American Journal of Botany 98(1): 109–121. 2011.
INFLUENCES OF LANDSCAPE AND POLLINATORS ON POPULATION
GENETIC STRUCTURE: EXAMPLES FROM THREE PENSTEMON
(PLANTAGINACEAE) SPECIES IN THE GREAT BASIN1
Andrea T. Kramer2,3,4,5, Jeremie B. Fant3, and Mary V. Ashley4
2 Botanic
Gardens Conservation International (U.S.), 1000 Lake Cook Road, Glencoe, Illinois 60022 USA; 3 Chicago Botanic
Garden, 1000 Lake Cook Road, Glencoe, Illinois 60022 USA; and 4 University of Illinois at Chicago, 845 West Taylor Street,
M/C 066, Chicago, Illinois 60607 USA
• Premise of the study: Despite rapid growth in the field of landscape genetics, our understanding of how landscape features interact with life history traits to influence population genetic structure in plant species remains limited. Here, we identify population genetic divergence in three species of Penstemon (Plantaginaceae) similarly distributed throughout the Great Basin region
of the western United States but with different pollination syndromes (bee and hummingbird). The Great Basin’s mountainous
landscape provides an ideal setting to compare the interaction of landscape and dispersal ability in isolating populations of different species.
• Methods: We used eight highly polymorphic microsatellite loci to identify neutral population genetic structure between populations within and among mountain ranges for eight populations of P. deustus, 10 populations of P. pachyphyllus, and 10 populations of P. rostriflorus. We applied traditional population genetics approaches as well as spatial and landscape genetics
approaches to infer genetic structure and discontinuities among populations.
• Key results: All three species had significant genetic structure and exhibited isolation by distance, ranging from high structure
and low inferred gene flow in the bee-pollinated species P. deustus (FST = 0.1330, RST = 0.4076, seven genetic clusters identified) and P. pachyphyllus (FST = 0.1896, RST = 0.2531, four genetic clusters identified) to much lower structure and higher inferred gene flow in the hummingbird-pollinated P. rostriflorus (FST = 0.0638, RST = 0.1116, three genetic clusters identified).
• Conclusions: These three Penstemon species have significant yet strikingly different patterns of population genetic structure,
findings consistent with different interactions between landscape features and the dispersal capabilities of their pollinators.
Key words: gene flow; landscape genetics; microsatellite; Penstemon; pollination syndrome; population genetic structure.
Landscape features interact with life history traits to either
enhance or truncate gene flow in ways that are not always predictable. The pattern and degree of population divergence will
depend largely on realized gene flow (Slatkin, 1985), which in
plants is determined by the composition, configuration, and matrix quality of the landscapes they inhabit (Manel et al., 2003;
Storfer et al., 2007; Holderegger and Wagner, 2008), as well as
life history traits such as pollination system and dispersal system (Hamrick and Godt, 1996; Richards, 1997; Duminil et al.,
2007). In animal-pollinated plants, landscape connectivity and
pollinator movement can affect population genetic structure in
unpredictable ways. For example, in two herbaceous species,
Lantana camara (butterfly pollinated) and Rudbeckia hirta (hymenoptera pollinated), greater habitat connectivity predictably
enhanced gene flow by facilitating greater pollinator movement
among populations, decreasing population genetic divergence
(Townsend and Levey, 2005). However, in the tropical forest
tree Dinizia excelsa (Dick et al., 2003) and temperate forest tree
1 Manuscript
Eucalyptus globulus (Mimura et al., 2009), loss of habitat connectivity unexpectedly enhanced gene flow, because primary
pollinators traveled greater distances between fragmented habitats. Significant differences in gene flow, and corresponding
effects on genetic divergence, can therefore exist among populations of the same species occupying different landscapes as
well as among different species occupying the same landscape.
For most species, the interacting effects of landscape features
and life history traits on population genetic structure are poorly
understood, but the growing field of landscape genetics is beginning to address this gap (Storfer et al., 2010).
The use of neutral genetic markers and traditional estimates
of genetic structure like FST (Weir and Cockerham, 1984) and
Nei’s genetic distance (Nei, 1978) provide insight into population genetic divergence, as do more recent Bayesian clustering
methods, which do not require a priori assignment of individuals to populations and thus allow cryptic population genetic
structure to be identified (Pritchard et al., 2000). Genetic distance between populations can be graphically represented via
hierarchical genetic clustering methods such as UPGMA
(Sneath and Sokal, 1973), or it can be combined with geographic distance information to detect patterns of isolation by
distance. In landscapes where topographic features such as
mountain ranges and arid valleys likely present obstacles to
gene flow even for neighboring populations, newly developed
methods allow genetic discontinuities between adjacent populations to be identified (e.g., BARRIER software, Manni et al.,
2004). Inference of gene flow patterns from population structure can be further enhanced by combining molecular maker
received 24 June 2010; revision accepted 22 November 2010.
The authors thank K. Havens and P. Olwell for project support, and
C. Newton, R. Tonietto, C. Flower, L. Jefferson, S. Karumuthil-Melethil,
E. Lukina, E. Sirkin, and J. Keller for field and laboratory assistance. They
also thank P. Wilson and an anonymous reviewer for comments on an
earlier version of this manuscript. This research was supported by the
Bureau of Land Management, Department of the Interior (Assistance
Agreement PAA-01–7035), and an EPA STAR Fellowship to A.T.K.
5 Author for correspondence (e-mail: andrea.kramer@bgci.org)
doi:10.3732/ajb.1000229
American Journal of Botany 98(1): 109–121, 2011; http://www.amjbot.org/ © 2011 Botanical Society of America
109
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American Journal of Botany
data directly with information on geographic landscape features
(Manel et al., 2003; Holderegger et al., 2006; Storfer et al.,
2007) and new statistical methods in spatial genetics (Guillot
et al., 2009). Interpreting genetic discontinuities in the context
of landscape features provides a powerful approach for understanding the interaction of plant dispersal systems, landscape,
and microevolutionary processes, yielding insights relevant to
evolutionary biology (Kay and Sargent, 2009).
