Conservation Genetics

, Volume 12, Issue 3, pp 793–803 | Cite as

Invasion genetics of Microstegium vimineum (Poaceae) within the James River Basin of Virginia, USA

Research Article

Abstract

Patterns of spatial genetic structure produced following the expansion of an invasive species into novel habitats reflect demographic processes that have shaped the genetic structure we see today. We examined 359 individuals from 23 populations over 370 km within the James River Basin of Virginia, USA as well as four populations outside of the basin. Population diversity levels and genetic structure was quantified using several analyses. Within the James River Basin there was evidence for three separate introductions and a zone of secondary contact between two distinct lineages suggesting a relatively recent expansion within the basin. Microstegium vimineum possesses a mixed-mating system advantageous to invasion and populations with low diversity were found suggesting a recent founder event and self-fertilization. However, surprisingly high levels of diversity were found in some populations suggesting that out-crossing does occur. Understanding how invasive species spread and the genetic consequences following expansion may provide insights into the cause of invasiveness and can ultimately lead to better management strategies for control and eradication.

Keywords

Microstegium vimineum Invasive species Genetic structure Founder effects Secondary contact 

Introduction

Colonization and subsequent invasion by exotic species into novel habitats is a major concern for conservation of natural ecosystems (Mack et al. 2000). Invasive species pose a serious threat to native biodiversity due to their ability to displace native species (Schooler et al. 2006) and alter ecosystem functions (D’Antonio and Vitousek 1992). Understanding how an exotic species colonizes and invades is important in determining accurate and realistic management strategies. Furthermore, reconstructing the invasion history using genetic information allows us to determine if geographic features also play a role in the spread of invasive species. Previous studies (Rejmanek and Richardson 1996; Gibson et al. 2002; Brown et al. 2002; Hiero et al. 2005) have sought to describe the processes invasive species undergo during introduction (i.e., founder effects and multiple introductions) and look for causation of invasiveness in introduced ranges (e.g., phenotypic plasticity, adaptive genotypes, enemy release). However, examining gene flow and demographic processes occurring during range expansion may shed new light onto the evolutionary processes that drive invasiveness.

Invasive species are often introduced into novel areas in relatively small numbers resulting in founder effects (see Barrett et al. 2008). Subsequent expansion will create a gradient in genetic diversity decreasing along the spatial axis of expansion (Austerlitz et al. 1997). This process often results in a pattern of isolation by distance (IBD; Wright 1943) whereby genetic differentiation and spatial separation of populations are asymptotically related. The general expectation of reduced diversity within newly colonized populations and/or correlation between genetic and physical distances can be used to discern both directionality and the relative rate of gene flow during the invasion process. Despite these expectations patterns of gene flow and direction of spread can be confounded by co-occurring demographic processes such as founder events, bottlenecks and multiple introductions.

Gene flow into recently established sites (i.e., secondary colonization) can increase within population genetic diversity. For example, Pappert et al. (2000) suggested that multiple introductions of kudzu (Pueraria lobata) across the Southeastern United States were a driving factor in the observed high levels of genetic diversity among populations. Homogenization due to multiple founding populations produces genetic patterns similar to what is observed under high levels of ongoing gene flow so it is important to consider several lines of independent evidence when reconstructing demographic histories.

During expansion invasive species are subject to not only demographic constraints but also may be influenced by geographic features acting as either barriers or corridors during expansion. Large landscape features such as mountains and rivers as well as localized abiotic factors (i.e., edaphic characteristics or micro-climate) may differentially influence the paths across the landscape by which expansion and ongoing gene flow occur. Van Looy et al. (2009) observed the genetic structure of Origanum vulgare (Oregano; Lamiaceae) populations located along the Meuse River. The seeds of Origanum vulgare are transported along the river following flood events but river regulation (i.e., dykes) prevented gene flow between populations. Van Looy et al. (2009) suggested that recent large flood events in response to removal of dykes resulted in genetic structure characterized by long-distance dispersal. Conversely, Zhang et al. (2007) found high differentiation in cpDNA of Vitex negundo (Chastetree; Verbenaceae) between opposite banks of the Yangtze River suggesting a barrier to gene flow via seeds. If landscape features are affecting gene flow then it is expected that spatial variation would be a significant contributor to the genetic variation seen across a geographic area.

