ABSTRACT
Cassondra R Thomas THE USE OF NETWORK ANALYSIS TO COMPARE THE
NITROGEN CYCLE OF THREE SALT MARSH ZONES EXPERIENCING
RELATI\'E SEA-LEVEL RISE (Under the direction of Dr Robert R Christian)
Department of Biology, August 1998
Network analysis was used to analyze the nitrogen cycles of three salt marshes on
the east coast of the US. A., Great Sippewissett in Massachusetts, Upper Phillips Creek in
Virginia, and Sapelo Island in Georgia A general nitrogen cycle model was constructed
after a preliminary review of literature on the Great Sippewissett marsh. This model
structure was used to construct 9 networks, one for each zone (creekbank, low marsh, and
high marsh) within each marsh, largely using data collected from the literature on the 3
marshes
The networks were analyzed to determine how nitrogen flowed through each zone
The factors used for analysis included how nitrogen import was exported, how imports
related to primary productivity, the amount of nitrogen that cycled within the system, and
how mature each zone was These results were then compared between marsh zones to
determine if trends existed. The Friedmans test, a nonparametric statistical test, was used
to determine the significance of the trends.
When precipitation and Tidal particulate nitrogen (PN) were the imports, export
via burial and denitrification significantly increased in importance moving across the marsh
from the creekbank to the high marsh. Nitrogen cycling also significantly increased from
creekbank to high marsh. The maturity of marsh was measured using the relative
ascendency index and a multicriteria analysis with the expectation that maturity would be
highest in the low marsh. Contrary to expectation, it was determined that maturity
increased moving across the marsh from the creekbank to the high marsh
These patterns were used to evaluate how a marsh may respond to increasing
relative sea-level rise. Key factors are the slope and sediment supply. If the marsh is able
to migrate overland, increasing the high marsh zone, nitrogen cycling will increase on a
per unit area basis, and the marsh will display more characteristics of a mature ecosystem.
If, however, the marsh stalls because of a steep slope, the amount of cycling will decrease
on a per unit area basis, and the marsh will act less mature If the supply of sediment is
great and the marsh progrades toward the sea, the nitrogen cycling and maturity of the
marsh may decrease.
THE USE OF NETWORK ANALYSIS
TO COMPARE THE NITROGEN CYCLES OF THREE SALT MARSH ZONES
EXPERIENCING RELATIVE SEA-LEVEL RISE
A Thesis
Presented to
the Faculty of the Department of Biology
East Carolina University
In Partial Fulfillment
of the Requirements for the Degree
Masters of Science in Biology
by
Cassondra R. Thomas
August, 1998
THE HSE OF NETWORK ANALYSIS
TO COMPARE THE NITROGEN CYCLES OF THREE SALT MARSH ZONES
EXPERIENCING RELATIVE SEA-LEVEL RISE
By
Cassoiulra R. Thomas
APPROVED :
DIRECTOR OF THESIS
COMMITTEE MEMBER
COMMITTEE MEMBER
COMMITTEE MEMBER
CHAIR OF THE
DEPARTMENT OF BIOLOGY
DEAN OF THE
GRADUATE SCHOOL
Thomas L. Feldbush, Ph.D.
ACKNOWLEDGMENTS
I would like to thank Dr. Robert Christian, my thesis director, for introducing me
to the world of network analysis and looking at the world through a different lens I
would also like to thank him for the time and effort he has put into this creation, and for
all the support he has provided I would also like to thank my committee members. Dr
Mark Brinson, Dr, Joe Luczkovich, and Dr. Iris Anderson for their willingness to indulge
me in this process 1 would like to thank Tracy Buck for helping out in the field upon
occasion and Debbie Daniel for helping out in the lab and running the CHN analyses I am
grateful to The Nature Conservancy for allowing me access to their property in order to
conduct my field experiments I am eternally grateful to Rick and Erica Inge for saving
my computer and my data from utter oblivion I would like to thank my parents, Robert
and Virginia Hahn, for their support and encouragement And finally, I would like to
thank my husband, Christopher Woodcock, for going along on this little adventure and for
reminding me to go out every once in a while.
TABLE OF CONTENTS
LIST OF FIGURES vi
LIST OF TABLES vii
TO INTRODUCTION 1
1 1 Nitrogen Cycling in Salt Marshes 1
1.2 Network Analysis 2
1.3 State Change and Sea Level Rise 2
1.4 Statement of the Problem 3
2 0 LITERATLÎRE REVIEW 4
2 1 Nitrogen Cycling in Salt Marshes 4
244.1 1 Standing Stocks 52.4.13..2 Imports 8214 Outputs 192.2 Mat12urity/Stability 222 2.1 Succession to Maturity and Stability 232.2 2 Ascendency and Developmental Capacity 252.2 3 Empirical Tests ofDevelopment Attributes 292.3 Network Analysis and Its Use Comparing Nitrogen Cycling 312 4 Sea-Level Rise 332 5 Ecosystem State Change 342 5 2 Expansion 382.5.3 Submergence 393 0 GOALS OF RESEARCH/HYPOTHESES 413.1 Research Goals 413.2 Hypotheses 414.0 METHODS AND MATERIALS 434.1 Research Design 434.2 Site Descriptions 434.2.1 Great Sippewissett Marsh 434.2 2 Upper Phillips Creek Marsh 454.2 3 Sapelo Island Marshes 454.3 Data Collection 464.3 1 Literature 46Field Sampling 474.4 Network Construction 49Assessment ofData Reliability 52
52 4 4 2 Balancing 544.5 Network Analysis 544.5.1 Input Environs Analysis 554.5.2 Total Contribution and Total Dependency hdatrices 554 5 3 Finn Cycling Index and Cycled Throughput 564.5.4 Information Indices 574.5.5 Mineralization 584.5.6 Average Path Length (APT) 584.6 Statistical Analysis 595.0 RESULTS 605.1 How Nitrogen Flows Through Each Marsh Area 615.1 1 Input Environs Analysis 615.13 Total Dependency ofPrimary Production on Tide andPrecipitation 78Nitrogen Cycling 81
5.3 Mineralization 83
5.3 1 Mineralization TST 83
5.3.2 Mineralization Production 83
5 3.3 Mineralization CT 86
5.4.1 Relative Ascendency 87
5 4.2 Overhead 87
5.4.3 Redundancy 90
5.4.4 Internal Ascendency 90
5 5 Total System Attributes 90
5 6 Reliability Factor 97
6.0 DISCUSSION Ill
6.1 Differences in nitrogen cycling in marsh areas Ill
611 Export Routes of Various Imports Ill
6 12 Total Contribution to Primary Production 113
6 .13 Total Dependancy ofPrimary Production 114
6.1.4 Groundwater 116
6 1.5 Nitrogen Cycling Indices 116
6 1.6 Mineralization 118
6.2 Maturity and Stability 118
6.2 1 Maturity Indices 120
6.2.2 Total System A ttrihutes 121
6.3 Comparisons Among Marshes 122
6 4 How Nitrogen Cycling May be Affected by Rising Relative
Sea-Level 125
6.4 1 State Change Model 125
6 4 2 Nitrogen cyclingpatterns across marsh zones 125
6 4.3 Hom’ a marsh's nitrogen cycle may respond to relative sea-level
rise 125
LITERATURE CITED 127
APPENDIX A GREAT SIPPEWISSETT ORIGINAL DATA 139
APPENDIX B GREAT SIPPEWISSETT CONVERTED DATA 152
APPENDIX C SAPELO ISLAND ORIGINAL DATA 165
APPENDIX D SAPELO ISLAND CONVERTED DATA 174
APPENDIX E UPPER PHILLIPS CREEK ORIGINAL DATA 183
APPENDIX F UPPER PHILLIPS CREEK CONVERTED DATA 187
APPENDING BALANCED MODELS 191
LIST OF FIGURES
1. Generalized Nitrogen Cycle Model for Salt Marshes 6
2. Systems with Different Average Mutual Information 27
3. Classes of Salt Marshes Responding to Rising Sea Level 35
4. Generalized Nitrogen Cycle Model for Salt Marshes 50
5. How Precipitation Import Leaves the Marsh 62
6. How Tidal Import ofNHj' is Exported 65
7. How Tidal Import of NOx is Exported 68
8. How Tidal Import of DON is Exported 70
9. How Tidal Import of PN is Exported 73
10. Total Contribution of Input to Primary Production 76
11 Total Dependency of Primary Production on Rain, Tide, and Recycling 79
12 Relative Mineralization 84
13. System Level Indices Relative to Capacity 88
14. Internal Ascendency and Redundancy 91
15. Cluster Analysis of System Level Attributes 94
16. Great Sippewissett % of # of Flows per RF 99
17. Upper Phillips Creek % of # of Flows per RF 101
18. Sapelo Island % of # of Flows per RF 103
19. Great Sippewissett % TST per RF 105
20. Upper Phillips Creek % TST per RF 107
21. Sapelo Island % TST per RF 109
LIST OF TABLES
1. Amount of Tidal Imports of NitrogenUsing Different Methodologies 8
2. Nitrogen fixation rates for different marshes 11
3. Aboveground primary production rates 13
4. Belowground primary production rates 14
5. Belowground Mineralization rates 17
6 Filter Feeding Rates of Geukensia demissa 18
7. Burial Rates 20
8. Denitrification rate 21
9. Odum’s (1969) 24 Attributes of Ecological Succession 24
10 Site Descriptions 44
11. Great Sippewissett Marsh Zone Characteristics 44
12. Upper Phillips Creek Marsh Zone Characteristics 46
13. Sapelo Island Marshes Zone Characteristics 46
14. Important Network Flows (g N m'‘ x yC') 60
15. Friedmans Test for Significant (°==0.05) Patterns in Precipitation Export Across
Marsh Zones 64
16 Percent of Tidal DON Import that is Exported by Various Routes in Upper Phillips
Creek 72
17 Percent of Groundwater Import Exported by Various Routes in Great
Sippewissett 72
18. Indicators of Cycling within Systems and Compartments 82
19. Mineralization Rates for Different Marsh Zones (g N x m'^ x yf') 83
viii
20. System Level Indices of Development (g N x bits x m'^ x yr’’) 87
21. Marsh Maturity/Stability Variables Used for Cluster Analysis and Ranking’ 93
22. Correlation Matrix of System Attribute Variables 96
24. Average RF and Standard Deviation for Marsh Zones 98
25 Flow Weighted Average RF for Marsh Zones 98
1.0 INTRODUCTION
Nitrogen is a limiting nutrient in salt marsh environments (Day et al., 1989) and
influences the productivity of marshes. Furthermore, how much nitrogen flows through
the system and in what direction it flows may affect a marsh’s ability to respond to
stressors related to relative sea-level rise. Network analysis can be used to assess and
comparatively analyze how nitrogen flows through marshes, what the important processes
are, and what the total system properties are (Wulff et al., 1989, Christian et al., 1996). I
apply network analysis to such an assessment and comparison
1.1 Nitrogen Cycling in Salt Marshes
Major sources of nitrogen to salt marshes include tidal flooding, nitrogen fixation,
and precipitation Groundwater can also be a major contributor depending on the
geomorphology (Valiela et al, 1978, Whitney et al, 1981), Nitrogen fixation is the only
microbially mediated source (Capone, 1983). The amount of tidal input to an area of
marsh is influenced by elevation of marsh, distance from source, and tidal amplitude.
Precipitation occurs throughout marshes. Nitrogen can enter marshes in several forms;
ammonium (NH4"), nitrate (NO3 ), nitrite (NO2 ), dissolved organic nitrogen (DON), and
particulate nitrogen (PN). The latter 2 are diverse sources that may include various
molecules in dissolved form, in organisms, in detritus, and attached to sediment The
species of imported nitrogen will determine initially what flow paths will be taken.
The dominant internal flows within a salt marsh include primary production and
mineralization (Whitney et al, 1981) Some flows are more important in different parts of
a marsh. For example, mussels tend to live where the marsh is frequently inundated
2
(Kuenzler, 1961), making filter-feeding more important in low than high marshes Also
whether a flow, such as decay of plant material, is located above- or belowground may
affect its relative importance to total cycling.
Nitrogen may leave a marsh in several ways. Hydrologic export is a major avenue
of removal. Numerous papers have addressed this “outwelling” (e g.. Teal, 1962, Odum,
1980, 1984), and whether or not it significantly contributes to an estuary’s food web.
Another potentially important export is denitrification (Whitney et al, 1981, Anderson et
al., 1997b, Valiela and Teal, 1979a). Denitrifying bacteria use NOj" to oxidize organic
matter with a by-product of nitrogen gas. Burial of nitrogen as organic matter is very
important for marsh maintenance against relative sea-level rise (Good et al., 1982)
1.2 Network Analysis
Different processes, such as those mentioned above, interconnect to form nitrogen
cycling. It is common to construct a diagram of the different flows and compartments to
help describe and analyze the nitrogen cycle (e g., Anderson et al., 1997b, Baird et al.,
1995). Network analysis is a tool that can be used to compare nitrogen cycles of different
systems (Christian et al., 1996). It is a group of analyses for evaluation of the structure of
a system, the trophic dynamics, cycling, and total system properties such as maturity and
stability. The reader is referred to Kay et al (1989) for a detailed description.
1.3 State Change and Sea Level Rise
Salt marshes located on the east coast of the United States can be divided into
different zones that reflect different communities and environmental conditions, the
creekbank where the tall form of Spartma alterniflora grows, the low marsh where the
short form of S. alterniflora dominates, and the high marsh which is dominated by
different plants depending on its geographical location. Some typical plants of the high
marsh are Juncus roemenanus, Distichlis spicata, and S. patens
Salt marshes respond to increased inundation caused by relative sea-level rise in
different ways depending on their slope and the amount of sediment supply (Brinson et al
1995) The creekbank either may prograde or erode, and the high marsh may either
migrate overland or stall. These changes cause specific areas to experience ecosystem
state changes from one zone type to another. As zones undergo change in state, the
nitrogen cycle may be altered. For example, primary production, a dominant flow within
the nitrogen cycle (Whitney et al., 1981), may be altered as each zone experiences state
change (Brinson et al., 1995).
1.4 Statement of the Problem
The purpose of this study is to analyze the nitrogen cycle of different salt marsh
zones and postulate how rising sea level may affect it. Three well-studied marshes were
used for this study. Great Sippewissett marsh in Falmouth, MA, Upper Phillips Creek
marsh near Nassawadox, VA, and Sapelo Island marshes in Georgia Given that the
nitrogen cycle may be altered by increasing sea-level rise, it is important to understand
how nitrogen flows through the system, and how it influences the marsh’s total system
properties. Total system properties such as maturity and stability are related to a marsh
zone’s ability to respond to stressors and disturbances.
2.0 LITERATURE REVIEW
There are a few well-studied marshes along the east coast of the United States,
where nutrient cycling was part of the focus. I chose 3 marshes to use for my study. Great
Sippewissett in Massachusetts, Upper Phillips Creek in Virginia, and Sapelo Island in
Georgia. There are many published studies regarding nitrogen processes in Great
Sippewissett and Sapelo Island that span almost 4 decades, 1960s-1990s. I used about 70
articles to create nitrogen cycle models for each marsh. The investigations at Upper
Phillips Creek have also produced much data However, little has been published to date
because most experiments were conducted within the last few years. To create models for
Upper Phillips Creek, I used the few published articles, the VCR/LTER database
(www.vcrlter.virginia.edu), and direct communication with scientists involved in studying
the area
2.1 Nitrogen Cycling in Salt Marshes
Each marsh was divided into 3 zones that represent different flooding regimes and
dominant macrophytes. The first zone. Tall, was where the tall form of S. alterniflora
dominates the plant species, and the area floods during almost every high tide. The
second zone. Short, was the low marsh where the short form of S. alterniflora dominates,
and the area is less frequently flooded by high tides. The third zone. High, was the high
marsh where one of several species may dominate including J. roemerianus, S. patens, and
D. spicata This part of the marsh is rarely flooded by high tides These zone divisions
were used to compare the nitrogen cycle in different areas of marsh From the literature, I
created a generalized nitrogen cycle for salt marshes (Figure 1).
5
2.1.1 Standing Stocks. Each compartment in Figure 1 represents the standing stock of
a potentially important component of the nitrogen cycle in a salt marsh. The
compartments that represent the macrophytes include roots/rhizomes, live shoots, and
standing dead. The surface water associated with tidal flushing and the sediment pore
water were divided into 4 nitrogen species, NH4*, NOx, DON, and PN. Surface PN
represents bacteria, protists, zooplankton, detritus, and nitrogen attached to sediment
particles. Pore PN represents decaying organic matter, microbes and meiofauna, and
nitrogen attached to sediment It also represents material that can be exported as PN
arising from standing dead Nitrogen-fixing bacteria and benthic microalgae were not
included in Pore PN but instead given their own compartments in order to better represent
their contribution to the nitrogen cycle. The benthic filter feeders are primarily
represented by the Atlantic ribbed mussel, Geukemia demtssa (Finn and Leschine, 1980,
Kemp et al., 1990a, Kuenzler, 1961), but theoretically could include all other filter feeders.
The Grazer/Nekton compartment is a composite of many common consumers within the
marsh system including different species of crabs (e g., Ucapugilator), snails (e g.,
Littorina irroratd), insects (e g., Orcheliumfidicinium), and birds (e g., Branta
canadensis) as well as mineralizers found on standing dead and litter However, data for
this compartment were very limited. It theoretically could also include the many species
of fish, deer, racoon, and fox, but data were not available in the literature for their
contribution to the nitrogen cycle Furthermore, most of the biological processing of
nitrogen is by plants and microbes (Christian and Day, 1989)
6
Figure 1. Generalized Nitrogen Cycle Model for Salt Marshes
 
8
2.1.2 Imports. The imports into this model include tidal flooding of different nitrogen
species, precipitation, groundwater, nitrogen fixation, and immigration by animals. Tidal
flooding (Figure 1) is one of the largest sources of nitrogen for the marsh zones. A variety
of methods were used to measure the amount of nitrogen within tidal water (Table 1).
The differences in estimates may be attributed to methodology and/or other differences
Table 1. Amount of Tidal Imports of Nitrogenllsing Different Methodologies
Unit of Duration of
Marsh Import study Methodology Source
Flux
Great Sippewissett 6740-6760 kg PN/yr 7 Years TSK flow meter/ Vahela et al.. 1978
nutrient analy sis
Sapelo Island 160 g C/(m2 x\t) simulation modehng Wiegert. 1986
(PN)*
Sapelo Island 1314 g C/(m2 X \t) 1 Year flume/persulfate Chalmers et al..
(PN)» oxidation using total 1985
carbon analyzer
Great Sippewissett 16300-16346 kg 7 Years TSK flow meter/ Vahela et al . 1978
DON/yr Kjeldahl
Sapelo Island 2890.8 g C/(m2 x 1 Year flume/persulfate Chalmers et al.,
yr) (DON)* oxidation using total 1985
carbon analyzer
Great Sippewissett 2620-2623 kg 7 Years TSK flow meter/ Vahela et al-, 1978
NH4/yT Techrucon
autoanalyzer
(Solórzano. 1969)
Great Sippewissett 540 kg NOx/yr 7 Year Technicon Vahela et al., 1978
autoanalyzer
(Strickland and
Parsons, 1968)
Concentration
Sapelo Island 14.79 agPN/1 1.5 Years micro-Dumas Haines, 1979
method using
nitrogen anal\'zer
9
Unit of Duration of
Marsh Import study Methodology Source
Sapelo Island 11.69 ug DON/1 1.5 Years ultraviolet oxidation Haines. 1979
(Strickland and
Parsons, 1972)
Sapelo Island 2.75 ug NH4/1 1.5 Years Indophenol blue Haines. 1979
method (Koroleff.
1970)
Phillips Creek 3.4 iimolNH4/l 5 Years autoanalvzer VCR/1,TER
(Solórzano. 1969) Database
Sapelo Island 1.89 ug NOxd 1.5 Years colormetricallv Haines. 1979
(Stnckland and
Parson. 1972)
Phillips Creek 4.25 ^mol NOx/1 5 Years autoanalyzer VCR/LTER
(Strickland and Database
Parson. 1972)
*I converted C values into N values using a C:N ratio of 9.5 (Valiela and Teal, 1979b)
Total tidal nitrogen import to the Tall zone ranged from 193.64 g N x m‘‘ x yr ' in
Sapelo Island to 80 74 g N x m'^ x yr"' in Upper Phillips Creek The dominant species of
nitrogen imported was DON in Great Sippewissett and Sapelo Tidal DON was not
measured in Upper Phillips Creek. In Upper Phillips Creek NOx import exceeded NH4'
import.
Nitrogen in precipitation is a very small import compared to tidal flooding.
However, it may have a significant influence on primary production (Keene and Galloway,
1997). Similar techniques were used by all scientists to analyze the various nitrogen
species found in precipitation. Most followed Strickland and Parsons (1972) and
Solórzano (1969) to analyze NOx, DON, and NH4"^ Total nitrogen import from
precipitation ranged from 0.3 g N x m'^ x yr* in Sapelo Island to 0.79 g N x m'* x yr'* in
Great Sippewissett
10
Groundwater import was a very important source of nitrogen in Great
Sippewissett. The other 2 marshes had very little information regarding the contribution
of groundwater to the amount of nitrogen imported. The nitrogen content of the
groundwater was measured using an autoanalyzer following Strickland and Parsons
(1972) and Solórzano (1969) (Valiela et al, 1978). Total nitrogen import ranged from
1131 to 14 84 g N X m'‘ X yr'' measured over a 7-year period (Valiela et al, 1978)
Nitrogen fixation is the microbially mediated process by which molecular nitrogen
in the atmosphere is reduced by bacteria and cyanobacteria to NTÍ4" (Capone, 1983) The
rate of nitrogen fixation is not evenly distributed among zones. For example, Hanson
(1977a) found that the activity in the tall S. alterniflora marsh was significantly higher
than in the short S. alterniflora marsh in Georgia. Carpenter et al (1978) found similar
patterns among zones in Great Sippewissett during midsummer They also found that the
high marsh had even lower rates.
Nitrogen fixation can be a significant source for a nitrogen-limited ecosystem. In
Great Sippewissett Marsh in Massachusetts, it can range from 9-20% of the total nitrogen
import (Capone, 1983). It was estimated for this marsh that enough nitrogen was fixed to
account for the maximum amount of nitrogen in the aboveground biomass (Van Raalte et
al, 1974) and is approximately a third of the needs of primary production (Teal et al,
1979). However, in Phillips Creek marsh, nitrogen fixation is only approximately 5% of
primary production (Anderson et al, 1997b) Though acetylene reduction technique (the
reduction of acetylene to ethylene as an indicator of the amount of N2 fixed) was used to
11
determine nitrogen fixation, there were several procedures that differed (Van Raalte et al,
1974, Anderson et al., 1997b).
Literature values are shown in Table 2 for each marsh. There is a wide range of
values produced not only within marshes but also between marshes. Some of the between
marsh variation may be due to climate as nitrogen fixation rates are higher during warmer
temperatures.
Table 2. Nitrogen fixation rates for different marshes
Unit of Input Duration of
Marsh (g N X X XT ') Study Methodology Source
Great Sippewissett 6.78 7 Years Acetylene reduction Vahela and Teal.
1979a
Great Sippewissett 6.43 3 Years Acetylene reduction Carpenter et al.
1978
Great Sippewissett 2.67 7 Years Acety lene reduction Valiela et al..
1978
Phillips Creek 1 2 Years Acety lene reduction Anderson et al..
1997b
Sapelo Island 22.2-52.4 1 Year Acety lene reduction Hanson. 1977b
Sapelo Island 6 1 Year Acetylene reduction Haines. 1976
Immigration of animals may be very small and is a little-studied pathway of
imported nitrogen to salt marshes. Valiela and Teal (1979a) attempted to quantify the
amount of nitrogen brought into the marsh by birds The amount of nitrogen associated
with immigration of other animals, including racoons and fishes, was not quantified by
Valiela and Teal (1979a).
2.1.3 Internal Flows. There are many internal flows of the nitrogen cycle Primary
production dominants and contributes to the other flows that dominate the system, such as
12
decay. Primary production may be considered the starting point of most cycles It is the
basis of the food web including the detrital food web as well as the detritus
formation/mineralization cycle. The amount of information regarding the cycles varies
among marshes and zones Some processes are very well studied, while others are not
Accumulation of nitrogen for aboveground primary production is translocated
from roots/rhizomes and senescing shoots to live shoots (Figure 1) and varies across
marsh zones due to different environmental conditions in each marsh zone (Morris, 1980)
In the Tall zone, -S', alterniflora grows between 1 and 3 m, and aboveground production
can range from 700 g x m'^ x yr"' in Florida (Kruczynski et al., 1978) to 3700 g x m'“ x yr"'
in Georgia (Stroud, 1976). In the Short zone the S. altermflora only grows up to 0 8 m,
and aboveground production ranges from 130 g x m'^ x yr'' in Florida (Kruczynski et al,,
1978) to 2895 g x m'^ x yr ’ in Louisiana (White et al., 1978). The High marsh zone’s
aboveground production depends on the dominant species J. roemerianus production
can range from over 3000 g x m’^ x yr’ in Louisiana (Hopkinson et al., 1980) to about 800
g X m'^ X yr"’ in Virgiiua (Tolley, 1996) and North Carolina (Christian et al., 1990). S.
patens can range from 4200 g x m'^ x yr'’ in Louisiana (Hopkinson et al., 1980) to 600 g x
m'^ X yr’ in Great Sippewissett (Valiela et al., 1976). D. spicata aboveground production
can range from 2000 g x m'^ x yr'’ in Louisiana (Hopkinson et al., 1980) to 600 g x m'* x
yr'’ in Great Sippewissett (Valiela et al., 1975).
Most aboveground primary production rates are measured in carbon or dry mass
using various harvest methods (Table 3). In some cases the Wiegert and Evans (1964)
13
method is applied to estimate production Production is estimated as the change in live
biomass over time plus the change in dead biomass including the disappearance of dead
material over time (Wiegert and Evans, 1964). However, Dai and Wiegert (1996) believe
that these values may be overestimations of aboveground primary production and
physiologically unlikely. They instead propose a canopy model that calculated primary
production based on physiology and plant demographics. Using the canopy model, they
estimate that primary production in Sapelo Island for tall S. alterniflora is 1421 g C x m'^
X yr’ and for the short form is 749 g C x m"^ x yr"' as compared to 1480 g C x m'“ x yr"'
for tall (Gallagher and Plumley, 1979) and 540 g C x m'* x yr'' for short (Gallagher et al.,
1980) based on Wiegert and Evans (1964) harvest techniques in the same marsh
Table 3. Aboveground primar> production rates
Unit of Flow
(g diy mass x m'^ Duration of
Marsh X yr') Study Methodology Source
Great Sippewissett 630 May-November Harvest (Loss of Vahela etal.. 1975
dead matter=NPP)
Great Sippewissett 423.7 May-Novembcr Regression (^\1= Vahela et al., 1976
0.074 X ht + 15.973)
Phillips Creek 442.56-955.7 May-September Harvest This studs
Phillips Creek 846.9 2 Years Harvest and Tagging ToUey, 1996
(NPP=Freq of
replacement leaves x
P/B X biomass)
Sapelo Island 1350-2840 1 Year Han'est (NPP= Schubauer and
biomass x Hopkinson. 1984
production:biomass)
(Gallagher et al..
1980)
Sapelo Island 1337-3711 2 Years Han'est (modified Gallagher et al.
Wiegert and Evans 1980
method (1964))
14
Nitrogen uptake during belowground production from pore water to
roots/rhizomes is also a dominant flow of nitrogen (Table 4) Belowground biomass
production tends to be higher in the Short zone than the Tall zone For example,
belowground production in Great Sippewissett’s creekbank is 3315 g x m'^ x yr’’ while in
the low marsh it is 3500 g x m'* x yf’ (Valiela et al, 1976). The high marsh’s primary
production depends on the dominant plant species In Georgia,./. roemehanus
belowground production was estimated to be 3360 g x m'' x yr’ (Gallagher and Plumley,
1979) S. patens ranges from 310 g x m'^ x yr"’ in Georgia to 3270 g x m'" x yf’ in New
Jersey (Good et al., 1982). D. spicata ranges from 1070 g x m'” x yf’ in Georgia to 3400
g X m'^ X yf’ in Delaware (Good et al., 1982)
Table 4. Belowground primary production rates
Unit of Flow Duration of
Marsh (g X m ^ X yr ') Study Methodology Source
Great Sippewissett 3291 7 Years '?'NHf Tracer/ White and Howes.
Harvest 1994a
Phillips Creek 676-2143 2 Years Litter Bag/Harvest Blum. 1993
Sapelo Island 2100 3 Years Harr’est Gallagher and
Plumley. 1979
Belowground primary production sampling techniques have not been perfected
Usually some sort of harvesting method is employed using corers of various diameters or
litter bags (Good et al., 1982, Blum, 1993) Significant sampling error can be introduced
throughout the entire process Cores may become compressed, washing may be
incomplete or too rigorous, which can cause a loss of fine root material, and separation
15
criteria of live and dead biomass may vary between investigators (Good et al ., 1982)
Physiological factors can also make production estimates difficult. Translocation from
aboveground to belowground, death, and aging factors cannot be measured using a
harvesting technique (Good et al,, 1982),
Translocation is a process, in response to protein breakdown, where aboveground
organic nitrogen relocates to belowground biomass (Figure 1 ) at the end of the growing
season for storage over the winter (Larcher, 1995), It can be a significant flow,
Hopkinson and Schubauer (1984) determined that potential translocation was
approximately 54% of aboveground production in Georgia, However, by comparing the
winter increase in belowground nitrogen with the total annual potential translocation (the
sum of death and leaching subtracted from aboveground production), they claim that
approximately 76% of the potentially translocated nitrogen was not used for winter
storage, but instead, was used to support growing-season leaf turnover. White and
Howes (1994c) determined that potential translocation, figured in the same manner as
Hopkinson and Schubauer (1984), was approximately 38% of aboveground production in
Great Sippewissett They determined that the majority of translocated nitrogen was not
used for the following year’s growth, but instead, became part of the dead macroorganic
matter where it was buried or mineralized. This difference in amount of aboveground
nitrogen supplied by translocation from belowground may reflect the longer growing
season in Georgia
Mineralization is the process of transforming organic nitrogen into ammonium
16
(Figure 1), the predominant nitrogen species taken up by salt marsh plants. This process
occurs on decaying matter both above- and belowground Newell et al (1989) found that
fungal biomass is the predominant (98%) microbe found on decaying S. altermflora leaves
left in the standing position. Nearly all of the dead-leaf nitrogen is incorporated into fungal
mass during the initial decay process (Newell et al., 1989). Very little nitrogen is lost to
the water column, but instead is translocated to rhizomes, consumed by Littonna, or falls
to the marsh surface as small particles (Newell et al., 1989).
Belowground mineralization is the result of root and rhizome decay and the
turnover of microbial biomass and its exudates (Anderson et al., 1997b). Anderson et al
(1997b) could only account for approximately half of the gross mineralization rate with
macroorganic matter or aboveground biomass available for decomposition. They propose
that the remaining mineralized nitrogen is the result of turnover of nitrogen previously
immobilized by bacterial biomass and associated exudates. Table 5 shows various
belowground mineralization rates determined with very different methodologies. The
litter bag and harvest experiments represent net mineralization, while isotope dilution
measures gross mineralization.
The rate of mineralization both above- and belowground is very important in
determining the availability of nutrients for plant uptake. If mineralization is inhibited, the
dead plant material will accumulate, and eventually the nitrogen will be buried. If
mineralization is very rapid there may be very little material available for bioaccretion In
such cases, the system would have to rely on sediment import to accrete at a rate
17
Table 5. Belowground Mineralization rates
Unit of Flow Duration of
Marsh (g N X tn^ X VT ') Study Methodology Source
Great Sif^wrssett 14.9-16.3 (net) 7 Years '?'N labeled Litter White and
Bag Howes. 1994c
Phillips Creek 84 '(gross) 2 Years 'NH4* isotope pool Anderson et al..
dilution 1997b
Sapelo Island 70 (net) Unknown Unknown Whitney et al.,
1981
Sapelo Island 19.7 (net) 1 Year Harvest Hopkinson and
Schubauer, 1984
sufficient to maintain marsh elevation relative to sea-level rise
Leaching from leaves and stems (Figure 1) is a loss of nitrogen for salt marsh
plants In Great Sippewissett, White and Howes (1994c) determined the leaching rate to
be 0.4 g N X m'^ X yr'' by adding a tracer to the sediment and then exposing the leaves
to deionized water In Sapelo Island, Hopkinson and Schubauer (1984) measured
leachate to be 0.7 g N x m'^ x yf' by placing S. altemiflora leaves in jars with seawater for
1 hour and then analyzing the water and leaf Anderson et al (1997b) did not consider
leaching in their model which may cause an underestimation of their belowground
production estimates (19 g N x m'^ x yr"'). However, given the small amount reported by
the above authors, Anderson et al’s (1997b) belowground production estimate would only
be changed by less than 4%.
Filter feeding by mussels can be a major flow in the Tall and Short zones within a
salt marsh The most common mussel is the Atlantic ribbed mussel, Geukemia demissa
(Jordan and Valiela, 1982) It filters particulates and DON from the water column (Figure
18
1). It is believed that filter feeding can have a significant effect on the microbial
population in the water column, and that it can link the water column with the sediment
(Kemp et al., 1990a). Jordan and Valiela (1982) measured the disappearance of
suspended particles ranging from 5-15 //m in diameter.in jars filled with seawater, thus
ignoring some bacteria (Table 6). Jordan and Valiela (1982) may have underestimated
filtration rates because their procedure did not account for a delay in initial filtering and a
slowing of filtering when particulates were scarce. Kemp et al (1990a) measured both
microbial mass and particulates They found filtration rates of 1.37 g N x m'^ x yr"’ were
associated with microbial biomass, while particulates removed were as high as 59.6 g N x
m'^ X yr'V Kemp et al (1990a) may have overestimated filtration rates because their
density of mussels was higher than field density
Table 6. Filter Feeding Rates of Geukensia demissa
Unit of Flow Duration of
Marsh (g N X m ' X yr ') Study Methodology Source
Great Sippewissett 11.8 2 Years Disappearance of Jordan and Vahela.
suspended particles 1982
in jars of seaw ater
Sapelo Island 1.37-59.6 1 Month Water sampling Kemp et al.. 1990a
within enclosed pots
Excretion and biodeposition by G. demissa (Figure 1 ) were measured in the same
manner as filter feeding by Jordan and Valiela (1982). They found excretion to be
approximately 3.24 g N x m'" x yr"’, while biodeposition was 5 9 g N x m'^ x yr'V Other
species’ feeding, excretion, and biodeposition are not widely studied in the context of
nitrogen flow
19
Nitrification is an aerobic microbial process performed in 2 steps where NH4" is
oxidized first to N02', and NOj' is then further oxidized to NOj'. These bacteria use the
oxidation of NH4' and N02' to fix CO2 and obtain energy (Atlas and Bartha, 1993)
Nitrification is a critical component of the nitrogen cycle (Figure 1). In the soil, nitrifiers
compete with plants for NH4*, but at the same time rely on them to create an oxidized
rhizosphere around their root system (White and Howes, 1994a, Taylor, 1995). The
nitrification process’s byproduct, NOj', is a source of terminal electron acceptors for
denitrifying bacteria so that they can oxidize organic matter (Atlas and Bartha, 1993).
Very few studies have been conducted that compare the rate of nitrification across marsh
zones However, Anderson et al (1997b) found a rate of 4 g N x m'" x yr'' for the Short
zone using the '^NOj' isotope pool dilution. The estimate for Great Sippewissett is 9.8-
19.92 g N X m'* X yr'* (Valiela, 1983, Finn and Leschine, 1980). However, direct
measurements were not made
2.1.4 Outputs. There are 4 main export routes of nitrogen from a salt marsh (Figure 1)
They include tidal flushing, burial, denitrification, and loss of animals either through
harvest or migration. Tidal flushing is the major source of nitrogen output from a salt
marsh Measurements of tidal output were made in the same way as tidal import in
Section 2.1.2. Therefore, wrack export is not considered
Burial may be an important loss of nitrogen from a salt marsh Burial helps a
marsh maintain its elevation relative to sea-level rise (Good et al., 1982) This may be
especially important in high marshes where low-frequency flooding causes peat formation
20
(Good et al., 1982). Table 7 shows the burial rates estimated in a variety of ways.
Assuming a C;N ratio of 38 (Gallagher and Plumley, 1979), Wiegert’s estimations equal
approximately 0.53-0.68 g N x m'^ x yr"’, significantly less than the 3.2-4.6 g N x m'^ x yr'
measured by White and Howes (1994c) for Great Sippewissett. Anderson et al’s (1997b)
estimate agrees well with the rates for Great Sippewissett.
Table 7. Burial Rates
Duration of
Marsh Unit of Flow Study Methodology Source
Great Sippewissett 3.24.6 g N X m'^ X 7 Years '?'N tracer/Ijtter Bag White and How es.
1994c
Phillips Creek 4.0 g N xm ’ X \T'' 2 Years Accretion=Sea-Level Anderson et al..
Rise 1997b
Sapelo Island 20-26 g C X m'^ X Simulation Modelmg Wiegert, 1979. 1986
\T'^
Denitrification is the only microbially mediated export of nitrogen from a salt
marsh It is the process used by facultatively anaerobic bacteria to oxidize organic material
using NOj' as a terminal electron acceptor The byproduct is dinitrogen gas which is
exported from the marsh system The creekbank area has the highest rate of
denitrification of the three marsh zones in Great Sippewissett (Kaplan et al., 1979). This
is believed to be the result of renewed NOj' supply by tidal flushing. The high marsh had
the lowest rate of denitrification (Kaplan et al., 1979). This can be a significant source of
nitrogen loss to the marsh and is not necessarily offset by nitrogen fixation input (Kaplan
et al., 1979). However, Anderson et al (1997b) found denitrification to be a fairly small
flow (0.6 g N X m'^ X yr"’). They considered that their estimate may be an underestimation
21
of the tme rate because denitrification may be constrained by nitrification rates due to low
concentrations of NO," in Phillips Creek But, they claim that denitrification is probably
not an important loss of nitrogen from the marsh (Anderson et al, 1997b). Taylor (1995)
found that denitrification increased when the area is disturbed by wrack in the high marsh
of upper Phillips Creek marsh, Va.
Table 8 shows the various methods employed by the authors to measure the rate of
denitrification. There is a wide range of rates given. According to Capone (1997), direct
measures of N2 fluxes, such as used by Kaplan et al. (1979) are not sensitive to small
increases because of the large N2 background Tracer techniques, as used by White and
Howes (1994a) and Anderson et al (1997b) offer more direct methods of measurements
but can be misled by artificially elevated substrate pools (Capone, 1997) Acetylene block
is viewed as a sensitive measure of denitrification except under conditions of low ambient
Table 8. Denitrification rate.
Duration of
Marsh Unit of Flow Study Methodology Source
Great Sippewissett 6.85-20.32 g N X 1 Year Bell jar/Gas partition Kaplan et al..
X >T'‘ 1979
Great Sip>pewssett 4.1-5.6 g N xm^ X 7 Years Mass balance using While and
"NH/ Howes. 1994a
Phillips Creek 0.6 gNxm'^XNT"' 2 Years '?'NjO isotope pool Anderson et al.,
dilution 1997b
Phillips Creek 0-75.8 wmolxm ’x 6 months Acet) lene block Taylor. 1995
hr'
Sapelo Island 12 g N xm'^ x\t ‘ 6 months N2 Flux Haines et al..
