Stand Age is Associated with Clonal Diversity, but Not Vigor

Wetlands DOI 10.1007/s13157-015-0678-7

ORIGINAL RESEARCH

Stand Age is Associated with Clonal Diversity, but Not Vigor, Community Structure, or Insect Herbivory in Chesapeake Bay Phragmites australis Eric L. G. Hazelton 1,2 & Melissa K. McCormick 1 & Matthew Sievers 1 & Karin M. Kettenring 1,2 & Dennis F. Whigham 1

Received: 8 July 2013 / Accepted: 15 July 2015 # Society of Wetland Scientists 2015

Abstract Invasions are dynamic as both the invading organism and the invaded ecosystem change. Intrinsic changes to the invader (invasion process) can involve population level genetic and reproductive changes. Extrinsic changes (invasion effect) occur to the environment that is invaded (e.g., alterations to the physical environment), to the invaded plant community (e.g., changes in species diversity and composition or evolutionary changes), or to insect herbivory. To investigate how invasions change through time, we investigated both the process and effect of a Phragmites australis invasion by comparing young and old P. australis stands within two Chesapeake Bay subestuaries. We quantified clonal richness of P. australis stands, vigor of the invader, herbivore damage, and plant community composition. Our results indicate that only the population-scale genetics (clonal richness, genetic distance) changed over the course of 40 years. Clonal richness was lower in the old P. australis stands, likely due to intraspecific competition and/or initial colonization by fewer genotypes. The mean genetic distance among clones within old stands was lower than within young stands, suggesting that clones within old stands were mostly closely-related, while young stands were likely established by seeds from nearby stands and so were more representative of the local area. Clones in different old stands were genetically more distant from each other than those in young stands were from clones in other young stands. This pattern suggested that old stands were established by independent colonization * Eric L. G. Hazelton [email protected] 1

Smithsonian Environmental Research Center, Edgewater, MD, USA

2

Ecology Center and Department of Watershed Sciences, Utah State University, Logan, UT, USA

events, while young patches were established by a mixture of seeds from local stands, which generated the lower average genetic distance between clones across young stands. We found that community composition, plant vigor, and herbivore damage to stems were similar across different age stands, which indicated that the effect of P. australis invasion becomes stable within a few decades. Over longer periods, the intrinsic invasion process may be more dynamic than the invasion effect. Keywords Allee effects . Clonal diversity . Clonal plants . Invasive plant . Microsatellite markers . Phragmites australis

Introduction The process and impacts of invasions change through time, in part, because inter- and intraspecific interactions are dynamic throughout the duration of an invasion. In wetland plants, the age of invading populations or time since invasion can influence plant community composition (Mitchell et al. 2011), physical processes (Rooth et al. 2003), and restoration success (Estrella and Kneitel 2011). Still, invasions are seldom studied at an appropriate temporal scale to discern their true effect or process (Strayer et al. 2006). A review of changes within invasions over time (Strayer et al. 2006) divided them into four components: (1) Changes in the species that invades; (2) Changes in the biological community that is invaded; (3) Cumulative changes in the abiotic environment that is being invaded; and (4) Interactions between the invading species and other variables that control the ecosystem. The first component is intrinsic; it is primarily the result of the population biology of the individual species during the time course of an invasion (sensu Vitousek 1990), which we consider the Bprocess^ of invasion. The remaining three components are

