United States
Environmental Protection
Agency
Environmental Research
Laboratory
Gulf Breeze FL 32561
EPA-600 3-78-092
October 1978
Research and Development
vvEPA
The Dynamics of an
Estuary as a Natural
Ecosystem, II
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into nine series. These nine broad cate-
gories were established to facilitate further development and application of en-
vironmental technology. Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface in related fields.
The nine series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
6. Scientific and Technical Assessment Reports (STAR)
7. Interagency Energy-Environment Research and Development
8. "Special" Reports
9. Miscellaneous Reports
This report has been assigned to the ECOLOGICAL RESEARCH series. This series
describes research on the effects of pollution on humans, plant and animal spe-
cies, and materials. Problems are assessed for their long- and short-term influ-
ences. Investigations include formation, transport, and pathway studies to deter-
mine the fate of pollutants and their effects. This work provides the technical basis
for setting standards to minimize undesirable changes in living organisms in the
aquatic, terrestrial, and atmospheric environments.
This document is available to the public through the National Technical Informa-
tion Service, Springfield, Virginia 22161.
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EPA-600/3-78-092
October 1978
THE DYNAMICS OF AN ESTUARY
AS A NATURAL ECOSYSTEM, II
by
F. J. Vernberg, W. Kitchens,
H. McKellar, K. Summers, and R. Bonnell
Belle W. Baruch Institute for Marine Biology and Coastal Research
University of South Carolina
Columbia, S. C. 29208
Grant No. R 804407-01
Project Officer
Gerald E. Walsh
Gulf Breeze Environmental Research Laboratory
Gulf Breeze, Florida
U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
ENVIRONMENTAL RESEARCH LABORATORY
GULF BREEZE, FLORIDA 32561
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DISCLAIMER
This report has been reviewed by the Environmental Research Laboratory,
Gulf Breeze, U.S. Environmental Protection Agency, and approved for
publication. Approval does not signify that the contents necessarily reflect
the views and policies of the U.S. Environmental Protection Agency, nor does
mention of trade names or commercial products constitute endorsement or
recommendation for use.
11
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FOREWORD
The protection of our estuarine and coastal areas from damage caused by
toxic organic pollutants requires that regulations restricting the introduc-
tion of these compounds into the environment be formulated on a sound
scientific basis. Accurate information describing dose-response relationships
for organisms and ecosystems under varying conditions is required. The EPA
Environmental Research Laboratory, Gulf Breeze, contributes to this informa-
tion through research programs aimed at determining:
• the effects of toxic organic pollutants on individual species and
communities of organisms;
• the effects of toxic organics on ecosystem processes and components;
• the significance of chemical carcinogens in the estuarine and marine
environments.
If we are to understand effects of pollutants, it is important to know
how unstressed ecosystems function. This paper describes the basic structure
and function of North Inlet Estuary, a large, relatively unpolluted salt
marsh in South Carolina. The work was designed to prepare a general data base
for future studies in polluted ecosystems. Concepts and generalizations given
here may be of help in planning and interpreting field data.
Thomas W. Duke
Director
Environmental Research Laboratory
Gulf Breeze, Florida
111
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ABSTRACT
Although estuaries and marshlands are valuable as a natural resource,
integrative scientific studies leading to the development of predictive
models are practically nonexistent. Such studies are necessary for effective
pollution control and long-term management of the estuarine ecosystem.
A research program was initiated to understand the dynamics of a
relatively undisturbed estuary-marshland ecosystem, the North Inlet Estuary,
near Georgetown, South Carolina. Because of the relative complexity of this
type of study, a five-year study was proposed; a summary of the first two
years' work has been published in the Ecological Research Series (EPA-600/
3-77-016, January 1977}. The present summary covers the next two years of
study.
This investigation consists of two separate but interrelated substudies:
an update of the macroecosystem model of the North Inlet Estuary and a
continuing study of experimental salt marsh microecosystems. The model is
being developed to help understand the interactions of various parts of a
natural ecosystem. The principal objective of the microecosystem study was
to develop and test replicate experimental salt marsh units at the micro-
ecosystem level as diagnostic tools for the assessment of both long- and
short-term pollution effects on the Spartina alterniflora salt marsh
community.
This report was submitted in fulfillment of Grant No. R 804407-01 by the
University of South Carolina, F. John Vernberg, principal investigator,
under the sponsorship of the U.S. Environmental Protection Agency. This
report covers a period from March 1, 1976 to February 28, 1978.
IV
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CONTENTS
Foreword ,..<, iii
Abstract iv
Figures 9 vi
Tables vii
Appendix viii
1. Introduction 1
2. Conclusions 3
3. Recommendations ..... ... 4
4. Review of Pertinent Estuarine Ecosystem Studies 5
5. Simulation Model of the Coupling of a Salt Marsh Ecosystem and
the Estuarine Water Column (H. McKellar, K. Summers, and R.
Bonnell) „ 7
6. Development of a Salt Marsh Microecosystem (W. Kitchens)... 56
7. Summary of Report (F. J. Vernberg) 71
References 72
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FIGURES
Number Page
1 Conceptual bases for overall model development 8
2 Conceptual diagram of the present simulation model indicating the
dynamic coupling of the salt marsh and water column 10
3 'Oscillation driving forces of the simulation model 11
4 Energy circuit diagram of the salt marsh subsystem showing the
inputs and interactions of sunlight and temperature as well
as the internal exchanges 13
5 .Energy circuit diagram of the water column submodel showing
inputs and interactions of sunlight (S), temperature (T),
tidal mixing (REMIX) and organic concentrations in the coastal
sea as well as internal exchanges among POM, DOM, and fish ... 16
6 Simulation response for major components of the salt marsh sub-
system 20
7 Simulation response for major components of the water column
subsystem 22
8 Generalized conceptual model depicting nutrient flow pathways
in the microecosystem 58
9 Cross section of a tank microecosystem unit 60
10 Schematic of seawater system 61
11 Schematic of air sampling system 53
12 Exemplary nutrient flux curves 68
13 Exemplary community metabolism curves 69
VI
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TABLES
Number Page
1 Estimates of Areal Coverage of Major Subsystem of the North
Inlet Estuary 9
2 Macrofauna 65
3 Total Meiofaunal Densities between Microecosystem and Control Site . 66
VI1
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APPENDIX
Number Page
List of all Table Titles 23
Al 24
A2 25
A3 29
A4 55
Vlll
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SECTION i
INTRODUCTION
The value to man of such natural resources as marshlands and estuaries is
indisputable and has been stressed by numerous national reports. Estuaries
and their surrounding marshlands have served as centers of population and are
heavily utilized for industrial development, shipping, fishing, and recrea-
tion. Furthermore, such usage is destined to increase in the future, with
the predictable result of increased competition for these limited natural
resources. Estuaries have been exploited, resulting in varying degrees of
environmental deterioration, the extreme being massive destruction of various
species. Despite recognition that estuaries are not an unlimited resource,
integrated scientific studies leading to the development of predictive models
are practically non-existent. Such integrated studies, rather than isolated
studies of individual species, are necessary for effective pollution control
and long-term management of the estuarine ecosystem. In particular, pro-
duction, energetics, and the mechanisms of various processes as influenced by
environmental perturbation are poorly understood, although the knowledge of
these factors has obvious economic and fundamental scientific value.
Great diversity in kinds and shapes of estuaries has been reported in
the scientific literature (Vernberg, 1976) . However, estuaries typically
have certain characteristics in common, such as tidal fluctuation, salinity
changes, and high concentrations of nutrients. Differences between estuaries
may be quantitative or qualitative, such as the amount of wetlands or the
amount and types of human habitation bordering their shores. Therefore, it
is important to develop and compare ecosystem-oriented models of the major
estuarine types if we are to assess the universal nature and differences
of estuarine dynamics.
This study was undertaken to understand the ecosystem dynamics of a
relatively undisturbed estuary, the North Inlet Estuary, near Georgetown,
South Carolina. This report describes the third year study of what was
designed to be a five-year project. After the project began, the study was
expanded to include a specific section on a microecosystem.
This study consisted of two separate but interrelated substudies, that
of the macroecosystem, and that of the microecosystem. For purposes of
clarity, objectives are presented separately.
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MACROECOSYSTEM STUDY
This was designed to study the dynamics of a relatively undisturbed
marsh-estuarine ecosystem. During the third year of study, the principal
objective was to develop and update a model of an estuarine ecosystem
which would predict probable effects of environmental perturbation.
MICRDECOSYSTEM STUDY
The prime objective was to develop and test replicate experimental
salt marsh units at the microecosystem level as diagnostic tools for the
assessment of both long- and short-term pollution effects on the Spartina
alterniflora salt marsh community.
Results of this integrated study add significantly to our understanding
of the marsh-estuarine ecosystem. Not only does this study provide better
insight into the functioning of estuarine processes in an undisturbed
estuary, but it also provides a basis for the development and validation of
the predictive models. These models are needed in making decisions on
environmental impact of man's activities in the estuarine environment
and in developing long-term management programs of this vital natural area.
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SECTION 2
CONCLUSIONS
Results in the third year of a proposed five-year analysis of a rel-
atively undisturbed marsh-estuarine ecosystem have included an update and
simulation studies of an energy flow model and more detailed investigation of
the applicability of a microecosystem for assessing environmental pertur-
bation. Because of budget cuts, no additional field data necessary for
model development were collected. The technology to use a new bioassay tool,
a salt-marsh microecosystem, was developed. However, additional work
needs to be undertaken to test the system after environmental perturbation
in order to evaluate the predictive capability of this method.
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SECTION 3
RECOMMENDATIONS
Since this study was designed as a five-year project, definitive
recommendations are premature. However, we suggest the following based on
three years' experience:
1) The study of relatively undisturbed ecosystems is necessary to
understand the dynamics of how systems work and to serve as a vital baseline
with which to compare perturbed areas.
2) Since natural environmental units are complex and it takes time and
effort to analyze them, it is recommended that granting agencies recognize
the need for supporting long-term projects.
3) To realize the maximal return on the investment to develop a salt
marsh microecosystem, this phase of the study should be continued in order
to accomplish the stated research goals.
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SECTION 4
REVIEW OF PERTINENT ESTUARINE ECOSYSTEM STUDIES
One of the first attempts to construct an energy flow diagram for an
estuarine-marsh ecosystem was that of Teal (1962) for the marshes of Sapelo
Island, Georgia. Teal proposed an energy flow diagram based on the data of
various investigators. During one year, the input of solar energy was
approximately 600,000 kcal/m2. This energy was estimated to be partitioned
as follows: most of the energy (93.9%) was dispersed as heat in photosynthe-
tic activity, and 6.1% was converted to organic matter in gross primary
production. After plant respiration, 1.4% of the incident light energy was
left as net primary production. Of the energy available to secondary
consumers, 55% was expended in respiration, while 45% of net production was
exported. Since this study, more detailed energy budgets have been published
for various individual species found in the estuarine-marsh ecosystem and
other estuarine systems have been studied as described below.
Recently, a detailed study of a New England salt marsh was made by
Nixon and Oviatt (1973). The two studies'differed in that Teal emphasized
energy flow in the marsh, while Nixon and Oviatt were concerned with energy
flow in marsh creeks and embayments. Since consumption for the embayment
exceeded production based on a yearly energy budget, this aquatic system
must depend on input of energy in the form of organic detritus from marsh
grasses. Production values of New England marsh grass were similar to those
from New York, but markedly lower than those of southern marshes. This
finding may reflect the substantial difference in climatic conditions
between these geographical regions. Marked seasonal differences in energy
flow patterns of New England ecosystems were observed. The flow of energy
is much more complex and values are higher during summer than in winter.
Thus, pollutants introduced at different times of the year might not only
have a greater differential seasonal effect on northern marshes, but north-
ern marshes might respond differently from those in southern regions.
In North Carolina the Newport River estuarine ecosystem-is being
studied by the Atlantic Estuarine Fisheries Center, National Marine Fisheries
Service, Beaufort, North Carolina.
An active program involving Georgia salt marshes has continued since
the earlier work of Teal (1962) , and recently Wiegert et al. (1975) pre-
sented a preliminary ecosystem model of a Georgia Spartina marsh.
Pomeroy et al. are continuing studies on the intermediary metabolism of a
salt marsh.
The dynamics of energy flow expressed as carbon in an estuarine-marsh
5
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ecosystem, Barataria Bay, Louisiana, was described by Day et al. (1973).
This study differs from the ones described above in that it deals in greater
detail with all parts of the estuarine-marsh complex. Like other marshes,
energy was available to be exported to the water, but unlike the findings of
Nixon and Oviatt, a net community production in the water column was reported.
Vernberg et al. (1977) have described the North Inlet marsh-estuarine
ecosystem using three sub-models: intertidal, benthic subtidal, and water
column. A detailed example of the intertidal oyster subsystem of the inter-
tidal submodel was described by Dame and Stevens (1977).
The report of the first two ye/ars of this proposed five-year study was
published by the Environmental Protection Agency in the Ecological Research
Series (Vernberg et al., 1977).
The present report is presented in two parts: 1) microecosystem studies,
and 2) the North Inlet marsh-estuarine model.
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SECTION 5
SIMULATION MODEL OF THE COUPLING OF A SALT MARSH ECOSYSTEM
AND THE ESTUARINE WATER COLUMN
H. McKellar, K. Summers, and R. Bonnell
INTRODUCTION
The long-range purpose of the ecosystem modeling effort is to provide an
overview of our evolving understanding of the North Inlet estuarine ecosystem.
In doing so, we hope to formulate a simulation model which incorporates the
major parts and processes which characterize southeastern salt marsh
estuaries in general.
The conceptual base for the model development is illustrated in Figure
1. The fully developed model of the total estuarine system will incorporate
inputs representing a wide spectrum of external•forcing functions from
terrestrial, atmospheric, and oceanic sources. Responses to inputs of sun-
light and temperature are of primary concern, especially with respect to
seasonal responses of biotic processes. The internal functioning of the
model represents a dynamic exchange of energy and materials among several
major subsystems as indicated in Figure 1. The intertidal subsystem is
dominated by the Spartina salt marsh but also includes extensive mud flats
and oyster reefs. The subtidal benthic subsystem is dominated by hetero-
trophic assemblages of macro- and meio-consumers. The water column
subsystem is dominated biologically by planktonic organisms but also receives
major inputs of energy and material from the contiguous marsh and underlying
benthic systems. The energy and material within the water column also
exchange with the sea and adjacent bays. This exchange takes place as a
tidally driven passive transport of materials which are dissolved or sus-
pended in the water mass as well as an active migration of nektonic organisms
through the inlets. The areal coverage at mid-tide of all major subsystems
has been estimated by planimetering existing maps of the region (Table 1).
