Flora 224 (2016) 42–49

Contents lists available at ScienceDirect

Flora

journal homepage: www.elsevier.com/locate/flora

Fragmentation and environmental constraints inﬂuence genetic

diversity and germination of Stipa pennata in natural steppes

a,∗ a,b a,c a

Steffen Heinicke , Isabell Hensen , Christoph Rosche , Dennis Hanselmann ,

d e b,f

Polina D. Gudkova , Marina M. Silanteva , Karsten Wesche

Institute of Geobotany and Botanical Garden, Martin-Luther University of Halle-Wittenberg, Am Kirchtor 1, 06108 Halle (Saale), Germany

German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany

Department of Botany, Faculty of Science, Charles University in Prague, Benátská 2, CZ-128 01 Prague, Czech Republic

Institute of Biology, Tomsk State University, Lenina Avenue 36, 634050 Tomsk, Russia

Institute of Botany, Altai State University, Prospekt Lenina 61, 656 049 Barnaul, Russia

Botany Department, Senckenberg Museum of Natural History Görlitz, PO Box 300 154, D-02806, Görlitz, Germany

a r t i c l e i n f o a b s t r a c t

Article history: Human impact and fragmentation often have negative effects on plant population sizes. This can lead

Received 24 November 2015

to declining genetic diversity due to restricted gene ﬂow and genetic bottlenecks, and eventually result

Received in revised form 5 June 2016

in reduced reproductive ﬁtness. Environmental conditions can also inﬂuence the genetic structure of

Accepted 6 June 2016

populations and directly affect their reproduction success.

Edited by W. Durka

For Stipa pennata, the key species of largely natural steppes in southern Siberia, using AFLP we tested

Available online 18 June 2016

whether genetic variability and germination are negatively inﬂuenced by fragmentation, and assessed

the inﬂuence of local environmental conditions. Genetic diversity was moderately high (mean percent-

Keywords:

Adaptation age of polymorphic bands = 38.4%), with high genetic differentiation occurring between populations

AFLP (ST = 0.547). Genetic variation was mainly partitioned (41.8%) between two distinct grassland types.

Kulunda-steppe Isolation negatively affected genetic diversity, highlighting that fragmentation had an impact on genetic

Population-genetic structure structure. Higher mean precipitation negatively inﬂuenced population size, population density and

Precipitation genetic diversity. The speed of seed germination was correlated positively with population size and neg-

Vegetation

atively with vegetation cover, while we found no evidence for negative effects of low genetic diversity on

percentage of seed germination. The presence of different genetic groups shows that populations have

adapted to a range of environments. Germination speed also differed between groups, as a consequence

of maternal effects or of adaption to certain environmental conditions.

Our results show that fragmentation can have potentially strong effects even in natural grasslands. We

recommend that any future restoration schemes take the observed pronounced genetic differentiation

into account.

1. Introduction to reduced reproductive ﬁtness (Ellstrand and Elam, 1993; Hensen

and Oberprieler, 2005; Leimu et al., 2006). However, the rele-

In a growing number of threatened species, human medi- vance of such population-wide genetic processes to persistence

ated fragmentation signiﬁcantly constrains population sizes and and conservation in endangered populations remains a topic of

increases among-population isolation due to restricted gene ﬂow much debate (Vernesi et al., 2008). Biotic and abiotic factors can

(Allendorf et al., 2012; Eckert et al., 2008; Hoffmann and Willi, also have an impact on genetic diversity in populations (Hamasha

2008). Fluctuations, colonisation events and genetic bottlenecks et al., 2013; Huebner et al., 2009; Wang et al., 2006), mainly as

(Castric and Bernatchez, 2003) may result in an increased risk of a result of adaptation to environmental conditions. For example,

inbreeding, genetic drift and accumulation of deleterious muta- increased drought stress led to increased genetic diversity in Stipa

tions (Dudash and Fenster, 2000; Frankham et al., 2002; Young krylovii (Zhao et al., 2006) and Hordeum spontaneum (Nevo et al.,

et al., 1996), while diminishing genetic diversity is known to lead 1998), while similar habitats can host genetically similar popu-

lations (Hamasha et al., 2013; Zhao et al., 2004). A given species’

adaptive potential to particular environmental conditions should

∗ depend heavily on the level of within-population genetic diver-

Corresponding author.

sity, which can in-turn have a direct bearing on the reproduction

E-mail address: steffen [email protected] (S. Heinicke).