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extrinsic, and are treated here as the Beffect^ of an invasion over time. We used the common reed, Phragmites australis Trin. Ex Steud (Poaceae), to simultaneously investigate how the age of a stand is related to both the process and effect of an invasion in Chesapeake Bay wetlands. Phragmites australis has been a component of North American wetlands for millennia (Niering et al. 1977; Orson et al. 1987), but in the last century it has been identified as a nuisance plant that colonizes and dominates disturbed sites and ditches (Tucker 1938). Recent decades have seen an aggressive expansion of P. australis in North America (Chambers et al. 1999), attributed to a cryptic invasion by an introduced Eurasian lineage of the common reed (Chambers et al. 1999; Saltonstall 2002), likely in combination with changes in anthropogenic land use that favor the invader (Bertness et al. 2002; Burdick and Konisky 2003; King et al. 2007; Chambers et al. 2008). Process of Invasion Once thought to reproduce primarily vegetatively (Bart et al. 2006; Rooth et al. 2003), recent evidence demonstrated that P. australis is also spreading via sexual reproduction and that seeds are responsible for its rapid, continent-wide expansion (Lavoie et al. 2003; Kettenring and Whigham 2009; McCormick et al. 2010a, b; Kettenring and Mock 2012). Since P. australis is only weakly self-compatible (Ishii and Kadono 2002; Lambert and Casagrande 2007; Kettenring et al. 2011), greater clonal richness within and among P. australis stands leads to increased viable seed production, and in turn, even greater clonal richness (McCormick et al. 2010a; Kettenring et al. 2010, 2011). As the invasion has progressed, increased outcrossing opportunities have led to greater numbers of genetic individuals, thereby creating a positive feedback. The positive feedback of increasing outcrossing on recruitment allowed reed populations to overcome Allee effects, negative effects on fitness at low densities (Allee 1931; Dennis 2002), that appear to have held its expansion in check for perhaps the first 100 years after its initial invasion of North America (McCormick et al. 2010a; Kettenring et al. 2011). In a clonal species like P. australis, these are ‘weak’ Allee effects (sensu Allee 1931), which allow long-term persistence through clonal growth but preclude extensive spread until increased genetic diversity results in a positive reproductive feedback. Clonal species generally do not exhibit the lower tipping point that characterizes ‘strong’ Allee effects, below which strictly sexual species decline. In this system, establishment of new stands via rhizome fragmentation and dispersal appears to be uncommon and dramatic increases in spread appear to be primarily seed-driven (McCormick et al. 2010a). Because seed production is, in turn, related to the presence of multiple genotypes in a stand (Kettenring et al. 2010, 2011), this relationship suggests that there is likely an

Allee effect with fitness consequences related to stand genetic diversity (see e.g., Taylor et al. 2004 for a similar example). In order to better understand the process of P. australis invasion (component 1: Bchanges in the species that invades^; Strayer et al. 2006), we investigated the population genetics and reproductive output of young and old P. australis stands. New stands of P. australis are typically formed by seeds and not vegetative fragmentation. If there were few opportunities for outcrossing and so few seeds were produced (i.e., weak Allee effects; McCormick et al. 2010a) early in P. australis invasion then there would have potentially been fewer recruits at the time of establishment for older stands. In turn, if the clones that initially established continue to dominate the stand genetic diversity well after establishment then older stands should have lower clonal richness than more recently established stands. Also, the genetic distance between individuals within- compared to among-stands should be less in older compared with younger stands. The initial pattern of clonal diversity could also change over time. Additional seedlings could be recruited or adjacent stands could grow together and merge, increasing diversity over time (see Eriksson 1989). If additional seedlings come from inside an existing stand, they will likely be more closely-related than if seeds originate from multiple potentially unrelated external stands. Alternatively, clonal diversity may decrease over time as available habitats become filled and intraspecific competition results in exclusion of some clones (Koppitz et al. 1997). Any or all of these processes may be occurring in P. australis stands and they would be expected to produce different patterns of clonal richness and genetic distance among clones with stand age. We set out to determine whether clonal richness was related to stand age and how genetic distance would vary within and among P. australis stands. How the process of invasion changes through time may also be reflected in sources of propagules that contribute to new stands. Seeds are capable of long distance dispersal. However, most seeds are expected to disperse locally (Bullock and Clarke 2000; McCormick et al. 2010b). Therefore we expect stands that are producing seeds and are close together to be genetically less distant than stands farther apart. However, if the processes that contribute to seed production have changed through time, then old stands and young stands may display different patterns of genetic distance. Because many seeds may simply fall to the ground within the stand that produced them, while seeds that disperse outside their stand of origin are subject to other dispersal processes (wind and water), we conducted separate analyses of the relationship between genetic and geographic distance. Within-stands, we predicted that if older stands were genetically less diverse than younger stands, this pattern would hold true at all distances. Among stands, we predicted that, regardless of age, stands closer together would be genetically less distant from each other than those farther apart. However, if

