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Freshwater Tidal Forest Communities Sampled in the Lower Savannah River Floodplain


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FRESHWATER TIDAL FOREST COMM UNITIES SAMPLED IN THE LOWER SAVANNAH RIVER FLOODPLAIN By JAMIE DUBERSTEIN 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 2004

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Copyright 2004 by Jamie Duberstein

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ACKNOWLEDGMENTS 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 my research. 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 Robinette. iii

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TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iii LIST OF TABLES............................................................................................................vii LIST OF FIGURES.........................................................................................................viii ABSTRACT.......................................................................................................................ix CHAPTER 1 INTRODUCTION AND STUDY AREA....................................................................1 Introduction...................................................................................................................1 Location of Study Area.................................................................................................3 Hydrology.....................................................................................................................6 Soil and Underlying Bedrock.......................................................................................9 Tree Species................................................................................................................10 Lower Floodplain History...........................................................................................11 2 METHODS.................................................................................................................12 Vegetative Sampling...................................................................................................12 Species-Area Curve.............................................................................................12 Sampling Design.................................................................................................13 Soil Analysis...............................................................................................................14 Chemical Constituents.........................................................................................15 Organic Matter Content and Bulk Density..........................................................16 Statistical Analyses.....................................................................................................17 Species Importance Values..................................................................................17 Insignificant Data Removal.................................................................................18 Rare species..................................................................................................18 Outlying plots...............................................................................................19 Insignificant environmental variables..........................................................20 A priori Landscape Grouping..............................................................................20 Exploratory Data Analyses..................................................................................21 Cluster analysis............................................................................................21 Indicator species analysis.............................................................................22 iv

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Multi-response permutation procedures.......................................................24 Nonmetric multidimensional scaling ordinations........................................25 Classification and Regression Tree.....................................................................26 3 RESULTS...................................................................................................................28 Introduction.................................................................................................................28 Exploratory Data Analyses.........................................................................................29 Cluster Analysis...................................................................................................29 Indicator Species Analysis..................................................................................30 Multi-response Permutation Procedures..............................................................34 NMS Ordinations.................................................................................................35 Autopilot.......................................................................................................35 Subsequent ordinations................................................................................37 Classification and Regression Tree Analysis.............................................................46 Descriptions of Communities.....................................................................................48 Shrub Community...............................................................................................48 Water Tupelo Community...................................................................................50 Swamp Tupelo Tag Alder Community............................................................51 Water Oak Swamp Bay Community................................................................52 4 DISCUSSION.............................................................................................................55 Tidal Forest Communities in Sampled Areas of the Savannah River Floodplain......56 Comparisons with Tidal Forests of the Lower Chesapeake Bay................................57 Community Description......................................................................................57 Environmental Factors.........................................................................................58 Comparisons with Tidal Forests of Floridas Gulf Coast and the Roanoke River, NC..........................................................................................................................59 Comparisons with Bottomland Hardwood Soils........................................................61 Community Description......................................................................................61 Soil Properties.....................................................................................................61 Future Research Needs...............................................................................................62 APPENDIX A TIDAL FOREST COMPUTATIONS BASED ON NATIONAL WETLAND INVENTORY.............................................................................................................64 B SPECIES NAMES AND ABBREVIATIONS...........................................................65 C SPECIES X PLOT DATA MATRIX.........................................................................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 v

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LIST OF REFERENCES...................................................................................................77 BIOGRAPHICAL SKETCH.............................................................................................82 vi

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LIST OF TABLES Table page 2-1 Species removed from analyses...............................................................................19 2-2 Environmental variables collected...........................................................................20 3-1 Monte 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 Pearsons coefficients of determination (r2) and Kendals tau values of environmental variables to axes for autopilot mode of NMS 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 vii

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LIST OF FIGURES Figure page 1-1 Locations of tidal forests documented in the United States.......................................1 1-2 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 NMS ordination: fetterbush and wax myrtle............................................................40 3-5 NMS ordination: water tupelo..................................................................................41 3-6 NMS ordination: swamp tupelo and tag alder..........................................................42 3-7 NMS ordination: water oak and swamp bay............................................................43 3-8 NMS ordination: biplot of axis 2 vs 1......................................................................45 3-9 NMS 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 viii

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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 By Jamie Duberstein August 2004 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 ix

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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 PO43-. 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, Cornus foemina 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 Persea palustris 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. x

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CHAPTER 1 INTRODUCTION AND STUDY AREA Introduction 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 Tidal forests documented Figure 1-1. Locations of tidal forests documented in the United States. All occur within the southeastern region of the country. 1

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2 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

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3 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

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4 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 0.1ppt, whereas salinities in the western stand were below 0.1ppt (Pearlstine et al. 1990). #################### #################### West East 1:10,0001:10,000 Atlantic OceanGeorgiaSouth CarolinaLittle Back RiverSavannah RiverSavannahSavannah National Wildlife Refuge East West 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.

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5 Savannah During Tide GateAfter Tide Gate During Tide GateAfter Tide Gate3.2 km (2 mi) East East West WestNew Cut 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 operation. Even though salinity at the 0.1ppt 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.

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6 Savannah, Georgia has an average annual temperature of 19C, with the highest monthly average of 27.8C 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 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 are unknown. 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

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7 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 1m 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 (PFO1/2T). 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

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8 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

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9 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. Year 19301940195019601970198019902000 Discharge (cubic feet per second) 0500010000150002000025000300003500040000 Discharge (cubic meters per second) 0200400600800100012001400Thurmond Dam Hartwell DamTide Gate InitiatedRussell DamTide Gate Decomissioned Figure 1-4. Mean annual discharge at USGS monitoring station #02198500 near Clyo, Georgia. 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

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10 (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). Tree Species 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. Marks rivers in Florida.

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11 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 River floodplain. Lower Floodplain History The lower basin has been severely altered to facilitate a variety of anthropocentric benefits. In the mid 1700s 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 wasnt 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.

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CHAPTER 2 METHODS Vegetative Sampling Species-Area Curve 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). 12

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13 Size of Quadrat (square meters) 25100225400251002254002510022540025100225400251002254002510022540025100225400251002254002510022540025100225400Number of Unique Species 05101520 Figure 2-1. Species-area curve computed with non-linear regression (r2=0.58) based on 10 points with 4 nested quadrat sizes. Sampling Design 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 I-95 and one south of the highway. Eight points were randomly placed in each section using the same tools

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14 as used in the other stand. That made for a total of thirty-two 10x10m plots in two stands. 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 between quadrats. Soil Analysis 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

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15 and frozen until processing. When processed, all samples were thawed and then oven dried at roughly 50C (120F) 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 ignition method. Chemical Constituents 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 HCl + 0.0125 M H2SO4. Electrical conductivity and chloride ion (Cl-) 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.

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16 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 carefully chosen. 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.

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17 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. Statistical Analyses 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

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18 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 follow. Rare species 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

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19 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 aquatica) W13 Laurel oak (Quercus laurifolia) W13 Outlying plots 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 aquatica)

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20 sapling (DBH < 1cm) 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 Pearsons 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 P 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

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21 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 non-parametric nature. Cluster analysis 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 ( = -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.