Previous progress toward establishing the data base for the North Inlet
estuary as well as the initial development of linear subsystem models are
presented earlier (Vernberg et al., 1977). We present in this report a non-
linear simulation model of the coupled interaction between the salt marsh and
the estuarine water column subsystems. This model is driven externally by
inputs representing seasonal oscillations of sunlight, temperature, and tidal
mixing. The proposed structure and functional interactions of the system are
described and the mathematical formulation is presented along with an appended
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OCEAN
03
NORTH INLET
ESTUARY
/ORGANICS,
I NUTRIENTS
X^'
•:•
• •. •«
• • ••••••
.;•••'.•:•.:•:
•. • •.
•«
•»
Subtidal
Benthic
Subsystem
Sediment
Nutrients
T
• •
••:•••
. •..•.:::
.!••••.*••
.•.:•••..•
• •**• *
* »*»^* * • * * ***
Seasonal
Program
Figure 1. Conceptual bases for overall model development. External driving forces are indicated by
the circle symbols and the internal subsystems are indicated by labeled boxes.
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TABLE 1. ESTIMATES OF AREAL COVERAGE OF MAJOR SUBSYSTEM OF THE NORTH INLET
ESTUARY
Subsystem
Symbol
Area (Hectares)
Intertidal
Salt Marsh
Oyster Reefs
Mud Flats
Water Column
Subtidal Benthic
Al
A3
A5
A2
A4
2513
137
82
709
779
documentation of the necessary references, calculations, and assumptions.
Simulation results are shown to indicate the seasonal behavior of the major
components of the modeled system.
THE SIMULATION MODEL
The model, as thus far developed, represents the dynamics of organic
matter in the coupled system of salt marsh and estuarine water mass. Organic
exchange processes between the water column and the sea are also included.
Figure 2 represents the conceptual status of this model as compared to the
overall concept diagrammed in Figure 1. This model includes oscillating
driving forces of sunlight, temperature, and tidal exchange. The simulated
inputs from the oyster reefs and the subtidal benthic subsystems are included
as constants since these subsystems have not yet been coupled as dynamic
components of the model. The concentrations of dissolved and particulate
organic matter in the coastal sea are also included as constants in these
initial simulations.
The Driving Forces
The programmed inputs of the oscillating driving forces (Fig. 3) repre-
sent periodic changes which are critical to the seasonal behavior of organic
exchange. The sunlight curve was programmed with data representing an
eleven-year average for the South Carolina coast (Charleston) (U. S. Depart-
ment of Commmerce, 1964).
The minimum and maximum water temperatures were programmed to lag 40
days behind the sunlight curve with an annual oscillation between 5°C and
30°C.
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SUNLIGHT
TEMPERATURE
INLET
SUBTIDAL BENTHIC
V^ COMMUNITY
(CONSTANT)
SEA
TIDAL
EXCHANGE
ORGANIC MATTER
(CONSTANT)
Figure 2.
Conceptual diagram of the present simulation model indicating the dynamic coupling of the
salt marsh and water column.
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MARSH-ESTUARINE FORCING FUNCTIONS
6000
1.0 T
0.5
Winter Spring Sunnier
Fall
Winter Spring Summer
Fall
Figure 3. Oscillation driving forces of the simulation model.
SUNLIGHT = 3945 + 1695 (SINE(.0172 x TIME))
TEMPERATURE= 17.5 + 12.5 (SINE(.0172 x (TIME - 40))
REMIXING
COEFFICIENT= .5 + .4 (SINE(.0172 X TIME))
11
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The tidal exchange characteristics include a "Remixing Coefficient"
which represents the relative fraction of the ebb tide volume which reenters
the estuary on the next flood tide. This function has significant conse-
quences on the actual amount of net exchange of material with the coastal
sea. For these simulations we examine the consequences of a sinusoidal
fluctuation of the "Remixing Coefficient." This function was assumed to
have a winter minimum of 10 percent reentry of the plume (when along-shore
currents are strong and there are general turbulent conditions at the inlet)
and a summer maximum of 90% (when along-shore currents are relatively weak
and calm conditions exist at the inlet). These preliminary assumptions were
based mainly on speculation and the actual behavior of this function is yet
to be determined (B. Kjerfve, personal communication).
The Salt Marsh Submodel
The detailed structure and functional interactions of the salt marsh
submodel are given as an energy flow diagram in Figure 4. The energy
circuit symbols used here are consistent with those formalized by Odum (1971,
1972) (See Appendix, Table Al).
Structure—
The state variables of the submodel include three primary producers,
X , Spartina biomass, both above and below ground;
X,,, biomass of marsh algae on the mud surface below the Spartina;
and
X,4, biomass of microbenthic algae on the mud flats
three marsh consumers,
X., macrofauna;
X5, meiofauna; and
Xgf birds
and three organic storage compartments,
X,, above ground detritus representing mainly dead Spartina and
attendant bacteria;
X6, sediment detritus and attendant bacteria; and
X_, dissolved organic matter (DOM) in the sediments.
Functioning—-
The inputs of sunlight and temperature are considered as main driving
forces for the model. These inputs control major systems functions of
primary production, respiration, and trophic transfers. Pathways of energy
transfer through the marsh include the major detrital pathways documented
for marsh-estuarine sytems as well as direct grazing on producers by meio-
and macro-consumers. The release of dissolved organic matter by all biotic
compartments is also indicated.
The vertical row of boxes on the right of Figure 4 identify the major
12
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Micuktmliic
MARSH SUBMODEL
Figure 4. Energy circuit diagram of the salt marsh subsystem showing the inputs and interactions of
sunlight and temperature as well as the internal exchanges. The boxes on the right
indicate pathways of exchange with other subsystems.
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routes by which energy is exchanged with the adjacent water column, oyster
reefs, and benthic subtidal systems.
The gross primary production of the energy in organic compounds by the
salt marsh flora was considered to be directly proportional to the product
of available sunlight, temperature, and producer biomass. (See Appendix,
Table A2 for the complete mathematical representation of model functions.)
For the marsh algae on the mud surface beneath the Spartina, the available
sunlight is attenuated by the above ground Spartina biomass. There exists
a definite shading effect of the Spartina on the marsh algae as documented
by Van Raalte et al. (1976). We considered the attentuation of sunlight as
it passes through the Spartina^ to be a general logarithmic extinction
function of above ground Spartina biomass. Since X in our model is total
Spartina biomass, we assume that the above ground portion is 60% of the total
so that
SS = (S) (e(-K8-AXi>
where S is the incident sunlight,
SS is the sunlight reaching the marsh algae,
K8 is the extinction coefficient, and
AX^ is the above ground portion o f X^ = (.6X,).
The extinction coefficient (K8) was calculated fron data presented by Van
Raalte et al. (1976) to be 0.0027. Thus, when Spartina biomass is high the
percent transmission of sunlight to the algae is correspondingly low.
Respiration of each biotic compartment represents a loss of organic
energy and is considered to be directly proportional to the product of water
temperature and the square of the component biomass. Thus, the effect of
temperature on respiration is combined with a quadratic function of biomass
representing a crowding effect of individuals within the compartment.
Quadratic respiratory functions have been used by O'Neill (1976) in his
general, 3-variable ecosystem model. Such functions for component respira-
tion are not fully substantiated by measurements but seem to produce valid
results in simulation models. All compartments in this submodel include this
respiratory function except the dissolved organic matter in the sediment
(DOM, X?).
Trophic transfers of the energy in organic compounds among compartments
are considered to be directly proportional to the product of the concentra-
tion of the food source, the biomass of the recipient, and water temperature.
The remaining exchanges of the energy in organic compounds described in
this model are considered to be linear, donor controlled exchanges. The
linear flows in' this model include:
- release of dissolved organic matter by all biotic components,
- death rates of all biotic components (i.e., transfer of living biomass
14
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to detritus-bacteria compartments (X., and Xg) , and
- export flows from the marsh to the water column subsystem. Again,
the vertical row of boxes on the right in Figure 4 identify the
external subsystem components to which the energy is exported as well
as the sources for energy import to the marsh from external components.
The imports of energy to the marsh from other subsystems include:
- food sources to the marsh birds from fish in the water column and from
organisms of the oyster reefs,
- the settling of particulate detritus onto the marsh floor with flood
tides.
The flow from oyster reef to marsh birds was evaluated and held constant in
this model since the oyster reef component is not yet a dynamic part of the
model.
The Water Column Submodel
Earlier development of a seven-compartment linear model of the water
column subsystem for North Inlet was presented by Bonnell (1977). Compart-
ments in this previous model have been aggregated into three functional units
for coupling with the other submodels of the North Inlet ecosystem. In
addition, several non-linear interactive terms have been specified. The
present status of development of the water column subsystem is given in
Figure 5.
Structure—
The three dynamic compartments in this submodel include:
- Particulate Organic Matter (X , POM), which represents an aggregation
of phytoplankton, zooplankton, detritus, and bacteria,
- Dissolved Organic Matter (X10, DOM), and
- Fish (XL1).
Future development of the water column model will perhaps include a separa-
tion of the living and non-living components of particulate organic matter.
This separation may be necessary to fully address issues concerning the be-
havior of planktonic organisms as separate from detritus. However, at this
stage of development, the model focuses more on the issues of total organic
exchange rather than on the details of plankton dynamics.
Functioning—
The functional relationships of the water column subsystem are similar
to those described for the salt marsh subsystem.
Gross primary production of organic energy in the water column repre-
sents an input to particulate organic matter. This function was considered
to be directly proportional to the product of sunlight and temperature as
indicated in Figure 5. Since much of the total POM is non-autotrophic, the
positive feedback function of Xg in controlling primary production was not
included.
15
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WATER COLUMN SUBMODEL
Figure 5. Energy circuit diagram of the water column submodel showing inputs
and interactions of sunlight (S), temperature (T), tidal mixing
(REMIX) and organic concentrations in the coastal sea as well as
internal exchanges among POM, DOM, and fish. The boxes on the
upper, lower, and left margins of the submodel indicate pathways
of exchange with other subsystems.
16
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Organic loss due to respiration is included for both POM and fish. Just
as for the salt marsh subsystem, respiration was considered to be directly
proportional to the product of temperature and the square of the biomass.
Trophic transfers in the water column are represented by the ingestion
functions of the fishes. Ingestion rates are considered to be directly
proportional to the product of the food source concentration, temperature,
and the biomass of the fishes.
Planktonic release of DOM as documented by Hellebust (1965) is included
in the model as a temperature-dependent flow from POM to DOM.
Planktonic uptake of DOM as documented and quantified by Crawford et al.
(1974) is represented by a temperature dependent exchange from DOM to POM.
Release of organic matter by the fish due to egestion of particulate
matter and the excretion of dissolved materials is included as temperature
dependent flows from fish to POM and DOM.
Organic exchange with the coastal sea is indicated in Figure 5 by the
"import-export" gates between the water column and the organic concentration
in the sea. As a focal point of this study, we consider a net rate of ex-
change in the model which is directly proportional to the concentration
gradient between the estuary and the sea. However, this net exchange is in-
hibited by the extent to which tidal remixing of the previous ebb-tide plume
occurs (see "Remixing Coefficient", Fig. 3). Thus, a maximum rate of net
exchange through the inlets would occur when tidal remixing is minimal
(i.e., winter conditions) and when a large concentration gradient exists
between the estuary and the sea. Mathematically stated
EX = K (X-X )(1-REMIX)
62C S
where EX is the net rate of exchange through the inlet,
K is a proportionality exchange coefficient,
GX
X is the concentration of a dissolved or suspended organic fraction
in the estuary,
X is the concentration of that fraction in the sea,
s
REMIX is the "Remixing Coefficient" as programmed in Figure 3.
This function for the exchange mechanism is a simplification of a very
complex process. Complete functional analysis of the dynamics of exchange
would require an extensive program of hydrodynamic sampling and modeling
techniques which is beyond the scope of this study. However, we believe
that the overall functioning of the exchange process between the estuary and
the sea can be approximated in the way described above. Continued work will
be directed toward refining this function in the model.
17
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Mathematical Formulation
Differential Equations—
Explicit mathematical expressions for each energy flow pathway shown in
Figures 4 and 5 were formulated as stated in the text. The terms represent-
ing input and output for each state variable were combined to describe the
time rate of change for the model compartments. The resulting set of
differential equations is listed in the Appendix (Table A2) as actual ex-
cerpts from the simulation program.
Energy Balance Factors—
The standing stocks of organic matter for each subsystem represented in
the model were evaluated in terms of energy content (kcal) per square meter
of each individual subsystem. Similarly, the exchange rates were evaluated
in terms of kcal per m^ of each subsystem per day. Thus, a mathematical
expression representing flow from one subsystem to another must incorporate
energy balance factors to account for the areal differences between sub-
systems. In effect, these energy balance factors are the ratios of the area
of the "donor" subsystem to the area of the "recipient" subsystem. For
example, consider an outflow of 1 kcal/m /day from a 2513 hectare salt marsh
(Table 1) to a 709 hectare surface area of estuarine water. The inflow to
the water mass would be 3.54 kcal/m2/day (2513/709 = 3.54). In this way,
the model conserves mass and energy at all transfers between modeled sub-
systems of varying areal coverage.
Evaluation of Coefficients—
In order to evaluate the coefficients for the model, a complete set of
estimates for the standing stocks of model compartments and exchange rates
were required. Much of the data needed to evaluate model parameters for the
water column subsystem were collected from the North Inlet estuary as re-
ported in Vernberg et al. (1977). Data from the North Inlet salt marsh is
still being synthesized for incorporation into the model. However, for
initial development of a functional salt marsh submodel we relied heavily on
published data from other salt marshes along the east and Gulf coasts. This
was necessary because there were not funds available in this grant for
gathering data on the North Inlet except as reported by Vernberg et al.
(1977). These data along with information generated from published labora-
tory experiments have provided an adequate quantitative base for the estab-
lishment of a functioning model. Detailed documentation of the data,
calculations, and assumptions used in the evaluation of model parameters is
given in the Appendix (Table A3). This documentation gives the initial
conditions for the model compartments and flow rates which represent average
annual conditions in the estuary.
