key to successful establishment of introduced populations, because of it increases the *genetic variance* necessary for adaptive responses (see Zheng & Ennos 1999 for an example in introduced pine populations). Experimental manipulation of gene flow in forest trees would provide valuable data to better understand its constraining or boosting effects on adaptation to local climate.

## Regional variation

Predicted shifts in *bioclimatic envelopes* imply that current populations at the trailing and leading edges of the range will face different adaptive challenges. Southern margin populations will face climatic conditions that currently do not permit species growth. Will these challenged populations have the evolutionary potential (Gomulkiewicz & Houle 2009) to adapt before going extinct? Their persistence will depend on whether the evolutionary or demographic constraints preventing current establishment in warmer or drier climates will be relaxed enough to enable enlargement of species' fundamental niches. Conversely, at the northern margins, new areas will become suitable for growth, but the success of colonization may depend on the genetic make-up of new population founders.

Gene flow can have contrasted consequences for populations at trailing and leading edges of a shifting range (Hampe & Petit 2005): populations at the leading edge or in the central part of the distribution are likely to receive “pre-adapted” genes from more southern populations, and gene flow may facilitate their adaptation (Hu & He 2006). The opposite may be true for populations at the rear end that encounter an entirely novel environment. The flow of pre-adapted genes from central populations is then not possible, which may increase maladaptation and extinction probabilities in populations at the southern margins. However, both demographic and genetic rescue effects of dispersal from larger populations within the species' range may help those marginal populations to persist. The precise balance of the multifarious effects of gene flow remains to be explored in this context.

## Balance between the effects of seed and pollen flow

Balance between the negative and positive effects of gene flow may also vary with the relative contribution of seed and pollen dispersal. Both pollen flow and seed flow contribute substantially to genetic diversity. On the one hand, pollen often disperses farther than seeds (see section 2 and Table 1) and in greater quantities. On the other hand, a single pollen grain carries half the number of alleles compared to a single seed, and only seeds can establish a new population in a remote habitat. Due to long generation times in trees, migrant seeds accumulate in a new population over years before the new generation reproduces, promoting high levels of diversity in recently founded populations (Austerlitz et al. 2000). Long

distance gene flow mediated by pollen in marginal habitats is therefore conditional on the successful establishment of shorter distance migrating seeds. The movement of alleles by pollen necessarily involves combining with existing genetic variation, which explains why seed and pollen dispersal may have different consequences for population divergence, maintenance of within-population diversity, and mean fitness (Hu & Li 2003; Lopez et al. 2008). When selection varies sharply in space, pollen dispersal could, in particular, generate higher *migration loads* than equivalent seed dispersal (Lopez et al. 2008). This happens because selection is less efficient at removing badly adapted immigrant alleles when their deleterious effects are partly masked in hybrids (Lopez et al. 2008). Selection at the gametophytic stage may further affect these differences (Hu & Li 2003; Hu & He 2006).

Most models of adaptation and migration in a heterogeneous environment (e.g. Pease et al. 1989; Kirkpatrick & Barton 1997; Polechova et al. 2009, see Box 2; but see Butlin et al. 2003) consider a single dispersal parameter. With pollen and seed dispersal, demographic migration is partially uncoupled from gene flow. Hu & He (2006) predicted that pollen dispersal could slow down or accelerate range expansion in some homogeneous environments by interfering with the spread of beneficial or deleterious mutations. At retracting range margins, seed and pollen dispersal may play very different roles on adaptation: seed dispersal enhances the probability of adaptation in a sink habitat, while pollen dispersal generally compromises it (Aguilée et al. unpublished). Conversely, both pollen flow and seed flow could have positive effects at expanding range margins. In the presence of pollen limitation, long-distance pollen flow could moreover prevent extinction in marginal populations (Butlin et al. 2003).

## Future research directions

### Develop new methods to trace pollen and seeds

Experimental dispersal studies monitoring LD pollen and seed dispersal have often been limited in spatial scale due to (i) overlapping of the *pollen/seed shadows* (glossary) masking LDD, (ii) dilution effect (LDD is rare and requires high power to observe, let alone measure), and (iii) large numbers of putative sources (characterizing their positions and genotypes is time- and cost-intensive). Using highly polymorphic genetic markers like microsatellites greatly overcomes the first point, and the advent of next-generation sequencing will improve power and resolution, however it is still necessary to conceive new experimental designs dealing with points (ii)-(iii). We propose potential strategies here, mostly relying on a stronger interaction with mechanistic approaches.

### Making use of meteorological data

For wind-mediated gene flow, available weather data could help determine the potential range of pollen and
seed dispersal within particular landscapes, regions, or
continents. Such an analysis requires regional
meteorological datasets, phenological observations over
a wide region and sufficient understanding of the
meteorological factors driving pollen and seed emission
and spread. Products of regional and global weather
reanalysis, such as the North American Regional
Reanalysis (NARR) dataset and the European Centre for
Medium Range Weather Forecast (ECMWF) data, offer
useful observational and model-based information on
wind, temperature, humidity, radiation and other
meteorological data (Schueler et al. 2005). On-line
interfaces for weather and radiation simulation tools can
also be used to evaluate conditions across large dispersal
ranges (Bohrerova et al. 2009). For pollen, phenological
data are available from pollen monitoring networks (e.g.
the European Aerobiology Network, EAN) or from
phenological observations (e.g. the European Phenological
Network). Model-driven weather reconstructions
(Solomon et al. 2007) can provide estimates of dispersal
potentials in past and future climates (Kuparinen et al.
2009; Nathan et al. 2011). Improved characterizations of
wind dispersal mechanisms accounting for interactions
between pollen/seeds and turbulent winds in relation to
weather conditions can be combined to determine annual
and multiannual wind-driven pollen and seed dispersal
patterns throughout large geographic regions (Muñoz et
al. 2004; Thompson & Katul 2008; Nathan et al. 2011).

To experimentally trace pollen or seed movement at the
continental scale, a joint use of weather data, weather
forecasting models and field observation of pollen/seed
pools seems most promising. Large-scale spatial
characterization of presence/absence of a species,
phenology, and airflows were already used to identify
temporal windows ideal for LDD and relate them to the
actual presence of pollen grains in physical captors
(Siljamo et al. 2008). A next step would be to measure the
diversity of origins in the effective pollen pools through
the genetic and/or phenotypic diversity of the seed
produced (Nilsson 1995).

Taking advantage of adequate landscape configurations

*Genetic assignment* (glossary) methods linking pollen, seeds or seedlings to candidate parental populations could be used to evaluate the effective rate and range of contemporary gene flow among discrete populations (Manel *et al.* 2005). Focus could be placed initially on isolated populations or trees, particularly informative about LDD because they are less subject to dilution effects. A recent study using *genetic assignment* in such demographic setting has revealed effective pollen gene flow over 100-km distances in a wind-pollinated species (Fig. 4). For species with extremely low densities, even *parentage analysis* (glossary) may prove efficient in detecting LD gene flow (Ahmed *et al.* 2009). Female plants, male-sterile or self-incompatible isolated individuals might prove useful traps for investigating the

composition of LD effective pollen clouds, and could be
distributed at specific positions during the pollination
period, e.g. using potted plants, flowering branches kept
alive or flowering grafts. High precision aerial photographs
and satellite images could be used to retrieve all potential
sources at the regional scale and avoid biases due to ghost
populations. An alternative solution, not requiring trap
plants but not assessing *effective dispersal* directly, is to
characterize the genetic content of the pollen pool by
genotyping single pollen grains (Matsuki et al. 2007),
sampled in volumetric traps from existing *aerobiology*
(glossary) networks or placed at specific sites in a
landscape. Note however that the atypical demographic
conditions of isolated trees that facilitate LDD assessment
may result in observed LDD patterns difficult to generalize
(e.g. dilution effects in large populations), for which
modelling approaches may be necessary.

Combining mechanistic and genetic models

Mechanistic and genetic tools for assessing dispersal have
been developed and applied virtually independently,
although they have complementary features and high
potential for synergy, particularly for the analysis of LDD.
For example, the ability of mechanistic approaches to
assess dispersal across multiple scales complements the
problematic extrapolation of genetic methods beyond the
small scale in which individuals were sampled. Genetic
methods, in turn, can provide data to validate
mechanistically-derived kernels, and to add the required
(and often hard to measure) component of post-dispersal
establishment effects needed to assess *effective dispersal*.
Mechanistically derived propagule transport probability
functions over different distances could also be
incorporated into the usual probabilistic (maximum-
likelihood or Bayesian) migration rate estimation
procedures based solely on genetic likelihoods, allowing
jointly estimation of migration rates and mechanistic
parameters that determine dispersal over long distances.

Developing the connectivity network

Current research on spatial patterns of dispersal and gene
flow is dominated by the *dispersal kernel* concept, which
bears significant disadvantages when applied to broad
scales. Studies using *dispersal kernels* generally require
sampling intensities that become unfeasible over long
distances. Moreover, they often assume isotropy (i.e., the
same *dispersal kernel* for all directions); although this
assumption is unrealistic for many systems in which the
dispersal vector moves in a directional manner, such as
many seasonal winds, downward flow of rivers, and
oriented movement of animals. Similarly, *genetic
assignment* methods for migration rate estimation
typically incorporate neither directional nor other kinds of
spatial information. *Lagrangian dispersal* (glossary)
simulations can account for dispersal anisotropy by
incorporating turbulence patterns (Bohrer et al. 2008),
and hourly, daily or seasonal variation in wind direction
(Wright et al. 2008); this computationally-intensive
approach, however, is practically limited to relatively short-
term small-scale applications. An alternative approach,
connectivity maps (glossary), depicts dispersal
probabilities between sites based on large-scale datasets
and/or models available for the primary dispersal vector,
for example, to assess wind connectivity of plants among
islands in the southern oceans (Muñoz et al. 2004). In
principle, this method could be adjusted to many plant
species in a variety of spatial scales, if the patterns of
movement of the dispersal vector can be estimated.
Because some key vectors such as wind, inland water
systems, ocean currents and migrating birds disperse
many plant species, efforts to develop vector connectivity
maps could advance the study of gene flow via pollen and
seeds for a large number of species.

Implement experimental approaches to assess
evolutionary changes in trees

Despite obvious biological constraints in trees, we
recommend setting up experiments that would allow
assessing evolutionary changes over a few generations.
Such experiments would not only provide estimates of
evolutionary rates, they would also offer the opportunity
to test evolutionary hypotheses regarding responses to
climate change. Existing *provenance tests* constitute in this
respect a precious source of data, allowing the
quantification of between and within sites genetic
diversity for climate adaptation and the putative
demographic impact of maladaptation (Box 1). Further
exploitation of such data should be encouraged.
Additional options can be foreseen:

*Testing for the effect of gene flow on the changes of population means and genetic and phenotypic variance over one generation.*

A straightforward design consists in conducting full sib
control “hybrid” crosses between distant and close
populations in comparison to “pure” within population
crosses. Offspring should then be raised under controlled
conditions mimicking different climatic scenarios. While
more difficult to implement, because of potentially small
sample sizes and unaccounted microenvironmental
variation, an alternative “in situ” experiment consists in
comparing “natural migrants” that have been identified by
parentage analysis or genetic assignment methods to
“local residents”.

*Testing the effects of the strength of selection over successive generations.*

We suggest installing short generation tree populations
(birch or willow) within open top chambers, and let the
population reproduce under such conditions. Strength of
selection can be set by manipulating conditions within the
open top chambers. Foreign pollen can be supplemented
at each generation to mimic gene flow.

*Measuring the strength of selection at various filtering stages over the life cycle.*

While filtering stages (i.e. stages with strong competition

and selective mortality) are well known in trees especially
at the young stage (from seeds to juvenile seedlings), the
changes induced by selection at each stage have only
rarely been assessed. We suggest to monitor population
means, and genetic variances of relevant *adaptive traits* as
well as allelic frequencies at genes of adaptive significance
after each filtering stage.

*Analysing adaptation in transferred populations.*

Artificial transfers of populations have been done in the past in forest trees and some of them are well documented (Fallour-Rubio et al. 2009). They can provide alternative ways of tracking evolutionary changes at contemporary time scales. In some cases these transfers actually mimicked climate changes, as populations were moved from cooler to milder climates. Well known examples are transfers of North American tree species to Europe, whose introduced populations have differentiated in so-called land races (Northern red oak, Daubree & Kremer 1993), or large scale transfers of native trees within Europe. Transferred populations have usually been deployed over larger areas than provenance tests and the transferred material has been tested in a real forestry context, rather than in experimental plantations.

Develop integrative theoretical approaches

*Extend evolutionary models of adaptation to climate change to the case of trees.*

Analytical models –as the example shown in Box 2–
provide conceptual insights into how gene flow,
adaptation and biotic interactions shape species ranges in
stable or changing environments (Pease et al. 1989;
Kirkpatrick & Barton 1997; Barton 2001; Polechova et al.
2009; Price & Kirkpatrick 2009). Available models,
however, rarely incorporate salient features of tree life
cycles, such as distinct dispersal modes, overlapping
generations, or fat-tailed dispersal kernels (glossary),
which may profoundly affect their evolutionary responses
to climate change. How the pace of adaptation in a
changing environment depends on variation in fitness
expressed before vs. after sexual maturity and on the
correlation between juvenile vs. adult traits is for instance
an important area for future research.

Exploring the effects of LD gene flow on adaptation also
requires modelling dispersal as a more complex process
than the simple homogeneous diffusion considered in the
models summarized in Box 2. What is the evolutionary
impact of rare long distance dispersal events well beyond
the average dispersal distance? While the effect of fat-
tailed dispersal kernels on rates of expansion (Thompson
& Katul 2008) and neutral diversity (Travis et al. 2010;
Fayard et al. 2009) have been explored, we lack similar
theoretical investigation of their effects on adaptive
diversity in the context of climate change. Answering this
question would also help identifying critical features of
pollen and seed dispersal distributions on which empirical
estimates should focus.
The idea that climate change is equivalent to simple spatial shift of local climatic conditions is also a gross simplification. Rather, climatic change may results in new combinations of precipitation patterns, temperature, photoperiod and biotic conditions that occur nowhere within the current range, imposing entirely new selection pressures, and favouring the assembly of novel genotypes (Williams & Jackson 2007). Adaptation to climate change may thus require the production of new phenotypic combinations. Reaching such combinations means that natural selection is acting on multiple traits simultaneously. Evolutionary models have often been limited to single traits. Multivariate adaptive responses depend on the amount of *genetic correlation* (glossary) among traits, which may limit or accelerate adaptation to climate change (e.g., Etterson & Shaw 2001). Modelling those responses as a univariate rather than multivariate process, as done so far, might thus fail to provide an accurate picture of species' adaptive capacities. Although we have begun to incorporate multivariate evolution into models of migration-selection balance (see, Guillaume & Whitlock 2007; Guillaume 2011; Duputié et al. in review), empirical data are direly missing on patterns of *genetic correlations* among key ecological traits in trees and on the spatial and temporal variation of their joint selection pressures.

Efforts should be made to fill these gaps and help calibrate models with real data, to ultimately be able to merge evolutionary approaches with niche- and process-based ecological forecasting of climate induced range shifts.

