progeny in this species and the potential role of stress-
induced epigenetic inheritance in evolution has been
discussed (Verhoeven et al. 2010). The involvement of a
histone variant (H2A.Z) was found to mediate short-term
adaptation to temperature change in *A. thaliana* (Kumar &
Wigge, 2010), and cold stress-induced hypomethylation
and transposition of a TE (Tam-3) has been observed in
*Antirrhinum* (Hashida et al. 2006).

Epigenetic recombinant inbred lines (epiRILs) have emphasised the relationship between response to environmental conditions and epigenetic phenomena. In *A. thaliana*, epiRILs have nearly identical genomes but display diverse mosaic epigenomes with variant DNA methylation patterns (Johannes et al. 2009; Reinders et al. 2009). The range of pathogen sensitivity/resistance within one isogenic epiRIL population exhibited 60% of the range of pathogen responses observed in natural, genetically varying *A. thaliana* accessions (Reinders et al. 2009). In the context of environmental challenges, such epigenetic modifications may be thought of as relatively “plastic” yet heritable marks that allow for rapid responses and adaptations and, at the same time, might avoid excessive genetic diversification (Boyko & Kovalchuk, 2008; Lira-Medeiros et al. 2010).

Epigenetic control in forest tree species

Relationship between epigenetic and phenotypic
plasticity

Forest trees are long-lived organisms with complex life
cycles, which must contend with a variable environment
over their long lifetimes (Rohde & Junttila, 2008). The long
generation times impose limits on natural selection under
rapidly changing climate conditions (Rehfeldt et al. 1999;
Rehfeldt et al. 2002). Consequently, trees must be highly
adaptable, displaying a wide range of phenotypes as a
function of their environments, known as phenotypic
plasticity (Nicotra et al. 2010). Phenotypic plasticity is likely
to be of great importance for both individual trees and
forest populations over near- and long-term timescales.
Despite this, knowledge of the extent and underlying
mechanisms of phenotypic plasticity in response to a
variety of stress responses and developmental traits in
trees is rudimentary (Rohde, 2009; Lira-Medeiros et al.
2010; Neale & Kremer, 2011).

In addition to the genetic component, epigenetic variation
has been suggested to contribute to the phenotypic
plasticity and adaptive potential of individuals and
populations (Bossdorf et al. 2008; Jablonka & Raz, 2009;
Herrera & Bazaga, 2010; Lira-Medeiros et al. 2010; Richards
et al. 2012). Insight into epigenetic variation, and its
relationship to phenotypic plasticity, will contribute to the
understanding of adaptive plant responses, and might
help to evaluate the risk of long-lived species to both
short-term and long-term fluctuations in the environment.
Moreover, understanding the interplay between

epigenetics and adaptation should enhance the
understanding of evolutionary trajectories, as natural
selection also directly targets the proportion of
phenotypic variation that is shaped by epigenetic
phenomena (Bossdorf et al. 2008; Herrera & Bazaga,
2010).

Epigenetic and phenotypic variation in natural
populations, ecotypes and species

Despite the substantial impact that epigenetics might have
in determining environmental compatibility, relatively few
studies have investigated the extent of natural epigenetic
variation and its relationship to phenotypic variation and
adaptation potential (Cervera et al. 2002; Bossdorf et al.
2008; Jablonka & Raz, 2009; Marfil et al. 2009; Herrera &
Bazaga, 2010; Lira-Medeiros et al. 2010; Paun et al. 2010).
Among the different epigenetic mechanisms that are
potentially involved in transgenerational inheritance and
natural epigenetic variation, DNA methylation represents
the most-studied modification (Akimoto et al. 2007;
Jablonka & Raz, 2009; Herrera & Bazaga, 2010; Lira-
Medeiros et al. 2010; Paun et al. 2010; Verhoeven et al.
2010). Two of the few published studies in higher plants
that considered the interplay between genetic, epigenetic,
as well as and phenotypic variation and environmental
factors, focused on a perennial violet species (Viola
cazorlensis) and orchids of the Dactylorhiza majalis
complex (Herrera, 1990; Herrera & Bazaga, 2010; Paun et
al. 2010). The studies detected coordinated genetic-
epigenetic adaptive differentiation, indicating the
involvement of epigenetic processes in adaptation and
evolution by influencing primary phenotypic diversity.

In tree species, natural variation in epigenetic marks and
the relation to phenotypic traits is still an under-explored
area. Insight into the role of epigenetics in determining tree
phenotype should identify key elements in the control of
growth traits and contribute to the understanding of
evolutionary capacity of tree species (Grattapaglia et al.
2009; Thumma et al. 2009; Lira-Medeiros et al. 2010). In
keeping with this, evidence for the correlation between
tree form and epigenetics is emerging. Trees of the white
mangrove (Laguncularia racemosa) can occur naturally in
contrasting habitats and can exhibit striking differences in
morphological traits (Lira-Medeiros et al. 2010). Tree-like
appearance was documented in a riverside habitat with
abundant fresh water and nutrient supply whereas in a
nearby salt marsh habitat mangrove plants were
characterised by abnormal growth and shrub-like
morphology. Notably, despite morphological
dissimilarities, analysis of DNA nucleotide sequences and
methylation patterns detected greater epigenetic than
genetic variation within and between populations in
contrasting environments, which indicates that epigenetic
variation in natural populations plays an important role in
long-term adaptation to different environments (Lira-
Medeiros et al. 2010).
FIGURE 1

Clone history shapes drought responses in poplar hybrids. Transcriptome-level responses to water withholding are influenced by geographic origin for two of the three genotypes, and are paralleled by differences in total (genome-wide) DNA methylation. Ramets of hybrid poplar genotypes (A) Okanese, (B) Walker, and (C) DN34 with distinct histories were obtained from two different locations for each of the genotypes. The response to water deficit was assessed under common, controlled environmental conditions. Okanese (A, D, G); Walker (B, E, H); DN34 (C, F, I). Tree appearance (A-C). Transcriptome-level responses (D-F). Heat maps represent relative abundance of drought responsive transcripts at pre-dawn for Okanese (D), Walker (E), and Okanese (F) obtained from two

locations each. The numbers indicated to the side of
the heat map correspond to transcripts with significant
treatment main effect only (gray) and with significant
treatment: location interactions (orange bar) (BH
adjusted, P<0.05). W, well watered samples; D, water-
deficient samples. Global DNA methylation levels as
percentage of 5mC under well-watered (shaded bars)
and water-limited conditions (white bars) for the
genotypes Okanese (G), Walker (H), and DN34 (I). L,
location effect; T, treatment effect; and LxT, location:
treatment interaction term (*P<0.05, **P<0.001, n=6,
SD bars). Locations are abbreviated as follows AB,
Alberta; SK, Saskatchewan; MB, Manitoba. Figure is
adapted from Raj *et al.* 2011.

<figure><img src="image_48.png" /></figure>
The lasting impact of previous environmental history on a tree's capacity to respond to a current environmental stimulus has recently been explored in *Populus* (Raj et al. 2011). Poplar trees are frequently propagated vegetatively through stem cuttings of branches containing dormant buds, generating genetically identical individuals or ramets of the same genotype. Clonally propagated poplar trees can be planted in different geographic locations, thus giving rise to populations of genetically identical ramets that are characterised by their own local environment and history. To study the lasting effect of clone history on current plant performance, cuttings of the same genotype were obtained from different geographic locations and grown under common environmental conditions, after which the transcriptome response to an important environmental stress, drought, was studied. Notably, differences in transcript abundance patterns in response to drought that were based on differences in geographic origin of clonally propagated trees were detected in two of the three investigated genotypes. These transcriptome-level patterns were paralleled by differences in genome-wide DNA methylation. Genotypes with the longest time since establishment and last common propagation showed the most pronounced location-specific patterns in transcriptome response and DNA methylation indicating a possible epigenomic basis for clone history-dependent transcriptome divergence (Fig.1). These findings underline the importance of epigenetic mechanisms related to the adaptation of long-lived species like poplar trees to the local environment (Raj et al. 2011).

The direct response of six hybrid poplar genotypes to water deficit revealed a relationship between epigenetic marks and the genotypic variability of phenotypic plasticity (Gourcilleau et al. 2010). Genotypic variation for both DNA methylation and traits related to biomass productivity was observed in hybrids (*Populus deltoides* × *P. nigra*), and a positive correlation was established among these variables in well-watered conditions (Fig.2). While poplar genotypes showed reduced growth in water deficit conditions, a significant genotype effect was observed for DNA methylation variations. This suggests that DNA methylation could participate in the fine-tuning of gene expression in poplar during water stress (Plomion et al. 2006; Bogeat-Triboulot et al. 2007; Bonhomme et al. 2009; Wilkins et al. 2009; Gourcilleau et al. 2010; Hamanishi & Campbell, 2011).

The potential link between natural epigenetic variation and phenotypic variability observed in trees is further supported by studies in ecotypes and individual populations of specific herbaceous plant species (Cervera et al. 2002; Marfil et al. 2009). Highly conserved DNA methylation patterns were detected within an *A. thaliana* ecotype (Ler) while clear DNA methylation differences existed between ecotypes that did not correlate with nucleotide sequence variation, but with their flowering

time (Cervera et al. 2002). Furthermore, variation in the floral phenotype of individuals from a single natural population of a wild hybrid potato (*Solanum ruiz-lealii*) were found to correlate with distinct DNA methylation patterns but not with DNA sequence variation (Marfil et al. 2009).

Most studies assessing epigenetic variation in natural populations, ecotypes or species focused on the extent of epigenetic variability and paid less attention to the functional consequences. Indication for a functional link between a specific epigenetic mark at a specific position in the genome and variation in a quantitative trait was discovered by analysing polymorphisms in an association population and a full-sib family of eucalypt (*Eucalyptus nitens*; Thumma et al. 2009). Making use of the low linkage disequilibrium in populations of forest trees, variation in cellulose content was linked to polymorphisms within a gene potentially involved in cellulose synthesis and deposition (functional polymorphisms). The COBRA-like gene *EnCOBL4A* was strongly associated with a QTL region for cellulose content and fine mapping revealed a significant association with a SNP in exon 5. Notably, allelic expression imbalance was linked to allele-specific cytosine methylation upstream of this SNP in a full-sibling family. A heritable epigenetic polymorphism is thus likely to influence phenotypic variation in cellulose content; however, further functional analyses are required (Thumma et al. 2009). The findings suggest that epigenetic variations might contribute to quantitative trait variation (Thumma et al. 2009), and it has been suggested that this phenomenon might be common (Johannes et al. 2008; Reinders et al. 2009; Thumma et al. 2009; Long et al. 2011).

To date, some prominent, shared observations have emerged from the few studies of natural epigenetic variation and phenotypic plasticity. These studies established that a) epigenetic variation occurs in natural populations, ecotypes and species, b) this variation can correlate with naturally occurring phenotypic variation, and c) there is a potential role for epigenetic variation in adaptation and potentially in evolution. Despite the commonalities that have emerged from these studies, many questions remain unresolved. For example, is epigenetic variation in natural populations a wide-spread phenomenon? Moreover, the key molecular mechanisms involved and how they are regulated remain to be determined. Finally, it is unclear to what extent epialleles arise and how stable they are when considered in an evolutionary context (Bossdorf et al. 2008; Herrera & Bazaga, 2010; Lira-Medeiros et al. 2010; Paun et al. 2010). Answers to such questions might also contribute to a better understanding of the adaptive capability of long-lived forest trees that might help to assess their susceptibility to rapidly changing environments (Grattapaglia et al. 2009).
Relation between epigenetic marks and the genotypic variability of phenotypic plasticity under limited water availability or not in six poplar hybrids. a. Experimental design; b. Stem biomass and DNA methylation levels in the shoot apex (center of morphogenesis). For each graph, g indicates the genotype effect, t the treatment effect and (gxt) genotype by treatment effect. Means

are accompanied by their standard errors SE (n = 6). Significant differences between well-watered and water deficit conditions are indicated by asterisk: * P ≤ 0.05, ** P ≤ 0.01 and *** P ≤ 0.001; c. Linear correlation (Pearson, r) between stem biomass and DNA methylation levels. Adapted from Gourcilleau *et al.* 2010.

a

Greenhouse experiment

Genetics

P. deltoides x P. nigra

6 hybrids

Carpaccio, Lambro, Soligo, Triplo,
I45-51, NL3972

Traits related to biomass
production

- Stem biomass

- Stem height

- Root biomass

- Leaf number

- Total leaf area

- Specific Leaf area

Environment

17 days

Well-watered

Soil Water Content > 32%

Water deficit

7%<SWC<13%, Ψpd = 0 MPa

Shoot apex

<figure><img src="image_49.png" /></figure>

Epigenetic traits

- Global DNA methylation

- DNA methyltransferase activity and isoforms

- Histone H3 and H4 acetylation

- Histone deacetylase activity and isoforms

b

<figure><img src="image_50.png" /></figure>

Well-watered

<figure><img src="image_51.png" /></figure>

Water deficit

<figure><img src="image_52.png" /></figure>

<figure><img src="image_53.png" /></figure>

c

<figure><img src="image_54.png" /></figure>
Epigenetic plasticity in growth and development

During their relatively long lifespans, trees must make
developmental adjustments while retaining flexibility to
match and synchronise growth and development with
prevailing environmental conditions. Epigenetic
mechanisms are proposed to contribute to such flexible
adjustments by generating transmittable and reversible
marks that constitute temporary "memory" systems
(Boyko & Kovalchuk, 2008; Kvaalen & Johnsen, 2008;
Yakovlev et al. 2010; Jaskiewicz et al. 2011). Tissue-, organ-
, and species-specific differences in DNA methylation
levels are well known (Fraga et al. 2002a; Fraga et al.
2002b; Valledor et al. 2007; Monteuuis et al. 2009;
Santamaria et al. 2009; Rodriguez Lopez et al. 2010;
Valledor et al. 2010; Vining et al. 2012; Lafon-Placette et al.
2012). Changes in epigenetic marks were found to
accompany morphological and physiological changes in
trees in a wide variety of processes, including ageing,
phase change, organ maturation, and bud set or burst
(Fraga et al. 2002a; Fraga et al. 2002b; Santamaria et al.
2009; Valledor et al. 2010).

Bud dormancy is a vital adaptation to seasonal changes, and release and induction of bud dormancy are complex processes that largely determine length of the growth season, and thereby affect annual tree productivity. Regulation of bud burst integrates endogenous and exogenous signals such as hormone levels, day length, light quality and temperature (Santamaria et al. 2009) and involves substantial changes in gene expression and epigenetic modifications (Ruttink et al. 2007; Rohde, 2009; Santamaria et al. 2009). In apical buds of a chestnut (Castanea sativa), a decrease in global DNA methylation level and concomitant increase in acetylation of histone 4 was observed during bud burst when conditions were favorable for active growth. The opposite pattern (i.e., DNA hypermethylation and lower histone acetylation levels), indicative of more repressive chromatin states, was detected during bud set when environmental conditions were less favorable for growth (Santamaria et al. 2009). The observed coordinated changes in DNA methylation and histone modifications are predicted to alter the control of gene expression to shape the processes of bud burst and bud set (Santamaria et al. 2009).

Ageing and maturation are characterised by altered
patterns of cell differentiation and organ formation
processes, and the potential role of DNA methylation in
maturation has been studied in some tree species (Fraga
et al. 2002a; Fraga et al. 2002b; Valledor et al. 2007;
Monteuuis et al. 2009). For example, studies in radiata pine
(Pinus radiata) support the involvement of DNA
methylation in this process. Changes in global DNA
methylation levels of up to 25% during maturation have
been reported in this species (Fraga et al. 2002a; Fraga et
al. 2002b). In juvenile plants without flowering capability,
young needle tissue was characterised by a markedly

lower extent of DNA methylation than corresponding
tissues in adult trees with reproductive ability. Regarding
histone modifications, decreased levels of euchromatin-
associated marks, such as histone 4 acetylation and
specific histone methylation (trimethylation of histone 3
on lysine 4 or H3K4me3) have been measured in mature
needles when compared with juvenile ones (Valledor et al.
2010). Moreover, the observed increase in DNA
methylation levels from juvenile to mature plants in
meristematic tissue could be directly linked to phase
change. Conversely, an increase in the degree of tree
reinvigoration by serial grafting, measured by the recovery
of morphogenetic competence, was accompanied by a
decrease in global level of DNA methylation in
meristematic tissue, thus pointing towards plasticity of
DNA methylation marks during ageing and maturation.
The degree of DNA methylation, as well as additional
biochemical characteristics, were proposed to serve as
suitable markers for ageing and reinvigoration in pine
(Fraga et al. 2002a; Fraga et al. 2002b). However,
differences between species and experimental systems
might exist. In another conifer, Larix laricina, age-related
changes in foliar traits were observed, while differences in
DNA methylation levels between juvenile and mature
scions could not be detected in DNA from whole needles
(Greenwood et al. 1989).

In angiosperms, heteroblastic tree species like *Acacia mangium* with distinct leaf morphologies of juvenile and mature stages provide excellent systems to study ageing. Small but significant differences between microshoots with juvenile (pinnate) and mature (phyllode) morphology were observed in this acacia species when analysing global DNA methylation levels in physiologically active apical buds of *in vitro* grown plant material. Here, the degree of DNA methylation was higher in juvenile than in mature tissue, and might be influenced by *in vitro* culture conditions (see Epigenetic and phenotypic plasticity in artificial system) in addition to maturation-related processes (Baurens *et al.* 2004; Monteuuis *et al.* 2009). Taken together, the aforementioned studies establish a clear relationship between DNA methylation levels and maturation for some tissue types and species in woody plants (Fraga *et al.* 2002a; Fraga *et al.* 2002b; Baurens *et al.* 2004; Valledor *et al.* 2007; Monteuuis *et al.* 2009). Observed differences might be attributable to differences in taxonomy, tissue type (meristematic vs. differentiated) or experimental system (*in vitro*, field conditions) and might also reflect underlying mechanistic differences in the relationship between ageing and epigenetic marks (Fraga *et al.* 2002b; Monteuuis *et al.* 2009). Furthermore, the data indicate that DNA methylation patterns are not static and can exhibit remarkable dynamics and plasticity during development and seasonal changes (Fraga *et al.* 2002b; Valledor *et al.* 2007; Monteuuis *et al.* 2009).

Evidence for the remarkable dynamics and plasticity of
epigenetic modification in tree species is growing.
FIGURE 3

DNA methylation profiles vary widely among chromosomes and among tissues at selected loci in *Populus trichocarpa*. Relative DNA methylation was determined using methylated DNA immunoprecipitation followed by Illumina sequencing (MeDIP-seq). The ratio

of MeDIP-seq read counts in immunoprecipitated (IP) samples versus non-IP control is plotted in 1 kb windows for chromosomes 10 and 19, and areas of tissue-differential methylation are expanded below each chromosome. Figures from Vining *et al*. (2012).

<figure><img src="image_55.png" /></figure>
Genome-level comparative analysis of cytosine methylation among differentiated poplar tissues revealed highly heterogeneous DNA methylation profiles among chromosomes, and a number of cases of tissue-specific methylation (Fig.3; Vining et al. 2012), many of them associated with gene bodies or promoters. Although a broadly similar chromosome methylation and gene expression profile was observed in poplar when compared to *A. thaliana* and other plant species, significant differences were also detected. For example, only in poplar was gene body (i.e., the entire gene from the transcription start site to the end of the transcript) methylation associated with greater repression of gene expression than was promoter methylation. In addition, Vining et al. (2012) observed a distinctive pattern of transposon and gene body methylation for male catkins compared to other tissues, including female catkins. Recently, analysis of the methylome of open chromatin in poplar meristematic cells found that 74% of poplar gene models had gene body methylation, and its intensity, as well as cytosine context, varied depending on gene size, redundancy in the genome (presence of paralogs), and extent of tissue-specific gene expression (Lafon-Placette et al. 2012).

Plasticity in tree epigenetic modification has also been observed in conifer species, specifically as it relates to phenology. Phenology responses of seedlings that were produced in warm or cold years vary within the same stands (Kohmann & Johnsen, 1994). In Norway spruce, a temperature-dependent epigenetic "memory" from the time of embryo development, which thereafter influences the timing of bud phenology and gene expression, has been discovered (Skrøppa & Johnsen, 2000; Johnsen et al. 2005; Yakovlev et al. 2010). Colder-than-normal conditions during embryogenesis and seed development advance the timing, whereas temperatures above normal delay the onset of these adaptive processes, and the altered performance is long lasting in the progeny. This phenomenon was initially discovered when ecotypes from northern Norway were transferred to a southern seed orchard where they produced progenies with a phenology similar to that of southern ecotypes (Johnsen et al. 1996; Skrøppa & Johnsen, 2000). Notably, differences in day length and temperature applied during pollen formation did not affect the progeny performance. Differences in the female flowering environment did affect progeny performance. The temperature during zygotic embryogenesis and seed maturation shifted the developmental program of the seeds, resulting in significant phenotypic changes, with the effect lasting as long as over 20 years (Skrøppa & Johnsen, 2000; Skrøppa et al. 2010; Yakovlev et al. 2010). The traits that are affected include the timing of dehardening and bud burst in the spring; leader shoot growth cessation in the summer and bud set and cold acclimation in the autumn. All processes are thus advanced or delayed as influenced by the

temperature during reproduction in progeny with identical genetic background. Similar effects have been observed in progeny from white spruce (*Picea glauca* × *Picea engelmannii*) crosses, Scots pine, Larix spp. and longleaf pine (Dormling & Johnsen, 1992; Greenwood & Hutchison, 1996; Stoehr et al. 1998; Webber et al. 2005) but there is lack of information regarding this phenomenon in angiosperm trees (Rohde & Junttila, 2008). In birch (*Betula pendula*), a small-scale study within a population revealed a close genetic relationship between trees that had established in a year of similar temperature (Kelly et al. 2003).

The importance of plastic epigenetic modification on phenology in conifer species extends beyond the individual to encompass the ecosystem. Epigenetic effects taking place during zygote development may create phenotypic diversity at the local community level, if temperature varies considerably amongst successive generations. This is particularly important as phenology traits are strongly genetically differentiated.

The molecular mechanism behind this striking epigenetic "memory" phenomenon is not yet clear, but transcriptional changes have been implicated (Johnsen et al. 2005; Yakovlev et al. 2010; Yakovlev et al. 2011). In progeny that differ epigenetically, transcriptional analysis revealed that seedlings from full-sib families produced at different embryogenesis temperatures under long and short day conditions differed. Suppressive subtracted cDNA libraries revealed considerable differences in their transcriptomes. MicroRNA pathway genes DICER-LIKE1 (PaDCL1), DICER-LIKE 2 (PaDCL2) and SUPPRESSOR OF GENE SILENCING 3-LIKE (PaSGS3), as well as transposon-related genes, had altered transcript abundance in epigenetically-different progeny with phenotypic differences in bud burst and bud set (Yakovlev et al. 2011). Norway spruce contains a set of conserved miRNAs as well as a large proportion of novel non-conserved miRNAs involved in temperature-dependent epigenetic "memory". Most of the miRNAs were targeted to previously unknown genes, or genes with no known function. The expression of seven conserved and nine novel miRNAs showed significant differences in transcript levels in progenies showing distinct epigenetic difference in bud set, but not in the progeny from a non-responding family without differences in bud set, making them excellent candidate miRNAs. The altered transcript abundance of specific miRNAs suggests their putative participation in epigenetic regulation (Yakovlev et al. 2010). This epigenetic phenomenon is not only generated in controlled Norway spruce crosses, but such epitypes can also be produced by somatic embryogenesis (Kvaalen & Johnsen, 2008). Genetically identical plants generated at different temperatures by zygotic embryogenesis expressed a difference in timing of terminal bud formation that was equivalent to a 4-6° latitudinal ecotypic difference.
The “memory” effects acting on phenological traits lasted for more than 20 years after germination and affected long-term growth under field conditions (Skrøppa et al. 2007). Notably, there was absence of any genetic segregation distortion in the progeny, strongly supporting that this “memory”, affecting the climatic adaptation in this species, is indeed an epigenetic phenomenon (Besnard et al. 2008). Thus distinct epitypes can be produced from the same genotype in Norway spruce, a process not well documented in other tree species so far. In view of rapid climate change, strategies to increase diversity for selection might be of prime importance for survival of species within their current geographic distribution, and therefore, this epigenetic “memory” mechanism is likely of evolutionary significance and has obvious practical implications.

