Remote Sensing of Glossy and Common Buckthorn (Frangula Alnus and Rhamnus Cathartica) in the Oak Openings Region of Northwest Ohio

Home , Rhamnus cathartica

A Thesis

entitled

Connecting the dots: Remote sensing of Glossy and Common Buckthorn (Frangula alnus

and Rhamnus cathartica) in the Oak Openings Region of Northwest Ohio

Kirk Zmijewski

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the

degree of Master of Science Degree in Geology

______Dr. Richard Becker, Committee Chair

______Dr. Johan Gottgens, Committee Member

______Dr. Jonathan Bossenbroek, Committee Member

______Dr. Patricia R. Komuniecki, Dean College of Graduate Studies

The University of Toledo May 2013

This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author

An Abstract of

Connecting the dots: Remote sensing of Glossy and Common Buckthorn (Frangula alnus and Rhamnus cathartica) in the Oak Openings Region of Northwest Ohio

Kirk A. Zmijewski

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Geology

The University of Toledo

May 2013

Glossy and common buckthorn (Frangula alnus and Rhamnus cathartica) are invasive woody shrubs that formed dense monoculture thickets in the Oak Openings

Region of NW Ohio. Conventional techniques of vegetation mapping are expensive and time-consuming. Thickets were identified using remotely sensed land surface phenology of buckthorn, that leafs out earlier and senesces later than native plant species. A tasseled-cap greenness index, which correlates with vegetation health and biomass, was calculated for 49 Landsat images across two time steps (2001-2006 and 2007-2011) to determine annual phenology. Areas of known thicket and aerial photography were used to classify buckthorn thicket. The satellite image classification showed a 37% increase in thicket extent (690 hectares to 945 hectares) between 2001-2011. P-plots were used to determine buckthorn thicket species composition and density. Sixty field sites were surveyed for presence/absence of thicket to determine accuracy and a kappa hat value of

0.73 was obtained.

iii

The classification image was used to model the spatial distribution of buckthorn in order to determine drivers causing monoculture thickets. A logistic regression was used against presence/absence of thicket as a function of the variables: height above river centerline, density of groundwater wells, and distance to agricultural sources. The model showed a pseudo-R2 value of 0.13 and predicted a 10-20% increase in likelihood of buckthorn thicket for each decrease of 30 cm elevation toward river center with a p-value of <0.01. Buckthorn thicket is not strongly driven by the variables modeled and other factors should be tested. Remote sensing identification of invasive species using phenology can be a very important tool for researchers and land managers that wish to remediate buckthorn invasions by understanding their landscape scale distribution.

For Mom and Dad, thanks for believing in me and helping me get this far.

Acknowledgements

First and foremost I must acknowledge my advisor Dr. Richard Becker without his guidance and assistance, this thesis or probably my entire academic career would not have been possible. He opened the door and I walked through it. Dr. Todd Crail also deserves thanks for nurturing my interest in invasive species and basically giving me the idea for this thesis. I hope that he and others may find the results of this work useful.

Dr. Jon Bossenbroek and Dr. Hans Gottgens were also very helping in providing me with direction and making sure I stuck with the proper scientific method. Dr. Jamie

Martin-Hayden may not have been on my final thesis committee, but he provided many good ideas and helped during the infancy of this thesis and inspired me towards my current path.

Also big thanks goes to Jon Sanders and Colleen Nagel for providing crucial field assistance because without their help climbing through awful buckthorn thickets, getting filthy and wet I would not have had the data I needed to complete this thesis. Lastly, all the current and past members of the Environmental Remote Sensing Lab have been there during this whole endeavor providing motivation and companionship to get the job done.

Contents

Abstract iii

Acknowledgements vi

Contents vii

List of Tables ix

List of Figures x

List of Abbreviations xi

1 Introduction and Problem Statement 1

1.1 Invasive Species…………………………………………………………….. 1

1.2 Study Area: Oak Openings………………………………………………….. 3

1.2.1 History…………………………………………………………... 3

1.2.2 Geology…………………………………………………………. 5

1.2.3 Ecology………………………………………………………….. 6

1.3 Buckthorn……………………………………………………………………. 7

1.3.1 Buckthorn in the Oak Openings………………………………… 8

2 Previous Methods, Hypothesis and Study Objectives 11

2.1 Remote Sensing Background……………………………………………… 11

2.1.1 Satellite Imagery and Sensor Resolution………………………. 12

2.2 Previous Methods………………………………………………………….. 13

vii

2.2.1 Phenology………………………………………………………. 14

2.3 Theoretical Approach…………………………………………………… 15

2.4 Study Objectives and Hypotheses……………………………………..... 16

3 Remote Sensing of Buckthorn: Image Processing and Field Confirmation 17

3.1 Image Selection………………………………………………………… 17

3.2 Image Processing and Classification…………………………………… 19

3.3 Buckthorn Thicket Quantification……………………………………… 23

3.3.1 Field Confirmation……………………………………………… 24

3.3.2 Accuracy Assessment…………………………………………… 25

4 Spatial Modeling of Invasibility and Invasive potential 29

4.1 Model Framework…………………………………………………….... 29

4.2 Variables Tested……………………………..…………………………. 31

4.3 Model Methodology……………………………………………………. 33

5 Results and Discussion 38

5.1 Remote Sensing Results………………………………………………… 38

5.2 Model Results…………………………………………………………… 42

5.3 Recommendations………………………………………………………. 45

References 47

A Summary of Buckthorn Thicket Confirmation Locations 53

B Summary of P-Plot Sampling Data 55

viii

List of Tables

3.1 A summary of the Landsat scenes used in the study ……………………………18

3.2 Summary of the p-plot data……………………………………………… ……...25

3.3 Kappa hat tests for all classification methods …………………………………..27

4.1 Summary of variables tested in the model………………………………… ……31

4.2 Descriptive statistics for all variables tested in the model ………………………37

5.1 Kappa hat of final classification method ……………………………………….38

5.2 Logistic regression model results ………………………………………………43

List of Figures

1-1 Study area location map .…………………………………………………………4

3-1 Land surface phenology curves …………………………………………………21

3-2 Pixel plot diagram ………………………………………………………………22

3-3 Pixel plot location map …………………………………………………………23

3-4 Buckthorn thicket confirmation location map ………………………………….26

4-1 A conceptual model of buckthorn invasive potential …………………………...30

4-2 Nutrient deposition flow event water sampling ....………………………………35

5-1 Regional buckthorn classification ….…………………………………………39

5-2 Buckthorn classification at Irwin Prairie State Nature Preserve ………………..41

List of Abbreviations

AGD……………….. Agricultural Path Distance

AVHRR……………. Advanced Very High Resolution Radiometer

BT………………….. Buckthorn (Glossy and Common)

EVI………………… Enhanced Vegetation Index

GLM……………….. Generalized Linear Model

GWD……………….. Shallow Groundwater Well Density

HAR……………….. Height Above River Centerline

IPSNP……………… Irwin Prairie State Nature Preserve

MODIS……………. Moderate Resolution Imaging Spectroradiometer

NDVI……………… Normalized Difference Vegetation Index

NLCD……………... National Land Cover Dataset

TC-G……………… Tasseled-Cap Greenness

TMx………………. Thematic Mapper (x is the corresponding band number)

USGS……………… United States Geological Survey

Chapter 1

Introduction and Problem Statement

1.1 Invasive Species

Invasive species have emerged in recent decades as a serious threat to ecosystems across the globe. An invasive species is defined as any species, plant or animal that establishes a new range outside of its natural habitat and proliferates to the detriment of the local ecosystem (Mack et al. 2000). Invasive species are one of the greatest threats to biodiversity and cause many local extirpations of native species (Vitousek et al. 1997).

Terrestrial plant species make up the majority of alien species introductions in the United

States at over 25,000 estimated species (Pimentel et al. 2005). Increased globalization and environmental modification have provided many new vectors for the spread of species outside of their natural range which would normally be constrained by environmental factors (i.e. mountains or oceans) (Hulme 2009). Within the United

States alone, the economic costs associated with invasive species simply for management and control, have been estimated to be as much as $137 billion annually (Pimentel et al.

2000). Factors such as biodiversity reduction, ecosystem service loss, species extinctions,

1 and aesthetic values when included may increase costs significantly but are much more difficult to quantify.

While many exotic species are introduced, only around 10% become established and 10% of these naturalized species becomes invasive (Lockwood et al. 2001;

Williamson 1996). The successful establishment of an invasive species depends on a number of factors including propagule pressure (total number of the introduced plants in an ecosystem), the characteristics of the plant itself, and invasibility of the ecosystem where the introduction occurs (Davis et al. 2000; Williamson and Fitter 1996).

Invasibility is generally defined as the susceptibility of an ecosystem to invasion including life-history traits that give a noxious invader a competitive advantage over native species (Rejmánek and Richardson 1996). While many species become naturalized the majority of exotics simply exist as a minor part of the plant communities while a few become noxious weeds that form monocultures to the detriment of native plant communities (Williamson and Fitter 1996). In the case of terrestrial plant species noxious invaders can become a community dominant with deleterious effects on the native plant species; phragmites (Phragmites australis), garlic mustard (Alliaria petiolata), and

European purple loostrife (Lythrum salicaria) are examples of noxious invaders (weeds) in the midwest United States (Czarapata 2005).

