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FRESHWATER TIDAL FOREST COMMUNITIES SAMPLED IN THE LOWER
SAVANNAH RIVER FLOODPLAIN
A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
UNIVERSITY OF FLORIDA
I thank my parents for their continuous support and encouragement, and for
instilling in me the belief that people can make positive contributions to the world in any
way they choose, so long as they set their mind to it and make the required effort. That
mindset is what gives me the freedom to pursue my dreams, while at the same time
driving me forward in my occupational development as a natural resources ecologist. I
also thank my sister for all of her support and assistance.
I thank my advisor, Dr. Wiley Kitchens. Wiley graciously gave me many
opportunities to use my experience, imagination, and background knowledge to propose
alternative solutions to situations. His timely reminders of the pertinent ecological
principles and statistical approaches were always helpful, and their blatancy sometimes
humbling. I am particularly grateful to him for passing on some of his knowledge of
wetland systems. I thank my committee members, Dr. William Conner and Dr. Michael
Binford, for their editorial contributions to my thesis, as well as comments given during
my defense. I also thank Dr. Conner for his recommendations during the early stages of
I sincerely thank Mark Parry, Janell Brush, Scott Berryman, Zach Welch, Adam
Cross, AnnMarie Muench, and Joey Largay for their help and dedication in the field. I
also thank the entire staff at the Savannah National Wildlife Refuge for all of their
logistic and moral support, particularly William "Russ" Webb, Robert Rahn, and John
TABLE OF CONTENTS
A C K N O W L E D G M E N T S ......... .................................................................................... iii
LIST OF TABLES ............................... .............. ................. vii
LIST OF FIGURES ............................................................. ......... viii
ABSTRACT ........ .............. ............. ...... ...................... ix
1 INTRODUCTION AND STUDY AREA .......................................... ..................1
In tro d u ctio n .................................................................................. 1
L location of Study A rea................................................................. ....................... 3
H ydrology ............. ...................... ........ .................... ...............
Soil and U underlying B edrock ............................................... ............................ 9
T re e S p e c ie s .......................................................................................................... 1 0
Low er Floodplain H istory........ ........ .. ................. ................... ............... 11
2 M E TH O D S ................................................................................................... 12
V egetative Sam pling ......... .... ........ .. ... ..................... ......... 12
Species-Area Curve ....... ...... .. ........ .......... .. .................. .............. 12
Sam pling D design ........................... .......... .. .... ..... ........... ..13
S o il A n a ly sis ......................................................................................................... 1 4
Chemical Constituents........................ ...... ........ .. .......... ....15
Organic M atter Content and Bulk Density ................. ................................ 16
Statistical A analyses .......... .... ........ .... ..... ......... .. .. .. ...... ....... 17
Species Im portance V alues........................................... ........................... 17
Insignificant Data Removal ............. ...... ........... ................... .... ............... 18
R are species................................................. 18
Outlying plots ........... ..... ............ ......... .........19
Insignificant environm ental variables .................................. ............... 20
A priori Landscape Grouping ............... .......................... .......... ............ 20
Exploratory D ata A nalyses......... ................. ............................. .............. .... 21
C luster analysis ......................................... ........... ............ 2 1
Indicator species analysis ............................ .. ............... .....................22
Multi-response permutation procedures................................................24
Nonmetric multidimensional scaling ordinations .....................................25
Classification and R egression Tree ........................................ .....................26
3 R E S U L T S .......................................................................... 2 8
Introduction ......................................... ......................................... 28
Exploratory D ata Analyses .......................................................... ............... 29
C lu ster A n aly sis.............................................. ................ 2 9
Indicator Species A analysis ........................................... ........................... 30
M ulti-response Permutation Procedures............... ........... ............................34
NMS Ordinations................... ...... ............................35
A u to p ilo t ............................................................................... 3 5
Subsequent ordinations ........................................ .......................... 37
Classification and Regression Tree Analysis .................................. ............... 46
D descriptions of Com m unities .............................................................................. 48
Shrub C om m unity ......................... ............................ .. ......... .... ............4 8
W ater Tupelo Com m unity ........................................................ ............. 50
Swamp Tupelo Tag Alder Community .................................... ............... 51
W ater Oak Swamp Bay Community ..................................... .................52
4 D ISCU SSION ...................................................................... .......... 55
Tidal Forest Communities in Sampled Areas of the Savannah River Floodplain......56
Comparisons with Tidal Forests of the Lower Chesapeake Bay.............................57
C om m unity D description ........................................................... .....................57
Environmental Factors.......................... .. ..... ... .................58
Comparisons with Tidal Forests of Florida's Gulf Coast and the Roanoke River,
NC .................. ........... ... .. ... ....................59
Comparisons with Bottomland Hardwood Soils ................................................61
C om m unity D description ........................................................... .....................6 1
S o il P ro p e rtie s ............................................................................................... 6 1
F future R research N eeds ...................................................................... ...................62
A TIDAL FOREST COMPUTATIONS BASED ON NATIONAL WETLAND
IN VEN TORY ..................................................... ............ .... ........64
B SPECIES NAMES AND ABBREVIATIONS....................................................65
C SPECIES X PLOT DATA M ATRIX ...................................................................... 66
D SOIL PROPERTY X PLOT DATA MATRIX .................................. ...............70
E CORRELATION OF SPECIES AND SOIL CONSTITUENTS TO AXES FOR
RUNS SUBSEQUENT TO AUTOPILOT MODE .............................................76
L IST O F R E F E R E N C E S ...................................... .................................... ....................77
B IO G R A PH IC A L SK E T C H ...................................................................... ..................82
LIST OF TABLES
2-1 Species rem oved from analyses. ........................................ .......................... 19
2-2 Environmental variables collected. ............................................... ............... 20
3-1 M onte Carlo results of species indicator value. ........................................... ........... 32
3-2 Significant indicators species in 4 clusters.................................... ............... 33
3-3 MRPP results for groups of plots. ......... ......... ............... 35
3-4 Pearson's coefficients of determination (r2) and Kendal's tau values of
environmental variables to axes for autopilot mode ofNMS ordination ...............36
3-5 Proportion of variance represented by axes in NMS ordination. ...........................37
4-1 Published nutrient values (mg/kg) of forested wetland soils. ................................62
LIST OF FIGURES
1-1 Locations of tidal forests documented in the United States. ......................................1
1-2 L location of study areas .............................................................. ....................... 4
1-3 Projected 0.1 ppt salinity zones during and after operation of tide gate .................5
1-4 Mean annual discharge at USGS monitoring station near Clyo, Georgia................9
2-1 Species-area curve .................. ..................................... .. ............ 13
2-2 Locations of plots and the a priori group they were placed in .............................22
3-1 Cluster dendrogram .................. .............................. .. ...... .. ........ .... 31
3-2 Summary of the 7 indicator species analyses ........................................................32
3-3 NMS ordination: dahoon holly and Virginia willow .............. ...........................39
3-4 NM S ordination: fetterbush and wax myrtle................................. ............... 40
3-5 N M S ordination: w ater tupelo ...................................................................... .. .... 41
3-6 NM S ordination: swamp tupelo and tag alder............................... ............... 42
3-7 NM S ordination: water oak and swamp bay ................................. ..................... 43
3-8 NM S ordination: biplot of axis 2 vs 1 ........................................... ............... 45
3-9 NM S ordination: biplot of axis 3 vs. 2 .................................................................. 46
3-10 Classification and regression tree................................ ................... ...... ........ 47
3-11 Community locations within the sample areas............................................. 49
3-12 Average stems per acre for communities and a-prior group ..................................53
Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Master of Science
FRESHWATER TIDAL FOREST COMMUNITIES SAMPLED IN THE LOWER
SAVANNAH RIVER FLOODPLAIN
Chair: Wiley M. Kitchens
Major Department: Natural Resources and Environment
Two freshwater tidal forest stands were sampled in the lower Savannah River
floodplain. Multivariate statistics were used to help describe community composition.
Plots were agglomerated using cluster analysis, indicator species characteristic of each
community were identified, and multiple response permutation procedures were used to
test significance differences between the groups. Trends were examined using nonmetric
multidimensional scaling ordinations of plots in species space with vector overlays of
edaphic factors. Finally, a classification and regression tree analysis was used both in a
confirmative fashion, to compare varying results based on community size, and in a
predictive fashion, characterizing communities based solely upon soil properties. Four
communities were found: 1) shrub 2) Nyssa aquatica 3) Nyssa biflora Alnus serrulata
and 4) Quercus nigra Persea palustris.
The shrub community has the most homogeneous mix of species and the highest
stem density per hectare of small diameter trees. This community also contains the rarest
species documented. It exists on substrate that has a very high organic matter content
(>78%), with high levels of Ca2+, Mg2+, Na+, and electrical conductivity. In general, this
community is found in areas relatively far removed from tidal creeks and drainages.
The Nyssa aquatica community has the highest density and greatest basal area of
Nyssa aquatica canopy trees among the communities in this study. Decreased
development of the shrub layer is a general maxim. The soil has, on average, the highest
concentration of P043. This community is found near tidal creeks and drainages in the
western study site.
The Nyssa biflora Alnus serrulata community has a well developed canopy in
terms of tree heights and abundances. Nyssa biflora dominates the canopy, along with
the highest amount of Taxodium distichum found in any of the communities. The shrub
layer of this community is relatively well developed and dominated by Alnus serrulata,
Cornusfoemina var.foemina, and Cephalanthus occidentalis. Soils of this community
have high electrical conductivity and Na+ concentration, though not nearly as high as the
shrub community. This community is generally found associated with tidal creeks and
drainages in both the eastern and western stands.
The Quercus nigra Perseapalustris community has a canopy layer with uniform
distribution of Nyssa biflora, Nyssa aquatica, ash, and Taxodium distichum. The shrub
layer of this community is dominated by smaller "tree" species rather than "shrub"
species. On average, the soils have the lowest values of organic matter, Na
concentration, Ca2+ concentration, and electrical conductivity found in this study. This
community is found in areas of greatest tide-water flux.
INTRODUCTION AND STUDY AREA
Along the coastal plain of the southeastern Unites States exists a unique mosaic of
forest habitats that are expressive of the junction of several water sources: an alluvial
river system, groundwater seepage, and a tidally driven hydrologic backflow of fresh
water. These freshwater tidal forests are little studied. The few accounts of tidal forests
include those found along the Pamunkey River in the lower Chesapeake Bay (Doumlele
et al. 1985, Rheinhardt 1991, 1992, Rheinhardt and Hershner 1992), the Roanoke River
in North Carolina (Wharton et al. 1982), the Altamaha (Wharton et al. 1982) and
Savannah rivers of Georgia, as well as the Apalachicola, Suwannee, St. Marks, and
Yellow rivers in the panhandle of Florida (Wharton et al. 1982) (Figure 1-1).
Tidal forests documented
Figure 1-1. Locations of tidal forests documented in the United States. All occur within
the southeastern region of the country.
These unique ecosystems occur where large river systems meet a tidally forced
backflow, situated just upstream of freshwater tidal marshes. Geographic extents of most
tidal forests have not been calculated, but they seem to be proportionally sized to the tidal
range for each river system, whether those relationships be directly related to depth of
overland flow or indirectly related to mean water-table height as a result of tidal
backpressure (Rheinhardt and Hershner 1992). The extents of tidal forests situated along
the Gulf of Mexico are likely relatively small while in the Virginia part of the lower
Chesapeake Bay there are a total of approximately 3500 ha on three different rivers
(Rheinhardt 1992). The Savannah River floodplain has a tidal range of up to 3 m,
resulting in comparatively large areas of tidal forest. There are approximately 3900 ha of
truly tidal forest in the Savannah River floodplain, with an additional 500 ha of
seasonally tidally flooded forest, and 150 ha of temporarily tidally flooded forest,
calculated from national wetlands inventory geographical information systems coverages
(Appendix A). The National Wetland Inventory, which follows the classification system
developed by Cowardin et al. (1979), has classified the northern portion of the western
study stand as being non-tidal, when it clearly is (personal observation). While the
National Wetland Inventory is useful for general habitat quantification such as this,
comparisons made using this information should be broad and take into consideration the
potential for minor misclassifications.
The objectives of this study were to identify the suite of tree species that occupy
tidal forests of the Savannah River basin, and explore factors that can help to explain
general tree communities. The working hypothesis is that species composition and
densities, together with substrate characteristics will be reflective of the topographical
and hydrologic history of the area. Further hypotheses regarding topographical factors
(elevation, ridge and swale location, etc.) and hydrological factors (hydroperiod,
groundwater vs. overland water source) may be formed once the biological and edaphic
factors measured in this study have been analyzed. To accomplish this I will look at:
how the plots naturally group into communities based on their species compositions,
specific species abundances that may be indicative of each community, general landscape
position of each plot, and soil characteristics that may be used to typify each community.
