Intensive study sites

Francois Lefèvre, Christian Pichot, E. Beuker, J. Kowalczyk, Jan Matras, Martin Ziehe, Marc Villar, M. Peter, Felix Gugerli, Christophe Orazio, et al.

<figure><img src="image_1.png" /></figure>

To cite this version:

Francois Lefèvre, Christian Pichot, E. Beuker, J. Kowalczyk, Jan Matras, et al. Intensive study sites. Evolution of trees and forest communities, PG Edition, 175 p., 2016, 978-2-9519296-3-9. hal-01608456

HAL Id: hal-01608456

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Submitted on 5 Jun 2020

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evoltree

A EUROPEAN RESEARCH GROUP

# Evolution of Trees and Forest Communities
Ten years of the EVOLTREE network

<figure><img src="image_4.png" /></figure>

CELEBRATING
10 YEARS

EDITED BY ANTOINE KREMER, STEPHANIE HAYES AND SANTIAGO C. GONZÁLEZ-MARTÍNEZ
<figure><img src="image_5.png" /></figure>

evoltree

A EUROPEAN RESEARCH GROUP

linking

Genomics
Genetics
Ecology
Evolution

To find out more about EVOLTREE
and how to get involved in its activities
or make the use of its services,
please consult the website:
www evoltree eu

<figure><img src="image_6.png" /></figure>

## AUTHORS

Dolores Abarca
Sally N. Aitken
Florian J. Alberto
Ricardo Alía
Catherine Bastien
Thomas Bataillon
Egbert Beuker
Gil Bohrer
Thomas Boivin
Aurore Bontemps
Roland Brandl
Katharina Bräutigam
Jon R. Bridle
Christian Brochmann
Carlo Calfapietra
Malcolm M. Campbell
Bastien Castagneyrol
Stephen Cavers
María-Teresa Cervera
Jean-Louis Churin
Cyrille Conord
François Courbet
Pierre-Emmanuel Courty
Hendrik Davi
Paolo De Angelis
Carmen Díaz-Sala
Marion Durand-Gillmann
François Ehrenmann

Bruno Fady
Mario Fernández Fraga
Silvia Fluch
Carl G. Fossdal
Alain Franc
Stephan Gaubitzer
Julie Gauzere
Sophie Gerber
Cindy Gidoin
Richard Gomulkiewicz
Santiago C. González-Martínez
Delphine Grivet
M. Ángeles Guevara
Felix Gugerli
Frédéric Guillaume
Jessica Gurevitch
José Gutiérrez Marcos
Heikki Hänninen
Stephanie Hayes
Hervé Jactel
Øystein Johnsen
Marie-Joe Karam
David F. Karnosky
Etienne K. Klein
Hans-Peter Koelewijn
Annegret Kohler
Dieter Kopecky
Jan Kowalczyk

Antoine Kremer
Anna Kuparinen
Jessy Labbé
Clément Lafon-Placette
Hadrien Lalagüe
Małgorzata Latałowa
François Le Tacon
François Lefèvre
Thomas Lenormand
Yunan Lin
Benoit Marçais
Francis Martin
Jan Matras
Stéphane Maury
Franco Miglietta
Marie Mirouze
Ran Nathan
Sylvie Oddou-Muratorio
Christophe Orazio
Martina Peter
Christian Pichot
Karin Pritsch
Kermit Ritland
Juan J. Robledo-Arnuncio
Alistair Rogers
Antje Rohde
Ophélie Ronce
Outi Savolainen

Johanna Schmidt
Hilke Schröder
Silvio Schueler
Federico Sebastiani
Eva Maria Sehr
Marinus J.M. Smulders
Christoph Sperisen
Michael Stierschneider
Steven H. Strauss
Nathaniel R. Street
Matthew James Tallis
Gail Taylor
Mari Mette Tollefsrud
Sara Torre
Inge van Halder
Giovanni G. Vendramin
Marc Villar
Kelly J. Vining
Willem O. van der Knaap
Otto Weichselbaum
Ross Whetten
Sam Yeaman
Mario Zabal-Aguirre
Jing Zhang
Birgit Ziegenhagen
Martin Ziehe

Photos: Fotolia, Bioversity, EFIATLANTIC, EVOLTREE.

Citation: Antoine Kremer, Stephanie Hayes and Santiago C. González-Martínez, Editors (2016).
Evolution of Trees and Forest Communities: Ten Years of the EVOLTREE network.
Document prepared by the EVOLTREE network. PG Edition - Bordeaux.
192 pages

ED. IEFC ISBN: 978-2-9519296-3-9

EVOLUTION OF TREES

as drivers of terrestrial biodiversity
# CONTENTS

<table><tr><td>AUTHORS</td><td>Cover 2</td></tr><tr><td>FOREWORD</td><td>2</td></tr><tr><td>INTRODUCTION</td><td>3</td></tr></table>

## EVOLTREE INFRASTRUCTURES, RESOURCES AND INITIATIVES 4

<table><tr><td>Intensive Study Sites (ISS)</td><td>6</td></tr><tr><td>Repository Centre</td><td>10</td></tr><tr><td>eLab</td><td>15</td></tr><tr><td>Training</td><td>20</td></tr><tr><td>TreeType</td><td>23</td></tr></table>

## RESEARCH HIGHLIGHTS: SCIENCE THAT MATTERS 26

<table><tr><td>Potential for evolutionary responses to climate change - evidence from tree populations</td><td>27</td></tr><tr><td>Epigenetic regulation of adaptive responses of forest tree species to the environment</td><td>45</td></tr><tr><td>Large-scale longitudinal gradients of genetic diversity: a meta-analysis across six phyla in the Mediterranean basin</td><td>61</td></tr><tr><td>Effect of poplar genotypes on mycorrhizal infection and secreted enzyme activities in mycorrhizal and non-mycorrhizal roots</td><td>75</td></tr><tr><td>Molecular footprints of local adaptation in two Mediterranean conifers</td><td>88</td></tr><tr><td>Community genetics in the time of next-generation molecular technologies</td><td>105</td></tr><tr><td>Long-distance gene flow and adaptation of forest trees to rapid climate change</td><td>115</td></tr><tr><td>Considering evolutionary processes in adaptive forestry</td><td>133</td></tr><tr><td>The transcriptome of Populus in elevated CO2 reveals increased anthocyanin biosynthesis during delayed autumnal senescence</td><td>150</td></tr><tr><td>Late Quaternary history of North Eurasian Norway spruce (Picea abies) and Siberian spruce (Picea obovata) inferred from macrofossils, pollen and cytoplasmic DNA variation</td><td>164</td></tr></table>

## EVOLTREE PARTNERS 175
# FOREWORD

EVOLTREE is ten years old this year (2016); but the idea of a European network in forest genetics dates well back before 2006. All current forest research involving genetics or genomics is transnational and it has become progressively apparent that better coordination of efforts and means is necessary if these issues are to be adequately addressed on a European level.

The forest genetics research community had felt the same need for a long time, but did not make the most of European Union funding opportunities at the beginning of the eighties.

It is unfortunate that pan-European initiatives, such as the comparison of provenances in plantations in the fifties and sixties, were not coordinated on a long-lasting scale in order to be able to answer current questions on assisted migration.

This historic reference highlights the necessity of coordinating research on forest genetics on a European level. The example of provenance tests is a past illustration of current needs in terms of means sharing in the field of genomics, the accessibility of as much biological and electronic resources as possible and the coordination of research efforts.

It is EVOLTREE's aim to rise up to this challenge. And since its creation ten years ago, this aim has been reaffirmed from year to year. We wish to celebrate the tenth anniversary by sharing the knowledge and results that we have accumulated during this period.

This book contains a selection of ten articles from the 165 listed on the Web of Science, which have been funded either by the EVOLTREE network of by other EVOLTREE initiatives, such as FORESTTRAC. It also describes the main physical and electronic infrastructures and other resources and initiatives, from which the forest genetics research community, in particular the network's present members can benefit.

ANTOINE KREMER
EVOLTREE Network Coordinator
INTRODUCTION

It all started in 2006, when twenty-five Universities and research institutes from fifteen European countries joined forces to set up EVOLTREE as a Network of Excellence. EVOLTREE aimed to link four major disciplines Ecology, Genetics, Genomics and Evolution to address global issues faced by European forests, such as environmental changes and the erosion of biodiversity.

Funded by the European Union within the 6th framework programme, it spent the
next four years developing and setting up the necessary experimental and monitoring
infrastructures and physical and electronic resources upon which long term research
could be built.

These network infrastructures and resources were therefore well in place and up and
running when EVOLTREE embarked on its next four year period in 2011. Twenty-three
research groups from thirteen European countries agreed upon and signed a new
consortium agreement. Now self-funding (based on financial contributions from some
partners and "in-kind" contributions from others in the form of running relevant
scientific training courses, for example), EVOLTREE was integrated into the European
Forest Institute's (EFI) network.

Instrumental in EU projects (such as NOVELTREE, PROCOGEN, FORGER, NOVELTREE, TreesForFUTURE and GENTREE) EVOLTREE's research activities address topical issues such as the discovery of genes with economic and ecological relevance and the evaluation of their genetic diversity in natural tree populations and associated species, as well as the evolution, conservation, restoration, breeding and management of tree populations subject to environmental change and human interference.

EVOLTREE offered its resources to European projects, but also benefited from
contributions of these projects by populating existing databases or upgrading
existing infrastructures. During this period, EVOLTREE stimulated the organisation of
workshops and summer schools on dedicated technical or broader issues related to
EVOLTREE's interests.

<figure><img src="image_7.png" /></figure>
<figure><img src="image_8.png" /></figure>

It was in the latter part of the second period of the network (2011-2014) that the open-science research initiative “TreeType” was created for the widespread collection of data on simple (but very relevant) phenotypic traits for European trees. Such data can provide important insights into the balance between local adaptation and phenotypic plasticity in tree populations. Indeed, at a time when large DNA sequence data of individual trees are becoming available, the missing component is standardised phenotypic data.

The project is open to participation by anyone with the enthusiasm and skills to record the data for the trees of their choice and data will be made openly available. The recording website (accessible via the EVOLTREE website) was launched at the end of 2014 in time for its utilisation in the current four-year term 2015-2018.

The current four year term (2015-2018) will see TreeType taking off and becoming a valuable long term resource, alongside the EVOLTREE e-resources, the DNA repository centre, the ISS field network and the training courses. Furthermore, EVOLTREE intends to increase its involvement in EU projects; for example, it will be involved in FORESTING, a networking research infrastructure for forest ecosystem and resources research in Environmental and Earth Sciences.

EVOLTREE's ambition to widen and strengthen the network has been achieved by welcoming new partners from different parts of Europe to the consortium and by increasing synergies with EFI. The summer schools of the previous terms have been incorporated into wider training programmes to include all year-round workshops and EVOLTREE now endeavours to become more present in, and to organise its own, scientific events.

