Analysis of the Composition and Structure of Eastern

Home , Acer rubrum, Mitchella, Shade tolerance, Tsuga, Tsuga canadensis, Zoar Valley

CURRENT COMPOSITION AND STRUCTURE OF EASTERN HEMLOCK

ECOSYSTEMS OF NORTHEASTERN OHIO AND IMPLICATIONS OF HEMLOCK

WOOLLY ADELGID INFESTATION

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in

the Graduate School of The Ohio State University

Thomas Daniel Macy, B.S.

Graduate Program in Environment and Natural Resources

The Ohio State University

2012

Thesis Committee:

Dr. David M. Hix, Advisor

Dr. P. Charles Goebel

Dr. Stephen N. Matthews

Thomas Daniel Macy

2012

ABSTRACT

Ohio’s eastern hemlock (Tsuga canadensis (L.) Carr.) ecosystems provide important ecological and economic benefits to the state. As a foundation species, eastern hemlock has a major influence on forest structure and functional processes. Eastern hemlock is also dominant in several popular outdoor recreation and tourist areas in Ohio.

Since its accidental introduction in 1951 hemlock woolly adelgid (Adelges tsugae

Annand; HWA), an invasive insect native to Japan, has been causing widespread mortality of eastern hemlock in an expanding portion of its range.

We sampled two types of hemlock forests that occur in northeastern Ohio: hemlock swamp forests of the Huron-Erie Lake Plain physiographic region (ELP) and stream-ravine forests of the Glaciated Allegheny Plateau physiographic region (GAP).

Vegetation and environmental data were collected from seven mature stands in northeastern Ohio. Information on the current composition and structure of these forests will be critical for predicting potential pathways of stand development in response to the possible decline of eastern hemlock and for informing management and restoration plans.

The objectives of our study were to: 1) compare the community composition and structure of eastern hemlock forest ecosystems of the ELP and GAP; 2) identify environmental factors influencing plant composition and structure; and 3) model HWA-

ii induced eastern hemlock mortality and predict changes in forest composition and structure using the Forest Vegetation Simulator (FVS) and the Hemlock Woolly Adelgid

Event Monitor.

Principal components analysis (PCA) showed clear separation between stands of the two physiographic regions based on slope percent and slope position, with stands of the ELP occupying flat bottomlands and stands of the GAP occupying middle- and upper-hillslope positions. Hierarchical cluster analysis failed to group trees, saplings, seedlings, or ground-flora by physiographic region, while multi-response permutation procedures (MRPP) detected a significant difference only in the seedling stratum (p =

0.03) between physiographic regions. The plant community composition and structure of stands in the ELP and GAP were similar due to the influence of eastern hemlock, which was dominant in both the tree layer (> 55% importance value for both physiographic regions) and the sapling layer (> 47% relative density for both physiographic regions), but was absent from the seedling layer. Redundancy analysis (RDA) revealed species- environment relationships consistent with species life-history traits and habitat requirements. FVS analysis forecast significant decreases (p < 0.05) in the total basal area and basal area of eastern hemlock following HWA infestation in both physiographic regions. FVS predicted decreases in eastern hemlock basal area up to 80% thirty years after infestation. The potential loss of this foundation species is forecast to drastically alter forest composition, structure, and functional processes.

iii

ACKNOWLEDGEMENTS

I would like to thank my advisor, Dr. David Hix, for his guidance and advice during both my undergraduate and graduate careers at Ohio State. I would also like to thank committee member Dr. Charles Goebel for providing very useful insights that improved this research. Their generosity in helping me to attend and present at various professional conferences has greatly increased my knowledge and broadened my understanding of natural systems. I would also like to thank committee member Dr.

Stephen Matthews, for his interest in this research and helpful comments. I am grateful to the School of Environment and Natural Resources for the opportunity to be a teaching associate, as well as the faculty and staff for their assistance. I would like to thank the

Ohio Agricultural Research and Development Center for awarding me a SEEDS Grant, without which, this research would not have been possible. Thank you to The Cleveland

Museum of Natural History, Friends of Wooster Memorial Park, The Holden Arboretum,

The Nature Conservancy, and Ohio State Parks for granting me access to their properties to conduct this research. Finally, I would like to thank Sarah Adams and my family for their love and support.

VITA

2010...... B.S. Forestry, Fisheries, and Wildlife, specialization in

Forestry and Wildlife Management, School of Environment

and Natural Resources, The Ohio State University

2010 to 2012 ...... Graduate Teaching Associate, School of Environment and

Natural Resources, The Ohio State University

Fields of Study

Major Field: Environment and Natural Resources

Area of Specialization: Forest Science

TABLE OF CONTENTS

Abstract ...... ii Acknowledgements ...... iv Vita ...... v List of Tables ...... viii List of Figures ...... x Chapter 1: Introduction ...... 1 Background ...... 1 Biology and Characteristics of Hemlock ...... 2 Hemlock Woolly Adelgid ...... 6 Research Justification and Objectives ...... 9 References ...... 12 Chapter 2: Plant Community Composition and Structure of Eastern Hemlock Forest Ecosystems of Northeastern Ohio ...... 19 Introduction ...... 19 Study Area ...... 21 Methods...... 23 Vegetation ...... 23 Environmental Attributes ...... 24 Data Analyses ...... 25 Results ...... 29 Principal Components Analysis ...... 29 Plant Community Composition and Structure ...... 29 Plant Community Diversity ...... 31

Downed Woody Debris...... 31 Hierarchical Cluster Analysis ...... 32 Multi-response Permutation Procedure ...... 32 Redundancy Analysis...... 32 Discussion ...... 34 References ...... 41 Chapter 3: Modeling Hemlock Woolly Adelgid-Induced Mortality and Predicting Stand Development Using the Forest Vegetation Simulator ...... 70 Introduction ...... 70 Study Area ...... 73 Methods...... 75 Vegetation ...... 75 Forest Vegetation Simulator ...... 75 Data Analyses ...... 76 Results ...... 77 Discussion ...... 79 Management Implications ...... 82 References ...... 84 Bibliography ...... 100 Appendix A: Tree Data ...... 111

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LIST OF TABLES

Table Page

2.1 Summary information for the three Huron-Erie Lake Plain (ELP) stands and the four Glaciated Allegheny Plateau (GAP) stands in northeastern Ohio ...... 47 2.2 Downed woody debris (DWD) decay classes and characteristics (adapted from Pyle and Brown 1998)...... 48 2.3 Mean relative dominance, relative density, importance value (IV), richness, basal area, and density (± 1 standard deviation) of tree species in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. Values for a given variable in a row followed by the same letter are not significantly different at p < 0.05 (Mann-Whitney test). Values followed by an asterisk indicate the species is an indicator of a physiographic region at p < 0.05 (Monte Carlo test) ...... 49 2.4 Number of trees cored and mean age of stands (± 1 standard deviation) of both physiographic regions in northeastern Ohio. Mean ELP stand age was significantly younger than mean GAP stand age (Mann-Whitney test; p < 0.001) ...... 50 2.5 Mean density (± 1 standard deviation) of sapling species in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. Values in a row followed by the same letter are not significantly different at p < 0.05 (Mann-Whitney test). Values followed by an asterisk indicate the species is an indicator of a physiographic region at p < 0.05 (Monte Carlo test) ...... 50 2.6 Mean importance percentage (± 1 standard deviation) of seedling species in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. Values in a row followed by the same letter are not significantly different at p < 0.05 (Mann-Whitney test). Values followed by an asterisk indicate the species is an indicator of a physiographic region at p < 0.05 (Monte Carlo test) ...... 51

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2.7 Mean importance percentage (± 1 standard deviation) of ground-flora species in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. Values in a row followed by the same letter are not significantly different at p < 0.05 (Mann-Whitney test). Values followed by an asterisk indicate the species is an indicator of a physiographic region at p < 0.05 (Monte Carlo test) ...... 52 2.8 Relative density of downed woody debris (DWD) decay classes in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. Values in a row followed by the same letter are not significantly different at p < 0.05 (Mann-Whitney test) ...... 53 2.9 Redundancy analysis (RDA) results for trees, seedlings, and ground-flora, for both physiographic regions of northeastern Ohio ...... 54 3.1 Summary information for the three Huron-Erie Lake Plain (ELP) stands and four Glaciated Allegheny Plateau (GAP) stands in northeastern Ohio ...... 90

LIST OF FIGURES

Figure Page

1.1 Distribution of HWA within the native range of hemlock as of 2011. Source: U.S.D.A. Forest Service (2012) ...... 18 2.1 Locations of the three Huron-Erie Lake Plain (ELP) stands (open circles) and four Glaciated Allegheny Plateau (GAP) stands (black dots) within northeastern Ohio...... 55 2.2 Sample plot design and orientation of the 500-m2 circular plot, nested 100-m2 circular plot, and four 1.0 x 1.0 m quadrats (not to scale) ...... 56 2.3 Principal components analysis (PCA) of five environmental variables for seventeen plots in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. Percentages are the amount of variation explained by each axis ...... 57 2.4 Mean tree diversity values (± 1 standard deviation) in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio ...... 58 2.5 Mean sapling diversity values (± 1 standard deviation) in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio ...... 59 2.6 Mean seedling diversity values (± 1 standard deviation) in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio ...... 60 2.7 Mean ground-flora diversity values (± 1 standard deviation) in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio ...... 61 2.8 Mean downed woody debris (DWD) characteristics (± 1 standard deviation) in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. There was a significant difference in the DWD volume per hectare between physiographic regions (Mann-Whitney test, p < 0.01) ...... 62 2.9 Dendrogram from hierarchical cluster analysis using tree species importance values (IV) for seventeen plots in the Huron-Erie Lake Plain (ELP; open circles) and Glaciated Allegheny Plateau (GAP; black dots) of northeastern Ohio. Plot abbreviations are provided in Table 2.1 ...... 63

2.10 Dendrogram from hierarchical cluster analysis using sapling species density (stems per ha) for eleven plots in the Huron-Erie Lake Plain (ELP; open circles) and Glaciated Allegheny Plateau (GAP; black dots) of northeastern Ohio. Plot abbreviations are provided in Table 2.1 ...... 64 2.11 Dendrogram from hierarchical cluster analysis using seedling species importance percentages (IP) for fifteen plots in the Huron-Erie Lake Plain (ELP; open circles) and Glaciated Allegheny Plateau (GAP; black dots) of northeastern Ohio. Plot abbreviations are provided in Table 2.1 ...... 65 2.12 Dendrogram from hierarchical cluster analysis using ground-flora species importance percentages (IP) for fifteen plots in the Huron-Erie Lake Plain (ELP; open circles) and Glaciated Allegheny Plateau (GAP; black dots) of northeastern Ohio. Plot abbreviations are provided in Table 2.1 ...... 66 2.13 Redundancy analysis (RDA) ordination diagram for tree species importance values (IV) for seventeen plots in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. Species abbreviations are provided in Table 2.3 ...... 67 2.14 Redundancy analysis (RDA) ordination diagram for seedling species importance percentages (IP) for seventeen plots in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. Species abbreviations are provided in Table 2.6 ...... 68 2.15 Redundancy analysis (RDA) ordination diagram for ground-flora species importance percentages (IP) for seventeen plots in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. Species abbreviations are provided in Table 2.7 ...... 69 3.1 Locations of the three Huron-Erie Lake Plain (ELP) stands (open circles) and four Glaciated Allegheny Plateau (GAP) stands (black dots) within northeastern Ohio...... 91 3.2 Sample plot design and orientation of the 500-m2 circular plot, nested 100-m2 circular plot, and four 1.0 x 1.0 m quadrats (not to scale) ...... 92 3.3 Forest Vegetation Simulator (FVS)-projected change in total basal area and hemlock basal area without hemlock woolly adelgid (HWA)-induced mortality 45 years into the future for forests of the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio ...... 93

3.4 Ten replicates of Forest Vegetation Simulator (FVS)-projected change in total basal area and hemlock basal area based on an eight-km per year spread of hemlock woolly adelgid (HWA) (infestation year = 2027) 45 years into the future for forests of the Huron-Erie Lake Plain (ELP) of northeastern Ohio ...... 94 3.5 Ten replicates of Forest Vegetation Simulator (FVS)-projected change in total basal area and hemlock basal area based on an eight-km per year spread of hemlock woolly adelgid (HWA) (infestation year = 2028) 45 years into the future for forests of the Glaciated Allegheny Plateau (GAP) of northeastern Ohio ...... 95 3.6 Ten replicates of Forest Vegetation Simulator (FVS)-projected change in total basal area and hemlock basal area based on a 13-km per year spread of hemlock woolly adelgid (HWA) (infestation year = 2021) 45 years into the future for forests of the Huron-Erie Lake Plain (ELP) of northeastern Ohio ...... 96 3.7 Ten replicates of Forest Vegetation Simulator (FVS)-projected change in total basal area and hemlock basal area based on a 13-km per year spread of hemlock woolly adelgid (HWA) (infestation year = 2021) 45 years into the future for forests of the Glaciated Allegheny Plateau (GAP) of northeastern Ohio ...... 97 3.8 Figure 3.8. Mean Forest Vegetation Simulator (FVS)-predicted total basal area and hemlock basal area (± 1 standard deviation) based on an eight-km per year spread of hemlock woolly adelgid 45 years into the future for forests of the Huron-Erie Lake Plain (ELP; infestation year = 2027) and Glaciated Allegheny Plateau (GAP; infestation year = 2028) of northeastern Ohio. Means that do not share a capital letter are significantly different in hemlock basal area between years and means that do not share a lowercase letter are significantly different in total basal area between years at p < 0.05 ...... 98 3.9 Figure 3.9. Mean Forest Vegetation Simulator (FVS)-predicted total basal area and hemlock basal area (± 1 standard deviation) based on a 13-km per year spread of hemlock woolly adelgid 45 years into the future for forests of the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio (infestation year = 2021). Means that do not share a capital letter are significantly different in hemlock basal area between years and means that do not share a lowercase letter are significantly different in total basal area between years at p < 0.05...... 99

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CHAPTER 1

INTRODUCTION

Background

The forests of eastern North America have been significantly altered over the past two centuries as a result of the introduction of non-native pests and diseases (Lovett et al.

