Waycott et al., 2009), the role of seagrass production
models has shifted to more of a synthesis-, forecast-, and
management-driven focus (JØrgensen, 1994).
Framework of seagrass productivity models
Mechanistic seagrass productivity models follow the flow
of carbon, the product of primary production, through
a modeled environment and track the flow as it is modified
by the defined model parameters (Short, 1980). Most
mechanistic seagrass production models follow a massbalance approach to quantify the change in a seagrass
population over time as a function of the rate of biomass
production from gross photosynthesis and loss due to
respiration, mortality, and herbivory (Madden and Kemp,
1996; Cerco and Moore, 2001). Mass-balance equations
are set up for all major, or state, variables and are collectively known as governing equations. Other examples of
common state variables found in seagrass production
models include epiphytes, phytoplankton, macroalgae,
zooplankton, and various fish species (da Silva and
Asmus, 2001; Biber et al., 2004; Baeta et al., 2011).
Modeling environmental factors that influence
seagrass production
Primary production is the result of gross photosynthesis,
which is the rate at which organic carbon and oxygen are
produced through the conversion of light energy into chemical energy (Marker and Westlake, 1980). Gross
photosynthesis (P) is often modeled as a function at a -
species-specific optimal photosynthetic rate (P max ) under
optimal or known fixed environmental conditions
(Vermaat and Hootsman, 1994; Madden and Kemp, 1996;
Cerco and Moore, 2001). Parameters that modify the rate
of processes in seagrass production models should have
a measureable impact on seagrass ecosystems, contain ecologically relevant equation coefficients, and avoid empirical
equations as much as possible (Best et al., 2001). While the
abiotic factors that influence P max vary, the availability of
light, water temperature, availability of nutrients, and initial
plant biomass are often included in most seagrass production equations (Carr et al., 1997).
Light
The primary factor impacting photosynthesis in coastal
seagrass ecosystems is light availability (Ralph et al.,
2007). However, not all light that reaches the surface of
the water within a seagrass bed is available for photosynthesis (Dunton and Tomasko, 1994; Zimmerman et al.,
1994). For most seagrass production models, the availability of light at the leaf surface depends on (1) the total
amount of available light, (2) the amount of light reflected
at the surface of the water, (3) water column light attenuation, (4) the amount of dissolved and suspended particles
in the water column, and (5) light reduction by epiphytes
on the leaf surface (Madden and Kemp, 1996).
Photosynthetic rates, described in the literature using
species-specific photosynthesis-irradiance or PI curves,
increase linearly with light up to a saturating level past
which photosynthesis no longer increases (Lee et al.,
2007). In several seagrass production models, the relationship between light and photosynthesis in seagrass models
is often defined by the Michaelis-Menten function or the
hyperbolic tangent function (Vermaat and Hootsman,
1994; Zimmerman et al., 1994; Madden and Kemp,
1996). Both functions assume that photo-inhibition does
not occur in seagrass beds and reduce P max either with
a light half-saturation constant (Michaelis-Menten) or by
a light-saturation threshold (hyperbolic tangent equation)
to account for the impact of low light conditions on
photosynthesis.
Temperature
Temperature impacts seagrass production by controlling
the rate of chemical reactions within the plant (Lee et al.,
2007). For most biological processes, there is an optimal
temperature range at which the process is the most efficient (Thornton and Lessem, 1978). As temperatures
depart from the optimum range, the rates of processes will
change, impacting overall production (Lee et al., 2007).
The relationship between temperature and physiological
processes is species, location, and seasonally specific
(Orth and Moore, 1986; Thom, 1990). Therefore no single
algorithm works for all species, and the selection of the
equation used to describe the impact of temperature on
seagrass productivity should be based on a wide range of
temperatures (Carr et al., 1997).
Nitrogen and phosphorus
Many seagrass production models also consider the
impact of nutrients on gross photosynthesis as an important growth-limiting factor (Zimmerman et al., 1987;
Bocci et al., 1997; Lee et al., 2007). Seagrasses are capable
of obtaining nutrients from both the water column and the
sediment, and uptake of nutrients by both roots and shoots
should be included when possible (Lee et al., 2007). Traditionally the Michaelis-Menten equation has been used
to model seagrass production response to nutrient limitations. The equation uses an estimate of the half-saturation
constant that corresponds with the nutrient concentration
where productivity is one-half P max (Madden and Kemp,
1996; Cerco and Moore, 2001). Half-saturation constants
will also vary between the roots/rhizomes and leaves,
and all values are species specific.
Modeling environmental factors that influence
seagrass biomass loss
Loss of seagrass biomass can be attributed to factors such
as physical disturbance, microbial decay, herbivory, high
rates of respiration, and mortality (Short, 1980; Madden
and Kemp, 1996; Cerco and Moore, 2001). While the
impacts of environmental factors such as temperature,
SEAGRASS PRODUCTION MODELS
543
models has shifted to more of a synthesis-, forecast-, and
management-driven focus (JØrgensen, 1994).
