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ABSTRACT
Title of Thesis:
The Effect of Adjacent Forests on Colonizing Tree Density
in Restored Wetland Compensation Sites in Virginia
Name of degree candidate:
Herman Wesley Hudson III
Degree and Year:
Masters of Science in Environmental Science, 2010
Thesis directed by:
Robert B. Atkinson, Ph.D., Professor of Biology,
Department of Biology, Chemistry and Environmental
Science
In Virginia, the success criterion for woody vegetation in forested wetland compensation
sites is typically met when the stem count reaches 495-990 stems/ha. Tree establishment
can be difficult and expensive but natural colonization is common in portions of some
sites and can increase compliance. The purpose of this study was to quantify and model
patterns of pioneer woody species colonization using variables measured within the
restoration area and from the adjacent forest, in 5 wetland compensation sites within the
Coastal Plain Province of Virginia. Colonization by three light-seeded species,
Liquidambar styraciflua, Acer rubrum and Pinus taeda would satisfy the woody
vegetation success criterion in 126 of the 160 (79%) sampled plots. Density of colonizing
trees was modeled using a forward stepwise regression which selected 5 of 10 variables
measured (r2=0.682, p<0.001). Understanding and predicting pioneer tree colonization
can improve forested wetland compensation efforts by informing planting strategies and
improving forest establishment.
The Effect of Adjacent Forests on Colonizing Tree Density in
Restored Wetland Compensation Sites in Virginia
By
Herman Wesley Hudson III
Thesis submitted to the Graduate Faculty of
Christopher Newport University in partial
fulfillment of the requirements
for the degree of
Master of Science
2010
Approved:
Robert B. Atkinson, Chair
______________________________
Michael D. Meyer
______________________________
Jessica S. Thompson
______________________________
DEDICATION
To my Poppa, Herman Wesley Hudson Sr. and my Grandma, Hazel Harvey Allen
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ACKNOWLEDGEMENTS
I would like to thank my friend and thesis advisor Dr. Robert B. Atkinson for all
of his guidance and support throughout my undergraduate and graduate career at
Christopher Newport University, with particular thanks for helping me to complete my
thesis. I would also like to thank my Thesis Committee members, Dr. Michael Meyer and
Dr. Jessica Thompson for their time and helpful insights. I would like to thank all of the
members in The Center for Wetland Conservation for their help in the field and lab and
everyone else who assisted me in the field. Particular big thanks to my fellow graduate
students Jess Campo and Jackie Roquemore who were there through this whole
experience struggling through their own thesis, but still finding time to assist me with
field work and writing support. This thesis would not have been possible without the
support and love of my parents and brother and the constant support and encouragement
from my fiancé Jeanna Kidwell, who survived minor home tick invasions and many
muddy hugs.
This research was part of a larger contract funded by The Nature Conservancy
and would not have been possible without the help from Jef DeBerry, who helped
establish the partnership between CNU and TNC. Lastly, I would like to thank my
extended family at CNU, the Biology Chemistry and Environmental Science Department,
for awarding me the Graduate Student Assistantship for 2008 and 2009, which allowed
me to complete my thesis and course work.
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TABLE OF CONTENTS
Section
Page
Dedication
ii
Acknowledgements
iii
List of Tables
vii
List of Figures
viii
Chapter 1 - Literature review, detailed site description and methods
1
Wetland Functions and Ecosystem Services
1
Wetland Regulations
2
Wetland Compensation
4
Wetland Compensation Site Success
5
Wetland Compensation Site Monitoring
9
Old Field Succession
11
Seed Dispersal Patterns
13
Seed Dispersal Quantities
14
Factors Limiting Survival and Growth of Colonizing Trees
15
Modeling Tree Colonization
17
Description of Pioneer Trees
19
A. Sweetgum (Liquidambar styraciflua) (FAC)
19
B. Red Maple (Acre rubrum) (FAC)
21
C. Loblolly Pine (Pinus taeda) (FAC-)
22
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Section
Page
Pioneer Tree Colonization in Post Agricultural Wetland Compensation Sites
23
Site Description
26
A. Site 1
28
B. Site 2
29
C. Site 3
29
D. Site 4
30
E. Site 5
31
Methods
32
Literature Cited
35
Chapter 2 - Research findings
45
Introduction
45
Methods
47
Results
50
Restoration Area
50
Adjacent Forest
52
Modeling Colonizing Tree Density
54
Discussion
58
Conclusion
61
Literature Cited
63
v
Section
Page
Chapter 3 - Further investigations
67
Tree Distribution Patterns
67
Self Thinning
68
Literature Cited
69
Appendix A - Site Maps
70
vi
LIST OF TABLES
Number
Page
Table 1-1. Wetland indicator status, categories, symbols and frequency
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of occurrence in wetlands as employed by USFWS (USFWS 1988).
Table 1-2. Description of wetland compensation sites.
28
Table 2-1. Description of wetland compensation sites.
49
Table 2-2. Variables selected by forward stepwise regression to
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predict colonization of all three species combined.
Table 2-3. Variables selected by forward stepwise regression to
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predict colonization of sweetgum.
Table 2-4. Variables selected by forward stepwise regression to
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predict colonization of red maple.
Table 2-5. Variables selected by forward stepwise regression to
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predict colonization of loblolly pine.
Table 2-6. Ranges of environmental conditions present at all sites.
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57
LIST OF FIGURES
Number
Page
Figure 1-1. Distribution of sweetgum in Virginia.
20
Figure 1-2. Distribution of red maple in Virginia.
22
Figure 1-3. Distribution of loblolly pine in Virginia.
23
Figure 1-4. Location of wetland compensation sites in Virginia.
27
Figure 2-1. The median colonizing density of sweetgum, red maple,
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loblolly pine, and all three species combined within the restoration areas.
Figure 2-2. The average density of sweetgum, red maple, and loblolly pine
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found at increasing distances from the adjacent forest.
Figure 2-3. The median density of sweetgum, red maple, loblolly pine,
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and all three species combined within the adjacent forests.
Figure 2-4. The mean basal area (m2/ha) of sweetgum, red maple, loblolly pine, 54
and all three species combined within the adjacent forests.
Figure 3-1. Distance from the adjacent forest edge and the colonizing
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density of sweetgum, red maple, and loblolly pine for all 160 plots.
Figure A-1. Site 1 plot location map.
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Figure A-2. Site 2 plot location map.
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Figure A-3. Site 3 plot location map.
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Figure A-4. Site 4 plot location map.
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Figure A-5. Site 5 plot location map.
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viii
Chapter 1 - Literature review, detailed site description and methods
WETLAND FUNCTIONS AND ECOSYSTEM SERVICES
Wetlands are areas identified by most state and federal agencies according to wetland
hydrology, hydric soils, and hydrophytic vegetation parameters. Between the 1780’s and
1980’s, prior to most wetland regulations, approximately 53% of the estimated
89,435,000 ha (221 million acres) of wetlands was lost in the lower 48 states. During the
same time period, Virginia lost approximately 42% of the estimated 748,263 ha
(1,849,000 acres) of wetlands present in the 1780’s (Dahl 1990). Most of the wetlands
lost in Virginia have been palustrine forested wetlands which are the most abundant type
of wetland in Virginia (Tiner and Finn 1986; United States Geological Survey, USGS
1999). Losses were mainly due to drainage activities associated with agriculture and
forestry practices, construction of reservoirs and urban/suburban development (Dahl and
Johnson 1991; Tiner and Finn 1986). The loss of wetlands from the landscape results in
the loss of wetland ecosystem services.
Wetland ecosystem services benefit humans and are derived from ecological functions
that occur in wetlands (Mitsch and Gosselink 2007). Wetland functions are generally
defined as processes that occur in wetlands (Hruby 1999). Wetland functions are
dependent on hydrologic and geomorphic conditions (Brinson 1993) and fall into four
general categories including hydrological, biogeochemical, plant community
maintenance and animal community maintenance (Brinson and Rheinhardt 1996).
Hydrologic wetland functions include improvement of water quality, short-term and
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long-term surface water storage, storage of subsurface water, moderation of groundwater
flow or discharge, and dissipation of energy and maintenance of high water tables
(National Research Council, NRC 1995; Smith 1995). Biogeochemical functions include
transformation and cycling of elements and nutrients; retention and removal of dissolved
substances and particulates; accumulation of peat; and accumulation of inorganic
sediments (NRC 1995; Smith 1995). Maintenance of species composition, abundance,
and age structure of plant and animal communities are also functions (NRC 1995; Smith
1995). The functions performed by wetlands vary by wetland type, landscape position,
season and other factors.
Examples of ecosystem services provided by forested wetlands include storing flood
waters (Brinson 1993), enhancing water quality (Sather and Smith 1984), retaining
nutrients (Fisher and Acreman 2004), providing wildlife corridors and habitat amenities
(Balcombe et al. 2005a; Shaw and Fredine 1956), providing erosion control along
streams (Silberhorn 1994), providing intrinsic values such as recreation opportunities
(Mitsch and Gosselink 2000; Office of Technology Assessment, OTA 1984), and
trapping waterborne sediments that help protect and restore sensitive aquatic ecosystems
such as the Chesapeake Bay (USGS 1999). As important functions and ecosystem
services began to be understood, destruction of wetlands became regulated by law.
WETLAND REGULATIONS
Wetland impacts are regulated by the 1972 Federal Water Pollution Control Act, which
was amended and renamed the Clean Water Act (CWA) in 1977 (33 U.S.C. 1344). The
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goal of the CWA is to maintain the biological, chemical and physical integrity of the
United States’ waters. Wetland impacts are specifically regulated under Section 404 of
the CWA which authorized the United States Army Corps of Engineers (USACOE)
under the direction of the United States Environmental Protection Agency (USEPA) to
issue permits regulating the discharge of dredged or fill material into “waters of the
United States” which includes wetlands (40 CFR Part 230.1). The mitigation sequence of
avoidance, minimization and compensation was devised to allow for permitted wetland
impacts while maintaining the integrity of water quality and the sequence was first
defined in the 1978 Council on Environmental Quality (CEQ) National Environmental
Policy Act (NEPA) clarification (CEQ 1978), but mitigation had been occurring prior to
1978 at the request of the United States Fish and Wildlife Service (USFWS) and the
National Marine Fisheries Service (NMFS) based on the Fish and Wildlife Coordination
Act and Endangered Species Act using techniques from the USACOE Dredged Material
Research Program (Hough 2009). After the 1978 CEQ clarification of NEPA, the USEPA
released new guidelines (Section 404(b)(1)) that required performance of an alternatives
test to identify the Least Environmentally Damaging Practicable Alternatives (LEDPA)
prior to completing the mitigation sequence (USEPA 1980). In 1990, a Memorandum of
Agreement (MOA) between the USEPA and the USACOE sought to clarify mitigation
regulations presented in the 1978 CEQ NEPA clarification and the Section 404(b)(1)
Guidelines, into one comprehensive document. The 1990 MOA clarified the three steps
in the mitigation sequence by combining the identification of LEDPAs with the
requirement of avoidance and by further defining regulations for minimization and
compensation.