http://dx.doi.org/10.1016/j.ﬂora.2016.06.003

S. Heinicke et al. / Flora 224 (2016) 42–49 43

Fig. 1. Global distribution range of Stipa pennata and location of the study populations within the Kulunda steppe.

and long-term survival of the population (Bauert et al., 1998; Brook et al., 1965–1992). Its populations have become increasingly frag-

et al., 2002; Potvin and Tousignant, 1996). mented throughout their European range (Fig. 1), resulting in

Germination is a key stage in reproduction, and is inﬂuenced reduced levels of genetic diversity (Durka et al., 2012; Hensen

by factors including genetic diversity and/or environmental con- et al., 2010). For S. pennata, high genetic differentiation in its Cen-

ditions (Baskin and Baskin, 1998; Gasque and García-Fayos, 2003). tral European range edge is associated with decreasing population

Germination behaviour and seed viability are both dependent on sizes and related to populations’ degree of isolation (Wagner et al.,

species’ capacity to adapt to changing environmental conditions 2012). Moreover, Stipa species are known to develop cleistogamous

(Fenner and Thompson, 2005; Ronnenberg et al., 2008). As such, ﬂowers, especially under drier conditions or in more arid regions

widely-distributed species in particular show local differentiation (Brown, 1952; Ronnenberg et al., 2011), and cleistogamous ﬂow-

in germination behaviour (Fenner and Thompson, 2005). Seed ger- ers of Stipa leucotricha produce a higher ratio of viable seeds than

mination is also limited by the environmental conditions required chasmogamous ﬂowers (Call and Spoonts, 1989).

to break dormancy (Baskin and Baskin, 2004), and unfavourable Working along a regional climate gradient, we tested (1)

climatic conditions can negatively inﬂuence germination in certain whether fragmentation of natural steppes in recent decades has

plants and lead to diminishing population sizes in the long-term, had a negative effect on genetic diversity and on germination, as

which in turn can lead to reduced genetic diversity (Montesinos a proxy for reproductive ﬁtness. Furthermore, we examined (2)

et al., 2009). whether genetic structure as well as germination differs among dif-

A large share of studies on genetics and germination of grass- ferent steppe habitats. We additionally investigated (3) whether

land species originates from European grasslands that have been the regional aridity gradient spanning from the wetter north-

fragmented for long periods of time and are largely secondary eastern to the drier south-western part of Siberia is associated with

in nature. Genetic structures in novel and peripheral ranges may increasing genetic diversity within populations and, consequently,

differ much from those in the natural range (Durka et al., 2012; increased germination.

Hensen et al., 2010; Wagner et al., 2012), and effects of frag-

mentation may thus also differ (Hampe and Petit, 2005). Many 2. Materials and methods

European grassland species have native ranges in temperate grass-

lands, such as steppes and prairies, which cover approximately 8% 2.1. Study species

of the global terrestrial surface (White et al., 2000). The steppes

of Eurasia are among the world’s largest continuous land biomes. The original description of Stipa pennata by Linnaeus (1753)

Gradients in aridity, groundwater availability and edaphic factors was ambiguous, resulting in different species, such as Stipa joannis

can cause both small- and large-scale differentiation among natural (Celakovskˇ y),´ occasionally being included under the name. After

steppe species (Boonman and Mikhalev, 2005; Box, 2002; Dieterich, Freitag (1985) had determined a new lectotype, S. pennata was

2000). Steppes have, however, been subject to large scale conver- accepted as a separate taxon.

sion (Henwood, 1998), which has resulted in severe fragmentation Stipa pennata is a typical grass species of steppes, meadow

throughout their distribution range. The Kulunda steppe of south- steppes or forest steppes (Nosova, 1975). It is perennial and

western Siberia covers an area of approximately 80,000 km , and tussock-forming and is commonly described as tetraploid (2n = 44;

it is a striking example of an ecosystem that has undergone wide- Krasnikov, 1991; Sheidai et al., 2006), which is probably the result

ranging land use change. Due to the rising demand for agricultural of a hybridization event (Johnson, 1945). Lemmas typically have

crops, approximately 80% of the natural steppes have been trans- an up to 30 cm long bigeniculate awn, which is covered in feath-

formed for cultivation (Hoekstra et al., 2005; Meinel, 2002; Wein, ery hairs. Caryopses (hereafter referred to as seeds) are dispersed

1985). As a consequence, native species have become increas- either by wind or via animal skin or fur.

ingly threatened, with many being included in the national Red-list

(Aleksandrova et al., 2006; Solotov, 2009).

