ORIGINAL PAPER

Variation components in leaf morphology of recruits of twohybridising oaks [Q. petraea (Matt.) Liebl. and Q. pyrenaicaWilld.] at small spatial scale

Unai Lopez de Heredia Æ Marıa Valbuena-Carabana ÆMarta Cordoba Æ Luis Gil

Received: 12 December 2008 / Revised: 28 May 2009 / Accepted: 3 July 2009 / Published online: 25 July 2009

� Springer-Verlag 2009

Abstract Leaf morphological variation was examined in

recruits of two hybridising oaks in a small sympatric area

from Central Spain. Nuclear microsatellites were used to

identify hybrids and assess the parental lineage. By

Bayesian clustering analysis, 5% of hybrids were found.

Principal component analysis was used to reduce 15 mor-

phometric variables to four components associated with

leaf size, lobation/pubescence and overall shape of the leaf.

The percentage of variance due to genetic factors was

evaluated through nested analysis of variance. As much as

70% of variance component was due to the factor ‘‘spe-

cies’’ for lobation/pubescence, suggesting high adaptive

value for these traits, possibly related to ecological con-

straints of the species. The genetic component of variance

for leaf size and overall shape of the leaf was below 33%.

Age and height of the recruits did not correlate with sun-

leaf morphology. Competition indexes and diameter of the

recruits showed slight, although significant, correlations

with leaf size and lobation/pubescence components,

pointing to some trade-offs between competition for light

and leaf morphology of Q. petraea and Q. pyrenaica

recruits.

Keywords Competition � Hybridisation �Leaf morphology � Multivariate analysis �Parentage analysis � Quercus

Introduction

Leaf morphology plays an important role in species adap-

tations related to photosynthetic ability of the plants: heat

dissipation, light interception and hydraulic resistance,

among others. In forest trees, leaf morphology has also

incidence in the ecosystem, because different leaf sizes and

shapes generate a variety of light environments, hence

different understory compositions. These patterns have led

to the development of a variety of explanations for the

adaptive value of leaf morphology and its plasticity. For

instance, Vogel (1968, 1970) showed that lobed leaves

increased heat dissipation per unit area and decreased the

dependence of heat dissipation on leaf orientation. Lower

hydraulic resistance in deeply lobed leaves may constitute a

mechanism for improving water balance under dry atmo-

spheric conditions (Siso et al. 2001). Gradients of leaf

lobing along the length of shoots may be significant in terms

of overall light interception (Niklas 1989). Lobed leaves are

structures with low production costs that are adaptive to

environments favouring the deciduous habit, and in light

gaps or early successional vegetation where rapid upward

growth and efficient competition for light are advantageous

(Givnish 1979). Other leaf traits, such as pubescence,

reduce leaf absorptance resulting in a smaller heat load and,

as a consequence, lower leaf temperatures and lower tran-

spiration rates (Ehleringer and Mooney 1978). The presence

of trichomes in the adaxial (upper) leaf surface allows the

maintenance of good photosystem II efficiency during the

summer (Morales et al. 2002). There are also inherent

Communicated by R. Matyssek.

U. Lopez de Heredia � M. Valbuena-Carabana � M. Cordoba �L. Gil

Ud. Anatomıa, Fisiologıa y Genetica Vegetal,

Dpto. Silvopascicultura, E.T.S.I. Montes,

Technical University of Madrid,

Ciudad Universitaria s/n, 28040 Madrid, Spain

U. Lopez de Heredia � M. Valbuena-Carabana � L. Gil (&)

Unidad mixta de Genomica y Fisiologıa Forestal

UPM-INIA, Madrid, Spain

e-mail: luis.gil@upm.es

123

Eur J Forest Res (2009) 128:543–554

DOI 10.1007/s10342-009-0302-6

interactions between photosynthetic capacity and leaf size,

because the former scales with stomatal conductance

(Schulze et al. 1994; Reich et al. 1999).

To elucidate the maintenance mechanism of inter- and

intra-specific variation, it is necessary to investigate the

relative contribution of genetic and non-genetic factors to

phenotypic variation observed in nature (Simms and

Rausher 1992). A detailed picture of variation in leaf

morphological traits related to ecological and genetic

determinants can be optimally obtained from progeny tri-

als. However, progeny trials for forest trees require a long

time to reach maturity, and they only allow the study of

seedling traits in most of the cases. Leaf morphology

variation in older trees can be studied in natural popula-

tions, but there are several limitations that may bias the

results: (1) the interaction of multiple non-genetic factors

derived from environmental heterogeneity, i.e. light avail-

ability, competition, soil composition, etc; (2) the degree of

dominance of the individual tree in the stand and the age of

the trees, which may interfere in leaf morphology unless

regular stands are analysed; (3) the lack of knowledge

about the seed source and the familiar structure of the

sampled trees usually hinder discerning the components of

variation in leaf morphology traits.

In order to minimise these limitations, we propose an

integrative approach that combines estimators of environ-

mental variability (degree of competition and soil compo-

sition) with measures on the own tree (diameter, height,

age) and the assessment of familiar relationships through

molecular analysis. As a case study, we have analysed leaf

morphological traits in two hybridising oak species.