1977
NO,", which is almost always true in salt marshes, or when S^" is present (Capone, 1997).
22
If this is the case, rates are likely to be underestimated (Capone, 1997).
Harvest of mussels from the marsh for human consumption is a very small export
route Valiela and Teal (1979a) quantified this route for Great Sippewissett. However,
their estimate for harvesting was very rough Many marshes do not have this export
route Emigration by animals is not considered here.
2.2 Maturity/Stability
Maturity can be defined in several ways, the state of age, development, and
perfection are some. For my purposes, I adopt the concept of Ulanowicz (1986) which
posits that within a mature system, the inputs, outputs, and interactions are organized in
such a manner as to pass units of flow efficiently and “most effectively participate in
autocatalytic activities” (Ulanowicz, 1997). Autocatalytic activities are positive feedback
cycles, where an increase in flow to one part of the cycle will increase the flow of the total
cycle A measurement of this definition of maturity is Ascendency (Ulanowicz, 1986).
Stability, on the other hand, is the ability of a system to resist destructive change caused
by perturbations. Perturbations may be caused by short term stressors, (e g., tropical
storms), or long term stressors, (e g., relative sea-level rise) The system’s ability to
recover or adapt to perturbations as appropriate is its level of stability. A measure of this
is Overhead (Ulanowicz, 1986) (Section 2.2.2).
There are theories about what a mature system is (Odum, 1969, Ulanowicz, 1986)
and how to measure the level of maturity of a system Odum (1969) developed a list of
criteria for ecological succession from developmental to mature stages based on old field
23
succession, sand dunes, and marine shores Ulanowicz (1986) developed some
measurements of maturity based on information theory and systems analysis. Christensen
(1994, 1995) evaluated “goal functions” for ecosystem maturity that can be measured He
analyzed several possible goal functions based on Odum’s list of maturity characteristics.
What follows is a summary of an attempt to define and characterize maturity and stability.
2.2.1 Succession to Maturity and Stability. In 1969, Odum published a seminal paper
entitled “The strategy of ecosystem development” in Science. In this paper, he attempted
to define ecological succession from a developmental standpoint. He believed that
succession followed three parameters, “( 1 ) succession was orderly, directional, and
predictable, (2) it resulted from modification to the environment by the community within
the constraints of physical factors, and (3) the end result was a stable community with
maximum biomass and symbiotic functions between organisms” (Odum, 1969). For an
ecosystem to undergo succession, there must be a fundamental shift in energy flows
toward maintenance. This shift is characterized by a phenomenon called “Maximum
Power” by H.T. Odum and Pinkerton (1955). The system that gets the greatest useful
energy per unit time is more likely to “survive” Maximum power is almost always less
than maximum efficiency, and usually no more than 50% of ideal reversible efficiency
(Odum and Pinkerton, 1955).
Using the above phenomenon as well as field studies of ecosystems, E P Odum
developed a list of 24 attributes he felt represented successional changes (Table 9). They
were divided into six areas, community energetics, community structure, life history.
24
nutrient cycling, selection pressure, and overall homeostasis. Odum proposed that these
attributes could help quantify mature stages of ecosystem development and help in testing
hypotheses. He felt that the attributes for overall homeostasis were the most likely to be
true for all ecosystem types (Odum, 1969). This model has become the basis for new
theories and methodologies concerning the maturity and stability of an ecosystem, such as
Ulanowicz.
Table 9. Odum’s (1969) 24 Attributes of Ecological Succession.
Ecosystem Attributes Developmental Stages Mature Stages
Community energetics
Gross fwoduction/communitv Greater or less than 1 Approaches 1
respiration (P/R ratio)
Gross production/standmg crop High Loyy
biomass (P/B ratio)
Biomass supported/unit energ\ flow Loyy High
(B/E ratio)
Net communiN production (\ield) High Loyy
Food chains Linear, predominantly grazing Weblike, predommantly detritus
Community structure
Total organic matter Small Large
Inorganic nutrients Extrabiotic Intrabiotic
Species diversity-variet> component Low High
Species diversity-equitabUity Low High
comjxinent
Biochemical diversity Low High
Stratification and spatial Poorly organized Well-organized
heterogeneity (pattern diversity )
Life History
Niche spiecialization Broad Narrow
Size of organism Small Large
25
Ecosystem Attributes Developmental Stages Mature Stages
Lile cycles Short. sunpJe Long, complex
Xutrienl cycling
Mineral cycles Open Closed
Nutrient exchange rate, between Rapid Slow
organisms and enwonment
Role of detritus in nutrient Unimportant Important
regeneration
Selection Pressure
Growth form For rapid growth (r-selection) For feedback control (K-selection)
Production Quantity Quality
Overall homeostasis
Internal symbiosis Undeveloped Developed
Nutrient conservation Poor Good
Stabüity (resistance to external Poor Good
perturbations)
Entropy High Low
Information Low High
2.2.2 Ascendency and Developmental Capacity. Ulanowicz has used information
theory to develop indicators of maturity and stability (1986), Based on Odum’s
hypothesis that ecosystem self-regulation is dependent upon the probable pathways taken
by a unit of flow within a system, Ulanowicz used the Shannon-Wiener Index of diversity
of flows scaled by the total amount of flow through a system or total system throughput
(TST) to determine an ecosystem’s Developmental Capacity (Ulanowicz, 1980) This
represents the ecosystems upper limit for self-organization.
To determine how much information is contained within a system, the average
mutual information (AMI) of flow structure is determined. This is a measure of constraint
26
exerted upon a random unit of flow as it moves from one compartment to another
(Ulanowicz, 1997). In Figure 2, two systems representing different AMIs are shown The
one with an even distribution of flows (A) has a lower AMI than the one with more
constrained flows (B). The system with the higher AMI is considered to contain more
information. Ulanowicz (1986) scaled the AMI by total system throughput (TST) and
called it Ascendency This represents the portion of Capacity that consists of flows that
contain information and thus, are considered organized It is postulated by Ulanowicz
(1986) to represent the maturity of a system Relative Ascendency is Ascendency divided
by Capacity (Ulanowicz, 1986) This measure of maturity can be used to compare
different systems or the same system over time (Ulanowicz and Wulff, 1991, Baird and
Ulanowicz, 1989).
The portion of Capacity not accounted for by Ascendency is called Overhead
This is the part of system complexity that is not organized. Overhead can be divided into
4 parts, uncertainty associated with inputs, uncertainty associated with output, uncertainty
associated with dissipations (respirations), and pathway redundancy (Ulanowicz, 1986).
Redundancy is believed to be an indicator of stress (Ulanowicz, 1997). For example, a
large number of redundant pathways present in a system may represent a system that is
adapting to stress.
Ascendency can grow at the expense of overhead Capacity is limited by a finite
source of new inputs and outputs and by the instability of small compartments (Ulanowicz,
1997). With a fixed capacity, as ascendency increases, overhead must necessarily
27
Figure 2. Systems with Different Average Mutual Information
Box A - The flow is evenly distributed and thus has a low AMI
Box B - The flow is more constrained and thus has a high AMI
28
2
29
decrease This could result in decreased redundancy, increased efficiency of imports,
increased efficiency of exports, or increased efficiency of dissipations However, a system
can never reach its total capacity. Overhead is needed as a buffer to perturbations
(Ulanowicz, 1997), Without it, the system will become brittle, and fall apart with
seemingly low levels of stress Therefore, overhead is believed to be a measure of stability
(Ulanowicz, 1997).
Ulanowicz has compared ascendency and capacity as a measure of ecosystem
development in relation to Odum’s (1969) 24 attributes of successional maturity (Table 9).
He believes that 15 attributes are in agreement with ascendency and the remaining 9 are
non-inconsistent (Ulanowicz, 1980). Cycling of material is a major contributor to
ascendency, as it is a source of organized flow (Ulanowicz, 1980). Several of Odum’s
(1969) attributes are related to cycling including greater retention of nutrients within the
system, increased reliance on detritus, lower P/B ratio, and greater proportions of
intrabiotic nutrients (Ulanowicz, 1980). Other attributes that correspond with an increase
in ascendency include species and biochemical diversity, specialization, higher information,
and internal symbiosis (Ulanowicz, 1980).
2.2.3 Empirical Tests ofDevelopment Attributes. Christensen (1994) has made an
attempt to evaluate indicators of maturity and stability in relation to Odum’s 24
developmental attributes (Odum, 1969). He referred to these indicators as “goal
functions” ( 1994), and he compared various goal functions, such as ascendency, to
Odum’s developmental attributes to determine if the indicator is in agreement with Odum
30
To do this, he used 41 static models of various aquatic environments from Christensen and
Pauly (1993b) (Christensen, 1994). The models were ranked based on 7 “goal functions”
of Odum’s attributes that could be measured by ECOPATH II, a software package for
network analysis. They were biomass/primary production (B/P), biomass/TST, the
proportion of flow originating from detritus, flow diversity, production/biomass (P/B),
average path length, and residence time. He examined how ascendency, relative
ascendency (ascendency/capacity), and exergy (amount of free energy of a system relative
to its environment), correlated with these 7 “goal functions” and hence, maturity sensu
Odum (Christensen, 1994). He found that relative ascendency was the most strongly
correlated with maturity sensu Odum, however, the correlation was negative (Christensen,
1994). Ascendency and exergy did not correlate with maturity. In an earlier paper,
Christensen and Pauly (1993b) evaluated the Finn Cycling Index (FCI) in relation to
maturity and found that it might be related to maturity.
In a later paper, Christensen (1995) expanded the goal functions to include total
overhead and internal redundancy. He found that total overhead, a measure of system
stability', was strongly correlated with maturity. However, internal redundancy, believed
by Ulanowicz to be a better indicator of stability than total overhead, did not correlate as
well with maturity (Christensen, 1995). He concluded that measures of stability are
probably also measures of maturity (Christensen, 1995).
Christensen’s (1995) comparison of ascendency to Odum’s (1969) maturity
attributes may not be adequately reflected in his maturity index. In the 1995 paper.
31
Christensen used 10 of Odum’s 24 attributes to represent maturity The other attributes
were removed from consideration after they were analyzed for cross-correlation
Christensen (1995) was concerned that highly correlated attributes would introduce bias
into the analysis if all were used. Of these 10 attributes selected to represent maturity,
Ulanowicz (1980) proposed that only 5 result in increased ascendency, P/B ratio, B/E
ratio, growth forms, and both variety and equitability species diversity (Table 9). Two of
the attributes used by Christensen (1995) are not reflected in the measure of ascendency,
the P/R ratio and the average size of organisms (Table 9) The remaining 3 attributes—
dominant food chain, nutrient exchange rate, and entropy—are ambiguous as to how they
relate to ascendency (Ulanowicz, 1980). Therefore, it may be unwarrented to make
comparisons of maturity and ascendency using maturity indicators that are not reflected in
the measure of ascendency. Of Odum’s (1969) 24 attributes of maturity, Ulanowicz
(1980) believed 15 were reflected in ascendency Christensen only focused on 5 (1995)
Of the 5 attributes Ulanowicz (1980) was unsure how they related to ascendency,
Christensen focused on 2 (1995). And of the 4 attributes Ulanowicz (1980) believed were
not reflected in ascendency, Christensen focused on 2 (1980). By trying to avoid bias,
Christensen may have weakened his index of maturity or at least his ability to compare it
to ascendency.
2.3 Network Analysis and Its Use Comparing Nitrogen Cycling
Ulanowicz developed a software program entitled NETWRK that uses network
analysis to evaluate a system’s structure including Information Indices such as capacity.
32
ascendency, and overhead. The latest version is NETWRK4.2 (Ulanowicz, 1998).
Network analysis is a group of analyses that employ a variety of mathematical techniques
to evaluate a system qualitatively and quantitatively. For a more detailed description, the
reader is referred to Section 4.5. of this paper, to Kay et al. (1989), and to the
documentation for NETWRK 4.2 (Ulanowicz, 1998).
A few investigators have used network analysis to analyze the nitrogen cycle of
aquatic systems, especially estuarine (Forés and Christian, 1993, Forés et al., 1994, Baird
et al., 1995, Christian et al., 1996, Christian et al., 1997). The 2 main approaches to
comparing static networks of systems are to compare the same system at different times
(Forés and Christian, 1993, Forés et al., 1994, Baird et al., 1995, Christian et al, 1997),
and to make comparisons among systems (Christian et al., 1996). Important aspects of
model comparison are that the models have the same units of medium (e g., g N x m‘“ x yr
'), and that they have similar topology, flow structure, and degree of aggregation (Baird
and Ulanowicz, 1993). This means that the models are similar in the number of
compartments, the way compartments are interconnected, and degree of aggregation (DIN
vs. NOj', NOj', and NH4*). Many indices respond to model structure For example, the
average path length (APL) is highly dependent on the number of compartments A 4-
compartment model may have a very different APL that a 20-compartment model, and
comparisons between the models would be ill advised.
In comparing the nitrogen cycle of 5 coastal systems, 2 of which were dominated
by rooted macrophytes, Christian et al (1996) determined that the life form and life cycle
33
of primary producer had a significant effect on cycling. Phytoplankton have a much
shorter turnover time than rooted macrophytes. This increases the amount of material
cycled within the system significantly (Christian et al., 1996),
Christian et al (1996) also discussed the meaning of cycling in the context of
foodweb models versus biogeochemical models, Foodweb models generally focus on
carbon flow as a substitute for energy flow. Cycles involve only organic matter
Biogeochemical models focus more on primary production and microbial processes
(Christian et al., 1996). Therefore indices that measure cycling such as the Finn Cycling
Index (FCI) (the percent of total flow that is involved in cycles) will have different
interpretations for the different model types (Christian et al., 1996). Baird and Ulanowicz
(1993) found in foodweb models that increased FCI was not an indicator of maturity but
of stress. As the system becomes more stressed food chains shorten, causing material to
cycle faster. However, in biogeochemical models, the foodweb is only a small part of the
total model. Christian et al (1996) found that stress in the form of eutrophication was
associated with a lower FCI Dead organic matter also plays a different role in
biogeochemical models than foodweb models. In biogeochemical models dead organic
matter can be one of several nonliving compartments, whereas in foodwebs, dead organic
matter is the only nonliving compartment.
2.4 Sea-Level Rise
Sea level has been rising since the end of the last glacial maximum approximately
18,000 years ago. There was a rapid rise in eustatic sea level during the early Holocene
34
period, but it slowed around 4,000 years ago to the current rate of approximately 0.11-
0 12 cm/year (Orson et al., 1985, Kana et al, 1984), Based on climate models, the rate of
sea-level rise is predicted to increase substantially over the next half century. Though
there are a number of model predictions based on different assumptions, most predict a
global average rise in sea level due to all causes of 0.3-0.6 cm/year (Warrick et al, 1996).
From another perspective, along the east coast of the United States, over the past
8,000 years, relative changes in sea-level ranged from 22 m in Virginia to 18 m in New
York, which equals 0 275 cm/yr for Virginia and 0.222 cm/yr for New York (Peltier,
1985). It is believed that the majority of this increase in sea level is due to glacial isostatic
adjustment rather than thermal expansion (Davis, 1987). For the time period 1940-1980,
the average relative sea-level rise for the east coast of the United States was 0 25±0.017
cm/yr (Davis, 1987),
2.5 Ecosystem State Change
Ecosystem state change is the transformation of one ecosystem class to another
(Brinson et al, 1995). Brinson et al (1995) recognized 5 distinct ecosystem classes in salt
marsh landscapes, upland or wetland forest, organic high marsh, mineral low marsh,
autotrophic benthic system, and heterotrophic benthic system. For a given rate of rising
sea level, the rate of state change from one class to another depends on slope, sediment
supply, bioaccretion rate, and inundation frequency.
The geomorphic settings of tidal marshes were divided by Brinson et al (1995) into
four types depending on slope and sediment supply (Figure 3). The first type of marsh is
35
Figure 3. Classes of Salt Marshes Responding to Rising Sea Level
(From Brinson et al., 1995)
Box A-Example of a marsh that is expanding as a result of increased inundation in two
directions, toward the creekbank due to high sediment supply and overland due to
a gentle slope
Box B-Example of a marsh that is expanding overland due to increased inundation but is
eroding at the creekbank due to a low sediment supply.
Box C-Example of a marsh that is stalling at a steep slope and expanding toward the
creekbank due to a high sediment supply.
Box D-Example of a marsh that is stalling at a steep slope and eroding at the creekbank
due to a low sediment supply
36
Sediment Supply
37
an expanding marsh It has a gentle slope and a high sediment supply. It responds to sea-
level rise in two ways. It progrades toward the estuary due to sediment surpluses, and it
transgresses overland into the terrestrial forest with rising sea level. This is exemplified
by the Barnstable Marsh in Massachusetts and along the Georgia coast (Redfield, 1972,
Pomeroy and Wiegert, 1981) The second type of marsh erodes at the creek bank but
transgresses toward the terrestrial forest. It is found in areas that have a gentle slope but a
low sediment supply It can be either a maintaining, expanding, or submerging marsh
depending on the rate of erosion and overland transgression. This type of marsh can be
found in the Mississippi Delta and the Virginia Coast Reserve (VCR) on the eastern shore
of Virginia (Brinson et al, 1995). The third type of marsh is another type of expanding
marsh. It is found in areas with steep slopes and a high sediment supply. It is unable to
transgress overland because of the slope This is called stalling Because of the high
sediment supply, it is able to prograde toward the estuary (Brinson et al., 1995). The last
type of marsh is a submerging marsh It is eroding at its creekbank and stalling at the
forest because of the steep slope. This type of marsh is relatively common at the VCR
(Brinson et al, 1995).
2.5.1 Maintenance. Marshes can maintain their level relative to sea-level rise when
there is sufficient sediment and/or peat accumulation. Increased flooding may bring in
additional sediments and nutrients to a marsh, which may increase primary production and
thus peat accumulation (The Working Group on Sea Level Rise and Wetland Systems,
1997). Major sources of mineral and organic sediment include: sand, silts, and clays from
38
the marine environment or upland erosion, particulate organic material from outside the
system, and organic material produced within the system such as roots, rhizomes, and
litter from vegetation (Orson et al., 1985).
A negative feedback loop is associated with wetland level maintenance. If marsh
elevation is relatively low, it is inundated frequently by tides This brings in sediment and
nutrients, which may enhance plant productivity. With increased plant productivity, there
is a greater build-up of organic matter within the sediment from root and rhizome growth
as well as litter from the plants. The part of primary production that is not decomposed
becomes peat. As the marsh vertically accretes, the tidal water is unable to penetrate the
marsh at the same level as before thus reducing the frequency and depth of flooding. This
reduces the amount of sediment and nutrients the marsh receives, decreasing primary
productivity. This decrease in primary production causes a slowing of the rate of
accretion because of decreased peat formation and sedimentation (The Working Group on
Sea Level Rise and Wetland Systems, 1997). This feedback mechanism maintains the low
marsh relative to sea level. It is when this maintenance feedback loop does not function
that marshes expand or submerge
2.5.2 Expansion. As can be seen in Figure 3, the marsh may prograde toward the
estuary and/or migrate overland (Brinson et al., 1995). Marshes that are able to increase
their area by migrating over upland areas begin when upland areas experience infrequent
tidal floods caused by extremes such as storms and hurricanes The salt water infiltrates
the soil at a high enough salinity (5-15%o) to result in water stress and possibly sulfide
39
toxicity (Brinson et al, 1995) If soil salinity increases enough, the upland plants will
begin to die The forest soils will be transformed to marsh soils by salinization, deposition
of marine sediments, and the accumulation of sulfide by sulfate reduction (Dame et al.,
1992) This allows high marsh plants to begin to invade the area. Slope of the land and
distance from the tidal creek determine the rate of the conversion from upland to high
marsh (Brinson et al., 1995). In areas where the slope is gentle and the creek is close to
the forest, salinity and H2S concentration in forest soils are higher than in areas where the
slope is steep and the creek is far from the forest (Hmieleski, 1994)
2.5.3 Submergence. A marsh can also be converted to open water in response to an
increased relative sea-level rise (Brinson et al., 1995) To maintain its elevation relative to
sea level, a marsh must accrete either through mineral sediment accumulation or peat
accumulation (Orson et al, 1985) When one of these two does not occur at a rate
sufficient to maintain its level relative to sea level, the marsh begins to submerge. When
tidal flooding increases, headward creek erosion and shoreline erosion can occur under
low sediment supply (Brinson et al., 1995) Erosion and wrack deposition can reduce or
eliminate vegetation resulting in a mud flat (Brinson et al., 1995). The mud flat may be
recolonized by S. alterniflora or become subaquatic (Brinson et al., 1995) In either case
macrophyte primary production is reduced.
The reduction in plant production also reduces the sediment trapping ability of the
marsh. The reduction in primary production results in fewer stems to baffle the incoming
waves, and thus less sediment is deposited on the marsh surface. There is also less
40
opportunity for bioaccretion (Orson et al., 1985) This creates a positive feedback loop
that further reduces primary production (Orson et al , 1985) As the marsh loses its
sediment-trapping ability and primary production, it is subjected to increased flooding and
erosion which reduces its sediment trapping ability and primary production. The marsh
will eventually become open w-ater (Orson et al., 1985, Brinson et al, 1995).
3.0 GOALS OF RESEARCH/HYPOTHESES
3.1 Research Goals
The three main goals of my research are to compare the nitrogen cycle of three
different ecosystem zones within salt marshes, assess how the nitrogen cycle may reflect
the zone’s maturity and stability, and determine how relative sea-level rise may affect the
nitrogen cycle of marshes. One aspect of the nitrogen cycle to be examined is how
imports of nitrogen are exported from the system and if there are any patterns across
marsh zones associated with the export routes. Another aspect to be examined is how
flows within the system relate to primary production and determine if any patterns exist
across marsh zones. The third aspect to be examined is how the nitrogen cycle reflects to
the marsh zone’s maturity and stability Using the outcome from these examinations, I
will postulate how the nitrogen cycle may be affected by rising relative sea-level and how
that may affect the maturity and stability of the entire marsh,
3.2 Hypotheses
I have postulated 3 specific relationships within the context of the overall study
My first hypothesis concerns the comparison of the amount of cycling within each marsh
zone I hypothesize that the relative amount of cycling will increase from the
creekbank/tall S. alterniflora marsh zone to the high marsh as tidal exchanges decrease.
The Finn Cycling Index and Average Path Length [(TST-Inputs)/Inputs] are used to
determine amount of cycling
The second hypothesis concerns that relative rate of mineralization As the import
of nitrogen decreases from creekbank to the high marsh resulting in lower TST, I
42
hypothesize that the relative rate of mineralization and its importance to primary
production will be highest in the high marsh. The relative mineralization rate will be
determined with respect to TST, cycled throughput, and primary production The
mineralization rate’s importance to primary production will be determined using the Total
Contribution and Total Dependency matrices within Netwrk4 ( Section 4 5,2).
The third hypothesis is related to the developmental maturity of each zone The
index used to compare marsh maturity is called relative ascendency and was developed by
R E Ulanowicz (1986) I hypothesize that relative ascendency (ascendency/capacity) will
be highest for the low/short -S', alterniflora marsh zone because under conditions of rising
sea level it is the zone that experiences the least extreme conditions associated with state
transition (Brinson et al., 1995),
4.0 METHODS AND MATERIALS
4.1 Research Design
I used network analysis to evaluate the differences in nitrogen cycling between
marsh zones Three well-studied marshes, representing different latitudes, were divided
into 3 different zones that represented various flooding regimes and plant communities
The first zone was the creekbank, referred to as “Tall ” In all three marshes selected, the
creekbank is considered to be flooded by all high tides and is dominated by the tall form of
S. alterniJJora The second zone was the low marsh, referred to as “Short.” This area
was considered to be flooded by 50% of all high tides and is dominated by the short form
of S. alterniflora in all three marshes. The third zone was the high marsh, referred to as
“High ” This area of the marsh is considered to be flooded by 10% of all high tides and is
dominated by plants locally found in high marsh areas. These include S. patens, D.
spicata, andy. roemenanus (Table 4 2 \)
4.2 Site Descriptions
The 3 marshes used to compare nitrogen cycles of different zones were Great
Sippewissett Marsh in Massachusetts, Upper Phillips Creek Marsh in Virginia, and Sapelo
Island Marshes in Georgia (Table 10 for site descriptions). All are located along the
eastern seaboard of the USA
4.2.1 Great Sippewissett Marsh. Great Sippewissett Marsh is located in Falmouth, MA
near Woods Hole (41 °35'N, 70°38'W) and is approximately 48 ha in size (Finn and
Leschine, 1980) (Table 11 for zone characteristics). It is tidally fed by Buzzards
44
Table 10. Site Descriptions
Great Sippewissett Phillips Creek Sapelo Island
Age 2,000 years (Valiela. 1983) 200 years (Chambers et al. 15.000 years (Hoy1. 1967)
1992)
Geomorphic Setting Mainland Mainland Bamer Island
Tidal Range (mean) 1.6 m (Valiela. et al.. 1.9 m (Anderson et al.. 2 .4 m (Schubauer and
1978) 1997b) Hopkinson. 1984)
Surface Water 32 pçit (Carpenter, et al.. 9-33 ppt (Hmieleski. 1994) 15-28 ppt (Pomeroy et al..
Saliniu 1978) 1972)
Interstitial Salinit> 28-38 ppt (Howes, et al.. 9-26 ppt (Anderson et al. 35-40 ppt (Nestler, 1977)
1986) 1997b)
Dominant Plants 5. alternijlora, D. spicata, S. alterniflora, D. spicata, S. alterniflora, J.
S. patens S. patens, J. roemerianus roemerianus
Fresh Water Sources Groundwater. Precipitation Precipitation Precipitation
Bay through a single entrance, Sippewissett Creek (Howes et a!., 1986) The marsh is
surrounded on three sides by glacial moraine and sand dunes on the fourth side The marsh
is accreting at 1 mm/year in the low and high marsh and as much as 14 mm/year in the
creekbank area (Valiela, 1983),
Table 11. Great Sippewissett Marsh Zone Characteristics
Tall Short High
Area (ha) 9.1 (Valiela and Teal, 12.3 (Valiela and Teal. 8.9 (Valiela and Teal,
1979) 1979) 1979)
Dominant Vegetation 5. alterniflora (Valiela and 5. alterniflora (Valiela and 5. patens, D. spicata
Teal, 1979) Teal. 1979) (Valiela and Teal. 1979)
Floodmg Frequency 100 (Jordan and Valiela. 50 (Meany et al., 1976) 10 (extrapolated from
(% of high tides) 1982) Valiela et al.. 1985)
Primary Production 30 27 29
(g N/m‘/\T)'
’ These numbers were averaged from the following sources: Finn and Leschine (1980),
Howes et al (1985), Teal et al. (1979), Valiela (1983), Valiela and Teal (1979a), Valiela
et al (1975), Valiela et al (1976), Valiela et al, (1978), and White and Howes (1994a).
45
4.2.2 Upper Phillips Creek Marsh. Upper Phillips Creek Marsh is located near
Nassawadox, VA, on the southern end of the Delmarva Peninsula (37°26' 38” N, 75°52'
05” W) (Blum, 1993), and is estimated to be over 1.2 ha by topographic survey
(Richardson et al., 1995) (Table 12 for zone characteristics). It is part of the Virginia
Coast Reserve (VCR) Long-Term Ecological Research Site (LTER) sponsored by the
National Science Foundation. The property is owned and managed by the Nature
Conser\'ancy It is tidally fed by Phillips Creek, a tributary of the Red Bank River that
feeds into Hog Island Bay The marsh originated from a Pleistocene sand ridge that was
breached by sea level rise within the last 200 years (Chambers et al., 1992). It is
surrounded by farm land to the south and pine forests to the north and west (Blum, 1993,
Hmieleski, 1994). The marsh grades gradually into the forested areas to the north and
more steeply into farmland to the south (Hmieleski, 1994). The marsh has increased in
size by 8% in the last 50 years due to the transition from upland areas to high marsh
(Kastler, 1993). Sediment is accreting at approximately 2 mm/year in the short S.
altertiiflora marsh (Kastler, 1993), which is considered sufficient to keep pace with the
rate of sea-level rise (Davis, 1987, Hayden et al., 1991).
4.2.3 Sapelo Island Marshes. Sapelo Island Marshes are located on Sapelo Island, GA
(31 ° 19”N, 81 ° 18”W) (Schubauer and Hopkinson, 1984) (Table 13 for zone
characteristics). The marshes total area is approximately 1140 ha (Kuenzler, 1961). The
marshes are fed by the Duplin River, which empties into the Doboy Sound (Imberger et
al, 1983). The barrier island was believed to have been formed as the result of beach
46
Table 12. Upper Phillips Creek Marsh Zone Characteristics
Tall Short High
Area (ha) 0.01 (Richardson et al., 0.54 (Richardson el al.. 0.65 (Richardson et al..
1995) 1995) 1995)
Dominant Vegetation S. altemiflora 5. altemiflora S. patens, Disticlis spicata,
J roemerianus
Flooding Frequenc> 100 (extrapolated from 52 .4 (extrapolated from 1-10.7 (Hmieleski. 1994)
(% of high tides) Blum. 1993) Anderson et al.. 1997b)
Primary Production 21.9 27.3 15.8
(g N/mVyr)'
’These numbers were averaged from the following sources: Anderson et al. (1997b),
Blum (1993), Blum and Christian (1997), Tolley (1996), and this study
ridges being intersected by sea level rise, which submerged the area landward of the ridges
during the late Holocene forming lagoons and islands (Hoyt, 1967).
Table 13. Sapelo Island Marshes Zone Characteristics
Tall Short High
Area (ha) 91.2 (Kuenzler. 1961. Dai 991.8 (Kuenzler. 1961. 57 (Kuenzler. 1961. Dai
and Wiegert. 1997) Dai and Wiegert. 1997) and Wiegert. 1997)
Dominant Vegetation 5. altemiflora S. altemiflora J. roemerianus. D. spicata
Flooding Frequency 92(Kneib. 1991) 50 (Kuenzler. 1961) 15 (Kuenzler, 1961)
(% of high tides)
Primaix Production 51.6 38.3 53,5
(g N/m'V\T)’
' These numbers were averaged from the following sources; Chalmers (1979), Chalmers
et al. (1985), Dai and Weigert (1996), Gallagher and Plumley (1979), Gallagher et al
(1980), Haines (1976), Haines et al. (1977), Hanson (1977b), Hanson (1983), Hopkinson
and Schubauer (1984), Kemp et al. (1990b), Schubauer and Hopkinson (1984), Weigert
(1979), Weigert (1986), and Whitney et al. (1981).
4.3 Data Collection
4.3.1 Literature. The majority of data was obtained from literature Great Sippewissett
Marsh and Sapelo Island Marshes were selected specifically because of the extensive
47
literature available regarding the nitrogen processes in these marshes. For Great
Sippewissett, 27 articles spanning from 1974 to 1994 were used to obtain data For
Sapelo Island Marshes, I used 42 articles spanning from 1959 to 1997. Much of the data
for Upper Phillips Creeks Marsh were also obtained from literature. However, because
this marsh has not yet been studied as extensively as the other two marshes, not as many
articles have been published. Four articles spanning from 1992 to 1998 were used.
Student theses and the VCR/LTER database were also used to obtain data for Phillips
Creek Marsh.
4.3.2 Field Sampling. Samples were taken from Upper Phillips Creek Marsh from May
through December 1997 in order to supplement information from the literature
Aboveground biomass of S. alterniflora was collected from two marsh zones (Tall
and Short) once in May and once in September. A total of 18 samples were clipped within
a 0.0625 m^ quadrant using hand clippers, stored in plastic trash bags, and transported to
the laboratory for processing The samples separated into live and dead based on the
present of green on the stems and leaves They were then weighed to 0.01-g accuracy on
an electronic scale to establish an initial mass, dried to a consistent mass at 85°C in an
AC-Lab Equipment convection oven, and then reweighed for calculation of g dry mass x
mThese samples were then ground in a Wiley mill through a 40-mesh screen Percent
nitrogen was determined using Leeman Labs Control Equipment 440 Elemental Analyzer
These masses were used to estimate aboveground biomass and primary production
Primary production was considered a very rough estimate because it was the subtraction
48
of May’s biomass from September’s biomass, thus underestimating production These
numbers, however, were averaged with literature values where they were available for the
creation of the networks
Belowground biomass was estimated in May, September, and December A 3.5
cm diameter aluminum corer was used to core marsh sediment to a core length of up to 28
cm. The samples were wrapped in aluminum foil for storage and transport. Both
macroorganic matter (MOM) (Gallagher, 1974) and bulk densities (Chalmers, 1979) were
measured. MOM was determined by cutting the core into two sections 0-10 cm and 10-
up to 28 cm. Each section was washed through a 1-mm mesh sieve. After all sediments
were visibly washed away, what remained was considered MOM Some samples were
used to determine the ratio of live: dead root matter Separation was based on color and
turgidity (Schubauer and Hopkinson, 1984) The samples were dried to consistent mass at
85°C in an AC-Lab Equipment convection oven and weighed to 0.01-g accuracy on an
electronic scale. They were then ground using a Wiley mill through a 40-mesh screen
Percent nitrogen was determined using Leeman Labs Control Equipment 440 Elemental
Analyzer. Bulk densities were determined by measuring the volume of the core, weighing
to 0.01-g accuracy on an electronic scale, drying to consistent mass at 85°C in an AC-Lab
Equipment convection oven, and reweighing the core.
To establish an estimate of the mussel, G. demissa, and snail, L. irrorata,
population, the number of mussels and snails within 0.0625 m^ quadrats were counted in
September by sampling three different areas of the marsh in triplicate to determine
49
populations per m^.
4.4 Network Construction
Networks were constructed for each zone of each marsh. The networks were
constructed by estimating values for all compartments and flows for the general box and
arrow diagram shown in Figure 4, Most of the diagram was created after conducting a
literature search for Great Sippewissett Marsh As more data were collected from Sapelo
Island and Upper Phillips Creek, the network was modified to reflect new important data
The diagram reflects the type of data available in literature, and therefore does not contain
all possible flows During network development, I noticed physical exchanges by tides
inordinately dominated over those due to biological processes To allow' the networks to
better reflect biological activity, tidal flushing was placed outside the system, and the
sedimentation/resuspension cycle was made into a net flow
The type of data used included imports to the marshes such as tidal flow',
precipitation, and nitrogen fixation, interactions such as primary production,
mineralization, and grazing, and outputs such as tidal flow, denitrification, and burial of
peat Biomasses of each compartment were gathered when available. However,
biomasses are not an intricate part of NETWRK4's programing and do not affect network
analysis output.
Each compartment represents a potentially important aspect of the nitrogen cycle
The surface water compartments represent the different species of nitrogen found in
surface water (Figure 4). Surface PN includes bacteria, algae, zooplankton, detritus, and
50
Figure 4. Generalized Nitrogen Cycle Model for Salt Marshes
51
TidalC 2Be)t
1
^
Surface N H 4 j SuiÉKJeîIOx 1 1 Su3Áce DON! Snrfhn» PH1 1
Á ^ w Á 1 ^ A À
Icn m igzation
\ 1 1
B enthic fiter feeders G zazers^ ekica
1 < ^
H aivest
St2aidx>g Dead^iUEr
1 1 p
Roota^hizcra ea |h2 Ffeets ; B enihfc A ^ea
n ^
/
j / \ j
/ '"\ //
z
CxoundwatBr
Burial
D enirifcatx>n
52
nitrogen attached to sediment particles. Fungi and bacteria associated with leaf decay are
part of the Standing Dead compartment. Benthic filter feeders are predominately
represented by G. demissa, the Atlantic ribbed mussel, but theoretically represent all filter
feeders in the marsh. The Grazer/Nekton compartment is a compilation of many different
types of animals. It includes crabs (e g., Ucapugilator), snails (e g., Littorina irrorata),
insects (e g., Orcheliumfidicinium), and birds (e g., Branta canadensis). However, it
could be expanded to fish, racoon, deer, or any other animal present in the marsh The
pore water compartments like the surface water compartments represent the different
nitrogen species found in pore water Pore PN, however, is an aggregation of many things
including decaying roots and rhizomes, decaying leaf litter, biodeposition, and nitrogen
attached to sediment particles
4.4.1 Assessment ofData Reliability. Each data point was assigned a number reflecting
its perceived reliability, referred to as the reliability factor (RF). A RF of 4 meant that
there was good confidence in the number. For example, a 4 would be assigned to a datum
that resulted from a nitrogen fixation experiment where the rate was directly measured
over a year or more and there was little to no manipulation needed for it to be
standardized to g N m'^ x yr"' A RF of 3 meant that I had good confidence in the original
data point but needed to manipulate it into the standard units For example, this may
result from a study directly measuring a carbon flow, and thus requiring a C:N ratio to
convert it to nitrogen A RF of 2 meant that the data point was an estimate or had to be
heavily manipulated by conversions or extrapolation of gaps to be in the standard units
53
This RF assignment would result from a study that had data as a rate per day or month,
and/or missing one or more months Data would have to be scaled up and/or estimated in
order to reflect a year’s worth of flow. A RF of 1 meant the data point was a very rough
estimate. A data point that was not directly measured but was roughly estimated based on
other measurements would receive this RF assignment. And, a RF of zero meant I derived
it by balancing the inputs and outputs of compartments. Each data point and its RF were
averaged with other like data points for the appropriate zone of the appropriate marsh
Thus, a flow or standing stock value for network construction could come from data
points from more than one source The averaged data and RF were then used to
construct each network. A data point was not assigned a RF of zero until the networks
were being balanced (Section 4.4 2).
The following is an example of the decision making process that occurred during
data manipulation. A data point for belowground production in Sapelo Island was given as
2100 g m'^ X yr"' (Gallagher and Plumley, 1979). In order to convert this to g N m'* x yr"',
the first bit of information needed was percent nitrogen or carbon. Percent carbon was
found to be 38.1 (Gallagher and Plumley, 1979). It was determined that belowground
production was 800 g C m'^ x yr*. The next step was to apply a C:N ratio to determine
the nitrogen content of the roots and rhizomes. The C;N ratio used was 38 (Gallagher and
Plumley, 1979). It could then be determined that the belowground production was 21 g N
m'^xyr'*. This conversion was given a RF of 3. C:N ratios, % N, and % C originated
from the marsh in question unless no information was available
54
4.4.2 Balancing. Once the initially estimated values for each network were obtained,
they were organized into a spreadsheet. Each compartment’s surplus or deficit nitrogen
flow was determined by adding all inputs to a compartment and subtracting all exports
from that compartment. To achieve steady state, each compartment’s inputs must equal
its outputs. The following rules involving REs were used as guidelines to help balance
each compartment. However, they were not strictly adhered to. If a data point had a RF
of 4, it was changed no more than 10% in either direction to help balance the
compartment. A RF of 3 was changed no more than 20%, 2 was changed up to 30%, 1
was changed up to 40%, and 0 was changed as needed to balance the compartment.
These percentages were arbitrarily chosen to help retain the integrity of the data during the
balancing process. These rules were violated when no other option was available to
balance the compartment. For example, in the Great Sippewissett Tall network the value
for denitrification was assigned a RF of 4 but was changed 50%. This was needed to
balance the compartment as no other realistic options were available Other input and
export routes had been manipulated as much as possible to account for the deficit nitrogen
flow without becoming unrealistic.
