ORIGINAL ARTICLE

doi:10.1111/j.1558-5646.2010.01064.x

POSTFIRE RESPONSE AND GENETIC DIVERSITYIN ERICA COCCINEA: CONNECTINGPOPULATION DYNAMICS ANDDIVERSIFICATION IN A BIODIVERSITYHOTSPOTJose Gabriel Segarra-Moragues1,2,3 and Fernando Ojeda4,5

1ARAID-UZ. Departamento de Agricultura y Economıa Agraria, Escuela Politecnica Superior de Huesca, Universidad de

Zaragoza. Carretera de Cuarte, Km 1, E-22071 Huesca, Spain3E-mail: j.gabriel.segarra@uv.es

4Departamento de Biologıa, Universidad de Cadiz, Campus Rıo San Pedro, E-11510 Puerto Real, Spain5E-mail: fernando.ojeda@uca.es

Received June 5, 2009

Accepted June 8, 2010

Understanding the processes of biological diversification is a central topic in evolutionary biology. The South African Cape fynbos,

one of the major plant biodiversity hotspots out of the tropics, has prompted several hypotheses about the causes of generation and

maintenance of biodiversity. Fire has been traditionally invoked as a key element to explain high levels of biodiversity in highly

speciose fynbos taxa, such as the genus Erica. In this study, we have implemented a microevolutionary approach to elucidate

how plant-response to fire may contribute to explain high levels of diversification in Erica. By using microsatellite markers, we

investigated the genetic background of seeder (fire-sensitive) and resprouter (fire-resistant) populations of the fynbos species Erica

coccinea. We found higher within-population genetic diversity and higher among-population differentiation in seeder populations

and interpreted these higher levels of genetic diversification as a consequence of the comparatively shorter generation times and

faster population turnover in the seeder form of this species. Considering that genetic divergence among populations may be

seen as the initial step to speciation, the parallelism between these results and the pattern of biodiversity at the genus level offers

stimulating insights into understanding causes of speciation of the genus Erica in the Cape fynbos.

KEY WORDS: Fynbos, generation time, population divergence, population turnover, resprouter, seeder.

Biodiversity hotspots are mostly circumscribed to tropical lati-

tudes (Myers et al. 2000). Out of the tropics, the most remark-

able biodiversity hotspot is the South African fynbos biome,

within the Cape Floristic Region (CFR). Its outstanding levels

2Present address: Centro de Investigaciones sobre Desertificacion

(CIDE, CSIC-UV-GV), Camı de la Marjal s/n, E-46470 Albal (Valencia),

Spain

of plant species richness and large concentration of endemic taxa

raise crucial questions regarding the causes of speciation and the

maintenance of biodiversity (Linder 2003; Latimer et al. 2005;

Barraclough 2006). The genus Erica, with ca. 700 species and a

high level of narrow endemism, is the epitome of biodiversity in

fynbos (Oliver et al. 1983; Linder 2003).

Fire has been traditionally invoked as a key element account-

ing for the high levels of biodiversity in fynbos woody plants

3 5 1 1C© 2010 The Author(s). Evolution C© 2010 The Society for the Study of Evolution.Evolution 64-12: 3511–3524

J. G. SEGARRA-MORAGUES AND F. OJEDA

(Cowling 1987; Cowling et al. 1996). Regarding their response to

fire, two basic regeneration modes, “seeder” (fire-sensitive) and

“resprouter” (fire-resistant), are recognized. Both of them confer

resilience to recurrent wildfires at the population level, but they re-

sult in two contrasting population dynamics. Although resprouter

populations are composed of multiple cohorts that survive follow-

ing fires (i.e., overlapping generation dynamics), populations of

seeder individuals tend to have even-aged cohorts (i.e., nonover-

lapping generation dynamics; Bond and van Wilgen 1996; Ojeda

et al. 2005). This is particularly true when germination from the

seed bank is triggered by fire (fire-recruiting species; Bond and

van Wilgen 1996), because the seed bank becomes depleted after

the passage of fire and seedling recruitment is thus always pre-

ceded by the adults’ death. Thus, seeder populations have shorter

generation times as well as distinct and faster population turnovers

(each fire cycle) than resprouter populations (Wells 1969; Ojeda

et al. 2005).

Wells (1969) suggested that higher levels of diversifica-

tion in seeder lineages are to be expected in woody taxa from

fire-prone ecosystems that contain both seeder and resprouter

species. Incidentally, species diversity and narrow endemism in

the speciose genus Erica are tightly associated with the seeder

habit (Ojeda 1998). Higher frequency and intensity of natural

selection in seeder populations, favored by their comparatively

faster generation turnovers, would increase genetic differentia-

tion among populations and, hence, speciation in seeder lineages

(Wells 1969). Certainly, genetic differentiation among popula-

tions within a given species may be seen as the initial step to spe-

ciation (Avise 2000; Coyne and Orr 2004). Several studies have

actually found a positive relationship between population genetic

differentiation and species diversity across latitudinal gradients

(Martin and McKay 2004; Eo et al. 2008).

Since Wells (1969), several authors have put forward more

indirect evidence to support his hypothesis (e.g., Ojeda 1998;

Cowling and Pressey 2001), or to challenge it (e.g., Bond and

Midgley 2003; Lamont and Wiens 2003; Perry et al. 2009).

Barraclough and Savolainen (2001) found a positive relation-

ship in flowering plants between the number of species within

a clade and the rate of neutral molecular evolution, and sug-

gested that rapid speciation is linked to faster rates of molecu-

lar change. Following this argument, Verdu et al. (2007) could

not find statistical support for either higher molecular evolution-

ary rates or higher diversification in seeder lineages than in re-

sprouter from Mediterranean-type ecosystems, concluding that

Wells’ hypothesis might not hold as a general rule. However,

more recently, Smith and Donoghue (2008) highlighted the ex-

istence of a general, consistent relationship between short gen-

eration times and high rates of molecular change in flowering

plants.