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WETLAND COMPENSATION
After a permittee avoids and minimizes wetland impacts, there are four methods of
compensating for wetland impacts including creation of new wetlands, restoration of
converted wetlands, or enhancement and preservation of existing wetlands (33 CFR
PART 332). This final step in the mitigation sequence, known as wetland compensation,
has become an essential regulatory mechanism for offsetting permitted losses of wetlands
(Balcombe et al. 2005b; Spieles 2005; Zedler 1996).
Three mechanisms for completing compensatory mitigation include purchasing credits
from mitigation banks, providing funds to an in-lieu-fee program, or direct completion of
the required mitigation by the permittee which is termed permittee-responsible
compensatory mitigation. A major distinction between mitigation banks and in-lieu-fee
programs is that mitigation banks must be established before credit sales can commence,
whereas credits from in-lieu-fee programs may be sold prior to completing compensation
activities. On March 31, 2008, the USEPA and USACOE released the “Final Rule” for
Compensatory Mitigation and announced the new priority of methods for satisfying
mitigation requirements (in order of decreasing priority) as purchasing credits from
mitigation banks, payment to an in-lieu-fee program, and permittee-responsible
compensatory mitigation (USACOE and USEPA 2008).
The USACOE in conjunction with other agencies including the Virginia Department of
Environmental Quality (VADEQ) and the United States Fish and Wildlife Service
(USFWS) are responsible for the wetland mitigation program in Virginia. The Virginia
Aquatic Resources Trust Fund (VARTF) was established in 1995 as a cooperative
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partnership between The Nature Conservancy (TNC) and the USACOE to provide an inlieu-fee program as an alternative to mitigation banks and permittee-responsible
mitigation in Virginia. Between 1995 and 2008, the VARTF has received $20,151,802 in
mitigation payments for 96.6 ha (238.7 acres) of non-tidal wetland impacts. Using these
funds the VARTF has restored 246.4 ha (608.8 acres) and protected 1,525.6 ha (3,769.8
acres) of non-tidal wetlands as of 2008 (TNC 2009).
WETLAND COMPENSATION SITE SUCCESS
In order for a wetland compensation site to successfully fulfill regulatory obligations the
site must meet yearly site specific criteria established before construction for a limited
number of years. The site specific criteria are mainly based on satisfying structural
hydrologic and vegetative parameters, including but not limited to, timing and duration of
saturation, prevalence of hydrophytic vegetation in the herbaceous stratum, and lack of
invasive species. The vegetative structural parameters are often measured once during the
growing season while water table measurements are often recorded over entire growing
seasons. Many local, regional, and national studies reported low success of compensation
sites in replacing wetland structure and fulfilling permit requirements. A national study
completed by the United States Government Accountability Office (USGAO) (2005)
found that the USACOE had failed to receive monitoring reports from 86% of the
surveyed permittee-responsible compensatory mitigation sites, 30% of the surveyed
mitigation bank sites, and 17% of the surveyed in-lieu-fee mitigation programs. A
separate national study completed by the NRC (2001), found that approximately 50% of
surveyed sites fail to meet prescribed monitoring criteria. In a study involving the review
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of 23 mitigation permits in Pennsylvania, 40% of investigated compensation sites were
not satisfying monitoring criteria 10 years after construction (Cole and Shafer 2002).
Common reasons why many sites do not fulfill compliance criteria are poor site selection
and poor site design (Sudol and Ambrose 2002). Compensation sites that are not fulfilling
regulatory obligations may not be successfully replacing lost wetland function and
ecosystem services. Wilson and Mitsch (1996) described two main ways to quantify
success of wetland compensation sites including fulfillment of regulatory obligations
(regulatory success) and successful functional wetland replacement (functional success)
and noted that successfully fulfilling regulatory obligations does not guarantee successful
functional wetland replacement and vice versa.
Several studies suggest comparing structural parameters in compensation wetlands to
structural parameters in adjacent or nearby natural reference wetlands as another measure
of regulatory success (Rheinhardt et al. 1997; Brinson 1996; Smith et al. 1995). When
comparing compensation sites to natural reference wetlands, several studies have also
found striking differences between structural parameters. Campbell et al. (2002) in a
study of 12 created wetlands in Pennsylvania, found that created wetlands exhibited less
organic matter content, greater bulk density, higher matrix chroma and more rock
fragments than reference wetlands. That study also found significantly higher vegetation
species richness, percent wetland plant species and total vegetative cover in the reference
wetlands compared to the created wetlands. In a study comparing soil characteristics in
constructed and natural wetlands in Virginia, the 4-7 year old constructed sites had lower
levels of organic carbon and nitrogen and lower cation exchange capacities (Stolt et al.
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2000). Two of the paired constructed-reference wetlands had significant differences in
depth to the water table which could lead to divergent vegetative composition and altered
functional performance. When constructed wetlands in the mid-Appalachian region were
compared to natural reference sites, Balcombe et al. (2005b) reported that plant species
richness, diversity, evenness, abundance of pioneer species, and dominance of non-native
species were greater in constructed wetlands. These inconsistencies in structural
parameters between natural and constructed wetland compensation sites show that
regulatory success is not often met and suggests that successful functional replacement
may not be occurring.
In order for a compensatory wetland site to be considered an ecological success, the
wetland functions lost during the permitted wetland impact must be replaced and the
wetland must be self sustaining. Determining successful functional wetland replacement
is more difficult than determining successful fulfillment of regulatory requirements,
because measuring functions of wetlands tends to be more difficult than measuring
structural parameters, functions of destroyed or reference wetlands are often unknown,
and long term monitoring is required. Thus, very few studies measure actual wetland
functions and very few compare wetland functions in constructed and natural wetlands.
The NRC (2001) investigation concluded that “the goal of no net loss of wetlands in not
being met for wetland functions by the mitigation program.” Race and Fonseca (1996)
summarized reasons why wetland compensation sites are not ecologically successful
including improper landscape position (elevation, contour, size), poor construction
methods, small site size, invasion by exotic species, or occurrence of natural
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catastrophes; furthermore, determining ecological or legal success of compensatory
wetlands could be problematic in sites younger than 15-20 years because they have not
developed completely and current models cannot predict their trajectories from an early
age (Wilson and Mitsch 1996).
Since wetland functions are difficult to measure directly, several studies, technical
reports, and wetland classification systems have suggested use of structural
characteristics as “indicators” of functions (Brinson 1993; Balcombe 2005b; Kentula et
al. 1992). In a review of available methods for wetland functional assessment and
characterization, Hruby (1999) concluded that structural characteristics such as plant
community, water regime, or soil characteristics of a compensation wetland indicate only
the potential that a function may be performed. A study by Cole (2002) investigating the
relationship between structural parameters and wetland functions found that composition
and cover of herbaceous plant species did not have a strong relationship with the
functions including, accumulation of inorganic sediments, retention or removal of
dissolved elements, transformation and cycling of elements, maintenance of a high water
table, long-term surface water storage, or short-term surface water storage. A study of 20year-old created wetlands found that despite exhibiting hydrophytic vegetation, wetlands
exhibited low rates of decomposition (Atkinson and Cairns 2001) and primary
productivity (Atkinson et al. 2010). Despite these findings, structural characteristics from
reference wetlands and compensation sites are still routinely used as indicators of
function.
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Another functional assessment method is the hydrogeomorphic classification system
developed by the USACOE (Brinson 1993). This method was developed to classify
wetlands into regional wetland subclasses and identifying groups of wetlands that
function similarly. Subsequently, the hydrogeomorphic approach began to be developed
as a method of functional assessment specifically for wetland compensation sites because
it can determine how a wetland impact will affect function, the baseline conditions before
an impact, compensation requirements, regulatory success, and how management actions
will impact function (Rheinhardt et al. 2002). However, this method does not measure
functions directly but relies on structural indicators of function.
WETLAND COMPENSATION SITE MONITORING
As a result of the national reports and independent evaluations concluding that wetland
compensation sites exhibit limited structural or functional success, an interagency team
(USACOE, USEPA, USFWS, NOAA, USDOT, and USDA NRCS 2002) issued the
National Wetlands Mitigation Action Plan (NWMAP). The goal of NWMAP was to
improve ecological performance of wetland compensation. Prior to the release of
NWMAP the Norfolk USACOE developed “Branch Guidance for Wetlands
Compensation, Permit Conditions and Performance Criteria” that was intended to provide
permit conditions for individual wetland permits requiring compensatory mitigation
(USACOE Norfolk District 1995). Due to concerns about the perceived inflexibility in
the 1995 mitigation guidelines and in accordance with the NWMAP, the Norfolk District
USACOE and the Virginia Department of Environmental Quality (VADEQ) jointly
released the “Norfolk District Corps-DEQ Wetland Mitigation Recommendations”
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(USACOE Norfolk District and VADEQ 2004). Based on these recommendations, each
forested wetland compensatory site in Virginia must meet a number of site specific
criteria in order to be considered compliant. When a forested wetland is impacted, a
common site goal for restored or created compensatory wetlands is a minimum woody
stem count of 495-990 stems/ha (200-400 stems/acre) until the canopy cover is 30% or
greater (USACOE Norfolk District and VADEQ 2004).