4.5 Network Analysis
Ecosystem Network Analysis was used to evaluate the structure of the 9 networks
using a variety of perspectives. The software package used to perform network analysis
was NETWRK4 (Ulanowicz, 1987) The package contains several subroutines in
FORTRAN for network analysis. I used the subroutine for Structure Analysis, specifically
55
Input Environs Analysis (Section 4.5 1) and matrices of Total Contribution and Total
Dependency (Section 4.5.2) I also used the subroutine for Biogeochemical Cycle
Analysis, specifically the Finn Cycling Index (FCI) to determine the amount of recycling in
the system (Section 4.5.3) I used the Information Indices, such as Relative Ascendency,
Capacity, and Overhead to determine maturity and stability (Section 4.5.4)
4.5.1 Input Environs Analysis. Input Environs Analysis computes the fraction of flows
within the system that results from the exogenous input of one unit of flow into a
compartment (Kay et al., 1989) The coefficients in each vector and matrix represent the
relative amounts of internal flows and outputs (or probability of flow) resulting from one
unit of input (Kay et al., 1989) I used this analysis to determine how inputs to the
systems were exported. I compared the distributions of exports between marsh zones and
between marshes to evaluate how nitrogen cycling may be different.
4.5.2 Total Contribution and Total Dependency Matrices. The total contribution
matrix evaluates the fraction of a compartment’s throughput that contributes to another
compartment’s throughput both directly and indirectly (Kay et al., 1989). For example,
the matrix can determine the fraction of the nitrogen that flows through the benthic filter
feeder compartment that will travel directly and indirectly to aboveground production In
this case there is no known direct flow from benthic filter feeders to aboveground
production, but a possible indirect flow would be from benthic filter feeders to Pore PN by
way of biodeposition (Figure 4) The Pore PN is then mineralized to Pore NH4 and taken
up by the plant. For the Great Sippewissett Tall network, this number is 0.0681 This
56
means that 6.81% of the total throughput of the benthic filter feeder compartment will go
through the aboveground production compartment.
The total dependency matrix evaluates the fraction of a compartment’s total
throughput that resided at some point in another compartment (Hannon, 1973). For
example, this matrix can determine the fraction of aboveground production’s nitrogen
throughput that came from benthic filter feeders both directly and indirectly Again, there
is no direct flow, but through the indirect flow, it can be determined what fraction of
aboveground production’s throughput came from benthic filter feeders. For the Great
Sippewissett Tall network, this number is 0.128 This means that 12.8% of aboveground
production’s throughput came from the benthic filter feeder compartment The diagonals
of both matrices can be used to determine the amount of material that is cycled back to a
compartment
These matrices were used to evaluate how important certain sources of imported
nitrogen are to various compartments, such as how important precipitation is to
belowground production This type of analysis can be achieved by making an input a
compartment. For example, instead of directing precipitation into the appropriate
compartments from outside the system, I created a compartment for precipitation thus
internalizing the input flows (Figure 4). It was also used to determine the amount of
material cycled through belowground production as an indicator of recycling
4.5.3 Finn Cycling Index and Cycled Throughput. Biogeochemical cycle analysis
employs graph theory to evaluate the cycles or positive feedback loops within a system
57
(Ulanowicz, 1986) The fraction of material that is involved in the feedback loops
compared to the total flow through the system (total system throughput) is called the Finn
Cycling Index (FCl) (Finn, 1980) When the FCI is multiplied by the total system
throughput (TST), the amount of material cycled, Cycled Throughput (CT) can be
determined (Christian, pers. com ). These two indices were used to determine the amount
of recycling within each marsh zone
4.5,4 Information Indices. Information indices developed by Ulanowicz (1987, 1997)
attempt to capture the emergent properties of a system I focused on ascendency,
overhead, redundancy, and internal ascendency to assess how nitrogen cycling may
influence system development (Section 2 2 2). Ascendency is the average mutual
information (AMI) within a system multiplied by the TST The AMI is a measure of the
amount of constraint on the flow of material (Ulanowicz, 1997). The more possible
pathways from a compartment or the more evenly distributed the flows between
compartments, the lower the constraint on the flow, and thus the lower the AMI.
Ascendency is believed to be an indicator of system maturity (Ulanowicz, 1997). Capacity
is TST multiplied by the Shannon Diversity of Individual Flows (Ulanowicz, 1997). The
Shannon Diversity of Individual Flows is a way of capturing the indeterminate complexity
or entropy of a system It is the sum of each flow’s potential contribution to system
complexity weighted by the frequency each flow occurs (Ulanowicz, 1997). The
difference between ascendency and capacity is referred to as overhead Overhead is the
uncertainty associated with inputs, output, dissipations, and internal flows (Ulanowicz,
58
1997), It is believed to be a measure of stability (Christensen, 1995). Redundancy is the
degree of internal flow associated with pathways that have similar functions and/or
evenness of flow. The higher the level of redundancy within a system the less benign or
more stressed an environment is believed to be (Ulanowicz, 1997). Internal ascendency is
a measure of maturity when inputs and outputs are removed from the AMI calculation.
These indices were used to establish a marsh’s level of maturity, stability, and level
of stress Relative ascendency was used to compare the maturity of different marsh zones
Overhead was used to compare the stability of the different marsh zones, and Redundancy
was used to compare levels of stress
4.5.5 Mineralization. Mineralization is an important part of nitrogen cycling within a
salt marsh. To determine what fraction mineralization was of total processing of nitrogen,
the net mineralization rate of Pore PN and DON to Pore NH4* was divided by TST To
determine if primary production’s uptake and/or requirements could be met by
mineralization, mineralization was divided by “belowground production” represented by
the uptake of NH4" and NOx. “Belowground production” was used because it contained
the total flow of nitrogen into the plant which then distributes both above- and
belowground Mineralization was also divided by CT to determine the fraction of CT
associated with mineralization
4.5.6 Average Path Length (APL). APL is another way of measuring cycling (Finn,
1980). It was originally developed to evaluate foodwebs, and is believed to be a measure
of stress as well as cycling. As a system becomes more stressed, the cycles tend to
59
become shorter thus reducing the APL (Kay et al, 1989), It is calculated with the
following formula: (TST-lnputs)/Inputs (Kay et al., 1989). It was used to determine
cycling and maturity/stability.
4.6 Statistical Analysis
It is assumed that the data are best analyzed using nonparametric statistics,
because the underlying data distributions are unknown The Friedman test was used to
determine the significance of various parameters in relation to marsh zone and marsh
(Potvin and Rolf, 1993). The Friedman test is a nonparametric statistical test that
analyzes within-subject effects based on rank It assumes within each block that errors are
mutually independent. Because the models were not true replicates, statistical interactions
were not determined The statistical software used was Systat 7.01 (SPSS, 1997).
Hierarchical Cluster Analysis was used to determine if the marshes clustered by
zone or by marsh. Distance metric was Euclidean distance and the single linkage (nearest
neighbor) method was used. The indices used for the cluster analysis were FCI, APL,
recycling within the belowground primary production compartment, relative ascendency,
input overhead, output overhead, redundancy, internal ascendency, and
mineralization/primary production A Pearson correlation matrix was also used on these
indices to compare maturity/stability indices and to insure that ranking of marsh zones was
not biased by a few indices
5.0 RESULTS
Overall, the 3 marshes showed consistent patterns among zones in some flows and
TST, but not in other flows. For example, TST, tidal imports, and tidal exports decreased
moving from Tall to High (Table 14). This was mainly the result of reduced tidal flushing
as it was assumed that the Tall zone was inundated by 100% of high tides. Short by 50%,
and High by only 10%. The largest internal flows were associated with primary
production and mineralization and did not show consistent patterns across marsh zones.
Table 14. Important Network Flows (g N % yr~^)
Great Sippewissett Upper Phillips Creek Sapelo Island
Tall Short High Tall Short High Tall Short High
Tidal Import 81.1 44.6 8.77 65.8 34.7 8.11 165 87.5 16.3
Precipitation 0.56 0.56 0.56 0.45 0.45 0.45 0.3 0.3 0.3
Groundwater 13.3 13.3 13 3 0.04 0.04 0,04 0.04 0.04 0.04
Nitrogen 5.03 2.75 5.87 1 1 1 39 8 23.7 4.5
Fixation
Tidal Export 87.9 45.6 8.83 63.1 30.5 5.6 162 74.5 16.1
Burial 6.13 9.51 11.4 3.6 5.16 3.53 1.44 1.35 1.2
Denitrification 9.58 7.54 8.58 0.6 0.6 0.6 41.6 35.7 3.91
Primary 30.2 27 29 21.1 27.7 15.8 51.6 38.3 53.5
Production
Translocation 1.4 1.26 1.26 7 7 7 16.2 14.8 13.3
Detritus 25.3 21.8 24.2 18.4 26.8 14.5 44.4 31.5 46.7
Formation
Mineralization 27.2 30.1 30 27.8 31.1 11.7 92.5 79.3 80.9
Nitrification 10 11.9 14.8 3.6 4 3.55 41.5 28.7 3.77
All other flow s 114 107 63.4 78.8 72.5 26.1 246 211 162
TST 412 323 220 290 242 98 902 627 403
Not only are there some differences between marsh zones but also between
61
marshes Sapelo Island marsh had considerably more nitrogen flowing through its system
than Upper Phillips Creek marsh Great Sippewissett was intermediate Primary
production, mineralization, and other microbial processes are also higher in Sapelo Island
marsh than the other two marshes
5.1 How Nitrogen Flows Through Each Marsh Area
5.1.1 Input Environs Analysis. I evaluated how imported N from major import routes
(Table 14) is exported from each marsh zone. Precipitation and tidal imports of each
nitrogen species were considered for analysis. Possible export routes included tidal export
of each nitrogen species, denitrification, burial, harvest of mussels, and volatilization
The following graphs depict the fraction of import that was exported by a
particular route in the different marsh zones (Figures 5-9) The output data from Input
Environs Analysis were placed in stacked bar graphs. Each bar section represents the
fraction of total import that was exported by each potential route. In all cases, harvest and
volatilization were very small fractions of export routes and usually are not large enough
to see on the bar graphs
The first import examined was precipitation In the Tall zone, more than half of
the precipitation was exported from the marsh by tide (Figure 5). Tidal export of
precipitation steadily decreased in importance moving across the marsh from Tall to High,
except for Sapelo Island Burial became a more important export route moving from the
Tall to High marsh zone Denitrification also generally increased in importance moving
across the marsh.
62
Figure 5. How Precipitation Import Leaves the Marsh
ITFmroapcttoiaofrntl
Tall Short High Tall Short High Tall Short High
Great Phillips Sapelo
Sippewisett Creek Island
Zone of Marsh
? Tidal NH4 ? Tidal NOx S Tidal DON 0 Tidal PN ? Burial ?Denitrification El Volatilization ? Harvest
64
To understand the importance of these patterns, the Friedmans Test was applied.
The increase in the importance of burial across marsh zones is a significant pattern. The
trends associated with tidal export and denitrification are not significant at the 0.05 level,
but there is a distinct pattern (Table 15). Given the limited number of samples (n=9), all 3
marshes must have the same rank order for significance at p=0 05 If one rank pair is
reversed, the p-value raised to 0.097. Therefore, in both tidal export in all forms and
denitrification, 2 of the marshes showed the same pattern and 1 had a reversal of rank
The reversal was at Sapelo Island between the Short and High zone This reversal w'as an
artifact of the way the Sapelo Island High marsh model was balanced (Appendix G)
Table 15. Friedmans Test for Significant («=0.05) Patterns in Precipitation Export
Across Marsh Zones. Numbers are p-values
Tidal Export Burial Denitrification
p-value 0.097 0.05 0.097
Tidal import of NH^* is a very important source of nitrogen to the marsh. Each
marsh processed the NH4" differently as can be seen in the different nitrogen species
exported tidally by each marsh, but there were some consistent patterns across marsh
zones (Figure 6). The importance of tidal export in each marsh across zones had no
significant trend (p=0.264). Burial increased in importance as an export route moving
from Tall to High in Great Sippewissett and Upper Phillips Creek However, this was not
a significant trend because of the pattern in Sapelo Island (p= 0.264). Denitrification
varied between each marsh zone with no consistent pattern (p=0.368) Therefore, the
export of NH4* shows no significant patterns across marsh zones
65
Figure 6. How Tidal Import of NH/ is Exported
IFTmroapctoiaofrntl
Great Phillips Sapelo
Sippewisett Creek Island
Zone of Marsh
B Tidal NH4 ? Tidal NOx S Tidal DON ? Tidal PN ? Burial ? Denitrification B Volatilization ? Harvest
67
Tidal import of NOx is also an important source of nitrogen to the marsh The
pattern of NOx processing across each marsh differed for each marsh (Figure 7). Tidal
flushing showed no significant patterns among marsh zones for all marshes (p=0.264).
Burial also shows no significant pattern across marsh zones and is essentially nonexistent
as an export route for Tidal NOx in Sapelo Island (p=0.368) Denitrification was a
dominant export route in Sapelo, but much less so in the other marshes, and there was no
significantly consistent pattern across marsh zones (p=0 264),
Tidal import of DON and its resultant cycling within the marsh is not very well
understood There were very few data regarding DON, so the networks essentially had
DON coming in and going out tidally with little transformation (Figure 8), There was no
significant trend when the other marshes are included in the statistical analysis (p=0.205)
Also at Upper Phillips Creek, burial increased in importance as an export route from Tall
to High (Table 16), but again significance testing did not support the trend overall
(p=0.205). Denitrification was only a very small export route for all marshes (Table 16)
and showed no significant trends (p=0.558)
Import of Tidal PN included bacteria, algae, zooplankton, detritus, and nitrogen
attached to sediment particles. Because of this diversity, it was processed within the
marsh in many different ways. There were, however, consistent patterns of export routes
(Figure 9) Tidal export of all forms decreased significantly in importance across marsh
zones from Tall to High (p=0.05). Burial and denitrification increased in importance
moving across the marsh from Tall to High Both trends were significant (p=0 05)
68
Figure 7. How Tidal Import of NOx is Exported
ITFmroapcttoiaofrnlt
Great Phillips Sapelo
Sippewisett Creek Island
Zone of Marsh
? Tidal NH4 ? Tidal NOx STidal DON ? Tidal PN ? Burial ?Denitrification ? Volatilization ? Harvest
70
Figure 8, How Tidal Import of DON is Exported
ITFmroapcttoaiofrnlt
Great Phillips Sapelo
Sippewisett Creek Island
Zone of Marsh
? Tidal NH4 ? Tidal NOx HTidal DON ? Tidal PN ? Burial ?Denitrification EVolatilization ? Harvest
72
Table 16. Percent of Tidal DON Import that is Exported by Various Routes in
Upper Phillips Creek.
Tall Short High
Tidal Export 88.3 81,8 52.2
Burial 11.1 17.5 28.5
Denitrification 04 0 54 2.54
Interestingly, the dominant export trends associated with Tidal PN were all significant,
whereas they were not for the other tidal imports
Groundwater was not considered for analysis because of the very small
contribution it makes to Upper Phillips Creek and Sapelo Island marshes. However, it is a
large import to the Great Sippewisset Marsh Like other imports, there is a decrease in
the importance of tidal export moving across the marsh from Tall to High (Table 17),
There is also an increase in importance in burial and denitrification from Tall to High
(Table 17).
Table 17. Percent of Groundwater Import Exported by Various Routes in Great
Tall Short High
Tidal Export 42.6 24.2 6.69
Burial 21.1 38 4 50.3
Denitrification 36.2 37.2 42.8
5.1.2 Total Contribution of Tide and Precipitation to Primary Production. The Total
Contribution Matrix was used to determine the fraction of precipitation and tidal inputs
that went to primary production, as most major nitrogen flows within marshes revolve
73
Figure 9. How Tidal Import of PN is Exported
IFTmroapcttoaiofrntl
Great Phillips Sapelo
Sippewisett Creek Island
Zone of Marsh
BTidal NH4 ? Tidal NOx HTidal DON 0Tidal PN ? Burial ?Denitrification 0Volatilization ? Harvest
75
around primary production. Uptake by belowground biomass represented plant primary
production, as it reflects production for both below- and aboveground production
numbers. In all 3 marshes and in all 3 zones, precipitation had a higher fraction of its
throughput go to primary production than each species of nitrogen in tidal import (Figure
10). The fraction increased significantly moving from Tall to High (p=0.05). In Upper
Phillips Creek High marsh almost 90% of nitrogen from precipitation went to primary
production. Tidal contributions also tended to increase from Tall to High (Figure 10).
As an exception, the percent of Tidal NH4' throughput contributing to primary production
tended to be least in the Short marsh and most in the High marsh at Great Sippewissett
and Sapelo Island but not at Upper Phillips Creek Therefore, there was no significant
trend (p=0.097). Tidal NOx behaved very differently in each marsh. For Great
Sippewissett and Upper Phillips Creek it followed the Tidal NH4" trend, but in Sapelo
Island there was no fraction of Tidal NOx that contributed to primary production
(p=0.105). Tidal DON contributed little if any of its throughput to primary production,
except in Phillips Creek This was a reflection of lack of data as stated above, as I did not
ascribe biological activity to this chemical species In Phillips Creek, tidal DON increased
its percent contribution to primary production moving across the marsh from Tall to High,
but there were no significant trends when all marshes were considered (p=0.097). Tidal
PN significantly increased its percent contribution moving across the marshes from Tall to
High (p=0.05)
In Great Sippewissett, much of the groundwater nitrogen was contributed to
76
Figure 10. Total Contribution of Input to Primary Production
rail Short High Tall Short High
(jreal Phillips Sapelo Island
Sippewissett Creek
Zone in Marsh
? Precipitation a Tidal NH4 ? Tidal NOx ? Tidal DON 0 Tidal PN
78
primary production. In the Tall zone, groundwater contributed 68.3% of its throughput to
primary production, the Short zone contributed 70.2%, and the High zone contributed
69 4%. No analyses were done on this pattern’s significance because of the lack of
groundwater flow in the other 2 marshes.
5.1.3 Total Dependency ofPrimary Production on Tide and Precipitation. A larger
percentage of primary production’s throughput originated from tidal import than
precipitation in all marsh zones (Figure 11). For example, precipitation only accounted for
0.1-9.5% of primary production’s throughput, but Tidal NH4* accounted for 3.1-41.5%.
However, precipitation significantly increased in importance moving across the marsh
from Tall to High (p=0.05). For example, in Sapelo Island, precipitation was a negligible
ifaction of primary production’s throughput in the Tall zone, but in the High zone it was
2 4%. Tidal imports varied in importance across marsh zones and between marshes. For
example. Tidal NH4' decreased in importance to primary production moving across the
marsh from Tall to High in Great Sippewissett, increased in Sapelo Island, but was highest
in the Short zone at Upper Phillips Creek (p=l 0) Tidal NOx varied greatly in its
importance to primary production between marshes and marsh zones In Great
Sippewissett, it was least important in the Short marsh and most important in the Tall
marsh In Phillips Creek, it decreased in importance moving across the marsh from Tall to
High And in Sapelo Island, primary production did not depend on Tidal NOx
Therefore, there was no significant trend across marsh zones (p=0.368) Primary
production depended very little on Tidal DON, except in Sapelo Island Short marsh, and
79
Figure 11. Total Dependency of Primary Production on Rain, Tide, and Recycling
o
00
Short High
Sapelo
Sippewiisett C’reek Island
Zone of Marsh
? Precipitation Cl Tidal NH4 ? Tidal NOx ? Tidal DON H Tidal PN ? Recycling
81
no cross zone patterns were found (p=0.472). Tidal PN played a relatively important role
in primary production in all marshes but not in a consistent way (p=0.717). It ranged from
4.3-59,2% of primary production’s throughput.
With the exception of Tidal NOx in Upper Phillips Creek, the dependencies were
less than 50% Therefore, recycling through belowground plant biomass was examined as
a source of nitrogen for primary production. This was evaluated by the diagonal
coefficient for belowground plant biomass within the Total Dependency matrix In each
marsh, recycling associated with primary production significantly increased in importance
moving across the marsh for Tall to High (p=0 05), and recycling was a predominant
source of flow in each case (Figure 11 ).
Primary production in Great Sippewissett was also very dependent on
groundwater In the Tall zone, 62.8% of primary production’s throughput came from
groundwater In the Short zone, it was 68.1%, and in the High zone, it was 62.6%.
Because groundwater was negligible in the other marshes, no comparison of marsh zones
was done.
5.2 Nitrogen Cycling
The amount of cycling can be measured in a number of other ways The Finn
Cycling Index (FCI) and Average Path Length (APL) are typically used (Kay et al., 1989)
Cycling can also be determined using the diagonals of the Total Dependency Matrix
(TDM), as was done above to determine recycling associated with primary production
Total system cycling as determined by FCI and APL increased moving across the
82
marsh from Tall to High (Table 18) Both FCI and APL significantly increased moving
toward the high marsh (p=0.05 for both).
Table 18. Indicators of Cycling within Systems and Compartments (based on Total
Dependancy Matrix). Numbers are the fractions of throughput recycled. APL is
average number of compartments unit of flow passes through.
Great Sippewissett Phillips Creek Sapelo Island p-value
Tall Short High Tall Short High Tall Short High «=0.05
FCI 0.297 0.365 0.471 0.361 0.5 0.532 0.408 0.415 0.801 0,05
APL 2,98 4.16 6,65 3.31 5.67 9.1 3 40 4.63 18 0.05
Mussels 0.19 0.218 0 068 0.029 0.069 0.000 0.01 0039 0.524 0.368
Grazers 0.11 0.162 0.11 0028 0.053 0.049 0.021 0.014 0.298 0.558
Shoots 0.108 0.18 0.229 0.483 0.456 0,712 0.489 0495 0.788 0.097
Roots 0.417 0.464 0.504 0.596 0667 0.807 0.600 0 615 0,889 0.05
Benthic 0 111 0 196 0.194 0 267 0.275 0.004 0.429 0.496 0.776 0.264
algae
Pore 0.152 0.197 0.257 0,252 0 344 0.400 0.000 0.001 0.002 0.05
NOx
Pore PN 0.447 0.509 0.529 0.543 0.65 0.71 0.622 0.65 0.906 0.05
Pore 0.421 0.504 0.533 0.549 0.65 0.71 0.623 0.65 0.906 0.05
NH4
Compartmental cycling is the fraction of a compartment’s throughput that starts in
the compartment, cycles through the system, and returns to the same compartment It
appeared to be significant only when related to primary production Recycling associated
with above- (“shoots”) and belowground (“roots”) biomass generally increased across the
marsh from Tall to High (Table 18). The trend was significant for belowground (p=0.05)
but not for aboveground (p=0,097) In Upper Phillips Creek, the Short zone had less
83
recycling than the Tall zone (Table 18), Recycling of the sediment nutrients that primary
production depends on also increased moving across the marsh from Tall to High This
trend was significant (p=0,05 for all) The other compartments, such as mussels, grazers,
and benthic algae, did not show any significant trends in their patterns of recycling.
5.3 Mineralization
Mineralization was evaluated because of its importance to the nitrogen cycle and
primary production In general, the actual mineralization rates varied little across marsh
zones because of a lack of data for different zones (Table 19) (Appendix A-D) To
determine the contribution of mineralization rate to each marsh zone in a comparable way.
it was divided by three factors, TST, primary production, and cycled throughput (CT)
Table 19. Mineralization Rates for Different Marsh Zones (g N x m ^ x yr *)
Tall Short High
Great Sippewissett 14.1 (net) 14 1 (net) 14.1 (net)
Phillips Creek 104 (gross) 104 (gross) 116 (gross)
Sapelo Island 70 (gross) 70 (gross) 70 (gross)
5.3.1 Mineralization/TST. Mineralization was considered as a percentage of TST In
all cases, it was 20 % of TST or less (Figure 12). In both Great Sippewissett and Sapelo
Island, the highest percentage was in the High marsh, whereas in Upper Phillips Creek, the
highest percentage was in the Short marsh. It was lowest in the Tall zone for each marsh
However, there were no significant trends (p=0.097)
5.3,2 Mineralization/Production. Mineralization was divided by primary production to
84
Figure 12. Relative Mineralization
in
oo
2
1.8
1.6
rail Short High lall Short High Tall Short High
(jTcat Phillips Sapelo Island
Sippewissetl Creek
Zone of Marsh
El Mineralization/TST H Mineralization/Productivity ? Mineralization/CT
86
determine if mineralization could provide primary producers with enough nitrogen to meet
their need In most cases, mineralization/production was> 1 0, meaning that
mineralization could meet the full demand of primary producers In Great Sippewissett and
Sapelo Island marshes, mineralization/production was highest in the Short marsh (Figure
12). In both Phillips Creek and Sapelo Island mineralization/production was lowest in the
High marsh, but in Great Sippewissett it was lowest in the Tall marsh. The trends across
zones were not significant (p=0 264).
5.3.3 Mineralization/CT. Mineralization was divided by CT to determine the percent of
CT that resulted from mineralization In all cases, mineralization was 30 percent or less of
CT (Figure 12) In both Phillips Creek and Sapelo Island, the Short marsh showed the
greatest amount of mineralization/CT. Great Sippewissett’s mineralization/CT was
highest in the High marsh These differences were not significant between marsh zones
(p=0 368).
5.4 Maturity/Stability
Maturity and stability were evaluated for each marsh zone using relative
ascendency, relative overhead, and relative redundancy. Relative ascendancy is a measure
of maturity, relative overhead is a measure of stability, and relative redundancy is a
measure of response to stress (Ulanowicz, 1997). Each of these indicators are a fraction
of the developmental capacity. Capacity is the Shannon Index of the diversity of flows
scaled by TST (Table 20) Capacity decreased from Tall to High, and was highest for
Sapelo Island and lowest for Upper Phillips Creek These indicators were used to
87
determine how nitrogen cycling reflects the system development potential of each marsh
zone
Table 20. System Level Indices of Development (g N x bits x m ^ x yr'*)
Great Sippewissett Upper Phillips Creek Sapelo Island
Tall Short High Tall Short High Tall Short High
CapaciW 1930.3 1576.4 1034.7 1247 1063.2 402.5 3841.4 2775.4 1624.3
Ascendency 1017.5 867.2 556.8 630.3 550.5 212,1 2410.1 1729.4 1016.7
Overhead 440.9 292.1 129 224.3 163 1 50.1 567.9 317,6 89,9
Redundancy 471.9 417.1 349 372.4 349.7 140.4 863.5 728.4 517.7
Internal 647.3 601.8 399,8 407.3 407,2 161.3 1448.4 1124.6 852.6
Ascendency
Internal 471.9 417.1 349 372.4 349.7 140.4 863.5 728.4 517.7
Redundanc\
5.4.1 Relative Ascendency. Relative ascendency exhibited different patterns for each
marsh (Figure 13), For Great Sippewissett, it was lowest in the Tall and highest in the
Short, For Phillips Creek, it was lowest in the Tall and highest in the High, And for
Sapelo Island, it was lowest in the Short and highest in the Tall Therefore, there were no
significant trends between marsh zones (p=0,717) The differences in relative ascendency
between the marsh zones was very small. They ranged from 50,5% of capacity in Phillips
Creek Tall to 62.5% in Sapelo Island Tall
5.4.2 Overhead. Overhead associated with exogenous flows (inputs, outputs, and
dissipations) consistently decreased moving across the marsh from Tall to High (Figure
13), Input overhead is consistent with this trend (p=0.05). Output overhead, which
decreased from Tall to High, was also significantly different between marsh zones
88
Figure 13. System Level Indices Relative to Capacity
ON
00
PCeoarpceanfctity
Sippewissetl
Zone of Marsh
? Ascendency CQ Input Overhead ? Output Overhead ? Dissipative Overhead 0 Redundancy
90
(p=0 05). Dissipative overhead was a very small portion of capacity in each marsh zone,
but it tended to increase across marsh zones.
5.4.3 Redundancy. Relative Redundancy, a part of total overhead, consistently
increased moving across the marsh from Tall to High (Figure 13). This trend was
significant (p=0.05). Redundancy ranged from 22.5% of capacity for Sapelo Island Tall to
34.9 % in Phillips Creek High marsh.
5.4.4 Internal Ascendency. Relative Internal Ascendency closely mirrored Relative
Ascendency for each marsh (Figure 14). The patterns for each marsh were different In
Great Sippewissett, internal ascendancy was highest in the Short marsh and lowest in the
High marsh. In Upper Phillips Creek, it was highest in the Short marsh and lowest in Tall
marsh. However, in Sapelo Island, it was lowest in the Short marsh and highest in the
Tall. Therefore, no significant trends were found (p=0.717)
5.5 Total System Attributes
To evaluate how the marsh zones ranked with respect to maturity/stability indices,
hierarchical cluster analysis was used. The variables used were FCl, APT, recycling
associated with primary production (PPR), relative ascendency (RA), input overhead (10),
output overhead (00), redundancy (Red), internal ascendency (lA), and
mineralization/primary production (M/P) (Table 21). The meaning of each number was
presented in Section 5.4
91
Figure 14. Internal Ascendency and Redundancy
(N
On
1
0.9
0.8
0.7
« 0.6
Q.
O
*5 0.5
C
O
'5 0.4
(0
w
U.
0.3
0.2
0.1
0
Tall Short High Short High Tall Short
Gfeat Phillips Creek Sapelo Island
Sippewissell
Zone of Marsh
? Internai Ascendancy ID Internal Redundancy
93
Table 21. Marsh Maturity/Stability Variables Used for Cluster Analysis and
Ranking*. See text for variable names referred to by abbreviation in table
Great Sippewissett Phillips Creek Sapelo Island
\'ariables Tall Short High Tall Short High Tall Short High
FCI 29.7 36,5 47.1 36.1 50 53.2 40.8 41.5 80.1
APL 2.98 4.16 6.65 3.31 5.67 9.1 3.4 4.63 18
PPR 0.417 0.464 0.504 0.596 0.667 0.807 0.6 0.615 0.889
RA 0.527 0.55 0.538 0.505 0.518 0.527 0.627 0.623 0.626
lO 0.119 0.089 0.06 0.094 0.07 0.052 0.09 0.068 0.029
OO 0.103 0.089 0.053 0.1 0.081 0.067 0.058 0.047 0.026
Red 0.244 0.265 0.337 0.299 0.329 0.349 0.225 0.262 0.319
lA 0.578 0.591 0.534 0.522 0.538 0.535 0.627 0.607 0.622
0.902 1,12 1.03 1.18 1.12 0.736 1.79 2.07 1 51
*Units for numbers are as follows: FCI=%TST, APL=#compartments, PPR=ffaction of
compartment flow, RA, 10, 00, Red, IA=fraction of capacity, M/P=fraction of primary'
production
A correlation matrix was created to determine if the variables used to do a cluster
analysis covaried (Table 22). Those variables with high positive correlation such as FCI
and APL were removed one at a time to run a cluster analysis New cluster analyses were
run with a highly correlated variable removed from the analysis for each new run It was
discovered that the marsh zones did not change their cluster pattern using this technique
The data presented show the results of the full cluster analysis (Figure 15),
Generally, Tall and Short marsh zones clustered together, eind High marshes
clustered together (Figure 15) Phillips Creek Short marsh clustered with the High
marshes Sapelo Island High marsh was very different from all other marshes and clustered
with none of the other marshes.
94
Figure 15. Cluster Analysis of System Level Attributes
Cluster Tree
GSTall Case 1
PC Tall Case 4n
GS Short Case 2
SI Tall Case 7 -n
SI Short Case 8 —^
GS High Case 3
PC Short Case 5
PC High Case 6
SI High Case 9
I 1 ^^ ^ ^ ^ ^ ^
01 2345678
Distances
96
Table 22. Correlation Matrix of System Attribute Variables (n=9)
FCI APL PPR RA lO OO Red U Ml»
FCI 1.00 0.971 0.869 0.399 -0.912 -0.769 .569 0.213 0.111
APL 1.00 0.811 0.362 -0.859 -0.712 .536 0.23 0.019
PPR 1.00 0.323 -0.825 -0.625 .542 0.1 0.135
RA 1.00 -0.355 -0.772 -0.421 0.903 0.853
lO 1 00 0.827 -0.693 -0.062 -0.135
OO 1.00 -0.229 -0.493 -0.547
Red 1.00 -0.655 -0.505
lA 1.00 0.714
1.00
To further assess the level of maturity using different variables believed to be
indicators of maturity, the marshes were also assigned a rank based on the above variables
used for cluster analysis. Each marsh zone was ranked from least mature to most mature
within a marsh using each variable as a stand-alone indicator of maturity A marsh zone
with a rank of 3 was the most mature, and 1 the least mature For example, the variable
FCI increased from Tall to High in all 3 marshes. It was assumed that the higher the FCI,
the more mature the zone. Therefore, the Tall zone in each marsh received a ranking of 1
and the High zone a ranking of 3. Zone maturity was only compared within a marsh The
Short zone in Sapelo Island could receive a rank of 1 for a variable, while the same zone in
Upper Phillips Creek received a 3 for the same variable Variables such as redundancy,
input overhead, and output overhead, were negatively correlated with maturity.
Therefore, a marsh zone with a high redundancy was given a rank of 1.
When ranking with respect to maturity/stability indices above from lowest maturity
97
to highest, it was found that the Tall marsh zones rank the least mature 1Q A% of the time
The Short marsh zones ranked at intermediate maturity 70.4% of the time, and the High
marsh zones ranked most mature 59.3% of the time (Table 23). Further, the mean rank
followed the same pattern.
Table 23. Marsh Zone Rankings Based on Maturity/Stability Variables (See Table
21 for Variables)
Mean Rank % Time Rank % Time Rank "/o Time Rank
Highest Second Lowest
Tall 1.48 18 5 111 70.4
Short 2.15 22.2 70.4 7.4
High 2.37 59 3 18.5 22.2
5.6 Reliability Factor
Each value used in each network was assigned a reliability factor (RE) as described
in Section 4.4.1. The RF for each marsh zone was averaged to determine the reliability of
the data used for each network (Table 24). Great Sippewissett had the highest level of
reliability, while Upper Phillips Creek had the lowest. This was a reflection of the intensity
of study on each marsh over the decades. The RFs were also weighted by flow to
determine if the majority of flows were associated with higher RFs (Table 25). The
weighted RFs show increased reliability of important flows in most cases. Only Great
Sippewissett Short decreased upon weighting. To better understand how the RFs related
to flow, the RPs were plotted against the percentage of numbers of flows that had a
particular RF and against the percentage of TST for each RF (Figures 16-21). Each
98
Table 24. Average RF and Standard Deviation for Marsh Zones
Tall Short High
Great Sippewissett 2.89±1.28 2,88±1.31 2.94±1.34
Upper Phillips Creek 2.07±1.73 2.12±1.73 1.67±1,77
Sapelo Island 2,25±1 78 2.22±1.77 2,25±1 79
Table 25. Flow' Weighted Average RF for Marsh Zones
Tall Short High
Great Sippewissett 3.06 2.75 3.02
Upper Phillips Creek 2 95 3 08 2 79
Sapelo Island 2.73 2 86 2.71
network contained a total of 60 flows For Great Sippewissett, there was a general
increase in percentage of number of flows associated with higher RFs (Figure 16)
However, for Upper Phillips Creek and Sapelo Island, Figures 17 and 18 show that the
data were generally either very reliable or were obtained by balancing the compartments
inputs and outputs. When RFs were compared to the percentage of TST a better picture
regarding reliability emerged (Figures 19-21). In all three marshes, the greatest
percentage of TST was associated with RFs of 3 and 4. Sapelo Island had the greatest
amount of flow associated with a RF of 0 of the 3 marshes. Over 20% of flows in Sapelo
Island’s Short and High zones were associated with a RF of 0. The rest of the marsh
zones were less than 20%
99
Figure 16. Great Sippewissett % of # of Flows per RF
60
50
40
30
20
10
0
^Tall Short High
101
Figure 1 . Upper Phillips Creek % of# of Flows per RF
60
50
40
30
20
10
—
0 1 1 1 ^ 1 ^ 1 i
0.5 1 1.5 2 2.5 3 3.5 4
RF
-•-Tall Short -A-High
103
Figure 18. Sapelo Island % of# of Flows per RF
o
RF
'?-Tall Short -a-High
105
Figure 19. Great Sippewissett % TST per RF
60
50
40
30
20
10
0
?-Tall Short ?- High
107
Figure 20. Upper Phillips Creek % TST per RF
60
50
40
30
20
10
0
^Tall Short High
109
Figure 21. Sapelo Island % TST per RF
50
45
40
35
30
25
20
15
10
5
0
Tall Short
6.0 DISCUSSION
6.1 Differences in nitrogen cycling in marsh areas
Many scientists have found differences between marsh zones for particular
nitrogen flows ( Section 2.1.2 - 2.1,4). For example, Hanson (1977a) found that nitrogen
fixation occurred at a higher rate in the Tall zone than the Short zone in Georgia. Many
have studied above- and belowground primary production throughout marsh zones (Blum,
1993, Dai and Weigert, 1996, Gallagher and Plumley, 1979, Schubauer and Hopkinson
1984, Valiela et al., 1975, White and Howes, 1994a). The general conclusion is that
aboveground production is higher in the Tall zone than the Short zone (Dai and Weigert,
1996, Gallagher and Plumley, 1979), but belowground production may be just the
opposite (Valiela et al., 1976). High marsh production depends on the dominant plant
species (Morris, 1980). There is also evidence that the mineralization rate is faster in the
Tall zone than the Short zone due to tidal flushing (Howarth and Hobbie, 1982) Again,
mineralization rates in the high marsh depend on the dominant plant species (Good et al.,
1982). Denitrification is also believed to be highest in the Tall zone and lowest in the
High zone associated with differences in tidal flushing (Kaplan et al, 1979) These
individual processes within the nitrogen cycle show differences between marsh zones
Therefore, one can conclude the entire nitrogen cycle will be different among marsh zones.
My contribution is in the evaluation of the integrated nitrogen cycle.
6.1.1 Export Routes of Various Imports. I found several patterns associated with the
export of different nitrogen import pathways. However, the only statistically significant
patterns were associated with precipitation and Tidal PN import Burial and
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denitrification significantly increased in relative importance across the marsh from Tall to
High when the import route was precipitation or Tidal PN This does not conflict with
Kaplan et al.’s (1979) findings that denitrification rates are faster in the Tall zone They
were measuring absolute rates. My findings consider % throughput within a marsh area I
also found that the tidal export of Tidal PN import significantly decreases in importance
moving across the marsh from Tall to High Patterns associated with precipitation are not
surprising Because of the decreased frequency of flooding in the high marsh zone, there
is more opportunity for marsh surface interaction However, in the Tall and Short zones,
flooding is more frequent and there is greater opportunity for the precipitation to be
flushed out by tide before there is contact with the marsh surface. Patterns related to
Tidal PN may reflect the sedimentation/resuspension cycle As the removal of tidal PN
import becomes less important moving across the marsh, there may be more opportunity
for particulates to settle out and become part of the marsh surface. In the Tall and Short
zones, the flooding frequency reduces the relative amount of net sedimentation, decreasing
the opportunity for significant marsh surface contact
In contrast, tidal imports of NH4^, NOx, and DON did not show consistent
patterns across marsh zones. Each marsh processed these nitrogen species very
differently. NOx in Upper Phillips Creek and DON in Great Sippewissett and Sapelo
Island were essentially flushed out of the marsh in the same manner NOx was largely
denitrified in Sapelo Island as a result of the high rate given by Whitney et al. (1981)
However, this rate is believed to be a potential rate rather than in situ (Whitney et al..
113
1981) NH4' was transformed into PN in Sapelo Island before it was flushed out There
was not much information regarding DON, so the lack of significant patterns is not that
surprising. The other 2 nitrogen species were very well studied for these marshes
Therefore, the lack of a pattern among marsh zones may be related to other aspects of the
marsh such as geomorphology, climate, or methodology problems.