In any case, at the within-species level, high rates of genetic

differentiation among populations are mainly driven by demo-

graphical stochasticity (Whitlock 1992) and local processes of

extinction and recolonization (i.e., population turnover), so long

as most individuals colonizing an extinct patch originate from

a single source population (the propagule-pool model; Slatkin

1977; Wade and McCauley 1988; Pannell and Charlesworth 1999,

2000). In this sense, postfire recruitment could be considered

as a particular case of recolonization from a single source (the

seed bank; see Gotelli 1991) which, moreover, does not con-

tribute to gene flow among sites, thus enhancing differentiation.

Hence, dynamics of fire-recruiting seeder populations would fit

a model of extinction and propagule-pool recolonization (i.e.,

rapid population turnover), whereas that of resprouter would

resemble the case with no local extinction (i.e., slow popula-

tion turnover). Accordingly, genetic differentiation among seeder

populations is expected to be higher than among resprouter

ones.

However, diversifying seeder populations—and potentially

“incipient species”—would also be prone to extinction, particu-

larly under unpredictably fluctuating environments, owing to their

non- (or little) postfire adult plant survival and subsequent lack

of reproductive storage potential over generations (Warner and

Chesson 1985; Higgins et al. 2000). Such counteracting higher

extinction probability would hence account for the apparent lack

of higher species diversification in seeder lineages reported by

Verdu et al. (2007; see also Perry et al. 2009). In fact, high

rates of species proliferation also require the existence of low

extinction risks (Dynesius and Jansson 2000; Mittelbach et al.

2007).

In this study, we used a microevolutionary approach to test

whether genetic differentiation among populations in the genus

Erica is more marked in seeder populations (short generation

times, rapid population turnover) than in resprouter populations

(long generation times, slow population turnover). We have fo-

cused on the fynbos species Erica coccinea L., which includes

distinct seeder and resprouter populations (Ojeda 1998), and thus

avoids possible confounding effects attributable to independent

phylogenetic histories. Specifically, we have analyzed the ge-

netic structure of seeder and resprouter groups of populations

of this species by means of microsatellite markers. Our results

provide evidence of a strong association between post-fire regen-

eration modes and genetic variation and differentiation among

populations. In a more general context, they illustrate the role

of contrasting population dynamics—rapid versus slow popula-

tion turnover—in the development of genetic divergence among

populations. Finally, this study offers stimulating insights into

the causes of diversification, and hence speciation, in the highly

speciose genus Erica in the CFR fynbos.

3 5 1 2 EVOLUTION DECEMBER 2010

SEGARRA-MORAGUES AND OJEDA-FIRE AND DIVERSIFICATION IN ERICA

Materials and MethodsSTUDY SPECIES AND DYNAMICS OF SEEDER

AND RESPROUTER POPULATIONS

Erica coccinea (Ericaceae) is a relatively abundant and

widespread heath species in the CFR fynbos under mesic con-

ditions. Besides its conspicuous variation in flower color (Rebelo

and Siegfried 1985; see Table 1), the most remarkable morpho-

logical feature of this species is the existence of distinct seeder

and resprouter individuals (Ojeda 1998; Bell and Ojeda 1999),

frequently (but not always) in disjunct populations. Both seeder

and resprouter populations are common in fynbos communities of

coastal mountains and hills of the south-western CFR, character-

ized by reliable winter rainfall and mild summer drought (Ojeda

1998; Ojeda et al. 2005). In more inland mountains, where win-

ter rainfall becomes less reliable and summer drought somewhat

more intense, seeder populations become less abundant owing to

their comparatively lower success under such climatic conditions

(Ojeda 1998; Ojeda et al. 2005). This accounts for an apparent

north-south latitudinal partitioning of seeder and resprouter pop-

ulations (Ojeda et al. 2005) although, apart from winter rainfall

reliability and summer drought strength, most environmental con-

ditions (e.g., soil, mean annual rainfall, fire regime) are similar

across the CFR mesic mountain fynbos (Campbell 1986; Ojeda

1998).

Resprouter adult plants are frequently multistemmed, indi-

cating post-disturbance regrowth, whereas seeder ones are sin-

gle stemmed. More importantly, resprouter individuals present

a swelling modification of the stem base (lignotuber) bearing

high bud activity on its surface and thick roots with abundant

tissue for starch storage, whereas seeder ones lack these features

(Fig. 1). These morphological, anatomical, and functional differ-

ences seem to have a strong genetic basis and are detected even

from early seedling stages (Verdaguer and Ojeda 2002, 2005),

making seeder and resprouter individuals readily distinguishable

from each other, even when they co-occur. Otherwise, seeder and

resprouter plants are similar in flower shape and reproductive

ecology—bird-pollination and passive (wind) seed dispersal—

although their flowering phenologies do not overlap. Flowering

spans from late January to early June in resprouter plants (peak

in February March), and from late August through November

in seeder plants (peak in September–October; F. Ojeda, unpubl.

data).

Both seeder and resprouter E. coccinea plants are fire-

recruiters (sensu Bond and van Wilgen 1996). They set flowers

and fruits every year after reaching sexual maturity but seeds re-

main dormant in a soil-stored seed bank until the passage of fire,

which breaks seed dormancy and, hence, triggers germination and

recruitment. Presumably, seed banks become depleted after post-

fire recruitment in both regeneration forms (Ojeda et al. 2005),

although this still needs empirical verification. Therefore, be-

cause a large proportion of resprouter adult plants survive fire and

regenerate their aboveground biomass, they may resume sexual

activity and again supply seeds into the seed bank, thus experi-

encing further postfire recruitment events. By contrast, offspring

recruitment in seeder plants is preceded by the succumbing of

adults to fire. Hence, it can be stated that seeder populations in

this species have shorter generation times and faster population

turnovers than resprouter populations.

SAMPLED POPULATIONS

Ten seeder populations (i.e., composed of only seeder individ-

uals) and 10 resprouter ones (i.e., composed of only resprouter

individuals) were sampled across a large part of the species’ ge-

ographical range (Fig. 2). They included 267 individuals from

the seeder populations (S01–S10) and 235 individuals from the

resprouter ones (R01–R04, R06–R11). Besides, one mixed pop-

ulation (R05) was sampled including 10 seeder and 27 resprouter

individuals, but were considered as separate seeder (R05s) and

resprouter (R05r) populations in all subsequent analyses. There-

fore, the overall sampling included 539 individuals, 277 and

262 from 11 seeder and 11 resprouter populations, respectively

(Table 1, Fig. 2). All populations except the R08 and R11 were

from mature fynbos stands, that is, more than 10 years after the

last fire, and in all instances only adult individuals were sampled

(resprouting adults in R08 and R11).

