The Role of Animals in Maintaining Forest Herb Diversity in Southeast Ohio

A thesis presented to

the faculty of

the College of Arts and Sciences of Ohio University

In partial fulfillment

of the requirements for the degree

Master of Science

Jennifer R. Philhower-Gillen

December 2014

2 This thesis titled

The Role of Animals in Maintaining Forest Herb Diversity in Southeast Ohio

JENNIFER R. PHILHOWER-GILLEN

has been approved for

the Department of Environmental and Plant Biology

and the College of Arts and Sciences by

Glenn R. Matlack

Associate Professor of Environmental and Plant Biology

Robert Frank

Dean, College of Arts and Sciences

3 ABSTRACT

PHILHOWER-GILLEN, JENNIFER R., M.S., December 2014, Plant Biology

The Role of Animals in Maintaining Forest Herb Diversity in Southeast Ohio

Director ofThesis: Glenn R. Matlack

Animal species have the ability to move the seeds of forest herbs long distances and into suitable sites for seedling success. Four forest herbs (Hydrastis canadensis,

Podophyllum peltatum, Maianthemum racemosum and Arisaema triphyllum) were monitored in southeast Ohio from June to October in 2008 and 2009. Animal vector visits were monitored using Buckeye Cam scouting cameras. Eighteen habitat variables were also measured to determine if forest herb species require specific habitat types, and if these habitat types affect animal visits. Birds removed red fruits and fruit visible from above more than dangling fruit and did not eat large fruit. Large mammals (i.e.

Odocoileus virginianus and Procyon lotor) removed large yellow fruit and did not take any small, red fruit. Sciurus caronlinensis, was photographed 101 times and only removed fruit once. Hylocichla mustelina removed fruit most frequently compared to all other animal species. No non-native animals were recorded removing fruit. Three plant species exhibited a preference in micro-habitat specificity, although these results were not profound.

4 DEDICATION

To my parents, my husband, and my children.

5 ACKNOWLEDGMENTS

I would like to thank my advisor Dr. Glenn Matlack for all of his support and assistance throughout this project. I would also like to thank all of my committee members, Dr. Phil Cantino, Dr. Brian McCarthy and Dr. Steve Reilly who all provided excellent advice and assistance throughout my research. A special thanks to Steve Reilly and Paul Strauss for allowing me to setup cameras and collect data on their property.

Thank you to the Ohio Department of Natural Resources for also giving me permission to photograph on state land, as well as to Vinton Furnace Research Station for allowing me access to their control site for research.

I could not have conducted my research without Athens Technical Specialist

Incorportated Athens, Ohio (Buckeye Cam) as they provided the cameras and any support needed at a moments notice to keep the cameras functioning. I would also like to thank Ohio Biological Survey for providing financial support that helped considerably with gas and other expenses.

Thank you Krista Sanders for carrying heavy field equipment and collecting data throughout the season. I would also like to thank Ryan Lima, Rebecca Van Valkenburgh, and Lisa Harlan for all of their assistance entering data and processing soils in the lab.

6 TABLE OF CONTENTS

Page

Abstract ...... 3 Dedication…………………………………………………………………………………4 Acknowledgments ...... 5 List of Tables ...... 7 List of Figures ...... 8 Introduction ...... 10 Methods ...... 18 Results……………………………………………………………………………………34 Discussion………………………………………………………………………………..58 References ...... 71 Appendix A. Buckeye Cam Images……………………………………………………..80

7 LIST OF TABLES

Page

Table 1: Ripe fruit morphology and presentations of the four forest herb species ...... 20

Table 2: Habitat variables measured for fruiting, non-fruiting, and control points for all four forest herb species……………………………………………………………….. .27

Table 3: Animal groups and species removing fruit from the four forest herb species..34

Table 4: Animal species’ contribution to the removal of forest herb fruits…………….44

8 LIST OF FIGURES

Page

Figure 1: Resolution trial results ...... 24 Figure 2: Field of view trial results...... 24 Figure 3: Time to first removal……………………………………………………….. 36 Figure 4a: Number of conspecifics within 0.5 m compared with days to removal……36 Figure 4b: Number of Conspecifics within 1m compared with days to removal ...... 37 Figure 4c: Mean Distance to nearest Conspecific compared with days to removal ..... 37 Figure 5a: Least Squares Analysis of Hydrasis canadensis. Conspecifics within 1m vs days to removal…………………………………………………………………………38 Figure 5b: Least Squares Analysis of Hydrastis canadensis. Log-transformed Conspecifics within 1m vs. days to removal……………………………………………39 Figure 5c: Least Squares Analysis of Hydrastis canadensis. Conspecifics within 0.5 m compared with days to removal…………………………………………………………39 Figure 5d: Least Squares Analysis of Hydrastis canadensis. Log-transformed Conspecifics within 1m compared with days to removal…………………………….…40 Figure 5e: Least Squares Analysis of Maianthemum racemosum. Conspecifics within 1m compared with days to removal……………………………………………………..40 Figure 5f: Least Squares Analysis of Maiantemum racemosum. Log-transformed conspecifics within 1m………………………………………………………………….41 Figure 6: Phenology of peak fruiting periods for all four species……………………...46 Figure 7: Timing of removal events for all four species………………………………..47 Figure 8: A Principle Component Analysis and ordination of Arisaema triphyllum and Maianthemum racemosum comparing removals vs. nonremovals……………………...49 Figure 9: Bar Charts comparing removals vs. nonremovals of Arisaema triphyllum and Maianthemum racemosum with two habitat variables…………………………………..50 Figure 10: Podophyllum peltatum and Hydrastis canadensis fruiting, nonfruiting and control treatments compared using sixteen habitat variables……………………...... 51 Figure 11: Bar charts comparing Fruiting, nonfruiting and control treatments of Podophyllum peltatum………………………………………………………………….52 Figure 12: Bar charts comparing Fruiting, nonfruiting and control treatments of Hydrasis canadensis………………………………………………………………………………53

9 Figures 13: Arisaema triphyllum fruiting, nonfruiting and control treatments compared using sixteen habitat variables……………………………………………………….....54 Figure 14: Bar charts comparing Fruiting, nonfruiting and control treatments of Arisaema triphyllum……………………………………………………………………55 Figure 15: Maianthemum racemosum fruiting, nonfruiting and control treatments compared using sixteen habitat variables………………………………………………56 Figure 16: Bar charts comparing Fruiting, nonfruiting and control treatments of Maianthemum racemosum……………………………………………………………..57 Figure 17: Buckeye Cam images of fruit removals for Hydrastis canadensis and Arisaema triphyllum……………………………………………………………………80 Figure 18: Buckeye Cam images of fruit removals for Podophyllum peltatum and Maianthemum racemosum……………………………………………………………..81

10 INTRODUCTION

An understanding of the dispersal ecology of herbaceous plants requires understanding how mechanisms work to place seeds into suitable sites for seedling success (Oswald and Neuenschwander 1993, Elmarsdottir et. al. 2003, Cousens et. al.

2008). In a forest community, dispersal allows herbaceous species to cope with the heterogeneous distribution of resources (Wijesinghe and Hutchings 1999, Purves et. al.

2007). Herbaceous composition varies on both a micro- and meso-scale due to a variation in light intensity, moisture, litter depth, landscape position, and slope aspect

(Peterson and Campbell 1993, Small and McCarthy 2002, Harrelson and Matlack 2006,

Albrecht and Mccarthy 2009). Assuming that the location of the parent plant is physically suited for seedling growth and survival, why do herbaceous species need to disperse their seeds away from the parent?

Two important hypotheses are relevant to the evolution of seed dispersal. The first, emphasizes the importance of ‘escaping’ or moving away from the parent plant for survival. Parasitism, predation, competition and other density-dependent threats are expected to decrease with distance from the parent (Janzen 1970, Jansen 2008).

Although escaping the area around the parent is important to seedling survival, dispersal outside of a resource patch (over-dispersion) can also reduce survival (Soons et. al.

2004), so evolution does not generally favor very long range dispersal.

Secondly, the colonization hypothesis (Platt and Weiss 1977) emphasizes that successful establishment of a dispersing plant is dependent upon arriving in a suitable micro-site. In a deciduous forest, a suitable micro-site could be a stream-side terrace or a tip-up mound created by a fallen tree (Peterson and Campbell 1993). If both dispersal

11 away from the parent and colonization of narrowly defined micro-sites are crucial to plant species reproduction, it is important to ask how seeds 'find' suitable micro-sites. We need to know how the behavior of vectors influences the distribution of seeds. In a closed-canopy forest ecosystem, the vectors that play this important role are often animals.

In some forested regions, vertebrate animals account for up to 90% of dispersal syndromes (Howe 1984, Andresen 2002). Why would a plant allocate energy resources for an ‘expensive’ animal-dispersed fleshy fruit as opposed to an ‘inexpensive’ dry propagule that floats in the wind? In a forest, an extensive closed canopy, as well as a multi-layered canopy structure, would slow the movement of wind and require other modes of dispersal (Mori and Brown 1994, Bolmgren and Eriksen 2005). Second, animals have been observed to move seeds long distances and potentially exhibit directed dispersal (Matlack 1994, Wenny and Levey 1998, Whitaker 1996, Clark 2001, Clark

2005, Cousens et. al. 2008). Directed dispersal is demonstrated by a particular vector moving seeds to a non-random subset of micro-sites that may or may not be physically suitable for germination and establishment (Cousens et. al. 2008). Birds in both tropical and temperate forest ecosystems have demonstrated a positive form of directed dispersal by consistantly dispersing seeds into habitat types where the dispersed plant thrives

(Wenny and Levey 1998, Bartuszevige and Gorchov 2006). Finally, gut passage through birds, mammals and reptiles has been shown to increase germination success of some plant species (Braun and Brooks 1987, Barnea et. al. 1991, Whitney et. al. 1998, Paulsen and Högstedt 2002). If plant species under a closed canopy are likely to benefit when dispersed by animals, producing a fleshy fruit is worth the expense.

12 Generalists and Specialists

What are the benefits of specialization? Is it beneficial to the plant to attract multiple animal vectors (e.g., a combination of mammals, birds, insects, and reptiles) or more specific vectors (e.g., only songbirds)? Some pollination studies suggest that flower specialization could help prevent local extinction of uncommon species if multiple species are competing for pollination attention (Stiles 1975). Therefore, plant species would benefit from specialization that encourages niche partitioning (Snow and Snow

1972, Stiles 1975). Fruit specialization that attracts a small number of animal species could be beneficial to rare or uncommon forest herbs competing for vectors.

