dissolved oxygen, and nutrients on respiration have been
investigated, the mechanistic relationships underlying
these impacts are not well known (Marsh et al., 1986;
Hemminga, 1998; Clavier et al., 2011). Therefore, many
seagrass models use empirical relationships or set loss
rates equal to a constant proportion of plant biomass to
drive processes like respiration and mortality that result
in biomass loss (Bocci et al., 1997; Carr et al., 1997; Cerco
and Moore, 2001). Additional research into the physiological response driving the mechanisms that result in the loss
of seagrass biomass is needed to accurately model these
important processes.
Current knowledge gaps
Beyond the factors that drive rates of production and loss
within seagrass beds, future production models need to
consider the complete life history strategy of seagrasses.
Exclusion of sexual reproduction in carbon-based models
has been accepted due to the dominance of vegetative
reproduction in seagrass beds and the relatively low carbon value of seeds (Harwell, 2000). However, recent
research has shown that sexual reproduction plays
a significant role in seagrass bed resilience and expansion
(Kendrick et al., 1999; Yang et al., 2013) and recovery
from large-scale declines (Plus et al., 2003; Jarvis and
Moore, 2010); therefore, key components of the bed
recovery and expansion dynamics may be missing from
seagrass production models when sexual reproduction is
excluded. In particular, information on seedling physiology is necessary to accurately parameterize the response
of seedlings to environmental conditions.
Future production models should also broaden their
focus beyond the impacts of light, temperature, and nutrients and further integrate sediment conditions through the
development of sub-diagenetic models (Eldridge and
Morse, 2000). Sediment characteristics including organic
content, redox conditions, grain size, nutrient concentrations, and sulfide levels have all been shown to impact
seagrass growth and survival (Goodman et al., 1995;
Borum et al., 2005; Wicks et al., 2009). Therefore, to
increase the forecasting ability of production models, the
impacts of the sediment environment on the mechanisms
that drive production and loss within these systems need
to be accurately defined (Eldridge and Morse, 2000).
Summary
Seagrass production models are important tools to
enhance our understanding of seagrass responses to
stressors (Biber et al., 2004), further define the role of
seagrass beds in coastal ecosystems (Baeta et al., 2011),
and highlight areas where future research is needed (Best
et al., 2001). Often seagrass production models provide
information vital to well-informed management of coastal
ecosystems that cannot be provided using field and laboratory data alone (JØrgensen, 1994; Best et al., 2001).
While the predictive capability of models can be a powerful tool for research and ecosystem management, care
needs to be taken to remember that predictions are limited
based on the model’s assumption of known interactions
(Best et al., 2001).
Bibliography
Bach, H. K., 1993. A dynamic model describing the seasonal variations in growth and the distribution of eelgrass (Zostera marina
L.) I. Model theory. Ecological Modelling, 65, 31–50.
Backhaus, J. O., and Verdiun, J. J., 2008. Simulating the interaction
of seagrasses with their ambient flow. Estuarine, Coastal and
Shelf Science, 80, 563–572.
Baeta, A., Niquil, N., Marques, J. C., and Patrício, J., 2011. Modelling the effects of eutrophication, mitigation measures and an
extreme flood event on estuarine benthic food webs. Ecological
Modelling, 222, 1209–1221.
Best, E. P. H., Buzzelli, C. P., Bartell, S. M., Wetzel, R. L., Boyd,
W. A., Doyle, R. D., and Campbell, K. R., 2001. Modelling submersed macrophyte growth in relation to underwater light climate: modelling approaches and application potential.
Hydrobiologia, 444, 43–70.
Biber, P. D., Harwell, M. A., and Cropper, W. P., Jr., 2004. Modelling the dynamics of three functional groups of macroalgae in
tropical seagrass habitats. Ecological Modelling, 175, 25–54.
Bocci, M., Coffaro, G., and Bendoricchio, G., 1997. Modelling biomass and nutrient dynamics in eelgrass (Zostera marina L.):
applications to the lagoon of Venice (Italy) and Øresund
(Denmark). Ecological Modelling, 102, 67–80.
Borum, J., Pedersen, O., Greve, T. M., Frankovich, T. A., Zieman,
J. C., Fourqurean, J. W., and Madden, C. J., 2005. The potential
role of plant oxygen and sulphide dynamics in die-off events of
the tropical seagrass, Thalassia testudinum. Journal of Ecology,
93, 148–158.