Therefore, at the age of ten, EVOLTREE is ready to take on new challenges and further its contribution to the field of genetics and genomics in Europe with fresh projects and ideas, and enthusiastic support and input from its partners and the forest genetics research community at large.
EVOLTREE infrastructures,
resources and initiatives

<figure><img src="image_9.png" /></figure>
F. LEFÈVRE¹, C. PICHOT¹, E. BEUKER², J. KOWALCZYK³, J. MATRAS³, M. ZIEHE⁴, M. VILLAR⁵, M. PETER⁶, F. GUGERLI⁶, C. ORAZIO⁷, R. CORDERO⁷, R. ALIA⁸, I. VAN HALDER⁹, S. C. GONZÁLEZ-MARTÍNEZ⁹, A. KREMER⁹.

1 URFM, INRA, 84914 Avignon, France; 2 LUKE, 58450 Punkaharju, Finland; 3 Dept. of Silviculture and Genetics of Forest Trees, IBL, 05-090 Raszyn, Poland; 4 Dept. of Forest Genetics and Forest Tree Breeding, University of Goettingen, 37077 Goettingen, Germany; 5 AGPF, INRA, 45075 Orléans, France; 6 WSL Swiss Federal Research Institute, 8903 Birmensdorf, Switzerland; 7 EFIATLANTIC, 33610 Cestas, France; 8 CIFOR, INIA, 28040 Madrid, Spain; 9 BIOGECO, INRA, University of Bordeaux, 33612 Cestas, France.

The Intensive Study Sites (ISS) are large-scale ecosystem plots of a few thousands of hectares, where large samples of trees and associated species are progressively mapped, genotyped and phenotyped. The sites comprise entire portions of landscapes, where trees are present in different configurations from single trees to edges and woods.

More detailed description of the ISSs, their organisation and their information system are available on the EVOLTREE website.

### Background and objectives of the ISS infrastructure

The recent revolution in high throughput technologies and methods in genetics has drastically changed the research perspectives and practices in ecology and evolutionary sciences. The availability of large genetic datasets, for a broad range of species far beyond the few so-called “model species”, together with the development of appropriate analytical tools, has transformed the genomes of living forms into immeasurable sources of information on the ecological and evolutionary processes that shaped biodiversity, at different spatial and temporal scales.

A side effect of this revolution has been the emergence of common tools and methods shared by different branches of life sciences that had previously tended to diverge, such as genomics, population genetics, quantitative genetics, functional ecology and community ecology. All these interconnected disciplines were represented within EVOLTREE.

The EVOLTREE scientific community recognised the considerable advance in forest genetics and ecology based on these tools and methods. However, the projects that followed these different lines of research were conducted independently from each other, highlighting the need for shared experimental infrastructures. By using shared infrastructures it is possible to accumulate genetic information from trees and their associated species with environmental data and other information of interest collected in different projects. They also help to address the effects of ecological processes and human activities on forest systems on the relevant spatial scales.

Furthermore, the EVOLTREE scientists were convinced that innovative knowledge integration across disciplines

often occurs *a posteriori* and does not always result from *a priori* planning. Thus, setting up adequate tools for sharing the information acquired by different research groups within the same experimental field sites seemed to be a priority.

The long-term EVOLTREE Intensive Study Sites (ISSs) infrastructure was therefore created with the following five main objectives:

1. To set up a European network of representative sites for long term research on the evolution of biodiversity in forest ecosystems at different hierarchical levels (from genes to phenotypes, from populations to communities) and with different management options.

2. To assess the spatial structure of biodiversity on various scales and at different hierarchical levels.

3. To monitor population dynamics in trees and their associated species, using demographic and genetic approaches, over different spatial scales.

4. To monitor the interaction between species (mainly trees, other plants, insects, and microorganisms).

5. To provide long-term and large-scale support for training, education and dissemination activities.

<figure><img src="image_10.png" /></figure>
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

Each ISS has intrinsic value, but has also added value in comparison with other ISSs, as they were chosen to build a network of representative forests in Europe. The ISSs are used in two ways. First, due to the local heterogeneity and gradients, the impact of the environment or management practices on the dynamics of diversity within each type of ecosystem can be studied. Beyond the “natural” local heterogeneity, such as altitudinal gradients, some ISSs also host short-term and long-term experiments such as irrigation, reciprocal transplantations, or different sylvicultural treatments. Second, the drivers of diversity change in different ecological regions can be compared across the ISSs.

## ISS integration in EVOLTREE research

Since the beginning of EVOLTREE, thirty-two international and national projects<fnref n="1" /> have made use of the ISS infrastructure (Table 1). As explained above, the ISSs were selected - amongst other criteria - on the basis of research activities carried out prior to the EVOLTREE Network (not mentioned here). During a first phase (i.e., that benefitting from financial contributions from the European Commission), EVOLTREE was directly funding research activities carried out within the ISSs. Hence, international partnership was mandatory and priority was given to projects that involved multiple sites. Since 2011 (i.e., after EVOLTREE became a European Research Group without EC financial support), eighteen new projects funded by other sources<fnref n="2" /> used the ISSs: national and mono-site projects, mainly based on local research groups, but also on international and multi-site projects.

These figures reflect a long-lasting interest in the ISS infrastructure and that the ISS network was not only useful to local groups, but also to others from a wide range of European research groups.

The research projects have made use of the ISSs in various ways connected to some of the infrastructure's five main objectives (listed above): simply as a reservoir of biological samples; as natural sites for observation and monitoring; and as appropriate ecological settings to establish experiments.

The research supported by the ISSs so far has addressed a broad range of scientific questions in various fields of evolutionary science and ecology represented within EVOLTREE; for example, the characterisation of genetic and genomic diversity in trees and associated species; the detection of genes involved in local adaptation and biotic interaction; the characterisation of local adaptation patterns and processes on various spatial and temporal scales; the assessment of the functional, demographic and genetic response to climate change on individual,

## THE ISS INFORMATION SYSTEM

The ISS Information System is designed to make the data collected in each ISS available. It uses metadata that follow the ISO 19115/19139 standard and are compliant with the EU directive INSPIRE. It proposes different ways to search for metadata: via geographic location, data categories (e.g., maps, datasets, pictures) or keywords selected from a dedicated thesaurus developed by the ISS partners. As well as general information about the sites, the system holds references to publications and information about research activities, permanent plots and transects, and permanent samples of individual trees georeferenced and tagged in the forest (from which DNA is available in the Repository Center).

<footnote n="1">1. A detailed list of these projects is available on the EVOLTREE website, and more information is accessible through the ISS information system</footnote>

<footnote n="2">2. EVOLTREE offers mobility grants to support activities in the ISSs</footnote>
FIGURE 1

In order to restrict the research activity to a few sites, we initially selected seven ISSs. An eighth site was added recently (2015). In a bottom-up process, potential sites were proposed by the EVOLTREE community and the ISSs were selected based on two sets of criteria:

**Selection criteria based on scientific characteristics**

* Within-site diversity of tree species, associated species, communities, population structures.

* Within-site comparability, i.e., temporal or spatial heterogeneity: historical records, environmental variability, diversity of management, comparative experiments.

* Within-site research history and available datasets, e.g., ecological records, climatic data, genetic data, management data.

* Potential for species comparisons across ISSs: presence of tree species in common with other ISSs.

* Network coverage of the range of terrestrial forest ecosystems across Europe: boreal, temperate, alpine, Mediterranean, riparian, untouched and intensively managed forests.

**Selection criteria based on infrastructure characteristics**

* Long-term perspectives, e.g., ownership of the land, legal and/or protection status, relation to other networks, interest for local managers, education and dissemination.

* Technical facilities, e.g., access to the site and to the biological samples.

* Expertise of the local partner institution and relationship with the local managers.

<figure><img src="image_13.png" /></figure>

The eight ISSs are, from North to South (Figure 1):

* **PUNKAHARJU**, Finland, Northern temperate and boreal forest (1,500 ha, includes Abies, Acer, Alnus, Betula, Larix, Picea, Pinus, Populus, Prunus, Quercus, Salix, Sorbus, Tilia)

* **BLIZYN**, Poland, Continental temperate untouched forest (17,000 ha, includes Abies, Acer, Alnus, Betula, Carpinus, Corylus, Crataegus, Fagus, Fraxinus, Larix, Picea, Pinus, Populus, Prunus, Quercus, Salix, Sorbus, Tilia, Ulmus)

* **SOLLING**, Germany, Continental temperate managed forest (25,000 ha, includes Betula, Fagus, Larix, Picea, Pinus, Quercus)

* **LOIRE**, France, riparian forest (96 ha, includes Acer, Alnus, Castanea, Corylus, Crataegus, Fraxinus, Populus, Prunus, Quercus, Salix, Tilia, Ulmus)

* **VALAIS**, Switzerland, montane to alpine forest (150,000 ha, includes Abies, Acer, Alnus, Betula, Carpinus, Corylus, Crataegus, Fagus, Fraxinus, Larix, Picea, Pinus, Populus, Prunus, Quercus, Salix, Sorbus, Tilia, Ulmus)

* **LANDES**, France, oceanic intensively managed forest (25,000 ha, includes Alnus, Betula, Castanea, Corylus, Crataegus, Fagus, Fraxinus, Pinus, Prunus, Quercus, Salix, Sorbus)

* **VENTOUX**, France, Mediterranean and South alpine forest (29,000 ha, includes Abies, Acer, Alnus, Betula, Castanea, Carpinus, Cedrus, Corylus, Crataegus, Fagus, Larix, Picea, Pinus, Quercus, Sorbus)

* **CALDERONA-ESPADÁN**, Spain, Mediterranean forest (49,000 ha, includes Celtis, Ceratonia, Juniperus, Olea, Pinus, Quercus, Salix)
<figure><img src="image_14.png" /></figure>

population and community levels; and the modeling of adaptive processes and responses to management practices. The ISSs also support projects developing multidisciplinary approaches with other scientific fields, including environmental and social sciences.

## What will the future bring?

The use of the ISSs as a research infrastructure has come of age. Beyond its role in supporting national research projects, the ISS infrastructure will continue to strengthen the long-lasting integration of research in the field of forest genetics and genomics on the pan-European level, e.g., via the H2020 research and innovation project GENTREE³ “Optimising the management and sustainable use of forest genetic resources in Europe” (2016-2020). Moreover, projects linking the ISSs with other long-term research infrastructures on forest ecology are underway, thus providing wider integration of multi-disciplinary knowledge on forest ecosystems on a pan-European scale.

An innovative use of the infrastructure is planned in the participative project TreeType⁴, in which citizens,

researchers or forest managers can contribute to collecting phenotypic data on individual trees, aiming to characterise the phenotypic variation in the forest. These data will then be analysed by the scientists to provide information on the genetic basis of traits and create a database for future research. This initiative will also support training and dissemination activities by providing easy-to-use protocols and tools to study adaptation in the wild that can be used as educational materials.

One of the major challenges for the future will be to assemble and share the extensive knowledge, results and original data that have been generated in the ISSs throughout the years. This contribution to “Open Science” will be facilitated by the regularly updated ISS information system (see Box) leading to the publication of datasets in data papers for the benefit of scientific progress.