American chestnut had been dominant, particularly riparian areas of the central and southern Appalachians, eastern hemlock (Tsuga canadensis (L.) Carr.; hemlock) replaced it as the foundation species (Elliott and Swank 2008). In certain habitats within the range of hemlock where American chestnut was not as prevalent, such as moist ravines and swamp forests (Gordon 1969, Black and Mack 1976; Godman and Lancaster 1990)

1 within the Unglaciated Allegheny Plateau and Huron-Erie Lake Plain physiographic regions of northeastern Ohio (Brockman 1998), hemlock has been a foundation species for a much longer period of time. Hemlock now faces a serious threat, much as chestnut did, since hemlock woolly adelgid (Adelges tsugae Annand; HWA), an Asian insect, is causing widespread mortality across portions of the range of hemlock (Figure 1.1).

Biology and Characteristics of Hemlock

Hemlock is one of the longest-lived and most shade tolerant tree species in eastern North America (Ward et al. 2004). Hemlock is found on nearly seven million hectares of forest and is the predominant species on one million hectares (McWilliams and Schmidt 2000). Its range extends from Nova Scotia south to Georgia and west to

Minnesota (Brisbin 1970; Ward et al. 2004). Hemlock can live for over 800 years and can grow to nearly 50 meters in height and 2 meters in diameter (Godman and Lancaster

1990). Hemlock can survive in the understory with as little as five percent of full sunlight and often develop very dense, nearly pure stands with deep canopies (Godman and Lancaster 1990). Hemlock can be found on a wide variety of sites, though it is intolerant of drought and most often found growing in moist, well-drained, acidic soils

(Goerlich and Nyland 2000; Ward et al. 2004). In the northern portion of its range, hemlock grows on shallow peat and muck soils on benches, flats, and swamp borders and in the southern portion it is restricted to north- and east-facing slopes and cool, moist valleys and coves (Godman and Lancaster 1990). Hemlock is a major component of the following eastern forest cover types: eastern white pine-hemlock, hemlock, hemlock-

2 yellow birch, and yellow-poplar-hemlock (Brisbin 1970; Godman and Lancaster 1990).

It is common but less prevalent in the white pine-northern red oak-red maple, eastern white pine, red spruce-yellow birch, red spruce-sugar maple-beech, red spruce, red spruce-balsam fir, and red spruce-Fraser fir forest cover types (Brisbin 1970; Godman and Lancaster 1990). Hemlock is a late-successional species that creates understory conditions conducive to its own regeneration and germination while excluding other tree species (Goerlich and Nyland 2000; McClure 2001). Forest floor conditions beneath hemlock stands tend to be cooler and more acidic than those of hardwoods (Godman and

Lancaster 1990; Goerlich and Nyland 2000; Ellison et al. 2005). It is possible for hemlock to withstand decades or even centuries of suppression from overtopping trees

(Brisbin 1970). Because of this characteristic, saplings exhibit little direct relationship between stem diameter and age, and individuals up to 100 years old may not exceed a few centimeters in diameter (Brisbin 1970; Goerlich and Nyland 2000). Hemlock responds rapidly to release at all stages of growth and development (Godman and

Lancaster 1990).

Several unique characteristics of eastern hemlock make it a very important component of the ecosystems in which it occurs. Its dense canopy and high degree of shade tolerance increase vertical and horizontal structural heterogeneity (Ward et al.

2004). Hemlock provides valuable cover throughout the year for game and non-game wildlife species including ruffed grouse (Bonasa umbellus L.), wild turkey (Meleagris gallopavo L.), white-tailed deer (Odocoileus virginianus Zimm.), and snowshoe hare

(Lepus americanus Erxleben) (McClure 2001). Nearly 100 bird species are associated

3 with hemlock in the eastern U.S. (Yamasaki et al. 2000; McClure 2001). Several avian species, including a few of conservation concern, are strongly associated with hemlock forest ecosystems (Yamasaki et al. 2000; McClure 2001; Rodewald 2007). Among these are black-throated green warbler (Dendroica virens Gmelin), blackburnian warbler

(Dendroica fusca Muller), and winter wren (Troglodytes troglodytes L.) (Howe and

Mossman 1996; Yamasaki et al. 2000). Unlike other conifers, hemlock’s shade tolerance allows its lower branches to retain foliage, providing valuable foraging and nesting sites for mid-canopy bird species (Howe and Mossman 1996). The furrowed bark of mature hemlock provides shelter to many insects, and in turn serves as a quality food source for woodpeckers and other bark-foraging birds like brown creeper (Certhia americana

Bonaparte) and red-breasted nuthatch (Sitta canadensis L.) (Howe and Mossman 1996).

The dense shade and accumulation of woody debris under nearly pure hemlock stands creates a cool, moist microclimate, promoting habitat for various understory wildlife species including winter wren (Howe and Mossman 1996).

Hemlock also has a major influence on aquatic systems. The wood of hemlock is very resistant to decay, much like the wood of American chestnut (Hedman et al. 1996).

This characteristic allows downed hemlock wood to remain in streams as large wood jams much longer than associated hardwoods (Hedman et al. 1996; Wallace et al. 2001;

Morris et al. 2006). Ross et al. (2003) found that trout species were at least twice as abundant in hemlock forests versus hardwood forests in headwater streams of the

Delaware River basin. The shaded, cool conditions created by the dense canopy of hemlock keep streams at a lower temperature, benefiting cold water fish species, such as

4 trout (Ross et al. 2003). Aquatic macroinvertebrates are primary consumers of leaf litter inputs to streams and constitute a major food source for fish and birds as well as other terrestrial wildlife species. The chemical composition and timing of hemlock litter inputs into streams differs from that of hardwoods, and influences aquatic macroinvertebrate communities differently. Streams draining hemlock forests have been shown to support more aquatic macroinvertebrate taxa than those draining hardwood forests (Snyder et al.

2002).

Because hemlock has a slow growth rate and its wood does not produce high- quality structural lumber or pulpwood (Brisbin 1970), it has not been intensively managed for commercial use. Management of hemlock often focuses on promoting wildlife habitat or reducing the negative effects of deer browse (Mladenoff and Stearns

1993; Goerlich and Nyland 2000; Reay 2000). Regeneration of hemlock has been achieved through the creation of small canopy gaps of adequate size to allow enough sunlight for hemlock to reach large sapling size, but not large enough that they will be overtopped by faster-growing hardwoods (Goerlich and Nyland 2000). Clearcutting and other even-aged management systems are not typically used to regenerate hemlock, as the high light levels and warmer, drier conditions allow more competitive hardwood species to dominate (Hix and Barnes 1984; Brissette and Kenefic 2000; Goerlich and Nyland

2000). In most situations, uneven-aged forest management systems are used to promote hemlock regeneration. Silvicultural practices such as single-tree selection and group selection regeneration methods have been successful in promoting sustainable recruitment of hemlock in the understory (Brissette and Kenefic 2000). Where hemlock

5 regeneration is poor, prescribed fire, soil scarification, or artificial regeneration through planting, possibly in combination with the selection regeneration method can be used to increase its presence (Hix and Barnes 1984; Goerlich and Nyland 2000).

Hemlock Woolly Adelgid

Hemlock woolly adelgid is an insect native to Japan (McClure 2001; U.S.D.A.

Forest Service 2005). In its native range, HWA is a common forest insect, but does not cause severe damage or mortality to Asian hemlock species for a number of reasons.

First, HWA and hemlocks native to Asia have coexisted for millennia, and so have coevolved allowing those trees to develop defense mechanisms. Another factor preventing HWA from causing widespread mortality of Asian hemlock is the presence of native predators, which keep HWA numbers in check (McClure 2001). HWA was first observed in western North America in the 1920s. Exactly how it was introduced to western North America is not clear, although an accidental introduction from Asia is likely (McClure 2001). Western hemlock (T. heterophylla (Raf.) Sarg.) and mountain hemlock (T. mertensiana (Bong.) Carr.), both found in western North America, are resistant to HWA (McClure 2001). The first report of HWA in the eastern U.S. was in

1951 near Richmond, VA, probably accidentally imported on ornamental stock from Asia

(U.S.D.A. Forest Service 2005). Since that time, HWA has dispersed in all directions within the native range of eastern hemlock. Both hemlock species found in the eastern

U.S., eastern hemlock and the less widespread Carolina hemlock (T. caroliniana

Engelm.) are hosts of HWA and have shown little or no resistance to the insect (McClure

2001; U.S.D.A. Forest Service 2005). Complete mortality of hemlock trees can occur in about four to 10 years (Orwig and Foster 1998; McClure 2001). Initial spread of HWA was slow but has increased along with HWA populations. The rate of spread to new areas is estimated to be 15.6 km per year south of Pennsylvania and 8.1 km per year in the northern portion of hemlock’s range (Evans and Gregoire 2007; Fajvan and Wood

2010). As of 2011, HWA was present in eighteen states from Maine to Georgia and has caused extensive tree mortality and decline throughout this area, but the most severe impact has been seen in areas of Virginia, New Jersey, Pennsylvania, and Connecticut

(U.S.D.A. Forest Service 2005).

Hemlock woolly adelgid is a tiny aphid-like insect, less than 1.6 mm in length and dark-brown to purple in color. Except for newly hatched nymphs, adelgids are covered in a waxy, wool-like coating of filaments to protect themselves and eggs from desiccation and predators. This “woolly” appearance is most conspicuous from late fall to early summer, when they are maturing and laying eggs in ovisacs (Ward et al. 2004; U.S.D.A.

Forest Service 2005). The HWA is parthenogenetic, meaning all individuals are females that reproduce asexually. HWA completes two generations every year. The winter generation, known as the sistens, develops from early summer to mid-spring of the following year (June-March). The summer generation, known as the progrediens, develops from spring to early summer (March-June), and so these two generations overlap during mid to late spring (Ward et al. 2004; U.S.D.A. Forest Service 2005).

HWA enter a dormant stage called aestivation during the hot summer months and once temperatures begin to drop, generally around October, they begin to feed again and

7 continue to do so through the winter (McClure 2001; Ward et al. 2004; U.S.D.A. Forest

Service 2005). Ovisacs of the winter generation can contain up to 300 eggs, while those of the spring generation generally contain 20-75 eggs. When hatched, the first instar nymphs, or “crawlers” make their way to suitable feeding sites at the base of hemlock needles and insert their stylets and begin to feeding on stored starches which are critical to the tree’s long-term growth and survival (U.S.D.A. Forest Service 2005). Crawlers can be dispersed between trees by wind and animals (Ward et al. 2004), although isolated infestations and long-distance dispersal are most likely due to accidental human transport of HWA on nursery stock (U.S.D.A. Forest Service 2005). Adelgids will remain at the feeding site for the remainder of their development. This life-cycle of two asexual generations per year and dormancy during harsh conditions allows HWA to be a prolific reproducer (Ward et al. 2004).

Being a non-native organism, HWA has not coevolved with the native biota of the eastern U.S. There are no known native predators or pathogens that are capable of maintaining HWA populations at non-damaging levels (McClure 2001; Ward et al.

2004). Silvicultural techniques to use with HWA generally focus on salvage cutting, which removes dead and dying trees to recover any economic value and reduce safety hazards. Removing bird feeders located near hemlock has been recommended, as birds are known to transport adelgids over long distances (Ward et al. 2004). On the individual-tree level, foliar sprays and horticultural oils can be effective. Systemic insecticides, such as imidacloprid are increasingly used through soil application or trunk injection to control HWA on individual high-value trees or in residential settings. These

8 control measures are not permanent and must be repeated at intervals of several months to a few years, and as stated previously, are not cost-effective at the landscape scale

(Ward et al. 2004). While pesticide application is an effective control measure for landscape trees, it is not practical at the landscape scale.

The most promising control measure at this time for large forest landscapes seems to be the introduction of biological predators (McClure 2001; Ward et al. 2004; U.S.D.A.

