Andaman Sea, Indian Ocean. In Stoddart, D. R. (ed.), Tsunamis
and Coral Reefs. Washington, DC: The Smithsonian Institution.
Atoll Research Bulletin, Publ, Vol. 544, pp. 81–92.
Quigley, M. C., Bastin, S., and Bradley, B. A., 2013. Recurrent liquefaction in Christchurch, New Zealand, during the Canterbury
earthquake sequence. Geology, 41, 419–422.
Ramachandran, S., Anitha, S., Balamurugan, V., Dharanirajan, K.,
and Vendhan, K. E., 2005. Ecological impact of tsunami on
Nicobar Islands (Camorta, Katchal, Nancowry and Trinkat).
Current Science, 89, 195–200.
Reed, D. J., 1990. The impact of sea-level rise on coastal salt
marshes. Progress in Physical Geography, 14, 465–481.
Reid, C. M., Thompson, N. K., Irvine, J. R. M., and Laird, T. E.,
2012. Sand volcanoes in the Avon–Heathcote Estuary produced
by the 2010–2011 Christchurch Earthquakes: implications for
geological preservation and expression. New Zealand Journal
of Geology and Geophysics, 55, 249–254.
Reinhardt, E. G., Nairn, R. B., and Lopez, G., 2010. Recovery estimates for the Rio Cruces after the May 1960 Chilean earthquake.
Marine Geology, 269, 18–33.
Thrush, S. F., Halliday, J., Hewitt, J. E., and Lohrer, A. M., 2008.
The effects of habitat loss, fragmentation, and community
homogenization on resilience in estuaries. Ecological Applications, 18, 12–28.
Wong, P. P., 2009. Impacts and recovery from a large tsunami:
coasts of Aceh. Polish Journal of Environmental Studies, 18,
5–16.
Cross-references
Fish Assemblages
Infauna
Macrofauna
Mangroves
Sediment Transport
Soft Sediment Communities
ECOLOGICAL MODELING
Mark J. Brush
1 and Lora A. Harris
2
1
Virginia Institute of Marine Science, College of William
and Mary, Gloucester Point, VA, USA
2
Chesapeake Biological Laboratory, University of
Maryland Center for Environmental Science, Solomons,
MD, USA
Synonyms
Ecosystem modeling
Definition
Ecological modeling (noun): The field of study in which
biological (i.e., biotic) and environmental (i.e., abiotic)
processes are represented mathematically to enable quantitative analysis and simulation of individuals,
populations, communities, and ecosystems.
See Box 1 for related definitions.
Introduction
A complex field such as oceanography tends to be subject to
two opposite approaches. The first is the descriptive, in
which several quantities are measured simultaneously and
their inter-relationships derived by some sort of statistical
method. The other approach is the synthetic one, in which
a few reasonable although perhaps oversimplified assumptions are laid down, these serving as a basis for mathematical
derivation of relationships. Gordon A. Riley (1946)
With this introduction, Gordon Riley embarked on his
description of the first mechanistic numerical model in
a marine ecosystem, in this case a simulation of phytoplankton biomass on Georges Bank. This classic work
illustrated the power of the synthetic approach in marine
science and laid the groundwork upon which the field of
estuarine ecosystem modeling was later built. Riley’s
work provided a significant development upon the
Lotka-Volterra equations of predator-prey dynamics
(Lotka, 1925, 1932; Volterra, 1926):
dN 1
dt
¼ N 1 r À gN 2
ð
Þ
ð 1Þ
dN 2
dt
¼ N 2 fgN 1 À d
ð
Þ
ð 2Þ
where N 1 and N 2 are the size of the prey and predator
populations, respectively, r is the intrinsic growth rate of
the prey, g is the grazing or attack rate of the predators, f
is the efficiency with which predators translate consumed
food into new offspring, and d is the death rate of the predators. The key advancement provided by Riley was to formulate these processes as functions of environmental
variables such as temperature, irradiance, and nutrient
concentration. By specifying measured values for these
“forcing functions,” Riley was able to predict annual
plankton cycles which matched the observations remarkably well. Riley’s models of phytoplankton (Riley, 1946)
and zooplankton (Riley, 1947) on Georges Bank were
subsequently combined into the first coupled nutrientphytoplankton-zooplankton (NPZ) model for the western
North Atlantic (Riley et al., 1949).
Riley’s mechanistic approach formed the basis for the
field of marine and estuarine simulation modeling which
developed in earnest in the 1960s and 1970s (Figure 1
and references therein; Wetzel and Wiegert, 1983; Hopkinson et al., 1988; Hofmann, 2000; Brush and Harris,
2010). Riley’s approach to mechanistic simulation of biological compartments (e.g., phytoplankton, zooplankton)
was combined with the similar mechanistic approach to
simulating abiotic state variables such as the StreeterPhelps dissolved oxygen model (Streeter and Phelps,
1925) to develop increasingly complete ecosystem
models. These early models were used primarily for
heuristic understanding of ecosystem structure and function and were gradually expanded from the original
NPZ structure to include multiple primary producers
(e.g., phytoplankton, sea grass, benthic algae) and
This is VIMS contribution number 3436.
This is UMCES contribution number 4980.