Restored or created compensatory wetland sites can satisfy the minimum woody stem
count through a combination of natural tree colonization and tree planting. There are
several approaches to compensatory mitigation site design and Mitsch and Wilson (1996)
contrasted two strategies termed “self-design” and “designer” approaches. The selfdesign approach involves introducing as many species as possible through planting and
natural colonization and understanding that natural forces will select for the most
appropriate species. This approach relies on the self organization principle described by
H. T. Odum (1989), that ecosystems select assemblages of plants, microbes, and animals
that are best adapted for existing site conditions. Site managers utilizing this approach
recognize the importance of natural woody colonization and adjust site design (Cronk and
Fennessy 2001; Mitsch and Wilson 1996). In contrast, the designer approach introduces
plant species and expects them to survive. Mitsch and Wilson (1996) suggest that
combining the self-design approach along with approximately 20 years of plant
community development will lead to more “successful” wetland compensation sites,
however, success was not clearly defined in that publication.
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Natural colonization in forested wetland restoration sites in Virginia is important because
they are subject to rapid colonization from adjacent forests which improves the potential
for natural colonization by woody species. Natural woody colonization is both a function
and an indicator that various other functions are occurring, including flood water storage,
mineral transformation and cycling, and primary production (Cole 2002). Cole (2002)
theorizes that measuring tree densities would provide more information about several
wetland functions than measuring herbaceous plant cover.
Woody colonization can be perceived as the process of tree species immigration and local
extinction of individuals that takes place over time as described in the theory of island
biogeography (MacArthur and Wilson 1967), which has been applied to situations as
diverse as protozoan colonization of artificial substrates (Cairns 1969) and insect
colonization of mangrove swamps (Simberloff and Abele 1976). Investigation into the
process of natural woody colonization into former agricultural fields has furthered the
understanding of secondary succession in abandoned agricultural fields (old field
succession). Understanding old field succession is valuable for predicting woody
colonization into post agricultural wetland compensation sites, especially in Virginia
where many compensation sites are constructed on former agricultural land.
OLD FIELD SUCCESSION
Theories addressing old field succession provide perspectives on revegetation and
reforestation of former agricultural fields that become wetland compensation sites.
Keever (1950) studied old field succession in the piedmont of North Carolina. The
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purpose of the Keever study was to determine the mechanisms that control changes in
herbaceous plant dominance. Her results confirmed the successional order of herbaceous
vegetation followed by woody colonization and mechanisms included competition for
light and water, germination rates, seed banks, and erosion (Keever 1950). The study
found that most abandoned fields are first dominated by herbaceous vegetation including
crabgrass (Digitaria sanguinalis), then horseweed (Conyza canadensis var. canadensis),
followed by aster (Symphyotrichum pilosum var. pilosum), and broomsedge (Andropogon
virginicus) and the first tree to colonize is typically loblolly pine (Pinus taeda). Loblolly
pine often appears in the third year and forms closed stands in 10 to 15 years (Keever
1950).
Other studies have also found that herbaceous vegetation dominates in the early
successional stages after agricultural abandonment because these species are present at
the time of abandonment either as seeds or as established plants (Gill and Marks 1991).
Woody plant species are usually not present in the buried seed pool in active agricultural
fields and tree colonization requires seed dispersal from adjacent seed sources (Egler
1954; Livingston and Allessio 1968; Myster and Pickett 1992; Oosting and Humphreys
1940). The first trees to colonize old fields are typically r-selected species as compared to
K-selected species. Species that are r-selected produce numerous offspring each of which
has a low probability of surviving to adulthood, have fast growth rates, and typically
colonize environments that are less crowded and unstable. In comparisons K-selected
species invest more energy in fewer offspring, each of which have a higher probability of
surviving to adulthood. K-selected species are often found in crowded conditions where
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competition is high (MacArthur and Wilson 1967). In addition to life strategies, there are
several factors influencing distribution, survival and growth of woody species in post
agricultural compensation sites including seed dispersal patterns, seed dispersal quantities
and factors limiting the survival and growth of colonizing trees.
SEED DISPERSAL PATTERNS
Trees primarily reproduce sexually through seed production (Fenner 1985) and seed
dispersal is a key adaptation for colonization. The first trees to colonize old fields are
anemochorous (wind-dispersed) followed by zoochorous (animal dispersed) species
(Fenner 1985; Pickett 1982). When observing seed dispersal patterns for most plants, the
highest density of seeds are found in close proximity to the parent plant and density of
seeds decreases exponentially as distance from the parent plant increases (Fenner 1985;
Harper 1977; Myster and Pickett 1992; Willson 1993). Anemochorous seeds that are
dispersed from dense vegetation, such as the edge of a forest, tend to have a uniform
negative exponential seed dispersal curve (Fenner 1985; Greene and Johnson 1996).The
shape of the seed dispersal curve of wind dispersed species depends upon the surrounding
vegetation, terminal velocity which is primarily determined by the shape and size of the
seed, the height of the release, as well as the wind speed and direction (Harper 1977;
Fenner 1985).
Willson (1993) compared observed seed dispersal patterns (seed shadows) for herbaceous
and woody species to the expected negative exponential decline pattern. Approximately
84% of the woody species surveyed fit the negative exponential pattern including several
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wind dispersed species. In an investigation of seed dispersal and colonization into a
disturbed northern hardwood forest, Hughes and Fahey (1988) confirmed that the
negative exponential decline patterns observed in seed dispersal curves are also observed
among tree saplings that become established during the early stages of old field
succession. However, in the longest study (31 years) of vegetation change after field
abandonment in North America (at the Hutcheson Memorial Forest Center in New
Jersey), Myster and Pickett (1992) found that the negative exponential woody stem
distribution pattern was significantly related to the forest edge, but only in the first 6
years after abandonment. The authors suggested that zoochorous dispersal and dispersal
from within the field, in addition to shading and self thinning, become increasingly
important later in succession.
SEED DISPERSAL QUANTITIES
In addition to seed distribution patterns, the amount of seeds dispersed is dependent upon
several factors, including seed tree abundance and size, adjacent forest area, edge to
interior ratio, seed size and dispersal mechanism, and other factors such as weather.
Gardescue and Marks (2004) investigated anemochorous seed dispersal in old fields
within the state of New York and found that seed density was positively correlated with
seed tree abundance and seed tree basal area. Greene and Johnson (1994) found that seed
production is positively correlated with basal area for several tree species within closed
stands. Compared to seed production within closed stands, Young (1995) found that
exposed canopy trees positioned near forest edges produce more seeds. Greene and
Johnson (1994) found that annual seed production is negatively correlated with seed
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mass, however, Moles et al. (2004) found that this relationship was only significant for
certain years and was not significant throughout the lifetime of several families of plants
including several tree species. Leishman (2000) suggested that the relationship between
seed size and abundance is an evolutionary compromise between producing fewer large
seeds and producing more seeds of a smaller size, consistent with the r/K selection
theory. One of the first models of this strategy in trees was developed by Smith and
Fretwell (1974) which concluded that the optimal strategy for tree reproduction is to
maximize the ratio of seedling probability for establishment to the amount of energy
allocated to each seed. However, not all species follow the predicted optimal strategy and
small-seeded wind-dispersed species have evolved to use less resources per seed but
produce many more in order to ensure survival, typical of r-selected species. In a study of
temporal variation of seed production in commercially important trees (14 conifers and
18 hardwoods), Greene and Johnson (2004) found that if seed production increased due
to dry, warm weather during bud differentiation, seed production the following year is
typically smaller; however this result was not always significant.
FACTORS LIMITING SURVIVAL AND GROWTH OF COLONIZING TREES
In addition to the amount of seeds distributed, seed and seedling predation, successful
seedling emergence and seedling competition with herbaceous vegetation for light, water
and nutrients limit the survival and growth of colonizing seedlings and influence the
eventual observed density and distribution of colonizing saplings. Seed and seedling
predation and successful seedling emergence are the first factors that influence the
eventual density and distribution of saplings following dispersal. Seed and seedling
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predation reduces the density of colonizing saplings while successful seedling emergence
increases the density of colonizing saplings (De Steven 1991a; Gill and Marks 1991;
Myster and Pickett 1993).
The presence of herbaceous vegetation influences seed and seedling predation and
seedling emergence and growth. In a study of old fields in New York, Gill and Marks
(1991) experimentally investigated the effect of herbaceous vegetation on the survival
and growth of Acer rubrum, Pinus strobus, Cornus racemosa, and Rhammus cathartica
during the first two growing seasons after dispersal. De Steven (1991a; 1991b)
experimentally investigated Pinus taeda, Acer rubrum, and Liquidambar styraciflua
establishment in North Carolina old fields, focusing on seedling emergence and seedling
survival and growth. Both of these studies found that the presence of herbaceous
vegetation increases successful seedling emergence (De Steven 1991a; Gill and Marks
1991) and reduces seedling growth (De Steven 1991b; Gill and Marks 1991). However,
Gill and Marks (1991) found that herbaceous vegetation increased seed and seedling
predation, while De Steven (1991a) found that herbaceous vegetation decreases seed
predation. De Steven (1991b) found that herbaceous vegetation reduces survival of
seedlings due to competition and predation while Gill and Marks (1991) found that
herbaceous vegetation does not have an effect on seedling survival (Gill and Marks
1991). De Steven (1991b) also found that herbaceous vegetation enhances germination
possibly due to the herbaceous cover regulating moisture extremes and providing moist
germination sites. Gill and Marks (1991) found that herbaceous vegetation decreases
seedling mortality due to frost and heat desiccation (Gill and Marks 1991). Overall,
16
herbaceous vegetation is very important in determining the pattern and amount of
colonizing trees present.
MODELING TREE COLONIZATION
Models have been developed that predict colonization rates and patterns in a variety of
organisms and ecosystems. Models of colonization are an important tool for
understanding ecosystem development. One of the earliest theories of colonization
addressed the simplest colonization question of how many species would colonize a
newly developed un-occupied island. The theory of island biogeography (MacArthur and
Wilson 1967) suggests that the amount of species that are found on an island is a result of
the balance between the rate at which new species immigrate and the rate at which
species on the island become extinct. The factors that affect the equilibrium are how far
the island is from mainland (source) and the size of the island. The distance from the
source determines which organisms can successfully reach the island. The size of the
island determines habitat diversity which can be described by a species area curve
(Jaccard 1912; Cain 1938). The extinction rate and immigration rate are dependent upon
the amount of species that are present on the island. If there is a low number of species
present on the island, such as during early stages of colonization then the immigration
rate is high and the extinction rate is low. If there is a large number of species present
then immigration is limited and the extinction rate is high. Further research into the
theory has found that “islands” do not have to be actual islands but can be unoccupied
areas of ecosystems where species can colonize. Cairns (1969) investigated colonization
of protozoan species on artificial substrates and found that the immigration rate slowed
17
and the extinction rate increased as the amount of species present increased, consistent
with island biogeography theory.