In this study, we examine the genetic structure of the invasive species Microstegium vimineum Trin. (Poaceae) within the James River Basin (JRB), a 27,000 km2 watershed extending from the Alleghany Mountains in the west eastward to the Chesapeake Bay along the eastern coast Virginia, USA. Microstegium vimineum, commonly referred to as Japanese stilt-grass, is an annual, C4, mesophillic grass native to Asia. This species was introduced into Tennessee, USA around 1919 and first recorded in Virginia in 1931 (Fairbrothers and Gray 1972). Since introduction, M. vimineum has invaded forests and roadsides in the eastern United States south of New York and east of the Mississippi River (Swearingen 2009). This species has also been found to severely retard forest re-generation and understory growth following a disturbance (Oswalt et al. 2007). There is even evidence that M. vimineum is displacing two other particularly invasive species Alliaria petiolata (Morrison et al. 2007) and Lonicera japonica (Gibson et al. 2002) in co-occurring areas.

Microstegium vimineum possesses a mixed-mating system that produces both cleistogamous (obligate selfing) and chasmogamous (potentially mixed mating) spikelets. Microstegium vimineum also exhibits significant phenotypic plasticity in response to heterogeneity in light conditions (Cheplick 2006). In low light conditions more biomass is allocated to leaves and chasmogamous seeds and in high light conditions more biomass is allocated to cleistogamous seeds. In addition to reductions in the overall genetic diversity as a result of relatively recent range expansion processes, the mating system for this species, with ample opportunity to produce selfed offspring, is predicted to maintain low levels of genetic diversity below that for purely out-crossing invasive species (e.g., Hamrick and Godt 1996).

Given the unique combination of life-history traits for this species and the relatively recent invasion of the JRB, we analyzed the spatial distribution of genetic variation both within and among populations of M. vimineum to address the following questions. First, does the distribution of within population genetic diversity provide evidence of the tempo and direction of invasion? Understanding direction and speed of spread allows for prediction and management of areas at risk of invasions. Second, how much genetic structure is in the JRB? Does the spatial distribution of the genetic structure exhibit: (a) Regional differences as would be expected from either multiple invasions or landscape features acting as sources of vicariance. If genetic structure is partitioned due to geographic features (i.e., rivers or regional climate), then boundaries or corridors to further expansion can be identified as areas of interest for invasive species management. Alternatively doe the genetic structure exhibit: (b) Spatial auto-correlation and/or IBD which suggest that dispersal is not panmictic across the sample area. If dispersal is not panmictic, then patterns of spread may be used to identify regions with higher probabilities of invasion.

Methods

Sampling

Individual M. vimineum plants were collected from state parks, wildlife management areas and roadsides along forests. Sites outside of the basin were also chosen for collection to be used as out-groups in all analyses to determine if populations outside of the basin influenced the genetic diversity and structure within the JRB. Sites were chosen based on presence of the grass as determined by communication with Department of Conservation and Recreation and the Virginia Department of Game and Inland Fisheries. Within sites, only patches that were 10 m × 10 m or larger and containing many individuals were selected as populations for sampling. A minimum of 20 and a maximum of 40 plants were collected from each population. Each population was geo-referenced and complete individuals were collected from within each location. Within the JRB 23 populations and 370 individuals were found at state parks, wildlife management areas, county parks, roadsides and the Virginia Commonwealth University’s Rice Center for Environmental Studies (Table 1). Four populations, consisting of 74 individuals, outside of the JRB were also sampled for use as out-groups in the analyses. Two of the out-group populations (POP1 and POP3) were north of the central basin (approximately 60 km and 90 km respectively). Another out-group population (POP2) was collected from a region 45 km southwest of the western-most reach of the JRB. The easternmost out-group (POP27) was located 1 km from the Atlantic Ocean and 78 km east of the nearest population within the JRB. Individual tissue samples were stored at −80°C until DNA extraction.
Table 1

Site identification, sampling locations, number of individuals collected from each population, spatial coordinates, regional membership, percent polymorphic loci (P) and Shannon’s index (I) for all populations of Microstegium vimineum

Site ID

Sampling site

Sample size

Latitude

Longitude

Region

P (%)