Genetically isolated populations may follow different evolutionary trajectories because of a combination of mutation, genetic drift, and/or natural selection. Extreme examples come
from high-elevation mountaintops like the Andes (Hughes and
Eastwood, 2006), rock outcrop “inselbergs” (Barbara et al.,
2007), and the edaphically diverse habitat of southern Africa’s
Karoo region (Ellis et al., 2006). With more than 100 separate
mountain ranges isolated by arid basins, the Great Basin region
of the western United States provides the opportunity to determine how a complex topography influences gene flow in species with different dispersal capabilities. Previous studies on
plants distributed throughout the region have shown that conifer species that rely on wind for pollen movement and birds for
seed dispersal display little population divergence (Johnson,
1975; Wells, 1983; Hamrick and Godt, 1996; Jorgensen et al.,
2002), whereas populations of terrestrial animals are more genetically isolated by the regions’ landscape (Floyd et al., 2005).
For animal-pollinated plants, pollinator movement is likely affected by the region’s steep elevational gradients, arid valleys,
and patchy distribution of plant communities (West, 1988).
Pollinators may navigate this landscape in different ways, with
varying impacts on the population genetic structure of the species they pollinate. For example, birds might be more effective
than bees at connecting distant populations via successful longdistance pollination (Graves and Schrader, 2008).
One of the most species-rich plant genera in the Great Basin
is Penstemon (Plantaginaceae). It is also North America’s largest endemic genus, with over 270 species. The great diversity in
this genus is hypothesized to be the result of a recent, rapid
evolutionary radiation centered in the western United States,
including the Great Basin region (Wolfe et al., 2006). Most
Penstemon species are long lived, primarily outcrossing perennial forbs with gravity-dispersed seeds, though wind might aid
dispersal along the ground (Fuller and del Moral, 2003). Realized gene flow is thus believed to be largely a function of pollinator movement. Primary pollinators for Penstemon species
include an array of insects (primarily bees) and hummingbirds.
Pollinators generally can be predicted by the pollination syndrome (the suite of floral traits that have evolved in response to
particular pollinators) displayed by each species (Thomson et
al., 2000; Wilson et al., 2004; Wilson et al., 2006). Penstemon
has been at the center of many studies on the validity and
application of pollination syndromes (Thomson et al., 2000;
Castellanos et al., 2003; Castellanos et al., 2004; Wilson et al.,
2004; Castellanos et al., 2006; Wilson et al., 2006; Wilson
et al., 2007; Thomson and Wilson, 2008). In particular, hummingbird pollination has arisen independently in the genus at least
10 times from bee pollination and possibly more than 20 times
(Wilson et al., 2007). Interestingly, this shift seems to occur in
only one direction; no occurrences have been reported of a shift
from hummingbird to other pollination syndromes nor of reversions back to the original bee-pollination syndrome (Wilson et
al., 2007). Similar biases have been detected in numerous other
genera: Mimulus (2 times; Beardsley et al., 2003), Erythrina (4
times; Bruneau, 1997), and Costus (7 times; Kay et al., 2005).
Here, we focus on three Penstemon species that are similarly
distributed throughout the Great Basin’s mountainous landscape and ask how landscape features affect their population
genetic structure. We use microsatellite markers and multiple
analyses to identify population genetic differentiation in these
species and to better understand how this structure relates to
landscape features. Because the species chosen for study have
different pollination syndromes but otherwise similar life history characteristics, we also identify how the population genetic
structure of each species may be differently affected by the interaction of landscape features with characteristics related to
their dispersal ability. We expect that genetic divergence will
be greater among mountain ranges than within mountain ranges
for all species but that the degree and structure of population
genetic differentiation may vary for species with different primary pollinators. Given previous findings for greater long-distance foraging in hummingbirds vs. bees, we expect that the
hummingbird-pollinated species will have lower population genetic divergence than the bee-pollinated species.
MATERIALS AND METHODS
Study species—This study focuses on three species: Penstemon deustus
Douglas ex Lindl. var. pedicellatus M. E. Jones, P. pachyphyllus A. Gray ex
Rydb. var. congestus (M. E. Jones) N. H. Holmgren, and P. rostriflorus Kellogg. All three species are considered common and widespread throughout
sagebrush–steppe habitat in the Great Basin (Kartesz, 1999). This habitat is
found primarily on the region’s sky islands at mid to high elevations, and within
it the distribution of each species is quite patchy: P. deustus and P. rostriflorus
often occur on rocky slopes, whereas P. pachyphyllus is primarily found on
sandy or rocky plateaus. Of the three species, P. deustus has the largest distribution throughout the Great Basin, whereas P. rostriflorus is found primarily
throughout the southern half of the region and P. pachyphyllus the southeastern
half. Study species are found on many but not all mountain ranges within their
distribution, so where their ranges overlap, two or even all three study species
can occur on the same mountain range. Although populations of each species
occasionally occur near each other, they do not grow at the same site. Within
each mountain range, as few as one and as many as five or more populations
may exist at distances ranging from 1 km to more than 30 km apart. Population
sizes and densities are similar for all three species, ranging from 100 to 500 or
more flowering plants per population.
These three species share most life history traits thought to affect population
genetic structure. They are all long-lived, herbaceous, perennial forbs that produce numerous, structurally similar protandrous flowers borne on multiple
flowering stalks. They have overlapping bloom times, but P. deustus and P.
pachyphyllus generally bloom earlier in the season than P. rostriflorus. The
species are not known to reproduce clonally and have mixed-mating systems
with at least some degree of self-compatibility (Kramer, 2008). Seeds produced
by Penstemon species are dispersed primarily by gravity but may tumble along
the ground with the wind until trapped (Fuller and del Moral, 2003). Seeds of
the three study species differ in size: P. deustus = 0.130 mg ± 0.028 mg;
P. pachyphyllus = 1.411 mg ± 0.472 mg; P. rostriflorus = 0.343 mg ± 0.095 mg
(Kramer, 2008).
The most notable difference between the three study species is in their
flower morphologies and predominant visitors. Both P. deustus and P. pachyphyllus have floral traits associated with bee syndromes (Thomson et al., 2000;
Fenster et al., 2004; Wilson et al., 2004): P. deustus produces small, pale flowers, which are visited predominately by small bees (many Osmia spp.) but also
bumblebees (Bombus spp.) at certain high-elevation sites (A. Kramer, personal
observations), and P. pachyphyllus produces larger purple flowers, which are
generally visited by larger bees, including bumblebees at most sites (A. Kramer,
personal observations). The third species, P. rostriflorus, has a hummingbird
syndrome, producing red, tubular flowers, which are visited primarily by many
of the hummingbird species found in the western United States (Thomson et al.,
2000; Wilson et al., 2004).