Location of Study Area
The Savannah River Bird Refuge was originally established on April 6, 1927, by
Executive Order Number 4626. That decree set aside a total of 953 ha as a preserve and
breeding ground for native birds. In 1940, Presidential Proclamation 2416 renamed the
refuge the Savannah National Wildlife Refuge (SNWR). Throughout the years, a variety
of parcels were added to the SNWR through several executive orders, acquisitions using
both duck stamp funds and Land and Water Conservation funds, exchange of spoilage
rights, exchanges for power line right-of-ways, and several purchases in title fee. The
current total acreage of the SNWR is now 11,239 ha (Graves 2001) situated along the
borders of Georgia and South Carolina.
The U.S. Army Corp of Engineers and the U.S. Geologic Survey (USGS), the
organizations from which all location designations and water data were obtained for
purposes of this study, use the river mile (RM) as the unit of measure for distances along
a river, and cubic feet per second (cfs) as a measure for discharge. Therefore, the
International System of Units (SI) convention for these measurements will be broken and
the current U.S. convention followed. The study area lies within SNWR boundaries
(Figure 1-2). Two forest stands were chosen based on projected salinities from a
hydrologic model used to predict interstitial salinities (Pearlstine et al. 1990). The
eastern stand is located 26 RM from the Atlantic Ocean adjacent to the Little Back River,
a distributary of the main Savannah River. The western stand is located 27 RM from the
mouth of the Atlantic adjacent to the main channel of the Savannah River. Salinities in
the eastern stand area (Figures 1-2 and 1-3) during the 14 years of operation of the tide
gate (described in later sections) were projected to be in excess of 0. lppt, whereas
salinities in the western stand were below 0. ppt (Pearlstine et al. 1990).
Carolina- !: I
:, ', ,.A tlantic
Figure 1-2. Location of study areas. Study plots are indicated by green dots. The
Savannah National Wildlife Refuge is indicated by the cross-hatched area.
[ During Tide Gate
[ After Tide Gate
3.2 km (2 mi)
Figure 1-3. Projected 0.1 ppt salinity zones during and after operation of tide gate.
Modified from Pearlstine et al (1990). Higher salinity occurs to areas south of
0.1 ppt zone during each time period. Inset picture shows tide gate in
Even though salinity at the 0. lppt level is quite low, it was believed that the
community compositions, particularly the subcanopy structure, in the two areas may vary
as a function of differing salinity stress during the tide gate era. Sampling points (plots)
and rationale will be described in Chapter 2.
Savannah, Georgia has an average annual temperature of 190C, with the highest
monthly average of 27.80C in July and lowest monthly average of 9.6C in January
(NOAA 2002). The average frost free season is 226 days long (90% confidence),
occurring between March 30th October 31st (NOAA 1988). Average annual
precipitation is 126 cm with an average high of 14 cm in June (NOAA 2002).
Hydrology has been widely recognized as the major factor in the determining the
community distributions of wetlands plants (Conner et al. 1981, Parsons and Ware 1982,
Wharton et al. 1982, Mitch and Gosselink 2000), as well as bottomland hardwood
community development and succession (Larson et al. 1981). The community
composition of freshwater tidal forests is also likely to be greatly affected by both
existing and past hydrologic conditions. Changes imposed upon the Savannah River
have been documented as the cause of vegetation shifts in marsh macrophytes (Latham
1990). Although tree species in swamps are unlikely to respond as quickly to changing
hydrologic conditions as compared to marshes, especially if annuals are an important
component of the marsh plant community (Rheinhardt and Hershner 1992), the long-term
effects of dam construction, tide gate installation and decommission, and rising sea level
Bottomland hardwood forests can be broken down into two main types based on
their primary source of water and subsequent nutrient load: blackwater swamps and
redwater swamps. Blackwater swamps arising in the coastal plain receive water inputs
principally through precipitation and are typically nutrient poor. Alluvial floodplain
forests, also known as redwater swamps, receive floodwater from rivers draining
Piedmont watersheds and are relatively nutrient rich due to the physical and chemical
breakdown of rock.
The lower Savannah River undergoes a regular, semidiurnal flooding regime and is
a salt-wedge type estuary (Hansen and Rattray 1966). Tidal range of the Savannah River
marshes is in excess of 3 m with flow reversals 28 RM upstream of the river mouth.
Tidal ranges at the study sites of the Savannah tidal forests, however, are only 1.5-2 m on
average, which are approximately comparable to the Im mean tidal range in the tidal
freshwater swamp along the Pamunkey River, Virginia (Doumlele et al. 1985).
Positioned upstream of tidal freshwater marshes and downstream of bottomland
hardwood forests, the tidally influenced forest of the Savannah River basin is classified
by U.S. Fish and Wildlife Service convention (i.e. Cowardin et al. 1979) as being a
palustrine system, forested wetland class, broad-leaved deciduous subclass with a
permanently flooded-tidal modifier (PFO 1/2T). Hydrologic conditions resulting from the
range and consistency of the semidiurnal tides keep soils saturated for the entire year in
most areas of the tidal forest, even during drought conditions (personal observation).
In 1977 a one-way tidal flap gate was installed at RM 14 as a mechanism for
minimizing the amount of maintenance dredging in the shipping channel (Front River) of
the Savannah River. In-flowing water was allowed to pass upstream through the gate
during the tidal flood stages. The one-way flap gate was shut at slack tide, and the entire
volume of entrained water was forced to flow through a diversion channel (New Cut,
Figure 1-3) and out the main channel during ebb tide, thereby increasing the velocity and
scour through the harbor area. However, the blockage caused salt water intrusion into the
Little Back River and Middle River portions of the Savannah River. With each tidal
cycle, the salt wedge was pushed further upstream, resulting in a dramatic shift in
vegetation from freshwater species to those that are more tolerant of oligohaline
conditions (Georgia Ports Authority 1998). Salinity projections by Pearlstine et al.
(1990) (Figure 1-3) indicate portions of the tidal forest of the Savannah River floodplain
area were impacted from operation of the tide gate. Although not likely as dramatic as the
diversion of the Santee River into the Cooper River that caused a reduced growth rate in
water tupelo (Nyssa aquatica) (R.A. Klawitter, personal communication, in Wharton et al.
1982), the increased salinity may have been a factor affecting the community makeup in
some areas of the Savannah River tidal forest.
In 1991 the tide gate was taken out of operation, with the subsequent closure of
New Cut in 1992. In 1993-94 the shipping channel was further deepened by 1.2 m. To
date, salinity levels in the tidal forest stretches of the lower Savannah River floodplain
have returned to below 0.5 ppt (personal observation), the level used to define a
"freshwater" system (Cowardin et al. 1979).
The Savannah River, arising in the southern Appalachian Mountains, is an alluvial
river and has the 5th largest discharge in the southeastern coast next to the Mississippi,
Alabama, Apalachicola, and Altamaha rivers. Freshwater inputs to the basin are from
inland runoff from the (approximately) 25,500 km2 drainage area. A comparably sized
watershed of an alluvial floodplain may be expected to flood from 18% to 40% of the
year (Bedinger 1981). Mean discharge of the Savannah River is 16,060 cfs at USGS
station #02198500 (Fig 1-4) located at RM 61 near Clyo, Georgia.
Aside from the natural seasonal and lunar fluctuations, the discharge of the
Savannah River is governed by a series of three dams: The J. Strom Thurmond Dam and
lake was constructed at RM 237.7 in 1954; The Richard B. Russell Dam and lake, located
at RM 275.1, was constructed in 1963; The Hartwell Dam and lake, at RM 304.7, was
constructed in 1983. Meade (1976) found that the reservoirs trap 85% to 90% of
incoming sediment. Sediment inputs to the continental shelf have been decreased by
50% since 1910 as a result of the reservoirs and dams. Therefore, the recharge of the
sediment load to the river south of the reservoirs must come from the river bed, banks,
and floodplain. This affects the tidal forest drastically by reducing sediment inputs to the
floodplain, as well as increasing erosion on the floodplain and tidal creeks.
Figure 1-4. Mean annual discharge
at USGS monitoring
station #02198500 near Clyo,
Soil and Underlying Bedrock
The underlying bedrock is geologically recent coastal plain sedimentary rocks
composed of marsh and lagoon deposits from the Pleistocene and Holocene epochs
(Quaternary Period). Technically referred to as the Pamlico Shoreline Complex, the
underlying bedrock is composed predominantly of sand and sandy clay with marsh and
lagoonal facies which were deposited at former high sea levels (GA DNR 1976, 1977).
General soil descriptions from the U.S. Department of Agriculture, Soil
Conservation Service, indicate the soils in the eastern stand are Levy Soils, which are
very poorly drained, nearly level soils on the lower coastal plain. The surface layer is
very dark gray silty clay loam 20 cm thick. The underlying material, to a depth of 152
cm, is gray silty clay over silty clay loam (USDA 1980).
General descriptions of soils in the western site indicate that they are composed of
Angelina and Bibb soils, also frequently flooded and poorly to very poorly drained.
These two soil series occur together, in approximately a 4:2:4 ratio of
Angelina:Bibb:other ("other" being Chipley, Kershaw, and Ocilla soils). They have been
formed in recent deposits of sediments washed from soils on the coastal plain. Surface
layers are very dark gray loam about 8 cm thick (Angelina) or light brownish gray loamy
sand about 46 cm thick (Bibb). The underlying areas are black to light-gray sand to silty
clay loam (Angelina) or mottled light-gray to greenish-gray coarse sand to sandy loam
(Bibb). The clay content between depths of 25 and 102 cm within the Bibb series is less
than 18 percent (USDA 1974).
Individual tree species of the Savannah River tidal forest (Appendix B) have been
documented as being part of many other forested wetland communities. The
communities include those described in the bottomland hardwood forest community
profile (Wharton et al. 1982): six zones of seasonally flooded bottomland forests, and
descriptions of freshwater tidal forests of the Suwannee and St. Mark's rivers in Florida.
Descriptions of freshwater tidal forests of the lower Chesapeake Bay (Doumlele et al.
1985, Rheinhardt 1991, Rheinhardt 1992, Rheinhardt and Hershner 1992) also contain
identical species, though community structure differs greatly from those in the Savannah
Lower Floodplain History
The lower basin has been severely altered to facilitate a variety of anthropocentric
benefits. In the mid 1700's much of the tidal portions of marsh and forest along the
Savannah River were transformed to rice cultivation. Through this process the trees were
cut down and moved out of the way or burned, and the stumps largely removed (Doar
1936). The presence of some remaining large stumps adjacent to Rifle Cut, a man-made
tidal creek, suggests that the tidal forest may have extended at least 5 RM further
downstream. After the 1863 issuing of the Emancipation Proclamation by President
Abraham Lincoln, and the subsequent ending of the Civil War in 1865, rice cultivation in
the tidal marshlands failed and much of the land was abandoned (McKenzie et al. 1980).
Aerial photography shows signs of rice field drainage creeks that had been constructed
without the associated land clearing that lie just south of the eastern site along the Little
Back River. This is likely where rice development in the tidal floodplain ended and it is
probable that the currently existing forest area wasn't logged for any reason. Cypress
stumps endure for many years, and their presence may indicate what the original forest
on a given site was like (Wharton et al. 1982). It follows that the absence of obviously
logged stumps throughout the area the tidal forest currently occupies is a good indicator
that this area was not logged in recent history.
A pilot study was undertaken to determine the tree species richness and diversity
within the study areas. It was quickly evident that the eastern site had greater diversity,
so efforts were focused within its boundaries. In all, ten nested quadrats were cataloged
for information regarding species and size. The smallest reasonable area to be quantified
was assumed to be 25m2 (5X5). By lengthening each quadrat by 5m on two ends, each
nested quadrat, then, consisted of one of each quadrat size: 25, 100, 225, and 400m2
With this information, a species-area curve (Cain 1938; Kent and Coker 1992) was
developed to determine the minimum quadrat area (equivalent to minimal area for the
community) for obtaining the data for this study. Unlike the traditional species-area
curves that use a progressive doubling of the quadrat size (Kent and Coker 1992), our
species-area curve necessitated an algorithm that could incorporate several samples of the
same quadrat size. To accomplish this, the computer program Sigma Plot 8.02 (SPSS
Inc. 2001) was used to perform a nonlinear regression, resulting in an optimal quadrat
size of 100m2, or 10x10m (Figure 2-1).
15 00 00
25 10 0 225 400
Size of Quadrat (square meters)
Figure 2-1. Species-area curve computed with non-linear regression (r2=0.58) based on
10 points with 4 nested quadrat sizes.