## Epigenetic and phenotypic plasticity in artificial systems

While epigenetic phenomena are clearly important for trees in a natural context, they also could be of great consequence during specific tree production processes integrated into the wood products chain. Long generation times and the out-crossing habit of a number of forest trees can make it difficult to rapidly propagate material and maintain valuable genotypes under natural conditions. Tissue culture can provide alternative means to keep desirable genotypes by vegetative propagation and to quickly produce commercial quantities of regenerants; therefore, micropropagation is widely used in forestry. It has been observed, however, that tissue culture can introduce variation in regenerated plants. This somaclonal variation, can result in subtle to drastic phenotypic variation and has been found to be attributable to genetic or epigenetic variations (e.g. reviewed in Kaeppler et al. 2000; Miguel & Marum, 2011). Somaclonal variation (heritable across mitotic and meiotic cell divisions) has been considered both beneficial and disadvantageous (Jaligot et al. 2000; Kaeppler et al. 2000; Schellenbaum et al. 2008), and a number of studies have focused on elucidating underlying mechanisms (Kaeppler et al. 2000; Rival et al. 2008; Schellenbaum et al. 2008; Rodriguez Lopez et al. 2010).

A well-studied example for somaclonal variants and their relation to epigenetic marks in a tree species is the *mantled* phenotype in somatic-embryo-derived oil palm (*Elaeis guineensis*). This phenotypic variant, found in about five percent of regenerants, is characterised by abnormal inflorescence development and has been associated with global DNA hypomethylation, but not to changes in genomic structure or nucleotide sequence (Jaligot *et al.* 2000; Rival *et al.* 2008). The exact mechanisms involved in generating somaclonal variants like the mantled phenotype remain largely unresolved. Ongoing studies of this phenomenon might help to better understand mechanisms of epigenetic responses to tissue-culture-induced stresses (Kaeppler *et al.* 2000; Rival *et al.* 2008).

It has also been observed that the ability to generate mature somatic embryos from cultured tissue can decrease as a culture ages and that somaclonal variation can increase with culture age (Phillips *et al.* 1994; Valledor *et al.* 2007; Krizova *et al.* 2009). In addition to other mechanisms, changes in DNA methylation were considered to contribute to the reduction of embryonic potential or organogenic potential in tissue culture and grafting procedures (Fraga *et al.* 2002b; Valledor *et al.* 2007). A detailed analysis of genetic and epigenetic variation in relation to callus age reports interesting plasticity in cocoa plants (*Theobroma cacao*) regenerated by somatic embryogenesis. Genetic variation was investigated using single sequence repeat (SSR) markers, and epigenetic variability was assessed by methylation-sensitive amplified polymorphism (MSAP), a method to detect genome-wide but anonymous DNA methylation patterns. Contrary to predictions, after an initial increase, a decrease in both genetic and epigenetic divergence between leaves of regenerants and the ortet plant was observed after the culture had reached an age of about 10 weeks (Rodriguez Lopez *et al.* 2010). One possible interpretation of the findings suggests a link between stable DNA methylation patterns and repression of *de novo* mutations during somatic embryogenesis (Rodriguez Lopez *et al.* 2010).

For many plant species, different physiological and developmental stages of diverse tissue explant types have been associated with distinct epigenetic characteristics, in particular DNA methylation (Fraga *et al.* 2002a; Fraga *et al.* 2002b; Monteuuis *et al.* 2009; Santamaría *et al.* 2009; Rodríguez López *et al.* 2010; Valledor *et al.* 2010). For example, some DNA methylation patterns and levels, characteristics of the source tissue used to start an *in vitro* culture, were retained in regenerants in acacia and cocoa (Monteuuis *et al.* 2009; Rodríguez López *et al.* 2010). This highlights the plasticity of DNA methylation marks under tissue culture conditions. Transitions from juvenile to adult phase are frequently accompanied by reduction or loss of morphogenetic ability in woody species (see Epigenetic plasticity in growth and development). Concomitant with maturation of pine needles, changes in epigenetic marks were measured when compared to immature needles. This finding could be in accordance with a less permissive and reprogrammable chromatin state and could account in part for the reduced organogenic capacity of explants from mature needles.

Generation of somaclonal genetic and epigenetic variants as well as plasticity in DNA methylation are widely documented outcomes of plant regeneration in tissue culture (Kaeppler et al. 2000; Marfil et al. 2009). Studying underlying mechanisms might be of relevance for basic research and applications in plant propagation such as the understanding of differentiation and dedifferentiation processes or the selection of appropriate *in vitro* culture conditions (Kaeppler et al. 2000; Marfil et al. 2009; Rodriguez Lopez et al. 2010).
# Strategic and technical approaches to study epigenetic processes

## Selection of appropriate systems

Different methods have been used in model plants to analyse epigenetic variation independently of genetic variation. These have included treatment with demethylating agents, analysis of natural epimutations, and study of DNA methylation-deficient mutants. Epigenetic recombinant inbred lines (epiRILs) have been developed in *A. thaliana* (Johannes et al. 2009; Reinders et al. 2009) using isogenic lines (wild types and mutant lines) differing only in the level and distribution of DNA methylation (see Epigenetic regulation in plant environmental responses). These lines represent a powerful tool to identify specific epigenomic regions that are associated with the observed phenotypic variation through epiQTL mapping approaches that are based on methylation sensitive markers. The epiQTL mapping approach requires the establishment of multiple plant generations, and may be difficult to apply to tree species that require a significant amount of time to reach sexual maturation.

To discern genetic and epigenetic effects, clonally propagated plants or systems that are characterized by reduced genetic variation, such as stone pine (*Pinus pinea*), represent ideal study subjects. To separate heritable from non-heritable epigenetic variation (resulting from developmental plasticity in response to different environments) it is necessary to study, when available, clonally propagated genotypes, the progeny of different natural populations or maternal families in a common environment, and to use the resemblance of epigenetic patterns among relatives as an indication of epigenetic inheritance (Bossdorf et al. 2008).

## Technical approaches

A wide variety of techniques have been developed to study epigenetic patterns and modifications. Histone modifications can be analysed by chromatin immunoprecipitation (ChIP) using antibodies that recognise specific histone modifications, followed by either microarray hybridisation (ChIP on chip) or by next generation sequencing (ChIP-Seq; Ku et al. 2011). DNA methylation at the genome level, the DNA methylome, can be investigated by methylated DNA immunoprecipitation (meDIP) or by bisulfite treatment of the DNA followed by hybridisation to a microarray, or by next generation sequencing (BS-Seq; Ku et al. 2011; Krueger et al. 2012; Cokus et al. 2008). Additionally direct detection of methylated residues using DNA synthesis technologies based on variable polymerase kinetics depending on the chemical modification of the template nucleotide (e.g. 5-methylcytosine vs. cytosine) represents a novel method to directly detect DNA methylation (Flusberg et al. 2010).

Next generation sequencing technologies enable mapping of epigenetic modifications at single base resolution. The nature and large amount of data generated by such technologies will demand new approaches in data analysis techniques. Inference of the methylation status of bisulfite-treated DNA by BS-Seq can be challenging as the data obtained do not exactly match the reference sequence. Consequently, both DNA strands must be considered separately, and methylation at a specific site can be a percentage rather than a total presence or absence. Nevertheless, a number of tools have been developed to facilitate these analyses and are now available for application to tree epigenomes (Chen et al. 2010; Lim et al. 2010; Krueger et al. 2012).

## Conclusion

Many questions remain about the mechanisms and roles of epigenetic processes in enabling rapid adaptation of plants to their environment, especially in forest trees. Recently, genome-wide studies of chromatin-bound proteins and epigenetic marks in *Drosophila melanogaster* and in *A. thaliana* have substantially revised our understanding of chromatin (Roudier et al. 2011; Van Steensel, 2011). The dogma of an uncompacted, transcriptionally-active euchromatin *versus* a compacted, silent heterochromatin is likely to be an oversimplification of the real chromatin architecture. It appears that chromatin might be composed of several types differing in their epigenetic marks as well as in their nuclear localisation and chromatin-associated proteins. These types could favor or prevent association with transcription factors, thus defining gene expression patterns. Whether these chromatin types exist in perennial species is not known, and the stability of these chromatin types in long-living organisms is to be established. Similarly, the maintenance of these types during clonal and *in vitro* culture propagation will give important clues about the effect of these biotechnologies on gene expression control.

It has been observed that the induction of alternative epigenetic states not only triggers the formation of new epialleles but also promotes the movement of DNA transposons and retroelements that are very abundant in plant genomes (Mirouze & Paszkowski, 2011). However, mechanisms counteracting accumulation of induced epialleles must also be in place, because otherwise we would be “constantly confronted with the inheritance of environmentally-induced phenotypic variation” (Richards, 2006). Additionally, in large genomes, such as those of conifer [with C estimates of DNA content ranging from 17 to 30 Gbp for pines and spruces of which more than 68% are attributed to repeated DNA (Rake et al. 1980; Ohri & Khoshoo, 1986)], cytosine methylation is implicated in genomic compartmentalisation, i.e., non-coding highly repeated sequences get separated from low-copy
sequence and transcriptionally active regions. The differential methylation of genic and non-genic regions observed across plant taxa, may be involved in decreasing transcriptional ‘noise’ (Rabinowicz et al. 2005). In large genomes, epigenetic mechanisms might be more prominent, as a means to control the repetitive parts of the genome. This might render their entire genomes more amenable to epigenetic regulation.

From an economic and ecological point of view, it is important to integrate information on epigenetic control of environmental and developmental processes in both forest resources management and breeding. In quantitative genetic studies, estimates of genetic variance over the total phenotypic variance are typically used to assess the heritability of a trait. Akin to other genetic characters, variance in epigenetic characters will contribute to genetic variance and/or phenotypic variance, but might go undetected in some studies, or might be confounded with normal Mendelian-based quantitative inheritance (Kalisz & Purugganan, 2004). Epigenetic effects may thus inflate the true genetic variation in traits. As a consequence, the genetic clines observed for many phenology traits, even in common garden experiments, may reflect more local adaptation than DNA sequence-based genetic differences among populations.

Recent developments show that both energy efficiency and energy homeostasis, which are integral parts of yield, have an epigenetic component that can be directed and stabilised by artificial selection (i.e. selective breeding; De Block & Van Lijsebettens, 2011). These findings open new possibilities for engineering plant metabolism and improving complex traits. For example, in addition to the unintended genetic and epigenetic variation imparted by *in vitro*-manipulation, it may be considered and utilised as a means to amplify or release epigenetic variation of value to breeding programs. Transgenic perturbation of epigenetic mechanisms might have similar effects; however, testing such effects using a transgenic approach with forest trees at a scale relevant to application and ecological variation are, at present, constrained by government regulations (Viswanath et al. 2012).

Genome perturbation, including epigenetic components, might be important for increasing the raw material for adaptive evolution under severe stress (Kalisz & Purugganan, 2004; Rapp & Wendel, 2005). Rapp & Wendel (2005) suggest that a population bottleneck, while reducing genetic diversity, might simultaneously create epigenetic novelty. In contrast to genetic alleles, epialleles might react more quickly to environmental change, be reversible, and persist for only a few generations (Kalisz & Purugganan, 2004). If a new epiallele were to cause a mild phenotype through alteration of gene expression, it might experience less strong selection than a loss-of-function sequence mutation (Kalisz & Purugganan, 2004) and thus enable rapid, yet fine-tuned,

trait modifications. The significance of epialleles in wild populations will depend on their frequency and stability (Rohde & Junttila, 2008).

The analysis of the epigenetic processes in an ecological context, known as "ecological epigenetics" is set to transform our understanding of the way in which organisms function on the landscape. Forest trees offer excellent opportunities to examine some of the most compelling questions of ecological epigenetics (Bossdorf et al. 2008), particularly those related to the interplay between epigenetic variation and phenotypic variation in natural populations, and the role of epigenetic variation in evolutionary processes. Ecological epigenetics could readily address such questions by capitalising on the advantageous features of forest trees, including their long-lifespans, their dominance of many ecosystems, their wide geographical distribution, and their life histories, especially reproductive traits like clonal propagation. Analysis of the epigenetics of forest tree species will significantly improve our understanding of the mechanisms underlying natural phenotypic variation, and the responses of organisms to environmental change, and may thereby inform efforts to manage and breed tree species to help them cope with environmental stresses.

## Acknowledgements

The preparation of this review was supported by funding from the FP7 UE projects FORESTTRAC (2440096-FP7) and ProCoGen (289841-FP7).

The authors are very grateful to Susana Ferrándiz for helping with the preparation of the manuscript.

## Conflict of interest

All authors disclose any potential source of conflict of interest or relationship (financial or otherwise) that might be perceived as influencing an author's objectivity.

## REFERENCES

* Akimoto K, Katakami H, Kim HJ, Ogawa E, Sano CM, Wada Y, Sano H (2007) Epigenetic inheritance in rice plants. *Annals of Botany*, **100**, 205-217.

* Bastow R, Mylne JS, Lister C, Lippman Z, Martienssen RA, Dean C (2004) Vernalization requires epigenetic silencing of FLC by histone methylation. *Nature* **427**, 164-167.

* Baurens FC, Nicolleau J, Legavre T, Verdeil JL, Monteuuis O (2004) Genomic DNA methylation of juvenile and mature *Acacia mangium* micropropagated in vitro with reference to leaf morphology as a phase change marker. *Tree Physiology*, **24**, 401-407.

* Besnard G, Acheré V, Jeandroz S, Johnsen Ø, Faivre Rampant P, Baumann R, Müller-Starck G, Skrøppa T, JM Favre (2008) Does maternal environmental condition during reproductive development induce genotypic selection in *Piceaabies*?. *Annals of Forest Science*, **65**, 109.
Birchler JA, Yao H, Chudalayandi S, Vaiman D, Vieti RA (2010) Heterosis. *The Plant Cell*, **22**, 2105-2112.

Bogeat-Triboulot MB, Brosché M, Renaut J, Jouve L, Le Thiec D, Fayyaz P, Vinocur B, Witters E, Laukens K, Teichmann T, Altman A, Hausman JF, Polle A, Kangasjärvi J, Dreyer E (2007) Gradual soil water depletion results in reversible changes of gene expression, protein profiles, ecophysiology, and growth performance in *Populus euphratica*, a poplar growing in arid regions. *Plant Physiology*, **143**, 876-892.

Bonasio R, Tu S, Reinberg D (2010) Molecular Signals of Epigenetic States. *Science*, **330**, 612-616.

Bond DM, Finnegan EJ (2007) Passing the message on: inheritance of epigenetic traits. *Trends in Plant Science*, **12**, 211-216.

Bonhomme L, Monclus R, Vincent D, Carpin S, Lomenech AM, Plomion C, Brignolas F, Morabito D (2009) Leaf proteome analysis of eight *Populus x euramericana* genotypes: genetic variation in drought response and in water-use efficiency involves photosynthesis-related proteins. *Proteomics*, **9**, 4121-4142.

Bossdorf O, Richards CL, Pigliucci M (2008) Epigenetics for ecologists. *Ecology, Letters*, **11**, 106-115.

Bourc'his D, Voinnet O (2010) A Small-RNA Perspective on Gametogenesis, Fertilization, and Early Zygotic Development. *Science*, **330**, 617-622.

Boyko A, Kovalchuk I (2008) Epigenetic control of plant stress response. *Environmental and Molecular Mutagenesis*, **49**, 61-72.

Cervera MT, Ruiz-Garcia L, Martínez-Zapater JM (2002) Analysis of DNA methylation in *Arabidopsis thaliana* based on methylation-sensitive AFLP markers. *Molecular Genetics and Genomics*, **268**, 543-552.

Chen PY, Cokus SJ, Pellegrini MBS (2010) Seeker: precise mapping for bisulfite sequencing. *BMC Bioinformatics*, **11**, 203.

Chinnusamy V, Zhu JK (2009) Epigenetic regulation of stress responses in plants. *Current Opinion in Plant Biology*, **12**, 133-139.

Chouard P (1960) Vernalization and its relations to dormancy. *Annual Review of Plant Physiology*, **11**, 191-238.

Cokus S J, Feng S, Zhang X, Chen Z, Merriman B, Haudenschild CD, Pradhan S, Nelson SF, Pellegrini M, Jacobsen SE (2008) Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. *Nature*, **452**, 215-219.

Cubas P, Vincent, C, Coen, E (1999) An epigenetic mutation responsible for natural variation in floral symmetry. *Nature*, **401**, 157-161.

De Block M, Van Lijsebettens M (2011) Energy efficiency and energy homeostasis as genetic and epigenetic components of plant performance and crop productivity. *Current Opinion in Plant Biology*, **14**, 275-282.

Demidov D, Hesse S, Tewes A, Rutten T, Fuchs J, Ashtiyani RK, Lein S, Fischer A, Reuter G, Houben A (2009) Auroral phosphorylation activity on histone H3 and its cross-talk with other post-translational histone modifications in Arabidopsis. *The Plant Journal*, **59**, 221-230.

Dormling I, Johnsen Ø (1992) Effects of the parental environment on full-sib families of *Pinus sylvestris*. *Canadian Journal of Forest Research*, **22**, 88-100.

Feil, R, Fraga MF (2011) Epigenetics and the environment: emerging patterns and implications. *Nature Reviews Genetics*, **13**, 97-109.

Flusberg BA, Webster DR, Lee JH, Travers KJ, Olivares, EC, Tyson AC, Korlach J, Turner, SW (2010) Direct detection of DNA methylation during single-molecule, real-time sequencing. *Nature Methods*, **7**, 461-465.

Fraga MF, Canal MJ, Rodriguez R (2002a) Phase-change related epigenetic and physiological changes in *Pinus radiata* D. Don. *Planta*, **215**, 672-678.

Fraga MF, Rodriguez R, Canal MJ (2002b) Genomic DNA methylation-demethylation during aging and reinvigoration of *Pinus radiata*. *Tree Physiology*, **2**, 813-816.

Gourcilleau D, Bogeat-Triboulot MB, Le Thiec D, Lafon-Placette C, Delaunay A, El-Soud WA, Brignolas F, Maury S (2010) DNA methylation and histone acetylation: genotypic variations in hybrid poplars, impact of water deficit and relationships with productivity. *Annals of Forest Science*, **67**, 208

Grattapaglia D, Plomion C, Kirst M, Sederoff RR (2009) Genomics of growth traits in forest trees. *Current Opinion in Plant Biology*, **12**, 148-156.

Greenwood MS, Hopper CA, Hutchison KW (1989) Maturation in larch: I. Effect of age on shoot growth, foliar characteristics, and DNA methylation. *Plant Physiology*, **90**, 406-412.

Greenwood MS, Hutchison KW (1996) Genetic after effects of increased temperature in Larix. In: Hom J, Birdsey R, O'Brian K eds. *Proceedings of the 1995 Meeting of the Northern Global Change Program*. Radnor: USDA Forest Service report 56-62.

Groszmann, M, Greaves IK, Albert N, Fujimoto R, Helliwell CA, Dennis. ES, Peacock WJ (2011a) Epigenetics in plants- vernalisation and hybrid vigour. *Biochimica et Biophysica Acta*, **1809**, 427-437.

Groszmann M, Greaves IK, Albertyn ZI, Scofield GN, Peacock WJ, Dennis ES (2011b) Changes in 24-nt siRNA levels in Arabidopsis hybrids suggest an epigenetic contribution to hybrid vigor. *Proceedings of the National Academy of Sciences of the United States of America*, **108**, 2617-2622.

Gutierrez-Marcos JF, Costa LM, Dal Prà M, Scholten S, Kranz E, Perez P, Dickinson HG (2006) Epigenetic asymmetry of imprinted genes in plant gametes. *Nature Genetics*, **38**, 876-878.

Ha M, Lu J, Tian L, Ramachandran V, Kasschau KD, Capman EJ, Carrington JC, Chen X, Wang XJ, Chen ZJ (2009) Small RNAs serve as a genetic buffer against genomic shock in Arabidopsis interspecific hybrids and allopolyploids. *Proceedings of the National Academy of Sciences of the United States of America*, **106**, 17835-17840.

Hamanishi ET, Campbell MM (2011) Genome-wide responses to drought in forest trees. *Forestry*, **84**, 273-283.

Hashida S N, Uchiyama T, Martin C, Kishima Y, Sano Y, Mikami T (2006) The temperature-dependent change in methylation of the Antirrhinum transposon Tam3 is controlled by the activity of its transposase. *Plant Cell*, **18**, 104-118.

Haun WJ, Laoueille-Duprat S, O'Connell MJ, Spillane C, Grossniklaus U, Phillips AR, Kaeppler SM, Springer NM (2007) Genomic imprinting, methylation and molecular evolution of maize Enhancer of zeste (Mez) homologs. *The Plant Jorunal*, **49**, 325-337.
Hauser M, Aufsatz W, Jonak C, Luschnig C (2011) Transgenerational epigenetic inheritance in plants. *Biochimica et Biophysica Acta*, **1809**, 459-468.

He G, Zhu X, Elling AA, Chen L, Wang X, Guo L, Liang M, He H, Zhang H, Chen F, Qi Y, Chen R, Deng XW (2010) Global epigenetic and transcriptional trends among two rice subspecies and their reciprocal hybrids. *Plant Cell*, **22**, 17-33.

Heo JB, Sung S (2011) Vernalization-mediated epigenetic silencing by a long intronic noncoding RNA. *Science*, **331**, 76-79.

Herrera CM (1990) The adaptedness of the floral phenotype in a relict endemic, hawkmoth-pollinated violet. 2. Patterns of variation among disjunct populations. *Biological Journal of the Linnean Society*, **40**, 275-291.

Herrera CM, Bazaga P (2010) Epigenetic differentiation and relationship to adaptive genetic divergence in discrete populations of the violet *Viola cazorlensis*. *New Phytologist*, **187**, 867-876.

Ito H, Gaubert H, Bucher E, Mirouze M, Vaillant I, Paszkowski J (2011) An siRNA pathway prevents transgenerational retrotransposition in plants subjected to stress. *Nature*, **472**, 115-119.

Jablonka E, Raz G (2009) Transgenerational epigenetic inheritance: prevalence, mechanisms, and implications for the study of heredity and evolution. *The Quarterly Review of Biology*, **84**: 131-176.

Jaligot E, Rival A, Beule T, Dussert S, Verdeil JL (2000) Somaclonal variation in oil palm (*Elaeis guineensis Jacq.): the DNA methylation hypothesis. *Plant Cell Reports*, **19**, 684-690.

Jaskiewicz M, Conrath U, Peterhansel C (2011) Chromatin modification acts as a memory for systemic acquired resistance in the plant stress response. *EMBO Reports*, **12**, 50-55.

Johannes F, Colot V, Jansen RC (2008) Epigenome dynamics: a quantitative genetics perspective. *Nature Reviews Genetics*, **9**, 883-890.

Johannes F, Porcher E, Teixeira FK, Saliba-Colombani V, Simon M, Agier N, Bulski A, Albuisson J, Heredia F, Audigier P, Bouchez D, Dillmann C, Guerche P, Hospital F, Colot V (2009) Assessing the impact of transgenerational epigenetic variation on complex traits. *PLoS Genetics*, **5**, e1000530

Johnsen Ø, Skrøppa T, Junttila O, Dæhlen OG (1996) Influence of the female flowering environment on autumn frost-hardiness of *Picea abies* progenies. *Theoretical and Applied Genetics*, **92**, 797-802.