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22 Proximal to main channel or large distributary Associated with tidal creeks Distant from drainagesLegend Proximal to main channel or large distributary Associated with tidal creeks Distant from drainagesLegend West EastLittle Back RiverMain Savannah RiverMiddle River West East West EastLittle Back RiverMain Savannah RiverMiddle RiverLittle Back River ################86101212131174315914516 ################86101212131174315914516 ################W8W6W10W12W1W2W13W11W7W4W3W15W9W14W5W16 Main Savannah River West ################86101212131174315914516 ################86101212131174315914516 ################W8W6W10W12W1W2W13W11W7W4W3W15W9W14W5W16 ################86101212131174315914516 ################86101212131174315914516 ################86101212131174315914516 ################86101212131174315914516 ################W8W6W10W12W1W2W13W11W7W4W3W15W9W14W5W16 Main Savannah River West EastLittle Back River ################NW3NW4NW2SW4SW2SW3SW1SE1SE3SE4SE2NE4NE1NE2NE3NW1 ################NW3NW4NW2SW4SW2SW3SW1SE1SE3SE4SE2NE4NE1NE2NE3NW1 ################NW3NW4NW2SW4SW2SW3SW1SE1SE3SE4SE2NE4NE1NE2NE3NW1 ################NW3NW4NW2SW4SW2SW3SW1SE1SE3SE4SE2NE4NE1NE2NE3NW1 ################NW3NW4NW2SW4SW2SW3SW1SE1SE3SE4SE2NE4NE1NE2NE3NW1 1:10,0001:10,000Little Back River East 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 Legendres (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

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23 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 (j) 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 chance.

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24 Each of the seven analyses resulted in different p-values for species as indicators for a given cluster. The p-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 optimal one. 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 the p-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 analysis.

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25 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.

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26 Classification and Regression Tree The statistical program S-Plus 2000 Professional Release 3 (Mathsoft 2000) with the TreesPlus add-in (Death 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 where Y = cluster X1 = landscape position X2 = organic matter content X3 = bulk density X4 = Ph X5 = phosphorous concentration X6 = calcium concentration X7 = magnesium concentration X8 = copper present X9 = electrical conductivity X10 = sodium concentration.

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27 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 clusters.

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CHAPTER 3 RESULTS Introduction 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. 28

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29 Exploratory Data Analyses Cluster Analysis 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

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30 community. Community designations will be described in detail in the following sections. 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 ( < 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 average p-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 (Persea palustris; PEPA), and water oak (Quercus nigra; QUNI).

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Distance (Objective Function)Information Remaining (%) 8E-03100 6.7E-0175 1.3E+0050 2E+0025 2.7E+000 NE1NE4NE2NW1NE3SE3NW2SE2SE4SE1NW4W11W16W7W5NW3SW4SW1W4W12W2W10SW2SW3W1W15W8W14W3W6W9 Landscape Position proximal to main channel/distributaryassociated with creeks/drainagesdistant from creeks/drainages Water TupeloShrubSwamp tupelo Tag AlderWater Oak Swamp bay 31 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.

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32 Number of Clusters 2345678Total p 3.03.54.04.55.0 Number of Significant Indicator Species 678910 Total p # 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-values. 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

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33 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). Cluster Species 1 (11) 2 (4) 3 (9) 4 (7) Average ACRU 26 9 27 28 22 ALSE 22 33 42 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 NYAQ 38 48 1 9 24 NYBI 3 10 44 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

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34 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, a p-value and an A-value are reported. The p-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 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

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35 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. General Criteria Mulitiple comparisons p-value A-value Stand 0.00042660 0.11826623 Region 0.00000142 0.23889437 river vs 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 vs 2 0.00018304 0.17175219 1 vs 3 0.00000747 0.38497760 1 vs 4 0.00001505 0.38976488 2 vs 3 0.00010362 0.30341608 2 vs 4 0.00088010 0.31202930 3 vs 4 0.00014051 0.14952343 NMS Ordinations Autopilot 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 Carlo p-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

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36 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. Pearsons coefficients of determination (r2) and Kendals tau values of environmental variables to axes for NMS ordination using autopilot mode. These correlations were used as the basis for removal of insignificant (r20.392) variables. 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 K 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

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37 Subsequent ordinations 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 a p-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. r2 Axis Increment Cumulative 1 0.025 0.025 2 0.407 0.433 3 0.527 0.960

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38 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.

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39 NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Axis 2Axis 3 Cluster234 NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Axis 2Axis 3 Cluster234 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 A NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Axis 2Axis 3 Cluster234 NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Axis 2Axis 3 Cluster234 B 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.

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40 NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Axis 2Axis 3 Cluster234 NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Axis 2Axis 3 Cluster234 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 A NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Axis 2Axis 3 Cluster234 NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Axis 2Axis 3 Cluster234 B Figure 3-4. NMS ordination. Relative importance values of fetterbush (A) and wax myrtle (B), as indicated by the size of triangles.

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41 NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Axis 2Axis 3 Cluster234 NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Axis 2Axis 3 Cluster234 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 Figure 3-5. NMS ordination. Relative importance values of water tupelo, as indicated by the size of triangles.

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42 NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Axis 2Axis 3 Cluster234 NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Axis 2Axis 3 Cluster234 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 A NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Axis 2Axis 3 Cluster234 NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Axis 2Axis 3 Cluster234 B Figure 3-6. NMS ordination. Relative importance values of swamp tupelo (A) and tag alder (B), as indicated by the size of triangles.

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43 NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Axis 2Axis 3 Cluster234 NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Axis 2Axis 3 Cluster234 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 A NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Axis 2Axis 3 Cluster234 NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Axis 2Axis 3 Cluster234 B Figure 3-7. NMS ordination. Relative importance values of water oak (A) and swamp bay (B), as indicated by the size of triangles.

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44 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

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45 negative correlation between Axis 2 and bulk density, potassium present, and copper present have little correlation to axis 3. Organic bulk P present Ca conc Mg conc Cu present Ec Na conc Axis 1Axis 2 Cluster234 Organic bulk P present Ca conc Mg conc Cu present Ec Na conc Axis 1Axis 2 Cluster234 1 1 Figure 3-8. Biplot of axis 2 vs 1. Strong correlations of all soil constituents to axis 2.

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46 NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Organic bulk P present Ca conc Mg conc Cu present Ec Na conc Axis 2Axis 3 Cluster234 NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Organic bulk P present Ca conc Mg conc Cu present Ec Na conc Axis 2Axis 3 Cluster234 NW3 SW1 SW2 SW3 SW4 W1 W10 W11 W12 W14 W15 W16 W2 W3 W4 W5 W6 W7 W8 W9 Organic bulk P present Ca conc Mg conc Cu present Ec Na conc Axis 2Axis 3 Cluster234 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 NE1 NE2 NE3 NE4 NW1 NW2 NW4 SE1 SE2 SE3 SE4 1 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 (Figure 3-10)

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47 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. Sodium concentration>353 mg/kg<353 mg/kgOrganic matter<78%>78% Electrical Conductivity>3 dS/m<3 dS/mShrubWater oak -Swamp baySwamp tupelo Tag alderWater tupelo(8)(11)(4)(8)Error: 0.2 CV Error (pick): 0.55 SE: 0.133 Misclassification rates: Null = 0.645 Model = 0.129 CV = 0.355 Cluster 3: Swamp tupelo Tag alder (9)Cluster 2: Water tupelo (4) Cluster 1: Shrub (11) Cluster 4: Water oak -Swamp bay (7) 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

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48 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. Shrub Community 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 stryraciflua), 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

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49 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. ###################8116102121122101174315914516 ###################8116102121122101174315914516 ###################8116102121122101174315914516 ####################NW 3NW 4NW 2SW 4SW 2SW 3SW 1SE 1SE 3SE 4SE 2NE 4NE 1NE 2NE 3NW 1 ####################NW 3NW 4NW 2SW 4SW 2SW 3SW 1SE 1SE 3SE 4SE 2NE 4NE 1NE 2NE 3NW 1 West EastLittle Back RiverMain Savannah RiverMiddle River West East West EastLittle Back RiverMain Savannah RiverMiddle River ShrubWater TupeloWater Oak Swamp baySwamp Tupelo Tag AlderCommunity Scale: 1:10,000Main Savannah RiverLittle Back River Figure 3-11. Locations of the 4 communities within the sample areas.

