ABSTRACT

BUTTERFLY MOVEMENTS AMONG ISOLATED PRAIRIE PATCHES: HABITAT EDGE, ISOLATION, AND FOREST-MATRIX EFFECTS

By David Jon Stasek

The spatial structure of a population is often determined solely by the frequency of interpatch movements. Landscape structural features and the behavioral response of organisms to these features affect animal movements among suitable habitat patches, but these factors have not been applied to spatial population studies. I recorded the movements, abundance, and behavioral response to the habitat edge of two species of butterflies, the great-spangled fritillary (Speyeria cybele) and the pearl crescent (Phyciodes tharos), among remnant prairie patches in south-central Ohio to determine the spatial structure of the populations and the mechanistic basis of movements among patches. The great-spangled fritillary exhibited characteristics of a patchy population because of the relatively high number of interpatch movements and its greater abundance at the patch edge. The pearl crescent moved infrequently among patches and was isolated within patches by the patch edge and all forest-matrix types, suggesting a classic metapopulation structure.

BUTTERFLY MOVEMENTS AMONG ISOLATED PRAIRIE PATCHES: HABITAT

EDGE, ISOLATION, AND FOREST-MATRIX EFFECTS

A Thesis

Submitted to the Faculty of Miami University in partial fulfillment of the requirements for the degree of Master of Science Department of Zoology by David Jon Stasek Miami University Oxford, OH 2006

Advisor ______Thomas O. Crist

Reader ______David L. Gorchov

TABLE OF CONTENTS

Acknowledgements iii

1 Literature Review 1

2 Butterfly movements among isolated prairie patches: habitat edge, isolation, and forest-matrix effects 10 Introduction 10 Methods 13 Results 21 Discussion 26

3 Synthesis 40

Figures 47

Appendix I 56

Appendix II 60

ii ACKNOWLEDGEMENTS

I would like to thank my faculty advisor, Dr. Tom Crist, for all of his valuable help throughout the course of this project. I would also like to thank my committee members, Dr. David Berg, Dr. David Gorchov, and Dr. Nancy Solomon, for all of their insightful comments and suggestions. I would also like to thank the staff of the Edge of Appalachia Preserve and the Nature Conservancy’s Ohio field office, especially Chris Bedel, Pete Whan, Mark Zloba, and Deni Porej, for allowing me to conduct my research on their property and for all of the valuable time and help they gave to me in the field and in the lab. Thanks also goes to Caitlin Bean for conducting the abundance counts and for collecting the patch quality data. Finally, I wish to thank Mark Meunier and Chad Eliason for their invaluable help in the field. This research was funded in part by the Ohio Biological Survey and the Miami University Zoology Summer Field Workshop.

iii CHAPTER 1 LITERATURE REVIEW

Habitat fragmentation results in the reduction of continuous areas of habitat into smaller, more isolated fragments (Wilcove et al. 1986, Wiens 1994, Laurance et al. 1997, Bender et al. 1998). Fragmentation alters the biotic diversity and abundance of organisms, nutrient cycling, and the above-ground biomass and microclimate within habitat patches (Klein 1989, Saunders et al. 1991, Laurance et al. 1997, Davies and Margules 1998). As continuous areas of habitat are reduced to smaller fragments, animals may have to disperse among fragments in order to find appropriate food sources and mating opportunities. The loss and isolation of habitat fragments have been studied from an island paradigm with island biogeography and metapopulation dynamics as the two dominant schools of thought. Island biogeography views the dynamics of communities and operates across regions (MacArthur and Wilson 1967, Hanski and Simberloff 1997). Metapopulation theory is likewise based on an island paradigm (Wiens 1997) but instead focuses on one or two species’ distributions among patches within landscapes (Hanski and Simberloff 1997, Turner et al. 2001). Metapopulation dynamics are often used to describe the occupancy or abundance of single species within highly fragmented landscapes using colonization and extinction rates among habitat patches (Moilanen and Hanski 2001). Classic metapopulation theory suggests that between 5 and 30 percent of the individuals in a population move among habitat patches with all patches being susceptible to extinction and recolonization (Levins 1969, Hanski et al. 1994, Hill et al. 1996). However, other models have been proposed for a set of isolated habitats that do not fit the classic metapopulation definition. An island-mainland metapopulation model has one habitat patch that is a source of colonists, while other patches in the system show local extinction and recolonization (Harrison et al. 1988). A patchy population has higher interpatch movement than a classic metapopulation. As a result, population dynamics are linked among patches, and the probability of local population extinctions is lessened (Harrison 1991, Harrison and Taylor 1997, Sutcliffe et al. 1997). A non-

1 equilibrium metapopulation occurs when local populations go extinct with few recolonizations, which will eventually lead to regional metapopulation extinction (Hanski 1997, Harrison and Taylor 1997). Non-equilibrium metapopulations can result when a disturbance disrupts the equilibrium of the metapopulation. The metapopulation may go extinct even if conditions remain constant after the disturbance (Tilman et al. 1994, Hanski 1997). There is not a clear delineation among these different models, and often a population will display characteristics of multiple models (Hill et al. 1996, Harrison and Taylor 1997, Sutcliffe et al. 1997). Nonetheless, these models can still be used to explain the spatial structure of a population within highly fragmented landscapes (Harrison and Taylor 1997). Both island biogeography and metapopulation dynamics have viewed the habitat fragments as islands surrounded by an inhospitable, uniform matrix (MacArthur and Wilson 1967, Hanski 1998, Tischendorf and Fahrig 2001). This is now considered an inadequate description since island theory does not consider edge effects (Laurance and Yensen 1991, Wiens 1994), nor does it recognize that fragments are often surrounded by species-rich habitats that may act as a source of propagules for establishment within the fragment (Janzen 1983, Doak and Mills 1994, Wiens 1994). Traditionally, island theory failed to incorporate landscape structural characteristics and connectivity and their importance to the local and regional dynamics of populations and communities. Recent metapopulation models, however, are beginning to incorporate landscape structure (see Lande 1987, Moilanen and Hanski 1998, Heino and Hanski 2001), suggesting a possible melding of metapopulation biology and landscape ecology (see Tischendorf and Fahrig 2001). Patch area and isolation have been shown to explain most of the variability in dispersal rates in metapopulation models (Gustafson and Gardner 1996, Moilanen and Hanski 1998). Often, metapopulation models only consider if the species is present or absent in a patch (Hanski 1994). Landscape approaches emphasize how animal movements are influenced by structural characteristics and the individual animal’s perception of the landscape (With and Crist 1995). In addition to patch area, other structural characteristics are known to influence animal movements (Thomas and Harrison 1992, Hill et al. 1996, McIntyre and Wiens 1999, Summerville and Crist 2001).

2 These include patch shape (Diamond 1975, Stamps et al. 1987), patch quality (Kuussaari et al. 1996, Moilanen and Hanski 1998, Haddad 1999, Thomas et al. 2001), boundary permeability of the patch edge (Stamps et al. 1987, Ries and Debinski 2001, Schtickzelle and Baguette 2003), and the surrounding matrix habitat (Ricketts 2001, Haynes and Cronin 2003, Schooley and Wiens 2004). The behavioral responses of animals to the structural characteristics of a landscape determine movements and dispersal across the landscape (With and Crist 1995). How connected or isolated a patch is depends on how the landscape structure facilitates or impedes movement among resource patches (Taylor et al. 1993, Fahrig and Merriam 1994), as well as individual response to the patch edge and matrix (Stamps et al. 1987, Tischendorf and Fahrig 2000, Ries and Debinski 2001, Ricketts 2001, Tischendorf and Fahrig 2001, Schtickzelle and Baguette 2003, Schooley and Wiens 2004). As a result, the connectivity of a landscape will be perceived differently by different taxa and species (Wiens et al. 1997, Turner et al 2001). Mammals and songbirds are the most-studied animals in fragmentation experiments (Debinski and Holt 2000), but insects can provide an excellent model system to assess fragmentation and matrix effects due to their high species diversity, occupation of various trophic levels, varying degrees of habitat specialization, and species abundance (Golden and Crist 1999, 2000, Ricketts 2001, Haynes and Cronin 2003). Butterflies, in particular, make good model organisms because of their well-described natural histories, with some being generalists and others specialists, and their ease of classification to species in the field (Kuussaari et al. 1996, Haddad 1999). Butterflies are important in food webs and species interactions as herbivores, pollinators, and food for vertebrates and other arthropods such as spiders (Summerville and Crist 2001, Summerville et al. 2002). They have also been used extensively in population studies because of their high dispersal ability, recapture rate, and their ease of marking (Hanski et al. 1994, Kuussaari et al. 1996, Haddad 1999). My aim was to determine the spatial population structure of two butterfly species, the great-spangled fritillary (Speyeria cybele Fabricius, Lepidoptera: Nymphalidae) and the pearl crescent (Phyciodes tharos Drury, Lepidoptera: Nymphalidae), at the Lynx Prairie in the Edge of Appalachia Preserve in Adams County, OH, USA. I also

3 determined the mechanistic basis of interpatch movements. I conducted a mark-recapture study to assess the permeability of the forest-matrix to movement among remnant prairie patches for the two butterfly species. To move among prairie patches, butterflies must cross the prairie-forest boundaries that comprise a large part of these relatively small prairie patches. Therefore, I quantified edge encounter rates of butterflies by conducting behavioral observations of butterflies at the edge. I also assessed the effects of patch size, isolation, and patch quality on butterfly abundance. As fragmented habitat patches become smaller and more isolated, the abundance of species within a patch decreases and the time spent moving through the unsuitable, intervening matrix increases (Kareiva 1985, Matter 1996, Steffan-Dewenter and Tscharntke 1999). The risk of mortality and the probability of not finding a suitable habitat patch increases as a result of increased patch isolation (Bonnet et al. 1999, Schtickzelle and Baguette 2003). Matrix effects have long been thought to be important but only recently have they been quantified empirically (Åberg et al. 1995, With and Crist 1995, Roland et al. 2000, Ricketts 2001, Haynes and Cronin 2003, Schooley and Wiens 2004). The model system of butterfly movement among remnant prairie patches provides a unique opportunity to investigate how matrix permeability affects population structure and dispersal in isolated prairie patches.