Forest Service 2005). Various Asian insect species that attack HWA have been investigated for their potential in controlling HWA populations. It is not expected that a single HWA predator will be successful in controlling the negative effects of HWA, but more likely a complex of natural predators (Ward et al. 2004; U.S.D.A. Forest Service

2005). Some of the most promising of these predators include the beetles Sasajiscymnus tsugae (previously Pseudoscymnus tsugae), native to Japan, and Laricobius nigrinus, native to western North America (McClure 2001; Conway and Culin 2007). While there have been some encouraging results after the release of some of these natural predators, it is still too early to tell whether these control measures will be able to successfully control

HWA (McClure 2001).

Research Justification and Objectives

In January of 2012, the first HWA infestation of a hemlock stand in Ohio was discovered in a state forest in Meigs County, Ohio (Ohio Department of Agriculture

2012a). In May of 2012, a second, larger infestation was discovered in Washington

County in southeastern Ohio (Ohio Department of Agriculture 2012b). While these

9 infestations were found early and a quarantine restricting the movement of hemlock material is in place, it is likely that HWA has gone undetected in other parts of Ohio and that additional infestations will be found. Ohio’s hemlock-dominated ecosystems are unique and significant not only to the ecology of the region, but the economy as well.

Forests at major tourist and recreational areas such as the Hocking Hills in southeastern

Ohio, Mohican State Park in north-central Ohio, and portions of the Cuyahoga Valley

National Park in northeastern Ohio are dominated by hemlock. The potential loss of this foundation species and the resulting effects on the ecosystem will likely have widespread impacts.

While all aspects of hemlock mortality are not fully understood, some research has shown that region-specific changes in forest development and nutrient cycling will occur (Jenkins et al. 1999; Eschtruth et al. 2006). For example, increased growth of the understory shrub Rhododendron maximum (L.) following hemlock mortality may prevent canopy tree recruitment and result in shrub thickets in the central and southern

Appalachians (Ellison et al. 2005). In New England, declining hemlock stands may transition to dominance by sweet birch (Betula lenta L.) in the short term (Orwig and

Foster 1998; Stadler et al. 2005). It is not known how Ohio’s hemlock forests, near the edge of the species’ natural range, will change after infestation by HWA. While there is much interest in finding biological controls for HWA (Cheah et al. 2004; Ward et al.

2004), further information regarding the extent and nature of hemlock-dominated forest ecosystems is necessary to begin restoration and management plans. Unlike with chestnut blight, there is now an opportunity to document and better understand the

10 composition and structure of northeastern Ohio’s hemlock forests before they are changed by a non-native pest.

The objectives of our study were to establish baseline conditions of these unique forest ecosystems before they are infested by HWA by examining the composition and structure of hemlock swamp forests of the Huron-Erie Lake Plain physiographic region

(ELP) and of the hemlock ravine forests of the Glaciated Allegheny Plateau physiographic region (GAP) in northeastern Ohio. Specifically, we 1) compared the plant community composition and structure of hemlock forests of the ELP and those of the GAP, 2) identified factors currently influencing the plant community composition and structure in both physiographic regions, and 3) modeled HWA-induced hemlock mortality and predicted changes in forest composition and structure using the Forest

Vegetation Simulator (Dixon 2002) with the Hemlock Woolly Adelgid Event Monitor developed by the U.S.D.A. Forest Service (Forest Health Technology Enterprise Team

2008).

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Eschtruth, A.K., N.L. Cleavitt, J.J. Battles, R.A. Evans, and T.J. Fahey. 2006. Vegetation dynamics in declining eastern hemlock stands: 9 years of forest response to hemlock woolly adelgid infestation. Canadian Journal of Forest Research 36:1435- 1450.

Jenkins, J.C., J.D. Aber, and C.D. Canham. 1999. Hemlock woolly adelgid impacts on community structure and N cycling rates in eastern hemlock forests. Canadian Journal of Forest Research 29:630-645.

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Fajvan, M.A. and P.B. Wood. 2010. Maintenance of eastern hemlock forests: Factors associated with hemlock vulnerability to hemlock woolly adelgid. In: Rentch, J.R. and T.M. Schuler, eds. Proceedings, Conference on the ecology and management of high-elevation forests in the central and southern Appalachian Mountains. General Technical Report NRS-P-64. U.S. Department of Agriculture, Forest Service, Newtown Square, Pennsylvania: 31-38.

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Goerlich, D.L. and R.D. Nyland. 2000. Natural regeneration of eastern hemlock: a review. In: McManus, K.A., K.S. Shields, and D.R. Souto, eds. Proceedings, Symposium on sustainable management of hemlock ecosystems in eastern North America. General Technical Report NE-267. U.S. Department of Agriculture Forest Service, Newtown Square, Pennsylvania: 14-22.

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Hedman, C.W., D.H. Van Lear, and W.T. Swank. 1996. In-stream large woody debris loading and riparian forest seral stage associations in the southern Appalachian Mountains. Canadian Journal of Forest Research 26:1218-1227.

Hix, D.M. and B.V. Barnes. 1984. Effects of clear-cutting on the vegetation and soil of an eastern hemlock dominated ecosystem, western Upper Michigan. Canadian Journal of Forest Research 14:914-923.

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Lovett, G.M., C.D. Canham, M.A. Arthur, K.C. Weathers, and R.D. Fitzhugh. 2006. Forest ecosystem responses to exotic pests and pathogens in eastern North America. Bioscience 56:395-405.

McClure, M.S. 2001. Biological control of hemlock woolly adelgid in the eastern United States. Forest Health Technology Enterprise Team 2000-08. U.S. Department of Agriculture Forest Service, Morgantown, West Virginia.

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Ohio Department of Agriculture. 2012a. Officials discover hemlock pest in southeast Ohio forest. Available online at http://www.agri.ohio.gov/public_docs/news/2012/01.20.12 ODA - ODNR HWA Joint News Release.pdf; last accessed Mar. 13, 2012.

Ohio Department of Agriculture. 2012b. Officials discover hemlock pest in Washington County. Available online at http://www.agri.ohio.gov/public_docs/news/2012/05.07.12 ODA - ODNR HWA Washington County Joint News Release.pdf; last accessed May 8, 2012.

Orwig, D.A. and D.R. Foster. 1998. Forest response to the introduced hemlock woolly adelgid in southern New England, USA. Journal of the Torrey Botanical Society 125:60-73.

Reay, R.S. 2000. Management of eastern hemlock for deer wintering areas. In: McManus, K.A., K.S. Shields, and D.R. Souto, eds. Proceedings, Symposium on sustainable management of hemlock ecosystems in eastern North America. General Technical Report NE-267. U.S. Department of Agriculture Forest Service, Newtown Square, Pennsylvania: 144-147.

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Snyder, C.D., J.A. Young, D.P. Lemarie, and D.R. Smith. 2002. Influence of eastern hemlock (Tsuga canadensis) forests on aquatic invertebrate assemblages in headwater streams. Canadian Journal of Fisheries and Aquatic Sciences 59:262- 275.

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Yamasaki, M., R.M. DeGraaf, and J.W. Lanier. 2000. Wildlife habitat associations in eastern hemlock - birds, smaller mammals, and forest carnivores. In: McManus, K.A., K.S. Shields, and D.R. Souto, eds. Proceedings, Symposium on sustainable management of hemlock ecosystems in eastern North America. General Technical Report NE-267. U.S. Department of Agriculture Forest Service, Newtown Square, Pennsylvania: 135-143.

Figure 1.1. Distribution of HWA within the native range of hemlock as of 2011. Source: U.S.D.A. Forest Service (2012).

CHAPTER 2

PLANT COMMUNITY COMPOSITION AND STRUCTURE OF EASTERN

HEMLOCK FOREST ECOSYSTEMS OF NORTHEASTERN OHIO

Introduction

The forests of eastern North America have been significantly altered over the past two centuries as a result of the introduction of non-native pests and diseases (Lovett et al.

2006). Some of the tree species lost to these introduced pests were dominant in their ecosystems and had a major influence on their structure and function. These species were considered “foundation species” (Ellison et al. 2005). The loss of these foundation species likely altered ecosystem functions in significant ways. One such species lost to a non-native disease is the American chestnut (Castanea dentata (Marsh.) Borkh.). This tree once dominated millions of hectares of forests of eastern North America, but was virtually eliminated by chestnut blight (Cryphonectria parasitica (Murr.) Bar.), an accidentally introduced pathogen from Asia (Anagnostakis 1987). The sustainability of another foundation species, eastern hemlock (Tsuga canadensis (L.) Carr.; hemlock), is currently threatened by a non-native, invasive insect, hemlock woolly adelgid (Adelges tsugae Annand; HWA). This small, aphid-like insect, native to Japan has been causing widespread mortality of hemlock in an expanding portion of its range since its accidental

19 introduction in 1951. (McClure 2001; U.S.D.A. Forest Service 2005).

In the northeastern United States, mortality of hemlock in stands infested by

HWA has exceeded 95% (Orwig and Foster 1998). Mortality has been shown to occur in

4-10 years after infestation, and generally occurs more rapidly in the southern portions of the range of hemlock (Ellison et al. 2005). While all aspects of hemlock mortality are not fully understood, some research has shown that region-specific changes in various forest dynamics and nutrient cycling will occur (Jenkins et al. 1999; Eschtruth et al. 2006). For example, increased growth of the understory shrub Rhododendron maximum (L.) following hemlock mortality may prevent canopy tree recruitment and result in shrub thickets in the central and southern Appalachians (Ellison et al. 2005). In New England, declining hemlock stands may transition to dominance by sweet birch (Betula lenta L.) in the short term (Orwig and Foster 1998; Stadler et al. 2005). It is not known how Ohio’s hemlock forests, near the edge of the species’ natural range, will change after infestation by HWA.

We have the opportunity in hemlock forests of northeastern Ohio, which we did not have with chestnut blight, to better understand these unique ecosystems before they are permanently altered. Our objectives were to compare the plant community composition and structure of hemlock forests in the Huron-Erie Lake Plain physiographic region (ELP) and in the Glaciated Allegheny Plateau physiographic region (GAP) of northeastern Ohio and to identify physical environmental factors influencing forest composition and structure. As HWA moves into Ohio and begins to alter the

20 composition and structure of hemlock-dominated ecosystems, information about their pre-altered conditions will be very important for restoration and management plans.

Most of the studies of the response of hemlock stands to HWA infestation have been conducted in the Appalachian region, particularly the northern and central

Appalachians. The northern and western portions of the native range of hemlock are quite different with respect to physiographic and edaphic factors compared with the

Appalachian region. It is likely that the potential effects of HWA infestation will be different in these areas than what has been documented in the northern and central

Appalachians. The ELP of northeastern Ohio contains stands of hemlock in poorly drained, low-lying “swamps,” more typical of forests found farther to the north (Aldrich

1943; Black and Mack 1976). Thus, these hemlock stands may be more similar to those of the Upper Great Lakes region and this study may provide important insights on how these forest ecosystem types might respond to HWA.

Study Area

A total of seventeen sample plots were established in seven mature stands located in northeastern Ohio on public and private land (Figure 2.1, Table 2.1). In order to examine the variation between hemlock forest types (Aldrich 1934; Black and Mack

1976), three hemlock swamp forest stands were located within the Erie Lake Plain district of the ELP and four stream ravine stands were located within the GAP of northeastern

Ohio (Brockman 1998). In Ohio, both of these physiographic regions fall within the

Beech-Maple Forest Region described by Braun (1950). Hemlock is largely confined to

21 the eastern half of Ohio (Sears 1925; Gordon 1969; Black and Mack 1976; Godman and

Lancaster 1990). Our efforts were focused on the northeastern part of the state, as other research examining hemlock forest composition and structure within the Unglaciated

Allegheny Plateau of southeastern Ohio is ongoing (Martin and Goebel 2011; Ohio

University 2012).

The four counties in which the stands were located (Ashland, Ashtabula, Geauga, and Wayne) have humid continental climates with relatively cold winters and generally warm summers. The county containing the ELP stands (Ashtabula) has a mean annual temperature of 9.3° C, with a mean July maximum temperature of 27.1° C and a mean

January minimum temperature of -8.1° C (Milliron et al. 2007). The counties containing the GAP stands (Ashland, Geauga and Wayne) have a mean annual temperature of 9.4°

C, with a mean July maximum temperature of 27.7° C and a mean January minimum temperature of -8.2° C (Redmond and Brown 1980; Williams and McCleary 1982;

Bureau et al. 1984). Average annual precipitation is 98 cm in Ashtabula County and 99 cm in the GAP counties, the majority of which falls between April and October

(Redmond and Brown 1980; Williams and McCleary 1982; Bureau et al. 1984; Milliron et al. 2007). The ELP is characterized by very low relief, elevation of 174-244 m, and lacustrine and till soils over Devonian- and Mississippian-aged shales and sandstones

(Brockman 1998). The soil map units associated with these stands (Table 2.1) are described as very poorly drained to well-drained (Soil Survey Staff 2012). The GAP is characterized by moderate relief, ridges and flat uplands dissected by steep valleys, elevation of 183-459 m, and Wisconsinan-age clay to loam till over Mississippian- and

Pennsylvanian-aged shales and sandstones (Brockman 1998). The soil map units associated with these stands (Table 2.1) are described as well-drained (Soil Survey Staff

2012).