2.2. Study area

Here, we focus on the feather grass Stipa pennata, which is a

characteristic biogeographical element of temperate western and

The Kulunda steppe represents the north-eastern distribution

middle Eurasian steppes (Lavrenko and Karamysheva, 1993 Meusel

range of S. pennata (Fig. 1). The region has been inﬂuenced by agri-

44 S. Heinicke et al. / Flora 224 (2016) 42–49

culture since the 1740s, when arable farming was introduced in tions F1–F9) contained all populations that had a grass cover of at

the north and subsequently spread southwards. Steppe conver- least 50% (mainly Stipa and Festuca species). The meadow steppe

sion accelerated in the 1950s (Meinel, 2002; Silanteva, personal group (Table 1, Fig. 1, populations M1–M12) in contrast, was char-

communication), while in the 1990s low grain prices resulted in acterized by lower grass cover and a higher proportion of forbs.

large-scale fallowing of former ﬁelds, especially in the south. Stipa Population sizes and densities were estimated by counting the tus-

species can, however, require up to 10 years before the ﬁrst indi- socks (per m , then extrapolated), and ranged between 140 and

viduals become established and begin to recolonize former ﬁelds 250,000 in M4 and F5, respectively (Table 1). Seeds were collected

(Boonman and Mikhalev, 2005). from six sites of the feather grass steppe group and ﬁve sites of the

The Kulunda steppe is characterised by declining precipita- meadow steppe group, and pooled from about 20 sampled indi-

tion towards the south (Bergmann and Frühauf, 2011). In the viduals across a given population. The total annual precipitation at

wetter northeast (annual precipitation 350–500 mm), degraded each site was obtained from the WorldClim model (Hijmans et al.,

chernozems underlie extensive forests which, travelling south- 2005). Populations were classiﬁed based on their degree of isola-

wards, give way to forest steppes with mosaics of steppe elements tion, which was used as a proxy for anthropogenic intervention. A

interspersed with groups of trees (Boonman and Mikhalev, 2005). high degree of isolation was assumed for populations surrounded

Further south, along a decreasing precipitation gradient, highly by barriers, such as forests or cultivated ﬁelds, and with no possi-

diverse meadow steppes overlying black chernozems become more bility for establishment of new individuals in the adjacent area.

dominant (Ronginskaja, 1963; Solotov, 2009), followed by typical Isolation was classiﬁed as medium if barriers were present but

steppes, which are considered the main habitat of Stipa species potential establishment sites existed in the adjacent area, such

and are deﬁned as “feather grass steppe” (Boonman and Mikhalev, as roadsides, while isolation was considered low if there were no

2005). apparent barriers (Table 1).

Due to the high level of agricultural conversion of the Kulunda

region, natural steppe vegetation now is almost exclusively

restricted to areas of less interest to agriculture, i.e. roadsides, ﬁeld 2.4. AFLP genotyping and analyses

edges, lake and river terraces and small refugia (Lapschina, 1992;

Solotov, 2009). We employed AFLP markers to facilitate direct comparisons

with previous studies on genetic variation of Stipa species. Extrac-

tion of DNA and AFLP analysis followed the protocol of Hensen

2.3. Sampling et al. (2012) with the following primer combinations being

employed: AAG (FAM)-CCA, AGC (HEX)-CCA and AGC (HEX)-CAA

In summer 2012, we collected leaves from 21 S. pennata popu- (Wagner et al., 2012). We analyzed samples using a MegaBACE

lations across the Kulunda steppe, with minimum and maximum 1000 sequencer (Amersham Bioscience, UK) and visualized peaks

distances between populations equalling six and 332 km, respec- between 60 and 400 bp in length with the program MegaBACE

tively (Fig. 1). A population was deﬁned as a group of individuals Fragment Proﬁler (Amersham Bioscience, 2003). For populations

separated by at least 1 km from another group. For each popula- M5 and M9, only 11 samples per population were used for further

tion, the 12 sampled individuals were distributed equally across analysis due to the limited quality of the band proﬁles.

the entire population area and were at least 1 m apart from one Using these binary data matrices, polymorphic DNA bands were

another. Sites were described by conducting vegetation surveys, given scores of “0” (absence) or “1” (presence). To determine

which then enabled us to distinguish between two habitat groups the most reliable of the automatically scored loci, we followed