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9 CHAPTER 2 Butterfly movements among isolated prairie patches: habitat edge, isolation, and forest-matrix effects

The study of animal populations in patchy environments has emphasized patch area and isolation as predictors of population persistence and dispersal, respectively, while ignoring the effects of the intervening matrix habitat and the patch boundary on population distribution. The matrix is not uniform, but rather is often a mosaic of habitat types that differ in their suitability and permeability to animal movement (Wiens 1997), and the composition of the patch edge will determine if the animal will enter the surrounding matrix (Stamps et al. 1987, Ries and Debinski 2001). Edge effects (Laurance and Yensen 1991, Wiens 1994), matrix effects (Pither and Taylor 1998, Ricketts 2001, Haynes and Cronin 2003), and an individual animal’s behavior at the patch edge and in the matrix (Tischendorf and Fahrig 2001, Schooley and Wiens 2004) may be critical to understanding the dynamics of patchily distributed populations. Thus, matrix and edge effects are significant factors that determine animal movements among suitable habitat patches. As suitable patches become more isolated, animals may spend more time in the matrix, thus raising the risk of mortality (Bonnet et al. 1999, Schtickzelle and Baguette 2003) and the probability of not finding a suitable habitat patch (Kareiva 1985, Matter 1996). Therefore, the composition of the matrix is important for predicting movement rates among habitat patches. Matrix effects are the focus of recent investigations of how the matrix influences movement patterns (see Åberg et al. 1995, Pither and Taylor 1998, Roland et al. 2000, Jonsen et al. 2001, Ricketts 2001, Haynes and Cronin 2003, Schooley and Wiens 2004). The matrix may form a barrier to movement based on vertical vegetation structure (Roland et al. 2000, Jonsen et al. 2001, Ricketts 2001, Schooley and Wiens 2004), the amount of unsuitable habitat between suitable habitat patches (Matter et al. 2004), light availability (Ricketts 2001, Ross et al. 2005), and temperature (Ricketts 2001). In some instances, however, the matrix has been shown to facilitate movement (Pither and Taylor 1998), or is even highly permeable to movement regardless of matrix type (Baguette and Neve 1994). Immigration and emigration rates among patches are the

10 most common methods for studying matrix effects (Roland et al. 2000, Jonsen et al. 2001, Ricketts 2001, Haynes and Cronin 2003), though comparisons of dispersal parameters among landscapes with differing matrix types have also been made (see Pither and Taylor 1998). The boundary permeability between the habitat edge and the matrix is also a major determinant to animal movement. If a habitat is surrounded by a “hard edge,” or a boundary that is impenetrable to movement, then animals are less likely to cross the edge into new habitats (Stamps et al. 1987). If, however, the habitat is surrounded by an edge that is permeable to movement, a “soft edge,” then animals are more likely to cross into the matrix and become established in new habitats (Stamps et al. 1987, Ricketts 2001, Berggren et al. 2002). The permeability of the matrix and patch edge differs among species based on habitat-specific preferences (Ricketts 2001) or movement behaviors at patch boundaries (Stamps et al. 1987, Ries and Debinski 2001, Schooley and Wiens 2004). In metapopulation studies, patch area and isolation are the best predictors of dispersal rates among patches (Gustafson and Gardner 1996, Moilanen and Hanski 1998), with larger patches in close proximity having a greater probability of occupancy and colonization (Thomas and Harrison 1992, Hill et al. 1996). Movement rates of individuals among isolated patches have important consequences for the spatial structure and dynamics of metapopulations. In a classic metapopulation, between 5 and 30% of individuals move among habitat patches (Levins 1969, Hanski et al. 1994, Hill et al. 1996), whereas a patchy population has higher interpatch movement rates among several patches and thus dynamics are linked among patches (Harrison 1991, Harrison and Taylor 1997, Sutcliffe et al. 1997). In addition to area and isolation, variation in patch quality may also be important (Kuussaari et al. 1996, Moilanen and Hanski 1998, Haddad 1999, Summerville and Crist 2001, Thomas et al. 2001, Summerville and Crist 2004). Landscape approaches to population dynamics have also noted that patch area influences the movement of animals through a landscape (McIntyre and Wiens 1999, Summerville and Crist 2001), but recognize that other factors influence movement patterns. Patch shape (Diamond 1975, Stamps et al. 1987), matrix and edge composition (Stamps et al. 1987, Ries and Debinski 2001, Ricketts 2001, Schtickzelle and Baguette

11 2003), and the presence or absence of movement corridors (Haddad 1999, Haddad and Baum 1999) influence whether an animal will move through a landscape. Together, these attributes of patch configuration, quality, and matrix composition determine the connectivity of the landscape. Landscape connectivity is a combination of the structural characteristics of a landscape, as well as the behavior of individuals in response to landscape structure (Taylor et al. 1993, Fahrig and Merriam 1994). Connectivity is often regarded as the inverse of isolation (Tischendorf and Fahrig 2000, Moilanen and Nieminen 2002), and the same landscape will be perceived differently by different species, so the connectivity of a landscape will vary among species (With and Crist 1995, Wiens et al. 1997, Turner et al 2001). I investigated the spatial population structure and the mechanistic basis for movement among habitat patches of two abundant butterfly species, the great-spangled fritillary (Speyeria cybele Fabricius: Lepidoptera: Nymphalidae) and the pearl crescent (Phyciodes tharos Drury: Lepidoptera: Nymphalidae), at the Lynx Prairie in the Edge of Appalachia Preserve in Adams County, OH, USA. The Edge of Appalachia Preserve is composed primarily of mixed mesophytic forest, but remnant prairie patches remain from when the Great Plains of the west extended further eastward (Braun 1928, Laycock 2003). These isolated prairies are located on soil derived from basic dolomite limestone, similar to the calcareous grasslands of Europe (van Swaay 2002, Laycock 2003). The soil composition may differ for areas surrounding the prairies which support mixed hardwood and pine forests. I conducted a mark-recapture study to assess the permeability of the forest-matrix to movement among patches for the two butterfly species. I quantified encounter rates of butterflies with the patch edges by conducting behavioral observations of butterflies along the prairie-forest boundary. I also assessed the effects of patch size, isolation, and patch quality on the abundance of butterflies within patches. I hypothesized that the permeability of the matrix and the patch edge would differ based on a species’ body size and vagility. I further hypothesized that differences in edge behaviors and permeability would influence the spatial distribution of abundance among patches. Specifically, I predicted the larger, more vagile great-spangled fritillary would cross the edge and matrix more frequently than the pearl crescent. I tested this hypothesis using data on both

12 interpatch movements and abundance to assess whether these two butterfly species show a patchy population structure with frequent interpatch movements or whether they have a classic metapopulation structure with limited interpatch movement.

Methods Study site This study was conducted May-September 2004 at the Lynx Prairie in the Edge of Appalachia Preserve in Adams County, OH, USA. The Edge of Appalachia Preserve (hereafter referred to as EOA) is jointly owned and managed by the Nature Conservancy and the Cincinnati Museum of Natural History. The EOA Preserve is unique because of its biogeographic history and present-day flora and fauna. It is situated at the intersection of three distinct ecoregions: the unglaciated Western Allegheny Plateau, the North- central Till Plain, and the Bluegrass Region, which is part of the Interior Low Plateau. This results in a mixing zone containing floral and faunal elements from the regions west, south, and north of the EOA Preserve (Braun 1928, Laycock 2003). Vegetation consisted of a diverse assemblage of mixed mesophytic species such as oaks, maples, ashes, and tulip tree in the coves and hollows. Along the sandstone ridge tops where the prairies are located, the forests are dominated by Virginia pine and several species of oaks (Strittholt and Boerner 1995). Eastern red cedar (Juniperus virginiana) is more commonly associated with limestone outcrops and frequently invades the margins of prairie patches, but it also occurs in more acidic soil. Approximately 0.2% of the Preserve was composed of remnant tallgrass prairie patches from when the Great Plains to the west extended further east during drier, cooler times (Braun 1928, Strittholt and Boerner 1995, Laycock 2003). These prairies occur along the edge of the Bluegrass Physiogeographic Region and the Western Allegheny Plateau (Strittholt and Boerner 1995). Prairies occur only on calcareous soils derived from dolomite limestone which are similar to the calcareous grasslands of Europe (Braun 1928, van Swaay 2002, Laycock 2003). The prairies are often dominated by perennial grasses such as big bluestem (Andropogon gerardii), little bluestem (Schizachyrium scoparium), and Indian grass (Sorghastrum nutans) in terms of overall cover, but over 200 species of flowering forbs also occur among patches (Laycock 2003). The 100+ remnant prairie patches at the EOA

13 Preserve are small in size (0.05-2.5 ha) and occur in clusters of 3-10 patches. These patches are also rich with butterfly diversity with 49 species recorded throughout the EOA Preserve’s prairie patches (C. Bean, unpublished data). The Lynx Prairie includes nine prairie patches that range in size from 0.1 to 0.71 ha (Figure 1). Patches are isolated by 12-440 m. The Lynx Prairie is intensively managed to prevent woody successional species from invading the prairies. Eastern red cedar is one of the first and most pervasive invaders from the forest edge. Woody plants are removed at the Lynx Prairie both through physical removal and regular prescribed burning of the prairies. Prairies 1, 2, and 3 were burned in April 2004 before the field season started. As a result of these management efforts, the prairies have a well-defined forest-edge. The forest between prairies 2 and 3 (Figure 1) has been recently cleared and now supports a mixture of prairie grasses, forbs, and remnant woody plants such as redbud and eastern red cedar.

Study species Two butterfly species were selected for intensive study to represent different life history characteristics such as body size and flight mode. These species are abundant at the EOA Preserve and throughout much of their geographic range (Opler and Malikul 1998). The great spangled fritillary (Speyeria cybele Fabricius, Lepidoptera: Nymphalidae) is a univoltine, highly vagile butterfly which uses various species of prairie and old field violets (Viola spp.) as host plants and milkweeds, thistles, and ironweed for adult nectar plants (Opler and Krizek 1984). It is a habitat generalist because it inhabits open habitats and the edges between prairies and forests, but it is a host-plant specialist because it uses a single genus of host plants (Swengel 1996). There is no sexual dimorphism in this species. Although dispersal distances for this species have not been documented, it is considered a highly vagile species because the congener S. mormonia has been documented to move up to 1.8 km (Boggs 1987, Ricketts 2001). It also has a large mean wing length (38-50 mm), which is a commonly used measure of vagility even though it has not been adequately tested (see Van Dyke and Matthysen 1999, Ricketts 2001).

14 The pearl crescent (Phyciodes tharos Drury, Lepidoptera: Nymphalidae) is a trivoltine butterfly in southern Ohio which uses various species of asters (Aster spp.) as host plants and asters, milkweeds, thistles, and sunflowers as nectaring sources (Opler and Krizek 1984, Swengel 1996). As with the great-spangled fritillary, it is a habitat generalist because it inhabits open and edge habitats, but it is a host-plant specialist because it uses a single genus of host plants (Swengel 1996). There is also no sexual dimorphism in this species. Again, using mean wing length as a correlate of vagility, this species was considered to be a low vagility species with a mean wing length of 15.5-20.5 mm.

Abundance measures Abundance counts were conducted once a month from May-September 2004. Pollard walks were used to measure abundance in eight of the nine Lynx Prairie patches to estimate butterfly abundance within the patches (Figure 1). This transect-based method to estimate the abundance of butterflies has been shown to be reliable for determining the relative abundance of species (Pollard 1977, Pollard and Yates 1993). Patch 4 was not sampled due to time constraints (Figure 1). Belt transects were stratified according to whether they were edge or interior habitat so that the same amount of interior and edge habitat was sampled. Equal numbers of 20 x 5 m segments were located within interior and edge habitats, and the total number of transect segments were scaled logarithmically to patch area so that the smallest patch (0.1 ha) had 4 segments and the largest patch (0.71 ha) had 8 segments. All butterflies within 2.5 m of the transect line were recorded while slowly walking each transect segment. Observations were conducted only when the temperature was >21ºC, sky condition was <40% cloud cover, and wind speed was <16 km/hr, as these are the optimal conditions for butterfly observation (Pollard 1977, Pollard and Yates 1993).

Patch quality Patch quality was assessed by determining the density of suitable host plants and nectaring sources within each patch. Vegetation sampling was conducted for the larval host plants of the two target butterfly species along the butterfly transects. Plant cover

15 was recorded by species within 0.25-m2 quadrats placed along the transect lines. Four quadrat samples were taken randomly within each 20 x 5 m transect segment. Vegetation was only sampled once during the field season. Each month, the total number of plants with flowers was recorded within 2.5 m to either side of the transect line. All plants with flowers were recorded since butterflies will often use a variety of nectar sources. The number of plants with flowers is a coarse estimate of nectar availability, but it has often been used instead of a more precise measure such as measuring the amount of nectar available at each flower (Kuussaari et al. 1996, Ricketts 2001). Patch-quality assessments of host-plant and flower availability were conducted in all patches except patch 4 due to time constraints.