DNA EXTRACTION, PCR AMPLIFICATION,

AND MICROSATELLITE GENOTYPING

Fresh leaves were sampled from each of the 539 individuals, dried

on silica gel (Chase and Hills 1991), and stored at −80◦C until

DNA extraction. Approximately 100 mg dry weight per sample

was used for DNA extraction. Dry material was reduced to fine

powder using 2.3 mm stainless steel beads on a Mini-beadbeater-

8 cell disrupter (BioSpec, Bartlesville, OK). DNA was extracted

using the SpeedTools plant DNA extraction kit (Biotools, Madrid,

Spain) and eluted in 50 μl in Tris-EDTA 0.1× buffer. Working di-

lution for PCR amplification was 1:10 of the eluted DNA solution.

Eight dinucleotide microsatellite loci were amplified following

Segarra-Moragues et al. (2009). PCR products were analyzed on

an ABI3730 automated sequencer (Applied Biosystems, Madid,

Spain) using LIZ500 as internal lane size standard and fragments

were assigned to allele classes using Genemarker version 1.80

software (Softgenetics, State College, PA).

DATA ANALYSES

Allele frequencies, mean number of alleles per locus (A), and ob-

served (HO) and unbiased expected (HE) heterozygosities (Nei

1978) were calculated for each population using GENETIX

EVOLUTION DECEMBER 2010 3 5 1 3

J. G. SEGARRA-MORAGUES AND F. OJEDA

Ta

ble

1.

Pop

ula

tio

nd

ata

and

gen

etic

div

ersi

tyin

dic

esin

11se

eder

and

11re

spro

ute

rp

op

ula

tio

ns

of

Eric

aco

ccin

eafo

rei

gh

tm

icro

sate

llite

loci

.

Hab

itan

dFl

ower

Lat

itude

Lon

gitu

deA

ltitu

dePo

pula

tion

N1

A1

HO

1H

E1

FIS

1

popu

latio

nsco

lor

(m)

size

Seed

erS0

1-T

MN

P.B

elow

Mac

lear

’sbe

acon

Yel

low

33◦

58′ 3

5′′ S18

◦25

′ 15′′ E

800

>50

030

7.88

0.63

30.

637

+0.0

06ns

S02-

Kle

inm

ond.

Kog

elbe

rgN

RY

ello

w34

◦19

′ 13′′ S

18◦

57′ 4

3′′ E65

>50

029

9.38

0.64

10.

758

+0.1

54∗∗

∗

S03-

Cal

edon

toH

erm

anus

.Sha

w’s

Pass

Yel

low

34◦

19′ 0

3′′ S19

◦24

′ 24′′ E

226

100–

200

198.

000.

711

0.80

0+0

.112

∗∗∗

S04-

Her

man

us.C

amph

illvi

llage

Yel

low

34◦

23′ 1

0′′ S19

◦13

′ 22′′ E

123

150–

300

247.

000.

703

0.71

4+0

.015

ns

S05-

De

Hoo

pN

R.P

otbe

rgY

ello

w34

◦22

′ 19′′ S

20◦

32′ 3

3′′ E28

7>

3000

030

9.00

0.64

20.

720

+0.1

09∗∗

∗

S06-

Cap

eA

gulh

as.S

andb

erg

Ora

ngis

h34

◦48

′ 11′′ S

19◦

58′ 3

4′′ E96

>30

000

3011

.00

0.69

60.

757

+0.0

80∗

S07-

Cap

eA

gulh

as.S

oeta

nysb

erg

Gre

enis

h34

◦45

′ 18′′ S

19◦

51′ 3

1′′ E80

>20

0030

11.5

00.

704

0.76

2+0

.076

∗

S08-

Nap

ier.

Nap

ierb

erg.

FMan

tenn

aY

ello

w34

◦31

′ 41′′ S

19◦

53′ 1

7′′ E55

850

0–10

0031

12.2

50.

714

0.75

7+0

.057

ns

S09-

Her

man

us.V

ogel

gatN

RY

ello

w34

◦24

′ 00′′ S

19◦

10′ 1

8′′ E12

520

0–30

024

9.25

0.64

60.

741

+0.1

28∗∗

S10-

TM

NP.

Cap

eof

Goo

dH

ope

Yel

low

34◦

21′ 0

6′′ S18

◦29

′ 07′′ E

105

100–

150

208.

000.

681

0.67

4−0

.012

ns

R05

s-B

otR

ivie

r.H

onin

gklip

?34

◦16

′ 53′′ S

19◦

08′ 5

2′′ E14

510

0–15

010

7.75

0.73

40.

785

+0.0

65∗

Res

prou

ter

R01

-TM

NP.

Bla

ckbu

rnra

vine

trai

lR

ed34

◦03

′ 21′′ S

18◦

22′ 1

9′′ E20

530

0–50

030

8.63

0.68

30.

732

+0.0

66∗∗

∗

R02

-TM

NP.

Dev

il’s

Peak

Red

33◦

57′ 1

3′′ S18

◦26

′ 21′′ E

953

200–

300

207.

500.

638

0.66

1+0

.036

ns

R03

-Tul

bagh

.Tul

bagh

Wat

erva

lNR

Red

33◦

22′ 4

3′′ S19

◦06

′ 48′′ E

491

200–

300

316.

750.

577

0.63

6+0

.093

∗

R04

-Fra

nsho

ek(F

hPa

ss)

Mon

tRoc

helle

Red

33◦

53′ 4

6′′ S19

◦09

′ 37′′ E

897

200–

300

197.

620.

770

0.72

9−0

.056

ns

R05

r-B

otR

ivie

r.H

onin

gklip

Red

34◦

16′ 5

3′′ S19

◦08

′ 52′′ E

145

>50

027

8.00

0.70

40.

759

+0.0

72∗

R06

-Ste

llenb

osch

.Sw

artb

oskl

oof

h.tr

ail

Red

33◦

59′ 5

6′′ S18

◦57

′ 48′′ E

558

>50

030

8.25

0.65

40.