<table><caption>TABLE 1</caption><caption>Number of projects conducted in the ISS since the beginning of EVOLTREE</caption><thead><tr><th></th><th>Mono-site projects</th><th>Multi-site projects</th></tr></thead><tbody><tr><td>EVOLTREE Phase I (2006-2010)</td><td></td><td></td></tr><tr><td>International partnership</td><td>3</td><td>10</td></tr><tr><td>EVOLTREE Phase II (2011-current)</td><td></td><td></td></tr><tr><td>International partnership</td><td>3</td><td>4</td></tr><tr><td>National partnership</td><td>11</td><td>0</td></tr><tr><td>TOTAL (2006-current)</td><td></td><td></td></tr><tr><td>International partnership</td><td>6</td><td>14</td></tr><tr><td>National partnership</td><td>11</td><td>0</td></tr></tbody></table>

## ISS COORDINATION

A Framework of Agreement defines the organisation of the ISSs, data and metadata supply and access policy. Each ISS has a local coordinator in charge of managing the ISS Information System and facilitating research activities, by interacting with local managers or helping with local logistics. In order to be able to access the infrastructure resources (data, samples, experiments), partners must accept the Agreement and contact the ISS coordinator before submitting the project.

<figure><img src="image_15.png" /></figure>

<footnote>3• Project website not yet available, see http://cordis.europa.eu/project/rcn/200286_en.html</footnote>

<footnote>4• www.treetype.org/, see specific review in this volume</footnote>
Michael Stierschneider<fnref n="1" />, Stephan Gaubitzer<fnref n="1" />, Johanna Schmidt<fnref n="1" />, Otto Weichselbaum<fnref n="1" />, Dieter Kopecky<fnref n="1" />, Antoine Kremer<fnref n="2" />, Silvia Fluch<fnref n="1" /> and Eva Maria Sehr<fnref n="1" />.

The Repository Centre is a centralised storage facility that hosts biological and genetic resources and corresponding metadata collected by EVOLTREE partners, to be used for research upon request by any interested lab.

It deals with resource management ranging from whole organisms (plant and animal material, bacteria, and fungi) to DNA samples of whole genomes, organellar and sub-genomic clones and BACs or genes (ESTs).

### Goal and objectives

For many decades, the importance of reference material for various types of plant based research from ecology to phylogenetic systematics has been acknowledged: whenever results need to be compared, the availability of reference material plays a critical role. Thus collections of reference material for plant breeding, biotechnology and biodiversity analyses have been established all around the world. These have traditionally been based on living plant material managed *ex situ* in seed banks, botanic gardens or *in situ* in nature reserves.

With the advent of DNA based analytics, a new and very powerful tool was uncovered to unlock the potential of the stored biological material. After all, genomic DNA samples represent the entire genetic information of an organism, from various resistance traits to phenotypic parameters, as well as records of their inherited characteristics and ancestral roots. Thus, since the discovery of the double helical structure of the DNA in the early 1950s (Watson and Crick, 1953), molecular data based on DNA sequences have become increasingly important for a variety of biological disciplines, including systematics, ecology, evolution and population genetics, genetic diversity assessment and data generation used as a basis for nature conservation.

Nowadays, the extraction of genomic DNA is easily done, even in high-throughput, and once purified, DNA can be stored for long periods of time. As a result, DNA based collections have become increasingly important, with biobanking in the human sector being the most prominent one; but natural history museums have also started engaging in uncovering the value of their historic collections by extracting DNA from various dried samples which have been collected and preserved for centuries (Droege *et al.* 2014).

Photos: AIT

<figure><img src="image_16.png" /></figure>

However, the key to success is standardisation. Standardised tools and techniques are increasingly applied in diverse initiatives, such as the 'International Barcode of Life' (iBOL) which entirely relies on DNA information generated from reference samples collected throughout the world, aims to reconstruct the phylogenetic relationships in the Tree of Life This project can only succeed if all the partners apply the same techniques and have access to the same reference DNA, as well as collected data.

In order to meet the demands of future high-impact research, reference material and larger sets of DNA samples, as well as corresponding data, have to be made widely accessible. Particularly in the area of ecological research, where findings are based on the fact that population genetic patterns are being compared across borders and over large geographical distances and gradients, it is essential that researchers have access to reference material and to the respective data generated from this material. Such biological material, data and tools

<footnote n="1">1 AIT Austrian Institute of Technology GmbH, Health & Environment Dept., Konrad-Lorenz-Street 24, 3430 Tulln, Austria</footnote>

<footnote n="2">2 INRA, UMR1202 BIOGECO, Cestas F-33610, France</footnote>
are needed in a standardised and freely accessible way in order to guarantee comparability of research results across Europe. When dealing with forest trees, sample collection is a laborious and time-consuming task, involving long trips to remote areas, as well as dangerous or difficult situations when accessing the material (e.g., tree climbing, shooting down twigs); therefore, the sharing of this material and generated data leads to more economical and time saving research.

In view of this, one objective of the EVOLTREE network was to build up a centralised and standardised storage and management facility, known as the Repository Centre. By storing research material at one physical site, the aim was to generate high impact in forest research in the disciplines of ecology, genetics, genomics and evolution, not only in Europe and during the course of the project, but also as an international reference site for forest genetic resources.

## Description of the facility

The Repository Centre gathers together all the available and dispersed research material in one reliable site and provides open-access to a continuously growing data-set. This high quality material is available to and provided by EVOLTREE partners and researchers outside the network.

Due to the huge number of samples which were already available at the EVOLTREE partner sites, special equipment for storing, managing and tracing material, together with a database for storage of all the corresponding (meta-) data, were prerequisites for the physical installation of the Repository Centre. The installation process for creating flexible and highly reliable workflows for DNA extraction, quality control and long-term storage at the Repository Centre laboratory was initiated in 2006. The goal was to have genomic DNA of populations, e.g., from the EVOLTREE Intensive Study Sites (Lefèvre et al. page 6, this book) as well as gene bank collections extracted and stored following the same extraction procedures. ESTs had to be available in the form of single clones, as well as spotted on micro arrays, in order to conduct large scale expression profiling in natural populations of non-model species. The most important features were guaranteed sample integrity, standardised quality of material and data, and sustainable and easy access to the material. To fulfil these requirements, a modular -20°C/-80°C fully automated storage system with a capacity for 11,230 microtiter plates at -20°C (Universal Store US-450; Nexus Biosystems) and 1,000 microtiter plates at -80°C was installed (BioBank™; Thermo Scientific).

The storage system comes with online monitoring and logging of the temperature status and includes an internal alarm system via SMS in case of any temperature failures. The redundant refrigeration systems are assembled

outside the building; each system being able to hold the set temperature without the second one.

Barcoded, heat sealed microtiter plates are stored on blue barcoded trays (6 plates/tray) which are positioned randomly within a shelf system. A rail-based robotic arm stores or retrieves the plates on request and plates within the trays can be rearranged by a separate plate picking module under freezing conditions to prevent needless sample thawing. Plate picking and re-insertion is logged by a database system

## Automation

To manage and distribute several thousand DNA resources, a high level of lab automation is required to guarantee failure-free sample and data handling, combined with high throughput and high quality. A newly introduced laboratory data management system, called *Material Administration and Preparation System* (MAPS™) serves as the backbone of the quality management system (Kopecky et al. 2012). It consists of a database which holds information about the lab processes and a web-based user interface for viewing and editing this information. Once the initial necessary Excel-based provider data has been imported into the system, the whole workflow being undertaken in the laboratory at the time (DNA extraction, electrophoresis images, PCR results, storage positions, DNA

<caption>FIGURE 1</caption>

Pie chart of the stored gDNA samples

<figure><img src="image_17.png" /></figure>
quantity/quality, and results of downstream analysis), together with all needed supply information (e.g., barcodes, volumes, well IDs), is logged within the system and can be retrieved accordingly.

Genomic DNA (gDNA) extraction protocols have been optimised for semi-automated (whereby the pipetting steps are run automatically and the centrifuging steps manually), high-throughput processing for 96-well format, using liquid handling platforms like *Tecan* or *Hamilton*. Supported by these infrastructures, the Repository Centre is able to execute up to 960 gDNA extractions per day.

Protocols for a wide range of raw plant material like leaves, needles, buds, roots, cambium, and wood have been established. In order to guarantee access to the reference material over time, lyophilised plant material is stored at room temperature alongside 2 x 100 µl of gDNA extracted from this material at concentrations of approximately 50 ng/µl. One of these 100 µl is located in the working copy deposited in the automated storage facility, whereas the second copy is used as a backup plate stored in another section of the building. This is due to the requirement of risk mitigation - so that in the case of any sort of accident (e.g., fire, system break down, etc.) which could destroy the working copy of a sample, the backup copy is still available for use.

As soon as the gDNA extraction is finished, an aliquot (mostly 1 µl) of each sample is verified and documented via agarose gel electrophoresis. Analysis of the gel images is carried out automatically using proprietary image analysis software (Bajla et al. 2005). This software enables the analysis of 96 individual samples (loaded on one gel) in parallel. Pixel intensities are measured and compared to a

pre-defined standardised mass ladder, whereby the gDNA yield is calculated. The corresponding picture as well as the estimated gDNA concentration and size are saved back to MAPS™.

In the case of failed samples, or samples showing degraded gDNA, the sample state is set to «failed» within MAPS™, and these samples will not be available for downstream analysis. This way, the quality and amount of gDNA is documented for each and every sample so that in the case of any problems occurring in downstream analyses, the data quality data of a sample can be retrieved and checked by the user directly via a central search portal.

At the request of a customer, single samples can be selected out of 96- or 384-well microtiter plates, a process known as cherry-picking. The requested samples are often distributed over a larger number of microtiter plates and their picking necessitates an error free workflow. To fulfil these requirements, worklists are generated by MAPS™ which are generated within the software and sent to the liquid handling platforms directly. The pipetting process monitored by barcode tracking starts according to the worklists and the generated log files provide information about possible sample manipulation at any time.

## Capacity

At the time of writing, 27,073 gDNA samples (Figure 1), 485,893 cDNA samples (Figure 2), 202,752 BACs, and 26,329 source samples (Figure 3) from various organisms of forest genetic resources and allied species (e.g., moths, caterpillars, fungi) are managed in the Repository Centre (Table 1). Of the capacity of 11,230 microtiter plates, currently 9,569 plates in both formats, 96- and 384- well, are stored. Most of the gDNA and tissue samples are stored in duplicates.

<figure><img src="image_18.png" /><figcaption>**FIGURE 2**</figcaption></figure>

**Pie chart of the stored cDNA samples**

Out of a total of 485,593 cDNA samples, 309,093 were generated in the framework of EVOLTREE.

<figure><img src="image_19.png" /></figure>
<figure><img src="image_20.png" /><figcaption>Pie chart of the stored tissue material</figcaption></figure>

## Data management and access

Databases are critical for efficient sample and data management, as well as for the efficient end utilisation of the DNA bank itself. To allow a quick and easy retrieval of a maximum amount of associated information (e.g., tissue type, collection conditions, date of collection, provider information, sample preparation, details on DNA quality and quantity), a tailor-made LIMS (Laboratory Information Management System) was developed in the framework of EVOLTREE to fulfil the demands of the Repository Centre. MAPSTM (Material Administration and Preparation System), a new concept of LIMS, meets these requirements with an innovative data model, as well as a modern service-oriented architecture, on top of state-of-the-art web technologies (Kopecky et al. 2012).