Study sites—Herbarium records were used to identify study sites on mountain ranges throughout the distribution of each study species in the Great Basin
January 2011]
Kramer et al.—Landscape genetics of Great Basin Penstemon
Q1
floristic region (Cronquist et al., 1972). Many of the larger mountain ranges in
the region contained at least one and often many herbarium records for each
species. Site selection for each species was targeted to mountain ranges with at
least two herbarium collections from different locations, spanning their distribution throughout the Great Basin. In the summer of 2003, six populations of P.
deustus and eight populations each of P. pachyphyllus and P. rostriflorus were
identified on four to six mountain ranges per species (Fig. 1, Table 1). Each
population contained more than 100 adult plants, and when possible, two populations were located on each mountain range (separated by at least 2 km). At
each study site, 5–10 g of fresh leaf tissue was collected from at least 32 haphazardly located individuals, spanning the range of the population and avoiding
sampling from adjacent plants. All leaf collections were dried in silica gel for
later DNA extraction, while GPS coordinates were recorded and voucher herbarium specimens collected for all species and sites. Vouchers were later deposited at the Nancy Poole Rich Herbarium (Chicago Botanic Garden) and the
Great Basin Herbarium (Utah State University, see Appendix 1). In 2006, collections were made following the same protocols at two additional populations
for P. deustus, while in 2008 collections were made at two additional populations each for P. rostriflorus and P. pachyphyllus to increase the number of
sampled populations (see Table 1).
Molecular data—Total genomic DNA was extracted from silica-dried leaf
material following a CTAB method modified from Doyle and Doyle (1987).
Genotypes were obtained for eight polymorphic nuclear (dinucleotide repeat)
microsatellite loci developed from P. rostriflorus (Pen02, Pen04, Pen05, Pen18,
Pen23, Pen24, Pen25; detailed in Kramer and Fant, 2007, as well as an eighth
locus, Pen06, developed following the same protocol; see Appendix 2 for details). DNA from all individuals was amplified with polymerase chain reaction
(PCR) by using fluorescently tagged forward primers (WellRed D2, D3, or D4;
Sigma-Proligo, St. Louis, Missouri, USA) following methods described in
Kramer and Fant (2007). Genotypes were scored with a CEQ 8000 Genetic
Analysis System and CEQ FRAGMENT ANALYSIS software (Beckman
Coulter, Fullerton, California, USA).
Statistical analysis—Microsatellite genotype data were formatted for analysis with CREATE software (Coombs et al., 2008). Descriptive parameters were
calculated in GDA (Lewis and Zaykin, 2001), including the following: P, proportion of polymorphic loci; n, mean sample size; A, mean number of alleles
per locus; Ap, total number of private alleles; HE, expected heterozygosity; HO,
observed heterozygosity; and Weir and Cockerham’s (1984) estimates of
Wright’s FIS (f; within-population inbreeding coefficient). Departures from
Hardy–Weinberg equilibrium (HWE) were tested for each species by using exact tests in GENEPOP (Raymond and Rousset, 1995) for each locus and population, as well as globally.
Population differentiation for each species was calculated by using Weir and
Cockerham’s (1984) estimate of FST (theta) in TFPGA 1.3 (Miller, 1997) and
Slatkin’s unbiased estimator of RST using RST_CALC (Goodman, 1997). For
FST, significance of values was determined by analyzing jackknife support over
all loci in TFPGA 1.3, and for RST, significance of values was tested across all
loci by permutation tests and bootstrapping to provide 95% confidence intervals
in RST_CALC. These two measures were chosen because FST assumes each
mutation can produce an allele of any size, and hence differences between populations are driven primarily by drift, whereas unbiased RST is thought to better
reflect differentiation at microsatellite data because it assumes stepwise mutation such that each allele mutates to one of the immediately neighboring alleles
with equal probability, incorporating mutation and drift (Olivier et al., 2003).
Insight into the drivers of population differentiation can then be gained by comparing values for FST and RST: values will be similar if genetic drift is the primary cause of population differentiation, whereas RST will be larger if
differentiation is also driven by stepwise mutation (Hardy et al., 2003).
The Bayesian clustering analysis software STRUCTURE v. 2.2 (Pritchard et al.,
2000; Falush et al., 2007) was used to provide insight into patterns of gene flow
(admixture, Q) and population subdivision (number of genetic clusters, K) in
each study species. This software uses individual multilocus genotypes to test
for the presence of population structure without a priori assignment of individual plants to populations. It does so by introducing population structure and
finding population groupings in the least possible disequilibrium (HWE and
linkage disequilibrium) by using a Markov chain Monte Carlo method. For
each species, we carried out 20 independent runs per K using a burn-in period
of 10 000 and collected data for 10 000 iterations for K = 1 to 16 (described in
Evanno et al., 2005). The most likely value of K was assessed by using the rate
of change in the log probability of data between corresponding K values (ΔK),
as detailed in Evanno et al. (2005). For each species, average and individual
111
admixture proportions (Q) were recorded for each study population by identified genetic cluster for the selected value of K. Finally, to provide a graphical
representation of genetic distance data and relations within and among mountain ranges for each species, Nei’s unbiased estimate of minimum genetic distance (Nei, 1978) was used for unweighted pair-group clustering based on
arithmetic averages (UPGMA), performed in TFPGA 1.3 (Miller, 1997).
For each species, genetic and spatial data were used to investigate isolation
by distance. First, pairwise genetic distance among populations was estimated
by calculating two measures in SPAGeDi (Hardy and Vekemans, 2002): (1)
Rousset’s linearized FST (FST /(1 – FST) (Weir and Cockerham, 1984; Rousset,
1997) and (2) unbiased RST. Isolation by distance was then tested in each species by regressing the resulting pairwise population comparisons of genetic
distance (FST and RST) on spatial distance (ln km, or distance between populations), and the significance of these relations was evaluated with Mantel (1967)
tests (103 permutations) in GENALEX (Peakall and Smouse, 2006).