6.1.2 Total Contribution to Primary Production. When examining what nitrogen
species contribute to primary production, patterns were found across marsh zones. All
imports tended to increase their relative contribution to primary production moving across
the marsh from Tall to High. However, there were only 2 imports that showed a
significant trend, precipitation and Tidal PN. Between 16 7% (Sapelo Island Tall) and
89.1% (Upper Phillips Creek High) of precipitation went to primary production, the
equivalent of 0.05-0.44g N/(m^ x yr'). The contribution of Tidal PN to primary
production ranged from 8 75% (Sapelo Island Tall) to 69.8% (Upper Phillips Creek
High), the equivalent of 0.20-6.68g N/(m^ x yr'). Tidal NTl4' showed an interesting trend
of contributing least to primary production in the Short zone and most in the High zone,
but this trend was not significant (p=0.097). It contributed from 4.0-48.9% of its
throughput to primary production
These trends may be related to the amount of interaction that each nitrogen species
has with the marsh surface. As discussed above, precipitation and Tidal PN have more
opportunity for marsh surface contact in the High marsh zone than in the Tall or Short
zones. Thus, there is a greater probability in the high marsh that nitrogen originating from
114
these sources will be taken up by the roots. The lack of a trend for Tidal DON is not
surprising given the lack of knowledge of how it is processed in the marsh. The lack of a
significant pattern for Tidal NOx and NH4' may result from different geomorphologies or
climate.
6.1.3 Total Dependancy ofPrimary Production. The amount of primary production’s
throughput that came from various sources was also examined. The only source of import
that showed a significant trend across marsh zones was precipitation It increased in
importance moving across the marsh from Tall to High, but was a very small percentage
of primary production’s throughput (0.2-9.6%, 0 14-2 26g N/(m^ x yr’)). Primary’
production was more dependent on the tidal imports, but showed no consistent pattern
across marsh zones
Tidal NH4' showed very different patterns across marsh zones for each marsh In
Great Sippewissett, it contributed most to primary production in the Tall zone and least in
the High zone. In Upper Phillips Creek, it contributed most to primary production in the
Short zone and least in the Tall zone. And in Sapelo Island, it contributed most in the
High marsh and least in the Short zone. In all cases except Sapelo Island High, primary
production received less than 18% of its total throughput from tidal NH4^. Given that
tidal NH4* contributes less than 25% (except Sapelo Island High) of its throughput to
primary production and that primary production gets less than 18% of its nitrogen from
tidal NH4 , the pore NH4^ that the plants depend on must come from transformations of
other nitrogen species.
115
Tidal NOx decreases in importance moving across the marsh from Tall to High in
Great Sippewissett and Upper Phillips Creek However, tidal NOx plays a larger role in
Upper Phillips Creek than in Great Sippewissett. No pattern was apparent in Sapelo
Island because of the relatively very small amount of nitrogen received by primary
producers from tidal NOx Likewise, primary production does not depend on tidal DON,
and thus no patterns were apparent Tidal PN is most important as a source of nitrogen in
the Short zone in Great Sippewissett and Upper Phillips Creek, but least important in that
zone in Sapelo Island It ranged from 4.3% to 59.2% of primary production’s throughput.
Of all the tidal imports, tidal PN contributes most to pore NH4*, and pore NH4‘ depends
the most on tidal PN as a source of nitrogen from the tide Thus, the sedimentation and
mineralization processes are very important for making tidal imports available to primary
producers.
The recycling of nitrogen within primary production, defined as the amount of
nitrogen that originated in the root/rhizome compartment that returned to that
compartment, showed a significant pattern across marsh zones It increased in importance
moving across the marsh from Tall to High. It accounted for between 417% and 88 9%
of primary production’s throughput This also points to mineralization being a very
important process for making nitrogen available to primary producers.
Though nitrogen fixation was not part of the statistical analysis, it is interesting to
note that there were no patterns associated with primary production’s dependence on
nitrogen fixation The dependency ranged from 4 7% in Upper Phillips Creek Tall zone.
116
supporting Anderson et al.’s (1997b) approximation of 5%, to 79.5% in Sapelo Island Tall
zone. Teal et al. (1979) estimated that nitrogen fixation was approximately a third of
Great Sippewissett’s primary production needs. I found a somewhat lower range of 13 9
% to 29.6% in Great Sippewissett marsh zones.
6.1.4 Groundwater. Groundwater was not subjected to statistical analysis because it is
a negligible source of input for Sapelo Island and Upper Phillips Creek marshes.
However, in Great Sippewissett, there were some interesting patterns. Groundwater
import followed similar export routes as the other imports did Tidal export decreased in
importance moving across the marsh from Tall to High, and burial and denitrification
increased in importance. A large portion of groundwater’s throughput goes to primary
production with the highest amount in the Short zone (70.2%) and the lowest in the Tall
zone (68 3%). Primary production also depends heavily on groundwater for nitrogen. In
each zone, more than 60% of primary production’s nitrogen came from groundwater.
Dependence was highest in the Short zone and lowest in the High zone. Valiela et al
(1978) recognized groundwater as a major source of nutrients for primary producers and
estimated that more nitrogen entered Great Sippewissett via groundwater than was needed
for total primary production Though my results do not support complete dependence on
groundwater, primary production received more than 60% of its nitrogen from
groundwater. It would be interesting to study other marshes Avith large amounts of
groundwater import to determine if these patterns can be generalized.
6.1.5 Nitrogen Cycling Indices. FCI and APL are both used to measure the amount of
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total cycling within a system. FCI is a measure of the total amount of material in the
system that is involved in cycling. APL is a measure of the average number of
compartments a unit of material passes through before exiting the system Both of these
indicators significantly increased moving across the marsh from Tall to High FCI ranged
from 29.7% in Great Sippewissett Tall zone to 80.1% in Sapelo Island High zone APL
ranged from 2 9 in Great Sippewissett Tall zone to 18.0 in Sapelo Island High zone. My
first hypothesis was that cycling would be highest in the High zone because of the reduced
amount of tidal import but relatively high primary production. The results support my
hypothesis that cycling will by highest in the High zone
As a subset of FCI, I also looked at compartmental recycling, the amount of
material that originates in a compartment, cycles through, and returns to that
compartment Recycling within the sediment compartments was significant. Pore NOx,
NH4', and PN all increased the amount of recycling within each compartment moving
across the marsh from Tall to High. Of these, Pore NOx is probably the least important as
recycling is lowest of these 3 compartments (Table 18). Recycling amounts were very
similar between Pore NH4' and Pore PN reflecting their role in the primary
production/mineralization cycle. The recycling within the belowground plant biomass also
significantly increased across the marsh. The trend for aboveground biomass recycling
was not statistically significant (p=0.097). The other compartments did not have any
significant trends across the marsh associated with recycling Therefore, cycling appears
to be closely linked to primary production, mineralization, and associated flows.
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6.1.6 Mineralizarían. Mineralization is a very important part of total nitrogen cycling
within the marsh It provides the much needed Pore NH4* for primary production. There
is a trend for mineralization rate to be higher in the Tall zone than the Short. However,
Blum (1993) did not find it to be significant. My second hypothesis was that relative
mineralization (mineralization/TST) would be highest in the high marsh When
mineralization was divided by TST, primary production, or CT, there were no significant
trends across marsh zones. Mineralization/TST tended to be highest in the High zone and
lowest in the Tall zone, but the trend was not significant (p=0.097) Therefore, 1 reject my
hypothesis
6.2 Maturity and Stability
Maturity and stability were measured using indicators developed by Ulanowicz
(1986) based on information theory Developmental Capacity is the total size and
complexity of a system’s flows Ascendency is the amount of flow within that system that
is organized and has been postulated to be an indicator of maturity. The difference
between Capacity and Ascendency is called overhead. When Ascendency is divided by
Capacity, it is called Relative Ascendency Ulanowicz (1986) proposes relative
ascendency is a good index to compare the maturity of different systems When only
internal flows are examined ascendency is referred to as internal ascendency and can be
scaled by the internal development capacity to get the relative internal ascendency
(Ulanowicz, 1986). This index can help determine the system’s reliance on exogenous
flows when compared to relative ascendency (Baird and Ulanowicz, 1993).
119
Christensen (1995) did an extensive examination of different indicators of maturity
as compared to E.P, Odum’s 24 attributes of succession. He found that relative
ascendency had a high correlation with other indicators of maturity. However, it was a
negative correlation He also found that total overhead had a very strong positive
correlation with maturity He concluded that indicators of stability were indicators of
maturity (Christensen, 1995) However, I believe that Christensen’s comparison of
Ulanowicz’s ascendency to Odum’s maturity attributes may not be a fair comparison
(Section 2.2.3).
The interpretation of maturity indicators also may be affected by the type of model
used to evaluate a system Foodweb models generally focus on carbon flow as a
substitute for energy flow. Cycles involve only organic matter. Biogeochemical models
focus more on primary production and microbial processes (Christian et al., 1996)
Therefore indices that measure cycling such as the Finn Cycling Index (FCI) will have
different interpretations for the different model types (Christian et al., 1996) Baird and
Ulanowicz (1993) found in foodweb models that increased FCI was not an indicator of
maturity but of stress As the system becomes more stressed food chains shorten, causing
material to cycle faster However, in biogeochemical models, the foodweb is only a small
part of the total model. Christian et al (1996) found that stress in the form of
eutrophication was associated with a lower FCI. Dead organic matter also plays a
different role in biogeochemical models than foodweb models In biogeochemical models
dead organic matter can be one of several nonliving compartments, whereas in foodwebs.
120
dead organic matter is the only nonliving compartment.
6.2.1 Maturity Indices. I used relative ascendency as the indicator of maturity. 1
believe that it adequately captures Odum’s (1969) attributes of a mature ecosystem. My
third hypothesis was that relative ascendancy would be highest for the Short zone because
under conditions of rising relative sea-level, this zone would experience a transition that
would be the least extreme (Brinson et al., 1995). This zone would be the least perturbed
by rising relative sea level, and therefore, be able to develop more efficient pathways for
material such as nitrogen to flow. However, relative ascendency did not show any
significant trends across marsh zones. Analysis does not support this hypothesis. Internal
Ascendency, the organization of a system once exogenous flows are removed was also
examined. Like relative ascendency, there were no significant patterns among marsh
zones.
Overhead was divided into input, output, and redundancy, to evaluate trends
across marsh zones. Both input and output overhead showed significant (p=0.05) trends
decreasing across the marsh from Tall to High. This may be interpreted as decreasing
stability moving across the marsh, increased susceptibility to perturbations, and increased
opportunity for state change to occur. Since there is no significant trend associated with
relative ascendency, it cannot be interpreted as a system increasing development at the
expense of overhead. However, redundancy, overhead associated with internal flows,
significantly increased across marsh zones from Tall to High (p=0.05). 1 propose that this
reflects a decreased reliance on exogenous inputs and more reliance on internal cycling in
121
the High zone.
6.2.2 Total System Attributes. Total system attributes attempt to capture the emergent
properties of a system such as stability and maturity Some have used these attributes to
compare systems either over time or space (Forés and Christian, 1993, Forés et al., 1994,
Baird et al., 1995, Christian et al., 1996, Christian et al., 1997). Sometimes indices have
to be scaled to a relative level in order to make comparisons between systems, such as
relative ascendency and FCI
Using scaled maturity/stability indices, hierarchical cluster analyses showed that the
Tall and Short zones tended to cluster together and that the High zones tended to cluster
together (Table 21 and Figure 15) This supports Brinson et al’s (1995) designation of
the low marsh The cluster pattern may be because of the similarity between the Tall and
Short zones, both of which are dominated by S. alteniiflora and received frequent tidal
flooding, 100% and 50% versus 10% of all high tides covering the marsh surface
When the marshes were ranked by the maturity/stability indices, there was a
distinct pattern of the Tall zone ranking the lowest. Short zone second, and the High zone
ranking highest in maturity. This did not follow either my prediction based on relative
ascendency (Ulanowicz, 1986), or overhead (Christensen, 1995). It actually was just the
opposite trend associated with overhead Input and output overhead decreased across the
marsh from Tall to High Overhead decreases when either the magnitude of associated
flows decreases or when these flows are partitioned more evenly (Ulanowicz, 1997). In
this case, the magnitude of tidal imports and exports were significantly decreased moving
122
across the marsh both in absolute and relative terms. Conversely to overhead, maturity
based on ranking of maturity/stability indices increased across the marsh Relative
ascendency and overhead were some of the indices used for the ranking but there were 9
indices used for ranking, 4 of which (i .e., FCI, APL, primary production recycling, and
mineralization/primary production) were unrelated to Ulanowicz’s Ascendency hypothesis.
Redundancy significantly increased (p=0.05) across the marsh from Tall to High. As
discussed earlier, I propose that this reflects a decreased reliance on exogenous inputs and
more reliance on internal cycling in the High zone, as both FCI and APL also significantly
increase moving across the marsh from Tall to High. This may make the High marsh more
stable and resistant to perturbations and state change. Christensen (1995) may have been
correct in stating that indicators of stability are indicators of maturity, but the best
indicator is not total overhead but instead is redundancy
6.3 Comparisons Among Marshes
The original intent was NOT to make comparisons among the different marshes
used in this study because there are several problems with comparing different marshes.
The most important is the use of different methodologies to measure processes such as
primary production, nitrification, mineralization and denitrification. Several different
methods were used to estimate these rates resulting in different estimates (Table 3) For
example, primary production is a major flow within the network and can significantly
influence the analysis of nitrogen flow. It was measured by several different methods
including Wiegert and Evans (1964) and regression (Dai and Wiegert, 1995). These
123
methodologies result in different estimates of primary production (Dai and Wiegert,
1995). Therefore, it cannot be known for sure if the differences among marshes are real
or artifacts of the methodology There were also cases where data were not available for
some marshes but were for others. Therefore, these factors must be kept in mind when
making comparisons among marshes.
Among marsh patterns were examined for FCI, APT, and recycling associated with
belowground plant biomass. There was a general but nonsignificant (p=0.097) pattern for
recycling to be lowest in Great Sippewissett and highest in Sapelo Island. The rough
estimates developed by Howarth and Hobbie (1982) for Great Sippewissett and Sapelo
Island would suggest the opposite finding given that primary production and
mineralization are major factors influencing the amount of recycling within a marsh zone.
They estimated microbial heterotrophy for short S. altermflora stands for both marshes
They found that microbial heterotrophy was much greater in Great Sippewissett (2590 g C
X m'^ X yr"’) than in Sapelo Island (870 g C x m'^ x yr '). They suggest this results from the
greater belowground primary production in Great Sippewissett than Sapelo However, I
found that belowground primary production and mineralization rates were both higher for
Sapelo Island than Great Sippewissett (Table 5 1 and Appendix A-F)
Mineralization/primary production tended to be highest in Sapelo Island and
lowest in Great Sippewissett but not significantly (p=0.097). The mineralization rate was
very different among marshes with the highest rate in Sapelo Island. This may be
explained by climatic effects However, Howarth and Hobbie (1982) found microbial
124
heterotrophy to be higher in Great Sippewissett than Sapelo Island. Though these were
rough estimates, they concluded that the inputs of carbon to the marsh soil were greater in
Great Sippewissett than Sapelo Island (Howarth and Hobbie, 1982)
Relative ascendency showed a significant trend among marshes (p=0 05). It was
lowest for Upper Phillips Creek and highest for Sapelo Island Coincidentally, if maturity
is defined as age, this pattern matches the ages of the marshes Sapelo Island is the oldest
marsh with an estimated age of 15,000 years (Hoyt, 1967) Great Sippewissett is
approximately 2,000 years old (Valiela, 1983), and Upper Phillips Creek is the youngest at
200 years (Chambers et al., 1992) The trend among marshes for internal ascendency was
similar to relative ascendency but was not significant (p=0.097).
Both relative input and output overhead tend to be highest in Great Sippewissett
and lowest in Sapelo Island. However, only the input overhead trend was significant
(p=0.05). Output overhead was nearly significant (p=0.097) This may be related to the
diversity of imports. Great Sippewissett relies on 3 major imports (tide, groundwater, and
nitrogen fixation) while Sapelo Island relies on 2 (tide and nitrogen fixation) and Upper
Phillips Creek relies mainly on 1 import (tide) The more the diversity of imports the less
information contained within the flows. However, when the Shaimon Index of diversity
was applied to the import flows, Sapelo Island showed the greatest diversity of flows
(18.06), Great Sippewissett second (9.27), and Upper Phillips Creek least as expected
(0.68).
There was also a significant trend associated with redundancy among marshes
125
(p=0.05) Redundancy was highest in Upper Phillips Creek and lowest in Sapelo Island
This may be related to the developmental stage of the marsh. Relative ascendency, a
measure of maturity, is lowest for Upper Phillips Creek and highest for Sapelo.
6.4 How Nitrogen Cycling May be Affected by Rising Relative Sea-Level
6.4.1 State Change Model. According to Brinson et al (1995), marshes will respond to
rising sea-level in a variety of ways. The proposed model is that a marsh zone will become
the adjacent marsh zone moving toward the creek. Forest will become high marsh, high
marsh will become low marsh, and low marsh will become subtidal As these changes take
place, the dominant plant species will change, and thus the amount and distribution of
plant biomass will change. The type of soil structure will also change depending on which
zone is considered (Brinson et al., 1995) And most obviously, the frequency of flooding
will change. All of these factors affect how nitrogen cycles through a marsh zone.
6.4.2 Nitrogen cycling patterns across marsh zones. I found statistically significant
nitrogen cycling patterns across marsh zones. Burial and denitrification increased in
importance as export routes moving across the marsh from Tall to High when the import
was precipitation or Tidal PN. The amount of recycling increased moving across the
marsh from Tall to High And maturity associated with nitrogen cycling, as measured by a
ranking of maturity/stability indicators, increased moving across the marsh from Tall to
High These patterns should be affected by an increase in relative sea-level rise
6.4.3 Htnv a marsh’s nitrogen cycle may respond to relative sea-level rise. As rising
relative sea level transforms a high marsh zone to the adjacent low marsh zone as modeled
126
by Brinson et al (1995), the zone will probably experience a decrease in the importance of
burial and denitrification as export routes, a decrease in recycling, and a decrease in
maturity But this does not mean that the total marsh is experiencing these patterns If the
marsh is migrating overland and maintaining its total area, there will be little change in its
overall cycling characteristics. If the marsh is migrating overland and increasing its total
area as is Upper Phillips Creek (Kastler, 1993), the marsh will be increasing cycling and
maturity in an average squared meter if the High zone is the area increasing in size If, as
is the case in Great Sippewissett (Valiela, 1983) and Sapelo Island (Pomeroy and Wiegert,
1981), the marsh is migrating overland and prograding toward the sea, the overall change
in the characteristic of the nitrogen cycle will depend on the rate at which each process
occurs. If prograding occurs more rapidly than overland migration, the marsh will
experience an overall decrease in nitrogen cycling and maturity in an average squared
meter If, however, the marsh is migrating overland faster than it is prograding, then there
will be an increase in cycling and maturity. If the marsh is stalling at a steep slope, the
marsh will cycle less nitrogen and decrease in maturity in an average squared meter
Depending on the steepness of the slope, the marsh may be replaced with open water.
In conclusion, the nitrogen cycle of salt marshes experiencing rising relative sea-
level will change. The degree and direction of change will depend on the landscape setting
of the marsh. Slope and degree of sediment supply will play key roles in determining how
the nitrogen cycle of a salt marsh will be affected by rising sea level
LITERATURE CITED
Anderson, I C ., W.D Miller, and S C. Neubauer 1997a. The effects of wrack deposition
and increased innundation frequency on production and respiration in a SparUna
patens Disticlis spicata salt marsh VCR/LTER All Scientist Meeting
(http ://www vcrlter.virginia.edu)
Anderson, I C., C R Tobias, B B Neikirk, and R L Wetzel 1997b Development of a
process-based nitrogen mass balance model for a Virginia (U S A.) Spartina alterniflora
salt marsh: implications for net DIN flux Marine Ecology Progress Series, 159:13-27.
Atlas, R M and R Bartha 1993 Microbial Ecology’: Fundamental and Applications,
3rd Edition. The Benjamin/Cummings Publishing Company, Inc., Redwood City. 563pp
Baird, D and R E. Ulanowicz 1989 The seasonal dynamics of the Chesapeake Bay
ecosystem Ecological Monographs, 59:329-364
Baird, D and R E. Ulanowicz 1993. Comparative study on the trophic structure, cycling
and ecosystem properties of four tidal estuaries Marine Ecology’ Progress Series,
99:221-237.
Baird, D., R E Ulanowicz, and W R Boynton 1995 Seasonal nitrogen dynamics in
Chesapeake Bay: a network approach Estuarine, Coastal and ShelfScience, 41:137-
162.
Blum, L.K 1997. Relating differential productivity and decomposiiton to rates of organic
matter accumulation VCR/LTER All Scientist Meeting
(http://www.vcrlter.Virginia edu).
Blum, L.K. 1993. Spartina alterniflora root dynamics in a Virginia marsh Marine
Ecology Progress Series, 102:169-178
Blum, L K., and R R Christian 1997 Belowground marsh grass production and decay
along a tidal/elevation gradient. VCR/LTER Database (http://www vcrlter.Virginia edu)
Brenner, D , I Valiela, C D Van Raalte, and E J Carpenter 1976 Grazing by
Talorchestia longicornis on an algal mat in an New England salt marsh Journal of
Experimental Marine Biology and Ecology’, 22 :161-169
Brinson, MM, R R Christian, and L K Blum 1995. Multiple states in the sea-level
induced transition from terrestrial forest to estuary. Estuaries, 18:648-659.
128
Capone, D G. 1983. Benthic nitrogen fixation \n Nitrogen m the Manne Environment
Academic Press, Inc p. 105-137.
Capone, D C. 1997. Microbial nitrogen cycling In: Hurst, C J , G.R. Knudsen, M J.
Mclnerney, L D Stetzenbach, and MV Walter (eds) Manual ofEnvironmental
Microbiology. American Society for Microbiology, Washington D C. p. 334-342
Carpenter, E.J., C D. Van Raalte, and I. Valiela. 1978. Nitrogen fixation by algae in a
Massachusetts salt marsh. Limnology and Oceanography, 23:318-327.
Chambers, R.M., J.W. Harvey, and W.E. Odum 1992. Ammonium and phosphate
dynamics in a Virginia salt marsh. Estuaries, 15:349-359.
Chalmers, A G. 1979 The effects of fertilization on nitrogen distribution in a Spartina
alterniflora salt marsh. Estuarine and Coastal Marine Science, 8 :327-337.
Chalmers, AG., E B. Haines, and B F Sherr 1976 Capacity of a Sfxxrtina salt marsh to
assimilate nitrogen from secondarily treated sewage Research Paper No. ERC-0776
University of Georgia Marine Institute, Sapelo Island, Georgia
Chalmers, A G., R G Wiegert, and P.L Wolf 1985 Carbon balance in a salt marsh:
interactions of diffusive export, tidal deposition and rainfall-caused erosion Estuarine,
Coastal and ShelfScience, 21:757-771
Christensen, V. 1994 On the behavior of some proposed goal functions for ecosystem
development. Ecological Modelling, 75/76:37-49.
Christensen, V. 1995. Ecosystem maturity - towards quantification Ecological
Modelling, 77:3-32.
Christensen,V and D Pauly 1993 a Trophic Models ofAquatic Ecosystems ICLARM
Conference Proceedings 26 390pp.
Christensen, V., and D Pauly 1993b Flow characteristics of aquatic ecosystems In:
Christensen, V , and D Pauly (eds) Trophic Models ofAquatic Ecosystems ICLARM
Conference Proceedings, 26:338-352.
Christian, R R., et al 1981 Aerobic microbes and meiofauna In: Pomeroy, L R , and
R G Wiegert (eds). The Ecology ofa Salt Marsh. Springer-Verlag, New York p 113-
136.
129
Christian, R.R , W.L. Bryant Jr , M.M. Brinson. 1990. Juncus roemenanus production
and decomposition along gradients of salinity and hydroperiod Marine Ecology Progress
Series, 68:137-145.
Christian, R R and J W Day, Jr 1989 Microbial ecology and organic detritus in
estuaries In: Day, J.W , Jr, C.A S Hall, W.M Kemp, and A Yanez-Arancibia (eds)
Estuarine Ecology. John Wiley and Sons, New York pp. 257-308.
Christian, R R., E Forés, F. Comin, P Viaroli, M Naldi, and I Ferrari 1996 Nitrogen
cycling networks of coastal ecosystems: influence of trophic status and primar>^ producer
form Ecological Mixiellmg, 87:111-129.
Christian, R.R., M. Naldi, and P Viaroli. 1997. Construction and analysis of static,
structured models of nitrogen cycling in coastal ecosystems. In Koch, A L., J A
Robinson, and G A Milliken (eds) Mathematical Modeling in Microbial Ecology
Chapman and Hall Microbiology Series, International Thomson Publishing pp. 162-195
Dai, T., and R G Wiegert 1996. Estimation of the primary productivity oí Spartina
a/tez-w/Zora using a canopy model Ecography, 19:410-423
Dame, R., D Childers, and E Koepfler. 1992 A geohydrologic continuum theory for
the spatial and temporal evolution of marsh-estuarine ecosystems. Netherlands Journal of
Sea Research 30:63-72
Davis, G.H. 1987. Land subsidence and sea level rise on the Atlantic Coastal Plain of the
United States. Environmental Geology and Water Science, 10(2) 67-80
Day, J W Jr, CAS Hall, W M Kemp, and A Yáñez-Arancibia 1989 Estuarine
Ecology. John Wiley & Sons, New York 558 pp
Finn, J T 1980 Flow analysis of models of the Hubbard Brook ecosystem Ecology,
61(3)562-571.
Finn, J T., and T.M. Leschine 1980 Does salt marsh fertilization enhance shellfish
production‘s An application of flow analysis. Environmental Management, 4(3): 193-203
Forés, E., and R R Christian. 1993 Network analysis of nitrogen cycling in temperate,
wetland ricefields. Oikos, 67:299-308
Forés, E., R R Christian, F.A Comin, and M. Menendez 1994. Network analysis on
nitrogen cycling in a coastal lagoon. Marine Ecology Progess Series, 106:283-290
130
Gallagher, J L. 1974. Sampling macro-organic matter profiles in salt marsh plant root
zones. Soil Science Society Proceedings, 38:154-155
Gallagher, J.L, 1975. Effect of an ammonium nitrate pulse on the growth and elemental
composition of natural stands of Spartina alterniflora and Juncus roemananus
American Journal ofBotany, 62:644-648.
Gallagher, J L. and F.G. Plumley. 1979. Underground biomass profiles and productivity
in Atlantic coastal marshes. American Journal ofBotany, 66:156-161.
Gallagher, J.L., R J. Reimold, R A Linthurst, and W.J Pfeiffer 1980. Aerial production,
mortality, and mineral accumulation-export dynamics in Spartina alterniflora and Juncus
roemananus plant stands in a Georgia salt marsh Ecology, 61:303-312.
Good, RE., N.F Good, and B R Frasco. 1982. A review of primary production and
decomposition dynamics of the belowground marsh component. In Kennedy, V.S (ed)
Estuarine Comparisons Academic Press, New York p 13 9-15 8
Gross, M.F., M A Hardisky, P L Wolf, and V Klemas 1991 Relationship between
aboveground and belowground biomass of Spartina alterniflora (smooth cordgrass)
Estuaries, 14:180-191
Haines, E B. 1976. Nitrogen content and acidity of rain on the Georgia coast. Water
Resources Bulletin, 12:1223-1231
Haines, E.B 1979. Nitrogen pools in Georgia coastal waters. Estuaries, 2 34-39.
Haines, E., A. Chalmers, R Hanson, and B Sherr. 1977. Nitrogen pools and fluxes in a
Georgia salt marsh In: Wiley, M (ed) Estuarine Processes: Volume H: Circulation,
Sediments, and Transfer ofMaterial in the Estuary Academic Press, New York p 241-
254.
Hannon, B 1973 The structure of ecosystems Jounial of Theoretical Biology, 41 535-
546.
Hanson, R B. 1977a Comparison of nitrogen fixation activity in tall and short Spartina
alterniflora salt marsh soils Applied and Environmental Microbiology, 33:596-602
Hanson, R.B. 1977b, Nitrogen fixation (acetylene reduction) in a salt marsh amended
with sewage sludge and organic carbon and nitrogen compounds Applied and
Environmental Microbiology, 3 3:846-852
131
Hanson, R.B. 1983. Nitrogen fixation activity (acetylene reduction) in the rhizosphere of
salt marsh angiosperms, Georgia, U.S A Botánica Marina, 26 49-59.
Hayden, BP, R D Dueser, J T Callahan, and H H Shugart 1991 Long-term research
at the Virginia Coast Reserve; modeling a highly dynamic environment BioScience,
41:310-318.
Hmieleski, J I. 1994 High marsh-forest transitions in a brackish marsh: The effects of
slope. Master’s Thesis East Carolina University, Greenville, NC. 129 pp.
Hopkinson, C.S., Jr., J.G Gosselink, and F T. Parrondo 1980. Production of coastal
Louisiana marsh plants calculated from phenometric techniques. Ecology, 61:1091-1098.
Hopkinson, C.S., and J P. Schubauer 1984. Static and dynamic aspects of nitrogen
cycling in the salt marsh graminoid Spartina aherniflora. Ecology, 65 :961-969.
Howarth, R W., and J E. Hobbie 1982 The regulation of decomposition and
heterotrophic microbial activity in salt marsh soils: a review In Kennedy, V S (ed)
Estuarine Comparisons Academic Press, Inc p 183-207.
Howes, B L , J W H Dacey, and D D Goehringer 1986 Factors controlling the growth
form of Spartina aherniflora feedback between above-ground production, sediment
oxidation, nitrogen and salinity. Journal ofEcology, 74:881-898
Howes, B.L , J W H Dacey, and J M Teal 1985 Annual carbon mineralization and
belowground production of Spartina aherniflora in a New England salt marsh. Ecology,
66:595-605.
Howes, B L , and D D Goehringer 1994 Porewater drainage and dissolved organic
carbon and nutrient losses through the intertidal creekbanks of a New England salt marsh
Marine Ecology Progress Series, 114:289-301
Hoyt, J H 1967 Barrier island formation Geological Society ofAmerica Bulletin,
78:1125-1136.
Imberger, J , T Berman, R R Christian, E B Sherr, D E Whitney, L R Pomeroy, R G
Weigert, and W.J Wiebe 1983 The influence of water motion on the distribution and
transport of materials in a salt marsh estuary Limnology and Oceanography 28:201-214
132
Jordan, T.E., and I Valiela. 1982. A nitrogen budget of the ribbed mussel, Geukemia
demtssa, and its significance in nitrogen flow in a New England salt marsh Limnology
and Oceanography, 27:75-90
Kana, T W,, J Michel, M S Hayes, and J R Jensen. 1984 The physical impact of sea
level rise in the area of Charleston, SC. In: Barth, M.C and J .G Titus (eds).
Greenhouse Effect and Sea Level Rise: A Challenge for this Generation. Van Nostrand
Reinhold Company, Inc. New York p. 105-114.
Kaplan, W., I. Valiela, and J.M Teal 1979. Denitrification in a salt marsh ecosystem
Limnology and Oceanography, 24:726-734
Kastler, J A 1993. Sedimentations and landscape evolution of Virginia salt marshes
Master’s Thesis. University of Virginia, Charlottesville, VA.
Kay, J J, L A. Graham, and R E Ulanowicz 1989. A detained guide to network analysis
In: Wulff, F., J G. Field, and K H Mann (eds) Network analysis in marine ecology:
methods and applications Coastal and Estuarine studies 32 Springer-Verlag,
Heidelberg p. 15-58.
Keene, W C , and J .N Galloway. 1997. Atmospheric deposition of inorganic nitrogen to
Hog Island VCR/LTER Database (http://www.vcrlter.virginia.edu).
Kemp, P.F., S .Y. Newell, and C Krambeck. 1990a. Effects of filter-feeding by the ribbed
mussel, Geukensia demissa on the water-column microbiota of a Spartina aherniflora salt
marsh Marine Ecology Progress Series, 59:119-131.
Kemp, P.F., S.Y Newell, and C.S. Hopkinson 1990b Importance of grazing on the salt-
marsh grass, Spartina aherniflora to nitrogen turnover in a macrofaunal consumer,
Littorina irrorata, and to decomposition of standing-dead Spartina Marine Biology,
104:311-319.
Kneib, R T 1991 Flume weir for quantitative collection of nekton from vegetated
intertidal habitats. Marine Ecology Progress Series, 75:29-38
Kruczynski, W L., C B. Subrahmanyam, and S H Drake 1978 Studies on the plant
community of a North Florida salt marsh Part 1 Primary Production Bulletin ofMarine
Science, 28:316-334
Kuenzler, E J 1961 Structure and energy flow of a mussel population in a Georgia salt
marsh Limnology and Oceanography, 6:191 -204
133
Larcher, W 1995. Physiological Plant Ecology’ 3rd Edition. Springer, Berlin. 506 pp.
Leschine, T.M. 1979 Salt marsh nitrogen analysis; fertilization and the allocation of
biological productivity Woods Hole Oceanographic Institution Technical Report WHOl-
79-29. Woods Hole, MA.
Meany, R.A., I Valiela, and J.M Teal. 1976. Growth, abundance and distribution of
larval tabanids in experimentally fertilized plots on a Massachusetts salt marsh. Journal of
Applied Ecology’, 13:323-332.
Montague, C .L. 1982. The influence of fiddler crab burrows and burrowing on metabolic
processes in salt marsh sediments In Kennedy, V.S. (ed). Estuarine Comparisons
Academic Press, Inc. p. 283-301.
Morris, J T. 1980. The nitrogen uptake kinetics of Spartina alterniflora in culture.
Ecology’, 61:1114-1121.
Neikirk, B B 1996 Exchanges of dissolved inorganic nitrogen and dissolved organic
carbon between salt marsh sediments and overlying tidal water MA Thesis College of
William and Mary, Gloucester Point, VA
Nestler, J. 1977. A preliminary study of the sediment hydrography of a Georgia salt
marsh using rhodamine WT as a tracer. Southeastern Geology’, 18:265-271.
Newell, S.Y., R.D. Fallon, and J D. Miller 1989. Decomposition and microbial dynamics
for standing, naturally posiitoned leaves of the salt-marsh grass Spartina alterniflora
Marine Biology, 101:471-481
Odum, E.P. 1969. The strategy of ecosystem development. Science, \6A.262-210.
Odum, E.P 1980 The status of three ecosystem-level hypotheses regarding salt marsh
estuaries: tidal subsidy, outwelling, and detritus-based food chains. In: Kennedy, V.
(ed) Estuarine Perspectives. Academic Press, Inc , New York p. 485-495.
Odum, H.T. and R.C. Pinkerton. 1955 Time’s speed regulator: the optimum efficiency
for maximum power output in physical and biological systems. American Scientist,
43:331-343.
Odum, W.E 1984. Dual-gradient concept of detritus transport and processing in
estuaries. Bullentin ofMarine Science, 35:510-521.
134
Orson, R., W. Panageotou, and S P. Leatherman. 1985. Response of tidal salt marshes of
the US. Atlantic and Gulf Coasts to rising sea levels Journal of Coastal Research, 1 29-
37.
Peltier, W.R 1985. Climatic implications of isostatic adjustment constraints on current
variations of eustatic sea level In: Glaciers, ice sheets, and sea level: Effects ofa CO2-
induced climatic change US Department of Energy, DOE/ERy60235-l, Attachment 3.
p 104-115.
Pomeroy, L R 1959 Algal productivity in salt marshes of Georgia Limnology and
Oceanography, 4:386-397.
Pomeroy, L.R , U R Shenton, R D. Jones, R J Reimold 1972. Nutrient flux in estuaries
In: Uikens, G E (ed) Nutrients and Eutrophication American Society ofLimnology’
and Oceanography Special Symposium,. 1 274-291
Pomeroy, UR, and R G Wiegert 1981 The Ecology ofa Salt Marsh Ecological
Studies 38. Springer-Verlag, New York 271p
Potvin, C , and D A. Rolf 1993 Distribution-free and robust statistical methods: viable
alternatives to parametric statistics. Ecology, 74:1617-1628
Redfield, A C. 1972. Development of a New England salt marsh. Ecological
Monographs 42:201 -23 7.
Reimold, R.J , J U Gallagher, R A Linthurst, and W.J Pfeiffer 1975 Detritus
production in coastal Georgia salt marshes In: Cronin, L E. (ed). Estuarine Research,
Volume 1. Academic Press, Inc. p. 217-228
Richardson, D L., S L Hunter, T J Wisecarver, and B P Hayden 1995 Topography,
structures, and vegetation: Upper Phillips Creek Marsh, Brownsville, Virginia
VCR/LTER, Charlottesville (http://www vcrlter Virginia edu)
Schubauer, J P , and C.S Hopkinson 1984 Above- and belowground emergent
macrophyte production and turnover in a coastal marsh ecosystem, Georgia. Limnology
and Oceanography, 29:1052-1065.
Solorzano, L 1969 Determination of ammonia in natural waters by the phenol
hypochlorite method Limnology and Oceanography, 14:799-801
135
Strickland, J D H., and T R. Parsons. 1972. A practical handbook of seawater analysis
Bull. 167 (2nd ed.). Fish Res. Bd. Canada, Ottawa 310p.
Stroud, L M. 1976 Net primary production of belowground material and carbohydrate
patterns in two height forms of Spartina alterniflora in two North Carolina marshes
Ph D Dissertation, North Carolina State University, Raleigh, NC
Taylor, J.H. 1995. The effects of altered inundation and wrack deposistion on
nitrificaiton, denitrificaiton, and the standing stocks of NOj" and NO,'. MS Thesis. East
Carolina University, Greenville, NC
Teal, J.M 1962 Energy flow in the salt marsh ecosystem of Georgia. Ecology,
624.
Teal, J M., I Valiela, and D. Berio. 1979. Nitrogen fixation by rhizosphere and free-
living bacteria in salt marsh sediments Limnology and Oceanography, 24; 126-132.
Tolley, P M 1996 Effects of increased innundation and wrack deposition on a salt
marsh plant community. MS Thesis East Carolina University, Greenville, NC
Ulanowicz, R E. 1980 An hypothesis on the development of natural communities
Journal of Iheoretical Biology, 85:223-245
Ulanowicz, R E 1986 Growth and Development: Ecosystems Phenomenology
Springer Verlag, New York, Inc., New York
Ulanowicz, RE 1987. NETWRK4: A Package ofComputer Algorithms to Analyze
Ecological Flow Networks University of Maryland, Chesapeake Bay Laboratory,
Solomons, MD
Ulanowicz, R E 1998 NETWRK4.2: A Package ofComputer Algorithms to Analyze
Ecological Flow Networks. University of Maryland, Chesapeake Bay Laboratory,
Solomons, MD
Ulanowicz, R E. 1997 Ecology, The Ascendent Perspective Columbia University Press,
Inc , New York 201pp
Ulanowicz, R E and F. Wulff. 1991 Comparing ecosystem strucutures the Chesapeake
Bay and the Baltic Sea In: Cole, J J., G M Lovett, and S E G. Findlay Comparative
Analyses ofEcosystems: Patterns, Mechanisms, and Theories. Springer-Verlag, New
York. p. 140-166.