MAPSTM reflects all workflow steps in the laboratory and provides possibilities for recording these steps electronically. MAPSTM further communicates with the storage system via an application programming interface (API) based on the *Java Messaging Service* (JMS). This way the end user can access the organisation and reporting capabilities of the storage device seamlessly, without the

need to work with two different applications in the laboratory. The user interface of MAPSTM has been implemented in a web-based way in *Java*. Access to the system therefore only requires a web-browser and no other specific software needs to be installed, so it is very easy to access MAPSTM from any computer available in the laboratory. The information in MAPSTM is recorded in a *PostgreSQL* database on a central server and regular backups of the data ensure its necessary integrity. Data access is only granted to laboratory workers and the administrator via user accounts with respective user rights. Should communication with external partners be required, MAPSTM offers an interface based on the *Simple Object Access Protocol* (SOAP), which provides standardised services for querying information about samples stored within the LIMS, as well as for adding new samples to the system. These services can also be made available across organisational borders, so that customers are able to query information about the stored samples via a central search portal, known as the eLab (Ehrenmann et al., page 15, this book). Due to security restrictions, however, the MAPSTM service, especially the storage system interface, is located in an internal network not reachable from the outside. The eLab services cannot directly access the MAPSTM databases, since they are located at the same institution (although in a different network). The MAPSTM database is therefore treated as an external database resource by the eLab that needs to be queried at regular intervals. The queried information is then inserted into the eLab search system, where it can be accessed by the users (Kopecky et al. 2010).

## Current and future use

Besides its central role as a storage and retrieval system for biological material, DNA, and data in the framework of the EVOLTREE network serving forest ecosystem research, the Repository Centre became part of the Trees4Future (http://www.trees4future.eu/) project. This was an Integrative European Research Infrastructure project that integrated forest tree breeding infrastructures to improve and enhance gains in the area of European forest tree breeding. In this framework, the repository centre and its links to the EVOLTREE data collections serves as an integrative hub for European forest ecosystem research and European tree breeding efforts.

<table><caption>TABLE 1</caption><caption>Overview of the sample types and number of samples stored in the Repository Centre</caption><thead><tr><th>Sample type</th><th>Nr. of samples stored</th></tr></thead><tbody><tr><td>Source material</td><td>26,329</td></tr><tr><td>gDNA</td><td>27,073</td></tr><tr><td>cDNA</td><td>485,593</td></tr><tr><td>BAC</td><td>202,752</td></tr></tbody></table>

<figure><img src="image_21.png" /></figure>
This way, the Repository Centre is currently the largest data
provider to the Global Genome Biodiversity Network
(GGBN) - a network of repositories of genomic sample
collections aiming at allowing open access to reference
material from botanical gardens as well as natural history
museums. Data of about 53,402 samples, divided into
27,073 gDNA and 26,329 tissue samples are accessible via
the GGBN portal (http://www.ggbn.org/) following the
ABCD standard used by the BioCASe provider software
(Holetschek et al. 2012). In addition to this, all georeferenced
EVOLTREE repository data sets can be found on GBIF, the
Global Biodiversity Information System (GBIF,
http://www.gbif.org/).

Being part of these networks enhances visibility and
contributes to the ever growing idea of open-access
resources and data in order to further the development of
DNA based forest ecosystem research. As can be seen by
all these activities, and as also requested by the European
Commission, open data and open material initiatives will
help future research to better integrate research results and
to enhance forest ecological understanding on a large scale.
By integrating genetic, as well as environmental data, we
will be able to generate forest systems modelling
approaches that will allow a more general understanding of
the complex ecosystem forest and will help to mitigate the
impact of climate change on one of the most important
socioeconomic factors in Europe.

REFERENCES

Bajla, I., Holländer, I., Fluch, S., Burg, K., Kollár, M., 2005. An alternative method for electrophoretic gel image analysis in the GelMaster software. Comput. Methods Programs Biomed. 77, 209-231. doi:10.1016/j.cmpb.2004.09.007

Droege, G., Barker, K., Astrin, J.J., Bartels, P., Butler, C., Cantrill, D., Coddington, J., Forest, F., Gemeinholzer, B., Hobern, D., Mackenzie-Dodds, J., Ó Tuama, É., Petersen, G., Sanjur, O., Schindel, D., Seberg, O., 2014. The Global Genome Biodiversity Network (GGBN) Data Portal. Nucleic Acids Res. 42, D607-D612. doi:10.1093/nar/gkt928

Holetschek, J., Dröge, G., Güntsch, A., Berendsohn, W.G., 2012. The ABCD of primary biodiversity data access. Plant Biosyst. - Int. J. Deal. Asp. Plant Biol. 146, 771-779. doi:10.1080/11263504.2012.740085

Kopecky, D., Schmidt, J., Fluch, S., 2010. Large-scale integration of distributed heterogeneous data sources within Evoltree, in: Proceedings of the EVOLTREE Conference on Forest Ecosystem Genomics and Adaptation. San Lorenzo de El Escorial, Madrid, Spain.

Kopecky, D., Weichselbaum, O., Fluch, S., 2012. MAPS - A service-oriented, customizable, multi-purpose LIMS. Nat. Methods Appl. Notes.

Watson, J.D., Crick, F.H., 1953. Molecular structure of nucleic acids. Nature 171, 737-738.
# EVOLTREE ELAB - AN INFORMATION SYSTEM FOR FOREST GENETICS

F. EHRENMANN<fnref n="1" />, S. GLAUBITZER², D. KOPECKY², J. SCHMIDT², S. FLUCH², E. MARIA SEHR², A. KREMER<fnref n="1" />.

1INRA, UMR1202 BIOGECO, Cestas F-33610, France; 2 AIT Austrian Institute of Technology GmbH. Health & Environment Dept., Konrad-Lorenz-Straße 24, 3430 Tulln, Austria.

The EVOLTREE web portal acts as a platform for information and data storage, retrieval, exchange and communication. It was set up in the early years of the network (between 2007 and 2008) in order to fulfill one of EVOLTREE's objectives of maintaining and reinforcing electronic and physical resources, repositories and infrastructures.

It comprises what is known as the “electronic Lab” (eLab) which was designed as a centralised search engine for databases that are stored in different servers located in different institutions in Europe.

The web portal can be accessed via the EVOLTREE website<fnref n="1" />.

## Background and objectives of the eLab

While some of the portal databases existed beforehand, most of the web applications corresponding to the databases were constructed in the first four years of the EVOLTREE network. These have been continuously updated and populated ever since by the member laboratories which host the databases and carry out this activity as part of their 'in-kind' contribution to the network.

The portal databases are connected through a standardised, HTTP transmittable interface (TAPIR - www.tdwg.org/activities/tapir/), so that queries can be made within the whole set of databases.

Given the number of tree species studied throughout Europe, it was decided to “virtually” subdivide the eLab into three major portals corresponding to the three major botanical forest tree families that are studied: the Quercus Portal (for species belonging to the Fagaceae family), the Pinus Portal (for the Pinaceae family), and the Populus Portal (for species belonging to Salicaceae). Depending on their field of interest, users can therefore enter the system and make queries via three channels:

* The individual database for queries targeting well-focused information.

* The eLab for an overall search across all the databases. Access via the eLab research engine is recommended if users do not know where - e.g., in which database - the information of interest is located.

* One of the family portals, for data corresponding to a particular species, or genera, of the Fagaceae, the Salicaceae, or the Pinaceae family. Databases concerning species not belonging to these families can be directly accessed via the eLab.

Photo: BioGeCo, INRA

<figure><img src="image_22.png" /></figure>

## The individual databases

Passport, phenotypic, genetic and genomic data corresponding to different research units (genes, individuals, populations, species) were stored in separate databases, some of which existed before the launch of the network. At the beginning of EVOLTREE it was decided to keep the decentralized structure of the databases and to connect them via an interoperable interface in order to benefit from the already existing resources and the contributions of different partners.

Table 1 provides a summary of the content of the largest and most completed individual databases. All databases can be accessed individually and queries can be made internally without using the overall research engine of the eLab. A few databases offer some additional features and in some cases provide internal data analysis; for example, genetic or QTL maps can be compared using Cmap. GD2 is dynamically linked with EUFGIS (http://portal.eufgis.org/), the georeferenced database of forest conservation units coordinated by EUFORGEN. The database connection is

<footnote>* These authors contributed equally to the construction of the eLab and its components.</footnote>

<footnote n="1">¹ The web portal can be accessed via the EVOLTREE website: www.evoltree.eu/index.php/e-recources/elab</footnote>
<table><caption>TABLE 1</caption><thead><tr><th>Acronyms</th><th>Main content</th><th>Additional features</th><th>Access via eLab</th><th>Access via family portal</th></tr></thead><tbody><tr><td>Map</td><td>Genetic and phenotypic records of trees belonging to mapping pedigrees.</td><td>Contains only data. Direct access to Cmap possible.</td><td>Yes</td><td>Yes, under the names of QuercusMap, PinusMap and PopulusMap.</td></tr><tr><td>Cmap</td><td>Position of markers and QTLs on genetic and QTL maps.</td><td>Comparison of different maps of different pedigrees.</td><td>Yes</td><td>Yes, under the same name (Cmap) or all three families (Fagaceae, Pinaceae, Salicaceae).</td></tr><tr><td>Treepop<br/>TreePop</td><td>Genetic and phenotypic records of trees belonging to natural or unstructured populations.</td><td></td><td>Yes</td><td>Yes, under the same name (Treepop) for all three families (Fagaceae, Pinaceae, Salicaceae).</td></tr><tr><td>Provenances</td><td>Passport, Genetic and phenotypic records of trees belonging to provenances established in provenance tests.</td><td>Also contains climatic data related to the provenance sources.</td><td>Yes</td><td>OakProvenance (exists only for oak).</td></tr><tr><td>GD2</td><td>Georeferenced data of allelic frequencies and diversity statistics in natural populations.</td><td>Is connected to the EUFGIS database.</td><td>Yes</td><td>Yes, under the same name (GD²) for all three families (Fagaceae, Pinaceae, Salicaceae).</td></tr><tr><td>SSR</td><td>Sequences of microsatellites motifs and their flanking regions.</td><td></td><td>Yes</td><td>Only in the Quercus Portal.</td></tr><tr><td>SNP</td><td>Sequences of the contigcontaining the SNP and the two flanking regions.</td><td></td><td>Yes</td><td>No</td></tr><tr><td>Candidate genes</td><td>Sequences of candidate genes.</td><td></td><td>Yes</td><td>No</td></tr><tr><td>ESTs</td><td>Expressed sequence tags of gene transcripts.</td><td></td><td>Yes</td><td>Yes, under the same name (EST) for all three families (Fagaceae, Pinaceae, Salicaceae).</td></tr></tbody></table>

carried out using the TAPIR interface. The landscape of genetic diversity near conservation units can be drawn as a result of the connection between both databases, thus potentially helping to refine the setup of the conservation units.