I

POP1

Caledon Natural Area

24

38.3534

−77.1516

a

27.78

0.148

POP2

Pulaski County

19

37.0355

−80.5582

Eastern JRBb

61.11

0.332

POP3

Mason Neck State Park

18

38.6404

−77.1978

Central JRBb

66.67

0.367

POP4

Douthat State Park

20

37.9358

−79.7818

Western JRB

69.44

0.373

POP5

Alleghany County

10

37.8253

−79.6633

Western JRB

44.44

0.253

POP6

Amherst County

15

37.7361

−79.2887

Western JRB

63.89

0.365

POP7

City of Buena Vista

20

37.7314

−79.3118

Western JRB

47.22

0.305

POP8

James River State Park

20

37.6264

−78.802

Western JRB

47.22

0.248

POP9

James River Wildlife Management Area

9

37.6688

−78.7265

Western JRB

33.33

0.182

POP10

Holiday Lake State Park

15

37.3955

−78.6404

Western JRB

44.44

0.254

POP11

Feather Fin Wildlife Management Area

9

37.3612

−78.5797

Western JRB

52.78

0.290

POP12

Briery Creek Wildlife Management Area

10

37.1772

−78.4473

Western JRB

19.44

0.089

POP13

Bear Creek Lake State Park

12

37.5314

−78.2648

Western JRB

38.89

0.235

POP14

Twin Lake State Park

20

37.1761

−78.2764

Central JRB

72.22

0.380

POP15

Sailor’s Creek State Park

11

37.3072

−78.2281

Central JRB

63.89

0.361

POP16

Powhatan Wildlife Management Area

20

37.5512

−78.0148

Western JRB

63.89

0.379

POP17

Amelia County

15

37.2473

−78.0095

Eastern JRB

33.33

0.193

POP18

Chesterfield County

12

37.3376

−77.7216

Eastern JRB

30.56

0.183

POP19

Pocohontas State Park

20

37.3664

−77.5744

Eastern JRB

77.78

0.350

POP20

VCU Rice Center

25

37.3312

−77.208

Eastern JRB

44.44

0.239

POP21

Chickahominy Wildlife Management Area

10

37.3124

−76.9338

Eastern JRB

27.78

0.176

POP22

Chippokes Plantation State Park

20

37.1456

−76.7383

Eastern JRB

38.89

0.205

POP23

York River State Park

20

37.4119

−76.7093

Eastern JRB

41.67

0.230

POP24

Hog Island Wildlife Management Area

8

37.1419

−76.6891

Western JRB

30.56

0.183

POP25

New Quarter Park

20

37.2954

−76.6354

Eastern JRB

63.89

0.343

POP26

York County High School

18

37.2024

−76.4997

Eastern JRB

52.78

0.273

POP27

Back Bay National Wildlife Refuge

24

36.6719

−75.9161

Eastern JRBb

36.11

0.196

aIndicates that this population belongs to its own region and b Indicates that this population was clustered with a region but that this population was excluded from spatial autocorrelation analyses involving the JRB populations only

Genomic DNA was extracted using the Qiagen DNeasy 96 Plant Kit (Valencia, CA, USA) using the protocol prescribed for frozen leaf tissue. Amplified Fragment Length Polymorphism (AFLP) procedures were conducted as described in Vos et al. (1995). Double stranded adapters used for ligation were EcoRI, 5′-CTCGTAGACTGCGTACC-3′ (forward), 5′-AATTGGTACGCAGTC-3′ (reverse), and MseI, 5′-GACGATGAGTCCTGAG-3′ (forward) 5′-TACTCAGGACTCAT-3′ (reverse). Pre-selective amplification was performed with an Eco+A and Mse+C primer. Two primer combinations were used for selective amplification Eco+AGC(5′-FAM)/Mse+CAA, Eco+ACG(TET)/Mse+CAA. AFLP reproducibility was assessed using the dual-tube method described in Bonin et al. (2004).

Capillary gel electrophoresis was conducted on a MegaBACE 1000 (Amersham Biosciences Pittsburgh, PA, USA) following the manufacturer’s recommendations. Selective amplification products were sized using a ROX dye-labeled 550 base pair sizing standard. Electropherograms were analyzed with Fragment Profilerv1.2 (Amersham Biosciences). AFLP loci were initially determined by selecting peaks with an intensity of 200 relative fluorescent units (rfu) or higher in at least one individual. Capillary electrophoresis was performed on samples from the same reaction three times to ensure consistency of genotype assignment and each genotype was verified by visual inspection in Fragment Profiler. AFLP genotypes were scored “1” for presence and “0” for absence of an allele.

