
1
Sulfide Toxicity
Thalassia
1
1
Halodule
Relative Growth
Halodule
Nitrogen
Uptake
0
Phosphorus
Uptake
1
2
Nitrogen Concentration (uM)
0
P vs I
1
1
2
Phosphorus Concentration (uM)
Temperature
Effect
Halodule
1
Thalassia
0
5
Halodule
Thalassia
10
15
Salinity Effect
1
Thalassia
Halodule
0
1000
(PAR)
2000
0
35
15
25
Temperature (°C)
0
10 20 30 40 50 60
Madden et al. (2005)
7
Integrating FATHOM and SAV Models:
Reconstruction of SAV Community: 1970-2003
8
Integrating FATHOM and SAV Models:
Reconstruction of SAV Community: 1970-2003
9
Integrating FATHOM and SAV Models:
Reconstruction of SAV Community: 1970-2003
10
Integrating FATHOM and SAV Models:
Reconstruction of SAV Community: 1970-2003
SAV
Salinity
Little Madeira Bay
Thalassia
Halodule
High Salinity
Salinity output from FATHOM Cosby et al. (2005)
SAV from Madden et al. (2006)
11
Phytoplankton Bloom Mapping 2005-2008
Generic pre-bloom
conditions
12
SAV-Phytoplankton Model Interactions
Advection
New N
Species
NH4 NO3
40%~80%
Grazing
7-15%
Recycled
N
Urea
Phytopl
Recycled
P
New
P
Epiphytes
Advection
SAV
Variable C:chla
Variable C:P
Advection
(TT=10-120 d)
Net
Settling
0-15%
Resuspension
Remin P
Geochemical
Sequestration
Sediment OM
Below
ground
Bound P
Available P
Sediment P
13
Characteristics of the Phytoplankton Module
Dependency on Recycling (40-80%)
Dependency on Residence Time (0-120 d)
Dependency on Grazing Rate (7-15%)
P Limitation/Injection and Bloom
N Substrate Preference
Mechanistically Incorporates Variables
Linked to Climate Change: depth, light,
temperature, salinity response
14
Phytoplankton: Basin Residence Time and
P Recycling Efficiency
12
12
Residence= 20d
6
6
12
12
Chla
ug/L
Chla
ug/L
Residence= 40d
6
Recycling= 0.4
Recycling= 0.6
6
12
12
6
6
Recycling= 0.8
Residence= 60d
5 YR
10 YR
5 YR
10 YR
15
Phytoplankton: Grazing (Sponges)
Filtration= 10% d-1
10
Chla
ug/L
5
10
Filtration= 15% d-1
5
16
Phytoplankton Parameter Sensitivity
Recycling Fraction (%)
% max annual biomass
Residence Time (d)
Grazing Rate (% d-1)
60
50
40
30
20
10
0
10 20 30 40 50 60 70 80
(d)
40
50
60
70
80
(% N&P recycled)
6
9
12
15
(% loss d-1)
17
Bloom Simulation and SAV: P injection in Yr 7
(Source: dead SAV, dead Mangrove, Hurricane)
20
1: chla conc
Chla
ug/L
1:
20
0% grazing;
Phytoplankton
50% recycling
10
1:
P injection
10
10% grazing;
50% recycling
1:
0
0.00
912.50
Page 1
1825.00
2737.50
3650.00
Days
1: PAR @ SAV leaf
Untitled
PAR
1:
2000
2000
PAR
1000
1000
1:
0
1:
0.00
912.50
Page 1
1825.00
2737.50
Days
Untitled
3650.00
10 YR
1: TAG
1:
50000
SAV mg
C/m2
50000
1:
1:
Thalassia
25000
25000
0
0.00
Page 1
912.50
1825.00
5 YR
Days
Untitled
2737.50
3650.00
10 YR
18
SAV Decline in Bloom Area: Six Year Trend
Frequency of quadrats with high density and sparse vegetation
1
0.9
dense SAV cover
Frequency
0.8
Oct 2005
0.7
0.6
0.5
0.4
0.3
0.2
sparse cover
0.1
Y
Freq(Bare+Sparse Tot)
Begin increase in
freq. of sparse cover
Oct. 2005-Jan 2006
01/2007
01/2006
01/2005
01/2004
01/2003
01/2002
01/2001
0
Freq(Dense Tot)
(DERM data; Rudnick et al. 2008)
19
Conclusions
Florida Bay is highly coupled, poised betw. alternate states
Model identifies buffered zones as well as inflection or
tipping points where potential to undergo state change
Sometimes small change = Big Response
Tool-wise are moving from a “Box of Models” to a Real
Model Synthesis
We can use simple transport and heuristic models to
understand complex ecological response
Model framework now being used to develop regulatory
freshwater requirements and to evaluate restoration
designs in the context of global climate change
20