Effects of Amur Honeysuckle (Lonicera Maackii

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Fall 2013 Effects of Amur Honeysuckle (Lonicera maackii [Rupr.] Herder) Invasion and Removal on Native Vegetation and White-Footed Mice (Peromyscus leucopus) in Mixed-Hardwood Forests of Indiana Joshua Michael Shields Purdue University

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Recommended Citation Shields, Joshua Michael, "Effects of Amur Honeysuckle (Lonicera maackii [Rupr.] Herder) Invasion and Removal on Native Vegetation and White-Footed Mice (Peromyscus leucopus) in Mixed-Hardwood Forests of Indiana" (2013). Open Access Dissertations. 60. https://docs.lib.purdue.edu/open_access_dissertations/60

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By Joshua M. Shields

Entitled Effects of Amur honeysuckle (Lonicera maackii [Rupr.] Herder) invasion and removal on native vegetation and white-footed mice (Peromyscus leucopus) in mixed-hardwood forests of Indiana

Doctor of Philosophy For the degree of

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Kevin Gibson

Patrick Zollner

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EFFECTS OF AMUR HONEYSUCKLE (Lonicera maackii [Rupr.] Herder) INVASION AND REMOVAL ON NATIVE VEGETATION AND WHITE- FOOTED MICE (Peromyscus leucopus) IN MIXED-HARDWOOD FORESTS OF INDIANA

A Dissertation

Submitted to the Faculty

Purdue University

Joshua M. Shields

In Partial Fulfillment of the

Requirements for the Degree

Doctor of Philosophy

December 2013

Purdue University

West Lafayette, Indiana

For my mother and step-father, father and step-mother, grandparents (Shields and

Castellon), and the rest of my family and friends who provided much love and

support during my time at Purdue.

iii

ACKNOWLEDGMENTS

First and foremost, I’d like to thank my primary advisor Dr. Michael Jenkins and my graduate committee members Dr. Michael Saunders, Dr. Patrick Zollner, Dr. John

Dunning, Jr., and Dr. Kevin Gibson for helping with every imaginable aspect of my research project at Purdue. I’d also like to thank Dr. Hao Zhang, Dr. Olin E. Rhodes, Jr.,

Dr. Marc Kéry, Dr. Gary White, Rob Morrissey, Justin Arseneault, Rita Blythe, and

Lindsey Reschke for help with the study design, data analyses, and/or for commenting on implications. Thanks also to Dr. Hao Zhang, Dr. Christopher Webster, and Dr. Robert

Swihart for reviewing earlier versions of manuscripts that will result from this dissertation. Thanks to Lindsay Jenkins, Rob Quackenbush, Kyle Leffel, Amanda Parks,

Michael Loesch-Fries, Brian Bailey, Caleb Brown, Justin Schmal, Brian Beheler, Oriana

Rueda Krauss, Seth Howard, Amy Wetzel, Taryn Clark, Crystal Bevis, Lindsey Purcell,

Jeff Clark, Dr. Rick Meilan, Shelly Newman (mother), and Randy Newman (step-father) for their help collecting/entering data, removing invasive species, loaning us equipment, and/or cleaning Sherman traps. Thanks to Matt Kraushar, Dr. Zach Lowe, Keith Ruble, and Greg Ressler for advice on herbicide application. I’d also like to thank Burk

Thompson, Andy Meier, Don Carlson, Brent Pursell, Dr. Kerry Rabenold, Mike Mycroft,

Mike List, Jeff Cummings, Dave Rader, Roy Dane, Doug Ucci, Jay Young, and Kevin

Leuck for helping find study sites and/or granting permission to work on properties.

Thanks to Mike Homoya and Sally Weeks for their help identifying plant specimens. iv

Thanks to Jennifer Spitznagle, Janis Gosewehr, Theresa Baker, Marlene Mann, Shelly

Opperman, and Julie Pluimer for their help with business-related tasks. I’d also like to thank Purdue University, Department of Forestry and Natural Resources and the

Hardwood Tree Improvement and Regeneration Center for their support. Other financial support was provided by the Purdue Doctoral Fellowship and the Frederick N. Andrews and Russell O. Blosser Environmental Travel Grant programs.

TABLE OF CONTENTS

Page

LIST OF TABLES ...... ix

LIST OF FIGURES ...... xii

ABSTRACT ...... xvi

CHAPTER 1 INTRODUCTION ...... 1

1.1 The Problem with Invasive Species ...... 1

1.2 Amur Honeysuckle ...... 4

1.3 Objectives of Dissertation ...... 8

CHAPTER 2 AGE DISTRIBUTION AND SPATIAL PATTERNING OF AN

INVASIVE SHRUB IN SECONDARY HARDWOOD FORESTS ...11

2.1 Introduction ...... 11

2.2 Methods ...... 14

2.2.1 Study Sites ...... 14

2.2.2 Data Collection ...... 15

2.2.3 Data Analyses ...... 17

2.3 Results ...... 23

2.4 Discussion ...... 25 vi

Page

CHAPTER 3 INFLUENCE OF INTENSITY AND DURATION OF INVASION

BY AMUR HONEYSUCKLE (Lonicera maackii [Rupr.] Herder)

WITHIN AND ACROSS MIXED-HARDWOOD FORESTS OF

INDIANA ...... 38

3.1 Introduction ...... 38

3.2 Methods ...... 41

3.2.1 Study Sites ...... 41

3.2.2 Data Collection ...... 43

3.2.3 Data Analyses ...... 45

3.3 Results ...... 50

3.4 Discussion ...... 53

CHAPTER 4 SHORT-TERM RESPONSE OF NATIVE FLORA TO THE

REMOVAL OF NON-NATIVE SHRUBS IN MIXED-HARDWOOD

FORESTS ...... 108

4.1 Introduction ...... 108

4.2 Methods ...... 112

4.2.1 Study Sites ...... 112

4.2.2 Experimental Design ...... 113

4.2.3 Data Collection ...... 114

4.2.4 Data Analyses ...... 116

4.3 Results ...... 119 vii

Page

4.3.1 Response of Amur honeysuckle ...... 119

4.3.2 Response of native flora and garlic mustard ...... 119

4.4 Discussion ...... 125

CHAPTER 5 EFFECTS OF AMUR HONEYSUCKLE (Lonicera maackii [Rupr.]

Herder) INVASION AND REMOVAL ON WHITE-FOOTED MICE

(Peromyscus leucopus) ...... 167

5.1 Introduction ...... 167

5.2 Methods ...... 172

5.2.1 Study Sites ...... 172

5.2.2 Experimental Design ...... 173

5.2.3 Data Collection ...... 175

5.2.3.1 White-footed mice ...... 175

5.2.3.2 Environmental variables ...... 177

5.2.4 Data Analyses ...... 179

5.2.4.1 Abundance of white-footed mice ...... 179

5.2.4.2 White-footed mice space use ...... 181

5.3 Results ...... 184

5.3.1 Abundance of White-footed Mice and Other Species ...... 184

5.3.2 White-footed Mice Space Use and Environmental Variables ...... 186

5.4 Discussion ...... 187

CHAPTER 6 CONCLUSIONS ...... 200 viii

Page

6.1 Spatial and Temporal Dynamics of Amur Honeysuckle ...... 200

6.2 Intensity and Duration ...... 202

6.3 Effects of Removals on Native Flora ...... 203

6.4 Influence on White-footed Mice ...... 204

6.5 Broader Impacts ...... 206

LIST OF REFERENCES ...... 208

VITA ...... 233

LIST OF TABLES

Table Page

Table 2.1. Dominant overstory (woody stems ≥ 10 cm diameter at breast height [dbh]) species, soil types, and mean (± 1 SE) densities of Amur honeysuckle (Lonicera maacckii [Rupr.] Herder) sapling (shrubs ≥ 1.37 m tall) and seedling (shrubs < 1.37 m tall) individuals (defined as a group of basal stems originating from the same rootstock) at six mixed-hardwood forests in Indiana...... 31

Table 3.2. Linear mixed-effects models, random effects coefficients by study site, and Akaike information criterion (AIC) for nine dependent variables at 12 mixed-hardwood forests in central Indiana...... 60

Table Page

Table 3.6. Importance value (IV = [{relative density + frequency}/2]*100) by species for seedling-layer individuals (woody individuals < 1.37 m tall) at 12 mixed-hardwood forests in Indiana...... 91

Table Page

Table 5.2. Mean (± 1 SE) values for environmental variables in reference and removal grids, during the summer and fall of 2010 and 2011, across six mixed- hardwood forests in Indiana...... 196

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LIST OF FIGURES

Figure Page

Figure 1.1. General location of 12 study sites in central Indiana...... 10

Fig. 2.2. Minimum age distributions for mature (with berries) and immature (without berries) Amur honeysuckle (Lonicera maackii [Rupr.] Herder) shrubs ≥ 0.5 m tall in 60 m x 60 m mapping areas at (a) FNR Farm, (b ) Pursell, and (c) Ross mixed-hardwood forests in Indiana...... 35

Fig. 2.3. Scatterplots of Amur honeysuckle invasion at 15, 20, and > 20 years following initial colonization at (a) FNR Farm, (b) Pursell, and (c) Ross mixed hardwood forests in Indiana...... 36

Fig. 2.4. Inhomogeneous L function for all Amur honeysuckle (Lonicera maackii [Rupr.] Herder) shrubs ≥ 0.5 m tall at (a) FNR Farm, (b) Pursell, and (c) Ross mixed-hardwood forests in Indiana; inhomogeneous cross-L function (examined if immature Amur honeysuckle shrubs [no berries] clustered around mature shrubs [with berries]) at (d) FNR Farm , (e) Pursell, and (f) Ross; inhomogeneous cross-L function (examined if Amur honeysuckle shrubs clustered around trees [diameter at breast height {dbh} ≥ 10 cm]) at (g) FNR Farm, (h) Pursell, and (i) Ross...... 37

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Figure Page

Figure 3.1. Nested plots for vegetation sampling. In the left diagram, diamonds denote variable-radius plots (basal area factor of 2.296 m2/ha) for sampling trees ≥ 10 cm diameter at breast height (dbh), spaced 40 m apart along transects (used for study site description). Large circles denote 40-m2 (radius of 3.57 m) sapling/shrub plots spaced 20 m apart along transects. Transects were spaced 20 m apart. Right diagram shows a closer view of a sapling/shrub plot (intersected by a transect) and a 2 m x 2 m quadrat to record percent covers of vascular vegetation and environmental variables...... 103

Figure 3.2. Mean percent cover (± 1 SE) of native plants (species combined as a group), Amur honeysuckle (Lonicera maackii [Rupr.] Herder), and other non- native plants (species combined as a group) in the lower vertical stratum (≤ 1 m) during spring (a) and summer (b), and the upper vertical stratum (1.01-5 m) during spring (c) and summer (d) at 12 mixed-hardwood forests in central Indiana...... 104

Figure 3.4. Box plots showing Amur honeysuckle (Lonicera maackii [Rupr.] Herder) duration (years) in fixed-area plots at 12 mixed-hardwood forests in central Indiana...... 106

Figure 4.2. Mean Amur honeysuckle (Lonicera maackii [Rupr.] Herder) sapling-layer densities (shrubs/ha for shrubs ≥ 1.37 m tall) in reference areas (a) and removal areas (b) and seedlings/ha (shrubs < 1.37 m tall) in reference areas (c) and removal areas (d), in 2010 and 2011, at six mixed-hardwood forests in central Indiana...... 161

xiv

Figure Page

Figure 4.3. Mean Shannon’s Diversity Index (H') in reference areas (a) and removal areas (b), mean taxonomic richness (S) in reference areas (c) and removal areas (d), and mean Pielou’s Evenness Index (J') in reference areas (e) and removal areas (f) during the spring seasons of 2010 and 2011 at six mixed- hardwood forests in central Indiana...... 162

Figure 4.4. Mean Shannon’s Diversity Index (H') in reference areas (a) and removal areas (b), mean taxonomic richness (S) in reference areas (c) and removal areas (d), and mean Pielou’s Evenness Index (J') in reference areas (e) and removal areas (f) during the summer seasons of 2010 and 2011 at six mixed-hardwood forests in central Indiana...... 163

Figure 4.5. Mean (± 1 SE) densities (stems/ha) of native seedlings (woody stems < 1.37 m tall) in reference and removal areas, in 2010 and 2011, at six mixed-hardwood forests in central Indiana...... 164

Figure 4.6. (a) Amur honeysuckle (Lonicera maackii [Rupr.] Herder) thicket showing no native plant regeneration on the forest floor; (b) bloody butcher (Trillium recurvatum Beck), (c) mayapple (Podophyllum peltatum L.), and (c) garlic mustard (Alliaria petiolata [M. Bieb.] Cavara & Grande) displayed positive responses to the removal of non-native shrubs...... 165

Figure 4.7. Mean percent cover (± 1 SE) of garlic mustard (Alliaria petiolata [M. Bieb.] Cavara & Grande) during the spring in reference areas (a) and removal areas (b), and during the summer in reference areas (c) and removal areas (d), in 2010 and 2011, at six mixed-hardwood forests in central Indiana..... 166

Figure 5.1. Example of one study site, showing two, 80 m x 80 m removal and reference areas for sampling small mammals and environmental variables...... 197

Figure 5.2. Small mammal trapping grid (left diagram; 80 m x 80 m removal/reference area boundary is denoted by line with large dashes). In the left diagram, filled circles denote the locations of 49 Sherman live traps (spaced 10 m apart). Diamonds denote four variable-radius plots (basal area factor of 2.296 m2/ha) for sampling trees ≥ 10 cm diameter at breast height (dbh), spaced 40 m apart along two transects (lines with smaller dashes running perpendicular to forest edge). Large circles denote 12, 40-m2 (radius of 3.57 m) sapling/shrub plots spaced 20 m apart along three transects. Transects were spaced 20 m apart. Right diagram shows a closer view of a sapling/shrub plot (intersected by a transect), Sherman live trap, and a 2 m x 2 m quadrat to record percent covers of vascular vegetation, coarse woody debris, leaf litter, and canopy cover...... 198

Figure Page

Figure 5.3. Mean abundance estimates of white-footed mice (Peromyscus leucopus) by grid type (removal or reference), trapping season (Summer or Fall), and year (2010 or 2011) at six mixed-hardwood forests in Indiana...... 199

xvi

ABSTRACT

Shields, Joshua M. Ph.D., Purdue University, December 2013. Effects of Amur honeysuckle (Lonicera maackii [Rupr.] Herder) invasion and removal on native vegetation and white-footed mice (Peromyscus leucopus) in mixed-hardwood forests of Indiana. Major Professor: Dr. Michael A. Jenkins.

The threat of non-native invasive species continues to compromise the ecological and economic integrity of our natural resources. Numerous investigators have documented the negative effects of invasive species on native biota. However, much work is still needed with regard to how invasive species spread in space and time, factors contributing to impacts on native biota within invaded ecosystems, and resultant effects of removing invasive species. In terms of invasion patterns, few studies have documented local patterns and rates of woody plant invasions, and even less is known about changes in spatial patterning and factors influencing structural characteristics

(diameter and height) of individuals as invasion phase progresses from establishment to saturation. Furthermore, little is known about how the duration of occupation by an invasive species, along with the amount of growing space it occupies, influences native biota within invaded areas (i.e., microsite-level impacts). Finally, few investigators have examined the effects of removing invasive species on native flora and fauna.

We (my graduate committee and I) examined the spread and ecological effects of

Amur honeysuckle (Lonicera maackii [Rupr.] Herder)—a non-native, invasive shrub that xvii has invaded many ecosystems throughout eastern North America—at 12 mixed- hardwood forests in central Indiana representing a range of invasion intensity and overstory composition.

At three of the mixed-hardwood forests we examined age distributions and spatial patterns of Amur honeysuckle invasions, and identified factors influencing life-stage characteristics of individual shrubs. Predicted age distributions indicated that Amur honeysuckle reached the exponential phase of invasion at ~10-15 years. Inhomogeneous

L and cross-L functions indicated that Amur honeysuckle exhibited a clustered spatial pattern; immature individuals (no berries) clustered around mature individuals (with berries). However, spatial relationships between honeysuckle and trees rarely exhibited a clustered pattern. Regression analyses with Amur honeysuckle diameter and height as response variables revealed that incorporating spatial autocorrelation provided a better model fit than models where spatial autocorrelation was not considered and caused otherwise significant predictor variables to become non-significant (p ≥ 0.05). Our results suggest that local-scale invasion by this species follows a predictable temporal sequence of population establishment and expansion via neighborhood diffusion and the forest-scale distribution of nascent foci. Furthermore, our results highlight the importance of considering spatial autocorrelation when evaluating life-stage characteristics of invasive populations.

At all 12 mixed-hardwood forests we examined the influence of density, percent cover, and duration of Amur honeysuckle (i.e., time since establishment), as well as other environmental factors, on native plant taxa. Overall, study sites with the highest taxonomic diversity (Shannon’s Diversity; Hˈ), richness (S), percent covers, and densities xviii of native vegetation also had the lowest percent covers of Amur honeysuckle in the upper vertical stratum (1.01-5 m). Based on linear mixed model analyses (random effect = study site), percent cover of Amur honeysuckle in the upper vertical stratum was consistently and negatively correlated with Hˈ, S, total percent cover, and woody seedling densities of native taxa at the microsite scale (mixed model p values < 0.05). While duration of Amur honeysuckle occupation at the microsite scale was not significant when percent cover of Amur honeysuckle in the upper vertical stratum was included in models, duration was significantly correlated with dependent variables and with upper-stratum honeysuckle cover, suggesting that greater Amur honeysuckle age at the microsite scale results in higher light competition from above for native ground flora species.

At six of the mixed-hardwood forests, we examined the short-term effects of removing Amur honeysuckle and other non-native shrubs on native herbaceous plants and woody seedlings, as well as white-footed mice (Peromyscus leucopus). Each study site contained two 80 m x 80 m sample areas―a removal area where all non-native shrubs were removed and a reference area where no treatment was implemented. Native and non-native vegetation was sampled in the spring and summer of 2010 (before removing non-native shrubs) and again in the spring and summer of 2011 (after removals). Percent cover and diversity of native species and seedling densities of native woody species increased after shrub removal (permutation p values ≤ 0.10). Conversely, changes in reference areas were typically much lower and often non-significant.

However, we also observed significant increases in Amur honeysuckle seedling densities and the percent cover of garlic mustard (Alliaria petiolata [M. Bieb.] Cavara & Grande) in removal areas. Our results suggest that removing Amur honeysuckle and other non- xix native shrubs allows the short-term recovery of native plant taxa across a range of invasion intensities. However, long-term recovery of native flora will likely depend on renewed competition with invasive species that re-colonize treatment areas, the influence of herbivores, and subsequent control efforts implemented by forest managers.

To examine effects on white-footed mice, 49 live traps were placed in each of the two, 80 m x 80 m areas (reference and removal areas). The number of white-footed mice was recorded using mark-release-recapture (MRR). Trapping was done for six nights in the summer of 2010 and four nights in the fall of 2010 (before exotic shrub removals), and again during the summer and fall of 2011 (after removals). For each 49-trap grid, we assumed population closure, and abundance was estimated using Bayesian parameter- expanded data augmentation, with time and individual heterogeneity (model Mth). For each season (summer or fall) and each grid type (removal or reference), we calculated differences in abundance by subtracting estimates in 2010 from estimates in 2011.

Permutation tests (assuming a paired design) were then used to test whether mean differences were significantly different from zero. For both trapping seasons, mean abundance increased from 2010 to 2011 (i.e., positive differences) in both removal and reference areas, but the magnitude of increase within removal areas was substantially greater (permutation p ≤ 0.05 for removal areas). For the feasible subset of mice, we calculated mean squared distance (MSD) as an index of space use. Linear regression was then used to determine how environmental variables influenced space use by individuals.

For mice captured in the summer, percent cover of leaf litter (p = 0.004) was the only significant predictor of MSD, whereas canopy cover (p = 0.001) and abundance (p =

0.003) were negatively correlated with MSD for mice captured in the fall. Our results xx suggest that management efforts to control the spread of Amur honeysuckle and other exotic shrubs may lead to short-term increases in the abundance of generalist rodents such as white-footed mice. Furthermore, factors such as leaf litter cover, canopy cover, and population-level abundance may influence space use by individual mice within invaded habitats. 1

CHAPTER 1 INTRODUCTION

1.1. The Problem with Invasive Species

The introduction of non-native, invasive organisms can be detrimental to native species and even cause significant economic losses. Not all exotic species are invasive and there are mixed opinions about the link between invasive species and declines of native flora and fauna (Gurevitch and Padilla 2004, Didham et al. 2005). For example,

Pyšek et al. (2012) presented a global overview of the impacts of 167 invasive species.

After reviewing 287 publications, these investigators concluded that, in general, invasive species cause a decrease in species- and community-level measures such as survival, abundance, and diversity of resident species, but effects are context dependent with no universal way to measure impact (Pyšek et al. 2012). Nonetheless, there are numerous case studies that exemplify the severe negative effects that can result from invasions by non-native species. In general, invasive species are considered nearly as important as habitat destruction in terms of impacts on native biota (Wilcove et al. 1998). For example, American chestnut (Castanea dentata [Marshall] Borkh.) is a foundation species (Ellison et al. 2005) that was functionally extirpated from forest ecosystems throughout the eastern U.S. due to chestnut blight (caused by the non-native fungus

Cryphonectria parasitica); the result was subsequent changes in forest structure where 2

American chestnut occurred historically (Vandermast and Van Lear 2002). Many other invasive species have or are causing large-scale changes in ecosystem composition, structure, and function in their invaded ranges. Some well-known examples include

Dutch elm disease (caused by non-native Ophiostoma fungi, vectored by native and non- native bark beetles; Gibbs 1978), emerald ash borer (Agrilus planipennis; Poland and

McCullough 2006), laurel wilt (caused by the non-native fungus Raffaelea lauricola, vectored by the non-native ambrosia beetle Xyleborus glabratus; Mayfield, III 2007), tamarisk (Tamarix spp.; Frasier and Johnsen, Jr. 1991) garlic mustard (Alliaria petiolata

[M. Bieb.] Cavara & Grande; Meekins and McCarthy 1999), common reed (Phragmites australis [Cav.] Trin. Ex Steud., non-native subspecies australis; Marks et al. 1994), purple loosestrife (Lythrum salicaria L.; Blossey et al. 2001), cheatgrass (Bromus tectorum L.; Knapp 1996), cogongrass (Imperata cylindrica [L.] P. Beauv.; Bryson and

Carter 1993), exotic mussels (zebra mussels [Dreissena polymorpha] and quagga mussels

[Dreissena rostriformis bugensis]; Ludyanskiy et al. 1993, Baldwin et al. 2002), Asian carp (silver carp [Hypophthalmichthys molitrix] and bighead carp [Hypophthalmichthys nobilis]; Herborg et al. 2007), and feral hogs (Sus scrofa; Siemann et al. 2009).

Equally important are the economic costs that result in damages and control costs associated with invasive species. For example, Pimentel et al. (2001) indicated that the damage and control costs of invasive species can be as high as $1.4 trillion per year worldwide (nearly 5% of the global economy). Furthermore, economic costs due to invasive species in the U.S. alone can exceed $120 billion in a single year (Pimentel et al.

2005). Such costs often fall short of money allocated towards controlling such problematic organisms. For example, in Fiscal Year 2011, approximately $2 billion was 3 allocated to eight U.S. departments/agencies for invasive species prevention, early detection and rapid response, control and management, research, restoration, education and public awareness, and leadership/international cooperation (The National Invasive

Species Council 2013); this is far less than the $120 billion in annual costs reported by

Pimentel et al. (2005).

Loo (2009) stated: “Impacts of non-indigenous invaders are expected to be greatest when the invading species performs a new ecological function in the invaded ecosystem.” In other words, invasion depends on the characteristics of the invader and the characteristics of the invaded ecosystem. However, much work is still needed to understand how and why invasive species establish and spread in their invaded environments and what this means in the context of control efforts. Specifically, scientists and land managers alike are still in need of a better understanding of effective control strategies, spatial and temporal invasion processes, factors contributing to successful invasion, and resultant changes to native biota and ecosystem processes in response to management strategies. To date, there is little information pertaining to small-scale (e.g., woodlot scale) patterns and rates of plant invasions, especially for woody invaders. Furthermore, we know very little about changes in spatial patterning and factors influencing characteristics of individuals as the invasion process progresses from establishment to saturation. Another missing piece of the puzzle is how both duration (in terms of age of invaders) and intensity (in terms of occupied growing space) affect native biota at microsite scales within an invaded area. Finally, while there is much anecdotal evidence pertaining to the consequences of implementing invasive species control programs, there is still a need for more scientifically-sound before-and- 4 after studies. All of this information is critical not only for predicting long-term effects on native floral and faunal communities but also for allowing forest managers to better prioritize invasive species-control efforts.

1.2 Amur Honeysuckle

Amur honeysuckle (Lonicera maackii [Rupr.] Herder) is a non-native shrub that was brought into North America from Asia in the 1890s (Luken and Thieret 1996). It is considered one of the most aggressive woody plant invaders and since its introduction it has colonized and spread throughout many ecosystems in the central U.S. and portions of

Ontario (Deering and Vankat 1999). This species reaches sexual maturity as early as five years (Deering and Vankat 1999); produces many berries (7,300 berries have been documented on a single shrub [Lieurance 2004]), which are readily dispersed by birds

(Bartuszevige et al. 2006), white-tailed deer (Odocoileus virginianus [Castellano and

Gorchov 2013]), and possibly small mammals (Williams et al. 1992). The species grows in full sunlight and tolerates shade (Luken et al. 1997); expands its leaves earlier in the year than other plants and retains them for longer (pre-adaption; Luken 1988); occupies the shrub stratum in forests that historically contained low densities of shrubs (taking available niche space); has no known, important natural enemies in its invaded range

(enemy release); and may have allelopathic foliage, which may inhibit other plants and deter herbivory (Cipollini et al. 2008). Because Amur honeysuckle is such an aggressive and widespread invader, investigators are afforded an opportunity to study its spread in space and time, as well as its ecological impacts. Gaining a better understanding of a 5 readily-observable species such as Amur honeysuckle may, in turn, result in a more thorough understanding of invasion patterns and ecological effects of woody invaders.

To date, several scientists have documented the negative impacts of Amur honeysuckle on native vegetation (Hutchinson and Vankat 1997, Gould and Gorchov

2000, Collier et al. 2002, Gorchov and Trisel 2003, Miller and Gorchov 2004, Meiners

2007, Cipollini and McClain 2008, Hartman and McCarthy 2008, McKinney and Goodell

2010). For example, Gorchov and Trisel (2003) found that competition with Amur honeysuckle led to increased mortality of native tree seedlings in an American beech

(Fagus grandifolia Ehrh.)-sugar maple (Acer saccharum Marsh.) forest in Ohio.

Hartman and McCarthy (2004) found that the survival of six planted, native tree seedling species was higher in areas where Amur honeysuckle was removed as compared to areas where Amur honeysuckle remained. It is unclear, however, how both the intensity and duration (i.e., combined effect of occupied growing space and age of Amur honeysuckle individuals) have impacted native plant communities. Of particular interest may be the influence that the density and duration of Amur honeysuckle have on seedling/sapling densities of commercially and ecologically valuable hardwood species, as well as its effects on sensitive herbaceous species. For example, because Amur honeysuckle expands its leaves earlier in the year than trees in the overstory stratum, it may be in direct competition for light with ephemeral herbs that otherwise take advantage of the leafless canopy during the spring. Is this effect exacerbated in forests where Amur honeysuckle has persisted longer?

Understanding the spatial distribution and rate of spread of Amur honeysuckle is also critical for determining its long-term effects on native plant communities. Deering 6 and Vankat (1999) found that Amur honeysuckle densities began to increase exponentially approximately 10 years after invasion; however, data were only collected in a single woodlot in Ohio. Flory and Clay (2006) found that the densities of Amur honeysuckle (as well as other non-native shrubs) decreased with increasing distance from roads in forests throughout Indiana, providing some insights into how spatial location can influence density. Castellano and Boyce (2007) examined the spatial distribution of

Amur honeysuckle in a Kentucky road cut (dominated by shrubs and small trees) and found that Amur honeysuckle exhibited a clustered pattern, immature honeysuckles clustered around mature honeysuckles, and the spatial distributions of Amur honeysuckle and eastern redcedar (Juniperus virginiana L.) were independent. Does Amur honeysuckle also exhibit these spatial patterns in mature forests? Do these patterns depend on the stage of invasion? Furthermore, does accounting for spatial autocorrelation of individuals within an invading population change which variables are most significantly influencing structural characteristics such as diameter and height?

These are questions that need to be addressed, not only to better understand ecological effects at small scales, but to help land managers more effectively design control strategies to help slow the spread of Amur honeysuckle.

Several investigators have examined the effects of removing Amur honeysuckle on native vegetation (See Luken et al. 1997, Gould and Gorchov 2000, Hartman and

McCarthy 2004, Owen et al. 2005, Runkle et al. 2007, Swab et al. 2008, Cipollini et al.

2009). However, most of these studies have employed relatively small experimental removals, and only Runkle et al. (2007) and Swab et al. (2008) employed removal areas larger than 40 m2. 7

With regard to animals, some investigators have shown that certain animal species serve as dispersal agents for Amur honeysuckle. For example, Amur honeysuckle flowers are pollinated by bees (including the introduced honey bee [Apis mellifera;

Goodell et al. 2010]) and both birds and white-tailed deer are known to consume and disperse Amur honeysuckle seeds (Bartuszevige et al. 2006, Castellano and Gorchov

2013). There is also some work indicating that Amur honeysuckle invasion may impact the abundance and survival of birds (Schmidt and Whelan 1999, McCusker et al. 2009,

Packett and Dunning 2009, Gleditsch and Carlo 2011) and amphibians (McEvoy and

Durtsche 2003, Watling et al. 2011a, Watling et al. 2011b). For example, in a study examining the use of stopover habitat by fall migratory birds in Indiana woodlots, Packett and Dunning (2009) found a significant positive correlation between bird densities and aggregate densities of Amur honeysuckle and rose (Rosa spp.). Furthermore, McCusker et al. (2009) found that in rural forests in Illinois, sites that contained non-native Lonicera spp. contained higher densities of understory bird species and lower densities of certain canopy bird species as compared to sites that were dominated by native shrubs and saplings. In terms of amphibians, Watling et al. (2011a) found that areas with high Amur honeysuckle densities had lower amphibian species richness and evenness than areas with lower densities of honeysuckle in an oak (Quercus spp.)-hickory (Carya spp.) forest in

Missouri and Watling et al. (2011b) found that leached extracts from honeysuckle foliage affected the behavior and survival of tadpoles of some species.

Small mammals, which have been used as bio-indicators of ecosystem sustainability (McLaren et al. 1998), are influenced by changes in microsite characteristics such as coarse woody debris, vegetative structure, and food availability 8

(Bowman et al. 2000, Schwieger et al. 2000, Greenberg 2002). Changes in the shrub layer induced by Amur honeysuckle invasion may therefore result in important environmental changes from the perspective of forest-dwelling small mammals. To date, few investigators have examined the influence of Amur honeysuckle on small mammals

(See Williams et al. 1992, Mattos and Orrock 2010, and Dutra et al. 2011). One question that has not been answered for any wildlife species is how removing exotic shrubs such as Amur honeysuckle affects animal abundance. Furthermore, there is little information pertaining to environmental variables that directly influence animals in ecosystems invaded by Amur honeysuckle.

1.3. Objectives of Dissertation

The primary objective of this research was to examine the impact of Amur honeysuckle on native plant communities and small mammal populations in 12 mixed- hardwood forests of Indiana (Figure 1.1). Specifically, we (my graduate committee and

I) examined: 1) the influence of intensity and duration of Amur honeysuckle on the diversity and abundance of native forest plants, 2) the spatial pattern and rate of spread of

Amur honeysuckle invasion at the forest/woodlot scale, and 3) the short-term effects of removing Amur honeysuckle and other non-native shrubs on the abundance and diversity of native plants and the abundance of white-footed mice (Peromyscus leucopus).

Specifically, at all 12 study sites we examined how the duration of Amur honeysuckle invasion, Amur honeysuckle abundance (percent cover and density), and other environmental factors influenced microsite-level abundance, taxonomic richness (S, 9 number of taxa), Pielou’s Evenness Index (J'; Pielou 1966), and Shannon’s Diversity

Index (H'; Shannon 1948) of native herbaceous/vine taxa, as well as densities of native seedlings. At three of the study sites we determined how Amur honeysuckle spreads in space and time by examining spatial patterns and minimum age distributions. We also identified factors influencing life-stage characteristics (diameter and height) of individual shrubs in the invading populations. At six of the study sites, we examined the short-term effects of removing Amur honeysuckle and other non-native shrubs on the abundance and diversity of native plant taxa, as well as the abundance of white-footed mice. We also determined which environmental factors influenced the space use of individual mice found in areas invaded primarily by Amur honeysuckle. 10

Figure 1.1. General locations of 12 study sites in central Indiana. One study site (a private woodlot) was located in Benton County (upper left star on map), five study sites (Purdue University Department of Forestry and Natural Resources Farm, Purdue University Department of Forestry and Natural Resources Martell Forest, Purdue University Meigs Farm South, a private woodlot, and Ross Biological Reserve) were located in Lafayette/West Lafayette (second-highest star on map), one study site (Ft. Harrison State Park) was located near Indianapolis (star near center of map), and five study sites (Fowler Park, Hawthorn Park, Pfizer Inc. Pond 5A forest, Pfizer Inc. Pond 5B forest, and Pfizer Inc. Rifle Range woodlot) were located near Terre Haute (lower left star on map). Indiana image from IndianaMAP (http://maps.indiana.edu). 11

CHAPTER 2 AGE DISTRIBUTION AND SPATIAL PATTERNING OF AN

INVASIVE SHRUB IN SECONDARY HARDWOOD FORESTS

2.1. Introduction

Invasion by non-native woody species has resulted in drastic changes in native ecosystems through the direct suppression of native plant regeneration, structural and compositional homogenization of communities, and altered ecosystem processes

(Pimentel et al. 2005, Webster et al. 2006). Invasions typically follow a particular chronological sequence, beginning with direct or indirect introductions, followed by establishment and expansion, and finally, saturation at one or more geographic scales

(Shigesada and Kawasaki 1997, Sakai et al. 2001). Control efforts can be especially challenging due to time lags early in the invasion process that make early detection and response difficult (Webster et al. 2006). Low population density during this initial lag or establishment phase gives way to rapid population growth during the expansion phase when control quickly becomes difficult. Understanding these changes in expansion rate and how they relate to the spatial patterning of individuals in a population is critical to predicting the dynamics of an invasion. Consequently, understanding the “when” of invasion (the rate of establishment and spread) is as important as understanding the

“where” (which communities or ecosystems are most vulnerable). Communities with 12

introduced species offer a unique opportunity to study pattern and rate of plant establishment and spread when the date of initial invasion can be determined or estimated with accuracy.

While a growing body of work has examined the spread of woody invasive species at the landscape scale (Bartuszevige et al. 2006, Wangen and Webster 2006,

Kuppinger et al. 2010), less is known about local patterns and rates of woody plant invasions or how spatial patterns change as invasion progresses from the establishment to the saturation phase. However, these widely-dispersed local populations serve as nascent foci that drive the geographic expansion of invasion (Moody and Mack 1988). At this local scale, questions remain about how rate and pattern of invasion are influenced not only by the characteristics of the invaded community, but also by life-stage characteristics (such as height and diameter growth, development of multiple basal stems, and fruit production) of the invading species. In forests, understanding the stand- and neighborhood-scale factors that influence the life-stage development (such as height and diameter growth) of an invasive species provides valuable insight into the mechanics of invasion, establishment, and persistence because life-stage characteristics determine population growth rate elasticity, stable stage distribution, and reproductive value

(Neubert and Caswell 2000).

Waldo Tobler’s (1970) first law of geography states: “Everything is related to everything else, but near things are more related than distant things.” Increasingly, ecologists are recognizing that observations that are closer in space may be more similar or less similar than observations that are farther away from one another (i.e., positive or negative spatial autocorrelation; Legendre 1993, Lichstein et al. 2002, Rangel et al. 13

2006). In plant populations and communities, interactions within local neighborhoods may exert a strong influence on life-stage development, which in turn influences survivorship, fecundity and dispersal; thus neighborhood interactions could have a strong effect on the pattern and rate of invasion. However, we are unaware of any studies that have focused on variables that influence life-stage characteristics of individuals in the invading population or examined how spatial autocorrelation within a population influences these characteristics.

In this study, we examined age distributions, spatial patterns of invasion, and factors influencing life-stage characteristics of individual shrubs in populations of Amur honeysuckle (Lonicera maackii [Rupr.] Herder), a non-native, invasive shrub species that has aggressively colonized many forests in the eastern U.S. and parts of Ontario, Canada

(Luken and Thieret 1996). Our specific objectives were to: 1) Examine age distributions to determine if there is a certain age when invading populations of Amur honeysuckle begin to spread at an exponential rate (i.e., expansion phase of invasion). We hypothesized that invasion rate would more dependent upon the life history of the invading species than the characteristics of the invaded community, and therefore, the rate of invasion (and the timing of invasion stages) would be constant across sample sites.

2) Determine whether the spatial distribution of Amur honeysuckle within a forest is purely random or if there is a more organized pattern to how it spreads. We hypothesized that pattern of invasion would display clustering dependent upon the location of existing honeysuckle and the presence of overstory trees. 3) Determine if Amur honeysuckle age distributions and spatial patterns differ depending on invasion intensity. We hypothesized that patterns would be less clustered in more intense (saturated) populations 14 as individuals begin to occupy a greater portion of the available growing space. 4)

Determine which variables influence the size (diameter and height) of individual shrubs in the invading population and whether accounting for the spatial autocorrelation of shrubs within a population changes the relative importance of these variables. We hypothesized that spatial autocorrelation would influence the importance of variables, likely due to small-scale competitive interactions within local neighborhoods.

2.2. Methods

2.2.1. Study Sites

From 2010 to 2012, we collected data in six, mature, second-growth mixed- hardwood forests in central Indiana, USA (range of study areas was approximately

39°20’ N to 40°26’ N and 86°57’ W to 87°26’ W)―1) Fowler Park, 2) Hawthorn Park,

3) Pfizer, Inc., Rifle Range woodlot (hereafter referred to as Rifle Range), 4) Purdue

University, Department of Forestry and Natural Resources Farm (hereafter referred to as

FNR Farm), 5) a private woodlot in Lafayette, Indiana (hereafter referred to as Pursell), and 6) Ross Biological Reserve (hereafter referred to as Ross). Across all study sites, landforms consisted of till plains, flood plains, loess hills, and dunes, all within the glaciated regions of Indiana (Soil Survey Staff, Natural Resources Conservation Service,

United States Department of Agriculture 2013). Parent materials were variable and consisted of loess, loess over loamy till, loess over loamy outwash, loamy alluvium over sandy and gravelly outwash, silty alluvium, loamy till, and loamy outwash over sandy 15 and gravelly outwash, with soils ranging from very poorly drained (e.g., Cohoctah loam) to excessively drained (e.g., Rodman sandy loam; Table 2.1; Soil Survey Staff, Natural

Resources Conservation Service, United States Department of Agriculture 2013).

Overstories at all study sites were dominated by mature, second-growth deciduous trees, with ≥ 85% canopy closure in the highest stratum (Table 2.1). At all study sites,

Amur honeysuckle was the dominant non-native invasive species and no major disturbance had occurred since the invasion of Amur honeysuckle. The six study sites we selected represented a gradient of Amur honeysuckle invasion in terms of density and size of individual shrubs and offered a variety of overstory compositions (Table 2.1).

2.2.2. Data Collection

In the fall/winter of 2010/2011, we harvested stem cross sections of Amur honeysuckle shrubs at all six study sites. Specifically, we placed three transects (spaced

20 m apart, with each transect extending from the forest edge to the interior) at each study site. Each transect contained four, 40-m2 (radius of 3.57 m) circular plots (sapling- layer plots) and four, 2 m x 2 m quadrats. The first sapling-layer plot was placed 5-10 m from the forest edge, with subsequent plots spaced 20 m apart along each transect. A 2 m x 2 m quadrat was placed to the upper right, upper left, lower right, or lower left of the sapling-layer plot center, with the corner of the quadrat occurring at plot center. In each sapling-layer plot, we collected cross sections from a maximum of eight Amur honeysuckle individuals (defined as a single stem or group of stems that originated from the same root burl) that were ≥ 1.37 m tall. Specifically, we collected cross sections from 16 the two largest shrubs (based on widest diameter at ground level) in each of the four quadrants (upper right, lower right, upper left, lower left) of each sapling-layer plot. In each 2 m x 2 m quadrat, we collected cross sections from the three largest Amur honeysuckle individuals that were 0.51-1.36 m tall (i.e., seedling-layer shrubs). We also collected cross sections from honeysuckles in the area between the forest edge and the first sapling-layer plot along each given transect. Three seedling-layer and the five sapling-layer Amur honeysuckle individuals closest to the forest edge that were rooted within one meter on each side of a given transect were chosen.

For each Amur honeysuckle that was destructively sampled, we also recorded diameter above root burl (diaburl; cm), diameter of largest living basal stem (diastem; cm), height (ht; m), number of living basal stems (nstem), and distance from nearest forest edge based on the drip line of the overstory canopy (edge; m). Diameter above root burl was measured on the widest portion of the individual (encompassing all basal stems) directly above the root burl. Diameter of largest basal stem was measured at the widest part of the basal stem, directly above the root burl.

At three sites (FNR Farm, Pursell, and Ross), we recorded the spatial location of every Amur honeysuckle individual ≥ 0.5 m tall and every overstory tree ≥ 10 cm diameter at breast height (dbh) in a 60 m x 60 m area where honeysuckles were not destructively sampled. These three study sites were chosen to represent a range of invasion intensity in terms of Amur honeysuckle density, size, and proportion of mature shrubs. Each 60 m x 60 m area was placed with one boundary along the forest edge. For each Amur honeysuckle and overstory tree within the 60 m x 60 area, we recorded the distance (m) and azimuth from a central location using the Haglöf PosTex® mapping 17 instrument (Haglöf Sweden AB, Långsele, Sweden); distances and azimuths were used to calculate XY coordinates for each honeysuckle and tree. For each honeysuckle, we also recorded diaburl, diastem, ht, edge, and whether it produced berries.