Johnsen Ø, Fossdal CG, Nagy N, Molmann J, Dælen OG, Skrøppa T (2005) Climatic adaptation in *Picea abies* progenies is affected by the temperature during zygotic embryogenesis and seed maturation. *Plant, Cell & Environment*, **28**, 1090-1102.

Jullien PE, Kinoshita T, Ohad N, Berger F (2006) Maintenance of DNA methylation during the Arabidopsis life cycle is essential for parental imprinting. *The Plant Cell*, **18**, 1360-1372.

Kaeppler SM, Kaeppler HF, Rhee Y (2000). Epigenetic aspects of somaclonal variation in plants. *Plant Molecular Biology*, **43**, 179-188.

Kalisz S, Purugganan MD (2004) Epialleles via DNA methylation: consequences for plant evolution. *Trends in Ecology Evolution*, **19**, 309-314.

Kelly CK, Chase MW, De Bruijn A, Fay MF, Woodward FI (2003) Temperature-based population segregation in birch. *Ecology Letters*, **6**, 87-89.

Kim DH, Sung S (2012) Environmentally coordinated epigenetic silencing of FLC by protein and long noncoding RNA components. *Current Opinion in Plant Biology*, **15**, 51-56.

Kohmann K, Johnsen Ø (1994) The timing of bud-set in seedlings of *Picea abies* from seed crops of a cool versus a warm summer. *Silvae Genetica*, **43**, 328-332.

Kouzarides T (2007) Chromatin modifications and their function. *Cell*, **128**, 693-705.

Krauss V (2008) Glimpses of evolution: heterochromatic histone H3K9 methyltransferases left its marks behind. *Genetica*, **133**, 93-106.

Krizova K, Fojtova M, Depicker A, Kovarik A (2009) Cell culture-induced gradual and frequent epigenetic reprogramming of invertedly repeated tobacco transgene epialleles. *Plant Physiology*, **149**, 1493-1504.

Krueger F, Kreck B, Franke A, Andrews SR (2012) DNA methylome analysis using short bisulfite sequencing data. *Nature Methods*, **9**, 145-151.

Ku CS, Naidoo N, Wu M, Soong R (2011) Studying the epigenome using next generation sequencing. *Journal of Medical Genetics*, **48**, 721-30.

Kumar SV, Wigge PA (2010) H2A.Z-containing nucleosomes mediate the thermosensory response in Arabidopsis. *Cell*, **140**, 136-47.

Kvaalen H, Johnsen Ø (2008) Timing of bud set in *Picea abies* is regulated by a memory of temperature during zygotic and somatic embryogenesis. *New Phytologist*, **177**, 49-59.

Lafon-Placette C, Faivre-Rampant P, Delaunay A, Street N, Brignolas F, Maury S (2012) Methylome of DNase I sensitive chromatin in *Populus trichocarpa* shoot apical meristematic cells: a simplified approach revealing characteristics of gene-body DNA methylation in open chromatin state. *New Phytologist*, in press. doi: 10.1111/nph.12026.

Lim SJ, Tan TW, Tong JC (2010) Computational epigenetics: the new scientific paradigm. *Bioinformation*, **4**, 331-337.

Lira-Medeiros CF, Parisod C, Fernandes RA, Mata CS, Cardoso MA, Ferreira PC (2010) Epigenetic variation in mangrove plants occurring in contrasting natural environment. *PLoS One*, **5**, e10326.

Lisch DR, Slotkin RK (2011) Strategies for silencing and escape: the ancient struggle between transposable elements and their hosts. *International review of cell and molecular biology*, **292**, 119-152.

Long Y, Xia W, Li R, Wang J, Shao M, Feng J, King GJ, Meng J (2011) Epigenetic QTL mapping in Brassica napus. *Genetics*, **189**, 1093-102.

Manning K, Tör M, Poole M, Hong Y, Thompson AJ, King GJ, Giovannoni JJ, Seymour GB (2006) A naturally occurring epigenetic mutation in a gene encoding an SBP-box transcription factor inhibits tomato fruit ripening. *Nature Genetics*, **38**, 948-952.

Marfil CF, Camadro EL, Masueli RW (2009) Phenotypic instability and epigenetic variability in a diploid potato of hybrid origin, *Solanum ruiz-lealii*. *BMC Plant Biology*, **9**, 21.
Miguel C, Marum L (2011) An epigenetic view of plant cells cultured in vitro: somaclonal variation and beyond. *Journal of Experimental Botany*, **62**, 3713-3725.

Mirouze M, Reinders J, Bucher E, Nishimura T, Schneeberger K, Ossowski S, Cao J, Weigel D, Paszkowski J, Mathieu O (2009) Selective epigenetic control of retrotransposition in *Arabidopsis*. *Nature*, **461**, 427-430.

Mirouze M, Paszkowski J (2011) Epigenetic contribution to stress adaptation in plants. *Current Opinion in Plant Biology*, **14**, 267-274.

Monteuuis O, Baurens FC, Goh DKS, Doulbeau S, Verdeil JL (2009) DNA methylation in *Acacia mangium* in vitro and ex-vitro buds, in relation to their within-shoot position, age and leaf morphology of the shoot. *Silvae Genetica*, **58**, 287-292.

Neale DB, Kremer A (2011) Forest tree genomics: growing resources and applications. *Nature Reviews Genetics*, **12**, 111-122.

Nicotra AB, Atkin OK, Bonser SP, Davidson AM, Finnegan EJ, Mathesius U, Poot P, Purugganan MD, Richards CL, Valladares F, van Kleunen M. (2010) Plant phenotypic plasticity in a changing climate. *Trends in Plant Science*, **15**, 684-692.

Ohri D, Khoshoo TN (1986) Genome size in gymnosperms. *Plant Systematics and Evolution*, **153**, 119-132.

Paun O, Bateman RM, Fay MF, Hedren M, Civeyrel L, Chase MW (2010) Stable epigenetic effects impact adaptation in allopolyploid orchids (*Dactylorhiza: Orchidaceae*). *Molecular Biology and Evolution*, **27**, 2465-2473.

Phillips RL, Kaeppler SM, Olhoft P (1994) Genetic instability of plant tissue cultures: breakdown of normal controls. *Proceedings of the National Academy of Sciences of the United States of America*, **91**, 5222-5226.

Plomion C, Lalanne C, Claverol S, Meddour H, Kohler A, Bogeat-Triboulot MB, Barre A, Le Provost G, Dumazet H, Jacob D, Bastien C, Dreyer E, de Daruvar A, Guehl JM, Schmitter JM, Martin F, Bonneu M (2006) Mapping the proteome of poplar and application to the discovery of drought-stress responsive proteins. *Proteomics*, **6**, 6509-6527.

Rabinowicz PD, Citek R, Budiman MA, Nunberg A, Bedell JA, Lakey N, O'Shaughnessy AL, Nascimento LU, McCombie WR, Martienssen RA (2005) Differential methylation of genes and repeats in land plants. *Genome Research*, **15**, 1431-1440.

Raj S, Bräutigam K, Hamanishi ET, Wilkins O, Schroeder W, Mansfield SD, Plant AL, Campbell MM (2011) Clone history shapes *Populus* drought responses. *Proceedings of the National Academy of Sciences of the United States of America*, **108**, 12521-12526.

Rake AV, Miksche JP, Hall RB, Hansen KM (1980) DNA reassociation kinetics of four Conifers. *Canadian Journal of Genetics and Cytology*, **22**, 69-79.

Rapp RA, Wendel JF (2005) Epigenetics and plant evolution. *New Phytologist*, **168**, 81-91.

Rehfeldt GE, Ying CC, Spittlehouse DL, Hamilton DA (1999) Genetic responses to climate in pinus contorta: niche breadth, climate change, and reforestation. *Ecological Monographs*, **69**, 375-407.

Rehfeldt GE, Tchebakova NM, Parfenova YI, Wykoff WR, Kuzmina NA, Milyutin LI (2002) Intraspecific responses to climate in *Pinus sylvestris*. *Global Change Biology*, **8**, 912-929.

Reinders J, Wulff BBH, Mirouze M, Mari-Ordonez A, Dapp M, Rozhon W, Bucher E, Theiler G, Paszkowski J. (2009) Compromised stability of DNA methylation and transposon immobilization in mosaic *Arabidopsis* epigenomes. *Genes & Development*, **23**, 939-950.

Richards EJ (2006) Inherited epigenetic variation - revisiting soft inheritance. *Nature Reviews Genetics*, **7**, 395-401.

Richards CL, Verhoeven KJF, Bossdorf O (2012) *Evolutionary significance of epigenetic variation*. In: Wendel JF, Greilhuber J, Dolezel J, Leitch IJ (eds.), Plant genome diversity Volume I. Springer, Vienna. pp 257-274.

Rival A, Jaligot E, Beule T, Finnegan EJ (2008) Isolation and expression analysis of genes encoding MET, CMT, and DRM methyltransferases in oil palm (*Elaeis guineensis Jacq.*) in relation to the 'mantled' somaclonal variation. *Journal of Experimental Botany*, **59**, 3271-3281.

Rodriguez Lopez CM, Wetten AC, Wilkinson MJ (2010) Progressive erosion of genetic and epigenetic variation in callus-derived cocoa (*Theobroma cacao*) plants. *New Phytologist*, **186**, 856-868.

Rohde A, Junttila O (2008) Remembrances of an embryo: long-term effects on phenology traits in spruce. *New Phytologist*, **177**, 2-5.

Rohde A (2009) Bud set - a landmark of the seasonal growth cycle in poplar. In: Gusta LV, Wisniewski ME, Tanino KK, eds. *Plant Cold Hardiness*. Cambridge, MA: CABI, 91-98.

Roudier F, Ahmed I, Bérard C, Sarazin A, Mary-Huard T, Cortijo S, Bouyer D, Caillieux E, Duvernois-Berthet E, Al-Shikhley L, Giraut L, Després B, Drevensek S, Barneche F, Dèrozier S, Brunaud V, Aubourg S, Schnittger A, Bowler C, Martin-Magniette ML, Robin S, Caboche M, Colot V (2011) Integrative epigenomic mapping defines four main chromatin states in Arabidopsis. *EMBO Journal*, **30**, 1928-1938.

Ruttink T, Arend M, Morreel K, Storme V, Rombauts S, Fromm J, Bhalerao RP, Boerjan W, Rohde A (2007) A molecular timetable for apical bud formation and dormancy induction in poplar. *The Plant Cell*, **19**, 2370-2390.

Santamaria ME, Hasbun R, Valera MJ (2009) Acetylated H4 histone and genomic DNA methylation patterns during bud set and bud burst in *Castanea sativa*. *Journal of Plant Physiology*, **166**, 1360-1369.

Schellenbaum P, Mohler V, Wenzel G, Walter B (2008) Variation in DNA methylation patterns of grapevine somaclones (*Vitis vinifera* L.). *BMC Plant Biology*, **8**, 78.

Schmitz RJ, Amasino RM (2007) Vernalization: a model for investigating epigenetics and eukaryotic gene regulation in plants. *Biochimica et Biophysica Acta*, **1769**, 269-275.

Schull GH (1946) Hybrid seed corn. *Science*, **103**, 547-550.

Skrøppa T, Johnsen Ø (2000) Patterns of adaptive genetic variation in forest tree species; the reproductive environment as an evolutionary force in *Picea abies*. In *Forest Genetics and Sustainability* (ed. C. Mátyás), pp. 49-58. *Kluwer Academic Publications*, Dordrecht, The Netherlands.

Skrøppa T, Kohmann K, Johnsen Ø, Steffenrem A, Edvardsen ØM (2007) Field performance and early test results of offspring from two Norway spruce seed orchards containing clones transferred to warmer climates. *Canadian Journal of Forest Research*, **37**, 515-522.
Skrøppa T, Tollefsrud M, Sperisen C, Johnsen Ø (2010) Rapid change in adaptive performance from one generation to the next in Picea abies-Central European trees in a Nordic environment. *Tree Genetics & Genomes*, **6**, 93-99.

Slotkin RK, Martienssen RA (2007) Transposable elements and the epigenetic regulation of the genome. *Nature Reviews Genetics*, **8**, 272-285.

Stoehr MU, L'Hirondelle SJ, Binder WD, Webber JE (1998) Parental environment after effects on germination, growth, and adaptive traits in selected spruce families. *Canadian Journal of Forest Research*, **28**, 418-426.

Thumma BR, Matheson BA, Zhang D, Meeske C, Meder R, Downes GM, Southerton SG (2009) Identification of a Cis-acting regulatory polymorphism in a Eucalypt COBRA-like gene affecting cellulose content. *Genetics*, **183**, 1153-1164.

Tsukahara S, Kobayashi A, Kawabe A, Mathieu O, Miura A, Kakutani T (2009). Bursts of retrotransposition reproduced in Arabidopsis. *Nature*, **461**, 423-426.

Valledor L, Hasbún R, Meijón M (2007) Involvement of DNA methylation in tree development and micropropagation. *The Plant Cell Tissue and Organ Culture*, **91**, 75-86.

Valledor L, Meijon M, Hasbun R, Jesus Canal M, Rodriguez R (2010) Variations in DNA methylation, acetylated histone H4, and methylated histone H3 during *Pinus radiata* needle maturation in relation to the loss of in vitro organogenic capability. *Journal of Plant Physiology*, **167**, 351-357.

Van Steensel B (2011) Chromatin: constructing the big picture. *The EMBO Journal*, **30**, 885-1895.

Verhoeven KJ, Jansen JJ, van Dijk PJ, Biere A (2010) Stress-induced DNA methylation changes and their heritability in asexual dandelions. *New Phytologist*, **185**, 1108-1118.

Vining KJ, Pomraning KR, Wilhelm LJ, Priest HD, Pellegrini M, Mockler TC, Freitag M, Strauss SH (2012) Dynamic DNA cytosine methylation in the *Populus trichocarpa* genome: tissue-level variation and relationship to gene expression. *BMC Genomics*, **13**, 27.

Viswanath V, Albrechtsen BR, Strauss SH (2012) Global regulatory burden for field testing of genetically modified tress. *Tree Genetics & Genomes*, **8**, 221-226.

Webber J, Ott P, Owens J, Binder W (2005) Elevated temperature during reproductive development affects cone traits and progeny performance in *Picea glauca* - *engelmannii* complex. *Tree Physiology*, **25**, 1219-1227.

Whittle CA, Otto SP, Johnston MO, Krochko JE (2009) Adaptive epigenetic memory of ancestral temperature regime in *Arabidopsis thaliana*. *Botany*, **87**, 650-657.

Wilkins O, Waldron L, Nahal H, Provart NJ, Campbell MM (2009) Genotype and time of day shape the *Populus* drought response. *The Plant Journal*, **60**, 703-715.

Yakovlev IA, Fossdal CG, Johnsen Ø (2010) MicroRNAs, the epigenetic memory and climatic adaptation in Norway spruce. *New Phytologist*, **187**, 1154-1169.

Yakovlev IA, Asante DKA, Fossdal CG, Junttila O, Johnsen Ø (2011) Differential gene expression related to an epigenetic memory affecting climatic adaptation in Norway spruce. *Plant Science*, **180**, 132-139.
# LARGE-SCALE LONGITUDINAL GRADIENTS OF GENETIC DIVERSITY: A META-ANALYSIS ACROSS SIX PHYLA IN THE MEDITERRANEAN BASIN

## BIOGEOGRAPHY OF GENES IN THE MEDITERRANEAN

Cyrille CONORD<fnref n="1" />,<fnref n="2" />; Jessica GUREVITCH<fnref n="3" />; Bruno FADY<fnref n="1" />

**Corresponding Author:**

Bruno FADY

INRA, UR629, Écologie des Forêts Méditerranéennes, Avignon, France,
bruno.fady@avignon.inra.fr,

Biodiversity is the diversity of life at all scales, from genes to ecosystems. Predicting its patterns of variation across the globe is a fundamental issue in ecology and evolution. Diversity within species, i.e. genetic diversity, is of prime importance for understanding past and present evolutionary patterns, and highlighting areas where conservation might be a priority. Using published data on the genetic diversity of species whose populations occur in the Mediterranean basin, we calculated a coefficient of correlation between within-population genetic diversity indices and longitude. Using a meta-analysis framework, we estimated the role of biological, ecological, biogeographical and marker type factors on the strength and magnitude of this correlation in six phylla. Overall, genetic diversity increases from west to east in the Mediterranean basin. This correlation is significant for both animals and plants, but is not uniformly expressed for all groups. It is stronger in the southern than in the northern Mediterranean, in true Mediterranean plants than in plants found at higher elevations, in trees than in other plants and in bi-parentally and paternally than in maternally inherited DNA makers. Overall, this correlation between genetic diversity and longitude, and its patterns across biological and ecological traits, suggests the role of two non-mutually exclusive major processes that shaped the genetic diversity in the Mediterranean during and after the cold periods of the Pleistocene: east-west recolonization during the Holocene and population size contraction under local Last Glacial Maximum climate in resident western and low elevation Mediterranean populations.

**Key words:** Biodiversity; biogeography; past-climate; genetic diversity; recolonization; Holocene; longitude; meta-analysis; Arthropod; Mollusc; Chordata; Bryophyte; Pteridophyte; Spermaphyte; phylogeography; Pleistocene.

Published in *Ecology and Evolution* (2012) 2: 2600-2614, with doi:10.1002/ece3.350

## Introduction

Biodiversity is the diversity of life at all scales, i.e. "the variability among living organisms from all sources including, *inter alia*, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; this includes diversity within species, between species and of ecosystems" (article 2 of the Convention on Biological Diversity1992). Although knowledge of the distribution of species is far from always being spatially accurate and detailed (Richardson & Whittaker 2010), species diversity and abundance is relatively well known for several taxonomic groups (e.g. mammals, birds, fishes, vascular plants) and particularly in the temperate regions of the world. This knowledge has helped shaping the delineation of hotspots of biological diversity (Myers et al. 2000) where conservation is most critical and is at the core of the field of conservation biogeography (Whittaker et al. 2005).

At a finer taxonomic scale, genetic diversity, diversity among individuals within species, yields valuable information for understanding how biodiversity changes within an evolutionary framework. Within the broad

context of biogeography, genetic diversity has been assessed either through ecogeographic or through phylogeographic perspectives (Avise, 2000). The ecogeographic view focuses on patterns produced by contemporary natural selection, as for example, the genetic structure of Mediterranean pine stands exposed to wild fires (Aravanopoulos et al. 2004). Conversely, the phylogeographic approach focuses largely on historical evolutionary processes such as the balance between vicariance and dispersal to examine genetic differentiation both among and within populations. Measures of genetic differentiation among populations, mapped against major geographical barriers, have been used to derive the most likely Quaternary glacial refugia and Holocene recolonization routes of major temperate species (Taberlet et al. 1998, Hewitt 1999). Measures of genetic diversity within population (*GDpop*) have been used to refine how Holocene recolonization occurred for multiple species and vegetation types, e.g. from southern refugia in Europe and North America (Petit et al. 2003, Soltis et al. 2006).

Large-scale phylogeographic studies have identified latitudinal Holocene recolonization as well as complex

<footnote n="1">¹ INRA, FR ECCOREV, UR629, Écologie des Forêts Méditerranéennes, Avignon, France.</footnote>

<footnote n="2">² Université de Saint-Etienne, Jean Monnet, Laboratoire BVpam, EA2061, 23 rue du Dr Michelon, F-42000 Saint-Etienne, France.</footnote>

<footnote n="3">³ Department of Ecology & Evolution, Stony Brook University, NY, USA.</footnote>
patterns of post-glaciation dispersal from refugia at landscape to regional scales as the major drivers of genetic diversity in the Northern Hemisphere (e.g., Petit et al. 2003, Brewer et al. 2002, Liepelt et al. 2009, for Europe). Phylogeographic studies at large scales have not often considered longitude as a potentially important ecological driver, even though refugia are distributed longitudinally in southern Europe (Stewart et al. 2010). In the Mediterranean basin, longitudinal trends are potentially an important factor in determining genetic diversity because of how the geography of southern Europe and the Mediterranean region is shaped. The Mediterranean Sea is a strong barrier to latitudinal movements of terrestrial species but also to longitudinal movements from one peninsula to the other in Europe, with potentially strong impacts in shaping contemporary biodiversity structures. Unveiling longitudinal patterns of diversity in the Mediterranean would be of great interest because, quoting from Atkinson et al. (2007), "longitudinal processes represent the raw material on which later latitudinal processes work" in Europe. The purpose of the present study is to examine whether longitudinal patterns of genetic diversity are important in the Mediterranean region and southern Europe across a large range of taxa. Such patterns may reveal a different perspective on post-glaciation colonization at large geographic and taxonomic scales.

In the same manner that a gene tree only depicts a very small part of the phylogenetic history of lineage, the population genetic structure of species can only represent a small slice of the history of a whole region or biome. Whether they have followed the ecogeographical or the phylogeographical approach, many empirical studies of population genetic structure provide GDpop estimates. Gathering and comparing the wealth of information contained in these empirical studies, using a proper statistical framework, represents an excellent opportunity to meet the challenge of testing processes determining genetic diversity patterns at regional scale or biome-wide, such as for the Mediterranean. We used meta-analysis to document and test the existence of cross-taxa longitudinal patterns of genetic diversity in the Mediterranean basin. No previous studies have examined genetic differentiation within and across populations at large geographic scales using the powerful statistical tools of meta-analysis.

Biogeographic genetic analyses have mostly focused on population structure and differentiation rather than on within-population diversity because genetic variation at neutral markers is not expected to respond to environmental effects (but see, e.g., Petit et al. 2003). Strong spatial gradients of neutral genetic differentiation are thus only expected as a consequence of historical effects such as directional dispersal during range expansions from refugia during global warming periods, which leads to marked population structure (Petit et al. 2003). However, because demographic changes can

impact GDpop (Young et al. 1996), strong gradients of GDpop can also be expected as an indirect response to clinal environmental effects, such as past climates.

GDpop is of fundamental importance in ecology and evolution because it is correlated with population demographic rates and, in numerous circumstances, with their potential for evolutionary adaptive change (Le Corre & Kremer 2003). GDpop is therefore important for identifying regions where evolutionary potential is either particularly low or high, thus providing insights for conservation strategies and planning (Schwartz et al. 2007). GDpop can be estimated in various ways, but the approaches fall within two general categories: "richness" (total amount of diversity, e.g. allelic richness, haplotypic richness) and "equitability" (the way diversity is distributed among samples, e.g. heterozygosity, Shannon's index, percentage of polymorphic loci). Whereas equitability measures are more sensitive to higher-frequency alleles and how they are distributed within populations, richness measures respond more to the presence and quantity of rare alleles. Thus, the two measures are needed concurrently to document and test for demographic events such as bottlenecks and expansions.

The Mediterranean basin is a hotspot of species diversity (Myers et al. 2000), and also a world region of unusually high GDpop (see Fady 2005 for conifers). Vascular plants are structured into regional hotspots of species diversity and endemism (Médail & Quézel 1997) often corresponding to glacial refugia (Médail & Diadéma 2009). The northern Mediterranean basin is made of south-north oriented peninsulas identified as independent Quaternary glacial refugia and starting points of Holocene recolonization for Europe (Hewitt 1999; Petit et al. 2003). The shoreline of the southern Mediterranean basin is more or less linear, without major peninsulas. Its western part, North Africa, is also recognized as a refugial zone (e.g. Cheddadi et al. 2009, Guzmán & Vargas 2009).