Within population diversity

The amount of within population genetic diversity was estimated from two complementary summary statistics. First, genetic diversity within populations was measured by percent polymorphic loci (P) and Shannon’s information index (I; Shannon 1948) using Genalex (Peakall and Smouse, 2006). These two summarizing parameters provided measures of diversity that were used as indicators of founder effects. It was expected that populations exhibiting founder effects would be at the front of the expansion and that more diverse populations would be at the core (e.g., Klopfstein et al.2006). Populations with low diversity are also expected to experience low levels of gene flow or considerable isolation from other populations whereas populations with high diversity are expected to experience high gene flow with other populations or are the products of multiple colonizations.

To identify gradients in diversity each genetically different region, as determined by Structure 2.2 (Pritchard et al. 2000; see Among Population Divergence), was separately analyzed for decreasing diversity concomitant with increasing physical distance to determine whether dispersal has been continuous across the basin. A best-fit regression test was performed for diversity parameters against longitude and latitude to determine directionality of dispersal. The expectation was that expansion has occurred in a west to east direction corresponding with a one-dimensional spread from the initial origin of invasion in Tennessee, USA and facilitated by water dispersal/hydrochory in the James River. The analyses were used to determine if changes in diversity were in a stepping-stone pattern or if they were partitioned by region due to multiple introductions or long-distance dispersal from outside of the JRB.

Among population divergence

Spatial discontinuity of genetic structure among populations was tested using Structure. Structure was run to determine if there was discontinuity in the spread of M. vimineum indicative of separate lineages colonizing the JRB. Structure was also used to examine levels of admixture representative of high gene flow between populations or regions of different descent. Populations with at least 20% of the individuals belonging to two or more different clusters were considered admixed. Structure parameters were: 100,000 iterations burn-in, 1,000,000 iterations run length, RecessiveAlleles = 1 (Falush et al. 2007), with all other parameters set to default for K = 1–20 clusters with each K run twice. Populations clustered together by Structure were considered regions and used for all subsequent hierarchical analyses.

Next, a hierarchical analysis of molecular variance (amova, Excoffier et al. 1992) was used to analyze among population and among region genetic differentiation. Among strata differentiation estimated among all populations (ΦST), all regions (ΦRT), and among populations within regions (ΦSR) was calculated using GeneticStudio (Dyer 2009). Pair-wise ΦST values for populations were also calculated using GeneticStudio and were used as a measure of differentiation between populations in conjunction with pair-wise genetic distances. Nei’s genetic distance (Nei 1978) was calculated using Genalex and was subsequently used to construct a neighbor-joining tree using Phylip 3.66 (Felsenstein 1993). Both an un-rooted neighbor-joining tree and a rooted tree (using out-group populations to root) was constructed. Clades in the neighbor-joining tree were compared to regional designations ascertained by Structure for congruence.

To examine localized gene flow that may be occurring after colonization, spatial auto-correlation was tested using Genalex to resolve the distance at which pair-wise population correlation became insignificant. These distances determined the spatial genetic structure indicative of gene flow between neighboring populations. A Mantel test was used to determine if genetic dissimilarity, as measured by Nei’s Distance, is correlated with spatial separation among populations resulting in IBD (Mantel 1967).

The degree of spatial structure influencing the distribution of among population genetic differentiation was determined by a step-wise analysis of molecular variance (stamova; Dyer et al.2004). The effects of spatial location were removed as a covariate prior to estimating the degree of among population differentiation, ΦST,Spatial, providing an estimate of the extent to which the observed genetic differentiation is a result of spatial location. Differences between the uncorrected ΦST, and the spatially corrected ΦST,Spaial, were estimated using GeneticStudio. In addition to trends across the JRB, the effect of spatial location on genetic variation was also tested at the regional level.