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50 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 2-1). 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 community. 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

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51 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 (Cornus foemina 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

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52 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

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53 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 3-6). DBH Size Class (cm) 20+15-19.910-14.95-9.9<2-4.9 Stems/ha 05001000150020006000800010000120001400016000 Shrub community Water tupelo community Swamp tupelo Tag alder community Water oak Swamp bay community Proximal to main channel Associated with creeks and drainages Distant from creeks and drainages Figure 3-12. Average number of stems per acre for each community and a-prior group.

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54 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. Community types Shrub (11) Water tupelo (4) Swamp tupelo tag alder (9) Water oak swamp bay (7) Species Red maple 4.07 1.96 4.76 5.15 Tag alder 12.77 18.59 23.71 1.87 Buttonbush 0.08 0.00 0.07 0.00 Ash 4.86 11.04 4.32 11.49 Dahoon holly 4.67 0.18 0.44 0.00 Virginia willow 0.43 0.00 0.00 0.00 Fetterbush 10.22 0.00 1.97 0.26 Wax myrtle 12.69 1.08 1.84 0.39 Water tupelo 38.54 49.01 1.99 12.79 Swamp tupelo 2.77 7.41 32.64 30.87 Bald cypress 5.72 3.26 14.12 9.19 Posssumhaw viburnum 0.13 0.00 0.04 0.00 Possumhaw 0.14 0.00 0.22 1.04 Lyonia 0.26 0.00 0.00 0.00 Swamp bay 0.17 0.35 2.79 6.50 Arrow wood 0.15 0.00 0.07 0.00 Sweet gum 0.99 0.00 0.82 1.69 Water oak 0.32 6.55 7.60 16.03 Swamp dogwood 0.10 0.20 0.96 0.13 Musclewood 0.00 0.37 1.35 2.34 Environmental parameters Organic matter (%) 86.26 49.76 52.98 42.62 Bulk density (g/cm3) 0.08 0.18 0.20 0.27 Electrical conductivity (dS/m) 7.81 3.54 3.92 2.48 Sodium concentration (mg/kg) 1033.00 315.30 435.49 261.91 Calcium concentration (mg/kg) 7326.00 4454.50 4746.44 3714.57 Magnesium concentration (mg/kg) 1817.15 539.00 787.96 510.43 Phosphorous present (g/cm3) 5.61 13.76 9.72 13.19 Copper present (g/cm3) 0.37 1.15 1.28 1.50 Summary Statistics Density (stems/ha) 18,209 6,425 9,611 4,043 Basal Area (m2/ha) 54 69 56 59

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CHAPTER 4 DISCUSSION 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 55

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56 Floridas Gulf coast, as well as one wind-tide dominated site along the Roanoke River, NC. 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 Floodplain 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.

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57 Comparisons with Tidal Forests of the Lower Chesapeake Bay Community Description 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 both locations. To facilitate a general comparison between Rheinhardts 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

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58 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). Environmental Factors 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 oakswamp 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 (Figure 3-8). 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

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59 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 observation). Comparisons with Tidal Forests of Floridas 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

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60 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 (Sabal palmetto) [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. Marks 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 <1m; Atlantic coast >1m). The Yellow River is unique in that Atlantic white cedar (Chamaecyparis thyoides) 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.

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61 Comparisons with Bottomland Hardwood Soils Community Description 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 zonation. Soil Properties 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

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62 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 Available* Total Coastal plain Piedmont Ash Blackgum Maple-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 7308740 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). 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

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63 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.

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APPENDIX A TIDAL FOREST COMPUTATIONS BASED ON NATIONAL WETLAND INVENTORY 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, and Hardeeville. 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 (5,200,007 m2). 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). 64

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APPENDIX B SPECIES NAMES AND ABBREVIATIONS Species naming follows Radford et al. 1968 Common name Latin name Code Red maple Acer rubrum Linnaeus ACRU Tag alder Alnus serrulata (Aiton) Willdenow ALSE Groundsel tree Baccharis halimifolia Linnaeus BAHA Musclewood Carpinus caroliniana Walter CACA Buttonbush Cephalanthus occidentalis Linnaeus CEOC Swamp dogwood Cornus foemina Miller var. foemina COFF Ash Fraxinus spp FRAX Dahoon holly Ilex cassine Linnaeus ILCA Possumhaw Ilex decidua Walter ILDE Inkberry Ilex glabra (Linnaeus) A. Gray ILGL American holly Ilex opaca Aiton var. opaca ILOO Black alder Ilex verticillata Linnaeus ILVE Virginia willow Itea virginica Linnaeus ITEA Fetterbush Leucothoe racemosa (Linnaeus) Gray LERA Sweetgum Liquidambar styraciflua Linnaeus LIST Lyonia Lyonia lucida (Lamarck) K. Koch LYLU Sweet bay Magnolia virginiana Linnaeus MAVI Wax myrtle Myrica cerifera Linnaeus MYCE Water tupelo Nyssa aquatica Linnaeus NYAQ Swamp tupelo Nyssa sylvatica (Marshall) var. biflora (Walter) Sargent NYBI Swamp bay Persea palustris (Rafinesque) Sargent PEPA Water elm Planera aquatica Walter ex. J.F. Gmelin PLAQ Water oak Quercus nigra Linnaeus QUNI Laurel oak Quercus laurifolia Michaux QULA Swamp rose Rosa palustris Marshall ROPA Swamp willow Salix caroliniana Michaux SACA Black willow Salix nigra Marshall SANI Bald cypress Taxodium distichum (Linnaeus) Richard TADI American elm Ulmus americana Linnaeus ULAM Highbush blueberry Vaccinium corymbosum Linnaeus VACO Possumhaw viburnum Viburnum nudum Linnaeus VINU Arrow wood Viburnum dentatum Linnaeus VIDE 65

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APPENDIX C 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 66

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67 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

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68 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

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69 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

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APPENDIX D 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 g/cm3. 70

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71 Organic Matter Bulk Density pH Phosphorous Concentration Phosphorous Present NE1 91.13 0.07 5.36 51.16 3.55 NE2 90.98 0.09 5.30 47.32 4.12 NE3 90.87 0.09 5.16 35.80 3.33 NE4 87.86 0.06 5.83 96.42 5.73 NW1 83.37 0.10 5.40 82.08 8.17 NW2 89.06 0.08 5.68 94.10 7.85 NW3 76.89 0.09 5.54 47.96 4.32 NW4 91.39 0.07 5.39 64.42 4.61 SE1 80.46 0.09 5.37 58.14 5.37 SE2 82.69 0.09 6.07 56.24 5.24 SE3 80.85 0.11 5.72 80.10 8.44 SE4 80.21 0.08 5.83 65.08 5.31 SW1 65.62 0.13 5.33 48.94 6.59 SW2 46.86 0.19 5.57 70.18 13.07 SW3 44.29 0.18 6.20 53.28 9.50 SW4 53.34 0.20 5.14 37.92 7.59 W1 42.64 0.18 5.59 64.18 11.67 W2 25.76 0.36 5.77 40.60 14.69 W3 56.60 0.14 6.03 53.66 7.77 W4 72.70 0.15 5.82 55.42 8.06 W5 42.16 0.22 5.72 55.20 12.18 W6 16.94 0.73 5.79 33.94 24.94 W7 65.32 0.13 5.63 58.08 7.65 W8 62.40 0.14 5.47 63.54 8.76 W9 48.93 0.19 5.68 62.10 11.70 W10 30.01 0.32 5.70 41.34 13.13 W11 40.87 0.19 5.31 90.48 17.16 W12 61.39 0.17 5.82 61.50 10.49 W13 46.63 0.20 5.38 55.96 11.29 W14 36.89 0.24 5.81 67.90 16.34 W15 33.95 0.25 5.88 44.16 11.16 W16 50.68 0.16 5.55 116.00 18.03