Given this data base, the model coefficients were empirically deter-
mined. For example, the rate of gross primary production of Spartina (FO1)
in the salt marsh subsystem was considered to be directly proportional to the
product of sunlight (SUN), water temperature (TEMP), and existing Spartina
biomass (Table A2). Mathematically,
FO1 = (Kl)(SUN)(TEMP)(XI)
18
-------
where Kl is the proportionality coefficient relating these parameters. Thus,
with adequate estimates of gross production, sunlight, temperature, and bio-
mass, the coefficient was then calculated as
Kl = F01
(SUN)(TEMP)(XI)
A complete list of coefficient values used in the preliminary simulations is
given in Table A4 in the Appendix.
Simulation Results
The model was programmed in CSMP-IV (Continuous System Modeling Program)
which is a specialized computer program developed by IBM and tailored for
the solution of systems of differential equations. The program was then
simulated on an IBM 370/165 digital computer. The simulated time span
started at the spring equinox (time when sunlight approximately equals the
annual mean) and lasted for 1500 days (approximately 4 years) with output
printed every 10 days. Beginning with output day 1010 (early winter of the
third year) the computer output was translated by hand to graph paper and
was plotted for an annual cycle.
The responses of the major compartments of the salt marsh subsystem
(Fig. 6) show stable annual changes which resemble the annual fluctuations
documented for several salt marsh systems (Nixon and Oviatt, 1973; Young,
1974; Kirby and Gosselink, 1976).
The simulated marsh community metabolism underwent a springtime surge
up to an early summer maximum and a more gradual decline to low winter
values. The integrated curves for daily metabolism yielded an annual pri-
mary production of around 33,500 kcal/m2. Considering the slight increase
in sunlight and temperature between South Carolina and Georgia, this value
representing the North Inlet system compares favorably with the 36,380
kcal/m2/yr documented for the Sapelo Island marsh (Teal, 1962). The simu-
lated annual net production of organic matter was about 3000 kcal/m2.
The Spartina compartment was the dominant simulated producer compart-
ment of the model. The annual response showed a spring increase in biomass
to an early summer maximum of about 2,700 kcal/m2, followed by a gradual
decline to a late winter maximum of around 700 kcal/m2.
Above ground detritus, composed primarily of dead Spartina showed a
phased lag response behind living Spartina biomass. This compartim <=^ -nd
a late spring increase to a rounded late summer and fall maximum foil
by a gradual decline to a late winter, early spring minimum.
The largest organic storage compartment in the model was the sediment
detritus and attendant bacteria. Output for this compartment was not plotted
since it began with an initial stock of 1.75 x 105kcal/m2 and remained rela-
tively constant throughout the simulation. The absolute value dropped by
less than 2% during the simulated four-year period.
19
-------
MARSH SUBSYSTEM
260
a
•a
m
u
1301
Community
Metabolism
—• Production
,*« Respiration
Annual P
Annual R
Net P
3.349 x 10
3.051 x 10^
= 2.98 x 10
Winter
Spring
Slimmer
Fall
3000.
15001
Live Spartina
Winter
Spring
Summer
Fall
1200
OJ
600 •
Above-Ground Detritus
(dead Spartina)
Winter
Spring
Summer
Fall
Marsh Consumers
Winter
Spring
Summer
Fall
CO
H
(0
o
u
Figure 6. Simulation response for major components of the salt
subsystem.
marsh
20
-------
The simulated macro-consumer biomass of the marsh, which in the field
is composed primarily of the fiddler crab (Uca), showed an oscillation be-
tween an |arly spring minimum of 5-6 kcal/m2 and a summer maximum of about
20 kcal/m . The meio-consumer biomass compartment, which was an order of
magnitude less than the macro-consumers, showed a bi-modal oscillation with
one sharp peak in the spring and an additional rounded peak over the late
summer and fall.
Consistent with Teal's previous analysis of energy flow in the salt
marsh (1962), this model incorporates a significant output of organic matter
from the marsh to the estuarine water column. This organic load to the
water column tends to stimulate heterotrophic growth and respiration (Odum,
1971; Wright and Hobbie, 1966). As a consequence, the model response for
water column metabolism (Fig. 7) shows a general dominance of respiration
over in situ production in the water. The model predicts an annual gross
primary production in the water of about 6000 kcal per m2 of water surface
and an annual respiratory organic consumption of about 11,000 kcal/m2.
The annual stock of simulated particulate organic matter in the water
is relatively constant compared to the simulated fluctuation of dissolved
organic matter. The DOM compartment reaches a summer peak which is more
than 2 times higher than its winter minimum.
The remixing coefficient is shown again in Figure 7 to illustrate the
relationship between organic concentration in the estuarine water, tidal
remixing, and the resultant exchange with the coastal sea. According to the
proposed relationships presently stipulated in the model, the simulated ex-
port rate of organic matter from the estuary is low during the summer, even
though the organic concentration in the estuary is at its, peak. This trend
results because of the large portion of the ebb-tide plume which is specu-
lated to reenter the estuary during subsequent flood tides during the
summer, thus inhibiting the net exchange with the sea. Conversely, when
turbulent conditions exist in the winter (tidal remixing is minimum) there
is a maximum rate of simulated net export.
SUMMARY
A non-linear, deterministic model of the interactions within and be-
tween the Spartina salt marsh and the estuarine water mass has been proposed
and simulated. Preliminary simulation results show that the modeled system
is stable and produces seasonal changes in simulated parameters which re-
semble measured fluctuations in Spartina-dominated estuaries. Whether or
not these simulated trends in metabolism, biomass, and export are actually
characteristic of the North Inlet estuary will be determined by continued
data analysis and collection. Regardless of the present validity of the
model it represents, the status of our understanding of the North Inlet marsh-
estuarine ecosystem with its complex interactions of physical, geochemical,
and biological processes.
21
-------
WATER SUBSYSTEM
CM
60T
So-
Production
Annual P
Annual R
Net P
6144 kcal/m2
10969 kcal/m2
-4825 kcal/m2
Winter Spring Summer
Fall
290T
CM
145- •
POM
DOM
+
Winter Spring Summer
1.0
Fall
Remixing
Coefficient
.5 .
Winter Spring Summer
Fall
20 T
10 •
a
u
Annual POM export =
2334
Annual DOM export =
1752 (kcal/m2-yr)
Winter Spring Summer
Fall
Figure 7. Simulation response for major components of the water column
subsystem. '
22
-------
APPENDIX
1. Table Al: Energy Circuit Symbols
2. Table A2: Differential Equations for the Simulation
Model
3. Table A3: Documentation of Data Used for Initial
Evaluation of Model Parameters with
Calculations, Assumptions, and References
4. Table A4: Coefficient Values for Preliminary
Simulations
23
-------
TABLE Al ENERGY CIRCUIT SYMBOLS
(Odum 1971, 1972)
SYMBOL
FUNCTION
External Driving Force
Internal Energy Storage
(state variable)
Pathway of Energy Flow
1
Heat Sink; Metabolic Energy
Loss in Respiration
"x
•—r_>—•"
-i-
Interactive Function off Two
Factors (A and B) causing a
Resultant Flow (C)
Primary Producer
Consumer
24
-------
TABLE A2 DIFFERENTIAL_EQUATIONS FOR THE STATE VARIABLES OF THE MODEL
NOTATION DESCRIPTION
X^DOT = time rate of change of energy storage in compartment i
FXiX-; = energy flow from compartment i to compartment j
RX^ = respiration of compartment i
FO. = primary production of compartment i
Kn = exchange coefficient
X^ = energy storage of compartment i.
EX^ = exchange of energy between compartment X^ and the sea
R = Remixing Coefficient
Spartina
X1DOT=F01-RX1-FX1X4-FX1X6-FX1X7-FX1X3-FX1X10
FO1=K1*SUN*TEMP*X1
RX1=K2*TEMP*X1**2
FX1X4=K3*TEMP*X1*X4
FX1X6=K4*X1
FX1X7=K5*X1
FX1X3=K6*X1
FX1X10=K7*X1
SUN=A+B*SIN(.0172*TIME)
TEMP=C+D*SIN(.0172*(TIME-40.))
SS=SUN*EXP(K8*AX1)
AX1=.6X1
(continued)
25
-------
TABLE A2 (continued)
Particulate Organic Matter in the Water
X9DOT=F04+((A1/A2)*(FX3X9+FX2X9))+((A5/A2)*FX14X9)+(((A1+A5).
/A2)*FX6X9)+((A3/A2)*(FX13X9+FX12X9))+FX10X9+FX11X9-FX9X6-FX9X11-.
FX9X12-FX9X17-FX9X10-RX9-EX9
FO4=K44*SUN*TEMP
FX13X9=K45*X130 ^
FX12X9=K46*TEMP*(FX9X12+X10X12)
FX10X9=K47*TEMP*X10*X9
FX11X9=K48*TEMP*X11
FX9X11=K49*TEMP*X9*X11
FX9X12=K50*TEMP*X9X120
FX9X17=K51*X9
FX9X10=K52 *TEMP*X9
RX9=K53*TEMP*X9**2
R=.5+.4*SIN(.0172*TIME)
EX9=3.*KEXC9*(1.-R) *((X9/3.)-POMS)
Dissolved Organic Matter in the Water
X10DOT=((A1/A2)*(FX7X10+FX1X10))+FX9X10+((A3/A2)*(X12X10+...
X13X10))+((A5/A2)*X14X10)*((A4/A2)*(X18X10+X15X10))+X11X10-FX10X9-
X10X12-E10
X12X10=K54*TEMP*X120
X13X10=K55X130
X18X10=K56*X10
X15X10=K57*TEMP*X150
X11X10=K58*TEMP*X11
X10X12=K59*TEMP*X10*X120
E10=3.*KEX10*(1.-R) *((X10/3.)-DOMS)
Fish
X11DOT=FX9X11+((A3/A2)*X12X11)+((A4/A2)*X15Xll)-FX11X8-FX11X9-
X11X10-RX11
X12X11=K60*TEMP*X120*X11
X15X11=K61*TEMP*X150*X11
RX11=K62*TEMP*X11**2
*
*This term in the equation for compartment X9 should have been ((A2/A3)*
(FX9X12)-«-X10X12. The results of a corrected simulation indicated that this
omission caused less than 5% error in the results.
(continued)
-------
TABLE A2 (continued)
Marsh Macrofauna
X4DOT=FX1X4+FX2X4+FX5X4+FX6X4-FX4X8-FX4X6-RX4-FX4X7
FX5X4=K19*TEMP*X5*X4
FX6X4=K20*TEMP *X6 *X4
FX4X8=K21*X4*X8
FX4X6=K22*X4
RX4=K23*TEMP*X4**2
.FX4X7=K24*X4
Marsh Meiofauna
X5DOT=FX2X5+FX6X5-FX5X4-RX5-FX5X6-FX5X7
FX6X5=K25*TEMP*X6*X5
RX5=K26*TEMP*X5** 2
FX5X6=K27*X5
FX5X7=K28*X5
Sediment Detritus and Bacteria (Marsh)
X6DOT=FX1X6+FX2X6+FX3X6+FX4X6+FX5X6+FX8X6+((A2/A1)*FX9X6)+.