Pubescence and lobation are amongst the most discrimi-

nant leaf morphological traits between Quercus petraea

and Q. pyrenaica (Franco 1990). Q. pyrenaica’s leaf

lobation and dense pilosity confers protection from high

levels of radiation, an advantage in Mediterranean habitats.

There is vast literature exploring the relationship

between leaf morphology and neutral variation in hybri-

dising oak species (see, for instance, Kremer et al. 2002;

Bruschi et al. 2000; Craft et al. 2002; Valbuena-Carabana

et al. 2007). With few exceptions (Gugerli et al. 2007),

these analyses have focused on overall patterns of variation

at wide–medium scales. Specifically, previous studies on

hybridisation between Q. petraea and Q. pyrenaica in the

Forest of Montejo analysed nuclear microsatellites varia-

tion in the adult cohort through a Bayesian analysis of

allele frequencies to identify putative hybrids (Valbuena-

Carabana et al. 2007). The authors found between 6 and

13% of hybrids depending on a relaxed (\0.85) or strict

(\0.95) probability criterion of species assignment. In

the present study, we have analysed the same nSSR on the

recruits. In addition, assignment of parent trees to the

recruits through parentage analysis adds information about

this source of variation to the data, frequently neglected

when studying natural populations.

Multivariate and regression analysis were used to

decompose variation in genetic and non-genetic compo-

nents in recruits’ leaf traits related to size, shape and

particular features such as leaf pubescence, to answer the

following specific questions: (1) Which part of leaf varia-

tion is under genetic control and which part is due to non-

genetic factors? (2) Is there a correlation between leaf

morphology and the studied non-genetic variables (i.e. age,

diameter, height, competition and soil composition)?

Materials and methods

Characterisation of the study plot

The study area is included within a Q. petraea–Q. pyrenaica

contact zone of 13 ha in northern Madrid (Central System

Range, Spain, 41� 60 20.300 N, 3� 290 26.400 W). The Forest of

Montejo (122 ha) is part of the UNESCO Biosphere Reserve of

the Sierra del Rincon. Until 1974, the Forest of Montejo had

been intensively managed for centuries like an open oak

woodland (dehesa) system for cattle grazing and firewood/

charcoal production (Pardo et al. 2004). Since that date,

traditional management was abandoned and open lands were

invaded by a dense regeneration of saplings. Today, Montejo

shows a combination of adult trees of high diameter [diam-

eter at breast height (DBH) [60 cm] at low density

(13.54 trees/ha), among which recruits (DBH \ 25 cm)

merge at extremely high density (2,362 trees/ha in the area of

the present study). The forest is at the southern edge of the

European distribution of Q. petraea, where stands of this

species consist of only a few 100 mature individuals.

Q. petraea grows here under marginal ecological conditions,

the water deficit during summer being a major limiting factor

to growth and survival. In contrast, Q. pyrenaica is wide-

spread in the region, the study plot being located in the

central range of its distribution. Oaks mainly appear in spe-

cies–group mixtures, occasionally forming aggregates.

Microhabitat segregation has been observed in the forest,

Q. petraea occurs in deeper and less rocky soils, and contact

zones between species are limited to only a few locations

(Pardo et al. 2004). Introgressed adult trees are mostly found

at these contact zones (Valbuena-Carabana et al. 2005).

An intensive study area of 1,875 m2 was fenced in at the

maximum contact zone between species (Fig. 1). All the

trees within the intensive plot (N = 443) were coded and

precise relative X, Y and Z coordinates were obtained with

a topographic total station (TOPCOM 500) (Fig. 2). Oaks

represented 65.7% of all trees (26.4% Q. petraea, 39.3%

Q. pyrenaica). Other tree and shrub species appeared at

lower frequencies (Fig. 2). As much as 7.4% of the trees

544 Eur J Forest Res (2009) 128:543–554

123

were dead at the sampling time, possibly due to the high

density of the intensive plot. Eight adult trees (60 cm \DBH \ 130 cm) were within the intensive plot, two of

them being Q. petraea and six Q. pyrenaica; the other oak

trees were recruits (5 cm \ DBH \ 35 cm).

A preliminary species assignment was performed de visu

to select 49 Q. pyrenaica and 48 Q. petraea recruits for

morphological and genetic analysis, according to the spe-

cies description in Flora Iberica (Franco 1990). Five to ten

sun leaves were collected from the top of the crown of each

recruit. DBH was measured with a digital forcipule (Hagloff

Digitech). Of the 97 recruits, 90 were cut in March 2007.

Slices were extracted from the base to the top of the tree at

intervals of 1 m. The basal slice was used to estimate the

age of the recruits by ring counting with a 109 microscope.

The height of the recruit was measured according to the

resultant number of slices. Height provides an indirect

estimate of the degree of dominance of the recruit and, thus,

of the light availability for the sun-leaves at the top of the

crown. At any moment in the successional process, being

taller than neighbours confers competitive advantage

through prior access to light (Westoby et al. 2002).

A systematic soil sampling was performed in six test pits

of 50 cm 9 50 cm 9 20 cm to test for edaphic hetero-

geneity within the plot. Standard protocols were used to

obtain estimates of pH, electric conductivity (EC), per-

centage of natural organic matter (NOM) and percentage of

nitrogen (N) in the organic horizon of the substrate (profile

I: 0–10 cm; profile II: 10–20 cm).