We used BARRIER 2.2 (Manni et al., 2004) to identify potential barriers to
gene flow between geographically adjacent populations. With the use of latitudinal and longitudinal coordinates and voronoi tessellation, BARRIER implements Monmonier’s algorithm to generate a neighborhood polygon around all
populations. Using pairwise matrices of Nei’s genetic distance, generated in
GENALEX for each locus (Peakall and Smouse, 2006), BARRIER assigns
each polygon edge a value on the basis of the genetic distance between the two
neighboring populations sharing it, and it separately ranks them from maximum
to least genetic difference for each locus. Starting at the polygon edge with the
highest pairwise difference between two neighboring populations, BARRIER
draws a line along the edges of the polygons, taking the path of highest genetic
distance until it transverses the sampling area, then it repeats this process with
the next-highest pairwise distance until all have been used. We restricted
BARRIER to draw only the three greatest disparities in genetic distance for
each locus across the sampling area. By repeating this process for each locus,
we identified which areas in the sampling range consistently showed high genetic distance across multiple loci, thereby identifying areas with a potential
barrier to gene flow. If genetic distance is associated purely with geographic
distances, then the two adjoining populations that are the farthest apart would
be expected to show the most barriers across all loci. However, if adjoining
population pairs have different origins, if there is strong genetic structure, or if
there is another physical or temporal barrier to gene-flow separating populations, these population pairs would also reveal a high number of barriers.
RESULTS
Descriptive statistics of loci— All eight microsatellite primer
pairs consistently amplified products and were highly polymorphic in all study species, with two exceptions: Pen25 did not
consistently amplify in P. deustus and P. pachyphyllus, so it
was not used in the analysis for these two species, whereas
Pen24 did not consistently amplify in P. rostriflorus, so it was
not used in the analysis for this species. Therefore, all analyses
were conducted on seven loci for each species. In all three species, these seven remaining loci were highly polymorphic; the
number of alleles ranged from 23 to 30 in P. deustus, seven to
26 in P. pachyphyllus, and 18 to 34 in P. rostriflorus.
Descriptive statistics of species and populations— When all
loci were combined, each species displayed a different pattern
of within-population diversity and heterozygosity (Table 2).
Both gene diversity (He) and mean alleles per loci were lowest
in P. pachyphyllus, followed by P. deustus and P. rostriflorus.
This may be the result of ascertainment bias (Hutter et al., 1998)
because the microsatellite library was developed from P. rostriflorus. Populations of all three species harbored between zero
and eight private alleles, with the exception of one P. deustus
population, Pd-SM1, which contained 23 private alleles. Measures of the population-level inbreeding coefficient (f) was significant in four P. deustus populations, three P. rostriflorus
populations, and two P. pachyphyllus populations. The average
inbreeding coefficient across all sampled populations was lowest in P. rostriflorus (0.015) and highest in P. deustus (0.06).
112
American Journal of Botany
[Vol. 98
Fig. 1. Study locations within the Great Basin region of the western United States, centered on the states of Nevada and Utah. Mountain ranges and
arid valleys are outlined and shaded in gray. Between 8 and 10 study populations (white circles; see Table 1 for additional information) were identified for
each of three Penstemon species: (A) P. deustus, (B) P. pachyphyllus, and (C) P. rostriflorus. Lines depict genetic discontinuities identified in BARRIER
(Manni et al., 2004).
Analysis of population genetic structure— All three study
species showed greater population differentiation with RST than
FST measures, suggesting mutation and genetic drift together
are driving population differentiation. However, because the
change in value between the two measures was different for
each species, their relative ranks changed depending on measure used. Of the three study species, P. rostriflorus showed
both the lowest population differentiation and the least change
in measures, with FST = 0.0639 ± 0.0125 and RST = 0.1116 ±
0.0219. For FST measures, P. pachyphyllus showed the greatest
population differentiation among the three study species (0.1896
± 0.0437), but it was only intermediate between the three species when RST was used (0.2531 ± 0.0250). Penstemon deustus
had the greatest difference between measures, suggesting a
strong role for mutation in driving divergence, with an intermediate FST value (0.1330 ± 0.0270) and the highest RST value
among the three species (0.4076 ± 0.0.063).
Bayesian analysis performed in STRUCTURE was used to
infer spatial population structure and estimate the number of
genetic clusters (K), or populations, into which the genotypic
data could be grouped. STRUCTURE results confirmed pronounced genetic structure in all three species, with generally
more structure in P. deustus and P. pachyphyllus than in P. rostriflorus. The modal value of the distribution of the true K identified a peak at ΔK = 7 for P. deustus and at ΔK = 4 for P.
pachyphyllus. In P. rostriflorus, the modal peak was at ΔK = 3,
January 2011]
Table 1.
Kramer et al.—Landscape genetics of Great Basin Penstemon
113
Q1
Detailed study site information for all three Penstemon study species collected throughout the Great Basin region of the western United
States.