2.2.3. Data Analyses

Using the cross sections, we determined the minimum age (agemin) of each destructively sampled shrub by counting the annual growth rings of the oldest basal stem.

For each shrub, diaburl, diastem, ht, number of living basal stems (nstem), and edge were used as independent variables to predict agemin, using a linear mixed effects model where study site was treated as a random effect. Specifically, we used the equation:

y = Xα + Zb + ϵ (1)

where y was a vector of observations for the dependent variable agemin (mean = E(y) =

Xα), α was the vector of fixed effects, b was the vector of random effects (mean = E(b) =

0, with a positive-definite covariance matrix), ϵ was the vector of identically distributed random error terms that may be correlated (mean = E(ϵ) = 0, with a positive-definite covariance matrix), and X and Z were the regression terms relating y to α and b (Laird and Ware 1982, Pinheiro and Bates 2000). To determine which predictor variables to include in the final linear mixed effects model, we first pooled the cross section data across all six study sites and developed a full multiple regression model using the aforementioned predictor variables. Variables were transformed as necessary to 18 minimize deviations from normality (Zar 1999) and a Variance Inflation Factor (VIF) was calculated for each predictor to assess multicollinearity (VIF values ≥ 5 were considered problematic). Next, using the best subsets regression approach (Miller 1984), we examined all subsets of the full multiple regression model (64 possible subsets from six predictor variables). We then selected those models with a delta Akaike information criterion (AIC; Akaike 1974) < 2 (Burnham and Anderson 2002) and ran each regression model as a linear mixed effects model. Of the candidate linear mixed effects models, we selected the model with all significant (p < 0.05) predictor variables as the final model to predict minimum age of Amur honeysuckle. Distributional assumptions for within-group errors and random effects for the final linear mixed effects model were examined by observing normality and residual plots.

To validate the final linear mixed effects model, 35 cross sections were randomly selected and excluded from the data set used to build the linear mixed effects model.

Spearman’s rank correlation coefficient (ρ; Myers and Well 2003) was then used to compare observed agemin of those 35 cross sections to the predicted values based on the mixed effects model. Finally, the linear mixed effects model was used to predict agemin of honeysuckles within the 60 m x 60 m mapping areas; these data were then used to create agemin distributions by study site.

For each mapping area, we used an inhomogeneous L function (Baddeley et al.

2000) to test whether the spatial distribution of Amur honeysuckle (based on XY coordinates) was different from random. An inhomogeneous function is appropriate when the intensity of a spatial point pattern is not homogeneous across the study area.

For our study, this was exemplified by a change in density of Amur honeysuckle across 19 each 60 m x 60 m sample area. The inhomogeneous L function is a square root transformation of the inhomogeneous K function (Baddeley et al. 2000) and used to stabilize variance and simplify graphical interpretations. It is calculated as:

{ } ( ) ̂ ( ) √∑ ∑ (2) ( ) ( )

where ̂ ( ) was the inhomogeneous L statistic for radius r of a circle surrounding a focal shrub i and including neighbor shrubs j, was the Euclidean distance from focal shrub i to neighbor shrub j, ( ) ( ) was the smoothing function used to characterize the inhomogeneous intensity (where λ was estimated using the leave-one-out kernel smoother), and ( ) was the edge correction factor used to reduce bias in estimates of L (Baddeley et al. 2000). A border edge correction was employed for the edge correction factor, and Confidence Envelopes (CE) of complete spatial randomness were generated using 999 Monte Carlo simulations (Dwass 1957, Barnard 1963). For a given r, if the estimated L was greater than the upper bound of the CE of complete spatial randomness, then the point pattern exhibited spatial clustering with statistical significance. If the estimated L was less than the lower bound of the CE for a given radius, then the point pattern was significantly more dispersed than a random distribution.

Estimated values of L that fell within the CE for a given radius suggested that the point pattern was a random Poisson point process for that radius (Ripley 1977, Ripley 1988,

Diggle 2003). 20

An inhomogeneous cross-L was used to test whether spatial independence existed between immature honeysuckles (those without berries) and mature honeysuckles (those with berries), and between Amur honeysuckle (mature and immature combined) and overstory trees. Like the inhomogeneous L function, the inhomogeneous cross-L is a square root transformation of the inhomogeneous cross-K function (Møller and

Waagepetersen 2004). Calculation of the inhomogeneous cross-L function is similar to the inhomogeneous L, except the focal point i is a different type of point than j. In our study, we were interested in whether immature honeysuckles (type j) clustered around mature honeysuckles (type i) and whether Amur honeysuckle (both mature and immature; type j) clustered around trees (type i). As with the inhomogeneous L, a border edge correction was employed (Baddeley et al. 2000), and 999 Monte Carlo simulations were used to generate confidence envelopes of complete spatial randomness (Dwass

1957, Barnard 1963). For a given r, if the estimated cross-L was greater than the upper bound of the CE, then the type j point clustered around the type i point with statistical significance. If the estimated cross-L was less than the lower bound of the CE for a given radius, then the type i and type j point patterns exhibited statistically significant spatial repulsion. Estimated values of cross-L that fell within the CE for a given radius suggested there was no relationship between the spatial distributions of the two types of point patterns.

For Amur honeysuckle individuals sampled at the mapped sites (FNR Farm,

Pursell, and Ross), regression analysis incorporating spatial autocorrelation (Pinheiro and

Bates 2000) was used to examine variables influencing the diameter and height of both immature and mature honeysuckles. For this analysis, we focused on variables that could 21 be measured on the ground by forest managers. Specifically, at each study site, we examined four dependent variables―1) diaburl of mature Amur honeysuckle (with berries), 2) ht of mature Amur honeysuckle, 3) diaburl of immature Amur honeysuckle (no berries), and 4) ht of immature Amur honeysuckle. For each dependent variable, we built a full multiple regression model and transformed variables as necessary to minimize heteroscedasticity and deviations from normality (Zar 1999). Predictor variables for each model where diaburl of mature Amur honeysuckle was the dependent variable included edge, number of nearest Amur honeysuckle shrubs within a 2-m radius of the focal shrub

(neighbor), distance to nearest overstory tree (m; distree), and nstem. With ht of mature

Amur honeysuckle as the dependent variable, predictor variables included edge, neighbor, distree, nstem, and diaburl. For each model with diaburl of immature Amur honeysuckle as the dependent variable, predictor variables included edge, neighbor, distree, distance to nearest mature Amur honeysuckle (m; disamur), and nstem. Finally, when ht of immature Amur honeysuckle was the dependent variable, predictor variables included edge, neighbor, distree, disamur, nstem, and diaburl.

For each full multiple regression model for the three mapped sites, we used best subsets regression (Miller 1984) to select the best model (three study sites × four dependent variables = 12 models total), using the criteria that the best model had the lowest AIC value, all significant (p < 0.05) predictor variables, and no predictor variables with a VIF ≥ 5. For each of these best models, we then used a semivariogram to examine whether the spatial proximity of Amur honeysuckle influenced the relationship between predictor variables and dependent variables (Pinheiro and Bates 2000). Specifically, we re-ran each best multiple regression model, using five theoretical semivariograms to 22 characterize the spatial structure of the standardized residuals―exponential, Gaussian, linear, spherical, and rational quadratic (Pinheiro and Bates 2000). The appropriate theoretical semivariogram was selected by running the multiple regression model with each theoretical semivariogram and choosing the model with the lowest AIC value

(Pinheiro and Bates 2000). Furthermore, based on examinations of empirical semivariograms, we incorporated a nugget effect (to account for abrupt changes in correlation at extremely small Euclidean distances) in each spatial model (Pinheiro and

Bates 2000). If characterizing the spatial structure of a multiple regression model caused otherwise significant (p < 0.05) predictor variables to become non-significant, then the newly non-significant variables were excluded and only significant independent variables were included in the final spatial model.

All statistical analyses were performed using the program R (R Core Team 2013).

Best subsets regression was performed using the R package MuMIn (Barton 2013), and

VIF values were calculated using the R package car (Fox and Weisberg 2011). Linear mixed effects models and multiple regressions using the semivariogram approach were built using the R package nlme (Pinheiro et al. 2012). The R package spatstat (Baddeley and Turner 2005) was used to generate spatial point patterns and to calculate the inhomogeneous L functions. Scientific names and authorities of plant species followed the U.S.D.A. Plants Database (USDA, NRCS 2013).

2.3. Results

The final linear mixed effects model to predict minimum age of Amur honeysuckle was: agemin = 0.12 + 0.04edge + 0.18diaburl + 1.40diastem + 1.17ht + random effects (n = 442, AIC = 2301.12, p < 0.001 for all predictor variables). The random effects coefficients for FNR Farm, Pursell, Ross, Rifle Range, Fowler, and Hawthorn were 1.26, -1.07, -0.26, -0.42, -0.36, and 0.85, respectively. While all significant predictor variables exhibited a positive relationship with agemin, the strongest predictor variables were diastem (p < 0.0001) and ht (p < 0.0001). Results from Spearman’s rank correlation analysis indicated that this model is accurate at predicting agemin (ρ = 0.84).

At FNR Farm, we recorded the spatial locations of 2,386 Amur honeysuckle shrubs (1,148 with berries and 1,238 without berries) and 231 trees (Figure 2.1a).

Compared to FNR Farm, Ross had fewer Amur honeysuckles overall (2,091), fewer shrubs that produced berries (442), more shrubs without berries (1,649), and fewer trees

(204; Figure 2.1c). Pursell had the fewest number of honeysuckles (1,552 overall; 274 with berries and 1,278 without berries) and trees (139; Figure 2.1b). The widest range in

Amur honeysuckle diaburl was observed at Pursell (0.3-28.5 cm), followed by FNR Farm

(0.3-27.8 cm) and Ross (0.3-21.4 cm). In terms of Amur honeysuckle ht, the tallest shrubs were found at FNR Farm (range = 0.5-7.4 m); range of ht at Pursell and Ross were

0.5-5.3 m. Based on predicted agemin, the oldest Amur honeysuckle shrubs were found at

FNR Farm (range of predicted agemin = 3-37 years), followed by Ross (1-28 years) and

Pursell (1-27 years; Figure 2.2). At all three study sites, agemin distributions indicated that Amur honeysuckle densities began to increase dramatically approximately 10-15 24 years after the oldest shrubs colonized the study areas (Figures 2.2 and 2.3). Twenty years after initial colonization, recruitment further intensified (Figure 2.3), with FNR

Farm (the oldest invasion) displaying the greatest level of saturation after 37 years.

The inhomogeneous L function indicated that Amur honeysuckle exhibited a clustered spatial distribution for all radii examined at Pursell and Ross (estimated L > upper CE of theoretical L of complete spatial randomness); however, at FNR Farm, clustering was only observed for radii < 8 m (Figure 2.4). The inhomogeneous cross-L function indicated that, at Pursell and Ross, immature honeysuckles clustered around mature honeysuckles for nearly all of the radii examined (Figure 2.4). At FNR Farm, immature honeysuckles clustered around mature honeysuckles at radii of < 4 m, but at larger radii, the pattern was either random or regular (Figure 2.4). The relationship between the spatial distributions of Amur honeysuckle and trees was strikingly different among study sites. At FNR Farm, the inhomogeneous cross-L function indicated that the spatial distributions of honeysuckle and trees were independent for small radii (< 1 m); however, honeysuckles tended to cluster around trees from radii of ~1 meter to ~6 meters

(Figure 2.4). At Pursell, honeysuckles exhibited some clustering around trees between radii of ~1 m and ~2.5 m (Figure 2.4). At Ross, the spatial distributions of Amur honeysuckle and trees were mostly independent of one another, but clustering was observed at radii > 12 m (Figure 2.4).

In most cases, incorporating spatial correlation structure in the regression models yielded a better fit (based on AIC) than when spatial correlation was not considered

(Table 2.2). Variables influencing Amur honeysuckle diameter and height depended on reproductive status of Amur honeysuckle, study site, and whether the spatial correlation 25 structure was accounted for in the model (Table 2.2). Diameter above root burl was a consistently significant (regression p < 0.05) predictor of ht, where shrubs with larger diaburl also tended to be taller (Table 2.2). Number of living basal stems was a consistently significant predictor of diaburl and ht, where more nstem equated to a larger diaburl but a shorter ht (Table 2.2). However, in some cases predictor variables that were otherwise significant became non-significant (p > 0.05) when spatial autocorrelation was accounted for in the model (Table 2.2).

2.4. Discussion

At the forest scale, we observed a sequence of invasion (establishment of early migrants, initial lag period, rapid expansion, and saturation) that has been documented at landscape and regional scales (Shigesada and Kawasaki 1997, Sakai et al. 2001). As we hypothesized, in all three mapped populations minimum age distributions indicated that

Amur honeysuckle densities began to increase dramatically after similar post-invasion time intervals; approximately 10-15 years following initial colonization. In a study of a single woodlot, Deering and Vankat (1999) also observed exponential growth approximately 10 years after invasion. When considered in conjunction with our three sites that differ in overstory composition, it appears that this 10-15 year lag may typify invasion by this species. This time lag has also been observed for Norway maple (Acer platanoides L.), another shade-tolerant, but wind-dispersed, invasive woody species

(Wangen and Webster 2006). At the forest/woodlot scale, this time lag of 10-15 years was likely a result of the time required for the first invasion cohort of Amur honeysuckle 26 to become sexually mature (as early as five years; Deering and Vankat 1999) and produce berries that germinated and became new individuals in the population. In terms of population invasion phase, this change in density with time represents a shift from initial colonization and establishment to population expansion, whereby new individuals are being added to the population at an exponential rate (Sakai et al. 2001). If left unchecked, populations typically enter the saturation phase, at which time control efforts may become unrealistic (Sakai et al. 2001). In particular, the high propagule pressure

(7,300 berries have been documented on a single shrub; Lieurance 2004), animal dispersal (Castellano and Gorchov 2013), shade tolerance (Luken et al. 1997), and early leaf expansion and longer leaf retention relative to native species (Luken 1988) make

Amur honeysuckle particularly problematic. In a forest setting, Minor and Gardner

(2011) found that the most widespread invasive plant species tended to be animal- or human-dispersed, while species that spread via abiotic means displayed more limited distributions. In addition, forests in the central United States lack a pervasive shrub layer and invasion by honeysuckle introduces an alien structural element through the occupation of an underutilized niche (Shea and Chesson 1992, Dietz and Edwards 2006).

This pervasive shrub layer has led to declines in native herbaceous and tree seedling diversity due to direct and apparent competition (Collier et al. 2002, Meiners 2007).

As we hypothesized, Amur honeysuckle exhibited a clustered pattern overall and immature honeysuckles tended to cluster around mature honeysuckles. However, the relationship between spatial distributions of Amur honeysuckle and overstory trees tended to be independent or regular. In general, clustering of Amur honeysuckle via neighborhood diffusion (as described by Shigesada et al. 1995) is likely due to seeds 27 being dispersed by birds as they perch on shrubs and/or from berries dropping to the ground from mature shrubs (animals rarely consumed all of the berries on Amur honeysuckles at our study sites; J.M. Shields, personal observation). At our less saturated sites (Pursell and Ross), this pattern was observed for all radii around focal shrubs, whereas at FNR Farm (our most saturated site), clustering of all Amur honeysuckles and of immature honeysuckles around mature shrubs only occurred at small radii, supporting our hypothesis that clustering becomes less pronounced in saturated populations. In highly saturated invasions, growing space decreases as the invasion front continues to expand and satellite sub-populations coalesce, thus reducing available growing space between sub-populations at larger radii. At FNR Farm and Pursell, clustering of Amur honeysuckle around trees was observed only for small radii (~1-6 m at FNR Farm and

~1-2.5 m at Pursell), and patterns were independent for nearly all radii examined at Ross.

As the availability of mature honeysuckle individuals increases, the importance of trees as perch sites for birds decreases. Therefore, as honeysuckle density increases, dissemination of honeysuckle seeds under mature individuals leads to a more variable spatial pattern prior to population saturation. A similar pattern of invasion was observed in a more open non-forest setting (Castellano and Boyce 2007), suggesting that mature honeysuckle stems serve as vectors of dissemination independent of a mature forest overstory.

Results from the linear mixed effects model indicated that structural characteristics were most important for predicting the minimum age of Amur honeysuckle. Specifically, changes in minimum age with per unit changes in predictor variables were strongest for diameter of the largest living basal stem (coefficient = 1.40, p 28

< 0.0001) and height (coefficient = 1.17, p < 0.0001). These results are not surprising given the perennial nature of a woody shrub such as Amur honeysuckle, where individuals are adding both diameter and height growth as they age. Distance from nearest forest edge also exhibited a positive relationship with minimum Amur honeysuckle age, suggesting that the oldest individuals in an invading population are not necessarily located along the forest edge. Birds such as the American Robin (Turdus migratorius) and Gray Catbird (Dumetella carolinensis), as well as white-tailed deer

(Odocoileus virginianus), are known to disperse Amur honeysuckle seeds (Bartuszevige et al. 2006, Castellano and Gorchov 2013); initial colonization patterns of an invading population are therefore likely to follow the feeding and defecation patterns of these animals, supporting a stratified dispersal model that incorporates both local diffusion and dispersal by long distance migrants (Shigesada et al. 1995). It is important to note that while distance from forest edge was a significant predictor of minimum age, the change in minimum age per unit distance was weak (coefficient = 0.04). Our results support those reported by Deering and Vankat (1999), who identified similar predictor variables in a single woodlot in Ohio. However, we observed such trends for six different forests with differing overstory composition and a gradient of invasion intensity. Given the high correlation (Spearman’s ρ = 0.84) between predicted minimum ages and the true minimum ages of the 35 cross sections that were used to validate the model, our results suggest that our model can be used to predict the age of Amur honeysuckle in a variety of forest types across the introduced range of the species.

Whether spatial autocorrelation was accounted for or not, number of living basal stems was largely the most important (in terms of consistency across models, 29 significance, and coefficient values) predictor variable for honeysuckle diameter and both number of living basal stems and diameter were the most important predictor variables for Amur honeysuckle height (Table 2.2). However, as we hypothesized spatial autocorrelation influenced the importance of variables used to assess honeysuckle height and diameter. Several predictor variables became non-significant when we accounted for spatial autocorrelation in the regression models (Table 2.2). For example, when spatial autocorrelation was not accounted for, distance to nearest forest edge was a weak

(coefficient = 0.003) but significant (p < 0.05) predictor of mature Amur honeysuckle diameter (square root transformed; Table 2.2). However, when the spatial correlation structure of the standardized residuals was modeled using a rational quadratic semivariogram, distance to nearest forest edge was no longer a significant predictor

(Table 2.2). Dormann (2007) and Lichstein et al. (2002) emphasized the importance of incorporating spatial autocorrelation into statistical models in order to properly estimate the effects of environmental variables on dependent variables. For example, Lichstein et al. (2002) documented a shift in the importance of habitat variables influencing the abundance of Neotropical migrant songbirds when spatial autocorrelation was incorporated into Gaussian spatial autoregressive models. While classical statistical approaches assume that residual errors are independent, response variables in ecological data are often spatially autocorrelated (Legendre and Fortin 1989, Lichstein et al. 2002).

Thus, if spatial autocorrelation is ignored, the result may be an inaccurate assessment of the effects of predictor variables (Lichstein et al. 2002). In our study, this was exemplified by a comparison of regression models where spatial autocorrelation was not accounted for to regressions where we used semivariograms to model spatial structure. 30

In the realm of invasive species management, this has important implications for forest managers who are interested in knowing which measurable variables may be most important for locating the most productive shrubs in an invading population. To our knowledge, our study is the first to examine variables influencing life-stage characteristics of woody invasive shrubs while also accounting for spatial autocorrelation at the forest stand scale. Future work should focus on an examination of additional study sites, habitat types, and a wider gradient of Amur honeysuckle invasion densities in order to build more generalizable regression models where spatial autocorrelation is incorporated.

Table 2.1. Dominant overstory (woody stems ≥ 10 cm diameter at breast height [dbh]) species, soil types, and mean (± 1 SE) densities of Amur honeysuckle (Lonicera maacckii (Rupr.) Herder) sapling (shrubs ≥ 1.37 m tall) and seedling (shrubs < 1.37 m tall) individuals (defined as a group of basal stems originating from the same rootstock) at six mixed-hardwood forests in Indiana. Overstory data were collected in variable-radius plots (Basal Area Factor [BAF] = 2.296 m2/ha, ≥ 12 plots per study site). Amur honeysuckle sapling data were collected in 24 fixed-area plots per study site (plot radius = 3.57 m), and Amur honeysuckle seedling data were collected in 24, 2 m x 2 m quadrats per study site. Soil series information was obtained from the U.S.D.A. Web Soil Survey (Soil Survey Staff, Natural Resources Conservation Service, Department of Agriculture 2013).

Amur Amur honeysuckle Study site Dominant overstory species* Soil types honeysuckle saplings/ha seedlings/ha

Fowler Park white ash (Fraxinus americana L.) Silt loams black walnut (Juglans nigra L.)

American elm (Ulmus americana L.) 2,500.0 ± 459.4 4,166 ± 911.2 black cherry (Prunus serotina Ehrh.) sassafras (Sassafras albidum [Nutt.] Nees)

FNR Farm black locust (Robinia pseudoacacia L.) Silt loams 10,416.7 ± American elm 3,135.4 ± 176.1 1,479.5

Hawthorn Park sassafras Silt loams blackgum (Nyssa sylvatica Marsh.)

tuliptree (Liriodendron tulipifera L.) 1,708.3 ± 256.1 4,687.0 ± 1,252.8 black cherry swamp white oak (Quercus bicolor Willd.)

Pursell osage orange (Maclura pomifera [Raf.] C.K. Loams Schneid.) Loamy sands black cherry Sandy loams 1,354.2 ± 254.9 6,770.8 ± 1,596.0 Silt loams

Rifle Range tuliptree Fine sandy

black cherry loams 2,375.0 ± 157.8 4,464.0 ± 911.2 sassafras

Ross black oak (Quercus velutina Lam.) Loams

tuliptree Sandy loams 1,041.7 ± 231.5 9,088.9 ± 1,855.3 Silt loams

*Species listed in descending order of importance value (IV = [relative basal area + relative density]/2). Dominant species at each study site included those with the highest IV and with cumulative IV of ≥ 50%.

Table 2.2. Multiple regression models where the spatial correlation structure was (Spat) and was not (NoSpat) accounted for, at three mixed-hardwood forests in Indiana (FNR Farm, Pursell, and Ross). Dependent variables examined were diameter (diaburl; cm) and height (ht; m) of immature (no berries) and mature (with berries) Amur honeysuckle (Lonicera maackii [Rupr.] Herder). Independent variables included distance to nearest forest edge (edge; m), number of neighbor honeysuckles within a 2-m radius of focal honeysuckle (neighbor), distance to nearest tree (distree; m), number of basal stems on focal honeysuckle (nstem), distance to nearest mature honeysuckle (distamur; m), and diaburl. For Spat models, we examined five theoretical semivariograms to characterize the spatial structure―exponential (Exp), Gaussian, linear, spherical, and rational quadratic (RQ). The theoretical semivariogram that resulted in the lowest Akaike information criterion (AIC) value was chosen for a given Spat model. Based on examinations of empirical semivariograms, a nugget effect was incorporated into all Spat models. If characterizing the spatial structure of a multiple regression model caused otherwise significant (p < 0.05) independent variables to become non-significant, then the newly non-significant variables were excluded and only significant independent variables were included in the final Spat model (i.e., all independent variables presented in this table were significant).

Spatial

correlation Model Type Model AIC structure

FNR Farm Immature Amur honeysuckle diameter (n = 1238) 0.5 0.5 0.5 NoSpat diaburl = 0.87 + 0.003edge – 0.06neighbor – 0.09distree + 0.68(nstem + 0.5) --- 1740.6 0.5 0.5 Spat diaburl = 0.43 + 0.007edge + 0.71(nstem + 0.5) RQ 1677.4 Immature Amur honeysuckle height (n = 1238) 0.5 0.5 0.5 NoSpat ht = 1.34 – 0.003edge – 0.03neighbor + 0.32diaburl – 0.09distamur– 0.29(nstem + --- -169.5 0.5)0.5 0.5 0.5 0.5 Spat ht = 1.14 + 0.26diaburl – 0.05distamur – 0.23(nstem + 0.5) Exp -190.9 Mature Amur honeysuckle diameter (n = 1148) 0.5 0.5 0.5 NoSpat diaburl = 1.48 + 0.003edge – 0.16neighbor + 0.95(nstem + 0.5) --- 1936.1 0.5 0.5 0.5 Spat diaburl = 1.59 – 0.16neighbor + 0.95(nstem + 0.5) RQ 1912.0 Mature Amur honeysuckle height (n = 1148) 0.5 0.5 0.5 NoSpat ht = 3.19 – 0.13neighbor + 0.85diaburl – 1.01(nstem + 0.5) --- 2828.0 0.5 0.5 Spat ht = 2.01 + 0.79diaburl – 0.64(nstem + 0.5) Exp 2380.8

Pursell Immature Amur honeysuckle diameter (n = 1278) 0.5 0.5 NoSpat diaburl = 0.44 + 0.002edge – 0.006neighbor + 0.75(nstem + 0.5) --- 1399.22 0.5 0.5 Spat diaburl = 0.50 – 0.007neighbor + 0.75(nstem + 0.5) RQ 1352.06 Immature Amur honeysuckle height (n = 1278) 0.5 0.5 NoSpat ht = 0.96 – 0.004edge – 0.005neighbor + 0.75diaburl – 0.07distamur – 0.43(nstem + --- 1707.80 0.5)0.5 0.5 0.5 Spat ht = 0.97 – 0.005neighbor + 0.68diaburl – 0.37(nstem + 0.5) RQ 1629.02 Mature Amur honeysuckle diameter (n = 274) 0.5 NoSpat diaburl = -0.35 + 0.03edge + 3.88(nstem + 0.5) --- 1450.59 0.5 Spat diaburl = -0.02 + 4.17(nstem + 0.5) RQ 1438.00 Mature Amur honeysuckle height (n = 274) 0.5 NoSpat ht = 2.84 + 0.14diaburl – 0.50(nstem + 0.5) --- 520.40 0.5 Spat ht = 2.77 + 0.13diaburl – 0.41(nstem + 0.5) RQ 511.04

Ross Immature Amur honeysuckle diameter (n = 1649) 0.5 0.5 0.5 NoSpat diaburl = 0.07 – 0.05distamur + 0.89(nstem + 0.5) --- 1461.00 0.5 0.5 Spat diaburl = 0.02 + 0.88(nstem + 0.5) RQ 1401.24 Immature Amur honeysuckle height (n = 1649) 33

Table 2.2 Continued

0.5 0.5 0.5 NoSpat ht = 0.92 + 0.37diaburl + 0.03distree – 0.25(nstem + 0.5) --- -868.32 0.5 0.5 0.5 Spat ht = 0.98 + 0.37diaburl – 0.26(nstem + 0.5) Exp -906.76 Mature Amur honeysuckle diameter (n = 442) 0.5 0.5 NoSpat diaburl = 1.06 – 0.01edge – 0.01neighbor + 1.02(nstem + 0.5) --- 589.46 0.5 0.5 Spat diaburl = 1.08 – 0.01edge – 0.01(neighbor + 1.01(nstem + 0.5) RQ 592.11 Mature Amur honeysuckle height (n = 442) 0.5 0.5 NoSpat ht = 2.52 + 0.90diaburl – 0.98(nstem + 0.5) --- 824.37 0.5 0.5 Spat ht = 2.43 + 0.88diaburl – 0.92(nstem + 0.5) RQ 810.59

Figure 2.1. Scatterplots of spatial locations of Amur honeysuckle (Lonicera maackii [Rupr.] Herder) shrubs ≥ 0.5 m tall and trees (diameter at breast height [dbh] ≥ 10 cm) in 60 m x 60 m mapping areas at (a) FNR Farm, (b) Pursell, and (c) Ross mixed-hardwood forests in Indiana. Open circles represent honeysuckles without berries, filled circles represent honeysuckles with berries, and white diamonds represent trees. Different size circles represent a gradient of diameter above root burl (cm) for Amur honeysuckle. For presentation purposes, data were pooled for all three study sites and quartiles were created based on median diameters (range across all three study sites was 0.3-28.5 cm). 35

Figure 2.2. Minimum age distributions for mature (with berries) and immature (without berries) Amur honeysuckle (Lonicera maackii [Rupr.] Herder) shrubs ≥ 0.5 m tall in 60 m x 60 m mapping areas at (a) FNR Farm, (b ) Pursell, and (c) Ross mixed-hardwood forests in Indiana. Minimum age was predicted using a linear mixed effects model based on destructively sampled Amur honeysuckle stem cross sections. 36

Figure 2.3. Scatterplots of Amur honeysuckle invasion at 15, 20, and >20 years following initial colonization at (a) FNR Farm, (b) Pursell, and (c) Ross mixed hardwood forests in Indiana. Based upon the age of the oldest individual, the invasion at FNR Farm spanned 37 years, vs. 27 years at Pursell and 28 years at Ross.

Figure 2.4. Inhomogeneous L function for all Amur honeysuckle (Lonicera maackii [Rupr.] Herder) shrubs ≥ 0.5 m tall at (a) FNR Farm, (b) Pursell, and (c) Ross mixed- hardwood forests in Indiana; inhomogeneous cross-L function (examined if immature Amur honeysuckle shrubs [no berries] clustered around mature shrubs [with berries]) at (d) FNR Farm , (e) Pursell, and (f) Ross; inhomogeneous cross-L function (examined if Amur honeysuckle shrubs clustered around trees [diameter at breast height {dbh} ≥ 10 cm]) at (g) FNR Farm, (h) Pursell, and (i) Ross. All spatial point patterns were based on 60 m x 60 m mapping areas at each study site. To simplify interpretation, L – r is presented on y-axes, where the theoretical L – r of complete spatial randomness is approximately equal to zero. The solid line represents the observed L – r function, the dotted line represents the theoretical L – r of complete spatial randomness, and the dashed lines represent the upper and lower bounds of the confidence envelope (CE) of complete spatial randomness, based on 999 Monte Carlo simulations. If the observed L – r (solid line) is above the upper bound of the CE for a given radius r, then there is statistically significant clustering at that radius. If L – r is within the CE, the point pattern is random for that radius, and if L – r is below the lower bound of the CE, there is statistically significant regularity or spatial repulsion 38

CHAPTER 3 INFLUENCE OF INTENSITY AND DURATION OF INVASION

BY AMUR HONEYSUCKLE (Lonicera maackii [Rupr.] Herder) WITHIN AND

ACROSS MIXED-HARDWOOD FORESTS OF INDIANA

3.1. Introduction

Invasions by non-native plant species have become one the most serious threats to the ecological and economic integrity of ecosystems worldwide (Higgins et al. 1999,

Mack et al. 2000, Mullin et al. 2000, Hunter and Mattice 2002, Chornesky and Randall

2003, Pimentel et al. 2005). Invasive plants may impact ecosystems in numerous ways, such as causing declines in native plant diversity and growth (Martin 1999, Jose et al.

2002, Fagan and Peart 2004, Webster et al. 2006), altering disturbance regimes and other ecological processes (Ehrenfeld et al. 2001, Brooks et al. 2004, Dukes and Mooney

2004), and even facilitating invasions by other non-native species (Simberloff and Von

Holle 1999, Heimpel et al. 2010). The result of such ecological impacts is often significant economic costs. For example, Pimentel et al. (2005) indicated that the invasive purple loosestrife (Lythrum salicaria L.) costs the United States $45 million per year due to control efforts and losses of wildlife forage species.

The specific response of a native plant community to non-native plant invasions is influenced by a multitude of factors pertaining to characteristics of both the invader and 39

the invaded ecosystem. For example, numerous investigators have proposed that non- native plants that become invasive, and therefore capable of displacing native species, possess characteristics that give them an advantage in their invaded environment, such as novel weapons (i.e., allelopathic properties; Callaway and Ridenour 2004) and high numbers of large propagules (Simberloff 2009). Impacts of invaders on native biota also depend on environmental factors such as corridors for effective propagule dispersal

(Christen and Matlack 2006), resource availability coinciding with propagule pressure

(Davis et al. 2000), disturbances that favor establishment (Stapanian et al. 1998,

Sutherland and Nelson 2010), and lack of natural enemies in the invaded range (enemy release; Keane and Crawley 2002). Equally important is how invasive plants spread in space and time. For plant invasions, it is generally thought that effects on native biota are least severe and control efforts are most effectively implemented during the establishment phase of invasion, prior to when an invader begins to spread at an exponential rate (Sakai et al. 2001, Webster et al. 2006). This great abundance of invasive plants has been shown to negatively affect native communities through competition (e.g. Gould and Gorchov 2000, Gorchov and Trisel 2003), but less is known about the effects of sustained presence by invasive plants. Recent research has shown that, in addition to direct competition, established populations of invasive plants may have indirect effects, including altering nutrient cycles and inhibiting fungal associates in native communities (Ehrenfeld et al. 2001, Wolfe and Klironomos 2006), that may become more acute with long-term exposure. Because the density of invasive plants varies at micro scales, it is critical to understand the combined effects of intensity and duration in order to more thoroughly understand the gradient of impacts on native species 40 that can occur within an invaded area. However, it remains unclear how microsite-level effects on native plants may vary within an invasion and whether these effects are exacerbated where the invader has persisted longer.

Throughout the eastern U.S. and parts of Canada, the non-native shrub Amur honeysuckle (Lonicera maackii [Rupr.] Herder) has invaded forest ecosystems and expanded its invaded range since its initial introduction in the 1890s; it is considered one of the most aggressive invasive plants in North America (Luken and Thieret 1996,

Deering and Vankat 1999). Numerous investigators have shown that Amur honeysuckle has negative impacts on native plants (Hutchinson and Vankat 1997, Gould and Gorchov

2000, Collier et al. 2002, Gorchov and Trisel 2003, Miller and Gorchov 2004, Meiners

2007, Cipollini and McClain 2008, Hartman and McCarthy 2008, McKinney and Goodell

2010), with some research providing insights into its invasion process in time and space

(Deering and Vankat 1999; also see Chapter 2 from this dissertation). However, we are unaware of any studies that have examined the combined effects of Amur honeysuckle invasion intensity and duration on native taxa in ecosystems invaded by this non-native shrub. As Amur honeysuckle and other long-lived woody invasives continue to expand within native communities and establish in new areas, an improved understanding of the gradient of impacts on native biota within invaded areas may improve the prioritization of control efforts at multiple scales.

The primary objective of this study was to examine microsite-level effects of

Amur honeysuckle intensity and duration of invasion on native plant communities in mixed-hardwood forests of Indiana. Specifically, we examined the influence of Amur honeysuckle density, percent cover, and duration of invasion, as well as other 41 environmental variables, on the diversity and abundance of native herbaceous-layer species and tree seedlings at microsites within 12 mixed-hardwood forests invaded by

Amur honeysuckle. We hypothesized that native diversity and abundance would be lowest at microsites where the density and percent cover of Amur honeysuckle were highest and where proximate Amur honeysuckle shrubs were oldest (i.e., greatest duration of invasion). Greater percent covers of Amur honeysuckle, especially from the largest shrubs, equates to more light competition for native taxa. We hypothesized that this effect would be especially pronounced for herbaceous plants that flower primarily in the spring given that Amur honeysuckle expands its leaves earlier in the year than native woody species and is therefore capturing light that would be available to these spring herbs under the leafless overstory canopy. In terms of duration, microsites where Amur honeysuckle shrubs have persisted longest may be subjected to higher amounts of allelochemicals found in honeysuckle foliage (McEwan et al. 2010) due to continued exposure to honeysuckle foliage falling to the forest floor compared to microsites where

Amur honeysuckle has not persisted as long.

3.2. Methods

3.2.1 Study Sites

This study was conducted in 12 mature, second-growth mixed-hardwood forests in the glaciated regions of central Indiana (forest locations ranged from 39°20’ N to

40°32’ N, and 86°00’ W to 87°26’ W)―1) Fort Harrison State Park (hereafter referred to 42 as Ft. Harrison), 2) Fowler Park (hereafter referred to as Fowler), 3) Hawthorn Park

(hereafter referred to as Hawthorn), 4) Pfizer, Inc., Pond 5 eastern forest (hereafter referred to as Pond 5A), 5) Pfizer, Inc., Pond 5 western forest (hereafter referred to as

Pond 5B), 6) Pfizer, Inc., Rifle Range woodlot (hereafter referred to as RR), 7) Purdue

University, Department of Forestry and Natural Resources Farm (hereafter referred to as

FNR Farm), 8) Purdue University, Department of Forestry and Natural Resources Martell

Forest (hereafter referred to as Martell), 9) Purdue University, Meigs Farm, south forest

(hereafter referred to as Meigs), 10) a private woodlot in Benton County (hereafter referred to as Leuck), 11) a private woodlot in Lafayette (hereafter referred to as Pursell), and 12) Ross Biological Reserve (hereafter referred to as Ross). Glacially-derived landforms varied across study sites and consisted of till plains, flood plains, loess hills, ground moraines, depressions on ground moraines, and dunes, with parent materials consisting of loess, loess over loamy till, loess over loamy outwash, loamy alluvium over sandy and gravelly outwash, silty alluvium, loamy alluvium, loamy till, and loamy outwash over sandy and gravelly outwash (Soil Survey Staff, Natural Resources

Conservation Service, United States Department of Agriculture 2013). Soil types ranged from very poorly drained loams to excessively drained sandy loams (Soil Survey Staff,

Natural Resources Conservation Service, United States Department of Agriculture 2013).

Although overstory species composition varied across study sites (Table 3.1), all canopy layers were characterized by mature, second-growth deciduous trees. Furthermore, all 12 study sites were selected using the criteria that 1) Amur honeysuckle was the dominant exotic shrub in terms of percent cover, density, and size of individuals and 2) sites represented a gradient of Amur honeysuckle percent cover, density, and size. 43

3.2.2. Data Collection

During 2010 and 2011, we sampled vegetation by placing a series of fixed-area plots along transects that extended from the forest edge to the interior. At each study site, we placed transects ≥ 20 m apart and along each transect, we placed ≥ two 40-m2 (radius of 3.57 m) sapling/shrub plots, each containing a 2 m x 2 m quadrat (to quantify seedlings and ground cover; Figure 3.1). Sapling/shrub plots were spaced 20 m apart along each transect, the first plot was placed 5-10 m from the forest edge. Each quadrat was placed to the upper right, upper left, lower right, or lower left of the sapling/shrub plot center

(direction was chosen randomly), with the quadrat oriented parallel to the transect. The number of transects at a given study site depended on the size of the forest/woodlot.

Likewise, the number of plots per transect depended on transect length, where each transect was extended until 1) it reached a distance that was more than halfway between the origin of the transect and another forest edge, 2) it reached a large stream or river, 3) topographic aspect changed ≥ 180˚, or 4) it reached 80 m in length (the most logistically feasible length in order to accomplish data collection objectives within and across study sites). The total number of nested plots (quadrats nested in sapling/shrub plots) placed at

FNR Farm, Fowler, Ft. Harrison, Hawthorn, Leuck, Martell, Meigs, Pond 5A, Pond 5B,

Pursell, Ross, and RR were 30, 24, 18, 24, 15, 12, 15, 19, 12, 30, 28, and 27, respectively.

In the sapling/shrub plots, we recorded the number of living saplings and shrubs

(woody plants < 10 cm dbh and ≥ 1.37 m tall) by species. A sapling/shrub that produced multiple stems from the same rootstock (e.g., Amur honeysuckle and northern spicebush

(Lindera benzoin [L.] Blume) was recorded as a single individual. In the quadrats we 44 recorded number of living seedlings (woody stems < 1.37 tall) by species and percent covers of living vascular herbaceous species, woody vines, shrubs, seedlings, saplings, coarse woody debris (CWD, midpoint diameter ≥ 10 cm), fine woody debris (FWD, midpoint diameter < 10 cm), dead leaves and herbaceous stems, and bare soil. Percent covers were categorized as follows (modified from Peet et al. [1998]): 1 = at study site but outside quadrats, 2 = 0-1%, 3 = 1-2%, 4 = 2-5%, 5 = 5-10%, 6 = 10-25%, 7 = 25-

50%, 8 = 50-75%, 9 = 75-95%, and 10 = > 95%. Percent covers were recorded in two vertical strata—lower (0-1 m) and upper (1.01-5 m). Percent cover estimates were performed by a single observer to reduce observer bias. Finally, we recorded total percent canopy cover (using a spherical densiometer) at each of the four corners of the quadrat (densiometer was held one meter above ground level).

Additional data were collected for a subset of Amur honeysuckle individuals in the sapling/shrub plots. Specifically, each sapling/shrub plot was divided into four quadrants and the following information was collected for the largest Amur honeysuckle individual (based on diameter at ground level) in each of those four quadrants: diameter at ground level just above the root burl (diaburl; cm), number of living basal stems (nstem), diameter of largest basal stem (diabasal; cm), height (ht; m), and distance to the overstory drip line of the nearest forest edge (edge; m).

Data were collected in 2010 at FNR Farm, Fowler, Ft. Harrison, Hawthorn, Pond

5A, Pond 5B, Pursell, Ross, and RR, whereas data at Leuck, Martell, and Meigs were collected in 2011. During each year, quadrats were visited in early May to coincide with the growth of spring-flowering herbaceous plants and again in July-August to coincide with growth of summer ground-layer species. Density data for woody stems in quadrats 45 were collected only during the summer sampling period. Sapling/shrub plots were also only visited once (in September). We were unable to classify some taxa to species because distinguishable features were not consistently present at all study sites. For those taxa, we grouped them into two- or three-species groups or classified them to genus.

Hereafter, individual species, species groups, and genera are collectively refered to as

“taxa”.