Methods

Vegetation

Sample plots were located within seven hemlock-dominated stands within both the ELP and the GAP. Depending on the size of the stand, two or three sample plots were established (Table 2.1). Sample plots consisted of 500-m2 circular plots containing a nested 100-m2 circular plot and four rectangular 1.0 x 1.0 m quadrats (Figure 2.2). The northeast corners of the quadrats were located 5.6 m from the center of the 500-m2 circular plot in four directions (N, S, E, W). Within the 500-m2 circular plot, diameter at breast height (1.37 m; dbh), species, and crown class (dominant, codominant, intermediate, and overtopped) (Smith et al. 1997) of living trees ≥ 10.0 cm dbh were recorded. Species and dbh of snags were also recorded. Species and dbh of saplings

>1.37 m in height and <10.0 cm dbh were recorded within the nested 100-m2 circular plot. Seedlings (tree species as described by Little 1979) and ground-flora (shrubs, vines, and herbaceous vascular plants) <1.37 m in height were sampled in each of the four 1.0 x

1.0 m quadrats on each plot. Both seedling and ground-flora species were visually assigned to cover classes using the following cover class codes: 1, <1%; 2, 1-5%; 3, 6-

10%; 4, 11-20%; 5, 21-40%; 6, 41-70%; 7, 71-100% (Figure 2.2).

Where permission was obtained (all but one stand; Little Mountain), increment cores were extracted from three to eight dominant or codominant trees per stand in order to estimate the average age of the stand. Increment cores were mounted on wooden boards with glue and sanded with progressively finer grit sandpaper to allow for accurate recognition of annual growth rings. Each increment core was scanned into a computer for analysis with WinDENDRO tree-ring analysis software (Regent Instruments, Inc.,

Quebec, Canada).

Environmental Attributes

At the center of each 500-m2 circular plot, slope steepness (%), aspect (azimuth in degrees), slope shape (concave, linear, or convex), and slope position (bottom, lower hillslope, middle hillslope, upper hillslope, or summit) were observed or measured using a clinometer, compass, and visual estimation, respectively. Length, end diameters, species, and decay class (Pyle and Brown 1998) were recorded for all downed woody debris (DWD) with at least one end diameter ≥ 10.0 cm within the 500-m2 circular plot

(Table 2.2). Density of DWD in each decay class was calculated for each physiographic region. Although not all DWD was identifiable to species, it was possible to determine the genus of all DWD. Elevation and latitude and longitude were recorded at each plot center using a Garmin 60CSx handheld GPS device. Soil pH was determined from a soil sample taken at each plot center using a Hellige-Truog soil pH test kit (Hellige, Inc.,

Garden City, New York). Percent canopy openness was determined using a Nikon

Coolpix 8400 digital camera with a Nikon FC-E9 fisheye lens mounted on a tripod

24 approximately 1.0 m above ground-level. The WinSCANOPY software program (Regent

Instruments, Inc., Quebec, Canada) was then used to analyze the hemispherical photos and calculate percent canopy openness.

Data Analyses

Principal components analysis (PCA) of five environmental variables (slope percent, slope aspect, slope shape, slope position, and soil pH) was used to examine differences in physical environment attributes of plots. Although PCA has severe limitations when examining community data, it is a useful technique to summarize and analyze environmental data alone (McCune and Mefford 1999). The variables were centered and standardized using a correlation cross-products matrix. Pearson and

Kendall correlation coefficients were calculated using PC-ORD (McCune and Mefford

1999) to examine the relationships among ordination scores and individual variables.

For each sample plot, importance value (IV) was calculated for living tree species as the sum of relative density and relative dominance (as expressed by basal area; ((dbh)2

* 0.00007854)) divided by two. Density (stems per ha) of sapling species was calculated for use in statistical analyses. Seedling and ground-flora data were summarized by calculating the importance percentage (IP) of each species as the sum of cover and relative frequency divided by two. Cover was derived by replacing the cover class code with the midpoint value of the corresponding cover class range. Relative frequency is the number of quadrats in which a species occurred in each sample plot. Volume of DWD

25 per plot was calculated using the formula for the volume of a conical frustum or truncated cone, as follows:

 h V  (R 2  Rr  r 2 ) 3 where h = height, or length of DWD, R = radius of larger end diameter, and r = radius of smaller end diameter. Density of DWD was also calculated. Slope aspect was cosine transformed (transformed aspect = (cos(45° – aspect) + 1)) for use in analysis, assigning relatively cooler and moister northeasterly aspects with a maximum value of 2.0 and relatively drier and warmer southwesterly aspects with a minimum value of 0.0 (Beers et al. 1966).

Species richness values, first-order jackknife estimates of species richness

(Palmer 1990), evenness (Pielou’s J) (Pielou 1969), Shannon-Weiner index values, and

Simpson diversity values were calculated for tree species (using IV), saplings (using density) and seedlings and ground-flora (both using IP) using the PC-ORD statistical software program (McCune and Mefford 1999). Differences in the means of these diversity measures as well as structural characteristics of the vegetation strata between

ELP plots and GAP plots were evaluated with Mann-Whitney tests using Minitab (2010).

The Mann-Whitney test is a non-parametric test for two samples that does not require assumptions of normality or equal variance (Kent and Coker 1992). A significance level of 0.05 was used to evaluate whether differences between physiographic regions were statistically significant.

Indicator species analysis was performed with PC-ORD (McCune and Mefford

1999), using Dufrene and Legendre’s (1997) method to determine if species’ distributions among the physiographic regions were significantly different from random. We used IV, density, and IP for species of trees, saplings, and seedlings and ground-flora, respectively, grouped them by physiographic region, and used a Monte Carlo test (4999 randomizations; p = 0.05) to evaluate statistical significance of individual indicator species.

To reduce “noise” and enhance detection of relationships (McCune and Grace

2002), those species that only occurred in a single plot (approximately 6% of plots), were not included in any of the following analyses. In order to examine the similarity between physiographic regions with respect to IV of tree species composition, density of saplings, and IP of seedlings and ground-flora, an agglomerative, hierarchical classification technique based on a Euclidean dissimilarity matrix was used to compare the two physiographic regions. Ward’s linkage method was used because of its compatibility with Euclidean distances (McCune and Grace 2002). Cluster analysis can be an effective method of identifying groups when dealing with ecological data and has been widely used in community ecology (McCune and Grace 2002). The analysis was completed using PC-ORD (McCune and Mefford 1999). The resulting dendrogram used to interpret the cluster analysis was scaled using Wishart’s (1969) objective function which measures the amount of information lost as clustering proceeds.

Multi-response permutation procedures (MRPP) (Meilke and Berry 2001) were used to test the hypothesis of no difference in IV of tree species, density of saplings, and

IP of seedling and ground-flora composition between the ELP and GAP. MRPP is a non- parametric technique that is useful for testing for a significant difference between two or more groups. It is an appropriate method to use when distributional assumptions, required of parametric tests that address similar questions, are not met (which is often the case with ecological community data) (McCune and Grace 2002). The chance-corrected within-group agreement (A) is a description of the within-group homogeneity (A = 1.0 when all items are identical within groups). A-values are often less than 0.1 when dealing with ecological community data (McCune and Grace 2002). Euclidean distance was used and groups were defined based on physiographic region. Statistical significance of the results was evaluated using a significance level of 0.05.

Detrended canonical correspondence analysis (DCCA) was conducted using the

CANOCO statistical software program (ter Braak and Smilauer 2002) to determine whether canonical correspondence analysis (CCA) or redundancy analysis (RDA) would be the more appropriate analysis (Leps and Smilauer 2003). Due to a relatively small data set, saplings were not analyzed. Generally, a gradient length less than 3.0 in a

DCCA suggests that a linear response model, such as RDA, is a more appropriate choice than CCA (Leps and Smilauer 2003). Results of DCCA gave a gradient length of 1.474 for IV of tree species, a gradient length of 2.194 for IP of seedlings, and a gradient length of 2.739 for IP of ground-flora; thus we conducted RDA to examine the influence of environmental factors on tree, seedling, and ground-flora species composition. RDA is a constrained, linear ordination method that shows those patterns in the species data that can be explained by the available environmental data (we used aspect, slope percent,

28 slope position, slope shape, canopy openness, and soil pH). Monte Carlo tests were used to assess the significance of the relationship between measured environmental variables and species distributions (499 randomizations; p = 0.05). Because variables were measured on different scales, the “center and standardize by species” option was used

(Leps and Smilauer 2003).

Results

Principal Components Analysis

PCA of five environmental variables revealed separation between plots of the

ELP and plots of the GAP (Figure 2.3). The first and second axes combined explained

74.1% of the variation in the data. The first axis was negatively correlated with slope percent, slope shape, and soil pH (p = 0.12; r = -0.67, r = -0.65 and r = -0.88, respectively). The second axis was positively correlated with aspect and slope position (p

= 0.06; r = 0.85 and r = 0.69, respectively).

Plant Community Composition and Structure

The tree species composition was dominated by hemlock in plots of both physiographic regions with respect to IV (Table 2.3). Similarly, hemlock was dominant with regard to both relative dominance and relative density, with values for both measures greater than 50%. After hemlock, northern red oak (Quercus rubra L.) and red maple (Acer rubrum L.) were the most important tree species in the ELP and red maple and white oak (Q. alba L.) were the most important tree species in the GAP. There were

29 no significant differences (p > 0.05) found in tree species between physiographic regions with respect to IV, relative dominance, or relative density. Significant differences were found in total basal area (p = 0.05), total density (p < 0.01), and dbh (p < 0.01) of trees between ELP plots and GAP plots.

A total of 31 trees were cored (Table 2.4). Analysis of increment cores revealed the age of trees ranged from 51-208 years. The average stand age in the ELP (76.3 years) was significantly younger (p < 0.001) than the average stand age in the GAP (120.2 years).

The sapling layer was also dominated by hemlock (Table 2.5). Hemlock and

American beech (Fagus grandifolia Ehrh.) were the only sapling species recorded in the

ELP. In the GAP, the most common sapling species were hemlock and red maple. No significant differences (p > 0.05) were found in the density of individual sapling species or total density of saplings between physiographic regions. The plots in the GAP tended to have greater densities of saplings (92 vs. 43 stems/ha; p = 0.19).

No significant differences (p > 0.05) were found in IP between physiographic regions for seedling species (Table 2.6). The most common species found in ELP plots were black cherry (Prunus serotina Ehrh.) and red maple, and red maple and white ash

(Fraxinus americana L.) in the GAP plots. A significant difference was found in the IP of black cherry seedlings between physiographic regions (p = 0.01). Hemlock was not found in the seedling layer of either physiographic region.

In the ground-flora layer, no significant differences (p > 0.05) were found in species’ IP between physiographic regions (Table 2.7). The most common species found

30 in ELP plots were Canada mayflower (Maianthemum canadense Desf.) and partridgeberry (Mitchella repens L.) and intermediate woodfern (Dryopteris intermedia

(Muhl. ex Willd.) A. Gray), and Canada mayflower in the GAP plots. The IP of partridgeberry in the ELP tended to be greater than in the GAP (21.7 vs. 7.6%; p = 0.06).

In the tree layer, cucumbertree (Magnolia acuminata L.) was identified as an indicator species of the ELP (p < 0.01) (Table 2.3). In the seedling layer, both black cherry and yellow-poplar (Liriodendron tulipifera L.) were found to be indicators of the

ELP (p < 0.01 and p = 0.05, respectively) (Table 2.6). In the ground-flora layer, partridgeberry was an indicator of the ELP (p = 0.05) and intermediate woodfern was an indicator of the GAP (p = 0.03) (Table 2.7).

Plant Community Diversity

No significant differences (p > 0.05) among evenness, Shannon-Weiner index, or

Simpson diversity were observed between physiographic regions for species of trees

(Figure 2.4), saplings (Figure 2.5), seedlings (Figure 2.6), or ground-flora (Figure 2.7).

The Shannon-Weiner index tended to be different (p = 0.06) for tree species between plots of the ELP and plots of the GAP. The first-order jackknife estimate of species richness for the ELP was 13.6 and for the GAP was 17.5.

Downed Woody Debris

Density of DWD was not significantly different (p > 0.05) between physiographic regions (Figure 2.8). Volume of DWD, however, was significantly greater in GAP plots

(p < 0.01). The majority of DWD in both physiographic regions was in decay classes 2 and 3 (Table 2.8). A significantly greater relative density of DWD in decay class 5 was detected in the ELP plots versus those in the GAP (p < 0.01).

Hierarchical Cluster Analysis

Hierarchical cluster analysis of trees (Figure 2.9), saplings (Figure 2.10), seedlings (Figure 2.11), and ground-flora (Figure 2.12) revealed no apparent separation of plots based on physiographic region. A relatively large amount of chaining was encountered in all resulting dendrograms, meaning many single plots were added to existing groups, and the analysis failed to form distinct groups.