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the process of invasion changed over time, old and young stands might have different patterns of genetic similarity with geographic distance. If old stands were established at a time when only a single, limited, seed source was available, they would be less genetically distinct than young stands. In contrast, if each old stand was established by an independent, unrelated, seed source (as might be expected if they were established by rare long-distance dispersal events) they would be more genetically distant than similarly close-together young stands. Young stands, having been established when multiple local sources of seeds could contribute to stand formation, would be expected to have a genetic distance that was more strongly related to geographic distance than older stands. Similarly, if old stands were seed sources for young stands then genetic distance would be less across different age stands than it would be if young stands were compared to other young stands or old stands to other old stands. We looked at differences in reproductive output with stand age as another component of the invasion process. Kettenring et al. (2011) demonstrated in a common garden experiment that outcrossing greatly improved viable seed production in P. australis compared with self-pollinated plants. Similarly, in the field they found that the greater the within-stand genetic diversity, the higher the likelihood that more seeds would be viable in that stand. In our study, we measured the reproductive success of stands with the premise that if clonal diversity proved to be lower in older stands, then seed production would also be reduced due to fewer opportunities for outcrossing. Effects of Invasion In addition to investigating the role of stand age on the process of invasion, we determined the role of stand age on the effect of invasion (Components 2, 3, and 4 from Strayer et al. 2006). Phragmites australis is considered an ecosystem engineer, variously altering nutrients (Findlay et al. 2003; Windham and Meyerson 2003), topography and hydrology (Farnsworth and Meyerson 1999; Rooth et al. 2003), and light availability (Minchinton et al. 2006). The introduced lineage of P. australis forms stands with dense, tall culms and a thick litter layer that intercept most of the solar radiation, resulting in low light levels, which limit the germination and growth of other wetland species and ultimately lead to lower levels of within-stand species diversity (Minchinton et al. 2006). We examined three potentially age-related consequences of P. australis invasion that were described by Strayer et al. (2006): 1. Changes in species composition and diversity of the plant community—an assessment of changes in the community that is invaded; 2. Increased vigor of P. australis (e.g., density and cover of living ramets and density of standing dead ramets that can alter light and sediment environments

(e.g., Rooth et al. 2003))—proxies for cumulative changes in the abiotic environment; and 3. changes in the effects of insect herbivory on P. australis—a measurement of interactions between the invading species and other variables that control ecosystem structure and function. We hypothesized that the diversity of other wetland species associated with stands of P. australis would decrease with stand age due to factors described above (e.g., increased shading and litter depth). The implication of this hypothesis is that stand age is related to the diversity of other species (species diversity decreases as the canopy closes and litter depth increases). In addition to age, genetic diversity within P. australis stands may have a negative impact on species diversity. Phragmites australis stands with higher clonal richness may be able to exploit a greater niche breadth than single-genet or low diversity stands (which can result either from few genets or from genets that are closely related), thereby having greater competitive ability and extirpating other species. We would therefore anticipate that high genetic diversity (i.e., younger) P. australis stands would have lower diversity of native plant species than low genetic diversity P. australis stands. Conversely, old stands may have had more time to expand and exclude other species or may have invaded lower diversity plant communities to begin with. We additionally recorded the vigor of P. australis stands to see if vigor changed with age, since as stands age they may develop greater capacity to acquire resources as they build perennial belowground resources. Finally, we quantified the frequency of herbivore attack to test the hypothesis that stand age would be positively related to the level of herbivore damage, and that herbivore damage would be inversely related to clonal richness. We quantified the impacts of herbivory on inflorescence production because many specialist herbivores damage P. australis meristems that produce inflorescences (Tewksbury et al. 2002). These specialist herbivores do not feed on other wetland dominants and have increased in abundance over the past few decades with P. australis invasion. Herbivores could impact genet richness by decreasing reproductive rates, such that heavily attacked stands would produce fewer seeds, recruit fewer seedlings, and therefore have fewer genets. Alternatively, lower diversity stands may have lower herbivore attack rates (as seen in Solidago spp. in Crutsinger et al. 2006) as there is lower habitat heterogeneity and are therefore fewer potential niches for herbivores.