3.2.3. Data Analyses

For percent cover data collected in quadrats, we calculated total percent cover and mean percent cover by taxon/variable, quadrat, and study site. Mean percent cover values were calculated separately for each season (spring, summer) and vertical stratum

(lower, upper). All mean values were calculated based on midpoint values of cover class estimates. We calculated mean individuals/ha by taxon and study site for woody taxa in the seedling and sapling layers. We also calculated Importance Values (IV) for herbaceous vegetation, vines, and environmental variables observed in the lower vertical stratum, for woody individuals observed in the seedling layer, and for woody individuals observed in the sapling layer at each study site. Importance values for each herb/vine taxon and each environmental variable were calculated as IV = ([{mean percent cover + frequency based on plots at a given study site}/2]*100); spring and summer observations were examined separately. For woody individuals in the seedling and sapling layers, IV

= ([{relative density + frequency based on plots at a given study site}/2]*100). 46

We calculated taxonomic richness (S, number of taxa), Pielou’s Evenness Index

(J'; Pielou 1966), and Shannon’s Diversity Index (H'; Shannon 1948) by quadrat and study site (mean value) using midpoint values from cover class estimates of native herbaceous and woody plants in the lower vertical stratum. Calculations of H', S, and J' for summer data included any native herbaceous and woody taxa observed. However, calculations for plants observed during spring included only native herbaceous taxa that flower primarily during the spring and early summer (March-June), based on descriptions from Yatskievych (2000). At our study sites, this included the following taxa: white baneberry (Actaea pachypoda Elliot), ramp/narrowleaf wild leek (Allium tricoccum

Aiton/Allium burdickii [Hanes] A.G. Jones), smooth rockcress (Arabis laevigata [Muhl.

Ex Willd.] Poir.), Jack in the pulpit (Arisaema triphyllum [L.] Schott), Canadian wildginger (Asarum canadense L.), cutleaf toothwort (Cardamine concatenata [Michx.]

Sw.), limestone bittercress/bulbous bittercress (Cardamine douglassii Britton/Cardamine bulbosa [Schreb. Ex Muhl.] Britton, Sterns & Poggenb.), Virginia springbeauty

(Claytonia virginica L.), bleeding heart (Dicentra sp.), eastern false rue anemone

(Enemion biternatum Raf.), dogtooth violet (Erythronium americanum Ker Gawl.), false mermaidweed (Floerkea proserpinacoides Willd.), spotted geranium (Geranium maculatum L.), American alumroot (Heuchera americana L.), eastern waterleaf

(Hydrophyllum virginianum L.), spring forget-me-not (Myosotis verna Nutt.), Clayton’s sweetroot/longstyle sweetroot (Osmorhiza claytonii [Michx.] C.B. Clarke/Osmorhiza longistylis [Torr.] DC.), roundleaf ragwort (Packera obovata [Muhl. Ex Willd.] W.A.

Weber & A. Löve), Miami mist (Phacelia purshii Buckley), wild blue phlox (Phlox divaricata L.), mayapple (Podophyllum peltatum L.), littleleaf buttercup (Ranunculus 47 abortivus L.), bristly buttercup (Ranunculus hispidus Michx.), bloodroot (Sanguinaria canadensis L.), nodding wakerobin (Trillium flexipes Raf.), bloody butcher (Trillium recurvatum Beck), toadshade (Trillium sessile L.), largeflower bellwort (Uvularia grandiflora Sm.), downy yellow violet (Viola pubescens Aiton), common blue violet

(Viola sororia Willd.), striped cream violet (Viola striata Aiton), and three-lobe violet

(Viola triloba Schwein.).

We used linear mixed effects models (see Laird and Ware [1982] for formulation of model) to examine the influence of environmental variables on nine dependent variables―spring H' (H'spring), spring S (Sspring), spring J' (J'spring), spring native percent cover in the lower vertical stratum (species combined as a group; NatLowerspring), summer H' (H'summer), summer S (Ssummer), summer J' (J'summer), summer native percent cover in the lower vertical stratum (species combined as a group; NatLowersummer), and native seedlings/ha (species combined as a group; Natseed). For the models with H'spring,

Sspring, J'spring, and NatLowerspring as dependent variables, we examined the following predictor variables: spring percent cover of Amur honeysuckle in the lower vertical stratum (AhLowerspring), spring percent cover of Amur honeysuckle in the upper vertical stratum (AhUpperspring), age of the oldest Amur honeysuckle shrub in the sapling/shrub plot (duration; Ahage), spring percent cover of other non-native species in the lower vertical stratum (species combined as a group; OtherExoticLowerspring), spring percent cover of other non-native species in the upper vertical stratum (species combined as a group; OtherExoticUpperspring), spring percent cover of native species in the upper vertical stratum (species combined as a group; NatUpperspring), spring canopy cover

(based on densiometer; canopyspring), spring percent cover of bare soil (soilspring), spring 48

percent cover of CWD (CWDspring), spring percent cover of FWD (FWDspring), spring percent cover of dead leaves and herbaceous stems (litterspring), Amur honeysuckle saplings/ha (Ahsap), saplings/ha of other non-native shrubs (species combined as a group;

OtherExoticsap), and native saplings/ha (species combined as a group; Natsap). For the models with H'summer, Ssummer, J'summer, and NatLowersummer as dependent variables, we examined the following predictor variables: summer percent cover of Amur honeysuckle in the lower vertical stratum (AhLowersummer), summer percent cover of Amur honeysuckle in the upper vertical stratum (AhUppersummer), Ahage, summer percent cover of other non-native species in the lower vertical stratum (OtherExoticLowersummer), summer percent cover of other non-native species in the upper vertical stratum

(OtherExoticUppersummer), summer percent cover of native species in the upper vertical stratum (NatUppersummer), summer canopy cover (based on densiometer; canopysummer) summer percent cover of bare soil (soilsummer), summer percent cover of CWD

(CWDsummer), summer percent cover of FWD (FWDsummer), summer percent cover of dead leaves and herbaceous stems (littersummer), Ahsap, OtherExoticsap, and Natsap. For the model with Natseed as the dependent variable, we examined the following predictor variables: NatLowersummer, AhLowersummer, AhUppersummer, Ahage,

OtherExoticLowersummer, OtherExoticUppersummer, NatUppersummer, canopysummer, soilsummer, CWDsummer, FWDsummer, littersummer, Ahsap, OtherExoticsap, and Natsap. For each model, Ahage was calculated by using a linear mixed effects model to predict the ages of the honeysuckle shrubs from which we collected additional structural information. The model used to determine age was based on the minimum ages (agemin; age of the oldest basal stem) of Amur honeysuckle shrubs that were measured and destructively sampled at 49 six of the study sites (FNR Farm, Fowler, Hawthorn, Pursell, Ross, RR) in a separate component of this study (see Chapter 2 from this dissertation). The specific model used to predict minimum age was agemin = 0.12 + 0.04edge + 0.18diaburl + 1.40diastem + 1.17ht

+ random effects for study sites (n = 442, AIC = 2301.12, p < 0.001 for all predictor variables; See Chapter 2). The accuracy of the age model was validated by using

Spearman’s rank correlation coefficient (ρ; Myers and Well 2003) to compare predicted minimum ages to actual minimum ages of 35 destructively-sampled Amur honeysuckle cross-sections that were not used to build the model; the resultant ρ = 0.84 (See Chapter

2). All random effects coefficients for the age model were < 2, but for FNR Farm,

Fowler, Hawthorn, Pursell, Ross, and RR, we added the study site random effects coefficients when calculating minimum age of Amur honeysuckle. However, for study sites where Amur honeysuckle was not destructively sampled (Ft. Harrison, Leuck,

Martell, Meigs, Pond 5A, Pond 5B), only the fixed-effects coefficients from the age model were used.

For the linear mixed effects models with H'spring, Sspring, J'spring, NatLowerspring,

H'summer, Ssummer, J'summer, NatLowersummer, Natseed as dependent variables, we examined data at the plot level, with study site as a random effect. To determine which predictor variables to include in the linear mixed effects model for each dependent variable, we first pooled data across all 12 study sites and developed a full multiple regression model.

When necessary, variables were transformed to minimize deviations from normality and constant variance (Zar 1999). Furthermore, a Variance Inflation Factor (VIF) was calculated for each predictor to assess multicollinearity (VIF values ≥ 5 were considered problematic). Using the best subsets regression approach (Miller 1984), we then 50 examined all subsets of the full multiple regression model, where the number of possible models = 2(number of predictor variables in full model). We then selected those models with the lowest delta Akaike information criterion (AIC; Akaike 1974) and all significant predictor variables (p < 0.05) and re-ran the multiple regression model as a linear mixed effects model with study site as the random effect. We observed normality and residual plots to examine distributional assumptions for within-group errors and random effects for each linear mixed effects model.

Statistical analyses were performed using the program R (R Core Team 2013).

Specifically, we used the R package MuMIn (Barton 2013) for best subsets regression and used the R package car (Fox and Weisberg 2011) to calculate VIF values. The R package vegan (Oksanen et al. 2013) was used to calculate H', S, and J'. Linear mixed effects models were built using the R package nlme (Pinheiro et al. 2012). All plant names and native/non-native classifications were based on the USDA Plants Database

(USDA, NRCS 2013).

3.3. Results

The variation in Amur honeysuckle percent cover, density, and age across study sites reflected our initial site selection criteria. During spring, mean percent cover of

Amur honeysuckle in the lower vertical stratum ranged from 1.4 ± 0.7 at Meigs to 26.0 ±

6.1 at Pond 5B (Figure 3.2). A similar trend was observed in the upper vertical stratum during spring, where the highest mean percent cover of Amur honeysuckle was observed at Pond 5A (68.0 ± 4.9%) and the lowest at Meigs (2.2 ± 0.8; Figure 3.2). During 51 summer, mean percent cover of Amur honeysuckle in the lower vertical stratum was highest at Pond 5B (26.0 ± 6.1) and lowest at Meigs (1.7 ± 0.8; Figure 3.2). In the upper vertical stratum, the highest mean percent cover of Amur honeysuckle was observed at

Pond 5A (70.5 ± 4.7) and the lowest at Meigs (5.0 ± 1.8; Figure 3.2). Seedling densities of Amur honeysuckle followed a similar pattern as observed for percent cover, where mean densities/ha of Amur honeysuckle in the seedling layer ranged from 0 (Meigs) to

11,447 ± 1,167 (Pond 5A; Figure 3.3). Likewise, mean densities/ha for Amur honeysuckle in the sapling layer were lowest at Meigs (533 ± 179) and highest at Pond

5B (3,354 ± 340; Figure 3.3). In terms of age as predicted from the linear mixed effects model, the oldest Amur honeysuckle shrubs (maximum predicted age = 32 years) were observed at FNR Farm and Pond 5A, with the youngest shrubs found at Martell, Meigs, and Ross (Figure 3.4).

Study sites with the highest diversity, percent covers, and densities of native vegetation also had the lowest percent covers of Amur honeysuckle in the upper vertical stratum (Figure 3.2, Figure 3.5). At the plot level, percent cover of Amur honeysuckle in the upper vertical stratum was negatively correlated with native H', native S, and total native percent cover during the spring and summer, as well as native seedling densities

(linear mixed effects models p < 0.05; Table 3.2). Environmental variables such as percent cover of bare soil and FWD were also important, but this varied depending on the response variable (Table 3.2). When plot-level duration of Amur honeysuckle was treated as the only predictor variable, it exhibited significant (p ≤ 0.05) negative correlations with native H', S, and total native percent cover in spring and summer, as well as native seedling densities, but was not significant in the best linear mixed effects 52 models where percent cover of Amur honeysuckle in the upper vertical stratum was included as a predictor variable (Table 3.2, Table 3.3). It is important to note that Amur honeysuckle duration also exhibited a positive, significant correlation with percent cover of Amur honeysuckle in the upper vertical stratum, both during spring and summer

(Table 3.3).

In terms of individual taxa in the lower vertical stratum, we observed 92 native forb taxa, eight native grass taxa, four native sedge taxa, five native fern taxa, 10 native vine taxa, 48 native tree/shrub taxa, 12 non-native forb species, one non-native grass species, five non-native vine species, and six non-native shrub species in quadrats across all study sites and both seasons (Table 3.4, Table 3.5). Across all study sites and seasons, environmental variables such as dead leaves/dead herbaceous stems, bare soil, and FWD had the highest IV (Table 3.4, Table 3.5). Herbaceous/vine taxa with the highest IV during spring were sanicle (Sanicula spp.), Virginia creeper (Parthenocissus quinquefolia

[L.] Planch.), and Virginia springbeauty (Table 3.4). During summer, herbaceous/vine taxa with the highest IV in the lower vertical stratum were sanicle, Virginia creeper, and jumpseed (Polygonum virginianum L.; Table 3.5). In terms of IV based on seedling-layer densities, the most important woody taxa in the seedling layer across study sites were

Amur honeysuckle, white ash/green ash (Fraxinus americana L./Fraxinus pennsylvanica

Marsh.), and multiflora rose (Rosa multiflora Thunb.; Table 3.6). In the sapling/shrub plots, we observed 46 native sapling/shrub taxa and eight non-native sapling/shrub taxa; taxa with the highest IV across study sites were Amur honeysuckle, sugar maple (Acer saccharum Marsh.), and hawthorn (Crataegus spp.; Table 3.7).

3.4. Discussion

Results from our study indicate that microsite-level diversity and abundance of native vegetation in mixed-hardwood forests may be largely driven by competition from above due to percent cover of Amur honeysuckle canopies, supporting our original hypothesis. However, the allelopathic properties of Amur honeysuckle may also be important. Differences in percent cover of Amur honeysuckle in the upper vertical stratum within a study site translated to differences in plant diversity and abundance across the gradient of Amur honeysuckle percent covers in the invading population. This was likely due to light competition from the larger honeysuckle shrubs, where decreased light levels near the forest floor at heavily infested microsites resulted in little available light for native herbaceous vegetation, vines, and seedlings to germinate and grow.

Another possible contributing factor is that microsites with higher percent covers of

Amur honeysuckle may contain greater amounts of honeysuckle foliage containing allelochemicals (McEwan et al. 2010), impeding the germination and growth of native ground-layer taxa. It is important to note that the effects of honeysuckle allelopathy have not been demonstrated in a field study, but this warrants further investigation.

The influence of competition from Amur honeysuckle is further supported by results presented in Chapter 4 of this dissertation. Specifically, removing 80 m x 80 m areas of Amur honeysuckle and other non-native shrubs at FNR Farm, Fowler, Hawthorn,

Pursell, Ross, and RR resulted in significant increases in native plant diversity (See

Chapter 4 from this dissertation). Several other studies have documented a negative effect of Amur honeysuckle on ground-layer vegetation. For example, Gorchov and 54

Trisel (2003) found that competition with Amur honeysuckle led to increased mortality of native tree seedlings in an American beech (Fagus grandifolia Ehrh.)-sugar maple forest in Ohio. Likewise, Collier et al. (2002) found that species richness, percent cover of herbaceous and woody species, and tree seedling densities were lower below the crowns of Amur honeysuckle as compared to away from the crowns of Amur honeysuckle.

Unlike previous investigators, we examined plot-level differences in native plant diversity and abundance across a gradient of Amur honeysuckle cover within a forest and across multiple forests. To our knowledge, we are the first to demonstrate that changes in native taxa diversity and abundance in response to Amur honeysuckle intensity and duration are heterogeneous within an invaded community.

When percent cover of Amur honeysuckle in the upper vertical stratum was included in the linear mixed effects models, honeysuckle duration was not important, which does not support our original hypothesis (Table 3.2, Table 3.3). However, we also found significant negative correlations between Amur honeysuckle duration and measures of native diversity and abundance, as well as between duration and percent cover of Amur honeysuckle in the upper vertical stratum (Table 3.3). Thus, it appears that duration of Amur honeysuckle at the microsite scale is important inasmuch that, at microsites where Amur honeysuckle has persisted longer, light competition from above is more intense for native-ground layer species. This is likely because older Amur honeysuckle shrubs may have larger, more developed crowns that induce more light competition from above or because longer duration equates to more sub-canopy growing space being filled due to the coalescing of the crowns from multiple shrubs as invasion progresses from establishment to saturation phases. It is widely acknowledged that plant 55 invasions reaching the expansion and saturation phases at the population scale incur higher ecological and economic costs, especially in terms of control efforts (Sakai et al.

2001, Webster et al. 2006). However, to our knowledge, we are the first investigators to examine the combined influence of invasive species duration and other environmental factors on native vegetation at the local scale within forests. From a management standpoint, information about microsite-level differences in diversity and abundance within an invaded area may help managers better prioritize control efforts given that existing sources of native propagules at microsites where Amur honeysuckle invasion is less intense may be critical to the long-term recovery of native communities after control efforts are implemented.

Table 3.1. Importance value (IV = [{relative basal area + relative density}/2]*100) by species for overstory trees (woody stems ≥ 10 cm diameter at breast height [dbh]) at 12 mixed-hardwood forests in Indiana―1) Purdue University, Department of Forestry and Natural Resources Farm (FNR Farm), 2) Fowler Park, 3) Ft. Harrison State Park, 4) Hawthorn Park, 5) privately-owned woodlot in Benton County (Leuck), 6) Purdue University, Department of Forestry and Natural Resources, Martell Forest (Martell), 7) Purdue University, Meigs Farm, south forest (Meigs), 8) Pfizer, Inc., Pond 5 eastern forest (Pond 5A), 9) Pfizer, Inc., Pond 5 western forest (Pond 5B), 10) privately-owned woodlot in Lafayette (Pursell), 11) Ross Biological Reserve, and 12) Pfizer, Inc. Rifle Range woodlot (RR). Data were collected using variable-radius plots (Basal Area Factor [BAF] = 2.296 m2/ha) at each study site. Present = documented, but outside variable-radius plots at a study site.

FNR Ft. Pond Pond Species Fowler Hawthorn Leuck Martell Meigs Pursell Ross RR Farm Harrison 5A 5B

boxelder present ------3.6 ------(Acer negundo L.) black maple present ------(Acer nigrum Michx. F.) red maple --- 0.6 --- 5.1 ------0.8 ------present (Acer rubrum L.) silver maple ------0.6 ------2.4 ------2.1 3.2 (Acer saccharinum L.) sugar maple 1.2 present 19.6 2.9 --- 44.9 ------13.0 --- 3.4 0.8 (Acer saccharum Marsh.)

Ohio buckeye ------2.4 ------2.4 ------(Aesculus glabra Willd.) river birch --- present ------(Betula nigra L.)

American ------present ------hornbeam (Carpinus caroliniana Walter) mockernut --- 3.4 ------0.7 ------hickory (Carya alba [L.] Nutt.) bitternut 0.9 present 5.4 1.6 ------10.3 --- present present hickory (Carya cordiformis [Wangenh.] K. Koch) pignut ------6.0 --- present ------present 1.0 present 2.4 hickory/red hickory (Carya glabra [Mill.] Sweet/Carya ovalis [Wangenh.] Sarg.) shagbark 8.4 0.6 ------6.0 present ------6.7 5.8 present 2.3 hickory (Carya ovata [Mill.] K. Koch)

Table 3.1 Continued common 1.8 4.4 --- 3.2 ------present 1.2 14.4 1.2 --- 5.5 hackberry (Celtis occidentalis L.) eastern redbud --- 8.8 1.7 ------present ------present (Cercis canadensis L.) flowering --- 0.8 4.0 present ------dogwood (Cornus florida L.) hawthorn present ------present --- present present --- 8.5 present 2.8 (Crataegus spp.) common --- 4.4 --- 2.9 ------3.1 ------persimmon (Diospyros virginiana L.)

American --- 0.6 --- present ------present present beech (Fagus grandifolia Ehrh.) white ash 2.7 18.0 15.6 2.2 17.1 5.4 --- 1.6 1.1 --- present 1.6 (Fraxinus americana L.) green ash present 1.6 ------1.6 ------1.0 (Fraxinus pennsylvanica Marsh.) blue ash ------present ------present --- (Fraxinus quadrangulata Michx.) honeylocust 0.5 0.6 --- 1.2 1.2 --- 1.5 present 2.7 ------1.3 (Gleditsia triacanthos L.) black walnut 8.5 10.8 7.5 0.5 18.8 2.6 33.0 13.0 1.2 1.2 2.4 1.2 (Juglans nigra L.) eastern --- present ------present --- redcedar (Juniperus virginiana L.) sweetgum ------3.5 ------(Liquidambar styraciflua L.) tuliptree --- 0.6 5.5 10.9 --- 6.8 --- 1.6 2.7 --- 20.8 21.1 (Liriodendron tulipifera L.) osage orange present ------32.4 --- 43.6 ------32.8 --- 0.4 (Maclura pomifera [Raf.] C.K. Schneid.) apple* 1.1 ------present ------4.5 --- (Malus spp.)

Table 3.1 Continued white ------present ------mulberry* (Morus alba L.) red mulberry present ------2.6 ------1.6 --- present (Morus rubra L.) blackgum --- 2.6 --- 11.3 ------0.9 (Nyssa sylvatica Marsh.) hophornbeam ------4.0 1.7 ------present --- (Ostrya virginiana [Mill.] K. Koch)

Virginia pine --- 2.3 3.3 ------(Pinus virginiana Mill.)

American --- 0.6 1.7 2.0 ------0.7 ------2.0 sycamore (Platanus occidentalis L.) eastern ------0.8 ------5.4 ------0.5 cottonwood (Populus deltoides Bartram ex Marsh.) black cherry 6.5 10.3 2.5 9.1 18.7 6.2 22.0 20.4 1.3 28.8 12.8 19.5 (Prunus serotina Ehrh.) white oak 0.5 1.8 --- present --- 7.5 ------1.3 1.1 4.4 (Quercus alba L.) swamp white ------6.1 ------present ------oak (Quercus bicolor Willd.) shingle oak present 5.6 --- 2.2 2.9 1.9 present --- present 8.3 --- 1.3 (Quercus imbricaria Michx.) bur oak --- present --- present 3.0 1.0 --- present 8.2 ------(Quercus macrocarpa Michx.) chinkapin oak ------1.7 0.6 --- 6.4 ------12.5 ------(Quercus muehlenbergii Engelm.) pin oak 0.5 0.5 --- 5.7 ------3.4 5.4 ------(Quercus palustris Münchh.)

Table 3.1 Continued northern red 0.8 present 3.1 1.8 --- 6.4 --- present 1.2 --- 5.7 5.9 oak (Quercus rubra L.)

Shumard’s ------0.4 ------oak (Quercus shumardii Buckley var. shumardii) black oak --- 0.6 --- 1.0 --- 1.4 --- 2.1 ------31.0 5.5 (Quercus velutina Lam.) black locust 46.5 ------(Robinia pseudoacacia L.) sassafras --- 9.5 --- 15.2 --- 3.2 --- 26.9 4.5 --- 16.2 9.5 (Sassafras albidum [Nutt.] Nees)

American 0.8 --- present ------2.3 ------present --- basswood (Tilia americana L.)

American elm 14.5 10.0 5.4 3.4 present present --- 7.4 --- 7.4 present 5.8 (Ulmus americana L.) slippery elm 4.7 1.0 15.7 --- present 1.5 --- 6.7 9.5 1.4 present 1.3 (Ulmus rubra Muhl.)

*Non-native to the U.S.

Table 3.2. Linear mixed-effects models, random effects coefficients by study site, and Akaike information criterion (AIC) for nine dependent variables—spring Shannon’s Diversity Index for native ground flora (H'spring), spring taxonomic richness for native ground flora (Sspring), spring Pielou’s Evenness Index for native ground flora (J'spring), spring native percent cover in the lower vertical stratum (species combined as a group; NatLowerspring), summer H' (H'summer), summer S (Ssummer), summer J' (J'summer), summer native percent cover in the lower vertical stratum (species combined as a group; NatLowersummer), and native seedlings/ha (species combined as a group; Natseed) —at 12 mixed-hardwood forests in central Indiana. Significant (p < 0.05) predictor variables included spring percent cover of Amur honeysuckle (Lonicera maackii [Rupr.] Herder) in the upper vertical stratum (AhUpperspring), spring canopy cover (based on densiometer; canopyspring), spring percent cover of bare soil (soilspring), spring percent cover of FWD (FWDspring), Amur honeysuckle saplings/ha (Ahsap), summer percent cover of Amur honeysuckle in the lower vertical stratum (AhLowersummer), summer percent cover of Amur honeysuckle in the upper vertical stratum (AhUppersummer), summer canopy cover (based on densiometer; canopysummer), summer percent cover of bare soil (soilsummer), summer percent cover of CWD (CWDsummer). Values were calculated for each nested plot within a study site, with study site treated as a random effect. Sample size (number of plots) for FNR Farm, Fowler, Ft. Harrison, Hawthorn, Leuck, Martell, Meigs, Pond 5A, Pond 5B, Pursell, Ross, and RR were 30, 24, 18, 24, 15, 12, 15, 19, 12, 30, 28, and 27, respectively.

Random effects coefficients

FNR Ft. Pond Pond Model Fowler Hawthorn Leuck Martell Meigs Pursell Ross RR AIC Farm Harrison 5A 5B

H'spring = 0.90 – 0.01FWDspring – 0.005AhUpperspring + random effects 346.6

-0.007 0.005 -0.007 -0.09 -0.03 0.78 0.20 -0.29 0.24 0.14 -0.47 -0.48

Sspring = 3.61 – 0.02AhUpperspring + random effects 962.3

-0.55 -0.25 -0.50 -0.58 0.96 5.55 0.41 -1.62 0.10 0.27 -1.90 -1.88

J'spring = 0.66 – 0.004FWDspring – 0.003AhUpperspring + random effects 240.1

0.02 0.06 0.06 -0.02 -0.03 0.13 0.12 -0.14 0.20 0.13 -0.19 -0.33

H'summer = 2.14 – 0.006AhUppersummer – 0.006soilsummer + random effects 336.7

-0.52 0.17 0.25 0.09 0.15 0.38 0.08 -0.13 -0.09 0.14 -0.07 -0.45

Ssummer = 12.37 – 0.05AhUppersummer – 0.03soilsummer + random effects 1325.3

-3.65 0.88 1.33 0.60 1.86 7.14 0.08 -2.43 -1.34 1.15 -1.84 -3.78

J'summer = 0.88 – 0.003soilsummer + random effects -113.6

-0.08 0.02 0.02 -0.002 -0.01 -0.005 0.005 0.06 0.01 0.004 0.01 -0.03

Table 3.2 Continued

0.5 (NatLowerspring + 0.5) = 8.39 – 0.03canopyspring – 0.02AhUpperspring – 0.02FWDspring – 0.01soilspring + random effects 968.9

-0.95 0.44 -0.09 1.17 0.91 1.46 0.67 -0.54 -0.45 0.13 -1.10 -1.66

NatLowersummer = 183.74 – 1.45canopysummer – 0.21AhUppersummer – 0.18CWDsummer – 0.12soilsummer – 0.002Ahsap + random effects 2156.1

-11.81 0.66 -1.07 7.20 7.68 17.70 12.22 -9.47 -5.77 7.88 -12.72 -12.51

0.5 (Natseed + 0.5) = -47.17* – 1.86canopysummer – 0.69AhUppersummer – 0.57AhLowersummer + random effects 2738.8

3.46 5.44 46.51 6.93 -41.46 46.76 -66.16 -16.08 4.64 -42.56 74.60 -22.07

*Intercept was not significant (p = 0.58)

Table 3.3. Linear mixed-effects models (study site = random effect), p values, and Akaike information criterion (AIC) for models comparing native plant diversity and abundance measures to Amur honeysuckle (Lonicera maackii [Rupr.] Herder) age (Ahage), as well as spring (AhUpperspring) and summer (AhUppersummer) percent cover of Amur honeysuckle in the upper vertical stratum (1.01-5 m) to Ahage, at 12 mixed-hardwood forests in central Indiana. Diversity and abundance measures included spring Shannon’s Diversity Index for native ground flora (H'spring), spring taxonomic richness for native ground flora (Sspring), spring Pielou’s Evenness Index for native ground flora (J'spring), spring native percent cover in the lower vertical stratum (species combined as a group; NatLowerspring), summer H' (H'summer), summer S (Ssummer), summer J' (J'summer), summer native percent cover in the lower vertical stratum (species combined as a group; NatLowersummer), and native seedlings/ha (species combined as a group; Natseed).

Model p AIC

H'spring = 0.84 – 0.01Ahage + random effects 0.04 347.6

S = 3.60 – 0.04Ah + random effects spring age 0.04 973.5

J' = 0.63 – 0.008Ah + random effects spring age 0.07 233.5

0.5 (NatLowerspring + 0.5) = 5.96 – 0.06Ahage + random effects 0.004 985.6

AhUpper = 0.08 + 2.54Ah + random effects spring age < 0.001 2326.6

H' = 2.02 – 0.01Ah + random effects summer age 0.02 368.3

S = 11.16 – 0.09Ah + random effects summer age 0.04 1370.5

J' = 0.88 – 0.004Ah + random effects summer age 0.07 -104.5

NatLower = 47.10 – 0.68Ah + random effects summer age 0.003 2217.3

AhUppersummer = 0.92 + 2.64Ahage + random effects < 0.001 2327.3

(Nat + 0.5)0.5 = 116.51 – 1.99Ah + random effects seed age 0.003 2760.9

Table 3.4. Importance value (IV = [{mean percent cover + frequency}/2]*100) by species for forbs, ferns, grasses, sedges, woody/herbaceous vines, and environmental variables in the lower vertical stratum (≤ 1 m tall) during spring at 12 mixed-hardwood forests in Indiana―1) Purdue University, Department of Forestry and Natural Resources Farm (FNR Farm), 2) Fowler Park, 3) Ft. Harrison State Park, 4) Hawthorn Park, 5) privately-owned woodlot in Benton County (Leuck), 6) Purdue University, Department of Forestry and Natural Resources, Martell Forest (Martell), 7) Purdue University, Meigs Farm, south forest (Meigs), 8) Pfizer, Inc., Pond 5 eastern forest (Pond 5A), 9) Pfizer, Inc., Pond 5 western forest (Pond 5B), 10) privately-owned woodlot in Lafayette (Pursell), 11) Ross Biological Reserve, and 12) Pfizer, Inc. Rifle Range woodlot (RR). Data were collected using 2 m x 2 m quadrats. The total number of quadrats placed at FNR Farm, Fowler, Ft. Harrison, Hawthorn, Leuck, Martell, Meigs, Pond 5A, Pond 5B, Pursell, Ross, and RR were 30, 24, 18, 24, 15, 12, 15, 19, 12, 30, 28, and 27, respectively. Present = documented, but outside quadrats at a study site.

Species FNR Fowler Ft. Hawthorn Leuck Martell Meigs Pond Pond 5B Pursell Ross RR Farm Harrison 5A

Native Plants

Ferns ebony spleenwort --- 4.2 ------1.9 1.8 (Asplenium platyneuron [L.] Britton, Sterns & Poggenb.) cutleaf grapefern --- 4.2 ------3.4 ------1.8 (Botrychium dissectum Spreng.) rattlesnake fern --- 4.2 --- 8.5 6.7 33.9 ------4.2 --- 5.6 --- (Botrychium virginianum [L.] Sw.) bladderfern present ------3.4 41.6 ------8.5 ------(Cystopteris spp.)

Christmas fern --- present ------present --- (Polystichum acrostichoides [Michx.] Schott)

Forbs white baneberry ------14.1 ------present ------(Actaea pachypoda Elliot)

Table 3.4 Continued white snakeroot --- 21.9 26.0 21.6 3.4 --- 24.1 5.3 4.2 17.1 17.2 10.9 (Ageratina altissima [L.] King & H. Rob. Var. altissima) tall hairy agrimony --- 4.3 --- 2.1 ------6.7 ------3.4 ------(Agrimonia gryposepala Wallr.) harvestlice --- present --- 4.2 ------(Agrimonia parviflora Aiton) beaked agrimony ------3.4 ------(Agrimonia rostellata Wallr.) meadow garlic 1.7 17.5 --- 4.3 ------10.6 present ------(Allium canadense L.) ramp/narrowleaf wild 5.2 --- 5.8 ------39.0 ------leek (Allium tricoccum Aiton/Allium burdickii (Hanes) A.G. Jones) great ragweed ------6.8 ------(Ambrosia trifida L.) smooth rockcress ------present (Arabis laevigata [Muhl. Ex Willd.] Poir.) green dragon --- 2.1 ------3.4 ------21.6 ------present (Arisaema dracontium [L.] Schott)

Jack in the pulpit 17.2 --- 22.7 --- 10.1 17.0 --- present 8.4 15.6 23.1 1.8 (Arisaema triphyllum [L.] Schott)

Canadian wildginger present --- 12.6 ------18.7 ------present --- (Asarum canadense L.) 64

Table 3.4 Continued hairy pagoda-plant ------3.6 ------(Blephilia hirsuta [Pursh] Benth.) smallspike false nettle ------2.2 ------2.7 ------(Boehmeria cylindrica [L.] Sw.)

Atlantic camas ------present ------(Camassia scilloides [Raf.] Cory)

American bellflower ------present --- (Campanulastrum americanum [L.] Small) cutleaf toothwort 8.4 2.1 2.8 21.2 --- 29.9 ------12.6 10.1 3.7 --- (Cardamine concatenata [Michx.] Sw.) limestone 5.1 ------8.5 3.9 4.2 ------bittercress/bulbous bittercress (Cardamine douglassii Britton/Cardamine bulbosa [Schreb. Ex Muhl.] Britton, Sterns & Poggenb.) broadleaf enchanter’s 1.7 2.2 8.5 8.5 30.4 4.2 --- 8.0 --- 17.0 22.9 7.3 nightshade (Circaea lutetiana L. ssp. candensis [L.] Asch. & Magnus)

Virginia springbeauty 13.5 12.6 14.2 12.6 43.7 29.6 37.7 5.3 38.0 21.9 13.1 19.7 (Claytonia virginica L.)

American cancer-root ------8.4 ------3.8 --- (Conopholis americana [L.] Wallr.)

Table 3.4 Continued

Canadian honewort 5.0 4.2 11.3 16.9 52.7 17.0 49.9 present present 13.4 3.7 1.8 (Cryptotaenia canadensis [L.] DC.) bleeding heart ------present ------4.2 ------(Dicentra sp.) eastern false rue ------12.8 ------anemone (Enemion biternatum Raf.) fleabane --- present ------(Erigeron spp.) dogtooth violet present present --- present ------29.5 ------(Erythronium americanum Ker Gawl.) sweetscented joe pye ------present ------weed (Eupatorium purpureum L.) false mermaidweed ------33.0 --- 6.7 ------10.4 ------(Floerkea proserpinacoides Willd.) strawberry ------4.2 ------(Fragaria spp.) stickywilly --- 25.4 14.0 12.9 45.4 42.3 52.3 2.6 present 15.2 3.8 --- (Galium aparine L.) rough bedstraw ------1.9 --- (Galium asprellum Michx.) shining bedstraw ------2.2 ------present --- (Galium concinnum Torr. & A. Gray)

Table 3.4 Continued lanceleaf wild licorice --- 2.1 14.1 ------12.6 ------7.5 --- (Galium lanceolatum Torr.) fragrant bedstraw --- 4.2 8.4 4.2 6.7 8.4 present 2.7 4.2 1.7 5.6 --- (Galium triflorum Michx.) spotted geranium ------present ------21.6 present --- (Geranium maculatum L.) avens 12.0 12.7 8.4 2.1 41.4 4.2 49.2 10.7 12.7 5.1 --- 5.4 (Geum spp.) hepatica ------12.7 ------(Hepatica nobilis Schreb.)

American alumroot ------4.2 ------(Heuchera americana L.) eastern waterleaf 1.7 --- 2.8 ------40.8 ------11.9 ------(Hydrophyllum virginianum L.) jewelweed/pale touch- 8.5 15.0 8.5 26.6 20.4 --- 3.4 33.7 --- 5.1 --- 7.3 me-not (Impatiens capensis Meerb./Impatiens pallida Nutt.)

Canadian woodnettle --- 2.1 --- present ------13.7 ------8.8 ------(Laportea canadensis [L.] Weddell) waterhorehound ------4.2 ------(Lycopus sp.)

Table 3.4 Continued feathery false lily of the 1.7 ------2.1 present 4.2 ------1.7 3.8 --- valley (Maianthemum racemosum [L.] Link ssp. racemosum)

Virginia bluebells ------present ------(Mertensia virginica [L.] Pers. Ex Link) twoleaf miterwort ------2.6 ------(Mitella diphylla L.) spring forget-me-not --- 2.1 ------(Myosotis verna Nutt.)

Clayton’s 8.5 21.4 --- 21.4 32.0 38.6 50.5 --- 4.3 35.8 11.3 1.8 sweetroot/longstyle sweetroot (Osmorhiza claytonii [Michx.] C.B. Clarke/Osmorhiza longistylis [Torr.] DC.) woodsorrel ------2.1 6.8 ------1.7 ------(Oxalis spp.) butterweed ------3.4 ------(Packera glabella [Poir.] C. Jeffrey) roundleaf ragwort --- 9.1 11.2 4.7 --- 8.5 ------present 1.9 --- (Packera obovata [Muhl. Ex Willd.] W.A. Weber & A. Löve)

American ginseng ------present ------(Panax quinquefolius L.)

Miami mist ------2.7 ------present (Phacelia purshii Buckley) 68

Table 3.4 Continued wild blue phlox ------34.2 ------8.4 ------(Phlox divaricata L.) lopseed ------3.4 4.2 ------5.6 3.6 (Phryma leptostachya L.) obedient plant ------present ------(Physostegia virginiana [L.] Benth.)

American pokeweed ------present present ------5.5 (Phytolacca americana L.)

Canadian clearweed --- 26.4 8.5 4.4 ------10.7 12.7 10.2 --- 23.8 (Pilea pumila [L.] A. Gray) blackseed plantain ------3.4 ------(Plantago rugelii Decne.) mayapple 20.9 18.6 5.8 25.8 8.0 29.8 present 5.3 4.5 10.5 4.2 7.6 (Podophyllum peltatum L.) smooth Solomon’s seal 1.7 8.5 14.2 --- 3.4 4.2 present ------present --- (Polygonatum biflorum [Walter] Elliott) hairy Solomon’s seal ------5.6 ------present --- 4.2 1.7 ------(Polygonatum pubescens [Willd.] Pursh) jumpseed 3.4 44.1 14.2 29.9 23.8 --- 37.6 21.4 12.7 34.8 3.8 7.3 (Polygonum virginianum L.) whiteflower leafcup ------2.8 ------12.7 ------(Polymnia canadensis L.) 69

Table 3.4 Continued

Norwegian cinquefoil --- present 2.8 ------(Potentilla norvegica L.) rattlesnakeroot ------21.7 3.4 31.5 ------1.9 --- (Prenanthes spp.) littleleaf buttercup --- 27.3 --- 2.1 27.0 12.6 50.8 present present 15.1 3.7 --- (Ranunculus abortivus L.) bristly buttercup ------4.2 10.1 ------(Ranunculus hispidus Michx.) bloodroot ------5.7 ------25.5 ------present 1.9 --- (Sanguinaria canadensis L.) sanicle 23.0 24.3 47.5 51.2 50.8 47.9 56.4 5.4 34.1 43.1 34.9 5.4 (Sanicula spp.) wreath goldenrod --- 4.2 present ------present --- (Solidago caesia L.) other goldenrod spp. ------3.4 ------(Solidago spp.) lady’s tresses ------present --- (Spiranthes sp.) celandine poppy ------present ------(Stylophorum diphyllum [Michx.] Nutt.) common blue wood ------2.8 2.1 --- 8.4 ------present aster (Symphyotrichum cordifolium [L.] G.L. Nesom)

Table 3.4 Continued white panicle aster ------present ------(Symphyotrichum lanceolatum [Willd.] G.L. Nesom ssp. lanceolatum var. lanceolatum) calico aster --- 4.2 2.8 6.3 ------4.2 3.4 ------(Symphyotrichum lateriflorum [L.] A. Löve & D. Löve var. lateriflorum)

Canada germander ------3.4 ------(Teucrium canadense L.) spiderwort ------2.2 --- 4.3 ------(Tradescantia spp.) nodding wakerobin ------present ------4.2 ------(Trillium flexipes Raf.) bloody butcher 8.4 6.4 2.8 14.9 23.7 42.5 ------12.6 5.1 3.8 3.6 (Trillium recurvatum Beck) toadshade ------present ------(Trillium sessile L.)

California nettle ------3.4 --- 6.9 ------3.4 ------(Urtica dioica L. ssp. gracilis [Aiton] Seland.) largeflower bellwort ------4.2 ------(Uvularia grandiflora Sm.) white vervain ------3.4 ------(Verbena urticifolia L.) wingstem ------10.2 ------1.7 ------(Verbesina alternifolia [L.] Britton ex Kearney) 71

Table 3.4 Continued downy yellow violet 1.7 4.2 5.6 2.2 23.7 25.5 3.4 5.3 4.2 10.2 ------(Viola pubescens Aiton) common blue violet 13.5 25.5 8.5 17.0 30.7 25.3 49.0 24.0 25.4 22.2 3.8 3.6 (Viola sororia Willd.) striped cream violet --- 10.8 22.5 4.2 ------(Viola striata Aiton) three-lobe violet ------present ------12.6 ------present --- (Viola triloba Schwein.) unknown forb spp. ------4.5 ------

Grasses bearded shorthusk ------2.1 --- 4.2 ------(Brachyelytrum erectum [Schreb. Ex Spreng.] P. Beauv.) sweet woodreed ------10.7 ------(Cinna arundinacea L.)

Bosc’s panicgrass ------8.5 ------(Dichanthelium boscii [Poir.] Gould & C.A. Clark) hairy wildrye ------4.2 ------(Elymus villosus Muhl. Ex Willd.) nodding fescue 1.7 4.2 11.3 12.7 --- 16.8 16.9 --- 4.2 30.6 ------(Festuca subverticillata [Pers.] Alexeev) fowl mannagrass ------3.9 ------(Glyceria striata [Lam.] Hitchc.)

Table 3.4 Continued whitegrass --- 14.9 --- 8.5 ------present 4.2 ------(Leersia virginica Willd.) woodland bluegrass ------2.9 2.1 ------3.4 ------(Poa sylvestris A. Gray) unknown grass spp. ------2.8 ------4.2 44.3 ------

Sedges eastern woodland 7.0 10.6 5.6 4.3 34.1 ------8.0 21.1 10.2 1.9 1.8 sedge/broad looseflower sedge/spreading sedge (Carex blanda Dewey/Carex laxiflora Lam./Carex laxiculmis Schwein.) pubescent sedge 1.7 2.2 ------3.4 ------(Carex hirtifolia Mack.)

James’ sedge ------8.5 ------(Carex jamesii Schwein.) eastern star sedge ------13.6 ------present 8.5 --- 1.9 --- (Carex radiata [Wahlenb.] Small) unknown Carex spp. ------3.6 --- 41.5 ------(Carex spp.)