((A5/A1)*FX14X6-RX6-FX6X4-FX6X5-FX6X7-FX6X9
FX8X6=K29*X8
FX9X6=K30*X9
FX14X6=K31*X14
RX6=K32*TEMP*X6**2
FX6X7=K33*X6
FX6X9=K34*X6
Dissolved Organic Matter in Marsh Sedii-ents
X7DOT=FX1X7+FX2X7+FX4X7+FX5X7+FX6X7-FX7X10
FX7X10=K35*X7
Birds
X8DOT=FX4X8+((A2/A1)*FX11X8)+((A3/A1)*FX12X8)-FX8X6-RX8
FX11X8=K36*X11*X8
FX12X8=K37*X120*X8
RX8=K38*X8**2
(continued)
27
-------
TABLE A2 (continued)
Microbenthic Algae on Mud Flats
X14DOT=F03-RX14-X14X15-X14X10-FX14X9-FX14X6
F03=K39*SUN*TEMP*X14
RX14=K40*TEMP*X14* *2
FX14X9=K42X14
X14X15=K41*TEMP*X14*X150
X14X10=K43*X14
Marsh Algae
X2DOT=FO2-RX2-FX2X4-FX2X5-FX2X6-FX2X9-FX2X7
FO2=K9*SS*TEMP*X2
RX2=K10*TEMP*X2* *2
FX2X4=K11*TEMP*X2*X4
FX2X5=K12*TEMP*X2*X5
FX2X6=K13*X2
FX2X9=K14*X2
FX2X7=K15*X2
Above Ground Detritus and Bacteria
X3DOT=FX1X3-RX3-FX3X6-FX3X9
RX3=K16*TEMP*X3**2
FX3X6=K17*X3
FX3X9=K18*X3
28
-------
TABLE A3 DOCUMENTATION OF DATA USED FOR INITIAL EVALUATION OF MODEI PARAMETERS WITH CALCULATIONS,
ASSUMPTIONS, AND REFERENCES
Compartment
or Flow
Value
Assumptions and Calculations
References
XI
1912.0
10
vo
1. Spartina alterniflora (Tall) = 3.3 kcal/g dwt 1. Nixon S Oviatt, 1973
Spartina alterniflora (Short) = 2.6 kcal/g dwt
2. Assuming 5.0 kcal/g dwt organic matter (OM):
S_._ alterniflora (Tall) =0.66 gOM/gdwt
£._ alterniflora (Short) = 0.52 gOM/gdwt
3. Extant linear regressions for S. alterniflora 3. Reimold et al., 1975
(Tall) and S^ alterniflora (Short):
(Tall) y = 167.6 + 0.2x (y = g dwt; x = g wwt)
(Short) y = 1.00 + 0.34x
4. Tall S. alterniflora comprises 58% of Sapelo 4. Teal, 1962
Island marsh and short S. alterniflora
comprises 42%
5. Standing crops for Sapelo Island marsh are:
Tall S_._ alterniflora = 1987.5 gwwt/m2
Short S. alterni flora = 660.0 gwwt/m2
6. Assuming #1.and #3 are applicable to Sapelo
Island Spartina alterniflora;
2
Short Spartina = .42 x .52 x 225.4 gdwt/m
= 49.23 gdwt OM/m2
5. Teal, 1962
Tall Spartina = .58 x .66 x 564.7 gdwt/i
= 216.17 gdwt OM/m2
m
(continued)
-------
TABLE A3 (continued)
Compartment Value
or Flow
Assumptions and Calculations
References
(%. marsh x organic matter conversion
x standing crop of Spartina dry weight)
7. Conversion to kilocalories :
(49.23 + 216.171 gdwt OM/m2 x 5 kcal/gdwt OM =
1326.97 kcal/m for above-ground biomass
8* Spartina roots comprise 58.5 gC/m2 = 585 kcal/m2 8. Wiegert et al., 1975
9. Total standing crop/m2 = 585.0 + 1326.97 =
1912.0 kcal/m2
10. Above-ground biomass = 69.4% of total standing
crop
FO1
94.74 1. Gross Production Spartina alterniflora
Net Production + Respiration
Net Production = 6580 kcal/m yr
Respiration = 28000 kcal/m2 yr
1. Teal, 1962
Smalley, 1959
Teal & Kanwisher, 1961
RX1
76.71
Gross Production = 34580 kcal/m yr
= 94.74 kcal/m2 day
1. Spartina alterniflora respiration = 28000 kcal/
m^ yr = 76.71 kcal/m^ day
1. Teal & Kanwisher, 1961
FX1X4
0.836 1. Herbivore grazing of Spartina = 305 kcal/m yr
= 0.836 kcal/m2 day
1. Teal, 1962
(continued)
-------
TABLE A3 (continued)
Compartment Value
or Flow
Assumptions and Calculations
References
FX1X6
FX1X7
u>
FX1X10
FX1X3
X2
5.83
1.17
0.167
10.027
75.92
1. Flow to sediment detritus from roots
mortality = Flow into roots compartment-
Flows out of roots compartment
= Production of roots - Respiration of
roots - Flow to Spartina leaves - Flow to
sediment DOM
2 2
= 43.3 kcal/m day - 35.1 kcal/m day -
1.2 kcal/m day - 1.17 kcal/m day
2
=5.83 kcal/m day
1. Flow to sediment DOM = Daily transfer rate
of 0.2%
2. .002 x Root Biomass (585 kcal/m2) =
1.17 kcal/m2 day
1. DOC release into water column by Spartina
leaves = 125 ug DOC/gdwt hr
2
2. Dry weight = 422.194 gdwt/m and leaves make
up 45% of above ground biomass; applicable
dry weight = 190.999 gdwt/m
1. Wiegert et al., 1975
1. Wiegert et al., 1975
1. Gallagher et al., 1976
2. Pfeiffer et al., 1973
3. 125 g DOC/gdwt hr x 190.999 gdwt/m x .7 hr/day 3. Gallagher et al., 1976
= .0167 pg DOC/m2 day = .167 kcal/m2 day
1. Inputs = Outputs (Steady State)
2. Mortality of Spartina = 10.027 kcal/m day
1. Average of 15.184 Ug Cl a/cm of marsh surface
1. Pomeroy, 1959
Estrada et al. 1974
(continued)
-------
TABLE A3 (continued)
Compartment
or Flow
Value
Assumptions and Calculations
References
F02
RX2
FX2X4
U)
FX2X5
FX2X6
FX2X9
4.93
0.49
0.312
0.1096
3.306
0.367
2. 15.184 g Cl a/cm2 - 0.15184 g Cl a/m2
3. 0.15184 g Cl a/m2 - 7.592 g C/in
4. 7.592 g C/m - 75.92 kcal/m2
1. Gross production of algae = 1800 kcal/
m year =4.93 kcal/m^ day
1. Respiration of marsh algae = Gross
production minus net production =
2 2
1800 kcal/m year - 1620 kcal/m year
=180 kcal/m year = 0.49 kcal/m2 year
1. Assume algal grazing by herbivores is 33%
of remaining macro-consumer consumption =
.333(342 kcal/m2 year) = 0.312 kcal/m2 day
1. Assume 33% of meio-consumer consumption
is algal grazing =
.333(110 kcal/m2 year) » .1096 kcal/m2 day
1. Assume 90% of senescent mortality goes to
sediment detritus =
.90(3.6729 kcal/m2 day) = 3.306 kcal/m2 day
1. Assume 10% of senescent mortality is washed
away by the tides =
.10(3.6729 kcal/m2 day) = 0.367 kcal/m2 day
3. Strickland, 1965
1. Poraeroy, 1959
1. Pomeroy, 1959
1. Teal, 1962
1. Teal, 1962
1. Teal, 1962
Pomeroy, 1959
1. Teal, 1962
(continued)
-------
TABLE A3 (continued)
Compartment Value
or Flow
Assumptions and Calculations
References
FX2X7
X3
FX1X3
RX3
u>
OJ
FX3X6
FX3X9
X4
1180.0
10.027
2.74
0.3451 1. DOM loss to sediment DOM pool equals 7%
of gross production
.07(4.93 kcal/m2 day) = 0.3451 kcal/m2 day
1. Standing crop of dead Spartina =
118.0 g C/m = 1180 kcal/m^
2. Assumes bacterial biomass negligible
1. Mortality of Spartina alterniflora;
See Compartment XI
1. Respiration of bacteria in dead Spartina
detritus = 1000 kcal/m^ year
= 2.74 kcal/m2 day
1. Flow to soil detritus is 10% of detrital
flow; calculated from input-output
analysis detrital flow equals 7.203
.10(7.203 kcal/m2 day) = .72 kcal/m2 day
T.483 1. Detrital flow to water column equals
remaining detrital flow or 90% of
total detrital flow
= 6.483 kcal/m2 day
19.77 :a. Insects: Respiratory Turnover
Time = 1.4 days (RTT)
lb. Tqsect respiration rate = 224 kcal/
•"• year
Ic. In.;t.-t standing crop =
1. Hellebust, 1965
0.72
1. Wiegert et al., 1975
1. Teal, 1962
la. Day et al., 1973
lb. Teal, 1962
(continued)
-------
TABLE A3 (continued)
Compartment
or Flow
Value
Assumptions and Calculations
References
00
A.
FX2X4
0.312
(Respiratory Rate)(Respiratory TT)
(# days/year)(kcal/g dwt organic matter)
(224 kcal/m2 year)(1.4 days)
(365 days)(5.0 kcal/g dwt OM)
= 0.17 gOM/m2
2a. Spiders; KTT = 1.4 days •
2b. Respiration rate = 23 kcal/m year
2c. Standing crop = 0.0176 gOM/m2
3a. Crabs (Uca_ spp. and Sesarma spp.)
RTT =20 days
3b. Respiration rate = 170.6 kcal/m2 year
3c. Standing crop = 1.87 gOM/m2
4a. Mussels; RTT = 20 days (oysters)
4b. Respiration rate = 39 kcal/m2 year
4c. Standing crop = .24 gOM/m2
5a. Mud crabs; RTT =20 days
5b. Respiration rate = 21.9 kcal/m2 year
5c. Standing crop = 24 gOM/m2
6a. Snails; RTT =31.2 days
6b. Respiration rate = 72 kcal/m year
6c. Standing crop = 1.23 gOM/m2
7. X4 = 5.0 kcal/g OM (0.17 + 0.0176 +
1.87 + .24 + .24 + 1.23) = 19.77 kcal/m
1. See X2
2a. Day et al., 1973
2b. Teal, 1962
3a. Day et al., 1973
3b. Teal, 1962
4a. Day et al., 1973
4b. Teal, 1962
5a. Day et al., 1973
5b. Teal, 1962
6a. Day et al., 1973
6b. Teal, 1962
(continued)
-------
TABLE A3 (continued)
Compartment
or Flow
FX1X4
FX6X4
Value
0
0
.836
.56223
Assumptions
1.
1.
See XI
Assume
and
Calculations
two- thirds
of
algal-detrital flow to
References
1. Teal,
1962
FX4X8
in
FX4X6
RX4
0.045
.1724
1.508
marsh macro-consumers is detritus of which
10% is meiofauna eaten in conjunction with
detritus
.6667{.9)(342 kcal/m2 yr) = 0.56223 kcal/m2 day
1. Input to birds via predation on macro-
consumers = 7.33 gOM/m2 yr
2. Assume 50% of this flow comes from fish and
50% from marsh community and oyster reefs
3. Assume 15% of marsh community flow comes from
oyster reef community (i.e., 7.5% of total from
oyster community)
4. Grazing on Macro-consumers by birds =
.5 x (.85) x (36.65 kcal/m2 yr) =
0.045 kcal/m2 day
1. Inputs = Outputs (Steady State)
2. Feeding via Spartina + Feeding via Algae +
Feeding via Detritus + Feeding via Meiofauna -
Predation by Birds - Respiration - DOM Excretion =
FX4X6 = 0.1724 kcal/m2 day
o
1. Insect Respiration = 224 kcal/m yr
Spider Respiration = 23 kcal/m yr
Crab Respiration =171 kcal/m yr
1. Day et al., 1973
1. Teal, 1962
Mussel Respiration = 39 kcal/m2 yr
(continued)
-------
TABLE A3 (continued)
Compartment Value
or Flow
Assumptions and Calculations
References
FX5X4
FX4X7
oo
X5
Snail Respiration = 72 kcal/m2 yr
Mud Crab Respiration = 21.9 kcal/m2 yr
2. RX4 = 550.9 kcal/m2 yr = 1.508 kcal/m2 day
0.6247 1. Ingestion of meiofauna with detritus is 10% of
detrital-meiofaunal component =
.6667(J.)(342 kcal/m2 yr) = .06247 kcal/m2 day
0.473 1. Excretion of DOM = .5 mg C/gdwt/hr
2. FX4X7 = .5 mg C/gdwt/hr (3.954 gdwt)(24hr)
= .0473 kcal/m2 day
1.372 1. Nematodes = 2.76 gwwt/m2
Assuming 10 gwwt = 1 gdwt
Biomass = .276 gdwt/m2
2. Assuming 90% organic matter/gdwt
Biomass = .2484 gOM/m2
Biomass = 1.242 kcal/m
3. Annelids = Winter: 33.56 individuals/.Olm2
Summer: 23.64 individuals/.Olm
Average weight = .1 mg wwt
Average wwt Biomass Winter = 335.6 mg/m
Average wwt Biomass Summer = 236.4 mg/m
o
4. Mean wwt Biomass = 286 mg/m
Biomass dwt = 28.6 mg dwt/m
5. Biomass = .026 gOM/m2
Biomass = .130 kcal/m
1. Johannes & Satomi, 1967
1. Teal, 1962
3. Teal, 1962
(continued)
-------
TABLE A3 (continued)
Compartment Value Assumptions and Calculations
or Flow
FX5X4
FX2X5
FX6X5
RX5
6.
.06247 1.
.1096 1.
.2203 1.
2.
.247 1.
X5 = (1.242 +
See X4
See X2
.130) kcal/m2 = 1.372 kcal/m2
References
Assume two-thirds of algal-detrital flow to
meioconsumers is detritus
.667(110 kcal/m2 yr) = .2203 kcal/m2 day
Meioconsumers
respiration = 90 kcal/m2 yr
2. Teal, 1962
1. Teal, 1962
UJ
FX5X7
FX5X6
X6
..014
.00653
1.75E+5
FX2X6 3.306
FX1X6 5.83
FX3X6 .72
2,
3.
1.
1.
1.
= .274 kcal/m2 day
Assume DOM excretion is 1% of standing crop/
day = .014 kcal/m2 day
Input = Output (Steady State)
Feeding via Algae + Feeding via Detritus -
Respiration - Predation by Macro-consumers -
DOM Excretion = FX5X6 = .00653 kcal/m2 day
Standing Crop Detritus in Sediments =
17.5E+3 g C/m2
o
Bacterial Biomass (54 kcal/m ) is negligible
X6 = 1.75E+5 kcal/m2
See X2
See XI
See X3
1. Wiegert et al., 1975
2. Wiegert et al., 1975
(continued)
-------
TABLE A3 (continued)
UJ
00
Compartment Value
or Flow
FX4X6
FX5X6
FX8X6
.1724
.00653
.0304
; Assumptions and Calculations --•
1.
1.
1.
See X4
See X5
2
Feces and Mortality of birds = 2.222 gOM/m yr
References
1. Day et al., 1973
FX6X7
RX6
FX14X6
FX6X4
FX6X5
FX9X6
0.54
5.483
10.283
0.56223
0.2203
1.155
= .0304 kcal/m day
1. Release of DOM to sediment pool is 1% of
bacterial standing crop =,.01(54 kcal/m )
= .54 kcal/m day
1. Wiegert et al., 1975
1. Respiration of marsh sediment = 2090 kcal/m2 yr 1. Teal & Kanwisher, 1961
- 5.726 kcal/m2 day
2. This includes meio-consumer respiration which
equals 0.247 kcal/m2 day
3. RX6 = 5.726 kcal/m2 day - 0.247 kcal/m2 day =
5.483 kcal/m2 day
1. Mortality to sediments from mudflat microalgae
equals 10.283 kcal/m2 day (See X14)
2. Prorated by area of mudflats (82E+6 m ) and area
or marsh (2513E+6 m2) = 0.3355 kcal/m2 day
1. Grazing of detritus by macro-consumers - see X4
1. Intake of detritus by meio-consumers - see X5
1. Sedimentation over marsh by water column =
0.326 kcal/m2 day
1. Wiegert et al., 1975
(continued)
-------
TABLE A3 (continued)
Compartment Value
or Flow
Assumptions and Calculations
References
u>
\D
FX6X9
X7
3.25
20.0
X8 0.22
FX8X6 0.0304
FX4X8 0.045
2. Prorated by the area of the marsh (2513E+6 m )
and the area of the water column (709E+6 m2) =
1.155 kcal/m2 day lost from water column
1. Input = Output (Steady State)
2
2. Difference here = 3.25 kcal/m day
1. DOM (Dissolved Organic Matter) in sediment
pool = 4.0 g C/m2 = 20.0 kcal/m2
1. Wiegert et al., 1975
FX1X7
FX2X7
FX4X7
FX5X7
FX6X7
FX7X10
1.17
.3451
.0473
.014
0.54
2.1164
1.
1.
1.
1.
1.
1.