136
Valida, I. 1983 Nitrogen in salt marsh ecosystems In: Nitrogen in the Marine
Environmeni Academic Press, Inc p.649-678
Valida, I , and J M Teal 1974 Nutrient limitation in salt marsh vegetation. In:
Reimold, R J., and W.H. Queen (eds). Ecology ofHalophytes. Academic Press, Inc.,
New York p. 547-563.
Valiela, I., and J M. Teal. 1979a The nitrogen budget of a salt marsh ecosystem.
Nature, 280:652-656
Valiela, I., and J M. Teal. 1979b Inputs, outputs, and interconversions of nitrogen in a
salt marsh ecosystem. In Jefferies, R L., and A.J. Davy (eds) Ecological Processes in
Costal Environments Blackwell Scientific Publication, London p. 399-414.
Valiela, I., J.M. Teal, S.D. Allen, R Van Etten, D. Goehringer, and S Volkmann 1985.
Decomposition in salt marsh ecosystems: the phases and major factors affecting
disappearance of above-ground organic matter Journal ofExperimental Marine Biology’
and Ecology, 89:29-54.
Valiela, 1., J.M Teal, and N Y. Persson. 1976. Production and dynamics of
experimentally enriched salt marsh vegetation belowground biomass Limnology and
Oceanography, 21:245-252
Valiela, 1., J.M Teal, and W.J. Sass 1975. Production and dynamics of salt marsh
vegetation and the effects of experimental treatment with sewage sludge. Biomass,
production, and species composition. Journal ofApplied Ecology’, 12:973-981
Valiela, I., J M. Teal, S Volkmann, D Shafer, and E J. Carpenter 1978 Nutrient and
particulate fluxes in a salt marsh ecosystem: tidal exchanges and inputs by precipitation
and groundwater. Limnology and Oceanography, 24:798-812.
Van Raalte, C D , I Valiela, E.J Carpenter, and J.M Teal 1974. Inhibition of nitrogen
fixation in salt marshes measured by acetylene reduction Estuarine and Coastal Marine
Science, 2:301-305.
Warrick, R A , C Le Provost, M E Meier, J Oerlemans, and P L Woodworth 1996
Changes in sea level In: Houghton, J T., L.G Meira Filho, B A Callander, N Harris, A
Kattenberg, and K. Maskell (eds) Climate Change 1995, The Science ofClimate Change
Contribution of Working Group 1 to the Second Assessment Report of the
Intergovernmental Panel on Climate Change. University Press, Cambridge, p.363-405.
137
UTiite, D A , T E Weiss, J.M, Trapani, and L.B. Thien, 1978 Productivity and
decomposition of the dominant salt marsh plant in Louisiana, Ecology, 59;751-759.
White, D S., and B L Howes. 1994a Long-term '^N-nitrogen retention in the vegetated
sediments of a New England salt marsh. Limnology and Oceanography, 39:1878-1892
Wfiite, D S., and B E. Howes. 1994b. Nitrogen incorporation into decomposing litter of
Spartina alterniflora. Limnology’ and Oceanography, 39:133-140.
White, D.S., and B E Howes 1994c Translocation, remineralization, and turnover of
nitrogen in the roots and rhizomes of Spartina alterniflora (Gramineae). American
Journal ofBotany, 81:1225-1234.
Whitney, DM, A G Chalmers, E B Haines, R B Hanson, L R Pomeroy, and B Sherr
1981. The cycles of nitrogen and phosphorus Pomeroy, L.R. and R G Wiegert (eds)
The Ecology ofa Salt Marsh. Springer-Verlag, New York, Inc. p. 163-182.
Wiegert, R G 1979 Ecological processes characteristic of coastal Spartina marshes of
the south-eastern U S A In Jeffries, R E and A J Davy (eds) Ecological Processes in
Coastal Environments Balckwell Scientific Publications, p 467-490
Wiegert, R.G. 1986. Modelling spatial and temporal variability in a salt marsh:
sensitivity to rates of primary production, tidal migration and microbial degradation In
Wolf, D A. (ed). Estuarine Variability. Academic Press, Inc., New York p 405-426
Wiegert, R G ., R R Christian, and R E Wetzel 1981 A model view of the marsh In:
Pomeroy, L.R., and R G Wiegert (eds) The Ecology ofa Salt Marsh Springer-Verlag,
New York, Inc p, 183-218
Wiegert, R G and F C Evans 1964 Primary production and the disappearance of the
dead vegetation on an old field in southeastern Michigan. Ecology, 45:49-63
Wiegert, R G , and R E Wetzel 1979 Simulation experiments with a fourteen-
compartment model of a Spartina salt marsh In: Dame, R F (ed). Marine-Estuarine
Systems Simulation Number 8. University of South Carolina Press, Columbia p 7-31
Working Group on Sea Level Rise and Wetland Systems 1997 Conserving coastal
wetlands despite sea level rise. Eos 78:257-261.
138
Wulff, F., J G. Field, and K.H. Mann (eds) 1989. Network analysis in marine ecology:
methods and applications Coastal and Estuarine Studies 32. Springer-Verlag,
Heidelberg
139
APPENDIX A. GREAT SIPPEWISSETT ORIGINAL DATA.
o
. —
Compartment Original Data Season Source
Abo\eground Biomass 400 g/m2/yr High Year Valielaetal, 1975
Aboveground Biomass 300 g/m2 Short Summer Valielaetal. 1976
Abo\ eground Biomass 270 g/m2 Short Summer Valielaetal, 1976
Aboveground Biomass 2.3 gN/m2 Short May White & Howes, 1994c
Aboveground Biomass *2 7gN/m2 Short June White & Howes, 1994c
Aboveground Biomass 3.4gN/m2 Short July White & Howes, 1994c
Abo\'eground Biomass 4.5 g N/m2 Short August White & Howes, 1994c
Aboveground Biomass 4,0 g N/m2 Short September White & Howes, 1994c
Aboveground Biomass 3 2 gN/m2 Short October White & Howes. 1994c
Abo\'eground Biomass 750 g/m2/yr Tall Year Valielaetal, 1975
Aboveground Biomass 350 g/m2/yr Tall Year Valielaetal, 1975
Aboveground Biomass 1700 g/m2 Tall Summer Valiela et al, 1976
Aboveground Dead 14720 kgN Total August Valiela& Teal, 1979b
Aboveground Live 1107 kg N Total August Valiela & Teal. 1979b
Aboveground Production 0 63 kg/m2/yr High Year Valielaetal, 1975
Aboveground Production 423.7 g/m2/yr Short Year Valiela et al, 1976
Aboveground Production 0.36 kg/m2/yr Short Year Valielaetal, 1975
Aboveground Production 3.8 g N/m2/yr Short Year White & Howes, 1994a
Abo\ eground Production 17 mol C/m2/yr Short Year Howes el al, 1985
Aboveground Production 1210 kg N/yr Short Year Leschine, 1979
Aboveground Production 2790 kg N/yr Total Year Finn & Leschine, 1980
Aboveground Production 2790 kg N/yr Total Year Finn & Leschine, 1980
Aboveground Production 51 kgN/d Total August Valiela & Teal, 1979b
Animal 5000 kg N/yr Total Year Valiela, 1983
Animals 9 kg N/yr Total Year Valiela & Teal, 1979b
Animals 9 kg N/yr Total Year Valiela & Teal, 1979b
Animals 1,700 kg Total Year Valiela, 1983
Arthropods 1 15 kgN/d Short June 16-Sept 30 Jordan & Valiela, 1982
Belowground Biomass 23 0 g/m2/growing season High Growing Season Valielaetal, 1976
Belowground Biomass 18 2 g/m2/growing season High Growing Season Valielaetal, 1976
Belowground Biomass 23.2 g/m2/growing season Short Growing Season Valielaetal, 1976
Belowground Biomass 58 9 g/m2/growing season Short Growing Season Valielaetal, 1976
Compartment Original Data Season Source
Belowground Biomass 970 dry mass g/m2 Short Year Howes et al, 1985
Belowground Dead 250 kg N Total August Vahela & Teal, 1979b
Belowground Lwe 493 kg N Total August Vahcla & Teal, 1979b
Belowground Production 3,291.0 g/m2/yr Short Year Valiela et al, 1976
Belowground F*roduction 18.6-20.4 g N/m2/yr Short Year White & Howes, t994a
Belowground Production 3500 gC/m2/yr Short Year Howes et al, 1985
Belowground Production 58 0-74.5 mol C/m2/yr Short Year Howes et al, 1985
Belowground Production 929-1022 g C/m2/yr Short Year White & Howes, 1994a
Belowground Production 3,921.7 g/m2/yr Short Year Valiela et al, 1976
Benthic algae production 5.0 g N/m2/yr Short Year White & Howes, 1994a
Benthic algae production 3.5 mol C/m2/yr Short Year Howes et al, 1985
Biodeposition 450 kg N/yr Short Year Leschine, 1979
Biodcposition 3154 kg N/yr Total Year Vahela, 1983
Biodcposition 1265 kg N/yr Total Year Finn & Leschine, 1980
Biodeposition 1265 kg N/yr Total Year Finn & Leschine, 1980
Burial 1,310 kg N/yT Short Year Jordan & Valiela, 1982
Bunal 4.4 g N/m2/yr Short Year White & Howes, 1994
Burial 7 4 mol C/m2/yr Short Year Howes et al, 1985
Bunal 3.2-4 6 g N/m2/yr Short Year White & Howes, 1994b
Bunal 3.7-4.1 g N/m2/yr Short Year White & Howes, 1994a
Burial 25 kg N/yr Short Year Leschine, 1979
Burial 2.7 g N/m2/yr Total Year Valiela, 1983
Burial 25 kg N/yr Total Year Vahela & Teal, 1979b
Bunal 1,295 kg N/yr Total Year Vahela, 1984
Burial 1,295 kg/yr Vegeti Year Vahala & Teal, 1979a
Burial 5% of annual production Year Vahcla et al, 1975
Decay 631 8 gbiomass/m2 High Year Leschine, 1979
Decay 1 kg N/m2 Higli Year Leschine, 1979
Decay 4200 kg N/yr Short Year Leschine, 1979
Decay 4 kg N/m2 Short Year Leschine, 1979
Decay 423.7 g biomass/m2 Short Year Leschine, 1979
Decay 0.4 g N/m2/yr Short Year While & Howes, 1994c
Compartment Original Data Zone Season Source
Decay 10 kg N/d Total August Vahela & Teal, 1979b
Decay 9440 kg N/yr Total Year Finn & Leschinc, 1980
Decay 9440 kg N/yr Total Year Finn & Leschinc, 1980
Decay 1,600 kg N/yr Total Year Vahela, 1983
Decomposer 70% of aboveground prod Total Year Vahela, 1983
Dcnitnfication 66 kg N/yr Algal Mat Year Kaplan et al, 1979
Denitrification 1.158 kg N/yT Creek Bottom Year Kaplan et al, 1979
Dcnitnfication 223 kg N/yr High Year Kaplan et al, 1979
Dcnitnfication 45 kg N/yr Pannes Year Kaplan et al, 1979
Demtnfication 25.2 mg N/m2/d Short May White & Howes, 1994a
Denitrification 4 1-5 6 g N/m2/y r Short Year White & Howes, 1994a
Dcnitnfication 1.371 kg N/yr Short Year Kaplan et al, 1979
Denitrification 2830 kg N/yr Short Year Leschinc, 1979
Dcnitnfication 153 kg N/yr Tall Year Kaplan el al, 1979
Dcnitnfication 9 kg N/d Total Year Vahela & Teal, 1979b
Dcnitnfication 6,940 kg/yr Total Year Vahala & Teal, 1979a
Dcnitnfication 1,558 kg N/yr Total Year Vahela & Teal, 1979b
Dcnitnfication 6940 kg N/yr Total Year Vahela, 1983
Denitrification 14.3 gN/m2/yr Total Year Vahela, 1983
Dcnitnficalion 8250 kg N/yr Total Year Finn & Leschinc, 1980
Dcnitnfication 6,940 kg N/yr Total Year Vahela, 1984
DIN 12 kg/d Total August/September Vahala & Teal, 1979a
DIN 0.05 kg N/d Total August Valiela & Teal, 1979b
Excretion 420 kg N/yr Short Year Leschinc, 1979
Excretion 7130 kg N/yr Total Year Vahela, 1983
Excretion 690 kg N/yr Total Year Finn & Leschine, 1980
Excretion 690 kg N/yr Total Year Finn & Leschine, 1980
Filtration 2650 kg N/yr Short Year Leschine, 1979
Filtration 2530 kg N/yr Total Year Finn & Leschine, 1980
Filtration 2530 kg N/yr Total Year Firm & Leschine, 1980
Fish 2.33 kg N/d Short June 16-Scpt 30 Jordan & Vahela, 1982
Grazers 10% of aboveground Prod Total Year Vahela. 1983
Compartment Original Data Zone Season Source
Grazers/Nekton 9 11 mussels/day Short Growing Season Seed, 1980
Groundwater 2455 kg N/yr Short Year Leschine, 1979
Groundwater 2.710 kg/yr Total Year Valíala & Teal, 1979a
Groundwater 460 kg/yr Total Year Valíala & Teal, 1979a
Groundwater 30 kg/yr Total Year Valíala & Teal, 1979a
Groundwater 2,920 kg/yr Total Year Valíala & Teal, 1979a
Groundwater 9 kg/d Total Junc/July Valíala & Teal, 1979a
Groundwater 6,120 kg/yr Total Year Valíala &. Teal, 1979a
Groundwater 5,471 kg N/yr Total Year Vahela & Teal, 1979b
Groundwater 2,495 kg N/yr Total Year Vahela & Teal, 1979b
Groundwater 29 kg N/yr Total Year Vahela & Teal, 1979b
Groundwater 492 kg N/yr Total Year Vahela & Teal, 1979b
Groundwater 2,455 kg N/yr Total Year Vahela & Teal, 1979b
Groundwater 12 6 g N/ni2/yr Total Year Vahela, 1983
Groundwater 2,921 kg N/yr Total Year Vahela et al, 1978
Groundwater 312 kj N/yr Total Year Vahela et al, 1978
Groundwater 458 kg N/yr Total Year Vahela et al, 1978
Groundwater 2,713 kg N/yr Total Year Vahela et al, 1978
Groundwater 6100 kg N/yr Total Year Vahela et al, 1978
Groundwater 7180 kg N/yr Total Year Finn & Leschine, 1980
Groundwater 15 kg N/yr Total Year Finn & Leschine, 1980
Groundwater 530 kg N/yr Total Year Finn & Leschine, 1980
Groundwater 2,921 kg N/yr Total Year Kaplan et al, 1979
Groundwater 6,120 kg N/yr Total Year Vahela, 1984
Leaching 0 4 kg N/d High June 16-Sept 30 Jordan & Vahela, 1982
Leaching 2.8kgN/d Short June 16-Sept 30 Jordan & Vahela, 1982
Leaching 0.4 g N/m2/yr Short Year White & Howes, 1994a
Leaching 73.5 mg N/m2/mo Short June White & Howes, 1994a
Leaching 78 7 mg N/m2/mo Short July White & Howes, 1994c
Leaching 70 6 mg N/m2/mo Short August White & Howes, 1994c
Leaching 59.6 mg N/m2/mo Short September White & Howes, 1994c
Ixaching 128 9 mg N/m2/mo Short October White & Howes, 1994c
Compartment Original Data Zone Season Source
Leaching 0.4 g N/m2/yr Short Year White & Howes, 1994c
Leaching 270 kg N/yr Short Year Leschine, 1979
Leaching 7 kg N/d Total August Valiela & Teal, 1979b
Leaching 1200 kg N/yr Total Year Valiela, 1983
Leaching 2830 kg N/yr Total Year Finn & Leschine, 1980
Leaching 630 kg N/yr Total Year Finn & Leschine, 1980
Leaching 7 kg/d Total August Valíala & Teal. 1979a
Litter 0-6.0 mol C/m2/yr Short Year Howes el al, 1985
Litter 2200 kg N/yr Total Year Finn & Leschine, 1980
Mineralization 14 9-16 3 g N/m2/yr Short Year White & Howes. 1994a
Mineralization 700 kg/yr Total Year Valíala & Teal, 1979a
Mineralization 700 kg N/yr Total Year Valiela, 1983
Mineralization 3280 kg N/yr Total Year Valiela, 1983
Mineralization 700 kg N/yr Total Year Finn & Leschine, 1980
Mussel 2528 kg N/yr Short Year Jordan & Valiela, 1982
Mussel 1264 kg N/yr Short Year Jordan & Valiela, 1982
Mussel 691 kg N/yr Short Year Jordan & Valiela, 1982
Mussel 261 kg N/yr Short Year i Jordan & Valiela, 1982
Mussel 7 49 kg N/d Short June 16-Sept 30 Jordan & Valiela, 1982
Mussel 2,530 kg N/yr Short Year Jordan & Valiela, 1982
Mussel 1,260 kg N/yr Short Year Jordan & Valiela, 1982
Mussel 165 kg N/yr Total Year Finn & Leschine. 1980
Mussel 165 kg N/yr Total Year Finn & Leschine, 1980
Mussel 1341 kgN Year Jordan & Valiela, 1982
Nitrification 0-10 kg N/d Total April-October Kaplan el al, 1979
Nitrification 4740 kg N/yr Total Year Valiela, 1983
Nitrification 9635 kg N/yr Total Year Finn & Leschine, 1980
Nitrogen Fixation 14,140 g N/yr Algal Mat Year Carpenter et al. 1978
Nitrogen Fixation 2.3 g N/m2/yr Algal Mat Year Valiela, 1983
Nitrogen Fixation 71.90 ng N/cm2/h Algal Mat May Van Raalte el al, 1974
Nitrogen Fixation 86.90 ng N/cm2/h Algal Mat May Van Raalte et al, 1974
Nitrogen Fixation 44 30 ng N/cm2/h Algal Mat July Van Raalte et al, 1974
Compartment Original Data 2U)nc Seas Source
Nitrogen Fi.xation 33.56 ng N/cm2/h Algal Mat July Van Raaltc et al, 1974
Nitrogen Fixation 7.3 ng N/cm2/h Algal Mat July Brenner et al, 1976
Nitrogen Fixation 155.7 ng N/cm2/h Algal Mat July Brenner et al, 1976
Nitrogen Fixation 104 kg N/yr Creek Bottom Year Kaplan et al, 1979
Nitrogen Fixation 1 9 ng N/cm2/h High March Teal et al^ 1979
Nitrogen Fi.xation 13.3 ng N/cm2/h High June Teal ét al, 1979
Nitrogen Fixation 28.2 ng N/cm2/h High July Teal et al. 1979
Nitrogen Fixation 67.9 ng N/cm2/h 'High August Teal et al, 1979
Nitrogen Fixation 18.7 ng N/cm2/h High September Teal et al' 1979
Nitrogen Fixation 16 6 ng N/cm2/h High November Teal et al, 1979
Nitrogen Fixation 10.3 ng N/cm2/h High November Teal et al, 1979
Nitrogen Fixation 1 9 ng N/cm2/h High March Teal et ai, 1979
Nitrogen Fixation 18.1 ng N/cm2/h High June Teal et al, 1979
Nitrogen Fixation 28 0 ng N/cm2/h High July Teal et al, 1979
Nitrogen Fixation 70.5 ng N/cm2/h High August Teal et al, 1979
Nitrogen Fi.xation 35.8 ng N/cm2/h High September féal étal, 1979
Nitrogen Fixation 19 4 ng N/cm2/h High November Teal et al, 1979
Nitrogen Fixation 5.8 ng N/cm2/h High November Teal et al7 l979
Nitrogen Fi.xation 114 kg N/yr High Year Kaplan et al, 1979
Nitrogen Fixation 1 2 g N/m2/yr High Year Vahela, 1983
Nitrogen Fi.xation 12.1 gN/ni2/yr High Year Valiela, 1983
Nitrogen Fi.xation 8,560 g N/yr [High Year Carpenter et al, 1978
Nitrogen Fixation 1.7 g N/m2/yr Muddy Creek Bottom Year Valiela, 1983
Nitrogen Fixation 14.140 g N/yr Pannes Year Ca^nter et al, 1978
Nitrogen Fi.xation 880 g N/yr Pink Sand Year Carpenter et al, 1978
Nitrogen Fixation 0 7gN/m2/yr Sandy Creek Bottom Year Valiela. 1983 ^
Nitrogen Fixation 1270 kg N/yr Short Year Leschine 1979
Nitrogen Fixation 0.8 g N/m2/yr Short Year Valiela, 1983
Nitrogen Fixation 8.4 n N/m2/y r Short Year Valiela, 1983
Nitrogen Fixation 1,096 kg N/>t Short Year Kaplan et al, 1979
Nitrogen Fi.xation 2.3 ng N/cm2/h Short March Teal et al, 1979
Nitrogen Fixation 27.7 ng N/cm2/h Short June Teal et al. 1979
Compartment Original Data Zone Season Source
Nitrogen Fixation 38.5 ng N/cm2/h Short July Teal et al, 1979
Nitrogen Fixation 47.5 ng N/cm2/h Short August Teal et al, 1979
Nitrogen Fixation 71.6 ng N/cm2/h Short September Teal etal, 1979
Nitrogen Fixation 17.3 ng N/cm2/h Short November Teal et al, 1979
Nitrogen Fixation 3.6 ng N/cm2/h Short November Teal el al, 1979
Nitrogen Fixation 2.8 ng N/cni2/h Short March Teal et al. 1979
Nitrogen Fixation 5.7 ng N/cni2/h Short June Teal et al, 1979
Nitrogen Fixation 2.8 ng N/cni2/h Short July Teal et al, 1979
Nitrogen Fixation 42 4 ng N/cm2/h Short August Teal et al, 1979
Nitrogen Fixation 44.9 ng N/cin2/h Short September Teal et al, 1979
Nitrogen Fixation 8 5 ng N/cm2/h Short November Teal et al, 1979
Nitrogen Fixation 5.6 ng N/cm2/h Short November Teal etal, 1979
Nitrogen Fixation 78,850 g N/yr Short Year Carpenter et al, 1978
Nitrogen Fixation 31,130 g N/yr Tall Year Carpenter et al, 1978
Nitrogen Fixation 63 kg N/yr Tall Year Kaplan etal, 1979
Nitrogen Fixation 0.6 ng N/cni2/h Tall March Teal et al, 1979
Nitrogen Fixation 2.2 ng N/cm2/h Tall June Teal et al, 1979
Nitrogen Fixation 2.9 ng N/cm2/h Tall July Teal et al, 1979
Nitrogen Fixation 7.5 ng N/cm2/h Tall August Teal et al, 1979
Nitrogen Fixation 2 0 ng N/cni2/h Tall September Teal etal, 1979
Nitrogen Fixation 0.7 ng N/cni2/h Tall November Teal et al, 1979
Nitrogen Fixation 0.2 ng N/cm2/h Tall November Teal et al. 1979
Nitrogen Fixation 12 ng N/cm2/h Tall March Teal et al, 1979
Nitrogen Fixation 1.9 ng N/cni2/h Tall June Teal et al, 1979
Nitrogen Fixation 2.8 ng N/cm2/h Tall July Teal et al, 1979
Nitrogen Fixation 0 4 ng N/cin2/h Tall August Teal et al, 1979
Nitrogen Fixation 2.0 ng N/cni2/h Tall September Teal et al, 1979
Nitrogen Fixation 0.4 ng N/cm2/h Tall November Teal et al, 1979
Nitrogen Fixation 0.2 ng N/cm2/h Tall November Teal et al. 1979
Nitrogen Fixation 174 kg N/yr Total Year Vahela & Teal, 1979b
Nitrogen Fixation 2,595 kg/yr Total Year Valíala & Teal, 1979a
Nitrogen Fixation 3.280 kg/yr Total Year Valíala & Teal, 1979a
Compartment Original Data Zone Season Source
Nitrogen Fixation 297 kg/yr Total Year Valíala & Teal. 1979a
Nitrogen Fixation 384 kg/yr Total Year Valíala & Teal. 1979a
Nitrogen Fixation 145kgN/yr Total Year Valiela & Teal, 1979b
Nitrogen Fixation 1.273 kg N/yr Total Year Valiela & Teal, 1979b
Nitrogen Fixation 8 kg N/d Total August Valieia & Teal, 1979b
Nitrogen Fixation 3280 kg N/yr Total Year Valiela, 1983
Nitrogen Fixation 145 kg N/yr Total Year Valida et al, 1978
Nitrogen Fixation 1,277 kg N/yr Total Year Valiela et al, 1978
Nitrogen Fixation 2600 kg N/yr Total Year Finn & Leschine, 1980
Nitrogen Fixation 3,280 kg N/yr Total Year Valiela, 1984
Nitrogen Fixation 1.592 kg N/yr Total Year Valiela & Teal, 1979b
Nitrogen Fixation 2600 kg N/yr Total Year Finn & Leschine
Nitrogen Fixation 10-20 mg N/m2/d Vegetative Summer Carpenter ct al, 1978
Nitrogen Fixation 186 ng N7cm2/h Vegetative June Van Raalte et al, 1974
Nitrogen Fixation 121 ng N/cm2/h Vegetative June Van Raalte et al. 1974
Nitrogen Fixation 106.8 ng N/cm2/h Vegetative June Van Raalte et al, 1974
Nitrogen Fixation 224.2 ng N/cm2/h Vegetative June Van^I^l^ et al, 1974
Nitrogen Fi.xation 161 0 ng N/cm2/h Vegetative May Van Raalte et aï^ 1974
Nitrogen Fixation 74 7 ng N/cm2/h Vegetative May Van Raalte et al, 1974
Nitrogen Fixation 46.0 ng N/cm2/h Vegetative May Van Raalte et al. 1974
Nitrogen Fixation 109.0 ng N/cm2/h Vegetative May Van Raalte et al, 1974
Nitrogen Fixation 230 0 ng N/cm2/h Vegetative May ]^n Raalte et aL 1974
Nitrogen Fixation 92.0 ng N/cm2/h Vegetative May Van Raalte et al, 1974
Nitrogen Fixation 3.8 ng N/cm2/h Vegetative August Van I^alte et al, 1974
Nitrogen Fixation 21.5 ng N/cm2/h Vegetative August Van Raalte et al^ 1974
Nitrogen Fixation 19.6 ng N/cm2/h Vegetative August Van Raalte et al, 1974
Nitrogen Fixation 3 .6 ng N/cm2/h Vegetative August Van Raalte et al, 1974
Nitrogen Fixation 293.5 ng N/cm2/h Vegetative June Van Raalte et al, 1974
Nitrogen Fixation 142.3 ng N/cm2/h Vegetative June Van Raalte et al. 1974
Nitrogen Fixation 78 8 ng N/cm2/h Vegetative June Van Raalte et al. 1974
Nitrogen Fixation 130 6 ng N/cm2/h Vegetative June Van Raalte et al, 1974
Nitrogen Fixation 463 2 ng N/cm2/h Vegetative June Van Raalte et al, 1974
Compartment Original Data Season Source
Nitrogen Fixation 14 8 ng N/cm2/h Veget June Van Raalte ct al, 1974
Other 9 kg N/yr Total Year Valiela, 1984
Other 26 kg N/yr Total Year Valiela, 1984
Particulate N 9 kg/yr Total Year Valíala & Teal, 1979a
Plant Uptake 155 kg N/yr High Year Leschinc, 1979
Plant Uptake 6990 ieg N/yr Short Year Leschinc, 1979
Plant Uptake 1055 kg N/yr Short Year Leschine, 1979
Plant Uptake 39 kg/d Total Junc/July Valíala & Teal, 1979a
Plant Uptake 4(K)0 kg N/yr Total Year Valiela. 1983
Plant Uptake 4100 kg N/yr Total Year Teal et al, 1979
Plant Uptake 4,100 kg N/yr Total Year Valiela ctal, 1978
Plant Uptake 16790 kg N/yr Total Year Finn & Leschine, 1980
Plant Uptake 11200 kg N/yr Total Year Finn & Leschine, 1980
Plant Uptake 5600 kg N/yr Total Year Finn & Leschine, 1980
Plant Uptake 4,000 kg N/yr Total Year Valiela, 1983
Plants 5,200 kg Total Year Valiela, 1983
Pore DON 19200 kg N/yr Total Year Finn & Leschine, 1980
Pore DON 18500 kg N/yr Total Year Finn & Leschine, 1980
Precipitation 90 kg N/yr Short Year Leschine, 1979
Precipitation 190 kg/yr Total Year Valíala & Teal, 1979a
Precipitation 70 kg/yr Total Year Valíala & Teal, 1979a
Precipitation 0.4 kg/yr Total Year Valíala & Teal, 1979a
Precipitation 110 kg/yr Total Year Valíala & Teal, 1979a
Precipitation 15 kg/> r Total Year Valíala & Teal, 1979a
Precipitation 380 kg/yr Total Year Valíala & Teal, 1979a
Precipitation 52 kg N/yr Total Year Valiela & Teal, 1979b
Precipitation 0.2 kg N/yr Total Year Valiela & Teal, 1979b
Precipitation 31 kg N/yr Total Year Valiela & Teal, 1979b
Precipitation 89 kg N/yr Total Year Valiela & Teal, 1979b
Precipitation 7 kg N/yr Total Year Valiela & Teal, 1979b
Precipitation 0.5 kg N/d Total August Valiela & Teal, 1979b
Precipitation 52 kg N/yr Total Year Valiela et al, 1978
O'
Compartment Original Data Season Source
Precipitation 0 2 kg N/yr Total Year Valielactal, 1978
Precipitation 31 kg N/yr Total Year Valida et al, 1978
Precipitation 89.2 kg N/yr Total Year Valielactal, 1978
Precipitation 6.5 kg N/yr Total Year Valida et al, 1978
Precipitation 178.9 kg N/yr Total Year Valida et al, 1978
Precipitation 380 kg N/yr Total Year Valida, 1984
Precipitation 179 kg N/yr Total Year Valida & Teal, 1979b
Rcsuspcnsion 23,000 kg N/yr Short Year Jordan & Valida, 1982
Resuspension 1250 kg N/yr Short Year Leschme, 1979
Rcsuspcnsion 1270 kg N/yr Short Year Leschine, 1979
Resuspension 60 mol/yr Tall Year Hoews & Goehringer, 1994
Resuspension 760 mol/yr Tall Year Howes &Doehringer, 1994
Resuspension 11945 kg N/yr Total Year Finn & lÆSchine, 1980
Resuspension 20380 kg N/yr Total Year Finn & Leschine, 1980
Sediment 19,100 kg N/yr Short Year Jordan & Valida, 1982
Sediment 40 % of uptake Total Year Valida, 1983
Sediment 46 kg N/d Total August Valida & Teal, 1979b
Sediment 15 kg N/d Total August Valida & Teal, 1979b
Sediment 10 kg N/d Total August Valida & Teal, 1979b
Sediment 116,800 kg Total Valíala & Teal, 1979a
Sediment 110,000 kg Total Year Valida, 1983
Sediment 49,000 kg N Total August Valida & Teal, 1979b
Sedimentation 490 kg N/yr Short Year Leschine, 1979
Sedimentation 505 kg N/yr Short Year leschine, 1979
Sedimentation 19100 kg N/yr Total Year Finn & Leschine, 1980
Sedimentation 19100 kg N/yr Total Year Finn & Leschine, 1980
Shellfish 454/m2 Tall Year Leschine, 1979
Shellfish 0.7 kg N/d Total August Valida & Teal, 1979b
Shellfish 5 kg N/d Total August Valida & Teal, 1979b
Shellfish 10 kg N/d Total August Valida & Teal, 1979b
Shellfish 214 kg N Total August Valida & Teal, 1979b
Snails 0 23 kg N/d Short June 16-Sept 30 Jordan & Valida, 1982
o
Compartment Original Data Zone Season Source
Tidal Water 3 kg Total Year Vahela. 1983
Tidal Water Exchange 16340 kg N/yr Short Year Leschine, 1979
Tidal Water Exchange 18480 kg N/yr Short Year Leschine, 1979
Tidal Water Exchange 6,760 kg N/yr Short Year Jordan & Valiela, 1982
Tidal Water Exchange 8,170 kg N/yr Short Year Jordan & Valiela, 1982
Tidal Water Exchange 2.0-3.2 gN/m2/yr Short Year White & Howes, 1994c
Tidal Water Exchange 1.6 g N/m2/yr Short Year White & Howes, 1994c
Tidal Water Exchange 1 1-1.2 gN/m2/yr Short Year White & Howes, 1994c
Tidal Water Exchange 8kg/d Total June/July Valíala & Teal, 1979a
Tidal Water Exchange 16,300 kg/yr Total Year Valiala & Teal, 1979a
Tidal Water Exchange 2,620 kg/yr Total Year Valíala & Teal, 1979a
Tidal Water Exchange 150 kg/yr Total Year Valíala & Teal. 1979a
Tidal Water Exchange 390 kg/yr Total Year Valiala & Teal, 1979a
Tidal Water Exchange 6,740 kg/yr Total Year Valiala & Teal, 1979a
Tidal Water Exchange 26,200 k^yr Total Year Valíala & Teal, 1979a
Tidal Water Exchange 26,252 kg N/yr Total Year Vahela & Teal, 1979b
Tidal Water Exchange 386 kg N/yr Total Year Vahela & Teal, 1979b
Tidal Water Exchange 154 kg N/yr Total Year Vahela & Teal. 1979b
Tidal Water Exchange 2,623 kg N/yr Total Year Vahela & Teal. 1979b
Tidal Water Exchange 16,346 kg N/yr Total Year Vahela & Teal, 1979b
Tidal Water Exchange 6,743 kg N/yr Total Year Vahela & Teal, 1979b
Tidal Water Exchange 2,623 kg N/yr Total Year Vahela el al, 1978
Tidal Water Exchange 386 kg N/yr Total Year Vahela et al, 1978
Tidal Water Exchange 154 kg N/yr Total Year Vahela el al, 1978
Tidal Water Exchange 16.346 kg N/yr Total Year Vahela et al, 1978
Tidal Water Exchange 26200 kg N/y'r Total jVear Finn & Leschine, 1980
Tidal Water Exchange 2620 kg N/yr Total Year Finn & Leschine, 1980
Tidal Water Exchange 6740 kg N/yr Total ¡Year Finn & Leschine, 1980
Tidal Water Exchange 4285 kg N/yr Total Year Finn & Leschine, 1980
Tidal Water Exchange 26,200 kg N/yr Total Year Vahela. 1984
Tidal Water Exchange 6,743 kg N/yT Total Year Vahela el al, 1978
Tidal Water Exchange 31,604 kg N/yr Total Year Vahela & Teal, 1979b
Compartment Original Data Zone Season Source
Tidal Water Exchange 1,215 kg N/yr Total Year Valiela & Teal, 1979b
Tidal Water Exchange 166 kg N/yr Total Year Valiela & Teal, 1979b
Tidal Water Exchange 3,539 kg N/yr Total Year Valiela & Teal, 1979b
Tidal Water Exchange 18,479 kg N/yr Total Year Valiela & Teal, 1979b
Tidal Water Exchange 8,205 kg N/jt Total Year Valiela & Teal, 1979b
Tidal Water Exchange 4 kg N/d Total August Valiela & Teal, 1979b
Tidal Water Exchange 4 kg N/d Total August Valiela & Teal, 1979b
Tidal Water Exchange 3,539 kg N/yr Total Year Valiela et al, 1978
Tidal Water Exchange 1,215 kg N/yr Total Year Valiela et al, 1978
Tidal Water Exchange 166 kg N/yr Total Year Valiela et al, 1978
Tidal Water Exchange 18,479 kg N/yr Total Year Valiela et al, 1978
Tidal Water Exchange 8,205 kg N/yr Total Year Valiela et al, 1978
Tidal Water Exchange 3540 kg N/yr Total Year Finn & Leschine, 1980
Tidal Water Exchange 8200 kg N/yr Total Year Finn & Leschine, 1980
Tidal Water Exchange 8320 kg N/yr Total Year Finn & Leschine, 1980
Tidal Water Exchange 20% of aboveground production Total Year Valiela et al, 1975
Tidal Water Exchange 31,600 kg N/yr Total Year Valiela, 1984
Tidal Water Exchange 31600 kg N/yr Total Year Finn & Leschine, 1980
Translocation 14 gN/m2/yr Short Year White & Howes, 1994c
Volatilisation of NH3 10 kg N/yr Short Year Leschine, 1979
Volatilisation of NH3 17 kg/yr Total Year Valíala & Teal, 1979a
Volatilisation of NH3 8 kg N/yr Total Year Valiela & Teal, 1979b
152
APPENDIX B. GREAT SIPPEWISSETT CONVERTED DATA.
RF=Reliability Factor
Compartment Zone Source g N/m2/yr RF Comments
Aboveground Biomass High Valielaetal, 1975 6 5 c 1.5% N content of dry mass (Vince et al, 1981)
Aboveground Biomass Short Valielaetal, 1976 4.5 5 c 15% N content of dry mass (Vince et al, 1981)
Aboveground Biomass Short Valielaetal, 1976 405 5 c 15% N content of dry mass (Vince et al, 1981)
Aboveground Biomass Short White & Howes, 1994c 2.5/may 4
Aboveground Biomass Short White & Howes, 1994c 2.7/jun 4
Aboveground Biomass Short White & Howes, 1994c .5,4/jul 4
Aboveground Biomass Short White & Howes, 1994c 4.5/aug 4
Aboveground Biomass Short White & Howes, 1994c 4 0/sep 4
Aboveground Biomass Short White & Howes, 1994c b.2/oct 4
Aboveground Biomass Tall Valielaetal. 1975 11.25 5 c 1.5% N content of dry mass (Vince et al, 1981)
Aboveground Biomass Tall Valielaetal, 1975 5 25 5 c 1.5% N content of dry mass (Vince et al, 1981)
Aboveground Biomass Tall Valielaetal, 1976 25.5 5 c 1.5% N content of dry mass (Vince ct al, 1981)
Aboveground Dead Total Valiela& Teal, 1979b 5045 4
Aboveground Live Total Valiela & Teal, 1979b 2.29 4
Aboveground Production High Valielaetal. 1975 9.45 4 c 1.5% N content of dry mass (Vince ct al. 1981)
Aboveground Production Short Valielaetal, 1976 6.56 5 c 1.5 % N content of dry mass (Vince et al, 1981)
Aboveground Production Short Valtela ct al, 1975 765 4 c 15% N content of dry mass (Vince et al. 1981)
Aboveground Production Short White & Howes, 1994a 5 8 4
Aboveground Production Short Howes et al, 1985 4.76 1 C:N=50 (White & Howes, 1994c)
Aboveground Production Short Leschine, 1979 12.55 2
Abov eground Production Total Finn & Leschine, 1980 5.77 4^
Abov eground Production Total Finn & Leschine, 1980 5.77 4
Abov eground Production Total Valiela & Teal, 1979b 5.27/aug 4
Animal Total Valiela, 198.5 10 55 2
Animals Total Valiela & Teal. 1979b 0.02 4
Animals Total Valiela & Teal, 1979b 0.02 2
Animals Total Valiela, 1985 5 51 2
Arthropods Short Jordan & Valiela, 1982 .58/summer 2
Belowground Biomass High Valielaetal, 1976 0 1 4 %N=.44. Hopkinson & Schubauer, 1984
Belowground Biomass High Valiela et al, 1976 008 4 %N=.44, Hopkinson & Schubauer, 1984
Belowground Biomass Short Valiela et al, 1976 0 1 4 %N= 44, Hopkinson & Schubauer, 1984
Belowground Biomass Short Valielaetal. 1976 0.26 4l %N=.44, Hopkinson & Schubauer. 1984
Compartment Zone Source g N/m2/yr RF Comments
Belowground Biomass Short Howes ctal, 1985 4 27 3 %N=.44, Hopkinson & Schubaucr, 1984
Belowground Dead Total Valiela & Teal, 1979b 0.52 4
Belowground Live Total Valiela & Teal, 1979b 1.02 4
Belowground Production Short Valida et al, 1976 14 48 3 %N=.44, Hopkinson & Schubauer, 1984
Belowground Production Short White & Howes, 1994a 186-20 4 4
Belowground Production Short Howes ctal, 1985 70 1 C/N=50. While & Howes, 1994c
Belowground Production Short Howes ctal, 1985 13.93-17 89 1 C/N=50. White & Howes, 1994c
Belowground Production Short While & Howes, 1994a 18 58-20.44 3 C/N=50, White & Howes, 1994c
Belowground Production Short Valida ct al, 1976 58.83 3 c 1.5% N content of dry mass (Vince ct al, 1981)
Benthic algae production Short White & Howes, 1994a 5 2
Benthic algae production Short Howes ct al, 1985 7.64 1 C/N=5.5, Valida, 1983
Biodeposition Short Lcschine. 1979 4.59 2
Biodcposition Total Valida, 1983 6.52 2
Biodeposition Total Finn & Lcschine, 1980 2.61 4
Biodcposition Total Finn & Leschinc, 1980 2 61 4
Burial Short Jordan & Valida. 1982 6 13 2
Bunal Short White & Howes, 1994 4 4 4
Burial Short Howes ctal, 1985 4.6 3 C/N=19.3, Valida, 1983
Burial Short White & Howes, 1994b 3.2-4 6 2
Bunal Short White & Howes, 1994a 3.7-4 1 4
Burial Short Leschinc, 1979 ! 0.26 2
'
Burial Total Valida, 1983 2.7 2
Burial Total Valida* Teal, 1979b j 0.05 2
Burial Total Valida. 1984 268 4
Burial Vegetative Valiala & Teal, 1979a 4.28 3
Burial 1 Valida el al, 1975 0 31 1 annual production from mean=6 22
Decay ; High Lcschine, 1979 9 48 2 %N=1.5
Decay High Lcschine, 1979 1000 2
Decay Short Lcschine. 1979 42.86 2
Dcca\ Short Lcschine, 1979 4000 2
Decay Short Lcschine, 1979 6.36 2 %N=1.5
Decay Short White & Howes. 1994c 04 2
Compartment Zone Source g N/m2/j r RF Comments
Decay Total Valida & Teal. 1979b .64/August 41
Decay Total Finn & Leschinc. 1980 19.51 4
Decay Total Finn & Leschinc, 1980 19 51 4!