Multi-response Permutation Procedure

MRPP indicated no significant differences in trees (p = 0.38; A < 0.01), saplings

(p = 0.35; A < 0.01), or ground-flora (p = 0.09; A = 0.05) between plots of physiographic regions. There was a significant difference found in seedling IP between physiographic regions (p = 0.03; A = 0.06).

Redundancy Analysis

RDA of tree species IVs indicated relationships among environmental variables, species, and plots of the ELP and the GAP (Figure 2.13). The first and second axes combined explained 33.3% of the variation in the species data (Table 2.9). The first axis was negatively associated with slope position (p = 0.01). Positively associated with the

32 second axis were canopy openness (p = 0.02) and aspect (p = 0.12) and negatively associated with the second axis were slope shape (p = 0.11), slope percent (p = 0.50), and soil pH (p < 0.01). The major trend in distribution of ELP and GAP plots ran along the slope position gradient, from the higher relative slope positions in the lower left to the lower relative slope positions in the upper right of the ordination diagram. Species distributions are generally consistent with individual species life-history characteristics and habitat requirements. Yellow birch (Betula alleghaniensis Britton) is positively correlated with aspect and canopy openness, cucumbertree and black cherry are negatively correlated with slope position, and white oak is negatively correlated with aspect and positively correlated with slope shape. Hemlock is positively correlated with slope position.

RDA of seedling species revealed clear patterns relating relationships among environmental variables, species, and plots of the ELP and GAP (Figure 2.14). The first and second axes combined explained 31.7% of the variation in the species data (Table

2.9). The first axis was positively associated with canopy openness (p = 0.42) and negatively associated with slope percent (p = 0.17). The second axis was positively associated with slope shape (p = 0.29) and slope position (p = 0.29). As with RDA of tree and ground-flora species, the trend in plot distribution follows a gradient of slope position, from relatively higher slope positions in the upper left to relatively lower slope positions in the lower right of the ordination diagram. Species distributions seem to reflect individual species habitat requirements and life-history characteristics. Black cherry, yellow-poplar, and northern red oak are positively correlated with canopy

33 openness. Red maple, sugar maple (Acer saccharum Marsh.), American beech, and white ash are positively correlated with both slope shape and aspect. Chestnut oak

(Quercus prinus L.) is positively correlated with slope position, white ash is negatively correlated with slope percent, and white oak is negatively associated with slope shape.

Hemlock was not found in the seedling layer of any plots.

RDA of ground-flora species also indicated relationships among environmental variables, species, and plots of the two physiographic regions (Figure 2.15). The first and second axes combined explained 43.0% of the variation in the species data (Table 2.9).

The first axis was positively associated with aspect (p < 0.01). Positively associated with the second axis was canopy openness (p = 0.47) and negatively associated with the second axis were slope percent (p = 0.09), slope shape (p = 0.33), soil pH (p < 0.01), and slope position (p = 0.24). The trend in the distribution of plots follows the slope position gradient, from higher relative slope positions in the bottom portion to lower relative slope positions in the upper portion of the ordination diagram. Partridgeberry is positively correlated with canopy openness and negatively correlated with slope percent, soil pH, and slope position. Smilacina racemosa (L.) and Rubus occidentalis (L.) are positively correlated with aspect.

Discussion

Despite physiographic and edaphic differences in stands of the two physiographic regions sampled, hemlock was the dominant tree and sapling species encountered. From our study, it appears that hemlock is not as influenced by these physical environmental

34 factors as might be expected. That hemlock is the dominant tree and sapling species in these stands when environmental factors were much different supports the idea that hemlock is a foundation species.

Hemlock was prevalent in the tree layer in all plots of both physiographic regions with respect to density (stems per ha) and dominance (basal area; m2 per ha). This is typically the case for a foundation species, which is locally abundant and tends to dominate an entire ecological community (Ellison et al. 2005). This overriding influence of hemlock in the tree layer likely prevented any significant groupings from being detected in cluster analysis (Figure 2.9), significant differences in MRPP, or any significant differences in diversity indices between physiographic regions (Figure 2.4).

Significantly greater mean tree species basal area and density and significantly smaller dbh were found in the ELP plots compared to the GAP plots. This may be due, in part, to an overall difference in the stage of development of the stands between the physiographic regions. Most of the stands in the ELP appeared to be in the stem exclusion stage of forest development, while most of the stands in the GAP appeared to be in the understory reinitiation stage of forest development (Oliver and Larson 1990).

The mean age of ELP plots, based on increment core analysis, was 76 years, whereas the mean age of GAP plots was 120 years. Younger forests in earlier stages of development typically have higher stem densities and smaller-size trees.

As seen in the tree layer, the sapling layer was dominated by hemlock in both physiographic regions. Greater density of saplings recorded in the GAP relative to the

ELP may also be due to differences in the developmental stages of forests sampled in the

ELP and GAP. Low sapling density, as was found in the ELP plots, is typical of the stem exclusion stage in which the canopy is composed of a single cohort and is too dense to allow for understory development. In the next stage of forest development, understory reinitiation, seedlings and saplings occur at greater densities, as some light is able to reach the forest floor when gaps are created due to canopy tree mortality (Oliver 1980;

Oliver and Larson 1990). The high-degree of variation in density of saplings recorded between plots may have prevented any statistically significant differences in diversity indices (Figure 2.5), MRPP, groups in cluster analysis (Figure 2.10), or sapling density

(Table 2.5) from being detected.

In their study on the composition and structure of old-growth forests on steep slopes lining Zoar Canyon, in western New York, Diggins and Catterlin (2010), found in the tree layer that hemlock was strongly associated with mesic north-facing slopes. They also found greater dominance by hemlock at higher slope positions compared to fluvial terraces. In our study, hemlock was dominant in the tree layer regardless of slope position or aspect. The steepest slope encountered on our plots was 57%, and its aspect was northwest-facing; we encountered no very steep south-facing slopes. It is likely that steep, south-facing slopes are too warm and dry to support hemlock, which is typically found on cool, moist sites (Godman and Lancaster 1990).

Black and Mack’s (1976) study examining hemlock-dominated forests throughout

Ohio found similar results as ours in terms of tree species composition and basal area and ground-flora species composition. In both studies, Dryopteris spp. seemed to be associated with increasing slope percent; we found it to be an indicator species of the

GAP, whose stands were consistently located on steeper slopes than those of the ELP.

Black and Mack (1976) sampled only one stand in the ELP, and found hemlock occurring on sandy hummocks. They also encountered a shrub layer of Prunus virginiana L.

While we did not encounter a distinct shrub layer in any stands, it did appear that hemlock was occurring on hummocks in these low-lying, ELP stands, though we did not record microtopographic factors. Aldrich (1943) found hemlock more commonly in northeastern Ohio on the Appalachian Plateau than in bogs and swamps of the ELP. It is possible, that at the time of his study, hemlock had not yet established at some of the stands in the ELP, as we found their mean age to be 76 years.

We found no regeneration of hemlock in the seedling layer (Table 2.6). Poor recruitment of hemlock is well-documented (Woods 1984; Alverson et al. 1988; Godman and Lancaster 1990; Mladenoff and Stearns 1993; O’Hanlon-Manners and Kotanen 2004;

Salk et al. 2011). Several reasons for poor hemlock seedling establishment include heavy browsing by white-tailed deer (Odocoileus virginianus Zimm.) (Gordon 1969; Woods

1984; Alverson et al. 1988; Godman and Lancaster 1990; Salk et al. 2011), low seed viability, very specific seedbed temperature and moisture requirements, and mortality caused by soil-borne pathogens (Godman and Lancaster 1990; O’Hanlon-Manners and

Kotanen 2004; Sullivan and Ellison 2006). Decaying logs and tree stumps (referred to as

“nurse logs”) often provide a critical substrate on which hemlock seeds experience moisture and temperature levels necessary for germination (Black and Mack 1976;

O’Hanlon-Manners and Kotanen 2004). White-tailed deer populations are significantly higher than historical levels across much of the eastern United States (Salk et al. 2011).

White-tailed deer density greater than six deer per square kilometer have been shown to negatively affect forest regeneration (Dougherty et al. 2001). Estimates of white-tailed deer density in the Cuyahoga Valley National Park in northeastern Ohio range from 12-

40 deer per square km, with an average of approximately 16 deer per square km

(Dougherty et al. 2001).

Hemlock-dominated forests typically have a ground-flora layer that is lower in species diversity than those of eastern hardwood forests (Braun 1950; Lewin 1974; Yorks et al. 2000; Ellison et al. 2005). The unique microclimate and dense shade created by hemlock limits the number of ground-flora species that occur there. Hemlock’s influence as a foundation species likely lead to similar ground-flora and soil conditions at plots of both physiographic regions, preventing significant differences between their ground-flora compositions from being detected. The ground-flora composition found in the current study is similar to those found in previous studies examining the plant community of hemlock-dominated ecosystems. Several species found in this study are closely associated with hemlock forests; particularly partridgeberry, Canada mayflower, and intermediate woodfern (Braun 1950; Gordon 1969; Black and Mack 1976; Hix and

Barnes 1984; Yorks et al. 2000).

We found a significantly greater volume of DWD on plots of the GAP than those of the ELP (Figure 2.3). Stage of stand development may be partially responsible for this observation; the amount of DWD tends to increase with stand age (Oliver and Larson

1990). One plot of the GAP in particular, was located adjacent to a large canopy gap created by the falling of multiple, large hemlock trees. These downed stems fell partially

38 within one of our study plots and thus, the volume of DWD calculated for this plot, and subsequently, the GAP may have been biased toward greater DWD volume. We also found significantly higher densities of DWD in advanced stages of decay (class 5) in the

ELP compared to the GAP (Table 2.). This may be attributable to differences in moisture regimes between the two physiographic regions. All of the plots in the ELP were located on flat bottomlands that may experience standing water at different times of the year, as opposed the plots in the GAP which were mostly located on middle- to upper-hillslope positions and do not experience standing water. Exposure to water and higher soil moisture levels, and subsequent leaching of DWD, accelerates decomposition (Harmon et al. 1986). This increased exposure to moisture of DWD in the ELP plots may explain the greater proportion of advance decay classes observed there.

Despite differences in physical environment attributes, hemlock-dominated forests of the ELP and GAP are similar in composition and structure. This suggests that hemlock does not respond to physiographic and edaphic factors as would be expected of many tree species and supports the idea that hemlock is a foundation species. While significant differences were detected in diversity measures and structure of the tree layer, composition of seedlings, and structure of DWD, these may be related to the differing stages of forest development between the two physiographic regions. These differences may not be present in forests of ELP and GAP that are in the same stage of forest development.

There could be many reasons for the possible differences in forest developmental stage between plots of the two physiographic regions examined in this study. One

39 possible explanation for this is the inherent differences in topography of the two physiographic regions. While the soils on which the ELP plots were located are not prime agricultural soils (Milliron et al. 2007), it is likely that these areas were logged for conversion to agriculture. They may have been abandoned after attempts at drainage failed or the soils proved unproductive for crop growth and were allowed to succeed to forest. The GAP is characterized by moderate relief, ridges and flat uplands dissected by steep valleys (Brockman 1998). The areas of the GAP in which study plots were located are mostly moderately to steeply sloped stream ravines which likely would have been much more difficult or impossible to convert to agricultural use. These areas may have been completely or selectively logged and (or) grazed by livestock and allowed to revert to forest at an earlier date than forests of the ELP. Further investigation into the historical land use of the areas in which the stands were located may provide information related to the current stage of forest development.

Further research into soil characteristics and disturbance histories may reveal additional factors that influence the composition and structure of the plant community of the hemlock forests of northeastern Ohio. This study provides valuable baseline information on the composition and structure of hemlock-dominated forests within two physiographic regions of northeastern Ohio prior to the arrival of the destructive pest,

HWA.

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Table 2.1. Summary information for the three Huron-Erie Lake Plain (ELP) stands and the four Glaciated Allegheny Plateau (GAP) stands in northeastern Ohio.