Old agricultural fields are similar to islands because they are colonized from outside
sources but differ in that agricultural fields are not surrounded by uninhabitable space and
the immigration rate is much higher than islands (MacArthur and Wilson 1967). Old
fields are typically surrounded by a variety of landscapes and therefore require a more
complex model than island biogeography to predict and understand species colonization.
The study of patch dynamics investigates spatial patterns, structures, functions, and
relationships between and within patches (Pickett and Thompson 1978; Pickett and White
1985; Thompson 1978). Patches are areas that have definable boundaries and are
distinguishable from the adjacent area, such as agricultural fields (Kotliear and Wiens
1990; Pickett and White 1985). Two general approaches to the study of patches include
the shifting mosaic approach and the landscape approach (Pickett and Rogers 1997). The
landscape/human land use approach is the most applicable because it incorporates human
created patches such as agricultural areas and incorporates many types of patches
simultaneously.
Many models and computer simulations have been created to predict forest dynamics
over time (Busing and Daniel 2004; Liu and Aston 1995), forest dynamics in response to
climate change (Iverson et al. 2004), forest patch dynamics (Levin and Paine 1974; Levin
1976), forest gap dynamics (Acevedo et al. 1995; Bugman 2001), long distance tree seed
dispersal (Bullock et al. 2000; Katul 2005), tree seed dispersal in closed canopies (Clark
18
et al. 1999; Nathan et al. 2002), tree seed dispersal in open landscapes (Green and
Johnson 1989; Nathan et al. 2002), and tree seed dispersal from adjacent forests into a
clearing (Green and Johnson 1996). These models and their associated equations have
been analyzed and compared using field data to determine appropriate uses (Canham and
Uriarte 2006; Green et al. 2004) including the density and distribution of seeds/saplings
in abandoned agricultural fields (Gardescu and Marks 2004; Johnson 1988). However,
the literature review from the current study was unable to find a model predicting tree
density and distribution into abandoned agricultural fields from adjacent forests.
DESCRIPTION OF PIONEER TREES
The woody species that colonize many post agricultural sites in the Coastal Plain of
Virginia are sweetgum (Liquidambar styraciflua L.), red maple (Acer rubrum L.), and
loblolly pine (Pinus taeda L.) (Monette and Ware 1983). In this study, these three species
represented 33.5% to 94.9% (average 66.4%) of all of the sampled trees at each site. In
the Coastal Plain of Virginia, wetland restoration attempts could be more effective if
there was a greater understanding of herbaceous and woody colonization and succession
in wetland compensations sites on old field sites.
A. Sweetgum (Liquidambar styraciflua) (FAC)
Sweetgum is a moderate to rapidly growing tree that reaches mature heights of 25 m to
37 m with diameters of 1 m to 1.2 m (Bormann 1953). It is an important commercial
hardwood product of the Southeast United States and often invades old fields and logged
areas in the Coastal Plain (Burns and Honkala 1990). Sweetgum is found from
19
Connecticut to Central Florida and Eastern Texas. In Virginia, sweetgum occurs in the
Coastal Plain and Piedmont Regions (Figure 1-1). Sweetgum is monoecious and blooms
from March to early May and becomes reproductively mature after 20 to 30 years. The
distinctive characteristic of sweetgum is the spiked fruiting heads (gumball) that contain
20 to 30 seeds per gumball. The gumballs mature in September and November and
release 5-7 mm long wind dispersed winged seeds. Sweetgum has an effective seed
dispersal distance of approximately 60 m but the maximum distance is 183 m (Bormann
1953; Burns and Honkala 1990). Germination of sweetgum seeds requires cold
stratification in order to overcome their moderate dormancy (Bonner and Farmer 1966).
This is accomplished naturally by seeds surviving winter and germinating the following
spring (Wilcox 1968). Germination is epigeal and will not occur if excess water is present
(Bonner and Farmer 1966).
Figure 1-1. Distribution of sweetgum in Virginia (Digital Atlas of the Flora of Virginia
2008). Dark counties correspond to presence of sweetgum.
20
B. Red maple (Acer rubrum) (FAC)
Red maple is one of the most adaptable and widespread trees of Eastern North America
and grows from Northern Canada to Southern Florida and east to Texas. This species can
tolerate varying hydrologic conditions, from dry ridges to peat bogs and swamps (Rodney
1995). In Virginia, red maple is found in every county (Figure 1-2). Red maple is one of
the first trees to flower in spring with the small flowers appearing in March through May.
Red maple can be entirely male, entirely female or monoecious and can begin producing
seed as young as 4 years old. The seed is a wind dispersed double samara that is released
for a one to two week period in April through July. The seed is able to germinate is low
light and moisture conditions during the first or second year after dispersal in the early
summer. Red maple can grow to be 18 m to 27 m tall and 0.5 m to 1 m in diameter
(Burns and Honkala 1990).
In a study of two abandoned agricultural fields (16 and 20 years old) in the Piedmont
province of North Carolina, red maple became established 6-9 years after abandonment
(Peroni 1994). In a study in the Piedmont region of New Jersey at the Hutcheson
Memorial Forest, Rankin and Pickett (1989) found that majority of the red maples
sampled were established within 7 years of agricultural abandonment (Rankin and Pickett
1989).
21
Figure 1-2. Distribution of red maple in Virginia (Digital Atlas of the Flora of Virginia
2008). Red maple is found in every county in Virginia.
C. Loblolly pine (Pinus taeda) (FAC-)
Loblolly pine is commercially the most important species in the southern United States
and is found from Southern New Jersey to Central Florida and west to Texas. In Virginia
it is found in the Piedmont and Coastal Plain provinces (Figure 1-3). Loblolly pine is a
monoecious conifer and begins flowering in June with the development of the male and
female strobili which remain in development until the following February when
pollination begins. The cones mature and the seeds ripen by the second October after
flowering. Each cone can contain fewer than 20 seeds to more than 200 seeds. Seeds have
a 20-mm wing and can be transported 23 m to 91 m from their source via wind dispersal.
The seeds are also an important food source for birds and small mammals. Successful
germination and establishment is dependent on soil moisture and un-compacted soil.
Young seedlings are moderately tolerant of shade but shade tolerance decreases with age
(Burns and Honkala 1990). Trees are reproductively mature after 7 years (Harper 1977).
22
In a study of abandoned old fields in North Carolina, Oosting (1942) found that loblolly
pine invades approximately 1-2 years after abandonment. Bormann (1953) found that in
upland abandoned agricultural sites in North Carolina, compared to other pioneer tree
species, loblolly pine is better fitted to tolerate old field conditions during the germination
period and first year of growth because it is tolerant of drier conditions. Also working in
North Carolina, De Steven (1991a) confirmed these results in her study of establishment
of pioneer trees in old field sites.
Figure 1-3. Distribution of loblolly pine in Virginia (Digital Atlas of the Flora of
Virginia 2008). Dark counties correspond to presence of loblolly pine.
PIONEER TREE COLONIZATION IN POST AGRICULTURAL WETLAND
COMPENSATION SITES
Herbaceous and woody species have been assigned a wetland indicator status by the
United States National Fish and Wildlife Service (USFWS) that indicates how frequently
a species occurs in wetlands (USFWS 1988). There are five indicator status categories
(Table 1-1). Three categories are subdivided by a plus (+) and minus (-) system which
more narrowly defines frequency of occurrence within each range such that a positive
23
sign indicates that a plant is found more frequently in wetlands and a negative sign
indicates less frequent occurrence in wetlands within each categorical range. Because the
percentage of time a species is found in wetlands can vary over geographic regions,
indicator status for a species may differ by region (Virginia is in USFWS Region 1,
Northeastern United States). According to the original USFWS national plant list in
Region 1, sweetgum and red maple have a FAC wetland indicator status and are equally
likely to occur in wetlands or non-wetlands (USFWS 1988). Using the 1987 Corps of
Engineers Wetland Delineation Manual (Environmental Laboratory 1987) species such as
loblolly with an indicator status of FAC – were not considered hydrophytic, however,
with the recent Regional Supplement to the 1987 Wetland Delineation for the Atlantic
and Gulf Coastal Plain Region (USACOE 2008), the plus and minus system is not used
and loblolly pine is considered a hydrophyte. This change in loblolly pine indicator status
has been a source of controversy in Virginia, particularly when completing wetland
delineations.
Table 1-1. Wetland indicator status, categories, symbols and frequency of occurrence in
wetlands as employed by USFWS (USFWS 1988)
Category
Obligate Wetland
Facultative Wetland
Facultative
Facultative Upland
Obligate Upland
Indicator Symbol
OBL
FACW
FAC
FACU
UPL
Occurrence In Wetlands
>99%
>67% to 99%
33% to 67%
1% to 33%
<1%
Hydrophytic vegetation is part of the three-parameter approach for identifying areas as
wetlands and is defined by USACOE (2008) as “present when the plant community is
24
dominated by species that can tolerate prolonged inundation or soil saturation during the
growing season.” Two examples of positive indicators of hydrophytic vegetation are the
Dominance Test and Prevalence Index (PI). The Dominance Test is passed when >50%
of the dominant plant species of all strata are hydrophytes, meaning they have indicators
of OBL, FACW, or FAC or are otherwise conspicuously adapted to survive in prolonged
saturated or inundated soil conditions (USACOE 2008). Prevalence Index is used when
the vegetation fails the Dominance Test and wetland hydrology and hydric soil criteria
are met. The vegetation is considered hydrophytic when the PI is <3. The PI calculation
is a weighted average that utilizes wetland indicator status and is weighted by an estimate
of dominance (Wentworth et al. 1988).
Sweetgum, red maple, and loblolly pine are found in various natural wetland types in
Virginia including broad-leaved deciduous/needle-leaved evergreen, temporarily flooded
(PFO1/4A) wetlands. These wetlands are typically inundated for three to six weeks
during the winter and early spring (Cowardin et al. 1979). Sweetgum, red maple and
loblolly pine often naturally colonize wetland compensation sites in Southeastern
Virginia at a rate that is great enough to reach the required woody stem count (495-990
stems/ha) when seed trees are present nearby. The purpose of this study was to
investigate the factors that influence the patterns of sweetgum, red maple and loblolly
pine colonization into post agriculture wetland compensation sites in the Coastal Plain of
Virginia, with the goal of improving tree establishment in forested wetland compensation
efforts by guiding planting strategies and predicting colonization into restored wetlands.