Methods Site Selection and Sampling Layout We sampled the clonal richness of P. australis stands, the diversity of the plant community of stands dominated by P. australis,

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herbivore damage to ramets, and P. australis vigor in 12 young and 11 old stands in the Rhode and South River subestuaries of Chesapeake Bay, Maryland, USA (Fig. 1). We first located 11 P. australis stands in the Rhode and South River subestuaries that were mapped by the Maryland Department of Natural Resources in 1971–2 (McCormick and Somes 1982), which are hereafter referred to as old stands. Most of the stands included in the study are identifiable as well established on aerial photographs from as early as 1962 (EH pers. obs.). We believe that all the old stands were decades old prior to the 1971–2 vegetation surveys. We selected 12 P. australis stands that became established since 1990 using multiple years of publicly available aerial photographs (hereafter, Byoung stands^). Phragmites australis in the Rhode River expanded from 5 stands in 1970 (McCormick and Somes 1982) to 212 stands in 2007 (McCormick et al. 2010b); P. australis on the South River expanded from 6 stands in 1971–2 to the numerous stands that are present today. The Rhode River expansion Fig. 1 Map of sampling sites on the Rhode and South Rivers, subestuaries of the Chesapeake Bay. Map shows location of young and old stands sampled in the study

yielded a 25-fold increase in P. australis area over 35 years (McCormick et al. 2010b), which is consistent with other subestuaries of Chesapeake Bay during that period (Chambers et al. 1999). All of the stands sampled in this study, described in more detail below, were of the introduced Eurasian lineage (Kettenring et al. 2010; McCormick et al. 2010a, b; EH and MM pers. obs.) We sampled each P. australis stand by establishing a single transect through the longest axis. All sampling was conducted along each transect in five quadrats established in a stratified random design (transect was divided into five equal lengths, and quadrats were randomly assigned within each segment) or at points mid-way between quadrats. Molecular Analysis To quantify the within-stand clonal richness and genetic distance, we collected one leaf from each of four corners of each

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1×1 m quadrat along each transect and also collected one leaf from a plant mid-way between each pair of quadrats along each transect (intermediate samples). Sampling along transects can result in an edge effect where large clones are highly likely to intersect a transect and be counted in the clonal richness assessment (Arnaud-Haond et al. 2007). However, such a nested sampling scheme was needed to accurately assess the relationship between genetic and geographic distances across a range of scales. The possible overestimation of absolute clonal richness using this methodology does not limit our ability to compare relative clonal richness among stands. We had a total of 24 leaf samples from each stand. Each collected leaf was frozen within 6 h of collection and lyophilized (Labconoco 195 Freeze dry system, Kansas City, MO). We extracted DNA from approximately 20 mg of lyophilized tissue (clipped into small fragments) using a BioSprint 96 with a BioSprint Plant DNA Kit (QIAGEN, Inc.; Valencia, California) following the supplied protocol. We used eight microsatellite primer pairs developed by Saltonstall (2003) to PCR amplify eight microsatellite DNA loci. PCR amplification was performed as in McCormick et al. (2010a) using a PTC-200 DNA Engine thermal cycler (MJ Research, Inc.; Waltham, Massachusetts, USA) programmed using the following conditions: an initial denaturation at 94 °C for 4 min, followed by 35 cycles of 94 °C for 30 s, 50–58 °C for 30 s and 72 °C for 10 s, with a final polymerization step at 72 °C for 2 min. The PCR was run as 12.5-μL reactions with concentrations as follows: 1 μL (20 ng) template DNA, 3.45 μL distilled water, 0.75 μL of each primer (10 μM), 0.3 μL 25 mM MgCl2, 6.25 μL RedMix Plus (Gene Choice, Inc.; Frederick, MD, USA). Annealing temperatures for each primer are listed in McCormick et al. (2010a). After amplification, PCR products with different fluorophores (FAM, HEX, and NED) and different expected fragment sizes were combined before sequencer analysis as follows: primers PaGT4, PaGT9, and PaGT16 were combined; primers PaGT12, PaGT13, and PaGT22 were combined; and primers PaGT14 and PaGT21 were combined. Amplicons were subjected to analysis on an ABI 3100 Automated Capillary DNA Sequencer (Applied Biosystems, Inc.; Foster City, California) at the Smithsonian’s Laboratory of Analytical Biology using a custom ROX500 size standard (DeWoody et al. 2004) as a reference. Fragment sizes were determined using GeneMapper v4.0 (Applied Biosystems, Inc.; Foster City, California). We aligned fragments for all samples using a TRFLP peak sorting function for Excel (Rees et al. 2004) and removed stutter peaks (smaller peaks 2 bp behind the actual peak) and pull-up peaks (those resulting from bleed-over fluorescence from large peaks at other loci with different fluorophores) manually. Phragmites australis is an allopolyploid (Raicu et al. 1972) that displays disomic inheritance in some loci (i.e., alleles behave as diploid, segregating with the DNA from a particular