Butterfly movements Butterflies were individually marked with a marking pen on the underside of the hind wing during systematic walks through the patches from June 2, 2004 to August 21, 2004. Patches were only sampled once per week due to a request from the Nature Conservancy to limit the human impact in the prairies. Butterflies were released at the capture point. If a butterfly was recaptured more than once per day, only the first capture was recorded if the recapture occurred in the same patch where it was marked. If a butterfly was recaptured in a different patch from where it was originally marked on the same day, then it was considered a movement and recorded as a recapture.

Forest-matrix structure Belt transects were used to measure the vegetation structure and composition of the forest matrix between patches (Figure 2). Vegetation was only sampled once. Transects were placed between adjacent pairs of prairie patches if the patches were separated by ≤110 m. Of the 10 belt transects between adjacent patches, the shortest transect was 12.2 m in length with the longest 110.0 m in length. Transects were 10 m wide and were divided into 10 x 10 m blocks. Two 5 x 5 m quadrats were randomly located within each 10 x 10 m block to sample tree saplings and shrubs. Shrubs and saplings were defined as woody species that were less than 10 cm dbh. Shrub and sapling height and the estimated percent cover within each 5 x 5 m block were recorded.

16 Shrubs and saplings were identified to species. Herbaceous vegetation was sampled using 2, 1 x 1 m quadrats randomly placed within each 10 x 10 m block. The percent cover within each 1 x 1 m quadrat was recorded. Since vegetation was only sampled once, it was not possible to identify all herbaceous plants to species because many of the plants had already senesced. Trees ≥10 cm dbh were measured along the entire transect and identified to species. Canopy cover was estimated by using a GRS densitometer. Readings were taken every 1 m within each 5 x 5 m quadrat. A total of 45-49 densitometer readings were taken for each 10 x 10 m quadrat.

Edge behavior Edge effects on butterfly behavior were determined by recording boundary- crossing behaviors of individuals in randomly chosen 10-m sections along the prairie- forest boundary. The number of sections chosen in a patch was determined by the total edge length: approximately 10% of the edge boundary was sampled. The location of the segment(s) along the patch edge was randomized for each observation period. Butterfly behavior was tallied according to whether (1) the butterfly approached the forest edge and crossed it; (2) the butterfly approached the edge but stayed in the patch; or (3) a butterfly immigrated into the patch from the forest-matrix. Butterflies that crossed from the prairie to the forest were observed until they had flown 10 m into the forest, at which point they were assumed to have left the patch. Edge-behavior observations were conducted once per week, and each edge segment was observed for 15 min.

Statistical analyses Overall population sizes in the Lynx Prairie were estimated using CAPTURE routines within program MARK (White and Burnham 1999), which assumes a closed population and uses generalized Lincoln-Peterson estimators. Capture probabilities are assumed constant, or allowed to vary among capture occasions or individual animals. The best-fitting models for mark-recapture data on both species were obtained using a Darroch time-dependent model that assumes all individuals have the same probability of capture on any occasion, but the probability of capture changes from one trapping

17 occasion to the next. The standard error of population estimates are based on a Poisson distribution of abundances. Abundance counts were tallied separately along transects from the patch interior and the forest edge. Using these counts, I tested for the main effect of whether a particular species was more abundant along the patch edge or in the patch interior and determined if there was an interaction between species (great-spangled fritillary vs. pearl crescent) and location (edge vs. interior) using a two-way test of independence. Mark-recapture data were summed according to whether butterflies were recaptured within their initial marking patch or in a different (migrant) patch. I determined if there was an interaction between species (great-spangled fritillary vs. pearl crescent) and whether it was recaptured in its initial patch or whether it was recaptured in a different patch (initial vs. migrant). This was also done using a two-way test of independence. I then modeled the probability of a butterfly moving as a function of distance traveled using an inverse power function after Ricketts (2001): a Tjk = z (Djk )

where Tjk is the number of butterflies emigrating from patch k along route j, Djk is the distance traveled, and a and z are fitted constants. I chose to use an inverse power function because inverse power functions have been shown to fit long-distance movement data better than negative exponential functions (Hill et al. 1996, Baguette 2003). Edge behavior observations for the great-spangled fritillary were tallied according to whether a butterfly was observed emigrating from a patch, immigrating into a patch, or approached the edge but stayed within the patch. I determined if there was a difference in the behavior of the great-spangled fritillary at the patch edge using a G-test for goodness of fit. “Immigration” and “emigration” were then combined to determine the probability of a great-spangled fritillary crossing the patch edge as opposed to approaching the edge and staying within the patch. This was also done using a G-test for goodness of fit. Insufficient observations of the pearl crescent at the patch edge did not permit for an analysis to be conducted.

18 To determine how the species composition of the forest-matrix influenced the frequency of interpatch movements, I performed a cluster analysis on the species composition to identify similarities in forest species composition among belt transects. Importance values were used in the cluster analysis and were calculated for tree species along the belt transects using the sum of relative frequency, relative density, and relative dominance (Mueller-Dombois and Ellenberg 1974, Appendix I). Relative frequency is the frequency of a species along a transect relative to the sum frequency of all species, relative density is the number of individuals of a species relative to the total number of individuals, and relative dominance is the dominance of a species relative to the dominance of all species (Mueller-Dombois and Ellenberg 1974). Dominance was defined as the product of mean basal area per species and the number of individuals per transect (Mueller-Dombois and Ellenberg 1974). The 9 tree species with the highest importance values that occurred in most of the belt transects were used in the cluster analysis. Shrubs and saplings were not included in the cluster analysis as there was no difference in the total cover of shrubs and saplings among transects of differing species composition (but see Appendix II for complete species list and importance values). The linkage method used was Group Average and the distance measure was Euclidean. PC- ORD (McCune and Mefford 1999) was used to conduct the cluster analysis. Herbaceous plant abundance was analyzed using a one-way ANOVA to detect for differences in plant abundance between forest-matrix types. Logistic regression was used to determine if there were patch characteristics that influence the probability of a butterfly staying within a patch or moving to a different patch. Patch area, host-plant density, flower density, and patch isolation were used as predictor variables to determine the probability of a butterfly moving among prairie patches. Model variables were screened for collinearity prior to analysis. Variables were assumed to be correlated when the Pearson correlation coefficient was ≥0.40. Patch isolation has often been measured as the probability of movement between suitable habitat patches, and connectivity has been regarded as the inverse of isolation (Taylor et al. 1993, Goodwin and Fahrig 2002, Moilanen and Nieminen 2002). Two different measures of isolation were used. The nearest neighbor metric is the simple linear distance between two patches:

19 Ii = dNN

where Ii is the isolation of patch i and dNN is the distance of the nearest neighboring patch. The incidence function model measure of connectivity takes into account distances to all possible habitat patches: S = exp(−αd ) i ∑ j ij where Si is the connectivity of patch i, d is the distance between patches i and j, and α is the inverse of the mean observed movement distances which scales the distance separating patches to movement (see Moilanen and Nieminen 2002). In my study, α= 0.0098. Models were estimated with all combinations of the four predictor variables. The best fitting model was selected based on the corrected Akaike’s Information Criterion value (AICc). The log-likelihood of each model was used to determine the AICc. The model with the lowest AICc value was determined to be the best-fitting model (Burnham and Anderson 2002). Poisson regressions were conducted to determine how butterfly abundance varied among patches. The models were estimated for each species using all combinations of the variables patch area, patch isolation, host-plant density, and flower density. Model variables were screened for collinearity before analysis. Both of the isolation measures listed above were used in separate runs of the model. The AICc was again determined for each model using the log-likelihood to determine the best-fitting model. Studies of interpatch movements often do not assess whether observed movement distances differ from the interpatch distances. Randomization tests were conducted to determine if the observed movement distances of recaptured butterflies were a random sample of distances between patches. A sample of the total number of movement distances observed for each butterfly species was drawn at random with replacement for 10,000 randomizations to determine if the randomized mean distances were less than the observed mean distances. Two types of randomizations were conducted: (1) all observed movement distances regardless of matrix type and (2) observed movement distances across forest-matrix composed of eastern red cedar and mesic hardwoods. When matrix type was not considered 18 interpatch distance samples were drawn for each randomization for the great-spangled fritillary and 6 for the pearl crescent. When only

20 forest-matrix composed of eastern red cedar and mesic hardwoods was considered, 7 random samples were drawn for the great-spangled fritillary and 5 for the pearl crescent. The p-values generated were from a one-tailed distribution (α= 0.025). There were insufficient recaptures across forest-matrix composed predominantly of Virginia pine and across forest-matrix composed of tulip poplar and redbud to permit analysis.

Results Abundance counts A total of 77 pearl crescent butterflies and 76 great-spangled fritillary butterflies were observed during Pollard walks conducted over five months. Of the 77 pearl crescent butterflies, 52 were observed within the interior of the patch while 25 were observed along the edge of a patch (Figure 3). Of the 76 great-spangled fritillary butterflies observed, 31 were observed within the interior of a patch, while 45 were observed along the edge of a patch. The abundance of the pearl crescent was significantly greater within the interior of a patch than at its edge (G=9.61, df =1, p=0.0019), while there was no difference for the great-spangled fritillary (G= 2.59, df=1, p=0.1100). The two-way interaction of abundance by species and habitat location showed that the two species differed in their frequency of occurrence between interior and edge locations within a patch (G=10.08, df=1, p=0.0015).

Edge Behavior Twelve of 52 great-spangled fritillary butterflies were observed immigrating into a patch. Ten great-spangled fritillary butterflies emigrated from a patch, and 30 great- spangled fritillary butterflies approached the edge but stayed within a patch (G= 82.96, df =1, p<0.0001). When immigrants and emigrants were combined and compared with those that approached the edge but stayed within a patch, there was no difference in whether butterflies moved or stayed within a patch (G= 1.2243, df =1, p = 0.2700). There were not enough observations of edge-behavior for the pearl crescent to conduct a statistical analysis. Only one of 14 pearl crescent butterflies was observed immigrating into a patch, 4 pearl crescent butterflies were observed emigrating from a patch, and 9 pearl crescent butterflies approached the edge but stayed in the patch.

21 Butterfly movements and population size A total of 136 great-spangled fritillary butterflies were marked during the study. Of the 29 recaptures, 18 were recaptured outside of their initial marking patch with one butterfly being recaptured twice outside of its initial patch (Figure 4, Figure 5a). There was no difference in whether the great-spangled fritillary was captured within its initial patch or in a different patch (G=2.79, df =1, p =0.0900). A total of 159 pearl crescent butterflies were marked. Of the 20 recaptures, 14 were recaptured within their initial patch (Figure 4, Figure 5b). There was no difference in whether the pearl crescent was captured inside of its initial patch or in a different patch (G= 3.22, df =1, p=0.0700). A two-way interaction showed that the great-spangled fritillary was recaptured significantly more outside of its initial patch and the pearl crescent more within its initial patch (G= 4.74, df =1, p= 0.0300). Program CAPTURE yielded estimates of population size of 392 (SE = 66.2) for the great-spangled fritillary and 688 (SE= 146.3) for the pearl crescent (Figure 6). Thus, approximately one-third of the estimated population of the great-spangled fritillary was captured and marked during the course of the study, whereas less than one-fourth of the estimated population of the pearl crescent was marked.