Two types of individual competition indexes were used

to estimate the effect on the leaf morphology of the

recruits. The first type considered the number of trees

around each recruit where morphology was measured (N).

The second type considered the basal area of all the trees

around the recruit of choice (AB). Both types of parameters

were obtained for a given radius around the tree. We

constructed competition indexes for 4, 5 and 6 m (i.e.

N.4M, N.5M, N.6M; AB.4M, AB.5M, AB.6M) by a

function programmed in S-PLUS 2000 Professional

Release 2 (�1998–1999, Mathsoft Inc.).

Species and lineage assignment

Genetic analysis was performed on genomic DNA extracted

from fresh leaf tissue with a modification of the protocol

by Doyle and Doyle (1990). Nine nuclear microsatellites

were analysed with fluorescence-labelled primers adapted

to a 4200 LI-COR automated DNA sequencer (LI-COR

Biosciences, Lincoln, NE, USA): QpZAG9, QpZAG36,

QpZAG110 (Steinkellner et al. 1997), QrZAG5, QrZAG7,

QrZAG11, QrZAG39 (Kampfer et al. 1998), MSQ4 and

MSQ13 (Dow et al. 1995). PCR and electrophoresis con-

ditions are detailed in Valbuena-Carabana et al. (2007).

Assignment of species and detection of the putative

hybrids was done using a Bayesian model-based clustering

method with STRUCTURE v. 2 (Pritchard et al. 2000). This

method detects the number of homogeneous clusters (two in

this case according to the existing species) and allows for the

presence of admixed individuals giving them a posterior

probability of belonging to any of the species clusters. Based

on individual probabilities at the 0.95 level (the strict cri-

terion of species assignment), trees were assigned to three

categories, Q. petraea, Q. pyrenaica or, otherwise, hybrids.

Assignment of the parent trees for each recruit was

performed through parentage analysis regarding the com-

plete set of adult individuals in 13 ha (94 Q. petraea and 92

Fig. 1 Spatial location of the

adult trees and the intensive

study plot (ISP). Identified

parent trees by parentage

analysis are indicated

Eur J Forest Res (2009) 128:543–554 545

123

Q. pyrenaica). The set of microsatellite markers was highly

polymorphic, resulting in high exclusion probabilities for

correct parent assignations. We used LOD scores and a

simulation procedure to infer single parents for each

recruit, as implemented in FAMOZ software (see details in

Gerber et al. 2000, 2003).

Morphological analysis

Five to ten fully developed leaves per tree were collected in

different branches from the sun-exposed upper part of the

crown before the cutting of the trees. The total set was

composed of 566 leaves. Fifteen morphological traits were

measured in the whole set of samples (Fig. 3). WINFOLIA

v. 2002a image analyser software (Regent Instrument Inc.,

Canada) was used to directly measure area (A), perimeter

(P), length (L), maximal width (MW), position of maximal

width (PMW), width at 50% of leaf length (W1), width at

90% of leaf length (W2), maximum lobe length (MLL) and

sinus depth below the maximum lobe (MSD). Ratios of

PMW/L, MSD/L and P/A were also calculated along with

circularity (4pA/P2), the mean value of all leaf widths (W)

and the difference between the MLL and MSD, as indirect

parameters. These characters represent leaf morphology in

size, overall shape and shape of some specific features (e.g.

lobation). In addition, pubescence of the upper (PU) and

lower (PL) surface of an intervein blade piece of 2 mm2 was

scored using a 109 microscope. Pubescence is considered

as a discrete character varying along a gradient between 1:

glabrous and 6: densely pubescent (Kissling 1977).

Statistical analysis

Median, mean, maximum/minimum values and standard

deviation were scored for each variable and specific

Fig. 2 Map of the intensive study plot for recruits. Square linesindicate the fence installed to avoid feeding by macromammals.

Samples outside the fence were considered for the estimation of

competition indexes. PetAd denotes Q. petraea adult trees. PyrAd:

Q. pyrenaica adult trees, PetM: Q. petraea for morphological

analysis, PyrM: Q. pyrenaica for morphological analysis, Pet: other

Q. petraea, Pyr: other Q. pyrenaica, Hyb: hybrid recruits, Fsy: Fagussylvatica, Pav: Prunus avium, other: other species in the plot [Fagussylvatica L. (7.5%), Prunus avium L. (9.9%), Ilex aquifolium L.