Population
Penstemon deustus
Pd-DM1
Pd-DM2a
Pd-SCR1
Pd-SCR2a
Pd-SM1
Pd-SM2
Pd-PNM1
Pd-PNM2
Penstemon pachyphyllus
Pp-MP1
Pp-MP2
Pp-WWM1
Pp-WWM2
Pp-WWM3b
Pp-SR1
Pp-SR2
Pp-SCR1
Pp-SCR2
Pp-AR1
Penstemon rostriflorus
Pr-MP1
Pr-MP2
Pr-MP3c
Pr-WWM1
Pr-WWM2
Pr-SR1
Pr-SR2
Pr-SCR1
Pr-PM1
Pr-PNM1
Mountain range
State
Latitude
Longitude
Elevation (m)
Population size
Desatoya Mountains
Desatoya Mountains
Schell Creek Range
Schell Creek Range
Steens Mountains
Steens Mountains
Pine Nut Mountains
Pine Nut Mountains
NV
NV
NV
NV
OR
OR
NV
NV
39.25
39.24
39.56
39.57
42.63
42.05
39.18
39.12
−117.68
−117.78
−114.64
−114.59
−118.53
−118.62
−119.53
−119.42
2025
1909
2649
2022
1793
1368
1861
1834
100–150
400–500
200–300
200–300
200–300
200–300
150–200
150–200
Markagunt Plateau
Markagunt Plateau
Wah Wah Mountains
Wah Wah Mountains
Wah Wah Mountains
Snake Range
Snake Range
Schell Creek Range
Schell Creek Range
Antelope Range
UT
UT
UT
UT
UT
NV
NV
NV
NV
NV
37.34
37.17
38.33
38.34
38.25
39.11
39.15
39.19
39.46
40.04
−113.08
−113.08
−113.59
−113.61
−113.58
−114.35
−114.33
−114.63
−114.67
−114.51
2122
1119
2560
2216
2430
2323
2227
2530
2103
1995
300–400
300–400
150–200
300–400
150–200
1000+
300–400
1000+
500+
300–400
Markagunt Plateau
Markagunt Plateau
Markagunt Plateau
Wah Wah Mountains
Wah Wah Mountains
Snake Range
Snake Range
Schell Creek Range
Pilot Mountains
Pine Nut Mountains
UT
UT
UT
UT
UT
NV
NV
NV
NV
NV
37.35
37.29
37.89
38.35
38.26
39.02
38.99
39.19
38.39
38.85
−113.08
−113.10
−112.73
−113.61
−113.58
−114.27
−114.22
−114.63
−118.03
−119.44
2092
1632
2037
2510
2455
2768
2147
2530
1919
1678
200–300
100–150
500+
100–150
150–200
150–200
100–150
100
100–150
200–300
Note: All populations were collected in 2003 unless otherwise indicated, and population size is shown as estimated number of flowering and nonflowering
plants at the time of collection. For state abbreviations, NV = Nevada, UT = Utah, and OR = Oregon.
a Collected in 2006.
b Collected in 2007.
c Collected in 2008.
which is at the lower limits at which STRUCTURE can detect
clustering using ΔK, however the value of ΔK = 3 was supported by large shifts in L(K) and Ln’(K) at K = 3 to K = 4 associated with true value of K, as described in Evanno et al.
(2005). For P. deustus and P. pachyphyllus populations, the average admixture proportion among identified “genetic clusters”
ranged from 80% to 98% assignment to a single cluster (Fig. 2).
Across the eight P. deustus populations, STRUCTURE identified strong genetic subdivision with limited admixture between
populations both within a mountain range and among mountain
ranges (Fig. 2). The exceptions were the two populations from
Schell Creek Range (Pd-SCR1 and SCR2), which clustered into
a single grouping. In P. pachyphyllus, STRUCTURE identified
strong genetic subdivision and limited admixture between
mountain ranges but no genetic structure between populations
within the same mountain range. In P. rostriflorus, K did not
correspond to number of mountain ranges nor number of populations sampled, and all populations had considerably more admixture among genetic clusters than the other two species.
Results from UPGMA analysis (Fig. 3) provide additional
resolution regarding genetic distances between populations on
the same mountain range and on increasingly distant mountain
ranges. Penstemon pachyphyllus collectively had the largest
overall genetic distances between populations, yet populations
on the same mountain range always grouped together. In this
species, Markagunt Plateau populations exhibited the greatest
genetic differentiation from each other and all other populations (Pp-MP1 and MP2). Genetic differentiation among P. deustus populations was generally similar to P. pachyphyllus.
Populations of P. deustus on the same mountain range grouped
together, with the exception of Steens Mountain populations
(Ps-SM1 and SM2), which were located 65 km apart. The UPGMA tree for P. rostriflorus was the shallowest, a result of relatively low genetic distances between all population pairs.
Effect of landscape on population genetic structure— Mantel tests revealed significant isolation by distance for all three
study species and measures of genetic differentiation used:
Penstemon pachyphyllus (FST P = .01; RST P = .05), P. deustus
(FST P = .02; RST P = .01) and P. rostriflorus (FST P = .01; RST
P = .03). The relation between different measures of genetic
distance and geographic distance varied by species and genetic
measures used (Fig. 4). When genetic distance was calculated as
FST, P. pachyphyllus showed the greatest increase in genetic
distance with geographic distance (y = 0.095× + 0.10, r2 = 0.47),
followed by P. deustus (y = 0.095× + 0.02, r2 = 0.30) and P. rostriflorus (y = 0.032× + 0.01, r2 = 0.38). However, when genetic
distance was calculated as RST (incorporating stepwise mutation
as well as drift), P. deustus showed a slightly greater increase in
genetic distance with geographic distance (y = 0.06× + 0.12,
114
Table 2.
[Vol. 98
American Journal of Botany
Summary statistics for seven microsatellite loci shown by population for each Penstemon study species.
Population
Penstemon deustus
Pd-DM1
Pd-DM2
Pd-SCR1
Pd-SCR2
Pd-SM1
Pd-SM2
Pd-PNM1
Pd-PNM2
Overall
Penstemon pachyphyllus
Pp-SR1
Pp-SR2
Pp-SCR1
Pp-SCR2
Pp-AR1
Pp-WWM1
Pp-WWM2
Pp-WWM3
Pp-MP2
Pp-MP1
Overall
Penstemon rostriflorus
Pr-SR1
Pr-SR2
Pr-SCR1
Pr-WWM1
Pr-WWM2
Pr-MP1
Pr-MP2
Pr-MP3
Pr-PM1
Pr-PNM1
Overall
Number of plants
Mean sample size
Mean alleles per locus
Private alleles
He a
HO b
fc
32
32
32
32
32
32
32
32
256
31.9
31.9
32.0
31.9
31.7
31.9
31.9
32.0
31.9
14.7
10.7
7.9
9.6
14.7
11.0
12.4
11.3
11.5
8
4
1
5
23
8
1
3
7
0.807
0.730
0.701
0.694
0.812
0.771
0.847
0.795
0.771
0.736
0.736
0.674
0.693
0.744
0.740
0.758
0.723
0.726
0.101***
–0.009
0.039
0.001
0.085***
0.041
0.107***
0.092***
0.060
32
32
31
32
32
32
32
32
32
32
319
32.0
31.7
30.6
30.3
31.7
31.0
31.9
30.7
31.7
31.9
31.7
7.6
8.6
8.6
9.3
6.3
8.3
9.7
8.2
9.4
7.3
8.2
0
5
5
7
0
4
7
4
6
7
5
0.530
0.519
0.613
0.650
0.608
0.617
0.642
0.640
0.772
0.612
0.620
0.518
0.474
0.598
0.675
0.563
0.560
0.620
0.635
0.761
0.647
0.605
0.023
0.087**
0.025
–0.040
0.076
0.094***
0.034
0.007
0.015
–0.058
0.025
32
32
29
32
32
32
32
32
32
32
317
31.9
31.3
28.6
32.0
32.0
31.9
31.9
31.7
30.7
31.6
31.6
11.3
13.0
13.9
16.1
14.6
16.4
12.3
17.6
12.6
7.9
13.0
2
1
3
3
0
6
0
7
4
2
3
0.797
0.825
0.835
0.866
0.870
0.906
0.799
0.913
0.779
0.665
0.813
0.816
0.804
0.801
0.835
0.879
0.870
0.776
0.887
0.786
0.645
0.801
–0.025
0.026
0.041***
0.036*
–0.012
0.041*
0.030
0.036
0.009
0.030
0.015
a Expected
heterozygosity.