The conditions required for a noxious invader to establish as a community dominant are widely variable. Factors that determine whether a plant will become a problem invasive can be split into two categories: life-history traits of the plant and external environmental factors. Contributing environmental factors include: disturbance,

2 habitat fragmentation, eutrophication, lack of natural fires, presence/absence of large herbivores, and a lack of natural predators or competitors (Cappuccino and Carpenter

2005; Hansen and Clevenger 2005; Lake and Leishman 2004). The life-history attributes of the plant that contribute to invasive potential include high fecundity, smaller seed size which allows for wide seed dispersal, high germination rates, efficient resource use, fast growth rates, a short juvenile period allowing for quick reproductive turnaround, and in a more general sense, plants from families not already represented in the local taxa

(Lockwood et al. 2001; Rejmánek and Richardson 1996).

This study investigates the extent of two invasive buckthorn species (Frangula alnus and Rhamnus cathartica) and identifies environmental factors that influence invasibility at the landscape scale.

1.2 Study Area: Oak Openings

1.2.1 History

The Oak Openings is located in Northwest Ohio and Southeast Michigan. Figure

1.1 shows the study area and total extent of the region based on the sandy soil type characteristic of the Oak Openings. Oak Openings is named for its large tracts of open

Oak Savannas which early settlers used to traverse the much more impassible Great

Black Swamp. Many of the sandy ridges were used as transportation and hunting trails by the displaced Ottawa Tribe and later the European settlers (Kaatz 1955). The Great Black

Swamp was formerly one of the largest wetlands in the country, but by 1850 much of the swamp had been drained by whites who began farming the land when the fertile nature of the soils underlying the swamp was discovered (Kaatz 1955). Today over 90% of Ohio‟s 3

Figure 1-1: A map showing the location and extent of the Oak Openings Region (label A) and the extent of the study area (label B). The Oak Openings region is outlined in gray.

4 wetlands have been lost, a large majority of which existed in northwest Ohio as the Great

Black Swamp (Dahl and Allord 1998). Scattered throughout the Oak Openings region of northwest Ohio are many parks and preserves representing Oak Savanna and Wet Prairies which formerly dominated the region. Irwin Prairie State Nature Preserve (IPSNP) is an example of wet prairie restoration and where field confirmation was conducted.

1.2.2 Geology

The Oak Openings region is situated on top of what is thought to be a freshwater barrier island complex that formed as the Wisconsinan glaciation ended 10,000 years ago. As the ice sheet receded, a series of recessional moraines formed. The Fort Wayne Moraine

(atop which modern day Fort Wayne, IN is built) is what impounded of the first of a series of proglacial lakes, known as Lake Maumee. Ice was completely out of northwest

Ohio by 10,000 years ago and lake levels also began to drop as new drainage outlets opened to the east and north (Calkin and Feenstra 1985). The Oak Openings sand ridge is up to 10 km wide and has sand a deep as 15 m in some places. The top of the sand ridge is covered with parabolic sand dunes which were active during multiple periods after deglaciation. The subtle topography that these dunes provide in tandem with the temperate climate provides a heterogeneous landscape which contributes to the diverse plant communities. Higher areas have sandy, well-drained soils while the lower areas commonly are wet and have muck type soils.

1.2.3 Ecology

The Oak Openings can be broadly defined as an assemblage of plant communities with sandy soils, however five specific plant communities make up the basis of the ecological zone including: oak savanna, oak barrens, mesic tallgrass prairie, twig rush wet prairie, and floodplain forests (Brewer and Vankat 2004). Oak species (Quercus sp.) historically made up the majority of the tree species because of their resistance to frequent fires that occurred in the oaks (from both lightning strikes and native peoples)

(Moseley 1928). Over 1300 plant species can be found in Oak Openings of which more than 100 are endangered or threatened within the state of Ohio (Sheaffer and Rose 1998).

A federally endangered species of butterfly, the Karner Blue (Lycaeides melissa samuelis) can also be found in the oaks and it depends on large areas of wild blue lupine

(Lupinus perennis) which grows in abundance in oak savannas.

While the Oak Openings region was not settled by immigrants historically because the sandy soils were not well suited for farming these well-drained, stable, sandy soils are ideal building locations for residential areas. For this reason it has seen increased pressures from urban sprawl since the 1950s-present push outward from the core of

Toledo. Natural fires were quickly suppressed because of the close proximity to human development. This lack of fires removes the competitive advantage for the oak species which were formerly dominant. Vegetation naturally succeeds to maple and beech (Acer and Fagus) forest, which are both less fire tolerant species but more tolerant of shade

(Abrams 1992). Because natural drainage in the area was poor and the sand is underlain by lacustrine clay groundwater fed wetland areas were common. Ditches were

6 constructed to remove water from agricultural fields and reduce standing water. These ditches have also provided a potential vector for nutrient rich agricultural runoff to enter a normally low-nutrient ecosystem. Furthermore, ditch construction and tile drainage installation have altered groundwater levels, which when lowered had deleterious effects on wet prairies in the region. However, it is unclear which of the factors described have led to the establishment of invasive plants in the Oak Openings, specifically Glossy and

Common buckthorn (Frangula alnus and Rhamnus cathartica).

1.3 Glossy and Common Buckthorn

Glossy buckthorn and common buckthorn are woody shrubs native to Eurasia.

They grow in forest understories, open fields, and sometimes dense monoculture thickets.

They have been commonly used as hedgerows because of their dense foliage and they most likely escaped cultivation in the early to mid-1800s after they were brought to

America by European immigrants (Knight et al. 2007). Glossy buckthorn is usually found in fringe wetland areas and can survive inundation for multiple weeks out of the year in contrast to common buckthorn which is found in drier areas. Both plants have very high fecundity and may have germination rates in excess of 85% (Frappier et al. 2003). The majority of the berries fall directly under the parent tree which can account for spread rates of up to 6m annually (Frappier et al. 2003). The berries of the plant float in water and can germinate even after weeks of inundation (Howell and Blackwell 1977).

Buckthorn has been known to leaf out earlier than native plants providing it with a competitive advantage, adding more than 60% of new plant biomass before overstory plants have leafed out (Knight et al. 2007).

Buckthorn also facilitates other invasive species. The European Starling (Sturnus vulgaris) has contributed to the spread of buckthorn and uses the buckthorn berries as food (Howell and Blackwell 1977). Common buckthorn has been called the keystone of a trophic cascade, meaning that it is the central plant facilitating other invasive species, in turn creating a positive feedback loop improving conditions for buckthorn. The soybean aphid (Aphis glycines) is a major agricultural pest crop, which uses buckthorn to survive cold winters. Buckthorn alters soil properties such as pH and soil moisture and provides shade for invasive Eurasian earthworms that preferentially feed on the leaf litter from the buckthorn further enriching the soil in nitrogen (Heneghan 2006). These factors may limit ecosystem recovery even after the buckthorn is removed.

1.3.1 Buckthorn in the Oak Openings

Buckthorn was first introduced in North America in the early to mid-1800s, but is not thought to have reached Ohio until the 1920s, some 80 years later (Torrey 1824). One of the first surveys of the Oak Openings by Edwin Moseley in 1929 showed buckthorn as

“rare” or “infrequent” and a similar survey 50 years later in 1978 showed that both species of buckthorn still were not found in high abundance (Easterly 1979; Moseley

1928). However, today buckthorn can be found in dense monoculture thickets where over

90% of the woody shrubs consist of glossy and common buckthorn (Becker et al. 2013).

Several changes have occurred in the last few decades that may have contributed to the shift in buckthorn to a community dominant including urban sprawl, land use change, hydrological regime changes (caused by increased aquifer demand), and a significant increase in agricultural nutrients applied to farm fields (Tilman et al. 2001).

Studies of agricultural crops have shown that plants may only utilize 30% of the fertilizer applied and the remainder can run off into streams and rivers or will bind to soil particles to be washed away later during large storm events (Tilman et al. 2001). While conservation tillage practices are employed on roughly 50% of agricultural lands in nearby drainage basins many of the rivers see large peak nutrient concentrations during early spring when runoff is high due to frozen ground. Some areas in close proximity to agricultural ditches are inundated regularly during spring flooding and excess nutrients become deposited there. In the Oak Openings buckthorn thickets are commonly found in these flood prone areas.

Research on the impact of glossy and common buckthorn in the Oak Openings is limited. However, some parallels may be drawn for the Oak Openings from other studies.

Reductions in biodiversity directly attributed to buckthorn at very high densities could be harmful to the overall stability of the ecosystem (Tilman 1996). Buckthorn does not always become a dominant invader in all ecosystems. Without external pressures buckthorn may simply coexist with native species. One study found that over 15 years buckthorn did little to change the species composition in a relatively undisturbed area

(Mills et al. 2009). However, in the Oak Openings there has been a shift from buckthorn simply existing as a community member to a monoculture in some areas. The dense nature of buckthorn thickets reduces aesthetic and recreation value areas that become overgrown. Many of the parks and preserves in northwest Ohio exist to preserve the Oak

Openings region. Successful management efforts require an understanding of the causes leading up to the shift of buckthorn from simply being present in the community to a dominant in the community that alters the ecosystem. Remote sensing can be employed 9 to identify buckthorn and aid in the determination of spatial variables that control the distribution of buckthorn which will be discussed in the next chapter.

Chapter 2

Previous Methods, Hypothesis and Study Objectives

2.1 Remote Sensing Background

Identifying and mapping the extent of an invasive species is important to understand how the species has spread and to be able to make predictions about areas that are vulnerable to expansion of an ongoing invasion. Field surveys of plants can be prohibitively expensive and access to areas in the case of buckthorn thickets is extremely limited due to the impassible nature of dense thickets. Remote sensing can be employed as a tool to map large areas for relatively low-cost with field work only necessary for in- situ confirmation of remotely sensed data. However, considerations such as plant extent, appropriate sensor resolution (spatially and temporally), and the ability to differentiate invasive plants from native species must be taken into account. Knowledge of the plant spectral properties as well as life history and phenological characteristics are necessary pre-requisites to any remote sensing approach. Buckthorn phenology is different than native plant communities within the Oak Openings region and this fact can be used to identify the plant using remote sensing.