Lastly, a Population Graph was created to describe the manner in which genetic variation is distributed amongst populations (Dyer and Nason 2004 and Dyer 2007). The resulting network topology was used to infer which populations may have in the past or are still experiencing gene flow. The population network was also used for assessing congruence with regions identified by Structure. The genetic distances between populations in the network were then regressed on Euclidean distance (using a Mantel approach) to estimate isolation-by-graph-distance (IBGD), a complementary measure to IBD (see Garrick et al. 2009 for more information). Isolation-by-graph-distance also provides an indication of two different categories of spatial genetic discontinuities. The first category consists of populations that are spatially closer than expected given their genetic covariance. In a Population Graph, the edges connecting these populations are “compressed” indicating potential locations of vicariance such as intervening mountain ranges. The second category of spatial genetic discontinuity are those populations who are spatially much further apart than expected given their genetic covariance. Here, the edges in the Population Graph are “extended” and are consistent with a scenario of long distance dispersal (Garrick et al. 2009).

Results

Sampling

From the 444 individual specimens collected, all loci were successfully amplified repeatably for 359 individuals (80%). Using the two primer combinations (EcoAGC/MseCAA and EcoACG/MseCAA) 52 loci were found of which 36 loci (69%) were highly reproducible. All 36 loci were also polymorphic across all populations with the exception of one private allele found only at out-group population POP1 with 22 individuals.

Within population diversity

The overall mean percent polymorphic loci (P) was 47.94 ± 3.06% SE, but there was a wide range of values for percent polymorphic loci with the highest percentage at POP19 (P = 77.78%) and the lowest at POP12 (P = 19.44%; Table 1). The mean diversity measured by Shannon’s I was 0.264 ± 0.010SE, with the highest observed at POP14 (I = 0.380) and the lowest at POP12 (I = 0.089; Table 1).

When examining patterns of diversity along spatial gradients (rather than as a function of pair-wise spatial separation) there was no significant relationship between longitude and any diversity measure within the entire JRB or within regions. Not surprisingly, given the orientation of the JRB and the proximity of sampled populations, there were also no relationships between latitude and I or P within the JRB or eastern region. However, there was a significant relationship in the west region between latitude and genetic diversity for both Shannon’s I and P (P = 0.0269, F = 6.7139, R2 = 0.3419; and P = 0.0232, F = 7.168, R2 = 0.3593). After performing a coordinate rotation of the spatial coordinates there was not a significant relationship between diversity and latitude.

Among population divergence

Five regions were inferred using Structure when all populations were included in the data set. However, only three regions (Western, Central and Eastern) were detected when the out-groups were removed and only the sites within the JRB were considered (Fig. 1). Clustering solutions were consistent between each K = 2 runs of Structure. Two of the out-groups (POP1 and POP2) were clustered as the only members of their respective region. The other two out-groups (POP3 and POP27) were clustered with the central and eastern regions, respectively. Due to close geographic proximity of POP27 to the eastern region it was included in the eastern region for all subsequent analyses. Within the confines of the JRB the three regions were grouped into geographically proximate clusters as follows: (1) Western region consisted of 11 populations with the easternmost population only 34 km from the nearest east region population and 22 km away from the nearest west region population. (2) The central region consisted of only two populations separated by 16 km, and (3) An eastern region consisted of 11 populations with the westernmost population being 27 km west of the nearest east region population and 34 km away from the nearest west region population. Two populations, POP24 and POP19 in the eastern region, showed a high degree of admixture (Fig. 1). This admixture resulted in POP24 and POP19 being most likely placed in the eastern region but with non-insignificant components of the western region for POP24 and the western and central regions for POP19. The remaining populations did not have appreciable levels of admixture between the three putative regional groups.
Fig. 1

(Above) Microstegium vimineum populations within the James River Basin of Virginia where shading denotes regional membership determined by Structure. (Below) Membership values (Q) determined by Structure for populations in the James River Basin listed in geographic order from west to east with membership shades corresponding to map colors. Refer to Table 1 for population information

Using the hierarchical clustering of populations suggested by Structure, significant differences were found in the genetic structure among regions (ΦRT = 0.4566; P < 0.005), among populations within regions (ΦSR = 0.1653, P < 0.005), and among all populations (ΦST = 0.5464, P < 0.005). When out-groups were considered in the hierarchical amova, there were similar results (ΦST = 0.5445, ΦRT = 0.4265, ΦSR = 0.2057) suggesting that out-group populations do not significantly affect the genetic structure within the JRB. Taken as a pair-wise analysis independent of the regional affiliations, all pairs of populations were significantly differentiated (ΦST ranged from 0.215 to 0.827). Within the west and east regions there was also significant structure among populations ΦST, West = 0.1133 and ΦST, East = 0.2331.