Plots were positioned in a stratified random manner in each of the two stands in
order to represent each stand in a way that would reflect the heterogeneity of the study
area. However, since differences between the two stands were evident by mere
observation, the stratification (see below) was unique to each stand. One obvious
difference was the greater structural diversity in tree communities within the eastern
stand. The gradient between understory and overstory is continuous for most of the
eastern stand whereas the majority of the western stand has a notable gap between the
understory and overstory. Soil conditions, principally stability of the substrate layer,
were also obviously more variable in the eastern stand. For these reasons, the
stratification of the eastern stand divided it into 4 equal sized quarters. Four points were
randomly placed in each of the quarters using a random number generator and a
geographical information systems (GIS) coverage of the area. The western stand was
divided into 2 approximately equal sized sections; one north of HWY 1-95 and one south
of the highway. Eight points were randomly placed in each section using the same tools
as used in the other stand. That made for a total of thirty-two 10x10m plots in two
A quadrat was flagged off at each point by measuring due north from the point
10m, then due east 10m, south 10m, and west 10m. Within each quadrat (plot) all tree
species > 1.38m (4.5 ft, or breast height) were identified and measured for diameter at
breast height (DBH). The canopy position of each tree was recorded as being in one of
three groups: understory, sub-canopy, or canopy. This was determined using relative,
rather than absolute heights since the overall community structure differed markedly
Sample nutrient concentrations, organic matter content, pH, and bulk density of
the general site area were obtained by taking samples from the substrate floor (i.e., not on
microtopographical highs or "hummocks") at each plot. Although Rheinhardt (1992)
found that there were no statistical differences between organic matter content of
hummocks and hollows (i.e., the substrate floor), I felt as though the soil properties in
hummocks would be more representative of the specific species living on them, rather
than the proximal tree community as a whole. It was also thought that the hummocks
may be more variable in regard to their nutrient concentrations. Further,
microtopographical highs contain a dense root structure, making sampling further biased
by both the higher values of organic matter, as well as increased effect of soil compaction
from excessive pressure on the soil corer.
Two samples were taken at each sampling location to a depth of 12.6 cm using a
6.9 cm diameter aluminum soil corer with holes drilled in the sides to allow for water
drainage and accurate measurement. All samples were placed in a freezer upon return
and frozen until processing. When processed, all samples were thawed and then oven
dried at roughly 50C (1200F) for at least 2 weeks to adequately remove all moisture.
From one of the two samples, material was passed through a 2mm sieve, homogenized,
and sent to the lab for analysis. Material passing through the sieve included mineral
matter as well as organic matter. The second soil sample was homogenized, weighed to
determine bulk density, and combusted to determine organic matter content by the loss on
All nutrient concentrations in the soil samples were determined by the Analytical
Research Laboratory at the University of Florida. Concentrations of phosphorous (P),
potassium (K), calcium (Ca), magnesium (Mg), zinc (Zn), manganese (Mn), sodium
(Na), and iron (Fe) were determined by Mehlich extraction with 5g (4cm3) soil to 20mL
0.05 HC1 + 0.0125 M H2SO4. Electrical conductivity and chloride ion (CE)
concentrations were determined using a 2:1 water to soil ratio with 250 cm3 soil. Values
were multiplied by the bulk density of the soil for each site to standardize the quantities
of nutrients present rather than just the concentration.
Macronutrients such as P, K, and N, are well established as being very important in
plant nutrient needs. The availability of C, N, and P may prove critical in determining
the health of a system (Salisbury and Ross 1992), and Fe and Mn concentration levels
have been found to be elevated in hydric soils (Gambrell et al. 1989). Soil pH has also
been found to be at least partially correlated to extractable Fe and Mn (Gambrell et al.
1989), therefore we recorded soil pH with an Oakton pH 6 Acorn series meter in the hole
created by removal of the soil core.
Organic Matter Content and Bulk Density
Soil organic matter (SOM) was assumed to be one of the most important soil
properties for analyzing soils-to-tree relationships. Wharton et al. (1982) found that
amount of SOM varies between the National Wetland Technical Council zones and is a
useful variable to examine when comparing blackwater and alluvial floodplains (Wharton
et al. 1977). Given the relationship to floodplain characteristics, and its assumed
relationships to species assemblages, the method of obtaining values for SOM was
The loss on ignition (LOI) method (Klawitter 1962) was used to determine SOM.
While earlier studies (eg. Wakeman and Stevens 1930, Robinson 1939) recommend the
Walkley-Black method (a chromic acid oxidation, Walkley and Black 1934) for
conventional soils, LOI is preferable for hydric or highly organic soils (Broadbent 1953,
Storer 1984, Deutsch 1998) of the type encountered in this study.
The LOI method involved combusting the samples in an ignition furnace at 500C
for eight hours. The formula for calculating percent organic matter for each plot was:
%OM = [(weight loss due to ignition)/(dry soil weight)]* 100
Two runs were done: one with a 2 g sub-sample, and one with an entire column of
soil. In the first run two 2 g sub-samples were averaged. The burnt sub-samples were
then added to the remaining sample for that site and sent to the laboratory for analysis
(see chemical constituents, above). In the second run the full amount of an additional
sample, collected June 2003, was used. Since this sample was composed of entirely
organic-free constituents after burning, and since chemical analyses were already done on
the previous run, the burnt remains were of no further use and discarded.
Bulk density was computed by dividing the dry weight of each soil sample by the
known volume of soil collected (470.9 cm3). Values of soil organic matter, bulk density,
nutrient concentration, electrical conductivity, and amount of nutrients present in each
plot are listed in Appendix D.
Species Importance Values
Accurately representing a particular tree species' contribution to the community
makeup of a given plot is perhaps the most important step in community analysis. When
studying distribution of tree species, two main factors must be taken into consideration:
how many, and how large. Due to inherent differences of plot structure, a method of
representing the competitive interactions at each plot is imperative. For example, one
plot may be comprised of many small, shrubby trees whose collective basal area is small.
Conversely, a plot may be made up of relatively few big trees with a large cumulative
basal area. The tidal forests along the Savannah River floodplain have structures
described in both scenarios.
Importance values are an optimal way of dealing with large differences in structural
diversity, while still accurately representing the importance of a species in a plot.
Originally developed by Curtis and McIntosh (1950, 1951), importance values have been
used in many studies of eastern North American forests (McCune and Grace 2002),
including studies of the tidal freshwater swamps of Virginia by Doumlele (1985),
Rheinhardt (1991, 1992) and Rheinhardt and Hershner (1992). One rationale for their
use is the fact that importance values are not overly sensitive to extremes of structural
diversity, as are measures of relative dominance or relative frequency alone. The
conversion of the species by plot data to importance values has yet another advantage. It
essentially is a standardization transformation of the data. Standardizations of this type
are widely used in gradient analyses because it increases the strength of the relationship
between species dissimilarity and ecological distance for moderate or long gradients
(Faith et al. 1987). For this study the importance values were computed in a manner
similar that of Curtis and McIntosh (1950, 1951), with the elimination of the relative
frequency term (for more information see Kent and Coker 1992). The value is the
average of two components:
1. Relative Density:
Number of individuals of a particular species 100
Total number of individuals of all species
2. Relative Dominance:
Average basal area of a particular species number of that species in that plot 100
Total basal area of all species in that plot
In this way, importance values summed over all species within a plot add up to 100.
Species importance values for each plot are listed in Appendix C.
Insignificant Data Removal
A full matrix of 28 species x 32 plots was modified by the removal of rare species
and an outlying plot. The resulting matrix, which will be referred to as the primary
matrix, contains 20 species and 31 plots. A second matrix containing all environmental
variables was edited in a way that only meaningful data were retained; the resulting
matrix will be referred to as the secondary matrix. Specifics of data scaling and deletion
Rare species were removed from the analyses in an effort to tighten patterns and
enhance the detection of relationships between community composition and
environmental factors. Using an approximate rule of thumb offered by McCune and
Grace (2002), those species that were present in fewer than 5% of the plots (i.e., 2 plots
or fewer) were removed from the analyses. Although deletion of rare species is
considered inappropriate when examining patterns in species diversity (Cao et al. 1999),
it is often helpful for multivariate analysis of community structure (McCune and Grace
2002) such as nonmetric multidimensional scaling ordination. In total, 8 species were
removed (Table 2-1).
Table 2-1. Species that were removed from analyses.
Species Plots found
Inkberry (Ilex glabra) NE3
Highbush blueberry (Vaccinium corymbosum) NE3, NW4
Sweet bay (Magnolia virginiana) NW1
Groundsel tree (Baccharis halimifolia) NW2, W14
Black alder (Ilex verticillata) SE1, W12
Black willow (Salix nigra) SE2
Water elm (Planera aquatic) W13
Laurel oak (Quercus laurifolia) W13
Following the removal of the 8 rare species, an outlier analysis was done to detect
entire plots that were functioning as outliers. This was done by calculating the average
distance, using the Sorensen distance measure, from each sample unit to every other
sample unit. Those plots that were more than 2 standard deviations from the mean for
average distance were considered outliers.
A plot located in the western site (W13) was removed from the analyses. This plot
was comprised of mostly canopy and sub-canopy trees, including (predominantly) swamp
tupelo (Nyssa sylvatica var. biflora), with some bald cypress (Taxodium distichum) and ash
(Fraxinus spp.) trees of similar canopy position. Relatively few shrubs were cataloged in
this plot, likely resulting in the outlying nature. A single water elm (Planera aquatic)
sapling (DBH < Icm) and a laurel oak (Quercus laurifolia) sapling (DBH 4.1 cm) were
also found in this plot only. Edaphic properties were not dissimilar to other plots.
Insignificant environmental variables
Environmental variables were first scaled to reflect the same order of magnitude as
the data in the primary matrix. To accomplish this task, values for particular variables
were multiplied or divided by orders of 10 so that the resultant value was as near the
range of 10-100 as possible. Following the relativization, NMS procedures were used to
determine the correlations between environmental variables and the main dissimilarity
matrix obtained from the primary matrix. In an effort to discern true relationships
between tree communities and soil properties, quantitative soil variables that had a
coefficient of determination (r2) less than 0.392 to any of the axes for any rotation were
removed from the environmental matrix (Table 2-2).
Table 2-2. Environmental variables collected in the tidal forests of the Savannah River
floodplain. Only variables with a Pearson's correlation (r2) of at least 0.392
were retained for further analyses.
Variables retained Variables removed
Organic matter pH Zn present
Ca concentration P concentration Mn concentration
Mg concentration K concentration Mn present
Electrical conductivity K present Cu concentration
Na concentration Ca present Fe concentration
Cu present Mg present Fe present
Bulk density Zn concentration Na present
A priori Landscape Grouping
Each plot was placed into one of three physiognomic categories based on their
landscape position and assumed hydrogeomorphologic differences (Figure 2-2): 1) Plots
that are proximal to either the main channel of the Savannah River or a large distributary.
These plots are likely to be of higher elevation and have higher mineral content since
they are associated with the natural levee of the river. 2) Those plots associated with
tidal creeks and drainages. Lower in elevation than the latter group, the proximity of
these plots to tidal rivulets in the floodplain likely results in intermediate drainage
conditions and soil mineralization as compared to the other two groups. 3) Plots
relatively far removed from tidal creeks and drainages and, therefore, from the main
channel of the Savannah River. These are essentially the backswamp sites furthest
removed from the main rivers, experiencing decreased water flux with each tidal cycle.
Relative isolation leads to very poor drainage, ponding, as well as increased residence
time and accumulation of organic matter and nutrients in the soil. A categorical variable
was added to the secondary matrix to reflect this grouping.
Exploratory Data Analyses
Unless otherwise stated, all exploratory analyses were done using the statistical
software PC-ORD for Windows, version 4.27 (McCune and Mefford 1999). Similarly,
unless otherwise noted, the distance measure used was Sorensen (Bray-Curtis) due to its
A hierarchical, polythetic (multiple species), agglomerative clustering was done on
sample units based upon the importance value of each species in each plot. The
clustering routine utilizes the Sorensen distance measure in combination with a flexible
beta (P = -0.25) linkage method (McCune and Grace 2002). Group memberships from
the cluster analysis were written to the secondary matrix and then used as categorical
variables to assist with an indicator species analysis.
A Distant from drainages
Associated with tidal creeks
Proximal to main channel
or large distributary
Figure 2-2. Locations of plots and the a priori group they were placed in.
Indicator species analysis
To assist with pruning of the cluster dendrogram, several indicator species analyses
were performed. The general procedure is based on Dufrene and Legendre's (1997)
method. The groups to which each plot belonged, computed from the cluster analysis,
were used as categorical variables in which to compute relative abundance and relative
frequency for each indicator species analysis. A requisite of this analysis is that each
group must be comprised of at least two or more plots, therefore the maximum number of
groups that could be analyzed with data from this study was eight. Logically, the
minimum number of groups was two, since placing all plots in one group leaves nothing
to compare and contrast. It follows that a total of seven separate indicator species
analyses were performed, ranging from 2 to 8 groups.