Matrix permeability The 9 tree species used in the cluster analysis were Virginia pine (Pinus virginiana), eastern red cedar (Juniperus virginiana), redbud (Cercis canadensis), green ash (Fraxinus pennsylvanica), tulip poplar (Liriodendron tulipifera), Chinquapin oak (Quercus muehlenbergii), white oak (Q. alba), red oak (Q. rubra), and sugar maple (Acer saccharum) (Appendix I). The cluster analysis revealed that there were three distinct types of forest-matrix between patches (Figure 7). One type of forest-matrix was composed primarily of Virginia pine with eastern red cedar and mesic hardwoods present in smaller numbers (hereafter referred to as VP forest-matrix). This type of forest-matrix occurred between patches 2 and 4, 7 and 8, 6 and 8, 6 and 7, and 3 and 4 (Figure 7). The second type of forest-matrix was composed primarily of eastern red cedar, Virginia pine, and mesic hardwoods such as green ash, Chinquapin oak, redbud, and tulip poplar (hereafter referred to as ERC forest-matrix after Strittholt and Boerner’s (1995)

22 description of this type of forest; see Appendix I for complete species list and importance values). This type of forest-matrix occurred between patches 1 and 2, 3 and 5, 6 and 9, and 8 and 9. The transect between patches 2 and 3 has been recently cleared of most trees and woody plants to permit expansion of the prairies. It was composed only of tulip poplar and redbud (hereafter referred to as TR forest-matrix). Virginia pine was the most important tree species, having the highest relative dominance, relative density, and relative frequency, on all VP forest-matrix transects (see Appendix I). After Virginia pine, tulip poplar, redbud, eastern red cedar, and other mesic hardwoods such as Chinquapin oak, white oak, and sugar maple were the next most important species along VP forest-matrix transects, but their importance values were far less compared to Virginia pine. The transects between patches 2-4 and 6-7 contained the same species composition with similar importance values leading to their pairing on the dendrogram (Figure 7). The transect between patches 6 and 8 was the most diverse transect containing mesic hardwood species such as sugar maple, white oak, and shagbark hickory (Carya ovata), yet Virginia pine was still the most important species (Figure 7). For the ERC forest-matrix transects, there was not one important species but several species that were important across transects. Tulip poplar, eastern red cedar, green ash, Chinquapin oak, and Virginia pine all displayed high importance values among the four transects (see Appendix I). Tulip poplar was the more important species across the TR forest-matrix with redbud having a much lower importance value (Appendix I). There was no difference in host-plant and nectaring source abundance among the three forest-matrix types (F =1.96, p =0.2107). Seven movements were observed across ERC forest-matrix, 9 movements were observed across VP forest-matrix, and 2 movements were observed across TR forest- matrix for the great-spangled fritillary. For the pearl crescent, 5 movements were observed across ERC forest-matrix, and 1 movement was observed across VP forest- matrix. No movements were observed across TR forest-matrix for the pearl crescent. Since the five shortest transects were composed of VP forest-matrix and four of the five longest transects were composed of ERC forest-matrix, randomization tests were conducted to determine if observed movement distances differed by matrix type. For movements across both matrix types for the great-spangled fritillary, the observed mean

23 was 101.7 m (SD = 95.0) and the mean of the interpatch distances from the null distribution was 196.5 m (SD = 30.3). When forest-matrix type was not considered, the great-spangled fritillary butterflies moved shorter distances than expected by the null distribution of inter-patch distances (p=0.0001, Figure 8a). When only ERC forest- matrix was considered for the great-spangled fritillary, the observed mean was 207.0 m (SD = 62.3) and the mean of interpatch distances from the null distribution was 228.3 m (SD= 43.3). There was no significant difference between the observed mean distance moved and the null distribution of inter-patch distances (p=0.3266, Figure 8b). This indicates that ERC forest-matrix was more permeable to movement for the great- spangled fritillary. For the pearl crescent, the observed mean across both matrix types was 103.9 m (SD = 91.5), and the mean of interpatch distances from the null distribution was 195.6 m (SD = 51.6). Inter-patch movements were shorter than the null distribution including both matrix types (p=0.0273, Figure 9a). When movements across only ERC forest- matrix for the pearl crescent were considered, the observed mean was 119.0 m (SD = 93.6), and the mean of interpatch distances from the null distribution was 229.0 m (SD = 52.2). Movements were also shorter than expected by the null distribution of inter-patch distances (p =0.0082, Figure 9b). This indicated that the pearl crescent was more isolated within patches regardless of matrix type. There were not enough interpatch movements through the VP forest-matrix or TR forest-matrix to conduct a randomization for either species.

Probability of Movement Using Ricketts’ (2001) inverse power function, I found that as distance increases, the probability of great-spangled fritillary movement decreases (Figure 10). At the shortest distance where a butterfly was observed moving, 12.2 m, the probability of a butterfly moving was approximately 14%. For the longest observed distance moved, 300 m, the probability of movement was approximately 6%. The probability does not go to zero, however, because fritillary species in the genus Speyeria can move several kilometers in their lifetime (Boggs 1987, Ricketts 2001). This function was only

24 modeled using the great-spangled fritillary because insufficient between-patch recaptures of the pearl crescent were obtained. Logistic regression analyses indicated that there was no habitat variable which accurately predicted the probability of emigration for the great-spangled fritillary. The model with the lowest AICc value for both the nearest-neighbor and scaled-distance

metrics contained only the isolation between patches (AICcNN= 29.23, p=0.1505,

AICcSD=29.23, p=0.1212). Of the two isolation metrics used, neither the nearest- neighbor nor the scaled-distance measure significantly predicted whether a great- spangled fritillary would move from its current patch. For the pearl crescent, a logistic regression model with only host-plant density predicted that movements were more likely from its initial patch when host-plant density was low (AICc = 23.33, p=0.0469, Figure 11). The power of the test was >0.99; therefore, the test had sufficient power.

Species Abundance The abundance of the two species was modeled using Poisson regression analysis to determine what variables best predicted differences in abundance among patches. For the great-spangled fritillary, abundance was best predicted by the model with only isolation included. The nearest-neighbor metric was a better predictor than the scaled- distance isolation metric. This suggested that as a patch becomes more isolated, great- spangled fritillary abundance decreases (AICc= -198.47, p=0.0025, Figure 12). The power of the test was 0.91; therefore, the test had sufficient power. For the pearl crescent, the best model included only the flower density. Abundance increased with greater flower density (AICc= -203.12, p=0.0012, Figure 13). The power of this test was >0.99, and therefore, the test had sufficient power. A possible competing model included patch area and flower density with increasing area and flower density leading to greater abundance (AICc= -202.75, p=0.0549, power = .33).

Spatial population structure The great-spangled fritillary displayed characteristics of a patchy population rather than a classic metapopulation or an island-mainland metapopulation. Two-thirds of recaptured great-spangled fritillary butterflies moved among patches (Figure 4, Figure

25 5a), which is more movement than in a classic metapopulation model (5-30% movement; see Hanski et al. 1994, Hill et al. 1996). The great-spangled fritillary also moved more frequently between patches that were closer together (Figure 10), and between patches separated by DL-matrix (Figure 8). There was also not a patch that served as a “mainland” or a single, large source of colonists, so it is unlikely that each individual patch supports a local population. A high rate of movements between patches suggests that butterfly population dynamics were likely linked among patches, although there is some isolation as distance increases based on the inverse-power distance function and the effects of increased isolation on abundance (Figure 10, Figure 12). A great-spangled fritillary was more isolated within a patch as the distance between patches increased, and abundance increased as isolation decreased. The pearl crescent was more consistent with a classic metapopulation structure since only 30% of recaptured pearl crescents moved between patches. (Figure 4, Figure 5b). This is the upper end of a classic metapopulation structure (Hanski et al. 1994), so dynamics of this metapopulation may not be sufficiently linked between patches as in a patchy population. Again for this species, there was no “mainland’ to serve as an infinite source of colonists. The few between-patch movements and the rare occurrence of the pearl crescent at the patch edge indicate that it displays characteristics of a classic metapopulation.

Discussion Matrix permeability My hypothesis that the matrix would differ in its permeability to movement based on a species’ body size and vagility was partially supported. In my study, there were three types of forest-matrix present, and each differed in its resistance to movement based on the species. The matrix types were classified based on a quantification of tree species characteristics, which has not been done in previous studies on matrix effects (see Pither and Taylor 1998, Roland et al. 2000, Jonsen et al. 2001, Ricketts 2001, Schooley and Wiens 2004). The great-spangled fritillary was able to move among prairie patches more easily when patches were separated by ERC forest-matrix rather than when patches were

26 separated by VP forest-matrix. The species composition of the forest-matrix influenced butterfly movement in addition to body size and vagility. The matrix has been shown to form a barrier to movement based on vegetation structure, light availability, temperature, and the amount of matrix separating habitat patches (Roland et al. 2000, Ricketts 2001, Haynes and Cronin 2003). The matrix has also been shown to facilitate movement when it is less structurally complex (i.e., pasture vs. forest, Pither and Taylor 1998). The matrix has also been shown to not impede movement at all (Baguette and Neve 1994). In my study, the forest-matrix types differed in their resistance based on the butterfly species and the species composition of the forest-matrix. Despite the fact that the five shortest transects separating patches were all on VP forest-matrix, the matrix permeability for the great-spangled fritillary was less for the VP forest-matrix transects than for ERC forest-matrix transects. Patch isolation did not have a significant effect on the probability of a great-spangled fritillary moving between patches regardless of the matrix type or the type of isolation metric used (nearest- neighbor or the scaled-distance metric); therefore, the structural characteristics of the matrix impeded the great-spangled fritillary as has been observed in other species (Kareiva 1985, Jonsen et al. 2001, Ricketts 2001). Isolation did, however, affect movement distances of the great-spangled fritillary (Gustafson and Gardner 1996, Hill et al. 1996, Roland et al. 2000). The great-spangled fritillary had a greater probability of moving between patches that were separated by shorter distances (Figure 10) with the most movement coming between patches 3 and 4 (28.6 m), which were separated by VP forest-matrix (Figure 5a). Other species of fritillary in the genus Speyeria have been documented as traveling almost 2 km within their lifetime, which was greater than the range of distances between patches in my study (Boggs 1987, Ricketts 2001). The tail of the inverse power function leveled off at about 6% probability of movement at a distance of 300 m, which was the maximum observed between-patch movement distance, although only one butterfly was observed moving this distance (Figure 10). This suggests that some great-spangled fritillary butterflies likely emigrated from the Lynx Prairie cluster to other patches further away. Randomization results for the smaller, less vagile pearl crescent indicated that it is more isolated within patches regardless of matrix type. This was likely a result of not

27 only the structural characteristics of the matrix, but also the pearl crescent’s behavior. Since the pearl crescent was rarely seen at the patch edge both in abundance counts and during behavioral observations of edges (Figure 3), this species appears to avoid the edge and may only emigrate when host-plants become limited. Between-patch movements of the congener Phyciodes campestris were impeded by multiple types of forest-matrix (Ricketts 2001) which parallels what I observed for the pearl crescent. The observed difference in permeability between different types of forest-matrix for the great-spangled fritillary and the pearl crescent may be an artifact of a small number of recaptures, especially for the pearl crescent. The small recapture sample sizes of both the great-spangled fritillary and the pearl crescent means that the tests may not have sufficient power to determine if a difference between observed and expected movement distances exists. Power was determined for the logistic regression and Poisson regression analyses. For these analyses, the power >0.90, and therefore these tests had sufficient power to detect if a Type II error was being committed. The observed between-patch movements may be due to a patch-quality effect that is confounded with the observed matrix effect (Haynes and Cronin 2004). However, I found no significant patch-quality effects on the movement probability for the great- spangled fritillary, and the observed between-patch movement distances differed from actual interpatch distances according to matrix type with ERC forest-matrix more permeable to movement than VP forest-matrix. Therefore, differences in great-spangled fritillary movements were in response to matrix structure rather than patch quality. For the pearl crescent, it was more difficult to evaluate whether the few between- patch recaptures were due to a matrix effect or a patch-quality effect. The pearl crescent only emigrated when host-plant density in the initial patch was low. High host-plant density leads to high residence times in a patch, and thus reduces the number of interpatch movements (Kuussaari et al. 1996, Moilanen and Hanski 1998, Matter and Roland 2002). However, the structure of the matrix also decreased the number of interpatch movements (Roland et al. 2000, Ricketts 2001, Haynes and Cronin 2003). Therefore, it is not possible to determine if the pearl crescent is responding to patch quality, the matrix, or both. Patch quality would need to be kept constant, or movement patterns within the forest-matrix would need to be recorded to determine if a patch-

28 quality effect or a matrix effect is present for the pearl crescent (Haynes and Cronin 2004).