Dcca> Total Valida, 1983 3.31 21
Decomposer Total Valida, 1983 4.35 11 !i aboveground from mcan=6 22
Denitrification Algal Mat Kaplan et al, 1979 10.65 4
Denitnficalion Creek Bottom Kaplan et al, 1979 24.58 4
Denitrification High Kaplan et al, 1979 7.8 4
Denitnfication Pannes Kaplan étal, 1979 20.55 4
Denitrification Short White & Howes, 1994a 756/may 4
Denitrification Short White* Howes, 1994a 4.1-5.6 4
Denitrification Short Kaplan et al, 1979 6.85 4
Denitrification Short Leschinc, 1979 28.88 21
Denitrification Tall Kaplan et al, 1979 20.32 41
Denitrification Total Valida & Teal, 1979b 0.58 4
Denitrification Total Valíala & Teal, 1979a 14.34 4
Denitrification Total Valida & Teal, 1979b 3.22 4
Dcnitrificaiion Total Valida, 198.3 14.34 2
Denitrification Total Valida, 1983 14 3 2
Denitrification Total Finn & Leschinc, 1980 17.05 4
Dcnilnfication Total Valida, 1984 14 .34 4
DIN Total Valíala & Teal, 1979a 1 51 4
DIN Total Valida & Teal, 1979b 0 4
Excretion Short Leschine, 1979 4 29 2
Excretion Total Valida, 1983 14.74 2^
Excretion Total Finn & Leschine, 1980 1.43 4
Excretion Total Finn & Leschinc, 1980 1 43 4
Filtration Short Leschine, 1979 27.04 2
Filtration Total Finn & Leschine. 1980 5.23 4
Filtration Total Finn & Leschinc, 1980 5,23 4
Fish Short Jordan & Valida, 1982 1 17/summer 2
Grazers Total Valida. 1983 0.62 1li¡aboveground from mcan=6 22
Compartment Zone Source g N/m2/j r RF Comments
Grazcrs/Nckton Short Seed, 1980 2
Groundwater Short Lcschine. 1979 25 05 2
Groundwater Total Valíala & Teal, 1979a 5.6 4
Groundwater Total Valíala & Teal, 1979a 0.95
,
Groundwater Total Valíala & Teal. 1979a 0.06 4
Groundwater Total Valiala & Teal, 1979a 6.04 4
Groundwater Total Valiala & Teal, 1979a 0.02 4
Groundwater Total Valíala & Teal, 1979a 12.65 4
Groundwater Total Vahcla&Teal, 1979b 1131 4
Groundwater Total Vahela & Teal, 1979b 5 16 4
Groundwater Total Valiela & Teal, 1979b 0.06 4
Groundwater Total Vahela & Teal. 1979b 1 02 4
Groundwater Total Valiela & Teal, 1979b 5.07 4
Groundwater Total Valiela, 1983 12.6 2
Groundwater Total Vahela ct al, 1978 6.04 4
Groundwater Total Vahela ct al, 1978 0.06 4
Groundwater Total Vahela ct al, 1978 0 95 4
Groundwater Total Vahela ct al. 1978 5 61 4
Groundwater Total Vahela el al. 1978 12.61 4
Groundwater Total Finn & Leschinc, 1980 14 84 4
Groundwater Total Finn & Lcschine, 1980 0 03 4
Groundwater Total Finn & Lcschine, 1980 11 4
Groundwater Total Kaplan ct al, 1979 6.04 4
Grounwatcr Total Vahela, 1984 12.65 4
Leaching High Jordan & Valiela, 1982 1.5/summcr 4
Leaching Short Jordan & Vahela, 1982 1.4/summcr 4
Leaching Short While & Howes, 1994a 0.4 4
Leaching Short While & Howes. 1994a 0735/jun 4
[.caching Short White & Howes. 1994c 0787/jul 4
Leaching Short White & Howes, 1994c 0706/aug 4
Leaching Short White & Howes. 1994c ()596/scp 4
Leaching Short White & Howes, 1994c 1289/ocl 4
Cumpartmcnt Zone Source g N/m2/yr RF Comments
Leaching Short White & Howes, 1994c 0 4 4
Leaching Short Leschinc. 1979 2 76 2
Leaching Total Valida & Teal, 1979b .45/aug 4
Leaching Total Valida, 1983 2 48 2
Leaching Total Finn & Leschinc, 1980 5 85 4
Leaching Total Finn & Leschinc. 1980 1.3 4
Leaching Total Valíala & Teal. 1979a 45/aug 4
Litter Short Howes ctal, 1985 0-3.6 1 C/N=20-60, Valida, 1983
Litter Total Finn & Leschinc, 1980 4 55 4
Mineralization Short White & Howes, 1994a 14.9-16.3 4
Mineralization Total Valíala & Teal, 1979a 1 45 4
Mineralization Total Valida. 1983 1 45 2
?
Mineralization Total Valida, 1983 6 78 2
Mineralization Total Finn & Leschinc, 1980 1 45 4
Mussel Short Jordan & Valida, 1982 11 84 4
Mussel Short Jordan & Valida, 1982 5.92 4
Mussel Short Jordan & Valida, 1982 3.24 4
Mussel Short Jordan & Valida, 1982 1.22 4'
Mussel Short Jordan & Valida, 1982 3 75/sumnicr 4
Mussel Short Jordan & Valida, 1982 11 84 4
Mussel Short Jordan & Valida, 1982 5.9 4
.
Mussel Total Finn & Leschinc, 1980 0.34 4
Mussel Total Finn & Leschinc, 1980 0.34 4
Mussel Jordan & Valida, 1982 6.28 4
Nitrification Total Kaplan et al, 1979 0-4 42/apr-ocl 2
Nitrification Total Valida, 1983 9.8 2
Nitrification Total Finn & Leschinc, 1980 19.92 4
Nitrogen Fixation Algal Mat Carpenter et al, 1978 2.28 4
Nitrogen Fixation Algal Mat Valida. 1983 2.3 2
Nitrogen Fixation Algal Mat Van Raaltc et al, 1974 005/inay 4
Nitrogen Fixation Algal Mat Van Raaltc et al, 1974 006/may 4
Nitrogen Fixation Algal Mat Van Raaltc et al, 1974 .003/iul 4
00
Compartment Zone Source g N/m2/yr RFj Comments
Nitrogen Fixation Algal Mat Van Raalte et al, 1974 ()02/jul 4
Nitrogen Fixation Algal Mat Brenner et al. 1976 ()005/jul 4
Nitrogen Fixation Algal Mat Brenner et al, 1976 012/jul 4
Nitrogen Fixation Creek Bottom Kaplan et al, 1979 2.21 4
Nitrogen Fixation High Teal et al, 1979 OOOl/mar 4
Nitrogen Fixation High Teal étal, 1979 .OOl/jun 4
Nitrogen Fixation High Teal et al, 1979 .002/jul 4
Nitrogen Fixation High Teal et al. 1979 .005/aug 4
Nitrogen Fixation High Teal et al, 1979 .001/sep 4
Nitrogen Fixation Higli Teal et al, 1979 OOl/nov 4
Nitrogen Fixation High Teal et al, 1979 .0008/nov 4
Nitrogen Fixation High Teal et al, 1979 OOOl/mar 4
Nitrogen Fixation High Teal et al, 1979 001/jun 4
Nitrogen Fixation High Teal et al, 1979 .002/jul 4
Nitrogen Fixation High Teal et al. 1979 ()05/aug 4
Nitrogen Fixation High Teal et al, 1979 ()026/scp 4
Nitrogen Fixation High Teal et al, 1979 OOl/nov 4
Nitrogen Fixation High Teal et al. 1979 ()004/nov 4
Nitrogen Fixation High Kaplan et al, 1979 3,99 4
Nitrogen Fixation High Vahela, 1983 1.2 2
Nitrogen Fixation High Valiela, 1983 12.1 2
Nitrogen Fixation High Carpenter et al. 1978 0.3 4
Nitrogen Fixation Creek Bottom Vahela, 1983 1.7 2
Nitrogen Fixation Pannes Carpenter et al. 1978 6 43 4
Nitrogen Fixation Pink Sand Carpenter et al, 1978 0 76 4
Nitrogen Fixation Creek Bottom Vahela, 1983 0.7 2
Nitrogen Fixation Short Leschine 1979 12.96 2
Nitrogen Fixation Short Vahela, 1983 0.8 2
Nitrogen Fixation Short Vahela, 1983 84 2
Nitrogen Fixation Short Kaplan cl al. 1979 5.48 4
Nitrogen Fixation Short Teal et al, 1979 .0002/mar 4
Nitrogen Fixation Short Teal et al, 1979 .0021/jun 4
Cumpartmcnt Zone Source g N/m2/yr RF Comments
Nitrogen Fixation Short Teal cl al. 1979 .()029/|ul 4
Nitrogen Fixation Short Teal etal, 1979 .()0,^5/aug 4
Nitrogen Fixation Short Teal cl al. 1979 .00-52/sep 4
Nitrogen Fixation Short Teal cl al. 1979 .001,1/nov 4
Nitrogen Fixation Short Teal cl al. 1979 .(X)0.3/nov 4
Nitrogen Fixation Short Teal etal, 1979 0002/inar 4
Nitrogen Fixation Short Teal ct al, 1979 .0004/) un
Nitrogen Fixation Short Teal et al, 1979 .0002/|ul 4
Nitrogen Fixation Short Teal ct al, 1979 .00.12/aug 4
Nitrogen Fixation Short Teal ct al. 1979 .0032/scp 4
Nitrogen Fixation Short Teal ct al, 1979 .0006/nov 4
Nitrogen Fixation Short Teal ct al, 1979 0004/nov 4
Nitrogen Fixation Short Carpenter et al. 1978 0,39 4
Nitrogen Fixation Tall Carpenter et al. 1978 0.35 4
Nitrogen Fixation Tall Kaplan ct al, 1979 8.38 4
Nitrogen Fixation Tall Teal el al, 1979 .00004/mar 4
Nitrogen Fixation Tall Teal etal, 1979 .0002/)un 4
Nitrogen Fixation Tall Teal cl al, 1979 .0002/jul 4
Nitrogen Fixation Tall Teal cl al, 1979 .0006/aug 4
Nitrogen Fixation Tall Teal et al, 1979 OOOl/scp 4
Nitrogen Fixation Tall Teal et al, 1979 .00005/nov 4
Nitrogen Fixation Tall Teal et al. 1979 00001/nov 4
Nitrogen Fixation Tall Teal ct al, 1979 00009/mar 4
Nitrogen Fixation Tall Teal ct al. 1979 0001/jun 4
Nitrogen Fixation Tall Teal et al, 1979 .0002/jul 4
Nitrogen Fixation Tall Teal et al, 1979 .00003/aug
Nitrogen Fixation Tall Teal ct al. 1979 OOOl/scp 4
Nitrogen Fixation Tall Teal et al, 1979 .00003/no\ 4
Nitrogen Fixation Tall Teal etal, 1979 OOOOl/nov 4
Nitrogen Fixation Total Valida & Teal, 1979b 0..36 4
Nitrogen Fixation Total Valíala & Teal, 1979a 5.36 4
Nitrogen Fixation Total Valíala & Teal, 1979a 6.78 4
o
o
Compartment Zone Source g N/m2/vr RF Comments
Nitrogen Fixation Total Valiala & Teal, 1979a 0.61 4
Nitrogen Fixation Total Valíala & Teal. 1979a I 0 79 4
Nitrogen Fixation ¡Total Valiela & Teal. 1979b ! 4
Nitrogen Fixation Total Valiela & Teal. 1979b 2 63 4
Nitrogen Fixation Total Valiela & Teal, 1979b .51/aug 1
Nitrogen Fixation Total Valiela, 198.'? 6.78 2
Nitrogen Fixation Total Valiela et al. 1978 0.3 2
Nitrogen Fixation Total Valiela et al, 1978 2 64 4
Nitrogen Fixation Total Finn & Leschine, 1980 -3.37 4
Nitrogen Fixation Total Valiela, 1984 6.78 4
Nitrogen Fixation Total Valiela & Teal, 1979b 3.29 4
Nitrogen Fixation Total Finn & Leschine .3.37 4
Nitrogen Fixation Vegetatixe Carpenter et al, 1978 1 23-2.46 4
Nitrogen Fixation Vegetative Van Raalte et al, 1974 0138/jun 4
Nitrogen Fixation Vegetative Van Raalte et al, 1974 .009/jun 4
Nitrogen Fixation Vegetative Van Raalte et al. 1974 .0079/jun 4
Nitrogen Fixation Vegetative Van Raalte et al, 1974 .0167/jun 4
Nitrogen Fixation Vegetative Van Raalte et al, 1974 .0116/may 4
Nitrogen Fixation Vegetative Van Raalte et al, 1974 .0034/niay 4
Nitrogen Fixation Vegetative Van Raalte et al, 1974 0033/may 4
Nitrogen Fixation Vegetative Van Raalte et al, 1974 .0078/niay 4
Nitrogen Fixation Vegetative Van Raalte et al, 1974 0166/may 4
Nitrogen Fixation Vegetative Van Raalte et al, 1974 0066/may 4
Nitrogen Fixation Vegetative Van Raalte et al, 1974 .000,3/aug 4
Nitrogen Fixation Vegetative Van Raalte et al, 1974 .0016/aug 4
Nitrogen Fixation Vegetative Van Raalte et al, 1974 .0013/aug 4
Nitrogen Fixation Vegetative Van Raalte et al. 1974 .0003/aug 4
Nitrogen Fixation Vegetative Van Raalte et al, 1974 .0218/jun 4
Nitrogen Fixation Vegetative Van Raalte et at, 1974 .0106/jun 4
Nitrogen Fixation Vegetative Van Raalte et al, 1974 i.0039/jun 4
Nitrogen Fixation Vegetatixe Van Raalte et al, 1974 .0097/jun 4
'
Nitrogen Fixation Vegetative Van Raalte et al, 1974 0343/jun 4
Compartment Ztme Source g N/m2/yr RF Comments
Nitrogen Fixation Vegetative Van Raaltc et al, 1974 OOll/jun 4
Other Total Vahela, 1984 0.02 4
Other Total Valida, 1984 0.05 4
Particulate N Total Valíala & Teal, 1979a 0.02 4
Plant Uptake High Leschine, 1979 5.54 2
Plant Uptake Short Leschine, 1979 71.33 2
Plant Uptake Short Leschine. 1979 10.77 2
Plant Uptake Total Valíala & Teal, 1979a 4 92/jun-jul 4
Plant Uptake Total Vahela, 1983 8 27 2
Plant Uptake Total Teal et al, 1979 8.47 2
Plant Uptake Total Vahela et al, 1978 8 47 2
Plant Uptake Total Finn & Leschine, 1980 34.7 4
Plant Uptake Total Finn & Leschine. 1980 23.15 4
Plant Uptake Total Finn & Leschine, 1980 11.58 4
Plant Uptake Total Vahela, 1983 8.27 2
Plants Total Vahela, 1983 10.75 2
Pore DON Total Finn & Leschine, 1980 3969 4
Pore DON Total Finn & Leschine. 1980 38 24 4
Precipitation Short Leschine. 1979 0.92 2
Precipitation Total Vahala & Teal. 1979a 0 39 4
Precipitation Total Valíala* Teal, 1979a 0.14 4
Precipitation Total Vahala & Teal, 1979a 0 4
Precipitation Total Vahala & Teal, 1979a 0.23 4
Precipitation Total Vahala & Teal, 1979a 0.03 4
Precipitation Total Vahala & Teal, 1979a 0.79 4
Precipitation Total Vahela* Teal, 1979b 0.11 4
Precipitation Total Vahela * Teal, 1979b 0 4
Precipitation Total Vahela * Teal, 1979b 0.06 4
Precipitation Total Vahela* Teal. 1979b 0 18 4
Precipitation Total Vahela * Teal. 1979b 0.01 4
Precipitation Total Vahela * Teal, 1979b .03/aug 4
Precipitation Total Vahela et al, 1978 Oil 4
(N
\o
Compartment Zone Source g N/m2/>T RF Comments
Precipitation Total Valida ctal. 1978 0 4
Precipitation Total Valida ctal, 1978 0.06 4
Precipitation Total Valida et al. 1978 0.18 4
Precipitation Total Valida et al, 1978 0.01 4
Precipitation Total Valida et al, 1978 0.37 4
Precipitation Total Valida. 1984 0.79 4
Precipitation Total Valida* Teal, 1979b 037 4
Rcsuspcnsion Short Jordan & Valida. 1982 107.68 2
Resuspension Short Leschinc, 1979 12 76 2
Rcsuspcnsion Short Lcschinc, 1979 12.96 2
Rcsuspcnsion Tall Hoews & Gochringer, 1994 0.28 1
Rcsuspcnsion Tall Howes &Doehringer, 1994 1.02 3
Rcsuspcnsion Total Finn & Lcschinc, 1980 24.69 4
Rcsuspcnsion Total Finn & Lcschinc, 1980 42 12 4
Sediment Short Jordan & Valida, 1982 8942 2
Sediment Total Valida, 198,3 5.88 1 uptake from mean=14 7
Sediment Total Valida* Teal, 1979b 2 95/aug 4
Sediment Total Valida* Teal. 1979b 96/aug 4
sediment Total Valida * Teal, 1979b 64/aug 4
Sediment Total Valiala * Teal. 1979a 241 42 4
Sediment Total Valida, 1983 227.37 2
Sediment Total Valida * Teal, 1979b 101.28 4
Sedimentation Short Lcschinc, 1979 5 2
Sedimentation Short Leschine, 1979 5.15 2
Sedimentation Total Finn* Lcschinc, 1980 39.48 4
Sedimentation Total Finn * Lcschinc, 1980 39 48 4
Shellfish Tall Leschine, 1979 2
Shellfish Total Valida* Teal, 1979b 04/aug 4
Shellfish Total Valida * Teal, 1979b .32/aug 4
Shellfish Total Valida * Teal, 1979b .64/aug 4
Shellfish Total Valida * Teal, 1979b 0 44 4
Snails Short Jordan * Valida. 1982 12/summer 4
vO
Compartment Zone Source g N/m2/yr RF Comments
Tidal Water Total Valida, 1983 0,01 2
Tidal Water Exchange Short Leschine, 1979 166.73 2
Tidal Water Exchange Short Lcschine, 1979 188.57 2
Tidal Water Exchange Short Jordan & Valida. 1982 31 65 2
Tidal Water Exchange Short Jordan & Valida, 1982 38 25 2
Tidal Water Exchange Short White & Howes, 1994c 2.0-3.2 2
Tidal Water Exchange Short White & Howes, 1994c 1.6 2
Tidal Water Exchange Short White & Howes, 1994c 11-12 4
Tidal Water Exchange Total Valíala & Teal. 1979a 1 01/jun-jul 4
Tidal Water Exchange Total Valíala & Teal, 1979a 33.69 4
Tidal Water Exchange Total Valiala & Teal, 1979a 5.42 4
Tidal Water Exchange Total Valíala & Teal, 1979a 0.31 4
Tidal Water Exchange Total Valiala & Teal, 1979a 0 8061 4
Tidal Water Exchange Total Valíala & Teal, 1979a 13 93 4
Tidal Water Exchange Total Valiala & Teal, 1979a 54 15 4
Tidal Water Exchange Total Valida & Teal, 1979b 54 26 4
Tidal Water Exchange Total Vahela & Teal, 1979b 0 8 4
Tidal Water Exchange Total Valida & Teal, 1979b 0 32 4
Tidal Water Exchange Total Vahela & Teal, 1979b 5 42 4
Tidal Water Exchange Total Valiela & Teal. 1979b 33.79 4
Tidal Water Exchange Total Vahela & Teal, 1979b 13.94 4
Tidal Water Exchange Total Vahela et al, 1978 5.42 4
Tidal Water Exchange Total Vahela et al, 1978 0 8 4
Tidal Water Exchange Total Vahela et al, 1978 0.32 4
Tidal Water Exchange Total Vahela et al, 1978 33.79 4
Tidal Water Exchange Total Finn & Leschine, 1980 54 15 4
Tidal Water Exchange Total Finn & Leschine, 1980 5 42 4
Tidal Water Exchange Total Finn& Leschine, 1980 13.93 4
Tidal Water Exchange Total Finn & Leschine. 1980 8.86 4
Tidal Water Exchange Total Valida, 1984 54 15 4
Tidal Water Exchange Total Vahela et al, 1978 13 94 4
Tidal Water Exchange Total Vahela & Teal, 1979b 65.32 4
'?o
Compartment Zone Source g N/m2/yr RF Comments
Tidal Water Exchange Total Valida & Teal. 1979b 2.51 4
Tidal Water Exchange Total Valida & Teal, 1979b 0.34 4
Tidal Water Exchange Total Valida* Teal, 1979b 7 32 4
Tidal Water Exchange Total Valida* Teal, 1979b 38.2 4
Tidal Water Exchange Total Valida * Teal, 1979b 16.96 4
Tidal Water Exchange Total Valida * Teal, 1979b .26/aug 4
Tidal Water Exchange Total Valida * Teal. 1979b .26/aug 4
Tidal Water Exchange Total Valida et al. 1978 7.32 4
Tidal Water Exchange Total Valida cl al. 1978 2 51 4
Tidal Water Exchange Total Valida el al, 1978 0.34 4
Tidal Water Exchange Total Valida cl al. 1978 38 2 4
Tidal Water Exchange Total Valida et al, 1978 16.96 4
1’idal Water Exchange Total Finn * Leschine, 1980 7.32 4
Tidal Water Exchange Total Finn * Lcschine, 1980 16.95 4
Tidal Water Exchange Total Finn * Leschine, 1980 17.2 4
Tidal Water Exchange Total Valida cl al. 1975 1 24 1 Aboveground production from mean=6.22
Tidal Water Exchange Total Valida, 1984 65.32 4
Tidal Water Exchange Total Finn * Leschine, 1980 65.32 4
Translocation Short While * Howes, 1994c 14 4
Volatilisation of NH3 Short Leschine. 1979 0.1 2
Volatilisation of NH3 Total Valiala * Teal. 1979a 004 2
Volatilisation of NH3 Total Valida* Teal. 1979b 0.02 2
APPENDIX C. SAPELO ISLAND ORIGINAL DATA.
'•o
\D
Compartment Original Data Z<mc Season Source
Abovcgound Biomass 760 g dvv7m2 Higli year Gallagher, 1975
Abovegound Biomass 3.14 g N/m2/ycar Short year Haines et al, 1977
Abovegound Biomass 651 g dry mass/m2 Short Summer Gross ct al, 1991
Abovegound Biomass 116.4 g dry' vveight/m2 Short Summer Montague, 1982
Abo\egound Biomass 4.2 gN/m2 Short year Hopkinson & Schubauer, 1984
Abov egound Biomass 4.7 g N/m2 Short year Whitney et al, 1981
Abo\ egound Biomass 471 gdvv/m2 Short year Gallagher, 1975
Abovegound Biomass 349 g dv\/m2 Short year Gallagher, 1975
Abov egound Biomass 8.28 g N/m2/year Tall year Haines et al, 1977
Abovegound Biomass 9.8gN/m2 Tall year Whitney et al, 1981
Abovegound Biomass 1124gdvv/m2 Tall year Gallagher, 1975
Abovegound Biomass 135gC/m2 Total year Wiegert & Wetzel, 1979
Abovegound Biomass 135gC/m2 Total year Wiegert et al, 1981
Abov egound Biomass 3 44 gN/m2 Total year Chalmers, 1979
Abovegound Biomass 557.96 g dvv/m2/y r Total year Schubauer & Hopkinson, 1984
Aboveground Biomass 1500 g dry vvt/m2 High year Pomeroy et al, 1981
Aboveground Biomass 2200 g dry wt/ni2 High year Pomeroy et al, 1981
Aboveground Biomass 2200 g dry wt/m2 Short year Pomeroy et al, 1981
Aboveground Biomass 1350 g dry vviym2 Short year Pomeroy et al, 1981
Aboveground Biomass 400 g dry vv1/m2 Short year Pomeroy et al, 1981
Aboveground Biomass 3300 g dry wt/m2 Tall Annual Pomerory et al, 1981
Aboveground Biomass 3700 g dry v\'l/m2 Tall year Pomerory et al, 1981
Aboveground Biomass 2000 g dry vviym2 Tall year Pomerory et al, 1981
Aboveground Dead 113.6 g dw/m2/yr Short year Chalmers, 1979
Aboveground Dead 459 g dry mass/m2 Short Summer Gross et al, 1991
Aboveground Dead j 184.4 g dry weightym2 Short Summer Montague, 1982
Aboveground Production 131 4 g N/m2 High year Whitney et al, 1981
Abov eground Production 2500 g dw/m2 High year Gallagher et al, 1980
Aboveground Production 2800 g dw/m2 High year Gallagher et al, 1980
Aboveground Production 2500 g dw'/m2 High year Gallagher el al, 1980
Aboveground Production 1500 g dvv/m2 High year Gallagher et al, 1980
Abov eground Production 2200 g dw/m2 Hlghj year Gallagher et al, 1980
r-*
VO
Compartment Original Data Zone j Season Source
Aboveground Production 13 4 g N/m2/yr Short lyear Kemp et al, 1990b
Aboveground Production 1488 g C/m2/yr Short year Wiegert. 1979
Aboveground Production 1573 g C/m2/yr Short year Wiegert, 1979
Aboveground Production 33.0 g N/m2/year Short year Hopkinson & Schubaurer, 1984
Aboveground Production 330 g /m2/year Short year Hason, 1977
Aboveground Production 1337 g/m2/year Short year Haines et al. 1977
Aboveground Production 18.6 g N/m2 Short year Whitney et al, 1981
Abo\ eground Production 1489 g C/m2/yr Short year Dai & Wiegert, 1996
Aboveground Production 1337 g dw/m2/yr Short year Chalmers, 1979
Aboveground Production 1350 g dw/m2/yr Short year Schubauer & Hopkinson. 1984
Aboveground Production 2840 g dw/m2/yr Short year Schubauer & Hopkinson, 1984
Aboveground Production 700 g dw/m2 Short year Gallagher et al, 1980
Aboveground Production 1500 g dvv/m2 Short year Gallagher et al, 1980
Aboveground Production 1600 g dw/ni2 Short year Gallagher et al, 1980
Aboveground Production 1200 g dw/m2 Short year Gallagher et al, 1980
Aboveground Production 1300 g dw'/m2 Short year Gallagher et al, 1980
Abov eground Production 2454 g C/m2/yr Tall year Dai & Wiegert. 1996
Aboveground Production 2596 g C/m2/yr Tall year Wiegert, 1979
Aboveground Production 1600 g /m2/year Tall year Hanson, 1977
Aboveground Production 3711 g/m2/ycar Tall year Haines et al, 1977
Aboveground Production 47.1 gN/m2 Tall year Whitney et al. 1981
Aboveground Production 3700 g dw/m2/yr Tall year Schubauer & Hopkinson, 1984
Aboveground Production 2300 g dw/m2 Tall year Gallagher et al, 1980
Aboveground Production 3000 g dw/m2 Tall year Gallagher et al, 1980
Aboveground Production 2700 g dw/m2 Tall year Gallagher et al, 1980
Aboveground Production 4400 g dw/m2 Tall year Gallagher et al, 1980
Aboveground Production 3700 g dvv/m2 Tall year Gallagher et al, 1980
Aboveground Production 2500 g C/m2/year Tall year Wiegert, 1986
Aboveground Production 2840 g dvv/m2/yr Total year Schubauer & Hopkinson 1984
Aboveground Production 1575 g C/m2/yr Total year Chalmers el al, 1985
Aboveground Production 1575 g C/m2/yr Total year Chalmers et al, 1985
Abov eground Production 13 4 g N/m2/ycar year Hanson, 1983
00
'?o
Compartment Original Data Zone Season Source
Aboveground Production 21 7 g N/m2/year year Hanson. 1983
Algae Biomass 1 g C/m2 Total year Wicgert & Wetzel, 1979
Algal Production 180 g C/m2/yr Short year Wicgerl, 1979
Algal Production 146 mg C/m2/day Short Jan-Feb Pomeroy, 1959
Algal Production 439 mg C/m2/day Short May-Jun Pomeroy, 1959
Algal Production 702 mg C/m2/day Short Jul-Aug Pomeroy, 1959
Algal Production 898 mg C/m2/day Short Sep-Oct Pomeroy, 1959
Algal Production 1094 mg C7m2/day Short Nov-Dcc Pomeroy, 1959
Algal Production 260 mg C/m2/day Short Mar-Apr Pomeroy. 1959
Algal Production 347 mg C/m2/day Tall Jan-Feb Pomeroy, 1959
Algal Production 222 mg C/m2/day Tall March-April Pomeroy, 1959
Algal Production 518mgC/m2/day Tall May-June Pomeroy, 1959
Algal Production 1086 mg C/m2/day Tall Jul-Aug Pomeroy. 1959
Algal Production 958 mg C/m2/day Tall Sep-Oct Pomeroy, 1959
Algal Production 830 mg C/m2/day Tall Nov-Dec Pomeroy. 1959
Algal Production 128 g C/m2/yr Total year Chalmers el al, 1985
Algal Production 128gC/m2/yr Total year Chalmers et al, 1985
Belowground Biomass 1340gC/m2 High year Pomeroy el al, 1981
Belowground Biomass 700 g C/m2 Short year Pomeroy el al, 1981
Belowground Biomass 3,3 g N/m2 Short year Hopkmson & Schubaucr, 1984
Belowground Biomass 11810gdw/m2 Short year Gallagher, 1975
Belowground Biomass 831 g dry mass/m2 Short Summer Gross ct al. 1991
Belowground Biomass 770 g C/m2 Tall year Pomeroy et al, 1981
Belowground Biomass 450 g C/m2 Total year Wiegert & Wetzel, 1979
Belowground Biomass 450 g C/m2 Total year Wicgert et al, 1981
Belowground Dead 3421 g dry wcight/m2 Short Summer Gross ctal, 1991
Belowground Production 397 g C/m2/yr Short year Dai & Wiegert, 1996
Belowground Production 2020 g dw/m2/yr Short year Schubaucr & Hopkinson, 1984
Belowground Production 4780 g dw'/m2/yr Short year Schubaucr & Hopkinson, 1984
Belowground Production 46.7 g N/m2 Tall year Whitney el al, 1981
Belowground Production 2110 g dw/m2/yr Tall year Schubaucr & Hopkinson, 1984
Belowground Production 872 g C/m2/yr Tall year Dai & Wiegert, 1996
ON
vO
Compartment Original Data Zone Season Source
Belowground Production 2100 g/ni2/year Total year Gallagher & Plumley, 1979
Belowground Production 4780 g dw/ni2/yr Total year Schubauer & Hopkinson, 1984
Belowground Production 70 g N/m2 Total year Whitney et al. 1981
Benthic algae 220 g C/m2 Total year Pomeroy et al 1981
Benthic algae 190gC/m2 Total year Pomeroy et al, 1981
Benthic algae 1 g C/m2 Total year Wiegertetal. 1981
Bentluc algae production 13 mg C/m2/hr Short Pomeroy et al, 1981
Benthic algae production 200 g C/m2/yr Short year Wiegert, 1979
Benthic algae production 34 mg C/ni2/hr Tall Pomeroy et al, 1981
Benthic algae production 20 g N/m2/year Total year Haines et al, 1977
Burial 26 g C/ni2/yr Short year Wiegert, 1979
Burial 20 g C/m2/year Total year Wiegert, 1986
Consumption 31 g C/m2/yr Short year Wiegert, 1979
Consumption 12% annual S.a. prod Total year Barlocher et al, 1989
Dead Biomass 2.85 gN/m2/year Short year Haines et al, 1977
Dead Biomass 6.79 g N/m2/year Tall year Haines et al, 1977
Dead Biomass 130gC/m2 Total year Wiegert & Wet/el, 1979
Death 14 4 g N/m2/yr Short year Hopkinson & Schubauer, 1984
Decay 69 g C/m2/yr Short year Wiegert, 1979
Decay 19.7 g N/m2/yr Short year Hopkinson & Schubauer, 1984
Denitrification 12 g N/m2/year Total year Haines et al. 1977
Denitnfication 65 g N/ni2 Total year Whitney et al, 1981
Denitrification 7 g N/m2/yr Total year Chalmers et al, 1976
Denitrification 31.7 ug/cm3/hour Sherr& Payne, 1979
Detritus 60% of biomass Short year Alberts et al, 1992
Detritus 12 g N/ni2/year Short year Haines et al, 1977
Detritus 113.6 g/ni2/nionth Short year Chalmers, 1979
Detntus 21 g N/m2/year Tall year Haines et al, 1977
Excretion 3.6 mg NH4/g/d Short year Montague, 1982
Filter Feeders 2.53 g dw/m2 High year Kuenzler. 1961
Filter Feeders 2 84 gdw/ni2 Short year Kuenzler, 1961
Filter Feeders 31 48gdw/m2 Tall year Kuenzler, 1961
o
Compartment Original Data Zone Season Source
Filter Feeders 445 mg dw/m2 Total year Kuenzler, 1961
Filter Feeders 1200 mg dvv/m2 Total year Kucnzlcr, 1961
Filter Feeders 820 mg d\v/in2 Total year Kuenzler, 1961
Filtration 4766.3 ug C/ni2/hr Short Aug and Sep Kemp et al, 1990a
Filtration 7.52 g C/m2/yr Short year Kemp et al, 1990a
Filtration 417gPOC/m2/yr Short year Kemp el al, 1990a
Grazer/Detritivore 400 indiv/m2 Barlocher et al, 1989
Gra/ers 1 g C/m2 Total year Wiegert & Wetzel, 1979
Grazers/Nekton 6.35gN/m2/yr Short year Kemp et al, 1990b
Grazcrs/Nckton .454 g N/m2/yr Short year Kemp et al, 1990b
Grazers/Nekton 75 indiv/m2 Short summer Kneib & Weeks, 1990
Grazers/Nekton .46 g N/m2 Short year Kemp et al, 1990b
Grazers/Nekton 243 indiv/m2 Short December Newell et al, 1989
Grazers/Nekton .005 indiv/m2 Short September Fritz & Wiegert, 1991
Grazers/Nekton 1.71 indi\7m2 Short year Kneib, 1991
Grazers/Nekton 119.7 g dry vv'tyni2 Short year Pfeiffer & Wiegert, 1981
Grazers/Nekton 125 indiv/m2 Tall Summer Kneib & Weeks, 1990
Grazers/Nekton 197 7 g dry wtym2 Tall year Pfeiffer & Wiegert, 1981
Grazers/Nekton 5% primary production Total year Dai & Wiegert, 1996
Grazers/Nekton 12% of net production Total year Smalley, 1960
Grazers/Nekton 50-700 indiv/m2 Total year Montague, 1982
Grazers/Nekton 1 g C/m2 Total year Wiegert et al, 1981
Grazers/Nekton 0.55 g du7m2 Total year Smalley, 1960
Leaching .7 g N/m2/yr Short year Hopkinson & Schubaucr, 1984
Litter 12 to 21 g N/m2 Total year Whitney ctal, 1981
Litter 130gC/m2 Total year Wiegert et al, 1981
Mineralization 70 g N/m2 Total year Whitney ctal, 1981
Mussel 4.1 g shell-free dry vvcightym2 Short Kemp et al, 1990a
Nitrogen Fixation 5 g N/m2 High year Whitney et al, 1981
Nitrogen Fixation 4.0 g N/m2 High year Whitney ctal, 1981
Nitrogen Fixation 13.1 g N/m2/ycar Short year Hanson, 1983
Nitrogen Fixation 5 8 g N/m2/ycar Short year Haines et al, 1977
Compartment Original Data Zone Season Source
Nitrogen Fixation 20-50 g N/m2/year Short year Hanson, 1977
Nitrogen Fixation 22.2-52.4 g N/m2/year Short year Hanson. 1977
Nitrogen Fixation 13 gN/m2 Short year Whitney et al, 1981
Nitrogen Fixation 13 1 g N/m2 Short year Whitney et al, 1981
Nitrogen Fixation 39.7 g N/m2/year Tall year Hanson, 1983
Nitrogen Fixation 40 g N/m2 Tall year Whitney et al, 1981
Nitrogen Fixation 39.7gN/m2 Tall year Whitney el al. 1981
Nitrogen Fixation 8 mg N/m2/day Total year Hanson, 1983
Nitrogen Fixation 146 mg N/m2/day Total year Hanson, 1983
Nitrogen Fixation 6 g N/m2/year Total year Haines, 1976
Nitrogen Fixation 15 gN/m2 Total year Whitney et al, 1981
Nitrogen Fixation 14.8 gN/m2 Total year Whitney et al, 1981
Plant Uptake 34.8 g N/m2/year Short year Hokinson & Schubauer, 1984
Plant Uptake 2.1 gN/m2/year Short year Haines et al, 1977
Plant Uptake 10.7 g N/m2/year Tall year Haines et al, 1977
Plant Uptake 11 g N/m2/year Total year Haines, 1976
Plant Uptake 22 g N/m2/year Total year Haines. 1976
Pore NH4 .21 gN/m2 Short year Chalmers et al, 1976
Pore NH4 .18gN/m2 Tall year Chalmers ct al. 1976
Pore NH4 30 to 70 uM Total year Whitney et al, 1981
Pore NH4 185.83 mg N/m2/yr Total year Chalmers, 1979
Pore NOx 0 04 g N/m2 Short year Chalmers el al, 1976
Pore NOx .03 g N/m2 Tall year Chalmers et al, 1976
Pore NOx 65 g N/m2 Total year Whitney ct al, 1981
Pore NOx 7 to 13 uM Total year Whitney et al. 1981
Pore PN 13.9 g N/m2/yr High year Gallagher et al, 1980
Pore PN 9.9 g N/m2/yr Short year Gallagher ct al, 1980
Pore PN 7.27 mg N/cc Short February Chnstian et al, 1981
Pore PN 486.54 g N/m2 Short year Chalmers et al, 1976
Pore PN 21 g N/m2/yr Tall year Gallagher et al, 1980
Pore PN 7 67 mg N/cc Tall February Chnstian et al, 1981
Pore PN 485.8 g N/m2 Tall year Chalmers et al, 1976
Compartment Original Data Zone Season Source
Pore PN 485 g N/m2 Total year Whitney et al, 1981
Pore PN 18.200 g C/m2 Total year Wiegertetal. 1981
Pore PN 3,780 g dw/m2 Total year Schubauer & Hopkinson, 1984
Precipitation 22.3% of TN Total year Haines, 1976
Precipitation .3 g N/m2/year Total year Haines, 1976
Precipitation 33 5%ofTN Total year Haines, 1976
Precipitation 0.3 gN/m2 Total year Whitney el al, 1981
Precipitation 0.3 g N/m2 Total year Whitney el al, 1981
Precipitation 44 2% ofTN Total year Haines, 1976
Sediment 493 g N/m2/year Short year Haines el al, 1977
Sediment 463 g N/m2/year Tall year Haines et al, 1977
Sediment 98 g N/m2 Total year Gallagher & Plumley, 1979
Sediment 18000 g C/m2 Total year Wiegert & Wetzel, 1979
Sediment DON 26 g C/m2 Total year Wiegert & Wetzel, 1979
Sediment NH4 6.3 umol N/1 Short March Montague, 1982
Sediment NH4 8 8 umol N/1 Short September Montague, 1982
Sediment NH4 .255 g N/m2/year Short year Haines el al, 1977
Sediment NH4 .202 g N/m2/year Tall year Haines et al, 1977
Sediment NOx .035 g N/m2/year Short year Haines el al, 1977
Sediment NOx 0 033 g N/m2/year Tall year Haines et al, 1977
Sedimentation 3.3 gN/m2 Total year Whitney ct al, 1981
Standing Dead 909 g dw/m2 High year Gallagher, 1975
Standing Dead 345 g dw/m2 Short year Gallagher, 1975
Standing Dead 397 g dw/m2 Tall year Gallagher, 1975
Surface DON 108 g C/m2/yT Total_^ year Chalmers el al, 1985
Surface DON 2-20 uM Total year Whitney et al, 1981
Surface NH4 5 uM Total year Whitney et al, 1981
Surface PN 3.2gN/m2 Total year Whitney et al, 1981
Surface PN 36 g C/m2/yr Total year Chalmers et al, 1985
Surface PN 208 g C/m2/yr Total year Chalmers el al, 1985
Surface PN 29 g C/m2/y r Total year Chalmers el al. 1985
Surface PN 5.6 g POC/m2 Total year Wiegertetal. 1981
Compartment Original Data Zone Season Source
Surface PN .1-30 uM Total year Whitney et al, 1981
Tidal DON 5.6 g C/m2 Total year Wiegert & Wetzel, 1979
Tidal PN 9 g C/m2 Total year Wiegert & Wetzel, 1979
Tidal Water Exchange 31.75 ug N/1 Short year Haines, 1979
Tidal Water Exchange 161 ug N/1 Short year Haines, 1979
Tidal Water Exchange .44 ug N/1 Short year Haines, 1979
Tidal Water Exchange 19.09 ug N/1 Short year Haines, 1979
Tidal Water Exchange 29.8 ug N/1 Short year Haines, 1979
Tidal Water Exchange 1075 g C/m2/yr Total year Wiegert, 1979
Tidal Water Exchange 247 g C/m2/yr Total year Wiegert, 1979
Tidal Water Exchange 782 g C/m2/yr Total year Wiegert, 1979
Tidal Water Exchange 213 Í6gC/m2/yr Total year Wiegert, 1979
Tidal Water Exchange 598 g C/m2/ycar Total year Wiegert, 1986
Tidal Water Exchange 1051.2 g C/m2/yr Total year Chalmers et al, 1985
Tidal Water Exchange 2715.6 g C/m2/yr Total year Chalmers et al, 1985
Tidal Water Exhchange 2.75 ug N/1 Short year Haines, 1979
Tidal Water Exhchange 1.47 ugN/1 Short year Haines, 1979
Tidal Water Exhchange .42 ug N/1 Short year Haines, 1979
Tidal Water Exhchange 11.69 ug N/1 Short year Haines, 1979
Tidal Water Exhchange 14.79 ug N/1 Short year Haines, 1979
Tidal Water Exhchange 2-3 umol N/l/tidc Short year Montague, 1982
Tidal Water Exhchange 46.6 g N/m2 Total year Whitney et al, 1981
Tidal Water Exhchange 3.1 gN/m2/ycar Total year Haines, 1976
Tidal Water Exhchange 160 g C/ni2/year Total year Wiegert, 1986
Tidal Water Exhchange 97.82 g C/m2/yr Total year Wiegert. 1979
Tidal Water Exhchange 1314gC/m2/yr Total year Chalmers et al, 1985
Tidal Water Exhchange 2890 8 g C/m2/yr Total year Chalmers et al, 1985
Translocation 54%ofN Short year Newell el al, 1989
Translocation 17.9 g N/m2/ycar Short year Hopkinson & Schubauer, 1984
174
APPENDIX D. SAPELO ISLAND CONVERTED DATA.