The analyses are based upon values for each species (i) as it pertains to that group
of plots (k): the relative abundance (RAjk) of a species in a group of plots; the relative
frequency (RFjk) of a species in a group of plots; and the indicator value of each species
to each group of plots, which is expressed as the percentage 100*(RAjk X RFjk). The
indicator values range from 0 (no indicator) to 100 (perfect indicator) with a perfect
indicator being faithful (always present) and exclusive to all plots in that group. The
largest indicator value for a given species across all groups is recorded as the indicator
value for that species (see Tables 3-1 and 3-2). A Monte Carlo test using 1000
randomized runs was then used to evaluate the statistical significance of the maximum
indicator value for given species across all groups. The probability of type I error (i.e.
the p-value) is the proportion of times, based on 1000 randomized runs, that the
maximum indicator value from the randomized data set equals or exceeds the maximum
indicator value from the actual data set. The null hypothesis being tested states that the
maximum indicator value is no larger than would be expected by chance (the indicator
value for the species would be 0), and there is no difference between groups (McCune
and Mefford 1999). Statistical significance implies that the species is occurring at a
significantly higher abundance and frequency than would be encountered by random
Each of the seven analyses resulted in different p-values for species as indicators
for a given cluster. Thep-values were then summed across all species for each of the
analyses, and used as a guide for choosing the optimum number of clusters (i.e., pruning
of the cluster dendrogram). Once the optimum number of groups was determined, all
groupings from the cluster analysis were removed from the secondary matrix except the
Multi-response permutation procedures
Multi-response permutation procedures (MRPP) was chosen to test the hypothesis
of no difference between groups. This nonparametric method was deemed more
appropriate to the community analyses than its parametric equivalent, discriminant
analysis and multivariate analysis of variance (MANOVA). MRPP supplements the
indicator species analysis; where the indicator species analysis describes how well each
species separates among the groups, the MRPP provides a test statistic (T) and its
associated p-value, as well as a chance-corrected with-in group agreement (A) value
(McCune and Grace 2002) for describing group differences. A-values range from 0 to 1,
and are indicative of the amount of homogeneity that plots within groups have compared
to what would be expected by chance (0). In this way, the A-value is representative of
effect size (McCune and Grace 2002). For community analyses, higher A-values (those
approaching 0.3) indicate that plots of the same group are not only significantly different,
as indicated by thep-value, but are composed of similar species. For the freshwater tidal
forest community data, MRPP methods were used to test the difference between forest
stands (East vs. West), a-priori landscape grouping, and groups defined by the cluster
Nonmetric multidimensional scaling ordinations
Indirect gradient analysis using nonmetric multidimensional scaling (NMS) is a
method for assessing dimensionality and ordination that is designed to deal with
scenarios inherent to this study. Specifically, NMS was chosen because it is best suited
for imbalanced designs, non-normal data, and relationships that are non-linear.
The software package PC-ORD was used to perform NMS ordinations based on
Sorensen distances calculated from the primary matrix. The first NMS run utilized the
autopilot mode in order to determine the appropriate number of axes to interpret, as well
as determining correlations between the primary matrix and all environmental variables.
A random number was generated for the starting configuration during this particular
ordination. While in autopilot mode, the software package recommends dimensionality
by comparing stress values among the best solutions for each of the 6 dimensional
possibilities it investigates. Once the optimal dimensionality is determined, the autopilot
mode does a final run with the appropriate dimensionality. While viewing ordination
graphs, biplots of variables in the secondary matrix overlaid onto the ordinations of plots
in species space, and correlations of the environmental variables to the axes can be
output. By analyzing these correlations, insignificant environmental variables can be
identified and removed (see insignificant environmental variables, above), thereby
making interpretation easier.
Subsequent NMS ordinations were run using data from the primary matrix, in
conjunction with the secondary matrix containing only important environmental
variables. These ordinations used a random starting configuration and were restricted to
the 3-D dimensionality determined by autopilot, with 100 runs using real data. The
Monte Carlo test used 100 runs of randomized data.
Classification and Regression Tree
The statistical program S-Plus 2000 Professional Release 3 (Mathsoft 2000) with
the TreesPlus add-in (De'ath 2002) was used classify plots into communities (clusters) by
using only the soil properties data (i.e., without species data). The classification and
multivariate regression tree approach was chosen as the final step in choosing how many
communities (clusters) to describe due to its predictive and descriptive ability to model
community composition with environmental correlates. It was also chosen for its ability
to handle interactions (correlations) among variables because only the single best
predictor is selected at each branch, while different predictors are still free to be selected
at other branches of the tree (Urban 2002).
Environmental variables are first rank-transformed. Recursive splitting of the data
minimizing the amount of within-partition heterogeneity for each side of the split is then
performed. After growing a tree of n-1 leaves (where n = the number of plots), the
appropriate number of leaves was chosen using the 1-SE method (Therneau and Atkinson
1997) based on cross-validation.
The model took the form: Y = X1 + X2 + X3 + X4 + X5 + X6 +X7 + X8 +X9 + X10
Y = cluster
Xi = landscape position
X2 = organic matter content
X3 = bulk density
X4 = Ph
X5 = phosphorous concentration
X6 = calcium concentration
X7 = magnesium concentration
Xs = copper present
X9 = electrical conductivity
Xo0 = sodium concentration.
After models had been run for each scenario (number of clusters), the cross-
validation standard errors were compared. Interpretability of each tree was also assessed
based on whether the tree gave a good representation of the corresponding number of
Statistical approaches were used to determine how many freshwater tidal forest
communities exist in the 2 stands sampled in the Savannah River floodplain, as well as an
aid in describing them. Plots were first agglomerated based on their relative species
compositions by using a cluster analysis, which was followed by indicator species
analyses for various numbers of groups (i.e., clusters). Multi-response permutation
procedures (MRPP) was used to test for differences in community makeup for various
numbers of groups, differences in a-priori designation of a plot based upon general
landscape position, as well as broad-scale site differences. Nonmetric multidimensional
scaling (NMS) was used to determine trends in soil characteristics (through biplots
overlays) and species importance values as they relate to individual plots. As a final step,
or cross-validation step, in determining the appropriate number of communities,
classification and regression trees (CART) analysis was used to recreate communities
based solely on environmental parameters.
Once the appropriate number of communities was determined, they were then
named based upon their respective indicator species, as determined by significantly high
relative abundance and relative frequency of a species in each community.
Exploratory Data Analyses
Cluster analysis was one of the many tools used to determine that four communities
comprise the tidal forest of the Savannah River floodplain. This analysis alone is
minimally informational. However, it is perhaps the single-most useful step in
determining and describing community compositions in a multi-step process and is the
first step in most statistical analyses of community makeup. The clustering routine
agglomerated sampling plots based upon the relative species makeup and Sorensen
distances computed from the primary matrix, resulting in a dendrogram with only 3.52%
chaining using the flexible beta linkage method. The resultant dendrogram (Figure 3-1)
depicts plot associations for all levels of grouping. The dendrogram was pruned at the
point where 50% of the information was remaining; this pruning is the key step in
determining how many communities exist. As noted previously, an entire suite of
statistical analyses were carried out on several groupings to determine where to prune.
The starting point for each of the routines was determining the group membership based
upon this cluster analysis.
An option in PC-ORD v. 4.27 (McCune and Mefford 1999) allows each plot to be
color coded according to some grouping variable (in the secondary matrix). Color coding
in Figure 3-1 shows how landscape position (Figure 2-2) can be used as an arbitrary
guide to assessing community makeup, even when the site has never been visited. The
plots labeled in black are relatively distant from creeks and drainages, and comprise
practically all plots in the "Shrub" community. Similarly, though not as strong a
relationship, the plots labeled in grey are proximal to either the main Savannah River or a
large distributary; they comprise over half of the plots in the "Water Oak Swamp bay"
community. Community designations will be described in detail in the following
Indicator Species Analysis
An example of one of the seven Monte Carlo runs of the indicator species analysis
is presented in Table 3-1. P-values were summed across all species for each of the seven
analyses. The lowest total p-values were 0.1667 and 0.1657, found in cluster sizes 4 and
2, respectively (Figure 3-2). The number of significant indicator species (a < 0.05) for
each analysis (cluster size) were also tallied and used as an aid for choosing the
appropriate cluster size (Figure 3-2). With cluster sizes of 5 or more the averagep-value
increases sharply, while the number of significant indicators drops dramatically,
indicating that 5 or more distinct communities probably do not exist in the freshwater
tidal forests of the Savannah River floodplain. Although cluster sizes 3 and 2 resulted in
the highest number of significant indicator species and had low total p-values, the cluster
size of 4 was chosen due to the fact that it has a very low total p-value, a high number of
significant indicators, and still allows detailed interpretation in further analyses. Later
analyses, including NMS ordinations and CART, further supported the choice of 4
clusters (i.e., communities) (following sections).
Monte Carlo results from testing the significance of no difference in species
indicator value [(RAjk* RFjk) 100] between groups based on 4 clusters and 1000 runs of
randomized data are presented in Table 3-1. Nine significant indicators were identified:
tag alder (Alnus serrulata; ALSE), dahoon holly (Ilex cassine; ILCA), virginia willow (Itea
virginica; ITEA), fetterbush (Leucothoe racemosa;LERA), wax myrtle (Myrica cerifera;
MYCE), water tupelo (NYAQ), swamp tupelo (NYBI), swamp bay (Perseapalustris;
PEPA), and water oak (Quercus nigra; QUNI).
Distance (Objective Function)
8E-03 6.7E-01 1.3E+00 2E+00 2.7E+00
Information Remaining (%)
100 75 50 25 0
SE3 ] Shrub
w1 2-1 / Swamp tupelo Tag Alder
w5 -- Water Oak -
w8 Swamp bay Landscape Position
Sm----- proximal to main channel/distributary
W9 associated with creeks/drainages
distant from creeks/drainages
Figure 3-1. Cluster dendrogram based on results of cluster analysis on matrix of 31 plots X 20 species. Plot names on the left
correspond to those depicted in figures 1-1 and 2-2. Landscape position of each plot corresponds to those depicted in
figure 2-2. Pruning of the dendrogram is indicated by the /, and community names are given for each of the four groups
based upon indicator species analysis.
10 A A 30
8 7 6 5 4 3 2
Number of Clusters
-- Total p
A # Indicators
Figure 3-2. Summary of the 7 indicator species analyses. P-values are based on Monte
Carlo randomization, then averaged over all species for each cluster size (x
axis) (see table 3-1). Blue circles denote cluster sizes with lowest average p-
Table 3-1. Monte Carlo results of species indicator value (IV) between the 4 groups. See
Appendix B for species abbreviations.
IV from randomized groups
Species Observed IV Mean Std Dev p
ACRU 27.7 32.0 4.71 0.822
ALSE 41.6 30.9 4.71 0.013 *
CEOC 14.3 15.5 8.47 0.466
FRAX 36.2 32.8 3.97 0.194
ILCA 80.2 24.5 9.22 0.001 *
ITEA 54.5 18.3 9.15 0.008 *
LERA 82.1 25.3 7.98 0.001 *
MYCE 79.3 26.2 7.81 0.001 *
NYAQ 47.9 29.6 5.60 0.002 *
NYBI 44.3 32.9 4.74 0.021 *
TADI 34.0 32.0 6.83 0.328
VINU 21.4 15.7 8.85 0.172
ILDE 21.2 17.4 9.26 0.242
LYLU 36.4 17.0 9.52 0.072
PEPA 56.8 29.3 9.21 0.013 *
VIDE 24.7 16.7 8.80 0.201
LIST 27.6 26.9 9.64 0.379
QUNI 52.5 30.6 7.21 0.011 *
COFF 38.3 21.7 10.64 0.069
CACA 24.7 21.9 9.64 0.318
After determining that 4 groups were going to be interpreted (through all methods
of analyses), the indicator species analysis was used to determine which species are
indicative of each of the 4 clusters (communities). Further, these species were used to
provide names to the communities (see Figure 3-1). Significant indicator species
pertaining to clusters are presented in Table 3-2. Cluster 1 has the following significant
indicators: dahoon holly (ILCA), virginia willow (ITEA), fetterbush (LERA), and wax
myrtle (MYCE); Cluster 2 has water tupelo (NYAQ) as an indicator; Cluster 3 has
swamp tupelo (NYBI) and tag alder (ALSE) as indicators; and Cluster 4 has water oak
(QUNI) and swamp bay (PEPA) as significant indicators.
Table 3-2. Indicator values for species in each of 4 clusters. Numbers in parentheses
indicate number of plots included in each cluster (community). Colored fields
correspond to significant indicators (see table 3-1).