See XI
See X2
See X4
See X5
See X6
Releasi
1. Release of dissolved organic matter from sediment
pool to water column
2. Input = Output (Steady State)
2
3. Difference equals 2.1164 kcal/m day
1. Bird Biomass = 0.044 gOM/m2 =0.22 kcal/m
1. See X6
1. See X4
1. Day et al., 1973
(continued)
-------
TABLE A3 (continued)
Compartment Value
or Plow
Assumptions and Calculations
References
RX8
FX12X8
X14
F03
0.07
0.0947
150.0
45.05
1. Bird Respiration =5.11 gOM/nr yr =
0.07 kcal/m2 day
1. Predation on oyster community by birds is
assumed to be 7.5% of total ingestion (see X4)
•\
2. Total Ingestion = 7.33 gOM/m2 yr
3. Ingestion via oyster community = 0.005 kcal/m2 day
1. Day et al., 1973
2. Day et al., 1973
4. Prorated over area of oyster communities (137E+6 m )
and area of marsh (2513E+6 m2) = .0947 kcal/m
day lost from oyster community
1. Biomass of benthic microalgae =
300 mg Cl a/m2
2. 0.3 g Cl a/m2 = 15.0 g C/m2 = 150.0 kcal/m2
1. Net Production = 685 g C/m2 yr
= 1.8767 g C/m day = 18.767 kcal/m2 day
2. Assuming night respiration = 40% of light
respiration and that light respiration =
Net Productivity; Total Respiration =
1.4 x Net Production = 26.274 kcal/m2 day
3. Gross Production = Net Production + Respiration
= 45.05 kcal/m2 day
1. Zingmark, Unpubl. Data
1. Zingmark, Unpubl. Data.
RX14
FX14X15
26.274 1. See above; FO3
1.915 1. Assume macrobenthic consumer intake of microbenthic
algae is 18.75% of total intake (i.e., algal and
(continued)
-------
TABLE A3 (continued)
Compartment
or Flow
Value
Assumptions and Calculations
References
FX14X10
FX14X9
FX14X6
macrophyte ingestion by benthic macro-
consumers represents 25% of total intake and
algal ingestion represents 75% of that flow
or 18.75%)
2. Total Macro-consumer intake = 1.074 kcal/m
day (see X15) k
3. .1875(1.074 kcal/m2 day) = 0.2014 kcal/m2 day
4. Prorated by area of benthos (799.9E+6 m2) and
area of mudflat (82E+6 m2) = 1.915 kcal/m2 day
3.15 1. DOM loss equals 7% of gross production
3.42774 1. Input = Output (Steady State)
2. Total Mortality of microalgal compartment =
13.711 kcal/m2 day
3. Assuming 25% of this is washed out by tides
FX14X9 = 3.42775 kcal/m2 day
10.28325 1. Total detrital flow from microalgal compartment
= 13.711 kcal/m2 day
2. Assuming 75% is trapped in sediment and thus
returns to sediment; FX14X6 = .75(13.711 kcal/m2
day = 10.28325 kcal/m2 day
3. Prorated by area of mudflats (82E+6 m ) and area of
marsh (2513E+6)(as sediment detritus computed
only for marsh and not mudflat; marsh area becomes
2595E+6 m2 and gain to sediment = 0.3355 kcal/m2 day
1. Hellebust, 1965
(continued)
-------
TABLE A3 (continued)
Compartment Value
or Plow
Assumptions and Calculations
References
X9
58.8
to
1. Water column particulate matter composed
of phytoplankton, detritus, attendant
microbes, and zooplankton
2. Phytoplankton = 4.02 mg el a/m
Assuming average depth of 3 meters/
= 12.06 mg Cl a/m
Assuming 50 mg C/mg Cl a,
= 603 mg C/m2
= 0.603 g C/m2 =6.03 kcal/m2
3. Zooplankton = 0.024 g C/m2
=0.24 kcal/m2
4. Total water column ATP =1.31 mg/m
Assuming 250 mg C/mg ATP and average depth
of 3 meters,
= 982.5 mg C/m2
Subtracting from this value the value of
phytoplankton and zooplankton;
0.9825 g C/m2 - 0.603 g C/m2 - 0.024 g C/m2
= 0.3555 g C/m2 (microbial biomass) =
3.555 kcal/m2
5. Detrital POC (particulate organic carbon) =
Total POC - Total ATP carbon
2. Erkenbrecher, 1976
Strickland, 1965
3. Coull, 1977
4. Erkenbrecher, 1976
Holm-Hansen S Paerl,
1972
(continued)
-------
TABLE A3 (continued)
Compartment
or Flow
Value
Assumptions and Calculations
References
FO4
15.0
FX3X9
6.483
FX2X9
0.367
Total POC = 1.96 g C/nr
5.88 g C/m2
Subtracting out living carbon;
5.88 g C/m2 - 0.9825 g C/m
= 4.8975 g C/m2 = detrital POC
= 48.975 kcal/m2
6. Total particulate organic matter in
water column = Phytoplankton + Zooplankton +
Microbial Biomass + Detrital Value
6.03 kcal/m2 +0.24 kcal/m2 + 3.555 kcal/m2 +
48.975 kcal/m2 =58.8 kcal/m2
1. Net productivity of estuarine phytoplankton =
273 g C/m2 yr = 0.75 g C/m2 day
2. Assuming total respiration (light and dark each)
equals net production; FO4 = 2(.75 g C/m2 day) =
1.5 g C/m2 day =15.0 kcal/m2 day
1. See X3
2. Prorated by the spatial areas of the water column
and the marsh surfaces;
FX3X9 = incoming flow of 22.979 kcal/m2 day
1. See X2
2. Prorated as above; FX2X9 = incoming flow of
1.3 kcal/m2 day
5. Erkenbrecher, 1976
1. Zingmark, 1977
Table 1
(continued)
-------
TABLE A3 (continued)
Compartment Value
or Flow
Assumptions and Calculations
References
PX14X9
FX6X9
FX13X9
FX12X9
FX10X9
3.42775 1. See X14
2. Prorated according to the areas of the water
and mudflats
3.25 1. See X6
2. Prorated by areas of marsh + mudflats
and water column;
2
FX6X9 = incoming flow of 11.89 kcal/m day
6.8148 1. Senescent mortality of maorobenthic algae
(see X13) = 6.8148 kcal/m2 day (by input-
output difference analysis)
2. Prorated by areas of oyster reefs and water
column
2
FX13X9 = incoming flow of 1.317 kcal/m day
27.66 1. Throughflow of filter-feeding organisms in
oyster reef community = 27.66 kcal/m2 day (see
X12)
2. Prorated by areas of oyster reefs and water
column;
2
FX12X9 = incoming flow of 5.345 kcal/m day
3.266 1. Average concentration of dissolved organic
matter in water column = 17.7 g C/m2 =
5.9 g C/m3 = 5.9 mg C/l = 5.9 x 103 ug C/l
Table 1
Table 1
1. Dame & Stevens, 1977
1. Erkenbrecher, 1976
(continued)
-------
TABLE A3 (continued)
Compartment
or Flow
Value
cn
FX11X9
.273
Assumptions and Calculations
References
2. Assuming all amino acids are equally distributed; 2. Crawford et. al., 1974
V(max) = 2.4143 ug C/l hr
K = 17.846 ug C/l
3. Using Michaelis-Menten equation:
v = V(max) x Concentration
K + Concentration
v = 2.4143 ug C/l hr x 5.9 x 103 ug C/l
17.846 ug C/l x 5.9 x 103 ug C/l
v = 2.407 mg C/m hr
v = 1.733 kcal/m2 day
4. Glucose uptake = V(max) = 2.13 ug C/l hr
K = 4.00 ug C/l
3 2
v = 2.1286 mg C/m hr = 1.5326 kcal/m day
2 2
5. FX10X9 = 1.733 kcal/m day + 1.5526 kcal/m
day = 3.2656 kcal/m2 day
1. Feces loss from fish equals 22.735 g OM/g OM
of fish yr
2. Standing Crop of Fish biomass = 0.876 g OM/m
(see Xll))
3. FX11X9 = (22.735 g OM/g OM fish yr) x 2
(0.876 g OM/m2) = 19.916 g OM/m yr =
0.273 kcal/m2 day
4. Crawford et. al., 1974
1. Day et. al., 1973
2. Moore, Unpubl. Data
(continued)
-------
TABLE A3 (continued)
Compartment
or Flow
Value
Assumptions and Calculations
References
FX9X6
FX9X11
FX9X12
FX9X17
FX9X10
1.155
0.551
9.066
4.62
1.21
1. Wiegert et. al., 1975
1. Dame & Stevens, 1977
1. Sedimentation rate (see X6)
1. See Xll
1. Filtration rate of oyster reef consumers =
46.9205 kcal/m2 day (see X12)
2. Prorated by areas of oyster reef and water
column;
FX9X12 = loss from water column of 9.066
2
kcal/nr day
1. See X17; found by input-output difference
analysis =4.62 kcal/m^ day loss from water
column
1. Phytoplankton excretion =7% (average) of
photoassimilated carbon
2. Gross Production of phytoplankton =15.0
kcal/m^ day = 1.5 g C/m^ day
3. .07(1.5 g C/m2 day) = 1.05 kcal/m2 day
4. Zooplankton excretion = (l.OxT) -5.9=
mg a-amino N/g Dwt-day
5. Mean temperature = 17.5 C
6. (1.0 x 17.5) - 5.9 = 11.6 mg a-amino N/g
dwt-day
7. Standing biomass of zooplankton = 48 mg dwt/m2 6. Coull, 1977
1. Hellebust, 1965
2. Zingmark, 1977
4. Johannes & Webb, 1965
5. National Weather Service
(continued)
-------
TABLE A3 (continued)
Compartment
or Flow
Value
Assumption and Calculations
References
EX9 17.48
(Export of
POM to coastal
waters)
8. 4 x a-amino N = Organic carbon
9. 11.6 mg a-amino N/g dwt-day = 46.4 rag
Org C/g dwt-day
2
10. 46.4 mg Org C/g dwt-day x 48 mg dwt/m =
2.227 mg Org C/m2-day
11. Zooplankton excretion = 0.0225 kcal/m -day
12. Substituting dwt of bacteria (microbes) in
above equation; Bacterial excretion =
0.139 kcal/m2-day
13. Total excretion of DOM by living particulate
matter = (1.05+ 0.0225 + 0.139) kcal/m -day
= 1.21 kcal/m -day
1. Assuming complete mixing on every tide and a
constant tidal prism of 40% of low water
estuarine volume;
2. Assuming that tidal remixing (the return of
water from the immediately preceding ebb flow)
is maximal in spring and summer (90%) and minimal
in fall and winter (10%) and is sinusoidal; i.e.,
R = .5 + .4sin(.0172 x TIME);
3. The concentration in an embayment after a full
tidal cycle can be described as:
C(p) = (Ve x Ce) + ((1-R) x VT x Co) + (R x VT x Ce)
Ve + VT
7. Johannes & Webb, 1965
12. Bonnell, 1977
1. Kjerfve, Pers. Comm.
2. Kjerfve, Pers. Comm.
(continued)
-------
TABLE A3 (continued)
Compartment Value Assumption and Calculations References
or Flow _........:.
where:
Cp » Concentration of x in tidal plume
Ve = Water volume in estuary at low water
Ce » Concentration in estuarine waters at
low water
R * Remixing coefficient
VT = Tidal volume (Prism volume)
Co « Concentration of x in coastal waters
4. Net exchange can be characterized as the difference
between Ce and Cp if total mixing occurs
5. Net C(exchange) - Ce - Cp
** 6. As tidal prism is constant; VT = .4Ve
oo
7. Substituting .4Ve for VT in Equation 3 gives:
Cp = (Ve x Ce) -i- ((1-R) x .4Ve x Co) + (R x .4Ve x Ce)
Ve + .4Ve
8. Factoring out Ve gives:
Cp = Ce + ((1-R) x .4Co) + (R x .4Ce)
1.4
9. Net C(exchange = Ce - (Ce + ((1-R) x .4Co) + (R x .4Ce))
1.4
10. Net C(exchange) can also be expressed equal to some
constant coefficient of exchange related to the difference
in estuarine and coastal water concentration;
Net C(exchange) = K(exc) x ((Ce - (((1-R) x Co) + (R x Ce)))
(continued)
-------
TABLE A3 (continued)
Compartment Value
or Flow
Assumption and Calculations
References
RX9
X10
FX7X10
28.17
177.0
2.116
11. Settling these two equations for net exchange
equal to each other allows a solution for K(exc):
K(exc) = 0.286/tide or 0.5714/day
12. Particulate organic matter concentration in
estuary = 58.8 kcal/m2 or 19.6 kcal/m^
13. Particulate organic matter concentration in
coastal water (Co) =2.14 kcal/m3
14. EX9 = 17.48 kcal/m2 day
1. By input-output analysis of difference
respiration of POM = 28.17 kcal/m2-day
1. Dissolved organic matter in water column
= 5.9 g C/m
2. Assuming average depth of 3 meters;
= 17.7 g C/m2 = 177 kcal/m2
1. See X7
2. Prorated by areas of marsh and water column
= Inflow of 7.501 kcal/m2-day
12. Erkenbrecher, 1976
13. Stevenson, Upubl. Data
1. Erkenbrecher, 1976
Table 1
FX9X10
FX1X10
1.21
.167
1.
1.
2.