2.1.1 Satellite Imagery and Sensor Resolution

The human eye sees colors as visible light from violet (400nm) to red (700nm) in the electromagnetic spectrum while cameras and sensors used on satellites and aircraft have specific bands of radiation that they detect based on the purpose of the sensor. The width of the band is described as the full width half maximum of the maximum intensity of light that a sensor has been calibrated to (e.g. Landsat MSS red band responds from

700 to 800 nm). The spectral resolution of a satellite is the total number of bands on the satellite and the width of the bands. Both Landsat 5 and 7 have three visible light bands

(Bands 1, 2, and 3), one near infrared (Band 4), two shortwave infrared (Band 5 and 7), and one thermal band (Band 6). A multispectral satellite has multiple bands while a hyperspectral satellite has hundreds of separate bands. Gaps are left between bands on the sensor because the atmosphere may absorb or scatter the reflected light due to water vapor and aerosols or the area does not provide additional information of interest.

Spatial resolution of the sensor is the smallest unit of area that can be resolved by the sensor. Landsat Thematic Mapper (TM) has a spatial resolution of 30 x 30 meters which corresponds to one pixel on the image produced by the satellite. Each pixel is an amalgamation of all the objects within the 30 x 30 meter area. Temporal resolution is the recurrence of images acquired. To establish a time series of images it is necessary to repeat the same path in order to compare changes across time. The temporal resolution of both Landsat 5 and 7 is 16-days, meaning it takes 16 days for the satellite to acquire an image over the same location. Any sensor requires a tradeoff between spatial or temporal resolution. To obtain higher spatial resolution data it requires lower altitude acquisition

12 and the total spatial extend of the image acquired will be smaller. This limits the repeatability of images because in the case of satellites it will require a much longer time to take an image at the same location.

2.2 Previous Methods

Remote sensing has been a common tool employed to identify invasive plant species using a variety of methods. Phenological differences during specific time periods have been used to identify leaf out or senescence (Bradley and Mustard 2006; Resasco et al. 2007). Other studies have developed phenological metrics such as accumulated degree days, relative humidity or the season length to identify plants (Brown and de Beurs 2008;

De Beurs and Henebry 2005; Tuanmu et al. 2010). Additionally, high spatial resolution satellite data such as IKONOS or Quickbird has been used to interpret plants from color infrared imagery (Müllerová et al. 2005). High temporal and low spatial resolution data such as Advanced Very High Resolution Radiometer (AVHRR) or Moderate Resolution

Imaging Spectroradiometer (MODIS) have been used to monitor variations in large scale phenology as a result of changing climate and other pressures (Justice et al. 1985;

Morisette et al. 2009). One study used a single Landsat image to determine sensitivity of different vegetation indices to identify an invasive species (Kandwal et al. 2009).

Numerous studies have used hyperspectral imagery to differentiate between invasive species based on their spectral properties (Andrew and Ustin 2008; Hestir et al. 2008;

Lawrence et al. 2006; Underwood et al. 2003). Even when high spectral resolution data is available, phenology allows for better differentiation between non-native and native plants

The previous methods described have identified invasive species against a homogeneous background land cover or plant type. The Oak Openings is not homogenous so many of these methods would be unsuitable in the study area. Using

Landsat imagery can allow discrimination with the appropriate spatial resolution (30m) and the appropriate temporal resolution (8-16 days). Historically, acquisition of images was prohibitively expensive with single scenes costing thousands of dollars. For this reason many methods employed the use of one or two scenes specifically targeting a time of year that would minimize cloud cover and maximize the amount of data found within the images. However, the Landsat archive has recently been made available to the public for free allowing large numbers of Landsat scenes to be used in analyses.

2.2.1 Phenology

Plant phenology describes the life cycle of a plant including budding, leaf out, growth and senescence. The phenology of buckthorn and other plants can be determined using a vegetation index that can be obtained from multispectral satellite imagery. Because most understory plants look similar in visible light due to strong absorption in blue and red light it is difficult to differentiate them using visible light. However, plants reflect well in near-infrared light and vegetation indices can be established comparing this difference.

The more leaves that a plant has the more it will reflect near-infrared light and can this is also a measure of vegetation health. The normalized difference vegetation index was the first vegetation index established shortly after the launch of the Advanced Very High

Resolution Radiometer (AVHRR) satellite (Rouse, 1974). A collection of vegetation index images throughout a calendar year can provide a phenology curve of plants. At a 30

14 meter scale many other objects occur within a pixel so the term land surface phenology is used.

2.3 Theoretical Approach

A collection of vegetation index images (NDVI or Tasseled-Cap) can be used to show the land surface phenology of buckthorn thickets and other objects. Other land covers such as pine forest, urban areas, water have a phenology that remains fairly constant throughout the year while other plant communities in the Oak Openings have a phenology that starts later and ends earlier than buckthorn in the case of oak savanna, oak blueberry forest or much less annual variation in the case of wet prairie and savanna.

Agricultural crops have a distinct phenology based on what type of crop is planted in a specific year. Pixels containing buckthorn thicket have a distinct phenology from that of other species and objects so that they should be readily identified using a time series of vegetation index images (i.e. Landsat).

Once areas of buckthorn thicket have been identified, the map produced can be used to model factors that affect the spatial distribution of buckthorn using a logistic regression approach. Other landscape factors such as distance to agricultural fields, elevation in relation to streams, habitat fragmentation, and density of groundwater wells can be compared to the spatial distribution of buckthorn thickets to see how these factors affect the distribution.

2.4 Study Objectives and Hypothesis

The main study objective is to identify buckthorn using Landsat imagery and remote sensing techniques. This will be accomplished using land surface phenology as determined by a series of Landsat images. To test the hypothesis that buckthorn can be mapped using remote sensing, different vegetation indices will be used on Landsat imagery and field confirmation of presence and absence of buckthorn will be conducted.

According to historical plant surveys of the Oak Openings, buckthorn existed as a noninvasive exotic species until recent decades where it has shifted to a monoculture thicket in some areas. Once buckthorn thicket locations are identified, a logistic regression will be used to explore any relationship between spatial variables and the current distribution of buckthorn as identified by remote sensing. In previous research, many invasive species have required disturbance, nutrient alteration, or have the ability to fill a previously unoccupied niche in a community to become invasive. The hypothesized variables that contribute to the invasive potential of buckthorn will be tested include: access to nutrients via agricultural runoff, habitat fragmentation, and alteration of the groundwater regime by anthropogenic usage.

Chapter 3

Remote Sensing of Buckthorn: Image Processing and Field Confirmation

3.1 Image Selection

The spectral characteristics of both buckthorn species are not sufficiently different from native species such as Quercus to distinguish using moderate spectral/spatial resolution sensors because they only have 7-10 bands. However, temporal differences in land surface phenologies associated with high density thickets are possible to differentiate. Buckthorn thickets identified in the study ranged from 50-100 meters across to some patches many hectares in size which makes them identifiable with a moderate spatial resolution sensor such as Landsat. In order to use phenology as a tool to distinguish buckthorn it is necessary to acquire satellite images during key times of the year such as early spring and late fall. Additional phenological data is gained by adding additional images to a sequence used to produce a more complete land surface phenology curve.

Forty-nine images were acquired from the USGS Glovis website

(http://glovis.usgs.gov). Both Landsat 5 TM and Landsat 7 ETM+ images were used in the study for path 20 and row 31. Only Landsat scenes with less than 10% cloud cover 17

Table 3.1: Summary of the scenes used in the analysis. Two annual phenology curves were established using images from 2001-2006 and also 2007-2011. The sensor used to acquire the images is shown and

2001-2006 Sensor Type 2007-2011 Sensor Type 14-Mar-01 ETM+ 7 4-Mar-09 ETM+ 7 22-Mar-01 TM 5 18-Apr-08 ETM+ 7 13-Apr-03 TM 5 24-Apr-07 TM 5 18-Apr-05 TM 5 5-May-11 TM 5 29-Apr-03 TM 5 10-May-10 ETM+ 7 1-May-01 ETM+ 7 18-May-07 ETM+ 7 4-May-05 ETM+ 7 20-May-08 ETM+ 7 23-May-06 TM 5 28-May-08 TM 5 16-Jun-03 TM 5 6-Jun-11 TM 5 24-Jun-06 TM 5 11-Jun-07 TM 5

7-Jul-02 ETM+ 7 24-Jun-09 ETM+ 7 15-Jul-02 TM 5 7-Jul-08 ETM+ 7 23-Jul-05 TM 5 15-Jul-08 TM 5 31-Jul-05 ETM+ 7 16-Aug-08 TM 5 8-Aug-05 TM 5 1-Sep-08 TM 5 21-Aug-01 ETM+ 7 15-Sep-10 TM 5 1-Sep-02 TM 5 25-Sep-08 ETM+ 7 6-Sep-04 TM 5 1-Oct-10 ETM+ 7 17-Sep-02 TM 5 9-Oct-10 TM 5

30-Sep-04 ETM+ 7 17-Oct-10 ETM+ 7 8-Oct-01 ETM+ 7 19-Oct-08 TM 5

24-Oct-01 ETM+ 7 2-Nov-10 ETM+ 7 12-Nov-05 TM 5 7-Nov-09 TM 5 20-Nov-05 ETM+ 7 1-Dec-09 TM 5 1-Dec-03 ETM+ 7

18 were included in the analysis. Any remaining clouds and associated shadows within each scene were masked out during the analysis. Table 3.1 shows a summary of the scenes used in the analysis.