Species 1(11) 2(4) 3(9) 4(7) Average
ACRU 26 9 27 28 22
OMl 22 33 M 1 25
CEOC 14 0 5 0 5
FRAX 15 35 14 36 25
ILCA 80 1 1 0 20
ITEA 55 0 0 0 14
LERA 82 0 5 0 22
MYCE 79 2 4 0 21
S 38 1 1 9 24
S3 10 42 25
TADI 14 5 34 24 19
VINU 21 0 2 0 6
ILDE 1 0 4 21 6
LYLU 36 0 0 0 9
PEPA 0 1 25 57 21
VIDE 25 0 4 0 7
LIST 13 0 13 28 13
QUNI 0 21 22 53 24
COFF 1 4 38 1 11
CACA 0 2 18 25 11
averages: 26 9 15 16 17
Multi-response Permutation Procedures
Testing for differences between groups was accomplished using multi-response
permutation procedures (MRPP) on the primary matrix, with groups defined by
categorical variables in the secondary matrix. For each test, ap-value and an A-value are
reported. Thep-value reported corresponds to the hypothesis of no difference between
groups. When statistically significant differences were found between groups, multiple
comparisons were done for further investigation. The A-value is the chance-corrected
within-group agreement (see chapter 2). A=0 when heterogeneity (species importance
values) within groups is what would be expected by random chance. As A approaches 1,
the homogeneity within a group is maximized and importance values for individual
species are identical for each of the plots within the group. Note, however, that
importance values do not need to be identical for all species within a plot. In this case of
maximum homogeneity the corresponding 6 value is equal to zero. Groups were based
upon the following three criteria:
1. Stand. Two groups of plots were made based upon their broad-scale placement
within the landscape. The eastern stand, composed of 16 plots, and the western
stand, composed of 15 plots, are located off the Little Back River and the main
channel of the Savannah River, respectively (Figure 1-1).
2. Floodplain physiography. Three groups were made based upon their proximity to
the river, as well as the size of the channel supplying tidewater (see a priori
grouping, chapter 2). The 3 groups consist of plots that are:
a. Proximal to the main river channel or a large distributary (n=4).
b. Associated with tidal creeks and drainages (n=17).
c. Backswamp sites relatively far removed (distant) from tidal creeks and
drainages and, therefore, from the main channels (n=10).
3. Cluster. The 4 groups of plots based on the cluster analysis. Groups were simply
given numbers as identifiers. Sizes of each group are listed in table 3-2. Although
only results from the 4-cluster analysis are presented, MRPP was performed for
clusters of size 2 and 3.
Results showed that all groupings are statistically different based upon the MRPP
analyses. Further, all multiple comparisons are also statistically significant (Table 3-3).
Table 3-3. MRPP results for groups of plots.
Criteria comparisons p-value A-value
Stand 0.00042660 0.11826623
Region 0.00000142 0.23889437
rivervs creek 0.01100517 0.06810518
river vs distant 0.00005802 0.37853775
creek vs distant 0.00004074 0.19628380
Cluster 0.00000000 0.40428521
1 vs2 0.00018304 0.17175219
1 vs 3 0.00000747 0.38497760
1 vs4 0.00001505 0.38976488
2 vs 3 0.00010362 0.30341608
2 vs 4 0.00088010 0.31202930
3 vs4 0.00014051 0.14952343
The NMS autopilot run with the primary matrix and all environmental data
indicated that a 3-dimentional solution was optimal. The probability that a similar final
stress could have been obtained by chance (i.e., the Monte Carlop-value) is 0.0196 for
the 3-D solution based upon 50 runs with randomized data. The final ordination for the
autopilot mode completed 82 iterations while analyzing the 3-D solution, resulting in a
stress value of 6.62099. This is well within the acceptable range (Kruskal 1964),
especially when statistics of this sort are applied to ecological community data (McCune
and Grace 2002).
Rotating the ordination graph allows correlations to be seen between the
environmental data (secondary matrix variables) and plot-species ordination via biplot
overlays. One such rotation allowed for all significant environmental variables (r2
>0.392) to be seen on one graph. Although other rotations change strengths of
environmental correlations to the axes (by changing species-plot placement and,
therefore, how the biplots are oriented along axes), no rotations resulted in correlations
>0.392 for any "insignificant" variables. Therefore, the rotation showing high
correlations (Table 3-4) for all significant variables was used as the basis for removal of
insignificant environmental variables (Table 2-2).
Table 3-4. Pearson's coefficients of determination (r2) and Kendal's tau values of
environmental variables to axes for NMS ordination using autopilot mode.
These correlations were used as the basis for removal of insignificant
Axis 1 Axis 2 Axis 3
r2 tau r2 tau r2 tau
Organic matter 0.248 0.295 0.651 -0.643 0.071 0.217
Bulk density 0.096 -0.371 0.395 0.572 0.002 -0.138
pH 0.012 -0.019 0.205 0.313 0.075 -0.145
P concentration 0.170 0.277 0.037 -0.092 0.019 -0.084
P present 0.041 -0.170 0.448 0.535 0.019 -0.187
Concentration 0.051 0.140 0.035 -0.196 0.001 0.071
K present 0.048 -0.194 0.332 0.435 0.001 -0.069
Ca concentration 0.192 0.258 0.404 -0.465 0.065 0.129
Ca present 0.012 -0.101 0.161 0.260 0.043 0.071
Mg concentration 0.256 0.277 0.471 -0.514 0.083 0.148
Mg present 0.005 0.135 0.014 -0.105 0.089 0.144
Zn concentration 0.061 0.153 0.140 -0.209 0.004 0.032
Zn present 0.047 -0.245 0.276 0.366 0.003 -0.108
Mn concentration 0.013 -0.138 0.007 0.112 0.087 -0.022
Mn present 0.081 -0.308 0.280 0.424 0.023 -0.092
Cu concentration 0.114 -0.241 0.103 0.220 0.195 -0.314
Cu present 0.139 -0.269 0.392 0.550 0.020 -0.231
Fe concentration 0.058 0.187 0.068 -0.148 0.017 -0.140
Fe present 0.030 -0.183 0.280 0.402 0.046 -0.226
Electrical conductivity 0.324 0.342 0.578 -0.563 0.055 0.139
Na concentration 0.362 0.385 0.572 -0.527 0.087 0.084
Na present 0.051 0.187 0.074 -0.200 0.074 0.213
Additional ordinations were run to fit a 3-D solution based on Sorensen distances
computed from data in the primary matrix with overlays of only the 8 important soil
properties. Monte Carlo results based on 100 runs of randomized data give ap-value =
0.0099. Ninety three iterations were used in the final solution, resulting in a final stress
of 6.62099 and a final instability of 0.00001.
Axes 2 and 3 represent the largest proportion of variance explained by the
ordinations (Table 3-5), and the plots separate into relatively concise groups of similar
communities (clusters) when viewing these axes. Therefore, most ordinations that follow
will show Axes 2 and 3 (See Appendix-E for correlations of species and soil constituents
to Axes 2 and 3). It follows that ordinations portraying species' importance in plots, as
well as overlays of variables from the secondary matrix can be interpreted easiest when
viewing these 2 axes. It is important to realize that, when viewing ordination graphs, the
axes are not a single variable, nor are they necessarily a summation of variables that have
been measured. Rather, they are best thought of as a synthesis of variables, both
measured and not measured, representing the relative differences between the plots that
have been sampled. In these ordination graphs large symbols correspond to higher
importance values for a given species as well as larger values for soil constituents.
Table 3-5. Proportion of variance represented by axes based on the r2 distance between
distance in the NMS ordination space and distance in the original space.
Axis Increment Cumulative
1 0.025 0.025
2 0.407 0.433
3 0.527 0.960
Indicator species importance within plots. A graphical representation of the
ordinations is useful for ease of depicting how the plots separate out in species space, as
well as perceiving relative importance of select species within plots. The indicator
species for Cluster 1 are represented in Figures 3-3 and 3-4. Dahoon holly and Virginia
willow are most important in plots of Cluster 1, with very little representation in other
plots (Figure 3-3). Likewise, fetterbush and wax myrtle are also highly important in plots
of Cluster 1, but they are also represented a bit more in plots of Cluster 3 (Figure 3-4).
Water tupelo is well represented in Clusters 1 and 2 (Figure 3-5) even though
statistical tests indicate that it is a significant indicator for Cluster 2 only.
The plots of Cluster 3 indicate swamp tupelo as one of the significant indicators,
yet it is clear that it is also relatively abundant in plots of Cluster 4 (Figure 3-6A).
Similarly, tag alder (Figure 3-6B) is an indicator species for plots in Cluster 3. Tag alder
is also the most prevalent of all species encountered in this study, but only reaches
maximum importance in plots of Cluster 3.
Water oak (Figure 3-7A) is an indicator for the plots of Cluster 4, yet the ordination
shows water oak as being most abundant in W10, a member of Cluster 3. Likewise, W10
has the largest importance value for swamp bay (Figure 3-7B), yet swamp bay is an
indicator for plots in Cluster 4.
\w5 SE4 1
SE A 2
A NW2 A3
W7 SE2 A 4
W 11 NE2 NE4
SE3 A A
W8 SWZ 2 SW4
W7 A 4
W8 SW2' SW4
Figure 3-3. NMS ordination. Relative importance values of dahoon holly (A) and
Virginia willow (B). Note the size of the triangle depicts the importance of
species in plots.
W8 W2 SW4
W14 W 9
W8 S S4
8 SWz2 SW4
Figure 3-4. NMS ordination. Relative importance values of fetterbush (A) and wax
myrtle (B), as indicated by the size of triangles.
W5 NW4 Cluster
SSE4 A A 1
SEI A 2
W7A NW2 A 3
W7 A A A 4
A SE2 NWI
W11 NE2 NE4
A SE3 A A
W16A A NE3 NE1
W6 A A
CO A W9
W8\ sw .' sw4
Figure 3-5. NMS ordination. Relative importance values of water tupelo, as indicated by
the size of triangles.
w \ NW4 Cluster
W7 SE1 NW2 A 4
11/ NE2 NE4
SW16 SE3 A
c AW14 AW9
ws sw4 /A
W6 16A A
W8 SWAV SW4
Figure 3-6. NMS ordination. Relative importance values of swamp tupelo (A) and tag
alder (B), as indicated by the size of triangles.
1S NW4 Cluster
A5 SE4 A 1
'W7 SE1 'NW2 A4
1 SE3 NE2 NE4
W6 W16A NE3 NEI
o AW14 N9
\ SW2 W SW4
W8 A A
Figure 3-7. NMS ordination. Relative importance values of water oak (A) and swamp
bay (B), as indicated by the size of triangles.
Soil properties. Although the secondary matrix has no affect on how the distance
matrix is calculated for the NMS ordination, it is helpful to see how soil constituents are
correlated, both to other constituents, and to plots. NMS graphs of plots in species space
with biplot overlays of soil constituent values illustrate how most soil properties are
closely correlated to other constituents, in both a positive and negative way (Figure 3-8).
Organic matter content, electrical conductivity, concentration of Ca, concentration of Mg,
and concentration of Na are all closely correlated with axis 2. At the same time, the
values for bulk density, K present, and Cu present are also correlated to axis 2, but
negatively correlated to the other soil parameters. With this rotation it is also easy to see
that all soil constituents are orthogonal to Axis 1 and, for all practical purposes, constitute
the majority of environmental differences associated with Axis 2. Note that lengths of
vectors in Figure 3-8 indicate strengths of relationships to plots. Biplots of organic
matter content, electrical conductivity, concentration of Ca, concentration of Mg, and
concentration of Na are all associated with high values in the plots of Cluster 1.
A view at axes 3 and 2 (Figure 3-9) allows the correlations to Axis 2 to be seen
again, but not as strict a correlation as in the previous graph (Figure 3-8). Partial
correlations to Axis 3 also exist. Viewing these 2 axes leads to a sense that the
appropriate number of clusters to interpret may have been 2, as indicated by the larger
solid line circles around the plots (Figure 3-9). This plot is an excellent demonstration of
the 2 vs. 4 groups distinction that was indicated in the indicator species analysis (Figure
3-2). There are essentially 2 broader groups that are comprised of 2 subgroups each that
can be distinguished as significantly different in the MRPP analysis (Table 3-3). The
negative correlation between Axis 2 and bulk density, potassium present, and copper
present have little correlation to axis 3.
A A 2
A A 3
Organic A 4
A Mg conc
Ca cone A A
bulk I P present
Figure 3-. Biplot of axis 2 vs Strong correlations of all soil constituents to axis 2.
< A A
A A A
Figure 3-8. Biplot of axis 2 vs 1. Strong correlations of all soil constituents to axis 2.
A-5 SE4 A 1
SE2 A 2
W7 SE1 ANW2 A 3
SSE3 A A
A Nacone NE3 NE1
AW6 Mg conc Ec A A
W14 Ca cone
T Cu present
\ A SW1
W8 SW2 W2 S2 4 4
Figure 3-9. Biplot of axis 3 vs. 2. Correlations of soil constituents are split between axes
2 and 3. Larger circles with solid lines indicate the 2 broad groups while
smaller circles with dashed lines encircle the 2 sub-groups.