(Heinicke, 2014): The feather grass steppe group (Table 1, popula- the approach of Ley & Hardy (2013) and compared automatically

Table 1

Overview of the collected S. pennata populations. Pop: populations, F: feather grass steppe group, Populations M: meadow steppe group; Geographic coordinates in decimal

degrees (LAT: latitude; LONG: longitude); P: annual precipitation (mm); n: number of individuals included in the analysis; S: population size (estimated number of adult

individuals); D: population density (tussocks per m ); I: spatial isolation, H: highest, M: middle, L: lowest; He: expected heterozygosity; PPB: proportion of polymorphic

bands; G: ﬁnal germination percentage (mean ± s.e.; n = 4); TI: Timson index (mean ± s.e.; n = 4).

Pop LAT LONG Region P n S D I He PPB G TI

F1 53.554 81.57 Kamenskij Rajon 388 12 50400 7 M 0.120 30.2 55 (±15) 17 (±5)

F2 53.434 78.955 Burlinskij Rajon 317 12 42000 6 L 0.208 79.7 – –

F3 53.228 79.788 Suetskij Rajon 323 12 24500 7 L 0.198 75.7 38 (±23) 15 (±9)

F4 53.108 79.832 Blagoweschtschenskij Rajon 318 12 30000 6 L 0.173 40.6 – –

F5 53.026 80.006 Blagoweschtschenskij Rajon 321 12 250000 10 H 0.100 28.7 35 (±19) 14 (±7)

F6 52.78 79.732 Blagoweschtschenskij Rajon 306 12 210000 7 H 0.104 30.7 38 (±14) 14 (±4)

F7 52.96 81.34 Sawjalowskij Rajon 385 12 195000 13 M 0.174 42.1 45 (±8) 6 (±2)

F8 53.249 83.941 Perwomajskij Rajon 440 12 30000 5 M 0.098 27.7 28 (±16) 14 (±7)

F9 52.646 82.152 Alejskij Rajon 449 12 2400 3 H 0.078 27.7 – –

M1 52.1 80.219 Woltschichinskij Rajon 319 12 9500 5 M 0.154 39.6 20 (±8) 8 (±3)

M2 52.776 82.658 Toptschichinskij Rajon 468 12 27000 3 M 0.109 30.7 40 (±12) 7 (±4)

M3 53.493 81.917 Schipunowskij Rajon 446 12 240 2 M 0.12 31.7 19 (±17) 19 (±7)

M4 52.815 81.752 Mamontowckij Rajon 421 12 140 1 H 0.098 30.7 18 (±7) 10 (±4)

M5 53.39 82.073 Schelabolichinskij Rajon 428 11 39000 3 M 0.114 31.2 – –

M6 53.351 81.614 Tjumenzewskij Rajon 399 12 51200 4 M 0.162 41.6 30 (±11) 12 (±5)

M7 52.872 81.612 Mamontowckij Rajon 410 12 11000 2 M 0.148 31.7 – –

M8 52.892 80.531 Blagoweschtschenskij Rajon 342 12 18000 3 M 0.191 63.4 – –

M9 52.206 81.635 Nowitschichinskij Rajon 434 11 16500 3 H 0.091 29.7 – –

M10 52.906 83.453 Kalmanskij Rajon 451 12 480 3 M 0.102 31.2 – –

M11 52.627 82.253 Altajskij Rajon 460 12 1500 2 M 0.103 31.2 – –

M12 52.627 82.344 Altajskij Rajon 467 12 1400 1 M 0.125 29.7 – –

Mean F 361 92700 7 0.139 42.6

Mean M 420 14663 3 0.126 35.2

Mean Total 395 48108 5 0.132 38.4

S. Heinicke et al. / Flora 224 (2016) 42–49 45

Table 2

Overview of the statistical models. PPB: percentage of polymorphic bands; TI: Timson’s index; LM: linear models; LMM: linear mixed.

Dependent variables Statistical models Explanatory variables Random effects No. populations

PPB LM log (population size) – 21

log (TI) LMM log (population size), PPB population 11

PPB LM vegetation cover*precipitation – 21

log (TI) LMM vegetation cover*precipitation population 11

scored genotypes of replicates in SPAGeDi 1.4 software (Hardy and in darkness (Hensen, unpublished data; Ronnenberg et al., 2008).