Vines trumpet creeper ------1.8 (Campsis radicans [L.] Seem. ex Bureau) wild yam --- present ------(Dioscorea villosa L.) 73

Table 3.4 Continued wild cucumber --- 2.1 --- 2.2 ------present ------(Echinocystis lobata [Michx.] Torr. & A. Gray) common moonseed ------2.1 ------8.5 ------1.8 (Menispermum canadense L.)

Virginia creeper 22.6 34.0 31.2 17.0 27.1 42.0 6.7 26.9 21.3 20.6 40.2 21.8 (Parthenocissus quinquefolia [L.] Planch.) climbing false ------2.2 ------buckwheat (Polygonum scandens L.) bristly greenbrier --- 2.1 5.6 8.5 6.7 12.6 3.6 --- 4.2 1.7 1.9 --- (Smilax tamnoides L.) eastern poison ivy 1.7 6.3 17.3 28.0 ------2.7 4.2 8.5 15.1 5.4 (Toxicodendron radicans [L.] Kuntze) grape 3.4 2.1 ------4.2 1.7 --- 5.6 (Vitis spp.)

Non-native Plants

Forbs garlic mustard 20.8 2.2 ------3.4 29.6 54.4 --- 4.2 34.2 7.5 --- (Alliaria petiolata [M. Bieb.] Cavara & Grande) wild garlic --- 10.6 --- present 3.4 ------(Allium vineale L.)

Table 3.4 Continued lesser burdock 1.7 ------(Arctium minus Bernh.) garden yellowrocket present ------(Barbarea vulgaris W.T. Aiton) caraway --- 4.2 ------present 4.2 51.2 ------(Carum carvi L.) ground ivy --- 2.1 ------(Glechoma hederacea L.) purple deadnettle ------6.8 --- 23.7 2.6 ------(Lamium purpureum L.) creeping jenny ------4.2 ------(Lysimachia nummularia L.) charity ------4.2 present ------(Polemonium caeruleum L.) spotted ladysthumb ------2.1 ------2.7 8.5 6.7 ------(Polygonum persicaria L.) common chickweed ------40.4 ------(Stellaria media [L.] Vill.) common dandelion --- 2.1 ------(Taraxacum officinale F.H. Wigg.)

Grasses

Kentucky bluegrass ------1.9 --- (Poa pratensis L.)

Table 3.4 Continued

Vines

Oriental bittersweet ------2.7 4.2 ------(Celastrus orbiculatus Thunb.) winter creeper ------1.8 (Euonymus fortunei [Turcz.] Hand.-Maz.)

Japanese hop ------2.6 ------(Humulus japonicus Siebold & Zucc.) morning-glory ------2.8 ------(Ipomoea sp.)

Japanese honeysuckle --- 21.4 2.8 ------(Lonicera japonica Thunb.)

Environmental Variables bare soil 66.0 52.8 54.1 41.6 53.8 51.0 48.8 69.8 62.0 54.5 36.6 63.1 coarse woody debris 19.1 9.3 15.5 10.2 7.8 19.2 --- 20.2 11.7 16.8 11.4 14.0 fine woody debris 54.1 53.2 53.1 52.4 52.2 50.0 45.5 59.3 60.0 47.4 47.9 54.8 dead leaves/dead 52.5 64.6 64.5 75.0 60.5 83.9 40.4 57.2 54.7 56.6 80.7 66.5 herbaceous stems

Table 3.5. Importance value (IV = [{mean percent cover + frequency}/2]*100) by species for forbs, ferns, grasses, sedges, woody/herbaceous vines, and environmental variables in the lower vertical stratum (≤ 1 m tall) during summer at 12 mixed-hardwood forests in Indiana―1) Purdue University, Department of Forestry and Natural Resources Farm (FNR Farm), 2) Fowler Park, 3) Ft. Harrison State Park, 4) Hawthorn Park, 5) privately-owned woodlot in Benton County (Leuck), 6) Purdue University, Department of Forestry and Natural Resources, Martell Forest (Martell), 7) Purdue University, Meigs Farm, south forest (Meigs), 8) Pfizer, Inc., Pond 5 eastern forest (Pond 5A), 9) Pfizer, Inc., Pond 5 western forest (Pond 5B), 10) privately-owned woodlot in Lafayette (Pursell), 11) Ross Biological Reserve, and 12) Pfizer, Inc. Rifle Range woodlot (RR). Data were collected using 2 m x 2 m quadrats. The total number of quadrats placed at FNR Farm, Fowler, Ft. Harrison, Hawthorn, Leuck, Martell, Meigs, Pond 5A, Pond 5B, Pursell, Ross, and RR were 30, 24, 18, 24, 15, 12, 15, 19, 12, 30, 28, and 27, respectively. Present = documented, but outside quadrats at a study site.

Species FNR Fowler Ft. Hawthorn Leuck Martell Meigs Pond Pond 5B Pursell Ross RR Farm Harrison 5A

Native Plants

Ferns ebony spleenwort --- 8.5 ------1.9 1.8 (Asplenium platyneuron [L.] Britton, Sterns & Poggenb.) cutleaf grapefern --- 12.7 ------3.4 ------3.6 (Botrychium dissectum Spreng.) rattlesnake fern --- 12.7 --- 8.5 6.7 33.9 ------4.2 --- 11.3 --- (Botrychium virginianum [L.] Sw.) bladderfern present ------3.4 39.0 ------8.6 ------(Cystopteris spp.)

Christmas fern --- present ------present --- (Polystichum acrostichoides [Michx.] Schott)

Forbs

Table 3.5 Continued white baneberry ------14.1 ------present ------(Actaea pachypoda Elliot) white snakeroot --- 24.1 32.4 22.1 24.2 --- 25.3 13.4 4.3 31.8 21.7 11.4 (Ageratina altissima [L.] King & H. Rob. Var. altissima) tall hairy agrimony --- 12.8 --- 2.2 ------6.8 ------5.1 ------(Agrimonia gryposepala Wallr.) harvestlice --- present --- 4.3 ------(Agrimonia parviflora Aiton) beaked agrimony ------3.4 ------1.9 --- (Agrimonia rostellata Wallr.) meadow garlic ------5.3 present ------(Allium canadense L.) ramp/narrowleaf wild ------5.6 ------33.5 ------leek (Allium tricoccum Aiton/Allium burdickii [Hanes] A.G. Jones) great ragweed ------11.5 ------(Ambrosia trifida L.) smooth rockcress ------present (Arabis laevigata [Muhl. Ex Willd.] Poir.) green dragon --- 2.1 ------3.4 ------21.1 ------present (Arisaema dracontium [L.] Schott)

Table 3.5 Continued

Jack in the pulpit 13.7 --- 8.5 --- 6.7 17.1 --- present 8.4 18.9 23.0 --- (Arisaema triphyllum [L.] Schott)

Canadian wildginger present --- 15.8 ------18.7 ------present --- (Asarum canadense L.) beggarticks ------1.7 ------(Bidens sp.) hairy pagoda-plant ------3.6 ------(Blephilia hirsuta [Pursh] Benth.) smallspike false nettle --- 2.1 --- 2.2 ------2.7 --- 1.7 --- 1.8 (Boehmeria cylindrica [L.] Sw.)

Atlantic camas ------present ------(Camassia scilloides [Raf.] Cory)

American bellflower ------3.4 present --- (Campanulastrum americanum [L.] Small) broadleaf enchanter’s 1.7 4.2 8.5 8.5 30.9 12.6 --- 8.0 --- 18.9 26.8 7.3 nightshade (Circaea lutetiana L. ssp. canadensis [L.] Asch. & Magnus)

American cancer-root ------8.4 ------5.8 --- (Conopholis americana [L.] Wallr.)

Canadian honewort 8.7 10.6 11.4 19.4 53.7 26.5 50.1 present present 34.4 5.7 1.8 (Cryptotaenia canadensis [L.] DC.) bleeding heart ------present ------(Dicentra sp.) 79

Table 3.5 Continued fleabane --- present ------(Erigeron spp.) dogtooth violet present present --- present ------(Erythronium americanum Ker Gawl.) sweetscented joe pye ------present ------weed (Eupatorium purpureum L.) strawberry ------4.2 ------(Fragaria spp.) stickywilly --- 23.3 14.1 12.7 44.4 42.2 50.9 2.7 present 15.2 3.8 --- (Galium aparine L.) rough bedstraw ------1.7 1.9 --- (Galium asprellum Michx.) shining bedstraw ------2.2 ------1.7 present --- (Galium concinnum Torr. & A. Gray) lanceleaf wild licorice --- 6.3 19.7 4.2 --- 12.7 ------11.3 --- (Galium lanceolatum Torr.) fragrant bedstraw --- 4.2 8.5 4.2 6.7 8.5 present 2.7 4.2 1.7 5.7 --- (Galium triflorum Michx.) spotted geranium ------present 4.2 ------21.0 present --- (Geranium maculatum L.) avens 12.1 19.2 17.0 4.3 45.2 4.2 53.1 16.1 12.9 27.6 --- 5.5 (Geum spp.)

Table 3.5 Continued beggarslice ------6.8 ------(Hackelia virginiana [L.] I.M. Johnst.) hepatica ------12.7 ------(Hepatica nobilis Schreb.)

American alumroot ------4.2 ------(Heuchera americana L.) eastern waterleaf 1.7 --- 2.9 ------38.4 ------11.9 ------(Hydrophyllum virginianum L.) jewelweed/pale touch- 8.7 8.6 8.5 25.9 21.0 --- 3.4 36.2 --- 8.5 --- 7.4 me-not (Impatiens capensis Meerb./Impatiens pallida Nutt.)

Canadian woodnettle --- 2.1 --- present ------14.5 ------14.1 ------(Laportea canadensis [L.] Weddell) waterhorehound ------4.2 ------(Lycopus sp.) feathery false lily of the 1.7 --- 8.5 2.1 3.4 4.2 ------1.7 3.8 --- valley (Maianthemum racemosum [L.] Link ssp. racemosum)

Virginia bluebells ------present ------(Mertensia virginica [L.] Pers. Ex Link) twoleaf miterwort ------2.6 --- 1.7 ------(Mitella diphylla L.)

Table 3.5 Continued

Clayton’s 8.5 21.2 --- 21.2 32.0 38.7 50.5 --- 4.2 37.5 11.3 3.6 sweetroot/longstyle sweetroot (Osmorhiza claytonii [Michx.] C.B. Clarke/Osmorhiza longistylis [Torr.] DC.) woodsorrel ------2.1 10.2 ------6.8 1.9 --- (Oxalis spp.) butterweed ------3.4 ------(Packera glabella [Poir.] C. Jeffrey) roundleaf ragwort 1.7 8.9 11.3 4.6 --- 8.5 ------present 1.9 --- (Packera obovata [Muhl. Ex Willd.] W.A. Weber & A. Löve)

American ginseng ------present ------(Panax quinquefolius L.)

Miami mist ------3.4 ------2.6 ------present (Phacelia purshii Buckley) wild blue phlox ------34.3 ------8.4 1.7 ------(Phlox divaricata L.) lopseed --- 6.3 --- 2.1 6.7 21.3 3.6 ------5.1 9.4 10.9 (Phryma leptostachya L.) obedient plant ------present ------(Physostegia virginiana [L.] Benth.)

American pokeweed --- 2.2 ------present present ------5.7 (Phytolacca americana L.) 82

Table 3.5 Continued

Canadian clearweed 1.7 28.1 8.5 4.3 27.8 --- 34.3 18.9 16.9 33.1 --- 29.2 (Pilea pumila [L.] A. Gray) blackseed plantain ------3.5 ------(Plantago rugelii Decne.) mayapple 1.7 ------21.1 present 5.3 4.3 8.6 3.8 7.3 (Podophyllum peltatum L.) smooth Solomon’s seal 1.7 6.3 14.2 --- 3.4 4.2 present ------present --- (Polygonatum biflorum [Walter] Elliott) hairy Solomon’s seal ------5.6 ------present --- 4.2 1.7 ------(Polygonatum pubescens [Willd.] Pursh) jumpseed 6.9 45.6 22.9 37.3 31.9 --- 48.6 21.8 13.0 48.3 3.8 9.2 (Polygonum virginianum L.) whiteflower leafcup ------2.8 ------12.9 ------(Polymnia canadensis L.)

Norwegian cinquefoil --- present 2.8 ------(Potentilla norvegica L.) rattlesnakeroot --- 2.1 --- 21.6 3.4 31.5 ------1.7 1.9 --- (Prenanthes spp.) littleleaf buttercup ------6.7 --- 13.4 present present ------(Ranunculus abortivus L.) bristly buttercup ------4.2 6.7 ------(Ranunculus hispidus Michx.) 83

Table 3.5 Continued bloodroot ------5.7 ------21.3 ------present 1.9 --- (Sanguinaria canadensis L.) sanicle 25.3 29.0 47.9 51.6 51.2 52.9 57.4 5.6 43.5 44.1 39.2 5.5 (Sanicula spp.) carpenter’s square ------5.2 ------(Scrophularia marilandica L.) wreath goldenrod --- 4.2 present ------present --- (Solidago caesia L.) other goldenrod spp. ------7.0 ------(Solidago spp.) lady’s tresses ------present --- (Spiranthes sp.) celandine poppy ------present ------(Stylophorum diphyllum [Michx.] Nutt.) common blue wood ------2.8 2.1 --- 8.4 ------present aster (Symphyotrichum cordifolium [L.] G.L. Nesom) white panicle aster ------present ------(Symphyotrichum lanceolatum [Willd.] G.L. Nesom ssp. lanceolatum var. lanceolatum) calico aster 1.7 6.3 5.6 6.4 ------4.2 10.1 ------(Symphyotrichum lateriflorum [L.] A. Löve & D. Löve var. lateriflorum) 84

Table 3.5 Continued

Canada germander ------6.7 ------(Teucrium canadense L.) spiderwort ------2.2 --- 17.1 ------(Tradescantia spp.) nodding wakerobin ------present ------(Trillium flexipes Raf.) toadshade ------present ------(Trillium sessile L.)

California nettle ------3.5 --- 7.0 ------6.9 1.9 --- (Urtica dioica L. ssp. gracilis [Aiton] Seland.) largeflower bellwort ------4.2 ------(Uvularia grandiflora Sm.) white vervain ------3.4 ------(Verbena urticifolia L.) wingstem --- 2.1 ------18.3 ------1.8 ------(Verbesina alternifolia [L.] Britton ex Kearney) downy yellow violet 5.1 4.2 8.5 2.2 23.8 25.6 3.4 5.3 4.2 10.3 ------(Viola pubescens Aiton) common blue violet 15.2 29.7 14.1 19.1 30.9 25.5 52.8 24.3 25.6 27.5 5.6 5.4 (Viola sororia Willd.) striped cream violet --- 10.9 22.6 4.3 ------1.7 ------(Viola striata Aiton) three-lobe violet ------present ------17.0 ------present --- (Viola triloba Schwein.) 85

Table 3.5 Continued unknown forb spp. ------4.5 ------

Grasses bearded shorthusk ------2.1 --- 4.2 ------(Brachyelytrum erectum [Schreb. Ex Spreng.] P. Beauv.) sweet woodreed ------12.9 ------1.9 --- (Cinna arundinacea L.)

Bosc’s panicgrass --- 2.1 --- 8.5 ------(Dichanthelium boscii [Poir.] Gould & C.A. Clark) hairy wildrye ------4.2 ------(Elymus villosus Muhl. Ex Willd.) nodding fescue 1.7 6.4 11.3 12.8 --- 16.9 16.9 --- 4.3 34.0 ------(Festuca subverticillata [Pers.] Alexeev) fowl mannagrass ------3.9 ------(Glyceria striata [Lam.] Hitchc.) whitegrass --- 17.1 --- 10.7 ------present 4.3 3.4 ------(Leersia virginica Willd.) woodland bluegrass ------2.9 2.1 ------3.4 ------(Poa sylvestris A. Gray) unknown grass spp. ------2.8 ------4.2 44.4 ------

Sedges

Table 3.5 Continued eastern woodland 7.0 17.0 8.5 8.5 34.2 ------8.0 21.2 15.5 1.9 1.8 sedge/broad looseflower sedge/spreading sedge (Carex blanda Dewey/Carex laxiflora Lam./Carex laxiculmis Schwein.) pubescent sedge 1.7 2.2 ------3.4 ------(Carex hirtifolia Mack.)

James’ sedge ------8.5 ------(Carex jamesii Schwein.) eastern star sedge ------13.6 ------present 8.6 --- 1.9 --- (Carex radiata [Wahlenb.] Small) unknown Carex spp. ------3.6 --- 41.6 ------(Carex spp.)

Vines trumpet creeper ------2.1 ------9.1 (Campsis radicans [L.] Seem. ex Bureau) wild yam --- present ------4.2 3.4 ------(Dioscorea villosa L.) wild cucumber --- 2.1 --- 2.2 ------present ------(Echinocystis lobata [Michx.] Torr. & A. Gray) common moonseed ------4.3 ------12.8 1.7 --- 1.8 (Menispermum canadense L.)

Table 3.5 Continued

Virginia creeper 22.7 38.4 31.5 19.2 28.1 48.4 10.6 30.1 25.9 22.3 40.7 22.2 (Parthenocissus quinquefolia [L.] Planch.) climbing false ------2.2 ------buckwheat (Polygonum scandens L.) bristly dewberry ------4.2 ------1.9 --- (Rubus hispidus L.) bristly greenbrier 1.7 10.6 14.2 10.7 13.5 17.3 10.4 --- 4.3 5.1 3.8 --- (Smilax tamnoides L.) eastern poison ivy 3.4 8.5 17.4 35.4 3.5 8.7 3.5 2.7 4.2 17.2 17.2 5.6 (Toxicodendron radicans [L.] Kuntze) grape 3.5 4.2 2.8 --- 3.4 --- 3.5 --- 4.2 3.4 --- 7.4 (Vitis spp.)

Non-native Plants

Forbs garlic mustard 20.6 2.1 ------3.5 34.2 54.4 --- 4.2 37.3 13.2 --- (Alliaria petiolata [M. Bieb.] Cavara & Grande) wild garlic ------present ------(Allium vineale L.) lesser burdock 1.7 ------(Arctium minus Bernh.) garden yellowrocket present ------(Barbarea vulgaris W.T. Aiton) 88

Table 3.5 Continued caraway --- 4.2 ------present 4.2 47.5 ------(Carum carvi L.) ground ivy --- 2.2 ------(Glechoma hederacea L.) purple deadnettle ------10.1 2.6 ------(Lamium purpureum L.) charity ------4.3 present ------(Polemonium caeruleum L.) spotted ladysthumb ------2.2 ------13.5 18.9 9.1 24.0 ------(Polygonum persicaria L.) common chickweed ------37.2 ------(Stellaria media [L.] Vill.) common dandelion --- 2.1 ------(Taraxacum officinale F.H. Wigg.)

Grasses

Kentucky bluegrass ------1.9 --- (Poa pratensis L.)

Environmental Variables bare soil 67.5 55.4 56.3 42.4 52.6 51.1 48.8 70.0 62.0 54.5 36.6 63.6 coarse woody debris 19.1 9.3 15.5 10.6 8.5 19.2 --- 20.2 11.7 16.8 11.4 14.0 fine woody debris 54.1 53.9 53.1 52.9 52.2 50.0 45.5 59.3 60.5 47.4 47.9 54.8

Table 3.5 Continued dead leaves/dead 52.0 58.8 60.3 71.7 53.9 82.9 43.9 54.9 52.6 54.3 80.6 62.0 herbaceous stems

Table 3.6. Importance value (IV = [{relative density + frequency}/2]*100) by species for seedling-layer individuals (woody individuals < 1.37 m tall) at 12 mixed- hardwood forests in Indiana―1) Purdue University, Department of Forestry and Natural Resources Farm (FNR Farm), 2) Fowler Park, 3) Ft. Harrison State Park, 4) Hawthorn Park, 5) privately-owned woodlot in Benton County (Leuck), 6) Purdue University, Department of Forestry and Natural Resources, Martell Forest (Martell), 7) Purdue University, Meigs Farm, south forest (Meigs), 8) Pfizer, Inc., Pond 5 eastern forest (Pond 5A), 9) Pfizer, Inc., Pond 5 western forest (Pond 5B), 10) privately-owned woodlot in Lafayette (Pursell), 11) Ross Biological Reserve, and 12) Pfizer, Inc. Rifle Range woodlot (RR). Data were collected using 2 m x 2 m quadrats. The total number of quadrats placed at FNR Farm, Fowler, Ft. Harrison, Hawthorn, Leuck, Martell, Meigs, Pond 5A, Pond 5B, Pursell, Ross, and RR were 30, 24, 18, 24, 15, 12, 15, 19, 12, 30, 28, and 27, respectively. Present = documented, but outside quadrats at a study site.

Species FNR Fowler Ft. Harrison Hawthorn Leuck Martell Meigs Pond Pond Pursell Ross RR Farm 5A 5B

Native boxelder ------3.2 --- 8.0 --- 5.1 ------(Acer negundo L.) black maple ------3.5 ------4.4 ------(Acer nigrum Michx. F.) red maple ------6.0 4.5 ------4.7 ------(Acer rubrum L.) silver maple ------(Acer saccharinum L.) sugar maple --- 7.2 57.1 ------29.0 ------4.7 --- 25.8 --- (Acer saccharum Marsh.)

Ohio buckeye ------3.2 ------13.9 ------(Aesculus glabra Willd.) serviceberry ------2.4 ------(Amelanchier spp.) pawpaw --- 5.5 --- 2.4 ------3.4 37.4 ------7.3 (Asimina triloba [L.] Dunal)

Table 3.6 Continued

American hornbeam ------6.7 ------(Carpinus caroliniana Walter) mockernut hickory ------(Carya alba [L.] Nutt.) bitternut hickory present 9.3 9.3 11.4 --- 23.4 ------9.5 5.8 5.9 2.1 (Carya cordiformis [Wangenh.] K. Koch) pignut hickory/red ------hickory (Carya glabra [Mill.] Sweet/Carya ovalis [Wangenh.] Sarg.) shagbark hickory present 7.2 ------4.0 4.7 ------3.9 5.9 --- (Carya ovata [Mill.] K. Koch) common hackberry 18.4 2.3 12.3 2.2 --- 13.3 12.0 3.4 10.0 22.3 16.2 23.1 (Celtis occidentalis L.) eastern redbud ------3.0 ------(Cercis canadensis L.) flowering dogwood --- 4.6 ------4.4 ------9.4 --- (Cornus florida L.) other dogwood ------(Cornus spp.)

American hazelnut ------2.4 ------(Corylus americana Walter) hawthorn 3.8 ------4.7 4.7 10.2 ------10.1 2.1 (Crataegus spp.) 92

Table 3.6 Continued

American beech ------9.3 ------(Fagus grandifolia Ehrh.) white ash/green ash 42.6 42.9 10.9 33.9 13.5 65.9 --- 3.8 14.7 5.7 74.5 10.1 (Fraxinus americana L./Fraxinus pennsylvanica Marsh.) blue ash ------42.0 ------(Fraxinus quadrangulata Michx.) honeylocust ------2.2 ------3.0 ------2.0 4.2 (Gleditsia triacanthos L.) black walnut ------5.1 ------(Juglans nigra L.) northern spicebush --- 5.1 3.0 16.9 ------10.0 --- (Lindera benzoin [L.] Blume) tuliptree ------3.2 2.2 ------6.0 2.1 (Liriodendron tulipifera L.) osage orange ------(Maclura pomifera [Raf.] C.K. Schneid.) red mulberry ------(Morus rubra L.) blackgum ------7.3 ------(Nyssa sylvatica Marsh.)

Table 3.6 Continued hophornbeam ------2.4 ------(Ostrya virginiana [Mill.] K. Koch)

American sycamore ------(Platanus occidentalis L.) black cherry 1.9 2.3 18.2 4.9 8.7 28.7 20.7 20.9 4.7 4.7 22.7 6.7 (Prunus serotina Ehrh.) white oak present ------2.0 --- (Quercus alba L.) swamp white oak ------(Quercus bicolor Willd.) bur oak ------(Quercus macrocarpa Michx.) chinkapin oak ------3.2 ------4.4 ------3.9 --- (Quercus muehlenbergii Engelm.) northern red oak present ------6.8 --- 4.4 ------4.7 --- 3.9 --- (Quercus rubra L.) black oak ------3.2 ------(Quercus velutina Lam.) currant --- 5.3 ------4.4 ------7.1 --- 4.5 (Ribes spp.)

Allegheny --- present 3.7 6.8 ------6.1 ------blackberry (Rubus allegheniensis Porter) 94

Table 3.6 Continued black raspberry ------12.1 --- present ------3.9 3.9 --- (Rubus occidentalis L.)

American black ------16.1 ------3.0 4.7 ------elderberry/red elderberry (Sambucus nigra L. ssp. canadensis [L.] R. Bolli/Sambucus racemosa L.) sassafras --- 20.8 3.2 18.4 ------13.2 ------26.5 12.2 (Sassafras albidum [Nutt.] Nees)

American basswood ------2.0 --- (Tilia americana L.)

American 7.9 5.3 6.2 7.1 8.7 38.1 12.0 ------10.5 12.2 --- elm/slippery elm (Ulmus americana L./Ulmus rubra Muhl.) mapleleaf viburnum ------3.0 ------(Viburnum acerifolium L.) blackhaw ------4.7 4.1 ------(Viburnum prunifolium L.) southern arrowwood ------14.1 --- 4.4 ------(Viburnum recognitum Fernald)

Non-native

Japanese barberry ------2.8 ------(Berberis thunbergii DC.) 95

Table 3.6 Continued autumn olive ------14.2 ------present 20.5 --- (Elaeagnus umbellata Thunb.) burningbush ------present 2.2 --- 4.7 ------(Euonymus alatus [Thunb.] Siebold)

European privet present present 9.3 ------5.0 --- 2.5 ------(Ligustrum vulgare L.)

Amur honeysuckle 70.8 42.5 48.3 35.6 46.4 24.2 --- 83.7 79.2 58.5 29.1 81.0 (Lonicera maackii [Rupr.] Herder) multiflora rose 7.9 29.3 12.3 7.1 37.6 25.1 38.1 ------38.4 24.3 --- (Rosa multiflora Thunb.)

Table 3.7. Importance value (IV = [{relative density + frequency}/2]*100) by species for sapling-layer individuals (woody individuals < 10 cm diameter at breast height [dbh] and ≥ 1.37 m tall) at 12 mixed-hardwood forests in Indiana―1) Purdue University, Department of Forestry and Natural Resources Farm (FNR Farm), 2) Fowler Park, 3) Ft. Harrison State Park, 4) Hawthorn Park, 5) privately-owned woodlot in Benton County (Leuck), 6) Purdue University, Department of Forestry and Natural Resources, Martell Forest (Martell), 7) Purdue University, Meigs Farm, south forest (Meigs), 8) Pfizer, Inc., Pond 5 eastern forest (Pond 5A), 9) Pfizer, Inc., Pond 5 western forest (Pond 5B), 10) privately-owned woodlot in Lafayette (Pursell), 11) Ross Biological Reserve, and 12) Pfizer, Inc. Rifle Range woodlot (RR). Data were collected using fixed-radius plots (radius = 3.57 m). The total number of plots placed at FNR Farm, Fowler, Ft. Harrison, Hawthorn, Leuck, Martell, Meigs, Pond 5A, Pond 5B, Pursell, Ross, and RR were 30, 24, 18, 24, 15, 12, 15, 19, 12, 30, 28, and 27, respectively. Present = documented, but outside plots at a study site.

FNR Pond Pond Species Fowler Ft. Harrison Hawthorn Leuck Martell Meigs Pursell Ross RR Farm 5A 5B

Native boxelder 1.8 --- 2.9 6.5 18.2 ------(Acer negundo L.) black maple ------2.9 ------(Acer nigrum Michx. F.) red maple ------6.7 ------(Acer rubrum L.) sugar maple 5.5 2.2 40.8 8.8 --- 48.2 ------18.6 --- 20.9 5.9 (Acer saccharum Marsh.)

Ohio buckeye ------30.9 ------(Aesculus glabra Willd.) serviceberry ------2.9 ------1.9 (Amelanchier spp.) pawpaw --- 5.1 --- 6.5 ------49.2 ------3.9 (Asimina triloba [L.] Dunal)

American hornbeam ------6.1 4.4 ------(Carpinus caroliniana Walter)

Table 3.7 Continued mockernut hickory --- 4.8 ------3.7 ------(Carya alba [L.] Nutt.) bitternut hickory --- 17.1 11.9 2.2 --- 5.3 ------4.3 ------1.9 (Carya cordiformis [Wangenh.] K. Koch) pignut hickory/red ------6.6 ------4.1 hickory (Carya glabra [Mill.] Sweet/Carya ovalis [Wangenh.] Sarg.) shagbark hickory --- 8.9 ------17.4 ------17.3 13.5 3.9 --- (Carya ovata [Mill.] K. Koch) common hackberry 7.7 --- 8.8 6.5 3.6 present ------35.8 11.3 --- 21.9 (Celtis occidentalis L.) eastern redbud --- 4.4 3.1 ------4.3 ------3.9 (Cercis canadensis L.) flowering dogwood --- 19.7 14.9 6.5 --- 25.9 ------1.8 21.1 --- (Cornus florida L.) other dogwood ------1.8 ------(Cornus spp.) hawthorn 1.8 --- 3.1 --- 23.0 4.7 54.5 2.9 --- 26.8 15.2 5.8 (Crataegus spp.) common persimmon ------present ------2.9 ------(Diospyros virginiana L.)

American beech --- 2.2 6.1 2.2 ------18.7 1.9 (Fagus grandifolia Ehrh.) 98

Table 3.7 Continued white ash 10.8 11.2 18.5 2.2 12.8 4.7 --- 8.8 13.0 --- 40.6 13.9 (Fraxinus americana L.) green ash 1.8 ------2.2 ------11.7 4.3 3.7 2.0 --- (Fraxinus pennsylvanica Marsh.) blue ash ------43.3 ------3.9 --- (Fraxinus quadrangulata Michx.) black walnut 1.8 ------7.2 --- 8.1 ------1.8 --- 1.9 (Juglans nigra L.) northern spicebush --- 16.7 18.3 22.7 ------14.4 --- (Lindera benzoin [L.] Blume) tuliptree ------6.7 ------1.9 (Liriodendron tulipifera L.) osage orange 1.8 ------7.4 --- 25.0 ------7.3 ------(Maclura pomifera [Raf.] C.K. Schneid.) red mulberry ------2.3 3.6 ------(Morus rubra L.) blackgum --- 2.2 --- 21.3 ------(Nyssa sylvatica Marsh.) hophornbeam ------2.9 4.3 --- present ------5.9 --- (Ostrya virginiana [Mill.] K. Koch)

American sycamore --- 4.4 ------

(Platanus 99 occidentalis L.)

Table 3.7 Continued black cherry 3.8 9.0 9.2 10.8 15.9 ------8.6 --- 27.8 2.0 9.9 (Prunus serotina Ehrh.) white oak --- 6.6 --- 2.2 ------2.0 present (Quercus alba L.) swamp white oak ------4.1 ------(Quercus bicolor Willd.) shingle oak --- 4.4 ------7.7 ------1.8 ------(Quercus imbricaria Michx.) bur oak ------21.8 ------8.7 ------(Quercus macrocarpa Michx.) chinkapin oak ------4.7 ------2.0 --- (Quercus muehlenbergii Engelm.) northern red oak --- 2.2 2.9 2.2 --- present ------6.2 1.9 (Quercus rubra L.) black oak ------2.0 3.9 (Quercus velutina Lam.) black locust 9.1 ------(Robinia pseudoacacia L.)

American black ------4.1 ------elderberry/red elderberry (Sambucus nigra L. ssp. canadensis [L.] R. Bolli/Sambucus

racemosa L.) 100

Table 3.7 Continued sassafras --- 15.9 --- 21.9 --- 4.7 --- 8.6 4.3 --- 27.0 13.9 (Sassafras albidum [Nutt.] Nees)

American basswood ------present --- 4.7 ------(Tilia americana L.)

American elm 18.5 11.5 18.2 14.3 --- present --- 5.7 4.3 18.8 8.1 12.0 (Ulmus americana L.) slippery elm 1.8 --- 8.8 10.9 --- present ------18.0 1.8 3.9 15.6 (Ulmus rubra Muhl.) blackhaw ------3.1 ------4.7 ------8.6 2.0 2.1 (Viburnum prunifolium L.) southern arrowwood ------18.3 ------(Viburnum recognitum Fernald)

Non-native

Japanese barberry ------2.2 ------(Berberis thunbergii DC.) autumn olive --- 4.6 ------15.9 ------6.3 49.2 --- (Elaeagnus umbellata Thunb.) burningbush ------2.2 ------(Euonymus alatus [Thunb.] Siebold)

European privet 3.6 2.3 5.9 ------3.1 ------3.9 --- (Ligustrum vulgare L.)

Amur honeysuckle 93.1 79.4 67.3 57.4 69.5 43.9 49.5 95.1 76.1 65.4 44.7 87.6 (Lonicera maackii [Rupr.] Herder) 101

Table 3.7 Continued apple 3.9 ------8.1 ------6.0 --- (Malus spp.) white mulberry ------3.6 ------(Morus alba L.) multiflora rose --- 21.4 3.1 --- 3.6 15.9 8.1 ------19.3 16.5 --- (Rosa multiflora Thunb.)

102

103

104

Figure 3.2. Mean percent cover (± 1 SE) of native plants (species combined as a group), Amur honeysuckle (Lonicera maackii [Rupr.] Herder), and other non-native plants (species combined as a group) in the lower vertical stratum (≤ 1 m) during spring (a) and summer (b), and the upper vertical stratum (1.01-5 m) during spring (c) and summer (d) at 12 mixed-hardwood forests in central Indiana.

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Figure 3.3. Mean seedling-layer densities (a) and mean sapling-layer densities (b) for native woody plants, Amur honeysuckle (Lonicera maackii [Rupr.] Herder), and other non-native woody plants (species combined as a group) at 12 mixed-hardwood forests in central Indiana. Seedling-layer densities included woody individuals < 1.37 m tall and sapling-layer densities included woody individuals ≥ 1.37 m tall and < 10 cm diameter at breast height (dbh). Error bars are ± 1 SE.

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Figure 3.4. Box plots showing Amur honeysuckle (Lonicera maackii [Rupr.] Herder) duration (years) in fixed-area plots at 12 mixed-hardwood forests in central Indiana. Sample size (number of plots) for FNR Farm, Fowler, Ft. Harrison, Hawthorn, Leuck, Martell, Meigs, Pond 5A, Pond 5B, Pursell, Ross, and RR were 30, 24, 18, 24, 15, 12, 15, 19, 12, 30, 28, and 27, respectively.

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Figure 3.5. Mean Shannon’s Diversity Index (H') (a), mean taxonomic richness (S) (b), and mean Pielou’s Evenness Index (J') (c) during the spring and summer at 12 mixed- hardwood forests in central Indiana. For spring, indices were based on percent covers of native herbaceous plants that flower from March-June. For summer, indices were based on percent covers of all native herbaceous and woody taxa. Error bars are ± 1 SE.

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CHAPTER 4 SHORT-TERM RESPONSE OF NATIVE FLORA TO THE

REMOVAL OF NON-NATIVE SHRUBS IN MIXED-HARDWOOD FORESTS

4.1. Introduction

Non-native (i.e., exotic) invasive species pose one of the most serious threats to ecosystems worldwide by causing direct and indirect changes to native biota and impeding management and control efforts (Wilcove et al. 1998, Mack et al. 2000, Molnar et al. 2008). Although the link between invasive species and declines of native flora and fauna have been debated (Gurevitch and Padilla 2004, Didham et al. 2005), numerous investigators have reported the ecological and economic effects of invasive species. For example, Wilcove et al. (1998) suggested that invasive species are the second-most important threat to native species designated as imperiled in the U.S., second only

108

tohabitat destruction. After reviewing 287 publications related to the impacts of 167 invasive species, Pyšek et al. (2012) concluded that, in general, invasive species cause a decrease in species- and community-level measures such as survival, abundance, and diversity of resident species, but effects are context dependent and difficult to quantify.

Nonetheless, there are numerous case studies that exemplify the severe negative effects of invasive species on native taxa (e.g., Gibbs 1978, Frasier and Johnsen, Jr. 1991,

109

Bryson and Carter 1993, Ludyanskiy et al. 1993, Marks et al. 1994, Knapp 1996,

Meekins and McCarthy 1999, Blossey et al. 2001, Baldwin et al. 2002, Poland and

McCullough 2006, Herborg et al. 2007, Mayfield, III 2007, Siemann et al. 2009). The ecological effects of invasive species and the cost of their control are expensive; Pimentel et al. (2005) indicated that the damage and control costs of invasive species can exceed

$120 billion in a single year in the U.S. In Fiscal Year 2011, approximately $2 billion was allocated to eight U.S. departments/agencies for invasive species prevention, early detection and rapid response, control and management, research, restoration, education and public awareness, and leadership/international cooperation (The National Invasive

Species Council 2013).

As land managers with limited resources strive to counteract the negative effects of invasive species, context-dependent information about the invader and the invaded ecosystem becomes more critical. Specifically, an understanding of effective control strategies, spatial and temporal invasion processes, and biotic and abiotic factors contributing to successful invasion are all critical to the prioritization of management actions. However, understanding positive and negative changes that result from these

actions is equally important. Developing this understanding depends upon scientifically 109 valid designed before-and-after studies as opposed to relying on anecdotal evidence of suspected impacts (Blossey 1999). While some investigators have examined the effects of invasive plant species management, a more comprehensive body of work is needed given the gap between scientific research and management programs (Reid et al. 2009).

Within forest ecosystems, woody invasive shrubs are particularly problematic because their longevity and persistence in the understory exerts considerable influence

110 over herbaceous-layer processes, including forest regeneration (Webster et al. 2006). For example, Amur honeysuckle (Lonicera maackii [Rupr.] Herder), an invasive shrub introduced from Asia (Luken and Thieret 1996) has become a serious management concern across much of eastern North America. Numerous studies have shown that this aggressive invader negatively affects native plant communities by causing reductions in the survival, reproduction, and growth of individual species (Gould and Gorchov 2000,

Gorchov and Trisel 2003, Miller and Gorchov 2004, Cipollini and McClain 2008,

McKinney and Goodell 2010), by inducing apparent competition (Meiners 2007), and by leading to decreases in the abundance and diversity of native flora (Hutchinson and

Vankat 1997, Collier et al. 2002, Hartman and McCarthy 2008). The treatment of this species has become a high priority for managers, highlighting the need to better understand the response of native plants to the removal of this widespread invasive shrub.

Although the effects of removing Amur honeysuckle on native vegetation have been examined (See Luken et al. 1997, Gould and Gorchov 2000, Hartman and McCarthy

2004, Owen et al. 2005, Runkle et al. 2007, Swab et al. 2008, Cipollini et al. 2009), most of these studies have employed relatively small experimental removals, with only two

studies (Runkle et al. 2007 and Swab et al. 2008) employing removal areas larger than 40 110 m2. However, because managers typically treat invasions at the forest or woodlot scale, an examination of larger contiguous removal areas where Amur honeysuckle has been removed provides a more accurate depiction of real-world environmental and dispersal conditions. Because plant community diversity can spatially vary due to a multitude of biotic and abiotic factors (Whigham 2004), pre- and post-treatment data are critical to

111 understanding treatment effects. However, with the exception of Owen et al. (2005) studies have lacked pre-treatment data for removal areas.

Biomass left on site after the removal of invasive shrubs can influence the light environment by providing shade, providing a source of allelochemicals, and creating refugia fromherbivory, all of which are important factors as native plant communities respond to control efforts (Cipollini et al. 2009). While one could assume that dead biomass was removed given the small size of treatment areas in most studies, few studies

(Luken et al. 1997, Hartman and McCarthy 2004, and Cipollini et al. 2009) explicitly stated whether dead biomass was left in place or dragged away from sample plots.

The primary objective of this study was to determine the response of native and non-native herbaceous and woody plants to the removal of Amur honeysuckle and other non-native shrubs in six mixed-hardwood forests in Indiana that represent a gradient of honeysuckle invasion intensities and overstory compositions. Specifically, we examined before-and-after changes in the species diversity of native spring and summer herbaceous flora and woody plant seedlings in removal areas (where Amur honeysuckle and other non-native shrubs were cut and removed) and reference areas (where non-native shrubs

were left intact). In removal areas, we used a combination of mechanical and chemical 111 treatments to kill non-native shrubs and removed all resultant slash to create a condition where aboveground biomass of woody invaders was absent from the ecosystem. We hypothesized that species diversity of native herbaceous plants and tree seedlings would increase following removal of non-native shrubs, likely due to decreased competition for light near the forest floor. We predicted a particularly dramatic increase for the vernal flora (herbaceous plants that flower primarily in the spring) given that Amur honeysuckle

112 expands its leaves earlier in the year than overstory trees, thus directly competing with ephemeral herbs that require the leafless canopy during spring. As observed by others

(Luken et al. 1997, Runkle et al. 1997, Cipollini et al. 2009), we also hypothesized that the percent cover of garlic mustard (Alliaria petiolata [M. Bieb.] Cavara & Grande) would increase following removal treatments given the presence of this species at our study areas prior to removing non-native shrubs (i.e., high propagule pressure).

4.2. Methods

4.2.1. Study Sites

We collected data in six mature, second-growth mixed-hardwood forests in the glaciated regions of central Indiana (locations ranged from 39°20’ N to 40°26’ N, and

86°57’ W to 87°26’ W―1) Fowler Park, 2) Hawthorn Park, 3) Pfizer, Inc., Rifle Range woodlot (hereafter referred to as RR), 4) Purdue University, Department of Forestry and

Natural Resources Farm (hereafter referred to as FNR Farm), 5) a privately owned

woodlot (hereafter referred to as Pursell), and 6) Ross Biological Reserve (hereafter 112 referred to as Ross). Landform types included till plains, flood plains, loess hills, and dunes; parent materials consisted of loess, loess over loamy till, loess over loamy outwash, loamy alluvium over sandy and gravelly outwash, silty alluvium, loamy till, and loamy outwash over sandy and gravelly outwash (Soil Survey Staff, Natural Resources

Conservation Service, United States Department of Agriculture 2013). Soil types varied across study sites, but included textures and drainage classes ranging from very poorly

113 drained loams to excessively drained sandy loams (Soil Survey Staff, Natural Resources

Conservation Service, United States Department of Agriculture 2013).