Data access is controlled via user accounts and a hierarchy of roles is granted depending on user access rights. A high level of confidentiality is maintained using table fields with assigned values depending on the related user groups. Thus, data can be kept confidential and restricted to a particular group of users before publication.

## Data input

In order to deal with the large amount of data created during the EVOLTREE project, it was decided to offer individual database-specific solutions for transferring data, using Excel or comma-separated text files (.csv). Most of

the database-driven web applications enable EVOLTREE users and/or database administrators to manage data and use templates to import data of the same type (for example markers, populations or sequences).

## Data output

EVOLTREE users can export data files (.csv) from the individual databases. Search menus are available in most of the database-driven web applications, with several criteria to avoid downloading issues for oversized datasets. The “download” buttons or sections are only available for registered users.

## The eLab (electronic Lab)

The eLab gathers the data from all the individual EVOLTREE databases, and provides an interoperable web-interface for
FIGURE 1

General view of the eLab and its components

The eLab search interface consists of three parts: (1) a full-text search interface, (2) a guided search interface, and (3) a tree-view of the data. Within the full-text search interface, users can search for textual terms that occur somewhere in the cache database; whether the term defines a species name, a genus name, an annotation, a marker name or something else (e.g., a comment). The synonyms found during the standardisation process (see above) are also integrated into the full-text search, so that users can retrieve their own terms with their original names.

<figure><img src="image_23.png" /></figure>

Data transfer is done over HTML using the TAPIR interface (http://www.tdwg.org/activities/tapir/). TAPIR is a XML-based protocol that can be used for information retrieval in distributed architectures. It is used to collect information from heterogeneous data sources in a pre-defined standardised format. TAPIR operates by harvesting all relevant information of the individual databases.

users to make queries against the data. Thus, the eLab functions as a clearinghouse mechanism enhancing the exchange of information and data throughout the different member labs of EVOLTREE. The relevant data parts of every database were defined when a new database was being integrated into the eLab.

The collected data is first stored locally and then transferred into a virtual cache database. This data collection is carried out at frequent intervals so that the latest information is always available in the cache. While transferring the data into the cache, the data is also merged into a standardised data format by using unique taxonomies; for example, different names for species (e.g., in English, German, French) are standardised so that only one name will be used in the cache database.

The implementation of a proper standardisation system played a major and important part in developing the eLab. The centralised search engine of the eLab only queries the cache database. Therefore, some specific information - only available in the individual databases and which was not considered to be relevant during the integration process - is not visible when using the eLab search engine. If users wish to access such additional information, their search will result in being redirected to the user interface of the corresponding individual database. During the redirection, the user information is encrypted when sent to the database (Figure 1).

Within the guided search interface, users can define more specific search queries. The existing data is presented in a web form and users can select their terms of interest (e.g.,

species, genus, institution, etc). It is also possible to refine queries further by selecting different pre-defined datatypes (e.g., genetic markers or population). The web-forms are updated dynamically when the selection changes. This way, users can, for example, search for all entries in the cache database that belong to a certain species, to a certain genus, or to a certain institution. The tree-view of the data represents a categorisation of the data available in the cache database. When transferring data into the cache, every data item is categorised according to pre-defined taxonomies. In the tree-view, users can browse through the hierarchical taxonomy and quickly find out how many, for example, data items for a certain sequence feature region exist. It is also possible to detect how many data items belong to a certain EVOLTREE partner.

In addition to the search tool, the eLab offers a reporting service. As they usually contain a large number of entries, the results are grouped together to get a better overview of the data. Each result entry is attributed a description to characterise it. If the user clicks on an entry, he will see all the information that is available for this entry in the cache database, which at this point may not be the "complete" data he is looking for. In order to view the "complete" data, the user can click on a second link and he will then be redirected to the external database the current result entry belongs to.

<figure><img src="image_24.png" /></figure>

Photo: EFIATLANTIC
<caption>FIGURE 2</caption>

## Main page of the Quercus Portal

The Quercus Portal comprises two sections:

A static section (left part of Figure 2) that provides general information regarding the biology, biogeography, phylogeny, botany and genetics of the botanical family and the different genera. The static page also comprises information about ongoing research and projects and links to their dedicated web pages.

A dynamic section that corresponds to the different databases related to the species or genera belonging to the Quercus family. They appear as different entry tabs in the headings of the webpage of the portal (upper part of Figure 2).

<figure><img src="image_25.png" /></figure>

## The family portals

To ease the queries of the user, the different databases were virtually subdivided into three families (Fagaceae, Pinaceae and Salicaceae); thus, users may directly enter one of the three portals (Quercus Portal, Pinus Portal or Populus Portal) and get direct access to the data they are looking for. As mentioned earlier, queries through the eLab retrieve the information stored in the cache database first and not the “complete” data stored in the individual database to which the user can be redirected; access via the portals is therefore much more rapid. As the different portals are designed in the same way, only the Quercus Portal is shown here, being the most complete at this stage (Figure 2).

The Quercus Portal has its own research engine (Global Search) which can be used to make queries across the databases hosted by the portal. An update of the current content of the different databases of the Quercus Portal is available in Table 2.

## Current and future use of the eLab and the portals

A web analytic service has tracked and reported the EVOLTREE website traffic ever since the beginning of the network. From 2007 to 2015, 68,838 sessions were recorded by 37,576 users. On average, every time a person visited the EVOLTREE site (a single session), they looked at 4.45 pages for a total of 307,000, and an average session duration of 2 minutes and 57 seconds.

While the main databases were constructed in the early years of EVOLTREE and the current portal structure was designed more recently, the main focus is now on the maintenance and regular updating of the databases. We anticipate, however, that very large data sets are still to come as a result of the development and applications of next generation sequencing (NGS) in population genomics of trees. Not all data collections corresponding to forest

<caption>FIGURE 3</caption>

## Interoperability and data flow between information systems in the field of genomics and forestry

<figure><img src="image_26.png" /></figure>
<caption>TABLE 2</caption>

Update of the content of Quercus Portal (March 31st 2016)

<table><thead><tr><th>Databases</th><th>Taxons</th><th>Data types</th><th>Features</th></tr></thead><tbody><tr><td rowspan="5">QuercusMap</td><td rowspan="5">Q. robur, Q. petraea, Q. robur x Q. petraea</td><td>Pedigrees</td><td>18</td></tr><tr><td>Genotypes</td><td>11,000</td></tr><tr><td>Traits</td><td>214</td></tr><tr><td>Genotypic data</td><td>515,000</td></tr><tr><td>Phenotypic data</td><td>335,000</td></tr><tr><td rowspan="3">Cmap</td><td rowspan="3">Q. robur, Q. petraea, Q. robur x Q. petraea, C. sativa</td><td>Geneticmap sets</td><td>24</td></tr><tr><td>QTL map sets</td><td>13</td></tr><tr><td>Maps</td><td>683</td></tr><tr><td rowspan="4">EST</td><td rowspan="4">Q. robur, Q. petraea, Q. robur x Q. petraea</td><td>Unigene sets</td><td>3</td></tr><tr><td>Contigs OCV1</td><td>69,514</td></tr><tr><td>Contigs OCV2</td><td>65,712</td></tr><tr><td>Contigs OCV3</td><td>91,000</td></tr><tr><td rowspan="5">TreePop</td><td rowspan="5">Q. robur, Q. petraea, Q. robur x Q. petraea</td><td>ISS</td><td>4</td></tr><tr><td>Association populations</td><td>7</td></tr><tr><td>Genotypes</td><td>4,729</td></tr><tr><td>Genotypic data</td><td>323,784</td></tr><tr><td>Phenotypic data</td><td>83,813</td></tr><tr><td rowspan="4">GD<sup>2</sup></td><td rowspan="4">106 distinct species for Quercus genus</td><td>Populations</td><td>4,017</td></tr><tr><td>Trees</td><td>24,160</td></tr><tr><td>Frequencymeasures</td><td>61,823</td></tr><tr><td>Diversitymeasures</td><td>6,902</td></tr><tr><td rowspan="5">Oak provenance</td><td rowspan="5">Q. robur, Q. petraea</td><td>Provenances</td><td>419</td></tr><tr><td>Provenance tests</td><td>60</td></tr><tr><td>Seed lots</td><td>464</td></tr><tr><td>Traits</td><td>1,874</td></tr><tr><td>Phenotypic data</td><td>1,883,677</td></tr><tr><td>SSR</td><td>Q. robur, Q. petraea</td><td>Genetic markers</td><td>669</td></tr><tr><td rowspan="2">Candidate genes</td><td rowspan="2">Q. robur, Q. petraea, Q. robur x Q. petraea</td><td>Genes</td><td>648</td></tr><tr><td>Traits</td><td>17</td></tr><tr><td>SNP</td><td>Q. robur, Q. petraea</td><td>SNP</td><td>7,576</td></tr></tbody></table>

trees can be hosted by international databases, such as GenBank, or dbSNP, and thus it is highly likely that in the future new databases will need to be constructed within the eLab.

In recent years, the eLab has also been connected to external data repositories related to either forestry or genomics. This is made possible by the use of a common set of exchange formats and of compatible protocols with the external repositories, similar to the TAPIR interface. Such interoperable protocols have now been installed with GnpIS (a multispecies integrative information system dedicated to genomic data of plants and fungi pests hosted and curated by INRA) and Trees4Future (an Integrative European Research Infrastructure in the field of Forestry) (Figure 3). Meetings, software demos and video conferences are organised to maintain the communication between collaborators and ensure a useful evolution of the information systems.

## ACKNOWLEDGEMENTS

We are grateful to Catherine Bastien, Zamira Betancourt, Noémie Emeriau, Audrey Jacques-Gustave, Véronique Jorge, Thierry Labbé, Mélina Millox, Christophe Plomion, Frédéric Raspail, Richard Séverin, and Jean-Paul Soularue for their contribution during the construction of the databases and the eLab.

Photo: EFIATLANTIC

<figure><img src="image_27.png" /></figure>
EVOLTREE TRAINING ACTIVITIES

F. GUGERLI¹, S. HAYES²

1 WSL Swiss Federal Research Institute, 8903 Birmensdorf, Switzerland;
2 EFIATLANTIC, 69, route d'Arcachon, 33612 Cestas, France.

One of EVOLTREE's four main Integration Activities (IAS) is dedicated to the training of students and young scientists.

For an integrative research consortium such as the Network of Excellence EVOLTREE, it is a prime responsibility to disseminate scientific knowledge and advances to the research community (e.g., through peer-reviewed articles) as well as to the public, but even more so directly to its constituting members; in particular to its students as the forthcoming generation of researchers.

One way in which EVOLTREE carries out such knowledge transfer is via the training opportunities created by its partners as part of their in-kind contribution to the network.

Transfer of knowledge within Evoltree –
and beyond

During the initial, EC-supported phase (2006-2010), every partner was invited to suggest training courses that would fit the scope of Evoltree research in a broad sense. The organisation of a course was financially supported by the network's own funds so that expenses, e.g., for invited teachers, could be covered.