Specialization benefits the plant in the case of directed dispersal. For example, if a plant is attracting Turdus migratorius (American Robin) or Procnias tricarunculata (Three- wattled Bellbird), these vectors are likely to travel to a habitat similar to where they are feeding, which is likely a suitable site for the plant species (Wenny and Levey 1998,

Bartuszevige and Gorchov 2006). Procyon lotor (raccoons) will often forage near or along streams and will sometimes defecate in a specific area (i.e., base of a tree, latrines)

(Page et. al. 2001, MacClintock 2003), which suggests that mammals are also capable of directed dispersal.

There are advantages to both generalist and specialist strategies. Plants can vary their temporal fruit maturation to increase the number of vector visits and thereby increase dispersal (Stiles 1980, Bolmgren and Eriksen 2005). However, a forest herb that only produces a single fruit or a single cluster may depend on attracting specific animal vectors that are likely to move the seed away from the parent while remaining within the mature forest. A forest herb that is low to the ground is more accessible to rodents

13 (Garcia-Robledo and Kuprewicz 2009) and is also concealed by overhead vegetation.

These two factors support the benefits of a specialist strategy if there is only one chance for dispersal and germination success.

Fruit Characters

Many fruit traits can influence which animals visit a fruiting plant species, but two traits that seem most important are fruiting phenology and fruit morphology (Stiles

1980, Stapanium 1982, Cousens et. al. 2008). Most plant species produce fruit at a very specific time of year (e.g., autumn), which increases the likelihood of birds foraging in preparation for migration (Stiles 1980, Stiles 1993, Herrara and Jordano 1981, Cousens et. al. 2008). Additionally, a plant species may produce or retain fruit at a time of year

(e.g., winter) when food resources are scarce, increasing the likelihood of vector visits

(Stampanium 1982, Cousens et. al. 2008). Conversely, a plant species that produces or retains fruit when food is scarce could also attract vectors (e.g., rodents) that often cache seeds during a food shortage, thereby limiting dispersal (Cousens et. al. 2008). Fruiting schedules could potentially result in the movement of seeds among widely distributed patches, as migrating birds have the ability to travel great distances in a short time (Stiles

1980, Cousens et. al. 2008). Although a single plant species rarely is adapted to attract a specific animal species as a dispersal vector (Cousens et. al. 2008), some fruit characters are likely to attract specific animal groups (Stiles 1982, Gautier-Hion et. al. 1985, Levey

1987, Cousens et. al. 2008). For example, red fruit is likely to attract birds (Stiles 1982,

Honkavaara et. al. 2004), but fruit size can also influence which animal species visit a plant (Gautier-Hion et. al. 1985). A study in Africa found that fruit eaten by mammals was almost three times the size of fruit eaten by birds (Knight and Siegfried 1983). It

14 seems that fruit characters are one of many strategies in maintaining reproductive success.

Defining what fruit characteristics (e.g., size, color, nutrition) as well as habitat components attract an animal to a fruiting plant can shed light on removal success by suggesting how plant species can influence animal behavior (McDonnell et. al. 1984).

Primarily frugivorous birds will select plants with higher lipid content, but different bird species react to plant species in different ways (Herrara and Jordano 1981, Stiles 1993).

The size of the reward (i.e., the number of fruits and size available) has also been shown to increase removal success of fruit-producing species (Welch et. al. 1997, Carlo and

Morales 2008, González-Varo 2010). In woody plant species, this could mean producing a large quantity of fruit per tree, but for a forest herb species, the reward relates to patch size or ‘neighborhood density’ (Cousens et. al. 2008, Carlo and Morales 2008). For example, a greater number of fruits available in a given patch could perhaps signal to a bird that the reward is worth compromising its safety by visiting the ground (Howe

1979). Birds that are primarily insectivorous will only remain long enough to feed for fear of vulnerability to predators, whereas frugivorous birds are likely to remain at the plant or revisit the plant if they are obligate frugivores (Howe 1979, Herrara and Jordano

1981, Pratt and Stiles 1983).

Adaptation to Animal Vectors

Most of what we know about animal dispersal of herbaceous plants is inferred from fruit morphology and presentation (Bolmgren and Eriksen 2005). A floristic survey conducted in Stroud’s Run State Park, Athens County, OH (Harrelson and Cantino 2006), revealed a large number of fleshy-fruited species in ravine habitats, ranging from

15 relatively large fruit (Podophyllum peltatum) to small fruit (Medeola virginiana), and showing a range of colors including blue (Caulophyllum thalictroides), yellow

(Podophyllum peltatum), and red (Arisaema triphyllum, Maianthemum racemosum).

Fruit size and color can allow specialization to a particular vector species (e.g., large yellow fruit, and large mammals) (Knight and Siegfried 1983, Cousens et. al. 2008); however fruit arrangement (e.g., only visible from above the plant or from the ground) can also be important.

An herbaceous plant that is near the ground relies more on fruit position than trees do to avoid predation and draw in animal vectors (Garcia-Robledo and Kuprewicz 2009).

An herbaceous species that produces only one fruit per plant, or has few neighbors, is less obvious than a woody plant species producing large quantities of fruit (Stiles 1980,

McDonnell et. al. 1984). In such cases, fruit presentation is critical; thus, fruit characters and presentation can adapt plants to particular groups of animal vectors (e.g., birds or large mammals) (Stiles 1980, Gautier-Hion et. al. 1985, Wheelright 1985).

Food is not the only factor that attracts an animal-habitat characteristics can also influence animal behavior (Bayne and Bryant 1994). Habitat parameters that have demonstrated importance to many animal species include food and water availability, tree access (escape cover), shrub cover (foraging cover), and distance to a forest edge (Howe

1979, Jordano and Schupp 2000, Bayne and Bryant 1994, Bartusizevige and Gorchov

2006). Just as a plant requires a particular micro-site for germination and growth, an animal must have access to essential resources within its home range. In order for an animal to find a fruiting plant in a heterogeneous environment, the plant needs to fall within a vector’s preferred habitat or home range.

16 Dispersal in the Eastern Deciduous Forest

Established deciduous forests are a good place to address questions regarding animal-mediated dispersal of forest herbs. Of the 50 woody plant species in the deciduous forests of northern New Jersey, 66% were found to be dispersed by birds and mammals (Armesto and Rozzi 1989). Plant species with ingestion-dispersal syndromes were more frequent in disjunct patches than wind-dispersed species, which suggests that ingested seeds have greater dispersal success across gaps (Matlack 1994). Animals that forage on fleshy fruits in the Eastern Deciduous Forest include white-tailed deer, small rodents, raccoons, and songbirds (Martin et. al. 1951). If, in fact, animals play such an important role in fruit removal and dispersal of plant species in deciduous forests, we should find out which animal species are contributing to the dispersal of forest herbs.

Identifying and observing the behavior of dispersal vectors of fleshy-fruit producing species sheds light on how seeds move around in a forest (Wenny and Levey 1998,

Vellend et. al. 2003, Bartusizevige and Gorchov 2006). Evidence of which animals disperse the seeds of forest herbs in the Eastern Deciduous Forest is still lacking (Stiles

1980, Vellend et. al. 2003). In fact, most direct observations of animal dispersal vectors of herbaceous plants within the temperate deciduous forest region come from anecdotal reports, and data that are available do not include many forest herbs found in the central

Appalachians (Martin et. al. 1951, Vellend et. al. 2003).

My research aimed to identify the animal vectors of four forest herb species

(Arisaema triphyllum, Podophyllum peltatum, Hydrastis canadensis, and Maianthemum racemosum), and characterize the plant-vector interaction. Which animals are removing the fruit? Are there temporal patterns of animal species visiting plant species? Is there a

17 relationship between fruit characters and animal vectors? In addition to plant characteristics, which habitat components influence animal visits and successful removal events? Recognizing which habitat components influence the presence of an animal group or species increases our understanding of plant-vector relationships.

Phenology of fruit production is important to note, as plant species may depend on groups of animals that utilize fruit as a primary food source at a certain time of year

(e.g., songbirds and migration). Thus, I asked, when do species produce fruit? Is there an interdependence between animal vectors and forest herbs in a particular season?

Where do forest herb species thrive in a mature forest? Having fruit removed from a plant species is only the first step in the process of establishment of forest herbs.

Plant species also require specific habitat characteristics, and they depend on vectors to disperse seeds into these micro-sites that exist in a heterogeneous landscape. To grasp this complex relationship, I needed to first identify the factors that define a suitable micro-site for an herbaceous species. Which habitat variables are important to forest herb species? The forest herb species that I chose to address these questions are Arisaema triphyllum, Podophyllum peltatum, Hydrastis canadensis, and Maianthemum racemosum.

18 METHODS

Study Areas

The study was carried out within the Mixed Mesophytic forest on the unglaciated section of the Allegheny Plateau (Braun, L. 1961). Soils within this region are highly leached ultisols derived from shale and sandstone with occasional limestone deposits, described as a silty or sandy loam depending on landscape position (NRCS 2000,

Sutherland and Hutchinson 2003, USFS 2005). The patchy sandstone, coal and limestone deposits create soil heterogeneity in the forest, which is reflected in plant species composition (Sutherland and Hutchinson 2003).

Average minimum temperatures in southeast Ohio fall within the hardiness zones of the Mississippi and Ohio valleys, with mean annual temperature for southeast Ohio as approximately 57 degrees Fahrenheit (National Weather Service Climate Report 2008).

Forest distribution in southeast Ohio has changed drastically over the last century due to logging, mining, farming, cutting for charcoal production, and a reduction in fire disturbance (Braun 1961, Clark and Hutchinson 1989, Sutherland and Hutchinson 2003).

Large areas cleared for agriculture and mining have returned to forest (Clark and

Hutchinson 1989). The forest canopies are dominated by hardwood species, including

Quercus spp, Carya spp., Liriodendron tulipifera, and Acer sacharum. Understory woody species include Lindera benzoin, Cornus florida, Ostrya virginiana, Asimina triloba, Acer rubrum, and Smilax spp. All of the sites in this study are rich in herbaceous plant diversity. I observed many forest herb species in north-northeast facing ravine habitats, consistent with Small and McCarthy (2002), who found greater forest herb abundance corresponding to greater soil moisture on a north-facing slope.

19 Plants were monitored in five forested locations within Athens, Vinton and

Meigs Counties in Ohio. Two of the sites were located on private properties near Shade,

Ohio (39.22N,82.03W) and Rutland, Ohio (39.08N, 82.17W). The other three sites were on state/federal land in Zaleski State Forest (39.29N, 82.39W), Stroud's Run State Park,

Ohio (39.35N, 82.03W) and Vinton Furnace Research Station (39.18N, 82.39W)

(Latitude and Longitude Finder 2014). All sites are mature second-growth forests. Most of the sites have experienced one or more of the disturbances listed above, and all sites have certainly experienced grazing by deer since their reintroduction in the early to mid-

1900’s.