(3.6%), Crataegus monogyna Jacq. (3.4%), Genista florida L. (0.9%),

Sorbus aucuparia (0.9%), Sorbus aria (L.) Crantz (0.7%), Ericaarborea L. (0.2%)]

Fig. 3 Morphological variables directly measured with WINFOLIA

v. 2002a image analyser software (Regent Instrument Inc., Canada):

area (A), perimeter (P), length (L), maximal width (MW), position of

maximal width (PMW), width at 50% of leaf length (W1), width at

90% of leaf length (W2), maximum lobe length (MLL) and sinus

depth below the maximum lobe (MSD) and ratios of PMW/L, MSD/L,

MLL–MSD and P/A were also calculated along with circularity (4pA/

P2) and the mean value of all leaf widths (W), as indirect parameters

546 Eur J Forest Res (2009) 128:543–554

123

category (Q. petraea, Q. pyrenaica and hybrids). Signifi-

cant differences in the means between Q. petraea and

Q. pyrenaica were tested through t tests for continuous

variables and the Wilcoxon rank sum test for discrete

characters. A principal component analysis (PCA) was

performed by scaling the original variables with the prin-

comp command from S-PLUS 2000 Professional Release 2

(1998–1999, Mathsoft Inc.). New variables were created

from the scores of the components, thus simplifying further

analysis. A hierarchical nested analysis of variance

(ANOVA) was performed on the new variables (principal

components scores) that mostly contributed to the overall

variation, to test for genetic factors explaining leaf

morphometric variation. The model was the following:

Yijk ¼ lþ ai þ bj ið Þ þ ck j ið Þð Þ þ eijkl

where l is the mean of the principal component, ai the

effect of the ‘‘species’’ i (SP), bi(j) the effect of the ‘‘par-

ent’’ j within the ‘‘species’’ i (P), ci(j(k)) the effect of the

‘‘tree’’ k within the ‘‘parent’’ j within the ‘‘species’’ i (T),

and eijkl the error (E) in the ‘‘leaf’’ l of the ‘‘tree’’ k in the

‘‘parent’’ j, in the ‘‘species’’ i. All the factors are inde-

pendent and show a normal distribution as follows: ai:

N(0, rSP); bj(i): N(0, rP); ck(j(i)): N(0, rT); eijk: N(0, r).

Estimators of rSP, rSP(P), rSP(P(T)) and rE were obtained

for the variables with the varcomp method from S-PLUS

2000 Professional Release 2 (1998–1999, Mathsoft Inc.)

using the restricted maximum likelihood method to identify

the factors that contribute significantly to variation in the

principal components of the model above.

The standardised residuals of the first three components

were used as new variables related to non-genetic variation.

Association of the residuals to ‘‘age’’, ‘‘DBH’’, ‘‘height’’

and competition indexes were tested by estimating Pear-

son’s correlation values. In addition, the step procedure of

S-PLUS 2000 Professional Release 2 (1998–1999, Math-

soft Inc.) was run to find the best variables fitting a linear

model.

Results

Species and parent assignment

Species assignment through STRUCTURE analysis

showed two clusters that corresponded to Q. petraea and

Q. pyrenaica. A low percentage of putative hybrids were

scored. Of 97 (5.2%) recruits, 5 showed posterior proba-

bility levels below 0.95 of belonging either to Q. petraea or

Q. pyrenaica, and four of them had probabilities below

0.85 (4.1%). This hybrid category included individuals

with traces of introgressive hybridisation. Figure 2 shows

that the hybrids are located in areas of maximum contact

for recruits of both species.

Parents were found both inside and outside the recruits’

plot, at distances ranging from 2.2 to 436 m. Single parent

assignation was gained for 39 Q. petraea, 41 Q. pyrenaica

and 3 hybrid recruits. As much as 21 Q. petraea adults and

22 Q. pyrenaica were identified as parents of the same

species’ offspring, according to the single-parent assign-

ment method. Two Q. petraea and one Q. pyrenaica adults

were identified as parent trees of the hybrid offspring. Each

identified parent tree was converted to a category for

further analysis. Recruits with the same parent tree are

half-sibs and may share leaf morphological features.

Leaf morphology of Q. petraea, Q. pyrenaica

and hybrid recruits

Table 1 shows the maximum/minimum, mean/median

values and standard deviation for each variable and specific

category. The t test showed significant differences in mean

values of Q. petraea and Q. pyrenaica (P \ 0.05) for all

the continuous morphological variables, except for the

perimeter of the leaf (P). The Wilcoxon rank sum test was

also significant (P \ 0.001) for differences in medians of

pubescence variables (PU, PL), with Q. pyrenaica having

denser pubescence. Overall, size-related parameters (A, L,

P, W, MW, W1, W2 and MLL) showed higher values for

Q. petraea than for Q. pyrenaica. Lobation-related traits

(MSD/L, 4pA/P2, P/A) showed higher lobation for Q. py-

renaica than for Q. petraea, but the difference between

MLL and MSD showed the opposite pattern, with signifi-

cantly higher values for Q. petraea than for Q. pyrenaica.

Significant differences in mean values of PMW and PMW/L

indicate that Q. petraea is sharper at the base than at the tip

of the leaf than Q. pyrenaica.

The PCA on morphological variables showed four

components accounting for 86% of the total variance:

PC1 = 49.5%, PC2 = 19.7%, PC3 = 9.4%, PC4 = 7.4%.

The loadings of the original variables for the four first

components are detailed in Table 2. Each principal com-

ponent is associated with one morphological attribute. PC1

shows the highest loading positive scores for variables

related to leaf size parameters (A, L, P, W, MW, W1 and

MLL). The variables with the highest weights in PC2 and

PC4 are related to pubescence (PU, PL) and lobation

parameters (MSD/L, 4pA/P2, P/A, MLL–MSD). Finally,

PC3 is indicative of the overall shape of the leaf (PMW/L).