In EVOLTREE's second phase as a European Research Group (2011 onwards), it was agreed that partners could offer training courses as in-kind contribution, an alternative to direct monetary support of the network activities. As a consequence of this formal change, the types of training opportunities broadened so that excursions, workshops, existing courses from within the curriculum of a university, etc. could be integrated into the programme.

As a great benefit of the resources available within the network during both phases, all participants from the EVOLTREE partner institutions can be reimbursed for their expenses up to a pre-defined limit., these training courses are not only open to students from registered EVOLTREE partners, but also to interested students from outside the network (at their own costs). This way, it is possible to foster cross-disciplinary education and to establish or strengthen contacts between complementary fields of research.

Wide-ranging expertise for a variety
of students

A multitude of disciplines are represented within the EVOLTREE partnership. Hence, training could greatly benefit from this broad range of expertise of leading scientists in their field within the consortium. To complement this competence in the training programme, EVOLTREE partners invited lecturers from a variety of

<figure><img src="image_28.png" /></figure>

Photo: Julien Dumercq, LabEx COTE
## AN INSIDER'S VIEW

Participating in an EVOLTREE training course has benefited many students over the past ten years. Students have appreciated the opportunities given by the network, be it the many topics explored within the various courses, or the depth and competence of the teaching in a particular course. This positive attitude is not only reflected by the often high numbers of participants, but also by respective feedback.

In their course feedback, participants have stated: "This course was beneficial for me and fulfilled my expectations. It was a good experience to learn interdisciplinary in approaching ecological problems. The necessity to combine natural, social and civil sciences to better understand the biodiversity loss and conservation was largely developed during the course." and "(...), a perfect place to forget about daily and mundane preoccupations and dedicate one's mind to the acquisition of new scientific skills". Such responses are great motivation for continuing our commitment to teaching our students and to dedicate time and resources towards these activities.

disciplines and institutions for these training courses, which also contributed to the exchange of knowledge among the researchers involved.

An appealing outcome of the training programme was to see that not only young students of forest ecology, e.g., at PhD or post-graduate levels, took advantage of the opportunities to learn about new techniques, types of analyses, or concepts, but that established scientists also participated in the training events and could thus learn from – and at the same time actively contribute with their own background to – the training offered by their colleagues.

Over the years, the EVOLTREE training programme has accumulated an immense breadth of topics covered in the various courses (Table. 1). Students have been able to, for examples, learn about fundamental analytical tools in population genetics, take first steps towards effectively using the bioinformatic toolbox, debate about conceptual issues of the coalescent theory, obtain insights into and perform meta-analyses, attempt to detect genomic signatures of adaptation, or discuss ecological consequences of global change on forest ecosystems.

## Outlook

The EVOLTREE community will continue to offer training opportunities that cover the entire breadth of EVOLTREE research and competence – and beyond. Benefiting from established courses or taking the opportunity for developing new teaching components, both researchers and their students of EVOLTREE partner organisations will be able to take part in the transfer of expertise and knowledge to the forthcoming generation of scientists in the fields of genetics, genomics, and ecology of forest ecosystems. These opportunities will also foster the integration of the European research laboratories taking part. Such personal contacts are fundamental and constitute a pre-requisite for continued integrative and interdisciplinary research.

<figure><img src="image_29.png" /><figcaption>Photo: Patricia Gonzalez Diaz</figcaption></figure>
<caption>TABLE 1</caption>

Overview of training opportunities during the Evoltree phase II
as a European Research Group (2011-2015)

<table><thead><tr><th>Year</th><th>Title/subject</th><th>Organising partner<fnref n="1" /></th></tr></thead><tbody><tr><td rowspan="5">2011</td><td>Next-generation sequencing</td><td>U Udine</td></tr><tr><td>Adaptation of forest management to climatic change</td><td>U West Hungary</td></tr><tr><td>Functions of microbial communities in soils</td><td>Hemholtz; TU Munich</td></tr><tr><td>Population genomics</td><td>U Oulu</td></tr><tr><td>Evolutionary quantitative genetics in forest ecosystems</td><td>INRA Pierroton</td></tr><tr><td rowspan="5">2012</td><td>Genetic data analysis</td><td>CZ U Life Sciences; N Carolina State U</td></tr><tr><td>Genome-wide association studies using mixed models</td><td>U Uppsala</td></tr><tr><td>An interdisciplinary perspective on biodiversity and ecosystem services</td><td>ALTER-Net</td></tr><tr><td>Ecophysiology techniques workshop</td><td>U Southampton</td></tr><tr><td>Population genetic and genomic approaches</td><td>U Göttingen</td></tr><tr><td rowspan="3">2013</td><td>Estimating mating system and gene flow in plants</td><td>U Bygdosz</td></tr><tr><td>NGS analysis for beginners</td><td>INIA; U Valladolid</td></tr><tr><td>Global Ecology for Global Change</td><td>LabEx COTE (INRA Pierroton)</td></tr><tr><td rowspan="4">2014</td><td>Transfers and interactions between ecosystems</td><td>LabEx COTE (INRA Pierroton)</td></tr><tr><td>NGS data analysis: from heaven to hell</td><td>U Udine</td></tr><tr><td>Georeferenced genetic data and their evaluation</td><td>TU Zvolen</td></tr><tr><td>Population structure and the architecture of quantitative traits</td><td>U Uppsala</td></tr><tr><td rowspan="6">2015</td><td>Forest genetic monitoring</td><td>U Thessaloniki</td></tr><tr><td>Approximate Bayesian Computation</td><td>U Uppsala</td></tr><tr><td>Ancestral graphs and SMC</td><td>U Uppsala</td></tr><tr><td>Coalescent today</td><td>U Uppsala</td></tr><tr><td>Ecology and society: biodiversity and global change</td><td>INRA Pierroton</td></tr><tr><td>Global change and the evolutionary potential of forest trees</td><td>U Copenhagen</td></tr><tr><td></td><td>NGS data for phylogenetics</td><td>U Marburg</td></tr></tbody></table>

<caption>FIGURE 1</caption>

Training courses combine expert lectures, hands-on computer work, guided discussions, poster sessions, and excursions — in a creative and stimulating environment

<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>

Photos: RensingLab, C. Rosique

<footnote n="1">1 Partner names in brackets indicate organisational link, but not full responsibility for course organisation; institutes in italics indicate non-Evoltree organisers.</footnote>
# TREETYPE: COLLECTING TREE PHENOTYPES IN THE WILD

S. CAVERS¹, S.C. GONZÁLEZ-MARTÍNEZ².

1 Centre for Ecology and Hydrology, CEH Edinburgh, Bush Estate, Penicuik, Midlothian EH26 0QB, UK; 2 BIOGECO, INRA, University of Bordeaux, 33610 Cestas, France.

TreeType is a citizen science initiative of the EVOLTREE network to collect standardised phenotypic data in forest trees, from individual trees to whole forests. Data collection is centralised through a web application.

Additional information and detailed recording protocols are available on the TreeType website, www.treetype.org

## Background and objectives

In an era when information from the genome is accessible as never before, the collection of standardised phenotypic data has become a limiting factor (Neale & Kremer, 2011). These data are essential if we are to establish the relationships between genes, phenotypes and natural selection in response to environmental drivers. For a wide variety of reasons, including breeding, climate change mitigation and conservation, we need to know how the genetic diversity observed in wild populations is translated, via the phenotype, into selectively relevant variation and how this reacts to the environment (in the broadest sense).

From individual genomes to populations and to species, most trees maintain very high levels of genetic diversity and, as a life history characteristic, have developed efficient mechanisms for dispersing that variation across spatially and temporally variable landscapes. Phenotypic data collected from experimental trial settings, whilst essential for establishing the genetic basis for traits, represents observation of unselected progeny and is always compromised by a lack of exposure to the home

site environment, where genotypes may perform quite differently. To harness the power of new genomic techniques to understand selection and adaptation in tree species, we must now take observation of plant phenotypes into the wild.

Analysing quantitative phenotypic data from wild populations is challenging, but the application of appropriate methodological approaches can help to disentangle plasticity and local adaptation, in particular if phenotypic data from the same trees are collected at different time points or if molecular marker data is collected from the same trees (e.g., Castellanos et al. 2015). For example, Phillimore et al. (2012) showed that the slope of phenology on temperature through time will be due to mean plasticity plus any association between this trait values and temperature, that is, adaptive microevolution, and that these two components can be separated using spaciotemporal data collected in a citizen study similar to TreeType.

Another interesting use of phenotypic data collected in wild populations is the study of trade-offs among traits, which could either favour or prevent adaptation to the new

<figure><img src="image_32.png" /></figure>

Photo: Fotolia
## STRUCTURE AND FUNCTION

TreeType is structured as an openly-accessible, web-based data entry portal available at www.treetype.org, via which datasets for individual trees or in bulk via spreadsheets can be entered. A core set of species has been selected, to get the platform under way, but new users have the option to suggest and vote for new species to be included in the portal and these will then be added once a sufficient critical mass of interest is demonstrated. For each species, a 'leader' will be identified who will promote and guide the collection of data including optimising the protocols for that species. Across all species, however, the project will aim to collect data on the same general set of traits, as well as locally-specific environmental data.

## INGREDIENTS FOR SUCCESS

The TreeType project was conceived using the 'citizen science' model, connecting a wide range of actors, from amateur naturalists to researchers, across the geographic ranges of target tree species and providing the infrastructure necessary for concerted data collection. Such approaches are now widespread and have been successfully deployed to gather data in many scientific fields, notably on phenology (e.g., Nature's Calendar, Track-a-tree).

When it works, citizen science has the great advantages of enabling data collection on a wide geographic scale at low cost. However, a number of critical factors must be taken into account to achieve success.

* Firstly, data quality depends on a careful selection of traits; they must be good indicators of the adaptive process in the wild, but at the same time easy to measure for the non-specialist.

* Secondly, a robust, simple platform for data collection is needed to promote participation.

* Finally, determined coordination, ensuring continual steering and promotion of the project is essential to see data collection through from project initiation to completion.

If these elements can be got right, then there is great potential to generate datasets of intrinsic value, as well as a resource on which to build future projects.

environments expected under impending climate change. Finally, the phenology and reproduction data collected by TreeType will increase our understanding of the interplay of demography and genetics for adaptation of forest trees to local environments.

## Project construction

Following a period of consultation with experts within the EVOLTREE network, it was decided that measurements should be taken on a set of four basic traits as a minimum (diameter, number of fruits, average seed mass, and age) and where possible this should be extended with the addition of up to 8 additional characters.

The essence of the selection was to try to cover a range of general categories, of importance to adaptation in different ways, namely growth, reproduction, phenology and defence. In each, a simple trait was identified that could both be recorded reliably by specialists and non-specialists alike. Protocols for each identified trait were designed based on internationally agreed standards such as Perez-Harguindeguy et al. (2013). Recorders are asked to target a set of at least five trees growing from open-pollinated naturally dispersed seed, although it is made clear that any record of any tree is welcome. A basic set of environmental data for each tree is requested along with a photograph, when possible.