25
SITE DESCRIPTION
The study was conducted at sites located in Southeastern and Central Virginia, U.S.A
(Figure 1-4, Table 1-2). All sites are part of the Virginia Aquatic Resource Trust Fund
(VARTF) in-lieu-fee program and were designed and implemented by The Nature
Conservancy (TNC). Sites for this study were selected from a group of sites monitored
for compliance under a contract between TNC and the Center for Wetland Conservation
at Christopher Newport University. Sites were selected based on age, size, amount of
woody colonization, adjacent forest size, and distance to adjacent forest edge. Sites with
little or no colonization were not used. Maps featuring aerial photographs and locations
of plots are located in Appendix A.
26
Figure 1-4. Location of wetland compensation sites in Virginia.
27
Table 1-2. Description of wetland compensation sites (The Center for Wetland
Conservation 2008, The Nature Conservancy 2009).
Site ID
County
Years
Sampled
Age
(2008)
Total Size
(ha)
Restoration
Size (ha)
Number
of Plots
Site 1
Chesapeake
2007
4
74.1
10.4
20
Site 2
Chesapeake
2007 & 2008
5
148.1
49.4
60
Site 3
Chesapeake
2007 & 2008
7
53.9
22.7
30
Site 4
Henrico
Virginia
Beach
2008
7
110.9
8.1
20
2007 & 2008
5&7
16.5
15.0
30
Site 5
A. Site 1
Site 1 is located in the Chowan Basin in Chesapeake, VA and is part of the Northwest
River Corridor (HUC: 03010203). Site 1 is 74.1-ha in total size with 10.4-ha of non-tidal
wetland restoration (Table 1-2). The site is prior-converted agricultural land and was
actively farmed until 2003. The purpose of this project site was to restore a non-riverine
wet hardwood forest thus restoring primary functions including wildlife habitat and water
quality enhancement. Construction began in late 2004 with the agricultural ditches being
filled and field crowns being removed (The Center for Wetland Conservation 2007). A
perimeter berm was also installed to protect adjacent properties from flooding and in
early 2005 the site was planted with 6,300 bare root tree seedling and 2,800 shrub
seedlings. Early investigations by TNC found that planted seedling survival was low
however the site did meet stem density requirements due to natural colonization (The
Nature Conservancy 2009). See Appendix A for Site 1 aerials photographs and plot
locations.
28
B. Site 2
Site 2 is located in the Chowan Basin in Chesapeake, VA and is part of a corridor that
connects the Northwest River and the Great Dismal Swamp National Wildlife Refuge
(GDSNWR) (HUC: 03010205). Site 2 is part of the 405-ha Green Sea Preserve and is
148.1-ha in total size with 49.4-ha of non-tidal wetland restoration (Table 1-2). This
investigation took place in the restoration area that was prior-converted agricultural land.
The purpose of this project site was to restore a non-riverine wet hardwood forest thus
restoring primary functions including wildlife habitat and water quality enhancement.
Construction began in 2003 when 50,500 bare root seedlings and 6,000 shrubs were
planted in the restoration area. In 2004 the agricultural field ditches were plugged and a
perimeter berm system was constructed. Early investigations by TNC found that the
survival of the planted seedling was high and that red maple and sweetgum were the
dominant colonizing trees (The Nature Conservancy 2009). See Appendix A for Site 2
aerials photographs and plot locations.
C. Site 3
Site 3 is located in the Chowan Basin in Chesapeake, VA and is part of a 485-ha
protected land corridor surrounding the Northwest River (HUC: 03010205). Site 3 is 53.9
ha in total size with 22.7 ha of non-tidal wetland restoration (Table 1-2). This
investigation took place in the restoration area which is prior-converted agricultural land.
The purpose of this project site was to restore a non-riverine wet hardwood forest thus
restoring primary functions including wildlife habitat and water quality enhancement.
The threatened and endangered species Virginia Least Trillium (Trillium pusillum var.
29
virginianum) and Canebrake Rattlesnake (Crotalus horridus) have been observed on the
site (The Center for Wetland Conservation 2008). Construction began in 2001 and
included plugging ditches and creating seasonally flooded ponds in the agricultural field.
A berm system was constructed to hold additional water and 15,000 bare root seedlings
were planted. Early investigations by TNC observed heavy colonization of loblolly pine
and small invasion of cattail (Typha latifolia) (The Nature Conservancy 2009). See
Appendix A for Site 3 aerials photographs and plot locations.
D. Site 4
Site 4 is located in the James River Basin in Henrico, VA and consists of abandoned river
meanders, swampland and six agricultural fields (HUC: 02080206). Site 4 is 110.9-ha in
total size with 8.1-ha of non-tidal wetland restoration (Table 1-2). This investigation took
place in the restoration area which is prior-converted agricultural land. The purpose of
this project site was to restore an alluvial floodplain coastal plain/piedmont bottomland
forest and mesic mixed hardwood forest. Construction consisted of blocking agricultural
ditches, plowing agricultural fields to increase water storage, and planting 13,000 bare
root seedlings and was completed in 2002. Initial investigation by TNC observed natural
woody colonization by red maple, sweetgum, bald cypress (Taxodium distichum) and
willow oak (Quercus phellos). Several invasive species including tree of heaven
(Ailanthus altissima), multiflora rose (Rosa multiflora), and Japanese honeysuckle
(Lonicera japonica) were observed in 2003 and 2004 in the wetland restoration areas and
in the upland field edges (The Nature Conservancy 2009).
30
E. Site 5
Site 5 is a combination of two sites that were restored at two separate times but are
directly adjacent to each other and are surrounded by and thus affected by the same
forests. Site 5 is located in the Chowan Basin in Virginia Beach, VA and is an served as
an opportunity to restore wetlands near the Back Bay National Wildlife Reserve (HUC:
03010205). The total size of Site 5 is 16.5-ha and the restoration area is 15-ha (Table 12). The purpose of this project site was to restore a non-riverine wet hardwood forest thus
restoring primary functions including wildlife habitat and water quality enhancement.
The older of the two sites consist of 11 prior-converted agricultural fields separated by
agricultural ditches totaling 7.3-ha of restoration area. Construction began in 2001 when
the agricultural ditches were plugged, 4,500 woody hardwoods were planted and a
perimeter berm system was installed to protect adjacent property from flooding and
enhance water storage. Early investigations by TNC observed good survival of planted
seedlings but the woody density was below 990 stems/ha suggesting low colonization
(The Nature Conservancy 2009).
The younger portion of Site 5 consists of 9.2-ha of prior-converted agricultural land with
7.7-ha of restoration area. Construction began in 2003 when a perimeter berm was
constructed with gaps to allow hydrologic connectivity between the two sites. In early
2004 the site was planted 5,500 bare root seedlings of seven hardwood species and 1,100
seedlings were installed using tree tubes and weed mats. Early investigations by TNC
observed low planted tree survivorship and low woody colonization possibly due to long-
31
duration inundation and herbaceous competition (The Nature Conservancy 2009). See
Appendix A for Site 5 aerials photographs and plot locations.
METHODS
The Virginia Aquatic Resources Trust Fund (VARTF) was established in 1995 as a
cooperative partnership between The Nature Conservancy (TNC) and the USACOE to
provide an in-lieu-fee program as an alternative to mitigation banks and permitteeresponsible mitigation in Virginia. During July and August of 2007 and 2008, 5 restored
wetland compensation sites (Table 1-2) that were prior-converted agricultural fields were
monitored for regulatory compliance and research goals. Planted and colonizing woody
species > 1 m tall were tallied in 160 10-m radius plots that were previously established
throughout the sites by TNC. Sweetgum, red maple, and loblolly pine trees were not
planted during site construction. Other parameters measured within the restoration area
included shortest distance from plot to forest edge, edge to interior ratio, total herbaceous
cover, herbaceous prevalence index (PI), and consecutive days of saturation within the
root zone (<30.5 cm) during the growing season. The growing season, which corresponds
to a 50% chance that temperatures will not drop below -2o C, in the Coastal Plain
Province of Virginia is defined as 260 days from March 1 to November 15. Relative
herbaceous cover and indicator status of each species in each plot were used to calculate
PI with the following formula:
PI = (y1u1 + y2u2 + . . . + ymum)/100
where y1, y2, . . . , ym are the relative cover estimates for each species and u1, u2, . . . , um
are the indicator status of each species.
32
The parameters that were measured in the adjacent forest in 2009 included seed tree
density, seed tree basal area, forest height, and forest size. Approximately 10% of the
adjacent forest edges were sampled in 10-m X 20-m rectangular plots established at
approximately 100 m intervals along the adjacent forest edge. Seed tree basal area was
calculated by measuring the diameter at breast height (DBH) of trees within each plot and
then calculating the area of each bole. Forest height was determined at each plot using a
Clinometer. Any forest that was directly bordering a restoration area was considered
adjacent forest and the contiguous area forest size was calculated using aerial
photographs and ArcGIS (ESRI 2008).
Within the restoration area woody colonization density and PI were sampled for two
consecutive years at Sites 2, 3, and 5 and stem density data and PI values were averaged
prior to further analysis. The statistical package SIGMA PLOT 11.0 (Systat Software,
Inc. 2008) was used for all hypothesis testing. Data collected from within the restoration
area and surrounding forest were non-normally and normally distributed (Shapiro-Wilk
Normality Test) thus both non-parametric tests and parametric tests were used for
hypothesis testing (p<0.05 was considered significant). Non-normally distributed data
were reported as medians ( ~
x ) and data with normal distribution were reported as means
( x ). Standard error of the mean (SE x ) was used throughout to describe variance.
Standard error of the mean was calculated by dividing the standard deviation (σ) by the
square root of sample size (n) (Zar 1998). In order to perform linear regressions,
colonizing tree density data were transformed using natural logarithms following Willson
33
(1993). All available measured variables were used to model colonizing tree density with
forward stepwise regression using SIGMA PLOT 11.0 (Systat Software, Inc. 2008).