The biplot of components PC2 and PC4 (Fig. 4)

shows a clear separation between leaves of the genetic

clusters corresponding to Q. petraea and Q. pyrenaica.

Leaves from the hybrid category, however, do not show

intermediate traits and cluster together with either

Eur J Forest Res (2009) 128:543–554 547

123

Ta

ble

1S

um

mar

yst

atis

tics

(mea

n,

med

ian

,m

inim

um

and

max

imu

mv

alu

esan

dst

and

ard

dev

iati

on

)o

fth

e1

6m

orp

ho

log

ical

trai

tso

nre

cru

its

of

Q.

pet

raea

,Q

.p

yren

aic

aan

dth

ep

uta

tiv

e

hy

bri

ds

PU

PL

AP

L4p

A/P

2W

MW

PM

WP

MW

/LW

1W

2M

SD

/LM

LL

P/A

ML

L–

MS

D

Qu

ercu

sp

etra

ea

Mea

nN

AN

A3

6.6

04

0.6

91

0.1

90

.28

6.0

15

.80

5.5

50

.54

5.0

82

.23

0.1

62

.99

1.1

61

.38

Med

ian

12

35

.25

39

.66

10

.05

0.2

75

.89

5.7

05

.36

0.5

45

.01

2.1

00

.15

2.9

51

.15

1.3

3

Min

12

12

.27

23

.00

6.3

80

.15

3.1

92

.95

2.4

20

.21

2.1

50

.80

0.0

61

.47

0.7

00

.36

Max

24

89

.54

69

.30

16

.26

0.5

59

.82

9.5

41

0.6

40

.74

9.0

04

.55

0.3

25

.01

1.9

22

.92

N2

58

25

82

58

25

82

58

25

82

58

25

82

58

25

82

58

25

82

58

25

82

58

25

8

SD

0.3

30

0.5

53

12

.95

49

.77

31

.90

40

.06

51

.23

11

.20

71

.49

40

.09

11

.13

80

.76

10

.05

10

.64

40

.19

80

.41

5

PU

PL

AP

L4p

A/P

2W

MW

PM

WP

MW

/LW

1W

2M

SD

/LM

LL

P/A

ML

L–

MS

D

Qu

ercu

sp

yren

aic

a

Mea

nN

AN

A2

9.8

24

0.5

19

.25

0.2

35

.81

5.5

84

.80

0.5

24

.62

1.9

80

.19

2.8

61

.45

1.0

7

Med

ian

45

27

.03

38

.18

8.9

80

.23

5.6

55

.40

4.6

80

.51

4.5

11

.87

0.1

92

.75

1.4

31

.03

Min

24

8.6

21

9.5

45

.51

0.1

02

.69

2.5

72

.04

0.2

41

.63

0.6

90

.06

1.4

80

.72

0.2

0

Max

56

75

.27

80

.02

15

.61

0.5

39

.77

9.6

39

.11

0.7

28

.79

4.5

30

.40

5.1

82

.27

3.5

8

N2

72

27

22

72

27

22

72

27

22

72

27

22

72

27

22

72

27

22

72

27

22

72

27

2

SD

0.4

56

0.5

38

12

.14

61

1.0

23

1.9

54

0.0

62

1.2

54

1.2

33

1.4

30

0.1

00

1.2

82

0.7

34

0.0

54

0.6

65

0.2

91

0.4

32

PU

PL

AP

L4p

A/P

2W

MW

PM

WP

MW

/LW

1W

2M

SD

/LM

LL

P/A

ML

L–

MS

D

Hy

bri

ds

Mea

nN

AN

A3

4.7

14

2.1

21

0.2

30

.25

5.8

35

.59

6.0

50

.59

4.7

82

.23

0.1

52

.92

1.2

91

.37

Med

ian

12

33

.31

40

.89

10

.38

0.2

75

.46

5.4

06

.00

0.5

84

.48

2.2

40

.14

2.7

21

.19

1.2

7

Min

12

17

.25

28

.79

6.9

20

.15

4.2

24

.08

3.3

70

.44

3.3

11

.08

0.0

81

.94

0.8

10

.61

Max

46

62

.37

65

.81

13

.19

0.4

08

.22

8.0

08

.54

0.7

27

.41

4.3

80

.29

4.1

11

.95

2.1

6

N2

72

72

72

72

72

72

72

72

72

72

72

72

72

72

72

7

SD

1.4

60

1.5

20

11

.93

69

.85

61

.90

90

.06

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548 Eur J Forest Res (2009) 128:543–554

123

Q. petraea’s or Q. pyrenaica’s leaves for pubescence-

lobation components.

The genetic component of leaf morphology variation

Analysis of variance was performed only for the first three

principal components (PC1, PC2 and PC3). Hybrids were

removed from the analysis. PC4 was not considered

because it includes the same leaf traits as PC2. The

ANOVA (Table 3) and the variance component plot

(Fig. 5) show different influence of genetic factors

depending on the leaf morphology attribute explained by

each response variable. The effect of the ‘‘species’’ is

highly significant for the three components, although for

PC1 and PC3 the variance component of the residuals is

over 67%. For PC2, the variance component of the resid-

uals is minimised (18.08%) and the ‘‘species’’ variance

component reaches 70.36%. ‘‘Parent within species’’ has

significant P values for PC2 and PC3, but the estimated

variance component is negligible (\5%). ‘‘Tree within

parent within species’’ shows significant P values for PC1

and PC3, being the variance components below 18%

(PC1 = 18.00%; PC3 = 11.87%).