See X9
See XI
Prorated by
= Inflow of
area of marsh and water
0.592 kcal/m -day
column ;
(continued)
-------
TABLE A3 (continued)
Compartment Value
or Flow
Assumptions and Calculations
References
FX12X10
o X13X10
X14X10
X18X10
0.100 1. Release of DOM by oysters = 0.100 kcal/m2-day
2. DOM release from mollusc = .505 mg a-amino
N/100 g dwt-day
3. 1 mg a-amino N = 4 mg Org C
o
4. Biomass of oyster community = 503.2 g dwt/nr
5. DOM release = 20.32 mg DOM/m -day =
0.1 kcal/m2-day
6. Prorated by areas of oyster reef and water
column; = Inflow of 0.019 kcal/m2-day
1.4532 1. Assuming benthic macrophytes lose an equivalent
amount of photoassimilated carbon (7%) as do
microbenthic algae; DOM loss = 1.4532 kcal/m2-day
(see X13)
2. Prorated by areas of oyster reef and water
column, = Inflow of 0.281 kcal/m2-day
3.15 1. See X14
2. Prorated by areas of mudflats and water column,
= Inflow of 0.364 kcal/m2-day
.0241 1. By input-output differences analysis, loss from
DOM sediment pool in benthos = 0.0241 kcal/m2-day
(see X18)
2. Prorated by areas of benthos and water column,
= Inflow of 0.0265 kcal/m2-day
2. Spitzer, 1937
3. Johannes & Webb, 1965
4. Dame & Stevens, 1977
(continued)
-------
TABLE A3 (continued)
Compartment Value
or Flow
Assumptions and Calculations
References
X15X10
0.348
X11X10 0.0438
FX10X9 3.266
X10X12 .0002
1. DOM loss by benthic macroconsumers =
0.348 kcal/m2-day (see X15)
2. Prorated by areas of benthos and water
column, = Inflow of 0.383 kcal/m2-day
1. DOM loss from fish = 0.0438 (see Xll)
1. See X9
1. oyster uptake of DOM = 15 ng DOC/hr-g
wwt at 50 yg DOC/ml
2 . DOM concentration in water column
=5.9 yg/ml
3 . Experimental concentration = 50 yg DOC/ml
= 100 yg DOM/ml
4. Ratio of Concentratibns = 0.059
5. Experimental uptake = 15 ng DOC/hr g wwt =
30 ng DOM/hr g wwt
6. Applying ratio of 0.059 to uptake gives
uptake of 1.77 ng DOM/hr g wwt
7. 1.77 ng DOM/hr-g wwt = 17.7 ng DOM/hr g dwt
8. Oyster reef predominately oyster biomass
= 503.2 gdwt/m2
2
9. Uptake by oysters = 0.000107 g DOM/m -day
= .0005 kcal/m2-day _
1. Johannes & Satomi, 1967
1. Swift et. al., 1975
1. Dame & Stevens, 1977
(continued)
-------
TABLE A3 (continued)
Compartment Value
or Flow
Assumptions and Calculations
References
EX10
Xll
FX9X11
X12X11
10. Prorated by areas of oyster reef and water
column, = Outflow of 0.0002 kcal/m -day from
water column
7.1395 1. Input-output difference analysis calculates
export to be 7.1395 kcal/m^-day at steady
state
2. Calculation of KEX10 (coefficient of exchange)
(for method see X9-ex9);
3. KEXC10+ = 8.4523E-2
4.38 1. Range of wet weights for fish in major
channels of North Inlet: 1 g wwt - 6 g wwt
2. 0.25 g wwt = dwt (Le., dwt = 25% wwt)
3. Biomass = 0.25 (3.5 g wwt/m2) = 0.875 g dwt/m2
=4.38 kcal/m2
0.0551 1. Input-output analysis shows planktivore and
detritivore intake = 0.0551 kcal/m2-day
.2417 1. Fish feeding habits can be apportioned as:
Herbivores and primary carnivores: 8.6% of
intake; Mid-carnivores: 48.6% of intake;
Carnivores: 43% of intake
2. Assuming 15% of mid-carnivore intake is from
oyster reef
3. Mid-carnivores consume 48.6% of fish intake
which is 0.747 kcal/m -day-gdwt
1. Moore, Pers. Comm.
2. Moore, Pers. Comm.
1. Day et. al., 1973
3. Day et. al., 1973
(continued)
-------
TABLE A3 (continued)
Compartment Value
or Flow
X15X11
u>
FX11X8
X11X10
FX11X9
.4911
.177
.0438
0.273
Assumptions and Calculations
References
4. X12X11 = .15(.486)(.747 kcal/m -day-gdwt)
(.876 gdwt/m2) = 0.0467 kcal/m2-day
5. Prorated by areas of oyster reef and
water column, = Outflow from oyster consumers
of 0.2417 kcal/m2-day
1. Assuming remaining 85% of mid-carnivore diet
and 50% of top carnivore diet is from macrobenthos
(remaining 50% of top carnivore diet internal)
o
2. Mid-carnivore diet = .2647 kcal/m -day
Top carnivore diet = 0.2755 kcal/m2-day
3. Input to fish equals 0.5402 kcal/m2-day
4. Prorated by area of subtidal benthos and water
column. = Outflow from Macrobenthos of 0.4911
kcal/m -day
1. See X8
2. Prorated by area of marsh and water column,
= Inflow to Birds of 0.05 kcal/m2-day
1. DOM loss in fish equals 1% of standing crop
2. .01(4.83) = .0483 kcal/m2-day
1. Feces loss from fish equals 22.735 g OM/m2 yr
2. Standing crop of fish = 0.876 g OM/m2
3. FX11X9 = (22.735 g OM/m2-yr-gOM fish) x
1. Wiegart et. al., 1975
1. Day et. al., 1973
(continued)
-------
TABLE A3 (continued)
Compartment Value
or Flow
Assumptions and Calculations
References
PX11
.147
X12
2516.
1. Pish respiration - 1 cal/g dwt-hr
2
2. Standing stock = 0.876 g dwt/m
3. RXll = 147 cal/m2-day - .147 kcal/m2-day
1. Average caloric value of oyster community
at North Inlet = 2516. kcal/m2
1. Nixon & Oviatt, 1973
1. Dame & Stevens, 1977
Ul
-------
TABLE A4
COEFFICIENT VALUES FOR PRELIMINARY SIMULATIONS*
Kl =
K2 =
K3 =
K4 =
K5 =
K6 =
K7 =
K8 =
K9 =
K10
Kll
K12
K13
K14
K15
K16
K17
K18
K19
K20
K21
K22
K23
K24
K25
K26
K27
K28
K29
K30
K31
K32
K33
K34
6.1470E-7
1.0651E-6
1.1227E-6
3.0492E-3
6.1192E-4
5.2442E-3
8.7343E-5
-2.6623E-3
1.2123E-5
= 4.3154E-6
= 1.05518E-5
= 5.3412E-5
= 4.3546E-2
= 4.8340E-3
= 4.5456E-3
= 9.939E-8
= 6.1017E-4
= 5.4941E-3
= 1.1691E-4
= 8.2490E-9
= 1.0346E-2
= 8.7203E-3
= 1.9585E-4
= 2.3925E-3
= 4.6575E-8
= 6.6607E-3
= 4.7595E-3
= 1.0204E-2
= 1.3818E-1
= 1.9643E-2
= 6.8553E-2
= 9.08802E-12
= 3.0857E-6
= 1.8571E-5
K35 = 1.0582E-1
K36 = 1.8369E-1
K37 = 1.7109E-4
K38 = 1.44628
K39 = 4.3503E-6
K40 = 6.6728E-5
K41 = 5.0312E-5
K42 = 2 2852E-2
K43 = 2.1E-2
K44 = 2.1727E-4
K45 = 4.7992E-2
K46 = 3.3688E-2
K47 = 1.7930E-5
K48 = 3.5616E-3
K49 = 1.2225E-5
K50 = 3.5018E-6
K51 = 7.8571E-2
K52 = 1.1759E-3
K53 = 5.6229E-4
K54 = 4.3152E-7
K55 = 1.0234E-2
K56 = 1.205E-3
K57 = 1.3714E-3
K58 = 5.7143E-4
K59 = 2.5663E-11
K60 = 1.2533E-6
K61 = 4.4187E-4
K62 = 4.3786E-4
KEX09 = 0.4444
KEX10 = 9.8748E-2
A = 3945
B = 1695
C = 17.5
D = 12.5
*lE-x is equivalent to 1 x 10~x
55
-------
SECTION 6
DEVELOPMENT OF A SALT MARSH MICROECOSYSTEM
W. Kitchens
INTRODUCTION
In order for governmental agencies to anticipate and respond to problems
arising from the ever-increasing encroachment of man on the invaluable, yet
vulnerable, coastal wetlands (Gosselink et al., 1973; Odum and Odum, 1972;
Odum, 1973; Vernberg, 1976) , two approaches have evolved for generating
predictive information for management strategies. Both approaches involve
defining structural and functional components of the systems in question.
The first approach is the development of ecosystem mathematical simulation
models. To this end, several models of varying complexities have been
generated for New England salt marshes (Nixon and Oviatt, 1973) , the Gulf of
Mexico salt marshes of the Mississippi River Delta (Day et al. , 1973), and
the Southeastern estuarine-salt marsh complexes (Vernberg et al.f 1977;
Weigert et al. , 1975) . These models are attempts to synthesize existing
knowledge into a systematic and integrative scheme that developers hope will
enable the modeler to make assessments at a holistic level regarding eco-
system responses to selected .perturbations. Although these models are
valuable in interpreting the interactive components of the ecosystem, as
well as pinpointing "sensitive" areas or control mechanisms (Weigert et al.,
1975) , they simply do not have the resolution required for simulation and
prediction of very specific environmental perturbations. Mann (in press)
has drawn attention to the limitations of these and other similar models in
application to these problems. He has suggested that one particular problem
area is the lack of realistic validation procedures for ecosystem models.
The second approach is the "living" model or microecosystem approach.
This approach also incorporates a holistic strategy to assess ecosystem
structural and functional properties as well as responses to perturbations.
Basically, this techniques involves "capturing" a viable part of the eco-
system in question and subjecting it to environmental regimes that simulate,
as closely as possible, those of the natural system while at the same time
maintaining some boundary control over these regimes for manipulative
purposes. Ideally, these systems should be large enough to incorporate as
many of the ecosystem components as possible without sacrificing ease of
section is in press in The International Journal of Environmental
Studies.
56
-------
replication, manipulation, and response measurements (Cooke, 1971). In the
past, this approach has been successfully employed to investigate aspects of
such fundamental ecosystem processes as nutrient cycling in aquatic systems
(Whittaker, 1961), patterns of community metabolism (Beyers, 1963, 1965),
patterns of ecological succession under various environmental regimes and
stress (Cooke, 1967; Wilhm and Long, 1969), response to low fresh water flow
regimes in estuaries (Cooper, 1970), and assorted community responses to
environmental alterations in flowing streams (Kevern and Ball, 1965; Lauff
and Cummings, 1964; Mclntire et al., 1964; Mclntire and Phinney, 1965; Odum
and Hoskin, 1958). In addition, these "living" models can be excellent tools
for the verification of mathematical models.
The purpose of this section is to present our design for a Spartina
alterniflora salt marsh microecosystem and includes a rationale for, and data
from, measurements detailing community structure and selected functional
processes within the microecosysterns.
The following criteria were incorporated within the design of the micro-
ecosystems: 1) the units would have to be replicable for use as a bioassay
tool; 2) the units would have to be practical enough to be installed at
various laboratory locations along the coast to assess local pollution
problems; 3) the holistic response measurements would have to incorporate as
much automation as feasible; 4) the construction and maintenance costs would
have to be kept at a minimum; and 5) the results of tests within the units
would have to be extrapolative to the natural ecosystem (community verifica-
tion would be required).
METHODOLOGICAL APPROACH
Rationale
Figure 8 represents a very simplistic conceptual model of the community
structure and energy flow pathways within the microecosystems. Inputs to
the microecosystems are indicated on the left while exports are indicated to
the right, except for Nitrogen. Since these systems are semi-enclosed by
tank walls, the net resultant or culmination of all the pathways is the
difference between inputs and exports. Since Copeland (1967) defines an
environmental stress as any factor that alters these normal pathways, we
have speculated that for these "living" models the best index of any stress
(whether directly applied or by natural means) is reflected as a discrepancy
between the input-output characteristics before and after any perturbations.
Hence, we have designed the seawater system in such a manner as to be able
to budget the fluxes of water and its constituents in and out of the systems
on any time scale (see following sections). We have initiated some prelim-
inary tidal budget studies for the fluxes of selected nutrients (see follow-
ing sections).
In addition to the nutrient flux studies, we have also designed the
systems for primary productivity studies for the following reasons: 1) they
have been shown to be a sensitive index of community imbalances in various
aquatic systems (Copeland, 1965, 1967; Copeland and Dorris, 1964); 2) re-
plicated laboratory aquatic systems do not differ significantly with respect
57
-------
Or
CO
TIDE
PROGRAM
SWITCH
PHOTOSYNTHESIS
IMUTPI-
EIMTS
rv-
SUN
FIXATION—*
Figure 8. Generalized conceptual model depicting nutrient flow pathways in the microecosystem
-------
to levels of community metabolism (Abbot, 1966) ; and 3) the ease of auto-
mation for the measurement of the response.
In addition to these holistic response determinations, we have selected
to monitor the macro- and meiobenthic faunal communities for the following
reasons: 1) since these organisms are relatively sessile, they are directly
subject to any environmental stress applied to the systems; 2) the communities
have sufficient regeneration times which would facilitate monitoring subtle
stress responses or recovery of the systems; and 3) these surveys allow one
to compare the microecosystems directly with the natural systems.
Design and Preliminary Results
Design—
The microecosystems consist of four square tanks, each containing
approximately six square meters of short Spartina alterniflora marsh. The
units function as both holding tanks and metabolism chambers for productivity
measurements. All associated support and monitoring systems are automated
to operate with a minimum of supervision.
The size of the marsh plots minimizes edge effects found in smaller
units without sacrificing sampling convenience and experimental control.
Marsh was transplanted from the high marsh zone in the form of square sod
blocks incorporating approximately 1000 cm^ of the marsh with substrate
intact to a depth of 20 cm. Observations during transplanting showed that
this was deep enough to include the majority of the root mass and
virtually all of the attendant faunal community. Sod blocks were reassembled
in the tanks by using a random numbering arrangement. Initial attempts at
establishing a viable marsh with the sod resting directly on the tank
bottoms failed due to water pooling which damaged the root stock. A bottom
layer of pea gravel was added to improve 4rainage and prevent pooling. This
type of drainage design simulates interstitial drainage as observed at the
control site. A cross section of a tank unit is illustrated in Figure 9.
The microecosystems were maintained with a flow-through seawater system
designed to simulate natural tidal regimes. A semi-diurnal tide, coincident
with the natural tide, is provided by initiating a flooding of the tanks at
12-hour and 20-minute intervals. Duration and depth of inundation repre-
sent average annual conditions at the control site. With minor variations,
daily tides 10 cm in depth inundate the substrate for two and one-half hours
per cycle. Water movement across the substrate simulates sheet flow. This
flow-through design maintains high water quality. The observed presence of
viable meroplankton insures natural recruitment of benthic organisms.
A diagram of the seawater system is shown in Figure 10. Water is
pumped continuously form the source creek adjacent to the control marsh to a
3000 liter head tank. Water flows into the bottom of the tank and overflows
through an outlet at the top into a holding pond. Residence time within the
head tank is approximately 15 minutes. To create a flood tide, water is
diverted from the head tank through a solenoid valve and into a branching
supply network to the tank inputs. After passing over the marsh surface,
water flows out through baffled drains into the holding pond. In line
59
-------
AC.