Target Species

The four plant species chosen for this study were easily located at all five field sites. Species included mayapple (Podophyllum peltatum), false Solomon’s seal

(Maianthemum racemosum), goldenseal (Hydrastis canadensis), and Jack-in-the-pulpit

(Arisaema triphyllum). These species exhibit variation in fruit size, color and presentation. Fruits are presented in clusters or singly. Fruits may be large (4-5 cm) or small (5-15 mm) in size, and fruit may only be visible from below or above (Table 1).

This variability in fruit size, color and presentation enables us to test for a contrast in dispersal service. Podophyllum peltatum produces fruit at least three times the diameter of those in the remaining three species, and it is the only yellow fruit. There is equal representation of clustering and fruit presentation (Table 1).

Table 1. Ripe fruit morphology and presentations of the four forest herb species.

Plant species Fruit Color Fruit Size Presentation Cluster vs Fruiting period Single

Arisaema triphyllum Red Small (8 mm) Pointed up Cluster (15-40 Sept. 1- berries/plant) Oct 30

Hydrastis canadensisRed Small-Medium Pointed up Dense Cluster June 15- (effectively July 30 (1.5 cm-cluster) single)

Maianthemum Red Small (5 mm) Up and/or Cluster (5-40 August berries/plant) 15- racemosum down October 30

Podophyllum Yellow Large (4-5 cm) Dangles downSingle June 25- August 1 peltatum

Phenology

Fruiting periods of each plant species were used to describe temporal niches.

Fruiting period is an important factor to note because some species produce fruit during the autumn migration of songbirds (Stiles 1980), which are potential long-distance dispersal vectors. Flowering and fruiting stages were monitored throughout the season.

Ten flowering individuals of each plant species were selected. To account for variation in sunlight, stand age, or land use history among study sites, all four species were monitored at each site. Monitoring began in April, 2008 when spring ephemeral species were first observed and ended in October when the plants senesced. Plants were visited

21 at weekly intervals, and plant size and developmental phase were noted. Fruiting stage was recorded as ripe or unripe, as determined by color and softness of the berry.

Monitoring Animal Vectors

The best way to observe animal behavior is through direct observation; however, human presence is known to influence animal behavior, and long periods of observation would be required to record infrequent events (Chapman et. al. 1992, Link and Di Fiore

2006, Prasad et. al. 2010). Alternatively, behavior can be monitored with non-invasive, remote-sensing scouting cameras (Beck and Terborgh 2002, Silveira et. al. 2003,

Rowcliffe et. al. 2008). Scouting cameras can monitor animals without affecting their behavior because scouting cameras are often silent when taking pictures, can be left alone for days (sometimes weeks), and need no manual trigger (Rowcliffe et. al. 2008, Prasad et. al. 2010). Scouting cameras are also less susceptible to error compared to researchers in the field, as they are consistent in their performance, making them a cost effective research tool (Silveira 2003, Kelly et. al. 2008). Scouting cameras are often used for monitoring and determining the population density of elusive animals (Silver et. al. 2004,

Kelly et. al. 2008, Rowcliffe et. al. 2008). However, scouting cameras have also been used in other ecological applications including plant-animal interaction studies (Beck and

Terbough 2002, Garcia-Robledo 2009, Prasad et. al. 2010).

The type of camera chosen for this study is called ‘Buckeye Cam: Orion’ (

Athens Technical Specialists Inc., Athens, Ohio), Buckeye Cam responds to both the movement of an object and the change in ambient temperature (infra-red), to ensure that pictures are taken of animals and not inanimate objects (e.g., a leaf). Buckeye Cam takes day and night photographs, is completely silent, and records time/date/moon phase on

22 each photograph. Additionally, Buckeye Cam has the ability to transmit the pictures to a PC base, which is especially useful when a researcher does not want to disturb the site and possibly prevent a visit from an animal. Eight cameras were loaned by Athens

Technical Specialists Inc. for the duration of the study.

Camera Testing

Before taking the cameras into the field, their sensitivity was tested. A refrigerator box was used as an enclosed test space to control light and temperature variation. A flat piece of cardboard was used as a partition within the box. The partition was used to separate my body from the camera, so I could control what triggered the camera. The side viewable from the camera was marked with a scale in both horizontal and vertical directions for peripheral sensitivity tests. Slots big enough to fit a flattened hand were cut in the partition horizontally and vertically at 10 cm increments away from the focal point of the camera. I determined sensitivity (i.e., triggered vs. not triggered) by recording whether my hand triggered the camera at each point on the scale. To find an optimal camera-plant distance, sensitivity was measured at 0.5 meter increments up to 4 m. At each distance, sensitivity was tested at 10 cm increments both horizontally and vertically.

The resolution performance of Buckeye Cam was tested using a form of ‘vision chart’. I used the largest font size available (72 mm) and typed rows of each size descending to 14 mm. The chart was placed 0.5 m away from the camera and tested at

0.5 m increments up to a distance of 4 m. At each distance, the number fonts legible in the photo were recorded. Finally, a test of resolution was performed in the field in the

23 spring of 2008. A camera was placed on the ground beneath a bird feeder. To determine sensitivity, bird visits were recorded at varying distances from the camera.

In the lab trial, the camera was easily triggered at all distances up to the maximum distance of 4 m. However, sensitivity was limited horizontally and vertically

(approximately 10 cm vertically in each direction and 20 cm in each horizontal direction).

Resolution was ideal between 0.5-2 m (Figure 1); however, 0.5 m was too close to record animals in peripheral locations (Figure 2). In the field, resolution for the identification of songbirds dropped off at around 4 m; however, some songbirds (e.g.,, Northern Cardinal) could be identified farther from the camera given their unique shape and color. In conclusion, the cameras should be placed 1-2 m from the plant, and the berry or cluster of fruit should be in the center of the photograph. This camera-plant distance is consistent with the findings of Garcia-Robledo and Kuprewicz (2009) who determined 1.5 m to be an optimal distance.

In addition to distance, the delay between photos will affect resolution (personal communication, ATSI Staff). For example, if there is a one-second delay, resolution is poorer than with a one-minute delay between photos. To assure capture of brief animal visits, a one second time interval was selected.

4 legible font (#) font legible

0 0 1 2 3 4 5 Distance to subject (m)

Figure 1. Resolution Trial Results. The number of legible font as distance from the camera increases. The ideal range for resolution was between 1-2 m.

(m) sq (m) 15

0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Distance from camera (m)

Figure 2. Field of View. As distance from the camera increases, field of view, (the area included) broadens. These tests were performed inside of a relatively narrow box, and the view was limited beyond 30 m2.

25 Monitoring Techniques

Eight cameras were used in the study. A camera was focused on each plant until fruit was removed or the plant ceased to be an ideal target. Some plant species and individuals had removals within one or two days of ripening; others did not have a removal for two weeks or more. Because of this delay and the limited number of cameras, the number of targets for each species varied from n = 7-13. The total number of camera days monitored varied from 139 to 257 days per species. The need for multiple replicates for statistical purposes, as well as the requirement of independence between target plants, figured into decisions regarding camera re-location.

When the fruit of a target plant species was located and determined, by color, to be ripe or close to ripe, a camera was placed 1-2 m from the plant. Most cameras were placed directly on the ground, as some fruits were within a few centimeters of the ground.

Lowering cameras did not compromise the recording of large animals, since digital scouting cameras are shown to be very sensitive (as confirmed in laboratory trials).

However, some fruits (Arisaema triphyllum) are elevated up to 0.5 m in height; therefore, cameras were sometimes mounted to a tree using mounting brackets and ratchet straps

(both provided by ATSI). The fruit of the plant was centered within the sensory field of the camera. Manual pictures were taken both by use of the computer’s manual trigger and by waving my hand in front of the camera to be sure the fruit was centered and within the sensory field. The camera was set to be in operation 24 hours a day, 7 days a week. A 1-sec time delay between photos ensured recording of removal events. The camera was locked to a nearby tree using a cable and master lock. Extra care was used in

26 camouflaging the camera and cables to secure the location and to avoid affecting animal behavior around the target plant species.

Security, number of plant individuals in an area, and the condition of the fruit were used to determine whether or not a camera should stay on a particular target. For example, some plant species (Arisaema triphyllum and Hydrastis canadensis) were abundant in locations such as Stroud’s Run State Park; however, some target plants were located too close to an edge or to a trail, which would compromise the security of a camera and therefore couldn't be used for monitoring. In some cases, the fruit of a particular target plant hadn’t been removed for two weeks; however, no other targets of that particular plant species had been located so the camera was left on that particular target. If fruit was degrading (i.e., brown or wrinkled), the camera was removed without a removal event occurring, and monitoring of that plant target stopped. If there were no removal events, animals that were within 2 m of a ripe fruit were counted as a rejection.

Micro-habitat Characterization

In order to identify micro-sites suitable for future studies of germination and establishment in other studies in a mature forest, habitat variables were measured for each target plant species. After the berries of a particular target plant were removed, microhabitat data were collected. Habitat variables were chosen based on the relevance to forest herb establishment and their potential to affect animal behavior (Table 2).

27 Table 2. Habitat variables measured for fruiting, non-fruiting, and control points for all four forest herb species.

Edaphic Variables Units of Measure Soil Moisture Percent of Soil Wet Weight Soil Depth Rod length (cm) until hitting an obstruction

Bulk Density Mg m-3 Proportion of Rocks Weight Percent Rocks sifted out of collected soil pH pH units Organic matter Weight percent of material incinerated Landscape variables Units of Measure Percent Canopy Cover Percent Closed Distance to Forest Edge m paced to the edge Local Slope cm (variation within 1 meter horizontal) Litter Depth cm Percent Litter Cover cover within 0.5 m radius Distance to the nearest tree m Landscape position Ridgetop, midslope, lowslope, riparian Coarse Woody Debris Total length (cm) within 2 m radius Percent Shrub Cover cover within 0.5 meter radius Percent Herbaceous Cover cover within 0.25 meter radius Distance to nearest stream m paced to nearest stream

Distance to nearest 3 Conspecifics within m 20 m # of Conspecifics within 1 m numerical count # of Conspecifics within 0.5 m numerical count

I selected 15 fruiting and 15 non-fruiting plants of each species for habitat data collection and analysis, as well as random points without plants, used as controls.