Two major causes can be hypothesized for longitudinal trends of GDpop, if such trends can be demonstrated, for natural populations in the Mediterranean (both in southern Europe and North Africa). First, longitudinal trends could result from genetic drift due to long distance dispersal and founder effects during Holocene recolonization from refugia (e.g., from eastern Mediterranean refugia as in the tree Pinus halepensis, Grivet et al. 2009, or in the wasp Andricus quercustozae, Rokas et al. 2003, from western Mediterranean refugia as in the tree Pinus sylvestris, Soranzo et al. 2000). However, both uni-directional recolonization patterns appear less likely than multi-refugium recolonization patterns (Taberlet et al. 1998). The second cause for longitudinal trends in this region could be genetic drift due to decreasing effective population size, given the existence of a climate of increasing severity from east to west in the Mediterranean during the last glacial cycle, particularly the Last Glacial Maximum (LGM)
21 000 years before present (van Andel 2002; Wu et al. 2007). There is also evidence of climatic instability over the North-Atlantic Ocean leading to several extreme cooling events over the Iberian Peninsula during the last glacial cycle (Sánchez-Goñi et al. 2002). The potential effect of such past climate trends on GDpop was described for gallwasps (Atkinson et al. 2007) and trees (Fady & Conord 2010).

Looking at patterns across multiple levels of biodiversity provides a framework to understand processes beyond the idiosyncrasy of case studies. Here, using a meta-analytical framework, we tested the existence of a longitudinal trend of within-population genetic diversity in the Mediterranean basin at multiple taxonomic levels in the tree of life. For each population genetic study we retrieved from the literature, we calculated a correlation coefficient between longitudinal coordinates and genetic diversity. We examined data on populations in the Arthropods, Mollusks, Chordata, Bryophytes, Pteridophytes, and Spermaphytes. Previous studies of genetic diversity at large spatial scales have generally focused on far smaller taxonomic groups (e.g., Riddle et al. 2000, vertebrates from Baja California, North America; Petit et al. 2003, trees from Europe; Kadereit et al. 2005, dicots from the Mediterranean basin).

We hypothesized that we would detect a west-east trend of increasing genetic diversity across all taxa if the demographic (and thus genetic) clinal imprint left by the climate of the last glacial cycle on resident populations in refugia was stronger than the imprints left by the incongruent Holocene recolonization patterns of different species from different refugia (Taberlet et al. 1998). In contrast, if recolonization from disparate refugia across multiple taxa is the dominant signal for current patterns of GDpop, we would not expect to find such a longitudinal imprint across taxa. Refugia have been identified in many different parts of the region. For example, Médail & Diadéma (2009) in their analysis of plant genetic patterns in the Mediterranean found that out of 52 refugia identified, 33 were in the western Mediterranean and 19 in the eastern Mediterranean (non-significantly different from an equal distribution in each zone). At species level, we expected different trends depending on where refugia were located. We expected that this trend would be weaker for studies using GDpop measures giving higher weight to frequent alleles because rare alleles are more likely to disappear with recolonization and bottleneck events than do frequent alleles.

We also predicted that, depending on their position in the tree of life, the different taxa would respond differently to longitude. We expected that their responses would depend on:

- their life-history traits (low versus high mobility);

- their bioclimatic requirements (particularly in plants, depending on their over-wintering abilities and their

temperature requirements, and thus their sensitivity to local glacial climate); and

- their location within distribution areas (islands versus continents and southern versus northern Mediterranean, for which demographic effects and migration possibilities are different).

# Material and methods

## 1. Gathering data from published sources

We collected published population genetic studies of terrestrial plant and animal species whose distributions were at least partially found within the Mediterranean basin, from endemic to widespread. For this, we searched the Web of Knowledge between for references published between Jan. 1980 and Oct. 2009. We used the following search expressions: 'population genetics', 'phylogeo*', 'genetic diversit*', 'Mediterr*' and individual Mediterranean country names. We also examined the references included in all retrieved publications for additional references.

## 2. Defining the geographic zone of interest and population sample size

From these papers, we selected all populations with GDpop estimates that were included within the Mediterranean basin. We used the delineation of the Mediterranean basin defined by Olson et al. (2001) which is the standard currently used by the World Wildlife Fund (WWF) to define the world eco-regions. We used a geographic information system (GIS) for selecting among published studies which population to allocate to that geographic envelope and to further qualify populations as continental versus insular and northern versus southern Mediterranean.

## 3. Constructing a database of GDpop estimates

We explain in details in Supplementary Material I how we constructed our database. The list of published studies used is referenced in Supplementary Material II.

## 4. Raw data analysis and calculation of effect-sizes

No published paper we retrieved had testing for a correlation between longitude and GDpop as its primary goal. We used the raw data from these studies to correlate the longitudinal position of each population with its GDpop. The statistics we used was the Pearson product-moment correlation coefficient.

The populations tested are not located on a strait longitudinal line but rather span a small latitudinal gradient (which reaches its maximum in each of the Mediterranean peninsulas). Also, not all organisms remained in the close vicinity of their glacial refugia after Holocene warming (receding edge populations may have moved farther away from refugia than rear edge populations or endemic species, Jump et al. 2009). Thus in order to account for the effect of latitude, we calculated a partial correlation coefficient which measured the degree of association of
*GDpop* with longitude while latitude was held constant. Partial correlation coefficients in meta-analysis represent the relationship between the independent and the dependent variable while controlling for other factors (Rosenthal & DiMatteo 2001, Keef & Roberts 2004). In our case, the controlling factor was always the same, latitude.

The partial correlation coefficients were transformed using Fisher's Z-transformation and used as effect-sizes: $Z = 0.5*\ln((1+r)/(1-r))$, where *r* is the partial correlation coefficient. Finally, effect-sizes (Z-transformed partial correlation coefficients) were weighted by the inverse of their asymptotic variance (see Aloe & Becker 2009 for weighting partial coefficients in meta-analysis) which was calculated as $Vz = 1/(n-3)$, where *n* is the number of sampled populations in the source study. Finally, summary-effects (*Zr*) were computed as the weighted mean of the individual effect sizes using a fixed-effect meta-analysis model. We chose a fixed-effect model (Borenstein *et al.* 2009) because we were interested in testing primary factors affecting *GDpop* that acted in a similar way across all species and were expressed non-randomly across the whole Mediterranean basin.

Because data in our primary dataset are not all independent (several papers in our dataset address the same species), we tested the effect of non-independence on Type I error rates as well as on the precision of summary-effects (Hartung *et al.* 2008) using several methods (see Supplementary Material III). As the redundancy of our raw data affected neither the direction of the relationship between *GDpop* and longitude nor its significance, we decided to use the entire dataset in the following meta-analyses and to not perform any statistical treatment to reduce redundancy.

Data processing, effect-size computations as well as sensitivity analyses were done using R packages *plyr* v0.1.9 (Wickham 2011), *MAc* v1.1 (Del Re & Hoyt 2010) and custom scripts for sensitivity analyses (available upon request), under R v2.11 (http://www.r-project.org/), whereas the meta-analysis procedure was performed with MetaWin (Rosenberg *et al.* 2000).

# Exploring moderators of the summary-effects

## 1. Moderators related to primary study design

### Effect of the sampling geographical range in the primary studies

If we hypothesize a general and homogeneous link of *GDpop* with longitude (supposing that longitude is a proxy for the same phenomenon for all species/studies), then we may expect that the wider the range of population sampling across the Mediterranean basin in the primary studies was, the greater the probability of detecting a positive mean effect-size *Zr* would be. Also, *Zr* might be affected by the position of the range within the

Mediterranean basin. We tested these relationships by regressing each *Zr* and their corresponding longitudinal span calculated as the absolute value of the difference in degrees of longitude between the easternmost and the westernmost populations and each *Zr* and their corresponding mean longitudinal coordinate.

### Choice of genetic marker and GDpop metric

The choice of the genetic marker may impact the sign of the correlation between *GDpop* and geography because they may reflect different processes acting at different spatial and time scales (ecological vs evolutionary). They may also reflect different demographic histories via their different effective population size or sex-related transmission. Discrepancies have classically been found by phylogeographers between the nuclear and the mitochondrial DNA (Petit & Vendramin 2007). We thus tested marker type effects by categorizing them depending on their inheritance type (male, female or bi-parental inheritance) which may be related to an effect of dispersal ability. Because foundation events or distance to refugia may be imprinted differently on the different types of *GDpop* measures (see the *Fagus sylvatica* example in Comps *et al.* 2001), we tested metric type effects by categorizing *GDpop* measures as either 'equitability' or 'richness' measures (see introduction).

## 2. Biogeographic effect (north vs south, continents vs islands)

We tested biogeographic effects by categorizing the effect-sizes as either northern or southern Mediterranean, and as either from continents or islands.

## 3. Plant species biological attributes and ecological requirements

There may be a strong confounding effect between taxonomy and marker type in our general dataset. Specifically, cpDNA effects may be due to the type of DNA used or to traits specific to plants as this type of DNA is not present in animals. Thus, we used the part of our dataset restricted to plants to retest for marker type effects on overall trends and also to test for the imprint of biological attributes and ecological requirements on *GDpop* in the Mediterranean.

Along with demographic processes, many life history traits can be responsible more or less deterministically for spatial *GDpop* differences. For example, generation time is a life history trait that will moderate the imprint of past demographic events on the measured *GDpop*. As a first general and very coarse proxy of those traits, we used taxonomy and we characterized all species by their family, class and kingdom names.

For each plant species, we also recorded several biological and ecological attributes using information from Quézel & Médail (2003), Pignatti (1982), Rameau *et al.* (2008) as well as the Telabotanica web database (www.telabotanica.org/ consulted in April 2009). We coded the
<caption>FIGURE 4</caption>

Square and error bars should be interpreted as indicated for Fig. 2a.

4a

<table><thead><tr><th>Moderator</th><th>Number of Sign. of effect-sizes</th><th>Q-test</th><th>Effect-size</th></tr></thead><tbody><tr><td>nuclear</td><td>291</td><td>***</td><td>0.07</td></tr><tr><td>plastidial</td><td>64</td><td>***</td><td>0.06</td></tr><tr><td>mitochondrial</td><td>37</td><td>*</td><td>0.08</td></tr><tr><td>genomic</td><td>36</td><td>**</td><td>0.05</td></tr></tbody></table>

<figure><img src="image_56.png" /></figure>

4a: Mean effect-sizes across the Mediterranean basin for marker type. The category "genomic" refers to unassigned marker types.

4b

<table><tr><td>Me</td><td>53</td><td>***</td><td>0.17</td></tr><tr><td>Su</td><td>39</td><td>***</td><td>0.06</td></tr><tr><td>Eurytherms</td><td>64</td><td>***</td><td>0.0033</td></tr><tr><td>Th</td><td>27</td><td>***</td><td>-0.01</td></tr><tr><td>Mt</td><td>82</td><td>***</td><td>-0.04</td></tr></table>

4b: Mean effect-sizes (ordered from most positive to most negative) of Mediterranean basin plants for ecological requirements. The categories refer to altitudinal belts where plant species are predominantly found: 'Me' is meso-Mediterranean, 'Su' is supra-Mediterranean, 'Eurytherms' refers to plant species found across several altitudinal belts, 'Th' is thermo-Mediterranean and 'Mt' is mountain-Mediterranean (see Quézel & Médail 2003). Square and error bars should be interpreted as indicated for Fig. 2a. Nota Benne: effect-sizes for ecological requirements do not add up to the total number of effect-sizes in plants because raw data communicated by some authors were pooled at the genus level or because data included species for which we were not able to retrieve their ecological requirement.

4c

<table><tr><td>Phanerophytes</td><td>192</td><td>***</td><td>0.07</td></tr><tr><td>Chamaephytes</td><td>25</td><td>***</td><td>-0.24</td></tr><tr><td>Hemicryptophytes</td><td>23</td><td>.</td><td>0.03</td></tr><tr><td>Therophytes</td><td>16</td><td>***</td><td>0.10</td></tr><tr><td>Geophytes</td><td>13</td><td>NS</td><td>0.12</td></tr></table>

4c: Mean plant effect-sizes (arranged in decreasing effect-size frequency per category) across the Mediterranean basin for Raunkiaer biological types. Phanerophytes are woody plants with over-wintering buds situated over 50 cm from the ground, chamaephytes are low growing perennials (often woody plants) with wintering buds below 50 cm in height, hemicryptophytes are (often two-year cycle) perennials with ground-level wintering buds, geophytes are plants with bulbs or rhizomes (wintering buds below ground level) and therophytes are annuals (wintering organs as seeds). Nota Benne: Bryophytes were not assigned a Raunkiaer type (9 effect-sizes).

4d

<table><tr><td>anemochorous</td><td>147</td><td>***</td><td>0.14</td></tr><tr><td>zoochorous</td><td>90</td><td>***</td><td>-0.02</td></tr><tr><td>barochorous</td><td>17</td><td>NS</td><td>0.16</td></tr><tr><td>Non assign.</td><td>12</td><td>NS</td><td>-0.43</td></tr><tr><td>hydrochorous</td><td>3</td><td>NS</td><td>-0.01</td></tr></table>

4d: Mean plant effect-sizes (arranged in decreasing effect-size frequency per category) across the Mediterranean basin for seed dispersal types. Anemochorous plants have wind-dispersed seeds, zoochorous plants animal-dispersed seeds, barochorous plants gravity-dispersed seeds and hydrochorous plants water-dispersed seeds.

4e

<table><tr><td>anemogamous</td><td>174</td><td>***</td><td>0.10</td></tr><tr><td>entomogamous</td><td>76</td><td>***</td><td>-0.03</td></tr><tr><td>Non assign.</td><td>12</td><td>NS</td><td>-0.43</td></tr><tr><td>hydrogamous</td><td>7</td><td>NS</td><td>-0.26</td></tr></table>

4e: Mean plant effect-sizes (arranged in decreasing effect-size frequency per category) across the Mediterranean basin for pollen dispersal types. Anemogamous plants have wind-dispersed pollen, entomogamous plants insect-dispersed pollen and hydrogamous plants water-dispersed pollen.
altitudinal thermo-climatic belts (from thermo- to oro-Mediterranean, see Fig. 4 for details) where each plant species was predominantly found. Bioclimatic requirements have been found to affect GDpop (Kadereit et al. 2005; Fady & Conord 2010; Soto et al. 2010). Species with higher temperature requirements may be more sensitive to demographic fluctuation in the Mediterranean as they will have been more strongly impacted by unfavorable cold climate during the cold cycles of the Quaternary. We also categorized each plant species according to their pollen and seed dispersal type (see Figs 4d and 4e for the detailed types within categories).

Seed dispersal type may influence GDpop as a result of migration, as illustrated by the comparison of beech vs hornbeam in Europe. Beech with its animal-dispersed nuts conserved more genetic diversity when crossing mountain barriers than did hornbeam with its winged seeds (Coart et al. 2005). Finally, each species in our database was categorized according to its Raunkiaer life-form, which is based on the position of the plant's buds during the unfavorable season and it therefore may be a proxy of life history traits playing an important role in the survival of the species under harsh conditions at the LGM.

FIGURE 2

2a

<table><thead><tr><th>Dataset</th><th>Number of Effect-sizes</th><th>Signif. of Q-test</th><th>Effect-size</th></tr></thead><tbody><tr><td>Southern Mediterranean</td><td>53</td><td>***</td><td>0.17</td></tr><tr><td>Northern Mediterranean</td><td>399</td><td>***</td><td>0.06</td></tr><tr><td>Mediterranean islands</td><td>108</td><td>***</td><td>0.02</td></tr><tr><td>Mediterranean continent</td><td>377</td><td>***</td><td>0.10</td></tr><tr><td>Mediterranean Basin</td><td>428</td><td>***</td><td>0.07</td></tr></tbody></table>

<figure><img src="image_57.png" /></figure>

2a: Mean effect-sizes across the Mediterranean basin and for different biogeographical envelopes.

Each square whose size is proportional to the number of effect-sizes, indicates mean values and each bar the 95% confidence interval around the mean. Bars not intercepting the Y-axis indicate a significant correlation between GDpop and longitude. When the mean is on the positive side of the X-axis, this correlation is positive, indicating that GDpop increases from west to east. Bars with arrows indicate that the 95% confidence interval falls outside the limits of the figure.

Diamonds highlight mean values for plants and animals. All Q-tests are highly significant (P < 0.0001), indicating that significant differences between effect sizes remain within each biogeographical category. Nota Benne: northern / southern and continent / island effect-sizes do not add up to the total number of effect-sizes in the Mediterranean basin because raw data studies may include species with populations present north and south of the Mediterranean and both on continents and on islands.

2b

**Moderator**

<table><tr><td>Monocotyledone</td><td>14</td><td>NS</td><td>0.15</td></tr><tr><td>Gymnosperm</td><td>122</td><td>***</td><td>0.11</td></tr><tr><td>Dicotyledone</td><td>121</td><td>***</td><td>0.03</td></tr><tr><td>Pteridophyte</td><td>3</td><td>NS</td><td>-0.01</td></tr><tr><td>Bryophyte</td><td>9</td><td>NS</td><td>-0.46</td></tr><tr><td><strong>Plants</strong></td><td><strong>269</strong></td><td>***</td><td><strong>0.04</strong></td></tr></table>

<table><tr><td>Crustacea</td><td>2</td><td>NS</td><td>0.56</td></tr><tr><td>Arachnida</td><td>10</td><td>NS</td><td>0.36</td></tr><tr><td>Insect</td><td>69</td><td>***</td><td>0.24</td></tr><tr><td>Reptile</td><td>8</td><td>NS</td><td>0.1</td></tr><tr><td>Mammal</td><td>33</td><td>*</td><td>0.05</td></tr><tr><td>Amphibian</td><td>29</td><td>***</td><td>0.02</td></tr><tr><td>Bird</td><td>5</td><td>*</td><td>0</td></tr><tr><td>Gasteropod</td><td>3</td><td>NS</td><td>-0.96</td></tr><tr><td><strong>Animals</strong></td><td><strong>159</strong></td><td>***</td><td><strong>0.14</strong></td></tr></table>

<figure><img src="image_58.png" /></figure>

Zr

2b: Mean effect-sizes across the Mediterranean basin for 5 plant and 8 animal classes, and mean values for the plant and animal kingdoms. Classes are arranged in decreasing effect size order. Square and error bars should be interpreted as indicated for Fig. 2a
# Results

## 1. Overall effect-sizes and role of range, taxonomy, and DNA markers

Overall, there was a positive and significant correlation between *GDpop* and longitude in the Mediterranean (Fig. 2a). Within-population genetic diversity decreases from east to west in the Mediterranean (Table 1; Supplementary Material IV). Out of the 428 effect sizes generated from 143 plant and animal species from 156 published studies in our meta-analysis, 54% showed a positive effect size. Considerable heterogeneity was found (Q = 1180, df = 427, *P* < 0.0001) leading to the tests of the categorical moderators reported below. The general longitudinal trend in *GDpop* was affected by sampling range and range type, by marker and metric types, by taxonomy (phylogeny), by biological traits and by ecological requirements.

### Sampling range and range type

Both sampling range span and the mean longitudinal position of the studies were significantly but weakly correlated with *Zr* (*r* = 0.0043, *P* < 0.0001 and *r* = 0.0086, *P* < 0.0001, respectively). Widely distributed species and species from the eastern part of the Mediterranean tended to have more significantly positive *Zr* than others. The *Zr* was five times higher for continents than for islands and almost three times higher for the southern than for the northern Mediterranean (Fig. 2a). However, *Zr* was positive and significant for all geographic envelopes except for Mediterranean islands. All tests based on Q statistics rejected the null hypothesis of homogeneity among

effect-sizes within category, i.e that all studies shared a common effect size (*P* < 0.0001, Supplementary Material Table IV-1). This suggested that there was more variability among the effect-sizes of a category than expected by chance and justified the search and testing of moderating variables.

Studies in our dataset sampled a majority of northern Mediterranean populations (399 effect sizes in the north compared to 53 for the south). Differences in *Zr* between the northern and southern Mediterranean were not biased by high order taxonomic differences. The number of effect-sizes belonging to the 6 different phyla of the database (Arthropods, Mollusks, Chordata, Bryophytes, Pteridophytes, Spermaphytes) were not significantly different between the northern and southern Mediterranean (contingency Chi-square test = 12.05, df = 5, *P* = 0.06), although they were between continents and islands (contingency Chi-square test = 15.448, df = 5, *P* = 0.01).

### Phylogeny and taxonomic group

The *Zr* computed for the animal and plant kingdoms were both positive and significant. *Zr* for animals was more than three times higher than that of plants. The two Q-tests rejected homogeneity of *Zr* within plants and animals (Fig. 2b), indicating that within each kingdom, finer level groups departed significantly from the positive trend. At a finer taxonomic level, 4 classes out of 13 had a summary-effect not intercepting the zero (Bryophytes, Gymnosperms, Arachnida and Insects, Fig. 2b). Heterogeneity tests were non-significant for all class levels with less than 10 effect-

<caption>TABLE 1</caption>

Description of the data set and summary statistics:
Geographic range (including islands) from Olson et al. 2001.

<table><thead><tr><th>Taxonomic group</th><th>Species nr.</th><th>Studies nr.</th><th>Effect-sizes nr.</th><th>mtDNA*</th><th>cpDNA*</th><th>cpDNA SSR*</th><th>nDNA*</th><th>nDNA Isozymes*</th><th>nDNA SSR*</th></tr></thead><tbody><tr><td>Bryophyte</td><td>2</td><td>4</td><td>9</td><td>-</td><td>0</td><td>0</td><td>5</td><td>4</td><td>0</td></tr><tr><td>Dicotyledone</td><td>47</td><td>40</td><td>121</td><td>-</td><td>33</td><td>13</td><td>88</td><td>56</td><td>16</td></tr><tr><td>Gymnosperm</td><td>17</td><td>55</td><td>122</td><td>-</td><td>28</td><td>28</td><td>93</td><td>76</td><td>4</td></tr><tr><td>Monocotyledone</td><td>7</td><td>6</td><td>14</td><td>-</td><td>2</td><td>2</td><td>12</td><td>9</td><td>3</td></tr><tr><td>Pteridophyte</td><td>1</td><td>1</td><td>3</td><td>-</td><td>0</td><td>0</td><td>3</td><td>0</td><td>3</td></tr><tr><td><strong>Total plants</strong></td><td><strong>74</strong></td><td><strong>106</strong></td><td><strong>269</strong></td><td>-</td><td><strong>63</strong></td><td><strong>43</strong></td><td><strong>201</strong></td><td><strong>145</strong></td><td><strong>26</strong></td></tr><tr><td>Amphibian</td><td>14</td><td>7</td><td>29</td><td>6</td><td>-</td><td>-</td><td>23</td><td>23</td><td>0</td></tr><tr><td>Arachnida</td><td>6</td><td>4</td><td>10</td><td>0</td><td>-</td><td>-</td><td>10</td><td>10</td><td>0</td></tr><tr><td>Birds</td><td>3</td><td>3</td><td>5</td><td>5</td><td>-</td><td>-</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Crustacea</td><td>1</td><td>1</td><td>2</td><td>2</td><td>-</td><td>-</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Gasteropod</td><td>1</td><td>1</td><td>3</td><td>0</td><td>-</td><td>-</td><td>3</td><td>3</td><td>0</td></tr><tr><td>Insect</td><td>30</td><td>26</td><td>69</td><td>17</td><td>-</td><td>-</td><td>54</td><td>27</td><td>18</td></tr><tr><td>Mammal</td><td>10</td><td>11</td><td>33</td><td>8</td><td>-</td><td>-</td><td>25</td><td>12</td><td>12</td></tr><tr><td>Reptile</td><td>4</td><td>4</td><td>8</td><td>0</td><td>-</td><td>-</td><td>8</td><td>5</td><td>3</td></tr><tr><td><strong>Total animals</strong></td><td><strong>69</strong></td><td><strong>57</strong></td><td><strong>159</strong></td><td><strong>36</strong></td><td><strong>-</strong></td><td><strong>-</strong></td><td><strong>123</strong></td><td><strong>80</strong></td><td><strong>33</strong></td></tr><tr><td><strong>Total</strong></td><td><strong>143</strong></td><td><strong>163</strong></td><td><strong>428</strong></td><td><strong>36</strong></td><td><strong>63</strong></td><td><strong>43</strong></td><td><strong>324</strong></td><td><strong>225</strong></td><td><strong>59</strong></td></tr></tbody></table>

<footnote>*: number of effect-sizes.</footnote>
sizes except for birds (Fig. 1b), suggesting consistent
responses among the members of these groups; however,
the Q test is not very powerful and may fail to detect true
heterogeneity, particularly in such small groups. The
remaining groups were highly significantly heterogeneous.
At the yet finer taxonomic level of the family, significant
longitudinal *GDpop* structures could be observed in the
Pinaceae, Cupressaceae, Poaceae and Asteraceae
(positive summary-effects) and in the Lamiaceae,
Nymphalidae and Pottiaceae (negative summary-effects,
Fig. 3). As the taxonomic sampling was unbalanced, we
ran the meta-analysis excluding successively the most
represented groups, from the higher taxonomic level to
the finer. Excluding the Phanerophytes (192 effect-sizes)
had no effect on the *Zr* computed for the whole dataset
and for the continental Mediterranean. However, *Zr* for the
northern Mediterranean became non-significant whereas
*Zr* for southern Mediterranean and for islands increased
and even became significant for islands. Then, excluding
gymnosperms (122 effect-sizes) confirmed the patterns
observed for the phanerophytes exclusion except for the
southern Mediterranean *Zr* which became non-significant.
In animals, two families showed a positive trend (Buthidae
and Tephritidae) whereas two other showed the opposite
trend (Plethodontidae and Torymidae) (Fig. 3).