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72 Potassium Concentration Potassium Present Calcium Concentration Calcium Present NE1 220.6 15.32 8050 559.00 NE2 245.6 21.38 5164 449.62 NE3 522.0 48.55 2676 248.90 NE4 239.2 14.22 9166 545.02 NW1 301.4 30.02 10380 1033.81 NW2 310.2 25.89 7062 589.37 NW3 192.8 17.36 10700 963.43 NW4 240.0 17.18 7886 564.36 SE1 788.2 72.81 7852 725.34 SE2 210.6 19.63 6832 636.92 SE3 186.4 19.63 9064 954.71 SE4 206.4 16.83 6454 526.30 SW1 175.2 23.59 4114 553.89 SW2 284.8 53.04 2960 551.27 SW3 186.8 33.32 4586 818.06 SW4 232.8 46.62 5924 1186.31 W1 494.4 89.87 3290 598.05 W2 108.6 39.30 1530 553.65 W3 166.2 24.07 4908 710.82 W4 263.4 38.32 5912 860.00 W5 179.0 39.49 5912 1304.43 W6 189.0 138.87 2402 1764.90 W7 199.6 26.28 3682 484.78 W8 242.2 33.38 4632 638.39 W9 280.2 52.78 4006 754.58 W10 168.8 53.63 2652 842.51 W11 296.0 56.13 3950 749.07 W12 238.6 40.69 4340 740.08 W13 203.6 41.07 4164 840.05 W14 201.6 48.51 3812 917.18 W15 257.4 65.05 2952 745.99 W16 333.6 51.86 4274 664.38

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73 Magnesium Concentration Magnesium Present Zinc Concentration Zinc Present NE1 1894.0 131.52 39.10 2.72 NE2 993.8 86.53 45.38 3.95 NE3 796.8 74.11 17.60 1.64 NE4 2236.0 132.95 13.30 0.79 NW1 2380.0 237.04 16.94 1.69 NW2 1242.0 103.65 22.84 1.91 NW3 1222.0 110.03 23.44 2.11 NW4 1706.0 122.09 13.68 0.98 SE1 2428.0 224.29 22.98 2.12 SE2 1546.0 144.13 19.58 1.83 SE3 2396.0 252.37 21.14 2.23 SE4 2370.0 193.26 16.30 1.33 SW1 734.6 98.90 10.94 1.47 SW2 660.4 122.99 24.68 4.60 SW3 899.4 160.44 20.72 3.70 SW4 1596.0 319.61 20.26 4.06 W1 519.4 94.42 11.18 2.03 W2 280.8 101.61 9.07 3.28 W3 550.2 79.68 19.90 2.88 W4 708.4 103.05 17.26 2.51 W5 585.6 129.21 29.38 6.48 W6 365.4 268.48 8.70 6.39 W7 451.0 59.38 18.42 2.43 W8 639.2 88.10 15.00 2.07 W9 558.6 105.22 12.18 2.29 W10 419.8 133.37 11.66 3.70 W11 516.4 97.93 10.58 2.01 W12 570.2 97.23 22.72 3.87 W13 514.6 103.82 19.60 3.95 W14 463.8 111.59 23.70 5.70 W15 476.4 120.39 9.40 2.38 W16 603.0 93.73 14.56 2.26

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74 Manganese Concentration Manganese Present Copper Concentration Copper Present NE1 135.60 9.42 5.40 0.37 NE2 210.20 18.30 6.56 0.57 NE3 155.40 14.45 8.23 0.77 NE4 104.00 6.18 3.47 0.21 NW1 278.20 27.71 2.21 0.22 NW2 65.56 5.47 3.59 0.30 NW3 305.20 27.48 3.06 0.28 NW4 96.84 6.93 4.82 0.34 SE1 554.40 51.21 4.69 0.43 SE2 191.40 17.84 2.98 0.28 SE3 301.20 31.73 2.47 0.26 SE4 306.40 24.99 4.44 0.36 SW1 339.60 45.72 6.21 0.84 SW2 412.00 76.73 8.28 1.54 SW3 689.00 122.91 7.79 1.39 SW4 334.80 67.05 3.03 0.61 W1 153.20 27.85 6.06 1.10 W2 180.40 65.28 8.24 2.98 W3 132.60 19.20 4.18 0.61 W4 211.00 30.69 5.54 0.81 W5 502.20 110.81 7.10 1.57 W6 213.60 156.95 5.11 3.75 W7 180.40 23.75 4.91 0.65 W8 262.40 36.16 7.36 1.01 W9 187.00 35.22 5.41 1.02 W10 214.40 68.11 5.74 1.82 W11 143.40 27.19 6.80 1.29 W12 93.92 16.02 7.40 1.26 W13 224.80 45.35 7.83 1.58 W14 284.40 68.43 6.92 1.66 W15 196.20 49.58 5.40 1.36 W16 163.00 25.34 7.02 1.09

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75 Iron Concentration Iron Present Sodium Concentration Sodium Present Electrical Conductivity NE1 1906.0 132.36 846.0 58.75 6.40 NE2 4176.0 363.59 994.4 86.58 7.12 NE3 2170.0 201.84 964.4 89.70 7.44 NE4 774.2 46.03 774.2 46.03 6.32 NW1 967.4 96.35 1028.0 102.39 8.88 NW2 1962.0 163.74 1008.0 84.12 6.88 NW3 1582.0 142.44 767.0 69.06 6.32 NW4 1290.0 92.32 1152.0 82.44 7.68 SE1 1724.0 159.26 1386.0 128.03 10.08 SE2 2256.0 210.32 1116.0 104.04 8.24 SE3 758.6 79.90 804.0 84.69 8.64 SE4 1424.0 116.12 1290.0 105.19 8.24 SW1 1692.0 227.80 525.2 70.71 4.32 SW2 2556.0 476.03 435.4 81.09 3.60 SW3 2090.0 372.82 444.0 79.20 3.44 SW4 730.8 146.35 680.8 136.33 3.60 W1 1176.0 213.77 352.2 64.02 2.88 W2 752.4 272.26 156.8 56.74 1.60 W3 1678.0 243.02 299.4 43.36 2.96 W4 1594.0 231.87 360.8 52.48 7.52 W5 2132.0 470.41 282.6 62.35 3.04 W6 808.8 594.28 167.8 123.29 1.68 W7 1752.0 230.67 351.0 46.21 3.52 W8 1504.0 207.28 297.8 41.04 3.28 W9 683.6 128.76 214.6 40.42 2.24 W10 543.4 172.63 195.0 61.95 1.84 W11 1540.0 292.04 265.2 50.29 3.28 W12 1154.0 196.79 354.4 60.43 3.04 W13 1596.0 321.98 254.4 51.32 2.88 W14 1468.0 353.21 245.4 59.04 2.24 W15 943.6 238.45 256.2 64.74 2.08 W16 1446.0 224.78 362.4 56.33 4.32

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APPENDIX E CORRELATION OF SPECIES AND SOIL CONSTITUENTS TO AXES FOR RUNS SUBSEQUENT TO AUTOPILOT MODE Select Species Correlations Axis: 2 3 r r-sq tau r r-sq tau ALSE 0.293 0.086 0.136 -0.290 0.084 -0.162 ILCA 0.702 0.492 0.736 0.368 0.136 0.384 ITEA 0.492 0.242 0.487 0.302 0.091 0.285 LERA 0.789 0.623 0.650 0.505 0.255 0.340 MYCE 0.786 0.618 0.645 0.525 0.275 0.313 NYAQ 0.480 0.231 0.291 0.964 0.930 0.867 NYBI -0.680 0.463 -0.495 -0.909 0.826 -0.706 PEPA -0.682 0.465 -0.556 -0.330 0.109 -0.436 QUNI -0.887 0.788 -0.768 -0.375 0.140 -0.352 Select Environmental Variable Correlations Axis: 2 3 r r-sq tau R r-sq tau Organic 0.812 0.660 0.634 0.454 0.206 0.256 bulk -0.639 0.409 -0.603 -0.232 0.054 -0.340 P_pres -0.637 0.406 -0.501 -0.157 0.025 -0.166 Ca_conc 0.645 0.416 0.482 0.410 0.168 0.228 Mg_conc 0.703 0.494 0.540 0.477 0.228 0.222 Cu_pres -0.633 0.401 -0.532 -0.327 0.107 -0.300 Ec 0.788 0.621 0.568 0.521 0.271 0.303 Na_conc 0.785 0.616 0.570 0.564 0.318 0.338 76