Abundance Patch abundance of the great-spangled fritillary was best modeled using patch isolation (Figure 12). The nearest-neighbor isolation metric was a slightly better predictor of abundance than was the scaled-distance metric, contrary to what Moilanen and Nieminen (2002) found for colonization probabilities of butterflies. Both metapopulation and island biogeography theory predict patches closer together have a higher probability of being colonized (MacArthur and Wilson 1967, Thomas and Harrison 1992, Hill et al. 1996, Roland et al. 2000). In a patchy population structure, the dynamics among patches are linked, and thus the system behaves as a single, large population (Harrison 1991, Harrison and Taylor 1997). As a result of high interpatch movement rates, isolation among the patches is decreased and the abundance in the local patches increases. Butterfly abundance has often been predicted by patch quality which considers both host-plant (Hanski et al. 1994, Haddad 1999, Thomas et al. 2001, Matter and Roland 2002) and flower availability (Feber et al. 1996, Kuussaari et al. 1996, Brommer and Fred 1999). For the pearl crescent, which is more isolated within patches, abundance is determined by the availability of nectaring sources (Figure 13). As the density of nectar sources increases, so does the abundance of the pearl crescent.

Edge behavior In addition to the permeability of the matrix, an animal’s response at the patch boundary will determine if it will move among patches (Taylor et al. 1993, Schooley and Wiens 2004). The probability of a great-spangled fritillary emigrating or immigrating from a patch was 0.42 compared to 0.47 for the congeners Speyeria atlantis and S. mormonia (Ricketts 2001). Since many individuals were observed to move through the edge and interpatch movements were common, the patch-forest edges can be considered “soft edges” to the great-spangled fritillary (Stamps et al. 1987, Ries and Debinski 2001).

29 The pearl crescent was rarely observed approaching the patch edge during edge- observation periods. Of the pearl crescent butterflies observed at the patch edge, the probability of a butterfly emigrating was 0.43 (5 of 14 butterflies) compared to 0.42 for the related species Phyciodes campestris and Chlosyne palla (Ricketts 2001). Despite the similar emigration probabilities between the species, the small sample size of the pearl crescent indicates that it rarely approached the patch edge, and therefore the prairie-forest boundary acted as a “hard edge” to the pearl crescent (Stamps et al. 1987, Ries and Debinski 2001). This further suggests that the pearl crescent is more isolated within a patch compared to the great-spangled fritillary. There are two possible reasons why the pearl crescent was observed infrequently at the patch edge. The interior of a patch may be of higher quality (i.e., more host plants and nectaring sources) than the edge. Also, the structure of the patch edge or matrix may deter the pearl crescent from approaching and crossing the edge (Stamps et al. 1987, Roland et al. 2000, Ricketts 2001, Ries and Debinski 2001, Haynes and Cronin 2003). Since the pearl crescent flies <1.5 m from the ground, it is not able to fly over the edge and matrix vegetation when it approaches an edge that it cannot fly through (Summerville et al. 2002).

Spatial population structure My hypothesis that body size and vagility would influence the number of interpatch movements was supported. The larger, more vagile great-spangled fritillary moved among patches more often than the smaller, less vagile pearl crescent due to its response to the patch edge and the forest-matrix. The frequency of interpatch movements and the butterflies’ responses to the patch edges and the forest-matrix led me to conclude that the great-spangled fritillary exhibits characteristics of a patchy population while the pearl crescent displays characteristics of a classic metapopulation. Studies assessing the spatial structure of populations often focus solely on the frequency of interpatch movements, though the mechanistic influences of interpatch movement such as the behavioral response of animals to the patch edge and the heterogeneity of the intervening matrix habitat are important (Thomas and Harrison 1992, Hanski et al. 1994, Hill et al. 1996, Sutcliffe et al. 1997, Wiens 1997, Mousson et al. 1999, Baguette et al. 2000, Wahlberg et al. 2002). The composition of the patch edge is

30 known to influence whether an animal will cross the edge, but quantification of behaviors at the patch edge has not been applied to metapopulation dynamics (Stamps et al. 1987, Ricketts 2001, Ries and Debinski 2001). Only one metapopulation study I am aware of examined whether the type of edge affects emigration rates, but the actual behaviors at the patch edge were not quantified (Kuussaari et al. 1996). The intervening matrix habitat is assumed to be uniform in metapopulation studies, but landscape studies have shown that the matrix is important in determining movement among patches (Levins 1969, Hanski et al. 1994, Hill et al. 1996, Pither and Taylor 1998, Ricketts 2001, Haynes and Cronin 2003). I have shown that different matrix types differ in their resistance to butterfly movement by quantifying the species composition of the forest-matrix. Species composition of the matrix has not been used to classify matrix habitat before, but an analysis of species composition can demonstrate why movements occur more frequently across a particular matrix type (see Pither and Taylor 1998, Roland et al. 2000, Ricketts 2001). In my study, the great-spangled fritillary moved more frequently across ERC forest-matrix because the forest was less dense than VP forest-matrix. The pearl crescent moved infrequently among patches regardless of matrix type. A patchy population is one where there are high interpach movement rates, offspring arise in a different patch from their parents, and no one patch is important for regional population persistence (Harrison 1991, Harrison and Taylor 1997). Other fritillary species have been described as spending ≈80% of their time in their natal patch and forming breeding populations within that patch even though there is considerable movement among patches (Hanski et al. 1994). Although I did not measure ovipositing behavior and frequency, the great-spangled fritillary exhibited a patchy population structure as a result of the high interpatch movement rates, with 66% of recaptured butterflies moving among patches (Figures 4 and 5a), and its willingness to cross the prairie-forest boundary. This supported my hypothesis that the great-spangled fritillary would move more among patches due to its behavior at the prairie-forest boundary and permeability of the forest-matrix. The dynamics among patches are linked, lessening the likelihood of local extinction, and the distances among patches are not important to regional population persistence (Harrison 1991, Harrison and Taylor 1997).

31 It has been noted that other species of fritillaries in the genus Speyeria can move almost 2 km (Boggs 1987, Ricketts 2001), so it was not surprising that the great-spangled fritillary moved throughout the Lynx Prairie. Due to time constraints, I only studied one cluster of prairie patches at the EOA Preserve. The mean distance among prairie clusters at the EOA Preserve is 2.93 km. Therefore, clusters of patches likely comprise the “local” populations and groups of clusters form a “regional” metapopulation for the great-spangled fritillary. The pearl crescent, in contrast, was found to be more isolated within the patch where it was first marked. The pearl crescent was rarely recaptured in a different patch from where it was first marked (Figures 4 and 5b). This complements my abundance counts and edge-behavior observations where the pearl crescent was observed significantly more within the interior of a patch and rarely approached the patch edge. It was interesting, however, that one pearl crescent was observed moving 221 m between patches. This suggests that they have the ability to move long distances, but do so infrequently, possibly due to a hard edge surrounding the habitat patches, the structure of the matrix, or the present patch is of high quality. Since the majority of recaptures for the pearl crescent were within the patch where they were first marked (14 of 20 butterflies), this species displays attributes of a classic Levins’ (1969) metapopulation with each patch operating as a separate, local population and each local population being subject to extinction and recolonization from other local populations. This again supported my hypothesis that the pearl crescent would move infrequently among patches due to its behavior at the patch edge and permeability of the forest-matrix.

Conservation implications The spatial structure of populations has implications for the management of threatened and isolated species. If the abundant great-spangled fritillary responds similarly to fragmentation as the threatened congener the regal fritillary (Speyeria idalia Drury: Lepidoptera: Nymphalidae), it may be possible to use movement data on the great-spangled fritillary to better manage the regal fritillary. The regal fritillary is both a habitat and host-plant specialist: it is restricted to tall-grass prairies and feeds on only two

32 Viola species (Swengel 1996, Ries and Debinski 2001). It is, however, highly vagile, which is unusual for specialists (Swengel 1996). In order for the great-spangled fritillary to be used as a substitute species, however, the level of fragmentation that allows both populations to persist must be determined as well as the key trait or traits in both the substitute and target species that affect this fragmentation threshold (Caro et al. 2005). Several years of study on both great-spangled fritillary and regal fritillary populations would be needed to identify the traits affecting their response to fragmentation and to determine if their responses to fragmentation are similar enough to allow the great- spangled fritillary to be considered an adequate substitute species.

Conclusions The spatial population structure of the great-spangled fritillary and the pearl crescent differed based on species behavior and landscape structural characteristics. The population structure of a species was determined by the number of interpatch movements, as has been done in other studies, but the mechanistic basis for movement was also determined, which has not been done in other spatial structure studies. Patch isolation, patch quality, permeability of the forest-matrix, the species’ responses to the prairie- forest boundary, and the edge and interior abundance of butterflies within a patch all contribute to the species’ spatial structure. The highly vagile great-spangled fritillary displayed a patchy population structure as a result of high interpatch movement rates and frequent crossing of the prairie-forest boundary. In contrast, few between-patch movements for the pearl crescent were observed, and it was observed infrequently at the prairie-forest boundary, therefore displaying more of a classic metapopulation structure. To increase connectivity between patches for species isolated by the forest- matrix, the EOA Preserve is clearing some of the forest-matrix between prairie patches. This will help less vagile species such as the pearl crescent to move among prairie patches and recolonize patches that may have undergone local extinction.

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39 CHAPTER 3 SYNTHESIS

Most metapopulation studies rely only on the frequency of interpatch movements and the area and isolation of a habitat patch to assess spatial structure (Harrison 1991, Thomas and Harrison 1992, Hanski et al. 1994, Hill et al. 1996, Sutcliffe et al. 1997, Mousson et al. 1999, Baguette et al. 2000, Leisnham and Jamieson 2002, Wahlberg et al. 2002). Other studies use census data as to whether habitat patches are occupied or vacant (Smith and Gilpin 1997, Biedermann 2000, Israely et al. 2005). The mechanistic basis of movements has not been applied to metapopulation studies, but the underlying reasons for movement are important to understanding population dynamics (Wiens 1997). The edge and interior abundance of organisms within a habitat patch, their behavior at the patch boundary, and landscape structural characteristics such as patch quality and the permeability of the matrix habitat all influence spatial population structure. Recent models and field studies have begun to incorporate patch quality and landscape structure suggesting a possible melding of landscape ecology and metapopulation dynamics (Lande 1987, Moilanen and Hanski 1998, Brommer and Fred 1999, Heino and Hanski 2001, Thomas et al. 2001, Tischendorf and Fahrig 2001, Őckinger 2006). My study is the first to use interpatch movements rates, abundance, edge-interior location within a patch, landscape structural characteristics such as the permeability of the matrix habitat and patch quality, and behavior at the patch boundary to assess the spatial population structure of two butterfly species. Together, the recapture data, butterfly observations at the patch edge, and observations during abundance counts suggest that the great-spangled fritillary exhibited a patchy population structure while the pearl crescent exhibited more of a classic metapopulation structure (Table 1). An animal must first approach the patch edge to emigrate to other patches. The pearl crescent was observed infequently at the prairie-forest boundary while the great- spangled fritillary was observed both at the patch edge and interior. Upon approaching the patch edge, an animal may remain within a patch or cross the edge into the unsuitable matrix habitat. The great-spangled fritillary perceived the edge as highly permeable, or a “soft edge“ (Stamps et al. 1987). The congeners Speyeria atlantis, S. mormonia and S.