*Use integrative simulation platforms.*

Trait-based, mechanistic models have recently been developed enabling predictions of species ranges under current and future non analogous climates (e.g., Morin et al. 2008). For instance the Phenofit model (Chuine & Beaubien 2001) predicts tree distributions based on existing phenological responses to local climate, drought and frost tolerance. Microevolutionary phenomena described above have only started to be incorporated in such ecological forecast models (Kearney et al. 2009; Kuparinen et al. 2010). There is therefore an urgent need to incorporate genetic and ecological concepts into integrated models to accurately predict the impact of environmental changes on species persistence over the next century and at the continental scale. Efforts should be dedicated to foster development of integrated computer simulation platforms with this aim. Individual-based, population and quantitative genetics simulation packages already exist (e.g. Nemo, Guillaume & Rougemont 2006; Metapop, e.g. Le Corre & Kremer 2003; Kremer & Le Corre, 2011) that could be extended to include the ecological and spatially explicit layers needed.

A key aspect of the modelling approach advocated here is the overlay of predictions from different processes; ecological niche and *bioclimatic envelope* modelling, variation of gene flow over geographical ranges, and

evolutionary adaptation of local populations. The basal layer, the climatic layer, defines how changes in climatic conditions over the species' geographical range modify the localization of suitable habitats (Thuiller 2003). The second layer describes spatial variation of pollen and seed dispersal and should integrate information from the climatic layer to model the changes of seed and pollen movements caused by climate change through modification of the *dispersal kernels* (Kuparinen et al. 2009; Nathan et al. 2011), pollen viability (Bohrerova et al. 2009), or the timing of pollination and female receptiveness. The third layer integrates information from the two previous ones to predict how local populations adapt to their shifting conditions (e.g., Kuparinen et al. 2010). Information from the climatic layer will set the strength of selection acting on different *adaptive traits* by indicating how far from its local optimum a population might be. Information on gene flow from the second layer will indicate hybridization rates and fitness effects, depending on the geographical origin of the migrants (Savolainen et al. 2007; Lopez et al. 2008; Yeaman & Guillaume 2009). It will also indicate the potential for colonization of new habitats. Finally, the outcome of local adaptation can be interpreted in terms of growth and persistence of local populations and how this feeds back into predictions of the intensity of gene flow over larger geographical scales.

## Conclusion

While much emphasis has been placed on the ability of tree populations to migrate fast enough in response to climate change, we have here examined the potential consequences of long distance gene flow on their adaptive response to climate change.

Many tree species have evolved dispersal syndromes enabling the effective flow of genetic information across distant populations inhabiting contrasting environments. We have argued how such exchanges, although potentially maladaptive in some evolutionary and demographic scenarios, may in the case of forest trees favour adaptation to changing climatic conditions, compensating for their long generation time.

Our understanding of the interaction between gene flow and local adaptation under realistic ecological, demographic and dispersal assumptions is however limited, and we have suggested potential theoretical and experimental avenues of research for the integration of dispersal biology, ecology and evolutionary quantitative genetics in a better predictive inferential framework.

## Acknowledgements

This article is a synthesis of a workshop organized within the Coordination and Support Action (CSA) FORESTTRAC "Forest ecosystem genomics research: supporting transatlantic cooperation" which took place in
Barsac (France) from September 27th to October 1st 2010. FORESTRAC was supported by the European Union under the FP7 Workprogramme Environment (FP7-244096). This is publication ISEM 2012-013 of the Institut des Sciences de l'Evolution Montpellier. This work was supported by the Agence Nationale de la Recherche, programme « 6th extinction », through the project EVORANGE (ANR-09-PEXT-011). FG was supported by a grant from the Swiss NSF (no. PZ00P3_121697). RN was supported by grants from the Israel Science Foundation (ISF-474 / 02, ISF-150 / 07 and ISF-FIRST-1316 / 05) and the Adelina and Massimo Della Pergola Chair of Life Sciences. JJRA was supported by a grant from the Spanish Ministry of Science and Innovation (CGL2009-09428).

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## GLOSSARY

**Adaptive divergence:** the differentiation among the mean phenotypes of populations subject to different selective pressures.

**Adaptive trait:** a phenotypic trait that enhances the fitness of an individual in a particular environment. Examples for trees are the time of flushing and bud set, and physiological traits determining water use efficiency.

**Aerobiology:** the study of airborne organisms and organic particles.

**Bioclimatic envelope:** the predicted potential geographic distribution of a species in a particular climatic scenario.

**Breeding value:** part of an individual's phenotype that can be transmitted to its offspring through the transmission of genetic material.

**Bud set:** formation of a terminal bud at the end of the vegetative period, the timing of which is heritable and determines cold tolerance in boreal and temperate trees.

**Connectivity map:** a map describing the cost or the probability of dispersal along possible trajectories linking a set of locations.

**Dispersal kernel:** a probability density function of dispersal distances or locations from a source point.

**Effective dispersal:** dispersal leading to successful establishment or reproduction.

**Evolutionary load:** the mean fitness loss in a population produced by the deviation of the mean phenotype from the local optimum due to varying selection in space and time.

**Fat-tailed dispersal kernel:** dispersal kernels with a slow probability decrease at long distances relative to a negative exponential.

**Genetic assignment:** the probabilistic ascertainment of the original population of an individual genotype.

**Genetic correlation:** non independent genetic variation for two phenotypic traits, which can be due in particular to the fact that the same genes affect variation of several traits.

**Genetic load:** the loss of mean fitness in a population due to the departure of individual phenotypes from the optimum in a given environment.

**Genetic variance:** part of the total phenotypic variance that is due to genetic differences between individuals.

**Heritability:** the proportion of phenotypic variation among the individuals of a population in a particular environment that is due to genetic variation.

**Heterosis:** the higher fitness of progeny obtained through crosses between populations rather than within the same population.

**Inbreeding depression:** reduced fitness of inbred individuals.

**Lag load:** the loss of mean fitness in a population due to the lagging response of the phenotypic mean to temporal changes in the optimum.

**Lagrangian dispersal model:** a mathematical description of the trajectories of individual dispersers.

**Migration load:** the contribution of immigrant genes to maladaptation.

**Outbreeding depression:** reduced fitness of individuals born to parents from different populations.

**Parentage analysis:** the probabilistic determination of the parents of an individual, frequently using genetic markers.

**Phenotypiccline:** a continuous change of a phenotypic trait along an environmental and/or geographical gradient.

**Pollen/seed shadow:** the density of pollen grains/seeds dispersed at different distances from an individual. It equals the product of the dispersal kernel by the individual's fecundity.

**Provenance:** the original geographic source of a population or group of individuals (used also to refer to such a population or group).

**Provenance test:** a common garden experiment, in one or more locations, where the genetic variation of different provenances is evaluated (see provenance).

**Reaction norm:** the set of phenotypes expressed by a particular genotype under a range of environments.

**Response to selection:** the difference between the mean phenotype of the offspring of a group of selected parents and the mean phenotype of the population before selection.

**Stabilizing selection:** selection that favours intermediate over extreme phenotypes.

**Standing load:** the loss of mean fitness in a population due to the phenotypic variance around the mean phenotype. Present even when the mean phenotype matches the optimum.
François Lefèvre*, Thomas Boivin, Aurore Bontemps,
François Courbet, Hendrik Davi, Marion Durand-Gillmann,
Bruno Fady, Julie Gauzere, Cindy Gidoin, Marie-Joe
Karam, Hadrien Lalagüe, Sylvie Oddou-Muratorio,
Christian Pichot.

**Corresponding Author:**

INRA, UR629 Ecologie des Forêts Méditerranéennes, URFM,
Domaine Saint Paul, Site Agroparc, 84914 Avignon Cedex 9, France.
* francois.lefevre@avignon.inra.fr

**Context:** Managing forests under climate change requires adaptation. The adaptive capacity of forest tree populations is huge but not limitless. Integrating evolutionary considerations into adaptive forestry practice will enhance the capacity of managed forests to respond to climate-driven changes.

**Aims:** Focusing on natural regeneration systems, we propose a general framework that can be used in various and complex local situations by forest managers, in combination with their own expertise, to integrate evolutionary considerations into decision making for the emergence of an evolution-oriented forestry.

**Methods:** We develop a simple process-based analytical grid, using few processes and parameters, to analyse the impact of forestry practice on the evolution and evolvability of tree populations.

**Results:** We review qualitative and, whenever possible, quantitative expectations on the intensity of evolutionary drivers in forest trees. Then, we review the effects of actual and potential forestry practice on the evolutionary processes. We illustrate the complexity of interactions in two study cases: the evolutionary consequences for forest trees of biotic interactions and of highly heterogeneous environment.

**Conclusion:** Evolution-oriented forestry may contribute adapting forests to climate change. It requires combining short-term and long-term objectives. We propose future lines of research and experimentation.

**Key words:** genetic resources ; silviculture ; adaptation ; climate change.

Annals of Forest Science (in press, accepted 07/02/2013)

Published in Annals of Forest Sciences (2014) 71: 723-739, with doi:10.1007/s13595-013-0272-1

Introduction

Ecosystem functioning depends on the adaptation of
living organisms to their physicochemical environment. In
particular, the maladaptation of trees to local conditions
can provoke ecosystem dysfunctions such as forest
dieback or failure of regeneration, and it can also affect
biotic interactions between trees and associated species.

Multi-site common-garden experiments, which allow
modelling the reaction norm of current tree populations
to climatic parameters, suggest that climate change will
lead to a high risk of maladaptation of tree species, at least
in some parts of the current distribution range (Savolainen
et al. 2007; St Clair and Howe 2007). To maintain forest
services under climate change, tree stands will have to
respond within one to ten generations to (1) more frequent
and more intense extreme climatic events, (2) changing
mean climatic parameters and (3) other related changes
such as parasite outbreaks (IPCC 2007). Consequently,
ecological services of the forests will depend on the
intensity and velocity of the evolution of tree populations
in response to climate change (Rehfeldt et al. 2001). Thus,
adaptation should be considered in a dynamic

perspective, as a bouquet of evolutionary processes that
change populations and communities to fit their
environment. Among these processes, genetic adaptation,
i.e. genetic change of a population responding to selection,
can be rapid and contribute to the ecological success of
species facing climate change including forest trees
(Aitken et al. 2008; Hoffmann and Sgrò 2011). A recent
review stressed the high potential of evolutionary response
to climate change in trees (Alberto et al. 2013). However,
evidence of lack of adaptation does also exist, e.g. niche
limits and empty niches, including for tree species that
have large population size and produce huge quantities of
seeds (Bradshaw 1991).

During the last century, foresters have succeeded in
adapting forest genetic resources to bioclimatic conditions
very different from their native range, obtaining good
survival, growth and reproduction in the new
environments. Emblematic examples are the worldwide
transfer of Pinus radiata (Yan et al. 2006) and the south to
north translocation of Picea abies (Skrøppa et al. 2010).
This adaptation was achieved in very few generations of
trees, it proceeds from plasticity and/or evolution. For
each adaptive trait, the phenotypic plasticity and the capacity of evolution depend on the genetic content and the environment of the population, which can both evolve (Pigliucci 2008). Within each population, genetic changes of mean trait value, plasticity and evolvability result from the combination of random and selectively oriented processes that can be affected by forestry practice. Whether immediate response to selection can hamper future evolutions, e.g. due to erosion of the genetic diversity, remains an open question. Evidence from breeding experience shows that the genetic responsiveness of populations submitted to continuous selection can be maintained through time for some traits: evolvability was maintained over more than 100 generations of selection for protein and oil content in the Illinois maize breeding population (Moose et al. 2004). No such long-term empirical evidence is available for trees. However, local adaptation that commonly emerged in most tree species over the course of post-glacial recolonisation provides another illustration of achieved evolution (Savolainen et al. 2007). Noticeably, this local adaptation did not completely erode within-population genetic variation of adaptive traits (Mimura and Aitken 2007; Alberto et al. 2013). The long-term maintenance of evolvability also depends on the genetic architecture of the traits under selection, and in the case of polygenic inheritance, Kremer and Le Corre (2012) showed that evolutionary changes first result from the selection of the fittest combinations of gene alleles before it reduces the allelic diversity at individual gene loci.

However, adaptation is not limitless. Futuyma (2010) reviewed the factors that can limit adaptation from the short term to the phylogenic time scale. Focusing on an ecological rather than geological time scale, we can retain here seven constraints to evolutionary changes. Firstly, developmental constraints result from functional interactions among traits involved in the elaboration of the performance. We use here "performance" as a generic term, referring either to fitness components in an ecological perception or to forestry objectives like wood quantity or quality in an agronomic perspective, or to any combination of these traits. Secondly, genetic constraints result from the genetic architecture of traits, with complex epistatic interactions between several genes on one trait or pleiotropic effects of one single gene on several traits. Actually, forestry practices have little (but not null) impact on these first two limiting factors. Then, Futuyma (2010) identified four limiting factors of adaptation on which forestry practice may have direct or indirect impact: lack of genetic diversity, demographic stochasticity (counter-acting directional selection), random genetic drift and asymmetric gene flow (e.g. at niche limits). In addition to these, Kuparinen et al. (2010) identified another limiting factor potentially affected by forestry practice: low mortality.

Deciphering the factors that determine adaptation in the real forest, from the genes to the traits and from the traits

to the performance, is complex. Each environment cannot be reduced to only one parameter, e.g. altitude combines temperature, soil, rainfall, biotic factors etc. Similarly, each performance, e.g. survival in stress conditions, can be achieved by different combinations of functional trait values. Finally, each value of a functional trait can be obtained by different combinations of gene alleles and interactions. As a consequence, one can hardly attribute a fixed intrinsic adaptive value to each physiological trait or to each gene allele. This complexity is also a chance for adaptation because it provides flexibility and there are multiple biological pathways to reach an ecological solution.

Forest management can enhance forest adaptation to climate change in three ways. Firstly, a full-control strategy consists in replacing the local population by a presumably better fit population. This is achieved through plantation of so-called forest reproductive material, which either comes from a breeding program or from a selected seed stand. This strategy allows for drastic stepwise evolutions, but it requires minimizing uncertainties about the ecological integration of the alien resource in the new site under future climates. Secondly, a driving strategy consists in guiding, i.e. supporting and accelerating, natural evolutionary processes using the local genetic resource, ecologically integrated within its current environment. This is achieved through natural regeneration. This strategy only produces progressive changes, limited by the evolutionary potential of the local resource, but it is flexible and relaxes the ecological uncertainty related to introduction of alien material. Thirdly, a combined strategy would follow the driving strategy after enrichment of the local resource with a certain amount of alien material in order to increase the evolutionary potential and to accelerate evolution while limiting the ecological uncertainty due to introduction. Since the first approach has already been treated elsewhere and deserves a complete treatment, e.g. see St Clair and Howe (2007) for a concrete experience in *Pseudotsuga menziesii*, we focus here on the second and third strategies. Three main questions emerge in this context: (1) How fast can tree populations respond to changes? (2) Will the populations keep their capacity to adapt to both continuous and unpredictable changes? (3) How can forestry practice affect, positively or negatively, the properties of adaptation and adaptability through time? Due to the complexity of evolutionary mechanisms interacting with highly diverse local conditions and climate change scenarios, the first two questions can only receive case-specific answers. Here, we call evolution-oriented forestry a particular form of adaptive forestry that integrates the enhancement of evolutionary processes among its possible objectives, and we propose a process-based approach to investigate the impact of silviculture on the evolution and evolvability of tree populations facing climate change.
In a first part, we describe the basic evolutionary mechanisms using a limited number of parameters in order to define a simple analytical grid. We show how these few parameters can help understand complex situations. The analytical grid also provides a mechanistic interpretation of the evolutionary constraints mentioned above. Secondly, we use this analytical grid to evaluate the potential effects of current forestry practice and to suggest other silvicultural options that could preserve as much as possible the objectives of forestry while driving the tree populations into faster evolutionary changes. Finally, we review the possible genetic tools available for monitoring adaptive changes and evolutionary processes and conclude with future perspectives for experimental management and research.