PAGE 87

LIST OF REFERENCES Bedinger, M.S. 1981. Hydrology of bottomland hardwood forests of the Mississippi Embayment. p. 161-176. In J.R. Clarke and J. Benforado (eds.) Wetlands of Bottomland Hardwood Forests. Elsevier Scientific Publishing Co., Amsterdam, The Netherlands. Brady, N.C. 1974. Organic soils (Histosols): Their nature, properties, and utilization. p. 353-371. In The Nature and Properties of Soil. Macmillan Publishing Co., Inc., New York, NY, USA. Broadbent, F.E. 1953. The soil organic fractions. Advanced Agronomy 5: 153-184. Cain, S.A. 1938. The species-area curve. American Midland Naturalist 19:3 573-581. Cao, Y., D.D. Willliams, and N.E. Williams. 1998. How important are rare species in aquatic community ecology and bioassessment? Vegetatio 42: 1403-1409. Conner, W.H., J.G. Gosselink, and R.T. Parrondo. 1981. Comparison of the vegetation of three Louisiana swamp sites with different flooding regimes. American Journal of Botany 68: 320-331. Cowardin, L.M., V. Carter, F.C. Golet, and E.T. LaRoe. 1979. Classification of wetland and deepwater habitats of the United States. FWS/OBS-79/31. Curtis, J.T. and R.P. McIntosh. 1950. The interrelations of certain analytic and synthetic phytosociological characters. Ecology 31: 434-455. Curtis, J.T. and R.P. McIntosh. 1951. An upland forest continuum in the prairie-forest border region of Wisconsin. Ecology 32:3 476-496. De'ath, G. 2002. Multivariate regression trees: a new technique for modeling species-environment relationships. Ecology 83:4 1105-1117. Deutsch, R. 1998. Soil Organic Matter; A Choice of Methods. The Agvise Analyst Newsletter. http://www.agviselabs.com/tech_art/om.php (accessed July 2004). Doar, D. 1936. Contributions from the Charleston Museum VIII: Rice and rice planting in the South Carolina low country. Milby Burton, ed. The Charleston Museum, Charleston, SC, USA. 77

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78 Doumlele, D.G., K. Fowler, and G.M. Silberhorn. 1985. Vegetative community structure of a tidal freshwater swamp in Virginia. Wetlands 4: 129-145. Dufrene, M., and P. Legenre. 1997. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecological Monographs 67: 345-366. Faith, D.P., P.R. Minchin, and L. Belbin. 1987. Compositional dissimilarity as a robust measure of ecological distance. Vegetatio 69: 57-68. Francis, J.K. 1986. The nutrient pool of five important bottomland hardwood soils. Research Note SO-327. U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station. New Orleans, LA, USA. Frost, C.C. 1987. Historical overview of Atlantic white cedar in the Carolinas. p. 257-264. In A.D. Laderman (ed.) Atlantic White Cedar Wetlands. Westview Press, Boulder, CO, USA. Gambrell, R.P., W.H. Patrick, and S.P. Faulkner. 1989. Extractable iron and manganese and redox changes in bottomland hardwood wetland-nonwetland transition zone soils. p. 117-122. In Hook, D.D., and L. Russ (eds.) Proceeding of the Symposium: The Forested Wetlands of the Southern United States held in Orlando, FL, July 12-14, 1988. General Technical Report SE-50. U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station. Asheville, NC, USA. Georgia Department of Natural Resources, Geologic and Water Resources Division. 1976. Geologic Map of Georgia. Williams & Heintz Map Corporation. Washington, DC, USA. Georgia Department of Natural Resources, Geologic and Water Resources Division. 1977. Geologic Map of Georgia. Williams & Heintz Map Corporation, Washington, DC, USA. Georgia Ports Authority. 1998. Environmental impact statement: Savannah harbor expansion feasibility study. http://www.sysconn.com/harbor/ (accessed July 2004). Graves, C. 2001. Avian use of tidal marshes across a salinity gradient at Savannah National Wildlife Refuge, Georgia-South Carolina. MS Thesis. University of Tennessee, Knoxville, TN, USA. Hansen, D.V., and M.Rattray, Jr. 1966. New dimentions in estuarine classification. Limnology and Oceanography 11:3 319-326. Kent, M., and P. Coker. 1992. Vegetation Description and Analysis: A Practical Approach. John Wiley & Sons Ltd., West Sussex, UK. Klawitter, R.A. 1962. Sweetgum, swamp tupelo, and water tupelo sites in a South Carolina bottomland forest. Ph.D. Dissertation. Duke University, Durham, NC, USA.

PAGE 89

79 Korstian, C.F. and W.D. Brush. 1931. Southern white cedar. U.S. Department of Agriculture, Washington, DC, USA, Technical bulletin 251. Larson, J.S., M.S. Bedinger, C.F. Bryan, S. Brown, R.T. Huffman, E.L. Miller, D.G. Rhodes, and B.A. Touchet. 1981. Transition from wetlands to uplands in southeastern bottomland hardwood forests. p. 225-273. In J.R. Clark and J. Benforado (eds.) Wetlands of Bottomland Hardwood Forests. Proceeding of a Workshop on Bottomland Hardwood Forest Wetlands of the Southeastern United States Held at Lake Lanier, Georgia, June 1-5, 1980. Developments in Agriculture and Managed-Forest Ecology, vol. 11. Elsevier Scientific Publishing Company, New York, NY, USA. Latham, P.J. 1990. Plant distributions and competitive interactions along a gradient of tidal freshwater and brackish marshes. Ph.D. Dissertation. University of Florida, Gainesville, FL, USA. Little, S., Jr. 1950. Ecology and silviculture of whitecedar and associated hardwoods in southern New Jersey. School of Forestry, Yale University, New Haven, CT, USA. Bulletin 56. Mathsoft, Incorportated. 2000. S-Plus Professional 2000 Professional. Release 3. Lucent Technologies, Incorporated, Murray Hill, NJ, USA. McCune, B., and J.B. Grace. 2002. Analysis of Ecological Communities. MjM Software Design. Gleneden Beach, OR, USA. McCune, B., and M.J. Mefford. 1999. PC-ORD. Multivariate Analysis of Ecological Data, Version 4.27. MjM Software Design, Gleneden Beach, OR, USA. McKenzie, M.D., J.V. Miglarese, B.S. Anderson, and L.A. Barclay. 1980. Ecological Characterization of the Sea Island Coastal Region of South Carolina and Georgia Volume 2: Socioeconomic Features of the Characterization Area. U.S. Fish and Wildlife Service, Washington, DC, USA. FWS/OBS-79/41. Meade, R.H. 1976. Sediment problems in the Savannah River Basin. p. 105-129. In B.L. Dillman and J.M. Stepp (eds.) The future of the Savannah River. Water Resources Research Institute, Clemson University. Clemson, SC, USA. Messina, M.G., and W.H. Conner. 1998. Southern Forested Wetlands. CRC Press LLC. Boca Raton, FL, USA. Mitch, W.J., and J.G. Gosselink. 2000. Wetlands, Third edition. John Wiley & Sons, Inc. New York, NY, USA. National Oceanic and Atmospheric Administration. 1988. Climatology of the U.S. No. 20, suppliment No. 1: Freeze/Frost data.