UNIT
INPUT
VALVE
FLOW
VALVE
1
AMBIENT AIR VENT
AIR
SAMPLING
VENT
MUD AND SAND SUBSTRATE
&;&v/£v:fi^
AIR
LINE
THROTTLE
.VALVES
HEAD
MEASURING
TUBE
Figure 9. Cross section of a Cank rfricroecosystem Unit
-------
CONSTANT
HEAD
TANK
FOOT
'ALVE
INTERSTITIAL
DRAI MAGE
SHEET
FLOW
SWIER
BOX.
-a—
OUTPUT
BOX
DRAIN
HOLDING
BOX
THROTTLE
• VALVES
„ — I~1T£3,
SOLENOID7
VALVE
INPUT
BAFFLE
X
— n —
INPUT
MANIFOLD
.n
f TO DRAIN POND
AC.
UNIT
MUD AND SAND SUBSTRATE
Figure 10. Schematic of seawater system.
61
-------
flowmeters on the input side and calibrated wiers on the output side provide
accurate measurements of water flow.
The air sampling system for the productivity measurements is also a
flow-through design. During productivity measurements, air trapped in the
chambers is continuously pumped through polyethylene tubing into the
laboratory, where the CO2 concentration is recorded by a Beckman 865
Infrared Gas Analyzer (IRGA) at hourly intervals. Air removed for analysis
is replaced by ambient air through vents in the tank walls. Ambient CC>2
levels are also measured hourly to correct for atmospheric carbon drawn in
through the wall vents. The constant addition of ambient CO2 prevents the
depletion of C02 in the chamber due to high photosynthetic rates. The air
sampling system is diagrammed in Figure 11.
Diaphragm air pumps situated in the laboratory draw air out the sampl-
ing vents, through drying columns and into the laboratory at a rate of
0.3m^ per hour. Air is then pumped into a switching manifold where it can
be routed to either an infrared gas analyzer or an exhaust vent. Each of
the five sample streams is diverted through the analyzer for a period of
twelve minutes. Halfway through each sampling period, the CC>2 concentration
of the sample is recorded along with the date, time, and air temperature
within the tank.
To trap air during productivity runs, translucent lids are bolted to
the chambers. The lids are constructed of fiberglass greenhouse panels, with
a transmittance of 95% in the visible light range. Since the lids are in
place only during productivity measurements, the microecosystems are
subjected to normal ambient weather conditions at all other times.
Window type air conditioners installed in the tank walls compensate
for heat buildup due to the greenhouse effect. Cooling is controlled by a
differential temperature controller situated on one of the tank walls.
Measured variations from tank to tank do not exceed 1°C and from tank to
ambient are within +2°C.
To test how well the microecosystems simulated the natural marsh
community, two separate benthic surveys were conducted. The first, a nine-
month macrobenthic survey, was initiated in June 1976 and terminated in
February 1977. Samples were taken and analyzed quarterly. Four core
samples were taken from the microecosystems (one sample/microecosystem).
These samples were selected randomly within each microecosystem by using a
grid and random number technique. In addition, eight random core samples
were extracted from the control site. Four of these cores were analyzed
and four were used as replacement cores for the microecosystem replicates.
Core samples were extracted with a 10 x 20 centimeter P.V.C. manual cylin-
drical coring device and were seived (one millimeter mesh). Numbers of
species and individuals were normalized to meter square values.
The other benthic survey was a meiobenthic survey initiated in July
1976 and terminated in May 1977. Samples for these surveys were made on a
monthly basis. By using a grid and random-number technique, twelve core
samples were randomly selected from the microecosystems (three samples/mi-
62
-------
AIR
LINE
AC I"
UNIT|_
AMBIENT
INTAKE
VENT
DRYING
COLUMN
SAMPLE
PORT
-Oi
TO LAB
WATER-TX
TRAP WA
AIR
PUMP
FLOW .1
METER
MANIFOLD
JLxCOLUMN
I V
1 \
\
V
TO ANALYZER
Figure 11. Schematic of air sampling system.
63
-------
croecosystem). Twenty-four random core samples were collected from the
control site. Twelve samples were analyzed and twelve were used as replace-
ment cores for the microecosystem replicates. A 50 cc syringe was util-
ized as a coring device and samples were seived (67 micron mesh). Numbers
of species and individuals were normalized to ten centimeter square values.
Preliminary Results—
Validation Studies. Table 2 represents both a ranked species list for
the control site and the microecosystem unit as well as an ANOVA of the
species densities (individuals/m^) and species diversity (Shannon-Wiener)
between the samples taken at the control site and the microecosystems. The
fiddler crab/ Uca pugilator, and the polychaetes, Heteromastus filiformis
and Nereis succinea, were clearly the dominant species in terms of numbers of
individuals per m2 in both the microecosystem samples and the control site.
These three species were encountered in all the samples for both sites
for all the time periods. The other species were only sporadically en-
countered although the relative abundances between the two sites remained
essentially the same. Inspection of the ANOVA tables (Table 2) reveals
that the communities within the microecosystems and the control site were
not statistically different at the 0.05 level, in terms of species densities,
with overall mean values of 114 individuals/m^ for the control site and 174
individuals/in^ within the microecosystems. The same results were found for
the species diversities (Shannon-Wiener index) between the microecosystems
IH = 0.57) and the control site (H = 0.52).
The results of the meiobenthic survey contrasting the microecosystems
with control site are summarized in Table 3. Again in terms of total species
densities the two sites are not statistically different at the 0.05 level
with a mean value of 525 individuals/10 cm^ for the control site and 352
individuals/10 cm^ for the microecosystems. However, examination of the
samples indicated a species composition shift in the microecosystem units
over time. Initially, both the control site and microecosystem were domin-
ated by copepod species. With time, nematode species became dominant while
the Harpacticoid copepod species numbers declined. This observation led to
an experiment begun in April 1976 and carried through for 30 days wherein a
nektonic component (Palaemonetes vulgaris) was added to a flow-through
refuge resevoir in the drain end of one of the tanks. Upon inundation
during flood tides, the Palaemonetes (approximately 100 individuals) would
swim out of the tank for excursion out over the substrate, presumably
selectively feeding on the larger meiobenthos (the nematodes). This con-
jecture was based upon the findings of Sikora (personal communication) that
nematodes comprised a major fraction of the gut content of Palaemonetes sp.
Prodigiously, the Palaemonetes found their way back into the refuge resevoir
(Fig. 10) during the ebbing of the waters in the microecosystems. The
results of this experiment reported elsewhere (Bell and Coull, in press) are
summarized in Table 3 as factorial ANOVA. Inspection of the ANOVA prior to
the addition of the" Palaemonetes indicates no significant differences be-
tween the microecosystems at the 0.05 level for any of the meiofaunal
components. Time was a significant factor and probably represents some
temporal succession within the units. After the addition of the Palaemonetes,
there was a significant difference (D < .05) between the control (no
64
-------
TABLE 2 MACROFAUNA
Species Listed in Order of Abundance
Control Site Microecosystem
Uca pugilator Uca pugilator
Hetefomastus filiformis Heteromastus filiformis
Nereis succinea
Nereis succinea
Uca pugnax
Geukensia dirnissa
Melampus bidentatus
Orchestia grillus
Uca pugnax
Melampus bidentatus
Corophium sp.
Orchestia grillus
Insects
ANOVA for Species Density (June 1976 - Feb. 1977)
df ss ms ' F
Treatment
Error
Total
1
22
23
40.04
298.58
338.62
40.04
13 . 57
2.95
F. 05(1, 22) =
4.30
Treatment
Error
Total
ANOVA for Species Diversity (June 1976 - Feb. 1977),
df ss ms F
0.05
F.05(1,22)= 4.30
1
22
23
ss
0.01
4.02
4.03
0.01
0.18
Treatments were control site samples vs. microecosystems samples
65
-------
TABLE 3 TOTAL MEIOFAUNAL DENSITIES BETWEEN MICROECOSYSTEM AND CONTROL SITE
ANOVA for (July 1976 - May 1977)
Treatment
Error
Total
df
1
69
70
ss
3001
261596
2618963
ms
3001
37912
F
0.08
F. 05(1,69} = 3.98
Prior to Adding Shrimp (July 1976-March 1977)
Mixed Model ANOVA, TO Test for Microecosystem Replicability and Time (n = 86)
Source of
Variation
Microecosystem
Time
Interaction
F
i
Total
df
3
6
18
Meiofauna
1
4
0
.27
.50**
.89
Nematodes
1
3
1
.23
.74*
.10
Polychaetes
1.
8.
1.
89
95**
04
Oligochaetes
0.
18.
0.
11
36***
31
Cope
0.
13.
0.
pods
60
5**
56
After Adding Shrimp (4 April-25 May 1977)
Model 1 ANOVA, To Test for Shrimp Effect (n = 18)
Source of
Variation
Microecosystem
Time
Interaction
df
1
2
2
Total
Meiofauna
9.96**
2.08
0.13
Nematodes
10.67**
0.24
0.60
Fs
Polychaetes
10.53**
1.66
1.19
Oligochaetes
7.40*
2.58
4.81*
Copepods
2.99
3.52
0.05
*Significant at P < 0.05
**Significant at P < 0.01
***Significant at P < 0.001
66
-------
Palaemonetes) and the test (Palaemonetes added) microecosystem. The shift in
species composition approximated the composition at the control site when the
study was ended.
Nutrient Fluxes and Community Metabolism—
In the following section we have presented an example of a nutrient budget
and community metabolism determination. The data are intended only to demon-
strate the type of data that may be obtained utilizing our experimental
design. Comprehensive evaluations will follow in subsequent papers.
Net fluxes of nutrients were determined by comparing areas under rate
change curves plotted for flood and ebb components of the tide. Water samples
from the input line and each outflow drain were collected every fifteen
minutes for the nutrient determinations as depicted in Figure 12. The flow
rate at each location was recorded, along with the date and time. By using
standard analytical techniques (Strickland and Parsons, 1972), concentrations
of the following nutrients were determined: nitrates, nitrites, ammonia,
total phosphorous; and reactive phosphate phosphorous concentration and flow
values were then used to calculate the nutrient flux rate at each sampling
interval. Rate change curves were plotted for the inflow and outflow of each
tank over the sampling period (Fig. 12). The curves were integrated, with a
comparison of the areas under the curves, yielding net values for import or
export of each nutrient in each tank. In this particular example, 24 mg of
total phosphorous was imported into the microecosystems; 0.1 mg of reactive
phosphorous was exported; 11 mg of total nitrogen was exported; 0.7 mg of
nitrate nitrogen was exported; and 15 mg of ammonia nitrogen was imported. We
do not have enough data to make a comprehensive statement regarding these
fluxes at this time. However, the trend of the total phosphorous import was
observed in every instance.
A similar, but more complicated method was used to determine community
metabolism or primary productivity. The average rate of change in the mass
of carbon in the enclosed air was determined in hourly increments in the
example provided (Fig. 13). These values were then corrected for changes
caused by net fluxes of carbon due to the flow-through air design. Negative
rates indicate net production and positive values show net respiration. The
corrected values were then used to plot rate change curves for the sampling
period. Comparisons of the integrated curves were then made to find net photo-
synthesis/respiration (P/R) ratios for each tank. An example set of curves
are illustrated in Figure 6. In this particular example, net photosynthesis
ranged between 0.14 gC/m^/day to 0.20 gC/m^/day for the four replicate micro-
ecosystems. Respiration values ranged between 0.12 gC/m2/day to 0.16 gC/m2/
day. One would expect some variations in these absolute values due to slight
discrepancies in the experimental conditions within each unit. The P/R ratio
normalizes these values and the values recorded for the four replicates varied
only between 1.1 and 1.2
CONCLUDING REMARKS
We feel we have accomplished the design and initial testing of a viable
and functional microecosystem or "living" model of a selected salt marsh
community. This model seems to simulate the natural marsh in terms of its
67
-------
200
24.42 MG IMPORTED
REACTIVE
PHOSPHATE-P
0.12 MG EXPORTED
10.66 MG EXPORTED
NITRATE-N
0.72 MG EXPORTED
AMMONIA-N
-10O .
1&53 MG IMPORTED
TIME OF DAY
Figure 12. Exemplary nutrient flux curves.
68
-------
TANK 1
1.1
TANK S
TANK 3
1.1
TANK
.3,
.2
.1
.1
2.
-.3
TIME OF DAY
1830
.3
.2
.1
0
-.1
-.2
-.3
003O
0630
1230
1830
0,20 gC
TIME OF DAY
1830
0030
063O
1230
1830
.2
.1
0
-.1
-.2
-3
0.12«C/2
NET
0.13
TIME OF DAY
1830
.3
.2
.1
-»-
0030
0630
1230
1830
-.1
-.2
-.3
CMS gC
NET
/
/m
0.19
Figure 13. Exemplary community metabolism curves.
69
-------
of its structural components. Tests related to certain functional aspects
such as nutrient assimilation and community metabolism between the model and
natural marsh should further validate the model. This design should be a
potentially valuable tool for the assessment and prediction of certain
environmental perturbations within the salt marsh ecosystem.
70
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SECTION 7
SUMMARY OF REPORT
F. J. Vernberg
This report represents the results of the third-year study of what was
to be a five-year investigation of a relatively undisturbed estuary-marshland
ecosystem, the North Inlet Estuary, Georgetown, South Carolina. A summary of
the first two years' work has been published in the Ecological Research Series
(EPAT600/3-77-016, January 1977).
The overall objectives of the planned five-year study were modified in
response to a reduced level of funding. Two substudies were undertaken: 1)
update of the macroecosystem model of the North Inlet Estuary; and 2) contin-
uing development of an experimental microecosystem.
A non-linear, deterministic model of the interactions within and between
the Spartina salt marsh and the estuarine water mass has been proposed and
simulated. Preliminary simulation results show that the modeled system is
stable and produces seasonal changes in simulated parameters which resemble
measured fluctuations in Spartina-dominated estuaries. Whether or not these
simulated trends in metabolism, biomass, and export are actually characteris-
tic of the North Inlet estuary will be determined by continued data analysis
and collection. Regardless of the present validity of the model it represents
the status of our understanding of the North Inlet marsh-estuarine ecosystem
with its complex interactions of physical, geochemical, and biological pro-
cesses.