Framework of seagrass productivity models
Mechanistic seagrass productivity models follow the flow
of carbon, the product of primary production, through
a modeled environment and track the flow as it is modified
by the defined model parameters (Short, 1980). Most
mechanistic seagrass production models follow a massbalance approach to quantify the change in a seagrass
population over time as a function of the rate of biomass
production from gross photosynthesis and loss due to
respiration, mortality, and herbivory (Madden and Kemp,
1996; Cerco and Moore, 2001). Mass-balance equations
are set up for all major, or state, variables and are collectively known as governing equations. Other examples of
common state variables found in seagrass production
models include epiphytes, phytoplankton, macroalgae,
zooplankton, and various fish species (da Silva and
Asmus, 2001; Biber et al., 2004; Baeta et al., 2011).
Modeling environmental factors that influence
seagrass production
Primary production is the result of gross photosynthesis,
which is the rate at which organic carbon and oxygen are
produced through the conversion of light energy into chemical energy (Marker and Westlake, 1980). Gross
photosynthesis (P) is often modeled as a function at a -
species-specific optimal photosynthetic rate (P max ) under
optimal or known fixed environmental conditions
(Vermaat and Hootsman, 1994; Madden and Kemp, 1996;
Cerco and Moore, 2001). Parameters that modify the rate
of processes in seagrass production models should have
a measureable impact on seagrass ecosystems, contain ecologically relevant equation coefficients, and avoid empirical
equations as much as possible (Best et al., 2001). While the
abiotic factors that influence P max vary, the availability of
light, water temperature, availability of nutrients, and initial
plant biomass are often included in most seagrass production equations (Carr et al., 1997).
Light
The primary factor impacting photosynthesis in coastal
seagrass ecosystems is light availability (Ralph et al.,
2007). However, not all light that reaches the surface of
the water within a seagrass bed is available for photosynthesis (Dunton and Tomasko, 1994; Zimmerman et al.,
1994). For most seagrass production models, the availability of light at the leaf surface depends on (1) the total
amount of available light, (2) the amount of light reflected
at the surface of the water, (3) water column light attenuation, (4) the amount of dissolved and suspended particles
in the water column, and (5) light reduction by epiphytes
on the leaf surface (Madden and Kemp, 1996).
Photosynthetic rates, described in the literature using
species-specific photosynthesis-irradiance or PI curves,
increase linearly with light up to a saturating level past
which photosynthesis no longer increases (Lee et al.,
2007). In several seagrass production models, the relationship between light and photosynthesis in seagrass models
is often defined by the Michaelis-Menten function or the
hyperbolic tangent function (Vermaat and Hootsman,
1994; Zimmerman et al., 1994; Madden and Kemp,
1996). Both functions assume that photo-inhibition does
not occur in seagrass beds and reduce P max either with
a light half-saturation constant (Michaelis-Menten) or by
a light-saturation threshold (hyperbolic tangent equation)
to account for the impact of low light conditions on
photosynthesis.
Temperature
Temperature impacts seagrass production by controlling
the rate of chemical reactions within the plant (Lee et al.,
2007). For most biological processes, there is an optimal
temperature range at which the process is the most efficient (Thornton and Lessem, 1978). As temperatures
depart from the optimum range, the rates of processes will
change, impacting overall production (Lee et al., 2007).
The relationship between temperature and physiological
processes is species, location, and seasonally specific
(Orth and Moore, 1986; Thom, 1990). Therefore no single
algorithm works for all species, and the selection of the
equation used to describe the impact of temperature on
seagrass productivity should be based on a wide range of
temperatures (Carr et al., 1997).
Nitrogen and phosphorus
Many seagrass production models also consider the
impact of nutrients on gross photosynthesis as an important growth-limiting factor (Zimmerman et al., 1987;
Bocci et al., 1997; Lee et al., 2007). Seagrasses are capable
of obtaining nutrients from both the water column and the
sediment, and uptake of nutrients by both roots and shoots
should be included when possible (Lee et al., 2007). Traditionally the Michaelis-Menten equation has been used
to model seagrass production response to nutrient limitations. The equation uses an estimate of the half-saturation
constant that corresponds with the nutrient concentration
where productivity is one-half P max (Madden and Kemp,
1996; Cerco and Moore, 2001). Half-saturation constants
will also vary between the roots/rhizomes and leaves,
and all values are species specific.
Modeling environmental factors that influence
seagrass biomass loss
Loss of seagrass biomass can be attributed to factors such
as physical disturbance, microbial decay, herbivory, high
rates of respiration, and mortality (Short, 1980; Madden
and Kemp, 1996; Cerco and Moore, 2001). While the
impacts of environmental factors such as temperature,
SEAGRASS PRODUCTION MODELS
543