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80 National Oceanic and Atmospheric Administration. 2002. Climatological Data Annual Summary for Georgia 106:13. Parsons, S.E., and S. Ware. 1982. Edaphic factors and vegetation in Virginia coastal plain swamps. Bulletin of the Torrey Botanical Club 109: 365-370. Pearlstine, L., P. Latham, W. Kitchens, and R. Bartleson. 1990. Development and Application of a Habitat Succession Model for the Wetland Complex of the SNWR: Volume II, Final Report. Submitted to the U.S. Fish and Wildlife Service, Savannah Coastal Refuges, by the Florida Cooperative Fish and Wildlife Research Unit, Gainesville, FL, USA. Radford, A.E., H.E. Ahles, and C. Ritchie Bell. 1968. Manual of the Vascular Flora of the Carolinas. The University of North Carolina Press, Chapel Hill, NC, USA. 1183 pp. Rheinhardt, R. 1991. Vegetation ecology of tidal swamps of the lower Chesapeake Bay, USA. Ph.D. Dissertation. Virginia Institute of Marine Science, School of Marine Science, College of William and Mary, Gloucester Point, VA, USA. Rheinhardt, R. 1992. A multivariate analysis of vegetation patterns in tidal freshwater swamps of lower Chesapeake Bay, USA. Bulletin of the Torrey Botanical Club 119:2 192-207. Rheinhardt, R.D., and C. Hershner. 1992. The relationship of below-ground hydrology to canopy composition in five tidal freshwater swamps. Wetlands 12:3 208-216. Robinson, W.O. 1939. Method and procedure of soil analysis used in the Division of Soil Chemistry and Physics. USDA Circular No. 139. 21pp. Salisbury, F.B., and C.W. Ross. 1992. Plant Physiology. Fourth Edition. Wadsworth Publishing Company, Belmont, CA, USA. SPSS, Inc. 2001. SigmaPlot 2002 for Windows, Version 8.02. Systat Software Inc., Richmond, CA, USA. Storer, D.A. 1984. A simple high sample volume ashing procedure for determination of soil organic matter. Communications in Soil Science and Plant Analysis 15:7 759-772. Therneau, T.M., and E.J. Atkinson. 1997. An introduction to recursive partitioning using the RPART routines. Mayo Foundation Technical report. http://www.mayo.edu/hsr/techrpt.html (accessed June 2004). Urban, D.L. 2002. Classification and Regression Trees. Pages 222-232 in McCune and Grace, Analysis of Ecological Communities. MjM Software Design, Gleneden Beach, OR, USA.

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81 U.S. Department of Agriculture, Soil Conservation Service. 1974. Soil Survey of Bryan and Chatham Counties, Georgia. USDA, Washington, DC, USA. U.S. Department of Agriculture, Soil Conservation Service. 1980. Soil Survey of Beaufort and Jasper Counties, South Carolina. USDA, Washington, DC, USA. U. S. Fish and Wildlife Service. 1993a. National Wetlands Inventory website: Hardeeville, SC. U.S. Department of the Interior, Fish and Wildlife Service, St. Petersburg, FL. http://www.nwi.fws.gov (accessed March 2004). U. S. Fish and Wildlife Service. 1993b. National Wetlands Inventory website: Limehouse, SC. U.S. Department of the Interior, Fish and Wildlife Service, St. Petersburg, FL. http://www.nwi.fws.gov (accessed March 2004). U. S. Fish and Wildlife Service. 1999a. National Wetlands Inventory website: Port Wentworth, GA. U.S. Department of the Interior, Fish and Wildlife Service, St. Petersburg, FL. http://www.nwi.fws.gov (accessed March 2004). U. S. Fish and Wildlife Service. 1999b. National Wetlands Inventory website: Rincon, GA. U.S. Department of the Interior, Fish and Wildlife Service, St. Petersburg, FL. http://www.nwi.fws.gov (accessed March 2004). Wakeman, S.A, and K.R. Stevens. 1930. A critical review of the methods for determining the nature and abundance of soil organic matter. Soil Science 30: 97-116. Walkley, A., and I.A. Black. 1934. An examination of the Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Science 37 29-38. Wharton, C.H., H.T. Odum, E. Ewel, M. Duever, A. Lugo, R. Boyt, J. Bartholomew, E. Debellevue, S. Brown, M. Brown, and L. Duever. 1977. Forested wetlands of Florida their management and use. Division of State Planning, State of Florida. Tallahassee, FL, USA. Wharton, C.H., W.M. Kitchens, E.C. Pendleton, and T.W. Sipe. 1982. The ecology of bottomland hardwood swamps of the southeast: A community profile. U.S. Fish and Wildlife Service, Biological Services Program, Washington, DC, USA. FWS/OBS-81/37. Woodwell, G.M. 1958. Factors controlling growth of pond pine seedlings in organic soils of the Carolinas. Ecological Monographs 38: 219-236.

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BIOGRAPHICAL SKETCH 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 Points 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. Jamies 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 countrys current administration and its attitude toward conservation, he is constantly reminded that he can no longer take these luxuries for granted. 82


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Title: Freshwater Tidal Forest Communities Sampled in the Lower Savannah River Floodplain
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Copyright Date: 2008

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FRESHWATER TIDAL FOREST COMMUNITIES SAMPLED IN THE LOWER
SAVANNAH RIVER FLOODPLAIN















By

JAMIE DUBERSTEIN


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


2004

































Copyright 2004

by

Jamie Duberstein















ACKNOWLEDGMENTS

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

my research.

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

Robinette.
















TABLE OF CONTENTS

page

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

CHAPTER

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

APPENDIX

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


Table page

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


Figure p

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

By

Jamie Duberstein

August 2004

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.














CHAPTER 1
INTRODUCTION AND STUDY AREA

Introduction

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).


W\est East











South -:.NT,
Carolina- !: I







:, ', ,.A tlantic
"an Ocantc

Georgia ocean

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.



























Cut


[ During Tide Gate

[ After Tide Gate


3.2 km (2 mi)


Savannah*

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
operation.

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

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

are unknown.

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.


25000
C-
o 20000
on

15000


10000


5000


1400



1200



1000 r


800



600

o

400



200


Ct


Figure 1-4. Mean annual discharge
Georgia.


Year


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).

Tree Species

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

River floodplain.

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.














CHAPTER 2
METHODS

Vegetative Sampling

Species-Area Curve

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).







13



20

*
** *
**



15 00 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.

Sampling Design


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

stands.

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

between quadrats.

Soil Analysis

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

ignition method.

Chemical Constituents

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

carefully chosen.

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.

Statistical Analyses

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

follow.

Rare species

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

Outlying plots

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
P 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

non-parametric nature.

Cluster analysis

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.





































Legend

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

chance.









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

optimal one.

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

analysis.









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

where

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.






27


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

clusters.














CHAPTER 3
RESULTS

Introduction

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

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

sections.

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

NE1
NE4
NE2
NW1 --
NE3
SE3 ] Shrub
NW2
SE2
SE4
SE]
NW4




w1 2-1 / Swamp tupelo Tag Alder
W12
W2 6
W10
SW5
SW3



w5 -- Water Oak -
W15


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



S/ -35



S8
040






50

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-
values.

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).
Cluster
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.
General Mulitiple
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

NMS Ordinations

Autopilot

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
(r2<0.392) variables.
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









Subsequent ordinations

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.
r2
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.













NW4 Cluster
\w5 SE4 1
SE A 2
A NW2 A3
W7 SE2 A 4
NW1


W 11 NE2 NE4
SE3 A A
W16 NE1
W6 NE3


W14 W9



W3


W10 NW3
A

.W15
WW \
W1
\W12 sw
W2
SW21
W8 SWZ 2 SW4
SW3 W4

Axis 2

A


Cluster
W5
A2

W7 A 4



11 A

16-
WW6 A


W14 W9



W3


W10 NW3


.W15
W1
W12wi
SW1
W2
W8 SW2' SW4
SW3 W4

Axis 2

B

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.





