Classification and Regression Tree Analysis
The Classification and Regression Tree (CART) analysis (Urban 2002) run in S-
Plus Release 3 (Mathsoft, Inc. 2000) was used to assist in the decision of how many
clusters to interpret, as well as aid in the description of the clusters. To accomplish this,
plots were classified into their respective cluster (for 2, 3, and 4 clusters) by using only
the soil nutrient values (i.e., without species data). The best fit of the model (Figure3-10)
explained 55% of the variation, which was accomplished when 4 clusters were classified,
reinforcing the earlier analyses that 4 different tree communities exist in the study area
and further, that these same 4 groups are communities are characterized, even
predictable, under given sets of environmental conditions.
Cluster 1: Shrub (11) Organic matter
<78% / >78%
Cluster 2: Water tupelo (4)
SCluster 3: Swamp tupelo Tag alder (9
Cluster 4: Water oak Swamp bay (7)
>353 mg/kg <353 mg/kg
>3 dS/m <3 dS/m
Swamp tupelo -Tag alder
Water tupelo Water oak Swamp bay
Error: 0.2 CV Error (pick): 0.55 SE: 0.133 Misclassification rates: Null = 0.645 Model = 0.129 CV = 0.355
Figure 3-10. Classification and regression tree (CART) depicting amounts of organic
matter, sodium concentration, and electrical conductivity characterizing
respective communities in plots. Parentheses indicate number of plots in
clusters and leaves.
Soil organic matter content makes a clear split when classifying the shrub
community. When soil organic matter is greater than 78%, the most likely community to
be found would be similar in composition to those sample plots that were in Cluster 1.
This was the strongest and most consistent result, even when 2, 3, and 4 clusters were run
in the model (not presented graphically). Further splitting was done based upon Na
concentration, followed by electrical conductivity. Most plots in cluster 3 have a soil
organic matter content less than 78% and a Na concentration of the soil greater than 353
mg/kg while most plots in cluster 2 have a soil organic matter content less than 78%, Na
concentration greater than 353 mg/kg, and electrical conductivity of the soil less than 3
dS/m. Finally, most plots in cluster 4 have soil properties similar to those of cluster 2
except for electrical conductivity, which is less than 3 dS/m.
Descriptions of Communities
After running the full suite of statistical analyses for each cluster size, it was
determined that 4 separate communities exist in the study area. Figure 3-11 depicts the
position of each community in the sample area.
These are the plots of Cluster 1. The shrub community is the most distinct of all the
communities described in this study. It is characteristically lacking of many tall trees and
occupies backswamp sites in all plots but 1 (Figure 3-11) with highly unconsolidated
hollows sparsely interspersed with hummocks where canopy and sub-canopy species tend
to grow. Canopy heights average only 13m, with the overall average of all trees being
4m tall and overall median height of 3m tall. Larger trees are found, but they are
infrequent and, given their height relative to the rest of the canopy, their presence does
not inhibit sunlight from penetrating to the forest floor. These supra-canopy individuals
are mostly water tupelo and bald cypress (rarely swamp tupelo, sweet gum (Liquidambar
i, yiu,, ,Ia), and red maple (Acer rubrum)) rooted on the larger sized hummocks. Their
large basal area relative to the other species results in large importance values for these
individuals in the plots within in this community. This, in turn, influences the results of
the cluster analysis, causing plots within this community to closely resemble the Water
tupelo community (Figures 3-3 through 3-7) when, in fact, they are quite different.
LIe i iI
IL I I 1.I,
L I .,.. !..Pi
Figure 3-11. Locations of the 4 communities within the sample areas.
The shrub community has the most homogeneous mix of species of all the
communities described in this study, as well as the highest stem density of individuals
that are smaller than 5cm DBH (Figure 3-12). The most common of these species are tag
alder, wax myrtle, regeneration-sized (sapling and sub-canopy) water tupelo, fetterbush,
dahoon holly, and red maple. These plots also contain the most uncommon of the species
analyzed: arrow wood (Viburnum dentatum), lyonia (Lyonia lucida), and possumhaw
viburnum (Viburnum nudum). This community also had many shrubby species that were
removed from the analyses due to their extreme rareness: inkberry (Ilex glabra), highbush
blueberry (Vaccinium corymbosum), sweet bay (Magnolia virginiana), groundsel tree
(Baccharis halimifolia), black alder (Ilex verticillata), and black willow (Salix nigra) (Table
Organic matter content is highest in the plots, as indicated in NMS biplots lengths
in Figure 3-9, the CART graph (Figure 3-10), and the means presented in Table 3-6. The
strong inverse relationship between soil weight and organic matter results in a bulk
density value that is the lowest of any community described in this study. Phosphorous
content of the soil is lowest in this community while concentrations of Na, Ca, and Mg
are the highest; it follows that electrical conductivity is also the highest in this
Water Tupelo Community
These are the plots of Cluster 2. This community was found entirely within the
western stand (Figure 3-11) and consists of only 4 plots, all of which are associated with
tidal creeks and drainages. Water tupelo is the defining species of this community,
occurring in higher numbers and greater basal area here than in any other community
type (Table 3-6). Canopy heights average approximately 15m with the overall average of
all trees being 6m and overall median height being 4m. Though these values seem
similar to those of the shrub community, there are far fewer stems/ha (Table 3-6) and
ample sunlight penetrates to the forest floor.
Decreased development of the shrub layer is a general rule for this community. No
fetterbush or possumhaw (Ilex decidua) were cataloged in any of the sample plots, nor
were there any sweetgum. There was average representation of trees such as water oak,
ash, maple, swamp dogwood (Cornusfoemina var. foemina), bald cypress, all of which can
eventually make it to the sub-canopy.
Phosphorous concentration is, on average, highest in this community. In that
respect it is most similar to the water oak swamp bay community, which also occurred
only within the western stand. According to the cluster analysis though, the water tupelo
community is most similar to the shrub community, further supporting the notion of 4
communities over 2 or 3.
Swamp Tupelo Tag Alder Community
These are the plots of Cluster 3. This is the most abundant of the communities,
being found in the majority of both the eastern and western stands that are associated
with tidal creeks and drainages. In fact, several of the plots were located right next to
tidal rivulets. It probably best represents the "typical" tidal freshwater forest community
that is found along the Savannah River floodplain, with its proximity to tidal rivulets and
fairly high floral diversity. This community has a well developed canopy in terms of tree
heights and abundances; there are several tall 18-21m trees, much like the water oak -
swamp bay community. However, the average height of canopy trees is still only 15m.
Similar to all communities, the overall average of all trees is 5m and overall median
height is 3m (10 ft.) tall. Swamp tupelo dominates the canopy, along with the highest
amount of bald cypress found in any of the communities (Table 3-6).
The shrub layer of this community is relatively well developed, as depicted in the
smaller DBH size classes of Figure 3-12. This layer is dominated by tag alder (Table 3-
6), as would be expected by the indicator species analysis, but also relatively abundant
are swamp dogwood and buttonbush (Cephalanthus occidentalis). The following species
are found in all layers of the canopy: red maple, ash, dahoon holly, fetterbush, wax
myrtle, possumhaw viburnum, possumhaw, swamp bay, sweet gum, water oak, and
musclewood (Carpinus caroliniana).
Electrical conductivity and Na concentration are quite high in this community
(Figure 3-10, Table 3-6), though not nearly as high as the shrub community.
Water Oak Swamp Bay Community
These are the plots of Cluster 4, found only in the western stand. Although this
community has plots associated with tidal creeks and drainages, it also represents all plots
associated with the main channel of the Savannah River. The canopy of this community
is well developed, similar to the swamp tupelo tag alder community, but is slightly
more diverse in terms of canopy tree diversity. Where the canopy layer of the swamp
tupelo tag alder community is dominated by swamp tupelo with relatively few other
individuals, the water oak swamp bay community canopy has a more uniform
distribution of swamp tupelo, water tupelo, ash, and bald cypress in the canopy layer.
The sub-canopy is also more uniform in terms of species composition with
regeneration of the canopy species mentioned above, as well as water oak, sweetgum, and
red maple. Although water oak and swamp bay are indicators for this community, there
are no true dominant species in this community. Water oak and swamp bay are
indicators, but as parts of the diverse understory that this community exhibits. The shrub
layer is not dominated by "shrub" species, but rather smaller "tree" species such as those
found in the canopy and sub-canopy. Notably absent is tag alder, which is perhaps the
most widely distributed of all the species.
On average, organic matter is lowest in this community (Table 3-6), with a range of
17% at W6 (very close to the main Savannah River) to 62% at the W8 plot (Figure 2-2).
Sodium concentration and electrical conductivity values are also, on average, the lowest
found in any of the community types (Figure 3-10), along with Ca concentration (Table
16000 m I I Shrub community
14000 Water tupelo community
Swamp tupelo Tag alder community
12000 m I 1 Water oak Swamp bay community
M Proximal to main channel
10000 I // Associated with creeks and drainages
8000 7 Y/ Distant from creeks and drainages
= 6000 /
<2-4.9 5-9.9 10-14.9 15-19.9 20+
DBH Size Class (cm)
Figure 3-12. Average number of stems per acre for each community and a-prior group.
Table 3-6. Averages of species importance values and environmental parameters within
a given community type. Values in parentheses indicates how many plots are
in the community and, therefore, used to average.
Organic matter (%)
Bulk density (g/cm3)
Electrical conductivity (dS/m)
Sodium concentration (mg/kg)
Calcium concentration (mg/kg)
Magnesium concentration (mg/kg)
Phosphorous present (gg/cm3)
Copper present (gg/cm3)
Basal Area (m2/ha)
Water oak -
This study found that 4 communities exist within the sampled plots along the
Savannah River floodplain. Exploratory data analyses were used to group sample plots,
identify species that are indicative of the groups, and explore relationships the groups
have to environmental parameters. Confirmatory data analyses were used to test
differences between groups, and a statistical model was developed to predict community
type based solely on soil properties. Although communities can be predicted based upon
certain soil parameters, the broad-scale landscape characteristics underlying the dynamics
of the system have not been analyzed.
There are a very limited number of available publications describing freshwater
tidal forests in the southeastern United States. Detailed descriptions are limited to this
study and studies done on the Pamunkey River in the lower Chesapeake Bay (Doumlele
et al. 1985, Rheinhardt 1991, Rheinhardt 1992, Rheinhardt and Hershner 1992).
Rheinhardt (1992) points out that the paucity of literature on freshwater tidal forests may
reflect their rarity. Freshwater tidal forests only seem to develop well in areas that
possess a large tidal range, voluminous river flow, and low coastal plain relief- factors
characteristic of several rivers in the southeastern United States (Altamaha, Santee,
Black, Pee Dee, etc.), including the lower Chesapeake Bay and the Savannah River
floodplains. The dearth of information on these ecosystems may stem more from the
historic interest in the areas, rather than lack of existence. Brief descriptions have been
compiled by Wharton et al. (1982) for 6 dominance types within 7 tidal forests of
Florida's Gulf coast, as well as one wind-tide dominated site along the Roanoke River,
Though there have been measures of soil organic matter, there is only one
publication (Rheinhardt 1992) that has values for the concentration of nutrients within
soils of freshwater tidal forests. Therefore, nutrient concentrations for non-tidal
bottomland hardwood forests were used as a basis for comparison (later section).
Tidal Forest Communities in Sampled Areas of the Savannah River
Chapter 3 outlines major differences between communities based upon species
importance, landscape position, canopy/sub-canopy/shrub layer development and
composition, and soil constituents. Although the underlying and regulatory nature of
these factors has not been formally investigated in this study, major trends can be seen.
Flooded conditions in remote areas of the landscape, where the largest portion of shrub
community is found (Figures 3-1 and 3-11), is likely maintained by tidal forcing of the
water-table. The shrub community has high values for all nutrients (Table 3-5), except P
and Cu. This is likely due to the increased residence time for the nutrients resulting from
limited overland flow. The water oak swamp bay community has the highest mean
value for bulk density. This is likely due to the fact that many of the plots included in
this community are immediately adjacent to the main channel of the Savannah River,
while those remaining are close to secondary creeks. A relatively large amount of silt
and clay likely gets deposited on the soils in these areas, resulting in high bulk densities.
Comparisons with Tidal Forests of the Lower Chesapeake Bay
Doumlele et al. (1985) catalogued 12 tree species in the freshwater tidal swamp
studied in the lower Chesapeake Bay of Virginia, only 9 species of which were 5cm DBH
or larger. They also found that 96% of the trees sampled were of 4 species: green ash
(Fraxinus pensylvanica), blackgum (Nyssa sylvatica), musclewood (Carpinus
caroliniana), and red maple (Acer rubrum). The freshwater tidal forests along the
Savannah River floodplain are comparatively more diverse with 28 species catalogued
(12 species 5cm DBH or larger).