34
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44
Chapter 2 – Research Findings
INTRODUCTION
Wetlands are areas identified by most state and federal agencies according to wetland
hydrology, hydric soils, and hydrophytic vegetation parameters. Between the 1780’s and
1980’s and prior to most wetland regulations, Virginia lost approximately 42% of the
estimated 748,263 ha (1,849,000 acres) of wetlands present in the 1780’s (Dahl 1990).
Most of the wetlands lost in Virginia have been palustrine forested wetlands which are
the most abundant class of wetlands in Virginia (Cowardin et al. 1979; Tiner and Finn
1986; USGS 1999). Permitted wetland impacts must follow the mitigation sequence of
avoidance, minimization and compensation; and wetland compensation sites are typically
restored or created. These wetland compensation sites have site specific structural goals
that are monitored for 5 to 10 years and monitoring hydrophytic vegetation presence and
abundance has been required for most of the regulatory program history (Atkinson et al.
1993). When a forested wetland compensation site is restored or created, a common site
goal is a minimum woody stem count of 495-990 stems/ha (200-400 stems/acre) until the
tree canopy is greater than 30% cover (USACOE Norfolk District 2004). While tree
planting is the principal strategy for achieving desired density, permits generally allow
natural tree colonization to meet the criterion. Natural tree colonization is potentially
important on recently abandoned agricultural fields which are a common former land use
for wetland restoration in Virginia.
Woody colonization into abandoned agricultural fields can be perceived as the process of
tree species immigration and local extinction of individuals that takes place over time as
described in the theory of island biogeography (MacArthur and Wilson 1967), which has
45
been applied to situations as diverse as protozoan colonization of artificial substrates
(Cairns 1969) and insect colonization of mangrove swamps (Simberloff and Abele 1976).
Other models have been developed that predict colonization rates and patterns of several
species in various ecosystems. Many models and computer simulations have been created
to predict forest dynamics over time (Acevedo et al. 1995; Bugman 2001; Busing and
Daniel 2004; Iverson 2004; Levin 1974; Levin 1976; Liu and Aston 1995). Seed
dispersal has been modeled for several tree species in various landscape positions
(Bullock 2000; Clark et al. 1999; Green and Johnson 1989; Green and Johnson 1996;
Katul 2005; Nathan et al. 2002). Several studies have investigated the density and
distribution of seeds/saplings around parent plants and found that density of seeds and
saplings decreases exponentially as distance from the parent plant increases (Clark et al.
1999; Fenner 1985; Gardescu and Marks 2004; Harper 1977; Johnson 1988; Myster and
Pickett 1992; Willson 1993). However, the literature review from the current study was
unable to find a model predicting tree density and distribution into abandoned agricultural
undergoing wetland restoration. The purpose of this study was to investigate the factors
that influence patterns of sweetgum (Liquidambar styraciflua L.), red maple (Acer
rubrum L.), and loblolly pine (Pinus taeda L.) colonization into post agriculture wetland
compensation sites in the Coastal Plain Province of Virginia, with the goal of improving
tree establishment in forested wetland compensation efforts by guiding planting strategies
and predicting colonization into restored wetlands.
46
METHODS
The Virginia Aquatic Resources Trust Fund (VARTF) was established in 1995 as a
cooperative partnership between The Nature Conservancy (TNC) and the USACOE to
provide an in-lieu-fee program as an alternative to mitigation banks and permitteeresponsible mitigation in Virginia. During July and August of 2007 and 2008, 5 restored
wetland compensation sites (Table 1-2) that were prior-converted agricultural fields were
monitored for regulatory compliance and research goals. Planted and colonizing woody
species > 1 m tall were tallied in 160 10-m radius plots that were previously established
throughout the sites by TNC. Sweetgum, red maple, and loblolly pine trees were not
planted during site construction. Other parameters measured within the restoration area
included shortest distance from plot to forest edge, edge to interior ratio, total herbaceous
cover, herbaceous prevalence index (PI), and consecutive days of saturation within the
root zone (<30.5 cm) during the growing season. The growing season, which corresponds
to a 50% chance that temperatures will not drop below -2o C, in the Coastal Plain
Province of Virginia is defined as 260 days from March 1 to November 15. Relative
herbaceous cover and indicator status of each species in each plot were used to calculate
PI with the following formula:
PI = (y1u1 + y2u2 + . . . + ymum)/100
where y1, y2, . . . , ym are the relative cover estimates for each species and u1, u2, . . . , um
are the indicator status of each species.
The parameters that were measured in the adjacent forest in 2009 included seed tree
density, seed tree basal area, forest height, and forest size. Approximately 10% of the
adjacent forest edges were sampled in 10-m X 20-m rectangular plots established at
47
approximately 100 m intervals along the adjacent forest edge. Seed tree basal area was
calculated by measuring the diameter at breast height (DBH) of trees within each plot and
then calculating the area of each bole. Forest height was determined at each plot using a
Clinometer. Any forest that was directly bordering a restoration area was considered
adjacent forest and the contiguous area forest size was calculated using aerial
photographs and ArcGIS (ESRI 2008).
Within the restoration area woody colonization density and PI were sampled for two
consecutive years at Sites 2, 3, and 5 and stem density data and PI values were averaged
prior to further analysis. The statistical package SIGMA PLOT 11.0 (Systat Software,
Inc. 2008) was used for all hypothesis testing. Data collected from within the restoration
area and surrounding forest were non-normally and normally distributed (Shapiro-Wilk
Normality Test) thus both non-parametric tests and parametric tests were used for
hypothesis testing (p<0.05 was considered significant). Non-normally distributed data
were reported as medians ( ~
x ) and data with normal distribution were reported as means
( x ). Standard error of the mean (SE x ) was used throughout to describe variance.
Standard error of the mean was calculated by dividing the standard deviation (σ) by the
square root of sample size (n) (Zar 1998). In order to perform linear regressions,
colonizing tree density data were transformed using natural logarithms following Willson
(1993). All available measured variables were used to model colonizing tree density with
forward stepwise regression using SIGMA PLOT 11.0 (Systat Software, Inc. 2008).
48
49
2008
2007 & 2008
Henrico
Virginia
Beach
Site 4
Site 5
5&7
7
7
2007 & 2008
Chesapeake
Site 3
5
2007 & 2008
Chesapeake
Site 2
4
Chesapeake
Site 1
Age
(2008)
2007
County
Site ID
Years
Sampled
2008, The Nature Conservancy 2009).
30
20
30
60
20
Number of
Plots
15
8.1
22.7
49.4
10.4
Site Size (ha)
1.3
210.5
349.7
712.9
219.8
Adjacent Forest
Size (ha)
63.1
38.1
143.7
78.7
99.5
Edge (m) /
Interior(ha)
Table 2-1. Description of wetland compensation sites (The Center for Wetland Conservation
RESULTS
Restoration Area
Woody colonization density was sampled within the restoration area for two consecutive
years at Sites 2, 3, and 5 and densities between the 2 years did not differ (p=0.173),
therefore stem density data were averaged prior to further analysis. PI between 2007 and
2008 at Site 5 did not differ (p<0.001) and PI was averaged prior to further analysis.
Sweetgum, red maple and loblolly pine represented at least 33.5% and up to of 94.9%
(average 66.4%) of all trees sampled within the restoration area at each site. Median
colonizing stem density of sweetgum, red maple and loblolly pine at all sites was 3360
stems/ha (SE x ± 1016 stems/ha). Of the 160 plots sampled, 126 (78.8%) had > 495
stems/ha satisfying the woody stem count through natural colonization of these species
(Figure 2-1). The median colonizing density of sweetgum at all sites was 1323 stems/ha
(SE x ± 887 stems/ha) which was significantly greater than the median colonizing
density of red maple ( ~
x = 419 stems/ha, SE x ± 238 stems/ha, p<0.001) which was
significantly greater than the colonizing density of loblolly pine ( ~
x = 171 stems/ha, SE x
± 222 stems/ha, p<0.001) (Figure 2-1).
PI at Site 1 (PI=3.3) was greater than at Site 2 (PI=2.7, p<0.001) but no other site
differences in PI were detected. Of the 160 plots in this study, 87 (54.4%) exhibited a PI
values less than 3 and were thus considered hydrophytic vegetation (USACOE 2008).
Median total herbaceous cover at Site 2 in 2007 ( ~
x = 116.2) was higher than in 2008 ( ~
x=
61.6, p<0.001); and median total herbaceous cover at Site 3 in 2007 ( ~
x = 94.2) was
50
higher than in 2008 ( ~
x = 26.3, p<0.001). There was not a difference in median total
herbaceous cover at Site 5 between 2007 ( ~
x = 59.2) and 2008 ( ~
x = 55.0, p=0.982).
Median total herbaceous cover at Site 1 was greater than Site 2 (p<0.001), Site 3
(p<0.001), Site 4 (p<0.001) and Site 5 (p<0.001).
5000
4500
Median colonizing density (stems/ha)
4000
3500
3000
2500
2000
1500
a
1000
b
500
c
0
Sweetgum
Red Maple
Loblolly Pine
3 spp.
Figure 2-1. The median colonizing density of sweetgum, red maple, loblolly pine, and all
three species combined within the restoration areas. Error bars represent standard error.
Horizontal line represents lowest required stem density (495 stems/ha). Medians with the
same letters were not different (p>0.05).
51
The highest percentage of sweetgum, red maple, and loblolly were found within 100 m of
the adjacent forest (Figure 2-2).
9000
8000
Average density (stems/ha)
7000
6000
5000
4000
3000
2000
1000
0
% Sweetgum
% Red Maple
% Loblolly Pine
<50 m
50 to 100 m
100 to 150 m
150 to 200 m
200 to 250 m
>250 m
5723
2844
1115
8128
2049
2515
4911
1327
1343
3768
711
228
3949
739
260
1962
829
205
Figure 2-2. Mean density of sweetgum, red maple, and loblolly pine found at increasing
distances from the adjacent forest.
Adjacent Forest
Mean height of adjacent forests did not differ among sites ( x =17.1 m, SE x ± 2.2 m,
p=0.239). The median stem density of sweetgum in the adjacent forest at all sites was 350
stems/ha (SE x ± 52.3 stems/ha) and did not differ from the density of red maple ( ~
x=
225 stems/ha, SE x ± 51.4 stems/ha, p=0.13), but was greater than the density of loblolly
pine ( ~
x = 50 stems/ha, SE x ± 14.6 stems/ha, p<0.001) (Figure 2-3).