705

+0.0

72∗

R07

-Cal

edon

.Sw

artb

erg

Red

34◦

13′ 1

0′′ S19

◦25

46′′ E

364

300–

400

176.

880.

699

0.70

7+0

.012

ns

R08

-Gre

yton

.Boe

sman

kloo

fh.

trai

lR

ed34

◦02

′ 07′′ S

19◦

37′ 2

3′′ E28

030

0–40

016

7.38

0.71

90.

689

−0.0

43ns

R09

-Sw

elle

ndam

.Mar

loth

NR

Red

+yel

low

33◦

59′ 5

6′′ S20

◦26

′ 47′′ E

225

>50

0030

7.88

0.68

30.

726

+0.0

58∗

R10

-Hei

delb

erg.

Gro

otva

derb

osch

NR

Red

+yel

low

33◦

58′ 5

2′′ S20

◦51

′ 08′′ E

315

200–

300

156.

380.

717

0.66

0−0

.086

ns

R11

-Riv

ersd

ale.

Kry

stal

Klo

ofh.

trai

lR

ed33

◦57

′ 43′′ S

21◦

15′ 1

5′′ E78

040

0–50

027

7.13

0.66

70.

732

+0.0

90ns

1N

=sam

ple

size

;A=m

ean

nu

mb

ero

fal

lele

sp

erlo

cus;

HO

,HE,o

bse

rved

and

exp

ecte

dh

eter

ozy

go

sity

,res

pec

tive

ly;F

IS=i

nb

reed

ing

coef

fici

ent.

3 5 1 4 EVOLUTION DECEMBER 2010

SEGARRA-MORAGUES AND OJEDA-FIRE AND DIVERSIFICATION IN ERICA

Figure 1. Representative image of the stem base and root sys-

tem of seeder and resprouter plants of Erica coccinea. Resprouter

adult plants are frequently multistemmed, indicating postdistur-

bance regrowth, whereas seeder ones are single-stemmed. Note

the conspicuous swelling modification of the stem base (lignotu-

ber) in the resprouter individual, bearing high bud activity on its

surface, and the thick roots, and the lack of these features in the

seeder individual.

version 4.05 (Belkhir et al. 1996–2004). Wright’s F-statistics

were estimated according to Weir and Cockerham (1984) us-

ing GENEPOP′007 (Rousset 2008) and tested for significance

by Fisher’s exact tests. This software was also used to check for

departures from Hardy–Weinberg equilibrium at each locus and

for genotypic linkage disequilibrium between pairs of loci within

each population using Fisher’s exact tests. Because multiple tests

were carried out, sequential Bonferroni corrections were used to

adjust P-values. To assess whether population diversity indices

differed between seeder and resprouter populations, average al-

lelic richness per locus (A∗), applying the rarefaction method of

Hurlbert (1971) adapted by El Mousadik and Petit (1996), HO,

expected heterozygosity (HS) and inbreeding coefficient (FIS)

within populations were compared between seeder and resprouter

groups of populations using FSTAT version 2.9.3.2 (Goudet 2001)

and tested for significance using 10,000 permutations. Pairwise

population differentiation estimates (FST) were also calculated by

FSTAT for all pairs of populations and average values between

seeder and resprouter groups of populations were compared in the

same manner.

Population genetic structure was investigated through a

Bayesian clustering method implemented in STRUCTURE ver-

sion 2.1 (Pritchard et al. 2000; Pritchard 2002). The program

allows the user to find the optimal number of genetic clusters

(K), and assigns individuals to the different clusters based on al-

lele frequencies at each locus. Our analyses were based on an

admixture ancestral model with correlated allele frequencies (be-

cause of high FISs in some populations; see Results section), for

a range of K values from 2 to the number of populations consid-

ered plus 2 (i.e., 24). In doing so, the proportion of membership

of each individual and population to the inferred K clusters were

Figure 2. Geographical location of the seeder (white dots) and resprouter (black dots) populations of Erica coccinea sampled in this

study. The gray dot (R05) corresponds to a location with both resprouter (R05r) and seeder individuals (R05s; see Material and Methods).

EVOLUTION DECEMBER 2010 3 5 1 5

J. G. SEGARRA-MORAGUES AND F. OJEDA

calculated. We used a burn-in period and a run length of the Monte

Carlo Markov Chain (MCMC) of 1 × 105 and 1 × 106 iterations,

respectively. Ten runs were carried out for each K to quantify the

amount of variation of the likelihood. The number of K present

in the dataset was evaluated according to Evanno et al. (2005).

This method uses an ad hoc parameter (�K) to estimate the rate

of change of likelihood values between successive K values.

Analyses of molecular variance (AMOVAs; Excoffier et al.

1992) were performed to partition the total variance into vari-

ance components using ARLEQUIN version 3.11 (Excoffier et al.

2005). These analyses were conducted for E. coccinea s.l., and at

different hierarchical levels. First, according to their regeneration

mode: between regeneration modes (i.e., seeder vs. resprouter),

among populations within regeneration mode, and within popula-

tions. Second, according to the K optimal genetic clusters detected

with STRUCTURE: between genetic clusters, among populations

within genetic clusters, and within populations. In all instances,

the significance of the variance components was obtained using

1000 permutations.

To explore the contributions of mutation and genetic drift

to differentiation between populations within each regeneration

mode (seeder and resprouter), and between seeder and resprouter

groups of populations, we used SPAGeDi version 1.2g (Hardy

and Vekemans 2002) to compare FST and RST statistics computed

on the same data (Hardy et al. 2003). Although FST is based on

allele identity (Wright 1951), RST also considers allele size infor-

mation (Slatkin 1995). SPAGeDi computes the statistic pRST by

using a randomization procedure in which allele sizes at a locus

are randomly permuted, while the allele identity information is

kept intact. Because allele identity, but not allele size, is taken into

account to estimate pRST, mean pRST values are equal to expected

FST ones (for further details, see Hardy et al. 2003). Therefore,

if RST is significantly larger than pRST (i.e., FST) it can be estab-

lished that genetic differentiation is the outcome of a stepwise-like

mutation process rather than a consequence of drift. Conversely,

if both RST and FST share equal expectations, it may be concluded

that differentiation is mainly caused by genetic drift (Hardy et al.