The non-genetic component of leaf morphology

variation

Although there were significant differences between pro-

files I and II of the organic horizon, no significant differ-

ences were obtained for edaphic parameters across the

intensive plot. Mean values for the plot show a slightly acid

pH (pHI = 5.68, SD = 0.326; pHII = 5.20, SD = 0.161)

and moderate values of conductivity (ECI = 0.19 mS/cm,

SD = 0.120; ECII = 0.05 mS/cm, SD = 0.013), percent-

age of NOM (NOMI = 10.00, SD = 3.447; NOMII =

5.05, SD = 1.128) and percentage of nitrogen (NI = 0.35,

SD = 0.058; NII = 0.21, SD = 0.038).

Table 2 Loadings of the principal components explaining 86.9% of

variance in leaf morphology

PC1 PC2 PC3 PC4

PU -0.10678361 0.40219579 -0.19665636 -0.52022794

PL -0.10117186 0.40069632 -0.18922370 -0.53337693

A 0.34539438 0.00003839 0.05561693 -0.04460930

P 0.30116521 0.22611475 -0.06320580 0.16328931

L 0.31797606 -0.01474115 -0.04545886 0.01633767

4pA/P2 -0.06823736 -0.42722863 0.23349752 -0.37126460

W 0.33114699 0.14943275 0.10033724 -0.04808334

MW 0.33063112 0.14689775 0.10154664 -0.03719994

PMW 0.27639859 -0.08634520 -0.45757111 0.08885703

PMW/L 0.08427364 -0.11454254 -0.68335220 0.14474097

W1 0.29648726 0.05483584 0.13544281 -0.04640915

W2 0.23992233 -0.01662059 -0.03332566 -0.09997696

MSD/L 0.00618722 0.43522601 0.26953509 0.23001790

MLL 0.31920345 0.14683451 0.13826073 -0.02993847

MLL–MSD 0.21335758 -0.29334296 -0.11047597 -0.35097872

P/A -0.25924335 0.28108991 -0.20420217 0.23755643

In bold the highest loading values for each component ([|0.25|)

Fig. 4 Graphical representation

over the biplot of principal

components PC2 and PC4

according to the genetic clusters

obtained with structure cluster 1

denotes Q. pyrenaica (opentriangle), Cluster 2: Q. petraea(Open circle), Cluster 3: hybrids

(dark filled square inside theopen square). Leaf shapevariation is evident from the

typical leaves for each side of

the biplot. Qpet denotes

Q. petraea and Qpyr denotes

Q. pyrenaica. Asterisk denotes

densely pubescent

Eur J Forest Res (2009) 128:543–554 549

123

The age of the recruits ranges between 24 and 51 years in

Q. petraea and between 21 and 52 years in Q. pyrenaica. The

peak of regeneration was reached in 1967 for Q. petraea and

in 1966 for Q. pyrenaica. Hybrids did not correspond to a

single regeneration event. The age of the recruits shows good

correlation with DBH (R2pet = 0.33; R2

pyr = 0.27) and height

(R2pet = 0.31; R2

pyr = 0.36) (Fig. 6). DBH of the trees ranges

between 2.20 and 24.20 cm for Q. petraea (mean =

9.96 cm) and between 3.70 and 32.00 cm for Q. pyrenaica

(mean = 11.07 cm). Height of the trees ranges between 3.35

and 17.56 m for Q. petraea (mean = 9.51 m) and between

2.46 and 14.32 m for Q. pyrenaica (mean = 8.29 m).

Hybrids show intermediate values for both DBH

(min = 5 cm; max = 21.10 cm; mean = 12.22 cm) and

height (min = 4.48 m; max = 15.57 m; mean = 10.06 m).

Table 4 shows Pearson’s correlations between environ-

mental variables and the residuals of the principal

components: RPC1, RPC2 and RPC3. For all the combi-

nations, Pearson’s correlation (r) was below 0.16. The

diameter of the recruits showed negative, although poor,

correlation with RPC1 (leaf size; rdiam = -0.092) and

RPC2 (lobation–pubescence; rdiam = -0.113). The number

of trees within a circle of 4 and 5 m radius around the recruit

were positively correlated with both RPC1 [r(N.4M) =

0.115; r(N.5M) = 0.096] and RPC2 [r(N.4M) = 0.153;

r(N.5M) = 0.104]. The basimetric area of the trees within a

circle of 6 m radius around the recruit showed negative

correlation with RPC2 [r(AB.6M) = -0.097]. Finally, height

and age of the recruits did not show significant correlations

with any of the variables. Given the poor correlation values,

the step procedure was unable to identify variables to

construct a multiple regression linear model.