Nei’s genetic distances ranged from 0.013 to 0.437 and were greater between populations from different regions than between populations from within the same region (mean within: West = 0.07, Central = 0.08, East = 0.07 SE = 0.004; mean among: West-Central = 0.30, West-East = 0.24, Central-East = 0.23 SE = 0.006). The neighbor-joining tree (Fig. 2) contains three general clades matching the three regions inferred by Structure. The neighbor-joining tree did not exhibit a pattern of serial nestedness (as in Nason et al. 2002) as would be predicted under an idealized one-dimensional stepping-stone model of range expansion. Instead, populations that were spatially proximate were not necessarily nearest neighbors on the tree.
Fig. 2

Neighbor-joining tree of Microstegium vimineum population of the James River Basin as well as out-group populations constructed using Phylip from Nei’s genetic distance. The east and west regions both form clades tat are in agreement with regions defined by Structure. Refer to Table 1 for population information

Localized spatial genetic structure, as measured by spatial auto-correlation, was found throughout the data. Within region auto-correlated patterns were observed with the eastern region where spatial auto-correlation was found up to a pair-wise distance among populations of 5 km (P < 0.001, Fig. 3a). The western region also exhibited a similar pattern of spatial auto-correlation, only slightly more intense, with significant spatial auto-correlation among pairs of populations separated by a distance of 10 km (P < 0.05, Fig. 3b). Auto-correlation was not estimated in the putative central region due to the small number of populations (K = 2).
Fig. 3

a Spatial auto-correlation for eastern cluster populations of Microstegium vimineum in the James River Basin indicating significant correlation of genotypes up to 5 km. Dashed lines indicate upper and lower bounds of a 95% confidence interval. Standard error bars are bootstrapped values around the observed population correlation coefficient. Correlation coefficients for distance classes are taken at the end-point to include all populations within the range of the distance class up to the start of the next distance class. b Spatial auto-correlation for western cluster populations of Microstegium vimineum in the James River Basin indicating significant correlation of genotypes up to 10 km

IBD, consistent with limited dispersal, was present when considering all populations in the JRB (Nei’s genetic distance; Mantel Test Z = 167.55, P = 0.001, ρ = 0.4226). When only considering populations in each region, there were conflicting results. There was a significant correlation between genetic distance and geographic distance in the west region (Mantel Z = 11.00, P < 0.05, ρ = 0.6286); however, there was no correlation in the east region (Mantel Z = 4.75, P = 0.290, ρ = 0.0781).

Given the potential for spatial influences on observed genetic structure the stamova model was used to determine what proportion of the observed genetic structure (ΦST) could be explained by spatial covariates. Removing the effects of spatial locations as a covariate reduced the observed differentiation among populations in the JRB by 23% (ΦST = 0.5464 vs. ΦST, Spatial = 0.3135). However, the west and east regions had much lower levels of variation attributed to spatial structure with 0.2 and 0.3% of the genetic variation explained by spatial location.

The distribution of genetic covariance among populations as depicted in the Population Graph revealed a topology that was elongated along the longitudinal axis and exhibited a compacted topology within putatively identified regions (Fig. 4). This topology is consistent with increased gene flow within but not among regions. Edges connecting populations in the Population Graph are indications of significant genetic covariance between populations. These connections are evidence of historical or contemporary gene flow. The west and east regions were connected through only two populations (POP19, POP24). The relationship between edge lengths, a measure of genetic covariance, and spatial separation among populations identified extended edges between POP4–POP1, POP5–POP16, POP5–POP24, POP7–POP24 and POP18–POP27, which would be consistent with the hypothesis of long-distance dispersal (Fig. 4). Conversely, compressed edges between POP3–POP1, POP17–POP18, POP17–POP19 and POP14–POP15 (Fig. 4) were found to be connecting populations that were more spatially proximate than expected given the genetic covariance indicating locations of potential intervening vicariance or potential zones of secondary contact.
Fig. 4