2003). To conduct these analyses, microsatellite allele sizes were

converted to number of repeats, as recommended in the SPAGeDi

manual, considering the number of repeats and sequence size of

the sequenced clones reported in Segarra-Moragues et al. (2009).

Pairwise genetic distances between populations were cal-

culated using Nei et al’s. (1983) DA, that assumes the infinite

alleles model (IAM, Kimura and Crow 1964), as implemented in

POPULATIONS version 1.2.3 beta (Langella 2000). The statisti-

cal robustness of the groupings was assessed by bootstrap anal-

ysis over populations with 1000 replicates (Felsenstein 1985).

Further genetic indices assuming the stepwise mutation model

(SMM, Kimura and Ohta 1978) were also assayed to have a pru-

dent range of evolutionary assumptions covered (Slatkin 1995).

Nonetheless, they were later discarded following the SPAGeDi

results (see below). The resulting distance matrix was used to con-

struct neighbor-joining (NJ) midpoint rooted trees using MEGA

4.0 software (Tamura et al. 2007) and to compute eigenvectors

and eigenvalues to perform principal coordinates analyses (PCO)

using NTSYSpc version 2.11a (Rohlf 2002). A minimum span-

ning tree was also constructed with NTSYSpc and superimposed

on the two-dimensional PCO.

Finally, isolation by distance was assessed separately for

seeder and resprouter groups of populations by matrix correla-

tion analyses between a matrix of pairwise linearized FST values

[i.e., FST/(1 − FST); Slatkin 1995] computed with ARLEQUIN

and a matrix of log-transformed pairwise geographical distances

between populations. Log transformation of pairwise geograph-

ical distances assumes a two-dimensional stepping-stone model

of population structure (Rousset 1997). Significance of the corre-

lation was tested for each group with Mantel tests (1000 permu-

tations) using NTSYSpc. Additionally, to search for possible dif-

ferences between seeder and resprouter groups in the increase of

genetic differentiation across geographical distance, the intercepts

and slopes of fitted line regressions to the data were compared by

means of standard analyses of covariance (ANCOVA).

ResultsGENETIC VARIATION IN ERICA COCCINEA

POPULATIONS

All eight microsatellite loci were polymorphic in all 22 analyzed

populations. Number of alleles amplified ranged from 12 (loci

Ecoc117 and Ecoc115) to 44 (locus Ecoc446) with a mean of

21.63 ± 9.85 (± SD) alleles per locus (see Appendix S1). Mean

number of alleles per locus ranged from 6.75 ± 2.49 in population

R03 to 12.25 ± 5.77 in population S08 (Table 1). Total number

of different SSR alleles scored was 173, 120 of which (69.3%)

were shared by seeders and resprouters, whereas 47 (27.2%) were

exclusive to seeders and only six (3.5%) to resprouters. Conse-

quently, seeders had a moderately higher average number of alle-

les per locus (6.44 ± 0.74) than resprouters (5.61 ± 0.73), these

differences being statistically significant (Table 2). Observed het-

erozygosities ranged from 0.577 ± 0.203 (R03) to 0.770 ± 0.309

(R04), and expected heterozygosities from 0.636 ± 0.152 (R03)

to 0.800 ± 0.060 (S03) (see Table 1). Seeder and resprouter pop-

ulations showed overall similar observed heterozygosity values

and slightly higher expected heterozygosity values, although dif-

ferences between the two groups were not statistically significant

(Table 2). Eighteen of the 22 populations showed HW deviations

toward heterozygote deficiency (i.e., positive mean FIS values).

However, these deviations were only significant in 12 popula-

tions (Table 1) and were not consistent across loci or populations.

3 5 1 6 EVOLUTION DECEMBER 2010

SEGARRA-MORAGUES AND OJEDA-FIRE AND DIVERSIFICATION IN ERICA

Table 2. Comparison of mean genetic polymorphism values be-

tween the seeder and resprouter populations of Erica coccinea.

Seeders Resprouters P-value

A1 6.440 5.609 0.0042HO

2 0.677 0.676 0.9357HS

2 0.733 0.706 0.1742FIS 0.076 0.043 0.1400FST 0.152 0.082 0.0083

1A=allelic richness calculated after the rarefaction method of El Mousadik

and Petit (1996) and based on minimum sample size of 10 individuals (cor-

responding to the smallest R05s population).2HO, HS, average observed and expected heterozygosity within popula-

tions, respectively.

Significant values based on 10,000 permutations are indicated in bold.

Seeders, N=11 populations; resprouters, N=11 populations.

This is suggestive of heterozygote deficiency being an artifact

due to the size of populations and the low allele frequency for the

highly variable loci and populations, rather than a reflection of

inbreeding. Accordingly, seeders and resprouters showed overall

FIS values close to zero, and differences between both groups

were statistically negligible (Table 2).

POPULATION STRUCTURE

Moderate to high levels of population differentiation were ob-

served among populations, with all pairwise FST values being

significantly different from zero (P < 0.001) and ranging from

a minimum of FST = 0.026 (pair R07–R08) to a maximum of

FST = 0.287 (pair S01–S10; data not shown). Average pairwise

FST (mean, 95%CI) was almost twice as high between seeder

populations (FST = 0.152, 0.095–0.221) than between resprouter

ones (FST = 0.082, 0.060–0.106), this difference being statisti-

cally significant (Table 2).

The optimal number of K genetic clusters present in the data

was difficult to estimate following the procedure described in

STRUCTURE software (Pritchard et al. 2000) because likelihood

values did not show a clear maximum, but increased with K until

a plateau was reached at approximately K = 12 (Fig. 3A). The

method of Evanno et al. (2005) showed a maximum modal value

of �K = 528.76 for K = 2 (Fig. 3B). This K = 2 clustering

separated fairly clearly seeder and resprouter populations with

high proportions of membership of each population to cluster 1

(composed of resprouters) or to cluster 2 (composed of seeders),

with the exception of seeder population S01, which showed a

higher proportion of membership to cluster 1 (Fig. 4).

A further maximum modal value of �K = 146.52 was ob-

tained for K = 4 (Fig. 3B). In this clustering most of the seeder

populations were grouped into two exclusive clusters (cluster 1

and 2) with high proportion of membership, whereas most of

the resprouter populations grouped into another exclusive cluster

(cluster 3) with similar high proportions of membership. Some

other resprouter populations grouped into a last cluster (cluster 4)

that also included one seeder population (S01) with high propor-

tion of membership (Fig. 4).