Fruiting plants have sufficient resources to reproduce, and comparing fruiting to nonfruiting plants with regard to habitat variability can help determine if fruiting plants have

28 more specific habitat requirements and, therefore, depend even more on dispersal effectiveness. Fruiting plants were selected if they had atleast one fruit-bearing pedicel; plants were considered to be non-fruiting if there was no pedicel found on the plant.

Because I was sampling on a micro-scale, control points were selected within 20 m of the target plant. Measuring control points within a short, defined distance will cause an autocorrelation in the results if viewed on the scale of the landscape (Lechowicz and Bell

1991). However, my focus was at a micro-scale where topographical changes will impact forest herbs (CerQueira and Freitas 1999). Forest herb habitat shows little autocorrelation when viewing plants at an individual scale between 1-5 m (Beatty 1984).

Habitat data were collected at the rooting point of the plant and assessed in a three dimensional view. Percent canopy cover was recorded using a canopy densitometer

(Lemmon 1956). A measurement of canopy cover was taken in each cardinal direction, and the mean of the four measurements was recorded. Litter depth within 0.5 m of the plant was determined by inserting the meter stick perpendicular to the ground and measuring the uncompressed depth of leaves. Three measurements were taken of litter depth and averaged to get a more accurate estimate of the plant’s local environment.

To determine how topography varied on a micro-scale, vertical distance was measured 50 cm from the target plant by placing the 50 cm point on a leveled meter stick over the fruiting or control point and measuring vertical distance between the tip of the meter stick and the soil surface (Dunne et. al. 1995, De Vries and Goold 2010). Total length of all woody debris > 2 cm diameter within a 1 m radius of the target plant/random point was measured. Percent litter cover was determined by a visual estimate within a

0.5 m radius. Herbaceous cover and shrub cover were estimated within a 0.25 and 0.5 m

29 radius, respectively (Chen 1999). Distance to the forest edge and to the nearest stream were estimated by ‘pace’, which was calibrated by pacing a known distance five times.

To determine if there is a relationship between animal visits and the density of plants in an area, conspecific density was measured for camera-target plants. Defining patch size is challenging; therefore, a neighborhood approach was used. Distance was measured to the nearest three conspecifics within 20 m, and the distances were used for analysis. The number of conspecifics was recorded within one meter of the target plant, and within 0.5 m.

Soil Collection and Analysis

A soil sample was collected at the base of the plant and at a control point for later analysis. A garden spade with cm increments marked on the blade was used to collect a

4 X 4 X 8 cm block of soil from each target plant. The soil was placed in a paper bag.

The soil samples were allowed to air-dry. Soil depth, moisture, organic matter, bulk density, pH, and proportion of rocks were measured for each sample. Soil variables were chosen for their correlation with other soil variables to save time and resources. For example, soil organic matter has often been correlated with sulfur concentrations, and pH is correlated with availability of several mineral nutrients (Waller and Adams 1957,

Nodvin et. al.. 1986, Matlack 2009).

Soil moisture and depth were recorded in the field using a Hydrosense probe

(Campbell Scientific, Queensland, Australia). The Hydrosense instrument measures soil moisture through a laboratory-calibrated probe that determines percent volumetric water content in a sample by conductivity. Soil depth was determined by inserting the probe until it stopped in three different locations surrounding the plant or control point to

30 eliminate false results due to random rocks or cavities in the soil. The remaining soil parameters were measured in the lab.

A standard protocol was used to analyze soil samples

(http://www.ohio.edu/plantbio/staff/McCarthy/Soils_Lab_Protocols.pdf).

Once the soil was air-dried, it was passed through a standard testing sieve with 4 mm openings. Soil chunks were broken up until all pebbles and rocks had been separated from the soil sample. Each soil sample was weighed before and after the rocks were sifted. Soil organic matter was determined by ‘loss of ignition’. Organic matter=100-% ash.

finalmassofcrucible − weightofemptycrucible %Ash = ( )×100 massofcrucible − weightofemptycrucible

A porcelain crucible was labeled using a number two pencil. Crucibles were filled within 3-5 mm of the rim and dried in a Despatch drying oven (Despatch

Industries: Lakeville, Mn) at 105 C for 2-3 hours. The soils were weighed using the

Mettler digital scale and recorded according to their corresponding crucible numbers and treatments. They were placed in a Thermolyne furnace (Barnstead-Thermolyne Corp:

Dubuque, IA ) at 550 C for 6 hours. Temperatures ranging from 440-600 degrees were shown to be sufficient for loss of ignition procedures by Andrejko et. al. (1983). Samples were left in the oven until the furnace cooled and then reweighed.

A standard 20 gram sample was used for pH testing, to which 20 ml of distilled water was added. The soil/water slurry (mixed at a 1:1 ratio) was allowed to sit for 5-10 min until the soil was broken up and suspended. A pHTestr3+ double junction meter

(Oakton Instruments: Vernon Hills, IL) was calibrated using both a 4.0 and 7.0 buffer

31 solution. The meter was then submerged in the soil slurry for 2-3 minutes until a reading was consistent on the meter.

Statistical Analysis

Phenology

I recorded the date of removal as well as the total number of removals for each species every week from June 1 to October 31. The availability of a dispersal vector could be a limiting resource, and selection to minimize competition through separated fruiting periods could enhance resource partitioning (Hutchinson 1959, Kronfeld-Schor and Dayan 1999). Using the phenology data to determine if temporal-niche partitioning occurs among these forest herbs, I analysed the data with a Temporal Overlap program

(Rango 2011) that calculates a Pianka index (Pianka 1973) to measure overlap of fruit removals over a period of time (Rango 2011).

Camera Monitoring

I ran a Kruskal Wallis Test in R (R Development Core Team 2011) to determine if the mean number of days to the first removal of each species was significantly different. To test whether fruit presentation or size affected removal events, (or the null hypothesis that removals reflect random vector encounters independent of fruit size or presentation), a Fisher exact test was used (R Development Core Team 2011). This method was chosen over the Chi-square analysis because some of the values for each category were less than five or a zero. Because Fisher Exact Tests require a two-by-two matrix of categorical variables, animals were lumped as “large mammals” (i.e., deer, raccoons) or “birds” (i.e., songbirds, Wild Turkey). Fruit presentation was categorized as

“fruit visible from above” or “fruit visible from below”.

32 To test the effect of microhabitat on fruit removals of Maianthemum and

Arisaema, a multivariate Principal Component Analysis (PCA) was used to ordinate plants in microenvironment space (PCORD software, MJM Design: Gleneden Beach,

OR). Both Maianthemum racemosum and Arisaema triphyllum had some individuals without a removal event, and they also shared presentation traits (i.e., color, cluster size and orientation). Therefore, I chose to lump these two species together to increase the sample size for statistical power. To determine whether aspects of a plant’s microenvironment have any effect on attracting animals, removals and non-removals were plotted in the ordination. Axis scores were tested for correlations with microenvironment variables to determine which variables most strongly influenced the ordination.

To determine whether three habitat variables shown in previous studies to influence animal behavior did so in this study as well, removals and non-removals were compared using Mann-Whitney U test in‘R’- Statistical Program (R Development Core

Team 2011). The Mann-Whitney U test was chosen after testing for equal variances using the “var.test”.

Microhabitat Analysis

To determine whether micro-environments of fruiting plants differ from random points in the forest, individual point data were ordinated using all sixteen habitat variables measured in the field and in the lab. Multivariate Non-metric Multi-

Dimentional scaling (NMDS) with PCORD software (Version 4.41MjM Software,

Gleneden Beach, Oregon, U.S.A) was chosen over a Principle Component Analysis for two reasons. Some of the Hydrastis canadensis plants were within a sanctuary, and

33 digging around the plants could affect plant health, therefore some soil data were missing in the combined matrices, violating assumptions of the PCA. Second, I was searching for relationships among multiple treatments using multiple variables, and this test is often recommended for ecology-based studies (McCune and Grace 2002).

Initially, each plant species and its corresponding controls were ordinated individually, but no separation of groups was observed. Following this negative result, all four species were combined in the analysis and ordinated together. Any groupings in ordination space were noted, and spearman correlation statistics were performed to correlate habitat variables with axis scores.

34 RESULTS

Fruit Removals

All four forest herb species had at least three fruit removals (Table 3). All three species that produce red fruit were eaten by birds, and birds ate only red fruits. The large yellow fruit, Podophyllum peltatum, was only eaten by large mammals (i.e., Procyon lotor (raccoon), Didelphis virginiana (opossum), and Odocoileus virginianus (white- tailed deer)). Small mammals (e.g., Tamias striatus (eastern chipmunk) and Peromyscus spp. (mice)) removed fruit from two of the three plant species producing small-medium red fruit. Hydrastis canadensis and Arisaema triphyllum were only eaten by birds while both birds and small mammals equally foraged on Maianthemum racemosum.

Table 3. Animal groups and species removing fruit from the four forest herb species. (Note: Some plants of Podophyllum peltatum were slow to ripen, and consequently the number of days monitored is much larger compared to other plant species).

A. M. H. P. peltatum triphyllum racemosum canadensis 7 replicates 12 replicates 13 9 replicates (Total Days (139) (186) replicates (257) Monitored) (180) Songbird Removals 3 5 4 0

Wild Turkey 0 0 3 0 Removals Mouse Removals 0 2 2 0

Chipmunk Removals 0 2 0 0

Gray Squirrel 0 1 0 0 Removals Omnivores 0 0 0 5 (Raccoon/Opossum) White-tailed Deer 0 0 0 4

There was no significant difference in removal rate among species (p = 0.31); however, Podophyllum peltatum did appear to have fruit removed quicker than

Maianthemum racemosum (Figure 3). Podophyllum peltatum had fruit removed within one or two days of ripening while Maianthemum racemosum waited up to ten to eleven days, which was the longest delay to fruit removal. The time to removal for Hydrastis canadensis was intermediate, varying depending on patch size (Figure 4a&b). While there was variation in timing of fruit removal for plant species in small patches, plant species in dense patches had consistent, rapid removals (Figures 4a.&b.). While there wasn’t a linear trend between removal and patch size for Hydrastis canadensis with conspecifics within 0.5 or 1 m (Figures 5a.b.c.&d.), Maianthemum did display a linear trend between days to removal and patch size within 1m (Figures 5e.&f.). Mean distance to the nearest conspecific was not a good predictor of how quickly a fruit was removed from an individual (Figure 4c.). However, conspecifics within one meter seemed to be the best of the three measures of neighborhood density in determining removal rates

(Figure 4b.).