Markers type and metric type

Nuclear and organelle markers showed a positive *Zr* of the
same order of magnitude although mitochondrial *Zr* was
not significantly different from zero (Fig. 4a). When
assessing the effect of the inheritance of the genetic
marker, we found that bi-parentally and paternally
inherited markers yielded a significant *Zr*. On the contrary,
*Zr* for maternally inherited markers (mitochondrial DNA in
all species of our dataset and plastidial DNA in
angiosperms) was positive but non-significant (not
shown), indicating that *GDpop* for maternally inherited
markers does not significantly increase with longitude. *Zr*
for both 'equitability' and 'richness' indices were
significant, positive and similar in magnitude. Excluding
island populations from the dataset increased *Zr* values.
*Zr* for 'equitability' was three times higher when restricting
the dataset to the southern Mediterranean envelope
whereas *Zr* for 'richness' stayed constant but became non-
significant (not shown).

2. Effect sizes in plants and role of ecological requirements and biological traits

The summary-effects for bi-parentally and maternally
inherited markers decreased and became (or remained in
the case of maternally inherited markers) non-significant
after excluding animals from the dataset (not shown). In

<figure><img src="image_59.png" /><figcaption>FIGURE 1</figcaption></figure>

Map of the Mediterranean Basin showing the eco-
climatic envelope as defined by Olson (2001) and the
geographic partition (North vs South vs islands) tested
in our study. Each black square represents a location
sampled in the meta-analyzed raw studies. Histograms

for latitudinal and longitudinal distributions of locations
sampled in raw studies are given above and to the right
of the map. The photographed tree in the upper right
corner is a *Cedrus brevifolia* individual on a ridge in its
natural habitat of the Trohodos mountains of Cyprus.
plants, positive and significant effects sizes were thus found for plastidial DNA and paternally-inherited DNA (gymnosperm plastidial DNA).

## Ecological traits

We were able to assign 201 effect-sizes of plant species to a bioclimatic belt without ambiguity (Fig. 4b). The remaining species being found in two or more belts were labeled as 'eurytherm' species (64 effect-sizes). The true

Mediterranean ecological group (meso-Mediterranean) had the highest positive Zr and was the only significant group. The supra-Mediterranean group had a positive but non-significant Zr. The category with the highest sample size (mountain-Mediterranean) had a negative and non-significant Zr, and the gymnosperms contributed predominantly to this group (70 of the 82 effect-sizes). The group with the highest requirements in terms of temperature (thermo-Mediterranean) had an almost null Zr. Heterogeneity was significant in all groups.

<caption>FIGURE 3</caption>

Mean effect-sizes across the Mediterranean basin for 12 plant and 17 animal families, arranged in decreasing effect size number per category. Out of 62 possible families represented in the dataset, only

the 29 with a number of effect-sizes over 5 are represented here. Square and error bars should be interpreted as indicated for Fig. 2a.

<figure><img src="image_60.png" /></figure>
## Biological traits

Phanerophytes (trees and shrubs) represented around 2/3 of the plants in our dataset and showed a positive and significant Zr (Fig. 4c). Among them, gymnosperms had a positive Zr (see above). Dicots among phanerophytes showed a contrasting pattern depending on the geographic envelope: at Mediterranean basin level, the effect was positive but non-significant; whereas it was strongly positive for the continental group (0.10 [0.04;0.16] 95% CI, N = 65) and strongly negative for islands (-0.56 [-0.71;-0.40] 95% CI, N = 17). Contrasting with the other moderator analyses, the southern group yielded a negative trend while the northern showed a positive one. Chamaephytes (low growing perennials with over-wintering buds below 50 cm) were the only group showing a negative (although non-significant) Zr.

Seed dispersal mode affected Zr values: Anemochorous (wind-dispersed, contributing to more than half of the effect sizes) and barochorous (gravity-dispersed) plants showed a significant and positive Zr while zoochorous (animal-dispersed) plants had a non-significant Zr (Fig. 4d). Pollen dispersal type also affected Zr values. Although calculated from a small sample size, the hydrogamous (water-dispersed pollen) plants showed a homogeneous negative trend (Fig. 4e) whereas entomogamous (insect-dispersed pollen) species had a non-significant Zr and anemogamous (wind-dispersed pollen) species had a positive and significant Zr.

## Discussion

Organization of genetic diversity in Europe mostly follows latitudinal routes of recolonization dating from the Holocene (Petit et al. 2003). In the Mediterranean, although a latitudinal imprint exists, our analysis demonstrates the existence of an overall longitudinal imprint on genetic diversity. Using a meta-analysis on 143 plant and animal species, we found that overall within-population genetic diversity of plants and animals increases significantly from West to East in the Mediterranean basin, both in southern Europe and in North Africa, and for continental but not for island species. The longitudinal trend was not found in all taxonomic groups, however. This result broadens the evidence provided by Fady & Conord (2010) beyond tree species to include such taxonomic groups as arthropods, well-represented in the dataset, gymnosperms, and monocots. Other well-represented groups including dicots, mammals and amphibians do not follow the trend. Several poorly represented groups such as gastropods and bryophytes demonstrate a significant opposite trend, or no trend (reptiles and ferns).

**What are the processes that shaped current genetic diversity longitudinally across the Mediterranean basin?**

Trying to detect events / processes responsible for longitudinal imprints on genetic diversity in the

Mediterranean may be challenging. The effects of older events such as divergence/diversification linked to vicariance during Pliocene (Blondel & Aronson, 1999; erection of geographical barriers) may coincide with that of younger events such as Last Glacial Maximum demographic bottlenecks, Holocene colonization events, admixture from secondary contact (Petit et al. 2003) or hybridization with closely related species (Papageorgiou et al. 2008).

However, it can be assumed that many current Mediterranean species have remained closer to their glacial refugia than their European counterparts and thus carry imprints (however attenuated) that commonly affected glacial refugia. A meta-analysis, using a fixed-effect model, precisely makes this assumption (Borenstein et al. 2009). It thus assumes that a common set of drivers affected GDpop across species along a longitudinal gradient in the Mediterranean basin. By analyzing the effect of moderators on summary-effect sizes, our goal was to discuss the most parsimonious explanations for such a trend.

**Effect of past climate on resident populations or effect of recolonization on genetic diversity in the Mediterranean**

**Biological and life history traits:**

We show that both plants and animals display a trend of increasing GDpop from west to east in the Mediterranean. We expected that, on average, the trend would be due more to migration effects (recolonization patterns) in mobile or high gene flow organisms, and more to local climatic effects in sessile or low gene flow organisms. With perhaps a difference in intensity between the two trends, that due to migration being less pronounced because of high gene flow and recurrent genetic exchange between populations blurring post recolonization foundation effects. Our data show that reality may be more complex because both highly mobile (insects) and more sessile (arachnids) animals showed trends similar in direction and magnitude. In arachnids, a group only comprising scorpions from the southern part of the Mediterranean basin in our dataset, the effect could be related to local past climate effects. The pattern shown by insects could reveal a link between their contemporary genetic structure and the mirrored structure of the plants they exploit. Recent studies have indeed illustrated the link between levels of diversity in keystone organisms such as trees and in their phytophagous associated organisms (Crutsinger et al. 2006, Whitham et al. 2006). The relationship has also been shown to hold true from local to region-wide scales (Bangert et al. 2006). Our work is certainly the first for trees and insects, indicating a clear common pattern of congruent GDpop at continental scale.

The absence of significant trend in mobile vertebrate groups such as birds, amphibians and mammals could reflect the coexistence of multiple Holocene recolonization routes from multiple refugia among species. For example,
in two species of rodents from the genus *Apodemus*, LGM survival had two very different outcomes, with *A. flavicollis* disappearing from the Iberian Peninsula whereas *A. sylvaticus* survived only there. The subsequent Holocene recolonization of Europe by these two currently sympatric species left two diverging imprints on genetic diversity (Michaux *et al.* 2005). In yet another species of rodent, the shrew *Crocidura russula*, refugial populations were located in North Africa from which western European Holocene populations derive (Cosson *et al.* 2005). As for the bank vole, *Clethrionomys glareolus*, most of its Holocene European range was recolonized from central European refugia although Mediterranean refugia existed (Deffontaine *et al.* 2005). In this group of mammals, the exception may be the rule in terms of LGM survival and Holocene recolonization, which is indicated by a non-significant trend of genetic diversity in our meta-analysis.

The only plant type (sensu Raunkiaer) displaying a negative trend in *GDpop* was the chameaphytes. Because of their ground level overwintering buds, chamaephytes are better suited to resist cold snowy winters than dry winters (Taulavuori *et al.* 2011). Snow may in fact be beneficial to their overwintering, thus potentially keeping larger populations during the LGM in the western than in the eastern Mediterranean.

The positive trend found in phanerophytes (i.e. woody plants having buds at least 50cm above the ground) overlaps with that of wind-dispersed species and may reflect both local demographic effects under *LGM* climate and long distance Holocene recolonization. Seed dispersal by gravity, however, does not allow for rapid and long-range dispersal. Thus, the positive trend found in gravity dispersed plant species matches the expectation of an effect of local *LGM* climate on genetic diversity. Conversely, animal-dispersed species depend on their dispersers' behavior for survival (Scofield *et al.* 2010). Their lack of trend in genetic diversity may mirror the diversity of vertebrate Holocene recolonization routes highlighted above for mammals.

**Ecological requirements in plant taxa:**

When assessing the impact of ecological (temperature) requirement on plants, we expected to find an increasing positive effect on *GDpop* from low to high elevation (from less to more cold tolerant) plant species. The rationale for this expectation was that the unfavorably cold *LGM* climate should affect more strongly population size in species with higher temperature requirements as they became trapped in reduced size habitats compared to those of lower temperature requirement species. In contrast, species adapted to colder climates such as *supra-* and *mountain Mediterranean* species for example, benefiting from larger habitats during the *LGM*, should not have suffered demographic bottlenecks as did the more truly Mediterranean group. Thus, a west to east oriented clinal climate at the *LGM* should result in a stronger west

to east clinal *GDpop* structure in true Mediterranean species ('Me' in Fig. 4b) than for species with other ecological requirements. The strength of the effects followed our expectations, with true Mediterranean plants showing a stronger cline of west-east increasing *GDpop* than *Supra-* and *Mountain-Mediterranean* plants ('Su' and 'Mt' in Fig. 4b). The group of plants with no precise thermal requirement showed no significant cline. The non-significant slightly negative summary-effect of the 'Th' group was more surprising. We expected this group of warm climate species to have been impacted even more strongly than other categories by cold climate at the *LGM* whether through strong bottlenecks or extirpation and subsequent migration / recolonization. Several explanations are possible. 'Th' species may have survived locally without stronger loss in *GDpop* in the west as compared with the east, although we have no evidence to support this explanation. 'Th' species could also have recolonized from glacial refugia not situated in the eastern Mediterranean, which was demonstrated for *Cistus ladanifer* (which came back into Spain from North Africa via the Strait of Gibraltar) by Guzmán & Vargas (2009). Finally, the thermo-Mediterranean belt is known to have endured severe human impact throughout the millennia, possibly obscuring clinal climatic and recolonization effects on *GDpop*. Interestingly, the two species generating most of the effect sizes in this group were species strongly impacted by humans (*Pinus pinea*, *Olea europaea*), both being valuable food crop.

**Marker and metric types:**

Studies using maternally-inherited markers are most of the time designed to detect phylogeographic signals and capture differentiation effects, e.g. those due to the imprints of contraction and recolonization to and from Pleistocene refugia. Studies using paternally-inherited (plastidial DNA in gymnosperms) or bi-parentally inherited markers are more often designed to detect local demographic signals (for example for conservation planning) resulting from current environmental drivers, in addition to phylogeographic signals. This confirms that the overall trend detected in our study is a within population demographic bottleneck effect, more weakly detected in maternally than paternally and bi-parentally inherited genomes. Phylogeographic studies have shown that the two different metric types we have used here, richness and equitability, can be negatively correlated along the distribution range of species (Comps *et al.* 2001; Petit *et al.* 2003). In Europe, from refugia to the newly colonized areas, heterozygosity increased (merging of recolonization routes that originated from different refugia) whereas allelic richness decreased (founder effects along the recolonization route) with local diversity peaks in suture zones. We found no such significant difference in our analysis. The two metric types had globally congruently positive and significant summary-effects. Taken together, the global positive effects we measured for marker and
metric types may indicate a stronger role of local climate over recolonization in shaping the genetic diversity of Mediterranean populations.

## Biogeographic effects: South vs North and continents vs islands

The Mediterranean basin has a highly heterogeneous and fragmented geography (Blondel & Aronson 1999). Its different geographical compartments have likely experienced different past ecological conditions and evolutionary histories. The northern Mediterranean flora and fauna contain predominantly Nordic, Asian and local elements whereas the southern Mediterranean is predominantly made of Tropical and local elements (Quézel & Médail 2003). Because of its peninsulas, migration may have been more restricted in the northern Mediterranean than in the southern Mediterranean. Also, one might expect stronger demographic bottlenecks and stronger scale and size effects on islands than on the continent. Although the number of populations originating from the southern Mediterranean was eight times lower than in the north, its summary-effect was more strongly positive than that of northern Mediterranean populations. Also, in the southern Mediterranean, the equitability metric type was more strongly positive than the richness metric type (which was actually non-significant). Although the gradient was more restricted in its longitudinal span (mostly but not entirely limited to populations of the Maghreb) in the southern than in the northern Mediterranean, these results suggest that factors linked to local *LGM* climate may have more strongly affected genetic diversity in the Southern Mediterranean than those linked to recolonization.

The weakly positive summary-effect of Mediterranean islands is in sharp contrast with that of continents. It may reflect a Mediterranean 'insularity syndrome' globally independent of climatic factors and more likely linked to high endemism (Médail & Diadema 2009) and/or early human impact (Vigne et al. 2009).

## Conclusions

Our population genetic diversity dataset covered an extensive range of animal and plant species. It also had the advantage of gathering data from species with strong economic importance (for example fruit trees and medicinal plants), strong ecological importance (for example forest trees) as well as endangered flagship species (for example butterflies and endemic plants). The taxonomic groups that were the most heavily sampled (gymnosperms for plants and arthropods for animals) showed a congruently positive summary-effect, i.e. an increasing genetic diversity from west to east. These abundant taxonomic groups are thus good models for detecting trends and patterns affecting biodiversity in general. The propensity of the genetic diversity of trees to be an excellent testimony of the imprint of ancient

evolutionary and demographic processes (given their long generation time) has been mentioned for some time (Petit et al. 2003; Petit & Hampe 2006) and it seems that arthropods can be added to this category.

Longitude is a strong structural element of biodiversity at gene level in the Mediterranean, both in southern Europe and in North Africa. Continental/oceanic longitudinal type gradients also exist in many parts of the world. The generally overlooked role of longitude in shaping species ranges and genetic diversity deserves stronger focus (Stewart et al. 2010). Taken together, our results suggest that, on top of a genetic structure inherited from the existence of glacial refugia (which phylogeography is increasingly demonstrating as being very complex, Leppanen et al. 2011), local climate during the *LGM* durably affected the demography of resident populations in the Mediterranean, observable as a weak but highly significant longitudinal cline of genetic diversity.

For conserving and sustainably managing biodiversity, global or region-wide assessments are needed beyond the idiosyncrasy of single species or single taxonomic groups to detect trends and large scale patterns. Meta-analyses, by making it possible to compare already available data acquired within unrelated studies, provide an interesting framework for these assessments. Already successfully used in ecology to test theoretical predictions (e.g. Rapoport's law predicting an increase of species range with latitude, Ruggiero & Werenkraut 2007), we have shown that meta-analyses can also be powerful to test the determinants of large-scale biodiversity patterns.

Finally, our findings can now be compared with other measurements of past, current and expected biodiversity (species and functional traits for example) and their congruence tested (Devictor et al. 2010), provided that appropriate databases (species, ecosystems, past and present climate) exist or can be constructed at relevant scales. Data available for estimating biodiversity at gene level remain critically insufficient in the Mediterranean. For example the Mediterranean comprises approximately 200 mammal species and more than 300 bird species (Blondel & Aronson, 1999) whereas our dataset only included 10 mammal and 3 bird species! This remains a major challenge in the poorly politically structured Mediterranean, but also in other regions of the world where biodiversity is high and rapidly declining.

## Acknowledgements

This research was made possible by financial support from the European Network of Excellence EVOLTREE (EVOLution of TREEs as drivers of terrestrial biodiversity) and from INRA Grant P-EFPA6 to Cyrille Conord. We would like to thank D. Betored and C. Pichot for help with database management and GIS analyses, and Drs F. Balfourier, D. Gömöry, C. Kerdelhué, R.J. Petit & G.G. Vendramin for kindly providing raw data.
## REFERENCES

Aloe AM, Becker BJ (2009) Teacher verbal ability and school outcomes: Where is the evidence? *Educational Researcher*, **38**, 612-624.

Aravanopoulos FA, Panetsos KP, Skaltsoyiannes A (2004) Genetic structure of Pinus brutia stands exposed to wild fires. *Plant Ecology*, **171**, 175-183.

Atkinson RJ, Rokas A, Stone GN (2007) Longitudinal patterns in species richness and genetic diversity in European oaks and oak gallwasps. In S. Weiss and N. Ferrand editors "Phylogeography of southern European refugia", pp. 127-151. Springer, Dordrecht, The Netherlands.

Avise JC (2000). *Phylogeography: The History and Formation of Species*. Harvard University Press, Cambridge, MA, USA. (447 pp.)

Bangert RK; Allan GJ, Turek RJ, Wimp GM, Meneses N, Martinsen GD, Keim P, Whitham TG (2006) From genes to geography: a genetic similarity rule for arthropod community structure at multiple geographic scales. *Molecular Ecology*, **15**, 4215-4228.

Blondel J, Aronson J (1999). *Biology and wildlife of the Mediterranean region*. Oxford University Press, New York.

Borenstein M, Hedges LV, Higgins JPT, Rothstein H, 2009. *Introduction to Meta-Analysis*. John Wiley and Sons.

Brewer S, Cheddadi R, de Beaulieu JL, Reille M (2002). The spread of deciduous Quercus throughout Europe since the last glacial period. *Forest Ecology & Management*, **156**, 27-48.

Crutsinger GM, Collins MD, Fordyce JA, Gompert Z, Nice CC, Sanders NJ (2006) Plant genotypic diversity predicts community structure and governs an ecosystem process. *Science*, **313**, 966-968

Cheddadi R, Fady B, François L, Hajar L, Suc JP, Huang K, Demarteau M, Vendramin GG (2009) Putative glacial refugia of *Cedrus atlantica* from Quaternary pollen records and modern genetic diversity. *Journal of Biogeography*, **36**, 1361-1371.

Coart E, Van Glabeke S, Petit RJ, Van Bockstaele E, Roldan-Ruiz I (2005) Range wide versus local patterns of genetic diversity in hornbeam (*Carpinus betulus* L.). *Conservation Genetics*, **6**, 259-273.

Comps B, Gomory D. Letouzey J, Thiebaut B, Petit RJ (2001). Diverging trends between heterozygosity and allelic richness during postglacial colonization in the European beech. *Genetics*, **157**, 389-397

Convention of Biological Diversity (1992). United Nations, New York, USA. http://www.cbd.int/doc/legal/cbd-en.pdf

Cosson JF, Hutterer R, Libois R, et al. (2005). Phylogeographical footprints of the Strait of Gibraltar and Quaternary climatic fluctuations in the western Mediterranean: a case study with the greater white-toothed shrew, *Crocidura russula* (Mammalia: Soricidae). *Molecular Ecology*, **14**, 1151-1162.

Deffontaine V, Libois R, Kotlik P, Sommer R, Nieberding C, Paradis E, Searle JB, Michaux J (2005). Beyond the Mediterranean peninsulas: evidence of central European glacial refugia for a temperate forest mammal species, the bank vole (*Clethrionomys glareolus*). *Molecular Ecology*, **14**, 1727-1739.

Del Re AC, Hoyt WT (2010). MAc: Meta-Analysis with correlations. R package version 1.1, available at

http://cran.r-project.org/web/packages/MAc/index.html

Devictor V, Mouillot D, Meynard C, Jiguet F, Thuiller W, Mouquet N (2010) Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: the need for integrative conservation strategies in a changing world. *Ecology Letters*, **13**, 1030-1040.

Fady B (2005) Is there really more biodiversity in Mediterranean forest ecosystems? *Taxon*, **54**, 905-910.

Fady B, Conord C (2010) Macroecological patterns of species and genetic diversity in vascular plants of the Mediterranean Basin. *Diversity & Distributions*, **16**, 53-64.

Grivet D, Sebastiani F, Gonzalez-Martinez SC, Vendramin GG. (2009) Patterns of polymorphism resulting from long-range colonization in the Mediterranean conifer Aleppo pine. *New Phytologist*, **184**, 1016-1028.

Guzmán B, Vargas P (2009) Long-distance colonization of the Western Mediterranean by *Cistus ladanifer* (Cistaceae) despite the absence of special dispersal mechanisms *Journal of Biogeography*, **36**, 954-968.

Hartung J, Knapp G, Sinha BK (2008) *Statistical meta-analysis with applications*. John Wiley, Sons.

Hewitt GM (1999) Post-glacial re-colonization of European biota. *Biological Journal of the Linnean Society*, **68**, 87-112.

Jump AS, Matyas C, Penuelas J (2009) The altitude-for-latitude disparity in the range retractions of woody species. *Trends in Ecology and Evolution*, **24**, 694-701

Kadereit JW, Arafeh R, Somogyi G, Westberg E (2005) Terrestrial growth and marine dispersal? Comparative phylogeography of five coastal plant species at a European scale. *Taxon*, **54**, 861-876.

Keef SP, Roberts LA (2004) The meta-analysis of partial effect sizes. *British Journal of Mathematical and Statistical Psychology*, **57**, 97-129.