## The basic mechanisms driving genetic changes as an analytical grid

Genetic diversity is continuously changing: each sexual reproduction event generates new and unique genotypic combinations, some of which are then eliminated by selection and random processes. The strength of selection and genetic drift can be efficiently approached with a limited number of parameters. A wide range of evolutionary scenarios can be obtained when considering the interactions between selection, genetic drift, gene flow and plasticity. Due to the short term considered here, we neglect the effect of mutation.

### Single and multitrait response to selection

Selection is the elimination of the less fit, due to low reproduction or mortality. A selection pressure on one trait can change the mean of the population (directional selection), or its variance (stabilizing or disruptive selection), or both. Although it does not integrate all the biological processes that effectively operate, the model of quantitative genetics (Falconer 1960), which assumes that a large number of genes interact with the environment to determine the phenotypic variation of each trait, has long proven a remarkable predictive value throughout all the practical achievements in plant and animal breeding programmes since mid twentieth century. This model predicts the rate of change of trait mean per generation under direct directional selection as:

$R = i.h.σ_A$ or, expressed in phenotypic standard deviation units, $R' = R/σ_p = i.h^2$

where

i is the intensity of selection, i.e. mean differential between the adults that contribute and those that do not contribute to the next generation expressed in standard deviation units (i directly relates to the proportion of selected individuals)

h is the square root of the narrow sense heritability, which is the ratio between the additive genetic variance and the phenotypic variance ($h^2=σ_A^2/σP^2$)

σA² is the additive genetic variance

σp² is the phenotypic variance

FIGURE 1

Expected rate of change per generation (R') for a single selected trait under direct selection, expressed in phenotypic standard deviation units, for different values of heritability ($h^2$) and different selection intensities (i). Assuming a Gaussian distribution of the trait, elimination of 25, 50 or 75% of the population corresponds to values of i = 0.42, 0.80 or 1.27, respectively. The range of empirical values of R' found in the literature by Gingerich (2009) lies between the dotted lines.

<figure><img src="image_63.png" /></figure>

Natural selection does not operate directly on the traits but on the global fitness of the individuals. Therefore, the response of each trait to natural selection is a type of indirect selection, and the previous equation is slightly modified as: $R = β.σA^2$ where β is the partial regression coefficient of the fitness on the trait, or selection gradient. This formulation extends to the multitrait case where the response on each trait integrates its correlations with other selected traits (Lande and Arnold 1983). Finally, selection is summarized with only four basic parameters: σP², σA², i (direct selection) or β (natural selection).

Based on a review of empirical studies in all kinds of organisms, Gingerich (2009) found relatively high rates of evolution (R') in the magnitude of 0.1 to 0.3 phenotypic standard deviation per generation, similar for long-term evolution and micro-evolution. Figure 1 shows the expected rate of change of a single trait under direct selection with different values of heritability and selection intensity. As explained in the next section, forest management can modify i and, therefore, proportionally change R'.

A trait will respond to selection if three conditions are simultaneously met (Endler 1986): (1) there is phenotypic variation within the population, (2) this variation is heritable and (3) this variation is correlated with the variation of fitness. Each term in the equation above is not fixed, but it varies depending both on the environment (Charmantier and Garant 2005) and the genetic background of the population (Carter et al. 2005). Natural selection in trees is a complex process because the
selection pressure varies between years and it can drastically change between life stages both in direction (e.g. shift in selective forces between a dense seedling patch under the canopy and the adult stage) and in intensity (e.g. mortality rate is much higher at juvenile stage while selection on reproductive success only occurs

at adult stage). Thanks to this variability of the parameters, the response to selection does not systematically correlates with a total exhaustion of the genetic variation within populations: the partitioning of the genetic variance of adaptive traits into between- and within-population components (Qst approach) has revealed that a large part

## BOX 1

### Raising-up complexity (1): the interplay between biotic interactions and evolutionary processes in forest trees

Trees interact with diverse mutualistic and antagonistic insect species. Mutualistic insects may be essential for plant reproduction during pollination, while antagonistic insects may be damageable to plants by consuming and removing plant parts and by selectively feeding on their reproductive tissues (Crawley 1989). The strength of such negative effects depends on the timing, the type and the amount of damage, as well as the stage of the plant's life cycle at which the damage occurs (Marquis 1992). By directly affecting tree reproduction or survival, the demographic and evolutionary consequences of the feeding activities of seed-specialized and tree-killing insect species are thus likely to differ from most forms of herbivory which only result in partial removal of tissues from individual plants (Hulme 1998).

Since seed predation leads to the eradication of individuals in a population, it plays a crucial role in plant population dynamics with possible genetic drift effects when population size is limited and potentially acts as a selective force driving the evolution of particular plant traits such as flowering synchrony, flowering phenology, inflorescence characteristics, flower size, flower longevity and mast seeding (Janzen 1971; Brody 1997; Fenner et al. 2002; Cariveau et al. 2004; Rose et al. 2005; Strauss and Whittall 2006). Many tree species suffer from large seed losses due to pre-dispersal seed predation, which can have significant effects on recruitment and plant population growth rate (Maron and Crone 2006; Kolb et al. 2007). However, the effect of such parasites on the long-term fitness of their host plant appears controversial (Crawley 1989; Horvitz and Schemske 2002), mainly due to the lack of data addressing this issue, especially on perennial plants. There is still a crucial need for studies examining the genetic consequences of massive seed losses within a host tree population, especially in a context in which pre-dispersal seed predation shows significant variation between trees. Indeed, seed loss due to seed-specialized chalcid wasps may vary from less than 1% to 100% between trees (Roques 1981; Rappaport et al. 1993). At the tree population level, this raises the question of how such local variation in pre-dispersal seed predation may increase or, reversely, decrease the variance of effective seed set among trees, which influences the effective population size.

Evidence of insects directly acting as selective agents on forest trees is still lacking in the literature. Despite the extremely high tree mortality rates recorded during population outbreaks of the bark beetle Dendroctonus ponderosae Hopkins, many trees escape or survive bark beetle attacks, regardless of their vigour, age and/or size

(Ott et al. 2011). Little is known about the heritability of tree traits involved in survival to bark beetle attacks such as resin acids (Baradat et al. 1978) and resin flow, viscosity and rate of crystallization (Nebeker et al. 1992), in the exception of monoterpene production, which has been shown to be under strong genetic control (Ott et al. 2011).

Understanding how abiotic and biotic disturbances and tree dynamics are interdependent is also crucial for predicting the overall impact of parasitism on tree evolution. Indeed, severe abiotic changes such as droughts and/or heat waves may affect trees and parasites, as well as their interactions (Jactel et al. 2012). Successive drought episodes can affect directly tree survival (Allen et al. 2010), or indirectly when higher temperatures and lower tree resistance trigger severe forest insect outbreaks (OFEFP 2005; Netherer and Schopf 2010; Durand-Gillmann et al. 2012). The interdependence between climate, biotic factors and tree dynamics remains complex to predict. Drought induced changes in tree nutritional quality (water, carbohydrates and nitrogen contents) or in tree defence mechanisms can limit the development and the damages of parasites (Rouault et al. 2006; Jactel et al. 2012; Forkner et al. 2004). Extreme droughts may even be directly involved in the collapse of herbivorous populations at wide scales (Yarnes and Boecklen 2005). But drought can also affect negatively tree physiology and decrease the effectiveness of tree resistance mechanisms to pathogens and parasites (McIntyre et al. 1996).

Fire ecology provides interesting additional examples of the complexity of integrating interdependencies between trees, biotic and abiotic factors. Bark beetle outbreaks and forest fires have indeed jointly increased in extent and severity during the last decades, raising concerns about their possible interactions (Parker et al. 2006; Simard et al. 2011). Bark beetle outbreaks may increase the probability and intensity of active crown fire because they create great quantities of dead and ladder fuels (Brown 1975; McCullough et al. 1998). However, Simard et al. (2011) suggest that active crown fire are less probable in the short-term after outbreaks due to insect-driven stand thinning, while the probability of passive crown fire does not change in the short term but greatly increases in the decades following an outbreak. Thus, bark beetles are likely to indirectly affect non-attacked trees through subsequent enhanced fire risks. This clearly illustrates the critical need to integrate the possible interplay between the abiotic environment, biotic interactions and trees dynamics when designing forest management strategies.
of the genetic variance of functional traits is maintained within population (Alberto et al. 2013, for a review), and an absence of within population genetic variance for a quantitative trait has exceptionally been reported in trees (Sáenz-Romero et al. 2006). Interestingly, the genetic architecture of a trait, i.e. the system of genes involved in the variation of the trait and their interactions with other traits, simultaneously determines long-term persistence of evolvability by a capacity to release cryptic variation in a new environmental or genetic context (Le Rouzic et al. 2007) as well as a potential limit to selection in case of detrimental genetic correlations (Walsh and Blows 2009). Functional constraints, resulting in environmental correlations between traits, can also limit the response to selection. Not included into this predictive model, epigenetic effects, i.e. environmentally determined heritable modification of gene expression, can also contribute to adaptation to sudden changes (Bossdorf et al. 2008; Skrøppa et al. 2010).

## Random changes due to genetic drift and mating system

Genetic drift accounts for the reduction of genetic diversity that occurs in small populations, in absence of selection, mutation or migration, due to the variation of allele frequencies after random sampling from one generation to the next. Furthermore, small populations are prone to increased inbreeding due to the higher probability of mating between relatives. Inbreeding has a twofold effect: it reduces fitness whenever inbreeding depression is present, and it retains non-random association of gene alleles (linkage disequilibrium) at higher rate which represents a reduction in the diversity of genotypic combinations. Non-random mating system can also affect inbreeding: the mating system varies among individuals and populations, including selfing rate (most of tree species are not dioecious) and diversity of pollen donors, depending on the relative fecundity and spatial distribution of reproducing trees.

Under the assumptions of Wright-Fisher's model population (Wright 1931), the reduction of gene diversity and the increase of inbreeding are driven by one single parameter, population size. Using this model as a reference, the effective population size (Ne) of a real population of size *N* that has a per-generation rate of reduction of gene diversity (*DHe*) or increase of inbreeding (*DF*) is such that: *DHe* = -1/2*Ne* or *DF* = 1/2*Ne*. It can be shown that, in the absence of dominance, Ne also measures the per-generation rate of reduction of additive variance: *DσA*² = -1/2*Ne*. Ne is defined on the rate of change of gene diversity or inbreeding, not on the actual population size *N*. Ne is most often not directly estimable in natural populations (unless longitudinal estimates of gene diversity, He or σA², or inbreeding, F), but its changes can be predicted and decreasing Ne means intensifying the intensity of genetic drift. When the actual population

only departs from the theoretical model by relaxing the assumption of Poissonian distribution of reproductive success, it can be shown that: $Ne = (4N-2)/(V+2)$ where V is the actual variance in reproductive success, i.e. Ne decreases substantially in proportion to this variance.

Tree populations are generally assumed to have large effective population size (Petit and Hampe 2006), in part because they are outcrossing and disperse their genes over long distances in particular through pollen (Ashley 2010); thus, they should not be too much affected by genetic drift. In their review, Schoen and Brown (1991) found Ne estimates for tree species in the range of other outbreeding plants: mean values around 3,000 for *Pseudotsuga menziesii* and >8,000 for *Pinus sylvestris* and *Picea abies*, with high variations among populations within each species. However, locally, seed and pollen contributions to reproduction are highly uneven among individuals (Burczyk et al. 2002; Krouchi et al. 2004; Oddou-Muratorio et al. 2005), and the great majority of the pollen disperses only in the close neighbourhood, which can greatly reduce the effective population size. In their review, Smouse and Sork (2004) found that the effective pollen pool size Nep, defined as the inverse of the probability that a female draws two offsprings from the same father, ranges from 2 to 200 in tree populations. Nep can be very small in some populations: fragmented populations of wind pollinated species of *Quercus humboldtii* (Fernandez-M and Sork 2005) and *Quercus alba* (Smouse et al. 2001) exhibit estimates of Nep around 6 and 8 respectively. By contrast, in continuous forest populations, several examples estimate high values of Nep: Robledo-Arnuncio et al. (2004) estimated a Nep >70 in a Spanish population of *Pinus sylvestris*. Fragmented populations, isolated populations and populations at low density have a higher risk of extinction due to the erosion of diversity by genetic drift (Goodell et al. 1997; Hardy et al. 2004; Robledo-Arnuncio et al. 2004; Aguilar et al. 2008).

## Interactions between selection, drift, gene flow and phenotypic plasticity

A well-known interaction between drift and selection is the vortex of extinction (Gilpin and Soule 1986): when there is a genetic load in the population, a rapid decrease in population size leads to increased genetic drift and increased inbreeding, resulting in reduced mean fitness that further reduces population size, which over time will result in extinction in a geometric decline. However, there is no experimental evidence to our knowledge that this kind of extinction vortex ever occurred in trees. Alternatively, resistant genotypes that emerge in the population increase their contribution to the next generation and can restore population growth if not eliminated at random, a process known as evolutionary rescue (Gomulkiewicz and Holt 1995). Whether
populations can be rescued depends on population size, genetic diversity and the degree of maladaptation to the new environment.

In the case when the environment changes both in space and time, gene flow can bring into the population pre-adapted genes (Pease et al. 1989; review by Kremer et al. 2012). Kuparinen et al. (2010) showed that pollen and seed dispersal at longer distance speed up the adaptation process. In tree populations, it is expected that pollen-mediated rather than seed mediated gene flow will contribute to this processes, with average pollination distances commonly being hundreds of meters (Ashley 2010), and maximum distance of 100km measured in *Pinus sylvestris* airborne transported pollen (Robledo-Arnuncio 2011). In this species, along a latitudinal gradient, Nilsson (1995) showed that long-distance pollen migration brings into the population new phenotypes with a phenology specific of other locations. Local individuals with extreme phenotypic values, in particular for flowering phenology, are keen to catch more long-distance pollen grains because they are better synchronized with source population of interest and also because they are less saturated by local pollen.

Abundant theoretical literature exists on the role of phenotypic plasticity in evolution; recent reviews of predictions on the interaction between selection and plasticity in the context of climate change can be found in Chevin *et al.* (2010, 2012), the second review also provides key references of empirical studies of plasticity in trees. Maladaptive plasticity is obviously detrimental to adaptation. When adaptive plasticity is not genetically variable, it is expected to slow down the genetic response to directional selection in each generation but also to allow

the phenotypes to track the environmental change more closely. The sustainability of this process of adaptation depends on the fitness cost of plasticity. When adaptive plasticity varies genetically, i.e. there is GxE interaction and plasticity can evolve, the amount of genetic variance of the plastic trait depends on the environment: if the new environment increases the genetic variance, then plasticity tends to accelerate the genetic response to selection and plasticity is itself selected for (Lande 2009). Considering a steep spatial environmental gradient, where gene flow interacts with selection and plasticity, the evolution of plasticity is expected to allow the population to explore a larger range, and marginal habitats are expected to show higher plasticity (Chevin and Lande 2011).

A further degree of complexity arises when considering the interplay between abiotic environment, biotic interactions and evolutionary processes (Box 1).