Carr, G. M., Duthie, H. C., and Taylor, W. D., 1997. Models of
aquatic plant productivity: a review of the factors that influence
growth. Aquatic Botany, 59, 195–215.
Cerco, C. F., and Moore, K. A., 2001. System-wide submerged
aquatic vegetation model for Chesapeake Bay. Estuaries, 24,
522–534.
Clavier, J., Chauvaud, L., Carlier, A., Amice, E., Van der Geest, M.,
Labrosse, P., Diagne, A., and Hily, C., 2011. Aerial and underwater carbon metabolism of a Zostera noltii seagrass bed in the
Banc d’Arguin, Mauritania. Aquatic Botany, 95, 24–30.
Coffaro, G., Bocci, M., and Bendoricchio, G., 1997. Application of
structural dynamic approach to estimate space variability of primary producers in shallow marine water. Ecological Modelling,
102, 97–114.
da Silva, E. T., and Asmus, M. L., 2001. A dynamic simulation
model of the widgeon grass Ruppia maritima and its epiphytes
in the estuary of the Patos Lagoon, RS, Brazil. Ecological
Modelling, 137, 161–179.
Dennison, W. C., Orth, R. J., Moore, K. A., Stevenson, J. C., Carter,
V., Kollar, S., Bergstrom, P. W., and Batiuk, R. A., 1993.
Assessing water quality with submersed aquatic vegetation: habitat requirements as barometers of Chesapeake Bay health. BioScience, 43, 86–94.
Dunton, K. H., and Tomasko, D. A., 1994. In situ photosynthesis in
the seagrass Halodule wrightii in a hypersaline sub-tropical
lagoon. Marine Ecology Progress Series, 107, 281–293.
Eldridge, P. M., and Morse, J. W., 2000. A diagenetic model for
sediment-seagrass interactions. Marine Chemistry, 70, 89–103.
Fourqurean, J. W., Duarte, C. M., Kennedy, H., Marbà, N., Holmer,
M., Mateo, M. A., Apostolaki, E. T., Kendrick, G. A., KrauseJensen, D., McGlathery, K. J., and Serrano, O., 2012. Seagrass
ecosystems as a globally significant carbon stock. Nature Geoscience, 5, 505–509.
544
SEAGRASS PRODUCTION MODELS
investigated, the mechanistic relationships underlying
these impacts are not well known (Marsh et al., 1986;
Hemminga, 1998; Clavier et al., 2011). Therefore, many
seagrass models use empirical relationships or set loss
rates equal to a constant proportion of plant biomass to
drive processes like respiration and mortality that result
in biomass loss (Bocci et al., 1997; Carr et al., 1997; Cerco
and Moore, 2001). Additional research into the physiological response driving the mechanisms that result in the loss
of seagrass biomass is needed to accurately model these
important processes.
Current knowledge gaps
Beyond the factors that drive rates of production and loss
within seagrass beds, future production models need to
consider the complete life history strategy of seagrasses.
Exclusion of sexual reproduction in carbon-based models
has been accepted due to the dominance of vegetative
reproduction in seagrass beds and the relatively low carbon value of seeds (Harwell, 2000). However, recent
research has shown that sexual reproduction plays
a significant role in seagrass bed resilience and expansion
(Kendrick et al., 1999; Yang et al., 2013) and recovery
from large-scale declines (Plus et al., 2003; Jarvis and
Moore, 2010); therefore, key components of the bed
recovery and expansion dynamics may be missing from
seagrass production models when sexual reproduction is
excluded. In particular, information on seedling physiology is necessary to accurately parameterize the response
of seedlings to environmental conditions.
Future production models should also broaden their
focus beyond the impacts of light, temperature, and nutrients and further integrate sediment conditions through the
development of sub-diagenetic models (Eldridge and
Morse, 2000). Sediment characteristics including organic
content, redox conditions, grain size, nutrient concentrations, and sulfide levels have all been shown to impact
seagrass growth and survival (Goodman et al., 1995;
Borum et al., 2005; Wicks et al., 2009). Therefore, to
increase the forecasting ability of production models, the
impacts of the sediment environment on the mechanisms
that drive production and loss within these systems need
to be accurately defined (Eldridge and Morse, 2000).
Summary
Seagrass production models are important tools to
enhance our understanding of seagrass responses to
stressors (Biber et al., 2004), further define the role of
seagrass beds in coastal ecosystems (Baeta et al., 2011),
and highlight areas where future research is needed (Best
et al., 2001). Often seagrass production models provide
information vital to well-informed management of coastal
ecosystems that cannot be provided using field and laboratory data alone (JØrgensen, 1994; Best et al., 2001).