Le Corre V, Kremer A (2003) Genetic variability at neutral markers, quantitative trait loci and trait in a subdivided population under selection. *Genetics*, **164**, 1205-1219.

Leppanen J, Vepsalainen K, Savolainen R (2011) Phylogeography of the ant *Myrmica rubra* and its inquiline social parasite. *Ecology and evolution*, **1**, 46-62.

Liepelt S, Cheddadi R, de Beaulieu JL, Fady B, Gömöry D, Hussendörfer E, Konnert M, Litt T, Longauer R, Terhürne-Berson R, Ziegenhagen B (2009) Postglacial range expansion and its genetic imprints in *Abies alba* (Mill.) - a synthesis from paleobotanic and genetic data. *Review of Palaeobotany and Palynology*, **153**, 139-149.

Médail F, Quézel P (1997) Hot-spots analysis for conservation of plant biodiversity in the Mediterranean basin. *Annals of the Missouri Botanical garden*, **84**, 112-127.

Médail F, Diadema K (2009) Glacial refugia influence plant diversity patterns in the Mediterranean Basin. *Journal of Biogeography*, **36**, 1333-1345.

Michaux JR, Libois R, Filippucci MG (2005). So close and so different: comparative phylogeography of two small mammal species, the Yellow-necked fieldmouse (*Apodemus flavicollis*) and the Woodmouse (*Apodemus sylvaticus*) in the Western Palearctic region. *Heredity*, **94**, 52-63.

Myers N, Mittermeier RA, Mittermeier CG, da Fonseca GAB, Kent J (2000) Biodiversity hotspots for conservation priorities. *Nature*, **403**, 853-858
Olson DM, Dinerstein E, Wikramanayake ED, Burgess ND, Powell GVN, Underwood EC, D'Amico JA, Itoua I, Strand HE, Morrison JC, Loucks CJ. Allnutt TF, Ricketts TH. Kura Y, Lamoreux JF, Wettengel WW, Hedao P, Kassem KR (2001) Terrestrial Ecoregions of the World: A New Map of Life on Earth. *BioScience*, **51**, 933-938.

Papageorgiou AC, Vidalis A, Gailing O, Tsiripidis I, Hatziskakis S, Boutsios S, Galatsidas S, Finkeldey R (2008) Genetic variation of beech (*Fagus sylvatica* L.) in Rodopi (N.E. Greece). *European Journal of Forest Research*, **127**, 81-88.

Petit RJ, Aguinagalde I, de Beaulieu JL, Bittkau C, Brewer S, Cheddadi R, Ennos R, Fineschi S, Grivet D, Lascoux M, Mohanty A, Muller-Starck GM, Demesure-Musch B, Palme A, Martin JP, Rendell S, Vendramin GG (2003) Glacial refugia: Hotspots but not melting pots of genetic diversity. *Science*, **300**, 1563-1565.

Petit RJ, Hampe A (2006). Some evolutionary consequences of being a tree. *Annual Review of Ecology Evolution and Systematics* **37**, 187-214.

Petit RJ, Vendramin GG (2007) Plant phylogeography based on organelle genes: an introduction. In S. Weiss and N. Ferrand editors "Phylogeography of southern European refugia", Springer, Dordrecht, The Netherlands, p 23-97.

Pignatti S (1982) *Flora d'Italia*. Edagricole, Bologna, Italy.

Quézel P, Médail F (2003) *Ecologie et biogéographie des forêts du bassin méditerranéen*. Elsevier, Paris.

Rameau JC, Mansion D, Dumé G, Gauberville C (2008) *Flore forestière française Tome 3: Région méditerranéenne*. IDF, Paris.

Richardson DM, Whittaker RJ (2010) Conservation biogeography - foundations, concepts and challenges. *Diversity & Distributions*, **16**, 313-320.

Riddle BR; Hafner DJ; Alexander LF, JR Jaeger (2000). Cryptic vicariance in the historical assembly of a Baja California Peninsular Desert biota. *Proceedings of the National Academy of Sciences*, **97**, 14438-14443.

Rokas A, Atkinson RJ, Webster L, Csoka G, Stone GN (2003) Out of Anatolia: longitudinal gradients in genetic diversity support an eastern origin for a circum-Mediterranean oak gallwasp *Andricus quercustozae*. *Molecular Ecology*, **12**, 2153-2174.

Rosenberg MS, Adams DC, J Gurevitch (2000) *MetaWin: statistical software for meta-analysis*. Version 2.0. Sinauer Associates, Sunderland, MA, USA.

Rosenthal R, DiMatteo MR, (2001) Meta-analysis: recent developments in quantitative methods for literature reviews. *Annual Review of Psychology*, **52**, 59-82.

Ruggiero A, Werenkraut V (2007) One-dimensional analyses of Rapoport's rule reviewed through meta-analysis. *Global Ecology, Biogeography*, **16**, 401-414.

Sánchez-Goñi MF, Cacho I, Turon JL, Guiot J, Sierro FJ, Peypouquet JP, Grimalt JO, Shackleton NJ (2002) Synchroneity between marine and terrestrial responses to millennial scale climatic variability during the last glacial period in the Mediterranean region. *Climate Dynamics*, **19**, 95-105.

Schwartz MK, Luikart G, Waples RS (2007) Genetic monitoring as a promising tool for conservation and management. *Trends in Ecology and Evolution*, **22**, 11-16.

Scofield DG, Sork VL, Smouse PE (2010). Influence of acorn woodpecker social behaviour on transport of coast live oak (*Quercus agrifolia*) acorns in a southern California oak savanna. *Journal of Ecology*, **98**, 561-572.

Soltis DE, Morris AB, Lachlan JSM, Manos PS, Soltis PS (2006) Comparative phylogeography of unglaciated eastern North America. *Molecular Ecology*, **15**, 4261-4293.

Soto A, Robledo-Arnuncio JJ, Gonzalez-Martinez SC, Smouse PE, Alia R (2010) Climatic niche and neutral genetic diversity of the six Iberian pine species: a retrospective and prospective view. *Molecular Ecology*, **19**, 1396-1409.

Soranzo N, Alía R, Provan J, Powell W (2000) Patterns of variation at a mitochondrial sequence-tagged-site locus provides new insights into the postglacial history of European Pinus sylvestris populations. *Molecular Ecology*, **9**, 1205-1211.

Stewart JR, Lister AM, Barnes I, Dalen L (2010). Refugia revisited: individualistic responses of species in space and time. *Proceedings of the Royal Society B*, **277**, 661-671.

Taberlet P, Fumagalli L, Wust-Saucy AG, Cosson JF (1998) Comparative phylogeography and postglacial colonization routes in Europe. *Molecular Ecology*, **7**, 453-464.

Taulavuori K, Bauer E, Taulavuori E (2011). Overwintering stress of Vaccinium vitis-idaea in the absence of snow cover. *Environmental and experimental botany*, **72**, 397-403.

Van Andel, TH (2002) The climate and landscape of middle part of Weichselian glaciation in Europe: the stage 3 project. *Quaternary Research*, **57**, 2-8.

Vigne JD, Zazzo A, Saliège JF, Poplin F, Guilaine J, Simmons A (2009) Pre-Neolithic wild boar management and introduction to Cyprus more than 11,400 years ago. *Proceedings of the National Academy of Sciences*, **106**, 16135-16138.

Whitham TG, Bailey JK, Schweitzer JA, Shuster SM, Bangert RK, Leroy CJ, Lonsdorf EV, Allan GJ, DiFazio SP, Potts BM, Fischer DG, Gehring CA, Lindroth RL, Marks JC, Hart SC, Wimp GM, Wooley SC (2006) A framework for community and ecosystem genetics: from genes to ecosystems. *Nature Reviews Genetics*, **7**, 510-523.

Whittaker RJ, Araujo MB, Paul J, Ladle RJ, Watson JEM, Willis KJ (2005) Conservation biogeography: assessment and prospect. *Diversity & Distributions*, **11**, 3-23.

Wickham H (2011) The Split-Apply-Combine Strategy for Data Analysis. *Journal of Statistical Software*, **40**, 1-29.

Wu HB, Guiot JL, Brewer S, Guo ZT (2007) Climatic changes in Eurasia and Africa at the last glacial maximum and mid-Holocene: reconstruction from pollen data using inverse vegetation modelling. *Climate Dynamics*, **29**, 211-229.

Young A, Boyle T, Brown T (1996) The population genetic consequences of habitat fragmentation for plants. *Trends in ecology and evolution*, **11**, 413-418.
Courty PE<fnref n="1" />,<fnref n="3" />*, Labbé J<fnref n="1" />, Kohler A<fnref n="1" />, Marçais B<fnref n="1" />, Bastien C<fnref n="2" />,
Churin JL<fnref n="1" />, Garbaye J<fnref n="1" />, Le Tacon F<fnref n="1" />

Corresponding Author:

Courty Pierre-Emmanuel

pierre-emmanuel.courty@unifr.ch

Poplar genotypes and secreted enzyme activities
in mycorrhizal and non-mycorrhizal roots

The impact of ectomycorrhiza formation on the secretion of exoenzymes by the host plant and the symbiont is unknown. Thirty-eight F1 individuals from an interspecific *Populus deltoides* (Bartr.) x *Populus trichocarpa* (Torr. & A. Gray) controlled cross, were inoculated with the ectomycorrhizal fungus *Laccaria bicolor*. The colonization of poplar roots by *L. bicolor* dramatically modified their ability to secrete enzymes involved in organic matter breakdown or organic phosphorus mobilization, such as N-acetylhexosaminidase, glucuronidase, cellobiohydrolase, glucosidase, xylosidase, laccase and acid phosphatase. The expression of genes coding for laccase, N-acetylhexosaminidase and acid phosphatase was studied in mycorrhizal and non-mycorrhizal root tips. Depending on the genes, their expression was regulated upon symbiosis development. Moreover, it appears that poplar laccase or phosphatase are poorly contributing to ECM metabolic activity. Enzymes secreted by poplar roots were added or substituted to enzymes secreted by *L. bicolor*. The enzymatic activities expressed in mycorrhizal roots differed significantly between the two parents, while it did not differ in non-mycorrhizal roots. Significant differences were found between poplar genotypes for all enzymatic activities measured on ectomycorrhizas except for laccase activity. On the contrary, no significant differences were found between poplar genotypes for enzymatic activities of non-mycorrhizal root tips except for acid phosphatase activity. The level of enzymes secreted by the ectomycorrhizal root tips is under the genetic control of the host. Moreover, poplar heterosis was expressed through the enzymatic activities of the fungal partner.

**Key words:** Poplar, *Laccaria bicolor*, secreted enzymes, heterosis, heritability, host genetic control.

Published in *Journal of Experimental Botany* (2011) 62: 249-260, with doi:10.1093/jxb/erq274

Introduction

The fine roots of tree species in temperate and boreal
forests are symbiotically associated with fungi, forming a
composite organ called ectomycorrhiza (ECM) (Smith and
Read, 2008). The establishment and the functioning of
ECM lead to complex morphological and physiological
changes in both the plant and the fungus (Martin and
Nehls, 2009, Courty et al. 2010a). The ECM symbiosis has
been described as a mutualistic association where the
autotrophic plant supplies photosynthates to the
heterotrophic fungus, which in turn supplies water and
nutrients to the host (Smith and Read, 2008). Several
studies also have shown that ectomycorrhizal fungi (ECMf)
are able to produce extracellular enzymes, such as
proteases, involved in the direct mobilization of nutrients
from organic substrates (Courty et al. 2005, 2006, 2010b;
Lindahl et al. 2005; Koide et al. 2008). In addition, a given

species may contribute to significant functional variations
through metabolic activities (Buée et al. 2007; Courty et
al. 2010b).

The ecological fitness and the metabolic activity of ECMf
depend on their genotypes, environmental factors (van
der Heijden and Sanders 2002; Smith and Read, 2008),
host plant genotypes (Barker et al. 2002; Linderman and
Davis, 2004), and the interactions between all these
factors (Khasa et al. 2002; Gehring et al. 2006; Karst et al.
2009). Recent studies also suggest that host plant
genome may play a role in determining the dominant
mycorrhizal type in dually colonized hosts (van der
Heijden & Kuyper, 2001; Khasa et al. 2002). However, no
studies have simultaneously examined the effect of host
plant genotypes and the metabolic activity of one ECMf
species in controlled conditions. In the *Laccaria
bicolor*/poplar ECM symbiosis, Tagu et al. (2005) have

<footnote n="1">¹ UMR 1136 INRA-Nancy Université, Interactions Arbres / Microorganisms, INRA-Nancy, 54280 Champenoux, France.</footnote>

<footnote n="2">² INRA Orléans, Unité Amélioration, Génétique et Physiologie forestières, Ardon, BP 20619, 45166 Olivet Cedex, France.</footnote>

<footnote n="3">³ Present address: University of Fribourg, Department of Biology, Rue Albert Gockel 3, CH-1700 Fribourg.</footnote>

<footnote>* These authors contributed equally to this work.</footnote>
shown that the host genotype impacts on root colonization by the fungus. The heritability of mycorrhizal colonization of poplar was also studied (Tagu *et al.* 2001, 2005). However, the metabolic activity of one ECM fungal genotype colonizing different genotypes of the same host species was never studied. In our study, the use of poplar as host tree model was motivated by the availability of large genetic and genomic resources for this species (Brunner *et al.* 2004; Tuskan *et al.* 2006). Moreover, the study of heritability and variability of physiological parameters (i.e. water use efficiency, dry weight, Leaf Maximum Area) at family level were intensively studied in poplar (Marron *et al.* 2005; Dillen *et al.* 2007).

In this study, we selected as relevant functional traits, a standard set of seven enzymatic activities routinely used in field studies (Courty *et al.* 2005). The enzyme activity of a secreted laccase, an oxidative enzyme involved in the degradation of recalcitrant plant residues, such as lignin, five secreted glycosyl hydrolases (cellobiohydrolase, β-glucosidase, β-xylosidase, β-glucuronidase, N-acetylglucosaminidase) acting on polysaccharides and a phosphomonoesterase involved in the mobilisation of phosphorus from soil organic matter were assessed. *L. bicolor* has a low set of glycosyl hydrolases able to hydrolyse plant cell wall polysaccharides (Martin *et al.* 2008). However, its genome encodes several carbohydrate-active enzymes able to degrade bacterial, fungal and animal polysaccharides (Martin *et al.* 2008).

The impact of the host genotype on the ECM metabolic activity is unknown. Here, the responding functional trait in focus is the capacity to produce secreted or cell wall bound enzymes. The first objective was to determine whether the enzymatic activities expressed in mycorrhizal roots differed significantly between two parents, *P. deltoides* and *P. trichocarpa*, and different poplar hybrid genotypes (*P. deltoides* × *P. trichocarpa*). The second objective was to determine the effect of host genotypes on fungal traits by measuring the heritability of enzymatic activities in mycorrhizal and non-mycorrhizal root tips and by assessing for these traits a possible heterosis among the progeny.

# Materials and Methods

## Plant material, strain and culture conditions

Poplar material consisted of 38 F1 individuals from an interspecific *P. deltoides* (female clone from Illinois, no. 73028-62) and *P. trichocarpa* (male clone from Washington, no. 101-74) controlled cross (family 54B) (Tagu *et al.* 2001, 2005). We have tested the ability of the two parents and the 38 breeds to form mycorrhizas by inoculating them with *Laccaria bicolor* S238N (Di Battista *et al.* 1996; Tagu *et al.* 2001). The 38 F1 genotypes were chosen at random among the 336 genotypes used for the construction of a genetic map (Cervera *et al.* 2001; Jorge *et al.* 2005). The *L. bicolor* S238N fungal strain, coming

from the INRA-Nancy collection of ECMf, was maintained on Pachlewski's. This model fungal strain was chosen for its ability to form ECMs with poplar and for the availability of genomic resources (Tagu *et al.* 2001; Martin *et al.* 2008). The inoculum of *L. bicolor* S238N was prepared by aseptically growing the mycelium in a peat-vermiculite nutrient mix in glass jars for 2 months in the dark at 25 °C, and kept at 4° C during 2 months before use (Le Tacon and Bouchard, 1986).

## Inoculation

Cuttings of one internode of each of the 38 poplar progenies and the two parents were rooted and individually inoculated at the same time, in 1-l pots containing a mixture of fungal inoculum (1:9 vol/vol) and calcinated attapulgite (Oil Dri US Special) during twelve weeks, in a greenhouse during spring with day-night temperatures of 28 and 15°C, respectively. Plants were watered during the whole experiment until measurements. From the second month, a low N, low P nutrient solution was applied weekly (Frey-Klett *et al.* 1997). In order to control environmental heterogeneity of the greenhouse, 8 replicates were done for each poplar genotype and were randomly distributed in 8 blocks. Each block contained one pot of each 38 progenies and the two parents.

## Root colonization

Entire root systems (except roots present 1 cm depth from cal) were carefully washed under tap water and cut into approximately 1-cm pieces. For each root system, 100 randomly selected root tips were examined and assessed as mycorrhizal or non-mycorrhizal under a stereomicroscope (magnification x40) for calculating ECM percentages.

## Chlorophyll content, leaf morphological measurement and dry weight

Before harvesting plants, chlorophylls a and b content was measured with a Minolta SPAD chlorophyll meter (Minolta Corp., Ramsey, N.J.). Three SPAD measurements were done on three leaves of each plant and then averaged (Monje and Bugbee, 1992). To convert SPAD measures into chlorophyll content, a standard curve was built by extracting chlorophylls with the dimethyl sulphoxide (DMSO) extraction technique (Monje and Bugbee, 1992; Richardson *et al.* 2002). Total leaf chlorophyll concentration (mg cm⁻²) of the extracts was calculated from this equation: $0.0202A_{645} + 0.00802A_{663}$. SPAD measurements were then converted to chlorophyll content using a third order polynomial equation: $-0.0064SPAD^3 + 0.5895SPAD^2 + 2.0891SPAD + 10.024$.

Once mycorrhizal infection had been determined, leaves, stems and roots were separated. The leaves were placed in plastic bags and kept at 4°C until leaf morphological measurements were completed. The leaf area (cm²) of all leaves of each plantlet was measured by using a LI-COR 3100 (Li-Cor Inc., Lincoln, NE, USA). Then, leaves, stems and roots were dried at 70 °C for 1 week (Mettler, Toledo
balance). The leaf mass area (LMA) was calculated for each clone using the relationship between the area of each leaf and its corresponding dry weight.

## Enzymatic activity profiling of ectomycorrhizal and non-mycorrhizal root tip

One mycorrhizal root tip and one non-mycorrhizal root tip were collected from each of the 320 cuttings in order to determine their potential enzymatic activities, using the high-throughput photometric and fluorimetric microplate assays described and detailed in Courty et al. (2005), and applied in previous studies (Buée et al. 2007; Courty et al. 2010b). As the variability of enzyme activities among ECM tips within a root system is low, one tip is sufficient to get a representative value (Courty et al. 2005). Each well of the 96-well micro-titration plate contained either one ectomycorrhizal root tip or one non-ectomycorrhizal root tip. Seven activities were successively measured on root tips: xylosidase (EC 3.2.1.37), glucuronidase (EC 3.2.1.31), cellobiohydrolase (EC 3.2.1.91), N-acetylglucosaminidase (EC 3.2.1.14), β-glucosidase (EC 3.2.1.3), acid phosphatase (EC 3.1.3.2), and laccase (EC 1.10.3.2) activities. The enzymes activities were expressed as pmol mm⁻² min⁻¹ of developed surface area of root tips. The developed surface area of the root tips was measured after scanning and image analysis using the Mac/Win Rhizo software (Regent Instruments, Quebec City, Canada). They correspond to the activities of enzymes present on the surface of the roots or mycorrhiza mantles and released in the medium during the incubation.

## Whole-genome expression oligoarray analyses

Genes coding for laccase, N-acetylglucosaminidase and acid phosphatase were known and characterized in the genome of *L. bicolor* and *P. trichocarpa*. As the genes involved in xylosidase, glucuronidase, cellobiohydrolase and β-glucosidase activity were not characterized, we were not able to measure the corresponding transcript expression. Accumulation of predicted laccase (*Lac*), N-acetylglucosaminidase (*Nag*) and acid phosphatase (*Pap*) transcripts was detected in free-living mycelium of *L. bicolor* S238N, and in ectomycorrhizal and non-mycorrhizal root tips of poplar using NimbleGen *L. bicolor* whole-genome expression oligoarray v2 (Martin et al. 2008) and NimbleGen *P. trichocarpa* whole-genome expression oligoarray (Tuskan et al. 2006). Data are available at the GEO platform GPL2699. The *L. bicolor* 4-plex whole genome expression array contained 18,653 gene models with three oligonucleotide probes for each gene model. For 4,702 gene models, technical duplicates were included on the oligoarray (A. Kohler & F. Martin, unpublished results). Average expression levels were calculated for each gene from the independent probes and were used for further analysis. To estimate the signal background and the resulting threshold value for significant expression, the mean intensity of 2,032 random probes present on the microarray was calculated. Gene

models with expression exceeding the threshold by three or more were considered to be transcribed. Raw array data were filtered for non-specific probes and renormalized using ARRAYSTAR software (DNASTAR). Three biological replicates were used. Therefore, the reported gene expression values corresponded to the mean intensity of hybridization signals obtained for the specific oligonucleotide probes. A student t-test with FDR (Benjamini-Hochberg) multiple testing corrections was applied on the data (P<0.05), using ARRAYSTAR software (DNASTAR).

## Statistical analysis

The percentage of mycorrhizal colonization was transformed by arcsin √X/100 function prior to variance analysis (ANOVA). Xylosidase, glucuronidase, cellobiohydrolase, chitinase, β-glucosidase, acid phosphatase and laccase activities, root, shoot and stem dry weight and LMA were also submitted to ANOVA. The following mixed linear model was applied on an individual basis to detect significant differences among the clones:

$$Y_{ijk} = \mu + B_i + G_j + \varepsilon_{ijk}$$

where μ is the overall mean, B is the block effect (fixed), G is the genotype effect (random), and ε is the random residual error.

Restricted maximum likelihood estimates of genetic, block and residual variance components (σ²G, σ²B and σ²ε) were computed, and for each trait, individual broad sense heritability (h²) was estimated as follows:

$$h^2 = \sigma^2G / (\sigma^2G + \sigma^2\varepsilon / n)$$ where n is the average number of replicates per genotype. Standard deviations (SD) were derived from classic estimation of SD for a ratio x/y where x = σ²G and y = σ²G + σ²ε / n.

All analyses were performed with the statistical programs JMP 5.0 (SAS Institute Inc., Cary, NC, USA) and R version 1.8.0 (R Development Core Team, 2006, www.R-project.org).

The genetic coefficient of variation (CVG) was used (Cornelius, 1994) to compare the relative amounts of genetic variation of traits with different means:

$$CVG = \sqrt{\sigma^2G / \mu}$$

Relationships between the different traits were also analysed by Pearson linear correlations.

Developed projected area of mycorrhizal and mycorrhizal root tips were compared between genotypes by ANOVA.

## Results

A total of 320 plants were harvested and studied in this experiment. Three dead plants were not used in the analysis. No significant block effect was found for any measured traits.

## Poplar ecophysiological traits

Significant differences (p<0.001) were found between plant genotypes for all measured traits (chlorophyll content, leaf maximum area, stem and root dry weight).
Significant differences were found between the parents for all measured traits except for leaf maximum area.

## Effect of poplar genotype on root colonization by *L. bicolor*

Twelve weeks after inoculation, progenies and parental clones were only colonized by *L. bicolor*. No other contaminant ectomycorrhizal fungi were found on roots. The two parental genotypes significantly differed in their mycorrhizal development, *P. trichocarpa* exhibiting a rate of colonization of 40% ± 8, and *P. deltoides* a rate of 16% ± 4. The percentages of colonization of the different genotypes (progenies) varied from 12% ± 8 to 64% ± 6, with an average of 31% (Fig. 1). The variance analysis showed a significant genotype effect and no block effect. The developed projected area of mycorrhizal or non-mycorrhizal root tips was not significantly different between genotypes.