## Potential effects of forestry practice on the rate of evolution of trees

### A global framework to assess the evolutionary impact of silviculture

Considering the evolutionary impact of silviculture is thus an additional requisite to adaptive forestry. While facing climate change and uncertain future, forestry practice should simultaneously accelerate genetic adaptation by helping tree populations to track the known environmental changes and preserve the genetic diversity as a reservoir of future options to respond to the next unknown changes. This is a classical challenge in breeding programs: obtaining a rapid genetic gain while keeping the potential for long-term response to selection. Breeders solve this problem by combining selection and recombination. Genetic adaptation occurs if better performing genotypes emerge during reproduction and if the best performing genotypes spread in the population before extinction. Therefore, we can assign three objectives to evolution-oriented forestry practice: (1) increase the chance of emergence of “innovative” genetic combinations, (2) facilitate the spread of the best adapted genotypes and (3) preserve genetic diversity for long-term response to selection. Acting simultaneously on the demography and the environmental conditions, silviculture has an impact on the parameters of selection and genetic drift. Due to the multiplicity and complexity of the processes involved and to the huge diversity of biological and environmental features among forests, it is more reliable to predict the effects of silviculture on the parameters of evolution rather than on the final state of the genetic diversity. Thus, silviculture should aim to limit the intensity of genetic drift, increase the diversity of mating pairs, avoid counter-selection and maintain selection pressure in the right direction, reduce inbreeding etc. Here, we briefly consider the evolutionary benefits, or risks, associated to current or future forestry practice. A more quantitative prediction of

<figure><img src="image_64.png" /><figcaption>**FIGURE 2**

Expected effects of forestry practice on evolutionary processes: schematic pathway from the forestry management in natural regeneration systems to the selection and genetic drift parameters. See text for explanation of the paths (σP² phenotypic variance, σA² additive genetic variance, β selection gradient for natural selection, i selection intensity for direct selection, s selfing rate, Nep effective pollen pool size, V variance of reproductive success, Ne effective population size, F inbreeding).</figcaption></figure>
their balanced effects could rely on simulation studies using process-based models that explicitly integrate demographic, genetic and biophysical processes and allow to simulate interventions (Kramer *et al.* 2008).

## Foreseen evolutionary impact of common forestry practice

A general consequence of silviculture on the drivers of evolution is the change of environmental conditions: competition and, possibly, other biotic or abiotic environmental factors. As mentioned above, the environmental conditions determines not only the phenotypic variance (σP²) of the traits but also the genetic variance (σA²), in case of GxE interactions, and the selection gradient that relates trait value to fitness (β) (Figure 2). Furthermore, increased canopy closure also affects pollen dispersal and mating system parameters: selfing rate (s), effective pollen pool size (Nep) and variance of reproductive success (V) (García *et al.* 2005; Milleron *et al.* 2012).

Beyond these general effects, thinning, whether systematic or selective, will affect the spatial clustering of genetically related reproducing trees and their allocation to reproduction and, therefore, the mating system (Figure 2). By removing neighbouring-related individuals, thinning may reduce spatial genetic structure and, consequently, inbreeding in the regeneration (Dounavi *et al.* 2002). Sagnard *et al.* (2011) showed that, when the density of seed trees is low (<16 trees.ha⁻¹), a clustered distribution of seed trees will produce less spatial genetic structure in the seedlings than a random or dispersed distribution. Robledo-Arnuncio *et al.* (2004), in *Pinus sylvestris*, and Restoux *et al.* (2008), in *Abies alba*, found at seed stage that low densities of reproductive trees simultaneously increase the probability of selfing (from <5% to >20%) and the diversity of the outcrossing pollen (*Nep*), which can be mechanistically explained by a higher rate of long distance pollen pollination (Klein *et al.* 2006). Therefore, lower seed trees density can result in higher genetic diversity at seedling stage, whenever pollen is not limiting and purge of inbreeding occurs early, but it can be a risk when pollen is limiting, which must be considered in the context of climate change.

When the local population size is limited, a genetic drift effect may result from the reduction of the number of reproducing trees, leading to a loss of rare alleles (among which currently deleterious genes). Such effect was observed in old-growth forests of *Pinus strobus* comparing pre- and post-harvest stands (>100 and <30 trees, respectively) (Buchert *et al.* 1997; Rajora *et al.* 2000), as well as in *Picea rubens* old growth forests (Mosseler *et al.* 2003). Konnert and Hussendörfer (2001) compared 16 even-aged and nine uneven-aged management systems in *Abies alba*, several forests for each group and also the two management systems within the same forest: they found a slightly higher number of rare alleles but smaller

number of different gametic combinations in the uneven-aged forests. However, there are too few studies of this type to draw general conclusions on an eventual intrinsic difference between these two management systems regarding their effects on the genetic diversity. The drift effect is not a priori limited to the managed population but it may also affect secondary tree species as observed for *Abies amabilis*, *Tsuga heterophylla*, *Thuya plicata* and *Pinus monticola* in *Pseudotsuga menziesii* forests by El-Kassaby and Benowicz (2000).

In naturally regenerated stands, reproductive trees usually result from successive selective thinnings for their phenotypic value such as stem vigour, health consideration, lack of defects like thick branches or forked or twisted trunk, as well as for their spatial distribution in order to reduce competition. In *Fagus sylvatica*, selective thinning favouring the most vigorous trees was found to increase the heterozygosity by 4% to 9% (Lauber *et al.* 1997; Dounavi *et al.* 2002), even when selection occurs at a very early stage (Thiebaut *et al.* 1992). However, this effect was not detected in other studies on *Abies alba* (Hussendörfer and Konnert 2000) or *Pinus contorta* (McDonald *et al.* 2001). We must remind here that an increase in heterozygosity during maturation of forest stands is frequently observed and that natural selection of seedlings can start at a very early stage in overstocked young regeneration (Pichot *et al.* 2006). Selective thinning favouring the best growing trees may act as selection for competing ability: assuming a Gaussian distribution of tree height, the elimination of the 25% (resp. 50%) smallest individuals represents a selection intensity i = 0.42 (resp. 0.80) on this trait. Selective thinning may also integrate a direct selection on other traits chosen by the forester (Figure 2). We need to understand more clearly which functional traits are indirectly selected for and how these traits relate to future fitness in the context of changing climate. One main question that still needs to be addressed is how far silviculture will intensify selection for juvenile vigour and, if this is the case, how far juvenile vigour is genetically positively or negatively correlated with drought resistance.

The intensity of genetic drift, through the variance in reproductive success, and the mating system not only depends on the number and spatial distribution of reproductive trees but also on their allocation to reproduction. As indicated in Figure 2, this allocation is influenced by local stand density as well as other practices like pruning (Ayari *et al.* 2012). Thinning and pruning also affect the plasticity, which will interact with selection processes as previously mentioned: the question here is whether the plastic response induced by the silviculture is adaptive or maladaptive regarding future climate change.

Multiple forestry practices may also be combined and interact to modify the microenvironmental conditions of development, generally as to reduce stress and
competition in order to favour growth (Forrester et al. 2012). Thus, after an extreme drought event in 1976, stand decline was reduced in *Picea abies* stands that had previously been thinned in 1971 (Misson et al. 2003): compared to the control plot, heavy thinning had a more beneficial impact than moderate thinning (thinning from 36 to 14 m².ha⁻¹ or to 20 m².ha⁻¹ basal area, respectively). We are not aware of any study of the effects this could have on plasticity (e.g. reduced acclimation to future stress) and selection. As a case study, we present the analysis of evolution and potential effects of silviculture in the situation of highly heterogeneous environment within the forest (Box 2).

## Evolutionary benefits and risks expected from some silvicultural recommendations related to climate change

New forestry practice is progressively implemented to reduce ecological and economic risks related to climate change (Legay and Mortier 2005; Yousefpour et al. 2012). From an evolutionary point of view, reducing environmental stress has a twofold effect. On the one hand, it reduces the damages and therefore contributes to increase the effective population size (Ne), which is a crucial issue when population size is already small or is expected to decrease drastically due to severe damages. But, on the other hand, it also slows down the genetic improvement in the next generation by reducing selection intensity (i) and it does not exploit potential adaptive

plasticity (no acclimation to future stress), which is an important issue to consider in large populations. We briefly consider here some of these practices, or changes in practices, from the evolutionary point of view and illustrate their possible balanced effects. In all cases, whenever applying new practice, it is crucial to keep precise records of what is done, how and when, in order to facilitate future evaluation *ex post*, in particular after marked climatic events.

Shortening rotation reduces the probability of the risk, e.g. to extreme climatic events, but it can also increase the vulnerability to the risk if shorter rotations select for higher juvenile vigour and if juvenile vigour is genetically negatively correlated with stress resistance. Both of these conditions still have to be investigated. The answer will probably depend on the species, on the environment and on the management system considered.

Reducing the density of stands is envisaged to reduce the effective drought stress supported by the trees. However, this immediate positive effect may be partly balanced by a long-term detrimental effect on selection by inducing a maladaptive phenotypic response and by reducing selective mortality (see first section). This risk is reduced if time is left for sufficient natural selection to proceed before thinning. More generally, from the evolutionary point of view, interventions occurring at juvenile stage raise the question of age-age correlations. Apart from the

## BOX 2

### Raising-up complexity (2): evolution and silviculture in a highly spatially heterogeneous environment

In trees, the selection process is complex due to the long life-cycle and the high within-stand spatial environmental heterogeneity. Firstly, for long-lived and sessile organisms, different selection pressures may occur successively from the juvenile stage to the adult stage, e.g. selection for competing ability in a young dense regeneration vs. selection for stress resistance in the adult stage. Secondly, in heterogeneous environment, the phenotypic correlation between parents and offspring do not only depend on genetic control of the phenotype but also on the difference in environmental effects between the parents' and offsprings' sites. Finally, the environmental heterogeneity induces spatial variation in selection pressure, eventually leading to different selection pressure between parents and offsprings. Thus, evolution-oriented forestry should take environmental heterogeneity into account.

As a general objective, evolution-oriented silviculture should aim at favouring the mating success of the best growing trees located in the patches where the highest desired selection pressure occurs. In homogeneous conditions where the selection pressure is uniform, this objective is directly achieved by classical selective thinning. In heterogeneous conditions, this objective could be achieved if enough trees are selected for seeding

within each patch where high selective pressure occurs, even though these trees may have lower growth than neighbours growing in more favourable conditions.

In such context, assessing and mapping the environmental heterogeneity among patches is essential to avoid confusion between micro-environmental and genetic effects on the performance of the trees. It is crucial not only to assess the individual performance but also the patch conditions with synthetic indicators, easy to use and independent of the competition, such as site index, species composition of vegetation etc. Local variations of site index can be assessed through spatial patterns of tree height; therefore it would be easier to identify and mark very early the 'good' phenotypes in highly selective areas, before a substantial reduction in number of trees happens due to other criteria.

Genetic improvement of the whole population will occur if these highly selected trees effectively contribute to the regeneration at stand level. If pollen or seed dispersal limits their effective contribution, considering here fecundity as included into the global dispersal process, it might be necessary to assist natural regeneration by local seed transfer from low to high selection patches within the stand.
temporal changes in environmental conditions, life stages
differ in their physiology and development. During the
complex and temporally changing selection process in
trees, juvenile-adult genetic correlations contribute to
determine how far a selection pressure (or release of
selection) during the juvenile stage will genetically affect
the adult population. This issue can hardly be addressed
*in situ*. Partial answer, here again, comes from the breeding
experience and early selection schemes. Studies on
growth and wood density in different *Pinus* species
revealed that genetic age-age correlation >0.8 is generally
achieved from the age of 10-12 years (Hannrup and Ekberg
1998; Gwaze *et al.* 2000; Wu *et al.* 2007; Bouffier *et al.*
2008). It varies greatly with environmental conditions, and
in *Pinus radiata*, this level of correlation can be reached as
soon as 2-5 years in certain sites (Gwaze *et al.* 2000; Wu
*et al.* 2007). Matheson *et al.* (2002) showed that genetic
age-age correlations in *Pinus radiata* also vary with the
genetic background with higher correlations in presence
of inbreeding. Thus, silviculture may have an effect on age-
age correlations through its effects on the environmental
conditions and on the genetic background. Further
investigations on age-age correlations of functional traits
are deeply needed.

In the case of massive dieback, sanitary logging is
necessary to reduce the spread of primary or secondary
parasites, and it can also be necessary for fire prevention
or for the protection of forest users. However, excessive
elimination of surviving trees could result in the elimination
of resistance to the pathogen (Burke 2011).

Evolution-oriented forestry, why not?

We imagined some specifically evolution-oriented forestry
practices in the case of natural regeneration management
system (Table 1). These interventions should not be directly
considered as recommendations or guidelines as such; we
rather propose them as case studies to illustrate innovative
adaptive forestry that would take into account short- and
long-term evolutionary potential, still to be associated with
other clues. This is not an exhaustive list, and any
combination of the proposed interventions can be
envisaged.

In order to reduce the intensity of genetic drift (increase Ne) in small populations, silviculture may be oriented towards reduced variance of fecundity (V) between trees: reducing V will not only increase Ne within each annual seed production, as mentioned above, but it will also reduce the fluctuation of effective contributions across years and thus increase pluri-annual Ne estimates (Krouchi et al. 2004). This would be another objective assigned to thinning and pruning. It requires a balance between keeping sufficient number of seedling trees and sufficient spacing between them (optimisation should be made on a case-by-case basis, depending on dispersal capacities). Due to the tree x year interaction effect on the variation in fecundity generally observed in trees, a general

recommendation would be to cumulate reproduction
during several years. Actually, current practice may already
be optimal for this purpose. A negative side effect is to
slow down the elimination of detrimental alleles (Couvet
and Ronfort 1994) and reduce the response to selection.
A compromise between preserving the genetic diversity
for the future (reduce genetic drift) and accelerating the
immediate response to selection could be to equalize the
mating success per patch, in particular when the
environment is spatially heterogenous (Box 2).

In order to reshuffle the local genetic diversity and
increase genetic recombination, silviculture could enhance
local gene flow, either through artificial dispersal of local
seeds or by assisting pollen dispersal. With the same
objective, isolated seed trees should be considered with
care: from one side, they may have a higher selfing rate
but, on the other side, they can capture long distance
pollen flow. If selfed seeds are eliminated at an early stage
of development (e.g. empty seeds in some conifer
species), the fertile seeds hamper a large genetic diversity.
In the case of heterogeneous environment within the
forest, areas for wood production and areas for evolution
could be spatially dissociated while maintaining gene flow
between these entities (Box 2).

In an environmental cline, typically an altitudinal cline, a
strategy might be to accelerate the migration of the
population towards more favourable areas. The velocity of
migration depends on the effective dispersal, and effective
dispersal is highly dependent on the local conditions for
seedling establishment conditions (Amm et al. 2012). We
can imagine to enhance seed germination and seedling
growth by preparing the soil or controlling competition
and predation at distance from the core of the population,
in the direction wanted for migration. For zoochorous
species, we can also imagine to attract seed dispersers
along the wanted migration route (Oddou-Muratorio et al.
2004; García et al. 2009; Schleuning et al. 2011).

Genetic enrichment of the local genetic resource by the
introduction of a limited amount of allochthonous material
from a putatively pre-adapted origin, through seed or
pollen introduction, could present a twofold benefit of
introducing gene alleles of interest and increasing the
global genetic diversity. To avoid gene swamping effect
and reduction of the effective population size, it is essential
to use a large genetic base of the introduced material
(Lefèvre 2004). For long-lived organisms, it is also
important to anticipate a possible trade-off between
adaptation to long-term climatic trend and adaptation to
current conditions and/or to annual fluctuations, such as
vulnerability to late frost of early flushing genotypes.

As a complete utopy, we can imagine future access to
intensive genotypic data on each adult tree. Inspired from
the marker-assisted selection strategies used in plant and
animal breeding, marker-assisted selective thinning could
combine the objectives of increasing the adaptive change for the target traits that are unambiguously identified while preserving the maximum diversity in the rest of the genome.