My protocol differed only slightly from that of Rheinhardt (1992). This study
utilized all life forms in the analyses while Rheinhardt analyzed canopy species (>10cm
DBH) separately from subcanopy/shrub species. Therefore, judicious comparisons can
still be considered legitimate. He characterized the Chesapeake Bay freshwater tidal
forest as having 2 general community types, separated primarily on the relative presence
of sweetgum (Rheinhardt 1992). Both of the communities closely resemble the swamp
tupelo-tag alder community of the Savannah River floodplain. Similarities include the
high importance of ash, red maple, swamp tupelo, and (in one Chesapeake Bay
community type) sweetgum in the canopy as well as the importance of tag alder, arrow
wood, fetterbush, swamp dogwood, and musclewood in the subcanopy/shrub layer for
To facilitate a general comparison between Rheinhardt's consonant analysis and
my own, I also summarized the basal area of trees greater than 10cm DBH sampled in the
Savannah River floodplain. Both sites are characterized by extremely low diversity of
canopy species. The Chesapeake Bay forests have 95% of the basal area of canopy
species consisting of only 5 species: ashes, swamp tupelo, red maple, sweetgum, and bald
cypress (Rheinhardt 1992). Similarly, 89% of the basal area (total DBH) of canopy
species in the Savannah River floodplain forests consists primarily of 3 species: water
tupelo (41%), swamp tupelo (33%), bald cypress (15%); the remaining 11% is composed
of water oak (4%), red maple (3%), ash (2%), sweetgum (1%) and a single individual
over 10cm DBH of both dahoon holly and musclewood.
Extensive tracts of cypress are found in tidal forests of north Florida as well as the
floodplain of the Chickahominy River, a tidal tributary of the James River (located just
south of the Pamunkey River) (Rheinhardt 1992). However, neither the Savannah River
floodplain nor tidal forests along the Pamunkey River, barring 2 sites, (Rheinhardt 1992)
contain substantial tracts of bald cypress. The abundance of cypress in the disparate 2
sites along the Pamunkey may be the expression of an old buried waterway (Wharton et
al. 1982). Detailed analysis of cypress abundance in relation to edaphic factors has not
been published, and continues to be an area of investigation regarding global climate
change and sea level rise (Tom Doyle, personal communication).
Although there are no significant differences, ordinations show that musclewood is
at least partially correlated to the concentration of P in the soil (Rheinhardt 1992) for tidal
forests in the lower Chesapeake Bay. Similarly, this study has found that the plots in the
water oak- swamp bay community have high average importance values for musclewood
as well as high concentrations of P (Table 3-5), a trend that is also evident in ordinations
The soils of this system vary from very mucky to almost upland in character
(Doumlele et al. 1985), ranging from 9.0 to 63.8% organic matter (Rheinhardt 1991) with
averages of 40.5% and 25.2% in ash-blackgum (Fraxinus spp-Nyssa biflora) and maple-
sweetgum communities, respectively (Rheinhardt 1992). Soils sampled in this study
range from 17% to 91% with a mean of 62%, clearly higher than most soils collected
along the Pamunkey River. As Rheinhardt (1992) points out, peat [organic matter]
content is a good indicator of the relative wetness of a tidal swamp. With that rationale, I
hypothesize that the root zone of soils in tidal forests of the Savannah River floodplain
are experiencing longer hydroperiods than those studied in the lower Chesapeake Bay.
Although the hollows may remain unflooded for several days (Rheinhardt 1992), a
tidally driven water table along the Pamunkey River results in a high mean water-table
depth in the root zone, which has been shown to influence the composition of tidal forests
in the lower Chesapeake Bay (Rheinhardt and Hershner 1992). It follows that the low
diversity is the Savannah River system is also likely due to tidally driven water table,
especially in remote areas of the landscape that do not experience above-ground tidal
flooding but maintained saturated soils during the drought years of this study (personal
Comparisons with Tidal Forests of Florida's Gulf Coast and the Roanoke River, NC
Published accounts of the tidal forests of north Florida and the Roanoke River, NC
are limited to lists of dominant species that occupy each. Although there is no
quantitative data, the community descriptions in Wharton et al. (1982) contain species
that are concurrently similar and dissimilar to the freshwater tidal forests along the
Savannah River. For instance, swamp tupelo is listed as a dominant species in all tidal
forests except those of the Apalachicola River (which contains water tupelo) and the
Yellow River (addressed below). Similarly, cypress is listed as a dominant species in 3
of the 6 dominance types. These are both species that are typically found in the wettest
environments (Zone II in Wharton et al. (1982)). The Savannah River tidal forest
communities have swamp tupelo prevalent in 16 of the 31 plots analyzed (Table 3-5) and
water tupelo in the other 15 plots. Bald cypress is found throughout the areas studied,
though probably in lesser numbers, and not dwarfed, as found in the Suwannee River
forest (as per Wharton et al. 1982).
Subtle differences in dominant communities exist between those described by
Wharton et al. (1982) and the Savannah River tidal forests. Many of the co-dominant
species listed are not found in the study area, including: southern red cedar (Juniperus
silicicola) and cabbage palm (Sabalpalmetto) [St. Marks River, Wakulla River, and
Wacissa River], sweet bay (Magnolia virginiana) [Suwannee River, Lafayette Creek, St.
Marks River, Wakulla River, and Wacissa River, Yellow River], groundsel tree
(Baccharis glomeruliflora) [St. Mark's River], and red bay (Persea borbonia)
[Apalachicola River]. The disparity in species between the tidal forests along the
Savannah River and those described above are not understood, but could be hypothesized
as being indirectly related to the tidal range (Gulf coast
The Yellow River is unique in that Atlantic white cedar (Chamaecyparis 1thyile%)
and sweet bay are listed as dominants. Since Atlantic white cedar is generally thought of
as disturbance-adapted (Wharton et al. 1982), the abundance of Atlantic white cedar may
be indicative of past fire, logging, flooding, or windthrow in the area (Korstian and Brush
1931, Little 1950, Frost 1987), which would explain why it is not fount in the tidal forest
along the Savannah River.
Comparisons with Bottomland Hardwood Soils
Although bottomland hardwood forests contain many of the same wetland-adapted
species as freshwater tidal forests, the communities that occupy each site differ
dramatically. This is likely due to the dramatic differences in hydrology (seasonal vs.
daily flooding) as well as soil properties. Due to these large differences, bottomland
hardwood communities will not be compared and contrasted to the freshwater tidal forest
in this study, aside from mention that most species found throughout the freshwater tidal
forests of the Savannah River floodplain are characterized as being in "Zone II"
(Wharton et al. 1982). For more information, see Wharton et al. (1982) and Messina and
Conner (1998) for complete descriptions of bottomland hardwood communities and
Soil organic matter (SOM) contents found in this study are some of the highest
values recorded. The highest reported SOM values are typically found on tidal forests
(40%) and peat systems (up to 44%), though SOM content for a tidal portion of the
Sopchoppy River [Wakulla River floodplain, Gulf coast, FL] is reported to be as high as
77% (Wharton et al. 1982). Pocosins are reported as having up to 66.8% SOM
(Woodwell 1958), while bottomland hardwood soils are typically less than 36% SOM
(Wharton et al. 1982).
Concentrations of P, K, Ca, and Mg in the freshwater tidal forests of the Savannah
River are roughly comparable to what Francis (1986) found for soils of bottomland
hardwoods in Mississippi, Arkansas, and Louisiana. Although Wharton et al. (1982)
notes that unusually high concentrations of calcium and magnesium are often found in
soils of spring-fed and tidal systems, Rheinhardt (1992) reported average values that are
much lower, particularly for Ca (Table 4-1). The high Ca concentration may be the result
of water entering swamps from seepage that has dissolved lime in its passage through the
subsoil and substratum of the surrounding uplands. Since decaying organic matter is
highly adsorptive and Ca ions plentiful, the resultant organic horizons cannot avoid the
presence of large amounts of exchangeable Ca ions (Brady 1974).
Table 4-1. Published nutrient values (mg/kg) of forested wetland soils.
Francis 1986' Wharton et al. 1982"' Rheinhardt 1992 This study
Coastal Ash Maple-
Available* Total plain Piedmont Blackgum Sweetgum Available
P 11.7-60.9 310-830 0.5 8 10.8 8.0 33-116
K 48.4-402.5 8030-19,050 9.3 56 67.5 66.5 108-788
Ca 404-5246 730- 8740 61 70 806 578 1530-10,700
Mg 153-1302 1810-10,510 33 21 N/A N/A 280-2428
* Values were published as g/mg, but were actually mg/kg (Francis, personal communication).
t Values listed are the range encountered for 5 soil series in Mississippi, Arkansas, and Louisiana.
"' Values listed are for bottomland hardwood forests that are neither tidal nor spring-fed.
Future Research Needs
Community composition in the tidal forest along the Savannah River floodplain is
determined, in part, by substrates and geomorphology, both of which are influenced by
hydrology. Although measures of organic matter can give good insight as to the degree
of flooding in an area, directly measuring surface and sub-surface water levels (e.g.,
depth, duration, and timing) could help explain the dynamics taking place within the
floodplain. CART approaches that incorporate geomorphologic variables (e.g. ridges and
swales, natural levees and channels (both remnant and recent), backswamps, etc) held in
a geographic information system along with digital elevation models and remotely sensed
imagery could prove very useful for generating habitat maps of communities (Urban
2002). More broad scale analysis, such CART models incorporating remote sensing
imagery, should include "field" examination and quantification of other tidal forest areas
along the Savannah River floodplain.
Studies focusing on the occurrence of species and minute changes in elevation in
relation to the water table and hollow surface may also give added explanation to
community arrangement. This may also be useful for modeling the effects of global
climate change in relation to community composition, an area of study that is
increasingly being investigated. Unfortunately, at the present time, the ability to
determine centimeter-level accuracy while under a full canopy is not yet available.
TIDAL FOREST COMPUTATIONS BASED ON NATIONAL WETLAND
These are computations for the total area of freshwater tidal forest in the Savannah
River floodplain. Designations are based upon Cowardin et al. 1979 (National Wetlands
Inventory, US Fish and Wildlife Service 1993a, 1993b, 1999a, 1999b). The following
GIS coverages were analyzed using ArcView 3.2: Limehouse, Port Wentworth, Rincon,
The following designations were considered tidal forest: PFO1T, PFO2/EM1T,
PFO2/SS1T, PFO2T, PSS1/2T, PSS1/EM1T, PSS1T, PEM1/FO2T, PEM1/SS1T,
PFO1/2T, PFO1/EM1T. Total area is 3874 ha (38,735,225 m2).
The following designations were considered seasonal tidal forests: PFO1/2R,
PFO1/3R, PFO1/4R, PFO1/SS1R, PFO1/SS3R, PFO1R, PFO3/1R, PFO3/4R, PSS1/3R,
PSS1/EM1R, PSS1R, PSS3/1R, PSS3R, PSS4/EM1R, PSS4R. Total area is 520 ha
The following designations were considered temporarily tidally flooded forests:
PFO1/4S, PFO1S, PFO4/1S, PFO4S, PSS3S. Total area is 155 ha (155,042 m2).
The following designations were considered semipermanently flooded forests:
PEM1/FO1F, PEM1/FO2F, PFO1/2F, PFO1/EM1F, PFO1F, PFO2/1F, PFO2/EM1F,
PFO2F, PSS1/2F, PSS1F. Total area is 4520 ha (45,204,251 m2).
SPECIES NAMES AND ABBREVIATIONS
Species naming follows Radford et al. 1968
Acer rubrum Linnaeus
Alnus serrulata (Aiton) Willdenow
Baccharis halimifolia Linnaeus
Carpinus caroliniana Walter
Cephalanthus occidentalis Linnaeus
Cornusfoemina Miller var. foemina
Ilex cassine Linnaeus
Ilex decidua Walter
Ilex glabra (Linnaeus) A. Gray
Ilex opaca Aiton var. opaca
Ilex verticillata Linnaeus
Itea virginica Linnaeus
Leucothoe racemosa (Linnaeus) Gray
Liquidambar -,,I 1tl, i Linnaeus
Lyonia lucida (Lamarck) K. Koch
Magnolia virginiana Linnaeus
Myrica cerifera Linnaeus
Nyssa aquatica Linnaeus
Nyssa sylvatica (Marshall) var. biflora (Walter) Sargent
Perseapalustris (Rafinesque) Sargent
Planera aquatica Walter ex. J.F. Gmelin
Quercus nigra Linnaeus
Quercus laurifolia Michaux
Rosa palustris Marshall
Salix caroliniana Michaux
Salix nigra Marshall
Taxodium distichum (Linnaeus) Richard
Ulmus americana Linnaeus
Vaccinium corymbosum Linnaeus
Viburnum nudum Linnaeus
Viburnum dentatum Linnaeus
SPECIES X PLOT DATA MATRIX
The following are importance values of all species in plots. Plot names are listed in
the furthest left column. Species abbreviations are listed along the top row and
correspond to scientific names in Appendix B.