Vekemans, 2002). Based on a dataset containing replicated individ- Twenty-ﬁve seeds each were placed in four Petri dishes per popu-

uals only (28.4% of 250 individuals), the broad-sense heritability of lation and incubated in a germination chamber under the following

◦ ◦

each marker was estimated as an FST value. Signiﬁcance of repro- conditions: 20 C/10 C at day/night in a 12 h cycle (in accordance

ducibility for the FST values was tested by executing 1000 random with Hensen and Hoffmann, unpublished data) and kept constantly

permutations. Markers with FST > 0.5 and P < 0.05 were selected for moist with demineralized water. Seeds were checked 10 times over

further statistical analysis. The ﬁnal dataset included 202 polymor- a 27 day period, with germinated seeds being removed as appro-

phic bands, with an error rate of 2.2%, which is within the expected priate. Final seed germination was determined in %. The modiﬁed

range of AFLP analysis (2–5%; Hansen et al., 1999). Timson Index (TI) was used to estimate germination speed, and was

Since different approaches exist for analysing AFLP data calculated as the cumulative sum of all germinated seeds divided

(Bonin et al., 2007), we determined key parameters follow- by the overall germination period (Pérez-Fernández et al., 2006).

ing: (1) Band-based approaches by counting polymorphic bands The Kruskal-Wallis test was also used to test whether the classi-

(PB), estimating the percentage of polymorphic bands (PPB) and ﬁcation of the degree of isolation had an impact on the reproductive

the Sørensen dissimilarity between individuals, and (2) Allele- ﬁtness of S. pennata. We used the Mann-Whitney U Test to deter-

frequency approaches for estimating Neiıs´ gene diversity (He) and mine signiﬁcant differences between the grassland types.

pairwise ST distance between populations, respectively. How-

ever, we were not able to precisely determine allele frequencies

2.6. Congruence between data on genetics, germination and

due to the dominance of the AFLP marker system and the poly-

environmental conditions

ploidy of S. pennata. As with previous studies on polyploid species

(e.g. Durka et al., 2012; Wagner et al., 2011), allele frequency was

A further Mantel test (9999 repetitions) was conducted with the

estimated as being equal to band frequency.

pairwise FST distance matrix and a dissimilarity matrix of the vege-

We estimated PB, PPB and He in accordance with Lynch and

tation data (Bray-Curtis dissimilarity) to examine whether genetic

Milligan (1994) using AFLP-SURV 1.0 (Vekemans et al., 2002). For

differentiation was associated with the plant community com-

further analyses, we consistently used the PPB as a parameter for

position. Correlations between population size, density, PPB and

genetic diversity, since its calculation is not based on any assump-

precipitation were calculated using Pearson’s r.

tion, in contrast to He (the calculation of which is based on allele

Effects of population size, precipitation and vegetation cover on

frequencies that can only be estimated with dominant markers

genetic diversity as well as on germination were tested with lin-

such as AFLP). Since the data of the tested genetic parameters

ear models and linear mixed effects models (Table 2) using the R

(He and PPB) between the grassland types were not normally

packages “MASS” (Venables and Ripley, 2002) and “nlme” (Pinheiro

distributed according to a Shapiro-Wilk test, we used the Mann-

et al., 2013). Further models were used to examine the relation-

Whitney U Test to assess the level of signiﬁcance. Effects of degree

ship between genetic diversity and germination. Population sizes

of isolation on gene diversity where analysed with Kruskal-Wallis

and TI were log-transformed to meet assumptions of normality. In

tests.

order to develop the minimum adequate model, all non-signiﬁcant

To calculate the partitioning of genetic variation within and

variables were incrementally excluded using a backward approach.

between populations, we performed Analyses of Molecular Vari-

Models of different complexity were compared using a Chi-square

ance (AMOVA, based on ST-distance, Excofﬁer et al., 1992) using

test for the LMM and an F-test for the LM, while parameter esti-

Arlequin 3.5 (Excofﬁer and Lischer, 2010). We included the two

mates were separately determined.

grassland types as an additional hierarchical level in the AMOVA.