Discussion

The overall description of leaf morphology in recruits of

Q. petraea and Q. pyrenaica from Central Spain does not

differ significantly from classic taxonomic descriptions

(Vicioso 1950; Schwarz 1964; Franco 1990). Overall,

Quercus pyrenaica’s leaves are more lobed, densely

pubescent and are slightly less sharp at the base than those

of Q. petraea that are almost glabrous, less lobed and

sharper at the base than at the tip of the leaf.

De visu assignment of species based on leaf lobation/

pubescence failed in identifying hybrids, which did not

show intermediate values for any trait. Our results show a

low percentage of hybrids based on neutral genetic variation

at a 0.85 probability criterion (4.1%), consistently with the

low hybrid rates obtained by Valbuena-Carabana et al.

(2007) for adult trees in the same area of study (6%) using

the same set of markers. For a probability criterion of 0.95,

recruits show a much lower percentage of hybrids than adult

trees (5.2 vs. 13%). For the three out of five cases where

parent assignment was possible, hybrid recruits showed

Table 3 Analysis of variance table for components PC1 (leaf size),

PC2 (leaf lobation/pubescence) and PC3 (overall leaf shape)

Factor df SS MS F P value

PC1: Leaf size

ai 1 276.338 276.3376 37.14067 0.0000000

bj(i) 2 24.894 12.4469 1.67290 0.1888447

ck(j(i)) 2 52.558 26.2788 3.53195 0.0300452

eijkl 459 3415.096 7.4403

PC2: Lobation/pubescence

ai 1 798.5402 798.5402 544.3102 0.0000000

bj(i) 2 38.3356 19.1678 13.0654 0.0000030

ck(j(i)) 2 1.4644 0.7322 0.4991 0.6074053

eijkl 459 673.3844 1.4671

PC3: Overall shape

ai 1 36.5961 36.59614 26.92444 0.0000032

bj(i) 2 33.4550 19.1678 13.0654 0.00000030

ck(j(i)) 2 11.3649 5.68247 4.18069 0.01587401

eijkl 459 623.8804 1.35922

Fig. 5 Variance components of the factors in the ANOVA model for

principal components PC1 (leaf size), PC2 (leaf lobation–pubescence)

and PC3 (overall leaf shape). rSP = Variance component of

‘‘species’’. rSP(P) = Variance component of ‘‘parent within species’’.

rSP(P(T)) = Variance component of ‘‘tree within parent within

species’’. rSP = Variance component of the residuals

550 Eur J Forest Res (2009) 128:543–554

123

similar leaf shapes as those of their parental species. These

results agree with Himrane et al. (2004) that analysed

morphological and ecophysiological characters in hybrids

of Q. pubescens 9 Q. faginea (i.e. Q. subpyrenaica) finding

a wide spectrum of characters, usually closer to those of the

assumed parental species.

Leaf shape and pubescence are under strong genetic

control for Q. petraea and Q. pyrenaica; therefore, they

have high adaptive value and conserve the identity of the

species through generations. Saintagne et al. (2004)

showed by quantitative trait loci analysis of genomic

regions that control leaf morphology that species’ differ-

entiation in oaks occurs at multiple sites within the genome

and is not confined to a few spots with limited recombi-

nation. Gugerli et al. (2007), following the genic view of

the process of speciation (Wu 2001), assumed that a rela-

tively small number of genomic regions under selection,

in concert with strong reproductive barriers and limited

seed dispersal, minimise interspecific gene flow between

Q. robur and Q. petraea and maintain taxon separation.

This would explain the absence of morphologically inter-

mediate recruits for Q. petraea and Q. pyrenaica.

Remarkably, the five hybrid recruits merge in areas of

maximum contact between recruits of both species in the

study plot, pointing to selection for micro sites where the

hybrid character could be advantageous. Unfortunately,

hybrid percentage is not sufficient as to extrapolate the

results to other areas or species complexes.

Fig. 6 Diameter at breast height–birth and height–birth plots for Q. petraea and Q. pyrenaica. The multiple R2 coefficient and the confidence

intervals at the 0.99 level for the corresponding fitted linear models are indicated