Due to the high frequency of cloud cover within the study area throughout the calendar year it was necessary to use multiple years to establish a single calendar year sequence of images especially when cloud cover was present during the spring green up in April-May (which is generally the part of the year in NW Ohio with the most rain and cloud cover). To minimize discrepancies between years, monthly Palmer-Drought

Severity Indexes (PDSI) were obtained for all the images and exceedingly wet or dry months for 2 or more months during the spring and summer were not used (Palmer 1965)

In total, 24 scenes were used all between the years 2007-2011.

3.2 Image Processing and Classification

All scenes were processed using ENVI IDL software. The images were first converted from raw data to radiance which corrects for differences in time of acquisition.

Atmospheric correction of the images was done using the FLAASH module within the

ENVI software package to correct for atmospheric variation and surface reflectance values were obtained (Cooley et al. 2002). Data for visibility used in the atmospheric correction for each scene was obtained from Toledo Express Airport which is near the center of the study area. Additional data including orbit parameters, scene center acquisition date/time, and the latitude and longitude of the scene center were obtained from the header data downloaded with each scene.

Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index

(NDVI) and Tasseled-Cap Greenness Index (TC-G) were calculated from each of the atmospherically corrected reflectance data (Crist and Kauth 1986; Huete et al. 1997;

Rouse 1974). The equations that were used are shown below:

( )

Where: TMx = Thematic Mapper (x denotes the corresponding TM band) G = 2.5(Gain Factor) L = 1 C1 = 6 C2 = 7.5

NDVI and EVI are both standard indices used for remote sensing of vegetation, but were not chosen for this study. NDVI values are generally compressed in forested regions and the index is very sensitive to heterogeneous backgrounds which over identifies buckthorn thicket. EVI was originally developed for MODIS and responds better to high biomass, Tasseled-Cap Greenness was found to be the most effective to identify buckthorn. The date for each scene was changed to a Julian day so cross-year comparisons could be made. The images were then stacked in sequence by day of year for use in further analysis.

Figure 3-1: Land surface phenology curves for buckthorn thickets and plant communities commonly found within the Oak Openings region. The dashed line of each color represents one standard deviation above and below the average TC-G of each plant community. Buckthorn thicket during the early and late seasons shows higher Tasseled-Cap Greenness values representing early leaf-out and late senescence. Parallelepiped classification is a supervised classification method that uses a decision rule based on an n-dimensional image where n is the number of bands included in that image (Richards 2012). A standard deviation is specified and classification is based on the value in each image that is part of the “stack”. Prior knowledge of buckthorn thickets and aerial photograph interpretation were utilized in conjunction as training sets in the parallelepiped classifications. The value of the TC-G for each scene was is averaged for training sets of known thickets at two locations (Irwin Prairie State Nature

Preserve and Bumpus Pond) for the entire time series of images. A standard deviation of two was selected for the parallelepiped method for comparing the average of the training sets in each scene. Figure 3-1 shows representative phenology curves for buckthorn and other land uses found within the study area. Pixels that were within two standard 21 deviations of the average in each scene of the stack were classified as buckthorn and pixels that did not meet the criteria were not classified.

For the 2001-2006 image stack, classification of buckthorn was done using the same method as the 2007-2011 image classification, but since field confirmation was not possible aerial photography acquired during the same time period by Ohio

Geographically Referenced Information Program (OGRIP) from 2005 was used to determine the extent of thickets (OGRIP 2012). Buckthorn thicket canopy in an aerial photograph has a mottled appearance and was compared with current thicket locations.

The same TC-G method was applied to the 2001-2006 image stack that was applied to the 2007-2011 image stack.

Figure 3-2:Diagram of the 30x30 meter Nested Pixel Plot (P-Plot) showing the location of each of the nested sub plots that were sampled and their dimensions.

3.3 Buckthorn Thicket Quantification

Many methods of vegetation sampling have been used in the past to obtain species data, distribution, and abundance within an ecosystem. A nested sampling plot uses a large area as the outline of the entire plot and instead of counting all plants within the plot statistically representative sub-plots within the larger plot are sampled. This simplifies field studies and saves lots of time and effort on the part of researchers.

Vegetation plots to characterize the species richness of plant communities can be done at multiple scales from 1m2 up to 1000m2 using a nested plot (Stohlgren et al.

1995). A modified version of this that is scaled for satellite data at a 30 meter resolution is a Nested Pixel Plot or P-Plot (Kalkhan et al. 2007). A diagram of the p-plot is shown

Figure 3-3: Location map showing each of the ten nested pixel plots surveyed in the study. Seven plots were measured at Irwin Prairie State Nature Preserve one of which was a control plot (Plot G) and the remaining three were located at Bumpus Pond, property owned by Toledo Metroparks.

23 in Figure 3-2. Each of the plots was oriented so that the top left corner of the plot was situated in the northwest corner and the axis of the plot lined up in a north-south line. The overall plot area was 30 x 30 meters containing one 10x10 meter subplot in the center, two 3x3 meter subplots in the upper left and lower right corners, and 10 1x1 meter subplots at specific intervals along the edges of the outside of the plot and along the edge of the 10 x 10 meter subplot.

At each 1 x 1 meter subplot all woody stems were counted in three categories: ground, understory, and canopy. Ground level was classified as any woody stems under

10 cm tall, understory was classified as woody stems from 10 cm to breast height, and the canopy included all woody stems taller than breast height. Woody species were also identified as glossy buckthorn, common buckthorn, or non-buckthorn. For the 3x3 meter plots all species were identified, but stems were not counted and in the 10 x10 meter plot in the center any woody species not present in any of the other plots were noted. In addition, a spherical densitometer was used to measure canopy closure/coverage for entire plot using the method described by Lemmon (1956). Figure 3-2 shows the locations of each of the p-plots. A summary of the P-Plot data is shown in Table 3.2.

3.3.1 Field Confirmation

After initial classifications, a field survey was done consisting of 50 semi-random points throughout the study area. Each of the sites was chosen based on proximity to a road in order to be able to cover a larger geographic region and to eliminate any problems gaining access to private property. Confirmation of presence/absence of thicket was determined based on a visual inspection of the thicket based on the p-plot observations.

50 points were chosen randomly based on a five meter buffer to public roads adjacent to areas likely to contain buckthorn (agricultural and urban lands were excluded from the analysis). Figure 3-4 is a map showing each of the 50 locations. Additionally Appendix A shows the GPS coordinates, presence/absence of thicket and estimated densities for each location.

Table 3.2: Summary of the p-plot data from the study showing overall buckthorn percentage, buckthorn in the overstory and total canopy closure as determined from a spherical densitometer. Buckthorn thicket density based on buckthorn in the overstory was divided into three categories: low (30% or less , medium (30-75%) and high (75% or more).

Plot ID Plot Name BT Overstory % BT Density BT Overall % % Canopy Closure A Irwin North Trees 2 22.73 Low 44.16 92.41 B Irwin South Trees 2 97.14 High 92.12 93.42 C Bumpus South Low Trees 96.00 High 75.71 92.85 D Bumpus Trees 1 59.70 Med 86.15 94.18 E Bumpus No Trees 93.84 HIgh 90.82 89.63 F Irwin East (Rear Back Prairie) 23.08 Low 73.71 82.97 G Irwin North Control 0.00 None 0.00 86.69 H Irwin East (Corner Bancroft) 77.78 High 78.67 86.04 I Irwin South Trees 89.66 High 96.12 94.72 J Irwin North Trees 86.11 High 87.03 87.83

3.3.2 Accuracy Assessment

In total six image stacks (three ach for 2001-2006 and 2007-2011) were produced using the method described in section 3.2 (NDVI, EVI, TC-G). Each of the methods used for the 2007-2011 image stack was assessed for accuracy using the field confirmation sites in addition to the p-plots for a total of 60 points. A kappa test, similar to an R2 value for classification images, was used for accuracy assessment as described by Congalton

(1991). TC-G was found to have the highest kappa hat value at 0.73 with overall

Figure 3-4: Map detailing the locations tested for presence or absence of buckthorn thickets. Points were chosen based on a random set of points within a 5 meter buffer of roads to make the sites accessible.

Table 3.3: Table showing the kappa hat tests for each of the classification methods tested. Tasseled-Cap Greenness had the best at 88.33% overall accuracy.

TC-G Class Buckthorn No Buckthorn Row Total: Buckthorn 15 4 19 No Buckthorn 3 38 41 Column Total: 18 42 60 Overall Accuracy= 88.33 Ommission Error= 16.67 9.52 Commission Error= 21.05 15.56 Xii= 53 Xi+ x X+i= 2064 Kappa hat = 0.727

NDVI Class Buckthorn No Buckthorn Row Total: Buckthorn 12 3 15 No Buckthorn 6 39 45 Column Total: 18 42 60 Overall Accuracy= 85.00 Ommission Error= 33.33 7.14 Commission Error= 20.00 13.33 Xii= 51 Xi+ x X+i= 2160 Kappa hat = 0.625

EVI Class Buckthorn No Buckthorn Row Total: Buckthorn 8 9 17 No Buckthorn 10 33 43 Column Total: 18 42 60 Overall Accuracy= 68.33 Ommission Error= 55.56 21.43 Commission Error= 52.94 19.51 Xii= 41 Xi+ x X+i= 2112 Kappa hat = 0.234

27 identification accuracy of 88%. Table 3.3 shows the value of the kappa tests for each image stack classification. The kappa test is discussed in more detail in Chapter 5.