## Genetic monitoring and study tools

In a recent publication, Hansen *et al.* (2012) reviewed the different tests and approaches for genetic monitoring of adaptive changes using phenotypic or molecular tools. Their focus was on the capacity to demonstrate the adaptive response and rule out alternative hypotheses that might explain the genetic change. In this section, we investigate how far recent genetic monitoring methods and tools can help to rationalize evolution-oriented forestry.

## What's new in molecular and phenotypic tools?

A comprehensive review of the genetic markers and their use in trees was published by Prat *et al.* (2006). Almost all kinds of markers have been developed on one or several tree species, and they were mainly used to infer on the neutral genetic diversity and neutral processes (drift, mating system, dispersal). With the classical genome sequencing projects, which started in the 1990s for trees, a gap in terms of available tools had progressively appeared between a very limited number of model tree species and the other species. In the last 3 years, recent advances in DNA sequencing have revolutionized the field of genomics making it possible to generate a large amount of sequences and markers in time- and cost-effective way. Nowadays, thanks to the emergence and evolution of the so-called next generation sequencing techniques and

<table><caption>TABLE 1</caption><caption>Some examples of evolution-oriented forestry practice, including re-orientation of usual interventions (no supplementary cost) and additional interventions</caption><thead><tr><th>Forestry practice</th><th>Expected benefits</th><th>Associated costs and risks</th></tr></thead><tbody><tr><td>Ne-oriented regulation of the density and spatial distribution to equalize reproductive success between trees in small populations</td><td>- Reduce the variance in reproductive success to reduce genetic drift<br/>- Reduce spatial genetic structure in the seedlings and inbreeding in next generation</td><td>- No supplementary cost<br/>- Risk to slow down the elimination of detrimental genes, prefer equalization of mating success per patch (compatible with the next line)</td></tr><tr><td>In heterogeneous environment, dissociate areas of production and areas of evolution (selection patches in harsh areas) and allow gene flow between these entities</td><td>- Increase the reproductive contribution of the trees that have survived to drastic selection pressure</td><td>- Limited supplementary cost<br/>- Requires preliminary simulation studies to estimate benefits in various contexts (strength and spatial structure of the environmental heterogeneity)</td></tr><tr><td>Save the lone tree, which cumulates long distance dispersal (in allo-pollinated seeds) and can be adapted to marginal conditions; collect seeds for local assisted regeneration</td><td>- Diversify the mating pairs to favour the emergence of new genotypic combinations<br/>- Promote adaptation to marginal conditions</td><td>- Limited supplementary cost<br/>- Requires a protocol for assisted regeneration<br/>- Risk of inbreeding if self-pollinated seeds are not purged at a very early stage (e.g. seed abortion)</td></tr><tr><td>Assisted local seed dispersal (e.g. collecting, possibly over several years, mixing and replanting seeds within the stand) or pollen dispersal (e.g. air flow used in seed orchards)</td><td>- Enhance local gene flow to diversify the mating pairs and favour the emergence of new genotypic combinations<br/>- Reduce inbreeding</td><td>- Potentially significant supplementary cost<br/>- Requires preliminary studies to estimate benefits in various contexts (genetic diversity and spatial structure)<br/>- Requires a protocol for assisted regeneration</td></tr><tr><td>Enhance local migration capacity by favouring seed dispersal and germination at distance from the main stand</td><td>- Speed-up colonisation of locally favourable habitats in an environmental gradient</td><td>- Potentially significant supplementary cost</td></tr><tr><td>Genetic enrichment by introduction of a limited amount of seeds or pollen from presumably pre-adapted allochthonous origins</td><td>- Introduce pre-adapted genotypes<br/>- Increase local genetic diversity</td><td>- Potentially significant supplementary cost<br/>- Risk of gene swamping and reduction of effective population size (Ne) if local population is small and if introduced material has low genetic diversity<br/>- Risk of unforeseen local maladaptation</td></tr><tr><td>Marker-assisted selective thinning (futurist)</td><td>- Increase selection intensity on target major genes while retaining genetic diversity in the rest of the genome</td><td>- High supplementary cost<br/>- Requires accurate genetic knowledge and high-troughput genotyping capacities</td></tr></tbody></table>
related bioinformatics (Metzker 2009; Kircher and Kelso 2010), full-genome sequencing, reduced-representation sequencing and targeted sequencing are in progress not only for model species but also for non-model species where molecular monitoring is becoming worth considering from scratch. Evidence of genetic changes at molecular level in trees has recently emerged from the direct study of genome wide DNA polymorphisms: evidence of the correlations between genotype frequencies and environmental gradients (Eckert et al. 2010) or climate-related traits (Grivet et al. 2011), sometimes completed with functional information on the detected genes (Holliday et al. 2010). See also Alberto et al. (2013) for a recent review of single nucleotide polymorphisms associated to climate related traits in trees. However, as Rockman (2012) very wisely stated, the extraordinary potential of these approaches should not be misleading: due to the genetic and environmental sensitivity of the response to selection as previously discussed, we should not expect to find many single gene alleles having large, constant and uniform effect in all populations. If such nucleotides are detected, we should not reduce the genetic variation to them because most of the genetic diversity of interest for adaptation to climate change will remain cryptic. These approaches will nevertheless be very useful in providing genetic indicators of the selection pressure.

The phenotypic approach of adaptation has also evolved in two directions. Firstly, the physiologists have produced proxies of physiological functions that can be measured in large sample size (hundreds of individuals). The relation between the measured trait and the actual function is generally indirect and requires careful interpretation, e.g. carbon isotope discrimination or ring density used as proxies of the response to drought (Osório and Pereira 1994; Tene et al. 2011). It is probably worth reminding that splitting an integrated phenotypic trait into simpler functional components does not resolve the complexity, e.g. functional components do not necessarily have higher heritability or simpler genetic determinism than the integrated trait, because new interactions and regulations appear at finer scale. Secondly, methodologies combining phenotypic and genotypic information have been developed to estimate genetic parameters (variances and correlations) *in situ*, i.e. in the natural environment, at any life stage and without requiring controlled pedigrees (Ritland 1996). To estimate selection gradients (β) in trees, a common approach is to use performance traits like survival, growth or reproductive traits as proxies of fitness and study the impact of functional traits on these performance traits in controlled *ex situ* progeny tests. Using this approach in *Quercus suber*, Ramirez-Valiente et al. (2011) detected significant heritability but non-significant selection gradient for carbon isotope discrimination, contrasting with very low heritability and significant selection gradient for specific leaf area. An

alternative approach of selection gradients through the assessment of actual reproductive success *in situ* was recently developed (Oddou-Muratorio et al. 2005; Burczyk et al. 2006; Klein et al. 2011). This method, based on the mixed-mating neighbourhood model, consists in estimating the reproductive success of individual adult trees using spatial genetic data of seedlings and their potential parents and then in relating this reproductive success to phenotypic traits. Bontemps (2012) used this method in a marginal population of *Fagus sylvatica*: in this case, the author found significant heritability and significant selection gradient for carbon isotope discrimination, contrasting with non-significant heritability and non-significant selection gradient for specific leaf area.

## Possible monitoring

Various sets of state and pressure indicators have been proposed for the monitoring of the genetic diversity in forest trees, based on direct genetic assessment or indirect observations through the demography and ecology of the populations (Namkoong et al. 1996; Brown et al. 1997; Koski et al. 1997; Lefèvre and Kajba 2001; Aravanopoulos 2011). Table 2 briefly reviews the possible uses of molecular and phenotypic tools, and the requirements of these uses, for genetic monitoring of adaptation. We distinguish two main objectives for genetic monitoring: (1) quantification and characterization of the genetic diversity and (2) monitoring of the drivers of genetic changes.

The genes controlling the variance of most adaptive traits (quantitative trait loci, QTL) are expected to be numerous with small individual effect. This is confirmed by empirical results, even though molecular tools only detect a small fraction of these QTLs. The QTLs and their genetic effects vary depending on the environment and the genetic background. Therefore, defining the adaptive genetic diversity as the whole set of QTLs potentially affecting fitness components, there is no strict frontier between neutral and adaptive genetic diversity: a gene polymorphism that is neutral in one context may become adaptive in another environmental or genetic context and vice versa. Thus, considering climate change and its uncertainties, quantification and characterization of both currently adaptive genetic diversity and currently neutral genetic diversity are needed. Classically, the overall genetic diversity is assessed with neutral markers (Buchert et al. 1997). Quantification and characterization of the current adaptive genetic diversity is assessed using phenotypic and molecular tools. Using only the phenotypic tools, the assessment of the genetic matrix of variances and covariances of adaptive traits requires known pedigrees and *ex-situ* controlled experiments (preferable in trees for which deep pedigrees are not available in natural populations like in other organisms). Candidate gene polymorphisms, which include direct functional polymorphisms as well as linked markers, provide direct
or indirect information on the diversity of target genes. When markers and phenotypic tools are combined, several approaches can be conducted: QTL mapping in known pedigrees and controlled conditions, QTL association to traits in controlled conditions and *in-situ* studies of QTL association to the environment (Neale and Savolainen 2004).

All kinds of molecular tools of known heredity may provide information on the neutral drivers of genetic changes: genetic drift, mating system and dispersal. More interesting is to investigate the changes in these drivers, which generally requires transgenerational sampling. Genome-wide markers as well as candidate genes polymorphisms are now commonly used to detect signature of past selection events by testing a departure of the diversity pattern from the neutral expectation. Monitoring ongoing selection processes requires to relate directly the trait to the fitness or at least to a performance trait (selection gradient *b*). With phenotypic tools alone, trait to performance mapping can be assessed *ex-situ* with known pedigrees. When molecular and phenotypic tools are combined, *in-situ* selection gradient studies can be performed.

## Conclusion

The adaptive capacity of tree populations is potentially huge, and silviculture can have a significant impact on the rate of phenotypic and genetic change per generation: the rate of change might probably be increased or reduced by a factor two depending on management interventions, which should not be neglected. The concept of evolution-oriented forestry that we introduced here does not pretend to allow for sufficient change in all cases. It should be considered as an option, with different associated benefits, risks and costs than those associated to the plantation strategy. Both strategies can also be combined. In any case, it is crucial to consider the potential evolutionary impact of silviculture when designing an adaptive forestry strategy. We proposed a simple framework to analyse and foresee the effects of forestry practice, and we identified a limited number of evolutionary processes and parameters that could be affected. Only few quantitative predictions can be made today, basically when evolutionary drivers are considered individually, and most expectations remain qualitative. Qualitative expectations can be used to draw research hypotheses. Quantitative predictions are needed to assess more precisely the cost effectiveness of forestry practice

<caption>TABLE 2</caption>

Objectives and requirements of genetic monitoring using molecular and phenotypic tools.

<table><thead><tr><th rowspan="2">Monitoring objectives</th><th colspan="3">Requirements</th></tr><tr><th>Molecular tools only</th><th>Phenotypic tools only</th><th>Combined approach</th></tr></thead><tbody><tr><td><strong>Quantification and characterization of the genetic diversity</strong></td><td></td><td></td><td></td></tr><tr><td>Quantify the global genetic diversity and characterize its organisation</td><td>Neutral markers</td><td>-</td><td>-</td></tr><tr><td>Decipher the genetic architecture of adaptive traits (QTLs, variances and correlations)</td><td>Known pedigrees, validated candidate genes polymorphisms</td><td>Known pedigrees, common garden experiments (ex situ)</td><td>QTL mapping or QTL association studies, ex situ experiments and in situ methods</td></tr><tr><td><strong>Monitoring the drivers of genetic changes</strong></td><td></td><td></td><td></td></tr><tr><td>Monitor recent changes in demography and genetic drift intensity</td><td>Neutral markers, multi-generation sampling</td><td>-</td><td>-</td></tr><tr><td>Monitor the mating system and hybridization</td><td>Neutral markers, multi-cohort sampling</td><td>-</td><td>-</td></tr><tr><td>Characterize pollen and seed dispersal functions</td><td>Neutral markers, seed or/and seedling samples</td><td>Pollen and seed traps</td><td>Combined approach possible</td></tr><tr><td>Detect signatures of past selection (β)</td><td>Genome wide markers and candidate genes polymorphisms</td><td>-</td><td>-</td></tr><tr><td>Monitor current selection gradient (i, β)</td><td>Validated candidate genes polymorphisms, multi-generation sampling</td><td>Known pedigrees, path analysis traits to performance, ex situ experiments</td><td>Selection gradient studies, in situ methods</td></tr></tbody></table>
under various climate change scenarios. Quantitative comparison of evolution-oriented forestry with other baseline management options will require further process-based modelling and simulation studies for different forest types and species, different biotic and abiotic environments and different climate change scenarios. For a better understanding of the limits of the response to selection, we suggest to couple demogenetic models with biophysical models or host-parasite models.

One challenge for forestry decision-making under climate change is to reach a compromise between short-term and long-term objectives, e.g. speed-up the response to current selection pressure while preserving diversity and evolvability for uncertain future. A safe guideline is to favour natural selection for certainly adaptive traits, like drought resistance in the areas where more severe drought is expected, avoid random genetic erosion and increase genetic mixing. Over-selection for undue traits should also be avoided. As we have shown, each forestry practice has an effect on several evolutionary drivers (a geneticist would say pleiotropic effects) and interaction effects of different practices on a single evolutionary driver also exist (epistasis in genetic terms). To understand the global impact of forestry practice, in parallel to modelling approaches, long-term silvicultural options should be experimented. These new experiments will provide to the next generation of foresters very informative results, complementary to those obtained from comparative studies of existing situations.

Local decision should rely on a case by case approach taking into account each particular situation. Following quantitative genetics theory, we expect that in most cases genotypic diversity rather than allelic diversity constrains evolutionary changes. Can we obtain higher rate of change by driving recombination and selection within the current population? Do we need to introduce allochthonous pre-adapted genotypes to accelerate the emergence and spread of adequate allelic combinations? To address these questions and make a diagnostic, combined genophenotypic approaches *in situ* are very promising and should be further developed. Molecular tools initially developed for model species now become available for non-model species. Similarly, methodologies and knowledge about the genetic architecture of traits and phenotype construction (e.g. genetic and environmental correlations, age-age correlations) should be generalized to non-model species to help better understanding the gene to trait and gene to fitness mapping.

## Acknowledgments

We particularly thank two anonymous reviewers and the editor for their relevant and useful comments on a previous version of the manuscript. The opinions expressed here benefitted from background experimental and modelling research funded by the EVOLTREE network

of excellence (www.evoltree.eu), the ERA-Net Biodiversa LINKTREE project, the FRB VARIADAPT project, the ANR MACBI project, the Agropolis Foundation BIOFIS project as well as Région Provence Alpes Côte d'Azur and Département Santé des Forêts.

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M.J. Tallis<fnref n="1" />, Y Lin<fnref n="1" />, A. Rogers<fnref n="2" />, J. Zhang<fnref n="1" />,<fnref n="3" />, N.R. Street<fnref n="1" />, F. Miglietta<fnref n="4" />, D.F. Karnosky<fnref n="5" />, P. De Angelis<fnref n="6" /> and G. Taylor<fnref n="1" />

**Present addresses:**

NR Street: Umeå Plant Science Centre, Department of Plant Physiology, University of Umeå, SE-901 87 Umeå, Sweden.

**Corresponding authors:**

Professor Gail Taylor
School of Biological Sciences, Bassett Crescent East,
University of Southampton, SO16 7PX
g.taylor@soton.ac.uk

**Key words:** Populus, autumnal senescence, cDNA microarray, anthocyanin biosynthesis, elevated CO₂, LDOX.

Published in *New Phytologist* (2010) 186: 415-428, with doi:10.1111/j.1469-8137.2010.03184.x

## Summary

Over recent decades the observed delay in autumnal senescence has been linked to rising temperatures. Here we suggest that increasing atmospheric CO₂ alone may partly account for extensions of the growing season and for the first time, through transcriptome analysis, identify gene expression changes associated with this delayed senescence.