parent during meiosis; Saltonstall 2003). For alleles that do not show disomic inheritance, it is difficult to determine how many copies of each allele are present and with which copy of parental DNA each allele segregates. As a result, we used genetic phenotypes instead of genotypes (Saltonstall 2003; McCormick et al. 2010a, b). Using genetic phenotypes, two individuals with the same alleles but different numbers of copies of each allele appear identical and so this method may underestimate the genetic diversity present. However, this method is necessary in a situation such as here, where it is impossible to correctly estimate the number of copies represented in alleles and avoids overestimating genetic diversity because of incorrect estimation of the number of alleles present. We considered all repeated genetic phenotypes within stands to be ramets of a single genet. However, it is possible that some sexually produced genets that shared microsatellite gene phenotypes made up of common alleles were incorrectly assumed to be ramets of a single clone. To determine the probability of shared genetic phenotypes arising sexually we calculated the probability of a repeated genetic phenotype arising by chance sexually rather than asexually as P = (Πpiqi)2h, where pi and qi are the frequencies of the two alleles at the ith locus and h is the number of heterozygous loci in the genotype (Pollux et al. 2007), using the original equation, but with the probabilities for gene phenotypes as the sum of different possible ways they could arise (i.e., X1/X2 in a tetraploid could be 1, 2, or 3 copies of allele X1 and 3, 2, or 1 copy of allele X2; see also McCormick et al. 2010b). We calculated P for all genetic phenotypes and identified all cases where genetic phenotypes were identified in multiple samples and also had a probability of being produced sexually of >0.001. This conservative cutoff was used to account for inbreeding that likely occurred within stands consisting of many related genets. In these cases, if the samples sharing a genetic phenotype originated from a single quadrat or immediately adjacent sampling locations along a transect they were assumed to represent a single genet. Probability calculations were made with and without cases of shared genetic phenotypes that were not in adjacent samples and had a probability of arising sexually >0.001 to determine whether they were altered by possible mis-assignment to shared clones. All cases where samples within stands differed by only one or two fragments were manually double-checked to rule out scoring errors. The mutation rate of microsatellites is relatively high, so closely adjacent samples (within a quadrat or in adjacent transect points) that differed by only one or two fragments could have potentially resulted from somatic mutation. To account for the possible inflationary effect of somatic mutation on clonal richness, we calculated clonal richness with and without these samples. Because P. australis is an allopolyploid, many calculations of genetic similarity

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were not appropriate (Obbard et al. 2006). We analysed all genetic phenotypes using a matrix of fragment sizes in Polysat for Rpackage (Clark and Jasieniuk 2011), which is specifically designed to account for difficulties in determining allele copy number in polyploid microsatellite data. Genetic diversity within each stand was calculated in two ways. First, we calculated the number of distinct genetic phenotypes in each stand divided by the total number of samples (i.e., PD, proportion of distinct genetic phenotypes; Ellstrand and Roose 1987) as a measure of recruitment of distinct genotypes through seeds or dispersed rhizome fragments. Second, we calculated the within-stand genetic distance between distinct clones using Bruvo’s D (Bruvo et al. 2004), which is effectively the average genetic distance between distinct clones in a stand, to distinguish possible sources of colonizing seeds (seeds originating at multiple external stands versus primarily seeds generated within the stand). Note that because we consider only genetic distance among distinct clones and not repeated measures of a single clone this metric is not confounded with the number of clones in a stand. We compared the proportion of distinct genetic phenotypes (PD) and the genetic distance (D) of young and old stands using an ANOVA (Systat 11 for Windows, Systat Software Inc., San Jose, CA, USA) to test for significant differences dependent on stand age. While PD addresses only the clonal richness, genetic distance addresses the relatedness of those clones. These two measures address two different processes: recruitment via seeds and rhizomes from other patches (richness; likely consisting largely of seed recruitment in this system (See McCormick et al. 2010a)) and the source of those seeds or rhizomes (local or distant—genetic distance). Correspondingly, the mean genetic distance (Bruvo’s D) between genets in different stands provides a measure of how closely related genets are, which can be interpreted as whether the stands were established by propagules (primarily seeds) from a single (genetically similar) or multiple (genetically distant) propagule pools. To determine whether stands that were close together were genetically similar, and whether this distance changed with stand age, we calculated genetic distance separately for older and newer stands as a function of geographic distance using Bruvo’s D at distances of 10, 25, 50, and 100 m for withinstand comparisons, and 50, 100, 250, 500, 1000, 5000, and >10,000 m for among-stand comparisons. As a secondary comparison, we also calculated genetic distance as a function of geographic distance between older and newer stands to determine whether older stands might be the source of seeds that established the younger stands. Geographic distance classes were chosen to incorporate a range of within and amongstand Euclidean distances while maintaining adequate numbers of sample pairs (>200) within each distance class. Dividing the continuous data into discrete distance classes was necessary to be able to compare the explicitly non-linear