ACRU ALSE CEOC FRAX ILCA ITEA LERA
NE1 3.03 17.45 0.28 0.55 11.38 0.84 17.33
NE2 6.33 9.38 0.24 4.24 4.22 0.00 9.41
NE3 1.47 10.58 0.00 1.25 9.58 0.23 5.74
NE4 5.54 21.88 0.00 1.42 9.80 2.00 9.41
NW1 7.09 10.87 0.00 4.19 1.19 0.00 12.11
NW2 1.79 10.95 0.00 6.57 3.53 0.82 11.45
NW3 3.32 18.45 0.00 2.00 4.00 0.00 8.03
NW4 3.85 4.01 0.00 2.00 7.66 0.00 11.50
SE1 6.82 16.41 0.00 6.24 2.11 0.53 6.92
SE2 5.12 13.59 0.36 9.76 1.51 0.36 12.81
SE3 2.85 12.61 0.00 8.25 0.39 0.00 4.30
SE4 0.93 12.72 0.00 9.04 0.00 0.00 11.41
SW1 3.44 21.93 0.00 5.94 0.00 0.00 0.00
SW2 2.80 33.29 0.00 2.67 0.00 0.00 0.37
SW3 9.81 36.62 0.00 1.74 0.00 0.00 0.00
SW4 4.32 7.06 0.00 1.51 0.00 0.00 9.32
W1 2.84 8.32 0.00 16.34 0.00 0.00 0.00
W10 3.52 21.71 0.65 2.12 0.00 0.00 0.00
W11 1.06 24.03 0.00 5.85 0.72 0.00 0.00
W12 11.65 21.89 0.00 9.10 0.00 0.00 0.00
W13 2.40 0.00 0.00 21.13 0.00 0.00 0.00
W14 3.78 0.00 0.00 11.94 0.00 0.00 0.00
W15 2.81 0.00 0.00 8.51 0.00 0.00 0.00
W16 2.89 27.61 0.00 6.77 0.00 0.00 0.00
W2 4.01 21.21 0.00 11.64 0.00 0.00 0.00
W3 14.75 3.88 0.00 15.32 0.00 0.00 0.00
W4 0.00 31.20 0.00 2.16 0.00 0.00 0.00
W5 3.89 5.92 0.00 9.35 0.00 0.00 0.00
W6 0.00 0.00 0.00 16.66 0.00 0.00 0.00
W7 0.00 16.78 0.00 22.21 0.00 0.00 0.00
W8 4.34 0.92 0.00 5.22 0.00 0.00 1.83
W9 7.52 0.00 0.00 6.45 0.00 0.00 0.00
MYCE NYAQ NYBI TADI VINU ILDE LYLU
NE1 17.12 25.62 5.85 0.28 0.28 0.00 0.00
NE2 18.41 30.24 10.87 3.96 0.00 1.57 0.20
NE3 11.35 27.91 2.70 20.70 0.00 0.00 1.60
NE4 11.02 33.70 1.63 2.23 0.33 0.00 0.73
NW1 14.84 37.35 1.93 7.67 0.00 0.00 0.00
NW2 11.25 44.88 0.00 8.21 0.00 0.00 0.00
NW3 12.79 6.94 21.40 20.76 0.32 0.00 0.00
NW4 15.10 51.36 1.75 1.51 0.00 0.00 0.29
SE1 5.08 48.76 0.00 0.00 0.00 0.00 0.00
SE2 9.56 44.43 1.01 0.00 0.00 0.00 0.00
SE3 18.33 26.66 3.19 14.96 0.81 0.00 0.00
SE4 7.57 52.99 1.56 3.37 0.00 0.00 0.00
SW1 2.20 0.00 36.5 15.00 0.00 0.00 0.00
SW2 0.00 3.88 47.65 3.94 0.00 0.00 0.00
SW3 0.00 0.00 43.70 0.00 0.00 0.00 0.00
SW4 1.55 0.00 27.28 42.29 0.00 0.00 0.00
W1 0.00 0.00 38.20 6.51 0.00 0.00 0.00
W10 0.00 7.13 25.85 0.00 0.00 0.65 0.00
W11 0.00 47.82 6.31 9.46 0.00 0.00 0.00
W12 0.00 0.00 36.68 3.46 0.00 0.00 0.00
W13 0.00 0.00 54.81 8.91 0.00 0.00 0.00
W14 0.00 26.21 32.78 2.73 0.00 3.52 0.00
W15 0.00 5.88 36.42 17.73 0.00 0.00 0.00
W16 0.00 44.92 12.09 0.00 0.00 0.00 0.00
W2 0.00 0.00 21.47 22.46 0.00 1.35 0.00
W3 0.00 12.42 32.99 0.00 0.00 0.00 0.00
W4 0.00 0.00 33.26 19.15 0.00 0.00 0.00
W5 0.00 58.89 4.25 0.00 0.00 0.00 0.00
W6 0.00 20.59 13.99 4.67 0.00 3.79 0.00
W7 4.32 44.42 6.99 3.58 0.00 0.00 0.00
W8 2.75 0.00 44.81 13.95 0.00 0.00 0.00
W9 0.00 24.40 16.89 18.74 0.00 0.00 0.00
PEPA ILGL VACO VIDE LIST MAVI QUNI
NE1 0.00 0.00 0.00 0.00 0.00 0.00 0.00
NE2 0.91 0.00 0.00 0.00 0.00 0.00 0.00
NE3 0.00 4.46 2.43 0.00 0.00 0.00 0.00
NE4 0.00 0.00 0.00 0.33 0.00 0.00 0.00
NW1 0.00 0.00 0.00 0.00 1.01 0.45 1.29
NW2 0.00 0.00 0.00 0.00 0.00 0.00 0.00
NW3 0.72 0.00 0.00 0.00 0.59 0.00 0.00
NW4 0.00 0.00 0.59 0.00 0.37 0.00 0.00
SE1 0.00 0.00 0.00 0.53 3.93 0.00 0.53
SE2 0.00 0.00 0.00 0.00 1.00 0.00 0.00
SE3 0.93 0.00 0.00 0.42 4.55 0.00 1.75
SE4 0.00 0.00 0.00 0.41 0.00 0.00 0.00
SW1 0.47 0.00 0.00 0.00 1.27 0.00 9.20
SW2 3.01 0.00 0.00 0.00 0.57 0.00 1.81
SW3 4.50 0.00 0.00 0.00 0.96 0.00 2.13
SW4 0.80 0.00 0.00 0.00 0.00 0.00 5.09
W1 5.72 0.00 0.00 0.00 1.93 0.00 19.22
W10 11.07 0.00 0.00 0.65 0.00 0.00 24.71
W11 0.00 0.00 0.00 0.00 0.00 0.00 3.95
W12 3.15 0.00 0.00 0.00 0.00 0.00 8.67
W13 2.77 0.00 0.00 0.00 1.88 0.00 1.00
W14 0.00 0.00 0.00 0.00 0.00 0.00 17.3
W15 4.10 0.00 0.00 0.00 1.03 0.00 22.48
W16 1.39 0.00 0.00 0.00 0.00 0.00 4.32
W2 1.35 0.00 0.00 0.00 0.00 0.00 7.22
W3 3.97 0.00 0.00 0.00 0.00 0.00 16.66
W4 0.00 0.00 0.00 0.00 3.95 0.00 9.59
W5 0.00 0.00 0.00 0.00 0.00 0.00 16.23
W6 13.42 0.00 0.00 0.00 0.00 0.00 19.48
W7 0.00 0.00 0.00 0.00 0.00 0.00 1.71
W8 7.26 0.00 0.00 0.00 2.07 0.00 8.90
W9 11.03 0.00 0.00 0.00 6.80 0.00 8.19
BAHA COFF ILVE SANI CACA PLAQ QULA
NE1 0.00 0.00 0.00 0.00 0.00 0.00 0.00
NE2 0.00 0.00 0.00 0.00 0.00 0.00 0.00
NE3 0.00 0.00 0.00 0.00 0.00 0.00 0.00
NE4 0.00 0.00 0.00 0.00 0.00 0.00 0.00
NW1 0.00 0.00 0.00 0.00 0.00 0.00 0.00
NW2 0.54 0.00 0.00 0.00 0.00 0.00 0.00
NW3 0.00 0.70 0.00 0.00 0.00 0.00 0.00
NW4 0.00 0.00 0.00 0.00 0.00 0.00 0.00
SE1 0.00 1.08 1.06 0.00 0.00 0.00 0.00
SE2 0.00 0.00 0.00 0.50 0.00 0.00 0.00
SE3 0.00 0.00 0.00 0.00 0.00 0.00 0.00
SE4 0.00 0.00 0.00 0.00 0.00 0.00 0.00
SW1 0.00 0.00 0.00 0.00 4.04 0.00 0.00
SW2 0.00 0.00 0.00 0.00 0.00 0.00 0.00
SW3 0.00 0.55 0.00 0.00 0.00 0.00 0.00
SW4 0.00 0.00 0.00 0.00 0.77 0.00 0.00
W1 0.00 0.92 0.00 0.00 0.00 0.00 0.00
W10 0.00 0.00 0.00 0.00 1.94 0.00 0.00
W11 0.00 0.81 0.00 0.00 0.00 0.00 0.00
W12 0.00 1.82 2.62 0.00 0.98 0.00 0.00
W13 0.00 0.00 0.00 0.00 5.20 1.00 0.90
W14 1.73 0.00 0.00 0.00 0.00 0.00 0.00
W15 0.00 0.00 0.00 0.00 1.05 0.00 0.00
W16 0.00 0.00 0.00 0.00 0.00 0.00 0.00
W2 0.00 4.85 0.00 0.00 4.45 0.00 0.00
W3 0.00 0.00 0.00 0.00 0.00 0.00 0.00
W4 0.00 0.69 0.00 0.00 0.00 0.00 0.00
W5 0.00 0.00 0.00 0.00 1.48 0.00 0.00
W6 0.00 0.00 0.00 0.00 7.40 0.00 0.00
W7 0.00 0.00 0.00 0.00 0.00 0.00 0.00
W8 0.00 0.00 0.00 0.00 7.95 0.00 0.00
W9 0.00 0.00 0.00 0.00 0.00 0.00 0.00
SOIL PROPERTY X PLOT DATA MATRIX
The following are values for soil samples collected. Plot names are listed in the
furthest left column. Organic matter is expressed as a percentage, bulk density units are
g/cm3, nutrient concentration units are mg/kg, and electrical conductivity units are ds/m.
Values for 'nutrients present' were obtained by multiplying the nutrient concentration by
the bulk density, resulting in ig/cm3.
CORRELATION OF SPECIES AND SOIL CONSTITUENTS TO AXES FOR RUNS
SUBSEQUENT TO AUTOPILOT MODE
Select Species Correlations
0.293 0.086 0.136
0.702 0.492 0.736
0.492 0.242 0.487
0.789 0.623 0.650
0.786 0.618 0.645
0.480 0.231 0.291
-0.680 0.463 -0.495
-0.682 0.465 -0.556
-0.887 0.788 -0.768
-0.290 0.084 -0.162
0.368 0.136 0.384
0.302 0.091 0.285
0.505 0.255 0.340
0.525 0.275 0.313
0.964 0.930 0.867
-0.909 0.826 -0.706
-0.330 0.109 -0.436
-0.375 0.140 -0.352
Select Environmental Variable Correlations
Organic 0.812 0.660 0.634
bulk -0.639 0.409 -0.603
P_pres -0.637 0.406 -0.501
Caconc 0.645 0.416 0.482
Mg_conc 0.703 0.494 0.540
Cu_pres -0.633 0.401 -0.532
Ec 0.788 0.621 0.568
Na cone 0.785 0.616 0.570
0.454 0.206 0.256
-0.232 0.054 -0.340
-0.157 0.025 -0.166
0.410 0.168 0.228
0.477 0.228 0.222
-0.327 0.107 -0.300
0.521 0.271 0.303
0.564 0.318 0.338
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Jamie Duberstein was born on May 28, 1974, in the city of Rhinelander,
Wisconsin. He grew up in the countryside in the town of Harshaw where, as a child, he
was constantly instructed by his parents to 'go play outside.' There he spent many days
hiking, building forts, and visiting the nearby lake to swim, fish, and catch tadpoles in the
knee-deep muck (known to him now as "soil organic matter"). The amount of time spent
adventuring outdoors as a child is certainly what taught him to enjoy and respect nature.
After graduating high school, he attended Nicolet Area Technical College (also in
Rhinelander), where he spent his first semester on track for an associate degree in
computer programming. He quickly learned that, although programming came naturally
to him, entering code did not interest him enough to make him vocationally happy. After
moving to Stevens Point, Wisconsin, he graduated from the University of Wisconsin -
Stevens Point's College of Natural Resources with majors in wildlife management and
biology. In 2000 he moved to South Florida, where he was given the chance to
demonstrate his capacity to utilize his knowledge and adaptive learning skills while
working on the demography of snail kites in the Everglades.
Jamie's plans for the future are linked to those things he took for granted as a child:
native plant and animal diversity, clean air, and clean water. Given the country's current
administration and its attitude toward conservation, he is constantly reminded that he can
no longer take these "luxuries" for granted.