Using a plantation of *Populus x euramericana* grown in elevated [CO₂] (e[CO₂]) using Free-air CO₂ Enrichment (FACE) technology, we investigated the molecular and biochemical basis underlying this response. Leaf biosynthetic pathways influenced by e[CO₂] during senescence were identified using a *Populus* cDNA microarray and an analysis of expression changes for genes representing multiple biochemical pathways, RT-qPCR, and leaf biochemical assays.

Both pathways for secondary metabolism and for the glycolytic pathway were significantly up-regulated by e[CO₂] during senescence. Within these pathways the two most significantly up-regulated ESTs in e[CO₂] represented transcripts for and *LDOX* and *DFR*, which regulate anthocyanin biosynthesis, with a normalised expression (e[CO₂] / a[CO₂]) of 39.6 and 19.3 respectively.

We propose that in e[CO₂] there is an association between increased autumnal sugar accumulation and changes in gene expression in genes determining anthocyanin biosynthesis. This prolongs leaf longevity during natural autumnal senescence through improved stress tolerance.

## Introduction

Evidence from phenological records suggests that recent global warming is leading to longer growing seasons. An analysis of over 1700 species showed significant shifts in plant phenology (Parmesan & Yohe, 2003) including

extension of the growing season (Menzel & Fabian, 1999; Menzel *et al.* 2006; Myneni *et al.* 1997 and Zhou *et al.* 2001) which has been attributed to rising air temperature (Menzel & Fabian, 1999). On average over the past 35 years autumnal senescence has been delayed across Europe by 1.3 days decade⁻<fnref n="1" /> (Menzel *et al.* 2006). However, whilst a strong correlation exists between atmospheric warming and an earlier spring phenophase, the correlation between warming and a later on-set of the autumn phenophase is very weak (Menzel *et al.* 2006). Understanding this process is important since changing phenology can alter bio-geochemical cycling and albedo both feeding back on climate change (Peñuelas, Rutishauser, and Filella, 2009). For example, an extended autumn has been reported to increase carbon storage in the boreal zone of Northern latitude forests (Lucht *et al.* 2002) and in the aspen boreal forests of North America (Chen *et al.* 1999)

Over the time period of the Menzel *et al.* (2006) study (1971-2000) atmospheric carbon dioxide has increased by 44 µmol mol⁻<fnref n="1" /> (13.5 %). We have shown previously that elevated atmospheric CO₂ (e[CO₂]) delays autumnal senescence in a forest canopy exposed for six years to e[CO₂] in a free air CO₂ exposure (FACE) ecosystem experiment. At the canopy level the decline in greenness (NDVI) and leaf area index (LAI) were both significantly reduced by e[CO₂]. Also significantly reduced was the decline in leaf chlorophyll indicating delayed senescence in these trees (Taylor *et al.* 2008). However, these finding are controversial since rising [CO₂] has been shown to shorten (Sigurdsson, 2001), extend (Li *et al.* 2000; Rae *et al.* 2005; Taylor *et al.* 2008) or have no effect (Herrick & Thomas, 2003) on forest senescence.

Natural autumnal senescence is regulated by day length, temperature, light, nitrogen supply and water supply and by plant carbon-nitrogen and source-sink balance (Wingler *et al.* 2006). The timing of which can be regarded as the result of a trade-off between the conflicting

<footnote n="1">¹School of Biological Science, Bassett Crescent East, University of Southampton, SO16 7PX.</footnote>

<footnote n="2">²Environmental Sciences Department, Brookhaven National Laboratory, Upton NY 11973, USA.</footnote>

<footnote n="3">³College of Life Sciences, Peking University, Beijing, 100871, P.R. China</footnote>

<footnote n="4">⁴Institute of Biometeorology-CNR, Via Caproni, 8 50145 Firenze, Italy.</footnote>

<footnote n="5">⁵School of Forest Resources & Environmental Science, Michigan Technological University, 1400 Townsend Drive, Houghton, Michigan 49931-1295 USA.</footnote>

<footnote n="6">⁶Department of Forest Environment and Resources (DISAFRI), University of Tuscia, Via S. Camillo De Lellis, 01100 Viterbo, Italy.</footnote>
requirements for optimizing the nitrogen and carbon status of the plant (Keskitalo et al. 2005). The strength of the plants sink for photosynthate can positively influence photosynthetic responses to e[CO₂] (Bryant, Taylor and Frehner, 1999; Ainsworth et al. 2004) and also reduce the rate of senescence (Wingler et al. 2004; Kaschuk et al. 2009). Recent studies using girdled sugar maple trees have shown that sugar accumulation in leaves resulted in the formation of anthocyanins (Murakami, Schaberg and Shane, 2008) and leaves with increased anthocyanin content were associated with a delayed senescence (Schaberg et al. 2008). In poplar over expressing an Arabidopsis sucrose phosphate synthase gene resulted in increased leaf sucrose content between August and throughout senescence which was associated with a delayed senescence (Park et al. 2009). These data indicate the complex interactions between the plants developmental state, source sink balance and rate of senescence. Nevertheless, the initiation and sequence of events during senescence are well conserved. The stimulus for the process of autumnal senescence in Populus is a shortening of the photoperiod initiating bud-set, at least for high-latitude trees (Böhlenius et al. 2006; Fracheboud et al. 2009; Keskitalo et al. 2005; Olsen et al. 1997) which is considered an adaptive trait related to plant fitness (Ingvarsson et al. 2006). Following this initial stimulus a well characterised sequence of cellular events has been reported (Keskitalo et al. 2005) from chloroplast breakdown, carotenoid and soluble sugar loss, to anthocynain production, a massive 80 % nitrogen remobilisation and leaf abscission. During this process in poplar 166 genes were classed as the most up-regulated revealing a shift from gene expression associated with anabolism to that of catabolism and an increased role of the mitochondria for energy generation as photosynthesis breaks down (Andersson et al. 2004).

The aim of this research was to understand how exposure to increased atmospheric CO₂ disrupts the process of autumnal senescence and to identify key changes in metabolism and gene expression associated with delayed senescence. We conducted our investigation at the POP/EUROFACE (Free Air CO₂ Enrichment) experiment (Miglietta et al. 2001) where trees had been grown for six years, since planting to canopy closure, in a fully open air environment at either ambient [CO₂] (a[CO₂]) or e[CO₂] (550 µmol mol⁻¹). Conducting this study at a FACE site allowed us to eliminate the potentially confounding problems of sink, N and water limitation that are common in experiments using other CO₂ fumigation techniques (McLeod & Long, 1999) which are known to influence the rate of senescence. The highly productive, fast growing trees reported to be non-resource limited (Liberloo et al. 2009) at the POP/EUROFACE experiment provided the ideal model system in which to investigate the changes in natural autumnal senescence of a forest canopy growing in e[CO₂]. Furthermore Populus is now recognised as a

model tree genus (Taylor, 2002; Tuskan et al. 2006; Jansson & Douglas, 2007) enabling genomic resources to be deployed to answer questions of ecological and evolutionary significance on plant response and adaptation to climate change.

# Materials and Methods

## The POP/EUROFACE site

The PopFACE experiment (9 ha) was situated on a nutrient rich, clay soil in Tuscania, Italy (42° 22' N, 11° 48' E; altitude 150 m a.s.l.; www.unitus.it/euroface). Three species of Populus, P. alba L. (clone 2AS-11), P. nigra L. (clone Jean Pourtet), and P. x euramericana (Dode) Guinier (clone I-214) were grown in the experiment within six experimental plots. The whole site was assigned to three blocks each containing two 314 m² octagonal plots assigned two treatments of [CO₂] (control or ambient CO₂, a[CO₂] and eleveated CO₂ e[CO₂] of 550 µmol mol⁻¹). Complete design characteristics of PopFACE are available elsewhere (Miglietta, et al. 2001; Scarascia-Mugnozza et al. 2006). During the period of this study trees had been planted for six years, coppiced after three years and a closed canopy was evident. Canopy characterisation and climatic data during this study have been described in detail (Taylor et al. 2008) and only the ambient nitrogen sub-plots were used in this study, the same treatments as Taylor et al. (2008). A strong chlorosis of the canopies in plots five and six was evident during this study so these plots were discounted from any further analysis as described in Liberloo et al. (2007). Day time CO₂ enrichment was provided from bud burst until bud-set except during this study when CO₂ enrichment was continued throughout. The e[CO₂] measured at 1 min intervals was within a 20% deviation from the target concentration of 550 ppm for 94% of the time during the first three year rotation, and for 78% of the time during the second rotation (Liberloo et al. 2009). The leaf sampling regime is described in the Supporting Information and all sampled leaves were instantly placed in foil, added to a weighted bag and dropped from the canopy to be placed in liquid N₂, from removal until placing in liquid N₂ was ~ 10 s.

## Canopy level spectral reflectance

Canopy reflectance was measured with a field portable spectroradiometer (GER 3700) (GER, Buffalo, NY, USA. Mod. 3700), and a chlorophyll specific NDVI (Gitelson and Merzlyak, 1994; Gamon and Surfus, 1999) was calculated as described in Taylor et al. (2008). Further details are given in Supporting Information.

## Microarray Hybridisation

Total RNA was extracted using the protocol of Chang et al. (1993) with the following modifications, 2.67 % β-Mercapto-ethanol was added to the CTAB extraction buffer instead of spermidine. After the overnight 4 °C LiCl precipitation and re-suspension in SSTE two additional
CHISAM (chloroform:isoamylalcohol, 24:1) extractions were performed. The RNA pellet was re-suspended in 20 µl of DEPC treated H₂O. RNA quantity was checked (NanoDrop® ND-1000 Spectrophotometer, Wilmington, DE, USA.) and RNA quality checked (Agilent 2100 bioanaylser, Agilent Technologies, USA). 100 µg of total RNA was denatured (65 ° for 10 minutes) with 2 µl of anchored oligo(dt)₂₀ primer then chilled on ice (max. 1 minute). A reverse transcription master mix was prepared. This consisted of 6 µl 5x -RT-buffer (first strand buffer), 3 µl of 10mM DTT, 1 µl of 50x dNTP mix (a mix of dA, dC and dG, and aa-dUTP and dTTP in a ratio of 4:1 aa-dUTP:dTTP) (Amersham UK; except aa-dUTP, Sigma UK), 1 µl RNase inhibitor and 2 µl of Superscript™ reverse transcriptase. The master mix was made in bulk such that 13 µl was added to each oligo (dt)₂₀ primed RNA sample for overnight cDNA synthesis at 48 °C. After overnight synthesis the reverse transcription reaction was inhibited by addition of 10 µl 0.5 M EDTA and any remaining RNA degraded by the addition of 10 µl 1 M NaOH and heating at 65 °C for 15 minutes. The remaining cDNA was then neutralised with 50 µl of 1M HEPES (pH 7.5). The cDNA purification was carried out according to the manufacturer's instructions (Qiagen PCR purification kit, Qiagen, Crawley, UK) with the following exceptions. A phosphate-ethanol wash buffer (PWB: pH 8.0, 5 mM KPO4) was used instead of buffer PE and two PWB steps were included. cDNA was then eluted via two elutions each with 30 µl of 0.1 M NaHCO₃ (pH 9.0), 1 µl of cDNA (60 µl total) was then taken for spectrometric quantification. The purified cDNA (59 µl total) was taken and 35 µl 100 mM sodium acetate (pH 5.2) added. Under minimal light, purified cDNA was added to an aliquot of CyDye™ ester (Amersham, Buckinghamshire, UK). Cy3 and Cy5 were added to control and treatment respectively, and for nearly 50 % of the samples this orientation was reversed to account for any dye binding bias. The samples were gently agitated and then left in the dark at room temperature for 2 hours. Following a dye coupled cDNA purification step (Qiagen protocol except an additional buffer PE wash step was included and two repeated elution steps were carried out) the labelled samples were randomly paired between control and treated samples. The total elute containing 200 µl of Cy3-and-Cy5 coupled cDNA was concentrated down to 25 µl in a spin concentrator (Eppendorf Concentrator 5301, Eppendorf, Cambridge, UK). The dye-labelled cDNA target (25 µl) was denatured by the addition of 50 µl formamide, 25 µl of hybridisation buffer (Amersham, UK) was added and the sample heated at 95 °C for 1 minute and then chilled on ice.

Microarrays slides were purchased from PICME (www.picme.at). The 26,915 ESTs spotted on glass slides were produced by INRA-Nancy (Rinaldi et al. 2007), INRA-Orleans (Dejardin et al. 2004), and University of Helsinki (Brosche et al. 2005) within the framework of the

LIGNOME and ESTABLISH programme respectively. This was estimated to represent approximately 10, 000 predicted gene models in the *P. trichocarpa* genome sequence (Rinaldi et al. 2007). Full MIAME-compliant details of the array content and production can be found at www.picme.at. An overview of the experimental design is illustrated in Figure 1. In approach (i), a direct comparisons between replicate senescent samples (18th October) exposed to either treated (elevated CO₂ e[CO₂]) or control ((ambient CO₂ (a[CO₂])) was conducted. Of the 12 leaves sampled per treatment on 18 October, RNA of sufficient quality was obtained from nine leaves per treatment. These nine samples were randomly paired so that each CO₂ treatment was represented in each pair. In approach (ii), comparisons between pre-senescent (31st August) and senescent material were undertaken using a common pre-senescent reference pool. For the progress of senescence in a[CO₂] the a[CO₂] reference pool was used and this was designated a[CO₂] senescence and in e[CO₂] the e[CO₂] reference pool was used and

FIGURE 1

A schematic representation of the microarray hybridisation design used. (i) Nine replicate hybridisations are shown for probes obtained from leaves sampled on 18th October 2004 with each CO₂ treatment represented in each hybridisation. (ii) Two hybridisations are shown for senescence within each CO₂ treatment. The pre-senescent probe for each treatment was created by pooling probes from seven replicate leaves sampled from within the respective CO₂ environment.

<figure><img src="image_65.png" /></figure>

<figure><img src="image_66.png" /></figure>
designated e[CO₂] senescence. Microarray hybridisations
were carried out to directly assess the transcriptome
changes occurring during senescence in a[CO₂] and in
e[CO₂]. RNA was extracted individually from pre-senescent
(31 August 2004) leaves and then pooled. The pool for
e[CO₂] consisted of seven samples (four from plot one and
three from plot four). The pool for a[CO₂] consisted of
seven samples (four from plot two and three from plot
three). In each pool 100 µg of total RNA was established
using 14.3 µg of total RNA from each replicate leaf.