For the image classification from 2001-2006, field confirmation was not possible because the study began well after this period. However, active management of buckthorn thicket during and between both time steps was recorded by local land managers and can be used as a proxy when comparing each of the time steps. This will be discussed further in the results section.

Chapter 4

Spatial Modeling and Invasibility/Invasive potential

4.1 Model Framework Within the landscape of the Oak Openings, buckthorn thickets identified by the remote sensing technique described in the previous chapter exhibit a pattern on the landscape that can be determined using landscape metrics. The buckthorn patches identified are considered “thickets” which means they have reached a certain threshold of mature buckthorn plants in each location so that they phenological signature is visible in a Landsat pixel (30x30 meters). The P-Plot data presented in the previous chapter when compared to the classification image show the method works when buckthorn is 75% or more of the canopy species which reduces the effect of mixed pixels. Figure 4-1 shows the conceptual model for the buckthorn to become an invasive species. This model is formulated around existing proposed models that track the shift from an exotic plant introduction to a problem invasive species (Lockwood et al. 2001). At tier one, an invasive species is introduced which in the case of buckthorn required European settlers to bring the plant during immigration. They used the plant as an ornamental and hedge rows and some plants inevitably escaped. To transition from a tier one to a tier two invasion three things are required: propagule pressure, ecosystem vulnerability, and advantage due to buckthorn‟s life history. Once buckthorn had ample propagule pressure 29

Figure 4-1: A conceptual model of buckthorn invasive potential. Each tier represents a different stage shifting towards buckthorn as a monoculture invasive “ecosystem engineer”. The proposed model inputs that contribute to the shift from tier two to tier three are shown. Variables were chosen based on a landscape scale analysis and availability of remotely sensed data. by distribution of berries it was able to establish reproductive populations within ecosystems that had either been disturbed or had a vacant ecological niche which buckthorn could fill.

The transition from tier two to tier three describes the transition of an invasive species from simply an established ecosystem member to an ecosystem engineer or community dominant. In the case of buckthorn, dense thickets form and control access to light and nutrients in the forest understory. Determining what factors influence this transition is the aim of this model. Because the data obtained in this study are the presence or absence of buckthorn thicket (as described by the remote sensing

30 classification method) a logistic regression was chosen to describe any relationships

(Harrell 2001). The dependent variable, buckthorn thicket, can be described as „1‟ for thicket present or „0‟ for thicket absent. Each of the independent variables tested must be a continuous variable rather than categorical so a mathematical relationship can be established. Other variables were considered, but due to the nature of this study were not tested as described below.

4.2 Variables Selected

In selecting variables for the model, only environmental factors were considered.

Buckthorn life history traits are well documented in the literature and both species of buckthorn have become established in most climatically suitable areas in the eastern

United States and can be found as far west as Idaho (USDA 2013). Table 4.1 shows the dependent and independent variables considered for the model.

Table 4.1: A summary of dependent and independent variables considered for modeling buckthorn thicket spatial dependence.

Dependent Variable Independent Variables Presence of Buckthorn Thicket Distance to Agricultural Nutrient Sources Distance to Roads Distance to Streams Height Above River Groundwater Well Density Due to availability of data and the spatial resolution of the Landsat imagery (30 x

30 m) some variables could not be tested in this analysis including: soil nitrogen, soil pH, and soil moisture. Soil variables vary widely across small scales and makes landscape scale modeling and remote sensing analysis of these variables very difficult (Brady and 31

Weil 1996). Septic fields contribute nitrates to unconfined aquifers however in the sand aquifer of the Oak Openings nutrients are used quickly by bacteria and would not likely be a significant source of nitrogen available to plants (Harman et al. 1996). Atmospheric deposition also may provide a significant source of nitrogen, but this process in the study area has not been monitored (Mooney and Hobbs 2000).

Disturbance also contributes to the spread of invasive plants and establishment of community dominants. To quantify disturbance, roads and streams were used to create a distance variable where core areas farther away from roads and streams are likely to be less disturbed. Use of groundwater in a shallow sand aquifer by residential and commercial interests can have effects on the water table and disturb native ecosystems

(Crooks 2002). Density of groundwater wells per square kilometer is a variable also included in the model.

Sources of nutrient eutrophication include agricultural fields (due to over- application of fertilizers that persist in runoff) and lawn fertilizers used in residential and commercial area. Without specific information on fertilizer application amounts it is difficult to quantify the total contribution of these sources to eutrophication. However, a general metric used that can be used is proximity to the source. The assumption is made that all agricultural areas use some type of nitrogen fertilizer. While this varies from year to year depending on crop type the majority of agricultural crops in this region consist of corn, soybeans, and wheat which all require application of nitrogen (NASS 2011).

However the same assumption cannot be made for residential and commercial areas because lawn fertilizer application is not ubiquitous therefore the variable was not tested.

An example of nutrient delivery was observed at IPSNP during a spring flood event March 2011. Surface water samples were collected within ditches at IPSNP when a

“backflow” event was observed. While water quality tests were not the main purpose of this study, the data collected provides some of the basis for modeling efforts discussed in this chapter. For most of the year at Irwin Prairie, water in ditches flows toward ditches in the center of the preserve, then toward the north and then west toward Wire Grass ditch.

During this “reversal” event, water rich in sediment and nutrients changed course opposite of the normal regime. Within three days the flood water subsided and the water was tested again showing decreased suspended solids and nutrients which had settled out in the stagnant water of the wet prairie. Figure 4-2 shows the data for this event. It is likely in areas with very low gradient streams within the Oak Openings this type of nutrient enrichment often occurs in low elevation areas with standing water during part of the year providing the basis for the variables used in the model.

4.3 Model Methodology

On the classification image produced by the method described in Chapter 3, 2154 pixels at a 30 meter resolution were identified as buckthorn thicket. Buckthorn thicket patches were defined as two or more orthogonally adjacent pixels. Within each patch 100 points were selected randomly with a minimum distance between points of 50 meters. To select for control points absent of buckthorn thicket, the 2006 National Land Cover

Dataset (NLCD) was used to limit land uses only to areas where buckthorn thickets were likely to occur (deciduous forest, coniferous forest, mixed forest, wooded wetland). All other land uses (e.g. agricultural and urban) were excluded from the analysis. Control

33 points were selected a minimum distance of 50 meters from pixels identified as buckthorn to account for exclusion within the classification and then were randomly chosen in the remaining area with a minimum distance between points of 500 meters to obtain a representative distribution of the entire study area. For each of the points all continuous variables were calculated using bilinear interpretation.

Figure 4-2: A spring backflow event shows nutrient deposition at Irwin Prairie State Nature Preserve. In the top panel, incoming water is high in suspended sediment and nitrogen from agricultural runoff to the west. Water slows down in inundated areas allowing settling out and deposition. Water reverses direction in the bottom panel to the normal flow and is much lower in suspended solids and nitrogen. 35

The presence or absence of buckthorn thickets was based on the classification images from 2007-2011. The independent variables tested in the logistic regression model included: elevation above river, distance to road centerline, distance to stream/ditch centerline, path distance to agricultural fields based on elevation and stream channels, and density of shallow aquifer (Oak Openings sand) groundwater wells. An f- test was used to determine if presence/absence in each of the variables was statistically significant. Table 4.2 summarizes the variable data for each model.

Groundwater well density and distance to roads failed the f-test in both instances so were not included in the model. Distance to streams was significantly different in one model at a 95% confidence interval, but not the other so it was included. Additionally, both sets of random points from A and B were also combined and used in a third model comparison. To reduce the multicollinearity between the interaction variables, each variable was scaled from 0 to 1 then subtracted from the mean value and multiplied against the other interaction term.

The R software function glm was used to perform the logistic regression (Everitt and Hothorn 2009). The formula used in the logistic regression was as follows: Presence

~ Height Above River + (Height Above River : Agricultural Distance) + (Agricultural

Distance : Stream Distance). Low elevation areas close to agricultural The results for the model are discussed in Chapter 5.

Table 4.2: Descriptive statistics of each variable tested in the model. A and B are two different sets of random points from where buckthorn thicket is present and absent.

A Variable Name M SD Min Max F-Test (p-value) Height Above River (meters) Present 0.4 1.2 -2.5 8.3 <0.0001 *** Absent 1.4 2.8 -17.8 11.6 Groundwater Well Density (wells per sq. km) Present 10.4 5.1 0.1 18.3 0.442 Absent 8.1 5.2 0.0 18.5 Distance to Streams (meters) Present 306 243 0 1185 0.127 Absent 324 273 8 1101 Distance to Roads (meters) Present 233 144 10 635 0.454 Absent 190 145 2 788 Agricultural Path Distance (cost distance) Present 26106 34289 181 199837 <0.001 *** Absent 49006 95017 0 658758

B Variable Name M SD Min Max F-Test (p-value) Height Above River (meters) Present 0.5 1.0 -0.7 4.8 <0.0001 *** Absent 2.2 3.1 -1.8 16.1 Groundwater Well Density (wells per sq. km) Present 9.9 5.4 0.1 18.2 0.106 Absent 7.1 4.8 0.0 17.8 Distance to Streams (meters) Present 298 234 1 1014 <0.001 *** Absent 327 331 4 2289 Distance to Roads (meters) Present 253 149 3 678 0.174 Absent 209 163 3 751 Agricultural Path Distance (cost distance) Present 23656 33689 267 175471 <0.0001 *** Absent 60114 110344 0 699627 Confidence intervals are denoted above as follows: * = 95% CI ** = 99% CI *** = 99.9% CI

Chapter 5

Results and Discussion

5.1 Remote Sensing Results

Landsat imagery was successfully used to identify Glossy and Common

Buckthorn (Frangula alnus and Rhamnus cathartica) when the number of adult plants reaches a certain threshold within the canopy of the study area in question. Phenology has been used before to identify invasive species, but the novelty of using many Landsat images has only been made possible in recent years after the USGS has made them available free of charge.