Stand name Plot Physiog. Slope Aspect Slope Slope position Canopy Soil Soil map unit and code region (%) (degrees in shape openness pH azimuth) (%) Armstrong 1 ELP 9 236 Convex Bottom 5.94 5.0 Kingsville loamy fine Swamp (AS) 2 8 208 Convex Bottom 6.02 5.0 sand, 0-2 percent slopes (KfA)

Cathedral 1 ELP 9 84 Concave Bottom 3.15 5.0 Colonie loamy fine sand, Woods (CW) 2 6 88 Convex Bottom 3.39 5.0 2-6 percent slopes (CoB)

Morgan 1 ELP 2 308 Convex Bottom 1.09 4.5 Caneadea-Canadice silt Swamp (MS) 2 1 214 Convex Bottom 3.64 4.5 loams, 0-2 percent slopes 3 2 270 Convex Bottom 3.50 4.5 (CeA)

Clear Fork 1 GAP 57 321 Convex Upper hillslope 3.56 4.5 Lordstown silt loam, 25- Gorge SNP 2 54 328 Convex Upper hillslope 2.92 4.5 40 percent slopes (LtF) (CFG) 3 49 315 Convex Middle hillslope 4.34 4.5

Little 1 GAP 3 125 Convex Summit 3.71 4.5 Lordstown loam, 2-6 Mountain 2 10 88 Convex Summit 2.87 4.5 percent slopes (LrB) (LM) 3 21 80 Concave Middle hillslope 2.22 4.5

Wooster 1 GAP 9 44 Convex Summit 2.92 5.0 Wooster-Riddles silt Memorial Park 2 17 34 Convex Upper hillslope 2.50 5.0 loams, 12-18 percent 1 (WMP 1) slopes, eroded (WuD2)

Wooster 1 GAP 18 262 Convex Upper hillslope 2.29 5.0 Wooster-Riddles silt Memorial Park 2 20 210 Convex Upper hillslope 1.47 5.0 loams, 12-18 percent 2 (WMP 2) slopes, eroded (WuD2)

Table 2.2. Downed woody debris (DWD) decay classes and characteristics (adapted from Pyle and Brown 1998).

Decay class Characteristics 1 Bark firmly attached Exposed wood has fresh color (not stained by weathering) Surface substrate: sound bark

2 Bark, if present, not firmly attached Wood generally solid Surface does not flake off when kicked perpendicular to log surface Log does not crush when considerable weight is placed on it Surface substrate: hard wood or decayed bark

3 Bark generally absent (except in Betula spp. and Prunus spp.) Log firm when kicked Wet surface of wood may compress and rebound like sponge Surface substrate: soft wood

4 Log no longer solid piece; large chunks remain Log will break apart when kicked perpendicular to log surface Log shape oval or flattened Surface substrate: very spongy wood or powder wood

5 Log predominantly powder wood Log shape flat or flat with some rounding Surface substrate: loosely aggregated powder wood

Table 2.3. Mean relative dominance, relative density, importance value (IV), richness, diameter at breast height (dbh), basal area, and density (± 1 standard deviation) of tree species in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. Values for a given variable in a row followed by the same letter are not significantly different at p < 0.05 (Mann-Whitney test). Values followed by an asterisk indicate the species is an indicator of a physiographic region at p < 0.05 (Monte Carlo test).

ELP GAP Species name Rel. dominance Rel. density IV Rel. dominance Rel. density IV Acer rubrum 6.8 (7.9)a 8.4 (10.9)a 7.6 (9.2)a 10.1 (16.3)a 10.3 (14.7)a 10.2 (15.3)a Acer saccharum 0.7 (1.7)a 0.3 (0.9)a 0.5 (1.3)a 0.0a 0.0a 0.0a Betula alleghaniensis 4.2 (5.4)a 5.9 (9.0)a 5.1 (6.9)a 0.7 (2.1)a 5.0 (15.8)a 2.8 (9.0)a Carya cordiformis 0.0a 0.0a 0.0a 0.1 (0.5)a 0.8 (2.4)a 0.5 (1.5)a Carya tomentosa 0.0a 0.0a 0.0a 0.3 (0.9)a 0.4 (1.1)a 0.3 (1.0)a Fagus grandifolia 2.1 (2.9)a 5.8 (7.6)a 4.0 (5.1)a 2.8 (8.1)a 2.2 (5.2)a 2.5 (6.5)a 49 Liriodendron tulipifera 3.0 (5.2)a 1.3 (2.2)a 2.1 (3.7)a 0.7 (1.6)a 1.2 (2.5)a 0.9 (2.0)a Magnolia acuminata 4.4 (4.0)a 3.8 (2.9)a 4.1 (3.3)a* 0.0a 0.0a 0.0a Pinus strobus 0.0a 0.0a 0.0a 3.0 (9.6)a 1.7 (5.3)a 2.3 (7.4)a Populus grandidentata 0.3 (0.7)a 0.4 (0.9)a 0.3 (0.8)a 0.0a 0.0a 0.0a Prunus serotina 2.5 (3.8)a 3.6 (4.4)a 3.0 (3.9)a 0.7 (1.7)a 2.1 (3.8)a 1.4 (2.4)a Quercus alba 0.0a 0.0a 0.0a 10.0 (23.0)a 5.1 (12.8)a 7.5 (17.8)a Quercus prinus 5.9 (15.5)a 3.1 (8.1)a 4.5 (11.8)a 2.2 (4.0)a 1.6 (2.6)a 1.9 (3.3)a Quercus rubra 17.1 (19.5)a 8.8 (10.4)a 13.0 (14.8)a 4.0 (7.7)a 1.6 (2.6)a 2.8 (5.0)a Quercus velutina 0.0a 0.0a 0.0a 1.9 (6.1)a 1.1 (3.4)a 1.5 (4.8)a Tsuga canadensis 53.1 (21.7)a 58.6 (16.3)a 55.9 (18.6)a 63.6 (25.3)a 67.1 (17.6)a 65.3 (18.0)a

Richness (no. of species) 11 13 Dbh (cm) 28.2 (14.2)a 34.8 (19.2)b Basal area (m2/ha) 51.6 (4.8)a 42.1 (10.0)b Density (stems/ha) 660 (171.7)a 340 (98.0)b

Table 2.4. Number of trees cored and mean age of stands (± 1 standard deviation) of both physiographic regions in northeastern Ohio. Mean ELP stand age was significantly younger than mean GAP stand age (Mann-Whitney test; p < 0.001).

Stand No. trees cored Mean age (years) ELP Armstrong Swamp 4 93.0 (29.8) Cathedral Woods 5 75.2 (13.0) Morgan Swamp 8 60.6 (9.4) ELP total 17 76.3 (16.2)

GAP Clear Fork Gorge 7 127.6 (27.9) Wooster Memorial Park 1 3 94.0 (11.0) Wooster Memorial Park 2 4 139.0 (59.0) GAP total 14 120.2 (23.4)

Table 2.5. Mean density (± 1 standard deviation) of sapling species in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. Values in a row followed by the same letter are not significantly different at p < 0.05 (Mann- Whitney test). Values followed by an asterisk indicate the species is an indicator of a physiographic region at p < 0.05 (Monte Carlo test).

Density (stems/ha) Species name ELP GAP Acer rubrum 0.0a 18.0 (50.3)a Acer saccharum 0.0a 10.0 (21.6)a Fagus grandifolia 11.4 (30.2)a 6.0 (13.5)a Hamamelis virginiana 0.0a 10.0 (31.6)a Prunus serotina 0.0a 4.0 (12.6)a Tsuga canadensis 31.4 (36.3)a 44.0 (65.9)a

Total density (stems/ha) 42.9 (43.9)a 92.0 (80.1)a Richness (no. of species) 2 6

Table 2.6. Mean importance percentage (± 1 standard deviation) of seedling species in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. Values in a row followed by the same letter are not significantly different at p < 0.05 (Mann-Whitney test). Values followed by an asterisk indicate the species is an indicator of a physiographic region at p < 0.05 (Monte Carlo test).

Importance percentage Species name Code ELP GAP Acer rubrum ACRU 19.74 (14.24)a 21.42 (18.81)a Acer saccharum ACSA 1.79 (4.75)a 1.63 (5.16)a Amelanchier arborea AMAR 0.00a 1.26 (3.97)a Carya cordiformis CACO 0.00a 1.26 (3.97)a Cornus alternifolia COAL 1.79 (4.75)a 0.00a Fagus grandifolia FAGR 0.00a 2.54 (5.36)a Fraxinus americana FRAM 1.79 (4.75)a 5.03 (12.14)a Liriodendron tulipifera LITU 7.18 (9.88)a* 0.00a Prunus serotina PRSE 21.54 (13.98)a* 3.77 (6.07)b Quercus alba QUAL 5.43 (14.36)a 1.26 (3.97)a Quercus prinus QUPR 0.00a 2.54 (5.36)a Quercus rubra QURU 9.06 (14.01)a 2.51 (5.30)a Sassafras albidum SAAL 5.43 (14.36)a 0.00a

Richness (no. of species) 9 10

Table 2.7. Mean importance percentage (± 1 standard deviation) of ground-flora species in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. Values in a row followed by the same letter are not significantly different at p < 0.05 (Mann-Whitney test). Values followed by an asterisk indicate the species is an indicator of a physiographic region at p < 0.05 (Monte Carlo test).

Importance percentage Species name Code ELP GAP Boehmeria cylindrica BOCY 0.00a 1.26 (3.97)a Cirsium vulgare CIVU 0.00a 1.26 (3.97)a Dryopteris intermedia DRIN 0.00a 19.43 (20.33)a* Lindera benzoin LIBE 3.59 (9.50)a 0.00a Maianthemum canadense MACA 23.42 (23.54)a 8.79 (18.77)a Medeola virginiana MEVI 1.8 (4.75)a 0.0a Mitchella repens MIRE 21.67 (15.76)a* 7.57 (8.80)a Monotropa uniflora MOUN 1.79 (4.75)a 0.00a Parthenocissus quinquefolia PAQU 0.00a 7.94 (17.99)a Phytolacca americana PHAM 0.00a 2.51 (7.95)a Podophyllum peltatum POPE 0.00a 2.58 (5.43)a Polygonum virginianum POVI 0.00a 1.26 (3.97)a Rubus allegheniensis RUAL 0.00a 1.29 (4.07)a Rubus occidentalis RUOC 1.79 (4.75)a 1.26 (3.97)a Smilax rotundifolia SMRO 0.0a 1.26 (3.97)a Smilacina racemosa SMRA 1.79 (4.75)a 2.51 (4.19)a Symplocarpus foetidus SYFO 1.84 (4.87)a 0.0a Toxicodendron radicans TORA 0.00a 2.54 (8.04)a Trillium grandiflorum TRGR 1.79 (4.75)a 0.00a Viola palmata VIPA 3.59 (6.13)a 2.51 (5.30)a Vitis aestivalis VIAE 5.38 (9.88)a 0.00a

Richness (no. of species) 11 15

Table 2.8. Relative density of downed woody debris (DWD) decay classes in the Huron- Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. Values in a row followed by the same letter are not significantly different at p < 0.05 (Mann-Whitney test).

Relative density (%) Decay class ELP GAP 1 6.3 (9.4)a 1.2 (2.8)a 2 24.0 (20.0)a 27.5 (16.7)a 3 40.4 (9.2)a 51.2 (16.8)a 4 17.1 (14.1)a 17.0 (16.4)a 5 12.2 (5.9)a 3.1 (3.8)b

Table 2.9. Redundancy analysis (RDA) results for trees, seedlings, and ground-flora for both physiographic regions of northeastern Ohio.

Axis 1 2 3 4 Trees Eigenvalues 0.213 0.120 0.086 0.062 Species-environment correlation 0.943 0.894 0.799 0.857 Cumulative percentage variance Of species data 21.3 33.3 41.9 48.1 Of species-environment relation 39.0 61.0 76.7 88.1

Seedlings Eigenvalues 0.173 0.145 0.086 0.038 Species-environment correlation 0.959 0.757 0.857 0.601 Cumulative percentage variance Of species data 17.3 31.7 40.4 44.2 Of species-environment relation 35.7 65.6 83.4 91.3

Ground-flora Eigenvalues 0.284 0.145 0.055 0.033 Species-environment correlation 0.950 0.680 0.714 0.652 Cumulative percentage variance Of species data 28.4 43.0 48.4 51.7 Of species-environment relation 52.6 79.5 89.6 95.7

Figure 2.1. Locations of the three Huron-Erie Lake Plain (ELP) stands (open circles) and four Glaciated Allegheny Plateau (GAP) stands (black dots) within northeastern Ohio.

Figure 2.2. Sample plot design and orientation of the 500-m2 circular plot, nested 100-m2 circular plot, and four 1.0 x 1.0 m quadrats (not to scale).

Figure 2.3. Principal components analysis (PCA) of five environmental variables for seventeen plots in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. Percentages are the amount of variation explained by each axis.

2.0 Evenness (Pielou's J) Shannon-Weiner Index 1.6 Simpson Index

1.2

0.8

0.4

0.0 ELP GAP

Figure 2.4. Mean tree diversity values (± 1 standard deviation) in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio.

0.7 Evenness (Pielou's J) 0.6 Shannon-Weiner Index Simpson Index 0.5

0.4

0.3

0.2

0.1

0 ELP GAP

Figure 2.5. Mean sapling diversity values (± 1 standard deviation) in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio.

2.0 Evenness (Pielou's J) Shannon-Weiner Index 1.6 Simpson Index

1.2

0.8

0.4

0.0 ELP GAP

Figure 2.6. Mean seedling diversity values (± 1 standard deviation) in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio.

2.0 Evenness (Pielou's J) Shannon-Weiner Index 1.6 Simpson Index

1.2

0.8

0.4

0.0 ELP GAP

Figure 2.7. Mean ground-flora diversity values (± 1 standard deviation) in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio.