AMOVA analyses conducted for E. coccinea s.l. attributed

13.46% of the total variation between populations. Hierarchical

AMOVA with populations arranged in two groups according to

their morphological membership (i.e., regeneration modes: seed-

ers and resprouters) revealed that only 3.23% of the total vari-

ation was attributable to differences between groups, whereas

11.56% of the variation was found among populations within

groups and 85.21% within populations (Table 3). Two further hi-

erarchical AMOVAs were conducted with populations grouped

in two (K = 2) and four (K = 4) genetic clusters retrieved from

STRUCTURE. For K = 2, the analysis resulted in 4.01% of the

variance distributed between clusters 1 and 2 and 11.11% of the

variance among populations within genetic clusters. The highest

proportion of variance among groups (5.20%) was obtained in the

hierarchical AMOVA with populations grouped according to four

genetic clusters (K = 4). This analysis also revealed the lowest

genetic variation among populations within each group (9.40%;

see Table 3).

Mean pairwise multilocus estimates of differentiation

(95%CI in brackets) were FST = 0.152 (0.095–0.221), RST =0.137 (0.063–0.154) between pairs of seeder populations and

FST = 0.082 (0.060–0.106), RST = 0.068 (0.034–0.101), between

resprouter ones, whereas differentiation between seeder and re-

sprouter populations were of FST = 0.135 (0.095–0.182), RST =0.115 (0.067–0.145) (see Appendix S2). All the observed RST val-

ues between seeder and resprouter populations were within the

95% confidence interval (CI) of pRST. For within-group pairwise

comparisons, only one locus within seeder (Ecoc446) and two

loci within resprouter (Ecoc142 and Ecoc431) showed signifi-

cantly higher RST values than pRST. These results show evidence

of a preponderant role of genetic drift in population differentiation

in E. coccinea. They also suggest that the microsatellite mutation

process does not follow an SMM and that, subsequently, FST is

a better estimator of population differentiation than RST in this

species.

Genetic distances based on the IAM clustered populations

more congruently with geographical distribution and morpholog-

ical types than those based on the SMM (results not shown).

This agreed with the little relevance of the SMM in the mutation

process reported by the SPAGeDi analysis (see above). There-

fore, only the results from Nei et al. (1983) DA genetic distance

will be commented on further. In the NJ tree constructed with

this distance method, resprouter populations formed a somewhat

homogeneous group, with populations showing shorter genetic

distances between them than seeder populations (Fig. 5). Seeder

EVOLUTION DECEMBER 2010 3 5 1 7

J. G. SEGARRA-MORAGUES AND F. OJEDA

Figure 3. (A) Log-likelihood of the eight microsatellite loci data for 22 E. coccinea populations given K clusters, obtained through 10

runs of the STRUCTURE analysis. (B) Corresponding �K estimation according to Evanno et al. (2005) showing maximum peaks of �K

values at K = 2 and K = 4, indicating that those are the optimal solutions for K given the data.

populations, by contrast, grouped into different clusters, and two

of them (S01 and R05s) were embedded into the cluster of re-

sprouters. Genetic distances between seeder populations were

larger than those between resprouter populations. Even the two

seeder populations that were genetically close to resprouter popu-

lations (S01 and R05s) showed larger distance to their respective

pairs than any of the other resprouter pairs (Fig. 5). The results

of this NJ cluster analysis support the effect of exclusive alleles

in increasing the genetic distance of seeder populations. Similar

results were obtained in the PCO analysis (results not shown).

A significant correlation between pairwise geographical dis-

tances and linearized FST values was found in both seeder (r =0.43, P = 0.017) and resprouter (r = 0.45, P < 0.001) populations

(Fig. 6), indicating that both morphological groups showed sig-

nificant isolation by distance. The intercept of the regression of

the seeder group (assuming standard line regression of the data)

was significantly higher than that of the resprouter (P < 0.0001;

ANCOVA), whereas no significant differences were detected be-

tween the slopes of both line regressions (P = 0.145; ANCOVA).

This can be interpreted as a reflection of higher genetic distance

values for seeder populations throughout the geographical range

but similar patterns of increase in genetic differentiation with ge-

ographical distance in both seeder and resprouter groups of pop-

ulations. However, despite the similarity in the slopes between

both groups, values were more scattered for seeder populations

throughout the geographical range (Fig. 6). This degree of scatter,

measured as the average of the absolute residuals from a standard

line regression, was in fact twice as large in seeder (0.053 ± 0.041)

than in resprouter populations (0.021 ± 0.018), being these dif-

ferences highly significant (P < 0.0001; Mann–Whitney U test).

DiscussionREGENERATION MODES AND GENETIC

BACKGROUND OF ERICA COCCINEA

The role of population dynamics may be crucial to explaining

levels of genetic differentiation in wild populations (Wade and

McCauley 1988; Pannell and Charlesworth 1999; Vitalis et al.

2004). In this sense, pairwise genetic differentiation levels in

E. coccinea were twice as high in seeder populations than in

3 5 1 8 EVOLUTION DECEMBER 2010

SEGARRA-MORAGUES AND OJEDA-FIRE AND DIVERSIFICATION IN ERICA

Figure 4. Bayesian analyses of genetic structure of 539 individuals from 22 populations of Erica coccinea. (A) Mean proportion member-

ship of each population to the predefined, K = 2 and K = 4 clusters with the highest �K values obtained following Evanno et al. (2005).

(B) Proportion of membership of individuals for K = 2 and K = 4 predefined clusters. Populations 1–11, seeders, 12–22, resprouters.

resprouter ones. Average genetic variance among seeder popula-

tions was even higher than across seeder and resprouter groups

of populations, which accounted for only slightly more than 3%

of the total variation (see Table 3). This challenges the possi-

bility of seeder and resprouter groups of E. coccinea actually

being two different cryptic taxa. It also reinforces the role of post-

fire regeneration modes in explaining genetic divergence among

populations in this species, rather than possible selective factors

associated with the apparent geographic partitioning of seeder

and resprouter populations (Ojeda et al. 2005; see Fig. 2). How-

ever, with the data presented in this study, we cannot rule out this

alternative explanation.