heterozygosity.
c Weir and Cockerham’s (1984) estimate of the within-population inbreeding coefficient.
* P < .05.
** P < .01.
***P < .001.
b Observed
r2 = 0.38) than P. pachyphyllus (y = 0.02× + 0.10, r2 = 0.19),
whereas P. rostriflorus again had the smallest increase in genetic distance with increasing distance (y = 0.01× + 0.01,
r2 = 0.25).
BARRIER was used to identify the largest difference in genetic distances between two adjoining populations, indicative
of potential barriers to gene flow (Fig. 1). In P. deustus, the
adjoining regions that produced the highest differences in genetic distance occurred between northern populations and all
others, and western populations and all others, suggesting limited gene flow north–south and east–west. Interestingly, there
was also a significant disparity in genetic distances between the
two populations in the Pine Nut Mountains (Pd-PNM1 and
PNM2) and between populations in the Steens Mountains (PdSM1 and SM2). For P. pachyphyllus, the most significant
boundaries were identified between mountain ranges. The only
other significant boundary identified for this species was between the two populations from the Markagunt Plateau (PpMP1 and MP2), located on the same mountain range but at very
different elevations (Table 1 and Fig. 1). For P. rostriflorus, the
only boundaries resulting from high genetic distance in three or
more loci were between populations on the Markagunt Plateau
(Pr-MP1, MP2, and MP3), and between the Markagunt Plateau
populations and all other populations (Fig. 1). The genetic distance between two populations on the Markagunt Plateau (PrMP1 and MP3) was comparable to the distance between
population pairs on other mountain ranges, whereas the largest
difference was identified between MP2 and the other two populations on the Markagunt Plateau (Pr-MP1 and MP3).
DISCUSSION
Gene flow maintains species cohesion. In its absence, populations isolated from one another will follow different evolutionary trajectories (Ellstrand, 1992; Morjan and Rieseberg,
2004). For plants, gene flow via the dispersal of seeds and pollen is therefore a critical determinant of current and future population genetic divergence (Kramer et al., 2008). Both physical
distance and landscape effects can interact with dispersal ability to enhance or deter gene flow. Our results show that the
Great Basin’s landscape significantly isolates the plant populations that occupy its sky islands. Further, results of our examination of three otherwise-similar Penstemon species with
January 2011]
Kramer et al.—Landscape genetics of Great Basin Penstemon
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115
Fig. 2. Identified genetic clusters (I–VII, as shown at the bottom of the figure) and Bayesian admixture proportions depicted for individual plants and
populations of all three Penstemon species (P. deustus K = 7, P. pachyphyllus K = 4, and P. rostriflorus K = 3). Squares outline individual samples for each
population. Population names correspond to study site information (Table 1), and populations on the same mountain range are similarly named, e.g., P.
deustus populations DM1 and DM2 both occur in the Desatoya Mountains.
different pollination syndromes suggest that pollinators do not
interact with landscape features in the same ways, creating different patterns and degrees of population genetic structure in
the species they pollinate.
We identified significant population genetic differentiation in
all three Penstemon species using RST, with greatest divergence
in P. deustus (RST = 0.4076) and lowest divergence in P. rostriflorus (RST = 0.1116). This divergence is likely driven by genetic drift and mutation, as RST values were higher than FST
values for all three species (e.g., FST = 0.1896 and RST = 0.2531
for P. pachyphyllus). In general, population genetic divergence
in these three species was greater than that reported for other
co-occurring plant species in the Great Basin region. For example, populations of eight wind-pollinated, bird-dispersed conifer species had an average GST ranging from 0.033 for pinyon
pine to 0.169 for bristlecone pine (Hamrick et al., 1994;
Jorgensen et al., 2002). A comparison of genetic differentiation
among our study species based on pollination syndrome revealed
greater structure in the bee-pollinated species, P. pachyphyllus
and P. deustus, compared with the hummingbird-pollinated
P. rostriflorus (Figs. 2–4). For both bee-pollinated species,
Bayesian clustering analysis independently identified each
sampled mountain range as a separate genetic cluster, with little
or no admixture between mountain ranges (Fig. 2). Other analyses, including UPGMA analyses and the identification of barri-
ers to gene flow via BARRIER, likewise revealed significant
genetic structure largely partitioned among sampled mountain
ranges. It is likely that bees either avoid crossing the Great Basin’s arid valley floors or, if they do cross it, are ineffective at
transferring pollen across these expanses. Many bees groom
pollen from their bodies at regular intervals, so even if they fly
long distances, they may not effect long-distance pollinations
(Wilson et al., 2004).
With far less genetic structure than its bee-syndrome counterparts, results from P. rostriflorus suggest that hummingbirds
are better than bees at maintaining cohesion between populations separated over much greater distances. This has often
been suggested but rarely substantiated (Graves and Schrader,
2008). Bird-mediated long-distance (>5 km) gene flow between
populations in fragmented agricultural habitat was recently
documented in the sunbird-pollinated Calothamnus quadrifidus
(Byrne et al., 2007), but the specific distances and terrains that
avian pollinators are capable of bridging are largely unknown.