Table 5.1: Table of the kappa hat statistic calculated for the Tasseled-Cap greenness method used for image classification. The method provided overall accuracy of 88% with a kappa hat value of 0.73

Tasseled-Cap Greenness Class Buckthorn No Buckthorn Row Total: Buckthorn 15 4 19 No Buckthorn 3 38 41 Column Total: 18 42 60 Overall Accuracy= 88.33 Omission Error (Producer's Accuracy)= 16.67 9.52 Commission Error (User's Accuracy)= 21.05 15.56 Xii= 53 Xi+ x X+i= 2064 Kappa hat = 0.730 38

Table 5-1: A table showing the results of the Kappa Hat function for the Tasseled-Cap Greenness classification. Overall accuracy for the method was 88% with a kappa hat statistic of 0.73. False positive rate for the method is 21% and 15% of thickets are not identified when using the method.

Figure 5-1: Map showing the regional identification of buckthorn thicket. Also shown are local area metroparks and nature preserves that actively manage for buckthorn and will use the data.

Figure 5.1 shows a regional map of identified buckthorn thickets and figure 5.2 shows buckthorn thicket in the area of IPSNP. Overall maximum accuracy of the method using the TC-G classification was 88 percent with a kappa hat value of 0.73. Table 5.1 shows the specifics of the kappa hat test. To ascertain accuracy of the method, the 2001-

2006 and 2007- 2011 image stacks were compared and historical management data were also used. Between 2002-2003, a 11.5-hectare management unit in the south-central portion of the nature preserve received multiple treatment methods to remove buckthorn thicket (Moser et al. 2005). In Figure 5-2, area „C‟, buckthorn thicket is not identified in either the 2001-2006 and 2007-2011 image stack (Figure 5-2, area „C‟). An additional 10 hectare area of buckthorn was removed in the north central part of the preserve using a hydraulic brush cutter in 2007, and this area shows buckthorn present in the 2001-2006 image stack, but not in the 2007-2011 image stack (Figure 5-2, area „A‟) . Area B final area was remediated in the fall of 2010, and this area show buckthorn thicket in the 2001-

2006 image, but is absent in the stack from 2007 – 2011 (Figure 5-2, area „B‟).

Comparison between the 2001-2006 and the 2007-2011 time steps showed a 37% percent increase in total area of buckthorn thicket from 690 ha to 945 ha. Within the study area this accounts for 0.31% and 0.43% respectively of total land cover. It should be noted that error is associated with any direct comparison between time steps due to the fact the study period began in 2011 and training sets were based mainly on aerial photo interpretation.

Figure 5-2: Buckthorn classifications at Irwin Prairie State Nature Preserve. The upper panel (gray) shows buckthorn thicket classification during 2001-2006 and the bottom panel (black) shows classification from 2007-2011.

Remote sensing is a valuable tool for the identification of terrestrial invasive plant species. The method used in this study is a quick low-cost method that can be employed with a few standard software packages and field confirmation data. In this study buckthorn “thicket” is defined as any plant community where a minimum of roughly 50% of the woody species found in a particular area are glossy or common buckthorn. The method used can successfully identify buckthorn thickets when roughly 70% or more of the canopy species in a pixel are composed of glossy or common buckthorn.

5.2 Modeling Results

The results of the logistic regression models are shown in Figure 5-2. The model created is additive, and models the relationship between height above river, the interaction between height above river/distance to agricultural sources, and the interaction between distance to streams/distance to agricultural sources. Height above river had a negative coefficient and was highly significant at the p < 0.001 level.

Buckthorn is 10-20% more likely to occur for each decrease in elevation of 30 cm above a ditch or stream. The overall model was significant at the p < 0.001 level for model B and the combined model and p < 0.05 for model A according to the Model chi-square statistics. The model correctly predicted 65.5%, 67%, and 66.75% of the responses for models A, B, and Both respectively. Agricultural distance and distance to streams did show significance in the model, but the odds ratio showed no determinative relationship for buckthorn thicket. The pseudo-R squared values for the model were 0.06, 0.16, and

0.13 respectively showing a weak relationship, but the model does explain up to 16% of the variance which was mostly the height above river center variable.

Table 5.2: A summary of the logistic regression models is shown with the variables height above river centerline (HAR), distance to agricultural sources (AGD), and distance to streams (STR_D).

A Variable Coefficients Odds Ratio, Exp(Coef.) 2.5% CI 97.5% CI P-value HAR -0.108 0.90 0.82 0.97 0.007 ** HAR:AGD 3.19E-07 1.00 0.99 1.00 0.377 AGD:STR_D -1.04E-08 1.00 1.00 1.00 0.041 * Intercept 0.447 1.56 1.08 2.29 0.0189 *

Overall Model Significance Chi-squared = 9.4 on 3 Degrees of Freedom P-value = 0.024 * Psuedo R-squared: 0.061 B Variable Coefficients Odds Ratio, Exp(Coef.) 2.5% CI 97.5% CI P-value HAR -0.212 0.80 0.72 0.88 <0.001 *** HAR:AGD 5.54E-07 1.00 0.99 1.00 0.263 * AGD:STR_D -1.27E-08 1.00 1.00 1.00 0.048 * Intercept 0.813 2.25 1.52 3.41 <0.001 ***

Overall Model Significance Chi-squared = 22.4 on 3 Degrees of Freedom P-value = <0.001 *** Psuedo R-squared: 0.162 Combined Variable Coefficients Odds Ratio, Exp(Coef.) 2.5% CI 97.5% CI P-value HAR -0.1987 0.82 0.76 0.88 <0.001 *** HAR:AGD 4.54E-07 1.00 0.99 1.00 0.067 AGD:STR_D -1.10E-08 1.00 1.00 1.00 0.005 ** Intercept 0.72 2.05 1.56 2.73 <0.001 ***

Overall Model Significance Chi-squared = 9.4 on 3 Degrees of Freedom P-value = <0.001 *** Psuedo R-squared: 0.134 Confidence intervals are denoted above as follows: * = 95% CI, ** = 99% CI, *** = 99.9% CI

It is important to consider that the relationship between height above river and buckthorn thicket may be attributed to the habitat preferences of the plant as well as nutrient delivery capability. Glossy buckthorn is known to be most aggressive in wet soil types (Czarapata 2005) while Common Buckthorn is more often found in well-drained soils. The interaction between stream distance and agricultural sources was significant in all three models and could be a mechanism of nutrient delivery to trigger a shift to buckthorn thicket. The dominant land use for the headwaters of streams and ditches that pass through the Oak Openings region is agricultural which provides a constant source of runoff nutrients. Some error also exists within the NLCD data which inherently passes error through any analysis conducted with this data. However, it is clear that wetland areas within the Oak Openings are at significant risk from buckthorn invasion.

The hypothesis was tested that a relationship exists between buckthorn thicket locations and variables that contribute to the invasive potential of buckthorn: access to nutrients via agricultural runoff, fragmented habitat, and alteration of the groundwater regime by anthropogenic usage. The hypothesis cannot be rejected because a strong relationship exists between height above river and presence of thicket. While this may be due to the habitat preferences of buckthorn, the fact that the majority of thickets occurred within a height of 1.5 meters or less above a river/stream still is a predictor of where buckthorn thicket is likely to be.

Distance to roads did not show statistically different populations to include in the models. This is most likely due to the fact that habitat fragmentation of the region is already very extensive and presence of thickets was not well documented until 1990 and beyond. It is likely that high propagule pressure for buckthorn already exists in most

44 areas resulting in a lack of any meaningful spatial relationship. While edge effect is expected to play a role in contributing to the high density nature of buckthorn thickets, many roadside thickets may not have reached the 50-100 square meter threshold for the

Landsat pixel to correctly identify the land surface phenology of the small thickets.

5.3 Recommendations

As of the writing of this thesis, large habitat management projects have been completed or are currently underway in the Oak Openings region to remove buckthorn thicket and restore wet prairie areas. Using the remotely sensed data produced by this thesis product in conjunction with the model results can help ecosystem recovery for areas that have been remediated. To expand the method to other areas is certainly possible but several factors must be kept in mind. A large latitudinal area or topographically heterogeneous area would contribute wide variation in phenology that would have to be accounted for, even perhaps across one Landsat scene. Because this study area was localized to the Ohio portion of the Oak Openings leaf-out and senescence are expected to be contemporaneous throughout the study area.

To move forward predicting where buckthorn will spread it is important to include other factors that were not tested in this model such as current location of buckthorn thickets or soil type. Soil properties such as pH, soil moisture, etc. are generally not remotely sensed data, but would also be worthwhile to test. Acquisition of new Landsat imagery can provide a new time step for the future and can also be used to monitor the progress of management efforts and as a tool to help refine historical time steps.

In conclusion, Landsat data provides an excellent tool for the identification of buckthorn species or any species that has a phenology different than native vegetation.

While a map of buckthorn invasive potential was the ultimate goal of this thesis, the relationships tested did not strongly support enough variables to allow enough assumptions to be made about buckthorn distribution. Understanding the driving forces behind invaders such as glossy and common buckthorn is critical for management and remediation efforts.