The above-mentioned low percentage of genetic variation

between regeneration modes assigned by the AMOVA (Table 3)

Table 3. Analyses of molecular variance (AMOVA) of Erica coccinea populations.

Source of Sum of squared df Variance Percentage of thevariation (groups) deviations (SSD) components total variance

1. Erica coccinea s.l.Among populations 517.466 21 0.44578 13.46Within populations 3026.218 1056 2.86574 86.54

2. Morphological membership: seeders (S01–S10, R05s) versus resprouters (R01–R11)Among groups 81.627 1 0.10862 3.23Among populations within groups 435.839 20 0.38875 11.56Within populations 3026.218 1056 2.86574 85.21

3. Genetic membership: Two clusters of STRUCTURE analysis; cluster 1 (S01, R01–R11) versus cluster 2 (S02–S10, R05s)Among clusters 94.954 1 0.13541 4.01Among populations within clusters 422.512 20 0.37512 11.11Within populations 3026.218 1056 2.86574 84.88

4. Genetic membership: four clusters of STRUCTURE analysis; cluster 1 (S02–S05, S07–S09, R05s) versus cluster 2 (S06, S10)versus cluster 3 (R04–R05r, R07–R11) versus cluster 4 (S01, R01–R03, R06)

Among clusters 191.755 3 0.17457 5.20Among populations within clusters 325.711 18 0.31536 9.40Within populations 3026.218 1056 2.86574 85.40

EVOLUTION DECEMBER 2010 3 5 1 9

J. G. SEGARRA-MORAGUES AND F. OJEDA

Figure 5. Neighbor-joining midpoint rooted tree based on DA ge-

netic distance (Nei et al. 1983) showing the relationships among

seeder (white dots) and resprouter (black dots) populations. Boot-

strap values obtained from 1000 permutations over populations

are shown above branches of the NJ tree when higher than 40%.

Figure 6. Isolation by distance analysis. Correlation between

log-transformed pairwise geographical (x-axis) and linearized FST

(Slatkin 1995) pairwise values (y-axis) of seeder (white dots) and

resprouter (black dots) populations. Correlation between matrices

was r = 0.436, P = 0.014, and r = 0.445, P = 0.001 for seeder and

resprouter populations, respectively; P values reported after 1000

random permutations Mantel tests.

seems at odds with the nearly accurate distinction between seeder

and resprouter populations provided by the STRUCTURE anal-

ysis (see Fig. 4). However, it shall be stressed that although

the AMOVA, as well as the NJ analysis are distance-based ap-

proaches, STRUCTURE is a coalescent clustering method based

on inferred allele frequencies between populations. Because both

seeder and resprouter seem to be conspecific, no distinct genetic

backgrounds, but just slight differences in frequencies of the most

common alleles are expected, which STRUCTURE is particularly

well suited to identify.

Genetic diversity in natural populations is shaped by a bal-

anced effect of mutation and migration (gene flow), which gener-

ate within-population variation, and natural selection and genetic

drift, which erode it and promote differentiation among popu-

lations (Hutchison and Templeton 1999; Hartl and Clark 2007).

High levels of genetic differentiation among populations are fre-

quently related to changes in allele frequencies and overall de-

clines in within-population genetic variation, mainly caused by

drift (Loveless and Hamrick 1984; Charlesworth 2003). Inter-

estingly, our results revealed that not only genetic differentia-

tion among populations, but also variation within populations (as

measured by the average allelic richness per locus) was higher in

seeder populations (see Table 2). These higher genetic diversity

values within seeder populations might be interpreted as the out-

come of presumably higher mutation rates in seeder populations

owing to their shorter generation times (Smith and Donoghue

2008; but see Verdu et al. 2007), which would subsequently ac-

celerate the generation of new alleles in populations.

Alternatively, higher gene flow rates (via seeds and/or pollen)

in seeder populations might be invoked to explain their higher

levels of (within-population) genetic diversity, assuming similar

levels of total diversity (HT) between the seeder and resprouter

group of populations. As a matter of fact, pairwise geographical

distances between seeder populations (78.7 ± 47.2 km; mean ±SD) were slightly but significantly shorter than those between

resprouter (110.6 ± 65.6 Km; P < 0.01; Mann–Whitney U test;

see Fig. 2). However, our analyses revealed significant patterns of

isolation by distance in both seeder and resprouter groups of popu-

lations, with similar, positive correlation values between pairwise

geographical and genetic distances in the two groups (Fig. 6). This

is not surprising because both seeder and resprouter plants share

equal reproductive traits. They both produce small, oval seeds

(F. Ojeda, pers. obs.) that are dispersed by ants or passively by

wind. These short range means of dispersal (wind dispersal dis-

tances of Erica seeds may not exceed 100 m; Bullock and Clarke

2000) would not overcome genetic patterns of isolation by dis-

tance. Regarding pollen, both seeder and resprouter plants exhibit

showy flowers with a distinct ornithophylous syndrome (Rebelo

and Siegfried 1985) and are actively and apparently equally vis-

ited by Nectariniidae (sunbirds) across their geographical range

3 5 2 0 EVOLUTION DECEMBER 2010

SEGARRA-MORAGUES AND OJEDA-FIRE AND DIVERSIFICATION IN ERICA

(F. Ojeda, pers. obs.). In any case, should the higher genetic di-

versity in seeder populations be attributed to a putatively higher

gene flow across populations, it would, in turn, cause differen-

tiation among populations to be lower (Zhivotovsky 2001; Hartl

and Clark 2007) in the seeder than in the resprouter group, while

the opposite has been found in this study. Besides, total diversity

values were slightly higher in the seeder (HT = 0.851) than the in

resprouter (HT = 0.760) group of populations.

