A Model of Seagrass Dynamics in Florida Bay: Evaluation and Application
Amanda A. McDonald, Christopher J. Madden
South Florida Water Management District, West Palm Beach, FL, USA
A mechanistic model of seagrass production in Florida Bay simulates seagrass
and epiphyte growth as influenced by nutrient availability, light, sediment sulfide
concentration, and salinity tolerance. Seagrass and epiphyte mortality and
subsequent decomposition stimulate both sulfide production (a stress) and nutrient
recycling (a benefit), which act as competing forces affecting seagrass production.
Salinity, light regime and nutrient loading also affect the response of modeled
seagrasses, epiphytes, and phytoplankton components. The model is solved
numerically using a 4th order Runge-Kutta method and a step size of 3 hours
(equal to 1/8 day). The goal is to couple this model with a water quality model to
predict effects of large-scale management practices in Florida Bay on a spatially
explicit landscape scale.
The model is initially parameterized for Thalassia testudinum. Intrinsic
parameter values such as maximum growth rate and sulfide tolerance were
determined from literature data and through targeted experiments. The model was
calibrated to specific Florida Bay sites using environmental data collected from
each site. Data include water column and sediment porewater nitrogen and
phosphate, photosynthetically active radiation (PAR) above and below the water’s
surface, sediment sulfide levels, sediment organic matter, water temperature, and
salinity. The model was initially forced with raw data to verify the accuracy of
the model mechanisms, and then data from each site was analyzed for annual
signals. These signals were then inserted as forcing functions in the predictive
model. The model was used in hindcast mode for a period of five years and we
tested results against historic seagrass biomass and nutrient concentration data at
each site. Residuals between predicted and actual biomass were not statistically
significant.
Sensitivity analyses were conducted on the model to evaluate the sources of
uncertainty and the confidence of model output. Three approaches were used to
evaluate the sensitivity of the model, the first being evaluation of sensitivity target
response variables to forcing functions. Model output was analyzed to determine
whether input signals were maintained, amplified, or damped and whether the
output resulting from variable forcing functions differed significantly from those
responding to constant forcings. The second approach was to introduce random
variability into forcing signals to represent environmental stochasticity. The
effect on the mean of the output (predicted value) and the variance of the output
over multiple iterations was calculated. Our third approach was to apply a
probability distribution to selected parameters to represent uncertainty in the
parameter estimates.
Uncertainty analysis was used to assess the confidence of the model output
assuming no environmental stochasticity. Parameters were treated singly, then in
combination. Uncertainty around the parameters was assumed to be a normal
random variable with mean of zero. Output variance relative to parameter
variance indicates the strength of the model's dependence on the selected
parameter. The probability density of the mean output is the confidence of the
model predictions. The mean and variance of output variables determine the
confidence interval of model predictions.
The model was used to simulate seagrass dynamics under different management
scenarios for several basins in Florida Bay: Little Madeira Bay, Rankin Lake,
Rabbit Key Basin, and Duck Key. The scenarios we examined were increased
and decreased salinity, increased and decreased phosphorous and nitrogen loads
and changes in the subsurface light regime. These scenarios correspond to
potential environmental responses to management practices being contemplated
for Everglades and Florida Bay restoration. Increasing or decreasing the salinity
out of optimal range resulted in reduced production, but not death, of Thalassia
plants. The model shows that reductions in Thalassia density will provide
opportunities for opportunistic pioneer species such as Halodule, and in the case
of reduced salinities, increases in salinity-intolerant species, such Ruppia and the
macro-alga Chara. Increased phosphorous load in the water column increased
epiphyte productivity, producing an oscillation in seagrass biomass, and gradual
reduction in below ground biomass. This condition led to an impairment of
community resiliency- the ability to withstand and rebound from stress or
conditions that cause temporary loss of above ground biomass. The model is
currently being refined to enable quantitative analysis of the threshold at which
nutrient introduction becomes problematic for the Thalassia community.
The model was also parameterized to describe Halodule wrightii dynamics. Since
the data for H. wrightii parameters is less available, some of the parameters are
adopted from similar species. Sensitivity analysis was conducted on these
parameters to determine model uncertainty. The quantitative verification of
model results was hindered by the lack of biomass data for H. wrightii in the
absence of T. testudinum. This version of the model will later be refined and
coupled with the T. testudinum model through experimentally determined
interaction terms to simulate community dynamics, interspecific competition,
community structure and succession.
Thus far, the model has been used only as a mean-field characterization of a
seagrass bed. Bed morphology and density distribution are two areas of interest
that are not directly addressed. By assigning relationships between biomass and
canopy morphology or shoot density, we anticipate building into the model the
ability to predict the effect of changing plant morphologies and seagrass bed
densities on nutrient uptake, light availability, sediment resuspensibility, and
ultimately, community survival.
Amanda A. McDonald, Florida Bay and Lower West Coast Division, South
Florida Water Management District, 3301 Gun Club Rd., West Palm Beach, FL,
33406. Phone:(561)753-2400 x4648 Fax:(561)791-4077, amcdonal@sfwmd.gov,
Question 4.