A viable and functional microecosystem or "living" model of a selected
salt marsh community has been designed and tested. This model seems to
simulate the natural marsh in terms of its structural components, and testing
certain functional aspects, such as nutrient assimilation and community
metabolism between the model and natural marsh, would further validate the
model.
71
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REFERENCES
Abbott, W. Microcosm studies on estuarine waters. I. The replicability of
microcosms. J. Water Pollut. Cont. Fed., 38:258-270, 1966.
Bell, S. and B. Coull. Field evidence that shrimp predation regulates
meiofauna. Oecologia (in press).
Beyers, R. J. A characteristic diurnal metabolic pattern in balanced micro-
cosms. Publ. Inst. Mar. Sci. Univ. Tex., 9:19-27, 1963.
. The pattern of photosynthesis and respiration in laboratory microeco-
systems. In: Primary Productivity in Aquatic Environments, R. R.
Goldman, ed. University of California Press, Berkeley, 1965. pp. 61-74.
Bonnell, R. D. Simulation model for North Inlet estuary. In: The Dynamics
of an Estuary as a Natural Ecosystem, F. J. Vernberg, R. Bonnell, B.
Coull, R. Dame, Jr., P. DeCoursey, W. Kitchens, Jr., B. Kjerfve, H.
Stevenson, W. Vernberg, and R. Zingmark. U. S. Environmental Protection
Agency, EPA-600/3-77-016, 1977. pp. 14-25.
Cooke, G. D. The pattern of autotrophic succession in laboratory microcosms.
BioScience, 17:717-721, 1967.
. Aquatic laboratory microecosystems and communities. In: The
Structure and Function of Freshwater Microbial Communities, J. Cairns,
ed., Am. Soc. Symp., Virginia Polytech. Inst., Blacksburg, Virginia, 1971.
Cooper, D. C. Responses of continuous-series estuarine microecosystems to
point-source input variations. Ph.D. Thesis, University of Texas,
Austin, Texas, 1970. 125 pp.
Copeland, B. J. Evidence for regulation of community metabolism in a marine
ecosystem. Ecology, 46(4}:563-564, 1965.
. Biological and physiological basis of indicator communities. In:
Pollution and Marine Ecology, T. A. Olson and F. J. Burgess, eds.
Interscience, New York, 1967.
and T. C. Dorris. Community metabolism in ecosystems receiving oil
refinery effluents. Limnol. Oceanogr., 9(3):431-447, 1964.
Coull, B. C. Composition and seasonality of the North Inlet zooplankton:
establishment of the baseline. In: The Dynamics of an Estuary as a
Natural Ecosystem. F. J. Vernberg, R. Bonnell, B. Coull, R. Dame, Jr.,
72
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P. DeCoursey, W. Kitchens, Jr., B. Kjerfve, H. Stevenson, W. Vernberg,
and R. Zingmark. U. S. Environmental Protection Agency, EPA-600/3-77-016,
1977.
Crawford, C. C. , J. E. Hobbie, and K. L. Webb. The utilization of dissolved
free amino acids by estuarine microorganisms. Ecology, 55:551-563, 1974.
Dame, R. F. and S. A. Stevens. A linear system model of an intertidal oyster
community. In: The Dynamics of an Estuary as a Natural Ecosystem.
F. J. Vernberg, R. Bonnell, B. Coull, R. Dame, Jr., P. DeCoursey, W.
Kitchens, Jr., B. Kjerfve, H. Stevenson, W. Vernberg, and R. Zingmark.
U. S. Environmental Protection Agency, EPA-600/3-77-016, 1977.
Day, J. W., W. G. Smith, P. R. Wagner, and W. C. Stowe. Community structure
and carbon budget of a salt marsh and shallow bay estuarine system in
Louisiana. Center for Wetland Resources, Louisiana State University,
Baton Rouge, Louisiana, 1973. 79 pp.
Duursma, E. K. Dissolved organic carbon, nitrogen,and phosphorus in the sea.
Neth. J. Sea Research, 1:1-148, 1961.
Erkenbrecher, C. W., Jr. The influence of environmental factors on the
distribution, composition and transport of microbial biomass and
suspended material in a salt marsh ecosystem. Ph.D. Thesis, University
of South Carolina, Columbia, South Carolina, 1976. 215 pp.
Estrada, M., I. Valiela, and J. M. Teal. Concentration and distribution of
chlorophyll in fertilized plots in a Massachusetts salt marsh. J. Exp.
Mar. Biol. Ecol., 14:47-56, 1974.
Gallagher, J. K., W. J. Pfeiffer, and L. R. Pomeroy. Leaching and microbial
organic carbon from leaves of Spartina alterniflora. Est. Coast Mar.
Sci., 4:467-471, 1976.
Gosselink, J. G. , E. P. Odum, and R. M. Pope. The value of the tidal marsh.
Prepublication draft. Urban and Regional Development Center.
Gainesville, Florida, 1973.
Hellebust, J. A. Excretion of some organic compounds by marine phytoplankton.
Limnol. & Oceanogr., 10:192-206, 1965.
Holm-Hansen, O. and H. W. Paerl. The applicability of ATP determination for
estimation of microbial biomass and metabolic activity. Mem. Inst. Ital.
Idrobiol., 29(Suppl.):149-168, 1972.
Johannes, R. E. and M. Satomi. Measuring organic matter retained by aquatic
invertebrates. J. Fish. Res. Board Canada, 24:2467-2471, 1967.
and K. L. Webb. Release of dissolved amino acids by marine zooplankton.
Science, 150:76-77, 1965.
73
-------
Kevern, N. R. and R. C. Ball. Primary productivity and energy relationships
in artificial streams. Limnol. Oceanogr., 10:74-87, 1965.
Kirby, C. J. and J. G. Gosselink. Primary production in a Louisiana Gulf
coast Spartina altemiflora marsh. Ecology, 57:1052-1059, 1976.
Lauff, S. H. and K. W. Cummings. A model stream for studies in lotic ecology.
Ecology, 45:188-190, 1964.
Mclntire, C. D., R. L. Garrison, H. K. Phinney, and C. E. Warren. Primary
production in laboratory streams. Limnol. Oceanogr., 9:92-102, 1964.
and H. K. Phinney. Laboratory studies of periphyton production and
community metabolism in lotic environments. Ecol. Monogr., 35:237-258,
1965.
Mann, K. H. Qualitative aspects of estuarine modelling. In: Marsh-Estuarine
Systems Symposium, R. Dame, ed. University of South Carolina Press,
Columbia, South Carolina. (in press).
Nixon, S. W. and C. A. Oviatt. Ecology of a New England salt marsh. Ecol.
Monogr., 43:463-498, 1973.
Odum, E. P. and H. T. Odum, Natural areas as necessary components of man's
total environment. Trans. North Amer. Wildlife and Nat. Res. Conf.,
37:178-189, 1972.
Odum, H. T. Environment, Power and Society. John Wiley and Sons, New York,
1971.
. An energy circuit language for ecological and social systems: its
physical basis. In: Systems Analysis and Simulation in Ecology, B. C.
Patten, ed., Vol. 2. Academic Press, New York, 1972.
and C. M. Hoskin. Comparative studies on the metabolism of marine
waters. Publ. Univ. Tex. Mar. Sci. Inst., 5:16-46, 1958.
Odum, W. E. Insidious alteration of the estuarine environment. Trans. Amer.
Fish. Soc., 99:836-847, 1973.
O'Neill, R. v. Ecosystem persistence and heterotrophic regulation. Ecology,
57:1244-1253, 1976.
Pfeiffer, W. J., R. A. Linthurst, and J. L. Gallagher. Photographic imagery
and spectral properties of salt marsh vegetation as indicators of canopy
characteristics. In: Symposium on Remote Sensing in Oceanography, P.
G. Teleki, ed., American Society of Photogrammetry, Falls Church,
Virginia, 1973.
Pomeroy, L. R. Algal productivity in salt marshes of Georgia. Limnol.
Oceanogr., 4:386-397, 1959.
74
-------
Reimold, R. J., J. L. Gallagher, R. A. Linthurst, and W. J. Pfeiffer.
Detritus production in coastal Georgia salt marshes. In: Estuarine
Research, L. E. Cronin, ed., 1975. pp. 217-228.
Skopintsev, B. A. Organic matter of seawater. Preprint Inter. Oceanogr.
Cong., 953-955, 1955.
Smalley, A. E. The growth cycle of Spartina and its relation to the insect
populations in the marsh. Proc. Salt Marsh Conf., Sapelo Island, Georgia,
1959.
Spitzer, J. M. Physiologisch-okologische utersuchungen uber den exkret-
stoffwechsel der mollusken. Zool. Jb., 50:457-496, 1937.
Strickland, J. D. H. Production of organic matter in the primary stages of
marine food chains. In: Chemical Oceanography, J. P. Riley and G.
Skirrow, eds., Academic Press, New York, 1965. pp. 477-610.
and T. R. Parsons. A practical handbook of seawater analysis. Bull.
167. Second Edition. Fish. Res. Bd. Canada, Ottawa, Canada, 1972.
Swift, M. L. , K. Conger, J. Exter, and S. Lakshmanan. Uptake of glucose and
orthophosphate by the American oyster. Life Sciences, 17:1679-1684,
1975.
Teal, J. M. Energy flow in the salt marsh ecosystem of Georgia. Ecology, 43:
614-624, 1962.
and J. Kanwisher. Gas exchange in a Georgia salt marsh. Limnol. &
Oceanogr., 6:388-399, 1961.
U. S. Dept. Commerce. Mean Daily Solar Radiation. Sheet of the National
Atlas of the U. S. Prepared by the Office of Meteorological Res. and
Office of Climatology. U. S. Government Printing Office, Washington,
D. C. 1964.
Van Raalte, C., I. Valiela, and J. M. Teal. Production of epiberithic salt
marsh algae: light and nutrient limitations. Limnol. S Oceanogr., 21:
862-872, 1976.
Vernberg, F. J. Characterization of the natural estuary in terms of energy
flow and pollution impact. In: Estuarine Pollution Control and
Assessment. Proc. of a Conf. Vol. 1. U. S. Environmental Protection
Agency; EPA-600/3-77-016, 1977.
Whittaker, R. H. Experiments with radiophosphorous tracer in aquarium
microcosms. Ecol. Monogr., 31:157-188, 1961.
Wiegert, R. G., R. R. Christian, J. L. Gallagher, J. R. Hall, R. D. H. Jones,
and R. L. Wetzel. A preliminary ecosystem model of coastal Georgia
Spartina marsh. In: Estuarine Research, L. E. Cronin, ed., Academic
Press, New York, pp. 583-601, 1975.
75
-------
Wilhm, J. L. and J. Long. Succession in algal mat communities at three
different nutrient levels. Ecology, 50(4):645-652, 1969.
Wright, R. T. and J. E. Robbie. Use of glucose and acetate by bacteria and
algae in aquatic ecosystems. Ecology, 47:447-464, 1966.
Young, D. L. Studies of Florida Gulf coast salt marshes receiving thermal
discharges. In: Thermal Ecology. J. W. Gibbons and R. R. Sharitz, eds.,
Atomic Energy Commission Symposium Proceedings. CONF 730505. NTIS,
Springfield, Virginia, 1974.
Zingmark, R. G. Studies on the phytoplankton and microbenthic algae in the
North Inlet estuary, S. C. In: The Dynamics of an Estuary as a Natural
Ecossytem. F. J. Vernberg, R. Bonnell, B. Coull, R. Dame, Jr., P.
DeCoursey, W. Kitchens, Jr., B. Kjerfve, H. Stevenson, W. Vernberg, and
R. Zingmark. U. S. Environmental Protection Agency, EPA-600/3-77-016,
1977.
76
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TECHNICAL REPORT DATA
(Pit-use read Instructions on the reverse before corn/lit ting)
1. REPORT NO.
EPA-600/3-78-092
2.
3. RECIPIENT'S ACCESSION NO.
4. TITLE ANDSUBTITLE
THE DYNAMICS OF AN ESTUARY AS A NATURAL ECOSYSTEM, II
5. REPORT DATE
6. PERFORMING ORGANIZATION CODE
7. AUTHOR1S)
F. J. Vernberg, W. Kitchens, H. McKellar, K. Summers,
and R. Bonnell
8. PERFORMING ORGANIZATION REPORT NO
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Belle W. Baruch Institute for Marine Biology and
Coastal Research, University of South Carolina,
Columbia, SC 29208
10. PROGRAM ELEMENT NO.
IEA615
11. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS
Environmental Research Laboratory
U. S. Environmental Protection Agency
Office of Research and Development
Gulf Breeze, Florida 32561
13. TYPE OF REPORT AND PERIOD COVERED
Final. March 1, 1976-Feb. 28,1
78
14. SPONSORING AGENCY CODE
EPA-ORD
15. SUPPLEMENTARY NOTES
16. ABSTRACT
This report describes two separate but interrelated substudies: an update of the
macroecosystem model of the North Inlet Estuary near Georgetown, SC and a continuing
study of experimental salt-marsh rtjicroecosysterns. The model is under development to
help understand the interactions of various parts of a natural ecosystem. The
principal objective of the study is to develop and test replicate experimental salt-
marsh units at the microecosystem level as diagnostic tools for assessing long- and
short-term pollution effects on the Spartina altemiflora salt-marsh community.
Because of the complexity, this study was conceived as a five-year work. A summary
of the first phase was published in the Ecological Research Series (EPA-600/3-77-016,
January 1977). The present summary covers subsequent two years of study, March 1,
1976 to February 28, 1978. This report was submitted in fulfillment of Grant No.
R804407-01 by the University of South Carolina, F. John Vernberg, principal
investigator, under the sponsorship of the U.S. Environmental Protection Agency.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
animal ecology
plant ecology
marshes
pollution
dynamics
18. DISTRIBUTION STATEMENT
UNLIMITED
b.lDENTIFIERS/OPEN ENDED TERMS
North Inlet Estuary,
South Carolina, Spartina
alterniflora, salt marsh
modeling, ecosystem,
microecosystem, macro-
ecosystem
19. SECURITY CLASS (ThisReport)
unclassified
20. SECURITY CLASS (Thitpagt)
unclassified
COSATI Held/Group
06/F
21. NO. OF fAGES
JJL
22. PRICE
EPA Form 2220-1 (Rev. 4-77) PREVIOUS EDITION is OBSOLETF
•U.S. GOVERNMENT PRINTING OFFICE] 1978-6^0-004/1699. Region
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