These somewhat unexpected results (i.e., both higher within-

population genetic variation and higher among-population diver-

gence in the seeder group) may indeed be reconciled by con-

sidering the contrasting population dynamics determined by the

seeder and resprouter regeneration modes (Bond and van Wilgen

1996; Ojeda et al. 2005). Each fire cycle means a complete gen-

eration turnover in seeder populations, whereas generation times

of resprouter are longer (several to many fire episodes). Hence,

faster rates of new alleles arising in the populations are to be ex-

pected in seeder populations of E. coccinea as a consequence of

their shorter generation times. At the same time, faster popula-

tion turnover rates in seeder populations may accelerate genetic

drift (Wade and McCauley 1988; Pannell and Charlesworth 1999),

causing allele loss and subsequent changes in allele frequencies

across populations. The wider scattered plot of the genetic versus

geographical distances in the seeder group shown in Figure 6 (see

also Results section) may be indicative of stronger genetic drift

in seeder populations (e.g., see Hutchison and Templeton 1999;

Johansson et al. 2006; Hamilton and Eckert 2007) compared to

resprouter ones, despite the overall shorter pairwise geographical

distances in the seeder group (see above). This random genetic

erosion would somewhat counterbalance the above-stated allele

gain boosted by the shorter generation times and would thereby

enhance genetic differentiation among seeder populations. Hence,

the contrasting population dynamics of seeder and resprouter pop-

ulations of E. coccinea may satisfactorily account for both the

higher within-population genetic diversity and the more marked

levels of differentiation among seeder populations found in the

present study.

The lower levels of differentiation among resprouter popula-

tions were clearly evidenced by the Bayesian analysis of genetic

structure, where resprouter populations diversified into fewer ge-

netic clusters (Fig. 4). Analogously, the NJ tree analysis clus-

tered all resprouter populations together (Fig. 5). By contrast,

the higher diversification among seeder populations is evidenced

in these analyses (Figs. 4 and 5), as a consequence of their

higher proportion of exclusive alleles and higher levels of genetic

differentiation.

EVOLUTIONARY IMPLICATIONS

Genetic differentiation among populations, considered the initial

step to speciation (Avise 2000; Coyne and Orr 2004), is frequently

associated with marked reductions in variation within populations

as a consequence of genetic drift (Loveless and Hamrick 1984;

Young et al. 1996; Charlesworth 2003). This genetic erosion of in-

dividual populations may compromise their long-term persistence

by increasing their risk of local extinction (van Treuren et al. 1991;

Newman and Pilson 1997), and would ultimately undermine pos-

sible speciation. Notwithstanding, here we have suggested that

genetic erosion in differentiation-prone seeder populations would

be counterbalanced by the allele gain boosted by their shorter

generation times, thereby sustaining or even increasing genetic

divergence (Figs. 4 and 5).

However, despite the apparent strength of seeder populations

against genetic erosion, they are still highly prone to extinction,

particularly under harsh and highly variable environmental con-

ditions that jeopardize postfire recruitment (e.g., severe summer

drought and winter rainfall unreliability; Ojeda et al. 2005). Due

to their nonoverlapping generation dynamics, seeder populations

lack reproductive storage potential over generations (Warner and

Chesson 1985; Higgins et al. 2000) and, unlike resprouter, they

are extremely vulnerable even to a single postfire recruitment

failure (Bond and Midgley 2001; Ojeda et al. 2005). In fact, the

apparent north-south geographical partitioning of resprouter and

seeder populations of E. coccinea in the CFR fynbos may be

rather explained by the higher extinction risk of seeder popu-

lations under climatic conditions different from those found in

coastal mountains of the south-western CFR (Ojeda 1998; Ojeda

et al. 2005).

High speciation rates certainly require the existence of low

extinction risks (Dynesius and Jansson 2000; Mittelbach et al.

2007). Consequently, higher genetic divergence of seeder popula-

tions might only translate into higher speciation when the risk of

(local) extinction is reduced. In this regard, it must be again em-

phasized that both species diversity and endemism in Erica reach

highest levels in coastal mountains of the south-western CFR

(Oliver et al. 1983; Ojeda 1998) and are tightly associated to the

seeder life form (Ojeda 1998; Cowling and Lombard 2002). Par-

ticular climatic conditions in this region reduce extinction rates of

seeder populations (Ojeda et al. 2005), thereby fostering specia-

tion in differentiation-prone seeder populations. Thus, we believe

that the results presented in this study contribute significantly to

our understanding of the high biodiversity levels of the genus

Erica in the South-African CFR fynbos.

ACKNOWLEDGMENTSWe thank A. Garcıa and R. Marriner for helping with field sampling.SANPARKS and Cape Nature authorities provided permits for collectingplant material. We also thank managers of Vogelgat (Hermanus) nature re-serve, and landowners of Brandfontein (Agulhas), Camphill (Hermanus)and Honingklip (Bot Rivier) farms for letting us collect samples in theirprivate properties. FO is deeply indebted to F. Conrad and K. Roux (KRC,SANBI, South Africa) for hosting and facilitating deliverables for plant

EVOLUTION DECEMBER 2010 3 5 2 1

J. G. SEGARRA-MORAGUES AND F. OJEDA

collection. S. Donat-Caerols for laboratory assistance. P. Linder, X. Pico,M. Verdu, R. Cowling, J. Pannell, M. Burd, and two anonymous refer-ess provided valuable comments to previous versions of the manuscript.Fieldwork was financed by travel grants to FO from the University ofCadiz (Plan Propio UCA de Investigacion) and the Junta de Andalucıa(subvencion de Incentivos de Actividades Cientıficas 2/2007). Financialsupport for laboratory work has been provided by project VAMPIRO(CGL2008-05289-C02-01/BOS; Spanish Ministerio de Ciencia e Inno-vacion), project P07-RNM-02869 (Junta de Andalucıa, Spain) and projectACOMP09/073 (Generalitat Valenciana, Spain). JGS-M was supportedfirst by an ARAID (Agencia Aragonesa para la Investigacion y el De-sarrollo, Spain) postdoctoral contract and next by a “Ramon y Cajal”(MICINN-RYC, Spain) postdoctoral contract.

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Associate Editor: M. Burd

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Supporting InformationThe following supporting information is available for this article:

Figure S1. Mean pairwise FST, pRST, and RST values between populations of Erica coccinea for populations belonging to the

same habit, seeder (A) or resprouter (B) or different habit (C).

Table S1. Allele frequencies for eight microsatellite loci in 11 seeder and 11 resprouter populations of Erica coccinea.

Supporting Information may be found in the online version of this article.

Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting information supplied by the

authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

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