Figure 2.8. Mean downed woody debris (DWD) characteristics (± 1 standard deviation) in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. There was a significant difference in the DWD volume per hectare between physiographic regions (Mann-Whitney test, p < 0.01).

Figure 2.9. Dendrogram from hierarchical cluster analysis using tree species importance values (IV) for seventeen plots in the Huron-Erie Lake Plain (ELP; open circles) and Glaciated Allegheny Plateau (GAP; black dots) of northeastern Ohio. Plot abbreviations are provided in Table 2.1.

Figure 2.10. Dendrogram from hierarchical cluster analysis using sapling species density (stems per ha) for eleven plots in the Huron-Erie Lake Plain (ELP; open circles) and Glaciated Allegheny Plateau (GAP; black dots) of northeastern Ohio. Plot abbreviations are provided in Table 2.1.

Figure 2.11. Dendrogram from hierarchical cluster analysis using seedling species importance percentages (IP) for fifteen plots in the Huron-Erie Lake Plain (ELP; open circles) and Glaciated Allegheny Plateau (GAP; black dots) of northeastern Ohio. Plot abbreviations are provided in Table 2.1.

Figure 2.12. Dendrogram from hierarchical cluster analysis using ground-flora species importance percentages (IP) for fifteen plots in the Huron-Erie Lake Plain (ELP; open circles) and Glaciated Allegheny Plateau (GAP; black dots) of northeastern Ohio. Plot abbreviations are provided in Table 2.1.

Figure 2.13 . Redundancy analysis (RDA) ordination diagram for tree species importance values (IV) for seventeen plots in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. Species abbreviations are provided in Table 2.3.

Figure 2.14. Redundancy analysis (RDA) ordination diagram for seedling species importance percentages (IP) for seventeen plots in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. Species abbreviations are provided in Table 2.6.

Figure 2.15. Redundancy analysis (RDA) ordination diagram for ground-flora species importance percentages (IP) for seventeen plots in the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio. Species abbreviations are provided in Table 2.7.

CHAPTER 3

MODELING HEMLOCK WOOLLY ADELGID-INDUCED MORTALITY

AND PREDICTING STAND DEVELOPMENT USING THE FOREST

VEGETATION SIMULATOR

Introduction

Since its introduction in 1951, hemlock woolly adelgid (Adelges tsugae Annand;

HWA), an invasive species native to Japan, has spread across much of the range of eastern hemlock (Tsuga canadensis (L.) Carr.; hemlock) and has had a major impact on composition, structure, and functional processes of forests from Maine to Georgia. In northeastern Ohio, hemlock occurs mainly in flat, poorly-drained sites in the Huron-Erie

Lake Plain physiographic region (ELP) and in stream ravines in the Glaciated Allegheny

Plateau physiographic region (GAP) (Braun 1950; Gordon 1969; Black and Mack 1976;

Brockman 1998). The first HWA infestation in Ohio was discovered in January of 2012, followed by a second, larger infestation discovered in May of 2012 (Ohio Department of

Agriculture 2012a; Ohio Department of Agriculture 2012b). While these infestations were found early and a quarantine restricting the movement of hemlock material is in place, it is likely that HWA has gone undetected in other parts of Ohio and that additional infestations will be found. As HWA moves into Ohio, it is not known how the

composition and structure of hemlock forests of the state will change in response to the adelgid. Before HWA reaches hemlock forests of the ELP and GAP in northeastern

Ohio, we want to predict changes in the composition and structure of these forests in response to likely HWA impacts.

The Forest Vegetation Simulator (FVS) is a software program developed by the

U.S.D.A. Forest Service and used mostly by governmental agencies and forest managers to predict forest stand dynamics and model the effects of stand management, wildfires, and insect and disease outbreaks (Dixon 2002). Geographical variants of the FVS have been developed for most of the forest of the United States based on location and incorporate growth and yield models tailored to that specific region. The appropriate variant for use in this study is the Northeast Variant, which covers Ohio north through

New England.

There are many “extensions” available for use with FVS. Extensions are linkable modules that work within the confines of FVS to simulate various insect and pathogen impacts, fire effects, and understory development, among others (Dixon and Keyser

2008). For this study we utilized the Hemlock Woolly Adelgid Event Monitor extension with the Northeast Variant of FVS to simulate hemlock mortality due to HWA in the forests we sampled. The HWA Event Monitor requires the user to input a year of infestation, calculated based on the distance from the nearest HWA infestation and rates of HWA spread documented in the literature (Trotter et al. 2008). Several studies have documented rates of HWA spread across the landscape (Souto et al. 1996; Orwig and

Foster 1998; Yorks et al. 2000; Evans and Gregoire 2007). Results of these studies

estimated rates of HWA spread up to 30 km per year. This high rate of spread is seen mainly in the southern Appalachians. Evans and Gregoire (2007) found that the rate of spread for Pennsylvania and northward could be as slow as 8.13 km per year or as fast as

12.5 km per year. In this study, we modeled HWA-induced hemlock mortality based on two rate of spread scenarios, eight km per year and 13 km year, to essentially “bracket” the estimated rate of spread found in the literature.

The HWA Event Monitor models the loss of hemlock basal area in five-year cycles. To simulate the effects of HWA on hemlock, the HWA Event Monitor determines the intensity of HWA outbreak based on a probability distribution. Five ranges of hemlock mortality can occur with each successive cycle. These pre-determined mortality ranges are “no infestation” resulting in 0% loss of hemlock, “low infestation” resulting in 0-5% loss of hemlock, “moderate infestation” resulting in 5-30% loss of hemlock, “high infestation” resulting in 30-70% loss of hemlock, and “catastrophic infestation” resulting in 70-90% loss of hemlock (Forest Health Technology Enterprise

Team (FHTET) 2008). The probability of a specific infestation severity is determined using the following probability distribution: low infestation, 40%; moderate infestation,

30%; high infestation, 20%; and catastrophic infestation, 10%. There is another version of the HWA Event Monitor for use with the Southern Variant of FVS, in which, probabilities of more severe infestations are greater, simulating more rapid decline seen in the southern portions of the range of hemlock. Once a stand is infested, a “no infestation” scenario is not allowed, as it assumed that once a stand is infested, it will remain infested. These population cycles are randomized to simulate some of the

stochasticity of infestation; however, a catastrophic infestation can occur only once in a simulation. This is done to simulate documented population dynamics of HWA, in which

HWA populations grow to a point that degrades the condition of the hemlock and subsequently reduces its ability to support large populations of HWA. This allows trees to recover, triggering a resurgence in HWA populations; however, they may not grow to the density of the initial infestation, due to the diminished capacity of hemlock to support such large populations. Over time, the decline of hemlock results in gradually reduced peaks in the HWA population (McClure 1991). Our objective was to simulate HWA- induced mortality of hemlock in both physiographic regions sampled, under two potential arrival date “scenarios,” based on the rate of spread of HWA from the nearest known infested county.

Study Area

A total of seventeen sample plots were established in seven mature stands located in northeastern Ohio on public and private land (Figure 3.1, Table 3.1). In order to examine the variation between hemlock forest types (Aldrich 1934; Black and Mack

1976), three hemlock swamp forest stands were located within the Erie Lake Plain district of the ELP and four stream ravine stands were located within the GAP of northeastern

Ohio (Brockman 1998). In Ohio, both of these physiographic regions fall within the

Beech-Maple Forest Region described by Braun (1950). Hemlock is largely confined to the eastern half of Ohio (Sears 1925; Gordon 1969; Black and Mack 1976; Godman and

Lancaster 1990). Our efforts were focused on the northeastern part of the state, as other

research examining hemlock forest composition and structure within the Unglaciated

Allegheny Plateau of southeastern Ohio is ongoing (Martin and Goebel 2011; Ohio

University 2012).

January minimum temperature of -8.1° C (Milliron et al. 2007). The counties containing the GAP stands (Ashland, Geauga and Wayne) have a mean annual temperature of 9.4°

C, with a mean July maximum temperature of 27.7° C and a mean January minimum temperature of -8.2° C (Redmond and Brown 1980; Williams and McCleary 1982;

Bureau et al. 1984). Average annual precipitation is 98 cm in Ashtabula County and 99 cm in the GAP counties, the majority of which falls between April and October

(Brockman 1998). The soil map units associated with these stands are described as very poorly drained to well-drained (Soil Survey Staff 2012). The GAP is characterized by moderate relief, ridges and flat uplands dissected by steep valleys, elevation of 183-459 m, and Wisconsinan-age clay to loam till over Mississippian- and Pennsylvanian-aged shales and sandstones (Brockman 1998). The soil map units associated with these stands are described as well-drained (Soil Survey Staff 2012).

Methods

Vegetation

Sample plots were located within seven hemlock-dominated stands within both the ELP and the GAP. Depending on the size of the stand, two or three sample plots were established at each stand (Table 3.1). Sample plots consisted of 500-m2 circular plots (Figure 3.2). Within each plot diameter at breast height (1.37 m; dbh), species, and crown class (dominant, codominant, intermediate, and overtopped) (Smith et al. 1997) of living trees ≥ 10.0 cm dbh were recorded.

Forest Vegetation Simulator

Before simulating HWA-induced mortality with FVS and the HWA Event

Monitor, year of HWA-infestation had to be determined. In order to estimate the arrival year of HWA to both the ELP and the GAP in northeastern Ohio, centroids of the plots of each physiographic region were calculated. This was done by averaging the latitude and longitude of all plots within each physiographic region to derive a geographic centroid

(Table 3.1). Year of infestation could then be calculated by dividing the distance from the centroids to the nearest HWA-infested county (Beaver County, Pennsylvania) by the rate of HWA spread. Based on previous studies (Souto et al. 1996, Orwig and Foster

1998, Yorks et al. 2000, Evans and Gregoire 2007), two HWA rate-of-spread scenarios were used in these calculations to “bracket” the anticipated arrival date of HWA; eight km/year and 13 km/year; thus, two anticipated HWA-arrival years were used in simulations for each physiographic region (Trotter et al. 2008).

Vegetation data gathered from the plots of each physiographic region were combined for use with FVS. The HWA Event Monitor was then used with the

Northeastern Variant (Dixon and Keyser 2008) of FVS to simulate HWA-induced mortality and predict forest development. We selected the Allegheny National Forest growth models for use with simulations, as it is the closest model to our stands available in the Northeast Variant. First, a control simulation was performed using the HWA

Event Monitor within FVS to simulate forest growth of ELP and GAP stands without any

HWA-induced mortality 45 years into the future. Next, the effects on the ELP and GAP stands were forecast using the FVS with the HWA Event Monitor to simulate HWA- induced mortality 45 years into the future using the two estimated arrival year scenarios.

Because of the stochastic nature of HWA-infestation intensity and population dynamics

(McClure 1991) modeled with the HWA Event Monitor, ten replicates were performed on each rate of spread scenario for both physiographic regions in order to stabilize variance estimates of the projected events and values (FHTET 2008). In all simulations, stand data was calculated at five-year intervals. For each interval following initial infestation, the event monitor assigned outbreak intensity based on the pre-determined probability function.

Data Analyses

For each sample plot, basal area per hectare was calculated for all living tree species and separately for hemlock (basal area = ((dbh)2 * 0.00007854)). The relative

dominance of hemlock was calculated by dividing the basal area of hemlock by the total basal area, and multiplying by 100 (Table 3.1).

For each physiographic region, FVS-generated hemlock basal area and basal area of all species were forecast. Differences in the mean basal area of hemlock and mean basal area of all species generated at each five-year interval were evaluated with one-way analysis of variance (ANOVA) and interval comparisons were accomplished with

Tukey’s method at p < 0.05 using Minitab (2010).

Results

Hemlock is the dominant tree species in plots of both the ELP and the GAP

(Table 3.1). The mean relative dominance of hemlock is greater than 50% in both physiographic regions. FVS-simulations of HWA-induced hemlock mortality indicate possible major changes in forest composition and structure following HWA infestation.

The control simulations, without HWA infestation, showed very little change in total basal area and hemlock basal area 45 years into the future for both physiographic regions

(Figure 3.3). Because the initial basal area of the ELP was above the maximum basal area limit used in the Northeast Variant, projections showed a slight decrease in basal area over the entire simulation, bringing basal area closer to the upper limit. Basal area of the GAP increased over the simulation, as its initial basal area was below the Northeast

Variant maximum basal area. The eight km/year rate of spread scenario for HWA gave an infestation year of 2027 for the ELP (129 km from the ELP centroid to Beaver

County, Pennsylvania) and 2028 for the GAP (134 km from the GAP centroid to Beaver

County, Pennsylvania). While there were varying degrees of hemlock mortality in each of the ten replicate simulations, hemlock basal area and total basal area in the ELP

(Figure 3.4) and the GAP (Figure 3.5) were greatly reduced following HWA infestation.

The greater relative dominance by hemlock in the GAP compared to the ELP (63.6 vs.

53.1%) may explain the slightly greater level of mortality forecast in the GAP. The 13 km/year rate of spread scenario for HWA gave an infestation year of 2021 for both the

ELP and the GAP. Similar to the eight km/year HWA-spread scenario, all ten simulation replications showed a major reduction in hemlock basal area and total basal area in the

ELP (Figure 3.6) and the GAP (Figure 3.7) following HWA infestation.