## What will the future bring?

The coordinators of TreeType are following an open-access principle. Therefore, any data collected through the TreeType project will be made freely available, subject only to users contacting the TreeType managers to confirm their interest and intentions. Any use of the data for publications or reports will make a clear reference to the project and those TreeType contributors who have been directly involved in the collection of relevant data will have

<figure><img src="image_33.png" /></figure>

Photo: Fotolia
the opportunity to be involved in collaborative work. Contributors are not automatically expected to be co-authors on any publications arising from the data, but they should be made aware of the data use, be offered an opportunity for involvement, and receive appropriate acknowledgement of any significant intellectual input. In addition to scientific publication, the data collected by TreeType will be regularly released to more general databases (e.g., the TRY database) and reported in data papers.

A phone application will be developed to facilitate data collections by citizens.

A concerted effort will be made to use the TreeType infrastructure to collect data for a set of key species, for which contributors will be actively sought. In parallel, the portal is essentially open to any enthusiastic researcher who feels it can fit their needs for data collection in their chosen species. We hope that initiating this project will enable and catalyse the establishment of some fundamental datasets which can be analysed in their own right, and which can form the basis of new projects and studies in the future, in particular those involving genome-wide genotyping.

## ACKNOWLEDGEMENTS

the TreeType project was developed in collaboration and consultation with many colleagues across the EVOLTREE network. For their essential contributions, we thank Stephan Gaubitzer, Egbert Beuker, Celine Blanc-Jolivet, Oliver Brendel, José Climent, Thomas Källman, Christian Pichot, Tommi Suominen, Jill Thompson and the EVOLTREE Executive Committee.

## REFERENCES

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

* Castellanos MC, González-Martínez SC, Pausas JG. 2015. Field heritability of a plant adaptation to fire in heterogeneous landscapes. *Molecular Ecology* 24: 5633-5642.

* Pérez-Harguindeguy N, Díaz S, Garnier E, et al. (32 more authors). 2013. New Handbook for standardized measurement of plant functional traits worldwide. *Australian Journal of Botany* 61: 167-234.

* Phillimore AB, Stâlhandske S, Smithers RJ, Bernard R. 2012. Dissecting the contributions of plasticity and local adaptation to the phenology of a butterfly and its host plants. *American Naturalist* 180: 655-670.

## RECORDED PHENOTYPIC TRAITS IN TREETYPE

1. **DBH (cm)**: Trunk diameter at 1.30 m, i.e., approximately at an adult's breast height.

2. **Height (m)**: Height from ground to tallest part of the crown.

3. **Crown size (m)**: Crown size is a measure of the footprint or plan area of the crown of a tree expressed as crown width.

4. **Stem form (class)**: Stem form is measured using a categorical classification based on straightness and verticality.

5. **Bark thickness (mm)**: Bark thickness is the thickness of the part of the stem that is external to the wood or xylem, including the vascular cambium.

6. **Number of fruits (units)**: Fruits counted from the ground using binoculars.

7. **Seed mass (g)**: Seed mass is the oven-dried mass of an average seed.

8. **First flowering date**: The date when the first male and/or female flower is observed.

9. **Bud flush date**: Bud flush or bud break is the first evidence of active growth resumption in the spring.

10. **SLA, Specific leaf area (cm²/g)**: Specific leaf area (SLA) is the one-sided area of a fresh needle, divided by its oven-dry mass.

11. **Foliar damage (%)**: Leave damage regardless of the cause of the damage.

12. **Tree age (years)**: The age of the tree. Age determination is very important to compare trees growing in different environments.

<figure><img src="image_34.png" /></figure>

Photo: S.C.Martinínez-González
Research highlights:
science that matters

<figure><img src="image_35.png" /></figure>
Florian J. Alberto<fnref n="1" />,<fnref n="2" />, Sally N. Aitken<fnref n="3" />, Ricardo Alía<fnref n="4" />,
Santiago C. González-Martínez<fnref n="4" />, Heikki Hänninen<fnref n="5" />,
Antoine Kremer<fnref n="2" />, François Lefèvre<fnref n="6" />, Thomas Lenormand<fnref n="7" />,
Sam Yeaman<fnref n="3" />,<fnref n="8" />, Ross Whetten<fnref n="9" />, Outi Savolainen<fnref n="1" />

**Corresponding Author:**

Outi Savolainen

Department of Biology and Biocenter Oulu

FIN-90014 University of Oulu

outi.savolainen@oulu.fi

**Key words:** quantitative genetics, adaptive traits, natural selection, local adaptation, phenotypic plasticity, provenance trials, conifers.

Tables S1, S2 and S3, along with the corresponding supporting information legends and references, can be downloaded here: http://onlinelibrary.wiley.com/wol1/doi/10.1111/gcb.12181/suppinfo

Published in *Global Change Biology* (2013) 19: 1645-1661, with doi:10.1111/gcb.12181

Evolutionary responses are required for tree populations to be able to track climate change. Results of two hundred years of common garden experiments show that most forest trees have evolved local adaptation, as evidenced by the adaptive differentiation of populations in quantitative traits, reflecting environmental conditions of population origins. Based on patterns of quantitative variation for 19 adaptation related traits studied in 59 tree species (mostly temperate and boreal species from the Northern hemisphere), we found that genetic differentiation between populations and clinal variation along environmental gradients were very common (respectively 90% and 78% of cases). Thus, responding to climate change will likely require that the quantitative traits of populations again match their environments.

We examine what kind of information is needed for evaluating the potential to respond, and what information is already available. We review the genetic models related to selection responses, and what is known currently about the genetic basis of the traits. We address special problems to be found at the range margins, and highlight the need for more modeling to understand specific issues at southern and northern margins. We need new common garden experiments, for less known species. For extensively studied species, new experiments are needed outside the current ranges. Improving genomic information will allow better prediction of responses. Competitive and other interactions within species and interactions between species deserve more consideration. Despite the long generation times, the strong background in quantitative genetics and growing genomic resources make forest trees useful species for climate change research. The greatest adaptive response is expected when populations are large, have high genetic variability, selection is strong, and there is ecological opportunity for establishment of improved genotypes.

Introduction

Populations can respond to environmental change through phenotypic plasticity, by moving to a new area corresponding to environmental conditions they are adapted to, by genetically adapting to the new conditions, or by combinations of these responses (Aitken *et al.* 2008). Most attention has been paid to range expansion or contraction (Chen *et al.* 2011, Parmesan, 2006), typically using models that assume the species are genetically

homogenous. The potential for genetic responses has often been neglected, for instance in the IPCC reports (IPCC, 2001, IPCC, 2007), even if it is well known that evolutionary changes, i.e. genetic responses, have historically accompanied changes in climate (Davis & Shaw, 2001). Further, it is also now understood that the rate of adaptation required by climate change varies among geographic regions (Loarie *et al.* 2009). Modeling work on the potential of populations and species to

<footnote n="1">¹ Department of Biology and Biocenter Oulu, University of Oulu, Oulu, FIN-90014 Finland</footnote>

<footnote n="2">² INRA, UMR1202 Biodiversité Gènes et Communautés, Cestas, F-33610, France and Université de Bordeaux, UMR1202 Biodiversité Gènes et Communautés, Talence, F-33410, France</footnote>

<footnote n="3">³ Department of Forest and Conservation Sciences and Centre for Forest Conservation Genetics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada</footnote>

<footnote n="4">⁴ Department of Forest Ecology and Genetics, INIA - Forest Research Centre, E-28040 Madrid, Spain</footnote>

<footnote n="5">⁵ Department of Biosciences, University of Helsinki, Helsinki, FIN-00014 Finland</footnote>

<footnote n="6">⁶ INRA, UR629 Ecologie des Forêts Méditerranéennes, URFM, Avignon, F-84914, France</footnote>

<footnote n="7">⁷ Centre d'Ecologie Fonctionnelle et Evolutive, CNRS, Université de Montpellier, UMR 5175, Montpellier F-34293, France</footnote>

<footnote n="8">⁸ Institute of Biology, Université de Neuchâtel, Neuchâtel, CH-2000, Switzerland</footnote>

<footnote n="9">⁹ Department of Forestry & Environmental Resources, NC State University, Raleigh NC 27695-8008, USA.</footnote>
respond genetically to recent climate change is advancing (see Franks & Hoffmann, 2012, Hoffmann & Sgrò, 2011, Shaw & Etterson, 2012 for recent reviews). The immediate responses via phenotypic plasticity have also been considered in the context of climate change (Nicotra et al. 2010).

Here we examine the importance of and potential for genetic responses to climate change in forest tree populations. Trees are ecologically key species in many terrestrial ecosystems, including boreal and temperate forests in Europe and North America. Their response to climate change can substantively impact the global carbon cycle. Local adaptation (Kawecki & Ebert, 2004) is more common in trees than in some other plant species. Tree species are adapted, to the current climate, and they are thus potentially greatly influenced by the rapid changes in climate (Savolainen et al. 2007). The long generation times are a challenge for research, but trees also provide some advantages for these studies, as described below.

First, adaptation to climate change will depend on phenotypic traits relevant in the new environments, such as timing of growth and drought or cold tolerance. There is an extraordinary wealth of information on the quantitative genetics and population differentiation of trees for these traits, based on 250 years of forestry common garden experiments, known as provenance trials (Langlet, 1971, Morgenstern, 1996), and on extensive tree breeding experience.

Secondly, the demographic history since the last glacial maximum has been reconstructed for several tree species by combining phylogeographic and palynological approaches with coalescent-based studies of population demography (Cheddadi et al. 2006, Eckert et al. 2010, Heuertz et al. 2006, Magri et al. 2006, McLachlan et al. 2005, Parducci et al. 2012, Petit et al. 2002, Soltis et al. 2006). Rates of past adaptation of trees to climate changes can be inferred from these studies (Hendry & Kinnison, 1999). The increasing knowledge of the molecular basis of quantitative trait variation (see Neale & Kremer, 2011 for references) can improve predictive models (see e.g., Wilczek et al. 2010). This body of background information allows us to examine the potential for adaptation in natural conditions better than in many other organisms. For instance, in butterflies, studies of responses to climate change have relied nearly exclusively on examining molecular marker variation (Hill et al. 2011). Trees have very long generation times, but they share population genetic characteristics with other outcrossing plants and animals with high levels of gene flow and large effective population sizes (Petit & Hampe, 2006). Trees are highly fecund, and may rapidly increase their population sizes. Because they are sessile, they generally have good tolerance of a range of environmental conditions and large plastic responses. There are ecologically and commercially important trees with large continuous distributions, such as *Picea abies*, *Pinus contorta*, and *Pinus sylvestris*, but also

species with small, fragmented distributions more susceptible to genetic drift. The dispersal capacity of tree species will play a crucial role in their potential for adaptation. Hybridization between closely related tree species can also influence their adaptive capacity out of their current range, as it has been shown in other organisms (Hoffmann & Sgrò, 2011, Olson-Manning et al. 2012 and references therein).