52
1800
Median seed tree density in the adjacent forest (stems/ha)
1600
1400
1200
1000
800
600
400
a
a
200
b
0
Sweetgum
Red maple
Loblolly Pine
3 spp.
Figure 2-3. Median density of sweetgum, red maple, loblolly pine, and all three species
combined within the adjacent forests. Error bars represent standard deviation. Medians
with the same letter were not different (p>0.05).
Median basal area of sweetgum in the adjacent forest at all sites was 20.4 m2/ha (SE x ±
3.2 m2/ha) which was greater than basal area of red maple ( ~
x = 8.4 m2/ha, SE x ± 1.6
m2/ha, p<0.001) and basal area of loblolly pine ( ~
x = 3.5 m2/ha, SE x ± 9.2 m2/ha,
p<0.001) (Figure 2-4).
53
Median seed tree basal area in the adjacent forest (m^2/ha)
60
50
40
30
a
20
b
10
b
0
Sweetgum
Red maple
Loblolly Pine
3 spp.
Figure 2-4. Median basal area (m2/ha) of sweetgum, red maple, loblolly pine, and all
three species combined within the adjacent forests. Error bars represent standard
deviation. Medians with the same letter were not different (p>0.05).
Modeling Colonizing Tree Density
Forward stepwise regression selected 5 of the 10 variables measured (r2=0.682, p<0.001)
(Table 2-2) to predict colonizing density of all three species:
PD=e^(-1.836+(0.0061*FS))+(0.781*A)-(0.00444*DE)+(0.818*PI)+(0.0169*BA)),
where PD is the colonizing density of sweetgum, red maple, and loblolly pine combined:
e is Euler's number (Zar 1998), FS is the adjacent forest size, A is the age of the
compensation site, DE is the distance from the plot to the forest edge, PI is the prevalence
54
index of the herbaceous stratum, and BA is the combined basal area of sweetgum, red
maple, and loblolly pine in the adjacent forest.
Table 2-2. Variables selected by forward stepwise regression to predict colonization of
all three species combined.
Step #
1
2
3
4
5
Variable
FS
A
DE
PI
BA
R^2
0.364
0.546
0.624
0.658
0.682
P Value
<0.001
<0.001
<0.001
<0.001
<0.001
To predict sweetgum colonization density, forward stepwise regression selected 5 of the
10 variables measured (r2=0.665, p<0.001) (Table 2-3) as follows,
SD=e^(-0.570-(0.00582*DE)+(0.00744*FS)+(0.443*A)+(0.824*PI)+(0.0187*SBA),
where SD is the colonizing density of sweetgum and SBA is the basal area of sweetgum in
the adjacent forest.
Table 2-3. Variables selected by forward stepwise regression to predict colonization of
sweetgum.
Step #
1
2
3
4
5
Variable
DE
FS
A
PI
SBA
R^2
0.478
0.596
0.62
0.649
0.665
55
P Value
<0.001
<0.001
<0.001
<0.001
0.008
To predict red maple colonization, forward stepwise regression selected 6 of the 10
variables measured (r2=0.632, p<0.001) (Table 2-4) as follows,
RMD =e^(-8.744-(0.00452*DE)-(0.0207*E/I)+(1.334*A)+(0.0110*FS)+(0.00131*RMSC)+(0.224*FH),
where RMD is the colonizing density of red maple, E/I is the edge to interior ratio, RMSC
is the red maple stem count from the adjacent forest and FH is the adjacent forest height.
Table 2-4. Variables selected by forward stepwise regression to predict colonization of
red maple.
Step #
1
2
3
4
5
6
Variable
DE
E/I
A
FS
RMSC
FH
R^2
0.347
0.525
0.563
0.597
0.615
0.632
P Value
0.01
0.001
<0.001
<0.001
0.034
<0.001
To predict loblolly pine colonization, forward stepwise regression selected 5 of the 10
variables measured (r2=0.717, p<0.001) (Table 2-5) as follows,
LPD=e^(53.296+(0.195*LPBA)-(4.065*FH)+(0.762*PI)+(0.102*E/I)+(1.128*A),
where, LPD is the colonizing density of loblolly pine, and LPBA is the basal area of
loblolly pine in the adjacent forest.
56
Table 2-5. Variables selected by forward stepwise regression to predict colonization of
loblolly pine.
Step #
1
2
3
4
5
Variable
LPBA
FH
PI
E/I
A
R^2
0.43
0.535
0.689
0.699
0.717
P Value
<0.001
<0.001
0.003
<0.001
<0.001
Accuracy of model equations is greater when environmental conditions fall within the
measured range of each parameter (presented in Table 2-6).
Table 2-6. Ranges of each environmental condition measured at all sites
Parameter
Location
Median
SE
Min
Max
3 spp combined density (stems/ha)
Restoration Area
3360
1017
0
68199
Sweetgum density (stems/ha)
Restoration Area
1323
887
0
62373
Red maple density (stems/ha)
Restoration Area
419
238
0
22581
Loblolly pine density (stems/ha)
Restoration Area
171
222
0
18868
Distance to forest edge (m)
Restoration Area
95.6
9.0
6.1
436.4
Edge to interior ratio (m/ha)
Restoration Area
78.7
2.6
38.1
143.7
Total herbaceous cover
Restoration Area
80.8
1.9
10.0
138.3
Prevalence Index
Restoration Area
2.92
0.04
1.41
3.99
Consecutive days of saturation
Restoration Area
24
1
2
88
Restoration size (ha)
Restoration Area
22.7
1.4
8.1
49.4
Years post restoration
Restoration Area
5
0.1
4
7
3 spp combined density (stems/ha)
Adjacent Forest
850
68
50
2900
Sweetgum density (stems/ha)
Adjacent Forest
350
29
50
1100
Red maple density (stems/ha)
Adjacent Forest
225
60
0
2300
Adjacent Forest
50
23
0
900
Adjacent Forest
45.3
2.9
3.3
125.1
Adjacent Forest
20.4
2.5
2.6
96.7
Adjacent Forest
8.4
1.5
0
52.7
Loblolly pine basal area (m /ha)
Adjacent Forest
3.5
2.4
0
101.9
Height (m)
Adjacent Forest
17.5
0.4
11.2
25.0
Forest Size (ha)
Adjacent Forest
219.8
117.6
1.3
712.9
Loblolly pine density (stems/ha)
2
3 spp combined basal area (m /ha)
2
Sweetgum basal area (m /ha)
2
Red maple basal area (m /ha)
2
57
DISCUSSION
Sweetgum, red maple and loblolly pine are found in various natural wetland types in
Virginia including broad-leaved deciduous/needle-leaved evergreen, temporarily flooded
(PFO1/4A) wetlands. These wetlands are typically inundated for 3 to 6 weeks during the
winter and early spring (Cowardin et al. 1979). In a study of old field succession in the
Coastal Plain Province of Virginia, Monette and Ware (1983) found that sweetgum, red
maple and loblolly pine are common pioneer species into abandoned agricultural fields.
These three species represented a large portion of the shrub/sapling stratum in the 5 sites
that were sampled and satisfied the woody stem requirement (495-990 stems/ha) in many
of the sampled plots. A large portion of the trees sampled were within 100 m of the
adjacent forest, suggesting that successful colonization of these three species is limited
beyond this distance possibly due to seed dispersal limitations. Bormann (1953) reported
an effective seed dispersal distance of 61 m for sweetgum with an average colonizing
density of 6178 stems/ha in recently abandoned old fields in the Piedmont Province of
North Carolina. Median sweetgum density in this study ( ~
x = 1323 stems/ha) was lower
than reported by Bormann ( x = 6178 stems/ha). However, colonization of sweetgum in
the current study occurred at distance of 435 m from the adjacent forest, 374 m further
than reported by Bormann (1953).
Median colonizing density of red maple in this study was 419 stems/ha but maximum
density was 22,580 stems/ha at 25 m from the adjacent forest edge. Gardescu and Marks
(2004) measured red maple seed input and seedling establishment in 4 to 18 year old
abandoned agricultural fields in New York and found that close to the forest edge (25 m)
58
the density of red maple seeds was 1,170,000 seeds/ha and that the average density of red
maples that emerged was 10,000 seedlings/ha to 50,000 seedlings/ha. The study also
concluded that red maple seedling survival was very low (0% to 23%) mainly due to dry
conditions and herbivory. Several years during the development of the sites in this study,
including 2007 and 2008, were considered drought years by the USACOE (The Nature
Conservancy 2009). McQuilkin (1940) reported an effective seed dispersal distance for
loblolly pine of 100 m and an average of 2471 stems/ha of abandoned agricultural fields
in the Piedmont Province of North Carolina and Virginia, ranging in age from 1 to >10
years post abandonment. Median loblolly pine density in this study was 171 stems/ha
with a maximum density of 18,868 stems/ha and 79.5% of sampled loblolly pine
occurring within 100 m of an adjacent forest.
Development of computer simulations modeling forest dynamics often does not take into
account variables that limit successful colonization of trees such as low seed and seedling
survival, intra- and inter- species competition, and resource limitations (Pacala et al.
1996). By quantifying the density and distribution of established trees within the
restoration area, the variables that effected the survival of seedlings can be taken into
account. The models presented in this study predict the density and distribution of these
pioneer colonizing trees on 4-7 year old abandoned agricultural fields that have been
restored to the approximate wetland conditions prior to their conversion to agriculture.