M W9











W12
Swi
W8 W2 SW4





A










W11

W6 16-


W14 W 9



W3


W10 NW3

.W15
W1
\W12
SWI1
W8 S S4
8 SWz2 SW4
SSW3 W4

Axis 2

B

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


W14
CO A W9



W3
A V


W10 NW3

,W15
W1
W12
SW1
W2
W8\ sw .' sw4
SW3 W4

Axis 2

Figure 3-5. NMS ordination. Relative importance values of water tupelo, as indicated by
the size of triangles.








42




w \ NW4 Cluster
SSE4 *
A2
SE2
A3
W7 SE1 NW2 A 4
NW1

11/ NE2 NE4
SW16 SE3 A




c AW14 AW9


W3






W1AW15
W12
A SWi

ws sw4 /A
SW3 A

Axis 2

A


S\ Cluster
W5
A


A
W7

SW11

W6 16A A



'W14 -W9


W3


W10
^A NW3A

\ W15
W1
W12
A SWi
A W2
W8 SWAV SW4

SW AW4
Axis 2

B
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
2
SE2 3
'W7 SE1 'NW2 A4
NW1

1 SE3 NE2 NE4

W6 W16A NE3 NEI



o AW14 N9


W3


W10
ANW3"


WV1AW15
W12
A SW1
\ SW2 W SW4
W8 A A
SW3
AW4
Axis 2

A


Cluste
-W5
A2

W7 4



w11

W6 W16,



W9
<
W14


W3


W10
0 NW3'

AW15
W1A
W12

WSWW2 SW4
swr
W4
Axis 2

B

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 Cluster
Al
A A 2
A A 3
Organic A 4
SNa conc
A Ec
A Mg conc
Ca cone A A
















bulk I P present
A A















Cu present
Axis 1
Figure 3-. Biplot of axis 2 vs Strong correlations of all soil constituents to axis 2.
C0

X A
< A A
A

A

A








A A A



A


Axis 1

Figure 3-8. Biplot of axis 2 vs 1. Strong correlations of all soil constituents to axis 2.













NW4 Cluster
A-5 SE4 A 1
SE2 A 2
W7 SE1 ANW2 A 3
NW1


SSE3 A A
W16 A
A Nacone NE3 NE1
AW6 Mg conc Ec A A
Organc
W14 Ca cone
A W9
SP present
bulk
T Cu present



W10 NW3


W15 """"

1 W12
\ A SW1
W8 SW2 W2 S2 4 4
SW3A W4


Axis



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)





Sodium concentration

>353 mg/kg <353 mg/kg


Shrub
(11)
Electrical Conductivity
>3 dS/m <3 dS/m

-I
Swamp tupelo -Tag alder
(8) I
Water tupelo Water oak Swamp bay
(4) (8)
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.

Shrub Community

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






49


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







A.SW


.~'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

2-1).

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

community.

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

3-6).



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
8000
= 6000 /

S 2000

1500

1000

500


<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.
Community types


Species
Red maple
Tag alder
Buttonbush
Ash
Dahoon holly
Virginia willow
Fetterbush
Wax myrtle
Water tupelo
Swamp tupelo
Bald cypress
Posssumhaw viburnum
Possumhaw
Lyonia
Swamp bay
Arrow wood
Sweet gum
Water oak
Swamp dogwood
Musclewood

Environmental parameters
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)

Summary Statistics
Density (stems/ha)
Basal Area (m2/ha)


Shrub
(11)

4.07
12.77
0.08
A 8R


38.54
2.77
5.72
0.13
0.14
0.26
0.17
0.15
0.99
0.32
0.10
0.00



86.26
0.08
7.81
1033.00
7326.00
1817.15
5.61
0.37



18,209
54


Water
tupelo
(4)


Swamp tupelo
tag alder
(9)


1.96
18.59
0.00
11.04
0.18
0.00
0.00
1.08

7.41
3.26
0.00
0.00
0.00
0.35
0.00
0.00
6.55
0.20
0.37


49.76
0.18
3.54
315.30
4454.50
539.00
13.76
1.15



6,425
69


4.76

0.07
4.32
0.44
0.00
1.97
1.84
1.99

14.12
0.04
0.22
0.00
2.79
0.07
0.82
7.60
0.96
1.35


52.98
0.20
3.92
435.49
4746.44
787.96
9.72
1.28



9,611
56


Water oak -
swamp bay
(7)

5.15
1.87
0.00
11.49
0.00
0.00
0.26
0.39
12.79
30.87
9.19
0.00
1.04
0.00

0.00
1.69

0.13
2.34



42.62
0.27
2.48
261.91
3714.57
510.43
13.19
1.50



4,043
59














CHAPTER 4
DISCUSSION

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,

NC.

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
Floodplain

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

Community Description

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

both locations.

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).

Environmental Factors

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

(Figure 3-8).

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

observation).

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 lm).

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

Community Description

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

zonation.

Soil Properties

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.














APPENDIX A
TIDAL FOREST COMPUTATIONS BASED ON NATIONAL WETLAND
INVENTORY

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,

and Hardeeville.

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

(5,200,007 m2).

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).
















APPENDIX B
SPECIES NAMES AND ABBREVIATIONS

Species naming follows Radford et al. 1968


Common name
Red maple
Tag alder
Groundsel tree
Musclewood
Buttonbush
Swamp dogwood
Ash
Dahoon holly
Possumhaw
Inkberry
American holly
Black alder
Virginia willow
Fetterbush
Sweetgum
Lyonia
Sweet bay
Wax myrtle
Water tupelo
Swamp tupelo
Swamp bay
Water elm
Water oak
Laurel oak
Swamp rose
Swamp willow
Black willow
Bald cypress
American elm
Highbush blueberry
Possumhaw viburnum
Arrow wood


Latin name
Acer rubrum Linnaeus
Alnus serrulata (Aiton) Willdenow
Baccharis halimifolia Linnaeus
Carpinus caroliniana Walter
Cephalanthus occidentalis Linnaeus
Cornusfoemina Miller var. foemina
Fraxinus spp
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


Code
ACRU
ALSE
BAHA
CACA
CEOC
COFF
FRAX
ILCA
ILDE
ILGL
ILOO
ILVE
ITEA
LERA
LIST
LYLU
MAVI
MYCE
NYAQ
NYBI
PEPA
PLAQ
QUNI
QULA
ROPA
SACA
SANI
TADI
ULAM
VACO
VINU
VIDE















APPENDIX C
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














APPENDIX D
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.












Phosphorous
pH Concentration


NE1
NE2
NE3
NE4
NW1
NW2
NW3
NW4
SE1
SE2
SE3
SE4
SW1
SW2
SW3
SW4
Wl
W2
W3
W4
W5
W6
W7
W8
W9
W10
Wll
W12
W13
W14
W15
W16


Organic
Matter
91.13
90.98
90.87
87.86
83.37
89.06
76.89
91.39
80.46
82.69
80.85
80.21
65.62
46.86
44.29
53.34
42.64
25.76
56.60
72.70
42.16
16.94
65.32
62.40
48.93
30.01
40.87
61.39
46.63
36.89
33.95
50.68


Bulk
Density
0.07
0.09
0.09
0.06
0.10
0.08
0.09
0.07
0.09
0.09
0.11
0.08
0.13
0.19
0.18
0.20
0.18
0.36
0.14
0.15
0.22
0.73
0.13
0.14
0.19
0.32
0.19
0.17
0.20
0.24
0.25
0.16


5.36
5.30
5.16
5.83
5.40
5.68
5.54
5.39
5.37
6.07
5.72
5.83
5.33
5.57
6.20
5.14
5.59
5.77
6.03
5.82
5.72
5.79
5.63
5.47
5.68
5.70
5.31
5.82
5.38
5.81
5.88
5.55


51.16
47.32
35.80
96.42
82.08
94.10
47.96
64.42
58.14
56.24
80.10
65.08
48.94
70.18
53.28
37.92
64.18
40.60
53.66
55.42
55.20
33.94
58.08
63.54
62.10
41.34
90.48
61.50
55.96
67.90
44.16
116.00