8 # of days 6

0 MR HC AT PP Plant Species

Figure 3. Time to first fruit removal for plants that had successful removals (MR = Maianthemum racemosum, HC = Hydrastis canadensis, AT = Arisaema triphyllum, PP = Podophyllum peltatum) M. racemosum and A. triphyllum individuals without removals are excluded.

20 Time to first removal 15 (# of days)

0 20 40 60 80 100 Number of Conspecifics within 0.5 m

AT HC MR

Figure 4a. Number of conspecifics growing within 0.5 m of the target plant compared with total days to first removal of fruit for each individual. AT = Arisaema triphyllum, HC = Hydrastis canadensis, MR = Maianthemum racemosum. (Note: patch size was not measured for plants monitored in 2008, so there are only two (both 0,4) of the three A. triphyllum fruit removals represented).

20 Time to first removal 15 (# of days)

0 20 40 60 80 100 120 140 160 180 Number of Conspecifics within 1 m

AT HC MR

Figure 4b. Number of conspecifics growing within 1 meter of the target plant compared with total days to first removal of fruit for each individual. These data include the three species that produce small-medium, red fruit (Hydrastis canadensis, Maianthemum racemosum and Arisaema triphyllum).

20 Time to first removal 15 (# of days)

0 1 2 3 4 5 Mean distance to nearest Conspecific

AT HC MR

Figure 4c. The number of days to the first fruit removal compared to the mean distance to the nearest conspecifics. These data include the three species that produce small to medium, red fruit (Hydrastis canadensis, Maianthemum racemosum and Arisaema triphyllum).

38 14 12 10 8 hdays 6 4 2 0

20 40 60 80 100

h1m

Figure 5a. Hydrastis canadensis number of days to removal vs. number of conspecifics within 1 m. Adjusted R2 = 0.059, Multiple R2 = 0.14, F-statistic = 1.697, p = 0.2219. Spearman p = 0.46.

39 14 12 10 8 hdays 6 4 2 0

2.0 2.5 3.0 3.5 4.0 4.5

log(h1m) Figure 5b. Hydrastis canadensis number of days to removal vs. log transformed number of conspecifics within 1 m. Multiple R2 = 0.126, Adjusted R2 = 0.03858. F-statistic = 1.441. p-value: 0.2576

14 12 10 8 hdays 6 4 2 0

0 20 40 60 80

h05 Figure 5c. Hydrastis canadensis number of days to removal vs. the number of conspecifics within 0.5m. Multiple R2 = 0.05056, Adjusted R2 = -0.04439, F-statistic = 0.5325. p-value = 0.4823. Spearman p = 0.99.

40 14 12 10 8 hdays 6 4 2 0

0 1 2 3 4

log(h05)

Figure 5d. Hydrastis canadensis number of days to removal vs log-transformed number of conspecifics within 0.5m. Multiple R2 = 0.00557, Adjusted R2 = -0.09387, F-statistic = 0.0560, p-value = 0.8177.

25 20 15 mrdays 10 5 0

0 20 40 60 80

mr1m Figure 5e. Maianthemum racemosum number of days to removal vs. number of conspecifics within 1m. Multiple R2, Adjusted R2 = 0.2257, F-statistic = 3.04, p-value = 0.131. Spearman p = 0.07.

41 25 20 15 mrdays 10 5 0

-6 -4 -2 0 2 4

log(mr1ml)

Figure 5f. Maianthemum racemosum number of days to removal vs log-transformed number of conspecifics within 1m. Multiple R2 = 0.3189, Adjusted R2 = 0.2054, F- statistic = 2.81, p-value = 0.1447.

Fruit removals also depended on fruit character and presentation. Fruit presentation (i.e.,, fruit visible from above or below), as well as fruit color (red, non-red), determined which animal species ate the fruit. The first Fisher Exact test analyzed fruit color and size. Red fruit that was also visible from above was most removed by birds

(birds n = 12, large mammals n = 0) with the exception of two visits from Peromyscus spp. (mice). Large, yellow fruit which dangled toward the ground were only eaten by large mammals (birds n = 0, large mammals n = 9) (p=0.000549). The second Fisher

Exact Test analyzed fruit (independent of color) that was visible from above versus those visible from below. Birds removed fruit visible from above significantly more than

42 mammals (birds n = 7, all mammals n = 2)(p=0.016). Fruits that were visible from the ground were removed significantly more by mammals (birds n = 5, all mammals n =

14)(p=0.016). The third Fisher Exact Test measured the same presentation as the second test, but with red fruit exclusively. Red fruit that was visible from above were more often removed by birds (birds n =7, all mammals n= 2). However, when visible from below, mammals and birds were equally drawn to the fruit (birds n = 5, all mammals n = 5) (p = 0.349).

Contribution According to Animal Species

Twelve mammal species and four bird species were observed (Table 4). Most of the animals recorded on camera are species that spend the majority of their time in or near a mature forest with the exception of Marmota monax (groundhog), which inhabits old fields and meadows (Whitaker 1996). No turtles were recorded, and since there was only one P. peltatum fruit removal unidentified, it isn’t clear if this animal group was present at all.

Animal species varied in their contribution to the removal/dispersal of forest herb fruits (Table 4). Birds removed fruit most often and most consistently (67-100% of visits resulting in a removal), followed by mammals (1-40% of visits resulting in a removal)

(Table 4). Of all animal species recorded, Hylocichla mustelina (Wood Thrush) removed fruit most often, and Wood Thrushes were recorded removing fruit much more often than other songbirds (Wood Thrush n = 8 , Northern Cardinal n = 2 , Ovenbird n = 1, unknown n = 2 ). Sciurus caroliniensis (gray squirrel) generally ignored all four forest herb species, although it was recorded removing one Maianthemum racemosum berry, and it removed fruit least often per visit (Table 4). Tamias striatus (eastern chipmunk)

43 was also recorded taking fruit of Maianthemum racemosum but not taking fruit from the other three species. Large mammals (Procyon lotor (raccoon), Didelphis virginiana

(opossum) and Odocoileus virginianus (white-tailed deer)) were the only animals that removed fruit of Podophyllum peltatum. However, in two camera recording events,

Tamias striatus examined an unripe Podophyllum peltatum fruit and left it alone.

Therefore, it is possible that small mammals will also take this large berry.

Table 4. Animal species’ contribution to the removal of forest herb fruits. Rejections were determined as a separate event if at least one hour passed between sightings of each animal species. Plant species that produced multiple berries on one plant (Arisaema triphyllum and Maianthemum racemosum) could experience more than one removal event. Animals were only counted once for Podophyllum peltatum and Hydrastis canadensis as these plant species only produce one berry, or dense cluster, per plant.

Animal Species # of # of Proportion of Removals Rejections visits resulting in removal Cardinalis cardinalis (Northern Cardinal) 2 0 100%

Seiurus aurocapillus (Ovenbird) 1 0 100%

Meleagris gallopavo (Wild Turkey) 3 0 100%

Hylocichla mustelina (Wood Thrush) 8 4 67%

Peromyscus (mouse) 2 5 40%

Tamias striatus (chipmunk) 2 5 40%

Odocoileus virginianus (white-tailed deer) 4 15 27%

Procyon lotor (raccoon) 4 24 17%

Didelphis virginiana (virginia opossum) 1 18 5%

Sciurus carolinensis (eastern gray squirrel) 1 100 1%

Canis latrans (coyote) 0 2 0%

Canis familiaris (domestic dog) 0 1 0%

Felis catus (feral cat) 0 3 0%

Marmota monax (groundhog) 0 3 0%

Mephitis mephitis(striped skunk) 0 1 0%

Sylvilagus floridanus (eastern cottontail) 0 1 0%

Unknown songbird 2 0 NA

45 Timing of Removal

All four forest herb species ripened fruit within a brief, well defined period

(Figure 6). Hydrastis canadensis and Podophyllum peltatum had distinct fruiting niches in July and August, respectively, and only slightly overlapped with the other plant species

(Figure 6). Arisaema triphyllum and Maianthemum racemosum have similar fruiting morphology and presentation and produce ripe fruit from late summer into fall, although

M. racemosum has a much broader fruiting period than A. triphyllum (Figure 6). During weeks 14 and 15 (Sept 4-15), I recorded three removal events for Arisaema triphyllum.

During this time, I did not record a removal event for the five Maianthemum racemosum individuals being monitored (Figure 6), although M. racemosum fruit was ripe (Figure 7).

Time overlap tests resulted in a Pianka index of zero (no overlap) between all species except for a substantial overlap of removals between Hydrastis canadensis and

Podophyllum in June (Figure 7). This latter overlap produced an overall result of non- separation (p > 0.05) despite the clear separation in all other species pairs.

100

60 Proportion ripe (%)

0 June July August September October Months Podophyllum peltatum Hydrastis canadensis Maianthemum racemosum Arisaema triphyllum

Figure 6. Phenology data reporting peak fruiting periods for the four forest herb species throughout the season (June 1st to October 31st).

4 Number of Removals 3

0 June July August September October Months Hydrastis canadensis Podophyllum peltatum Maianthemum racemosum Arisaema triphyllum

Figure 7. Timing of removal events for each forest herb. The number of removal events that occurred throughout the fruiting season from week 1 (June 1-8th) to week 20 (October 24-31st).

Time of day was important for some plant-animal interactions and not for others.

Animals that removed Podophyllum peltatum fruit were crepuscular and nocturnal in their foraging habit, and there were no removals between the hours of 10 a.m. and 8 p.m.

Specifically, Procyon lotor and Didelphis virginiana were both recorded from dusk to dawn. Hydrastis canadensis was removed by turkey, songbirds and mice, and it had fruit removed during daylight hours; however, turkeys (n = 3) only removed fruit in the early morning. Maianthemum racemosum had fruit removed during daylight hours, with the exception of two rodent removals overnight. Arisaema triphyllum only had fruit removed in the early morning by songbirds; however, there were only three removals for this plant species. Most birds removed fruit between 6 a.m. and 12 noon but were

48 sometimes recorded foraging in the afternoon. All animal groups recorded on camera

(i.e., songbirds, rodents, deer), with the exception of Procyon lotor, Peromyscus sp. and

Didelphis virginiana, were recorded during the day between 0500 and 1900.

Animal Response to Microhabitat

Habitat characteristics may help to advertise fruit to vectors. Unlike Podophyllum peltatum and Hydrastis canadensis, some Maianthemum racemosum and Arisaema triphyllum individuals did not have a removal event, and so I was able to compare habitat data between M. racemosum and A. triphyllum individuals with and without removals.