40 idalia have also been shown to frequently cross the habitat edge (Ricketts 2001, Ries and Debinski 2001). The pearl crescent was rarely observed at the prairie-forest boundary, and therefore the edge was perceived to be a “hard edge.“ Quantification of the behavioral repsonse of animals to the patch edge has not been applied to metapopulation dynamics, but these responses are important in determining if an animal will cross the edge and potentially move among habitat patches (Wiens 1997). Upon crossing the edge, an animal must the move through the intervening matrix habitat. Previous studies in natural systems have determined matrix types by assuming that different vegetation types differ in their resistence to movement (see Jonsen et al. 2001, Ricketts 2001). In other studies, where the matrix is composed of one vegetation type, the permeability of the matrix is measured by the number of interpatch movements (Roland et al. 2000, Schooley and Wiens 2004). At the Lynx Prairie, there was not a clear delineation between matrix types, such as in Ricketts’ (2001) study with butterflies where the matrix was either conifer or willow forests, or in Jonsen et al’s (2001) study with flea beetles where the matrix was either grass or shrub. The forest-matrix at the Lynx Prairie was similar in tree species composition, but the cluster anlaysis revealed that three distinct forest-matrix types were present based on species composition. In previous studies, forest-matrix species composition has not been quantified (see Pither and Taylor 1998, Roland et al. 2000, Ricketts 2001). The quantification of the species composition of the matrix provides a mechanistic basis as to why one matrix type is more permeable than another. In systems where the matrix is of one vegetation type such as alpine forest (Roland et al. 2000), or short-grass steppe (Schooley and Wiens 2004), quantification of species composition can determine if more than one type of matrix is present, as was the case in my study. This information can then be used to predict movements among suitable habitat patches across different matrix types and why more movements are observed across one matrix type compared to another. The great-spangled fritillary was more likely to cross the forest-matrix habitat when the forest was composed of eastern red cedar, Virginia pine, and mesic hardwoods (ERC forest-matrix), while the pearl crescent was isolated within patches regardless of matrix type. The mortality rate of the pearl crescent is not known, but the congener Phyciodes phaon has a life expectancy of two weeks in the laboratory (Genc et al. 2003);

41 in nature, both the pearl crescent and the great-spangled fritillary are likely similar to the regal fritillary with approximately 10% of the population dying per day with a life expectancy of about 10 days (Nagel et al. 1991). A higher than expected mortality of the pearl crescent, however, would explain the lower recapture rate. Another possibility is that the pearl crescent moved out of the Lynx Prairie to other prairie clusters, but this seems unlikely given the low patch emigration rates in general. The ERC forest-matrix was more permeable to movement for the great-spangled fritillary than was the forest-matrix composed primarily of Virginia pine (VP forest- matrix). This could be due to the more open forests. Since many great-spangled fritillary butterflies were observed flying over the forest canopy, at least some interpatch movements of this species may not be influenced by the structure of the matrix. Metapopulation models often only consider the incidence of an animal within a habitat patch, and patch area and isolation have been determined to be the best predictors of dispersal rates (Hanski 1994, Gustafson and Gardner 1996, Moilanen and Hanski 1998). Recent models have begun to incorporate patch quality and landscape structure (Lande 1987, Moilanen and Hanski 1998, Heino and Hanski 2001, Thomas et al. 2001). A more realistic model is one that incorporates species abundance within patches and landscape structural characteristics that influence movements. Hanski et al.’s (2000) virtual migration model uses mark-recapture data on individual animals to estimate the probability of an individual emigrating, the probability of surviving migration between patches, and the probability of migrating between two patches based on patch area and connectivity. Matter et al. (2004) modified the model to include the connectivities of two different matrix types. I attempted to use this model to determine the probability of a butterfly moving between two patches. However, there were too many pairs of patches where movement did not occur in both directions (i.e., a butterfly moved from patch 3 to patch 5, but not patch 5 to patch 3). If more recaptures had been obtained, this would be a very useful model to determine not only the probability of a butterfly moving between patches, but also the persistence of butterfly populations within the Lynx Prairie and other prairie clusters at the EOA Preserve. Another modeling approach would be to use individual-based movements models which incorporate edge-boundary permeability, patch quality, and matrix effects (Stamps et al. 1987, With and Crist 1995, Moilanen and

42 Hanski 1998, Hanski et al. 2000, Heino and Hanski 2001, Matter et al. 2004), as I have shown that all of these influence the spatial structure of butterfly populations.

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46

Lynx 1 0.1 ha

Lynx 2 0.71 ha

Lynx 4 Lynx 3 0.12 ha 0.22 ha

Lynx 6 Lynx 9 Lynx 5 0.14 ha 0.15 ha 0.22 ha Lynx 7 0.11 ha Lynx 8 0.20 ha 100m

Figure 1. Lynx Prairie, Adams County, OH, USA. All prairies occur on soil derived from dolomite limestone. Patches are separated by 12 to 440 m of forest-matrix. The forest-matrix between patches 2 and 3 was recently cleared to allow prairies 2 and 3 to expand.

10 meters

10 meters 5 meters

Figure 2. Sample belt transect for woody and herbaceous vegetation measurements. Yellow squares are 5 x 5 m quadrats for measuring shrubs and saplings. Red squares are 1 x 1m quadrats for measuring herbaceous vegetation.

47 60

Pearl crescent 50 Great-spangled fritillary

40 viduals 30

No. of indi 20

10

0 Edge Interior

Figure 3. Abundance of the great-spangled fritillary and the pearl crescent at the patch edge and in the interior. Counts are summed across all Pollard walk counts.

20

18 Great-spangled fritillary 16 Pearl crescent

14 s e r

u 12

10 ecapt r

of 8 No. 6

4

2

0 initial migrant

Figure 4. Number of recaptures of the great-spangled fritillary and the pearl crescent in their initial (initial) patch and in a different (migrant) patch. One migrant great-spangled fritillary was recaptured twice. All other butterflies were only recaptured once.

48 a.)

Lynx 1 0.1 ha

Lynx 2 0.71 ha

Lynx 4 Lynx 3 0.12 ha 0.22 ha

Lynx 6 Lynx 9 Lynx 5 0.14 ha 0.15 ha 0.22 ha Lynx 7 0.11 ha Lynx 8 0.20 ha 100m b.)

Lynx 1 0.1 ha

Lynx 2 0.71 ha

Lynx 4 Lynx 3 0.12 ha 0.22 ha

Lynx 6 Lynx 9 Lynx 5 0.14 ha 0.15 ha 0.22 ha Lynx 7 0.11 ha Lynx 8 0.20 ha 100m

Figure 5. Movements of the a.) great-spangled fritillary and b.) pearl crescent among the patches at the Lynx Prairie. One tailed arrows indicate movement in one direction between patches. Two-tailed arrows indicate movement in both directions between patches. The thickness of the arrow indicates the relative movement rates between two patches.

49

900 800 s 700 Pearl crescent dual i 600 v Great-spangled

ndi 500 fritillary i

of 400 r e

b 300 m

u 200 N 100 0 Population estimate Captures

Figure 6. Population size estimates and the total number of captured butterflies for the great-spangled fritillary and the pearl crescent in the Lynx Prairie. Error bars are ±1 SE.

Distance (Objective Function) 7.1E+02 1.8E+04 3.6E+04 5.3E+04 7.1E+04 Information Remaining (%) 100 75 50 25 0

1-2 3-5 6-9 8-9 2-4 6-7 6-8 3-4 7-8 2-3

Figure 7. Dendrogram of tree species composition. The transects between patches 1-2, 3-5, 6-9, and 8-9 occur in forests composed of eastern red cedar, Virginia pine, and mesic hardwoods. The transects between patches 2-4, 7-8, 6-8, 6-7, and 3-4 occur in forests composed primarily of Virginia pine. The transect between patches 2 and 3 had recently been cleared of most woody plants to permit expansion of the prairies and was composed only of tulip poplar and redbud.

50

a.)

1400

1200

1000

800 Observed

equency 600

Fr mean 400

200

0 110 130 150 170 190 210 230 250 270 290 310 Interpatch Distance (m) b.)

1200 Observed mean 1000

800

600 equency

Fr 400

200

0

0 0 0 0 0 0 0 0 0 0 0 0 3 10 1 16 19 22 25 28 31 34 37 40 43 Interpatch distance (m)

Figure 8. Distribution of randomized interpatch distances for the great-spangled fritillary a.) regardless of matrix type and b.) eastern red cedar forest-matrix. Eighteen interpatch distances were drawn at random with replacement 10000 times, and the observed mean movement distance is 101.7 m for 8a (p=0.0001). Seven interpatch distances were drawn at random with replacement 10000 times, and the observed mean was 207.0 m for 8b (p=0.3266).

51 a.)

900 800 700 600

500 Observed 400 mean equency

Fr 300 200 100 0

0 0 0 0 0 0 0 0 0 60 90 12 15 18 21 24 27 30 33 36 Interpatch Distance (m)

b.)

900 800 700 600 500 Observed 400 mean

Frequency 300 200 100 0

0 0 0 0 0 0 0 0 0 0 0 0 6 90 7 12 15 18 21 24 2 30 33 36 39 42 Interpatch distance (m)

Figure 9. Distribution of randomized means for the pearl crescent a.) regardless of matrix type and b.) eastern red cedar forest-matrix . Six interpatch distances were drawn at random with replacement 10000 times, and the observed mean was 103.9 m in 9a (p= 0.0273). Five interpatch distances were drawn at random with replacement 10000 times, and the observed mean was 119.0 m for 9b (p= 0.0082).

52 0.3

0.25

0.2

expected 0.15 observed Probability 0.1

0.05

0 0 50 100 150 200 250 300 350 distance (m)

Figure 10. Probability of emigration for the great-spangled fritillary modeled with an inverse power function. Shortest distance between patches where movement was observed was 12.2 m and the longest distance was 300 m (z =0.26, R2 = 0.30).

1.2

1 n o i t 0.8 a r g i

em Expected f 0.6 Observed y o t i l i ab

b 0.4 o r P

0.2

0 0 0.01 0.02 0.03 0.04 0.05 0.06 Host-plant density (plants/m2)

Figure 11. Probability of emigration of the pearl crescent as a function of host-plant density from logistic regression. The smallest host-plant density was 0 plants/m2 and the 2 largest density was 0.0525 plants/m (β0 =0.83).

53 18

16

14

12

10 Expected Observed 8 bundance A

6

4

2

0 0 102030405060708090 Distance (m)

Figure 12. Great-spangled fritillary abundance as a function of the distance between patches from Poisson regression. The nearest-neighbor isolation metric was used because it was a better predictor than the scaled-distance metric. The greatest abundance was 16 butterflies while the smallest was 3 butterflies.

18

16

14

12

10 Expected Observed 8 bundance A

6

4

2

0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Density of plants with flowers (plants/m2)

Figure 13. Pearl crescent abundance as a function of the density of plants with flowers from Poisson regression. The smallest density was 0.23 plants/m2 and the largest density was 0.70 plants/m2.

54

Table 1. Summary of major findings on movements and distributions of two butterfly species among the nine prairie patches at the Lynx Prairie, Adam County, OH, USA. Measures marked with “*” indicate measures where Patch 4 was not sampled. GSF= great-spangled fritillary, PC = pearl crescent.