(Above) Populations of Microstegium vimineum in Virginia and the James River Basin with extended (dashed lines) and compressed edges (solid lines) between populations where numbers above populations correspond to numbers in the Population Graph. (Below) Population Graph of all sampled populations of Microstegium vimineum (node size indicates within population variance, node shade corresponds to clusters in Fig. 1 and thin black lines are retained edges indicating genetic covariance. Refer to Table 1 for population information

Discussion

The most striking result from our analysis was that within the JRB, there is evidence of a zone of secondary contact between separate lineages. It was thought that expansion of M. vimineum was continuous across the basin in a westward direction. However, regional partitioning determined by Structure in conjunction with the NJ Tree and Population Network (Figs. 2 and 4, respectively) indicates three separate introductions with subsequent expansion creating a zone of secondary contact between east and west regions. Multiple shared edges in the Population Network (Fig. 4) between western populations and POP24 and POP19 in the east region is indicative of gene flow. Furthermore, the partitioning inferred by Structure was strong for all populations except for POP24 and POP19, which are of admixed descent.

The admixture seen in populations along the border of the east and west regions (i.e., POP19 and POP24; Fig. 1) is similar to the admixture seen in two divergent lineages of Astronium urundeuva (Aroeira; Anacardiaceae) experiencing secondary contact as suggested by Caetano et al. (2008). There is also evidence that this secondary contact is recent, genetic distance and differentiation (ΦST) was greater among populations within different regions than within their own regions except for POP24 and POP19 populations. Gene flow between groups, even when connected through two populations, over time should serve to homogenize the genetic variation between clusters. Also, if there was evidence of admixture in populations directly connected to POP24 or POP19 then time since secondary contact would be considerably longer.

Regional partitioning in genetic structure in M. vimineum indicates that multiple colonization events have led to the current distribution of this invasive grass. This finding suggests that more precautions need to be taken to prevent further introductions and that because of multiple introductions the spread of this grass has the potential to become quite rapid compared to the temporal scales needed for natural diffusive spread. Secondary contact between the two separate lineages has potential implications for the ongoing controversy of why invasive species are so successful. Zones of secondary contact are excellent opportunities to study evolutionary rates in invasive species. If the genetic differentiation between lineages is not ameliorated over time by gene flow than rates of evolution are relatively fast. A continuous barrier demonstrates that populations have become adapted to the particular environment they occupy. In M. vimineum, which has a relatively recent introduction (ca. 100 years; Fairbrothers and Gray 1972), continuing divergence indicates rapid evolution and adaptation to the environment (e.g., Latta and Mitton 1999). Alternatively, if differentiation is reduced then this demonstrates the ability of F1 hybrids to be successful in intermediate habitats. Both of these scenarios lend insight into why invasive species are so prolific in introduced areas.

Fine-scale patterns of genetic diversity produced during an invasion are influenced by founder events and gene flow. However, the levels of genetic diversity within the JRB varied with some populations being genetically depauperate while others were surprisingly diverse. The level of diversity found in populations such as POP19, POP14 and POP4 was higher than expected for recent colonization (Table 1). The level of diversity suggests that gene flow via out-crossing may be occurring more frequently than expected based on mating-system. A comparison of allozyme diversity levels in plants with various mating systems has shown that high diversity is prevalent in obligate out-crossing species (Hamrick and Godt 1996). While comparisons of diversity levels between allozymes and AFLPs cannot be direct, similar trends in diversity should occur across mating systems regardless of the molecular marker. The JRB also possessed populations with very low levels of diversity indicative of either low gene flow between other populations, high rates of inbreeding or self-fertilization or recent founder events.

For both invasive as well as non-invasive plant species, gene flow consists of two separate dispersal vectors, each of which may have unique influences on the spatial distribution of genetic variance (Odduo-Muratorio et al. 2001). In M. vimineum, pollen is dispersed passively via wind. If the spatial scale of pollen-mediated gene flow is smaller than the distribution of our sample sites, we expected to find patterns of IBD (Loveless and Hamrick 1984) throughout the basin and indeed we did. Seed dispersal is also passive and M. vimineum has been reported to be dispersed via water (Barden 1987; pers. obs. S.A. Baker). This may result in episodic long-distance dispersal events resulting in discontinuities in genetic variation. A major consequence of seed-mediated gene flow is that if it is limited, independent of the range of pollen-mediated gene flow, then spatially auto-correlated patterns will emerge. Both IBD and spatial auto-correlation are viewed as separate patterns because they are generated by alternate processes but nonetheless can be inferred from the genetic data.