While the predictive capability of models can be a powerful tool for research and ecosystem management, care
needs to be taken to remember that predictions are limited
based on the model’s assumption of known interactions
(Best et al., 2001).
Bibliography
Bach, H. K., 1993. A dynamic model describing the seasonal variations in growth and the distribution of eelgrass (Zostera marina
L.) I. Model theory. Ecological Modelling, 65, 31–50.
Backhaus, J. O., and Verdiun, J. J., 2008. Simulating the interaction
of seagrasses with their ambient flow. Estuarine, Coastal and
Shelf Science, 80, 563–572.
Baeta, A., Niquil, N., Marques, J. C., and Patrício, J., 2011. Modelling the effects of eutrophication, mitigation measures and an
extreme flood event on estuarine benthic food webs. Ecological
Modelling, 222, 1209–1221.
Best, E. P. H., Buzzelli, C. P., Bartell, S. M., Wetzel, R. L., Boyd,
W. A., Doyle, R. D., and Campbell, K. R., 2001. Modelling submersed macrophyte growth in relation to underwater light climate: modelling approaches and application potential.
Hydrobiologia, 444, 43–70.
Biber, P. D., Harwell, M. A., and Cropper, W. P., Jr., 2004. Modelling the dynamics of three functional groups of macroalgae in
tropical seagrass habitats. Ecological Modelling, 175, 25–54.
Bocci, M., Coffaro, G., and Bendoricchio, G., 1997. Modelling biomass and nutrient dynamics in eelgrass (Zostera marina L.):
applications to the lagoon of Venice (Italy) and Øresund
(Denmark). Ecological Modelling, 102, 67–80.
Borum, J., Pedersen, O., Greve, T. M., Frankovich, T. A., Zieman,
J. C., Fourqurean, J. W., and Madden, C. J., 2005. The potential
role of plant oxygen and sulphide dynamics in die-off events of
the tropical seagrass, Thalassia testudinum. Journal of Ecology,
93, 148–158.
Carr, G. M., Duthie, H. C., and Taylor, W. D., 1997. Models of
aquatic plant productivity: a review of the factors that influence
growth. Aquatic Botany, 59, 195–215.
Cerco, C. F., and Moore, K. A., 2001. System-wide submerged
aquatic vegetation model for Chesapeake Bay. Estuaries, 24,
522–534.
Clavier, J., Chauvaud, L., Carlier, A., Amice, E., Van der Geest, M.,
Labrosse, P., Diagne, A., and Hily, C., 2011. Aerial and underwater carbon metabolism of a Zostera noltii seagrass bed in the
Banc d’Arguin, Mauritania. Aquatic Botany, 95, 24–30.
Coffaro, G., Bocci, M., and Bendoricchio, G., 1997. Application of
structural dynamic approach to estimate space variability of primary producers in shallow marine water. Ecological Modelling,
102, 97–114.
da Silva, E. T., and Asmus, M. L., 2001. A dynamic simulation
model of the widgeon grass Ruppia maritima and its epiphytes
in the estuary of the Patos Lagoon, RS, Brazil. Ecological
Modelling, 137, 161–179.
Dennison, W. C., Orth, R. J., Moore, K. A., Stevenson, J. C., Carter,
V., Kollar, S., Bergstrom, P. W., and Batiuk, R. A., 1993.
Assessing water quality with submersed aquatic vegetation: habitat requirements as barometers of Chesapeake Bay health. BioScience, 43, 86–94.
Dunton, K. H., and Tomasko, D. A., 1994. In situ photosynthesis in
the seagrass Halodule wrightii in a hypersaline sub-tropical
lagoon. Marine Ecology Progress Series, 107, 281–293.
Eldridge, P. M., and Morse, J. W., 2000. A diagenetic model for
sediment-seagrass interactions. Marine Chemistry, 70, 89–103.
Fourqurean, J. W., Duarte, C. M., Kennedy, H., Marbà, N., Holmer,
M., Mateo, M. A., Apostolaki, E. T., Kendrick, G. A., KrauseJensen, D., McGlathery, K. J., and Serrano, O., 2012. Seagrass
ecosystems as a globally significant carbon stock. Nature Geoscience, 5, 505–509.
544
SEAGRASS PRODUCTION MODELS