Measure GSF PC______

Location within patch* Edge and Interior Interior

Edge permeability High Low

Matrix structure Permeable across Impermeable to movement forest-matrix composed of eastern red cedar, Virginia pine, and mesic hardwoods

Abundance predictor* Nearest-neighbor metric Flower density

Emigration predictor None Host-plant density

55 Appendix I. Tree composition of the forest-matrix at the Lynx Prairie. Importance values (IV) are the sum of relative dominance (Dominance), relative density (Density), and relative frequency (Frequency). “Transect” is the patch numbers between which the transect was placed. “Matrix” is the matrix type separating patches. VP= Virginia pine, ERC = eastern red cedar, TR= Tulip poplar and redbud. The means and standard deviations per transect are listed below all species in the transect.______

Transect Matrix Species Dominance Density Frequency IV Rank______2-3 TR Liriodendron tulipifera 90.03 66.67 50.00 206.7 1 Cercis canadensis 9.972 33.33 50.00 93.27 2

Mean 50.00 49.98 50.00 150.0 Standard deviation 56.61 23.59 0.000 80.20______

1-2 ERC Fraxinus pennsylvanica 31.21 26.92 18.75 76.88 1 Liriodendron tulipifera 22.46 11.54 12.50 46.49 2 Ulmus rubra 5.640 19.23 18.75 43.62 3 Platanus occidentalis 14.33 11.54 6.251 32.13 4 Quercus alba 12.47 7.690 6.251 26.41 5 Cercis canadensis 1.547 7.690 12.50 21.74 6 Acer saccharum 8.545 3.850 6.251 18.65 7 Juniperus virginiana 2.278 3.850 6.251 12.38 8 Quercus muehlenbergii 0.6435 3.850 6.251 10.74 9 Ulmus americana 0.8801 3.850 6.251 10.98 10

Mean 10.00 10.00 10.00 30.00 Standard deviation 10.29 7.730 5.269 20.85______

Appendix I cont. Transect Matrix Species Dominance Density Frequency IV Rank______6-9 ERC Juniperus virginiana 32.26 26.92 23.53 82.71 1 Liriodendron tulipifera 17.43 15.38 17.65 50.46 2 Quercus rubra 20.55 11.54 11.76 43.85 3 Quercus muehlenbergii 1.680 19.23 11.76 32.67 4 Fraxinus pennsylvanica 11.71 7.690 5.882 25.28 5 Ulmus americana 2.152 7.690 11.76 21.61 6 Fraxinus americana 6.593 3.850 5.882 16.33 7 Pinus virginiana 5.939 3.850 5.882 15.67 8 Carya ovata 1.682 3.850 5.882 11.41 9

Mean 11.11 11.11 11.11 33.33 Standard deviation 10.50 8.030 6.200 22.71______3-5 ERC Pinus virginiana 23.44 22.78 14.63 60.86 1 Juniperus virginiana 16.46 21.52 19.51 57.49 2 Quercus muehlenbergii 13.84 20.25 19.51 53.61 3 Fraxinus pennsylvanica 11.89 12.66 12.20 36.75 4 Quercus rubra 13.75 5.100 7.317 26.17 5 Liriodendron tulipifera 11.15 3.800 4.878 19.83 6 Acer rubrum 2.476 3.800 4.878 11.15 7 Cercis canadensis 1.576 3.800 4.878 10.25 8 Ulmus americana 1.303 2.530 4.878 8.711 9 Carya glabra 2.148 1.270 2.439 5.857 10 Acer saccharum 1.321 1.270 2.439 5.030 11 Carya ovata 0.6421 1.270 2.439 4.351 12

Mean 8.330 8.340 8.330 25.00 Standard deviation 7.690 8.520 6.450 21.72______

Appendix I cont. Transect Matrix Species Dominance Density Frequency IV Rank______8-9 ERC Liriodendron tulipifera 33.71 22.72 19.23 75.67 1 Juniperus virginiana 20.66 22.72 23.08 66.67 2 Pinus virginiana 24.63 25.00 15.38 65.01 3 Quercus muehlenbergii 11.93 11.36 15.38 38.67 4 Cercis canadensis 2.496 9.090 11.54 23.12 5 Fraxinus pennsylvanica 1.612 4.545 7.692 13.85 6 Fraxinus americana 3.068 2.272 3.846 9.186 7 Acer rubrum 1.890 2.272 3.846 8.008 8

Mean 12.50 12.50 12.50 37.50 Standard deviation 12.45 9.64 7.05 28.01______2-4 VP Pinus virginiana 86.65 60.00 33.33 179.9 1 Liriodendron tulipifera 5.125 10.00 33.33 48.46 2 Cercis canadensis 4.434 10.00 16.67 31.10 3 Juniperus virginiana 3.792 10.00 16.67 30.46 4

Mean 25.00 22.50 25.00 72.50 Standard deviation 41.10 25.00 9.623 72.14______3-4 VP Pinus virginiana 84.73 57.14 42.86 184.7 1 Cercis canadensis 14.34 39.29 42.86 96.49 2 Magnolia cuminate 0.9330 3.570 14.28 18.79 3

Mean 33.33 33.33 33.33 100.0 Standard deviation 45.01 27.28 16.49 83.02______

Appendix I cont. Transect Matrix Species Dominance Density Frequency IV Rank______6-7 VP Pinus virginiana 85.71 65.00 33.33 184.0 1 Cercis canadensis 6.382 20.00 33.33 59.71 2 Juniperus virginiana 5.339 10.00 16.67 32.01 3 Liriodendron tulipifera 2.567 5.000 16.67 24.23 4

Mean 25.00 25.00 25.00 75.00 Standard deviation 40.51 27.39 9.622 81.79______7-8 VP Pinus virginiana 63.28 50.00 33.33 146.6 1 Quercus muehlenbergii 24.06 25.00 33.33 82.40 2 Juniperus virginiana 12.65 25.00 33.33 70.99 3

Mean 33.33 33.33 33.33 100.0 Standard deviation 26.56 14.43 0.000 40.77______6-8 VP Pinus virginiana 85.04 41.94 27.78 154.8 1 Acer saccharum 3.581 19.35 22.22 45.15 2 Juniperus virginiana 5.648 16.13 11.11 32.89 3 Quercus alba 1.890 6.450 11.11 19.45 4 Fraxinus pennsylvanica 1.341 6.450 11.11 18.90 5 Carya ovata 1.549 3.220 5.556 10.32 6 Acer rubrum 0.5083 3.220 5.556 9.284 7 Ulmus americana 0.4457 3.220 5.556 9.221 8

Mean 12.50 12.50 12.50 37.50 Standard deviation 29.36 13.41 8.270 49.04______

Appendix II. Shrub and sapling composition of the forest-matrix at the Lynx Prairie. Importance values (IV) are the sum of relative dominance (Dominance), relative density (Density), and relative frequency (Frequency). “Transect” is the patch numbers between which the transect was placed. “Matrix” is the matrix type separating patches. VP= Virginia pine, ERC = eastern red cedar, TR= Tulip poplar and redbud. The means and standard deviations per transect are listed below all species in the transect.______

Transect Matrix Species Dominance Density Frequency IV Rank______2-3 TR Cercis americana 57.64 50.00 15.79 123.4 1 Quercus muehlenbergii 12.12 11.44 13.16 36.72 2 Ostrya virginiana 10.31 13.18 9.210 32.70 3 Rhamnus caroliniana 8.802 9.000 11.84 29.64 4 Quercus rubra 1.286 1.740 6.580 9.605 5 Quercus imbricaria 1.583 2.736 5.263 8.582 6 Fraxinus pennsylvanica 0.6962 1.000 5.263 6.959 7 Fraxinus quadrangulata 0.5470 1.000 3.947 5.494 8 Quercus macrocarpa 1.159 1.700 2.632 5.492 9 Rhus copallina 1.284 1.490 2.632 5.406 10 Quercus alba 0.6433 1.244 2.632 4.520 11 Rhus glabra 0.6962 1.000 2.632 4.328 12 Prunus serotina 0.6464 1.000 2.632 4.279 13 Sassafras albidum 0.6430 0.7460 2.632 4.021 14 Viburnum dentatum 0.3978 0.5000 2.627 3.525 15 Fraxinus americana 0.2984 0.5000 2.632 3.431 16 Hypericum prolificum 0.2984 0.5000 2.632 3.431 16 Corylus americana 0.1989 0.5000 2.632 3.331 18 Acer rubrum 0.2486 0.5000 1.315 2.064 19 Lindera benzoin 0.4953 0.2490 1.315 2.060 20

Mean 5.000 5.001 5.000 15.00 Standard deviation 12.90 11.26 4.196 27.61______

Appendix II cont. Transect Matrix Species Dominance Density Frequency IV Rank______1-2 ERC Ostrya virginiana 15.99 12.54 8.988 37.52 1 Lindera benzoin 16.13 9.830 10.11 36.07 2 Physocarpus opulifolius 10.21 13.22 2.248 25.68 3 Viburnum prunifolium 5.581 9.830 6.742 22.15 4 Viburnum dentatum 7.854 9.490 3.371 20.71 5 Cercis americana 8.855 4.410 6.742 20.01 6 Carpinus caroliniana 9.7950 2.710 4.494 17.00 7 Quercus rubra 2.490 5.760 7.865 16.11 8 Cornus spp. 5.177 4.410 3.371 12.96 9 Fraxinus americana 0.9968 3.400 6.742 11.14 10 Corylus americana 1.362 4.070 4.494 9.926 11 Fraxinus pennsylvanica 2.583 2.370 4.494 9.447 12 Fraxinus quadrangulata 1.291 2.030 5.619 8.939 13 Quercus muehlenbergii 2.721 2.710 3.371 8.802 14 Ulmus rubra 1.021 3.730 2.248 6.999 15 Prunus serotina 2.157 1.020 3.371 6.548 16 Rhamnus caroliniana 1.630 2.030 2.248 5.908 17 Ulmus americana 0.8849 1.356 3.371 5.612 18 Quercus alba 0.4412 1.690 2.248 4.379 19 Carya ovata 0.6827 1.020 2.248 3.950 20 Liriodendron tulipifera 0.9839 0.7000 1.123 2.807 21 Juniperus virginiana 0.6807 0.6780 1.123 2.482 22 Celtis occidentalis 0.2731 0.340 1.123 1.736 23 Hypericum prolificum 0.1365 0.3400 1.123 1.600 24 Juglans nigra 0.06827 0.3400 8.330 1.531 25

Mean 4.000 4.000 4.000 12.00 Standard deviation 4.789 3.904 2.596 10.18______

Appendix II cont. Transect Matrix Species Dominance Density Frequency IV Rank______3-5 ERC Cercis americana 30.36 26.27 11.98 68.60 1 Ostrya virginiana 14.41 17.56 12.58 44.54 2 Quercus muehlenbergii 9.809 15.82 11.38 37.01 3 Fraxinus pennsylvanica 8.507 11.55 10.78 30.84 4 Rhamnus caroliniana 7.026 8.070 9.581 24.68 5 Fraxinus quadrangulata 9.095 4.430 5.988 19.51 6 Acer rubrum 9.042 0.1600 0.5994 9.802 7 Corylus americana 1.689 2.060 4.191 7.940 8 Fraxinus americana 1.125 1.900 4.191 7.216 9 Quercus imbricaria 1.023 1.900 2.995 5.917 10 Prunus serotina 0.7388 1.420 3.593 5.752 11 Hypericum prolificum 0.8419 1.580 2.995 5.146 12 Physocarpus opulifolius 1.401 1.420 1.796 4.617 13 Liriodendron tulipifera 0.7414 0.9500 2.395 4.087 14 Ulmus americana 0.3322 0.6330 2.395 3.360 15 Ulmus rubra 0.2277 0.4700 1.796 2.493 16 Rhus copallina 0.5731 0.6310 1.198 2.402 17 Carya ovata 1.292 0.1600 0.5994 2.051 18 Sassafras albidum 0.2783 0.4700 1.198 1.946 19 Amelanchier arborea 0.2067 0.3200 1.198 1.724 20 Quercus rubra 0.1292 0.3200 1.198 1.647 21 Juniperus virginiana 0.1033 0.3200 1.198 1.621 22 Vaccinium stamineum 0.2797 0.6300 0.5994 1.509 23 Quercus macrocarpa 0.2584 0.1600 0.5981 1.016 24 Carya glabra 0.07751 0.1600 0.5981 0.8356 25 Viburnum prunifolium 0.07751 0.1600 0.5981 0.8356 25 Pinus virginiana 0.02584 0.1600 0.5994 0.7853 27 ____