Table 4 Pearson correlations of the residuals of the first three prin-

cipal components, age, height, diameter and competition indexes

within circles of 4, 5 and 6 m radii around the recruits

RPC1 RPC2 RPC3

Age 0.039 0.0162 0.063

Height -0.018 -0.096 0.023

Diameter -0.092 -0.113 0.053

N.4M 0.115 0.153 0.000

N.5M 0.096 0.104 0.019

N.6M 0.065 0.074 0.043

AB.4M 0.089 0.018 -0.083

AB.5M 0.810 -0.069 -0.058

AB.6M 0.044 -0.097 -0.021

In bold the correlations significant at 95%

Eur J Forest Res (2009) 128:543–554 551

123

The genetic component of morphological variation is

highly significant for lobation–pubescence and overall

shape parameters. Conversely, leaf size, although sig-

nificant, has a negligible variance component for ‘‘spe-

cies’’. Lobation and pubescence are strongly correlated

with the ecological constraints of the species. Leaf

hydraulic resistance in oaks is negatively related to leaf

lobation and it is suggested that a lower hydraulic

resistance in lobed leaves may constitute a mechanism

for improving water balance under dry atmospheric

conditions (Siso et al. 2001). The highly lobed and

pubescent Q. pyrenaica is a strictly sub-Mediterranean

species adapted to inter- and intra-annual dry periods

that characterise the Mediterranean climate. In contrast,

Q. petraea is less adapted to drought and grows in the

forest of Montejo under marginal ecological conditions,

the water deficit during summer being a major limiting

factor to growth and survival. Interestingly, although P/A

shows a more profuse lobation for Q. pyrenaica than for

Q. petraea, the mean difference between MLL and MSD

reveals that Q. petraea’s maximum lobes are deeper than

those of Q. pyrenaica, which are shallower but with

more open angles, thus allowing more heterogeneous

light incidence in the understory at a comparable crown

allometry (Valladares and Niinemets 2007). As compared

to the mean values for this variable in other European

stands, Q. petraea from the mountains of Central Spain

shows similar scores (1.32) than in mountain forests

from Italy (1.29) (Bruschi et al. 2000), but higher than

the values reported for flat stands in Germany (1.14)

(Kleinschmit et al. 1996) and Northern Spain (0.98)

(P. Goikoetxea personal communication), suggesting that

deeper sinuses are performed in mountain Mediterranean

forests of Q. petraea.

Regarding a potential effect of the ‘‘parent tree’’ in leaf

morphology, sampled recruits are the progeny of at least 23

Q. petraea and 23 Q. pyrenaica adults. These parent trees

are located at distances ranging from 2.2 to 346 m, sug-

gesting that secondary animal-mediated dispersal has

pooled a seed rain coming from a wide area in the forest.

Therefore, the potential bias of analyzing recruits from

limited sources is minimised. Parentage analysis usually

considers the mother plant as the most likely parent when a

single parent is found (Dow and Ashley 1998; Asuka et al.

2005; Valbuena-Carabana et al. 2005; Gonzalez-Martınez

et al. 2006; Goto et al. 2006; Hardesty et al. 2006), a rather

logical assumption in species with contrasting seed/pollen

sizes, like oaks. Maternal effects can have a substantial

influence on an individual’s phenotype at the early seedling

stage, and these effects diminish over time (Roach and

Wulff 1987). Under this assumption, our results point to

the lack of traces of maternal effects, because the variance

estimates due to the parent tree (i.e. mother) are very small

for lobation/pubescence and overall shape of the leaves and

non-significant for leaf size.

We only detected significant effects of the individual’s

genotype on leaf morphology for leaf size and overall

shape. A significant 18.1% of leaf size variation was due to

the effect of the individual within parent tree and within

species, indicating that leaf size varies among recruits to

some extent, probably due to the their different growing

conditions and, hence, due to micro-environmental factors

affecting each recruit.

As much as 67% of variation in leaf size was due to

environmental or stochastic factors. We found poor,

although significant, negative correlation of diameter with

leaf size. In addition, the presence of other trees around the

recruit at distances less than 5 m was positively correlated

with leaf size. The extremely high density of the recruits in

the plot may promote strategies focused on the maximi-

sation of sun-leaves’ size to increase photosynthetic rates

in detriment to diameter growth. Early successional trees

have to compromise between extensive branching and

rapid vertical growth (Givnish 1979), modifying the rela-

tionships of support biomass distribution in leaves and

stems (Niinemets et al. 2006).

Only the 18.1% of variation for lobation/pubescence is

due to non-genetic factors. We found some poor, but sig-

nificant, correlations that may indicate some trade-offs

between competition for light and leaf lobation/pubes-

cence. The highest Pearson correlation index was obtained

for the number of individuals around the recruits

(r = 0.153), suggesting that lobation and pubescence

increase with competition for light resources. This result

makes sense in highly competitive habitats, given that

lobed and pubescent leaves reduce transpiration rates

(Ehleringer and Mooney 1978) and may be advantageous

in competition for light in drought-prone habitats (Givnish

1979).

Interestingly, we did not find any relationship between

the age of the recruits and the shape of the leaf. Leaf shape

has been shown to vary with plant ontogeny (Kerstetter and

Poethig 1998; Lynn and Waldren 2001), but recruits from

present study had already surpassed the seedling stage.

Indeed, the recruits had ages that ranged from 21 to 53

years, the majority of samples being 40 and 41 years for

Q. pyrenaica and Q. petraea, respectively. There was no

significant correlation between the age of the recruits and

leaf morphology, which might suggest the stabilisation of

the latter on reaching maturity.

The low correlations between variables may suggest

another mechanism acting on leaf morphology variation.

Among others, responses to drought stress (Niinemets

2001) or branching architecture (Corner 1949; Ackerly and

Donoghue 1998) have been recognised as factors having an

effect in leaf morphology.

552 Eur J Forest Res (2009) 128:543–554

123

Acknowledgments The Autonomic Government of Madrid Region,

the Technical University of Madrid and the Spanish Ministry of

Education and Science provided financial assistance to the following

projects: CAM 07M/0011/2000, CAM 07M/0012/2002, R05/11065

and AGL2006-00813. We wish to thank Drs N. Nanos and J. Rod-

rıguez-Calcerrada for valuable suggestions and comments on the

manuscript, J. Alonso and M.C. Garcıa for field assistance and

M. Venturas for suggestions on English grammar and style.

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