One-way ANOVA indicated significant reductions in mean total basal area and hemlock basal area (p < 0.0001) of both physiographic regions between five-year intervals immediately preceding and following HWA infestation in both the eight km/year HWA spread scenario (Figure 3.8) and the 13 km/year HWA spread scenario

(Figure 3.9). In the first five-year interval after HWA infestation in the eight km/yr

HWA spread scenario, mean hemlock basal area was reduced by 61.7% and 44.5% in the

ELP and the GAP, respectively. In the first five-year interval after HWA infestation in the 13 km/yr HWA spread scenario, mean hemlock basal area was reduced by 46.1% and

33.6% in the ELP and the GAP, respectively. From the five-year interval immediately preceding HWA infestation to the final five-year interval for the eight km/year HWA spread scenario, hemlock basal area was reduced by 78.3% and 83.6% in the ELP and the

GAP, respectively. From the five-year interval immediately preceding HWA infestation

to the final five-year interval for the 13 km/year HWA spread scenario, hemlock basal area was reduced by 85.9% and 84.1% in the ELP and the GAP, respectively.

Discussion

Our FVS simulations reveal significant predicted decreases in hemlock basal area within five years of HWA infestation and continued mortality for several decades thereafter. These trends are supported by other studies that have documented the rapid decline of hemlock following HWA-infestation (Orwig and Foster 1998; Stadler et al.

2005; Eschtruth et al. 2006). Spaulding and Rieske (2010) found similar results from the

FVS and HWA Event Monitor based on central Appalachian stands sampled in

Kentucky. Some of the replicates showed evidence of the documented “reduced peaks” of HWA abundance (McClure 1991) over the course of the simulation, as hemlock basal area decreased drastically, then seemed to recover, and was eventually further reduced

(Figure 3.5, Figure 3.6, Figure 3.7).

It is important to note, however, several important limitations of the FVS and the

HWA Event Monitor. The FVS HWA Event Monitor simulates mortality of hemlock based on the intensity of initial infestation and subsequent HWA population cycles in the absence of any kind of forest management. Modeling of regeneration is not possible with the HWA Event Monitor. While other studies suggest a transition to sweet birch (Betula lenta L.) in the Northeast (Orwig and Foster 1998; Stadler et al. 2005) and great laurel

(Rhododendron maximum L.) and (or) yellow-poplar (Liriodendron tulipifera L.) in the central and southern Appalachians (Ellison et al. 2005b), there has been very little

research examining how hemlock forests at the edge of the species’ range will respond to

HWA infestation. Further research examining the sapling and seeding layers as well as the dormant soil seed bank will be important in forecasting forest composition post-HWA infestation and the subsequent decline of hemlock.

The HWA Event Monitor was created based on studies of population dynamics of

HWA in the Northeast (Trotter et al. 2008). It is not known how HWA spread and population dynamics will occur in Ohio, at the edge of hemlock’s range and with different climatic, geographic, and plant community characteristics than those regions on which the event monitor was based. Many factors affecting growth and mortality are not accounted for in the FVS and the variability in individual tree mortality typically seen in response to HWA-infestation cannot be adequately modeled. Therefore, simulation results may be more useful for evaluating relative differences in HWA-infestation between stands, rather than absolute responses for specific stands (FHTET 2008).

It is also important to mention various factors affecting the movement of HWA that could not be modeled in this study. Rates of HWA spread used in this study were based on studies conducted hundreds of miles from northeastern Ohio. Factors such as land use (i.e., urban, agricultural, forested), recreational activities, and climate variables will certainly affect the rate of spread of HWA into and through northeastern Ohio.

Much of northeastern Ohio is in agricultural land-use or highly-developed. Whether or not this will facilitate or hinder the spread of HWA remains to be seen. Lack of large stands of hemlock in these agricultural or developed areas may lessen the ability of HWA to survive there. On the other hand, increased movement of people and goods could

hasten the arrival of the insect to hemlock in planted landscape settings or hemlock- dominated forests of the region. Hemlock forests happen to be a major component of popular recreational areas in northeastern Ohio. This fact could be very important; campgrounds and picnic areas are often the destination for firewood brought from long distances and could facilitate the unintended movement of HWA into these areas.

In addition to major changes in the composition and structure of northeastern

Ohio’s hemlock forests post-HWA infestation, significant changes in functional processes will likely occur. While it is not known which tree species will become dominant in northeastern Ohio hemlock stands affected by HWA, it will likely be a deciduous, non-conifer tree species. This transition from hemlock- to deciduous, non- conifer-dominated stands could eliminate a suite of wildlife species dependent on hemlock (Howe and Mossman 1996; Yamasaki et al. 2000; Tingley et al. 2002; Ellison et al. 2005a), alter nutrient cycling (Jenkins et al. 1999; Yorks et al. 2000), and change hydrological regimes (Ellison et al. 2005b).

Our FVS-generated simulations of HWA-induced mortality show significant reductions in hemlock and total basal areas immediately following, and decades after,

HWA infestation. These changes will clearly have a major impact on the structure, composition, and functional processes of northeastern Ohio’s hemlock-dominated forests.

The likely transition from hemlock to deciduous tree species will have widespread consequences for wildlife, soil nutrient cycling, and hydrological regimes. The increase in standing dead trees and (or) their removal may require trail closures and restrictions on access for the public in popular recreational areas. Not only will the loss of hemlock

impact the ecology of northeastern Ohio, but it may impact the economy as well. The aesthetic qualities of hemlock forests attract visitors in many recreational areas. Those areas that are currently dominated by hemlock may lose their appeal, negatively effecting tourism. While more research is needed to answer the question of what species will replace hemlock after the arrival of HWA, this study provides important insights into how the hemlock resource of northeastern Ohio will be impacted by this invasive insect.

Management Implications

Control of HWA is possible at the individual tree-level through the use of various insecticides, although this is not a permanent solution (i.e., applications must be repeated, at the most, every few years). This is also generally not an economically viable option when dealing with an entire forest. One option for managers is to perform a salvage harvest of dead or dying hemlocks after HWA-infestation (Ward et al. 2004). Orwig and

Kittredge (2005) stress delaying harvesting until after infestation by HWA so as not to unknowingly eliminate potentially resistant trees. By leaving these stands alone, any surviving hemlock could potentially persist and provide a future seed source, should an effective biological control of HWA be found. Some of the forests in which study plots for this research were located are popular recreational areas, meaning public use would need to be restricted during harvesting. Ward et al. (2004) suggest engaging the public for input prior to any harvest operations to avoid controversy. In Ohio, hemlock is not a species of commercial value, thus, it may be cost-prohibitive to conduct any type of harvesting (Eric McConnell, personal communication 2012). Studies have shown that in

many cases, preemptive or pre-salvage logging can have greater impacts on the ecosystem than the insect outbreak (Kizlinski et al. 2002; Foster and Orwig 2006).

Performing no management practices in areas impacted by HWA will allow individual trees to gradually decline and die over a period of several years, resulting in standing snags and downed woody debris that will benefit a wide variety of wildlife (Orwig and

Kittredge 2005). Even if no management is conducted after the arrival of HWA, these areas may still need to be closed to recreation due to the danger posed by falling snags.

Another result of HWA-induced mortality of hemlock is the possibility for colonization by opportunistic invasive plant species. Canopy gaps created by declining crowns of hemlock and mortality following HWA-infestation allow more light to reach the forest floor and may facilitate invasion and establishment by non-native species (i.e.,

Ailanthus altissima (Mill.) Swingle or Microstegium vimineum (Trin.) A. Camus) (Orwig and Foster 1998). Invasive species may outcompete native species, decrease diversity and structure relative to the native community, and can form undesirable single-species monocultures (Meekins and McCarthy 1999; Hejda et al. 2009). Management practices including the use of herbicides and (or) mechanical treatments may be necessary to control invasive species.

REFERENCES

Aldrich, J.W. 1943. Biological survey of the bogs and swamps of northeastern Ohio. American Midland Naturalist 30:346-402.

Black, R.A. and R.N. Mack. 1976. Tsuga canadensis in Ohio: synecological and phytogeographical relationships. Vegetatio 32:11-19.

Braun, E.L. 1950. Deciduous forests of eastern North America. Blakiston, Philadelphia, Pennsylvania.

Brockman, C.S. 1998. Physiographic regions of Ohio (map). Ohio Division of Geological Survey, Columbus, Ohio.

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Table 3.1. Summary information for the three Huron-Erie Lake Plain (ELP) stands and the four Glaciated Allegheny Plateau (GAP) stands in northeastern Ohio.

Physiographic Stand name Plot Latitude Longitude Basal area of Total basal Relative dominance region hemlock (m2/ha) area (m2/ha) of hemlock (%) ELP Armstrong Swamp 1 41.92433 -80.57583 41.9 56.2 74.6

2 41.92467 -80.57672 46.4 54.6 84.9 Cathedral Woods 1 41.93754 -80.63002 12.4 48.0 25.8 2 41.93716 -80.62982 31.1 49.9 62.3 Morgan Swamp 1 41.64071 -80.89408 20.1 55.0 36.6 2 41.64084 -80.89467 16.4 43.2 37.9 3 41.64066 -80.89335 27.0 54.4 49.7 ELP average 41.80656 -80.72778 27.9 51.6 53.1

90 GAP Clear Fork Gorge 1 40.60864 -82.30577 32.5 45.8 70.9 2 40.60824 -82.30609 31.4 55.0 57.1 3 40.60798 -82.30659 11.2 25.2 44.5 Little Mountain 1 41.64016 -81.27411 16.0 38.7 41.4 2 41.63981 -81.27285 17.0 29.9 57.0 3 41.63983 -81.27184 50.7 55.4 91.6 Wooster 1 40.82080 -82.03506 45.7 46.3 98.5 Memorial Park 1 2 40.82094 -82.03482 34.8 35.0 99.5 Wooster 1 40.81820 -82.02853 21.2 47.4 44.8 Memorial Park 2 2 40.81795 -82.02837 12.9 42.6 30.3 GAP average 41.00225 -81.88640 27.3 42.1 63.6

Figure 3.1. Locations of the three Huron-Erie Lake Plain (ELP) stands (open circles) and four Glaciated Allegheny Plateau (GAP) stands (black dots) within northeastern Ohio.

Figure 3.2. Sample plot design and orientation of the 500-m2 circular plot, nested 100-m2 circular plot, and four 1.0 x 1.0 m quadrats (not to scale).

Figure 3.3. Forest Vegetation Simulator (FVS)-projected change in total basal area and hemlock basal area without hemlock woolly adelgid (HWA)-induced mortality 45 years into the future for forests of the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio.

Figure 3.4. Ten replicates of Forest Vegetation Simulator (FVS)-projected change in total basal area and hemlock basal area based on an eight-km per year spread of hemlock woolly adelgid (HWA) (infestation year = 2027) 45 years into the future for forests of the Huron-Erie Lake Plain (ELP) of northeastern Ohio.

Figure 3.5. Ten replicates of Forest Vegetation Simulator (FVS)-projected change in total basal area and hemlock basal area based on an eight-km per year spread of hemlock woolly adelgid (HWA) (infestation year = 2028) 45 years into the future for forests of the Glaciated Allegheny Plateau (GAP) of northeastern Ohio.

Figure 3.6. Ten replicates of Forest Vegetation Simulator (FVS)-projected change in total basal area and hemlock basal area based on a 13-km per year spread of hemlock woolly adelgid (HWA) (infestation year = 2021) 45 years into the future for forests of the Huron- Erie Lake Plain (ELP) of northeastern Ohio.

Figure 3.7. Ten replicates of Forest Vegetation Simulator (FVS)-projected change in total basal area and hemlock basal area based on a 13-km per year spread of hemlock woolly adelgid (HWA) (infestation year = 2021) 45 years into the future for forests of the Glaciated Allegheny Plateau (GAP) of northeastern Ohio.

Figure 3.8. Mean Forest Vegetation Simulator (FVS)-predicted total basal area and hemlock basal area (± 1 standard deviation) based on an eight-km per year spread of hemlock woolly adelgid 45 years into the future for forests of the Huron-Erie Lake Plain (ELP; infestation year = 2027) and Glaciated Allegheny Plateau (GAP; infestation year = 2028) of northeastern Ohio. Means that do not share a capital letter are significantly different in hemlock basal area between years and means that do not share a lowercase letter are significantly different in total basal area between years at p < 0.05.

Figure 3.9. Mean Forest Vegetation Simulator (FVS)-predicted total basal area and hemlock basal area (± 1 standard deviation) based on a 13-km per year spread of hemlock woolly adelgid 45 years into the future for forests of the Huron-Erie Lake Plain (ELP) and Glaciated Allegheny Plateau (GAP) of northeastern Ohio (infestation year = 2021). Means that do not share a capital letter are significantly different in hemlock basal area between years and means that do not share a lowercase letter are significantly different in total basal area between years at p < 0.05.

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