Appendix II cont. Transect Matrix Species Dominance Density Frequency IV Rank______3-5 cont. ERC

Mean 3.448 3.448 3.448 10.34 Standard deviation 6.487 6.389 3.893 16.14______

6-9 ERC Cercis canadensis 36.67 24.15 10.47 71.29 1 Ostrya virginiana 19.49 22.64 11.63 53.76 2 Prunus serotina 6.566 6.000 6.977 19.54 3 Quercus muehlenbergii 2.718 8.300 6.977 17.99 4 Physocarpus opulifolius 4.773 4.150 4.651 13.57 5 Fraxinus pennsylvanica 1.358 3.770 8.140 13.27 6 Viburnum prunifolium 2.813 5.660 4.651 13.12 7 Corylus americana 1.421 3.770 6.977 12.17 8 Rhamnus caroliniana 2.023 4.150 5.814 11.99 9 Carpinus caroliniana 8.796 0.7500 2.326 11.87 10 Ulmus americana 2.619 2.260 5.814 10.69 11 Hypericum prolificum 2.419 4.530 3.488 10.44 12 Fraxinus quadrangulata 1.011 2.640 5.814 9.465 13 Acer rubrum 3.944 1.130 2.326 7.400 14 Juniperus virginiana 0.4457 1.900 4.651 6.997 15 Lindera benzoin 1.579 0.3770 1.163 3.119 16 Viburnum rufidulum 1.579 0.3770 1.163 3.119 16 Carya ovata 0.5680 1.130 1.163 2.861 17 Ulmus rubra 0.1885 0.7500 1.163 2.101 18 Sassafras albidum 0.01579 0.3770 1.163 1.698 19 Liriodendron tulipifera 0.09475 0.3770 1.163 1.635 20 Quercus alba 0.09475 0.3770 1.163 1.635 20 Quercus imbricaria 0.09475 0.3770 1.163 1.635 20______

Appendix II cont. Transect Matrix Species Dominance Density Frequency IV Rank______

6-9 cont. ERC Mean 4.348 4.345 4.348 13.04 Standard deviation 8.226 6.403 3.179 16.76______

8-9 ERC Cercis canadensis 33.65 20.53 10.43 64.61 1 Ostrya virginiana 19.93 14.58 9.816 44.32 2 Rhamnus caroliniana 7.551 18.34 9.816 35.71 3 Quercus muehlenbergii 10.56 9.100 9.202 28.86 4 Corylus americana 4.038 8.150 7.362 19.55 5 Fraxinus pennsylvanica 2.609 6.580 9.816 19.00 6 Prunus serotina 8.400 1.720 4.294 14.41 7 Sassafras albidum 2.169 4.545 4.294 11.01 8 Hypericum prolificum 0.9787 3.610 6.135 10.72 9 Lindera benzoin 1.489 1.720 4.907 8.117 10 Fraxinus quadrangulata 0.4443 1.250 4.294 5.989 11 Ulmus americana 1.403 1.250 3.068 5.720 12 Ulmus rubra 0.9554 1.880 2.454 5.289 13 Liriodendron tulipifera 3.115 0.6270 1.227 4.969 14 Fraxinus americana 0.3223 0.784 3.068 4.174 15 Juniperus virginiana 0.1398 1.570 2.454 4.164 16 Viburnum prunifolium 0.5692 0.9404 1.841 3.350 17 Carpinus caroliniana 0.6441 0.4700 1.227 2.341 18 Viburnum dentatum 0.2569 1.250 0.6140 2.121 19 Fagus grandifolia 0.08592 0.3135 1.227 1.626 20 Acer rubrum 0.5379 0.1570 0.6140 1.309 21 Quercus rubra 0.04296 0.3135 0.6140 0.9704 22 Juglans nigra 0.1076 0.1570 0.6140 0.8785 23 Pinus virginiana 0.01076 0.1570 0.6140 0.7817 24______

Appendix II cont. Transect Matrix Species Dominance Density Frequency IV Rank______

8-9 cont. ERC Mean 4.167 4.166 4.167 12.50 Standard deviation 7.792 5.894 3.475 16.06______

2-4 VP Cercis canadensis 32.23 10.73 8.696 51.66 1 Prunus serotina 19.37 7.340 6.522 33.23 2 Rhamnus caroliniana 3.635 15.25 8.696 27.58 3 Acer rubrum 15.64 3.950 6.522 26.11 4 Hypericum prolificum 2.950 14.12 6.522 23.59 5 Juniperus virginiana 2.152 9.600 6.522 18.27 6 Fraxinus pennsylvanica 6.573 3.390 4.348 14.31 7 Pinus virginiana 1.538 7.910 4.348 13.80 8 Viburnum prunifolium 2.657 4.520 4.348 11.52 9 Vaccinium stamineum 2.070 4.520 4.348 10.94 10 Fraxinus quadrangulata 2.657 3.390 4.348 10.39 11 Fraxinus americana 1.650 2.260 4.348 8.258 12 Amelanchier arborea 1.673 1.690 4.348 7.711 13 Viburnum rufidulum 0.9789 1.130 4.348 6.457 14 Quercus rubra 0.1952 1.690 4.348 6.233 15 Sassafras albidum 0.3350 2.820 2.174 5.329 16 Quercus alba 0.2517 2.260 2.174 4.686 17 Crataegus macrosperma 0.8391 0.5650 2.174 3.578 18 Ulmus americana 0.8391 0.5650 2.174 3.578 18 Viburnum dentatum 0.8391 0.5650 2.174 3.578 18 Nyssa sylvatica 0.5594 0.5650 2.174 3.298 19 Liriodendron tulipifera 0.2797 0.5650 2.174 3.019 20 Corylus americana 0.08391 0.5650 2.174 2.823 21______

Appendix II cont. Transect Matrix Species Dominance Density Frequency IV Rank______2-4 cont. VP Mean 4.348 4.346 4.348 13.04 Standard deviation 7.757 4.409 2.073 12.20______

3-4 VP Cercis canadensis 41.81 18.27 8.334 68.41 1 Fraxinus pennsylvanica 16.84 17.00 8.334 42.17 2 Rhamnus caroliniana 8.900 9.000 6.944 24.84 3 Quercus muehlenbergii 4.740 13.14 6.944 24.82 4 Prunus serotina 4.848 9.900 8.334 23.08 5 Acer rubrum 8.259 5.100 6.944 20.30 6 Ostrya virginiana 2.567 5.130 8.334 16.03 7 Juniperus virginiana 3.857 5.780 5.555 15.19 8 Quercus alba 0.4600 3.500 5.555 9.515 9 Fraxinus quadrangulata 0.7651 1.920 5.555 8.240 10 Hypericum prolificum 0.6339 1.900 5.555 8.089 11 Sassafras albidum 0.6939 1.600 5.555 7.849 12 Fraxinus americana 1.761 1.900 2.778 6.438 13 Amelanchier arborea 1.957 0.6400 2.778 5.375 14 Quercus rubra 0.2847 1.600 2.778 4.662 15 Vaccinium stamineum 0.4982 0.9600 2.778 4.236 16 Fagus grandifolia 0.7117 0.6400 2.778 4.129 17 Viburnum prunifolium 0.3114 0.9600 1.389 2.661 18 Pinus virginiana 0.05338 0.6400 1.389 2.083 19 Corylus americana 0.05338 0.3200 1.389 1.763 20

Mean 5.000 5.000 5.000 15.00 Standard deviation 9.613 5.585 2.525 16.31______

Appendix II cont. Transect Matrix Species Dominance Density Frequency IV Rank______6-7 VP Rhamnus caroliniana 1.676 43.33 14.29 59.29 1 Juniperus virginiana 1.546 16.00 14.29 31.83 2 Cercis canadensis 2.824 15.33 14.29 32.44 3 Acer rubrum 1.729 7.333 14.29 23.35 4 Sassafras albidum 0.8751 8.000 7.143 16.02 5 Fraxinus quadrangulata 0.04450 2.000 50.00 9.187 6 Quercus muehlenbergii 0.04450 2.000 7.143 9.187 6 Pinus virginiana 0.5932 1.333 7.143 9.069 8 Liriodendron tulipifera 0.4449 1.333 7.143 8.921 9 Toxicodendron radicans 0.1780 2.000 3.571 5.749 10 Quercus alba 0.0449 1.333 3.571 4.949 11

Mean 0.9091 9.091 9.091 19.09 Standard deviation 0.9180 12.63 4.334 16.62______

7-8 VP Cercis canadensis 41.34 40.54 22.22 104.1 1 Rhamnus caroliniana 28.94 28.38 22.22 79.54 2 Quercus muehlenbergii 21.85 24.32 22.22 68.39 3 Sassafras albidum 4.917 2.700 11.11 18.73 4 Fraxinus quadrangulata 2.557 2.700 11.11 16.37 5 Acer rubrum 0.3933 1.350 11.11 12.85 6

Mean 16.67 16.67 16.67 50.00 Standard deviation 16.66 16.68 6.086 39.06______

Appendix II cont. Transect Matrix Species Dominance Density Frequency IV Rank______6-8 VP Cercis americana 29.54 13.42 10.10 53.06 1 Rhamnus caroliniana 11.59 27.29 10.10 48.98 2 Juniperus virginiana 5.365 17.45 9.091 31.91 3 Acer rubrum 22.99 1.342 5.051 29.38 4 Prunus serotina 6.956 11.63 9.091 27.68 5 Quercus muehlenbergii 3.832 7.830 9.091 20.75 6 Fraxinus pennsylvanica 2.800 4.470 7.071 14.34 7 Ostrya virginiana 3.354 3.580 4.040 10.97 8 Hypericum prolificum 1.101 2.460 6.601 9.622 9 Fraxinus americana 0.8608 2.010 6.061 8.931 10 Fagus grandifolia 6.700 0.4470 1.010 8.157 11 Fraxinus quadrangulata 2.065 1.570 4.040 7.676 12 Sassafras albidum 0.5516 1.120 5.051 6.722 13 Pinus virginiana 0.2155 1.342 3.030 4.588 14 Quercus rubra 0.3114 0.8950 2.020 3.227 15 Liriodendron tulipifera 0.3113 0.6710 2.020 3.003 16 Quercus alba 0.1916 0.6711 2.020 2.883 17 Viburnum prunifolium 0.2393 0.4470 2.020 2.706 18 Corylus americana 0.7147 0.8900 1.010 2.615 19 Ulmus rubra 0.2398 0.2240 1.010 1.474 20 Ulmus americana 0.07195 0.2240 1.010 1.306 21

Mean 4.762 4.761 4.762 14.28 Standard deviation 7.818 7.082 3.260 15.40______