Canopy layers across all study sites were characterized by mature, second-growth deciduous trees (Table 4.1) with ≥ 85% canopy closure. We also selected study sites where Amur honeysuckle was the dominant non-native invasive shrub (Amur honeysuckle comprised > 88% of exotic shrubs/ha across all study sites) and that the last major canopy disturbance (e.g., timber harvest) pre-dated invasion by honeysuckle.

Furthermore, our six study sites were chosen to represent a gradient of Amur honeysuckle density and size of individual shrubs as well as a range of overstory composition (Table

4.1).

4.2.2. Experimental design

We sampled vegetation in two, 80 m x 80 m areas (extending from the forest edge into interior) at each study site. Specifically, we placed one boundary of a given sample area along the forest edge, with the other three boundaries located ≥ 10 m from any edge.

In one of the 80 m x 80 sample areas at each study site (hereafter referred to as removal 113 area), we removed Amur honeysuckle and all other non-native shrubs rooted inside the sample boundary (Figure 4.1). While Amur honeysuckle was the dominant invasive species at all sites, we also removed all other non-native shrubs because this represents a more realistic management practice. In the other sample area (hereafter referred to as reference area), non-native shrubs were left intact. From November 2010 through March

2011 we hand-pulled or cut all non-native shrubs rooted within removal areas. Shrubs

114 were cut at ground level using a gas-powered clearing saw and stumps were treated with herbicide (20% Garlon 4® triclopyr, 1% Stalker® imazapyr, and 79% Ax-it® basal oil).

Large slash (diameter ≥ 5 cm) was removed and chipped using a wood chipper, placed ≥

50 m from both removal and reference areas, added to pre-existing slash piles, or placed in honeysuckle thickets immediately surrounding the sample areas. Slash < 5 cm diameter was scattered on the forest floor, but noticeable piles were not created. The purpose of removing the large slash was to create a condition where dead biomass was not influencing the light environment via shading or providing physical obstructions to herbivores such as white-tailed deer (Odocoileus virginiana). This is likely the future condition in the most effective management programs, where the largest standing biomass is cut during the first entry, re-sprouts and new seedlings are killed in successive years, and the large slash from the first entry decays, eventually resulting in an environment where there is little to no living or dead biomass from invasive shrubs.

4.2.3. Data collection

At each study site, vegetation data were collected using a series of fixed-area plots 114 located along transects. Specifically, each removal or reference area contained three transects (spaced 20 m apart) extending from the forest edge into the interior. Along each transect, we placed four, 40-m2 (radius of 3.57 m) circular plots and four, 2 m x 2 m quadrats (See Figure 5.2 from Chapter 5 of this dissertation). The circular plots were spaced 20 m apart along a given transect (the first plot was placed 5-10 m from the forest edge along a given transect). Each quadrat was randomly placed to the upper right, upper

115 left, lower right, or lower left of the circular plot and oriented parallel to the transect.

This design resulted in 12 circular plots and 12 quadrats per 80 m x 80 sample area.

In the 40-m2 circular plots, we recorded number of individuals from each woody species in the sapling layer (woody stems < 10 cm diameter at breast height [dbh] and ≥

1.37 m tall). An individual was defined as a single stem or a clump of stems originating from the same point at ground level. In the 2 m x 2 m quadrat, data were recorded in two strata―lower (≤ 1 m) and upper (1.01-5 m). In the lower stratum, we recorded percent covers of native and non-native flora (herbaceous and woody species), coarse woody debris (CWD; midpoint diameter ≥ 10 cm), fine woody debris (FWD; midpoint diameter

< 10 cm), dead leaves/dead herbaceous stems, and bare soil. We also recorded number of seedlings and shrubs < 1.37 m tall in the lower stratum. In the upper stratum, we recorded percent covers of native and non-native trees, shrubs, and vines. All percent cover estimates were based upon categories modified from Peet et al. (1998): 0-1%, 1-

2%, 2-5%, 5-10%, 10-25%, 25-50%, 50-75%, 75-95%, and > 95%. Percent cover estimates were performed by a single individual to reduce observer bias.

Quadrats in removal and reference areas were visited in the spring (April) and

summer (July-August) of 2010 (before non-native shrubs were removed) and again in the 115 spring and summer of 2011 (after removals). Densities of seedlings and shrubs were quantified during the summer of each year. Circular plots were visited in September of both years. For some taxa, we did not consistently observe distinguishable features across all sample plots so we were not able to separate species; these taxa were therefore put into two- or three-species groups or identified to genus. Common names and

116 scientific names of all plant species were based on the USDA Plants Database (USDA,

NRCS 2013).

4.2.4. Data analyses

For ground-layer vegetation and environmental variables, we calculated percent cover by taxon and quadrat using midpoint values from cover classes. Mean percent cover values were also calculated by stratum, sample area (removal, reference), study site, season (spring, summer), and year (2010, 2011). For woody individuals in the sapling layer and for seedlings and shrubs < 1.37 m tall, we calculated mean stems/ha for each taxon within a plot, as well as mean values by sample area, study site, and year.

Quadrat-level taxonomic richness (S, number of taxa), Pielou’s Evenness Index

(J'; Pielou 1966), and Shannon’s Diversity Index (H'; Shannon 1948) were calculated based on the percent covers of native herbaceous and woody plants in the lower stratum.

We also calculated mean H', S, and J' by sample area, study site, season, and year. For summer data, any native taxa recorded in the quadrats were considered. However, for

spring data calculations were only made for the following herbaceous taxa, which flower 116 primarily in the spring and early summer (March through June), based on descriptions from Yatskievych (2000): white baneberry (Actaea pachypoda Elliot), narrowleaf wild leek (Allium tricoccum Aiton/Allium burdickii [Hanes] A.G. Jones), smooth rockcress

(Arabis laevigata [Muhl. Ex Willd.] Poir.), jack-in-the pulpit (Arisaema triphyllum [L.]

Schott), cutleaf toothwort (Cardamine concatenata [Michx.] Sw.), limestone bittercress/bulbous bittercress (Cardamine douglassii Britton/Cardamine bulbosa

117

[Schreb. Ex Muhl.] Britton, Sterns & Poggenb.), Virginia springbeauty (Claytonia virginica L.), false mermaidweed (Floerkea proserpinacoides Willd.), spotted geranium

(Geranium maculatum L.), eastern waterleaf (Hydrophyllum virginianum L.), spring forget-me-not (Myosotis verna Nutt.), Clayton’s sweetroot/longstyle sweetroot

(Osmorhiza claytonii [Michx.] C.B. Clarke/Osmorhiza longistylis [Torr.] DC.), roundleaf ragwort (Packera obovata [Muhl. Ex Willd.] W.A. Weber & A. Löve), wild blue phlox

(Phlox divaricata L.), mayapple (Podophyllum peltatum L.), littleleaf buttercup

(Ranunculus abortivus L.), bristly buttercup (Ranunculus hispidus Michx.), bloodroot

(Sanguinaria canadensis L.), bloody butcher (Trillium recurvatum Beck), downy yellow violet (Viola pubescens Aiton), common blue violet (Viola sororia Willd.), and striped cream violet (Viola striata Aiton).

We used permutation tests (Pesarin 2001) to examine differences between 2010 and 2011 in removal areas and reference areas for the following response variables: density of Amur honeysuckle shrubs in the sapling layer, density of Amur honeysuckle shrubs < 1.37 m tall, percent cover of garlic mustard in spring and summer, spring and summer native S, J', and H', density of native seedlings (species were combined as a

group), percent cover of vegetation groups based on native classification and growth 117 habit, and percent cover of environmental variables (CWD, FWD, dead leaves/dead herbaceous stems, bare soil). The USDA Plants Database (USDA, NRCS 2013) was used to create the following groups based upon native/non-naitve status and growth habit: native forbs, native grasses, native sedges, native ferns, native vines, native trees and shrubs, non-native forbs, non-native grasses, non-native vines, and non-native shrubs.

For each response variable, we calculated differences in plot-level values between 2010

118 and 2011 by sample area and study site. For example, 12 plots in a treatment area × 6 study sites = 72 “differences” between 2010 and 2011 for a given response variable. The mean of these differences was then calculated and treated as the observed mean of differences. A permutation test was then used to determine if the observed mean of differences was significantly different from zero for a given treatment area. Specifically, we specified 99 iterations for each permutation test, and for each iteration the signs of the differences in plot-level values between 2010 and 2011 were randomly shuffled and a mean of differences was calculated. The result was a null population consisting of 99 means of differences from the shuffling procedure plus the observed mean of differences.

Based on 99 iterations, the two-sided p-value = (number of times mean of differences from null population was ≥ observed mean of differences)/100. For data that do not adhere to parametric assumptions of normality such as the data observed in this study, it is more advantageous (and more appropriate) to use non-parametric permutation tests to examine before-and-after changes as opposed to using parametric procdures such as paired t-tests or Analysis of Variance. The program R was used to perform all statistical analyses (R Core Team 2013). We used the R package vegan (Oksanen et al. 2013) to

calculate H', S, and J' and to perform permutation tests. 118

119

4.3. Results

4.3.1. Response of Amur honeysuckle

Our results show that removal treatments were successful in removing Amur honeysuckle from the sapling layer; however, seedlings exhibited a strong, positive response. Specifically, in 2010 (before removing non-native shrubs), mean Amur honeysuckle density was 2,372 ± 183 shrubs/ha in the sapling stratum in removal areas

(Table 4.2); however, we found no honeysuckle shrubs ≥ 1.37 m tall in the removal areas in 2011 (Table 4.2, Figure 4.2). Conversely, sapling-layer honeysuckle densities in reference areas changed little from 2010 to 2011 (mean of differences was 6.94 shrubs/ha, permutation p = 0.75; Table 4.2, Figure 4.2). Changes in the density of Amur honeysuckle seedlings (shrubs < 1.37 m tall), between 2010 and 2011 were positive and much greater in removal areas compared to reference areas (Table 4.2, Figure 4.2).

Removal areas contained a mean of 7,743 ± 796 shrubs/ha in 2010, but increased to

28,299 ± 8,594 shrubs/ha by 2011 (permutation p value = 0.03; Table 4.2). In reference

areas, mean Amur honeysuckle seedling density increased from 5,382 ± 836 shrubs/ha in 119

2010 to only 6,424 ± 889 shrubs/ha in 2011 (permutation p = 0.03; Table 4.2).

4.3.2. Response of native flora and garlic mustard

For native vegetation groups categorized by growth habit, changes (from 2010 to

2011) in mean percent cover in the lower vertical stratum were generally higher and more

120 positive in removal areas as compared to reference areas, both in spring (Table 4.3) and summer (Table 4.4). In the lower stratum, changes in the percent cover of forbs were particularly noticeable. In spring, mean percent cover of forbs in the lower stratum in reference areas increased from 20.6 ± 2.6 in 2010 to 22.6 ± 2.8 in 2011 (permutation p =

0.10) whereas in removal areas, mean percent cover increased from 17.2 ± 2.0 in 2010 to

31.0 ± 2.4 in 2011 (permutation p = 0.05; Table 4.3). Summer mean forb cover was largely unchanged (20.9 ± 2.3 in 2010, 20.1 ± 2.2 in 2011) in reference areas

(permutation p = 0.70), but increased from 22.7 ± 2.5 in 2010 to 37.8 ± 3.5 in 2011 in removal areas (permutation p = 0.03; Table 4.4). Changes for groups examined in the upper stratum (native trees/shrubs and native vines) were less pronounced than those in the lower stratum. In the upper stratum, the only moderately significant change observed was in the reference areas, where mean percent cover of native trees/shrubs increased from 9.8 ± 1.9 in 2010 to 10.9 ± 1.8 in 2011 during the spring (permutation p = 0.07;

Table 4.3).

Native H', S, and J' generally increased from 2010 to 2011 in both spring and summer, but changes were greater in removal areas (Figure 4.3, Figure 4.4). During

spring, means of differences (2011 minus 2010) in reference areas were 0.05 for H' 120

(permutation p = 0.04), 0.2 for S (permutation p = 0.02), and 0.009 for J' (permutation p

= 0.09). In removal areas during spring, means of differences were 0.4 for H'

(permutation p = 0.04), 1.6 for S (permutation p = 0.04), and 0.3 for J' (permutation p =

0.03). Trends in the summer were similar as those observed in spring; means of differences were -0.04 for H' (permutation p = 0.58), 0.1 for S (permutation p = 0.61), and 0.04 for J' (permutation p = 0.04) in reference areas and 0.4 for H' (permutation p =

121

0.05), 5.2 for S (permutation p = 0.01), and 0.04 for J' (permutation p = 0.26) in removal areas.

At all study sites, native seedlings as a group exhibited a positive response to the removal of non-native shrubs whereas in reference areas, seedling densities changed little between 2010 and 2011 (Figure 4.5). In removal areas, the mean of differences in seedling density between 2010 and 2011 was 20,174 seedlings/ha (permutation p = 0.05) whereas in reference areas, mean of differences was 417 seedlings/ha (permutation p =

0.15). In terms of individual taxa, the largest changes in reference areas were observed for white ash/green ash (Fraxinus americana L./Fraxinus pennsylvanica Marsh; change in mean seedlings/ha from 2010 to 2011 = -139), common hackberry (Celtis occidentalis

L.; change = -139), sassafras (Sassafras albidum [Nutt.] Nees; change = -104), and tuliptree (Liriodendron tulipifera L.; change = 104.2; Table 4.2). Changes in removal areas were greater than in reference areas, where the largest changes were observed for black cherry (Prunus serotina Ehrh; change = 8,611.1), tuliptree (change = 5,660), and northern spicebush (Lindera benzoin [L.] Blume; change = 1,458; Table 4.2).

In terms of percent covers of individual taxa in the lower stratum (≤ 1 m), we

observed a total of 66 native forb taxa, seven native grass species, five native sedge 121 species, ten native vine taxa, and 42 native tree/shrub taxa across all sample areas, seasons, and years (Table 4.5). In the lower stratum, we observed fewer non-native taxa as compared to native taxa, including 11 non-native forb taxa, one non-native grass species, three non-native vine species, and five non-native shrub species (Table 4.5). In the upper stratum we observed three native vine taxa, 34 native tree/shrub taxa, one non- native vine species, and three non-native shrub species (Table 4.6).

122

In the lower stratum, changes in abundance of native flora were generally positive and greater in removal areas as compared to reference areas, indicating a positive response by native taxa to the removal of Amur honeysuckle and other non-native shrubs

(Figure 4.6). When examining changes in mean percent cover (2011 minus 2010) during the spring seasons, 54 taxa exhibited a positive change and 10 taxa exhibited a negative change in reference areas whereas in removal areas, 88 taxa exhibited a positive change and seven taxa exhibited a negative change (Table 4.5). The largest changes in reference areas in the spring were observed for white snakeroot (Ageratina altissima [L.] King &

H. Rob. Var. altissima; change in mean percent cover from 2010 to 2011 = 0.7), jumpseed (Polygonum virginianum L.; change = 0.61), and white ash/green ash (change

= 0.6; Table 4.5). Changes in removal areas were greater than in reference areas for spring, where the largest changes in removal areas were observed for sanicle (Sanicula spp.; change = 2.8), mayapple (change = 1.5), and Clayton’s sweetroot/longstyle sweetroot (change = 1.2; Table 4.5). Changes in mean percent cover during the summer seasons were similar to those observed in spring. Specifically, in reference areas 26 taxa exhibited a positive change and 37 taxa exhibited a negative change whereas in removal

areas, 72 taxa exhibited a positive change and 26 taxa exhibited a negative change (Table 122

4.5). The largest changes in reference areas in the summer were observed for jumpseed

(change in mean percent cover from 2010 to 2011 = -0.5), Jack-in-the-pulpit (change = -

0.4), white ash/green ash (change = -0.4), and sanicle (change = -0.4; Table 4.5). As with spring flora, changes in removal areas were greater than in reference areas during the summer, where the largest changes in removal areas were observed for Canadian

123 clearweed (Pilea pumila [L.] A. Gray; change = 4.2), sanicle (change = 2.2), and Virginia creeper (Parthenocissus quinquefolia [L.] Planch.; change = 1.9; Table 4.5).

Changes in abundance of taxa in the upper stratum were more variable than those observed in the lower stratum. During the spring in reference areas, 14 taxa exhibited a positive change in mean percent cover from 2010 to 2011, and four taxa exhibited a negative change in reference areas. In removal areas, 10 taxa exhibited a positive change and 12 taxa exhibited a negative change (Table 4.6). The largest changes in reference areas in the spring were observed for flowering dogwood (Cornus florida L.; change in mean percent cover from 2010 to 2011 = 0.3), tuliptree (change = 0.3), and common hackberry (change = -0.3; Table 4.6). Changes in removal areas were similar to those observed in reference areas for spring, where the largest changes in removal areas were observed for northern spicebush (change = -1.0), American elm/slippery elm (Ulmus americana L./Ulmus rubra Muhl.; change = -0.9), and blackgum (Nyssa sylvatica

Marsh.; change = -0.7; Table 4.6). In the summer, fewer taxa exhibited changes as compared to spring. Specifically, in reference areas three taxa exhibited a positive change and five taxa exhibited a negative change whereas in removal areas, three taxa

exhibited a positive change and 10 taxa exhibited a negative change (Table 4.6). The 123 largest changes in reference areas in the summer were observed for common hackberry

(change in mean percent cover from 2010 to 2011 = -0.4), American beech (Fagus grandifolia Ehrh.; change = -0.1), and northern spicebush (change = -0.1; Table 4.6). In summer, changes in removal areas were similar to changes in reference areas (as observed during the spring), where the largest changes in removal areas were observed for sugar maple (Acer saccharum Marsh.; change = -0.3), common hackberry (change = -

124

0.3), white oak (Quercus alba L.; change = -0.1), and pawpaw (Asimina triloba [L.]

Dunal; change = 0.1; Table 4.6).

Garlic mustard also exhibited increased cover following the removal of non- native shrubs (Figure 4.7), but this depended on the level of propagule pressure (as represented by pre-treatment percent cover), which varied across study site. Garlic mustard was not detected in either sample area at Rifle Range or Hawthorn before or after removal treatments and was not detected in the reference area during spring or summer at Fowler (Figure 4.7). Garlic mustard did occur in all other sample areas, and changes in mean percent cover from 2010 to 2011 were higher in removal areas than reference areas (Figure 4.7). Specifically, in reference areas where garlic mustard occurred, means of differences in percent cover (2011 minus 2010) were 0.1 during the spring (permutation p = 0.72) and 0.5 during the summer (permutation p = 0.27).

Conversely, in removal areas, means of differences were 1.6 during the spring

(permutation p = 0.06) and 3.1 during the summer (permutation p = 0.13).

We also observed changes in some environmental variables. Mean percent cover of bare soil from 2010 to 2011 decreased in reference areas and removal areas during

both spring and summer, but changes in mean values were higher in removal areas (Table 124

4.3, Table 4.4). Conversely, mean percent cover of FWD increased from 2010 to 2011 during both seasons and in both removal and reference areas, but permutation p values were ≤ 0.05 only in removal areas (change in spring = 2.0, p = 0.02; change in summer =

3.6, p = 0.05; Table 4.3, Table 4.4)

125

4.4. Discussion

Our results suggest that removing Amur honeysuckle and other non-native shrubs allows the recovery of native herbaceous and woody plant communities, at least in the short term. Removal areas exhibited significant increases in the cover and diversity of native herbaceous and woody species in both spring and summer, as well as increased densities of native seedlings, whereas reference areas did not, supporting our original hypothesis. Furthermore, we observed significant decreases in the percent cover of bare soil in removal areas in spring and summer but no significant changes in the cover of dead leaves/dead herbaceous stems, indicating that growing space was being filled by native vegetation. These subsequent increases in native flora were documented at all six study sites, which represented a wide range of pre-treatment Amur honeysuckle densities.

For example, pre-treatment honeysuckle densities in the sapling layer ranged from <

1,000 shrubs/ha at Ross to > 4,000 shrubs/ha at Fowler, but removing non-native shrubs led to increased cover of native forbs (during both spring and summer), increased H' and

S of native vernal and aestival species, and increased native seedling densities across all

study sites. Thus, our results suggest that even at sites with invasions in the advanced 125 expansion phase or saturation phase, control efforts can allow the short-term recovery of native taxa. This was particularly noticeable in the removal area at RR, where the mean density of Amur honeysuckle shrubs ≥ 1.37 m tall was 2,354 ± 249 shrubs/ha (Figure 4.2) and percent cover in the sub-canopy stratum was near 100% in a majority of the sample area prior to implementing the removal treatment. The depauperate ground layer (Figure

4.6a) that occurs under such conditions could lead a forest manager to believe that the

126 area is completely void of native vegetation, with little hope for recovery in response to control efforts. However, removing Amur honeysuckle and other non-native shrubs in the removal area at RR and our other sites caused dramatic increases in the abundance of native flora, particularly for spring-flowering herbs. Based upon a review of the literature, our study is the first to examine before-and-after effects across a range of forest compositions and invasion intensities in post-treatment stands that contained little to no aboveground biomass from non-native shrubs following removal treatments. As such, our study adds to a limited, but growing, body of work that documents the recovery potential of post-treatment forests (e.g. Runkle et al. 2007, Owen et al. 2005).

The major environmental change resulting from removing non-native shrubs was likely an increase in light availability near the forest floor, allowing forest herbaceous plants and tree/shrub seedlings to utilize that resource. Increased light availability may have played a particularly important role for the recovery of native vernal herbs. Because

Amur honeysuckle expands its leaves earlier in the year than other woody plants in its invaded range (Luken 1988), it is in direct light competition with spring-flowering forest herbs that require the light resource made available because of the leafless spring canopy

(Schemske et al. 1978). This would explain the dramatic increases in H' and S observed 126 for spring-flowering forest herbs after non-native shrubs were removed. Furthermore, this increase in light availability may have been further enhanced by the removal of slash which can increase shading of the forest floor following woody invasive control treatments (Cipollini et al. 2009).

For those ground-layer taxa that exhibited increased post-removal cover, dispersal undoubtedly occurred via a combination of sexual and vegetative reproduction

127

(Bierzychudek 1982). The most dramatic increases were observed for taxa that exhibited high levels of propagule pressure and effective dispersal mechanisms, which allowed them to exploit the newly available light resource. For example, during the summer, generalist species such as Canadian clearweed, sanicle, and Virginia creeper (Table 4.5) exhibited the greatest increases in cover. It is not surprising that generalists would respond to reduced competition from non-native shrubs. However, cover of generalists was quite low prior to removal treatments, suggesting that invasions by Amur honeysuckle and other shrubs alter the availability of light and other resources to the degree that even the most resilient native forest herbs are impacted. As with ground- layer species, increased densities of seedlings were likely due to a combination of increased light levels near the forest floor, availability of microsites for germination, adequate propagule pressure, and effective dispersal mechanisms. Although honeysuckle removal allowed seedlings to establish, additional factors will influence the capability of seedlings to recruit to canopy and sub-canopy layers. For example, while black cherry seedlings exhibited the largest increases from 2010 to 2011 in removal areas, this species is classified as shade intolerant and typically survives for only three to five years under a

closed canopy (Marquis 1990); disturbances resulting in a more open canopy would 127 therefore be necessary for these seedlings to recruit to the overstory stratum. Northern spicebush also exhibited one of the largest increases in seedlings/ha after removing non- native shrubs. Because this species occupies the same stratum as species such as Amur honeysuckle (Luken et al. 1997), control efforts that prevent non-native shrubs from recruiting to the sub-canopy layers should favor spicebush.

128

Persistence of native ground-layer communities and recruitment of native trees and shrubs following removal treatments will depend on propagule pressure and competitive abilities of native taxa, pressure from herbivores, and future management decisions. In our study areas, long-term recovery of native taxa will be largely influenced by competition with non-native species and herbivory by white-tailed deer. Management actions aimed at controlling invasive species and favoring the recruitment of desirable tree species are critical to the long-term recovery and persistence of these forests. As originally hypothesized, we observed a post-harvest influx of Amur honeysuckle seedlings and garlic mustard, indicating that un-desirable outcomes may also result from the implementation of control efforts. With Amur honeysuckle, increased seedling densities were likely the result of germination from the short-lived seed bank in response to the removal treatments, and animal dispersal of seeds from surrounding honeysuckle thickets. Given that honeysuckle seeds are readily dispersed by birds (Bartuszevige et al.

2006), white-tailed deer (Castellano and Gorchov 2013), and possibly Peromyscus mice

(Williams et al. 1992), effective management will require the perpetual control of new seedlings in treatment areas. Garlic mustard, like native taxa, responded to the increased

light levels and possibly the soil disturbance associated with cutting non-native shrubs 128 and dragging slash (Bartuszevige et al. 2007). Several other investigators have also documented high abundances of garlic mustard following removal of Amur honeysuckle

(See Luken et al. 1997, Runkle et al. 2007, and Cipollini et al. 2009). Even in the absence of Amur honeysuckle and other non-native shrubs, garlic mustard invasion is particularly problematic for forest managers given its shade tolerance, high seed production (~15,000 seeds/m2; Anderson et al. 1996), and ability to disrupt belowground

129 mutualisms between native taxa and fungi (Stinson et al. 2006). Another important consideration is the influence of herbivory by white-tailed deer. It is widely acknowledged that overabundant deer populations can cause declines in native taxa through intense herbivory (Russell et al. 2001), and sometimes help perpetuate the dominance of invasive species by preferentially browsing on native plants (Webster et al.

2008, Knight et al. 2009). In our study areas, effects from white-tailed deer may be more pronounced because we removed the large slash, which otherwise may have served as refugia for native taxa by serving as physical barriers to deer (Cipollini et al. 2009).

However, it is important to note that when slash is left onsite following woody invasive control management, the benefits it provides as refugia for native taxa is transient given that the slash will eventually decay. As mentioned previously, one of the objectives of our study was to create a condition whereby aboveground biomass of non-native shrubs was absent from the system, a condition that would be observed under successful long- term management of invasive woody plants. Nonetheless, leaving slash is a more practical approach when implementing control efforts; whether the short-term positive effects of leaving slash is of consequence to the long-term recovery of native taxa

warrants further investigation. For example, does leaving slash result in a quick pulse of 129 allelochemicals that may actually hinder the long-term recovery of native plants?

130

Table 4.1. Importance value (IV = [{relative basal area + relative density}/2]*100) by species for overstory trees (woody stems ≥ 10 cm diameter at breast height [dbh]) at six mixed-hardwood forests in Indiana―Purdue University Department of Forestry and Natural Resources Farm (FNR Farm), Fowler Park, Hawthorn Park, privately-owned woodlot (Pursell), Ross Biological Reserve, and Pfizer, Inc. Rifle Range woodlot (RR). Data were collected using ≥ 12 variable-radius plots (Basal Area Factor [BAF] = 2.296 m2/ha) per study site.

Study Site

Species FNR Farm Fowler Hawthorn Pursell Ross RR

red maple --- 0.6 5.1 ------(Acer rubrum L.) silver maple (Acer saccharinum L.) ------0.6 --- 2.1 3.2 sugar maple (Acer saccharum Marsh.) 1.2 --- 2.9 --- 3.4 0.8 mockernut hickory (Carya alba [L.] Nutt.) --- 3.4 --- 0.7 ------bitternut hickory (Carya cordiformis [Wangenh.] K. Koch) 0.9 --- 1.6 ------pignut hickory/red hickory (Carya glabra [Mill.] Sweet/Carya ovalis [Wangenh.] Sarg.) ------6.0 1.0 --- 2.4 shagbark hickory (Carya ovata [Mill.] K. Koch) 8.4 0.6 --- 5.8 --- 2.3 common hackberry (Celtis occidentalis L.) 1.8 4.4 3.2 1.2 --- 5.5 eastern redbud (Cercis candensis L.) --- 8.8 ------flowering dogwood (Cornus florida L.) --- 0.8 ------hawthorn (Crataegus spp.) ------8.5 --- 2.8 common persimmon (Diospyros virginiana L.) --- 4.4 2.9 ------

130 American beech

(Fagus grandifolia Ehrh.) --- 0.6 ------white ash (Fraxinus americana L.) 2.7 18.0 2.2 ------1.6 green ash (Fraxinus pennsylvanica Marsh.) --- 1.6 ------1.0 honeylocust (Gleditsia triacanthos L.) 0.5 0.6 1.2 ------1.3 black walnut (Juglans nigra L.) 8.5 10.8 0.5 1.2 2.4 1.2 tuliptree (Liriodendron tulipifera L.) --- 0.6 10.9 --- 20.8 21.1

131

Table 4.1 Continued

osage orange (Maclura pomifera [Raf.] C.K. Schneid.) ------32.8 --- 0.4 apple (Malus spp.) 1.1 ------4.5 --- red mulberry (Morus rubra L.) ------2.6 1.6 ------blackgum (Nyssa sylvatica Marsh.) --- 2.6 11.3 ------0.9 hophornbeam (Ostrya virginiana [Mill.] K. Koch) ------1.7 ------

Virginia pine (Pinus virginiana Mill.) --- 2.3 ------

American sycamore (Platanus occidentalis L.) --- 0.6 2.0 ------2.0 eastern cottonwood (Populus deltoides Bartram ex Marsh.) ------0.5 black cherry (Prunus serotina Ehrh.) 6.5 10.3 9.1 28.8 12.8 19.5 white oak (Quercus alba L.) 0.5 1.8 --- 1.3 1.1 4.4 swamp white oak (Quercus bicolor Willd.) ------6.1 ------shingle oak (Quercus imbricaria Michx.) --- 5.6 2.2 8.3 --- 1.3 chinkapin oak (Quercus muehlenbergii Engelm.) ------0.6 ------pin oak (Quercus palustris Münchh.) 0.5 0.5 5.7 ------northern red oak (Quercus rubra L.) 0.8 --- 1.8 --- 5.7 5.9

Shumard oak (Quercus shumardii Buckley var. shumardii) ------0.4 ------131 black oak (Quercus velutina Lam.) --- 0.6 1.0 --- 31.0 5.5 black locust (Robinia pseudoacacia L.) 46.5 ------sassafras (Sassafras albidum [Nutt.] Nees) --- 9.5 15.2 --- 16.2 9.5

American basswood (Tilia americana L.) 0.8 ------

American elm (Ulmus americana L.) 14.5 10.0 3.4 7.4 --- 5.8 slippery elm (Ulmus rubra Muhl.) 4.7 1.0 --- 1.4 --- 1.3

Table 4.2. Mean (± 1 SE) densities/ha of native and non-native woody plants in the sapling layer (woody plants ≥ 1.37 m tall and < 10 cm diameter at breast height [dbh]) and the seedling layer (woody plants < 1.37 m tall) in reference and removal areas, during the spring and summer of 2010 and 2011, at six mixed-hardwood forests in central Indiana. For each taxon, mean values were calculated by pooling data across all six study sites (n = 72 sample plots for each species in a given treatment type, year, and season).

Reference Removal

Species 2010 2011 2010 2011

Sapling layer

Native boxelder (Acer negundo L.) 69.4 ± 48.8 69.4 ± 48.8 69.4 ± 48.8 69.4 ± 48.8 red maple (Acer rubrum L.) 208.3 ± 146.3 208.3 ± 146.3 ------sugar maple (Acer saccharum Marsh.) 833.3 ± 631.3 868.1 ± 665.6 694.4 ± 258.0 694.4 ± 238.3 pawpaw (Asimina triloba [L.] Dunal) 138.9 ± 84.0 138.9 ± 84.0 312.5 ± 191.1 277.8 ± 182.1

American hornbeam (Carpinus caroliniana Walter) ------104.2 ± 77.2 104.2 ± 77.2 mockernut hickory (Carya alba [L.] Nutt.) 69.4 ± 48.8 69.4 ± 48.8 173.6 ± 142.7 104.2 ± 77.2 bitternut hickory (Carya cordiformis [Wangenh.] K. Koch) 312.5 ± 177.9 312.5 ± 177.9 451.4 ± 238.8 451.4 ± 258.5 pignut hickory/red hickory (Carya glabra [Mill.] Sweet/Carya ovalis [Wangenh.] 208.3 ± 118.6 208.3 ± 118.6 104.2 ± 77.2 69.4 ± 48.8 Sarg.) shagbark hickory (Carya ovata [Mill.] K. Koch) 520.8 ± 177.9 520.8 ± 177.9 34.7 ± 34.7 69.4 ± 48.8

132

Table 4.2 Continued

common hackberry

(Celtis occidentalis L.) 520.8 ± 226.3 486.1 ± 168.7 451.4 ± 124.4 590.3 ± 234.5 eastern redbud (Cercis candensis L.) 69.4 ± 48.8 69.4 ± 48.8 34.7 ± 34.7 34.7 ± 34.7

flowering dogwood

(Cornus florida L.) 1,284.7 ± 479.4 1,215.3 ± 447.8 347.2 ± 158.7 277.8 ± 135.9 other dogwood (Cornus spp.) ------34.7 ± 34.7 69.4 ± 48.8 hawthorn (Crataegus spp.) 868.1 ± 389.9 868.1 ± 389.9 763.9 ± 282.5 833.3 ± 296.7

American beech (Fagus grandifolia Ehrh.) 347.2 ± 166.3 347.2 ± 166.3 347.2 ± 173.5 347.2 ± 150.8 white ash/green ash (Fraxinus americana L./Fraxinus pennsylvanica Marsh.) 1,458.3 ± 418.9 1,423.6 ± 413.2 1,736.1 ± 491.1 2,152.8 ± 963.1 blue ash (Fraxinus quadrangulata Michx.) 34.7 ± 34.7 34.7 ± 34.7 34.7 ± 34.7 --- black walnut (Juglans nigra L.) 69.4 ± 48.8 69.4 ± 48.8 34.7 ± 34.7 34.7 ± 34.7 northern spicebush (Lindera benzoin [L.] Blume) 902.8 ± 317.8 937.5 ± 322.1 1,006.9 ± 311.4 1,041.7 ± 311.8 tuliptree (Liriodendron tulipifera L.) 208.3 ± 128.5 208.3 ± 128.5 34.7 ± 34.7 69.4 ± 48.8 osage orange (Maclura pomifera [Raf.] C.K. Schneid.) 138.9 ± 68.0 138.9 ± 68.0 ------red mulberry (Morus rubra L.) 104.2 ± 104.2 104.2 ± 104.2 ------blackgum (Nyssa sylvatica Marsh.) 138.9 ± 109.3 173.6 ± 142.7 1,180.6 ± 458.5 1,041.7 ± 424.7

133

Table 4.2 Continued

hophornbeam (Ostrya virginiana [Mill.] K. Koch) 69.4 ± 48.8 69.4 ± 48.8 34.7 ± 34.7 34.7 ± 34.7

American sycamore (Platanus occidentalis L.) 69.4 ± 48.8 69.4 ± 48.8 ------black cherry (Prunus serotina Ehrh.) 868.1 ± 227.5 868.1 ± 227.5 520.8 ± 163.5 416.7 ± 148.3 white oak 138.9 ± 68.0 138.9 ± 68.0 69.4 ± 48.8 69.4 ± 48.8 (Quercus alba L.)

shingle oak

(Quercus imbricaria Michx.) 69.4 ± 48.8 69.4 ± 48.8 34.7 ± 34.7 34.7 ± 34.7 chinkapin oak (Quercus muehlenbergii Engelm.) ------34.7 ± 34.7 34.7 ± 34.7 northern red oak (Quercus rubra L.) 34.7 ± 34.7 34.7 ± 34.7 277.8 ± 152.9 277.8 ± 152.9 black oak (Quercus velutina Lam.) 104.2 ± 59.3 104.2 ± 59.3 ------black locust (Robinia pseudoacacia L.) 208.3 ± 118.6 208.3 ± 118.6 ------sassafras (Sassafras albidum [Nutt.] Nees) 1,180.6 ± 270.7 1,145.8 ± 261.4 798.6 ± 208.8 659.7 ± 209.8

American elm (Ulmus americana L.) 1,527.8 ± 403.4 1,493.1 ± 388.3 972.2 ± 245.0 1,041.7 ± 235.9 slippery elm (Ulmus rubra Muhl.) 312.5 ± 130.2 520.8 ± 270.6 277.8 ± 93.2 277.8 ± 93.2 blackhaw (Viburnum prunifolium L.) 486.1 ± 230.0 451.4 ± 222.9 34.7 ± 34.7 34.7 ± 34.7 southern arrowwood (Viburnum recognitum Fernald) 416.7 ± 221.1 381.9 ± 195.7 381.9 ± 213.6 173.6 ± 114.1

134

Table 4.2 Continued

Non-native

Japanese barberry (Berberis thunbergii DC.) ------34.7 ± 34.7 --- autumn olive (Elaeagnus umbellata Thunb.) 1,458.3 ± 483.9 1,597.2 ± 544.6 1,493.1 ± 427.3 138.9 ± 109.3 burningbush (Euonymus alatus [Thunb.] Siebold) ------34.7 ± 34.7 ---

European privet (Ligustrum vulgare L.) 208.3 ± 95.8 243.1 ± 122.7 ------

Amur honeysuckle (Lonicera maackii [Rupr.] Herder) 1,666.7 ± 162.3 1,659.7 ± 160.5 2,371.5 ± 182.8 ---

apple

(Malus spp.) 277.8 ± 160.7 277.8 ± 160.7 34.7 ± 34.7 34.7 ± 34.7 multiflora rose (Rosa multiflora Thunb.) 798.6 ± 241.4 868.1 ± 262.4 972.2 ± 351.6 34.7 ± 34.7

Seedling layer

Native boxelder (Acer negundo L.) --- 34.7 ± 34.7 --- 69.4 ± 48.8 red maple (Acer rubrum L.) 34.7 ± 34.7 34.7 ± 34.7 34.7 ± 34.7 --- sugar maple (Acer saccharum Marsh.) 868.1 ± 343.2 833.3 ± 312.7 173.6 ± 124.4 381.9 ± 182.8 serviceberry (Amelanchier spp.) ------69.4 ± 69.4 34.7 ± 34.7

135

Table 4.2 Continued

pawpaw (Asimina triloba [L.] Dunal) 69.4 ± 69.4 69.4 ± 69.4 416.7 ± 209.8 486.1 ± 260.0

American hornbeam (Carpinus caroliniana Walter) ------69.4 ± 69.4 69.4 ± 69.4 bitternut hickory (Carya cordiformis [Wangenh.] K. Koch) 243.1 ± 87.9 208.3 ± 82.0 347.2 ± 133.7 312.5 ± 130.2 shagbark hickory (Carya ovata [Mill.] K. Koch) 243.1 ± 100.9 243.1 ± 100.9 34.7 ± 34.7 69.4 ± 48.8 common hackberry (Celtis occidentalis L.) 1,006.9 ± 230.1 902.8 ± 222.8 625.0 ± 189.9 1,111.1 ± 312.3

eastern redbud

(Cercis canadensis L.) ------104.2 ± 77.2 flowering dogwood (Cornus florida L.) 243.1 ± 132.3 208.3 ± 107.8 486.1 ± 394.5 486.1 ± 341.3 other dogwood (Cornus spp.) ------34.7 ± 34.7

American hazelnut (Corylus americana Walter) ------69.4 ± 69.4 69.4 ± 69.4 hawthorn (Crataegus spp.) 277.8 ± 105.5 277.8 ± 116.6 104.2 ± 104.2 69.4 ± 48.8 white ash/green ash (Fraxinus americana L./Fraxinus pennsylvanica Marsh.) 8,194.4 ± 1,554.5 8,055.6 ± 1,514.7 6,562.5 ± 1,567.2 6,909.7 ± 1,340.2 honeylocust (Gleditsia triacanthos L.) 34.7 ± 34.7 --- 104.2 ± 59.3 69.4 ± 48.8 northern spicebush (Lindera benzoin [L.] Blume) 486.1 ± 201.7 451.4 ± 199.8 451.4 ± 173.6 1,909.7 ± 400.2 tuliptree (Liriodendron tulipifera L.) 208.3 ± 95.8 312.5 ± 147.8 --- 5,659.7 ± 1,370.4

136

Table 4.2 Continued

blackgum (Nyssa sylvatica Marsh.) ------243.1 ± 149.7 520.8 ± 220.8 black cherry (Prunus serotina Ehrh.) 868.1 ± 232.8 902.8 ± 253.6 381.9 ± 189.3 8,993.1 ± 1,901.5 white oak (Quercus alba L.) ------34.7 ± 34.7 34.7 ± 34.7 chinkapin oak (Quercus muehlenbergii Engelm.) ------69.4 ± 48.8 138.9 ± 109.3 northern red oak (Quercus rubra L.) 34.7 ± 34.7 34.7 ± 34.7 173.6 ± 90.2 208.3 ± 95.8

currant

(Ribes spp.) 173.6 ± 124.4 173.6 ± 124.4 312.5 ± 177.9 416.7 ± 203.9 black locust (Robinia pseudoacacia L.) ------347.2 ± 173.5

Allegheny blackberry (Rubus allegheniensis Porter) 173.6 ± 102.9 173.6 ± 102.9 69.4 ± 48.8 34.7 ± 34.7 black raspberry (Rubus occidentalis L.) ------104.2 ± 59.3 208.3 ± 107.8

American black elderberry/red elderberry (Sambucus nigra L. ssp. canadensis [L.] R. Bolli/Sambucus ------69.4 ± 48.8 racemosa L.) sassafras (Sassafras albidum [Nutt.] Nees) 1,423.6 ± 356.0 1,284.7 ± 346.1 763.9 ± 207.7 1,979.2 ± 395.4

American basswood (Tilia americana L.) ------34.7 ± 34.7 ---

American elm/slippery elm (Ulmus americana L./Ulmus rubra Muhl.) 347.2 ± 142.5 347.2 ± 150.8 763.9 ± 219.1 1,805.6 ± 325.1 blackhaw (Viburnum prunifolium L.) 104.2 ± 77.2 104.2 ± 77.2 ------137

Table 4.2 Continued

southern arrowwood (Viburnum recognitum Fernald) 34.7 ± 34.7 --- 347.2 ± 158.7 416.7 ± 197.8

Non-native

Japanese barberry (Berberis thunbergii DC.) ------173.6 ± 173.6 243.1 ± 210.7 autumn olive (Elaeagnus umbellata Thunb.) 312.5 ± 163.5 312.5 ± 163.5 347.2 ± 142.5 347.2 ± 150.8 burningbush (Euonymus alatus [Thunb.] Siebold) ------34.7 ± 34.7 104.2 ± 59.3

Amur honeysuckle (Lonicera maackii [Rupr.] Herder) 5,381.9 ± 835.8 6,423.6 ± 888.5 7,743.1 ± 796.1 28,298.6 ± 8,593.8 multiflora rose (Rosa multiflora Thunb.) 1,805.6 ± 487.6 1,909.7 ± 522.1 1,423.6 ± 338.4 1,909.7 ± 459.9

138

Table 4.3. Mean percent cover (± 1 SE) and permutation results for native and non-native plants (grouped according to growth habit) and environmental variables in reference and removal areas, during the spring seasons of 2010 and 2011 at six mixed-hardwood forests in central Indiana. Difference represents difference in mean percent cover between years (2011 minus 2010) for each group/environmental variable and treatment type (reference or removal area). Permutation p values were calculated to examine significance of differences between 2010 and 2011, based on 99 iterations for each group/environmental variable. For each group/environmental variable, mean values were calculated by pooling data across all six study sites (n = 72 sample plots for each species in a given treatment type, year, and season). Plant data were collected in the lower stratum (≤ 1 m) and upper stratum (1.01-5 m) whereas environmental data were collected only in the lower stratum.