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Appendix A

Summary of Buckthorn Thicket Confirmation Locations

A summary of all locations sampled for presence or absence of buckthorn thicket is shown. Included is latitude and longitude coordinates, presence or absence of “thicket” as defined in Chapter 3, and the density of the buckthorn present as defined in Chapter 3.

Site # Latitude Longitude Presence BT Density Site # Latitude Longitude Presence BT Density 1 41.6695 -83.7610 NO LOW 26 41.6513 -83.7649 YES HIGH 2 41.6683 -83.7610 NO NONE 27 41.6514 -83.7632 NO LOW 3 41.6632 -83.7609 NO NONE 28 41.6471 -83.7619 NO LOW 4 41.6594 -83.7645 NO NONE 29 41.6442 -83.7678 NO LOW 5 41.6614 -83.7705 NO HIGH 30 41.6444 -83.7761 NO MED 6 41.6621 -83.7736 YES HIGH 31 41.6443 -83.7776 NO LOW 7 41.6655 -83.7821 NO MED 32 41.6443 -83.7796 NO MED 8 41.6655 -83.7820 NO NONE 33 41.6441 -83.7833 NO NONE 9 41.6674 -83.7897 NO LOW 34 41.6437 -83.7876 NO LOW 10 41.6639 -83.7884 NO NONE 35 41.6440 -83.7892 NO LOW 11 41.6607 -83.7901 NO LOW 36 41.6420 -83.7888 NO HIGH 12 41.6587 -83.7937 NO NONE 37 41.6411 -83.7891 NO HIGH 13 41.6474 -83.8179 NO NONE 38 41.6395 -83.7890 NO MED 14 41.6439 -83.8178 NO NONE 39 41.6383 -83.7886 NO MED 15 41.6438 -83.8078 NO LOW 40 41.6356 -83.7889 YES HIGH 16 41.6435 -83.8044 NO LOW 41 41.6311 -83.7888 NO LOW 17 41.6434 -83.7987 NO LOW 42 41.6298 -83.7896 NO LOW 18 41.6485 -83.7988 NO NONE 43 41.6295 -83.7928 NO NONE 19 41.6502 -83.7993 YES MED 44 41.6295 -83.7960 NO NONE 20 41.6508 -83.7926 YES MED 45 41.6268 -83.7979 NO NONE 21 41.6512 -83.7861 NO NONE 46 41.6259 -83.7978 NO NONE 22 41.6512 -83.7822 YES HIGH 47 41.6242 -83.7982 NO NONE 23 41.6511 -83.7784 YES MED 48 41.6218 -83.7977 NO LOW 24 41.6511 -83.7771 YES LOW 49 41.6187 -83.7976 NO NONE 25 41.6512 -83.7736 YES HIGH 50 41.6175 -83.7975 NO NONE

Appendix B

Summary of Nested Pixel-Plot (P-Plot) Plant Survey Data

A summary of the P-Plot plant survey is shown. Each sub-plot is the 1x1 meter plant count as per the diagram in Figure 3-2. The sub-plots are numbered beginning in the southwest corner of each p-plot. Plots 1-6 are along the outside edge of the 30x30 meter plot and 7-10 are located on the edge of the 10x10 meter sub-plot. The plots are labeled A-J as shown in Figure 3-3.

P-Plot A Subplot #1 Subplot #2 Subplot #3 Subplot #4 Subplot #5 Species O U G Total O U G Total O U G Total O U G Total O U G Total Frangula alnus 2 4 43 49 0 1 47 48 0 7 7 14 3 5 18 26 1 4 2 7 Rhamnus cathartica 5 3 20 28 6 3 9 18 0 6 12 18 0 0 0 0 0 0 0 0 Lindera sp. 0 0 0 0 0 1 0 1 0 2 0 2 0 0 0 0 0 0 0 0 Parthenocissus sp. 0 0 2 2 0 0 19 19 0 0 5 5 0 0 23 23 0 0 0 0 Fraxinus sp. 0 0 0 0 0 2 0 2 0 3 0 3 0 0 0 0 0 0 0 0 Lonicera sp. 0 2 7 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acer sp. 0 0 8 8 0 0 4 4 0 0 0 0 0 0 0 0 3 0 0 3 Crataegus sp. 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cornus sp. 0 0 0 0 0 0 2 2 0 1 0 1 0 0 0 0 0 0 0 0 Betula sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Toxicodendron sp. 0 0 0 0 0 0 6 6 0 0 1 1 0 0 0 0 0 0 0 0 Viburnum sp. 0 0 0 0 0 0 2 2 0 0 0 0 0 0 2 2 0 0 2 2 Ligustrum sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosa sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Quercus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Populus sp. 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 Rubus sp. 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1

P-Plot A (Part II) Subplot #6 Subplot #7 Subplot #8 Subplot #9 Subplot #10 Species O U G Total O U G Total O U G Total O U G Total O U G Total Frangula alnus 0 0 3 3 2 2 14 18 0 0 12 12 0 4 0 4 2 6 15 23 Rhamnus cathartica 0 0 0 0 2 1 18 21 0 1 0 1 2 2 1 5 0 0 0 0 Lindera sp. 2 1 1 4 0 2 0 2 0 1 4 5 0 3 5 8 0 0 0 0 Parthenocissus sp. 0 0 2 2 0 0 0 0 0 0 6 6 0 0 5 5 0 0 5 5 Fraxinus sp. 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 Lonicera sp. 0 0 0 0 0 0 0 0 1 4 0 5 0 1 0 1 0 0 0 0 Acer sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 0 0 3 3 Crataegus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cornus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Betula sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 Toxicodendron sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 Viburnum sp. 0 0 0 0 0 0 3 3 0 0 0 0 4 0 0 4 0 0 0 0 Ligustrum sp. 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 Rosa sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 0 0 2 2 Quercus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Populus sp. 0 0 0 0 0 1 3 4 0 1 0 1 0 0 0 0 0 0 0 0 Rubus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

P-Plot B Subplot #1 Subplot #2 Subplot #3 Subplot #4 Subplot #5 Species O U G Total O U G Total O U G Total O U G Total O U G Total Frangula alnus 4 3 0 7 4 1 0 5 5 8 1 14 9 3 1 13 16 6 2 24 Rhamnus cathartica 0 0 0 0 0 0 0 0 0 0 0 0 2 0 1 3 0 0 0 0 Betula sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Fraxinus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Lindera sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rubus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Ligustrum sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 Cornus sp. 0 1 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 Toxicodendron sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Viburnum sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 3 0 0 0 0 Quercus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosa sp. 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 Prunus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosaceae sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Parthenocissus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Physocarpus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Crataegus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

P-Plot B (Part II) Subplot #6 Subplot #7 Subplot #8 Subplot #9 Subplot #10 Species O U G Total O U G Total O U G Total O U G Total O U G Total Frangula alnus 2 0 385 387 12 8 5 25 3 1 0 4 1 2 2 5 12 3 4 19 Rhamnus cathartica 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Betula sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Fraxinus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Lindera sp. 0 2 2 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rubus sp. 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Ligustrum sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cornus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Toxicodendron sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Viburnum sp. 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Quercus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosa sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Prunus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosaceae sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Parthenocissus sp. 0 0 24 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Physocarpus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Crataegus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

P-Plot C Subplot #1 Subplot #2 Subplot #3 Subplot #4 Subplot #5 Species O U G Total O U G Total O U G Total O U G Total O U G Total Frangula alnus 2 7 18 27 2 8 5 15 9 16 0 25 1 14 24 39 11 13 4 28 Rhamnus cathartica 0 0 5 5 0 8 1 9 0 0 1 1 0 0 1 1 0 0 1 1 Cornus sericea 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Fraxinus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Viburnum sp. 0 2 1 3 0 0 1 1 0 2 0 2 0 0 1 1 0 0 0 0 Spirea sp. 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Smilax sp. 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 Acer sp. 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Parthenocissus sp. 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Physocarpus sp. 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 Rosa sp. 0 0 0 0 0 0 0 0 0 2 1 3 0 0 0 0 0 0 0 0 Lindera sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Quercus sp. 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 Rubus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 5 0 0 0 0 Ligustrum sp. 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 Lonicera sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosaceae sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cedrus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Populus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Prunus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

P-Plot D Subplot #1 Subplot #2 Subplot #3 Subplot #4 Subplot #5 Species O U G Total O U G Total O U G Total O U G Total O U G Total Frangula alnus 2 12 7 21 3 28 39 70 8 38 2 48 0 7 2 9 0 7 0 7 Rhamnus cathartica 13 0 0 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cornus sericea 2 1 1 4 2 0 0 2 0 0 0 0 0 1 0 1 0 0 0 0 Lonicera sp. 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Spirea sp. 0 0 0 0 0 4 0 4 0 0 0 0 0 0 0 0 0 0 0 0 Viburnum sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 Quercus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Prunus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Physocarpus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Populus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Ligustrum sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acer sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Berberis sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Unknown sp. 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 Unknown sp. 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

P-Plot D (Part II) Subplot #6 Subplot #7 Subplot #8 Subplot #9 Subplot #10 Species O U G Total O U G Total O U G Total O U G Total O U G Total Frangula alnus 13 20 7 40 2 1 0 3 0 9 0 9 4 14 8 26 8 4 10 22 Rhamnus cathartica 0 0 2 2 0 0 0 0 1 0 2 3 0 0 0 0 0 0 0 0 Cornus sericea 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1 Lonicera sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Spirea sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Viburnum sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Quercus sp. 0 0 0 0 1 0 0 1 1 0 0 1 1 0 0 1 0 0 0 0 Prunus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 3 Physocarpus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Populus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Ligustrum sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acer sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Berberis sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Unknown sp. 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Unknown sp. 2 0 0 0 0 4 2 0 6 0 0 0 0 0 0 0 0 0 0 0 0