Pairwise FST-values among populations and their signiﬁcance

3. Results

(10,000 permutations) were determined using AFLP-SURV 1.0. Fur-

ther AMOVAs were calculated for three subsets of data comprising

3.1. Genetic diversity and differentiation

populations from the three levels of isolation. We tested for genetic

isolation by (geographical) distance with a Mantel test using the

Population PPB ranged between 28.7% and 79.7% (Table 1), while

R-package “ade4” (Dray and Dufour, 2007) and using a pairwise

Neiıs´ gene diversity (He) ranged between 0.091 and 0.208. Both

FST distance matrix along with a geographical distance matrix. Sig-

measures of genetic diversity were highly correlated (Pearsonıs´

niﬁcance of the resulting standardized Mantel correlation rM was

r = 0.82, p < 0.001). Mean genetic diversity across all populations

tested with 9999 permutations. We obtained a dissimilarity matrix

was moderately high at PPB = 38.4% and He = 0.132. In addition,

of square-root transformed Sørensen coefﬁcients between indi-

Kruskal-Wallis tests showed that an increase of the degree of isola-

viduals, which places emphasis on shared bands (Legendre and

tion resulted in reduced genetic diversity (PPB: = 9.28, p < 0.01,

Legendre, 1998). This was visualised by Principal Coordinate Anal-

Fig. 3; He: = 12.19, p < 0.005). Limited power of pair-wise post hoc

ysis (PCoA) with the package “vegan” (Oksanen et al., 2009) in R

non-parametric comparisons resulted in moderate initial p-values

3.1.2 (R Development Core Team, 2013).

(p ≈ 0.025 for all three tests), which did not pass the scrutiny of

Bonferroni-type (Holm) corrections.

2.5. Reproductive ﬁtness: germination experiments According to the Mann-Whitney U Test, populations from the

two grassland types did not differ with respect to genetic diversity.

One hundred seeds per population were used for a germina- However, population sizes and densities were signiﬁcantly smaller

◦

tion experiment following cold stratiﬁcation at 5 C for 10 weeks in the meadow steppe group (Mann-Whitney U = 16; p < 0.01;

46 S. Heinicke et al. / Flora 224 (2016) 42–49

Fig. 2. Principal Coordinate Analysis (PCoA) for all AFLP data of S. pennata populations; Axes 1 and 2 explain 35.8% and 12% of the total variance, respectively. Genetic

data were analysed based on square-root transformed Sørensen dissimilarity (ellipses indicate the three main clustered groups). Different symbols and colours indicate

the different populations and their classiﬁcation within the main habitat types (blue: meadow steppe group; orange: feather grass steppe group). (For interpretation of the

references to colour in this ﬁgure legend, the reader is referred to the web version of this article.)

Fig. 3. Relationship between the percentage of polymorphic bands and the degree of isolation. Differences were overall signiﬁcant (PPB: = 9.28, p < 0.01), while limited

statistical power resulted in non-signiﬁcant pair-wise post hoc comparisons if Holm-Bonferroni-correction was applied.

Mann-Whitney U = 5; p < 0.001, respectively). In addition, we did ing to the ﬁrst group, and M2, M3, M4, M5, M7, M9, M10, M11 and

not ﬁnd a relationship between population size and PPB. According M12 to the second group. The less important second axis differ-

to the linear regression models, in wetter regions vegetation cover entiated a third group (Fig. 2), with populations F3 and F4. Along

−

had a negative impact on PPB (parameter estimate = 0.01, F = 7.00, axis 1, individuals of populations F2 and F7 were assigned between

p < 0.05). the ﬁrst and third group. Differences along the second axis were

The hierarchical AMOVA (Table 3) indicated that the largest limited and largely caused by the presence/absence of two private

share of molecular variance was partitioned within populations alleles. The ﬁrst and second group corresponded to populations

of S. pennata (43.7%), followed by variances among the two grass- from the feather grass steppe group and the meadow steppe group,

land types (41.8%). Variation among populations within grassland respectively.

types was comparatively small (14.5%). Populations were strongly Genetic structures were related to plant community compo-

differentiated overall (AMOVA, ST = 0.547, Table 3). All pairwise sition (rM = 0.32, p < 0.005), and genetically similar populations

ST distances among populations were highly signiﬁcant. Sepa- consequently tended to be similar with respect to their vegeta-

rate AMOVAs showed that ﬁeld-based estimates of isolation were tion cover and species composition. Univariate Pearson correlations

reﬂected in genetic structures; the most strongly isolated popula- showed that precipitation negatively affects population size

tions had a total ST = 0.653 compared to ST = 0.270 among those (r = −0.49, p < 0.05), population density (r = −0.60, p < 0.01) and PPB

with low isolation. The Mantel test revealed a modest yet signiﬁcant (r = −0.58, p < 0.01).

correlation between genetic differentiation among populations and

geographic distance (rM = 0.19, p = 0.032). The PCoA revealed strong

3.2. Germination

geographical differentiation, with the ﬁrst two ordination axes

accounting for 48% of the genetic variation (explained variances

Germination success differed strongly among populations, with

axis 1 = 36% and axis 2 = 12%). It also indicated the presence of three

the lowest germination being attributed to population M4 (18%)

major groups (Fig. 2), with populations F1, F5, F6, F8 and F9 belong-

and the highest (55%) to F1 (Table 1). However, the percentage of

S. Heinicke et al. / Flora 224 (2016) 42–49 47

Table 3

Results of the analysis of molecular variance (AMOVA). I: hierarchical models, populations nested in habitats; II: separate AMOVAs for subsets of populations according to

isolation level.