Reference Removal

Group 2010 2011 Difference p 2010 2011 Difference p

Lower stratum (≤ 1 m)

Native forbs 20.62 ± 2.61 22.60 ± 2.80 1.98 0.10 17.24 ± 1.99 31.02 ± 2.44 13.78 0.05 Native grasses 0.60 ± 0.18 0.66 ± 0.20 0.06 0.77 0.52 ± 0.14 0.60 ± 0.13 0.08 0.49 Native sedges 0.15 ± 0.05 0.15 ± 0.05 0.00 --- 0.49 ± 0.25 0.85 ± 0.27 0.35 0.15 Native ferns 0.16 ± 0.05 0.36 ± 0.09 0.20 0.11 0.16 ± 0.07 0.38 ± 0.11 0.22 0.12 Native vines 0.73 ± 0.17 1.06 ± 0.18 0.33 0.14 0.83 ± 0.29 1.41 ± 0.30 0.58 0.09 Native trees and shrubs 2.81 ± 0.54 4.61 ± 0.67 1.80 0.06 3.79 ± 0.66 5.31 ± 0.60 1.51 0.09 Non-native forbs 0.54 ± 0.17 0.67 ± 0.15 0.13 0.05 1.01 ± 0.31 2.28 ± 0.46 1.27 0.09 Non-native grasses 0.02 ± 0.02 0.01 ± 0.01 -0.01 1.00 ------Non-native shrubs 12.01 ± 1.91 12.28 ± 1.89 0.27 0.57 18.70 ± 2.20 3.34 ± 0.61 -15.36 0.04 Non-native vines 0.33 ± 0.12 0.33 ± 0.12 0.00 --- 0.09 ± 0.06 0.15 ± 0.06 0.06 0.46

Upper stratum (1.01-5 m)

Native trees and shrubs 9.78 ± 1.89 10.93 ± 1.81 1.15 0.07 9.32 ± 1.42 9.70 ± 1.32 0.38 0.35 Native vines 0.15 ± 0.11 0.11 ± 0.10 -0.04 1.00 0.13 ± 0.06 0.08 ± 0.05 -0.06 0.45

Non-native shrubs 35.53 ± 3.83 35.58 ± 3.98 0.05 0.99 40.60 ± 3.65 0.05 ± 0.05 -40.56 0.03 139

Table 4.3 Continued

Non-native vines ------0.05 ± 0.05 0.00 ± 0.00 -0.05 1.00

Environmental variables

Bare soil 15.58 ± 2.34 14.74 ± 2.20 -0.84 0.73 13.58 ± 2.09 10.08 ± 1.89 -3.50 0.10 Coarse woody debris 5.22 ± 1.50 5.08 ± 1.49 -0.14 1.00 3.04 ± 0.99 3.19 ± 0.99 0.15 0.56 Fine woody debris 7.89 ± 0.75 8.08 ± 0.78 0.19 0.41 8.56 ± 0.79 10.58 ± 0.94 2.03 0.02 Dead leaves/dead 35.05 ± 2.98 36.65 ± 3.01 1.60 0.61 27.82 ± 3.02 26.69 ± 3.08 -1.13 0.51 herbaceous stems

140

Table 4.4. Mean percent cover (± 1 SE) and permutation results for native and non-native plants (grouped according to growth habit) and environmental variables in reference and removal areas, during the summer seasons of 2010 and 2011 at six mixed-hardwood forests in central Indiana. Difference represents difference in mean percent cover between years (2011 minus 2010) for each group/environmental variable and treatment type (reference or removal area). Permutation p values were calculated to examine significance of differences between 2010 and 2011, based on 99 iterations for each group/environmental variable. For each group/environmental variable, mean values were calculated by pooling data across all six study sites (n = 72 sample plots for each species in a given treatment type, year, and season). Plant data were collected in the lower stratum (≤ 1 m) and upper stratum (1.01-5 m) whereas environmental data were collected only in the lower stratum.

Reference Removal

Group 2010 2011 Difference p 2010 2011 Difference p

Lower stratum (≤ 1 m)

Native forbs 20.92 ± 2.30 20.10 ± 2.18 -0.83 0.70 22.69 ± 2.45 37.81 ± 3.50 15.11 0.03 Native grasses 0.85 ± 0.27 0.76 ± 0.22 -0.10 0.50 0.74 ± 0.19 0.83 ± 0.20 0.09 0.36 Native sedges 0.15 ± 0.05 0.21 ± 0.08 0.06 1.00 0.86 ± 0.28 0.96 ± 0.29 0.10 0.24 Native ferns 0.38 ± 0.10 0.38 ± 0.09 -0.01 1.00 0.25 ± 0.09 0.35 ± 0.10 0.10 0.13 Native vines 1.39 ± 0.25 1.44 ± 0.25 0.06 0.88 2.06 ± 0.48 2.69 ± 0.42 0.63 0.15 Native trees and shrubs 5.71 ± 0.80 5.03 ± 0.67 -0.68 0.16 6.39 ± 0.89 8.71 ± 0.88 2.32 0.10 Non-native forbs 0.63 ± 0.18 0.99 ± 0.22 0.37 0.07 1.10 ± 0.24 3.34 ± 0.72 2.24 0.14 Non-native grasses 0.02 ± 0.02 0.02 ± 0.02 0.00 ------Non-native shrubs 11.95 ± 1.78 12.70 ± 1.88 0.75 0.09 19.85 ± 2.18 4.68 ± 0.64 -15.17 0.04 Non-native vines 0.33 ± 0.12 0.44 ± 0.17 0.11 1.00 0.19 ± 0.08 0.25 ± 0.12 0.06 1.00

Upper stratum (1.01-5 m)

Native trees and shrubs 13.22 ± 2.24 12.62 ± 2.14 -0.60 0.38 12.38 ± 1.65 11.58 ± 1.51 -0.81 0.43

Native vines 0.15 ± 0.11 0.15 ± 0.11 0.00 --- 0.33 ± 0.16 0.17 ± 0.08 -0.16 0.27 141

Table 4.4 Continued

Non-native shrubs 37.17 ± 3.95 37.31 ± 3.94 0.14 0.77 42.38 ± 3.58 0.33 ± 0.25 -42.04 0.03 Non-native vines ------0.05 ± 0.05 0.00 ± 0.00 -0.05 1.00

Environmental variables

Bare soil 17.75 ± 2.61 14.97 ± 2.19 -2.78 0.06 15.11 ± 2.03 8.36 ± 1.46 -6.75 0.04 Coarse woody debris 5.22 ± 1.50 5.27 ± 1.50 0.06 1.00 3.32 ± 1.08 3.44 ± 1.01 0.12 0.47 Fine woody debris 8.33 ± 0.85 8.33 ± 0.78 0.00 --- 8.93 ± 1.01 12.48 ± 1.56 3.55 0.05 Dead leaves/dead 30.00 ± 2.95 31.64 ± 2.83 1.64 0.27 22.17 ± 2.87 19.41 ± 2.80 -2.76 0.44 herbaceous stems

142

Table 4.5. Mean percent cover (± 1 SE) of native plants, non-native plants, and environmental variables in the lower stratum (≤ 1 m) in reference and removal areas, during the spring and summer of 2010 and 2011, at six mixed-hardwood forests in central Indiana. For each taxon/environmental variable, mean values were calculated by pooling data across all six study sites (n = 72 sample plots for each species in a given treatment type, year, and season).

Reference Removal

2010 2011 2010 2011

Species Spring Summer Spring Summer Spring Summer Spring Summer

Native Plants

Ferns

ebony spleenwort (Asplenium platyneuron [L.] 0.08 ± 0.04 0.10 ± 0.05 0.10 ± 0.05 0.10 ±0.05 --- 0.02 ± 0.02 0.01 ± 0.01 0.01 ± 0.01 Britton, Sterns, & Poggenb.) cutleaf grapefern (Botrychium dissectum Spreng.) 0.05 ± 0.03 0.14 ± 0.05 0.13 ± 0.05 0.15 ± 0.05 ------rattlesnake fern (Botrychium virginianum [L.] 0.03 ± 0.02 0.14 ± 0.06 0.11 ± 0.05 0.11 ± 0.05 0.16 ± 0.07 0.23 ± 0.08 0.38 ± 0.11 0.35 ± 0.10 Sw.) bladderfern (Cystopteris spp.) ------0.02 ± 0.02 0.02 ± 0.02 ------

Forbs white baneberry 0.03 ± 0.02 0.03 ± 0.02 (Actaea pachypoda Elliot) ------

white snakeroot (Ageratina altissima [L.] King & 0.92 ± 0.20 1.90 ± 0.38 1.58 ± 0.35 1.91 ± 0.38 1.04 ± 0.25 2.31 ± 0.50 1.40 ± 0.26 2.42 ± 0.35 H. Rob. Var. altissima)

143

Table 4.5 Continued

tall hairy agrimony 0.09 ± 0.06 0.18 ± 0.08 0.15 ± 0.07 0.18 ± 0.08 0.03 ± 0.02 0.11 ± 0.06 0.06 ± 0.03 0.09 ± 0.04 (Agrimonia gryposepala Wallr.)

harvestlice 0.04 ± 0.03 0.10 ± 0.07 0.04 ± 0.03 0.07 ± 0.05 (Agrimonia parviflora Aiton) ------

beaked agrimony 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.01 0.01 ± 0.01 (Agrimonia rostellata Wallr.) ------

meadow garlic (Allium canadense L.) 0.27 ± 0.13 --- 0.27 ± 0.13 0.02 ± 0.02 0.38 ± 0.25 --- 0.39 ± 0.25 0.05 ± 0.05 ramp/narrowleaf wild leek (Allium tricoccum Aiton/Allium ------0.17 ± 0.12 --- 0.37 ± 0.25 0.01 ± 0.01 burdickii [Hanes] A.G. Jones)

great ragweed ------0.04 ± 0.03 0.03 ± 0.02 (Ambrosia trifida L.)

smooth rockcress

(Arabis laevigata [Muhl. Ex ------0.01 ± 0.01 0.01 ± 0.01 Willd.] Poir.)

green dragon (Arisaema dracontium [L.] Schott) 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 ------0.02 ± 0.02 ---

Jack in the pulpit 0.96 ± 0.23 0.83 ± 0.22 1.01 ± 0.22 0.40 ± 0.11 0.49 ± 0.17 0.40 ± 0.13 0.99 ± 0.33 0.28 ± 0.09 (Arisaema triphyllum [L.] Schott)

beggarticks (Bidens sp.) ------0.02 ± 0.02 ------smallspike false nettle (Boehmeria cylindrica [L.] Sw.) 0.10 ± 0.10 0.15 ± 0.11 0.13 ± 0.11 0.13 ± 0.11 --- 0.04 ± 0.03 0.01 ± 0.01 0.07 ± 0.05

American bellflower

(Campanulastrum americanum ------0.04 ± 0.03 0.06 ± 0.05 0.06 ± 0.05 [L.] Small)

cutleaf toothwort

(Cardamine concatenata [Michx.] 0.30 ± 0.09 --- 0.31 ± 0.09 --- 0.17 ± 0.06 --- 0.33 ± 0.10 ---

Sw.) 144

Table 4.5 Continued

limestone bittercress/bulbous bittercress (Cardamine douglassii Britton/Cardamine bulbosa 0.11 ± 0.06 --- 0.11 ± 0.06 --- 0.04 ± 0.03 --- 0.18 ± 0.07 --- [Schreb. Ex Muhl.] Britton, Sterns & Poggenb.) broadleaf enchanter’s nightshade (Circaea lutetiana L. ssp. 0.48 ± 0.13 0.58 ± 0.14 0.54 ± 0.14 0.57 ± 0.14 0.49 ± 0.15 0.67 ± 0.19 0.69 ± 0.18 0.86 ± 0.28 canadensis [L.] Asch. & Magnus)

Virginia springbeauty (Claytonia virginica L.) 0.37 ± 0.08 --- 0.33 ± 0.06 --- 0.14 ± 0.04 --- 0.86 ± 0.13 ---

American cancer-root

(Conopholis americana [L.] ------0.01 ± 0.01 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 Wallr.)

Canadian honewort 0.24 ± 0.06 0.86 ± 0.17 0.45 ± 0.10 0.69 ± 0.15 0.07 ± 0.03 0.63 ± 0.17 0.77 ± 0.19 0.97 ± 0.21 (Cryptotaenia canadensis [L.]

DC.)

false mermaidweed (Floerkea proserpinacoides 0.15 ± 0.08 --- 0.12 ± 0.07 ------0.14 ± 0.11 --- Willd.) stickywilly 0.43 ± 0.14 0.32 ± 0.08 0.43 ± 0.14 0.36 ± 0.10 0.28 ± 0.08 0.24 ± 0.06 0.67 ± 0.16 0.58 ± 0.15 (Galium aparine L.)

rough bedstraw (Galium asprellum Michx.) --- 0.02 ± 0.02 ------0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 shining bedstraw (Galium concinnum Torr. & A. Gray) ------0.05 ± 0.05 0.07 ± 0.05 0.06 ± 0.05 0.06 ± 0.05

lanceleaf wild licorice 0.06 ± 0.04 0.15 ± 0.05 0.13 ± 0.05 0.13 ± 0.05 0.04 ± 0.03 0.11 ± 0.06 0.13 ± 0.06 0.18 ± 0.11 (Galium lanceolatum Torr.)

fragrant bedstraw 0.08 ± 0.04 0.08 ± 0.04 0.08 ± 0.04 0.08 ± 0.04 0.06 ± 0.03 0.11 ± 0.06 0.10 ± 0.05 0.27 ± 0.12 (Galium triflorum Michx.)

145

Table 4.5 Continued

spotted geranium 0.64 ± 0.28 0.64 ± 0.28 0.64 ± 0.28 0.69 ± 0.30 0.17 ± 0.12 0.12 ± 0.07 0.42 ± 0.27 0.28 ± 0.15 (Geranium maculatum L.)

avens 0.21 ± 0.11 0.55 ± 0.16 0.31 ± 0.12 0.50 ± 0.15 0.31 ± 0.09 0.64 ± 0.15 1.24 ± 0.26 1.17 ± 0.23 (Geum spp.)

beggarslice

(Hackelia virginiana [L.] I.M. --- 0.02 ± 0.02 0.02 ± 0.02 0.05 ± 0.05 --- 0.02 ± 0.02 0.15 ± 0.11 0.47 ± 0.16 Johnst.)

eastern waterleaf 0.10 ± 0.07 0.10 ± 0.07 0.10 ± 0.07 0.08 ± 0.05 0.06 ± 0.05 0.06 ± 0.05 0.15 ± 0.08 0.13 ± 0.07 (Hydrophyllum virginianum L.)

jewelweed/pale touch-me-not (Impatiens capensis 1.22 ± 0.54 0.78 ± 0.27 1.20 ± 0.54 1.08 ± 0.30 0.35 ± 0.14 0.38 ± 0.15 0.89 ± 0.24 2.01 ± 1.19

Meerb./Impatiens pallida Nutt.)

Canadian woodnettle (Laportea canadensis [L.] 0.20 ± 0.12 0.83 ± 0.43 0.31 ± 0.16 0.83 ± 0.43 0.07 ± 0.05 0.31 ± 0.25 0.17 ± 0.12 0.75 ± 0.54

Weddell) feathery false lily of the valley (Maianthemum racemosum [L.] 0.04 ± 0.03 0.04 ± 0.03 0.05 ± 0.03 0.06 ± 0.04 0.02 ± 0.02 0.02 ± 0.02 0.13 ± 0.05 0.13 ± 0.05

Link ssp. racemosum) twoleaf miterwort (Mitella diphylla L.) --- 0.01 ± 0.01 ------spring forget-me-not (Myosotis verna Nutt.) 0.01 ± 0.01 --- 0.01 ± 0.01 0.01 ± 0.01 ------0.01 ± 0.01 ---

Clayton’s sweetroot/longstyle sweetroot (Osmorhiza claytonii [Michx.] 0.94 ± 0.20 0.76 ± 0.16 0.90 ± 0.18 1.01 ± 0.20 0.60 ± 0.12 0.60 ± 0.11 1.77 ± 0.27 1.92 ± 0.27

C.B. Clarke/Osmorhiza longistylis [Torr.] DC.) woodsorrel (Oxalis spp.) --- 0.03 ± 0.02 --- 0.01 ± 0.01 0.04 ± 0.03 0.09 ± 0.06 0.06 ± 0.03 0.13 ± 0.06 roundleaf ragwort (Packera obovata [Muhl. Ex 0.60 ± 0.30 0.41 ± 0.19 0.47 ± 0.21 0.47 ± 0.21 0.26 ± 0.24 0.31 ± 0.25 0.38 ± 0.26 0.38 ± 0.26

Willd.] W.A. Weber & A. Löve) 146

Table 4.5 Continued

wild blue phlox (Phlox divaricata L.) --- 0.01 ± 0.01 0.01 ± 0.01 0.02 ± 0.02 ------0.02 ± 0.02 0.11 ± 0.06 lopseed 0.03 ± 0.02 0.19 ± 0.07 0.11 ± 0.04 0.14 ± 0.05 0.03 ± 0.02 0.24 ± 0.08 0.10 ± 0.03 0.29 ± 0.07 (Phryma leptostachya L.)

American pokeweed 0.02 ± 0.02 0.05 ± 0.05 0.02 ± 0.02 0.05 ± 0.05 0.07 ± 0.05 0.26 ± 0.15 0.35 ± 0.25 0.54 ± 0.27 (Phytolacca americana L.)

Canadian clearweed 0.63 ± 0.18 1.03 ± 0.30 0.69 ± 0.17 1.07 ± 0.30 0.97 ± 0.53 2.19 ± 0.80 0.67 ± 0.28 6.44 ± 2.20 (Pilea pumila [L.] A. Gray)

blackseed plantain (Plantago rugelii Decne.) ------0.04 ± 0.03 0.10 ± 0.07 0.04 ± 0.03 0.06 ± 0.04 mayapple (Podophyllum peltatum L.) 4.10 ± 1.34 0.20 ± 0.09 3.94 ± 1.33 0.36 ± 0.13 1.28 ± 0.36 --- 2.77 ± 0.73 0.82 ± 0.28 smooth Solomon’s seal

(Polygonatum biflorum [Walter] 0.08 ± 0.04 0.03 ± 0.02 0.01 ± 0.01 0.01 ± 0.01 --- 0.06 ± 0.04 0.06 ± 0.04 0.10 ± 0.06 Elliott)

hairy Solomon’s seal

(Polygonatum pubescens [Willd.] 0.01 ± 0.01 0.01 ± 0.01 0.01 ± 0.01 0.01 ± 0.01 ------Pursh)

jumpseed 1.31 ± 0.27 3.06 ± 0.52 1.92 ± 0.36 2.53 ± 0.45 2.01 ± 0.45 3.56 ± 0.60 2.30 ± 0.42 3.94 ± 0.60 (Polygonum virginianum L.)

cinquefoil (Potentilla spp.) ------0.01 ± 0.01 0.01 ± 0.01 rattlesnakeroot 0.12 ± 0.07 0.08 ± 0.04 0.14 ± 0.07 0.06 ± 0.03 0.49 ± 0.18 0.51 ± 0.18 0.72 ± 0.22 0.67 ± 0.22 (Prenanthes L.)

littleleaf buttercup (Ranunculus abortivus L.) 0.10 ± 0.03 --- 0.13 ± 0.04 0.02 ± 0.02 0.15 ± 0.04 --- 0.39 ± 0.08 0.01 ± 0.01 bristly buttercup (Ranunculus hispidus Michx.) ------0.02 ± 0.02 0.02 ± 0.02 bloodroot (Sanguinaria canadensis L.) ------0.05 ± 0.05 0.05 ± 0.05 0.10 ± 0.10 0.02 ± 0.02 147

Table 4.5 Continued

sanicle (Sanicula spp.) 4.47 ± 0.84 5.53 ± 0.95 4.53 ± 0.84 5.16 ± 0.87 5.63 ± 1.31 6.78 ± 1.30 8.40 ± 1.51 9.00 ± 1.67 carpenter’s square (Scrophularia marilandica L.) ------0.15 ± 0.11 0.01 ± 0.01 0.02 ± 0.02 wreath goldenrod (Solidago caesia L.) 0.04 ± 0.03 0.04 ± 0.03 0.04 ± 0.03 0.04 ± 0.03 ------

smooth hedgenettle ------0.06 ± 0.05 (Stachys tenuifolia Willd.)

common blue wood aster

(Symphyotrichum cordifolium [L.] ------0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 G.L. Nesom)

calico aster (Symphyotrichum lateriflorum [L.] 0.06 ± 0.04 0.13 ± 0.06 0.10 ± 0.06 0.12 ± 0.06 0.05 ± 0.03 0.10 ± 0.05 0.09 ± 0.04 0.16 ± 0.06 A. Löve & D. Löve var. lateriflorum)

Canada germander (Teucrium canadense L.) ------0.05 ± 0.05 spiderwort (Tradescantia sp.) 0.05 ± 0.05 0.05 ± 0.05 0.05 ± 0.05 0.05 ± 0.05 ------bloody butcher (Trillium recurvatum Beck) 0.27 ± 0.08 --- 0.28 ± 0.08 --- 0.17 ± 0.07 --- 0.39 ± 0.10 ---

California nettle

(Urtica dioica L. ssp. gracilis --- 0.05 ± 0.05 ------0.02 ± 0.02 0.16 ± 0.11 0.05 ± 0.03 0.03 ± 0.02 [Aiton] Seland.)

wingstem

(Verbesina alternifolia [L.] Britton --- 0.02 ± 0.02 0.02 ± 0.02 0.05 ± 0.05 0.05 ± 0.05 0.10 ± 0.10 0.05 ± 0.05 0.13 ± 0.11 ex Kearney)

downy yellow violet 0.04 ± 0.03 0.04 ± 0.03 0.04 ± 0.03 0.04 ± 0.03 0.20 ± 0.08 0.26 ± 0.09 0.38 ± 0.11 0.40 ± 0.12 (Viola pubescens Aiton)

common blue violet 0.44 ± 0.11 0.62 ± 0.13 0.52 ± 0.11 0.69 ± 0.14 0.59 ± 0.13 0.74 ± 0.14 1.13 ± 0.20 1.24 ± 0.21

148 (Viola sororia Willd.)

Table 4.5 Continued

striped cream violet 0.31 ± 0.15 0.37 ± 0.16 0.26 ± 0.12 0.29 ± 0.13 --- 0.02 ± 0.02 0.17 ± 0.11 0.19 ± 0.12 (Viola striata Aiton)

Grasses

bearded shorthusk

(Brachyelytrum erectum [Schreb. 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 ------Ex Spreng.] P. Beauv.)

sweet woodreed 0.19 ± 0.10 0.24 ± 0.13 0.22 ± 0.10 0.22 ± 0.10 0.02 ± 0.02 0.07 ± 0.05 0.02 ± 0.02 0.02 ± 0.02 (Cinna arundinacea L.)

Bosc’s panicgrass (Dichanthelium boscii [Poir.] 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 0.09 ± 0.06 0.08 ± 0.04 0.10 ± 0.06 0.22 ± 0.12

Gould & C.A. Clark)

hairy wildrye ------0.05 ± 0.05 (Elymus villosus Muhl. Ex Willd.)

nodding fescue (Festuca subverticillata [Pers.] 0.15 ± 0.05 0.20 ± 0.07 0.16 ± 0.05 0.19 ± 0.07 0.29 ± 0.09 0.40 ± 0.11 0.36 ± 0.09 0.41 ± 0.12 Alexeev) whitegrass 0.19 ± 0.06 0.35 ± 0.11 0.22 ± 0.07 0.29 ± 0.10 0.13 ± 0.11 0.19 ± 0.11 0.12 ± 0.06 0.13 ± 0.06 (Leersia virginica Willd.)

woodland bluegrass 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 ------(Poa sylvestris A. Gray)

Sedges

eastern woodland sedge/broad looseflower sedge/spreading sedge (Carex blanda Dewey/Carex 0.13 ± 0.05 0.13 ± 0.05 0.13 ± 0.05 0.16 ± 0.07 0.42 ± 0.25 0.79 ± 0.28 0.73 ± 0.27 0.84 ± 0.28 laxiflora Lam./Carex laxiculmis Schwein.)

149

Table 4.5 Continued

pubescent sedge (Carex hirtifolia Mack.) ------0.07 ± 0.05 0.07 ± 0.05 0.10 ± 0.07 0.10 ± 0.07

eastern star sedge 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 0.05 ± 0.05 ------0.02 ± 0.02 0.02 ± 0.02 (Carex radiata [Wahlenb.] Small)

Vines

trumpet creeper

(Campsis radicans [L.] Seem. ex --- 0.05 ± 0.05 0.02 ± 0.02 0.05 ± 0.05 --- 0.06 ± 0.04 0.04 ± 0.02 0.07 ± 0.05 Bureau)

wild yam (Dioscorea villosa L.) --- 0.05 ± 0.05 0.05 ± 0.05 0.05 ± 0.05 --- 0.02 ± 0.02 0.01 ± 0.01 0.01 ± 0.01 wild cucumber (Echinocystis lobata [Michx.] 0.05 ± 0.05 0.05 ± 0.05 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 0.01 ± 0.01 0.01 ± 0.01

Torr. & A. Gray) common moonseed (Menispermum canadense L.) 0.02 ± 0.02 0.09 ± 0.06 0.06 ± 0.05 0.04 ± 0.03 0.02 ± 0.02 0.05 ± 0.05 ------

Virginia creeper (Parthenocissus quinquefolia [L.] 1.25 ± 0.19 1.67 ± 0.23 1.27 ± 0.19 1.71 ± 0.24 1.54 ± 0.24 2.11 ± 0.28 1.81 ± 0.24 3.98 ± 0.45

Planch.) climbing false buckwheat (Polygonum scandens L.) 0.05 ± 0.05 0.05 ± 0.05 0.06 ± 0.05 0.06 ± 0.05 ------0.01 ± 0.01 0.01 ± 0.01

bristly dewberry --- 0.03 ± 0.02 0.03 ± 0.02 0.03 ± 0.02 --- 0.02 ± 0.02 0.01 ± 0.01 0.01 ± 0.01 (Rubus hispidus L.)

bristly greenbrier 0.06 ± 0.04 0.13 ± 0.06 0.11 ± 0.05 0.15 ± 0.07 0.09 ± 0.06 0.24 ± 0.08 0.22 ± 0.07 0.34 ± 0.09 (Smilax tamnoides L.)

eastern poison ivy (Toxicodendron radicans [L.] 0.38 ± 0.10 0.73 ± 0.17 0.50 ± 0.11 0.86 ± 0.21 0.65 ± 0.27 1.58 ± 0.41 0.94 ± 0.29 1.53 ± 0.33

Kuntze) grape 0.17 ± 0.12 0.22 ± 0.12 0.22 ± 0.12 0.17 ± 0.11 0.04 ± 0.03 0.07 ± 0.05 0.18 ± 0.07 0.70 ± 0.27

(Vitis spp.) 150

Table 4.5 Continued

Trees and Shrubs

boxelder (Acer negundo L.) ------0.02 ± 0.02 0.02 ± 0.02 ------0.01 ± 0.01 0.01 ± 0.01 red maple (Acer rubrum L.) 0.05 ± 0.05 0.05 ± 0.05 0.05 ± 0.05 0.05 ± 0.05 0.01 ± 0.01 0.01 ± 0.01 0.01 ± 0.01 --- sugar maple 0.29 ± 0.13 0.39 ± 0.16 0.39 ± 0.16 0.39 ± 0.16 0.04 ± 0.03 0.07 ± 0.05 0.06 ± 0.04 0.10 ± 0.05 (Acer saccharum Marsh.)

serviceberry (Amelanchier spp.) ------0.02 ± 0.02 0.02 ± 0.02 0.01 ± 0.01 pawpaw 0.02 ± 0.02 0.10 ± 0.10 0.02 ± 0.02 0.05 ± 0.05 0.07 ± 0.05 0.19 ± 0.11 0.12 ± 0.06 0.40 ± 0.26 (Asimina triloba [L.] Dunal)

American hornbeam (Carpinus caroliniana Walter) ------0.10 ± 0.10 0.10 ± 0.10 0.10 ± 0.10 0.10 ± 0.10 bitternut hickory (Carya cordiformis [Wangenh.] K. 0.02 ± 0.02 0.22 ± 0.09 0.21 ± 0.09 0.23 ± 0.09 0.10 ± 0.05 0.31 ± 0.13 0.16 ± 0.07 0.19 ± 0.08

Koch) shagbark hickory 0.03 ± 0.02 0.18 ± 0.08 0.18 ± 0.08 0.18 ± 0.08 0.02 ± 0.02 0.05 ± 0.05 0.03 ± 0.02 0.03 ± 0.02 (Carya ovata [Mill.] K. Koch)

common hackberry 0.08 ± 0.03 0.33 ± 0.08 0.27 ± 0.07 0.24 ± 0.07 0.05 ± 0.03 0.23 ± 0.07 0.17 ± 0.05 0.25 ± 0.08 (Celtis occidentalis L.)

eastern redbud (Cercis canadensis L.) ------0.01 ± 0.01 flowering dogwood 0.04 ± 0.02 0.08 ± 0.05 0.06 ± 0.03 0.06 ± 0.03 0.01 ± 0.01 0.16 ± 0.11 0.04 ± 0.02 0.08 ± 0.05 (Cornus florida L.)

other dogwood ------0.02 ± 0.02 (Cornus spp.)

American hazelnut ------0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 (Corylus americana Walter)

151

Table 4.5 Continued

hawthorn

(Crataegus spp.) 0.05 ± 0.03 0.07 ± 0.03 0.07 ± 0.03 0.07 ± 0.03 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 0.01 ± 0.01 white ash/green ash (Fraxinus americana L./Fraxinus 1.12 ± 0.32 2.29 ± 0.47 1.67 ± 0.36 1.91 ± 0.37 1.73 ± 0.46 2.47 ± 0.51 1.71 ± 0.36 2.38 ± 0.49 pennsylvanica Marsh.) honeylocust (Gleditsia triacanthos L.) --- 0.01 ± 0.01 ------0.02 ± 0.01 0.03 ± 0.01 0.01 ± 0.01 northern spicebush 0.47 ± 0.27 0.54 ± 0.27 0.53 ± 0.27 0.52 ± 0.27 0.48 ± 0.26 0.61 ± 0.28 0.38 ± 0.14 0.83 ± 0.22 (Lindera benzoin [L.] Blume)

tuliptree

(Liriodendron tulipifera L.) 0.01 ± 0.01 0.08 ± 0.04 0.05 ± 0.03 0.05 ± 0.03 0.44 ± 0.11 ------blackgum (Nyssa sylvatica Marsh.) ------0.04 ± 0.03 0.12 ± 0.07 0.06 ± 0.04 0.26 ± 0.13 hophornbeam

(Ostrya virginiana [Mill.] K. ------0.01 ± 0.01 Koch)

black cherry 0.17 ± 0.05 0.28 ± 0.08 0.25 ± 0.07 0.31 ± 0.09 0.15 ± 0.11 0.27 ± 0.13 0.83 ± 0.22 1.15 ± 0.20 (Prunus serotina Ehrh.)

white oak (Quercus alba L.) ------0.01 ± 0.01 0.01 ± 0.01 0.01 ± 0.01 chinkapin oak (Quercus muehlenbergii Engelm.) ------0.01 ± 0.01 0.06 ± 0.05 0.03 ± 0.02 0.06 ± 0.05 northern red oak (Quercus rubra L.) --- 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 0.05 ± 0.03 0.06 ± 0.03 0.04 ± 0.02 0.05 ± 0.03 currant 0.15 ± 0.11 0.21 ± 0.15 0.15 ± 0.11 0.21 ± 0.15 0.22 ± 0.13 0.22 ± 0.13 0.24 ± 0.13 0.38 ± 0.17 (Ribes L.)

black locust (Robinia pseudoacacia L.) ------0.14 ± 0.11 0.14 ± 0.11

Allegheny blackberry 0.07 ± 0.05 0.12 ± 0.07 0.09 ± 0.06 0.10 ± 0.07 0.02 ± 0.02 0.04 ± 0.03 0.05 ± 0.05 0.05 ± 0.05

152 (Rubus allegheniensis Porter)

Table 4.5 Continued

black raspberry (Rubus occidentalis L.) ------0.01 ± 0.01 0.08 ± 0.05 0.07 ± 0.05 0.16 ± 0.08

American black elderberry/red elderberry

(Sambucus nigra L. ssp. 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 ------0.02 ± 0.02 0.04 ± 0.03 canadensis [L.] R. Bolli/Sambucus racemosa L.) sassafras 0.11 ± 0.06 0.56 ± 0.13 0.42 ± 0.11 0.44 ± 0.11 0.04 ± 0.03 0.44 ± 0.12 0.16 ± 0.06 0.71 ± 0.16 (Sassafras albidum [Nutt.] Nees)

American basswood (Tilia americana L.) ------0.05 ± 0.05 ------

American elm/slippery elm

(Ulmus americana L./Ulmus rubra 0.10 ± 0.06 0.13 ± 0.06 0.11 ± 0.06 0.11 ± 0.06 0.07 ± 0.04 0.22 ± 0.06 0.22 ± 0.05 0.31 ± 0.07 Muhl.)

blackhaw (Viburnum prunifolium L.) 0.02 ± 0.02 0.03 ± 0.02 0.03 ± 0.02 0.04 ± 0.03 ------

southern arrowwood 0.01 ± 0.01 0.01 ± 0.01 ------0.56 ± 0.29 0.56 ± 0.29 0.57 ± 0.29 0.49 ± 0.27 (Viburnum recognitum Fernald)

Non-native Plants

Forbs

garlic mustard (Alliaria petiolata [M. Bieb.] 0.40 ± 0.15 0.39 ± 0.13 0.47 ± 0.14 0.65 ± 0.17 0.95 ± 0.31 0.66 ± 0.17 1.99 ± 0.44 2.72 ± 0.67

Cavara & Grande) wild garlic (Allium vineale L.) 0.07 ± 0.04 --- 0.07 ± 0.04 0.01 ± 0.01 0.02 ± 0.02 --- 0.05 ± 0.05 0.05 ± 0.05 lesser burdock (Arctium minus Bernh.) ------0.02 ± 0.02 0.05 ± 0.05 0.05 ± 0.05 0.10 ± 0.10

153

Table 4.5 Continued

caraway 0.01 ± 0.01 0.01 ± 0.01 0.01 ± 0.01 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 0.06 ± 0.03 0.04 ± 0.02 (Carum carvi L.)

Queen Anne’s lace (Daucus carota L.) ------0.02 ± 0.02 0.02 ± 0.02 ground ivy (Glechoma hederacea L.) 0.02 ± 0.02 0.05 ± 0.05 0.05 ± 0.05 0.05 ± 0.05 0.01 ± 0.01 --- 0.01 ± 0.01 0.01 ± 0.01 purple deadnettle (Lamium purpureum L.) ------0.01 ± 0.01 0.01 ± 0.01 ------0.01 ± 0.01 --- spotted ladysthumb 0.04 ± 0.03 0.21 ± 0.11 0.10 ± 0.04 0.28 ± 0.13 0.01 ± 0.01 0.42 ± 0.17 0.14 ± 0.05 0.49 ± 0.17

(Polygonum persicaria L.) common chickweed

(Stellaria media [L.] Vill.) ------0.02 ± 0.02 --- common dandelion (Taraxacum officinale F.H. Wigg.) 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 ------

clover ------0.01 ± 0.01 0.01 ± 0.01 (Trifolium spp.)

Grasses

Kentucky bluegrass 0.02 ± 0.02 0.02 ± 0.02 0.01 ± 0.01 0.01 ± 0.01 ------(Poa pratensis L.)

Vines

Oriental bittersweet (Celastrus orbiculatus Thunb.) ------0.05 ± 0.05 0.05 ± 0.05 0.07 ± 0.05 winter creeper

(Euonymus fortunei [Turcz.] ------0.02 ± 0.02 0.04 ± 0.03 0.03 ± 0.02 0.01 ± 0.01 Hand.-Maz.)

Japanese honeysuckle 0.33 ± 0.12 0.33 ± 0.12 0.33 ± 0.12 0.44 ± 0.17 0.07 ± 0.05 0.10 ± 0.06 0.07 ± 0.04 0.17 ± 0.11

(Lonicera japonica Thunb.)

154

Table 4.5 Continued

Shrubs

Japanese barberry (Berberis thunbergii DC.) ------0.24 ± 0.24 0.24 ± 0.24 0.10 ± 0.10 0.11 ± 0.10 autumn olive 0.35 ± 0.25 0.38 ± 0.25 0.38 ± 0.25 0.38 ± 0.25 0.15 ± 0.07 0.32 ± 0.14 0.16 ± 0.07 0.19 ± 0.08 (Elaeagnus umbellata Thunb.)

burningbush

(Euonymus alatus [Thunb.] ------0.02 ± 0.02 0.03 ± 0.02 0.05 ± 0.03 Siebold)

Amur honeysuckle 8.63 ± 1.50 8.92 ± 1.49 8.93 ± 1.49 9.28 ± 1.48 16.72 ± 2.00 17.51 ± 1.99 1.61 ± 0.17 2.80 ± 0.32 (Lonicera maackii [Rupr.] Herder)

multiflora rose 3.04 ± 1.26 2.65 ± 1.05 2.97 ± 1.25 3.04 ± 1.26 1.60 ± 0.63 1.76 ± 0.64 1.44 ± 0.56 1.54 ± 0.56

(Rosa multiflora Thunb.)

Environmental Variables

bare soil 15.58 ± 2.34 17.75 ± 2.61 14.74 ± 2.20 14.97 ± 2.19 13.58 ± 2.09 15.11 ± 2.03 10.08 ± 1.89 8.36 ± 1.46

coarse woody debris 5.22 ± 1.50 5.22 ± 1.50 5.08 ± 1.49 5.27 ± 1.50 3.04 ± 0.99 3.32 ± 1.08 3.19 ± 0.99 3.44 ± 1.01

dead leaves/dead herbaceous stems 35.05 ± 2.98 30.00 ± 2.95 36.65 ± 3.01 31.63 ± 2.83 27.82 ± 3.02 22.17 ± 2.87 26.69 ± 3.08 19.41 ± 2.80

fine woody debris 7.89 ± 0.75 8.33 ± 0.85 8.08 ± 0.78 8.33 ± 0.78 8.56 ± 0.79 8.93 ± 1.01 10.58 ± 0.94 12.48 ± 1.56

155

Table 4.6. Mean percent cover (± 1 SE) of native and non-native vines, trees, and shrubs in the upper vertical stratum (1.01-5 m) in reference and removal areas, during the spring and summer of 2010 and 2011, at six mixed-hardwood forests in central Indiana. For each taxon, mean values were calculated by pooling data across all six study sites (n = 72 sample plots for each species in a given treatment type, year, and season).

Reference Removal

2010 2011 2010 2011

Species Spring Summer Spring Summer Spring Summer Spring Summer

Native Plants

Vines

Virginia creeper --- 0.12 ± 0.07 0.07 ± 0.05 0.07 ± 0.05 --- 0.05 ± 0.05 ------(Parthenocissus quinquefolia [L.] Planch.) eastern poison ivy 0.15 ± 0.11 0.15 ± 0.11 0.11 ± 0.10 0.15 ± 0.11 0.07 ± 0.05 0.15 ± 0.11 --- 0.10 ± 0.07 (Toxicodendron radicans [L.] Kuntze) grape ------0.06 ± 0.04 0.17 ± 0.12 0.05 ± 0.08 0.07 ± 0.04 (Vitis spp.)

Trees and Shrubs boxelder 0.05 ± 0.05 0.10 ± 0.10 0.10 ± 0.10 0.10 ± 0.10 0.87 ± 0.87 0.87 ± 0.87 0.87 ± 0.87 0.87 ± 0.87 (Acer negundo L.) red maple 0.34 ± 0.25 0.40 ± 0.27 0.40 ± 0.27 0.40 ± 0.27 ------(Acer rubrum L.) silver maple 0.10 ± 0.10 0.24 ± 0.24 0.24 ± 0.24 0.24 ± 0.24 ------(Acer saccharinum L.) sugar maple 0.35 ± 0.26 0.35 ± 0.26 0.35 ± 0.26 0.35 ± 0.26 0.70 ± 0.31 1.09 ± 0.45 0.32 ± 0.76 0.78 ± 0.32

(Acer saccharum Marsh.) 156

Table 4.6 Continued pawpaw ------0.26 ± 0.15 0.50 ± 0.28 0.27 ± 0.42 0.64 ± 0.36 (Asimina triloba [L.] Dunal)

American hornbeam ------0.07 ± 0.05 0.10 ± 0.07 0.07 ± 0.10 0.10 ± 0.07 (Carpinus caroliniana Walter) mockernut hickory (Carya alba [L.] Nutt.) ------0.05 ± 0.05 0.10 ± 0.10 0.10 ± 0.10 0.10 ± 0.10

bitternut hickory 0.10 ± 0.10 0.10 ± 0.10 0.10 ± 0.10 0.10 ± 0.10 0.08 ± 0.04 0.44 ± 0.27 0.12 ± 0.19 0.33 ± 0.25 (Carya cordiformis [Wangenh.] K. Koch) pignut hickory/red hickory 0.05 ± 0.05 0.10 ± 0.10 0.05 ± 0.05 0.10 ± 0.10 0.10 ± 0.10 0.10 ± 0.10 0.10 ± 0.10 0.10 ± 0.10 (Carya glabra [Mill.] Sweet/Carya ovalis [Wangenh.] Sarg.) shagbark hickory 0.26 ± 0.15 0.31 ± 0.16 0.31 ± 0.16 0.25 ± 0.13 ------(Carya ovata [Mill.] K. Koch) common hackberry 0.67 ± 0.53 1.02 ± 0.87 0.40 ± 0.27 0.67 ± 0.53 0.85 ± 0.43 1.13 ± 0.62 0.42 ± 0.80 0.85 ± 0.42 (Celtis occidentalis L.) eastern redbud 0.40 ± 0.27 0.80 ± 0.38 0.66 ± 0.31 0.80 ± 0.38 0.05 ± 0.05 0.10 ± 0.10 0.10 ± 0.10 0.10 ± 0.10 (Cercis canadensis L.) flowering dogwood 0.31 ± 0.18 0.64 ± 0.36 0.64 ± 0.36 0.64 ± 0.36 0.10 ± 0.10 0.10 ± 0.10 0.10 ± 0.10 0.10 ± 0.10 (Cornus florida L.) other dogwood ------0.10 ± 0.10 0.10 ± 0.10 0.10 ± 0.10 0.10 ± 0.10 (Cornus spp.) hawthorn 0.15 ± 0.11 0.15 ± 0.11 0.15 ± 0.11 0.15 ± 0.11 0.15 ± 0.11 0.21 ± 0.15 0.11 ± 0.15 0.15 ± 0.11 (Crataegus spp.)