Spatial auto-correlation and IBD can determine the pattern of spread within regions that could indicate why secondary contact has been recent. There was significant IBD as indicated by the Mantel test indicating that populations within regions are not experiencing high rates of gene flow. Also, population similarity measured by spatial auto-correlation revealed that populations in the west region were only significantly similar at a distance of 10 km and 5 km in the east region (Fig. 3b, a). Together, these findings indicate that both pollen and seed dispersal is occurring at maximum distances of 5–10 km. This is contributing to the heterogeneity of populations and also why gene flow following secondary contact has not been sufficient to homogenize the two lineages. The use of spatial auto-correlation across regions is an inappropriate analysis as these regions were identified as separate by Structure. Analyzing both regions for spatial auto-correlation violates the assumption of stationarity (Legendre 1993 and Wagner and Fortin 2005) because different genetic and demographic processes are shaping the populations in different clusters. What can be determined is that the low level of similarity over great distances paired with IBD indicates that the expansion of M. vimineum has been a slow diffuse spread leading to a patchy mosaic of genetic structure. This slow diffuse spread with gene flow occurring under 10 km allows for a focusing of management and eradication plans on nearby areas of ecological interest.

The Population Graph, NJ tree, and within population genetic diversity suggests a patchy diffuse spread of M. vimineum within regions inside the basin however, there is also evidence of long-distance dispersal occurring within the JRB. If expansion was continuous then the NJ tree should show serially nested populations in the direction of spread however this was not the case for populations within the JRB. The Population Graph was elongated along the axis of the JRB but regions were visibly grouped together with many edges between populations in the same region. Within regions there is evidence of long-distance dispersal as indicated by extended edges on the Population Graph. However, these connections do not imply directionality (e.g., the edges are based upon covariance that is symmetric between populations) but they do indicate that populations separated by large distances are genetically more similar than expected based on the dataset. Klopfstein et al. (2006) predicted that populations at the front of an expansion should be lower in diversity than those at the core of the expansion. Within each region there appeared to be no obvious front or core where population diversity varied and as indicated by the lack of significance following coordinate rotation, correlation between latitude and diversity was an artifact of sampling along a north–south gradient. This suggests that there is no correlation between latitude or longitude and population diversity and thus expansion has not occurred in a stepping-stone fashion. A lack of a stepping-stone pattern in genetic structure with no obvious expanding invasion front further supports the notion that management should be focused on populations in close proximity due to diffusive spread and non-directionality in spread.

In conclusion, we have shown that within the JRB there has been more than one introduction and a few instances of long distance dispersal but no obvious direction of spread. While some might be tempted to focus on preventing further invasions or long distance dispersal, we argue that this should not be the only approach. The spread of M. vimineum within the JRB has also been diffuse, with gene flow occurring over relatively short distances (5–10 km) suggesting that efforts should be more concerned with the movement of propagules over short distance and connectivity with nearby populations. Low levels of within population diversity, evidence of gene flow over short distances within regions and limited gene flow between separate lineages suggests the current spread of Microstegium vimineum is occurring from within the JRB. We suggest that future management plans for M. vimineum should focus on preventing the long distance dispersal of seeds as well as eradication of newly established patches surrounding larger populations.

Notes

Acknowledgments

The authors thank Associate Editor Andrew Young and the two anonymous reviewers for their helpful insights and comments on the initial version of this manuscript. In addition SAB would also like to thank the Virginia Department of Forestry for identification of sampling sites, the Virginia Department of Conservation and Recreation and the Virginia Department of Game and Inland Fisheries for permission to sample from state parks and wildlife management areas. Portions of this study were supported by a VCU Rice Center Research Grant to SAB (Virginia Commonwealth University Rice Center Contribution #16).

Conflict of interest

None.

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of BiologyVirginia Commonwealth UniversityRichmondUSA
  2. 2.Department of BiologyVirginia Commonwealth UniversityRichmondUSA

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