Source of Variation df Sum of Squares Variance components Variation (%) Fixation Index

I: Populations

in habitats

among habitats 1 1487.69 11.63 41.8 CT = 0.418

among populations 19 1146.42 4.05 14.5 SC = 0.250

within populations 229 2789.46 12.18 43.7 –

total 249 5423.57 27.86 100 ST = 0.547

II: Populations

of different

isolation levels

high among populations 4 664.64 13.48 65.3

within populations 54 386.89 7.17 34.7

total 58 1051.53 20.64 100.0 ST = 0.653

medium among populations 12 1186.01 7.29 38.0

within populations 142 1690.49 11.91 62.0

total 154 2876.50 19.20 100.0 ST = 0.380

low among populations 2 235.00 7.99 27.0

within populations 33 712.08 21.58 73.0

total 35 947.08 29.57 100.0 ST = 0.270

germinated seeds did not differ between the main grassland types In our study, degree of isolation was used as a measure of direct

according to Mann-Whitney U tests. anthropogenic intervention, which has been shown to strongly

Seeds of larger populations germinated faster (parameter esti- affect genetic diversity within a population, with gene ﬂow inhi-

mate = 0.13, = 11.24, p < 0.001), while germination rate was not bition leading to genetic bottlenecks, inbreeding and resultant

signiﬁcantly affected by the degree of isolation ( = 0.77, p = 0.680). genetic drift (Dudash and Fenster, 2000; Frankham et al., 2002). In

Neither PPB nor precipitation had a signiﬁcant effect on TI, while accordance, populations which were subject to high isolation due

vegetation cover had a signiﬁcantly negative impact (LMM, param- to intervening forests or cultivated ﬁelds, tended to show lower

eter estimate = −0.02, = 7.11, p < 0.01). genetic diversity than that of the other populations, and also had

higher values of ST. This afﬁrms a negative inﬂuence of isolation;

mainly as a result of habitat fragmentation.

With regard to genetic diversity within populations, no signiﬁ-

4. Discussion

cant differences were found between the meadow steppes and the

feather grass steppe group. However, we found that populations

4.1. General patterns of genetic diversity and differentiation

with similar vegetation composition were genetically similar to one

and other, and the PCoA supports the suggestion that local habitat

Our data revealed moderately low levels of genetic diversity

can play a crucial role in genetic structuring (Boulding and Hay,

(mean PPB = 38.4%) and strong genetic differentiation (AMOVA,

2001). As such, populations of the two different steppe types may

ST = 0.547) for S. pennata within the Kulunda steppe. Our results

have adapted to the respective environmental conditions, resulting

correspond with those for non-clonal grass species with mixed

in the different genetic patterns. As both main vegetation types are

mating systems (mean PPB = 38%; ST = 0.45, according to AFLP

natural to the area, S. pennata may have developed different eco-

data compilation in Wagner et al., 2011). Several other AFLP stud-

types for the prevailing habitats (e.g. Manel et al., 2012; Hamasha

ies on Stipa species detected lower values for genetic diversity,

et al., 2013). There were, however, both intermediate individuals

e.g. for S. pulcherrima (PPB 9%, Durka et al., 2012), S. capillata (PPB

and even entirely intermediate populations. During genetic analy-

21%, Wagner et al., 2011) or S. pennata (PPB 21%, Wagner et al.,

sis, samples were randomised rendering effects of methodological

2012). However, our results on genetic differentiation are in the

artefacts unlikely. Intermediate populations such as F2, F7, M6, M1

same order of magnitude as that reported for Stipa species else-

and M8 are at meadow steppe sites potentially suitable for agri-

where, e.g. Stipa arabica, Stipa lagascae (AFLP; ST = 0.57, ST = 0.60

culture with respect to soil and moisture conditions. Among these

respectively, Hamasha et al., 2013) and S. pennata (AFLP; ST = 0.34,

only F7 has some private alleles. Such sites have been used for

Wagner et al., 2012).