patterns of genetic and geographic distance among different categories of P. australis stands. Plant Community Composition and Phragmites Vigor Plant community composition in each quadrat (N=5 per transect) was assessed as a visual estimate of percent cover of each species. All species were recorded as absolute cover (not scaled) in July 2011 by a single observer. We determined P. australis vigor using several metrics: mean stem height, stem density, density of flowering stems, and visual estimates of percent cover of live stems. Additionally, we recorded percent cover of standing dead and lodged (horizontal) dead stems. Mean stem height was recorded as the average of the four stems closest to each corner of the quadrat. Stem density was measured within each 1 m2 quadrat. We differentiated between standing dead (upright) and lodged (horizontal) dead stems while determining visual covers since they may have different impacts on the physical environment within the quadrats. The density of flowering stems was determined as the number of stems with inflorescences that were not damaged by herbivores, since herbivory typically eliminates flowering. All reproduction measurements were made in October 2011, when no additional stems were being produced and all inflorescences were fully developed. In order to determine the impact of herbivores on ramets (see Tewksbury et al. 2002 for a review of herbivores and herbivore impacts on P. australis), we manually counted the number of stems that had been damaged by stem boring insects in 0.25 m2 quadrats (N=5 per transect, at location of vegetation quadrats). Only stems with galls or obvious exit holes containing frass, that resulted in the death of the terminal parts of shoots (i.e., eliminated the potential of a shoot to produce an inflorescence) were counted. We were particularly interested in the impacts of insects on flowering because of the importance of seed production in the invasion process (Kettenring et al. 2011). We did not investigate the occurrence of direct seed predators (e.g., Ahee et al. 2013), but limited observations to herbivores that prevent flowering on an entire ramet. The insects that were encountered in this study were from several undetermined species within the Dipteran genera: Lasioptera, Giraudiella, Calamomyia, or the Hymenopteran, Tetramesa that all attack stems and are capable of preventing flowering in P. australis (Tscharntke 1999; Tewksbury et al. 2002). Most of the damage was, however, done by Lipara spp. (Tewksbury et al. 2002; Lambert et al. 2007), identified by their galls or by flagging of the uppermost leaf (Blossey et al. 2002). We collected inflorescences in late October 2011 from one stem in each quadrat (N=5 per stand). The sample size was dictated by logistical necessity, and we acknowledge that there is low statistical power. We removed the florets from the inflorescences, and used a 0.15 g subsample from each

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inflorescence to determine the number of viable seeds present (as per Kettenring and Whigham 2009). Seed samples were cold stratified at 5 °C for 6 weeks in moist soil and then germinated on moist sand under controlled conditions (7 °C/25 °C; on a 16/8 h light/dark schedule; modified from Kettenring and Whigham 2009). Seedlings were removed daily from each sample and counted for 3 weeks, at which point germination had ceased. We compared data on P. australis vigor (shoot density, shoot height, inflorescence density, seed viability), shoot damage due to herbivores, and species richness in young and old stands using a one way ANOVA in JMP (SAS Institute). To visualize vegetative communities in young and old stands, we used nonmetric multidimensional scaling (NMDS) and Monte Carlo analysis in Labdsv, Biostats, and Vegan (metaMDS) packages for RPackage2.13. Species present in fewer than 5 quadrats (of 115) were omitted. Additionally, we conducted a perMANOVA in Primer6 to determine statistical significance of differences in community composition (Anderson et al. 2008). In order to determine if stand age influenced the variability of the traits measured, we conducted a Levene test in JMP (Legendre and Legendre 2012). Additionally, we compared Bruvo’s diversity and clonal richness to seed viability and insect attack by linear regression in JMP.

Results Process: Distinguishable Clones and Reproductive Output Within stands, young stands had greater clonal richness (young: PD=0.45±0.03, old: 0.29±0.02, F=26.06, DF=1, P