American beech 0.45 ± 0.28 0.59 ± 0.35 0.45 ± 0.28 0.45 ± 0.28 0.55 ± 0.29 0.66 ± 0.31 0.28 ± 0.50 0.65 ± 0.31 (Fagus grandifolia Ehrh.)

157

Table 4.6 Continued white ash/green ash 0.76 ± 0.32 0.94 ± 0.34 0.74 ± 0.25 0.94 ± 0.34 0.63 ± 0.22 0.71 ± 0.23 0.18 ± 0.45 0.71 ± 0.23 (Fraxinus americana L./Fraxinus pennsylvanica Marsh.) black walnut ------0.05 ± 0.05 0.02 ± 0.02 0.05 ± 0.05 (Juglans nigra L.) northern spicebush 2.09 ± 1.22 2.28 ± 1.24 2.15 ± 1.23 2.15 ± 1.23 1.45 ± 0.66 1.78 ± 0.81 0.49 ± 1.17 1.78 ± 0.81 (Lindera benzoin [L.] Blume) tuliptree 0.10 ± 0.07 0.45 ± 0.28 0.40 ± 0.27 0.45 ± 0.28 0.05 ± 0.05 0.23 ± 0.15 0.15 ± 0.23 0.23 ± 0.15 (Liriodendron tulipifera L.) osage orange ------0.10 ± 0.10 0.10 ± 0.10 0.10 ± 0.10 (Maclura pomifera [Raf.] C.K. Schneid.) red mulberry (Morus rubra L.) ------0.05 ± 0.05 0.05 ± 0.05 0.05 ± 0.05 blackgum ------1.10 ± 0.62 1.18 ± 0.62 0.43 ± 0.88 1.18 ± 0.62 (Nyssa sylvatica Marsh.)

American sycamore --- 0.02 ± 0.02 0.05 ± 0.05 0.05 ± 0.05 ------(Platanus occidentalis L.) black cherry 0.53 ± 0.34 0.66 ± 0.36 0.66 ± 0.36 0.66 ± 0.36 0.10 ± 0.10 0.10 ± 0.10 0.10 ± 0.10 --- (Prunus serotina Ehrh.) white oak ------0.29 ± 0.25 0.49 ± 0.34 0.26 ± 0.35 0.35 ± 0.26 (Quercus alba L.) chinkapin oak ------0.05 ± 0.05 0.05 ± 0.05 0.05 ± 0.05 0.10 ± 0.10 (Quercus muehlenbergii Engelm.) northern red oak ------0.10 ± 0.10 0.10 ± 0.10 0.10 ± 0.10 0.10 ± 0.10 (Quercus rubra L.) currant --- 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 0.11 ± 0.10 0.13 ± 0.11 --- 0.13 ± 0.11 (Ribes spp.)

158

Table 4.6 Continued

Allegheny blackberry --- 0.02 ± 0.02 0.02 ± 0.02 0.05 ± 0.05 ------(Rubus allegheniensis Porter) sassafras 0.51 ± 0.34 0.58 ± 0.35 0.58 ± 0.35 0.58 ± 0.35 --- 0.02 ± 0.02 0.02 ± 0.02 0.02 ± 0.02 (Sassafras albidum [Nutt.] Nees)

American elm/slippery elm 1.96 ± 0.71 2.83 ± 0.97 1.86 ± 0.56 2.85 ± 0.97 1.42 ± 0.50 1.74 ± 0.55 0.55 ± 1.76 1.76 ± 0.51 (Ulmus americana L./Ulmus rubra Muhl.) blackhaw 0.56 ± 0.30 0.56 ± 0.30 0.56 ± 0.30 0.56 ± 0.30 ------(Viburnum prunifolium L.) southern arrowwood 0.05 ± 0.05 0.05 ± 0.05 0.05 ± 0.05 0.05 ± 0.05 0.07 ± 0.05 0.13 ± 0.11 0.10 ± 0.10 0.13 ± 0.11 (Viburnum recognitum Fernald)

Non-native Plants

Vines

Japanese honeysuckle ------0.05 ± 0.05 0.05 ± 0.05 ------(Lonicera japonica Thunb.)

Shrubs autumn olive 0.44 ± 0.27 0.47 ± 0.27 0.41 ± 0.26 0.41 ± 0.26 0.66 ± 0.36 0.69 ± 0.36 0.05 ± 0.05 --- (Elaeagnus umbellata Thunb.)

Amur honeysuckle 34.26 ± 3.87 35.99 ± 4.00 34.32 ± 4.04 36.04 ± 3.99 39.56 ± 3.70 41.28 ± 3.65 ------(Lonicera maackii [Rupr.] Herder) multiflora rose 0.83 ± 0.43 0.72 ± 0.37 0.85 ± 0.43 0.85 ± 0.43 0.39 ± 0.26 0.41 ± 0.26 --- 0.33 ± 0.25 (Rosa multiflora Thunb.)

159

160

Figure 4.1. One of the six 80 m x 80 m areas (at Purdue University, Department of Natural Resources Farm [FNR Farm]) where Amur honeysuckle (Lonicera maackii [Rupr.] Herder) and other non-native shrubs were removed. Right side of photo shows a portion of the cleared area and left side of photo shows bordering thicket of Amur honeysuckle. Photo by Joshua Shields.

161

162

163

164

Figure 4.5. Mean (± 1 SE) densities (stems/ha) of native seedlings (woody stems < 1.37 m tall) in a) reference areas and b) removal areas, in 2010 and 2011, at six mixed- hardwood forests in central Indiana.

165

a )

b c d ) ) )

165

166

167

CHAPTER 5 EFFECTS OF AMUR HONEYSUCKLE (Lonicera maackii [Rupr.]

Herder) INVASION AND REMOVAL ON WHITE-FOOTED MICE (Peromyscus

leucopus)

5.1. Introduction

Introductions of non-native, invasive species have led to subsequent effects on native wildlife species (Usher et al. 1986, Gibbon et al. 2000, Clavero and García-

Berthou 2005). In some cases, direct negative effects are apparent, such as when an invasive animal consumes and/or acts aggressively toward native animals (Savidge 1987,

Conover and Kania 1994, Caut et al. 2008, Dove et al. 2011). A classic example of this type of effect was documented by Savidge (1987), who examined the impacts of the invasive brown tree snake (Boiga irregularis) on island avifauna in Guam and found that predation rates were higher in areas where bird populations had declined as compared to

167

areas where population numbers were stable. In other cases, impacts of invasive species are indirect and more complex (Kuhns and Berg 1999), particularly in the case of plants

(Thompson 1996, Masters and Sheley 2001, Webster et al. 2008). For example,

Thompson (1996) found that removing the invasive spotted knapweed (Centaurea stoebe

L. ssp. micranthos [Gugler] Hayek) influenced the foraging activity of elk (Cervus elaphus) in a Montana grassland. Specifically, elk were observed moving through

168 knapweed-infested areas, but they foraged only on palatable species (e.g., grasses) that grew in areas where knapweed had been removed (Thompson 1996). Positive associations between invasive organisms and native species have also been observed, such as the use of tamarisk (Tamarix spp.) by the Southwestern Willow Flycatcher

(Empidonax traillii extimus; Sogge et al. 2008). Given the multitude of potential impacts that may result from the introduction of an invasive species, it is important to elucidate such impacts in order to gain a better understanding of subsequent effects on ecological processes and population dynamics of native biota. This, in turn, will help land managers develop more comprehensive adaptive management strategies as invasive species populations continue to expand.

Amur honeysuckle (Lonicera maackii [Rupr.] Herder) is an invasive shrub that was introduced to North America in the 1890s (Luken and Thieret 1996). Since its introduction, it has aggressively colonized many forests throughout the central and eastern U.S. (Deering and Vankat 1999). In many forests throughout its invaded range,

Amur honeysuckle forms a shrub layer that is more dense and persistent than what existed historically. This aberrant layer alters the habitat structure of these forests, which

may in turn alter the population dynamics and spatial pattern of occurrence for native 168 wildlife species.

While numerous studies have documented the negative impacts of Amur honeysuckle on native vegetation (Gould and Gorchov 2000, Collier et al. 2002, Gorchov and Trisel 2003, Hartman and McCarthy 2004), less is known about how Amur honeysuckle affects native wildlife species and communities. Some research has shown that certain wildlife species serve as dispersal agents for Amur honeysuckle. For

169 example, flowers are pollinated by bees (including the introduced honey bee [Apis mellifera; Goodell et al. 2010]), and seeds are dispersed by birds (Bartuszevige et al.

2006), white-tailed deer (Odocoileus virginianus; Castellano and Gorchov 2013), and possibly small mammals such as Peromyscus spp. (Williams et al. 1992). A few studies have also examined the effects of Amur honeysuckle invasion on abundance and behavior of wildlife, with a majority of these studies focusing on birds (Schmidt and

Whelan 1999, McCusker et al. 2009, Packett and Dunning 2009, Gleditsch and Carlo

2011). These studies have documented a positive relationship between Amur honeysuckle density and abundance of certain Neotropical and temperate fall migrants, notably American Robin (Turdus migratorius) and Gray Catbird (Dumetella carolinensis;

Packett and Dunning 2009, Gleditsch and Carlo 2011). However, Amur honeysuckle berries are considered nutritionally poor compared to berries produced by native woody species (Ingold and Craycraft 1983), and some bird species (e.g., American Robin) experience higher levels of nest predation when nesting in Amur honeysuckle as compared to nesting in native shrubs (Schmidt and Whelan 1999). Areas with high Amur honeysuckle densities have been associated with lower amphibian species richness and

evenness than areas with lower densities of honeysuckle (Watling et al. 2011a), 169 potentially as a result of leached extracts from foliage (Watling et al. 2011b). Studies relating Amur honeysuckle invasion to small mammals are scarce and have mainly focused on feeding preferences (Williams et al. 1992) and foraging and anti-predator behavior (Mattos and Orrock 2010, Dutra et al. 2011). Consequently, little information is available pertaining to the impacts of honeysuckle invasion on mammal abundance or environmental variables influencing mammalian use of habitat in invaded ecosystems.

170

The primary objective of this study was to examine the influence of Amur honeysuckle and co-occurring exotic shrub species on the abundance and space use of the white-footed mouse (Peromyscus leucopus), a habitat generalist found throughout North

America (Batzli 1977, Glazier 1980, Adler and Wilson 1987). While many studies focus on rare, threatened, or endangered species, it is also critical to consider impacts of environmental changes on abundant generalist species such as white-footed mice. White- footed mice and other Peromyscus species are an important food source for numerous predators, including canids (Knable 1970, Philips and Hubert 1980), mustelids (Hamilton

1933, Lampe 1982), raptors (Errington 1932, Armstrong 1958), and snakes (Reinert et al.

1984, Kurta 1995). Furthermore, white-footed mice play critical roles as primary consumers by feeding on plant seeds (Whitaker, Jr., 1966, Ostfeld et al. 1997) and mycorrhizal fungi (Maser and Maser 1987), as predators by consuming bird eggs

(Maxson and Orig 1978, DeGraaf and Maier 1996) and insects (Anderson and Folk 1993,

Elkinton et al. 1996), and as preferred hosts for the spirochete bacteria that cause Lyme disease (Donahue et al. 1987). Therefore, fluctuations in population abundance and changes in habitat use by species such as white-footed mice have important implications

for the population dynamics of producers, consumers, and predators across trophic levels. 170

Aggressive invasion by Amur honeysuckle may represent a drastic environmental change for white-footed mice, whereby changes in native plant diversity and structural characteristics lead to changes in numbers of mice and how they are using habitat resources. To date, only Mattos and Orrock (2010) have investigated how invasion by

Amur honeysuckle influences the abundance of white-footed mice, but this study focused on differences in mouse abundance between invaded and un-invaded areas. In the

171 context of habitat management, it is still unclear how white-footed mice respond to the management practice of removing invasive species such as Amur honeysuckle.

Furthermore, we are aware of only one study where investigators examined how environmental variables influence movement patterns of individual mice in invaded habitats, but the influence of exotic shrubs was not directly examined (Klein and

Cameron 2012).

To examine how an invasive shrub influences the abundance and habitat use of white-footed mice, we 1) examined changes in the abundance of white-footed mice following removal of Amur honeysuckle and other exotic shrubs in six mixed-hardwood forests in Indiana where Amur honeysuckle was the dominant invasive species, and 2) examined the relationship between environmental variables and space use (using an index of movement) for individual mice, across a gradient of Amur honeysuckle invasion intensity. Given the dense sub-canopy layer produced by Amur honeysuckle and the fact that Peromyscus spp. are known to feed on honeysuckle berries, we hypothesized that removing this shrub would lead to decreases in white-footed mice abundance due to the removal of structure and food in the lower stratum of the forest. We also hypothesized

that space use by individual mice would have a positive association with canopy cover, 171

Amur honeysuckle density, and amount of coarse woody debris (CWD) because these features provide protection from predators and microsites for hiding, nesting and feeding

(Kaufman et al. 1983).

172

5.2. Methods

5.2.1. Study Sites

We sampled white-footed mice and environmental variables in mature, second- growth mixed-hardwood forests in the glaciated regions of central Indiana (forest locations ranged from 39°20’ N to 40°26’ N, and 86°57’ W to 87°26’ W). Specifically, six forests were examined―1) Fowler Park, 2) Hawthorn Park, 3) Pfizer, Inc., Rifle

Range woodlot (hereafter referred to as Rifle Range), 4) Purdue University, Department of Forestry and Natural Resources Farm (hereafter referred to as FNR Farm), 5) a private woodlot in Lafayette, Indiana (hereafter referred to as Pursell), and 6) Ross Biological

Reserve (hereafter referred to as Ross). Till plains, flood plains, loess hills, and dunes were the typical landforms, with parent materials consisting of loess, loess over loamy till, loess over loamy outwash, loamy alluvium over sandy and gravelly outwash, silty alluvium, loamy till, and loamy outwash over sandy and gravelly outwash (Soil Survey

Staff, Natural Resources Conservation Service, United States Department of Agriculture

2013). Soil types ranged from very poorly drained loams to excessively drained sandy 172 loams (Soil Survey Staff, Natural Resources Conservation Service, United States

Department of Agriculture 2013).

At all study sites, canopy layers were characterized by mature, second-growth deciduous trees. Species composition of canopy trees varied across forests, but all study sites contained sources of hard and soft mast (See Table 4.1 in Chapter 4 from this dissertation). Ground-layer community composition was more consistent across study

173 sites. Specifically, the most frequent species in the lowest stratum were Amur honeysuckle, sanicle (Sanicula spp.), Virginia creeper (Parthenocissus quinquefolia [L.]

Planch.), jumpseed (Polygonum virginianum L.), white ash/green ash (Fraxinus americana L./Fraxinus pennsylvanica Marshall), white snakeroot (Ageratina altissima

[L.] King & H. Rob.), Clayton’s sweetroot/longstyle sweetroot (Osmorhiza claytonii

[Michx.] C.B. Clarke/Osmorhiza longistylis [Torr.] DC.), common blue violet (Viola sororia Willd.), Canadian clearweed (Pilea pumila [L.] A. Gray), multiflora rose (Rosa multiflora Thunb.), eastern poison ivy (Toxicodendron radicans [L.] Kuntze), and

Canadian honewort (Cryptotaenia canadensis [L.] DC.); See Chapter 4 from this dissertation). All six study sites were selected using the criterion that Amur honeysuckle was the dominant exotic species in terms of percent cover, density, and size of individuals in the population. For example, Amur honeysuckle comprised 97.7%, 90.6%,

98.8%, 84.4%, 100%, and 51.0% of mean exotic shrubs/ha (≥ 1.37 m tall) at FNR Farm,

Fowler Park, Hawthorn Park, Pursell, Rifle Range, and Ross, respectively.

5.2.1. Experimental Design

173

At each study site, we delineated two, 80 m x 80 m sample areas (extending from the forest edge to interior) to sample small mammals and environmental variables (Figure

5.1). For each sample area, one boundary was placed along the forest edge and the other three boundaries were placed ≥ 10 m from any edge (Figure 5.1). When possible, sample areas were placed ≥ 150 m apart so that the distance between areas exceeded the typical home range size of white-footed mice (Mace and Harvey 1983), thus ensuring

174 independence between sample areas (Figure 5.1). In November 2010 through March

2011, Amur honeysuckle and all other exotic shrubs were removed in one of the sample areas at each study site. The other 80 m x 80 m sample area (reference area) was not manipulated. Specifically, all exotic shrubs rooted within the removal area were either pulled by hand or cut at ground level with a gas-powered clearing saw. Stumps were treated with herbicide (20% Garlon 4® triclopyr, 1% Stalker® imazapyr, and 79% Ax-it® basal oil) and all large slash (≥ 5 cm basal diameter) was removed in order to create a condition where Amur honeysuckle and other exotic shrubs were absent. Specifically, we piled slash ≥ 50 m away from both the removal and reference areas, added it to pre- existing slash piles or thickets of Amur honeysuckle surrounding the removal area, or removed it from the study site and chipped it using a wood chipper. The purpose of piling large slash ≥ 50 m away or incorporating it into existing slash piles was to minimize the influence these slash piles would otherwise have on abundance of white- footed mice, which would compromise our ability to attribute changes in mice abundance to the treatment effect of removing exotic shrubs. We chose 50 m as a distance because at sites where it was not logistically feasible to use a wood chipper, 50 m represented the

maximum, universal distance (across study sites) from both sample areas that we could 174 place the slash. Smaller slash (< 5 cm basal diameter) was scattered on the forest floor without creating noticeable piles. As mentioned previously, we selected study sites where Amur honeysuckle was the dominant exotic shrub so any observed responses by white-footed mice would be due mainly to the removal of Amur honeysuckle. However, we chose to remove all exotic shrubs because this represented a more realistic invasive species management practice.

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5.2.3. Data Collection

5.2.3.1. White-footed mice

To examine the short-term effects of removing Amur honeysuckle and other exotic shrubs on the abundance of white-footed mice, we placed 60 m x 60 m trapping grids at sample areas where exotic shrubs were removed (hereafter referred to as removal grids) and at each area that was not manipulated (hereafter referred to as reference grids;

Figure 5.2). Each grid contained 49 Sherman live traps (H.B. Sherman traps,

Tallahassee, FL), spaced 10 m apart (Figure 5.2).

White-footed mice were sampled in removal grids and reference grids prior to removal of Amur honeysuckle and other exotic shrubs (summer and fall 2010) and again the year after removal (summer and fall 2011). The purpose of the reference grid was to examine whether abundance would differ between years if Amur honeysuckle and other exotic shrubs remained a component of the habitat. Thus, if significant changes in abundance were observed in removal grids, then the reference data would help reinforce

whether or not these changes could be directly attributable to changes in the density of 175 exotic shrubs.

Small mammal sampling prior to removals began in June 2010. One hundred ninety-six Sherman traps were placed at two of the six study sites (49 traps/grid × two grids/study site × two study sites) on 14 June 2010. For six nights, each trap was set and opened in the afternoon, checked the following morning, and closed until that afternoon

(Sutherland 1996). Each trap was baited with sunflower seeds and polyester filling was

176 added to provide insulation and nesting materials for captured animals. On 22 June 2010,

Sherman traps were moved to other study sites and the process of trapping for six nights was repeated until we sampled all six study sites, resulting in 3,528 potential trap nights for the summer period (49 traps × two grids/study site × six study sites × six nights of trapping). In early October 2010, we placed 196 Sherman traps at two of the study sites using the same criteria used during the summer trapping period, except that traps were opened for four nights at each site (2,352 potential trap nights = 49 traps × two grids/study site × six study sites × four nights of trapping). In June and July 2011 and

October 2011 (after the removal treatment was implemented), we trapped white-footed mice using the same trapping grids and criteria used in 2010.

For white-footed mice, and other members of Muridae, Dipodidae (jumping mice), and eastern chipmunks (Tamias striatus), captured individuals were marked with a uniquely-numbered, size 1 Monel ear tag (National Band and Tag Company, Newport,

KY) upon first capture and released back into the population (mark-release-recapture

[MRR]; Williams et al. 2002). Prior to release, we recorded species, weight (grams, using a Pesola® scale), sex, and reproductive status (for males, whether or not testes were

descended and for females, whether or not nipples were distinct). Any captured animals 176 other than Muridae, Dipodidae, and eastern chipmunks were identified and released immediately without being marked. Trapping and handling procedures followed those outlined by Bookhout (1994) and Gannon and Sikes (2007). Trapping and handling was conducted with an approved Indiana Scientific Purposes License and protocol (#07-032) from the Purdue Animal Care and Use Committee (PACUC). Scientific names of mammals followed Whitaker and Mumford (2009).

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5.2.3.2. Environmental variables

Environmental variables were collected in each trapping grid using a series of plots located along transects (Figure 5.2). In each grid, three transects were placed 20 m apart, with each transect extending from the forest edge to the interior (Figure 5.2).

Along two transects, we placed four variable-radius plots (basal area factor of 2.296 m2/ha), spaced 40 m apart (Figure 5.2). We placed four, 40-m2 (radius of 3.57 m) circular plots (sapling/shrub plots) and four, 2 m x 2 m quadrats along all three transects

(Figure 5.2). Sapling/shrub plots were spaced 20 m apart along each transect, with the first plot located 5-10 m from the forest edge (Figure 5.2). Each quadrat was placed either to the upper right, upper left, lower right, or lower left of the sapling/shrub plot center (direction was chosen randomly) and oriented parallel to the transect (Figure 5.2).

Variable-radius plot centers coincided with sapling/shrub plot centers (Figure 5.2). Every plot contained a Sherman trap, but not all Sherman trap locations contained a plot (Figure

5.2).

In the variable-radius plots, we recorded species and diameter at breast height

(dbh; 1.37 m) of overstory trees (woody stems ≥ 10 cm dbh). In the sapling/shrub plots, 177 we recorded the number of structural individuals of saplings and shrubs (woody stems <

10 cm dbh and ≥ 1.37 m tall) by species. A sapling or shrub that produced multiple stems from the same rootstock was considered one structural individual. In the 2 m x 2 m quadrats, we recorded percent covers of herbaceous species, woody vines, seedlings

(woody stems < 1.37 m tall), coarse woody debris (CWD; midpoint diameter ≥ 10 cm), and leaf litter. Percent covers were categorized as follows (modified from Peet et al.

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[1998]): 0-1%, 1-2%, 2-5%, 5-10%, 10-25%, 25-50%, 50-75%, 75-95%, and > 95%.

Percent covers were estimated in the vertical stratum of 0-1 m, and estimates were performed by a single observer to reduce observer bias. Finally, we recorded combined percent cover of the overstory canopy and sub-canopy (e.g., Amur honeysuckle canopy) by using a spherical densiometer held one meter above the ground in each of the four corners of the quadrat.

Quadrats were first sampled in July-August 2010 to coincide with growth of summer vegetation and summer small mammal trapping, and in October 2010 to coincide with fall small mammal trapping and to capture vegetation that flowered late in the season. Variable-radius plots and sapling/shrub plots were visited once in 2010 (in

September) and these data served as estimates for both summer and fall. All plots were re-sampled in 2011 (after the removal treatment) in the same manner as in 2010.

Scientific names of plant species were based on the U.S.D.A. Plants Database (USDA,

NRCS 2013).

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5.2.4. Data Analyses

5.2.4.1. Abundance of white-footed mice

For this study, we considered small mammal populations to be closed (Williams et al. 2002). Important assumptions of a closed population are 1) that no additions (from birth or immigration) or losses (from death or emigration) occur during a trapping period,

2) marks (in this case ear tags) are not lost or overlooked, and 3) capture probabilities are modeled appropriately (Williams et al. 2002).

Abundance of white-footed mice was estimated for each trapping grid.

Specifically, we used encounter history data from each reference and removal trapping grid at each study site to estimate the abundance of white-footed mice in each reference and removal area at each study site. Furthermore, for each reference or removal area, we calculated a separate abundance estimate by season (summer or fall) and year (2010 or

2011). For each abundance estimate, white-footed mice data were pooled due to an overall low sample size (i.e., sex category and reproductive classes were not examined

separately). We estimated abundance using closed-capture model Mth (Otis et al. 1978). 179

Model Mth was chosen because, 1) during every trapping session we observed changes in temperature, cloud cover and/or precipitation, which can influence the movements of

Peromyscus (Drickamer and Capone 1977, Vickery and Bider 1981), and 2) mice data that were pooled included different ages, genders, and reproductive classes so we assumed there would be some degree of individual-level variation in capture probabilities. Furthermore, because number of unique mice captured during a given

180 trapping session was often < 20, we chose not to use estimation procedures based on

Maximum Likelihood Estimation (MLE), which can be unreliable with small sample sizes (White et al. 1982). Instead, we used parameter-expanded data augmentation (PX-

DA), whereby encounter history data for a given trapping session was augmented by adding all-zero encounter histories and a new model was calculated using Bayesian analysis (Kéry and Schaub 2012, Royle and Dorazio 2012). Specifically, 300 all-zero rows were added to each encounter history data set and model Mth was fit to the augmented data set using a Bayesian approach, where time was treated as an additive fixed effect and individual was treated as a random effect (Kéry and Schaub 2012). To produce posterior distributions of white-footed mice abundance (N), we specified the

Markov chain Monte Carlo (MCMC) settings as three chains with 25,000 iterations, a burn-in of 5,000, and a thinning rate of two.

Permutation tests (Pesarin 2001) were used to test for differences in white-footed mice abundance prior to removal of exotic shrubs and after removals (n = six study sites).

Specifically, for each trapping grid, we used the mean value of the posterior distribution of white-footed mice abundance as a point estimate and calculated the difference in mean

values between years (2011 minus 2010). We then ran four permutation tests—1) testing 180 whether mean of differences (from 2010 to 2011) for mouse abundance was significantly different from zero in removal grids in the summer, 2) testing whether mean of differences (from 2010 to 2011) for mouse abundance was significantly different from zero in reference grids in the summer, 3) testing whether mean of differences (from 2010 to 2011) for mouse abundance was significantly different from zero in removal grids in the fall, and 4) testing whether mean of differences (from 2010 to 2011) for mouse

181 abundance was significantly different from zero in reference grids in the fall. For each permutation test, 64 iterations were specified. Specifically, we examined all possible combinations of shuffling 2010 and 2011 abundance estimates for a given trapping grid.

For each iteration, we then calculated a mean of differences, resulting in a null population of means of differences. We chose 64 iterations because with six study sites, the number of possible ways to shuffle abundance estimates to create the null population is 26 = 64.

Based on 64 iterations (which included the observed mean of differences), the permutation p value was calculated as p = (number of times mean of differences from null population was ≥ observed mean of differences)/64. For data that do not adhere to parametric assumptions of normality such as the data observed in this study, it is more advantageous (and more appropriate) to use non-parametric permutation tests to examine before-and-after changes as opposed to using parametric procdures such as paired t-tests or Analysis of Variance.

5.2.4.2. White-footed mice space use

For a subset of white-footed mice, we examined the influence of environmental 181 variables on space use. Mean squared distance (MSD; Calhoun and Casby 1958, Slade and Swihart 1983) was used as an index of space use, calculated as

(( ) ∑( ̅) ) (( ) ∑( ̅) ) , where n = number of captures

for a given mouse, = x coordinate of a Sherman trap for a given capture, = y coordinate of a Sherman trap for a given capture, and ̅ and ̅ were the coordinates for a

182 given mouse’s center of activity (Hayne 1949). For MSD calculations, the subset of mice consisted of resident mice (captured ≥ two times; Bennett 1990) that were captured in a minimum of two different Sherman traps in a given trapping grid, with a center of activity ≥ 15 m from forest interior boundaries of the grid. We chose to exclude mice with centers of activity within 15 m of the forested boundaries of the sample areas because many of their movements could have occurred outside of the area being trapped, thus biasing the calculation of MSD for those mice (Klein and Cameron 2012).

With MSD as the dependent variable, we used linear regression (Zar 1999) to determine how environmental variables influenced space use by individual mice.

Because no individual was observed in more than one study site, grid type, season, or year, we pooled mice across reference and removal grids and years. However, because mice may respond to different microsite variables depending on season, we analyzed summer and fall data separately. For each MSD model type (summer or fall), we examined the influence of the following predictor variables: abundance estimate of white-footed mice for a given grid, distance from center of activity of focal mouse to nearest known neighbor’s center of activity (m), distance from center of activity to

nearest forest edge (m), percent cover of native vegetation, percent cover of exotic 182 vegetation, percent cover of CWD, percent cover of leaf litter, density of native saplings/shrubs, density of exotic shrubs, canopy cover, and overstory basal area (BA).

Not all Sherman live trap locations contained vegetation plots, therefore predictor variable values derived from fixed-area plot data were calculated by first assigning sapling/shrub plots and quadrats to one of 12 areas of influence (Figure 5.2). For each mouse, we then delineated a minimum convex polygon (MCP; Mohr 1947) based on

183

Sherman trap visits. Finally, for a given predictor variable, a mean value was calculated from fixed-area plot data representing all areas of influence intersected by the boundary of the MCP; this was the predictor value used in the regression model. The procedure for examining the influence of BA was the same, except there were only four areas of influence, where each variable-radius plot represented one quarter of the trapping grid

(Figure 5.2). For each MSD model type, we first ran the full multiple regression model using all 11 predictor variables. Assumptions of normality and constant variance were assessed by examining quantile-quantile plots and standardized residuals (Zar 1999) and when necessary, variables were transformed. We assessed potential problems with multicollinearity by calculating a Variance Inflation Factor (VIF) and excluding predictor variables with a VIF ≥ 5. Best subsets regression (Miller 1984) was then used to examine all subsets of the full multiple regression model. With 11 predictor variables, this equated to 211 = 2,048 possible models. Of those models, we chose the model with the lowest delta Akaike information criterion (AIC; Akaike 1974, Burnham and Anderson

2002) and all significant (p < 0.05) predictor variables.

All analyses were performed in R (R Version 2.15.1, http://www.R-project.org,

accessed 20 Feb 2013) and WinBUGS (WinBUGS Version 1.4.3, www.mrc- 183 bsu.cam.ac.uk/bugs/winbugs, accessed 20 Feb 2013). Specifically, abundance estimates of white-footed mice were calculated using the R package R2WinBUGS (Sturtz et al.

2005) to call WinBUGS. We performed best subsets regression using the R package

MuMIn (Barton 2013), and VIF values were calculated using the R package car (Fox and

Weisberg 2011).

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5.3. Results

5.3.1. Abundance of White-footed Mice and Other Species

We captured a total of 532 white-footed mice across grid types, seasons, and years (Table 5.1). Overall, more mice were captured during the summer trapping periods than the fall trapping periods (332 in summer as compared to 200 in fall; Table 5.1).

From 2010 to 2011, numbers of white-footed mice increased in both grid types for both trapping seasons, but changes were noticeably greater in removal grids (Table 5.1). The greatest change occurred in removal grids in summer, where number of mice increased from 72 in 2010 to 105 in 2011 (Table 5.1). Conversely, the smallest change occurred in reference grids in fall, where the number of mice increased from 44 in 2010 to 48 in 2011

(Table 5.1). While the white-footed mouse was the focus of this study and the only species captured enough to use for data analyses, we also captured and marked 24 individual eastern chipmunks (Tamias striatus) and two individual meadow jumping mice (Zapus hudsonius). Several other species were also captured (but not marked),

including the eastern gray squirrel (Sciurus carolinensis; one capture), North American 184 red squirrel (Tamiasciurus hudsonicus; four captures), northern short-tailed shrew

(Blarina brevicauda; 112 captures), Virginia opossum (Didelphis virginiana; two captures), tufted titmouse (Baeolophus bicolor; seven captures), Carolina chickadee

(Poecile carolinensis; nine captures), and American toad (Bufo americanus; one individual).

185

Abundance estimates of white-footed mice exhibited a similar pattern as number of individuals captured (Table 5.1, Figure 5.3). For summer trapping in 2010, abundance was highest in the reference grid at Pursell (¯x = 37, 95% Credible Interval [CI] = 29-53 mice), whereas the lowest estimate was in the reference grid at Rifle Range (¯x = 7, 95%

CI = 6-12; Figure 5.3). Trends in the summer of 2011 were similar, where the highest abundance was in the removal grid at Pursell (¯x = 46, 95% CI = 42-56) and the lowest abundance was in the removal grid at Ross (¯x = 8, 95% CI = 7-12; Figure 5.3).

Abundance estimates for fall trapping were generally lower than those for the summer.

In the fall of 2010, estimated abundance was highest in the reference grid at Pursell (¯x =

21, 95% CI = 15-35 mice) and lowest in the reference grid at Rifle Range (¯x = 8, 95%

CI = 6-15; Figure 5.3). Finally, during the fall of 2011, the highest abundance was in the removal grid at Pursell (¯x = 43, 95% CI = 31-71) and lowest in the reference grid at

Hawthorn Park (¯x = 4, 95% CI = 3-8; Figure 5.3).

As with number of individuals, changes in mean abundance estimates before

(2010) and after (2011) the removal treatment were much greater in removal grids than in reference grids, a trend observed for both summer and fall trapping (Figure 5.3).

Specifically, changes in mean abundance estimates in reference grids were small and 185 non-significant for both the summer trapping period (observed mean of differences between 2010 and 2011 = 0.45 mice, permutation p = 0.36) and the fall trapping period

(observed mean of differences = 0.97, permutation p = 0.44). Conversely, significant changes from 2010 to 2011 were observed in removal grids during the summer (observed mean of differences = 5.9 mice, permutation p = 0.05) and fall (observed mean of differences = 6.2 mice, permutation p = 0.02). It is important to note that at Ross,

186 abundance decreased in both the reference and removal grids during summer trapping

(Figure 5.3).

5.3.2. White-footed Mice Space Use and Environmental Variables

Across all study sites, Amur honeysuckle comprised > 88% of individuals/ha of exotic shrubs in 2010 (before the removal treatment was implemented). In reference areas, densities of exotic shrubs were similar in 2010 (1,944 ± 176 individuals/ha) and

2011 (1,958 ± 177 individuals/ha; Table 5.2). However, in removal areas, densities of exotic shrubs decreased by > 99% from 2010 to 2011 (Table 5.2), indicating that the removal treatment was successfully implemented, at least in the short term.

Environmental variables were similar across all study sites but noticeable changes were observed between seasons and after exotic shrubs were removed (Table 5.2). For example, tree BA ranged from 22.0 ± 1.2 m2/ha to 23.3 ± 1.3 m2/ha across all six study sites (Table 5.2). Canopy cover was higher in the summer than in the fall (summer range

= 90.9 ± 0.3% to 91.7 ± 0.3%, fall range = 83.0 ± 1.5% to 88.5 ± 0.4%; Table 5.2).

Percent cover of leaf litter was much higher in the fall (range = 40.4 ± 2.9% to 63.2 ± 186

1.8%) as compared to summer (range = 19.4 ± 2.8 to 31.6 ± 2.8; Table 5.2). The percent cover of CWD was similar across all study sites and both years (range = 3.3 ± 1.1% to

5.6 ± 1.6%; Table 5.2). Densities of native saplings/shrubs and percent covers of native and exotic vegetation were similar across study sites (Table 5.2). However, in removal areas, percent covers of native vegetation increased from 35.1 ± 2.8 prior to removing exotic shrubs to 55.3 ± 3.4 after removals during the summer (Table 5.2). Likewise,

187 native percent cover increased from 17.1 ± 1.6 prior to removing exotic shrubs to 33.2 ±

2.9 after removals during the fall (Table 5.2).

In terms of space use of individual white-footed mice, MSD for mice captured during the summer trapping periods ranged from 33.3 m2 to 1,850 m2, whereas MSD for mice captured during the fall periods ranged from 33.3 m2 to 1,250 m2. Important predictor variables for MSD differed depending on season. For mice captured in the summer, the best linear regression model contained percent cover of leaf litter as a significant predictor variable (p = 0.004). Specifically, the simple linear regression

2 model was ln(MSD) = 4.66 + 0.03(percent cover of leaf litter); R = 0.21, F1,37 = 9.61, n

= 39 mice. For mice captured in the fall, the best regression model contained percent canopy cover (p = 0.01) and estimated abundance of white-footed mice (p = 0.003) as predictor variables, and both were negatively associated with MSD. For this multiple linear regression model, ln(MSD) = 14.41 – 0.10(percent canopy cover) – 0.05(estimated

2 abundance of white-footed mice); Adjusted R = 0.31, F2,24 = 6.92, p = 0.004, n = 27 mice.

5.4. Discussion 187

Contrary to our hypothesis, removing Amur honeysuckle and other exotic shrubs will led to a short-term increase in the abundance of white-footed mice in our study sites within the Central Hardwoods Region. In both the summer and fall, abundance of white- footed mice showed positive, significant changes between 2010 and 2011 in areas where removal treatments were implemented whereas changes were much lower and non-

188 significant in reference grids (Figure 5.3). The only other study examining the influence of Amur honeysuckle on white-footed mice abundance (Mattos and Orrock 2010), found no statistically significant differences in mean number of individuals captured between forest sites invaded by Amur honeysuckle and sites that were not invaded (Mattos and

Orrock 2010). However, it is important to note that we examined changes in white- footed mice abundance in response to removing exotic shrubs in the same habitat (i.e., before-and-after) rather than comparing habitats with different levels of invasion intensity. To our knowledge, our study is the first to examine the effects of removing

Amur honeysuckle on the abundance of white-footed mice or any wildlife species.

The increase in white-footed mice abundance in response to removal of exotic shrubs is somewhat surprising given that Amur honeysuckle populations provide dense cover in the sub-canopy in the summer and both cover and food in the fall. In our study, the observed increases in abundance of white-footed mice were likely due to subsequent changes in habitat structure and quality following removal of exotic shrubs. While dense sub-canopy layers of Amur honeysuckle may promote higher levels of foraging activity for white-footed mice (Mattos and Orrock 2010, Dutra et al. 2011), increased foraging

activity does not necessarily translate to increases in population-level abundance. At our 188 study sites, white-footed mice may prefer using ground-layer vegetation and other features such as logs, stumps, and rocks, for hiding, nesting, or as corridors for traveling between feeding and nesting sites, as opposed to using individual honeysuckle shrubs, where they may be more exposed to predators. For example, Greenburg (2002) documented a positive association between white-footed mice capture success and coarse woody debris at the microsite level. Thus, additional microsite features at ground level,

189 even under dense sub-canopies of Amur honeysuckle, may be critical for the small-scale dispersal of these mice. No temporal changes in coarse woody debris were observed in our study; however, the cover of native vegetation in the lowest vertical stratum (≤ 1 m) was much lower before exotic shrubs were removed as compared to after removals

(Table 5.2). While cover of native vegetation in the lowest vertical stratum did vary across study sites, we observed post-removal increases in cover at all study sites (See

Tables 4.3 and 4.4 in Chapter 4 from this dissertation). It is possible that this increase in ground cover resulted in more available corridors to travel between potential nesting sites, which, in turn, resulted in a greater number of mice utilizing the habitat. Certain ground-layer species also serve as sources of nesting material (Whitaker and Mumford

2009) and food for white-footed mice (Whitaker 1966); both are important for reproduction. At our study sites, we observed numerous ground-layer species that produce seeds consumed by white-footed mice, including trumpet creeper (Campsis radicans [L.] Seem. ex Bureau), sedge (Carex spp.), wildrye (Elymus spp.), fleabane

(Erigeron spp.), geranium (Geranium spp.), avens (Geum spp.), touch-me-not (Impatiens spp.), woodsorrel (Oxalis spp.), American pokeweed (Phytolacca Americana L.),

knotweed (Polygonum spp.), buttercup (Ranunculus spp.), blackberry (Rubus spp.), 189 greenbrier (Smilax spp.), eastern poison ivy, and grape (Vitis spp.; Whitaker 1966, Jones

1970). Many of these taxa increased in percent cover after we removed exotic shrubs

(See Table 4.5 in Chapter 4 from this dissertation).