## Enzymatic activity patterns of the parental and the hybrids root system

For each plant, the seven enzymatic activities were measured successively on one mycorrhizal and on one non-mycorrhizal root tip (40 poplar genotypes x 8 plant replicates x 2 root tips). Mycorrhizal root tips never lost their ability to secrete the seven enzymes in the conditions of the test, even if sometimes at a very low level (e.g. for xylosidase, glucuronidase, laccase).

The seven enzymatic activities expressed in non-mycorrhizal roots did not differ significantly between the two parents (Table 1a). The seven enzymatic activities measured on ECM root tips differed significantly between the two parents (Table 1a): five activities (xylosidase, cellobiohydrolase, β-glucosidase, acid phosphatase, and laccase) had a higher level in *P. trichocarpa* and two (N-acetylhexosaminidase and glucuronidase) had a higher level in *P. deltoides*.

Enzyme activity patterns of mycorrhizal and non-mycorrhizal root tips of the parents and of their progeny were different (Figure 2). Significant differences were found between plant genotypes for all activities measured on ECM root tips except for laccase activity (Table 1b). No significant differences were found between plant genotypes for any activities measured on non-mycorrhizal root tips except for acid phosphatase activity (Table 1b). Six of the enzymatic activities differed significantly between mycorrhizal and non-mycorrhizal roots, while no laccase activity could be detected in non-mycorrhizal roots (Table 1b). Compared to non-mycorrhizal root tips, N-acetylhexosaminidase activity was increased by more than 100 in mycorrhizas, while glucuronidase, cellobiohydrolase and glucosidase activities were multiplied by a factor ranging between 50 and 100, and xylosidase and acid phosphatase between 15 and 50 (Table 1b).

<caption>FIGURE 1</caption>

Percentage of root colonization of the different Poplar clones (n = 40). Genotypes are ranked in mean percentage of root colonization. Bars represent SE

(n = 8). Grey corresponds to *Populus trichocarpa* (male) and black to *Populus deltoides* (female).

<figure><img src="image_61.png" /></figure>
<figure><img src="image_62.png" /><figcaption>FIGURE 2</figcaption></figure>
## Heritability

Heritability values ($h^2$) of plant phenotypic characters ranged from 0.21 to 0.48. The highest values of heritability were found for leaf maximum area (0.48 ± 0.01), chlorophyll content (0.45 ± 0.01), and stem and leaf dry weight (0.43 ± 0.01 and 0.50 ± 0.01, respectively). The lowest value was found for root dry weight (0.21 ± 0.01). A value of 0.45 ± 0.02 was found for the percentage of mycorrhizal colonization. Heritability values of enzymatic activities were similar for ectomycorrhizal and non-mycorrhizal root tips, except for laccase activity, which was not detected on non-mycorrhizal root tips (0.29 ± 0.01 in mycorrhizal root tips; 0 in non-mycorrhizal root tips). The highest heritabilities were found for N-acetylhexosaminidase (mycorrhizal root tips, 0.42 ± 0.01, non-mycorrhizal root tips 0.40 ± 0.01), acid phosphatase

(mycorrhizal root tips, 0.41 ± 0.01; non-mycorrhizal root tips, 0.40 ± 0.01), glucosidase (mycorrhizal root tips 0.36 ± 0.01; non-mycorrhizal root tips, 0.34 ± 0.01) and cellobiohydrolase (mycorrhizal root tips, 0.33 ± 0.02; non-mycorrhizal root tips, 0.31 ± 0.02) activity. The lowest value was found for glucuronidase activity (mycorrhizal root tips 0.04 ± 0.01; non-mycorrhizal root tips, 0.04 ± 0.01). A medium value was found for xylosidase activity (mycorrhizal root tips 0.16 ± 0.01; non-mycorrhizal root tips, 0.19 ± 0.01).

## Heterosis

For each trait, we have calculated the ratio between the average of the hybrids and the best parent (a) or between the average of the hybrids and the average of the two parents (b) (Table S1). The leaf maximum area exhibited a high positive heterosis (a = + 46, b = + 51), while the leaf

<caption>TABLE 1</caption>

Average enzymatic activities of ectomycorrhizal and non-mycorrhizal root tips of poplar. Enzyme activities are expressed as pmol.mm⁻².min⁻¹ (Courty et al. 2005). Mean and SE are given for each activity. Significant

differences (S, *p* < 0.001) are indicated by an asterisk (*). The effect of plant genotype on enzyme activities was assessed for mycorrhizal root tips and non-mycorrhizal root tips.

### (a) Comparison of the parents: *P. trichocarpa* (Pt) and *P. deltoides* (Pd)

<table><thead><tr><th rowspan="2"></th><th colspan="3">Mycorrhizal root tips</th><th colspan="3">Non-mycorrhizal root tips</th></tr><tr><th>Pt</th><th>Pd</th><th>S</th><th>Pt</th><th>Pd</th><th>S</th></tr></thead><tbody><tr><td>Xylosidase</td><td>25.43 ± 1.67</td><td>14.04 ± 1.17</td><td>*</td><td>0.94 ± 0.52</td><td>0.46 ± 0.14</td><td></td></tr><tr><td>Glucuronidase</td><td>23.29 ± 1.82</td><td>35.47 ± 1.47</td><td>*</td><td>0.69 ± 0.41</td><td>0.81 ± 0.51</td><td></td></tr><tr><td>N-acetylhexosaminidase</td><td>297.83 ± 13.86</td><td>412.81 ± 17.72</td><td>*</td><td>3.02 ± 0.70</td><td>4.1 ± 1.42</td><td></td></tr><tr><td>Cellobiohydrolase</td><td>86.51 ± 2.21</td><td>22.42 ± 1.61</td><td>*</td><td>0.57 ± 0.17</td><td>0.62 ± 0.24</td><td></td></tr><tr><td>Glucosidase</td><td>358.94 ± 16.96</td><td>119.77 ± 9.77</td><td>*</td><td>4.13 ± 1.47</td><td>2.78 ± 1.45</td><td></td></tr><tr><td>Acid phosphatase</td><td>368.46 ± 19.47</td><td>155.94 ± 12.84</td><td>*</td><td>25.33 ± 5.15</td><td>25.77 ± 4.47</td><td></td></tr><tr><td>Laccase</td><td>5.64 ± 0.95</td><td>4.76 ± 0.72</td><td>*</td><td>0</td><td>0</td><td></td></tr></tbody></table>

### (b) Comparison of the 40 plant genotypes (the two parents and the 38 progenies).

Significant differences in enzymatic activities between mycorrhizal and non-mycorrhizal root tips are also reported

<table><thead><tr><th rowspan="2"></th><th rowspan="2">n</th><th colspan="2">Mycorrhizal root tips</th><th colspan="2">Non-mycorrhizal root tips</th><th colspan="2">Mycorrhizal versus non-mycorrhizal</th></tr><tr><th>Values</th><th>S</th><th>Values</th><th>S</th><th>Ratio</th><th>S</th></tr></thead><tbody><tr><td>Xylosidase</td><td>317</td><td>31.32 ± 1.76</td><td>*</td><td>1.23 ± 0.07</td><td></td><td>25.5</td><td>*</td></tr><tr><td>Glucuronidase</td><td>317</td><td>41.41 ± 2.90</td><td>*</td><td>0.82 ± 0.06</td><td></td><td>50.5</td><td>*</td></tr><tr><td>N-acetylhexosaminidase</td><td>317</td><td>418.30 ± 17.26</td><td>*</td><td>3.14 ± 0.16</td><td></td><td>133.2</td><td>*</td></tr><tr><td>Cellobiohydrolase</td><td>317</td><td>85.07 ± 6.48</td><td>*</td><td>0.87 ± 0.06</td><td></td><td>97.8</td><td>*</td></tr><tr><td>Glucosidase</td><td>317</td><td>306.78 ± 16.93</td><td>*</td><td>4.68 ± 0.52</td><td></td><td>65.5</td><td>*</td></tr><tr><td>Acid phosphatase</td><td>317</td><td>396.65 ± 26.42</td><td>*</td><td>24.81 ± 1.35</td><td>*</td><td>16</td><td>*</td></tr><tr><td>Laccase</td><td>317</td><td>4.8 ± 0.2</td><td></td><td>0</td><td></td><td></td><td>nd</td></tr></tbody></table>
dry weight exhibited a negative one (a = - 41, b = - 36). The percentage of mycorrhizal colonization also exhibited a positive heterosis (a = + 25, b = + 75). In mycorrhizal and non-mycorrhizal root tips, all the enzymatic activities displayed a positive heterosis at least for the b values, with the exception of laccase activity in mycorrhizal root tips (a = - 15, b = - 8) and acid phosphatase activity in non-mycorrhizal root tips (a = - 4, b = - 3).

## Gene expression

Gene expression of laccase (Lcc), N-acetylglucosaminidase (Nag) and acid phosphatase (Pap) was assessed by whole-genome expression oligoarray analyses in poplar and *L. bicolor* (Table 2). In poplar, gene expression was compared between non-mycorrhizal and mycorrhizal roots. Thirty-two laccases (Lcc1 to Lcc32) were detected in poplar. Three of them are mitochondrial and 27 have a signal peptide meaning that they belong to secreted pathways. On the 32 genes coding for laccases in poplar, the expression of 11 could be assessed. On the 11 expressed and also putatively expressed, one (Lcc6) was significantly up regulated in mycorrhizas and two were down regulated in mycorrhizas (Lcc16 and Lcc31). Two N-acetylglucosaminidase (*Nag1* and *Nag2*) genes were expressed and a signal peptide was found for both of them. The expression of *Nag1* and *Nag2* was not modified by mycorrhizal establishment. Seven acid phosphatase genes (*Pap1* to *Pap7*) were expressed and a signal peptide was found for five of them (*Pap2*, *Pap3*, *Pap5*, *Pap6*, *Pap7*). Two (*Pap5* and *Pap7*) were significantly up expressed in mycorrhizas.

For *L. bicolor*, gene expression was compared between mycorrhizal roots of *P. trichocarpa* and *P. deltoides*, and mycelium growing in pure culture. On the 9 laccases previously described (Courty et al. 2009), one was mitochondrial (Lcc5) and 8 had a signal peptide meaning that they belong to secreted pathways. Seven of these laccase genes were expressed, while two were not (Lcc2 and Lcc7). Lcc5 was only expressed in the free living mycelium. Lcc9 was significantly down expressed in *P. deltoides* and *P. trichocarpa* mycorrhizas, while Lcc6 was significantly down regulated only in *P. deltoides* mycorrhizas and Lcc8 up regulated in *P. trichocarpa* mycorrhizas. The other laccases were not significantly regulated. The only acid phosphatase (*Pap1*) expressed displayed a peptide signal. Its expression was not significantly different between mycorrhizas and free-living mycelium. Among the two N-acetylglucosaminidase (*Nag1* and *Nag2*) expressed, only *Nag2* exhibited a signal peptide. The expression of *Nag2* was higher in the free-living mycelium than in *P. trichocarpa*-L.bicolor and *P.deltoides*-L.bicolor mycorrhizas.

## Correlations between the different traits

No poplar trait was correlated with enzymatic activities of non-mycorrhizal root tips (Table 3). LMA, stem and roots dry weight were not correlated with any activities either

from ectomycorrhizal and non-mycorrhizal root tips (Table 3). Chlorophyll content was significantly negatively correlated with xylosidase, cellobiohydrolase and glucosidase activities, three enzymes involved in cellulose and hemi-cellulose catabolism.

Enzymatic activities of ectomycorrhizal root tips were not correlated with those of non-mycorrhizal root tips. All the enzymatic activities of mycorrhizal root tips were correlated between them, except for laccase activity. Similarly, except for acid phosphatase, all the enzymatic activities of non-mycorrhizal root tips were correlated between them. Xylosidase activity of mycorrhizal roots was the only activity positively correlated with the percentage of mycorrhizal infection (Table 3). Stem and root dry weights also were significantly correlated with the percentage of root colonization.

## Discussion

### Enzymatic activities of non-mycorrhizal root tips and mycorrhizas

The potential activities of enzymes involved in organic matter breakdown or organic phosphorus mobilization measured on poplar root tips colonized or not by *L. bicolor* were significantly different. Here, we found that the ectomycorrhizal complex adds or substitutes enzymes secreted from poplar roots. Compared to non-mycorrhizal root tips, N-acetylhexosaminidase activity was multiplied by more than 100 in mycorrhizas, while glucuronidase, cellobiohydrolase and glucosidase activities were multiplied between 50 and 100 and xylosidase and acid phosphatase between 15 and 50. Moreover, laccase activity could not be detected on non-mycorrhizal roots. By degrading organic compounds, including those from their own mycelia, and channelling nutrients directly to the host tree, ECMf have the capacity to shorten mineralization pathways in which free-living decomposers are involved. It is well known that ECMf are able to secrete enzymes, which allow the release of nutrients from soil organic matter (Cullings and Courty, 2009; Courty et al. 2010a). However, this study was not driven to understand the role of ECMf in the release of nutrients. Enzymes we measured should be considered as functional traits to study effects of soil or host tree parameters on ECMf. It is the first time that the breath of the modifications induced by the symbiotic association in the potential enzymatic secretion by the root system is measured.

### Expression of genes involved in enzymatic activities

Although the complete sequences of the *P. trichocarpa* and *L. bicolor* genome are available, all genes putatively encoding the proteins responsible for the measured activities were not identified. The correspondence between enzymatic assay and gene expression could be done for three of them: laccase (Lcc), N-acetylglucosaminidase (Nag) and acid phosphatase (Pho). Two genes code for a secreted N-acetylglucosaminidase
<caption>TABLE 2</caption>

Quantification by exon expression array of the transcript levels of laccase (Lcc), N-acetylglucosaminidase (Nag) and acid phosphatase (Pap) genes in different conditions. The length (bp) of signal peptide (Signal-P) was predicted with Signal P 3.0 (http://www.cbs.dtu.dk/services/SignalP/). The prediction of the subcellular location of the proteins

(a) Poplar genes. The NimbleGen array analysis was carried out using *P. trichocarpa* root tips mycorrhizal or not by *L. bicolor*. Transcript levels in non-mycorrhizal root tips were used as the control values. NT: not transcribed. (-): gene not on the array or no reliable probe left.

<table><thead><tr><th></th><th>Protein ID</th><th>Signal-P</th><th>Target-P</th><th>Ratio</th></tr></thead><tbody><tr><td>Lcc1</td><td>820390</td><td>24</td><td>S</td><td>1.0</td></tr><tr><td>Lcc2</td><td>557962</td><td>24</td><td>S</td><td>-</td></tr><tr><td>Lcc3</td><td>797646</td><td>22</td><td>S</td><td>NT</td></tr><tr><td>Lcc4</td><td>576931</td><td>23</td><td>S</td><td>-</td></tr><tr><td>Lcc5</td><td>762473</td><td>25</td><td>S</td><td>-</td></tr><tr><td>Lcc6</td><td>767563</td><td>28</td><td>S</td><td>4.3*</td></tr><tr><td>Lcc7</td><td>219290</td><td>29</td><td>S</td><td>1.4</td></tr><tr><td>Lcc8</td><td>759686</td><td>-</td><td>S</td><td>1.2</td></tr><tr><td>Lcc9</td><td>653089</td><td>24</td><td>S</td><td>-</td></tr><tr><td>Lcc10</td><td>819177</td><td>26</td><td>S</td><td>NT</td></tr><tr><td>Lcc11</td><td>797888</td><td>31</td><td>S</td><td>NT</td></tr><tr><td>Lcc12</td><td>579478</td><td>32</td><td>S</td><td>-</td></tr><tr><td>Lcc13</td><td>235935</td><td>32</td><td>S</td><td>-</td></tr><tr><td>Lcc14</td><td>831900</td><td>34</td><td>S</td><td>NT</td></tr><tr><td>Lcc15</td><td>235930</td><td>32</td><td>S</td><td>-</td></tr><tr><td>Lcc16</td><td>768177</td><td>26</td><td>S</td><td>0.4*</td></tr><tr><td>Lcc17</td><td>548008</td><td>32</td><td>S</td><td>-</td></tr><tr><td>Lcc18</td><td>783559</td><td>32</td><td>S</td><td>NT</td></tr><tr><td>Lcc19</td><td>822366</td><td>32</td><td>S</td><td>0.4</td></tr><tr><td>Lcc20</td><td>560853</td><td>23</td><td>S</td><td>NT</td></tr><tr><td>Lcc21</td><td>592533</td><td>23</td><td>S</td><td>-</td></tr><tr><td>Lcc22</td><td>832603</td><td>31</td><td>S</td><td>0.3</td></tr><tr><td>Lcc23</td><td>738903</td><td>-</td><td>M</td><td>-</td></tr><tr><td>Lcc24</td><td>574533</td><td>30</td><td>S</td><td>-</td></tr><tr><td>Lcc25</td><td>777748</td><td>23</td><td>S</td><td>0.8</td></tr><tr><td>Lcc26</td><td>738893</td><td>-</td><td>M</td><td>-</td></tr><tr><td>Lcc27</td><td>571858</td><td>33</td><td>S</td><td>-</td></tr><tr><td>Lcc28</td><td>574985</td><td>28</td><td>S</td><td>-</td></tr><tr><td>Lcc29</td><td>205176</td><td>-</td><td>M</td><td>1.6</td></tr><tr><td>Lcc30</td><td>569758</td><td>28</td><td>S</td><td>-</td></tr><tr><td>Lcc31</td><td>420672</td><td>17</td><td>S</td><td>0.4*</td></tr><tr><td>Lcc32</td><td>774519</td><td>-</td><td>S</td><td>0.2</td></tr><tr><td>Nag1</td><td>772972</td><td>27</td><td>S</td><td>1.4</td></tr><tr><td>Nag2</td><td>202916</td><td>25</td><td>S</td><td>1.5</td></tr><tr><td>Pap1</td><td>821155</td><td>-</td><td>S</td><td>1.4</td></tr><tr><td>Pap2</td><td>831269</td><td>28</td><td>S</td><td>0.6</td></tr><tr><td>Pap3</td><td>818768</td><td>27</td><td>S</td><td>0.5</td></tr><tr><td>Pap4</td><td>816041</td><td>-</td><td>S</td><td>1.4</td></tr><tr><td>Pap5</td><td>272725</td><td>22</td><td>S</td><td>2.1*</td></tr><tr><td>Pap6</td><td>259486</td><td>23</td><td>S</td><td>1.9</td></tr><tr><td>Pap7</td><td>825753</td><td>32</td><td>S</td><td>1.8*</td></tr></tbody></table>

was performed with TargetP 1.1 available on the webpage (http://www.cbs.dtu.dk/services/TargetP/); M=mitochondrial, S=secreted and -=unknown. Three biological replicates were used for each treatment with NimbleGen oligoarrays (v.2.0; NG2). A Cyber-T test was performed on the mean for each transcript (*, P < 0.05).

(b) L. bicolor genes. The NimbleGen array analysis was carried out using *P. trichocarpa* (Pt) and *P. deltoides* (Pd) mycorrhizal by *L. bicolor*. Transcript levels in the mycelium grown in pure culture were used as the control values. NE: not expressed in mycorrhizas (signal under background). (-): gene not on the array or no reliable probe left.

<table><thead><tr><th>Ratio Pd</th><th>Protein ID</th><th>Signal-P</th><th>Target-P</th><th>Ratio Pt</th></tr></thead><tbody><tr><td>Lcc1</td><td>399743</td><td>17</td><td>S</td><td>0.1</td></tr><tr><td>Lcc2</td><td>399744</td><td>17</td><td>S</td><td>-</td></tr><tr><td>Lcc3</td><td>399745</td><td>17</td><td>S</td><td>4.4</td></tr><tr><td>Lcc4</td><td>399746</td><td>18</td><td>S</td><td>0.8</td></tr><tr><td>Lcc5</td><td>399747</td><td>19</td><td>M</td><td>NE</td></tr><tr><td>Lcc6</td><td>399748</td><td>20</td><td>S</td><td>0.4</td></tr><tr><td>Lcc7</td><td>399750</td><td>19</td><td>S</td><td>-</td></tr><tr><td>Lcc8</td><td>399749</td><td>22</td><td>S</td><td>2.0*</td></tr><tr><td>Lcc9</td><td>399751</td><td>16</td><td>S</td><td>0.3*</td></tr><tr><td>Pap1</td><td>310810</td><td>21</td><td>S</td><td>0.6</td></tr><tr><td>Nag1</td><td>309753</td><td>-</td><td>S</td><td>0.6</td></tr><tr><td>Nag2</td><td>182604</td><td>18</td><td>S</td><td>0.4*</td></tr></tbody></table>
(Nag) involved in chitin catabolism in poplar and also two in *L. bicolor*. In this work, poplar *Nag1* and *Nag2* were expressed both in root tips and mycorrhizas but were not regulated by the symbiosis. On the contrary, the expression of the *L. bicolor* *Nag1* and *Nag2* were down regulated in mycorrhizas. Nevertheless, the activity of N-acetylglucosaminidase was multiplied by more than 130 times in the mycorrhizas compared to non-mycorrhizal root tips. We can make the assumption that (i) *L. bicolor* N-acetylglucosaminidases are secreted outside the mycelium of the sheath in mycorrhizas, while the Poplar N-acetylglucosaminidase are not secreted outside the root tips, (ii) *L. bicolor* N-acetylglucosaminidases can be involved in nitrogen mobilization from chitin by degrading its own mycelia and in defence against soil pathogenic fungi, (iii) *L. bicolor* can have the ability as *Trichoderma asperellum* (Ramot *et al.* 2004) to store a high amount of this enzyme in an active form and secrete it when mycelium is sensing the substrate.

In the *L. bicolor* genome, nine genes coding for laccases were characterized (Courty *et al.* 2009). In this experiment, six putatively secreted were expressed and three (*Lcc1*, *Lcc3*, *Lcc4*) were not significantly regulated by symbiosis.

One (*Lcc8*) was significantly over expressed in mycorrhizas and two (*Lcc6* and *Lcc9*) under expressed. In *P. trichocarpa* genome, 32 genes are coding for laccases, 27 display a signal peptide meaning that they belong to secreted pathways and, in this work, 20 were expressed in root tips. Despite this large number of laccase genes which were expressed, no laccase activity was detected by the ABTS test on non-mycorrhizal root tips. This means that poplar laccases are not secreted in the rhizosphere. In *Arabidopsis thaliana*, only a few of the laccase genes were expressed in a pattern that could be considered consistent with a major role for these enzymes in lignin deposition (McCaig *et al.* 2005). Poplar laccases seems to be not cell wall bound, nor secreted outside the cells. They are probably involved in the polymerization of lignin precursors or in other functions.