Pre-hybridisation, hybridisation and scanning of the PICME microarray slides are described in Street et al. (2009). The transcript profile data were also analysed according to Street et al. (2009). Linear models with B statistics implemented in the LIMMA (Smyth, 2004: http://bioinf.wehi.edu.au/limma/) package for the statistical software 'R' (http://www.r-project.org) were used to identify ESTs that may be differentially expressed (Diaz et al. 2003). The model contained only one specified factor for treatment either (i) e[CO₂] for the 18th October direct comparison between exposure to a[CO₂] and e[CO₂] or (ii) expression on 18th October for the comparison between the pre-senescent reference pools and the senescent material. Calculated B and P values are adjusted for multiple testing with a false discover rate (FDR) of 0.05 (Benjamini & Hochberg, 1995) considered a very conservative statistical analysis for FACE experiments of field acclimated material (Leakey et al. 2009a). As the influence of senescence on transcript abundance can be very large for some transcripts (Andersson et al. 2004) this conservative statistical analysis was considered appropriate. ESTs considered as significantly differentially expressed in e[CO₂] compared to a[CO₂] late in senescence (18 October 2004) and those differentially expressed between pre-senescent and senescent tissues were those with a B value of ≥ 3. A B value of zero equals a 50:50 probability of differential expression where as a B value of 3 represents approximately 95% certainty of differential expression (exp[3] / (1+exp[3])) = 0.95, or 95 %). We used a B value of ≥ 3 and a two-fold change in mean normalized expression as our threshold for declaring an EST as significantly differentially expressed. Sequence annotation was obtained using the tblastx algorithm run by the DOE Joint Genome Institute (JGI, http://genome.jgi-psf.org). All microarray data generated have been deposited in the Gene Expression Omnibus (GEO) database as Series GSE15874.

Analysis of expression changes for genes
representing multiple biochemical pathways

Microarray data was further analysed by MapMan (version
2.2.0, downloaded from (http://gabi rzpd.de/projects/
MapMan/). Mapman software displays large data sets onto
diagrams of metabolic pathways or other processes
(Thimm et al. 2004). *Arabidopsis thaliana* ortholog ids
were obtained using the gene model ids of each EST

sequence on the PICME array and the ortholog extractor
function in PopGenIE (Sjödin et al. 2009). The mean Log2
ratio for all the *Populus* ESTs representing a single ortholog
Arabidopsis gene model were used in the Mapman
pathway analysis. The Wilcoxon Rank Sum Test was used
within MapMan to identify any functional group of genes
that exhibit a different behaviour in terms of expression
profile compared to all the other remaining functional
groups. Data were Benjamini Hochberg corrected in
MapMan and *P* ≤ 0.05 was considered the cut-off for
identifying functional groups considered to have a
different behaviour in terms of expression profiles. The
pathway diagrams for anthocyanin biosynthesis were
based on that described for Arabidopsis (Solfanelli et al.
2006) and additional information for Populus were
obtained from Tsai et al. (2006).

Real-time quantitative PCR

The selection and validation of the internal reference gene
is described in Supporting Information. ESTs to be
validated were searched by their EST name in the PICME
database and the EST information was extracted from
NCBI using Accession Number. Gene model information
was downloaded from JGI. Real-time qPCR primers were
designed using Beacon Design 5.0 (PREMIER Biosoft
International) and the following criteria: Tm of 55-60°C
and PCR amplicon lengths of 115 to 160 bp, yielding primer
sequences with lengths of 19 to 22 nucleotides and GC
contents of 45% to 55%. Primers were also designed to
amplify close to the 3' end of the transcripts or EST, and
at least one primer of a pair was designed to cover an
exon-exon junction if possible. All primers used in this
study were synthesized and desalted by Sigma-Genosys.

The protocols for cDNA synthesis and SYBR Green qPCR
are described in supplementary materials and methods.
The primer pairs used and mean ct values of the reference
gene are also reported in supplementary materials and
methods.

Leaf Biochemistry

Anthocyanin content

Frozen leaves were ground and 50 mg from each sample
was used for analysis according to the method of Martin
et al. (2002). Data were calculated from the mean of three
technical repeats for eight replicate leaves per treatment
(four per plot).

Soluble carbohydrates and starch

Extraction and measurement of glucose, fructose, sucrose
and starch content has been described previously (Rogers
et al. 2006). Glucose, fructose and sucrose were extracted
from frozen ground material using sequential incubations
in ethanol. Starch was extracted from the residual material
and converted to glucose. Glucose, fructose, sucrose and
the glucose resulting from the starch degradation were
then assayed using a continuous enzymatic substrate
assay.
## Statistical analysis of leaf biochemistry and spectral reflectance

Responses of leaf biochemistry during the progression of autumnal senescence were subjected to general linear model ANOVA (using Minitab® 15.1.0.0. Minitab Inc., Philadelphia), with the model: Response = Block' | CO₂ and leaf was considered the unit of replication. No significant Block * CO₂ interactions were detected. Responses of canopy spectral reflectance during the progression of autumnal senescence were analysed by a two-way ANOVA with repeated measures in SPSS (SPSS 16.0 for windows) with the model: Response = time | CO₂

## Results and Discussion

The trees used in this study exhibit both a net increase in photosynthesis (Tricker et al. 2005; Liberloo et al. 2007) increased biomass production (Liberloo et al. 2009) a significantly reduced leaf N (on a leaf mass basis) (Liberloo et al. 2007) and were considered not to be resource limited (Liberloo et al. 2009). We have previously established that the field grown poplar trees in this study, exhibit delayed autumnal senescence in response to growth at e[CO₂] (Taylor et al. 2008). Figure 2a shows digital photography examples of one a[CO₂] and one e[CO₂] plot for two years and a detailed analysis of the LAI for all experimental plot canopies is given in Taylor et al. (2008) where a significant delay in the decline of LAI was reported in e[CO₂]. A canopy modified NDVI (a chlorophyll specific NDVI) (Gitelson and Merzlyak, 1994; Gamon and Surfus, 1999) used to estimate changes in whole canopy chlorophyll content during October 2004 is reported in Figure 2b. Modified NDVI significantly declined with time (F<sub>2,6</sub> = 115.2 *P* ≤ 0.001, as expected during senescence). At all time points modified NDVI was significantly greater in e[CO₂] (F<sub>1,3</sub> = 104.1 *P* ≤ 0.01) indicating that during this time the canopy contained more chlorophyll in e[CO₂] than in a[CO₂] this is in agreement with extracted leaf chlorophyll content reported in Taylor et al. (2008) there was no significant interaction with CO₂ treatment and time. A more detailed report addressing canopy changes during senescence in e[CO₂] is given in Taylor et al. 2008. This study aims to provide the first snap-shot of what may be occurring within the metabolism of the plants during senescence in e[CO₂]. In order to do this the focus was initially on genes that exhibited a statistically significant change in regulation in e[CO₂] during senescence as evidenced from the microarray EST data. This data identified a significant up-regulation of key transcripts in anthocyanin biosynthesis. Additional to this individual EST data, functional groups of genes were examined which together exhibited a significant change in regulation. These data from an e[CO₂], a[CO₂] direct comparison late in senescence (18th Oct) are presented in Figure 3. Changes in the leaf transcriptome between non-senescent and senescent leaf material in either e[CO₂] or a[CO₂] were

<caption>FIGURE 2</caption>

The influence of e[CO₂] on canopy chlorophyll content and leaf anthocyanin content during senescence. (a) Digital images of an a[CO₂] and an e[CO₂] plot during senescence and on two separate years. (b) The chlorophyll specific NDVI (modified NDVI) measured over the e[CO₂] (black bars) and a[CO₂] (open bars) canopies at three time-points through October 2004 mean (+/- SE; n = 4). (c) Extractable leaf anthocyanin content in leaves senescing in either e[CO₂] (black bars) or a[CO₂] (open bars), mean (+/- SE; n = 8).

<figure><img src="image_67.png" /></figure>

<figure><img src="image_68.png" /></figure>
also examined and a change in leaf metabolism was identified. These data representing the changes between non-senescent and senescent material are presented in Figures 4 and 5. Leaf anthocyanin content and soluble and insoluble carbohydrate contents were also measured to support the data identified from the microarrays.

## Identifying the genes most significantly influenced by e[CO₂] late in autumnal senescence

The effect of senescence in e[CO₂] on transcript abundance, represented by ~ 23,000 ESTs accounting for approximately 10,000 predicted gene models in the P. trichocarpa genome sequence (Rinaldi et al. 2007) was tested using the PICME microarray (http://www.picme.at/). Differential gene expression

between e[CO₂] and a[CO₂] grown leaves was assessed on 18th October during the latter phase of senescence. Canopy Leaf Area Index (LAI) at this time had declined by 43% and 37% from 31st August in a[CO₂] and e[CO₂] treatments respectively (data not shown). Using nine replicate microarray hybridisations each of ~ 23,000 ESTs then by random chance ~ 1150 ESTs would be classed as significantly differentially expressed within each microarray (P ≤ 0.05) using a traditional ANOVA framework. In order to account for this a robust statistical analysis was applied to identify the most consistently and significantly differentially expressed ESTs during the heterogeneous process of canopy senescence. Bayesian statistics were used with a FDR of 0.05 considered by some as very stringent for such FACE experiments (Leaky

FIGURE 3

Differential gene expression involved in (a) metabolism and (b) anthocyanin biosynthesis from replicate microarray data for probes prepared from leaf samples taken late in senescence (18th October 2004). (a) The differences in transcript abundance for Arabidopsis othologs of the *Populus* gene models. Each coloured square represents the mean Log₂ expression data for the Arabidopsis ortholog calculated from the *Populus* EST expression data. The resulting file was loaded into the MapMan Image Annotator module to generate the metabolism overview map. The logarithmic colour scale bar ranging from ~ 1.5 (dark blue, representing a three-fold down-regulated gene in e[CO₂]) to + 1.5 (dark red, representing a three-fold up-regulated gene

in e[CO₂]) is given.. (b) Log₂ transcript abundance data for Populus gene models involved in anthocyanin biosynthesis, from the nine replicate microarray hybridizations as a function of (e[CO₂] / a[CO₂]) expression. Genes coding for enzymes in this pathway were identified using the *Populus* EST sequence data and annotation and obtaining the Arabidopsis ortholog gene model. Coloured circles represent *Populus* gene models predicted to code for enzymes involved in each metabolic step. Pathway diagrams were constructed using Solfanelli *et al.* (2006) and Tsai *et al.* (2006). The expression data and annotations for Figure 3b are given as Supplementary Table S3.

<figure><img src="image_69.png" /></figure>
et al. 2009a) for example, in the study by Taylor et al. (2005) zero ESTs would have been classed as significantly differentially expressed at this level of significance. Sixty six ESTs were classed as significantly differentially expressed using a Bayesian Log odds (B-stat) cut-off value of ≥ 3. Of these 66 ESTs, 15 were significantly up-regulated in e[CO₂] 13 of which were also ≥ two-fold up-regulated and these are given as supplementary information (Table S1) and 51 were significantly down-regulated, 38 of which were also ≥ two-fold down-regulated and these are given as supplementary information (Table S2). The two most significantly differentially expressed ESTs showing the greatest increase in abundance in the e[CO₂] treatment were annotated *leucoanthocyanidin dioxgenase* (LDOX, clone id R71B12) and *dihydroflavonol reductase* (DFR, clone id RSH03D11) exhibiting a normalised change in transcript abundance (e[CO₂] / a[CO₂]) of 39.6 (5.3 log₂) and 19.3 (4.3 log₂) respectively (Table S1). The gene models for the two Arabidopsis orthologs of the above were identified (LDOX gene model at4g22880 and DFR gene model at5g42800) and the mean normalised increase in transcript abundance (e[CO₂] / a[CO₂]) for all (not only those classed as significant) of the *Populus* ESTs showing homology to these Arabidopsis orthologs were 16.7 (4.1 log₂) and 30.2 (4.9 log₂) respectively. These two transcripts code for enzymes in the anthocyanin biosynthetic pathway of *Populus* (Tsai et al. 2006). The influence of this change in gene expression on leaf anthocyanin content was investigated. Anthocyanin content was measured on three occasions in August, October and November. Irrespective of CO₂ treatment, leaf anthocyanin content increased over time from late August to early-November, as expected during senescence (Keskitalo et al. 2005). Although not significant, anthocyanin content had increased by 413 % in e[CO₂] compared to a 342 % increase in a [CO₂] between 31st August and 4th November resulting in a 23 % increase in the anthocyanin content by 4th November in e[CO₂] compared with a[CO₂] (Figure 2c).

Identifying the pathways most significantly
influenced by e[CO₂] late in autumnal
senescence.

Although a relationship between mRNA and protein levels
can be inferred here for anthocyanin biosynthesis this may
often not be the case (Feder and Welser, 2005). Therefore,
by grouping genes into functional categories Andersson
et al. (2004) considered that mean values should
represent a good approximation of the relative effort that
plants are making to synthesize the proteins of the
respective categories. Using the pathway analysis software
Mapman (http://gabi.rzpd.de/projects/MapMan/) the
metabolism overview map was explored against the
Arabidopsis TAIR 8 database. Functional groups of genes
which together exhibit a statistically significantly different
behaviour in terms of expression profiles compared to all

the other remaining functional groups were identified. The
mean expression data were calculated for each EST
passing the quality controls from the late senescent
replicate microarray hybridisations. Of the 13,241 ESTs with
expression data, 12,491 exhibited homology with the
Arabidopsis genome, and unique Arabidopsis ortholog
gene models numbered 4,712. The mean Log₂ expression
data for each Arabidopsis ortholog was imported into
MapMan and the functional groups within the metabolism
class were analysed (Figure 3a). The statistically
differentially regulated functional groups (BINs) were BIN
16.8 secondary metabolism of flavoniod biosynthesis P
≤0.02 (mean normalised expression ratio, e/a = 4.56 (2.19
log₂,)) and BIN 4 glycolysis P ≤0.04 (mean normalised
expression ratio, e/a = 2.39 (1.26 log₂)). This approach
provided further support for up-regulation of secondary
metabolism leading to anthocyanin biosynthesis in e[CO₂]
compared to a[CO₂]-grown leaves. Absent from the
individual EST statistical analysis, an up-regulation of the
glycolytic pathway was also identified.

In *Populus* the flavoniod biosynthetic pathway of
secondary metabolism leading to anthocyanin
biosynthesis contains increased gene copy numbers for
many enzymes when compared with Arabidopsis (Tsai *et al.* 2006). Therefore when analysing this pathway further
the mean expression data for each *Populus* gene model
were used. Figure 3b shows that transcript abundance for
enzymes catalysing the biosynthetic pathway from
phenylalanine to anthocyanin were generally increased in
e[CO₂]. Supplementary Information Table S3 gives the
expression data for each EST, the *Populus* gene models
and the Arabidopsis orthologs used in this figure.
Although post-transcriptional processes play an important
role in regulating metabolism, the greater transcript
abundance for nearly the entire pathway, not just a few
enzymes, provides strong evidence for a transcriptionally
driven mechanism responding to e[CO₂] and stimulating
anthocyanin biosynthesis. Anthocyanin pigments have a
multi-faceted protective role in leaves, including protection
from UV damage, pathogens, photoinhibiton, photo-
oxidative stress and scavenging free radicals (Gould,
2004). The increased anthocyanin biosynthesis seen here
is consistent with the idea that induction of stress
responsive pathways can extend the viability of senescing
cells (Buchanan-Wollaston *et al.* 2005). Diaz *et al.* (2006)
suggest that anthocyanin may influence Arabidopsis leaf
lifespan by protecting from photo-oxidative stress and
Schaberg *et al.* (2008) identified a delay in abscission layer
formation during autumnal senescence in sugar maple
leaves containing increased anthocyanin.

Identifying the genes most significantly
influenced by e[CO₂] during the progression
of autumnal senescence.