Additionally, the territoriality of some bird species (hummingbirds) may actually limit long-distance pollen movement (Parra
et al., 1993). Nine hummingbird species are recorded in the
Great Basin region, including Broad-billed (Cynanthus latirostris), Magnificent (Eugenes fulgens), Black-chinned (Archilochus alexandri), Anna’s (Calypte anna), Costa’s (C.
costae), Calliope (Stellula calliope), Broad-tailed (Selasphorus
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American Journal of Botany
[Vol. 98
Fig. 3. UPGMA clustering using Nei’s unbiased estimate of minimum genetic distance (1978) for each species showed generally similar levels of
genetic distance between populations in the bee-pollinated Penstemon deustus and P. pachyphyllus, with much lower genetic distances between populations
in the hummingbird-pollinated P. rostriflorus.
platycercus), Rufous (S. rufus), and Allen’s (S. sasin) hummingbirds (Johnsgard, 1983). Our study reveals that at least
some of these species appear capable of moving pollen over
large distances across the Great Basin. Results of Bayesian
cluster analyses (Fig. 2) indicate hummingbird-assisted admixture between populations separated by at least 19 km within
mountain ranges (e.g., Pr-WWM1 and WWM2) and over 100
km between mountain ranges, particularly when populations
were at high elevations (e.g., Pr-MP1 and WWM1). A study of
two Streptocarpus species in South Africa also reported significantly lower genetic differentiation in a primarily sunbird-pollinated species than in a fly-pollinated species (Hughes et al.,
2007), providing another case in which bird pollination maintains greater population cohesion than insect pollination.
Our results highlight an important but often overlooked interaction between pollinators and landscape features like topography
and distance on the evolutionary trajectory of a species. The three
Penstemon species studied share most life history traits, and it is
unlikely their gravity-dispersed seeds contribute much to gene
flow among populations on different mountain ranges. Therefore,
differences in primary pollinators are a likely explanation for the
differences in population structure among species. Our results
suggest that pollination syndromes do not just summarize the floral architecture and functional pollinator group of a species
(Thomson et al., 2000) but also have important impacts on population structure and genetic isolation of populations. As Kay and
Sargent (2009) recognized, “floral isolation alone is rarely a
complete barrier (Chari and Wilson, 2001; Ramsey et al., 2003;
January 2011]
Kramer et al.—Landscape genetics of Great Basin Penstemon
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117
Fig. 4. Relations between different measures of pairwise genetic distance (FST and RST,, both linearized) and geographic distance (ln km) for each
species. Although significant isolation by distance was detected with Mantel tests for each study species and genetic distance measure, the degree of isolation by distance varied. In (A), when only genetic drift was incorporated (FST), Penstemon pachyphyllus had a steeper slope than P. deustus However, in (B),
that pattern was reversed with RST (incorporating drift and stepwise mutation), and P. deustus had a greater slope than P. pachyphyllus. Regardless of measure used, P. rostriflorus had a smaller slope than either of the two species.
Kay, 2006) but can act in concert with isolating factors to reduce
the homogenizing effects of gene flow, allowing divergent lineages to persist and further diverge.” Although our results agree
with predictions from previous reports of pollinator dispersal distances, additional studies that compare species with different dispersal agents in differing landscapes are needed to clarify the role
of pollinators in shaping the genetic structure of plant populations.
Our findings are of particular relevance to recent interest in
evolutionary shifts between bee- and bird-pollination syndromes (Cronk and Ojeda, 2008; Thomson and Wilson, 2008;
Kay and Sargent, 2009), as they provide a possible explanation
for unidirectional shifts from bee- to bird-pollination syndromes, particularly in areas with high topographic and edaphic
variation (Kay and Sargent, 2009). Our results suggest that in
bird-pollinated species, greater functional connectivity exists
among distant populations, and this greater gene flow likely
constrains additional local adaptive divergence (Hendry et al.,
2002; Hendry and Taylor, 2004; Moore et al., 2007; Räsänen
and Hendry, 2008). This constraining effect may explain why
reversion back to a bee syndrome or toward another syndrome
has not been documented once a bird-pollination syndrome has
arisen in Penstemon species (Wolfe et al., 2006).
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American Journal of Botany
An alternative hypothesis for the differing levels of population structure in these three species might involve the timing of
speciation and the origin of each species. For example, lower
levels of genetic structure in P. rostriflorus might reflect a more
recent origin than those of P. deustus and P. pachyphyllus. Phylogenetic work on the genus (Wolfe et al., 2006) might allow
the relative timing of origins to be inferred, as all three study
species were included in an analysis using nucleotide sequence
data from ITS (nuclear DNA) as well as trnC–D and trnT–L
(chloroplast DNA) regions. Results show the phylogenetic
placement and branch length of each study species varies depending on the region analyzed. With ITS, the bird-pollinated
P. rostriflorus appears to be more recently derived than the beepollinated P. deustus but not as recently derived as P. pachyphyllus, and with trnC–D/T–L, the bee-pollinated species
actually appear to be more recently derived than the bird-pollinated P. rostriflorus. Thus, phylogenetic work to date does not
support a recent origin for P. rostriflorus as an explanation for
its low levels of population differentiation.
Whereas the bees pollinating our study species appear to be
less effective than hummingbirds at mediating long-distance gene
flow between Great Basin mountain ranges, our cluster analysis
also indicated that bees pollinating P. pachyphyllus are more effective at moving pollen between populations on the same
mountain range than are bees pollinating P. deustus (Fig. 2).
Numerous bee visitors to P. deustus have been reported, including
Osmia species, Anthophora and similar small nectar-collecting
bees, as well as larger nectar-collecting Bombus species (Wilson
et al., 2004). Visitors to P. pachyphyllus flowers have not been
well documented, but flowers are similar to those of the wellstudied P. strictus, so we would expect a similar range of visitors.
This includes the same functional groups as in P. deustus but extends to a broader range of generally larger bee and wasp visitors (Wilson et al., 2004). The different pollinators observed for
each species may affect long-distance pollen flow over different scales for various reasons, including (1) larger bee pollinators of P. pachyphyllus may move pollen more effectively
between distant populations on the same mountain range than
smaller bee pollinators of P. deustus, or (2) larger bees have
greater energy requirements than smaller bees (Westphal et al.,
2006), requiring that they use a broader foraging area and leading to greater long-distance gene flow. However, the higher
level of admixture for P. pachyphyllus compared with P. deustus for populations on the same mountain range (Fig. 2) may
be in part a reflection of our study design. The two withinmountain populations of P. pachyphyllus were separated by
less than 5 km, whereas all comparisons for P. deustus were
separated by more than 5 km. Resolving this question would
require incorporating additional populations on each mountain
range at a variety of geographic distances.