Another reason for our observed increases in mice abundance in removal grids, particularly during the fall, may be related to food quality. While consumption of Amur honeysuckle berries by white-footed mice has not been adequately demonstrated in the

190 field, we observed several individuals consuming berries in this study. Amur honeysuckle does produce many berries during the fall (as many as 7,300 berries have been recorded on a single shrub; Lieurance 2004), which may provide an abundant food source for mice utilizing invaded habitats. However, as mentioned previously, Amur honeysuckle berries are considered nutritionally poor (Ingold and Craycraft 1983). In habitats heavily invaded by Amur honeysuckle, mice may key in on this widely available but nutritionally poor food source, especially in habitats where numbers of honeysuckle berries far exceed higher-quality seeds from native vegetation. Thus, removing Amur honeysuckle may have led to a shift in foraging behavior by mice whereby individuals in a population more frequently search for and consume higher-quality food sources (e.g., acorns, hickory nuts, black cherry seeds, insect larvae), resulting in higher mouse productivity and/or potential increases in abundance. Jones (1970) examined the stomach contents of 489 white-footed mice in Indiana and found that the highest volume of food was comprised of starchy materials such as acorns and hickory nuts (mean of 30.1% of total volume), followed by insect adults and larvae (mean of 23.7%), unidentified seeds

(mean of 7.5%), and Prunus seeds (5.3%), with the remaining food being a combination

of woody and herbaceous plant seeds. Interestingly, Lonicera seeds constituted only 190

0.3% of the total volume of food found in the stomachs of these mice (Jones 1970). In terms of mast availability in our study, shifts in feeding behavior following removal of exotic shrubs would have differed across sites given the variability in species that dominated the overstory stratum. For example, at Fowler Park, the highest 75% of cumulative importance values for overstory species were represented by white ash, black walnut (Juglans nigra L.), black cherry (Prunus serotina Ehrh.), American elm (Ulmus

191 americana L.), sassafras (Sassafras albidum [Nutt.] Nees), eastern redbud (Cercis canadensis L.), shingle oak (Quercus imbricaria Michx.), and common persimmon

(Diospyros virginiana L.), with 16 additional species occupying the overstory stratum

(See Table 4.1 in Chapter 4 from this dissertation). Conversely, FNR Farm contained 16 total species representing the overstory stratum, with only four species comprising the highest 75% of cumulative importance values (black locust [Robinia pseudoacacia L.],

American elm, black walnut, and shagbark hickory [Carya ovata {Mill.} K. Koch]).

From the perspective of white-footed mice, these differences in overstory composition across study sites equate to differences in mast quality. It is important to note that neither diet nor consumption rates were measured in this study. Therefore, future research should focus on changes in the diets of white-footed mice in response to removal of exotic shrubs and how this is related to changes in population-level abundance.

Observed increases in abundance in removal grids were likely influenced by mice that also utilized habitat still infested with Amur honeysuckle and other non-native shrubs. As mentioned previously, removal treatments were implemented in 80 m x 80 m areas but at all study sites, these areas were surrounded by forest that still contained

exotic shrubs. While mice with centers of activity within 15 m of a forest boundary of 191 sample areas were excluded from MSD analyses, all captured mice were considered when calculating abundance. Thus, mice captured in the outer perimeter of a trapping grid were likely using habitat features inside and outside the removal areas. Furthermore,

MSD analyses indicated that dispersion from center of activity for some mice was large enough to include both invaded and un-invaded habitats. While our study has provided valuable insights into the response of white-footed mice to invasive species management

192 at the scale of habitat patches, future research should incorporate management-driven removal of exotic shrubs from entire forests/woodlots in order to better understand how this species might respond to larger-scale removal treatments.

In terms of space use by individual mice, we hypothesized that higher MSD would be afforded by greater canopy cover from trees and exotic shrubs (resulting in decreased perceived predation risk), as well as higher percent covers of CWD (critical as travel corridors and nesting sites). For white-footed mice captured in the fall, MSD was negatively correlated with canopy cover (opposite to what we hypothesized) and abundance. Higher canopy cover near the center of activity may result in a mouse concentrating its foraging movements within that area, where perceived predation risk is lower. Conversely, if the average canopy cover is low within the home range of a mouse, then there may be a need to move farther from the center of activity to find multiple protected patches to search for food. During the fall trapping periods in 2010 and 2011, a portion of trees at our study sites had already dropped leaves for the season so there was patchiness in canopy cover within the home ranges of many of the white-footed mice.

Furthermore, for mice captured in both grid types in 2010 (before the removal treatment)

and in reference grids in 2011, most of the fall canopy cover was provided by Amur 192 honeysuckle (which retains its leaves longer than other woody species in its invaded range; Luken 1988), indicating that the influence of canopy cover on MSD was primarily driven by honeysuckle cover. It is not surprising that habitat-level abundance of white- footed mice was negatively correlated with MSD. As densities of mice increase, competition for resources may result in smaller areas of activity for individuals in the population. This may be especially true for white-footed mice given that they are solitary

193 during the summer and fall and typically exhibit interference competition (Korytko and

Vessey 1991, Kurta 1995). This aggressive behavior between individuals may be exacerbated in the fall, when white-footed mice are hoarding food for the winter (Kurta

1995). Wolff (1985) also observed this density-dependent effect by documenting a negative, albeit marginal, correlation between home range size and population density of

Peromyscus spp.

For mice captured in the summer, only percent cover of leaf litter was significantly associated with MSD, with a higher percent cover of leaf litter equating to a higher MSD by individual mice. This was not entirely surprising given that the mean percent cover of leaf litter during the summer periods across both grid types and years didn’t exceed 32% (Table 5.2). Dispersal distance around a focal mouse’s center of activity may therefore be highly influenced by proximate patches of leaf litter used as safe travel corridors or to search for seeds, ground-dwelling arthropods, and other food sources located in these litter layers. Klein and Cameron (2012) also examined how microsite variables influenced MSD of white-footed mice in an eastern deciduous forest in Ohio that contained Amur honeysuckle and other exotic shrubs. These investigators

found that female MSD was positively correlated with distance from edge and diameter 193 of trees, and negatively correlated with vertical cover (Klein and Cameron 2012). We did not see an edge-to-interior effect on MSD of mice examined in our study, possibly due to the fact that we pooled males and females to have a larger sample size for our analyses.

However, for a given trapping season (summer or fall), we also pooled mice across years, meaning that the edge-to-interior environment for mice captured before exotic shrubs were removed would be different than the environment for mice captured in removal

194 grids in 2011 (after the removal treatment was implemented). Where exotic shrubs were not removed, microhabitat quality may have changed little from forest edge to interior, especially given that Amur honeysuckle, due partly to its tolerance of shade, was the main source of vertical complexity along the forest edge and interior. For this same reason, removing exotic shrubs in removal grids may have induced a consistent change in habitat quality, where edge-to-interior gradients were less prevalent compared to forests with a more drastic change in edge-to-interior structure due to lower densities of native, light-demanding shrubs in the forest interior.

While our results differed from those of Klein and Cameron (2012), both studies suggest that small-scale environmental variables associated with food quality, potential sites for nesting and hiding, and perceived predation risk are important drivers of localized space use by white-footed mice in forest ecosystems. A few other studies have also examined the influence of microhabitat variables on small-scale movement patterns of white-footed mice (Myton 1974, Wolff 1985), but our study is the first that we are aware of that explicitly examined the influence of Amur honeysuckle and other microhabitat features on space use of white-footed mice in hardwood forests.

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Table 5.1. Number of unique white-footed mice (Peromyscus leucopus) captured (using Sherman live traps) in reference and removal grids, during the summer and fall of 2010 and 2011, at six mixed-hardwood forests in Indiana— Purdue University Forestry and Natural Resources Farm (FNR Farm), Fowler Park (Fowler), Hawthorn Park (Hawthorn), privately owned woodlot (Pursell), Pfizer, Inc., Rifle Range woodlot (RR), and Ross Biological Reserve (Ross). For a given grid, number of trap nights = (number of potential trap nights) – (number of times a trap was sprung and empty + number of times a species other than white-footed mouse was captured). Number of potential trap nights for a grid in the summer was 294 trap nights (49 traps * six nights of trapping). Number of potential trap nights for a grid in the fall was 196 (49 traps * four nights of trapping).

Reference Grids

Number of individuals and trap nights, Summer Number of individuals and trap nights, Fall

2010 2011 2010 2011 2010 2011 2010 2011 Study Trap Trap Trap Trap Individuals Individuals Individuals Individuals Site nights nights nights nights

FNR 8 228 11 252 11 175 8 163 Farm

Fowler 13 250 12 261 5 166 7 175

Hawthorn 11 235 9 269 6 170 3 160

Pursell 28 218 33 270 14 176 23 185

RR 6 244 7 260 3 156 2 145

Ross 9 264 8 271 5 170 5 182

Removal Grids

Number of individuals and trap nights, Summer Number of individuals and trap nights, Fall

2010 2011 2010 2011 2010 2011 2010 2011 Study Trap Trap Trap Trap

Individuals Individuals Individuals Individuals 195 Site nights nights nights nights

FNR 11 236 13 247 8 148 15 185 Farm

Fowler 8 249 12 265 4 174 5 155

Hawthorn 11 221 15 259 5 171 6 179

Pursell 24 226 41 259 14 172 29 175

RR 10 260 17 254 6 175 8 142

Ross 8 256 7 269 3 174 5 184

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Table 5.2. Mean (± 1 SE) values for environmental variables in reference and removal grids, during the summer and fall of 2010 and 2011, across six mixed-hardwood forests in Indiana. Variables included basal area (BA; m2/ha) of trees ≥ 10 cm diameter at breast height (1.37 m; dbh), canopy cover (%), density (individuals/ha) of native saplings/shrubs (woody stems ≥ 1.37 m tall and < 10 cm dbh), density (stems/ha) of Amur honeysuckle (Lonicera maackii [Rupr.] Herder) and other exotic shrubs, ground cover of native vascular plants (%), ground cover of exotic vascular plants (%), ground cover of coarse woody debris (CWD; %), and ground cover of leaf litter (%). BA was estimated based on four variable-radius plots (basal area factor of 2.296 m2/ha) per grid (n = 24 across all six study sites for a given grid type and year). Native and exotic sapling/shrub density estimates were based on 12, 40-m2 (radius of 3.57 m) fixed-area plots per grid (for each variable, n = 72 across all six study sites for a given grid type and year). All other estimated values were based on 12, 2 m x 2 m quadrats per grid (for each variable, n = 72 across all six study sites for a given grid type and year). For canopy cover, the point estimate for a given quadrat was based on mean canopy cover from four measurements taken with a spherical densiometer (one measurement at each corner of the quadrat). Quadrats were visited during the summer and fall of each year. Sapling/shrub plots and variable-radius plots were visited only once per year (estimates from those plots were used for both summer and fall for a given year).

Summer trapping periods Fall trapping periods

2010 2011 2010 2011 2010 2011 2010 2011 Variable Reference Reference Removal Removal Reference Reference Removal Removal

23.3 ± 23.3 ± 23.3 ± 23.3 ± BA 22.0 ± 1.2 22.0 ± 1.2 22.0 ± 1.2 22.0 ± 1.2 1.3 1.3 1.3 1.3

91.7 ± 90.9 ± 83.7 ± 83.0 ± Canopy cover 91.0 ± 0.4 90.8 ± 0.3 83.2 ± 0.7 88.5 ± 0.4 0.3 0.3 0.8 1.5

Native 1,409.7 ± 1,406.3 ± 1,142.4 1,125.0 1,409.7 ± 1,406.3 ± 1,142.4 1,125.0 saplings/shrubs 150.4 177.3 ± 110.0 ± 141.1 150.4 153.7 ± 110.0 ± 141.1

1,944.4 ± 1,958.3 ± 2,628.5 20.8 ± 1,944.4 ± 1,958.3 ± 2,628.5 20.8 ± Exotic shrubsa 175.5 177.3 ± 175.4 11.9 175.5 177.3 ± 175.5 11.9

Native ground 35.1 ± 55.3 ± 17.1 ± 33.2 ± 31.1 ± 2.6 29.6 ± 2.5 14.3 ± 1.3 22.5 ± 1.9 cover 2.8 3.4 1.6 2.9

Exotic ground 21.1 ± 19.9 ± 12.9 ± 1.8 14.2 ± 1.9 8.3 ± 1.0 11.9 ± 1.8 13.8 ± 1.9 7.8 ± 1.0 cover 2.2 2.2

CWD 5.2 ± 1.5 5.2 ± 1.5 3.3 ± 1.1 3.4 ± 1.0 5.2 ± 1.5 5.6 ± 1.6 3.3 ± 1.1 4.0 ± 1.2 196

22.2 ± 19.4 ± 60.4 ± 51.1 ± Leaf litter 30.0 ± 3.0 31.6 ± 2.8 63.2 ± 1.8 40.4 ± 2.9 2.9 2.8 3.0 3.9

aAmur honeysuckle comprised > 88% of exotic shrubs/ha across all study sites in 2010 (prior to the removal treatment).

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Figure 5.1. Example of one study site, showing two, 80 m x 80 m removal and reference areas for sampling small mammals and environmental variables. One boundary of each sample area was placed along the forest edge, with the other three boundaries being ≥ 10 m from any forest edge. In removal areas, Amur honeysuckle (Lonicera maackii [Rupr.] Herder) and other exotic shrubs rooted within the area were removed between November 2010 and March 2011. Reference areas were not manipulated. When possible, areas were placed ≥ 150 m apart to ensure independence between sampled populations of white-footed mice (Peromsycus leucopus). Map is not to scale.

198

3.57 m) sapling/shrub plots spaced 20 m apart along three transects. Transects were 198 spaced 20 m apart. Right diagram shows a closer view of a sapling/shrub plot (intersected by a transect), Sherman live trap, and a 2 m x 2 m quadrat to record percent covers of vascular vegetation, coarse woody debris, leaf litter, and canopy cover. Quadrats were placed either to the upper right, upper left, lower right, or lower left of the sapling/shrub plot center. Canopy cover was measured at each corner of the quadrat, using a spherical densiometer. Note that not all Sherman live trap locations contained vegetation plots. To examine the influence of fixed-area plot data on mean squared distance (MSD) of white-footed mice (Peromyscus leucopus), sapling/shrub plots and quadrats were separated into 12 areas of influence (1-12 on the left diagram). To examine the influence of overstory basal area on MSD, the trapping grid was separated into four equal quarters (upper left, upper right, lower left, and lower right), where each variable-radius plot represented one quarter of the trapping grid.

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Figure 5.3. Mean abundance estimates of white-footed mice (Peromyscus leucopus) by grid type (removal or reference), trapping season (summer or fall), and year (2010 or 2011) at six mixed-hardwood forests in Indiana—Purdue University Forestry and Natural Resources Farm (FNR Farm), Fowler Park (Fowler), Hawthorn Park (Hawthorn), privately owned woodlot (Pursell), Pfizer, Inc., Rifle Range woodlot (RR), and Ross Biological Reserve (Ross). Abundance estimates are based on Bayesian parameter- expanded data augmentation using closed-population model Mth, where time was treated as a fixed effect and individual was treated as a random effect. Each error bar represents upper bound of 95% Credible Interval from posterior distribution.

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CHAPTER 6 CONCLUSIONS

With regard to invasions by non-native organisms, Elton (1958) stated: ‘‘We must make no mistake: we are seeing one of the great historical convulsions in the world’s fauna and flora.” Results from our work with Amur honeysuckle (Lonicera maackii

[Rupr.] Herder) certainly provide additional lines of evidence in support of such a statement.

6.1. Spatial and Temporal Dynamics of Amur Honeysuckle

In this study we found that Amur honeysuckle reached the exponential phase of invasion at ~10-15 years at all three study sites examined; age distributions in this case were based on an accurate predictive model (Spearman’s ρ = 0.84; See Chapter 2 from this dissertation) used to predict minimum ages of individual Amur honeysuckle shrubs.

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Deering and Vankat (1999) found a similar pattern for Amur honeysuckle in an Ohio woodlot, suggesting that this may be a generalizable pattern of temporal population spread for this non-native, invasive shrub. In terms of spatial spread, we found that Amur honeysuckle spreads in a clustered pattern overall and that immature shrubs cluster around mature shrubs within invaded forests (i.e., expansion via neighborhood diffusion).

While Castellano and Boyce (2007) observed a similar pattern in an ecosystem

201 dominated by immature trees, we are the first to report such a pattern in mature forests.

Furthermore, our results revealed that accounting for spatial autocorrelation changed which factors most influenced life-stage characteristics of individual Amur honeysuckle shrubs. We are unaware of any other studies that have examined variables influencing life-stage characteristics of woody invasive shrubs while also accounting for spatial autocorrelation at the forest stand scale.

From a management perspective, these findings provide valuable information.

First, our quatification of the lag between initial invasion and exponential growth will help mangers determine treatment priorities following initial invasion. Given the high correlation between predicted minimum ages and true minimum ages of honeysuckle stem cross sections that were used to validate the model, forest managers should be able to use our model (or slightly modify it) to predict the age of Amur honeysuckle in a variety of forest types across the introduced range of the species. Information about the importance of spatial autocorrelation should help forest managers who are interested in knowing which measurable variables may be most important for locating the largest and tallest(and likely most fecund) Amur honeysuckle shrubs in an invading population. It is

important to note that while future research should include more study sites, and a wider 201 gradient of Amur honeysuckle invasion densities; this work is an important step towards more generalizable conclusions in terms of the temporal and spatial dynamics of Amur honeysuckle invasion.

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6.2. Intensity and Duration

We found that forests with the highest taxonomic diversity (Shannon’s Diversity;

Hˈ), richness (S), percent covers, and densities of native vegetation also had the lowest percent covers of Amur honeysuckle in the upper vertical stratum (1.01-5 m).

Furthermore, linear mixed model analyses results indicated that percent cover of Amur honeysuckle in the upper vertical stratum was negatively correlated with Hˈ, S, total percent cover, and seedling densities of native taxa at the microsites within forests.

Interestingly, duration of Amur honeysuckle at the microsite scale was not significantly associated with measures of plant diversity and abundance when percent cover of Amur honeysuckle in the upper vertical stratum was included in models. However, when we included duration as the only predictor variable, it was significantly correlated with dependent variables and with upper-stratum honeysuckle cover; this suggests that greater

Amur honeysuckle age at the microsite scale results in higher subcanopy percent cover and therefore light competition from above for native ground flora species.

To our knowledge, no other investigators have attempted to examine the

combined influence of invasive species duration and other environmental factors on 202 native vegetation at microsite-level scales within forests. By knowing that changes in native taxa diversity and abundance in response to Amur honeysuckle intensity and duration are heterogeneous within an invaded community, forest managers may better prioritize control efforts given that existing sources of native propagules at microsites where Amur honeysuckle invasion is less intense may be critical to the long-term recovery of native communities after control efforts are implemented.

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6.3. Effects of Removals on Native Flora

We found that the percent cover and diversity of native ground-layer flora and seedling densities of native woody species increased significantly after removing Amur honeysuckle and other non-native shrubs across the range of Amur honeysuckle invasion intensities observed across study sites. Such changes were not observed in reference areas, suggesting that removing non-native shrubs in forests dominated by Amur honeysuckle in the subcanopy stratum may lead to the short-term recovery of native plant taxa. It is also important to note that we also observed significant increases in the abundance of Amur honeysuckle seedlings and garlic mustard (Alliaria petiolata [M.

Bieb.] Cavara & Grande) following removals. Thus, forest managers are presented with a double-edge sword whereby control efforts may lead to the short-term recovery of desirable plants while simultaneously leading to subsequent invasions by other invasive species such as garlic mustard.

It is clear that the long-term recovery of native flora in forests like those observed in our study will depend on many factors, including renewed competition with invasive

species that re-colonize treatment areas, the influence of herbivores, and subsequent 203 efforts implemented by forest managers. For example, even in the absence of subsequent invasions by Amur honeysuckle, garlic mustard, or other invasive species, the recruitment of native tree seedlings to the overstory stratum will depend on the creation of canopy openings due to anthropogenic and non-anthropogenic disturbance.

Furthermore, successful recruitment of native taxa will be largely influenced by abundance of white-tailed deer (Odocoileus virginiana), where overabundant deer

204 populations can hinder the development of advanced regeneration of certain native plant species. One of the key components of this study was that we removed large slash to create a condition devoid of aboveground biomass of non-native shrubs; our intent was to mimic the long-term conditions observed after successfully implementing an invasive species control program. However, for some native plants, removing the large slash may have increased the vulnerability to herbivory by white-tailed deer due to the lack of physical barriers provided by the dead biomass. Furthermore, leaving slash is certainly more practical when implementing control efforts. While the positive effects of leaving slash are clear, it remains unclear whether there are any negative consequences of doing with regard to the long-term recovery of native plant communities following invasive shrub removals. Reseach has shown that Amur honeysuckle possess allelochemicals and leaving slash may create short-term, but acute, additions of these chemicals in much greater concentration than typically occurs from above and below litter inputs.

6.4. Influence on White-footed Mice

For both trapping seasons, mean abundance of white-footed mice (Peromyscus 204 leucopus) significantly increased from 2010 to 2011 in areas where we removed Amur honeysuckle and other non-native shrubs, whereas changes were more variable and non- significant in the reference areas that were not manipulated. Using mean squared distance (MSD) as an index of space use, we also found that space use of individual mice across a gradient of Amur honeysuckle invasion intensities was positively correlated with percent cover of leaf litter during summer and negatively correlated with overall canopy

205 cover (including canopy cover by Amur honeysuckle and other shrubs) and mice abundance during fall.

Amur honeysuckle and other non-native, invasive plant species continue to aggressively colonize vegetation communities, displacing native plant species, altering habitat quality, and disrupting ecosystem processes. Because our removal treatments created conditions where Amur honeysuckle and other exotic shrubs were effectively absent, the clear response of white-footed mice suggests that exotic shrubs are likely having negative impacts even on wildlife species that are considered generalists and capable of adapting to change. With regard to white-footed mice, this is a compelling finding given the importance of the species as a food source and consumer. This finding is particularly relevant to fragmented hardwood forests where the white-footed mouse is the often the only Peromyscus species present. In terms of space use, our results suggest that Amur honeysuckle cover is contributing to increased movements from centers of activity during the fall, when mice are hoarding food to prepare for winter. As pointed out by Mattos and Orrock (2010), a major implication of this type of behavior may be competitive interactions between Amur honeysuckle and native plant species.

Specifically, as white-footed mice feed heavily on native seeds under the crowns of 205 honeysuckle, they reduce and potentially deplete native seed sources, thereby reducing competition for growing space and favoring the establishment of honeysuckle reproduction (Mattos and Orrock 2010). Additional research is needed to determine if white-footed mice will shift their feeding and nesting behavior and/or exhibit increasingly lower population numbers as Amur honeysuckle continues to change habitat quality. Changes in forest ecosystems that result from interactions between small

206 mammals and exotic shrub species will likely contribute to region-wide changes resulting from shifting management techniques, introduced pests and pathogens, and increased deer populations (McShea et al. 2007).

6.5. Broader Impacts

This study will have important implications at a multitude of levels. From a scientific standpoint, this study will help advance the field of ecology by improving our understanding of how non-native invasive plants affect other trophic levels, and help us better understand the process of biotic homogenization resulting from disturbance suppression and invasive species. The effects of Amur honeysuckle on hardwood forests will also be of interest to land managers and Indiana residents. Indiana hardwood forests provide habitat for wildlife, help protect watersheds and water quality, sequester atmospheric carbon, and provide a multitude of recreational opportunities such as hunting, hiking, and bird watching. Furthermore, the hardwood forest industry has a total economic impact of ~17 billion dollars and directly employs more than 38,000 people in

Indiana, making it the largest economic sector of Indiana agriculture (Indiana Department 206 of Natural Resources 2006). Approximately 85% of the forests that produce these economic benefits are privately owned (Indiana Department of Natural Resources 2006).

Thus, a more thorough understanding of how Amur honeysuckle spreads in both time and space as well as its effects on biota such as native vegetation and small mammals will not only be of interest to the academic community, but also to state and federal agencies, conservation organizations, extension and consulting foresters, and private landowners.

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With improved knowledge, control efforts can be more efficiently prioritized and more comprehensive strategies can be developed to better protect multiple ecosystem components. Given the documented evidence that Amur honeysuckle has severe impacts on native biota (including results reported in our study), if forests invaded by Amur honeysuckle are left alone and no control efforts are implemented, then these forests may cease to be forests in the future, but rather degrade into a homogeneous, non-native shrub land that provides few benefits to humans and other organisms.

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Education Purdue University, West Lafayette, Indiana Doctor of Philosophy in Forest Biology, December 2013 Dissertation: Effects of Amur honeysuckle (Lonicera maackii [Rupr.] Herder) invasion and removal on native vegetation and white-footed mice (Peromyscus leucopus) in mixed-hardwood forests of Indiana

Michigan Technological University, Houghton, Michigan Master of Science in Forest Ecology and Management, August 2006 Thesis: Initial effects of group-selection harvesting with yellow birch (Betula alleghaniensis) retention on biodiversity in northern hardwoods

Michigan Technological University, Houghton, Michigan Bachelor of Science in Applied Ecology and Environmental Science, May 2004

Certifications, Awards, and other Honors  Exemplary Graduate Student Service Award, Department of Forestry and Natural Resources, Purdue University (2013)  1st place in Ph.D. Research category, Purdue University Department of Forestry and Natural Resources Annual Research Symposium (2013)  Ontario Tree Marker Training (2012)  Committee for Education of Teaching Assistants Teaching Award, Purdue University (2012)  Purdue Doctoral Fellowship, Purdue University (2009-2010, 2010-2011)  Game of Logging Phase 1 and Phase 2 Chainsaw Safety (2010)  Grinter Fellowship, University of Florida (2008-2009)  Member of the Year, Michigan Environmental Stewardship AmeriCorps (2008)  Wilderness First Aid (2007)

 Honorable Mention, Midwestern Association of Graduate Schools Distinguished Master’s Thesis 233 Competition (2007)  Distinguished Master’s Thesis (1st place across all departments), Michigan Technological University (2006)  Graduate Teaching Assistant of the Year, School of Forest Resources and Environmental Science, Michigan Technological University (2005)  Outstanding Senior, Applied Ecology and Environmental Science program, Michigan Technological University (2004)  2nd place, Upper Midwest Forestry Capstone Competition (2004)  Forest and Environmental Resource Management Team of the Year, School of Forest Resources and Environmental Science, Michigan Technological University (2004)

Professional and Academic Memberships  The Wildlife Society, Purdue University Chapter (Graduate Student Advisor from September 2012-August 2013)

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 Ecological Society of America (2011-present)  Indiana Academy of Science (2011)  Xi Sigma Pi (Forestry Honor Society), Alpha Eta Chapter (Associate Forester from January 2006- May 2006; member from 2004-2006)  Phi Kappa Phi (College Honor Society) (2004)

Research Skills  Oral and written communication skills  Ecological census methods  Equipment: angle gauge, chainsaw, clearing saw, clinometer, D-net, diameter tape, drip torch, fish electrofishing equipment, GPS receivers (Garmin, Magellan, and Trimble), herbicide application equipment (hand sprayers, backpack sprayers), Hess sampler, hypsometer, increment borer, logger’s tape, prism, radio telemetry, Secchi dish, Sherman live traps, soil auger, soil corer, water conductivity meter, water velocity meter, wading rod  Computer Skills: Microsoft Office Analytical/Statistical Software: COFECHA, Forest Vegetation Simulator (FVS), MIWILD, PAST, PC-ORD, Program Distance, Program MARK, R, SAS, SigmaPlot, Statistix, WinBUGS, WinDENDRO, GIS and GPS Software: ArcMap, GPS Pathfinder Office, GPS Utility

Extracurricular Skills  Acoustic guitar performance/songwriting (1995-present)  Martial arts , including experience with full-contact competitive fighting (1998-present)  Outdoor activity skills, including backpacking/camping, basic primitive living/survival skills, bowhunting, fishing, and orienteering (1995-present)

Work Experience

University of Wisconsin-Stevens Point – Stevens Point, WI Visiting Lecturer (September 2013-present)  Instructor for three courses – Forest Biometry (lecture and lab), Forest Mensuration (lab), Dendrology and Silvics (lab)

Biodiversity and Productivity Assessment in Wisconsin Grasslands – Southern Wisconsin; Michigan Technological University

Research Technician (May 2009-August 2009) 234 Field Crew Leader (April 2007-September 2007)  Determined the aerial percent covers of grassland vegetation by species  Quantified breeding male bird densities using distance sampling techniques  Mapped territories of singing male birds using spot-mapping and flushing techniques  Searched for bird nests and quantified and monitored the number of eggs  Captured and identified families of arthropods  Entered data into an MS Excel database

Etoniah Creek State Forest Hardwoods Study – Northern Florida; University of Florida Graduate Research Assistant (August 2008-May 2009)  Collected seedling and sapling density data  Recorded redbay (Persea borbonia) mortality rates due to laurel wilt disease  Analyzed overstory, sapling, seedling, ground-layer vegetation, and redbay mortality data using statistical methods

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 Completed four graduate-level courses (Environmental Soil, Water, and Land Use; Hardwoods Ecology; GIS for Research; Physiology of Forest Trees)

Michigan Environmental Stewardship AmeriCorps (MESA) – Genesee, Lapeer, and Shiawassee Counties, MI AmeriCorps Environmental Educator (October 2007-October 2008)  Conducted environmental risk assessment exercises with homeowners  Collected water samples from private wells to test for drinking water quality standards  Presented watershed and water quality concepts to K-12 and college students, environmental clubs, and citizen groups  Taught a high school Envirothon team about plant and animal ecology and trained them to conduct field work  Trained volunteers to plant trees, plant rain gardens, collect and identify aquatic organisms, and conduct environmental risk assessments  Distributed information about water quality and other ecosystem topics via pamphlets and brochures and by publishing articles in local newspapers and interviewing on local radio

Bark Beetle Trapping Project – Lower Peninsula of MI; Michigan Technological University Laboratory Technician (January 2007-May 2007)  Identified species of captured bark beetles

Eagle River Watershed Habitat Assessment - Eagle River, MI; Houghton Keweenaw Conservation District, Michigan Technological University Ecological Consultant (December 2006-April 2007) Field Crew Leader (June 2006-September 2006)  Quantified the density, diameters, and heights of trees and saplings by species  Quantified seedling density and percent covers of ground flora species  Inventoried coarse woody debris  Measured the lengths and weights of fish by species  Sampled aquatic invertebrates using D-nets and a Hess sampler  Measured water velocity, conductivity, water depths, and wetted widths  Created a GIS database using ArcView GIS software

Emerald Ash Borer Research Project – Lower Peninsula of MI; Michigan Technological University Research Technician (September 2006-November 2006)  Quantified emerald ash borer larval galleries throughout Lower Michigan

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Teaching Experience

Forest Biometry, Forest Mensuration, Dendrology and Silvics – Stevens Point, WI; University of Wisconsin-Stevens Point Visiting Lecturer (September 2013-present)  Teach lectures and labs in Forest Biometry, where students learn about basic statistical analyses  Teach labs in Forest Mensuration, where students learn about different methods of measuring forest resources  Teach labs in Dendrology and Silvics, where students learn how to identify woody plants

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Forest Ecosystems – West Lafayette, IN; Purdue University Graduate Teaching Assistant (August 2010 – December 2010, August 2011 – December 2011, August 2012-December 2012)  Assisted with lab exercises dealing with identification of tree species and herbaceous plants and how to collect vegetation data using fixed-area plot sampling and transects  Graded lab reports  Lectured on the use of Analysis of Variance (ANOVA) and Tukey’s multiple comparisons to analyze vegetation data  Lectured on non-native, invasive species and their role in forest ecosystems

Forestry and Wildlife Summer Practicum – Covenant Point, Iron River, MI; Purdue University Volunteer Teaching Assistant (May 2010-June 2010, May 2011, May 2012-June 2012, May 2013- June 2013)  Led field exercises dealing with the identification of plants and forest ecosystems  Demonstrated the use of increment borers and how to calculate site index  Lectured on the short-term, ecological effects of group-selection timber harvesting in northern hardwoods  Demonstrated the use of Sherman live traps to trap small mammals and how to properly handle, mark, and release captured mammals

Natural Resources Measurement – West Lafayette, IN; Purdue University Graduate Teaching Assistant (January 2010 – May 2010)  Assisted with lab exercises dealing with the identification of tree species and how to collect forestry data  Graded lab quizzes and lab practical  Lectured on the use of GPS in the field of forestry

Ornithology – Western Upper Peninsula of MI; Michigan Technological University Teaching Assistant (January 2007-May 2007)  Led labs dealing with the identification of Michigan bird species  Set up study skins for indoor laboratory exercises  Graded lab quizzes and lecture exams

Integrated Field Practicum – Ford Forestry Center, Alberta, MI; Michigan Technological University Graduate Teaching Assistant (August 2004-November 2004, August 2005-November 2005)  Answered student questions and assisted professors in lectures and laboratory exercises with regard to 10 undergraduate environmental science courses  Lectured on the concept of plant species diversity and various ways to calculate diversity indices 236

 Inventoried, monitored, and maintained all equipment necessary for field exercises  Enforced rules applicable to the behavior and responsibilities of the students as a resident advisor at the Ford Forestry Center

Volunteer Work, Mentoring, and Outreach  Oral presentation (2013). “Invasive species”. Forestry field day with Hoosier Ag Science Academy (HASA), June 19, 2013, John S. Wright Forestry Center, Purdue University.  Instructor (2013), basic forestry inventory training for high school students in Hoosier Ag Science Academy (HASA), July 17, 2013, Martell Forest, Purdue University.  Oral presentation (2013). “Ecological and economic effects of non-native, invasive species”. Invasive Species Field Day, June 12, 2013, John S. Wright Forestry Center, Purdue University.

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 Field exercise (2013). “Managing an invasive species problem”. Invasive Species Field Day, June 12, 2013, John S. Wright Forestry Center, Purdue University.  Article (2013). “Ecological impacts and spatial dynamics of Amur honeysuckle in Indiana forests”. Purdue University Department of Forestry and Natural Resources Compass magazine, Spring 2013 issue.  Instructor (2013), invasive species training for Purdue chapters of The Wildlife Society and Society of American Foresters, February 23, February 24, March 2, 2013, Martell Forest, Purdue University.  Graduate student mentor (2012 – present), Cole Bleke’s undergraduate research project, Wildlife Program, Department of Forestry and Natural Resources, Purdue University.  Graduate student advisor (2012-2013), Purdue chapter of The Wildlife Society.  Article pertaining to graduate research project, Ross Biological Reserve Newsletter, Fall 2012 issue.  Oral presentation (2011). “Ecological and economic effects of non-native, invasive species”. Invasive Species Training and Ecological Impacts, October 5, 2011, John S. Wright Forestry Center, Purdue University.  Field exercise (2011). “Managing an invasive species problem”. Invasive Species Training and Ecological Impacts, October 5, 2011, John S. Wright Forestry Center, Purdue University.  Oral presentations pertaining to watershed and water quality concepts (2007-2008); given as an AmeriCorps Environmental Educator with Michigan Environmental Stewardship AmeriCorps and presented to K-12 and college students, environmental clubs, and citizen groups.  Articles pertaining to ecosystem health and water quality concepts (2007-2008); given as an AmeriCorps Environmental Educator with Michigan Environmental Stewardship AmeriCorps; published in Argus-Press (Owosso, MI), Independent (Owosso, Michigan), Shiawassee Conservation District Newsletter (Owosso, MI), Friends of the Shiawassee River Newsletter (Owosso, MI), Flint Journal (Flint, MI), Lapeer Area View (Lapeer, MI), and newsletters for Kiwanis Club and Rotary Club in Shiawassee, Genesee, and Lapeer Counties in Michigan.  Volunteer (2008-2009), Friends of the Shiawassee River and The Nature Conservancy.  Volunteer consultant (2006-2008); conducted habitat assessments, forest inventories, and wildlife population censuses (both terrestrial and aquatic) for private landowners in Lower Peninsula Michigan.  Co-author (2004), three web-based teaching modules (Forest Ecosystems, Schoolyard Ecosystems, and Wetlands) for K-12 students; developed by Michigan Technological University and Michigan Environmental Education Curriculum.  Volunteer field assistant (2002), graduate student research project, Michigan

Technological University. 237

Peer-Reviewed Publications Shields, J.M., Jenkins, M.A., Zollner, P.A., and Saunders, M.R. 2013. Effects of Amur honeysuckle invasion and removal on white-footed mice. In Review at Journal of Wildlife Management. Shields, J.M., Jenkins, M.A., Saunders, M.R., Zhang, H., Jenkins, L.H., and Parks, A.M. 2013. Age distribution and spatial patterning of an invasive shrub in secondary hardwood forests. In Review at Forest Science. Jenkins, L.H., Jenkins, M.A., Webster, C.R., Zollner, P.A., and Shields, J.M. Herbaceous layer response to 17 years of controlled deer hunting in forested natural areas. In Review.

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Zellers, C.E., Saunders, M.R., Morrissey, R.C., Shields, J.M., Bailey, B.G., Dyer, J., and Cook, J. 2012. Development of allometric leaf area models for intensively managed black walnut (Juglans nigra L.). Annals of Forest Science 69, 907- 913. Shields, J.M., Jose, S., Freeman, J., Bunyan, M., Celis, G., Hagan, D., Morgan, M., Pieterson, E.C., and Zak, J. 2011. Short-term impacts of laurel wilt on redbay (Persea borbonia (L.) Spreng.) in a mixed evergreen-deciduous forest in northern Florida. Journal of Forestry 109, 82-88. Bump, J.K., Webster, C.R., Peterson, R.O., Vucetich, J.A., Shields, J.M., and Powers, M.D. 2009. Ungulate carcasses perforate ecological filters and create biogeochemical hotspots in forest herbaceous layers allowing trees a competitive advantage. Ecosystems 12, 996-1007. Webster, C.R., Huckins, C.J., and Shields, J.M. 2008. Spatial distribution of riparian zone coarse woody debris in a managed northern temperate watershed. American Midland Naturalist 159, 225-237. Shields, J.M., Webster, C.R., and Storer, A.J. 2008. Short-term community-level response of arthropods to group selection with seed-tree retention in a northern hardwood forest. Forest Ecology and Management 255, 129-139. Shields, J.M. and Webster, C.R. 2007. Ground-layer response to group selection with legacy- tree retention in a managed northern hardwood forest. Canadian Journal of Forest Research 37, 1797-1807. Shields, J.M., Webster, C.R., and Glime, J.M. 2007. Bryophyte community response to silvicultural opening size in a managed northern hardwood forest. Forest Ecology and Management 252, 222-229. Shields, J.M., Webster, C.R., and Nagel, L.M. 2007. Factors influencing tree species diversity and Betula alleghaniensis establishment in silvicultural openings. Forestry 80, 293-307.

Journal Reviewer  Natural Areas Journal (1 article)

Presentations (Posters and Oral Presentations)

Invited Shields, J.M., Jenkins, M.A., Saunders, M.R., Zhang, H., Jenkins, L.H., and Parks, A.M. 2013. Oral Presentation. Age distribution and spatial patterning of an invasive shrub in secondary hardwood forests. North American Forest Ecology Workshop (NAFEW), Bloomington, IN, June 16-20, 2013. Shields, J.M., Jenkins, M.A., Zollner, P.A., and Saunders, M.R. 2013. Poster. Abundance and space use of white-footed mouse (Peromyscus leucopus) in 238

Indiana mixed-hardwood forests invaded by Amur honeysuckle (Lonicera maackii (Rupr.) Herder). North American Forest Ecology Workshop (NAFEW), Bloomington, IN, June 16-20, 2013. Jenkins, L.H., Jenkins, M.A., Webster, C.R., Zollner, P., and Shields, J.M. 2013. Oral Presentation. Vegetation community recovery following more than a decade of deer reductions in Indiana State Parks. North American Forest Ecology Workshop (NAFEW), Bloomington, IN, June 16-20, 2013. Contributed Shields, J.M., Jenkins, M.A., Zollner, P.A., and Saunders, M.R. 2013. Poster. Abundance and space use of white-footed mouse (Peromyscus leucopus) in Indiana mixed-hardwood forests invaded by Amur honeysuckle (Lonicera maackii (Rupr.) Herder). Purdue University Department of Forestry and Natural Resources Research Symposium, West Lafayette, IN, April 12, 2013.

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Bleke, C.A., Rodkey, J.T., Bienz, C.R., Zollner, P.A., Shields, J.M., and Kraushar, M.S. 2013. Poster. Abundances estimates of Microtus and Peromyscus across vegetation treatments within Indiana prairie habitat. 2013 Joint Spring Meeting of the Indiana Chapters of The Wildlife Society, Society of American Foresters, and the American Fisheries Society, West Lafayette, IN, March 19-20, 2013. Shields, J.M., Jenkins, M.A., Saunders, M.R., Zhang, H., Jenkins, L.H., and Parks, A.M. 2012. Oral Presentation. Spatial characteristics of an Amur honeysuckle (Lonicera maackii (Rupr.) Herder) invasion in an Indiana mixed-hardwoods forest. 97th Ecological Society of America Annual Meeting, Portland, OR, August 5-10, 2012. Shields, J.M., Jenkins, M.A., and Saunders, M.R. 2012. Poster. Short-term effects of removing the non-native, invasive Amur honeysuckle (Lonicera maackii (Rupr.) Herder) on spring-flowering forest forbs in Indiana. Purdue University Department of Forestry and Natural Resources Research Symposium, West Lafayette, IN, April 13, 2012. Shields, J.M., Jenkins, M.A., Saunders, M.R., and Zellers, C.E. 2011. Poster. Diversity and composition of ground-layer vegetation in Indiana mixed-hardwood forests invaded by the non-native Amur honeysuckle (Lonicera maackii (Rupr.) Herder). 96th Ecological Society of America Annual Meeting, Austin, TX, August 7-12, 2011. Jenkins, L.H., Jenkins, M.A., Webster, C.R., Zollner, P.A., and Shields, J.M. 2011. Poster. Evaluating the recovery of vegetation communities in Indiana state parks after over a decade of white-tailed deer population reduction. 96th Ecological Society of America Annual Meeting, Austin, TX, August 7-12, 2011. Jenkins, L.H., Jenkins, M.A., Webster, C.R., and Shields, J.M. 2011. Oral Presentation. Evaluating the recovery of vegetation communities in Indiana state parks after over a decade of white-tailed deer population reduction. Indiana Academy of Science 126th Annual Academy Meeting, Indianapolis, IN, March 4-5, 2011. Dyer, J.H., Saunders, M.R., Morrissey, R.C., Bailey, B.G., Zellers, C., Shields, J., and Arseneault, J. 2010. Poster. Leaf area and growth dynamics of black walnut (Juglans nigra L.), Indiana, USA. Seventeenth Central Hardwood Forest Conference. Lexington, KY, April 5-7, 2010. Shields, J.M. and Webster, C.R. 2006. Poster. Initial effects of group selection with seed tree retention on tree species diversity in hemlock-northern hardwoods. 2nd Annual Ecosystem Science Center/Biotechnology Research Center Graduate Research Forum, School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, February 24, 2006.

Grants  Frederick N. Andrews Environmental Travel Grant, Purdue University - $1,500.00 (2011) 239

 AmeriCorps Week Mini-Grant - $600.00 (2008)