To determine whether this shift in metabolism late in
senescence was in response to delayed senescence, the
changes in leaf transcript abundance and selected
metabolites during the progress of autumnal senescence
were examined. Differential gene expression between the
pre-senescent (31 August) and the senescent canopies (18
October) was investigated using RNA from a pre-
senescent reference pool, similar to the approach of
Andersson *et al.* (2004). During senescence in a[CO₂]
normalised transcript abundance was calculated as (18 Oct
/ 31 Aug) and 921 ESTs representing 148 unique Populus
gene models were significantly differentially expressed (≥

3 Bvalue and ≥ 2-fold change) whilst in the e[CO₂] treated canopies those ESTs significantly differentially expressed numbered 494 representing 49 unique *Populus* gene models. These differences are interesting as they suggest that the senescence transcriptional programme was disrupted by the e[CO₂] treatment. Using a different microarray platform, Andersson *et al.* (2004) identified 201 ESTs as the most up-regulated during senescence in *Populus tremula* and these represent 166 unique *Populus* gene models, an amount similar to that identified in

FIGURE 4

Mapman metabolism overview maps showing
changes in transcript abundance during senescence
in both e[CO₂] and a[CO₂] as a function of expression
data (18 Oct / 31 Aug) within each CO₂ environment
for Arabidopsis othologs of the *Populus* ESTs. The
mean normalised expression ratio (log₂) is also given
for those functional groups that exhibited a
significantly different behaviour in terms of expression
profiles compared to all the other remaining groups.
Figures (a) and (b) represent gene expression
changes during senescence in a[CO₂] and e[CO₂]
respectively. The logarithmic colour scale bar ranges
from - 1.5 (dark blue, representing a three-fold down-
regulated gene on 18th Oct) to + 1.5 (dark red,
representing a three-fold up-regulated gene on 18th
Oct) is given.. The normalized expression values for all
ESTs classed as significantly differentially expressed

through the process of senescence in e[CO₂] are
reported in Supplementary Tables 4 and 5 and those
during senescence in a[CO₂] in Supplementary Tables
6 and 7. (c) Ratio for all functional groups classed as
significantly differentially expressed (Benjamini
Hochberg corrected Wilcoxon Rank Sum test in
MapMan) during senescence in e[CO₂] in terms of
expression profiles compared to all the other
remaining groups. The expression ratio for the same
group is given for the a[CO₂] and P values are
reported as \* P ≤ 0.05; \*\* P ≤ 0.01; \*\*\* P ≤ 0.001; \*\*\*\* P ≤ 0.0001. Functional groups are annotated in
MapMan as: (1) PS. light reactions, (2) Secondary
metabolism. Flavonoids, (3) Secondary metabolism.
Phenylpropanoids, (4) Glycolysis, (5) Amino acid
metabolism. Synthesis. aromatic aa. Chorismate, (6)
TCA cycle.

<figure><img src="image_70.png" /></figure>
a[CO₂]. The ESTs exhibiting a two-fold change in normalized transcript abundance and a significant Bvalue and unique to senescence in each CO₂ environment are reported as supplementary tables (Tables S4 to S7). For example Table S4 gives the details of all 75 ESTs significantly up-regulated during senescence in e[CO₂] and Table S6 the 274 ESTs in a[CO₂] while 161 up-regulated ESTs were common to both environments. Table S5 reports the 67 down-regulated ESTs in e[CO₂] and table S7 the 295 EST down-regulated in a[CO₂] while 191 down-regulated ESTs were common to both environments. Although the Andersson et al. (2004) study and the data reported here used different microarray platforms of the significantly up-regulated ESTs during senescence in e[CO₂] only seven had commonality with Andersson's up-regulated list, whilst in a[CO₂] this numbered 15, data for these ESTs are reported in table S8. Table S4 reports the ESTs significantly up-regulated during senescence in e[CO₂] and ESTs representing four Arabidopsis ortholog gene models in the anthocyanin biosynthetic pathway were among those most abundant. These represented the Arabidopsis ortholog genemodels; at4g22880 (LDOX), at5g42800 (DFR), at2g37040 (Phenylalanine ammonia lyase, PAL1) and at5g05270 (chalcone-flavanone isomerise, CHI). The mean normalised increases in transcript abundance in e[CO₂] were 20.26 (4.3 log₂), 12.75 (3.7 log₂), 8.36 (3.1 log₂) and 6.31 (2.7 log₂) respectively for each of these gene models. Of these gene models both those expressing the products LDOX and DFR were among those at least four-fold up-regulated and CHI was between two - four fold up-regulated during the development of bud dormancy in Populus (Ruttink et al. 2007).

## Identifying the pathways most significantly influenced by e[CO₂] during the progression of senescence.

The MapMan software was used to identify functional groups of genes which together exhibited a significantly different behaviour in expression during the progression of senescence in either a[CO₂] (18.10.04 / 31.08.04) or e[CO₂] (18.10.04 / 31.08.04). To identify changes in transcript abundance between senescence in a[CO₂] and senescence in e[CO₂] the sequences for all expression data from ESTs existing in both CO₂ environments and passing quality control were taken. These numbered 7404 ESTs, 7171 showed homology to the Arabidopsis genome and of these sequences, 2148 were classed as unique Arabidopsis ortholog gene models. Figure 4a represents functional groups within the metabolism overview in a[CO₂] and Figure 4b in e[CO₂]. In both CO₂ environments these figures show a significant down-regulation of genes involved in the photosynthetic regulation of the light reactions, as would be expected during senescence. It is notable that the mean down-regulation of this functional group is greater in e[CO₂] than in a[CO₂] (Figure 4c). It is possible that anthocyanin accumulation results in a

stabilisation of photosynthetic proteins and pigments in e[CO₂] and thus delayed functional senescence despite induction of the senescence programme at the transcriptional level. During senescence in e[CO₂] the phenylpropanoid and flavonoid biosynthetic pathways were significantly up-regulated (18th Oct / 31st Aug) 1.84 (0.88 log₂) and 1.93 (0.95 log₂) respectively while during senescence in a[CO₂] the flavonoid biosynthetic pathway was down-regulated 0.63 (-0.67 log₂) (Figure 4c). Studies of developing and mature soybean have shown an e[CO₂] induced increase in transcripts associated with glycolysis (Ainsworth et al. 2006; Leakey et al. 2009b). Data here indicates that both the glycolytic pathway and the TCA cycle were significantly up-regulated during senescence in e[CO₂] as were genes for enzymes of the shikimate pathway leading to chorismate biosynthesis, a pre-cursor for the aromatic amino acids such as phenylalanine (Figure 4c). These data provide support for the up-regulation of flavonoid biosynthesis and glycolysis obtained from a snap-shot late in senescence. Together these data indicate a shift in metabolism between senescence in a[CO₂] and senescence in e[CO₂] which appears to coincide with an up-regulation of glycolysis and secondary metabolism. As carried out for the late senescence samples the whole anthocyanin biosynthetic pathway was analysed. The change in transcript abundance was calculated from EST expression data as a function of (senescence in e[CO₂] / senescence in a[CO₂]) and the mean expression data for Populus gene model were reported (Figure 5a). The pathway analysis (Figure 5a) and data from the EST statistical analysis (Table S4) all indicate an active up-regulation of the anthocyanin pathway during the progress of senescence in e[CO₂] compared with that in a[CO₂]. This supports the transcript data obtained from a direct comparison between CO₂ growth environments late in senescence (Figure 3b) and the leaf anthocyanin content data (Figure 2c).

## Leaf carbohydrate contents

As photosynthate production declines during the active process of senescence, energy is generated by mitochondrial respiration through processes such as beta-oxidation (Andersson et al. 2004). During senescence in e[CO₂] it could be postulated that metabolism through glycolysis was still sufficient for energy generation, and the products of this metabolism were used in flavonoid biosynthesis. If this were the case then substrate for glycolysis should be present in the leaves of e[CO₂] exposed leaves and beta-oxidation could be expected to be up-regulated in the a[CO₂] leaves. Although, not classed as a significantly up-regulated functional group, those genes comprising BIN 11.9.4.2: (lipid metabolism.lipid degradation-beta-oxidation) exhibited an increase in mean normalised expression during senescence in a[CO₂] compared with e[CO₂], of 4.20 (2.07 log₂) and 3.07 (1.62 log₂) respectively (Figure 4a and b). Sucrose and starch content were increased in e[CO₂] this was only significant at two time points and only for sucrose (Figures 5 b and
c). A gradual increase in sucrose content was apparent from August to November in e[CO₂] (Figure 5b). This is in contrast to leaves sampled in the growing season prior to the on-set of senescence during a preceding year where in e[CO₂] no accumulation of sucrose or hexoses were observed (Davey et al. 2006). Similarly the measured starch content was higher in leaves grown in e[CO₂] (Figure 5c) consistent with previous measurements on these trees (Davey et al. 2006). During senescence starch content declined in all leaves, suggesting that catabolism contributed to the energy requirements of the leaf (Figure 5c). Glucose and fructose contents are reported as supplementary Figure 1 (Figure S1). During senescence the glucose content of leaves decreased and no significant difference was reported between treatments for either glucose or fructose.

### Additional microarray confirmation using real-time quantitative PCR (RT-qPCR)

Transcript abundance data derived from the microarray hybridisations for the pathways reported here is consistent with the findings from leaf biochemical assays of anthocyanin and carbohydrate contents. Real-time quantitative PCR (RT-qPCR) was used to further assess the reliability of the microarray data and particularly to observe the CO₂ influences on LDOX transcript abundance. Primers were designed against the sequences of three genes up-regulated and three down-regulated in e[CO₂] on the 18th October. The expression of these genes was quantified using RT-qPCR and normalised with PDF1 as a reference gene. Supplementary Figure 2 (Figure S2) shows good confirmation between the microarray data

FIGURE 5

The influence of e[CO₂] on gene expression within the anthocyanin biosynthetic pathway and leaf sucrose and starch content during senescence (between 31st August and 18th Oct). (a) Log₂ expression data for Populus gene model from two replicate hybridizations for each CO₂ treatment calculated as (( e[CO₂] 18 Oct / 31 Aug) / (a[CO₂] 18 Oct/ 31 Aug)). Dark red circles represent a Log₂ value > 2 (a > four-fold increase in transcript abundance) for that gene during senescence in e[CO₂] compared with senescence in a[CO₂]. Green

circles represent a Log2 value between -1.5 and -2 (a three to four-fold decrease in transcript abundance). The pathway diagram was constructed using Solfanelli et al. (2006) and Tsai et al. (2006). The expression data and annotations for figure 5a are given as Supplementary Table S9.(b) and (c) Data for leaf sucrose and starch content respectively in both a[CO₂] and e[CO₂], n = 8, *P* values are reported as *P* ≤ 0.05; **P** ≤ 0.01; ***P** ≤ 0.001 where no *P* value was reported data were not significant.

<figure><img src="image_71.png" /></figure>
and those from RT-qPCR. The relative expression of *LDOX* between the late growing season and late senescence samples was also examined using RT-qPCR. A clear up-regulation of *LDOX* during senescence in e[CO₂] can be seen, with the opposite occurring in a[CO₂] (Supporting Information (Fig S2 inset).

Summary

Once autumnal senescence in *Populus* is initiated by a change in photoperiod (Olsen *et al.* 1997; Keskitalo *et al.* 2005; Böhlenius *et al.* 2006) the balance between reactive oxygen species (ROS) production and ROS scavenging can determine the rate of senescence (McKersie *et al.* 1988; Buchanan-Wollaston *et al.* 2003; Gepstein *et al.* 2003). We have shown that soluble carbohydrates and starch were increased by exposure to e[CO₂] at a concentration predicted for 2050, and that this may act as a signal to stimulate the synthesis of anthocyanin. This supports findings in several contrasting plant species and organs and is supported by work on the mutant *pho3* (Lloyd & Zakhleniuk, 2004). In this sucrose-export mutant (*pho3*) LDOX and DFR were up-regulated by 190 and 31 times respectively, again linking leaf sucrose content and anthocyanin biosynthesis. Direct sugar induction of anthocyanin biosynthesis in Arabidopsis has also been reported (Teng *et al.* 2005; Solfanelli *et al.* 2006). Furthermore, the association between e[CO₂] and the partitioning of carbon to the synthesis of secondary metabolites was evident in tobacco plants exposed to an e[CO₂] of 1000 ppm (Matros *et al.* 2006). The Matros *et al.* (2006) study provides evidence for a direct link between e[CO₂], an increased leaf C:N ratio and an increased activity of Phenylalanine ammonia lyase (PAL), a key enzyme catalyzing the first committed step in the biosynthesis of phenylpropanoids, with a concomitant increase of secondary metabolites. Long-lived trees, such as *Populus*, have evolved strategies for defence, dormancy and wood formation that may not be well represented in the genomes of annuals such as Arabidopsis. Enzymes involved in the flavonoid biosynthetic pathway leading to anthocyanin production are coded by multiple copy genes in *Populus* and generally single copy genes in Arabidopsis for example, *LDOX* and *DFR* (Tsai *et al.* 2006). This study further highlights the importance of using *Populus* as a model to study natural autumnal senescence (Jansson & Douglas, 2007) and the open-field environment as essential to gain a mechanistic understanding of how trees may respond in the natural environment (Taylor *et al.* 2005; Sjödin *et al.* 2008).

In conclusion, we have identified an association between a delayed autumnal senescence in e[CO₂] a change in leaf carbohydrate status, gene expression profiles and anthocyanin content. It is possible that this may be a secondary response to other factors. For example, canopy temperature is often increased during growth in e[CO₂] at FACE sites, as is evident for soy bean (Long *et al.* 2006). Nevertheless the data reported here begin to identify

processes by which climate change can influence plant phenology. Using transcript profiling we identified a number of genes that changed in expression during senescence in an atmosphere enriched with CO₂ and the most conspicuous of these were genes involved in the biosynthetic pathway of anthocyanin; they were strongly and significantly induced in e[CO₂] resulting in an increased leaf anthocyanin content. The carbohydrate content of senescing leaves was also increased in e[CO₂] and we propose that this provided both a signal and a fuel for anthocyanin biosynthesis. Additional leaf anthocyanin then provides increased protection to the senescing leaves in e[CO₂] and extends the senescent phase. This CO₂ stimulated shift in metabolism is consistent with the growth/differentiation balance hypothesis extended by Herms and Matteson (1992) and observed in forest studies (Harding *et al.* 2005; Mattson *et al.* 2005; Cseke *et al.* 2009). Evidence from this study allows us to postulate that excess carbon in e[CO₂] is available to be partitioned to carbon rich secondary metabolites. These provide a protective role in senescing leaves, so extending leaf longevity, as the carbon demand from growth declines in the autumn. We have begun to identify the genetic mechanisms for adaptation to future CO₂, but the long-term consequences of such changes for forest ecosystem function and micro-evolutionary adaptation remain uncertain.

Acknowledgements

This research was supported by the European Commission through the Directorate General Research within the Fifth Framework for Research - Quality of Life and Management of the Living Resources Programme, contract number QLK5-CT-2002-00953 (POPYOMICS), coordinated by the University of Southampton. The PopFACE experiment within the EUROFACE infrastructure was supported by the EC through its Environment R and D programme within the Fourth Framework as a research contract ENV4-CT97-0657 (PopFACE) and within the Fifth Framework as contract EVR1-CT-2002-40027 (EUROFACE) coordinated by Giuseppe Scarascia-Mugnozza at the University of Viterbo. This research was also supported by the Office of Science (BER), US Department of Energy, Grant No. DE-FG02-04ER63792, 'POPGENICS' awarded to GT and DFK. AR was supported by the US Department of Energy Office of Science grant no. DEFG02-04ER63792 and by contract no. DE-AC02-98CH10886 to Brookhaven National Laboratory. Research in the laboratory of GT was also supported by the DEFRA project BEGIN and the Seventh Framework Network of Excellence, EVOLTREE. The authors thank B Gielen, C Calfapietra, T Oro, GJJ Clarkson, M Pecchiari and C Steynen for help in the field, and LE Graham and J Tucker for help with the microarray hybridisations. A Zaldei is thanked for support of the infrastructure at the POPFACE field site.
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