RF=Reliability Factor
Compartment Zone Source g N/m2/yr RF Comments
Abovegound Biomass High Gallagher. 1975 9 12 3 %N=1.2, Gallagher, 1975
Abovegound Biomass Short Haines el al, 1977 ,3.14 4
Abo\egound Biomass Short Gross et al, 1991 10.94 3 %N= 1.68, Kemp el al, 1990b
Abovegound Biomass Short Montague, 1982 1.22 3 %N=1 05, Hopkmsons & Shubauer, 1984
Abovegound Biomass Short Hopkinson & Schubauer, 4.2 4
Abovegound Biomass Short Whitney et al, 1981 4.7 4 Ecology of Salt Marsh
Abo\egound Biomass Short Gallagher, 1975 3.77 3 %N=0.8, Gallagher, 1975
Abovegound Biomass Short Gallagher, 1975 2.79 3 %N=0.8, Gallagher, 1975
Abovegound Biomass Tall Haines et al. 1977 8.28 4
Abovegound Biomass Tall Whitney et al. 1981 9 8 4 Ecology of Salt Marsh
Abovegound Biomass Tall Gallagher, 1975 7.87 3 %N=0.7, Gallagher, 1975
Abo\egound Biomass Total Wiegert & Wetzel, 1979 3.55 3 C;N=38, Gallagher & Plumley, 1979
Abovegound Biomass Total Wiegertetal, 1981 4 Ecology of Salt Marsh
Abovegound Biomass Total Chalmers, 1979 3.44 2
Abovegound Biomass Total Schubauer & Hopkinson, 9.37 3 %N=1.68, Vince et al, 1981
Aboveground Biomass High Pomeroy et al, 1981 4 Ecology of Salt Marsh
Aboveground Biomass High Pomeroy et al. 1981 4 Ecology of Salt Marhs
Aboveground Biomass Short Pomeroy et al. 1981 4 Ecology of Salt marsh
Aboveground Biomass Short Pomeroy et al, 1981 4 Ecology of Salt marsh
Aboveground Biomass Short Pomeroy et al, 1981 4 Ecology of salt Marsh
Aboveground Biomass Tall Pomerory et al, 1981 4 Ecology of Salt marsh
Aboveground Biomass Tall Pomerory et al, 1981 4 Ecology of Salt marsh
Aboveground Biomass Tall Pomerory et al, 1981 4 Ecology of Salt marsh
Aboveground Dead Short Chalmers, 1979 0.87 3 %N=0 77, Hopkinson & Schubauer, 1984
Aboveground Dead Short Gross et al, 1991 3.53 3 %N=.77, Kemp et al, 1990b
Aboveground Dead Short Montague, 1982 1 42 3 %N=.77, Kemp et al, 1990b
Aboveground Production High Whitney et al, 1981 31.4 4 Ecology of Salt Marsh
Aboveground Production High Gallagher et al, 1980 30 3 %N=1.2, Gallagher, 1975
Aboveground Production High Gallagher et al, 1980 33.6 3 %N=1.2, Gallagher, 1975
Aboveground Production High Gallagher et al, 1980 30 3 %N=1 2, Gallagher, 1975
Aboveground Production High Gallagher et al, 1980 18 3 VoN=1.2, Gallagher, 1975
Aboveground Production High Gallagher et al, 1980 26.4 3 %N=1 2, Gallagher, 1975
Compartment Zone Source N/m2/yr RF Comments
Aboveground Production Short Kemp ct al. 1990b 13,4 4
Aboveground Production Short Wicgert, 1979 39.16 3 C:N=38, Gallagher & Plumley, 1979
Aboveground Production Short Wiegcrt, 1979 41.39 3 C:N=38. Gallagher & Plumley, 1979
Aboveground Production Short Hopkinson & Schubaurer, 1984 33 4
Abo\'eground Production Short Hason, 1977 3.47 3 %N=1 05, Hopkinson & Schubauer. 1984
Aboveground Production Short Haines et al, 1977 35.18 3 C:N=38, Gallagher & Plumley, 1979
Aboveground Production Short Whitney ct al, 1981 18.6 4 Ecology of Salt Marsh
Aboveground Production Short Dai & Wiegert, 1996 29.78 3 C:N=50 White & Howes, 1994c
Aboveground Production Short Chalmers. 1979 22.46 3 %N=1.68, Vince et al, 1981
Aboveground Production Short Schubauer & Hopkinson, 1984 22.68 3 %N=1 68, Vince ct al, 1981
Aboveground Production Short Schubauer & Hopkinson, 1984 47.71 3 %N= 1.68, Vince et al. 1981
Aboveground Production Short Gallagher et al, 1980 11,76 3 %N=1 68, Vince et al, 1981
Aboveground Production Short Gallagher et al, 1980 25.2 3 %N=1.68, Vince el al, 1981
Aboveground Production Short Gallagher et al. 1980 26 88 3 %N=1 68, Vince el al, 1981
Aboveground Production Short Gallagher et al, 1980 20 16 3 %N=1 68, Vince et al, 1981
Aboveground Production Short Gallagher et al, 1980 21 84 3 %N=1.68, Vince et al, 1981
Aboveground Production Tall Dai & Wiegert, 1996 64.58 3 C:N=38, Gallagher & Plumley, 1979
Aboveground Production Tall Wiegert, 1979 68.32 3 C:N=38, Gallagher & Plumley, 1979
Aboveground Production Tall Hanson, 1977 16 8 3 %N=1 05, Hopkinson & Schubauer, 1984
Aboveground Production Tall Haines et al, 1977 97.66 3 C:N=38, Gallagher & Plumley, 1979
Aboveground Production Tall Whitney et al. 1981 47.1 4 Ecology of Salt Marsh
Aboveground Production Tall Schubauer & Hopkinson, 1984 62 16 3 %N=1 68, Hopkinson & Schubauer, 1984
Aboveground Production Tall Gallagher et al, 1980 3864 3 %N=1.68, Vince et al, 1981
Abov eground Production Tall Gallagher el al, 1980 50.4 3 %N=1.68, Vince et al, 1981
Aboveground Production Tall Gallagher et al. 1980 45.36 3 %N=1.68, Vince et al, 1981
Aboveground Production tali Gallagher el al, 1980 73 92 3 %N=1.68, Vince et al, 1981
Aboveground Production Tall Gallagher et al, 1980 62.16 3 %N=1 68, Vince et al, 1981
Aboveground Production Tall Wiegert, 1986 65.79 3 C:N=38, Gallagher & Plumley, 1979
Aboveground Production Total Schubauer & Hopkinson 1984 47.71 3 %N=1.68, Vince et al, 1981
Aboveground Production Total Chalmers el al, 1985 26.46 3 %N=1.68, Vince et al. 1981
Aboveground Production Total Chalmers et al. 1985 41 45 2 C:N=38, Gallagher & Plumley, 1979
Aboveground Production Hanson, 1983 13 4 4
Cumpartmcnt Zone Source g N/m2/yr RF Comments
Aboveground Production Hanson. 1983 217 4
Algae Biomass Total Chalmers et al, 1985 23 27 3 C:N=5.5, Vahela, 1983
Algae Biomass Total Wiegert & Wetzel. 1979 0.18 4 C:N=5.5, Vahela, 183
Algae Production Short Wiegert. 1979 32.73 3 C:N=5 5, Vahela, 1983
Algae Production Short Pomeroy, 1959 1 57/Jan-Feb 3 C:N=5.5, Vahela, 1983
Algae Production Short Pomeroy, 1959 4.87/May-Jun 3 C:N=5.5, Vahela, 1983
Algae Production Short Pomeroy, 1959 7.91/Jul-Aug 3 C:N=5.5, Vahela, 1983
Algae Production Short Pomeroy, 1959 9.96/Scp-Oct 0 C:N=5.5, Vahela, 1983
Algae Production Short Pomeroy, 1959 12 1.3/Nov-Dcc 3 C:N=5.5, Vahela, 1983
Algae Production Short Pomeroy, 1959 2 88/Mar-Apr 3 C;N=5.5, Valida, 1983
Algae Production Tall Pomeroy. 1959 3.72/Jan-Feb 3 C:N=5 5, Vahela, 1983
Algae Production Tall Pomeroy, 1959 2 46/mar-apr 3 C:N=5.5, Vahela, 1983
Algae Production Tall Pomeroy. 1959 5.75/May-Jun 3 C;N=5.5, Vahela, 1983
Algae Production Tall Pomeroy, 1959 12.24/Jul-Aug 3 C:N=5.5, Vahela, 1983
Algae Production Tall Pomeroy, 1959 10 63/Sep-Oct 0 C:N=5.5, Vahela, 1983
Algae Production Tall Pomeroy. 1959 9.21/Nov-Dec 3 C:N=5.5, Vahela, 1983
Algae Production Total Chalmers et al, 1985 23.27 2 C:N=5.5, Vahela, 1983
Belowground Biomass High Pomeroy et al, 1981 4 Ecology of Salt Marsh
Belowground Biomass Short Pomeroy et al, 1981 4 Ecology of Salt Marsh
Belowground Biomass Short Hopkinson & Schubauer, 1984 3.3 4
Belowground Biomass Short Gallagher, 1975 51.96 3 %N=0.44, Hopkinson & Schubauer, 1984
Belowground Biomass Short Gross et al, 1991 3.66 3 %N= 44. Hopkinsons& schubauer, 1984
Belowground Biomass Tall Pomeroy et al, 1981 4 Ecology of Salt Marsh
Belowground Biomass Total Wiegert & Wetzel. 1979 9 3 C:N=50, White & Howes, 1994c
Belowground Biomass Total Wiegert et al, 1981 4 Ecology of Salt Marsh
Belowground Dead Short Gross et al, 1991 34.21 3 %N=1. Moran et al, 1989
Belowground Production Short Dai & Wiegert, 1996 7.94 3 C:N=50, White & Howes, 1994c
Belowground Production Short Schubauer & Hopkinson, 1984 8 89 3 %N=0.44, Hopkinson & Schubauer, 1984
Belowground Production Short Schubauer & Hopkinson, 1984 21.03 3 %N=0 44, Hopkinson & Schubauer, 1984
Belowground Production Tall Whitney et al, 1981 46.7 3 Ecology of Salt Marsh
Belowground Production Tall Schubauer & Hopkinson, 1984 9.28 3 %N=0 44, Hofrfdnson & Schubauer, 1984
Belowground Production Tall Dai & Wiegert, 1996 17 44 3 C:N=50 White & Howes, 1994c
00
Compartment Zone Source g N/m2/yr RF Comments
Belowground Production Total Gallagher & Plumley, 1979 2106 3 %C=38 1 C:N=38, Gallagher & Plumley, 1979
Belowground Production Total Schubauer & Hopkinson, 1984 21.03 3 %N=0 44, Hopkinson & Schubauer, 1984
Belowground Production Total Whitney et al. 1981 70 4 Ecology of Salt Marsh
Benthic Algae Short Pomeroy el al, 1981 4 Ecology of Salt Marsh
Benthic Algae Tall Pomeroy el al. 1981 4 Ecology of Salt Marsh
Benthic Algae Total Pomeroy et al 1981 4 Ecology of Salt Marsh
Benthic Algae Total Pomeroy et al. 1981 4 Ecology of Salt Marsh
Benthic Algae Total Wiegert el al, 1981 4 Ecology of Salt Marsh
Benthic algae production Short IWiegerl 1979 36 36 3 C:N=5 5, Valiela 1983
Benthic algae production Total Haines et al. 1977 20 4
Burial Short Wiegert. 1979 135 3 C:N=19.3, Valiela. 1983
Burial Total Wiegert. 1986 1 04 3 C:N=19.3, Valiela. 1983
Consumption Short Wiegert. 1979 0.82 3 C:N=38, Gallagher & Plumley, 1979
Consumption Total Barlocher et al, 1989 2 for Louisiana
Dead Biomass Short Haines ct al, 1977 2.85 4
Dead Biomass Tall Haines et al. 1977 6,79 4
Dead Biomass Total Wiegert & Wct/.el, 1979 1 49 3 C:N=87, White & Howes, 1994b
Death Short Hopkinson & Schubauer. 1984 14 4 4
Decay Short Wiegert, 1979 1 82 3 C:N=38. White & Howes, 1994b
Decay Short Hopkinson & Schubauer, 1984 19.7 4
Demtnfication Total Haines et al. 1977 12 4
Denitrification Total Whitney et al. 1981 65 4 Ecology of Salt Marsh
Denitrification Total Chalmers el al, 1976 7 4
Denitrification Sherr & Payne, 1979
Detritus Short Alberts et al. 1992 4
Detritus Short Haines et al, 1977 12 4
Detritus Short Chalmers, 1979 10 5 3 %N=.77 Hopkinson & Schubauer, 1984
Detritus Tall Haines el al, 1977 21 4
Excretion Short Montague, 1982 12.2 3
Filter Feeders High Kuenzler, 1961 0 25 3 %N=10, Leschine, 1979
Filter Feeders Short Kuen/ler, 1961 0.28 3 %N=10, Leschine, 1979
Filter Feeders Tall Kuen/ler, 1961 3.15 3 %N=10, Leschine, 1979
o^
Compartment Zone Source g N/m2/yr RF Comments
Filler Feeders Total Kuenzlcr, 1961 0.04 3 %N=10, Leschine, 1979
Filter Feeders Total Kuenzler, 1961 0.12 3 %N=10, Leschine, 1979
Filter Feeders Total Kuenzler, 1961 0.08 3 %N=10, Leschine, 1979
Filtration Short Kemp et al, 1990a 1.27 /aug&sep 3 C:N=5.5 Valiela, 1983
Filtration Short Kemp et al. 1990a 1.37 3 C:N=5.5, Valiela, 1983
Filtration Short Kemp et al. 1990a 59.57 3 C;N=7, Valiela & Teal, 1979b
Grazer/Detritivore Barlocher et al, 1989 2
Grazers Total Wiegert & Wetzel, 1979 0.06 3 C;N=17, Valiela, 1983
Grazers/Nekton Short Kemp el al, 1990b 6 35 4
Grazers/Nekton Short Kemp el al, 1990b 045 4
Grazers/Nekton Short Kncib & Weeks, 1990 4
Grazers/Nekton Short Kemp el al, 1990b 0.46 4
Grazers/Nckton Short Newell elal, 1989 4
Grazers/Nekton Short Fnlz & Wiegert, 1991 4
Grazers/Nekton Short Kneib, 1991 4
Grazers/Nekton Short WeilTer & Wiegert, 1981 4 Ecology of Salt Marsh
Grazers/Nekton Tall Kneib & Weeks, 1990 4
Grazers/Nekton Tall Pfeiffer & Wiegert, 1981 4 Ecology of Salt Marsh
Grazers/Nekton Total Dai & Wiegert, 1996 2
Grazers/Nekton Total Smalley, 1960 3
Grazers/Nekton Total Montague, 1982 4
Grazers/Nekton Total Wiegert ctal, 1981 4 Ecology of Salt Marsh
Grazers/Nekton Total Smalley, 1960 3
Leaching Short Hopkinson & Schubauer, 19 0.7 4
Litter Total Whitney et al, 1981 1210 21 4 Ecology of Salt Marsh
Litter Total Weigert et al, 1981 4 Ecology' of Salt Marsh
Mineralization Total Whitney et al, 1981 70 4 Ecology of Salt Marsh
Mussel Short Kemp et al, 1990a 4
Nitrogen Fixation High Whitney et al, 1981 5 4 Ecology of Salt Marsh
Nitrogen Fixation High Whitney et al, 1981 4 4 Ecology of Salt Marsh
Nitrogen Fixation Short Hanson, 1983 13.1 4
Nitrogen Fixation Short Haines elal. 1977 5.8 4
o
00
Compartment Zone Source g N/m2/yr RF Comments
Nitrogen Fixation Short Hanson, 1977 20-50 4
Nitrogen Fixation Short Hanson, 1977 22.2-,52.4 4
Nitrogen Fixation Short Whitney et al, 1981 13 4 Ecology of Salt Marsh
Nitrogen Fixation Short Whitney et al, 1981 13 1 4 Ecology of Salt Marsh
Nitrogen Fixation Tall Hanson, 1983 39.7 4
Nitrogen Fixation Tall Whitney et al, 1981 40 4 Ecology of Salt Marsh
Nitrogen Fixation Tall Wliitney et al, 1981 39.7 4 Ecology of Salt Marsh
Nitrogen Fixation Total Hanson, 1983 2.92 4
Nitrogen Fixation Total Hanson, 1983 53.29 4
Nitrogen Fixation Total Haines, 1976 6 4
Nitrogen Fixation Total Whitney et al, 1981 15 4 Ecology of Salt Marsh
Nitrogen Fixation Total Whitney et al. 1981 14.8 4 Ecology of Salt Marsh
Plant Uptake Short Hokinson & Schubauer. .34.8 4
Plant Uptake Short Haines et al, 1977 2.1 4
Plant Uptake Tall Haines el al, 1977 10.7 4
Plant Uptake Total Haies, 1976 11 4
Plant Uptake Total Haines, 1976 22 4
Pore NH4 Short Chalmers et al, 1976 0.21 4
Pore NH4 Tall Chalmers et al, 1976 0.18 4
Pore NH4 Total Whitney et al, 1981 4 Ecology of Salt Marsh
Pore NH4 Total Chalmers, 1979 0.19 2
Pore NOx Short Chalmers et al, 1976 0.04 4
Pore NOx Tall Chalmers et al, 1976 0.03 4
Pore NOx Total Whitney et al, 1981 65 4 Ecology of Salt Marsh
Pore NOx Ibtal Whitney et al, 1981 4 Ecology of Salt Marsh
Pore PN High Gallagher et al, 1980 13.9 4
Pore PN Short Gallagher et al, 1980 9.9 4
Pore PN Short Christian et al, 1981 727000 3 Ecology of Salt Marsh
Pore PN Short Chalmers et al, 1976 486.54 4
Pore PN Tall Gallagher et al, 1980 21 4
Pore PN Tall Christian et al, 1981 767000 g/m2 r 3 Ecology of Salt Marhs
Pore PN Tall Chalmers et al, 1976 485 8 4
GO
Compartment Zone Source g N/m2/yr RF Comments
Porc PN Total Whitney et al, 1981 485 4 Ecology of Salt Marsh
Pore PN Total Wiegertctal, 1981 4 Ecology of Salt Marsh
Pore PN Total Schubauer & Hopkinson, 1984 16.63j 3 %N=0 44, Hopkinson & Schubauer, 1984
Precipitation Total Haines, 1976 0.07 4 TN=.3 g N/m2/year
Precipitation Total Haines, 1976 0.3 4
Precipitation Total Haines, 1976 0.1 4 TN=.3 g N/m2/year
Precipitation Total Whitney et al, 1981 0.3 4 Ecology of Salt Marsh
Precipitation Total Whitney et al, 1981 0.3 4 Ecology of Salt Marsh
Precipitation Total Haines, 1976 0 13 4 TN=.3 g N/m2/year
Sediment Short Haines el al, 1977 493 4
Sediment Tall Haines et al, 1977 463 4
Sediment Total Gallagher & Plumley. 1979 98 4
Sediment Total Wiegerl & Wetzel, 1979 932.64 4 C:N=19.3, Valiela, 1983
Sediment EXDN Total Wiegert & Wetzel, 1979
Sediment NH4 Short Montague, 1982 4
Sediment NH4 Short Montague, 1982 4
Sediment NH4 Short Haines el al, 1977 0.255 4
Sediment NH4 Tall Haines et al, 1977 0.202 4
Sediment NOx Short Haines et al, 1977 0.035 4
Sediment NOx Tall Haines et al, 1977 0.033 4
Sedimentation Total Whitney et al, 1981 3.3 4 Ecology of Salt Marsh
Standing Dead High Gallagher, 1975 7.27 3 %N=0.8, Gallagher, 1975
Standing Dead Short Gallagher, 1975 2.42 3 %N=0.7, Gallagher, 1975
Standing Dead Tall Gallagher, 1975 2.78 3 %N=0.7, Gallagher, 1975
Surface DON Total Chalmers et al, 1985 17.65 3 C:N=10 2, Hopkinson & Schubauer, 1084
Surface DON Total Whitney el al, 1981 4 Ecology of Salt Marsh
Surface NH4 Total Whitney et al, 1981 4 Ecology of Salt Marhs
Surface PN Total Whitney et al, 1981 3.2 3 Ecology of Salt Marsh
Surface PN Total Chalmers et al, 1985 6.55 2 C;N=5.5, Valiela, 1983
Surface PN Total Chalmers el al, 1985 21.89^ 3 C:N=9 5, Valiela & Teal, 1979b
Surface PN Total Chalmers ct al, 1985 5.27 3 C:N=5.5, Valiela, 1983
Surface PN Total Wiegerl et al, 1981 3 Ecology of Salt Marsh
r^i
00
Compartment Zone Source g N/m2/yr RF Comments
Surface PN Total Whitney et al, 1981 4 Ecology of Salt Marsh
Tidal DON Total Wiegert & Wetzel. 1979
Tidal PN Total Wiegert & Wetzel, 1979 1.29 3 C:N=7. Vahela & Teal, 1979b
Tidal Water Exchange Short Haines, 1979 4
Tidal Water Exchange Short Haines, 1979 4
Tidal Water Exehange Short Haines, 1979 4
Tidal Water Exchange Short Haines. 1979 4
Tidal Water Exchange Short Haines. 1979 4
Tidal Water Exchange Total Wiegert, 1979 153.57 3 C:N=7, Vahela & Teal, 1979b
Tidal Water Exchange Total Wiegert. 1979 .54.89 3 C:N=4.5 Vahela, 1983
Tidal Water Exchange Total Wiegert, 1979 111 71 3 C:N=7, Valeila & Teal, 1979b
Tidal Water Exchange Total Wiegert, 1979 30.45 3 C:N=7, Vahela & Teal, 1979b
Tidal Water Exchange Total Wiegert. 1986 85.43 3 C:N=7, Vahela & Teal, 1979b
Tidal Water Exchange Total Chalmers et al. 1985 191 13 3 C:N=5 5, Vahela, 1983
Tidal Water Exchange Total Chalmers et al. 1985 226.24 3 C:N=10.2, Hopkinson & Schubauer, 1984
Tidal Water Exhehange Short Haines, 1979 4
Tidal Water Exhehange Short Haines, 1979 4
Tidal Water Exhehange Short Haines, 1979 4
Tidal Water Exhehange Short Haines, 1979 4
Tidal Water Exhehange Short Haines, 1979 4
Tidal Water Exhehange Short Montague, 1982 4
Tidal Water Exhehange Total Whitney et al, 1981 46.6 4 Ecology of Salt Marsh
Tidal Water Exhehange Total Haines, 1976 3.1 4
Tidal Water Exhehange Total Wiegert. 1986 9.41 3 C:N=17, Vahela, 1983
Tidal Water Exhehange Total Wiegert. 1979 13.971 3 C:N=7, Vahela & Teal, 1979b
Tidal Water Exhehange Total Chalmers el al, 1985 138.32 3 C:N=9 5, Vahela & Teal, 1979b
Tidal Water Exhehange Total Chalmers et al. 1985 283.41 3 C:N=10 2, Hopkinson & Schubauer, 1984
Translocation Short Newell et al. 1989 4
Translocation Short Hopkinson & Schubauer. 1984 17 9 4
APPENDIX E. UPPER PHILLIPS CREEK ORIGINAL DATA.
00
Compartment Original Data Zone Season Source
Aboveground Biomass 722 82 g/m2 High Year Tolley, 1996
Aboveground Biomass 27g/m2 High Year Blum, 1997
Aboveground Biomass 60.72 g dw/m2 Short Year Blum, 1993
Aboveground Biomass 8 g/m2 Short Year Blum, 1997
Aboveground Biomass 35 g/m2 Tall Year Blum, 1997
Aboveground Biomass 156.34 g dw/m2 Tall Year Blum, 1993
Aboveground Production 800 g C/m2/\ r High Year Anderson et al, 1997a
Aboveground Production 846.9 g/m2/yr High Year Tolley. 1996
Aboveground Production 14 gN/m2/yr Short Year Anderson et al, 1997b
Aboveground Production 442.56 g dw/m2/yr Short Growing Season this study
Aboveground Production 955.68 g dw/m2/yr Tall Growing Season this study
Bacterial Uptake 2.74 g N/m2/yr Short Year Neirkirk, 1996
Bacterial Uptake 315 umol/m2/tidc Short Tidal Cycle Chambers et al, 1992
Belowground Biomass 250 g/m2 High Year http: //WWW vcrlter.Virginia,edu/elecvol
Belowground Biomass 0 79gN/m2 Short Year this study
Belowground Biomass 900 g/m2 Short Year http://www.vcrlter.virginia.edu/elecvol
Belowground Biomass 0 1 g N/m2 Tall Year this study
Belowground Biomass 2(K) g/m2 Tall Year http.7/www vcrlter virginia.edu/elecvol
Belowground Dead 2.89 gN/m2 Short Year this study
Belowground Dead 1.35gN/m2 Tall Year this study
Belowground Production 20.15gN/m2/yr High Year this study
Belowground Production 33 gN/m2/yr Short Year Anderson et al, 1997b
Belowground Production 2140 g dw/m2/yr Short Year Blum, 1993
Belowground Production 900 g AFDW/m2/yr Short Year Blum & Christian, 1997
Belowground Production 680 g dw/m2/yr Tall Year Blum, 1993
Benthic Algae 3.06 ug chi aycm3 High Year Anderson et al, 1997a
Benthic Algae 10.12 mg/m2 Short Year Neikirk, 19%
Benthic Algae Production 5.0 g Ñ/m2/yr Short Year /knderson et al, 1997b
Benthic Filter Feeders 18.67/m2 Short Year this study
Benthic Filter Feeders 16/m2 Tall Year this study
Burial 4.0 g N/m2/yr Short Year Anderson et al, 1997b
Decay k=-0.254/yr (50% of decay at 2.7 years) Year Chnstian et al, 1990
00
Compartment Original Data Zone Season Source
Decay 66.3% remain of NPP High Year Blum & Chnstian. 1997
Decay 7 g N/m2/yr Short Year Anderson et al. 1997b
Decay 26 g N/m2/y r Short Year Anderson cl al, 1997b
Dcca> 76.9% of abo\cground production Short Year Blum, VCR/l-TER Database
Decay 69.7% of belowground production Short Year Blum. VCR/LTER Database
Decay 133.44 ^g dw/m2/yr Short Year this study
Decay 69.15% remain of NPP Short Year Blum & Chnstian. 1997
Decay 80 8 % of production Tall Year Blum, VCR/LTER Database
Decay 68 4 % of belowground production Tall Year Blum. VCR/LTER Database
Decay 479.47g dv\/m2/yr Tall Year this study
Decay 59 9% remain of production Tall Year Blum & Christian, 1997
Denitrification 0.6 g N/m2/y r Short Year Anderson ct al. 1997b
Detritus formation 2.05 g N/m2/y r High Year Buck, personal communication
Filter Feeding 16.04 g N/biomass/y r Short Year this study
Filter Feeding 0.032 g N/biomass/y r Short Year this study
Filter Feeding 11.79 g N/biomass/yr Tall Year this study
Filter Feeding 0.032 g N/biomass/y r Tall Year this study
Gra/ers 141 33/m2 Short Year this study
Gra/ers 8/m2 Short Year this study
Grazing 2% of NPP High Year VCR 1998 All Scientist Meeting
Grazing 2% of NPP Short Year VCR 1998 All Scientist Meeting
Grazing 2% of NPP Tall Year VCR 1998 All Scientist Meeting
Leaching 0.96 g N/m2/y r High Year this study
Leaching 0.62 g N/m2/yT Short Year this study
Leaching 0.82 gN/m2/yr Tall Year this study
Mineralization L3.27gN/m2/yr High Year Anderson et al, 1997a
Mineralization 1 14 mg N/m2/h High July Anderson et al, 1997a
Mineralization 20.21 gN/m2/yr Short Year Neirkirk. 1996
Mineralization 84 g N/m2/y r Short Year Anderson et al, 1997b
Nitrification 7.09 g N/m2/y r High Year Anderson et al, 1997a
Nitrification 0 9 mg N/m2/h High July Anderson el al. 1997b
Nitrification 0.0 g N/m2/y r High Summer Tavlor. 1995
-o
00
Compartment Original Z<me Season Source
Nitrification 121 gN/m2/yr Short Year Neirkirk. 1996
Nitrification 4.0 g N/m2/yr Short Year Anderson et af 1997b
Nitrogen Fixation 1 0 g N/m2/yr Short Year Anderson et al. 1997b
Pore NH4 . 11 g N/m2 High Year Anderson et al. 1997a
Pore NH4 4.62 uM High Year Anderson et al. 1997a
Pore NH4 0.92 gN/m2 Short Year this study
Pore NH4 0.18 g N/m2 Tall Year this study
Pore NH4 32 uM Tall Year http://w"vvxv.vcrlter.Virginia.edu/elecNol
Pore NOx 11 g N/m2 High Year Taylor. 1995
Pore NOx .02 g N/m2 High Year Anderson et al. 1997a
Pore NOx 1.01 uM High Year Anderson et al. 1997a
Precipitation 0.25 g N/m2/yr Total Year Anderson et al. 1997b
Precipitation 0.18gN/m2/yr Total Year Anderson et al. 1997b
Precipitation 0.31 gN/m2/yr Total Year Keene & Galloway. 1997
Precipitation 0.15 gN/m2/yr ¡Total Year Keene & Galloway. 1997
Sediment release of NH4 846.6 umol/m2/tidc Short Tidal Cycle Chambers et al. 1992
Sediment release of NOx 6.31 gN/m2/yr Short Year Neirkirk. 1996
Sediment uptake of NH4 0.41 gN/m2/yr Short Year Neirkirk. 1996
Sedimentation 1.25gN/m2/yr Short Year Anderson et al. 1997b
Standing Dead 806.82 g/m2 High Year Tolley. 1996
Standing Dead 4.35gN/m2/yr ¡High Year Buck, personal communication
Standing Dead 413.47 g dvv/m2 Short Year this study
Standing Dead 652 g dw/m2 Tall Year this study
Surface PN 19.2 ug chi a/1 Short Year Neikirk. 1996
Tidal Water Exchange 3.4 umol/1 Tall Year VCR/LTER Database
Tidal Water Exchange 4 25 umol/1 Tall Year VCR/LTER Database
Tidal Water Exchange 2.98 umol/1 Tall Year VCR/LTER Database
Tidal Water Exchange 2.48 umol/1 Tall Year VCR/LTER Database
Translocation 7 g N/m2/> r Short Year Anderson et al, 1997b
187
APPENDIX F. UPPER PHILLIPS CREEK CONVERTED DATA.
RF=Reliability Factor
oo
00
Compartment Zone Source g N/m2y r RF Comments
Abov eground Biomass High Tolley- 1996 11 3.3 3 %N=1.57 Vince ctal, 1981
Aboveground Biomass High Blum. 1997 0 42 3‘%N= 1.57 Vince ctal. 1981
Aboveground Biomass Short Blum, 1993 0 64 3 %N=1 05 PPopkinson & Schubauer. 1984
Aboveground Biomass Short Blum, 1997 0 13 3:%N=1.57 Vince et al, 1981
Aboveground Biomass Pall Blum. 1997 0.5.S 3:%N=1 57 Vince ctal. 1981
Abov eground Biomass Tall Blum. 1993 1.64 3Í%N=1 05 PPopkinson & Schubauer, 1984
Aboveground Produelion High Anderson ct al. unpublished 16 4'C N=50 White & Howes, 1994
Aboveground Production High Tolley. 1996 13 3 3l%N=l 57 Vinccetal. 1981
Abov eground Production Short Anderson ct al. 1998 14 4'
Abov eground Production Short this study 5.22 3 %N= 118 this study
Aboveground Production Tall this study 17.58 3 j%N= 1 84 this study
Bacterial Uptake Short Neirkirk. 19% 2.74 2'
Bactenal Uptake Short Chambers ct al. 1992 .3.22 3'
Belowground Biomass High http://wvvw .vcrltcr Virginia edu/clccvol 11 3j%N=0.44 PPopkinson & Schubauer, 1984
Belowground Biomass Short this study 0.79 3!
Belowground Biomass Short http://wvvw vcrltcr Virginia edu/clccvol .3.96 3j%N=0 44 Plopkinson & Schubauer, 1984
Belowground Biomass Tall this study 0 1 3|
Below ground Biomass Tall http://wwAv.vcrlter virginia.edu/elccvol 0 88 3 %N=0 44 PPopkinson & SchuPrauer. 1984
Belowground Dead Short this study 2 89 -3!
Belowground Dead Tall this study 1 35 3!