Phosphorous
Present
3.55
4.12
3.33
5.73
8.17
7.85
4.32
4.61
5.37
5.24
8.44
5.31
6.59
13.07
9.50
7.59
11.67
14.69
7.77
8.06
12.18
24.94
7.65
8.76
11.70
13.13
17.16
10.49
11.29
16.34
11.16
18.03














NE1
NE2
NE3
NE4
NW1
NW2
NW3
NW4
SE1
SE2
SE3
SE4
SW1
SW2
SW3
SW4
W1
W2
W3
W4
W5
W6
W7
W8
W9
W10
Wll
W12
W13
W14
W15
W16


Potassium
Concentration
220.6
245.6
522.0
239.2
301.4
310.2
192.8
240.0
788.2
210.6
186.4
206.4
175.2
284.8
186.8
232.8
494.4
108.6
166.2
263.4
179.0
189.0
199.6
242.2
280.2
168.8
296.0
238.6
203.6
201.6
257.4
333.6


Potassium
Present
15.32
21.38
48.55
14.22
30.02
25.89
17.36
17.18
72.81
19.63
19.63
16.83
23.59
53.04
33.32
46.62
89.87
39.30
24.07
38.32
39.49
138.87
26.28
33.38
52.78
53.63
56.13
40.69
41.07
48.51
65.05
51.86


Calcium
Concentration
8050
5164
2676
9166
10380
7062
10700
7886
7852
6832
9064
6454
4114
2960
4586
5924
3290
1530
4908
5912
5912
2402
3682
4632
4006
2652
3950
4340
4164
3812
2952
4274


Calcium
Present
559.00
449.62
248.90
545.02
1033.81
589.37
963.43
564.36
725.34
636.92
954.71
526.30
553.89
551.27
818.06
1186.31
598.05
553.65
710.82
860.00
1304.43
1764.90
484.78
638.39
754.58
842.51
749.07
740.08
840.05
917.18
745.99
664.38














NE1
NE2
NE3
NE4
NW1
NW2
NW3
NW4
SE1
SE2
SE3
SE4
SW1
SW2
SW3
SW4
Wl
W2
W3
W4
W5
W6
W7
W8
W9
W10
Wll
W12
W13
W14
W15
W16


Magnesium
Concentration
1894.0
993.8
796.8
2236.0
2380.0
1242.0
1222.0
1706.0
2428.0
1546.0
2396.0
2370.0
734.6
660.4
899.4
1596.0
519.4
280.8
550.2
708.4
585.6
365.4
451.0
639.2
558.6
419.8
516.4
570.2
514.6
463.8
476.4
603.0


Magnesium
Present
131.52
86.53
74.11
132.95
237.04
103.65
110.03
122.09
224.29
144.13
252.37
193.26
98.90
122.99
160.44
319.61
94.42
101.61
79.68
103.05
129.21
268.48
59.38
88.10
105.22
133.37
97.93
97.23
103.82
111.59
120.39
93.73


Zinc
Concentration
39.10
45.38
17.60
13.30
16.94
22.84
23.44
13.68
22.98
19.58
21.14
16.30
10.94
24.68
20.72
20.26
11.18
9.07
19.90
17.26
29.38
8.70
18.42
15.00
12.18
11.66
10.58
22.72
19.60
23.70
9.40
14.56


Zinc
Present
2.72
3.95
1.64
0.79
1.69
1.91
2.11
0.98
2.12
1.83
2.23
1.33
1.47
4.60
3.70
4.06
2.03
3.28
2.88
2.51
6.48
6.39
2.43
2.07
2.29
3.70
2.01
3.87
3.95
5.70
2.38
2.26














NE1
NE2
NE3
NE4
NW1
NW2
NW3
NW4
SE1
SE2
SE3
SE4
SW1
SW2
SW3
SW4
Wl
W2
W3
W4
W5
W6
W7
W8
W9
W10
Wll
W12
W13
W14
W15
W16


Manganese
Concentration
135.60
210.20
155.40
104.00
278.20
65.56
305.20
96.84
554.40
191.40
301.20
306.40
339.60
412.00
689.00
334.80
153.20
180.40
132.60
211.00
502.20
213.60
180.40
262.40
187.00
214.40
143.40
93.92
224.80
284.40
196.20
163.00


Manganese
Present
9.42
18.30
14.45
6.18
27.71
5.47
27.48
6.93
51.21
17.84
31.73
24.99
45.72
76.73
122.91
67.05
27.85
65.28
19.20
30.69
110.81
156.95
23.75
36.16
35.22
68.11
27.19
16.02
45.35
68.43
49.58
25.34


Copper
Concentration
5.40
6.56
8.23
3.47
2.21
3.59
3.06
4.82
4.69
2.98
2.47
4.44
6.21
8.28
7.79
3.03
6.06
8.24
4.18
5.54
7.10
5.11
4.91
7.36
5.41
5.74
6.80
7.40
7.83
6.92
5.40
7.02


Copper
Present
0.37
0.57
0.77
0.21
0.22
0.30
0.28
0.34
0.43
0.28
0.26
0.36
0.84
1.54
1.39
0.61
1.10
2.98
0.61
0.81
1.57
3.75
0.65
1.01
1.02
1.82
1.29
1.26
1.58
1.66
1.36
1.09














NE1
NE2
NE3
NE4
NW1
NW2
NW3
NW4
SE1
SE2
SE3
SE4
SW1
SW2
SW3
SW4
W1
W2
W3
W4
W5
W6
W7
W8
W9
W10
Wll
W12
W13
W14
W15
W16


Iron
Concentration
1906.0
4176.0
2170.0
774.2
967.4
1962.0
1582.0
1290.0
1724.0
2256.0
758.6
1424.0
1692.0
2556.0
2090.0
730.8
1176.0
752.4
1678.0
1594.0
2132.0
808.8
1752.0
1504.0
683.6
543.4
1540.0
1154.0
1596.0
1468.0
943.6
1446.0


Iron
Present
132.36
363.59
201.84
46.03
96.35
163.74
142.44
92.32
159.26
210.32
79.90
116.12
227.80
476.03
372.82
146.35
213.77
272.26
243.02
231.87
470.41
594.28
230.67
207.28
128.76
172.63
292.04
196.79
321.98
353.21
238.45
224.78


Sodium
Concentration
846.0
994.4
964.4
774.2
1028.0
1008.0
767.0
1152.0
1386.0
1116.0
804.0
1290.0
525.2
435.4
444.0
680.8
352.2
156.8
299.4
360.8
282.6
167.8
351.0
297.8
214.6
195.0
265.2
354.4
254.4
245.4
256.2
362.4


Sodium
Present
58.75
86.58
89.70
46.03
102.39
84.12
69.06
82.44
128.03
104.04
84.69
105.19
70.71
81.09
79.20
136.33
64.02
56.74
43.36
52.48
62.35
123.29
46.21
41.04
40.42
61.95
50.29
60.43
51.32
59.04
64.74
56.33


Electrical
Conductivity
6.40
7.12
7.44
6.32
8.88
6.88
6.32
7.68
10.08
8.24
8.64
8.24
4.32
3.60
3.44
3.60
2.88
1.60
2.96
7.52
3.04
1.68
3.52
3.28
2.24
1.84
3.28
3.04
2.88
2.24
2.08
4.32
















APPENDIX E
CORRELATION OF SPECIES AND SOIL CONSTITUENTS TO AXES FOR RUNS
SUBSEQUENT TO AUTOPILOT MODE

Select Species Correlations


2
r r-sq


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


3
r r-sq


-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


Axis:


R r-sq


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


Axis:


ALSE
ILCA
ITEA
LERA
MYCE
NYAQ
NYBI
PEPA
QUNI















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BIOGRAPHICAL SKETCH

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.