Both of these species produce a cluster of red berries and both are primarily dispersed by birds. All observed Maianthemum racemosum and Arisaema triphyllum individuals were ordinated on the basis of above-ground habitat variables. Individuals without a removal were clustered at high values on axis one (Figure 8). Axis one accounted for

24% of the variation, and axis two accounted for 16% of the variation in the PCA.

Individuals with fruit removed had a significantly higher percentage of shrub cover (p =

0.017) and were closer to an edge (p = 0.04) than plants without a removal (Figure 9).

However, the distance of an individual from a water source (stream) was not significantly related to removal success.

Axis 2 0

-2

-4 -4 -2 0 2 4 Axis 1

Not Removed Removed

Figure 8. An ordination using 16 variables to compare M. racemosum and A. triphyllum plant individuals with removals against those without. Distance to tree and distance to conspecifics are positively and negatively, respectively, correlated with the first axis. Distance to tree was positively correlated (Pearson’s r = 0.79) with scores on the first axis, and number of conspecifics within 0.5 and 1 meter were negatively correlated with scores on the first axis (Pearson’s r = -0.74, -0.74) respectively.

100

120

80 100

80 60

60 40

40 % CoverShrub Distance to Edge(m) Distance to

20 20

0 0 Non-Removal Removal Non-Removal Removal Treatments Treatments Figure 9. Two measures of habitat that are significant factors in removal, non-removal events. Displayed are the mean and Standard Error of each group. a. The distance to the edge of a forest patch from Arisaema and Maianthemum individuals. b. Percent shrub cover surrounding A. triphyllum and M.racemosum individuals.

Forest Herb Response to Habitat

Microhabitat did not appear to differ between fruiting and non-fruiting individuals for any of the four forest herb species when compared with paired controls. However, using all controls, fruiting plants form a cluster within the points for three of the plant species (Figures 10 & 15). Arisaema triphyllum, however, did not show a difference among the three treatments (Figure 13). This could suggest the plants are using a subset of the environment. Podophyllum peltatum ordination results produced two dimensions, required 55 iterations, and resulted in a final stress = 15.33307 and instability = 0.00010.

Hydrastis canadensis ordination results produced 2 dimensions, required 79 iterations, and resulted in a final stress = 17.08722 and instability = 0.00006. Maianthemum racemosum ordination results produced 3 dimensions, required 78 iterations, and resulted

51 in a final stress = 10.61936 and a final instability=0.00008. Arisaema triphyllum ordination results produced 3 dimensions, required 56 iterations, and resulted in a final stress = 10.28513 and a final instability = 0.00009.

3 3

2 2

1 1

Axis 2 Axis 2 0 0

C -1 -1 F NF

-2 -2

-3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 Axis 1 Axis 1

Figure 10a. NMS ordination: Podophyllum peltatum fruiting, nonfruiting and control treatments compared using 16 habitat variables, exhibiting axis 1 vs 2. Distance to edge (r = -0.545), Proportion of Rock (r = 0.457), distance to stream (r = 0.432), percent organic matter (r=0.402), and woody debris ( r= -0.76) are all correlated with the first axis. Woody debris is correlated with the second axis (r=-0.85). Axis 1 accounts for 44 percent of the variance. Axis 2 accounts for 45 percent of the variance. Proportion of rock is the only matrix score > 0.5. 10b. Hydrastis canadensis fruiting, nonfruiting and control treatments compared using sixteen habitat variables, exhibiting axis 1 vs 2. Axis 1 represents 75 percent of the variance, and Axis 2 represents 11 percent. Woody Debris is correlated with the first axis (r = 0.92). Distance to edge (r = -0.63) and percent litter cover (r = 0.43) are correlated with the second axis. Woody debris is the only matrix score > 0.5.

Visual inspection of Podophyllum peltatum bar charts suggest fruiting plants more often occur closer to trees and with less woody debris than controls. Hydrastis canadensis bar charts showed little variation between fruiting plants and controls.

(Figures 11 & 12).

Soil Depth Soil Moisture Distance to Tree (m) Distance to Edge (m)

10 1.4 100 7

1.2 80 8 6

1.0 5 60 6 4 0.8 40 3 0.6 4 SoilDepth 2 0.4 (m) toedge Distance 20 Soil(%) Moisture Distance to tree (m) totree Distance 1 2 0.2 0 Fruit Nonfruit Control 0 0.0 Fruit Nonfruit Control Treatments 0 Fruit Nonfruit Control Treatments Fruit Nonfruit Control Treatments Treatments

Litter Cover Woody Debris (cm) Shrub Cover Distance to Stream (m)

500 100 80 100

400 80 80 60

60 300 60 40

40 40 200

Shrub(%) cover 20 Litter Cover (%) Cover Litter 20 20 Woody(cm) debris

100 (m) tostream Distance

0 0 0 0 Fruit Nonfruit Control Fruit Nonfruit Control Fruit Nonfruit Control Fruit Nonfruit Control Treatments Treatments Treatments Treatments

Herb Cover Canopy Cover Local Slope (cm) Litter Depth

(cm) 50

6 120 10 40 5 100 8 30 80 4 6 3 60 20

4 40 2 Litter Depth (cm) Depth Litter

Localslope(cm) 10 Canopy(%) Cover Herbaceous cover (%) Herbaceouscover 20 2 1

0 0 0 0 Fruit Nonfruit Control Fruit Nonfruit Control Fruit Nonfruit Control Fruit Nonfruit Control Treatments Treatments Treatments Treatments

Figure 11. Podophyllum peltatum: Character of the environment at Control and Plant microsites assessed by twelve environmental variables. Error bars indicate one standard deviation. Fruit (n = 22), Nonfruit (n = 12), Control (n = 22).

Soil Depth Soil Moisture Distance to Tree (m) Distance to Edge (m)

100 12 120 1.6 10 80 1.4 100 1.2 8 80 60 1.0 6 0.8 60 40 0.6 4 40 Soil Depth (cm) Distance to Edge (m) to Distance (%) Soil Moisture

Distance to tree (m) 0.4 20 2 20 0.2

0.0 0 0 0 Fruit Nonfruit Control Fruit Nonfruit Control Fruit Nonfruit Control Fruit Nonfruit Control Treatments Treatments Treatments Treatments

Woody Debris (cm) Shrub Cover Distance to Stream Litter Cover (m)

100 400 100 80

80 300 80 60 60

60 40 200 40

Litter cover (%) 40 20 20 Distance to stream (m) Woody debris (cm)

100 Shrub Cover (%) 20 0 0 Fruit Nonfruit Control Fruit Nonfruit Control 0 Treatments 0 Fruit Nonfruit Control Treaments Fruit Nonfruit Control Treatments Treatments

Local slope (cm) Litter Depth (cm) Herbaceous cover Canopy Cover

100 100 8 2.5 80 80 2.0 6 60 60 1.5 4 40 40 1.0 Herbaceous cover (%) cover Herbaceous Litter depth (cm) 20 Local slope (cm) Local slope Canopy Cover (%) 20 2 0.5 0 0.0 Fruit Nonfruit Control 0 0 Fruit Nonfruit Control Treatments Fruit Nonfruit Control Fruit Nonfruit Control Treatments Treatments Treatments

Figure 12. Hydrastis canadensis: Character of the environment at Control and Plant microsites assessed by twelve environmental variables. Error bars indicate one standard deviation. Fruit (n = 23), Nonfruit (n = 11), Control (n = 23).

1 1

Axis 2 Axis 3 0 0 Control Fruit Nonfruit -1 -1

-2 -2 -2 -1 0 1 2 -2 -1 0 1 2 Axis 1 Axis 2 Figures 13. Arisaema triphyllum Fruiting, nonfruiting and control treatments compared using sixteen habitat variables, exhibiting axis 1 vs 2, and 1 vs. 3. Axis 1 accounts for 14 percent of the variance, axis 2 accounts for 47 percent, and axis 3 accounts for 32 percent. Percent herbaceous cover (r = 0.53), soil depth ( r = 0.445), distance to stream (r=-0.50), and proportion of rock (r = 0.632) are all correlated with the first axis. Distance to edge (r = -0.624) and woody debris (r = -0.809) are correlated with the second axis. Woody debris (r = -0.854) is correlated with the third axis. Proportion of rock is the only matrix score > 0.5.

Visual inspection of Arisaema triphyllum bar charts show little variation between fruiting plants and controls (Figure 14). Nonfruiting plants are consistently greater than fruiting and control in mean values of all variables. However, variance makes categories difficult to separate statistically

Soil Depth Soil Moisture Distance to tree (m) Distance to Edge (m) 50 50 50 50

40 40 40 40

30 30 30 30

20 20 20 20 Soil depth (cm)

Distance to Edge (m) 10 Soil moisture (%) 10 Distance to tree (m) 10 10

0 0 0 Fruit Control Nonfruit Fruit Control Nonfruit 0 Fruit Control Nonfruit Fruit Control Nonfruit Treatments Treatments Treatments Treatments

Litter Cover Woody Debris (cm) Shrub Cover Distanc e to Stream

100 100 50 120

80 80 40 100

80 60 60 30

60 40 40 20

40 Litter (%) cover Shrub cover Shrub (%) cover 20 20 Distance to (m) stream Woody Woody debris (cm) 20 10

0 0 0 Fruit Control Nonfruit Fruit Control Nonfruit Fruit Nonfruit Control 0 Treatments Fruit Control Nonfruit Treatments Treatments Treatments

Canopy Cover Local Slope (cm) Litter Depth (cm) Herb Cover

100

50 50 100 80

40 40 80

60 30 60 30

40 40 20 20

Canopy Cover (%) 20 Litter depth (cm) Local slope (cm) Herbaceous (%) cover 20 10 10

0 0 Fruit Control Nonfruit Fruit Nonfruit Control 0 0 Fruit Control Nonfruit Fruit Control Nonfruit Treatments Treatments Treatments Treatments

Figure 14. Arisaema triphyllum: Character of the environment at Control and Plant microsites assessed by twelve environmental variables. Error bars indicate one standard deviation. Fruit(n=20), Nonfruit (n=12 ), Control (n = 20).

Control Axis 2 Fruit Axis 3 0 NF

-1

-2

-2 -1 0 1 2

-2 -1 0 1 2 Axis 1 Axis 1

Figure 15. Maianthemum racemosum fruiting, nonfruiting and control treatments compared using 16 habitat variables, exhibiting axis 1 vs 2 and 1 vs 3. Axis 1 represents 37 percent of the variance, axis 2 represents 28 percent, and axis 3 represents 29 percent. Woody debris (r = -0.62), proportion of rock (r = 0.45), percent litter cover (r = 0.445), percent shrub cover (r = 0.458), percent herbaceous cover (r = 0.462) and distance to stream (r = -0.53) are all correlated with the first axis. Distance to edge (r = -0.63) and woody debris (r = -0.82) areboth correlated with the third axis. Proportion of rock is the only matrix score > 0.5.