P-Plot E Subplot #1 Subplot #2 Subplot #3 Subplot #4 Subplot #5 Species O U G Total O U G Total O U G Total O U G Total O U G Total Frangula alnus 19 68 42 129 2 31 65 98 0 0 1 1 0 9 32 41 8 39 20 67 Rhamnus Cathartica 0 0 0 0 0 1 2 3 0 6 2 8 0 1 0 1 0 0 0 0 Populus sp. 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acer sp. 0 1 0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 Lonicera sp. 0 0 0 0 0 0 0 0 1 5 0 6 0 0 0 0 0 0 0 0 Cornus sericea 1 1 0 2 0 0 0 0 0 2 0 2 3 4 1 8 0 11 0 11 Prunus sp. 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 Physocarpus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 Rubus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Quercus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Viburnum sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosa sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Spirea sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cedrus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosaceae sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Salix sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Fraxinus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosa palustris 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

P-Plot E (Part II) Subplot #6 Subplot #7 Subplot #8 Subplot #9 Subplot #10 Species O U G Total O U G Total O U G Total O U G Total O U G Total Frangula alnus 23 72 7 102 29 24 26 79 23 53 9 85 0 11 79 90 33 5 2 40 Rhamnus Cathartica 0 0 0 0 0 0 0 0 0 3 0 3 0 4 0 4 0 0 2 2 Populus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acer sp. 0 0 1 1 0 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 Lonicera sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cornus sericea 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Prunus sp. 0 0 0 0 0 0 0 0 0 2 1 3 0 1 0 1 0 0 0 0 Physocarpus sp. 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 3 0 0 0 0 Rubus sp. 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Quercus sp. 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 Viburnum sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 Rosa sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Spirea sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 0 0 0 0 Cedrus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosaceae sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Salix sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Fraxinus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosa palustris 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

P-Plot F Subplot #1 Subplot #2 Subplot #3 Subplot #4 Subplot #5 Species O U G Total O U G Total O U G Total O U G Total O U G Total Frangula alnus 3 2 3 8 4 3 61 68 1 54 53 108 1 0 85 86 0 6 9 15 Rhamnus cathartica 0 0 0 0 5 1 5 11 0 1 1 2 0 0 0 0 0 0 0 0 Hamamelis sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cornus sp. 0 2 0 2 2 1 0 3 1 2 0 3 0 1 0 1 1 1 0 2 Viburnum sp. 0 0 0 0 0 1 0 1 0 0 0 0 1 0 3 4 0 0 0 0 Parthenocissus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Toxicodendron sp. 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosa sp. 0 3 3 6 0 0 0 0 0 2 0 2 0 0 0 0 0 0 0 0 Rubus sp. 0 1 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Physocarpus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 Rosaceae sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 10 0 0 0 0 Prunus sp. 0 0 0 0 0 1 2 3 0 0 0 0 0 0 0 0 2 0 1 3 Lindera sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Crataegus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Ribes sp. 0 0 0 0 0 0 0 0 0 5 4 9 0 0 0 0 0 0 0 0 Lonicera sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Populus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Quercus sp. 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 Acer sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 Spirea sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

P-Plot F (Part II) Subplot #6 Subplot #7 Subplot #8 Subplot #9 Subplot #10 Species O U G Total O U G Total O U G Total O U G Total O U G Total Frangula alnus 0 0 20 20 0 0 25 25 0 1 78 79 0 2 4 6 0 6 22 28 Rhamnus cathartica 0 3 0 3 0 0 0 0 0 21 11 32 1 5 7 13 0 0 0 0 Hamamelis sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cornus sp. 0 0 0 0 0 0 0 0 4 0 0 4 0 0 0 0 0 0 0 0 Viburnum sp. 0 0 0 0 0 0 0 0 3 2 0 5 0 0 0 0 0 0 0 0 Parthenocissus sp. 0 1 12 13 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 0 Toxicodendron sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosa sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 0 0 0 0 Rubus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Physocarpus sp. 0 0 0 0 0 0 0 0 3 0 0 3 0 0 0 0 0 0 0 0 Rosaceae sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 3 Prunus sp. 4 1 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Lindera sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Crataegus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Ribes sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Lonicera sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Populus sp. 2 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 Quercus sp. 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 Acer sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Spirea sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2

P-Plot G Subplot #1 Subplot #2 Subplot #3 Subplot #4 Subplot #5 Species O U G Total O U G Total O U G Total O U G Total O U G Total Fagus sp. 0 5 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Viburnum sp. 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 3 0 3 Lindera sp. 0 0 0 0 1 1 1 3 3 1 0 4 0 0 0 0 1 2 1 4 Parthenocissus sp. 0 0 0 0 0 1 7 8 0 0 2 2 0 0 1 1 0 0 0 0 Sambucus sp. 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 Prunus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Quercus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Ribes sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Lonicera sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Ligustrum sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Fraxinus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosa sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acer sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

P-Plot G (Part II) Subplot #6 Subplot #7 Subplot #8 Subplot #9 Subplot #10 Species O U G Total O U G Total O U G Total O U G Total O U G Total Fagus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Viburnum sp. 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Lindera sp. 1 2 0 3 0 0 2 2 1 0 9 10 0 0 0 0 2 2 0 4 Parthenocissus sp. 0 4 6 10 0 0 1 1 0 0 0 0 0 0 6 6 0 0 4 4 Sambucus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Prunus sp. 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 Quercus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 Ribes sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Lonicera sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Ligustrum sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Fraxinus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosa sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acer sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

P-Plot H Subplot #1 Subplot #2 Subplot #3 Subplot #4 Subplot #5 Species O U G Total O U G Total O U G Total O U G Total O U G Total Frangula alnus 4 9 0 13 9 3 1 13 7 3 0 10 2 13 21 36 0 2 0 2 Rhamnus cathartica 0 0 0 0 0 0 0 0 0 0 0 0 0 9 2 11 0 0 0 0 Fraxinus sp. 2 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosa sp. 0 0 0 0 0 0 1 1 0 0 0 0 0 0 6 6 0 0 0 0 Populus sp. 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 Physocarpus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Lindera sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 2 0 2 Cornus sp. 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 3 0 0 0 0 Quercus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 Viburnum sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 Rosaceae sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Prunus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Parthenocissus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Ribes sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Sambucus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

P-Plot H (Part II) Subplot #6 Subplot #7 Subplot #8 Subplot #9 Subplot #10 Species O U G Total O U G Total O U G Total O U G Total O U G Total Frangula alnus 0 0 0 0 2 2 0 4 4 6 1 11 9 9 41 59 5 0 13 18 Rhamnus cathartica 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Fraxinus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 Rosa sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 Populus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Physocarpus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Lindera sp. 3 2 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 Cornus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Quercus sp. 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 Viburnum sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosaceae sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 Prunus sp. 2 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Parthenocissus sp. 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 Ribes sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Sambucus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

P-Plot I Subplot #1 Subplot #2 Subplot #3 Subplot #4 Subplot #5 Species O U G Total O U G Total O U G Total O U G Total O U G Total Frangula alnus 0 0 0 0 3 7 8 18 9 8 28 45 0 0 0 0 9 8 2 19 Rhamnus cathartica 0 0 0 0 0 0 0 0 2 0 1 3 0 0 0 0 0 0 0 0 Cornus sp. 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 Quercus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Viburnum sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Smilax sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acer sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Toxicodendron sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Prunus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Crataegus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Fraxinus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Subplot #6 Subplot #7 Subplot #8 Subplot #9 Subplot #10 Species O U G Total O U G Total O U G Total O U G Total O U G Total Frangula alnus 2 6 1 9 1 0 0 1 1 0 2 3 0 0 0 0 1 3 0 4 Rhamnus cathartica 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cornus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Quercus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Viburnum sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Smilax sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acer sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Toxicodendron sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Prunus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Crataegus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Fraxinus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

P-Plot J Subplot #1 Subplot #2 Subplot #3 Subplot #4 Subplot #5 Species O U G Total O U G Total O U G Total O U G Total O U G Total Frangula alnus 0 1 0 1 0 0 0 0 5 5 23 33 7 7 34 48 4 2 1 7 Rhamnus cathartica 0 0 0 0 0 0 0 0 0 0 1 1 0 2 0 2 0 0 0 0 Lindera sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 3 10 13 0 1 0 1 Prunus sp. 0 0 0 0 2 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 Viburnum sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Parthenocissus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosa sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosaceae sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 0 0 0 0 Acer sp. 0 0 0 0 0 0 0 0 1 0 1 2 0 0 3 3 0 0 0 0 Fraxinus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 3 Betula sp. 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 Quercus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Populus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Sambucus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

P-Plot J (Part II) Subplot #6 Subplot #7 Subplot #8 Subplot #9 Subplot #10 Species O U G Total O U G Total O U G Total O U G Total O U G Total Frangula alnus 1 0 2 3 4 5 0 9 1 0 8 9 0 2 122 124 9 9 3 21 Rhamnus cathartica 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Lindera sp. 0 0 1 1 0 0 0 0 0 0 0 0 0 0 3 3 0 0 1 1 Prunus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Viburnum sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Parthenocissus sp. 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 Rosa sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rosaceae sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Acer sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 Fraxinus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Betula sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Quercus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Populus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Sambucus sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0