arable land for over 200 years (Silanteva, personal communica-

Durka et al. (2012) highlighted that a population’s history

tion), and after fallowing the spontaneous steppe vegetation has

(including ﬂuctuations in range extent), size and density as well

often been used for livestock grazing or hay production. This might

as its associated degree of isolation and cleistogamous breeding

have resulted in transport of propagules from various accessions,

system can all result in dramatically reduced genetic diversity

resulting in the formation of mixed individuals over subsequent

and high genetic differentiation, especially at the range edge of

generations. The intermediate geographic location of these popula-

a species’ distribution. The high genetic differentiation revealed

tions supports this assumption. Even more distinct are populations

within our study area supports this ﬁnding, as does our modest

F3 and F4 which are at small sites not suitable for mechanical agri-

correlation between geographic distance and genetic differentia-

culture. Perhaps these have never been ploughed or at least have

tion in the Mantel test. However, we did not ﬁnd any correlation

not been subject to seed input during industrial hay-making.

between estimated population size and genetic diversity, which

Furthermore, we found that S. pennata population size and den-

may be explained by historic variations or ﬂuctuations in popu-

sity decreased with increasing precipitation. Towards the wetter,

lation sizes, or by the longevity of species (Honnay et al., 2007;

more northern extent of the study range, the species is replaced by

Schiemann et al., 2000; Wagner et al., 2012). Likewise, the rela-

other plants better adapted to more moist environmental condi-

tively high genetic diversity within the Kulunda steppe indicates

tions and which presumably tolerate higher levels of interspeciﬁc

that populations have not been strongly affected by range edge

competition. The resultant negative effect of higher precipitation

effects, such as that found for populations in Europe (Durka et al.,

on genetic diversity is also described by Hamasha et al. (2013)

2012; Wagner et al., 2012).

48 S. Heinicke et al. / Flora 224 (2016) 42–49

for various Stipa species. It is however in contrast to that found as a proxy for reproductive ﬁtness, germination was found to be

for other species, e.g. Nevo et al. (1998) found that for H. spon- unaffected by genetic diversity, yet it did respond to local habitat

taneum, drier and hotter environments led to decreasing genetic conditions. The observed genetic patterns are therefore probably

diversity. Different patterns may therefore reﬂect different selec- partly caused by adaptation to different prevailing environmen-

tion and/or adaptation to harsher environmental conditions (Nevo tal conditions across the steppe; neutral effects may have played

et al., 1998; Wang et al., 2006). Nevertheless, the observed relation- an additional role given the modest effect of isolation on genetic

ship between genetic diversity and environmental conditions for S. structure.

pennata in our study is largely in line with studies on other grass The recent period of dramatic habitat loss should be regarded as

species (e.g. Hamasha et al., 2013; Wang et al., 2006; Zhao et al., a crucial challenge to be addressed by conservation policy (Durka

2006). et al., 2016). In the region, S. pennata plays a major role in any habi-

tat restoration, and it is vital that any restoration incorporates the

use of locally adapted accessions to ensure successful establish-

4.2. Reproductive ﬁtness and environmental conditions

ment of the species across the Kulunda steppe.

While germination differed strongly among the populations in

Acknowledgements

our study, mean germination was 33%, and all observed germi-

nation percentages were higher than those reported for Central

We would like to thank the Russian cooperation partners for

European populations of S. pennata (Treiber, 2009; 1.6%–25.6%).

assistance in the ﬁeld work, especially Aleksey Prays. We also thank

Our results may be explained by both adaption of the populations

B. Müller for helping with the lap work; C. Voigt for assistance in the

to prevailing environmental conditions and/or genetic effects (e.g.

germination experiment; G. Seidler for compiling Fig. 1, and, M. Al-

Baskin and Baskin, 1998; Gasque and García-Fayos, 2003; Murdoch

Gharaibeh for helpful hints. The manuscript beneﬁtted much from

and Ellis, 1992). These may also explain why populations from the

comments by W. Durka and two anonymous referees. The work was

different grassland types did not differ with respect to the percent-

funded by the funding measure “Sustainable land management” of

age of germinated seeds.

the Federal Ministry of Education and Research (BMBF) within the

While germination speed (TI) was neither affected by genetic

KULUNDA project.

diversity nor by the level of isolation in our study, it increased with

population size and decreased with vegetation cover. This can be

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