Visual inspection of Maianthemum racemosum bar charts shows little variation between fruiting plants and controls (Figure 16). However nonfruiting plants were consistently lowest in most variables.

Distance to Tree (m) Soil Depth Percent Soil Moisture Distance to Edge (m)

100

100 100 100 80

80 80 80 60

60 60 60 40 40 40 40

Distance to tree (m) 20 Distance to edge (m)

Soil depth (cm) 20 Soil moisture (%) 20 20

0 0 Fruit Control Nonfruit Fruit Control Nonfruit 0 0 Fruit Control Nonfruit Treatments Fruit Control Nonfruit Treatments Treatments Treatments

Woody Debris (cm) Shrub Cover Distance to Stream (m) Litter Cover

100 100 100 100

80 80 80 80

60 60 60 60

40 40

40 (%) cover Litter

20 Shrub cover Shrub (%) cover Woody debrisWoody (cm) 20 20

20 Distance to stream (m) 0 Fruit Control Nonfruit 0 0 Treatments Fruit Control Nonfruit 0 Fruit Control Nonfruit Fruit Control Nonfruit Treatments Treatments Treatments

Litter Depth (cm) Herbaceous Cover Canopy Cover Local Slope (cm)

100 100 7 100

6 80 80 80 5

60 60 4 60

40 3 40 40

Canopy Cover (%) 2 Litter depth (cm) Litter

20 Local slope (cm) 20 1 20 0 Herbaceous (%) cover Fruit Control Nonfruit 0 0 Fruit Control Nonfruit Fruit Control Nonfruit Treatments 0 Treatments Treatments Fruit Control Nonfruit Treatments

Figure 16. Maianthemum racemosum: Character of the environment at Control and Plant microsites assessed by twelve environmental variables. Error bars indicate one standard deviation. Fruit (n = 9), Control (n = 9), Nonfruit (n = 9).

58 DISCUSSION

As many forest herb species in Ohio grow low to the ground and do not have the large fruiting display exhibited by some tree and shrub species, dispersal away from the parent is challenging. In addition to being inconspicuous, fruit low to the ground is accessible to woodland rodents, which are sometimes seed predators (Borchert and Jain

1978, Jensen 1985) and are not likely to move the seeds very far due to their small home range (Harestad and Bunnel 1979). Furthermore, the plant species I studied are vulnerable to trampling, as seen on camera. Consequently, forest herbs rely on attracting animal species that have the potential to move seeds away from the parent (e.g., songbirds, large mammals) and place the seeds into suitable sites.

In the four plant species studied, I found that animals specialize on fruits according to fruit characters, presentation, and timing. Birds only removed small to medium red fruits, and medium to large mammals only removed large yellow fruit

(Podophyllum peltatum). This relationship is consistent with other studies examining fruit size and color preferences of vector species (Knight and Siegfried 1983, Gautier-

Hion et. al. 1985, Cousens et. al. 2008). For some animal species, gape size limits what a bird or small mammal can eat (Wheelwright 1985, Cousens et. al. 2008). However, some large animals (e.g., Procyon lotor) that are known to eat smaller fruits such as Phytolacca americana (Martin et. al. 1951) rejected the fruit of three of the forest herbs that present small to medium fruit. It is difficult to determine why exactly an animal would ignore a food resource that is readily available. Welch et. al. (1997) found that bears ignored small patches of berries, suggesting that expending energy for that resource was not worth the reward. The small, red berries produced by the forest herbs in this study may

59 not have been worth the energy of consumption for larger animals. However, it is likely that some fruits are distasteful, or simply did not have an odor to draw in mammal vectors that have strong olfactory senses (Feldhammer et. al. 2004). In a feeding trial,

Terrepene carolina did sample Arisaema triphyllum, but rarely ate the fruit compared to the other berries offered (Braun and Brooks 1987). This is likely because of the painful oxalate crystal that some animals find irritating when chewed (Cheeke 1995). However, it is important to recognize that large mammals may occasionally take a single small, red fruit, and that the results of this study demonstrate patterns, and are not absolute.

The sample size for determining removals for all four plant species is small, due to a limited number of cameras and a delay in some removal events. Turtles and perhaps foxes likely play a role in dispersal in southeast Ohio, as they have in other regions

(Braun and Brooks 1987, Matias et. al. 2010), but they were not recorded in this study.

Among the thousands of hours of observation in a mature forest, it is interesting that these animals were never recorded. Turtles may have been undetected by the heat- sensitive cameras due to their low body temperatures. Coyotes were seen on camera, and this could have affected the presence of other wild canids. Nevertheless, there were only two unidentifiable removal events on camera, and I believe I have caught much of the story of forest herb dispersal due to the extensive monitoring that began in June and ended in late October.

Plant-vector Specialization

Animal groups appeared to specialize in response to fruit character and presentation. Podophyllum peltatum produces a large, dangling yellow berry and attracted large mammals. The red fruits of Arisaema triphyllum and Hydrastis

60 canadensis, which are visible from above, often cannot be seen from below because of their leaf arrangement. They were primarily eaten by birds, which suggests that red fruits with upward presentation are more likely to attract birds. Birds are capable of seeing red fruits (Hart 2001), whereas mammals are likely responding to olfactory senses (taste, smell) ( Feldhammer et. al. 2004) especially in the case of diurnal and nocturnal mammals (i.e., raccoons, opossums, mice etc) (Gregg et. al. 1929). Maianthemum racemosum varies in presentation when compared with the other three forest herb species. Maianthemum racemosum produces a red berry that is usually visible from all directions, but dangles toward the ground, and was eaten by both birds and mammals.

Visibility from below explains why more rodents foraged on the fruit, while the red fruit color, visible from above when not shielded by vegetation, also attracted birds. Sciurus spp. are able to distinguish differences in red and green (MacDonald 1992), while Tamias striatus do not respond as much to color as they do safety (cover) and accessibility

(Johnson et. al. 2009). The rejection of fruit from Sciurus carolinensis is presumably a result of the fruits being distasteful to this animal. While the smaller rodent, Tamias striatus is foraging on foods within a refuge. Although there were only four forest herb species monitored in this study, we can extrapolate presentation-vector relationships to other plant species such as the rare forest herb Panax quinquefolius (ginseng). Because

Panax quinquefolius’ small red fruit is visible from above, we can infer that in southeast

Ohio, birds, specifically, Hylocichla mustelina and Meleagris gallopavo, likely disperse the fruit.

How would a plant species benefit from attracting a single animal group (e.g., only songbirds or only mammals) rather than taking advantage of all possible vectors?

61 Specialization in seed dispersal vectors potentially benefits forest herbs by attracting specific animal groups (e.g., songbirds) that maximize dispersal distance. It is also possible that plant species are reducing the likelihood of visits by seed predators such as rodents (Borchert and Jain 1978, Jensen 1985). In addition to fruit characters and presentation, forest herb fruit-vector specialization can also be in the form of temporal presentation.

Vector Limitation

A temporal separation was evident among all four forest herbs, although two of the species overlapped in the fall. Even though I cannot statistically support the resource partitioning hypotheses, the distinct time frames and removal rates strongly suggest that at least three of the four species are competing for vectors. Hydrastis canadensis and

Arisaema triphyllum both display red fruit pointing upward and produced ripe fruit at two very different times. Hydrastis canadensis and Podophyllum overlapped in temporal fruit display, but these two species aren't likely to compete as their fruit arrangement (pointed up vs. dangling down), color and size were all different, and they attracted different animal vectors.

Podophyllum peltatum was removed relatively quickly, on average, within one or two days of ripening. (Podophyllum peltatum even had fruit removed when it was unripe, by Odocoileus virginiana.) Podophyllum peltatum's odor likely attracted large mammals so quickly because of their strong olfactory senses (Feldhammer et. al. 2004).

In contrast to Podophyllum peltatum, some Hydrastis canadensis individuals did not have a removal for up to a week or more, and some Maianthemum racemosum and Arisaema triphyllum never had a removal. When fruiting periods overlapped, Arisaema triphyllum

62 had removal events during the same week that Maianthemum racemosum had none.

These observations suggest that availability of animal vectors is sometimes limited. A delay in a removal could be detrimental as the plant is vulnerable to predation, trampling or senescing the longer it retains fruit (Cousens et. al. 2008).

Many plant species produce fruit in the fall, suggesting a large crop of fruit available in a short period. Assuming no increase in numbers of the animal vectors, this might lead to intense competition for vectors. Therefore, to increase chances of removal, a plant species benefits by providing fruit at a time when fewer forest-interior species are fruiting. Although many other plant species fruit during the summer months, there were fewer species observed fruiting in the forest interior during the summer when compared to fall. If Podophyllum peltatum, a plant species presenting only one berry per individual, produced fruit later in the season while large quantities of fruit were available in the forest interior, competition for vectors would increase for this plant species.

Podophyllum peltatum attracted large animal vectors (i.e., Odocoileus virginiensis,

Procyon lotor). Forest interior woody plant species producing fruit that attracts forest mammals, include Quercus spp, Fagus grandifolia, Prunus serotina, Cornus florida,

Celtis sp., Asimina triloba and Diospyros virginiana (Apsley and Ghert 2010). All of these species produce ripe fruit in the fall, whereas Podophyllum peltatum produced ripe fruit between late July and mid- August, which suggests that P. peltatum has found a favorible temporal niche. Another animal documented as a dispersal vector for

Podophyllum peltatum is Terrepene carolina (eastern box turtle) (Braun and Brooks

1987). Eastern box turtle's preferred temperature is between 28-30 C (Dodd 2002); therefore, removal probability by box turtles is likely to increase in days that are

63 consistently warmer. Of the four forest herb species studied, Podophyllum peltatum is unique in its color, size, presentation, and animal vectors, suggesting a distinct class of vector specialization and phenology.

Hydrastis canadensis produced ripe fruit in early summer (mid-June to early July) and also didn’t overlap temporally with any of the other red-fruited forest herbs studied.

Why is this time of year special, and how does this species benefit? Stapanian (1982) found that frugivorous birds were most numerous in the fall but unpredictable, and less numerous during breeding season but more predictable. His argument is that there is a need for quick energy resources that are easy to digest during the breeding season as adult birds are caring for nestlings, and that some songbirds are likely to forage on fleshy fruits as well as insects to satisfy the demand for water and fats. He also found an interesting trend of summer-fruiting species having fruit removed more rapidly than fall- fruiting species.