Acid phosphatases, able to free phosphate groups from complex organic compounds, are widespread in living organisms. Both ECMf and plants secrete acid phosphatases in the rhizosphere. In most of the studies, mycorrhizas secrete more phosphatases than non-colonized roots (Colpaert *et al.* 1997; Conn and Dighton, 2000). Nevertheless, there are some exceptions (Cumming,

<table><caption>TABLE 3</caption><caption>Correlation matrix (Pearson Correlation r) between poplar traits, enzymatic activities and percentage of mycorrhizal colonization. Correlation is significant for p < 0.01 (light grey cells). Abbreviations: %, percentage of mycorrhizal colonization; Chl, Chlorophyll (g/m²); DW, Dry Weight (g); LMA, Leaf maximum area (m²); M, mycorrhizal root tips; NM, non-mycorrhizal root tips; Pho, acid phosphatase; Nag, N-acetyl-glucosaminidase; Gls, β-glucosidase; Cel, cellobiohydrolase; Xyl, β-xylosidase; Lac, laccase; Glr, β-glucuronidase.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">%</th><th rowspan="2">Chl</th><th colspan="2">LMA</th><th colspan="8">M Gl's</th><th colspan="4">NM</th></tr><tr><th>Stem</th><th>Roots</th><th>Xyl</th><th>Glr</th><th>Nag</th><th>Cel</th><th>Pho</th><th>Lac</th><th>Xyl</th><th>Glr</th><th>Nag</th><th>Cel</th><th>Glus</th><th>Pho</th></tr></thead><tbody><tr><td>%</td><td>1</td><td>-0,09</td><td>0,20</td><td>0,20</td><td>0,06</td><td>0,22</td><td>0,14</td><td>0,13</td><td>0,07</td><td>0,13</td><td>0,04</td><td>0,05</td><td>0,05</td><td>0,13</td><td>0,02</td><td>-0,02</td><td>0,06</td><td>0,13</td></tr><tr><td>Chl</td><td>1</td><td>-0,12</td><td>-0,12</td><td>-0,01</td><td>-0,19</td><td>-0,01</td><td>-0,07</td><td>-0,18</td><td>-0,18</td><td>-0,05</td><td>0,01</td><td>-0,00</td><td>0,02</td><td>0,08</td><td>0,08</td><td>0,00</td><td>0,00</td><td>0,11</td></tr><tr><td>DW Stem</td><td>1</td><td></td><td>0,42</td><td>-0,28</td><td>-0,05</td><td>-0,04</td><td>0,04</td><td>-0,07</td><td>-0,13</td><td>-0,02</td><td>-0,01</td><td>-0,01</td><td>0,09</td><td>0,12</td><td>0,00</td><td>-0,02</td><td>0,01</td><td></td></tr><tr><td>DW Roots</td><td></td><td></td><td>1</td><td>-0,20</td><td>0,05</td><td>0,04</td><td>0,06</td><td>-0,01</td><td>0,02</td><td>-0,03</td><td>0,06</td><td>-0,07</td><td>0,00</td><td>-0,09</td><td>-0,09</td><td>-0,10</td><td>-0,02</td><td></td></tr><tr><td>LMA</td><td></td><td></td><td></td><td>1</td><td>0,14</td><td>0,09</td><td>0,14</td><td>0,06</td><td>0,10</td><td>0,12</td><td>0,13</td><td>-0,08</td><td>-0,13</td><td>-0,04</td><td>-0,07</td><td>-0,06</td><td>0,04</td><td></td></tr><tr><td>M Xyl</td><td></td><td></td><td></td><td></td><td>1</td><td>0,12</td><td>0,41</td><td>0,64</td><td>0,56</td><td>0,25</td><td>0,25</td><td>0,07</td><td>0,02</td><td>-0,03</td><td>0,11</td><td>-0,03</td><td>0,09</td><td></td></tr><tr><td>M Glr</td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0,27</td><td>0,18</td><td>0,15</td><td>0,30</td><td>0,15</td><td>0,01</td><td>0,07</td><td>-0,12</td><td>-0,13</td><td>-0,07</td><td>0,15</td><td></td></tr><tr><td>M Nag</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0,35</td><td>0,33</td><td>0,47</td><td>0,31</td><td>-0,11</td><td>-0,08</td><td>-0,04</td><td>-0,01</td><td>-0,19</td><td>0,32</td><td></td></tr><tr><td>M Cel</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0,69</td><td>0,22</td><td>0,16</td><td>0,06</td><td>-0,04</td><td>0,02</td><td>0,05</td><td>-0,06</td><td>0,05</td><td></td></tr><tr><td>M Gl's</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0,25</td><td>0,27</td><td>0,01</td><td>-0,05</td><td>-0,07</td><td>0,06</td><td>-0,05</td><td>0,09</td><td></td></tr><tr><td>M Pho</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0,29</td><td>0,01</td><td>0,08</td><td>-0,01</td><td>0,02</td><td>0,01</td><td>0,36</td><td></td></tr><tr><td>M Lac</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>-0,12</td><td>-0,05</td><td>-0,14</td><td>-0,06</td><td>-0,10</td><td>0,21</td><td></td></tr><tr><td>NM Xyl</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0,47</td><td>0,27</td><td>0,43</td><td>0,35</td><td>-0,03</td><td></td></tr><tr><td>NM Glr</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0,25</td><td>0,39</td><td>0,29</td><td>0,04</td><td></td></tr><tr><td>NM Nag</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0,24</td><td>0,31</td><td>-0,03</td><td></td></tr><tr><td>NM Cel</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0,28</td><td>-0,03</td><td></td></tr><tr><td>NM Gl's</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0,07</td><td></td></tr><tr><td>NM Pho</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td></td></tr></tbody></table>
1996). ECMf exhibit high phosphatase release in their environment, particularly under mineral phosphorus deficiency (Dighton, 1983; Nygren and Rosling, 2009). The *L. bicolor* genome comprises only one gene coding for a putative secreted acid phosphatase, while *P. trichocarpa* genome contains five. The phosphatase *Pap1* from *L. bicolor* was not regulated by symbiosis. *Pap5* and *Pap7* from poplar were significantly highly expressed under mycorrhizal conditions, whereas the three others (*Pap2*, *Pap3*, *Pap6*) were not significantly regulated.

Ezawa et al. (2005) have shown, on *Tagetes petala* in symbiosis with *Archaeospora leptoticha*, that the levels of transcripts of the *T. petala* acid phosphatase (*TpPAP1*) was increased eight times by *A. leptoticha* colonization. Our results support the hypothesis of Ezawa et al. (2005) on the fungal activation of the low-phosphate adaptation system of the plant partner and seem to show that the same mechanism of plant phosphatase activation exists both in arbuscular mycorrhizas and ectomycorrhizas.

Another hypothesis could be involved in the explanation of the differences in enzyme secretion between non-mycorrhizal and mycorrhizal roots. Inside the mycorrhizas, the root tissues, being isolated from the external medium by the fungal sheath, probably poorly contribute to enzyme secretion. The ability to degrade cellulose, hemicelluloses and lignin is widespread among fungi and soil bacteria (i.e. *Streptomyces* sp., *Bacillus* sp., *Cellulomonas* sp., Lynd et al. 2002). However, it is assumed that most of cellulose degradation in soil is performed by fungi (de Boer et al. 2005). Even if it has been shown that laccase genes were present in bacteria (Kellner et al. 2008), bacterial lignin degradation appears to be negligible in terrestrial environments compared to fungal lignin degradation (Peng et al. 2002). This is supported by the fact that we did not find any laccase activity on non-mycorrhizal root tips. Among bacteria, *Collimonas* sp. display chitinolytic activities (de Boer et al. 2004). However, these bacteria are present under specific conditions, completely different from greenhouse experiments with artificial substrate. Thus, despite the fact that this experiment was performed in non-axenic conditions, we may assume that the secreted enzymes that we measured in mycorrhizas were mainly due to fungal activity.

## Host genetic control of ECM enzyme secretion

The enzymatic activities expressed in mycorrhizal roots differed significantly between the two parents, while it did not differ in non-mycorrhizal roots. Significant differences were found between poplar genotypes for all enzymatic activities measured on ECMs except for laccase activity. On the contrary, no significant differences were found between poplar genotypes for enzymatic activities of non-mycorrhizal root tips except for acid phosphatase activity. Heritability values of enzymatic activities were similar for ectomycorrhizal and non-mycorrhizal root tips, except for glucuronidase in both types of roots and for laccase that

was not detected on non-mycorrhizal root tips. It is remarkable to find a high heritability value among the poplar genotypes for the enzymatic secretions of mycorrhizal roots, which are mainly due to fungal activity. Several previous studies have demonstrated significant genetic variability within plants and/or fungal species for symbiotic capability in mycorrhizal interactions. Rosado et al. (1994) reported a high value of heritability for colonization of *Pinus ellilotii* by the ECMf *Pisolithus tinctorius*, and moderate heritability for the development of *P. tinctorius* extramatrical mycelium. *Eucalyptus grandis*, *E. globulus*, *E. marginata* and *Pinus muricata* varied greatly in their growth response to different *Pisolithus* and *Rhizopogon* genotypes, respectively (Tonkin et al. 1989; Burgess et al. 1994; Piculell et al. 2008). Tagu et al. (2003, 2005) have already shown that the ability of poplar to form ECMs is under its genetic control. Other studies with contrasting results have found that plant genotype can play a dominant role in controlling the associate soil microbial communities (Mari et al. 2003; Korkama et al. 2007). Short-term experiment have either shown variations in mycorrhizal colonization, in microbial and in mycorrhizal communities (Gehring et al. 2006; Barbour et al. 2009; Lojewski et al. 2009) or few differences in arbuscular fungal and bacterial communities (Bever et al. 1996; Madritch and Hunter, 2002). Here, the degree of fungal enzymatic secretion is modulated according to poplar genotype. An explanation could be that the host genotype controls the amount of fungal tissue in the mantle and that enzyme activity is determined by the amount of fungal tissue present on the root. However, we found no significant differences in the projected area of mycorrhized or non-mycorrhized root tips between genotypes. So, it means that the amount of mycelium in the root tip is similar whatever the genotype. These results suggest the potential for poplar genome to drive the microbial-plant interaction, to create environments to which ECM fungi can respond and that could be explained by the "extended phenotype" phenomenon (Schweitzer et al. 2008; Whitham et al. 2008). As defined by Whitham et al. (2003), the heritable genetic variation within individual species (poplar in our study) has community and ecosystem consequences. In addition, we found a high positive heterosis for the capacity of poplar to form mycorrhizas ($h^2 a = + 45 \%$). We also found positive heterosis for characters such as leaf maximum area, dry weight of roots and for five of the seven enzymatic activities of mycorrhizal roots. Heterosis for poplar hybrids is a well-known phenomenon (Li and Wu, 1997; Marron et al. 2006). Heterosis is determined by non-mutually exclusive mechanisms, including genome-wide dominance complementation, locus-specific overdominance effects and epistasis, although the relative contribution of each of these mechanisms is still unclear (Lippman and Zamir, 2007). But it is also the first time that it is shown that plant heterosis could be expressed through the physiological activity of the fungal partner.
## Conclusion

The genetic diversity in tree species can influence fluxes of nutrients as well as interactions with soil microorganisms. Assessing tree genotype x environment interactions is a major challenge in functional ecology. In this paper, our data linked and quantified the general relationships between poplar plant genetics, ECM fungal infection, and physiological parameters. In the association *L. bicolor* /poplar, variations in plant and fungal responses in these controlled conditions illustrate the broad plasticity of the interaction. In this study, the role of poplar genetics in determining both poplar growth characteristics and fungal activities has been highlighted.

## Acknowledgements

Courty PE was supported by a grant of the French Ministry of Ecology and Sustainable Development and Labbé J by a scholarship of Région Lorraine/INRA; part of this research has been supported by the Biological Invasions program of the same Ministry. This project was also supported by the European Network of Excellence EVOLTREE. **We thank Joseph Armento and Krista Plett for language corrections and helpful comments on the manuscript,** and Francis Martin for valuable discussion.

## REFERENCES

Barbour RC, Baker SC, O'Reilly-Wapstra JM, Harvest TM, Potts BM. 2009. A footprint of tree-genetics on the biota of the forest floor. *Oikos*, **118**, 1917-1923.

Barker SJ, Duplessis S, Tagu D. 2002. The application of genetic approaches for investigations of mycorrhizal symbioses. *Plant and Soil*, **244**, 85-95.

Bever JD, Morton J, Antonovics J, Schultz P. 1996. Host specificity and diversity of glomalean fungi: an experimental approach in an old-field community. *Journal of Ecology*, **84**, 71-82.

de Boer W, Folman LB, Summerbell RC, Boddy L. 2005. Living in a fungal world: impact of fungi on soil bacterial niche development. *FEMS Microbiology Review*, **29**, 795-811.

de Boer W, Leveau JHJ, Kowalchuk GA, Gunnewiek PJAK, Abeln ECA, Figge MJ, Sjollema K, Janse JD, van Veen JA. 2004. *Collimonas fungivorans* gen. nov., sp. nov., a chitinolytic soil bacterium with the ability to grow on living fungal hyphae. *International Journal of Systematic and Evolutionary Microbiology*, **54**, 857-864.

Brunner AM, Busov VB, Strauss SH. 2004. Poplar genome sequence: functional genomics in an ecologically dominant plant species. *TRENDS in Plant Science*, **9**, 49-56.

Buée M, Courty PE, Mignot D, Garbaye J. 2007. Soil niche effect on species diversity and catabolic activities in an ectomycorrhizal fungal community. *Soil Biology and Biochemistry*, **39**, 1947-1955.

Burgess T, Dell B, Malajczuk Thompson N. 1994. Variation in mycorrhizal development and growth stimulation by 20 *Pisolithus* isolates inoculated on to *Eucalyptus grandis* W. Hill ex Maiden. *New Phytologist*, **127**, 731-739.

Cervera MT, Storme V, Ivens B, Gusmao J, Liu BH, Hostyn V, Hostyn Van Slycken J, Van Montagu M, Boerjan W. 2001. Dense genetic linkagmaps of three Populus species (*Populus deltoides*, *P. nigra* and *P. trichocarpa* based on AFLP and microsatellite markers. *Genetics*, **158**, 787-809.

Colpaert JV, van Laere A, van Tichelen KK, van Assche JA. 1997. The use of inositol hexaphosphate as a phosphorus source by mycorrhizal and non-mycorrhizal scots pine (*Pinus sylvestris*). *Functional Ecology*, **11**, 407-415.

Conn C, Dighton J. 2000. Litter quality influences on decomposition, ectomycorrhizal community structure and mycorrhizal root surface acid phosphatase activity. *Soil Biology and Biochemistry*, **32**, 489-496.

Cornelius J. 1994. Heritabilities and additive genetic coefficients of variation in forest trees. *Canadian Journal of Forest Research*, **24**, 372-379.

Courty PE, Franc A, Garbaye J. 2010a. Temporal and functional pattern of secreted enzyme activities in an ectomycorrhizal community. *Soil Biology and Biochemistry*, submitted.

Courty PE, Buée M, Diedhiou AG, Frey-Klett P, Le Tacon F, Rineau F, Turpault MP, Uroz S, Garbaye J. 2010b. The role of ectomycorrhizal communities in forest ecosystem processes: new perspectives and emerging concepts. *Soil Biology and Biochemistry*, **42**, 679-698.

Courty PE, Hoegger P, Kilaru S, Kohler A, Buée M, Garbaye J, Martin F, Kües U. 2009. Phylogenetic analysis, genomic organization and expression analysis of multicopper oxidases in the ectomycorrhizal basidiomycete *Laccaria bicolor*. *New Phytologist*, **182**, 736-750.

Courty PE, Pouysegur R, Buée M, Garbaye J. 2006. Laccase and phosphatase activities of the dominant ectomycorrhizal types in a lowland oak forest. *Soil Biology and Biochemistry*, **38**, 1219-1222.

Courty PE, Pritsch K, Schloter M, Hartmann A, Garbaye J. 2005. Activity profiling of ectomycorrhiza communities in two forest soils using multiple enzymatic tests. *New Phytologist*, **167**, 309-319.

Cullings K, Courty PE. 2009. Saptrotrophic capabilities as functional traits to study functional diversity and resilience of ectomycorrhizal communities. *Oecologia*, **161**, 661-664.

Cumming JR. 1996. Phosphate-limitation physiology in ectomycorrhizal pitch pine (*Pinus rigida*) seedlings. *Tree Physiology*, **16**, 977-983

Di Battista C, Selosse MA, Bouchard D, Stenström E, Le Tacon F. 1996. Variations in symbiotic efficiency, phenotypic characters and ploidy level among different isolates of the ectomycorrhizal basidiomycete *Laccaria bicolor* strain S238. *Mycological Research*, **100**, 1315-1324.

Dighton J. 1983. Phosphatase production by mycorrhizal fungi. *Plant and Soil*, **71**, 455-462.

Dillen SY, Marron N, Bastien C, Ricciotti L, Salani F, Sabatti M, Pinel MPC, Rae AM, Taylor G, Ceulemans R. 2007. Effects of environment and progeny on biomass estimations of five hybrid poplar families grown at three contrasting sites across Europe. *Forest Ecology and Management*, **252**, 12-23.

Ezawa T, Hayatsu M, Saito M. 2005. A new hypothesis on the strategy for acquisition of phosphorus in arbuscular mycorrhiza: up-regulation of secreted acid phosphatase gene in the host plant. *Molecular Plant-Microbe Interactions*, **18**, 1046-1053.
Frey-Klett P, Pierrat JC, Garbaye J. 1997. Location and survival of mycorrhiza helper *Pseudomonas fluorescens* during establishment of ectomycorrhizal symbiosis between *Laccaria bicolor* and Douglas fir. *Applied and Environmental Microbiology*, **63**, 139-144.

Gehring CA, Mueller R, Whitham TG. 2006. Environmental and genetic effects on the formation of ectomycorrhizal and arbuscular mycorrhizal associations in cottonwoods. *Oecologia*, **149**, 158-164.

Gehring CA, Whitham TG. 2002. *Mycorrhiza-herbivore interactions: population and community consequences*. In: van der Heijden MGA, Sanders IR (eds) Mycorrhizal ecology. Springer, Berlin Heidelberg New York, pp 295-320.

Jorge V, Dowkiw A, Faivre-Rampant P, Bastien C. 2005. Genetic architecture of qualitative and quantitative *Melampsora larici-populina* leaf rust resistance in hybrid poplar: genetic mapping and QTL detection. *New Phytologist*, **167**, 113-127.

Kellner H, Luis P, Zimdars D, Kiesel B, Buscot F. 2008. Diversity of laccase-like multicopper-oxidase genes in forest and grassland Cambisol soil samples. *Soil Biology and Biochemistry*, **40**, 638-648.

Khasa DP, Hambling B, Kernaghan G, Fung M, Ngimbi E. 2002. Genetic variability in salt tolerance of selected boreal woody seedlings. *Forest Ecology and Management*, **165**, 257-269.

Karst J, Jones MD, Turkington R. 2009. Ectomycorrhizal colonization and intraspecific variation in growth responses of lodgepole pine. *Plant Ecology*, **200**, 161-165.

Koide RT, Sharda JN, Herr JR, Malcolm GM. 2008. Ectomycorrhizal fungi and the biotrophy-saprotrophy continuum. *New Phytologist*, **178**, 230-233.

Korkama T, Fritze H, Pakkanen A, Pennanen T. 2007. Interactions between extraradical ectomycorrhizal mycelia, microbes associated with the mycelia and growth rate of Norway spruce (*Picea abies*) clones. *New Phytologist*, **173**, 798-807.

Le Tacon F, Bouchard D. 1986. Effects of different ectomycorrhizal fungi on growth of larch, Douglas fir, Scots pine and Norway spruce seedlings in fumigated nursery soil. *Oecologia Applicata*, **7**, 389-402.

Li B, Wu R. 1997. Heterosis and genotype×environment interactions of juvenile aspens in two contrasting sites. *Canadian Journal of Forest Research*, **27**, 1525-1537.

Lindahl BD, Finlay RD, Cairney JWG. 2005. *Enzymatic activities of mycelia in mycorrhizal fungal communities*. In: Dighton J, White JF, Oudemans P, eds. The fungal community: its organization and role in the ecosystem, 3rd edn. Boca Raton, FL, USA: CRC Press, 331-348.

Linderman RG, Davis EA. 2004. Varied response of marigold (Tagetes spp.) genotypes to inoculation with different arbuscular mycorrhizal fungi. *Scientia Horticulturae*, **99**, 67-78.

Lippman ZB, Zamir D. 2007. Heterosis: revisiting the magic. *Trends in Genetics*, **23**, 60-66.

Lojewski NR, Fischer DG, Bailey JK, Schweitzer JA, Whitham TG, Hart SC. 2009. Genetic basis of aboveground productivity in two native Populus species and their hybrids. *Tree Physiology*, **29**, 1133-1142.

Lynd LR, Weimer PJ, van Zyl WH, Pretorius IS. 2002. Microbial cellulose utilization: fundamentals and

biotechnology. *Microbiology Molecular Biology Review*, **66**, 506-577.

Madritch MD, Hunter MD. 2002. Phenotypic diversity influences ecosystem functioning in an oak sandhills community. *Ecology*, **83**, 2084-2090.

Mari S, Jonsson A, Finlay R, Ericsson T, Kähr M, Eriksson G. 2003. Genetic variation in nitrogen uptake and growth in mycorrhizal and nonmycorrhizal *Picea abies* (L.) Karst. seedlings. *Forest Science*, **49**, 258-267

Marron N, Bastien C, Sabatti M, Taylor G, Ceulemans R. 2006. Plasticity of growth and sylleptic branchiness in two poplar families grown at three sites across Europe. *Tree Physiology*, **26**, 935-946.

Marron N, Villar M, Dreyer E, Delay D, Boudouresque E, Petit JM, Delmotte FM, Guehl JM, Brignolas F. 2005. Diversity of leaf traits related to productivity in 31 *Populus deltoides-Populus nigra* clones. *Tree Physiology*, **25**, 425-435.

Martin F, Nehls U. 2009. Harnessing ectomycorrhizal genomics for ecological insights. *Current Opinion in Plant Biology*, **12**, 508-515.

Martin F, Aerts A, Ahrén D, et al. 2008. The genome sequence of the basidiomycete fungus *Laccaria bicolor* provides insights into the mycorrhizal symbiosis. *Nature*, **452**, 88-92.

Monje OA, Bugbee B. 1992. Inherent limitations of nondestructive chlorophyll meters: a comparison of two types of meters. *Hortscience*, **27**, 69-71.

McCaig B, Meagher RB, Dean JFD. 2005. Gene structure and molecular analysis of the laccase-like multicopper oxidase (LMCO) gene family in *Arabidopsis thaliana*. *Planta*, **221**, 619-636.

Nygren CMR, Rosling A. 2009. Localisation of phosphomonoesterase activity in ectomycorrhizal fungi grown on different phosphorus sources. *Mycorrhiza*, **19**, 197-204.

Peng X, Masai E, Kitayama H, Harada K, Katayama Y, Fukuda M. 2002. Characterization of the 5-carboxyvanillate decarboxilase gene an dits role in lignin-related biphenyl catabolism in *Sphingomonas paucimobilis* SYK-6. *Applied and Environmental Microbiology*, **68**, 4407-4415.

Piculell BJ, Hoeksema JD, Thompson JN. 2008. Interactions of biotic and abiotic environmental factors in an ectomycorrhizal symbiosis, and the potential for selection mosaics. *BMC Biology*, **6**, 23.

**R Development Core Team.** 2006. *R: A Language and Environment for Statistical Computing*. R Foundation for Statistical Computing, Vienna, Austria.

Ramot O, Viterbo A, Friesem D, Oppenheim A, Chet I. 2004. Regulation of two homodimer hexosaminidases in the mycoparasitic fungus *Trichoderma asperellum* by glucosamine. *Current Genetics*, **45**, 205-213.

Richardson AD, Duigan SP, Berlyn GP. 2002. An estimation of non-invasive methods to estimate foliar chlorophyll content. *New Phytologist*, **153**, 185-194.

Rosado SCS, Kropp BR, Piche Y. 1994. Genetics of ectomycorrhizal symbiosis. I. Host plant variability and heritability of ectomycorrhizal and root traits. *New Phytologist*, **126**, 105-110.

Schweitzer JA, Bailey JK, Fischer DG, LeRoy CJ, Lonsdorf EV, Whitham TG, Hart SC. 2008. Plant-soil-microorganism interactions: Heritable relationship between plant