One exception exists to our finding of higher connectivity
between populations within mountain ranges than among them,
and it occurred in the same location for two of our study species. BARRIER identified strong landscape-level barriers to gene
flow between population pairs on the Markagunt Plateau for both
P. pachyphyllus (bee pollinated) and P. rostriflorus (hummingbird pollinated). Despite the small geographic distance separating these populations (each less than 1 km apart), there was
relatively high genetic distance. This may be attributable to large
elevational differences between sites: nearly 500 m in elevation
for P. rostriflorus and more than 1000 m in elevation for P. pachyphyllus. For P. rostriflorus, this elevation difference, as well as
differences in aspect, resulted in population-level differences in
peak flowering time of 1–2 mo (Kramer, unpublished data).
These results suggest that, regardless of pollination syndrome,
phenological differences caused by topography are also important in driving genetic divergence between populations.
Our results can be used to guide ecological restoration efforts
for our study species and those that share similar characteristics
found throughout the unique landscape of the Great Basin region.
Findings of high genetic diversity and significant population genetic structure in all three species support ongoing efforts to bank
seeds of multiple populations to store genetic diversity for future
restoration and research efforts (DeBolt and Spurrier, 2004). Our
findings of significant genetic structure also caution against
broad-scale movement and mixing of populations for restoration
purposes. Given the greater among-population gene flow identified in the hummingbird-pollinated P. rostriflorus, the large-scale
movement of seeds to restore populations of hummingbird-pollinated species may pose less risk to the success of a restoration
than in bee-pollinated species. However, we recommend additional research to investigate adaptive divergence in quantitative
traits, which can occur even in the presence of gene flow (Endler,
1973) and may influence the success or failure of a restoration.
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Appendix 1.
Taxon—Population name, Voucher specimen, Collection locale; Herbarium.
Penstemon deustus Douglas ex Lindl. var. pedicellatus M.E. Jones—Pd-DM1,
Tietmeyer et al. s.n., NV; CHIC, UTC. Pd-DM2, Fant et al. s.n., NV;
CHIC. Pd-SCR1, Tietmeyer et al. s.n., NV; CHIC, UTC. Pd-SCR2, Fant
et al. s.n., NV; CHIC. Pd-SM1, Fant et al. s.n., OR; CHIC, UTC. Pd-SM2,
Fant et al. s.n., OR; CHIC, UTC. Pd-PDNM1, Tietmeyer et al. s.n., NV;
CHIC, UTC. Pd-PNM2, Tietmeyer et al. s.n., NV; CHIC, UTC.
Penstemon pachyphyllus A. Gray ex Rydb. var. congestus (M.E. Jones)
N.H. Holmgren—Pp-MP1, Fant et al. s.n., UT; CHIC, UTC. Pp-MP2,
Tietmeyer et al. s.n., UT; CHIC, UTC. Pp-WWM1, Tietmeyer et al. s.n.,
UT; CHIC, UTC. Pp-WWM2, Tietmeyer et al. s.n., UT; CHIC, Pp-WWM3,
Fant et al. s.n., UT; CHIC. Pp-SR1, Fant et al. s.n., NV; CHIC, UTC.
Pp-SR2, Fant et al. s.n., NV; CHIC, UTC. Pp-SCR1, Fant et al. s.n., NV;
CHIC. Pp-SCR2, Fant et al. s.n., NV; CHIC. Pp-AR1, Fant et al. s.n., NV;
CHIC, UTC.
Penstemon rostriflorus Kellogg—Pr-MP1, Tietmeyer et al. s.n., UT; CHIC,
UTC. Pr-MP2, Fant et al. s.n., UT; CHIC, UTC. Pr-MP3, Fant et al. s.n.,
UT; CHIC. Pr-WWM1, Tietmeyer et al. s.n., UT; CHIC. Pr-WWM2,
Tietmeyer et al. s.n., UT; CHIC, UTC. Pr-SR1, Tietmeyer et al. s.n., NV;
CHIC, UTC. Pr-SR2, Tietmeyer et al. s.n., NV; CHIC, UTC. Pr-SCR1,
Fant et al. s.n., NV; CHIC. Pr-PM1, Fant et al. s.n., NV; CHIC, UTC. PrPNM1, Tietmeyer et al. s.n., NV; CHIC, UTC.
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Q1
Appendix 2. Locus name, repeat type, GenBank accession number, primer sequence, and size range (bp) for eight Penstemon microsatellite loci.
Locus
Repeat
GenBank accession number
Pen02
(TC)14(CA)13
DQ917423
Pen04
(TC)22
DQ917425
Pen05
(TC)25
DQ917426
Pen06
(TG)9(GA)12
GU902974
Pen18
(CT)20(CA)20
DQ917428
Pen23
(GA)21
DQ917430
Pen24
(GT)9(GA)22
EF203408
Pen25
(CT)29
DQ917431
Primer sequence (5′–3′)
F:
R:
F:
R:
F:
R:
F:
R:
F:
R:
F:
R:
F:
R:
F:
R:
TTCTATGCTTCGTTAACCCAAAA
GGTCGTATTGGTCCTTTCCA
GATGGAAAATGTGCCAGGAC
CTCTGCGGTGCATGAAAGTA
CAGATAGGGTGGAGGGGCTA
CAACCCAATCTGGTCGATCT
TGTTGACAGTTTTAATTGAAAGGAA
GAGGCCAGAAATGTTCCAAA
CTCATGATGATTGTGCGGATA
ACAACTCTCGCACTCTCACG
TGGTCTGATTTCAGGAAAAGC
TGCTCAAGACGATAATAAAAGTGC
TCAAATTGAGAAAATGAGTGAAAGTC
ATATGGTGGGACCTTTCGTG
GATGATCACCCAAGTTGCTT
CCTAATGCACGAGGCAAACT
Size range (bp)
163–245
209–287
159–245
185–253
530–616
148–206
145–225
120–176