Although no other plant was observed producing ripe, fleshy fruit in the vicinity of Hydrastis canadensis plants, there are other species that fruit in early summer such as

Morus rubra (Stapanian 1982). Hydrastis canadensis could be limiting competition by producing fleshy fruit when fewer species are fruiting. Hydrastis canadensis attracted

Melegris gallopavo (Wild Turkey) to three separate plant individuals/patches. Turkeys were only recorded on camera near fruiting Hydrastis canadensis. Podophyllum peltatum and Maianthemum racemosum individuals were within the same area of the Hydrastis canadensis plants that attracted turkeys. Turkeys were recorded five separate times within large patches of Hydrastis canadensis, suggesting they were drawn to the specific area to forage on this fruit. Cameras did not photograph Turkeys near Podophyllum

64 peltatum or Maianthemum racemosum later in summer when H. canadensis fruits were gone. Perhaps fruiting in early summer is a better strategy than fruiting later in the season. If frugivorous birds such as Hylocichla mustelina (Wood Thrush) and Wild

Turkeys are present in the forest patch, an early summer fruiting period limits competition with other plant species for dispersal vectors.

Of the native animal species that ignored or rejected the fruit of the four forest herbs, certain rodent species were clearly not interested in the four types of fruit. Sciurus carolinensis (eastern gray squirrel) was photographed 101 times near fruiting plants and only removed fruit once from Maianthemum racemosum, which was also removed by other rodents. My conclusion is that although rodents will occasionally take the fruit of forest herbs, as a group they are not a major dispersal vector of these four forest herbs.

Rodents are not an ideal vector as they are often seed predators (Borchert and Jain 1978,

Jensen 1985) and have a limited range. Furthermore, the two forest herbs producing ripe fruit in the fall coincide with hard mast production, which comprises a large portion of the diet of both Sciurus carolinensis and Tamias striatus (eastern chipmunk) (Whitaker

1996). Additionally, Arisaema triphyllum produces a calcium oxalate compound in the fruit, that is irritating to mammals and would deter them from eating the fruit (Cheeke

1995).

Removal Success

The size of the reward, represented here by patch size, has been a factor in successful removals in multiple studies (Welch et. al. 1997, Cousens et. al. 2008, Carlo and Morales 2008). This could explain the positive relationship observed here between the patch size and time to removal in Hydrastis canadensis. However, high stem density

65 isn’t always beneficial, since the availability of more fruits in a particular patch potentially causes an animal to stay close to the patch and travel shorter distances (Carlo and Morales 2008). A smaller patch is likely to cause an animal to forage and travel a greater distance if there is a limited amount of that food source (Carlo and Morales 2008).

However, for species such as Arisaema triphyllum and Maianthemum racemosum that had some individuals without a removal, a short dispersal distance is better than no dispersal.

Patch size or reward can also refer to multiple plant species in a patch. Although not quantified, Arisaema triphyllum and Maianthemum racemosum were both observed producing mature fruit near dense patches of Lindera benzoin, a shrub that produces red, fleshy fruits during the same time of year. As discussed, competition for vectors may be reduced if Arisaema triphyllum and Maianthemum racemosum had separate fruiting periods, as does Podophyllum peltatum; however, there are advantages to fruiting in the fall. Plant species producing red fleshy fruit in the fall are attracting birds that are building fat reserves for migration (Smith et. al. 2007). Perhaps red-fruited shrubs, such as Lindera benzoin, not only draw songbirds seeking food and cover, but also draw vectors to forest herbs conveniently near the shrubs, thereby increasing their chances of dispersal. Furthermore, it is possible that some Arisaema. triphyllum and Maianthemum. racemosum individuals without a removal did not present a large enough reward or weren’t within an animal’s preferred microhabitat. Therefore, environmental factors affecting removals become important.

Plant individuals without a removal had less shrub cover, were farther from an edge, were farther from the nearest tree, and were in a small patch or solitary. These

66 habitat variables are also shown to be important to songbird habitat (Howe 1979,

Jordano and Schupp 2000, Bayne and Bryant 1994, Bartusizevige and Gorchov 2006).

Foraging cover is important for certain animal groups, such as songbirds, as it provides them with a sense of security while foraging (Cueto and de Casenave 2002). Shrubs and trees provide an escape from the ground if pursued by a predator, while large patches of forest herbs provide a reward, as well as cover (Cueto and de Casenave 2002, Bayne and

Bryant 1994, Carlo and Morales 2008). Thus, animal defense behavior may affect the structure of forest herb populations.

Non-random Distribution of Forest Herbs

Perhaps more important to forest herbs than dispersal distance is the potential for directed dispersal. If forest herbs are specialized to attract a single group of animal vectors, through presentation or through fruiting phenology, they are potentially fine- tuning the location to which seeds are dispersed. Certain bird species were seen on camera removing fruit more often than other animals. Of the songbirds photographed,

Hylocichla mustelina (Wood Thrush) removed red fruit more frequently than other songbirds. They were also the only animal species that visited three of the four plant species. Hylocichla mustelina is mostly insectivorous in summer months, but in late summer, it switches to include more fleshy fruit in preparation for migration (Blake and

Loiselle 1992, Brooks and Johns 2005). Hylocichla mustelina resides mostly in mature forests and prefer moist habitats and moderate shrub cover (Brooks and Johns 2005,

McDermott and Wood 2010). Although our study did not follow animal vectors after they consumed fruit, this overlap in habitat preferences could suggest directed dispersal, as other studies have shown directed dispersal to suitable sites up to 100 percent of the

67 time (Mack 1995, Cortes et. al. 2009). It is also suggested that if there is one animal species removing fruit more often than other species, as in the case of Hylocichla mustelina, directed dispersal may be more likely for that plant species (Wenny 2001).

Furthermore, three of the four plant species presented a preference in habitat compared to random points.

Forest herbs exist within an environment of heterogeneous resources. Dispersal vectors potentially play an important role in placing seeds into suitable micro-sites for seedling success (Wijesinghe and Hutchings 1999, Purves et. al. 2007). Which microhabitat parameters are important to the four plant species in this study wasn’t completely clear, but some habitat variables were correlated in ordinations for three of the four species. Except in the case of A. triphyllum, the amount of woody debris surrounding an individual, the proportion of rock in the soil in which an herb is growing, percent litter cover, and the distance to the forest edge were significant factors potentially affecting germination and growth of forest herbs. It appears that these forest herbs are responding on a micro-site scale and a broad landscape scale. From the plant species’ perspective, dispersal within a heterogeneous landscape relies on vectors that are likely to place seeds within a mature forest and to a suitable micro-site. This does not mean that the animal species is always going to drop the seed into soil with fewer rocks or less woody debris; however, it is likely that the animal spends a disproportionate amount of time within this coarse-scale environment where forest herbs are likely to grow. All four plant species were commonly found in study sites. While this suggests that the animal-mediated dispersal of these four species is effective, Podophyllum peltatum and Hydrastis

68 canadensis are also capable of forming large clones, and seed dispersal with successful establishment in a new micro-site could be rare.

Conclusion

Just as animal species select fruit according to seed size and color (Wheelwright

1985), forest herbs are attracting specific animal vectors. Producing a fleshy fruit is more expensive than a dry propagule, and in some cases, the parent plant only produces one fleshy dispersal unit, allowing only one chance for a successful dispersal event. It appears that these four forest herbs have adapted to their vulnerable, yet concealed, forest floor environment through fruit characters that increase the likelihood of a removal event by a particular group of vectors. However, there is an interesting paradox created by fruit specialization. If animal vectors are limited as they appear to be, why wouldn’t a generalist fruiting strategy be more common? Vectors may be limited as a result of specialization. There does not seem to be a limit of fruit for birds in the fall, but a limit of frugivores. Although specialization seems risky, the forest herbs in this study were commonly found in the study sites. Why were they common? Podophyllum peltatum and Hydrastis canadensis are presumably well established because of clonal reproduction as well as seed dispersal. Arisaema triphyllum and Maianthemum racemosum present several berries, allowing for more than one dispersal event, and this could account for the establishment of these species. For population stability in second-growth forests, perennial herbs need one successful dispersal event that may happen during a year when we’re not watching. Even if species are common within my study sites, it is important to note the crucial impact dispersal vectors have on maintaining forest herb populations.

69 Although southeast Ohio is rich in herbaceous plant diversity, some forest herbs are in decline. Since documentation in 1845, about half of Hydrastis canadensis populations in Ohio have gone extinct (Mulligan and Gorchov 2004). Although much of this population decline is due to over-harvesting for medicinal purposes, some of it is due to deforestation (Mulligan and Gorchov 2004). Forest herb species are dependent on both a suitable micro-site and the vectors that place them into these micro-sites.

Trophic-level impacts regarding animal-mediated dispersal have been shown to be critical to plant populations in delicate ecosystems (Wheelwright 1988, Chimera and

Drake 2010). With the extinction of the Dodo bird (Ralphus cuculatus) over 300 hundred years ago, the seeds of some plant species, such as the Calvaria tree, have failed to germinate due to lack of scarification through the Dodo’s digestion (Wheelwright 1988).

In addition to failed germination, the extinction of gamphotheres and other large megafauna caused drastic changes in genetic variation, as well as greatly reduced geographical distributions of plant species (Guimaraes Jr. and Galetti 2008). In the present study, no non-native birds or mammals were photographed with the exception of a domestic dog and two feral cats in one location. The absence of non-native animal species on camera suggests that only our native animal species play a major role in seed dispersal of forest herbs. The local extinction of animals such as Hylocichla mustelina could greatly impact the local flora in forest patches, especially if there are only one or two specific animal vectors for a particular plant species (Wenny 2001). If southeast

Ohio forest herb species are primarily dispersed by native animals, resource managers will need to use a more holistic approach to protect forest herbs. Specifically, reducing human activity in protected areas when fruit is likely to be removed (daily or seasonally)

70 and providing safe corridors for dispersal vectors should be considered in management schemes. Although there were only four forest herbs studied, many more plant species persist in the forests of southeast Ohio, and we can use these relationships to study and protect them.

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APPENDIX A. BUCKEYE CAM IMAGES OF FRUIT REMOVALS

Figure 17. Top: Wild Turkey removing Hydrastis canadensis fruit. Bottom: Wood Thrush removing Arisama triphyllum fruit.

Figure 18. Top: White-tailed deer removing Podophyllum peltatum fruit. Bottom: Wood Thrush removing Maianthemum racemosum fruit.

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