Of the 10 variables measured, 8 were selected by forward stepwise regression as
predictors of colonizing density of either all three species combined or individual species
59
colonizing densities. Size of the adjacent forest, density of seed trees in the adjacent
forest and basal area of seed trees in the adjacent forest were all positively correlated with
colonizing tree densities in the current study. Several authors have reported positive
relationships between these variables and the amount of seeds that can be dispersed into
abandoned agricultural fields (Gardescue and Marks 2004; Greene and Johnson 1994;
Young 1995). The height of the adjacent forest was negatively correlated with the
colonizing density of red maple and loblolly pine. The height the seed trees determines
the height of seed release and influences the terminal velocity and the distance individual
seeds can travel, influencing seed dispersal patterns (Harper 1977; Fenner 1985). The
edge to interior ratio was positively correlated with the colonizing density of red maple
and loblolly pine. The length of forest edge in relation to the amount of interior
influences the density and distribution of seeds. Young (1995) found that exposed canopy
trees positioned near forest edges produce more seeds and Battaglia et al. (2007) found
that large bottomland hardwood forest restoration sites may not be colonized due to low
edge to interior ratios. The distance to the forest edge was negatively correlated with the
colonizing density all three species combined and the colonizing density of sweetgum
and red maple. The distance from the forest edge influences seed distribution patterns
because anemochorous seeds that are dispersed from dense vegetation, such as the edge
of a forest, tend to have a uniform negative exponential seed dispersal curve (Fenner
1985; Greene and Johnson 1996). The herbaceous PI quantifies the amount of
hydrophytic vegetation and is an indication of hydrologic conditions in wetland
compensation sites (Atkinson et al. 1993) which can influence tree seed germination (De
Steven 1991), competition (Davis et al. 1998), and seedling survival (Wallace 1996). In
60
this study, herbaceous PI was positively correlated with colonizing densities of all three
species combined and colonizing densities of sweetgum and loblolly pine. The number of
years after an agricultural field has been abandoned influences the density of trees
because colonization proceeds as self-thinning commences (MacArthur and Wilson
1967). In this study, the number of years after a site was abandoned was positively
correlated with colonizing densities of all three species combined and separate. These 8
variables are important to consider when selecting forested wetland restoration sites
where specific woody stem densities are required.
CONCLUSION
The colonizing densities and distribution of sweetgum, red maple, and loblolly pine
assisted in reaching the required woody stem count and were similar to reported densities
and distributions found in other abandoned agricultural fields. Heavy colonization by
these three species can limit early colonization by less aggressive and late successional
tree species.
Based on the colonizing tree density models, the wetland restoration site characteristics
that increase tree colonization densities would be; sites that have large adjacent forests
with high density and large basal area of seed trees, high edge to interior ratio and sites
that are smaller in overall size. The hydrologic conditions present at the restoration site
would influence the overall species composition and density of the three species
investigated in this study. More detailed testing of the model at additional sites and across
a greater range of environmental conditions would increase confidence in model
61
predictions. Wetland restoration practitioners in Southeastern Virginia might find these
models particularly useful in old field restoration projects and based on these models
results, have more confidence on where tree planting should occur to reach the required
woody stems counts.
62
LITERATURE CITED
Acevedo, M. F., D. L. Urban and M. Ablan. 1995. Transition and gap models of forest
dynamics. Ecological Applications 5:1040-1055.
Atkinson, R. B., J. E. Perry, E. Smith and J. Cairns, Jr. 1993. Use of created wetland
delineation and weighted averages as a component of assessment. Wetlands
13:185-193.
Battaglia, L. L., D. W. Pritchett and P. R. Minchin. 2007. Evaluating dispersal limitation
in passive bottomland forest restoration. Restoration Ecology 16:417-424.
Bormann, F. H. 1953. Determining the role of loblolly pine and sweetgum in early oldfield succession in the piedmont of North Carolina. Ecological Monographs
23:339-358.
Bugmann, H. 2001. A review of forest gap dynamics. Climate Change 51:259-305.
Bullock, J. M. and R. T. Clarke. 2000. Long distance seed dispersal by wind: Measuring
and modeling the tail of the curve. Oecologia 124:506-521.
Busing, R. T. and M. Daniel. 2004. Advances in spatial, individual-based modeling of
forest dynamics. Journal of Vegetation Science 15:831-842.
Cairns, Jr., J., M. L. Dahlberg, K. L. Dickson, N. Smith and W. T. Waller. 1969. The
relationship of fresh-water protozoan communities to the MacArthur-Wilson
equilibrium model. The American Naturalist 103:439-454.
Center For Wetland Conservation. 2008. 2008 Monitoring Reports. Center For Wetland
Conservation, Christopher Newport University, Newport News, VA, USA.
Clark, J. S., M. Silman, R. Kern, E. Macklin and J. HilleRisLambers. 1999. Seed
dispersal near and far: Patterns across temperate and tropical forests. Ecology
80:1475-1494.
Cowardin, L. M., V. Carter, F. C. Golet and E. T. LaRoe. 1979. Classification of
wetlands and deepwater habitats of the United States. United States Department
of the Interior, Fish and Wildlife Service, Office of Biological Services,
Washington D.C., USA.
Dahl, T. E. 1990. Wetland losses in the United States 1780's to 1980's. United States
Department of the Interior, Fish and Wildlife Service, Washington, D. C., U.S.A.
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Davis, M. A., K. J. Wrage and P. B. Reich. 1998. Competition between tree seedlings and
herbaceous vegetation: Support for a theory of resource supply and demand.
De Steven, D. 1991. Experiments on mechanisms of tree establishment in old-field
succession: Seedling emergence. Ecology 72:1066-1075.
ESRI Inc. 2008. ESRI ArcMap 9.3. Redlands, CA, USA.
Fenner, M. 1985. Seed Ecology. Chapman and Hall Ltd. New York, NY, USA.
Gardescue, S. and P. L. Marks. 2004. Colonization of old fields by trees vs. shrubs: Seed
dispersal and seedling establishment. Journal of the Torrey Botanical Society
131:53-68.
Greene, D. F. and E. A. Johnson. 1989. A model of wind dispersal of winged or plumed
seeds. Ecology 70:339-347.
Greene, D. F. and E. A. Johnson. 1994. Estimating the mean annual seed production of
trees. Ecology 75:642-647.
Greene, D. F. and E. A. Johnson. 1996. Wind dispersal of seeds from a forest into a
clearing. Ecology 77:595-609.
Harper, J. L. 1977. Population biology of plants. Academic Press, London.
Iverson, L. R., M. W. Shwartz and A. M. Prasad. 2004. Potential colonization of newly
available tree-species habitat under climate change: an analysis for five eastern
US species. Landscape Ecology 19:787-799.
Johnson, W. C. 1988. Estimating dispersibility of Acer, Fraxinus and Tilia in fragmented
landscapes from patterns of seedling establishment. Landscape Ecology 1:175187.
Katul, G. G., A. Porporato, R. Nathan, M. Siqueira, M. B. Soons, D. Poggi, H. S. Horn
and S. A. Levin. 2005. Mechanistic analytical models for long-distance seed
dispersal by wind. The American Naturalist 166:368-381.
Levin, S. A. 1976. Population dynamic models in heterogeneous environments. Annual
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Levin, S. A. and R. T. Paine. 1974. Disturbance, patch formation, and community
structure. Proceedings of the National Academy of Science 71:2744-2747.
Liu, J. and P. S. Ashton. 1995. Individual-based simulation models for forest succession
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MacArthur, R. H. and E. O. Wilson. 1967. The theory of island biogeography. Princeton
University Press. Princeton, New Jersey.
McQuilkin, W. E. 1940. The natural establishment of pine in abandoned fields in the
piedmont plateau region. Ecology 21:135-147.
Monette, R. and S. Ware. 1983. Early forest succession in the Virginia Coastal Plain.
Bulletin of the Torrey Botanical Club 110:80-86.
Myster, R. W. and S. T. A. Pickett. 1992. Effects of palatability and dispersal mode on
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and S. A. Levin. 2002. Mechanisms of long-distance dispersal of seeds by wind.
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2008. The Nature Conservancy, Charlottesville, VA, USA.
Pacala, S. W., C. D. Canham, J. Saponara, J. A. Silander Jr., R. K. Kobe and E. Ribbens.
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United States Army Corps of Engineers Norfolk District and Virginia Department of
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hydrology and soils. Restoration Ecology 4:33-41.
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66
Chapter 3 - Further investigations
TREE DISTRIBUTION PATTERNS
In order to test if the tree distribution pattern matched the expected negative exponential
decline in stem density as distance from the adjacent forest increased, the colonizing
density was transformed using the natural logarithm and a linear regression was
performed (Willson 1993) (Figure 3-1).
ln(Average sweetgum, redmaple, loblolly pine colonizing density
(stems/ha))
12
10
8
6
4
2
y = -0.005x + 8.2939
R² = 0.0628
0
0
50
100
150
200
250
300
350
400
450
500
Distance from forest edge (m)
Figure 3-1. Distance from the adjacent forest edge and the colonizing density of
sweetgum, red maple, and loblolly pine for all 160 plots. Regression of the natural
logarithm of the colonizing density of all three species combined and the distance from
the forest edge yields R2 = 0.0628 (p=0.001).
Distance from the adjacent forest edge was not a good predictor of the combined
colonizing density of all three species and the distribution of colonizing trees does not
67
match the expected negative exponential decline (R2 = 0.0628) (Figure 3-1). Myster and
Pickett (1992) found that the negative exponential woody stem distribution pattern was
significantly related to the distance from the forest edge, but only in the first 6 years after
abandonment. The authors suggested that zoochorous dispersal and dispersal from within
the field, in addition to shading and self thinning, become increasingly important later in
succession.
SELF THINNING
Self thinning did occur at Sites 2, 3 and 5 which ranged in age from 5-7 and were
monitored in 2007 and 2008. At sites 2, 3, and 5 the average stem density of sweetgum
decreased by 1873 stems/ha between 2007 and 2008. The average density of red maple
decreased by 222 stems/ha between 2007 and 2008 while the average density of loblolly
pine increased by 329 stems/ha. The plots that experienced self thinning between 2007
and 2008 had reached an average density of 10,141 stems/ha of sweetgum, 3392 stems/ha
of red maple and 2013 stems/ha of loblolly pine. The expected distribution pattern may
not be observed at these sites because self thinning was occurring in some of the
restoration areas.
68
LITERATURE CITED
Myster, R. W. and S. T. A. Pickett. 1993. Effects of litter, distance, density and
vegetation patch type on postdispersal tree seed predation in old fields. Oikos
66:381-388.
Willson, M. F. 1993. Dispersal mode, seed shadows, and colonization patterns. Vegetatio
107/108:261-280.
69
Appendix A – Site Maps
Figure A-1. Site 1 plot location map.
70
Figure A-2. Site 2 plot location map.
71
Figure A-3. Site 3 plot location map.
72
Figure A-4. Site 4 plot location map.
73
Figure A-5. Site 5 plot location map.
74