Belowground Production High this study 20.15 1 ! Average SI & GS per unit biomass
Belowground Production Short Anderson ct al. 1998 33 4Í
Belowground Production Short Blum. 1993 942 3 %N=0.44 PPopkinson & Schubauer, 1984
Belowground Production Short Blum & Chnstian. 1997 3.96 3 %N=0 44 PPopkinson & Schubauer, 1984
Belowground Production Tall Blum. 1993 2.99 3 !%N=0 44 PPopkinson & Schubauer, 1984
Benthic Algae High Anderson ct al. 1996 0 18 pjchl a conversion Fhckncy 1994; C:N=5.5 Valida, 198.3
Benthic Algae Short Neikirk. 1996 5 88 3 chi a conversion Fhckncy 1994; C:N=5 5 Valida, 1983
Benthic Algae Production Short Anderson ct al. 1998 5 4
Benthic filter feeders Short this study 0.26 3 1 musscl=0 0138 g N
Benthic filter feeders Tall this study 0.22 3 ! 1 musscl=0.01.38 g N
Burial Short Anderson ct al. 1998 4 4
Dcca> High Christian cl al. 1990 1 09 4 For Cedar Island, NC
O'
oo
Compartment Zone Source g N/m2yr RF Comments
Decay High Blum & Christian, 1997 6.79 1 NPP=20 15
Decay Short Anderson ct al, 1998 i 7 4
Decay Short Anderson et al, 1998 26 4
Decay Short Blum. VCR/LTER Database 10.77 3 NPP=14 gN/m2/yr
Decay Short Blum, VCR/LTER Database 6.57 3 NPP=9 42 g N/m2/yr
Decay Short this study 1 27 .3 %N=0 95 this study
Decay Short Blum & Christian. 1997 ! 2.06 3 NPP=6.69
Decay Tall Blum. VCR/LTER Database ! 5.07 3 NPP=6.27 g N/m2/yr
Decay Tall Blum, VCR/LTER Database ’ 2.05 3 NPP=2.99 gN/m2/yr
Decay Tall this study 3.07 3 *M>N=0.64 this study
Decay Tall Blum & Christian. 1997 ' 12 3 NPP= 2.99 ?
Denitrification Short Anderson
?
ct al, 1998 0.6 4
Detritus formation High Buck, personal communication ! 2.05 3 ‘fi)N=0.52 Hopkinson & Schubauer, 1984
Filter Feeding Short this study 1 4 1.3 1 biomass=0.26
Filter Feeding Short this study I 0.01 1 biomass=0.26
Filter Feeding Tall this study ¡ 2.6 1 biomass=0.22
Filter Feeding Tall this study 0 01 1 biomass=0.22
1
Grazers Short this study 4
Grazers Short this study LL3 4
Grazing High VCR 1998 All Scientist Meeting 0 27 2 used Tolley. 1996 data
Grazing Short VCR 1998 All Scientist Meeting 0.28 2 NPP=14 g N/m2/yr
Grazing Tall VCR 1998 All Scientist Meeting ! 0..35 2 NPP=17 58 gN/m2/yr
Leaching High this study ! 0.96 1 estimate from GS and SI
Leaching Short this study i 0.62 1 estimate from GS and SI
Leaching Tall this study 082 1 estimate from GS and SI
^
Mineralization High Anderson ct al, unpublished 13.27 4
Mineralization High Anderson ct al, 1997 ; 9.99 4
Mineraliz.ation Short Ncirkirk, 1996 1 20.21 2
Mineralization Short Anderson el al, 1998 ! 84 4
'
Nitrification High Anderson cl al, unpublished 709 4
Nitrification High Anderson et al, 1997 I 7.88 4
Nitrification High Taylor. 1995 ! 0 2
o
o
Compartment Zone Source g N/m2j r RF Comments
*
Nitrification Short Neirkirk, 19% 1.21 2
Nitrification Short Anderson et al. 1998 4 4
Nitrogen Fixation Short Anderson et al. 1998 1 4
Pore NH4 High Anderson et al, unpublished 0.111 4
Pore NH4 High Anderson et al. 1997 0,01 3
Pore NH4 Short this stud\ ^ 0.92 3
’
Pore NH4 Tall this study 0.181 3
Pore NH4 Tall http://w'wxv.vcrlter.virgmia.edu/elea'ol ^ 0 04 3
'
Pore NOx High Taylor. 1995 0.11 4
'
Pore NOx High Anderson et al. unpublished 0,02 4
Pore NOx Higli Anderson et al. 1997 ¡ 0.01 3
Precipitation Total Anderson et al. 1998 1 0.25 4
Precipitation Total Anderson et al. 1998 0.18 4
Precipitation Total Keene & Galloway, 1997 0.31 4
'
Precipitation Total Keene & Galloway. 1997 0.15 4
*
Sediment release of NH4 Short Chambers el al, 1992 8.66 3 mean of given data
Sediment release of NOx Short Neirkirk, 1996 6.31 2
Sediment uptake of NH4 Short Neirkirk. 1996 0.41 2
Sedimentation Short Anderson et al. 1998 125 4
Standing Dead High Tolley. 1996 4.2 3 %N=0.52 Hopkmson & Schubauer, 1984
Standing Dead High Buck, personal communication 4.35 3 %N=0 52 Hopkinson & Schubauer. 1984
Standing Dead Short this study 1 3.93 3 %N=0.95 this study
Standing Dead Tall this study 4 17" 3 %N=0.64 this study
Surface PN Short Neikirk, 1996 i 0.01 2 1 g C=2 g dw; C:N=5.5
Tidal Water Exchange Tall VCR/LTER Database 34.77 3
'
Tidal Water Exchange Tall VCR/LTER Database 43.47 3
’
Tidal Water Exchange Tall VCR/LTER Database 30.44 3
Tidal Water Exchange Tall VCR/LTER Database ] 25.36' 3
Translocation Short Anderson et al, 1998 7 4
191
APPENDIX G. BALANCED MODELS.
Standing Stock in g N x m‘“
Flows in g N X m'‘ X yf’
The first column of numbers represents the averaged data obtained from literature The
balanced number column reflects the numbers used in the model after the model was
balanced The % difference represents the percentage the balanced number was changed
from the original data inorder to balance the model
The first column shows the flows from one compartment to another For example, F1-2 is
a flow from compartment 1 to compartment 2 FO-1 is an input to compartment 1, and
F1-0 is an output from compartment 1
F=flow
R=respiration
Final Balancing the Great Sippewisset Tall Low Model
Compartment Standing, Stock Reliability Factor Balanced Xumhers % Difference
Benthic filler feeders 1 6.28 4.00 6.28 0,00
Grazers/Nekton 2 3,51 2.00 3.51 0.00
Li\e Shoots 3 25.50 3,00 25.50 0.00
Standing Dead/Litter 4 30,43 4.00 30.43 0.00
Roots 5 4.27 3.00 4.27 0.00
Benthic algae 6
N2 Fixers 7
Precipitation 8
Surface NH4 9
Surface NOx 10
Surface DON 11
Surface PN 12
Pore NOx 13
Pore DON 14
Pore PN 15 190,54 4.00 190 54 0.00
PoreNH4 16
Groundwater 17
Inputs Reliability' Factor Inputs
F()-8 0,56 3.98 0.65 16.07
F()-17 13,27 3.70 16 91 27 43
FO-2 0.02 4.00, 0.02 0.00
FO-7 4 19 3.09 5,03 20.05
FO-12 1748 3.60 12.13 -30.61
FO-9 5.42 4 00‘ 5.90 8.86
FO-IO 3.13 400 3 44 9.90
FO-11 67,00 3.50 5963 -11.00
Outputs Reliability Factor Outputs
FI 5-0 6.13 3.00 6.13 0.00
Fl-0 0.02 2.00 0.02 0.00
F12-0 15.33 2.83 15 92 3.85
F9-0 7.32 4.00 5.56 -24.04
F10-0 7.75 4.00 6.98, -9.94
FI 1-0 ; 53.45 3.60 59 47 11.26
R9-0 0.05 2.00 0.05 0.00
R13-0 20.32 4.00 9.58 -52.85
Flows Reliability Factor Flows
F8-12 ¡ 0.01 4.00 0,01 0.00
F8-9 1 0.03 4.00 0.03 0.00
F8-10 0.07 4.00 0.07 0.00
F8-11 0 21 4.00 0.21 0.00
F17-16 i 1.01 4.00 1.11 9.90
F17-13 5,88 4,00 6.47 10.031
F17-14 10.33 3:5(r 9.30 -9.97
F17-15 0.03 4.00 0.03 0.00
F12-1 11 92 3.33 13.11 9 98
Fll-1 0.34 4 00 0.37 8.82 193
F3-2 0.62 1.00 0.68 9.68
F4-2 4.35 1.00 1 00 -77.01
Fl-9 2.32 3.75 2.09 -9.91
F2-9 13.75 2.00 8.90 ?-35.27
Fl-15 462 4 00 4 16 -9.96
F16-6 6.32 1.50 4.42 -30.06
F5-3 928 2.75 6.51 -29.85
Fn-5 11.58 4.00 11.58 0.00
F16-5 23.24 2.57 18 59 -20.01
F3-5 1.40 4.00 1.40 0.00
F3-9 2 76 2.00 2.76 0.00
F5-16 0.40 4 00 0 44 10.00
F3-4 6.36 2.00 1.67 -73.74
F4-15 21.94 2.75 0.67 -96 95
F15-16 12.66 3.33 17.72 39.97
F14-16 1 45 3.33 9 49 554.48
F16-13 14 86 3.00 10.00 -32.71
F15-12 107 68 2 00 10.07 -90.65
F12-15 8942 2.00 0 00 -100.00
F6-15 0.00 4.41
F7-16 4 19 3.09 5.03 20.05
F6-2 0.00 0.01
F5-15 0.00 24 62
Fl-2 26 97 1 00 7.21 -73.27
F2-15 0 02 2.00 0.02 0.00
F9-10 0.00 8 07
F9-12 0.00 6.82
F13-10 0.28 1 00 0 17 -39.29
F16-9 1.02 3.00 0 82 -19.61
F8-14 0.21 4.00 0.19 -9.52
F8-13 0.08 4.00 0.09 12.50
F8-16 0.04 4 00 0 04 0.00
F8-15 0.01 4 00 0.01 0.00
Fl()-13 0.00 4.77
Average 2.89 1.28 -1.91
Final Balancing the Great Sippewisset Short Low Model 194
Compartment Standing Stock Reliability Factor Balanced Xumhers % Difference
Benthic filler feeders 1 6.28 4.00 6 28 0.00
Grazers/Nekton 2 3.51 2 00 3.51 0.00
Live Shoots 3 25.50 3 00 25.50 0.00
Standing Dead/Litter 4 3043 4.00 30.43 000
Roots 5 4.27 3.00 4.27 0.00
Benthic algae 6
N2 Fixers 7
Precipitation 8
Surface NH4 9
Surface NOx 10 |
Surface DON 11
Surface PN 12
Pore NOx 13
Pore DON 14
Pore PN 15 190.54 4 00 190 54 0 00
Pore NH4 16
Groundwater 17
Inputs Reliability Factor Inputs
FO-8 0.56 3.98 0 66 17.86
F()-17 13.27 3.70 14 69 10 70
F()-2 0 02 400 0.02 0.00
FO-7 2.75 3.09 2.75 0.00
FO-12 8.74 3.60 11 48 31.35
FO-9 2.71 4.00 1 60 -40.96
FO-10 1.57 4.00 1 25 -20.38
FO-11 33.50 3.50 30 25 -9.70
Outputs Reliability Factor Outputs
F15-0 4.71 3.00 9.51 101.91
Fl-0 ' 0.02 2.00 0.02 0.00
F12-0 7.67 2.83 4 93 -35.72
F9-0 3.66 4 00 4.77 30.33
FlO-0 3.88 4.00 5 87 51.29
Fll-0 26.73 3.60 29 99 12.20
R9-0 0.05 2.00 0 07 40.00
R13-0 6.85 4.00 7 54 10.07
\fIohs Reliability Factor \FIoms
F8-12 0.01 4.00 0 01 0 00
F8-9 ! 0.02 4.00 0.01 -50.00
F8-10 ~1 0.04 4.00 0 04^ 0.00
F8-11 0.11 4.00 0.11 0.00
F17-16 1.01 4.00 111 9.90
F17-13 i 5.88 4.00 5.29 -10.03
F17-14 10.33 3.50' 8 26 -20.04
F17-15 ' 0.03 4.00, 0 03 0.00
F12-1 11 92 3.33 14 30 19 97
Fll-l 0.34 4.00 0.37 8.82 195
F3-2 0,62 1.00 0.93 50.00
F4-2 4.35 1.00 2.61 -40.00
Fl-9 2,32 3.75 2.09 -9 91
F2-9 13.75 2.00 11 03 -19 78
Fl-15 4.62 4.00 5 08 9.96
F16-6 6.32 1 50 6.32 0.00
F5-3 4 28 2.50 9.26 116.36
F13-5 11.58 4.00 10.75 -7 17
F16-5 23.24 2.57 16.27 -29.99
F3-5 1 40 4 00 1.26 -10.00
F3-9 1 41 3.33 1,27 -9,93
F5-16 0.40 4.00 0.44 10.00
F3-4 3.63 1.88 5.80 59.78
F4-15 4 56 2.60 3.19 -30.04
F15-16 12 66 3.33 21 56 70.30
F14-16 1 45 3.33 8.58 491 72
F16-13 14 86 3 00 11.89 -19,99
F15-12 12 86 2.00 2.16 -83.20
F12-15 10.70 2.00 0.00 -100.00
F6-15 0 00 6.31
F7-16 2.75 3.09 2.75 0.00
F6-2 0.00 0 01
F5-15 0.00 18.58
Fl-2 26 97 0 00 7 48 -72.27
F2-15 0.02 2.00 ' 0.02 0 00
F9-10 0.00 5.59
F9-12 ^ 0.00 5.58
F13-l() ; 001 1.00 0.01 0.00
F16-9 0.01 3.00 0.01 0.00
F8-13 0.11 4.00 0.10 -9 09
F8-14 0.32 4.00 0,32 0.00
F8-15 0.02 4 00 0.02 0 00
F8-16 0.05 4.00 0.05 0.00
FlO-13 0.00 1.02
Average 2.88 131 9.71
Final Balancing the Great Sippewisset High Marsh Model 196
Compartment Standing Stock Reliability Factor Balanced Xumhers % Difference
Benthic filter feeders 1 0 44 400 0.44 0.00
Grazers/Nekton 2 3.51 2 00 3.51 0 00
Live Shoots 3 6.00 3 00 6.00 0 00
Standing Dead/Litter 4 30 43 4 00: 30,43 0.00
Roots 5 1 02 4.00 1.02 0.00
Benthic algae 6
N2 Fixers 7
Precipitation 8
Surface NH4 9
Surface NOx 10
Surface DON 11
Surface PN 12
Pore NOx 13
Pore DON 14
Pore PN 15 190 54 4 00 190 54 0.00
PoreNH4 16
Groundwater 17
Inputs Reliability Factor Inputs
F()-8 0 56 3 98 0 64 14 29
FO-17 13 27 3 70 13 56 2 19
FO-2 0 02 4 00 0 02 0.00
FO-7 5.87 2 95 5.87 0.00
FO-12 1,75 3.60 1,93 10.29
F()-9 0.54 4 00 0.53 -1 85
FO-IO 0 31 4 00 0.28 -9 68
FO-11 6.70 3.50 6.03 -10 00
Outputs Reliability Factor Outputs
F15-0 1 38 2.40 11.38 724.64
Fl-0 0.02 2.00 0.02 0.00
F12-0 1 53 2 83 1.22 -20.26
F9-0 0.73 4 00 0.73 0.00
FlO-0 0 78 4.00 0.86 10.26
FI 1-0 5.35 3.60 6.02 12.52
R9-0 0.05 2.00 0.05 0.00
R13-0 7.80 4.00 8.58 10.00
Flows Reliability Factor Flows
F8-12 0.01 4.00 0.01 0.00
F8-9 0.01 4.00 O.OL 0.00
F8-10 001 4.00 0.01 0.00
F8-11 002 4 00 0.02 0 00
F17-16 1.01 4.00 1.01 0.00
F17-13 5 88 4.00 5.29 -10 03
F17-14 10,33 3.50 7.23 -30.01
F17-15 0.03 4.00 0.03 0.00
F12-1 1,19 3.33 1 43 20 17
Fll-l 0.03 4.00 0.03 0.00 197
F3-2 0.62 1 00 0.37 -40.32
F4-2 4.35 1.00 2.61 -40.00
Fl-9 0.23 3.75 0.21 -8.70
F2-9 1 24 2.00 1.61 29.84
Fl-15 043 4 00 0.39 -9.30
F16-6 6.32 1.50 6 32 0.00
F5-3 7.50 3.00 9.00 20 00
FI 3-5 11.58 4.00 12.74 10.02
F16-5 23.24 2.57 16.27 -29.99
F3-5 1.40 4.00 1.26 -10.00
F3-9 1 50 4.00 1.35 -10.00
F5-16 0.40 4.00 0 44 10.00
F3-4 9.48 2.00 6.02 -36.50
F4-15 21.94 2.75 3.41 -84 46
F15-16 12 66 3.33 22.40 76.94
F14-16 1 45 3.33 7.59 423.45
F16-13 14 86 3.00 14 78 -0.54
F15-12 3.34 4.00 -100.00
F12-15 3 95 4.00 0.55 -86.08
F6-15 000 6.31
F7-16 5.87 2 95 5.87 0.00
F6-2 0.00 0.01
F5-15 0.00 20.83
Fl-2 0 00 0 84
F2-15 0.22 2 00 2.24 918 18
F9-10 0.00 1 68
F9-12 0.00 1.26
F13-10 i 000 0.01
F16-9 0.00 0.01
F8-13 ^ 0.14 4.00 0.14 0.00
F8-14 : 0.40 400 0.36 -10.00
F8-15 1 0.02 400 0.02 0.00
F8-16 0.07 4.00 0 07 0.00
FlO-13 ' 0.00 1.12
Average ^ 2.94 1.34 34 22
Final Balancing the Sapelo Island Tall Low Model
Compartment Standing Stock Reliahilit} Factor Balanced Sumhers % Difference
Benthic filter feeders 1 1.01 3.25 1.01' 0.00
Grazers/Nekton 2 5.70 3.48 5.70 0.00
Live Shoots 3 8.08 3.50 8.08 0.00
Standing Dead/Litter 4 4.56 4.00 4.56 0.00
Roots 5 21.42 3.33 21 42 0.00
Benthic algae 6 0 18 4.00 0 18 0.00
N2 Fixers 7
Precipitation 8
Surface NH4 9 35 69 4.00 35.69 0.00
Surface NOx 10
Surface DON 11 52 69 3.67 52.69 0.00
Surface PN 12 54.29 3.50 54.29 0.00
Pore NOx 13 0.04 4 00 0.04 0.00
Pore DON 14 2.55 3.00 2.55 ‘ 0.00
PorePN 15 548 37 4.00 548 37 0.00
PoreNH4 16 0.17 4.00 0.17 0.00
Groundwater 17
Inputs Reliability Factor Inputs
FO-8 0.30 4.00 0.32 667
FO-17 0.00 004
F()-2 0.00 0 01
FO-7 39.80 400 39 80 0.00
FO-12 ' 49.60 3.33 61 31 23.61
'f0^^9 ^ 1.77 4 00 1.59 -10 17
FO-10 ^ 0.14 4 00 0.13 -7 14
FO-11 ! 142 13 3.50 101.95 -28 27
• Outputs Reliability Factor Outputs
FI 5-0 1.20 3.00 1.44 20.00
Fl-O 0.00 0.01
F12-0 70.33 3.00 57.35 -18 46
F9-0 2.32 4 00 2.55 9.91
F10-0 0.15 4.00 0.17 13.33
Fll-0 75.04 3.25 101.98 35.90
R9-0 0.00 0.01
R13-0 1 35.35 3.80 41.64, 17.79
Flows Reliability Factor Flows
F8-12 0.00 0.01
F8-9 0.05 4.00 0.05 0.00
F8-10 007 4.00 ' 0.07 0.00
F8-11 : 0.04 4.00 0.04, 0 00
F17-16 0.00 0.01
F17-13 1 0.00 0.01
F17-14 0.00 0.01
F17-15 0.00 001
F12-1 j 21.90 3.00 16.30 -25.57
Fll-l 0.00 0.01 199
F3-2 1.76 2.75 1 23 -30.11
F4-2 5.44 3.00 4 74 -12 87
Fl-9 6.10 3.00 7.32 20.00
F2-9 6 10 3.00 7.32 20.00
Fl-15 0.00 8.86
F16-6 49.08 3.50 39.26 -20.01
F5-3 50.38 3.37 40 31 -19 99
F13-5 0.00 0.01
F16-3 64.42 3.33 51 54 -19 99
F3-5 14 76 4.00 16.24 10.03
F3-9 0.70 4.00 0.78 11 43
F5-16 0.00 0.44
F3-4 15 15 3.50 22.06 45.61
F4-15 ^ 2Í.Ó0 4.00 17.32 -17.52
F15-16 1 70.00 4.00 92 44 32.06
F14-16 0.00 004
F16-13 0 00 41.54
F15-12 0.00 0.00
F12-15 ' 3.25 3.50 2.60 -20.00
F6-15 0.00 38 03
F7-16 39 80 4.00 39.80 0.00
F6-2 0.00 1.23
F5-15 0.00 27.04
Fl-2 0 12 3.00 0 12 0 00
F2-15 0 00 0.01
F9-10 0.00 0 01
F9-r2 0 00 14 93
F13-l() 0.00 0.01
F16-9 0.00 044
F8-13 0.06 4.00 0.06 0.00
F8-14 0.03 4.00 0.03 0.00
F8-15 0.01 4.00 0.01 000
F8-16 0.05 4.00 0.05 0.00
FlO-13 0.00 0.05
Average 2.25 1 78 1 04
Final Balancing of the Sapelo Island Short Low Model
1
( ompartment Standing Stock Reliability Factor Balanced Sumhers '% Difference
Benthic filter feeders 1 1.01 3.25 1 01 0.00
Grazers/Nekton 2 5.70 3.48 5,70 0.00
Live Shoots 3 4.39 3.43 4.39 0.00
Standing Dead/Litter 4 2.79 3.00 in'! 0.00
Roots 5 21 42 3.33 21 42 0.00
Benthic algae 6 0 18 4.00 0.18 0 00
N2 Fixers 7
Precipitation 8 '
Surface NH4 9 35.69 4 00 35.69 0.00
Surface NOx 10
Surface DON 11 52.69 3.67 52 69 0 00
Surface PN 12 54 29 3.50 54.29 0.00
Pore NOx 13 0.04 4.00 0.04 0,00
Pore DON 14 2.55 3.00 2.55 0 00
Pore PN 15 548.37 4.00 548,37 0,00
PoreNH4 16 0.17 4 00 0 17 0.00
Groundwater 17
Inputs Reliability Factor Inputs
FO-8 0 30 4.00 0.33 10 00
FO-17 0.00 0 04
FO-2 0.00 0.01
FO-7 23.70 4 00 23 70 0 00
FO-12 24 80 3.33 29.76 20.00
FO-9 0 89 4.00 0 80 -10 11
FO-10 0.07 4.00 0 06 -14.29
FO-11 71 07 3.50 56.86 -19.99
Outputs Reliability Factor Outputs
F15-0 1.35 3.00 1.35. 0.00
Fl-0 0.00 0.01
F12-0 35.17 3.00 28.14 -19.99
F9-0 1 16 4.00 1.28 10.34
FlO-0 0.08 4.00 0.09 12.50
FI 1-0 37.52 3.25 45.02 19 99
R9-0 0.00 0.01.
R13-0 35.35 3.80 35.66 0.88
Flows Reliability Factor \Flows
F8-12 0.00 ; 0.01
F8-9 0.02 4.00, 0.02 0.00
F8-10 0.05 4.00 0.04 -20.00
F8-11 0 02 4.00 0 02 0.00
F17-16 0.00. 0.01
F17-13 0.00 0.01
F17-14 0.00 0,01
F17-15 0.00 0.01
F12-1 30 47 3.00 5.93 -80 54
Fll-1 0.00 11 86
F3-2 1.76 2.75 1.76-^ 0.00
F4-2 5.44 3.00 4.35 -20.04
Fl-9 6 10 3.00 7.32 20.00
F2-9 6.10 6.23 2.13
Fl-15 0.00 10.32
F16-6 36.14 2.67 36.14 0.00
F5-3 25.35 ïï? 30.48 20.24
FI 3-5 0.00 aôT^
F16-5 38.32 3.25 38.32 0.00
F3-5 14.76 4.00 14.76' 0.00
F3-9 0.70 4.00 0.70 0.00
F5-16 0.00 0.01
F3-4 9.88 3.80 13.26 34.21
F4-15 9.90 4.00 8.91 -10.00
F15-16 70.00 4.00 79.27 13.24
F14-16 0.00 0.06
F16-13 0.00 28.66
F15-12 0.00 0.00
F12-15 3.25 3.50 2.60 -20.00
F6-15 0.00 36.13
F7-16 23.70 4.00 23.70 ÔÔÔ
F6-2 0.00 0.01
—
F5-15 0.00 22.60
FI-2 0.12 3.00 0.14 16.67
F2-15 0.00 0.04
F9-10 0.00 6.89
F9-12 0.00 6.90
F13-10 0.00 0.01
F16-9 0.00 0.01
F8-13 0.10 4.00 0.10 0.00
F8-14 0.05 4.00 0.05 0.00
F8-15 0.01 ÂOÔ 0.01 0.00
F8-16 0.08 4.00 0.08 0.00
FlO-13 0.00 6.91 j
Average 2.22 1.77 -0.99
Final Balancing the Sapelo Island High Marsh Model
1
Compartment Standing Stock Reliability Factor Balanced Numbers % Difference
Benthic filter feeders 1 1,01 3.25 ToT^ 0.00
Grazers/Nekton 2 5.70 3.48 5^ 0.00
Live Shoots 3 9.12 3.00 9d7 0.00
Standing Dead/Litter 4 2.42 0.00
Roots 5 21.42 3.33 21 42' 0.00
Benthic algae 6 0.18 4 0,18' 0,00
i?
N2 1Fixers 7
Precipitation 8
Surface NH4 9 35.69 4.00 35.69 0.00
Surface NOx 10
Surface DON 11 52.69 3.67 52.69 0.00
Surface PN 12 54.29 3.50 54.29 0^
Pore NOx 13 0.04 4.00 0.04 âôô
Pore DON 14 2,55 3,00 2,55 0,00
Pore PN 15 548.37 4.00 548.37 0 00
PoreNH4 16 0 17 4.00 0 17 0.00
Groundwater 17
Inputs Reliability Factor Inputs
FO-8 0.30 4.00 0.35 16.67
FO-17 0.00 0.04
FO-2 0.00 0.01
FO-7 4,50 4.00 4 50 0.00
FO-12 4.96 3.33 5.95 19.96
FO-9 0.18 4.00 0.16 -11.11
FO-10 0.01 4.00 0.01 ÔÔÔ
FO-11 14.21 3.50 10.19 -28.29
Outputs Reliability Factor Outputs
F15-0 1.20 3.00 1.20 0.00
Fl-0 0,00 0.01
F12-0 7.03 3.00 5 62 -20.06
F9-0 0,23 4.00 0.25 8.70
FlO-0 0.02 4.00 0.02 0.00
Fll-0 7^ 3.25 10,19 35.87
R9-0 0.00 0.01
R13-0 35.35 3.80 3.91 -88.94
Flows Reliability Factor Flows
F8-12 0.00 0.01
F8-9 0.01 4.00 0.01 0.00
F8-10 0,01 0.01 0.00
F8-11 0.01 4.00 0.01 0.00
F17-16 0.00 0.01
F17-13 ÔÔÔ^ ÔÏÏÎ
F17-14 0,00 0.01
F17-15 0.00 0.01
F12-1 21.90 3.00 11.77 -46.26
Fll-1 0.00 0.01 203
F3-2 1.76 2.75 1.76 0.00
F4-2 5.44 ÏÔÔ^ 4.35 -20.04
Fl-9 ^ 6.10 3.00 7.32 20.00
F2^9 6.10 3.00 6.10 0,00
Fl-15 00(? 4.33
F16-6 28.22 ïïïT 28.22' 0.00
F5-3 26 73 3.38 32.08 20.01
F13-5 0.00 0.01
F16-5 53.53 4.00 53.53 0.00
F3-5 14 76 4.00 13,28 -10.03
F3-9 0.70 4^ otiT 0.00
F5-16 0.00 0.01
F3-4 0.00 16.34
F4-15 13,90 4.00 11.99 -13.74
F15-16 70.00 4.00 80.83 15.47
F14-16 0.00 0.08
F16-13 0.00 3.77
F15-12 0.00 0.00
F12-15 3.25 3.50 2.60 -20,00
F6-15 0.00 28.21
F7-16 4.50 4.00 4.50 0.00
F6-2 0,00 0.01
F5-15 0.00 34.73
FI-2 0.12 3.00 0.12 0.00
F2-15 0.00 0.15
F9-10 0.00 0.01
F9-12 0.00 14.03
F13-10 0.00 0.01
F16-9 0.00 0.01
F8-13 0.12 4.00 0.13 8,33
F8-14 0.07 4.00 0.07 0.00
F8-15 0.01 4.00 0.01 0.00
F8-16 0,10 4.00 0.10 0.00
FlO-13 0.00 0.02
Average 2.25 1 79 -3.34
Final Balancing of the Phillips Creek Tall Low Model
Compartment Standing Stock Reliability Factor Balanced Numbers % Difference
Benthic filter feeders 1 0.22 3.00 0.22 0.00
Grazers/Nekton 2 1 13 3.00 Oí 0.00
Live Shoots 3 6.27 3.00 627 0.00
Standing Dead/Litter 4 4.17 ïtxT 4.17 0.00
Roots 5 0.49 3.00 0.49 0.00
Benthic algae 6 5.88 ÏOtT 5.88 0.00
N2 Fixers 7
Precipitation 8
Surface NH4 9 0.05 3.00 0.05 0.00
Surface NOx 10 0.06 3.00 0.06 0.00
Surface E)ON 11
Surface PN 12
Pore NOx 13 0.05 3.66 0.05 0.00
Pore DON 14
Pore PN 15 1.35 3.00 1 35 0 00
Pore NH4 16 0.11 3.00 0.11 0.00
Groundwater 17
Inputs Reliability Factor Inputs
FO-8 045 4.00 0.48 6.67
FO-17 0.00 0.04
FO-2 0.00 0.01
FO-7 OO o o o o
1
FO-12 2.50 4.00 2.50 0.00
FO-9 3477 3.00 27 82 -19.99
FO-10 43.47 3.00 35 48 -18 38
FO-11 0.00 0.01
Outputs Reliability Factor Outputs
F15-0 4.00 4 00 3.60 -10.00
Fl-0 0.00 0.01
F12-0 0.00 0.69
F9-0 30.44 3.00 34.25 12.52
FlO-0 25.36 3.00 28.17 11.08
Fll-0 0.00 0.01
R9-0 0.00 0.01
R13-0 0.60 4.00 0.60 ÔÔÔ
Flows Reliability Factor Flows
F8-12 0.00 0.01
F8-9 0.08 4.00 0.08 0.00
F8-10 0.14 4.00 0.14 0.00
F8-11 0 00 0.01
F17-16 0.00 0.01
F17-13 0.00 0.01
F17-14 0.00 0.01
F17-15 0.00 0.01
F12-1 2.60 1.00 2 60 000
Fll-1 001 1.00 0.01 0.00
F3-2 0.35 2 00 0.35 0.00
F4-2 0.00 0.01
Fl-9 0.54 1.00 0.54 0.00
F^9 0.00 ooT
Fl-15 0.06 1.00 0.78 1200.00
F16-6 5.00 4.00 5.00 0.00
F5-3 17.58 3.00 15.83 -9.95
F13-5 0.00 11.81
F16-5 9.26 4.00 9.26 0.00
F3-5 7.00 4.00 7.70 10.00
F3-9 0 82 1.00 1.64 100.00
F5-16 0.00 0.65
F3-4 3.07 1.00 6.14 100.00
F4-15 4.07 3.00 6.13 5061
FI5-16 84.00 4.00 24.76 -70.52
F14-16 20.21 4.00 0.02 -99.90
F16-13 4.00 4.00 3.60 -10.00
F15-12 0.00 0.00
F12-15 2.50 4.00 2.50 0.00
F6-15 0.00 4.99
F7-16 1 00 4.00 1.00 0.00
F6-2 0.00 0.01
F5-15 1 63 3.00 12.29 653.99
Fl-2 0.00 1.28
F2-15 0.00 1.65
F9-10 1.21 4.00 1.21 0.00
F9-12 2.98 4.00 3.28 10.07
F13-10 6.31 4.00 6.31 0.00
F16-9 8.66 4.00 8 66 0.00
F8-13 0.14 4.00 0.14 0.00
F8-14 0.00 0.01
F8-15 0.00 0.01
F8-16 0.08 4.00 0.08 0.00
FlO-13 000 14.97
Average 2 07 1.73
Final Balancing of the Phillips Creek Short Low Model
f
Compartment Standing Stock Reliability Factor Balanced Numbers % Difference
Benthic filter feeders 1 0,17 3.00 0.17 0.00
Grazers/Nekton 2 1.13 3.00 1.13 0.00
Live Shoots 3 5.79 3.00 5.79 0.00
Standing Dead/Litter 4 3.93 3.00 3.93 0.00
Roots 5 2.38 3.00 2.38 0.00
Benthic algae 6 5.88 locT 5.88 0.00
N2 Fixers 7
Precipitation 8
Surface NH4 9 0.05 3.00 0.05 0.00
Surface NOx 10 0.06 3.00 0.06 0.00
Surface DON 11
Surface PN 12 0.01 2.00 0.01 0.00
Pore NOx 13 0.07 4.00 ' 0.07 0.00
1
Pore DON 14
Pore PN 15 2.89 3.00 2.89 0.00
Pore NH4 16 0,92 3.00 0.92 0.00
Groundwater 17
Inputs Reliability Factor Inputs
FO-8 0 45 4.00 0.48 6.67
FO-17 0.00 0.04
FO-2 0.00 0.01
FO-7 1.00 4.00 1.00 0,00
FO-12 1.25 4.00 1.38 10.40
FO-9 17.38 3.00 13 88 -20,14
FO-10 21.73 3.00 19.46 -10.45
FO-11 0.00 0.01
Outputs Reliability Factor Outputs
F15-0 4.00 4.00 5.16 29.00
Fl-0 0.00 0.01
F12-0 0.00 0.01
F9-0 15,22' 3.00 18 26 19.97
FlO-0 12.68 3,00 12.20 -3.79
Fll-0 0.00 0.01
R9-0 0.00 0.01
R13-0 0.60 4.00 0.60 0.00
Flows Reliability Factor Flows
F8-12 0.00 0.01
F8-9 0.04 4.00 0.04 0.00
F8-10 0,07 4,00 0.07 0.00
F8-11 0.00 0.01
F17-16 0.00 0.01
F17-13 0.00 0.01
F17-14 0.00 0.01
F17-15 0.00 ÔÔT
F12-1 4.13 1.00 ML -17.43
Fll-1 0.01 1.00 0.01 0.00
F3-2 0,28 icxT 0.28 0.00
F4-2 0.00 0.01
Fl-9 0.54 1.00 0.54 0.00
F2-9 0.00 0.01
Fl-15 0.06 1.00 0.78 1200.00
F16-6 5.00 4.00 5^ 0.00
FW 9.61 3.00 12.04 25.29
F13-5 ôôo^ 12,28
F16-5 15.46 166" 15.46' 0.00
F3-5 7.00 4^ 7.00 0.00
F3-9 0.62 1,00 06? 0.00
F5-16 0.00 0.01
F3-4 4.14 3.50 4 14 0.00
F4-15 6.35 3.33 4.13 -34.96
F15-16 84.00 4.00 31.09 -62.99
F14-16 20,21 4.00 0.02 -99 90
F16-13 4.00 ÂôïT 4.00: 0.00
F15-12 0.00 0.00
F12-15 1 25 4.00 1.25 0.00
F6-15 0.00 4.99
F7-16 1.00 4.00: 1,00 0.00
F6-2 0.00 0.01
F5-15 11.54 3.33 22.69 96 62
Fl-2 0.00 2.09
—
F2-15 0.00 2.39
F9-10 4.00 1.33 9.92
F9-12 2.98 4.00 3.28 10.07
F13-10 631 4.00 6.31 0.00
F16-9 8.66 4.00 7.79 -10.05
F8-13 0.21 AOÔ 021 0.00
F8-14 0.00 ÔÔT
F8-15 0.00 0.01
F8-16 0.12 4,00 0.12 0.00
FlO-13 6 31 3.00 14.97 137.24
Average 2,13 1.72
Final Balancing of the Phillips Creek High Model
Compartment Standing Stock Reliability Factor Balanced Numbers % Difference
Benthic filter feeders 1
1
Grazers/Nekton 2
Live Shoots 3 5.89 3.00 5.89 0.00
Standing Dead/Litter 4 4.28 4.28 0.00
Roots 5 1.10 3.00 1.10 0.00
Benthic algae 6 0.18 0.18 0.00
N2 Fixers 7
Precipitation 8
Surface NH4 9 0.05 3.00 0.05 0.00
Surface NOx 10 0.06 3.00 0.06 0.00
Surface DON 11
Surface PN 12 0.01 2.00 0.01 0.00
Pore NOx 13 0.05 3.66 0.05 0.00
Pore DON 14
Pore PN 15
Pore NH4 16 0.06 3.50 0.06 0.00
Groundwater 17
Inputs Reliability Factor Inputs
FO-8 0 45 4.00 0.49 8.89
FO-17 0.00 0.04
FO-2 0 00 0.01
FO-7 1.00 4.00 1 10 10.00
FO-12 0.25 4 00 0.26 4.00
FO-9 3.48 3.00 3.48 0.00
FO-10 4.35 3.00 4.35 0.00
FO-11 0.00 0.02
Outputs Reliability Factor Outputs
F15-0 4.00 4.00 3.53 -11.75
Fl-0 0.00 0.01
1
F12-0 0.00 ôôT
F9-0 3.04 3.00 3.04 0.00
FlO-0 2.54 3.00 2.54 0.00
Fll-0 ÔÔÔ 0.01
R9-0 0.00 0.01
R13-0 0.60 4.00 0.60 0.00
Flows Reliability Factor Flows
F8-12 0.00 0.01
F8-9 0.01 4.00 0.01 0.00
F8-10 0.01 4.00 0.01 0.00
F8-11 0.00 0.01
F17-16 0.00 0.01
F17-13 ôoô ÔÔT
F17-14 0.00 0.01
F17-15 0.00 0.01
F12-1 0.00 0.02
Fll-l 0.00 0.02 209
F3-2 0.30 2.00 0.30 0.00
F4-2 0.00 0.01
Fl-9 0.00 0.01
F2-9 0.00 0.01
Fl-15 ÔÔÏ^
F16-6 0 15 1.00 0.02 -86,67
F5-3 14,65 13.18 -10.03
F13-5 0,00 6,47
F16-5 15.60 2.33 9.35 -40.06
F3-5 7.00 4.00 7.70 10.00
F3-9 0.96 1.00 0.96 0.00
F5-16 0.00 0.01
F3-4 2,05 3 00 4.22 105.85
F4-15 1.09 4,00 4,21 286.24
F15-16 11.63 4.00 11.63 0,00
F14-16 0.00 0 02
F16-13 3.94 4.00 3.55 -9.90
F15-12 0.00 0.00
F12-15 0.25 4.00 0.25 0.00
F6-15 0,00 0,01,
F7-16 1.00 4.00 1.10 10.00
F6-2 0.00 0.01
F5-15 6.79 1.00 10.33 52,14
Fl-2 0.00 0.01
F2-15 0,00 0.33
F9-10 7.09 4.00 1.42 -79.97
F9-12 0.00 0.01
F13-10 0.00 0.01
F16-9 0.00 0.01
F8-13 0.27 4.00 0.27 0.00
F8-14 0.00 ôm
F8-15 0.00 0.01
F8-16 0.16 4.00 0.16 0,00
FlO-13 3.25
Average 1.67 1.77