214
ECOLOGICAL MODELING
and Coral Reefs. Washington, DC: The Smithsonian Institution.
Atoll Research Bulletin, Publ, Vol. 544, pp. 81–92.
Quigley, M. C., Bastin, S., and Bradley, B. A., 2013. Recurrent liquefaction in Christchurch, New Zealand, during the Canterbury
earthquake sequence. Geology, 41, 419–422.
Ramachandran, S., Anitha, S., Balamurugan, V., Dharanirajan, K.,
and Vendhan, K. E., 2005. Ecological impact of tsunami on
Nicobar Islands (Camorta, Katchal, Nancowry and Trinkat).
Current Science, 89, 195–200.
Reed, D. J., 1990. The impact of sea-level rise on coastal salt
marshes. Progress in Physical Geography, 14, 465–481.
Reid, C. M., Thompson, N. K., Irvine, J. R. M., and Laird, T. E.,
2012. Sand volcanoes in the Avon–Heathcote Estuary produced
by the 2010–2011 Christchurch Earthquakes: implications for
geological preservation and expression. New Zealand Journal
of Geology and Geophysics, 55, 249–254.
Reinhardt, E. G., Nairn, R. B., and Lopez, G., 2010. Recovery estimates for the Rio Cruces after the May 1960 Chilean earthquake.
Marine Geology, 269, 18–33.
Thrush, S. F., Halliday, J., Hewitt, J. E., and Lohrer, A. M., 2008.
The effects of habitat loss, fragmentation, and community
homogenization on resilience in estuaries. Ecological Applications, 18, 12–28.
Wong, P. P., 2009. Impacts and recovery from a large tsunami:
coasts of Aceh. Polish Journal of Environmental Studies, 18,
5–16.
Cross-references
Fish Assemblages
Infauna
Macrofauna
Mangroves
Sediment Transport
Soft Sediment Communities
ECOLOGICAL MODELING
Mark J. Brush
1 and Lora A. Harris
2
1
Virginia Institute of Marine Science, College of William
and Mary, Gloucester Point, VA, USA
2
Chesapeake Biological Laboratory, University of
Maryland Center for Environmental Science, Solomons,
MD, USA
Synonyms
Ecosystem modeling
Definition
Ecological modeling (noun): The field of study in which
biological (i.e., biotic) and environmental (i.e., abiotic)
processes are represented mathematically to enable quantitative analysis and simulation of individuals,
populations, communities, and ecosystems.
See Box 1 for related definitions.
Introduction
A complex field such as oceanography tends to be subject to
two opposite approaches. The first is the descriptive, in
which several quantities are measured simultaneously and
their inter-relationships derived by some sort of statistical
method. The other approach is the synthetic one, in which
a few reasonable although perhaps oversimplified assumptions are laid down, these serving as a basis for mathematical
derivation of relationships. Gordon A. Riley (1946)
With this introduction, Gordon Riley embarked on his
description of the first mechanistic numerical model in
a marine ecosystem, in this case a simulation of phytoplankton biomass on Georges Bank. This classic work
illustrated the power of the synthetic approach in marine
science and laid the groundwork upon which the field of
estuarine ecosystem modeling was later built. Riley’s
work provided a significant development upon the
Lotka-Volterra equations of predator-prey dynamics
(Lotka, 1925, 1932; Volterra, 1926):
dN 1
dt
¼ N 1 r À gN 2
ð
Þ
ð 1Þ
dN 2
dt
¼ N 2 fgN 1 À d
ð
Þ
ð 2Þ
where N 1 and N 2 are the size of the prey and predator
populations, respectively, r is the intrinsic growth rate of
the prey, g is the grazing or attack rate of the predators, f
is the efficiency with which predators translate consumed
food into new offspring, and d is the death rate of the predators. The key advancement provided by Riley was to formulate these processes as functions of environmental
variables such as temperature, irradiance, and nutrient
concentration. By specifying measured values for these
“forcing functions,” Riley was able to predict annual
plankton cycles which matched the observations remarkably well. Riley’s models of phytoplankton (Riley, 1946)
and zooplankton (Riley, 1947) on Georges Bank were
subsequently combined into the first coupled nutrientphytoplankton-zooplankton (NPZ) model for the western
North Atlantic (Riley et al., 1949).
Riley’s mechanistic approach formed the basis for the
field of marine and estuarine simulation modeling which
developed in earnest in the 1960s and 1970s (Figure 1
and references therein; Wetzel and Wiegert, 1983; Hopkinson et al., 1988; Hofmann, 2000; Brush and Harris,
2010). Riley’s approach to mechanistic simulation of biological compartments (e.g., phytoplankton, zooplankton)
was combined with the similar mechanistic approach to
simulating abiotic state variables such as the StreeterPhelps dissolved oxygen model (Streeter and Phelps,
1925) to develop increasingly complete ecosystem
models. These early models were used primarily for
heuristic understanding of ecosystem structure and function and were gradually expanded from the original
NPZ structure to include multiple primary producers
(e.g., phytoplankton, sea grass, benthic algae) and
This is VIMS contribution number 3436.
This is UMCES contribution number 4980.
214
ECOLOGICAL MODELING