The focus of this review is on predicting evolutionary responses, with as much evolutionary, genetic, and ecological realism as possible. We examine the models needed for prediction, starting with the simplest models of evolution in individual populations, and continuing to more complex and more realistic models involving multiple populations in heterogeneous environments. We discuss what data are needed for realistic prediction of genetic responses, what information is already available, and what additional information we need in terms of new models, new data, or new analyses of existing data (Lindner et al. 2010). Quantitative genetic models of evolutionary response deal with traits that will confer adaptation to future environments. While it is not easy to predict what traits will be most important in the future, it is reasonable to examine traits related to climate, such as the timing of growth and reproduction (Hänninen & Tanino, 2011, Rohde & Bhalerao, 2007) or cold and drought tolerance (Niinemets, 2010).

# Evolution in one isolated population

## A single population: the breeder's equation

According to the breeder's equation, the simplest model governing response to directional selection on a single trait, the response in a large population with no gene flow depends on the strength of selection, on the amount of genetic variation, and its ratio to total phenotypic variation (heritability; (see Falconer & Mackay, 1996). If there is no genetic variation, any change in phenotype in a novel environment inducing directional selection would be due to phenotypic plasticity alone. Forest tree populations harbor considerable genetic variability in many quantitative traits (Cornelius, 1994, Howe et al. 2003, Morgenstern, 1996) as well as at the DNA level (Savolainen & Pyhäjärvi, 2007). While tree breeders can control the intensity of selection and predict responses in breeding populations, it is much more difficult to make such predictions in the wild. Environmental variances will be higher, and heritabilities generally lower (Conner et al. 2003). Methods for estimating heritabilities in the wild are improving because of much better estimates of relatedness and improved methods (Ritland, 1996, Sillanpää, 2011), and will be of critical importance to understanding responses to climate change.

Assessing the strength of directional selection is a demanding task, as we do not even know exactly which traits are most important for fitness, and the longevity of
trees makes lifetime fitness estimates unattainable in a
realistic timeframe. Estimates of directional selection are
available for natural populations (Kingsolver & Diamond,
2011, Kingsolver et al. 2001), but studies on forest trees are
lacking. Further, selection is likely to be variable across
environments, years, and life stages. In natural populations
the traits are also subject not just to directional selection
but also to stabilizing and disruptive selection, not
included in this simplest model. Thus, for most natural
situations, the breeder's equation is far from the reality of
populations responding to natural selection.

Temporal variation in selection

Two general classes of quantitative genetic models have
been developed to study the risk of extinction in single
populations: models with a sudden single step-change in
the optimum phenotype (Gomulkiewicz & Holt, 1995,
Gomulkiewicz et al. 2010, Gomulkiewicz & Houle, 2009,
Pease et al. 1989), and models with a continuous change
in the optimum phenotype (Björklund et al. 2009, Burger
& Lynch, 1995, Chevin et al. 2010, Lynch & Lande, 1993). In
single step-change models, extinction occurs as a
consequence of decreasing population size due to
selective deaths as the population adapts to the change
in environment. In the continuous-change models, by
contrast, extinction is assumed to occur when the pace of
adaptation lags behind the rate of change in the optimum
phenotype (see Aitken et al. 2008 for further discussion).
There are several interesting ways in which these models
could be extended to increase biological realism. Most of
these models assume that the strength of selection does
not vary with population density, which is unrealistic for
most forest trees, as competition is likely greatly reduced
at low densities (see Björklund et al. (2009) for a
simulation model incorporating density dependent
selection). Also, failing to account for changes in biotic
interactions that may be associated with climatic change
could cause models to under- or over-estimate extinction
risks (Gilman et al. 2010). Climate change may result in the
introduction of new pests, as for instance the mountain
pine beetle (Robertson et al. 2009) or new pathogens
(Netherer & Schopf, 2010), but also losses of current
competitors, insects, or diseases caused for example by
phenological shifts between trees and associated pests
(van Asch & Visser, 2007).

While it is possible to parameterize some of these models
to make quantitative predictions about extinction risk, the
assumptions involved greatly limit the faith that should be
placed in any such predictions (see Aitken et al. 2008 for
further discussion). Rather, they seem most useful as
heuristic tools to identify the most likely factors causing
population extinction and to compare relative risk among
species. Generally, these models find that the probability
of extinction decreases for species with large population
sizes, high fecundity, high heritabilities, and high amounts
of standing genetic variation. While many forest trees
present such characteristics, extra effort should be made
to study species that are on the low end of the spectrum

for any of these characteristics. Some examples of species
that may be particularly vulnerable due to their small
population sizes are *Pinus torreyana* in North America, or
*Abies pinsapo* in Europe. More study is necessary to see
whether such vulnerable species also have lower levels of
standing variation.

Genetic basis of adaptive trait variation

The expected genetic responses in many models depend
on the genetic architecture of the trait (e.g. Gomulkiewicz
et al. 2010). While the traditional polygenic model of Fisher
(Fisher, 1918, Fisher, 1930) is based on small effects at a
very large number of loci, some models of selection
predict larger effect sizes (Orr, 1998, Yeaman & Whitlock,
2011). Overall, quantitative trait locus (QTL) studies in
forest trees have generally found large numbers of loci
with relatively small effect sizes, compared to some crop
plants (Barton & Keightley, 2002, Howe et al. 2003, Laurie
et al. 2004). Association studies have further confirmed
this view of moderate effect sizes (summarized in Fig. 2),
e.g. in *Pinus taeda* (Cumbie et al. 2011, Quesada et al. 2010),
in *Populus tremula* (Ingvarsson et al. 2008), *Picea
sitchensis* (Holliday et al. 2010a), and *Pseudotsuga
menziesii* (Eckert et al. 2009). These findings are
consistent with the small effect sizes of flowering time and
leaf trait variation loci in maize (Buckler et al. 2009, Tian
et al. 2011), and human height (Hill et al. 2008). In contrast,
Atwell et al. (2010) found large effect SNPs for many
phenotypic traits of *Arabidopsis*. There may also be major
effect loci for disease resistance, such as for rust disease
caused by fungal pathogens in North American conifers
(Kayihan et al. 2010). The associated loci may well differ
between environments due to genotype by environment
interactions (Jermstad et al. 2003) or because of different
genetic basis in different areas (Goldstein & Holsinger,
1992, Hancock et al. 2011). In many conditions, the
phenotypic differences between populations can be due
to combined effects of several loci rather than
differentiation at the level of individual loci (Kremer & Le
Corre, 2012, Latta, 1998, LeCorre & Kremer, 2003).

Weak genetic correlations allow traits to respond to
selection independently, whereas genetic correlations
opposing the direction of selection will delay the response
(Etterson & Shaw, 2001), and reinforcing correlations will
accelerate it. Under stabilizing selection, responses are
facilitated, if the selection is weak (Duputie et al. 2012). The
underlying causes of genetic correlations are so far not
known in trees.

Overall, the limited findings so far suggest that the
response to strong selection on phenotypes will often be
based on many loci with small effects, and fairly weak
selection on individual loci, as has been also found in
humans (Turchin et al. 2012). If larger effect loci are
involved, response predictions could then use specific
information on such loci. Alternatively, genomic selection
methods could be used to build predictive models that do
not need to identify the particular loci underlying adaptive
genetic responses (Grattapaglia & Resende, 2011, Holliday
et al. 2012, Iwata et al. 2011, Resende et al. 2012).

We do not know whether most adaptations in trees are
due to existing variation or new mutations. During
interglacial periods, tree populations have repeatedly
colonized northern areas and have rapidly adapted to
those conditions, likely because the north-adapted
variants may have remained in southern populations at
lower frequencies (De Carvalho et al. 2010, Savolainen et
al. 2011). Typically large effective population sizes in forest
trees would have contributed to rapid fixation of adaptive
variants. This supports an interpretation of evolution from
standing rather than de novo variation.

Phenotypic plasticity and adaptation

Trees exhibit a high degree of phenotypic plasticity with
respect to climatic variation. Phenological shifts of bud
flush in response to recent increases in temperatures have
been widely documented (Menzel & Fabian, 1999, Menzel
et al. 2006, Parmesan, 2006). Arid years or an arid
microsite may favor the development of deeper and
denser root systems (Kozlowski & Pallardy, 2002). In such
a context, adaptive plasticity can buffer the impact of
changing conditions on population size (Lynch & Lande,
1993). However, these plastic changes may take time to
develop (as in the root example above). In addition, more
plasticity also means less intense selection, causing
populations to genetically track changing optima more
slowly. Recent models have shown that the decreased
selection is more than compensated for by the increased
phenotypic match allowed by plasticity (Chevin & Lande,
2010). In fact, the evolution of plasticity can provide
populations with a transient and efficient response to large
environmental changes (Lande, 2009).

Multiple-site provenance trials can be used to examine the
plastic responses of populations in new environments. This
can be quantified with response functions for individual
populations, which describe the change in a trait as a
function of transfer distance or change in environmental
factors (Rehfeldt et al. 2002, Rehfeldt et al. 1999).
Provenance trials have been planted in sites that vary with
respect to many environmental variables, such as
temperature or water availability (Kramer, 1995,
Morgenstern, 1978, Rehfeldt et al. 2002, Rehfeldt et al.
1999, Reich & Oleksyn, 2008, Shutyav & Giertych, 1997,
Vitasse et al. 2010, Worrell et al. 2000). Transfers to the
south have been used to predict responses to a warming
climate (Beuker et al. 1998, Rehfeldt et al. 2002, Wang et
al. 2006) even if the future conditions may be different
(e.g. photoperiod). Further, these experiments take place
in spaced plantings of seedlings, and thus ignore
germination, establishment, and early intra- and inter-
specific competitive effects. Response functions of
individual populations have been developed for growth
using very large datasets of multiple trials including more
than a hundred populations available for Pinus contorta
(Rehfeldt et al. 2001), Pinus sylvestris (Rehfeldt et al. 2002)

and *Larix occidentalis* (Rehfeldt & Jaquish, 2010). Recently,
Wang *et al.* (2010) developed a universal response
function for Pinus contorta, which integrated populations
and environment effects and can be used to predict the
performance of any population in any climatic conditions.
Incorporating provenance trial data on local adaptation
and phenotypic plasticity in models predicting future
distributions reduced dramatically the extinction risk in
southern populations (Benito-Garzón *et al.* 2011, Morin &
Thuiller, 2009). The plastic response of different traits (e.g.
phenology in trees) to variation in climate is, however,
often much more complex than in heuristic models of
adaptation (see e.g. Caffarra *et al.* 2011, Hänninen & Tanino,
2011, Valladares *et al.* 2007).

Finally, epigenetic effects on phenotypic plasticity and
inheritance of phenotypic variation need further
investigation. Epigenetic variation can be partly inherited
from one generation to the next while being still sensitive
to environmental variation (Richards et al. 2010). Maternal
epigenetic effects are known in *Arabidopsis* (Johannes et
al. 2009), but so far their nature has not been studied
much in trees (Bräutigam et al. 2013). Epigenetic effects
can also occur during seed maturation. Temperature
differences during embryogenesis caused differences in
phenology in *Picea abies* (Skrøppa & Kohmann, 1997) and
the molecular mechanisms involved are being studied
(Yakovlev et al. 2010). They could have significant
implications for the interpretation of provenance trial data,
explaining some of the phenotypic variation among
populations that is commonly interpreted as genetic
variation.

Evolution in multiple populations

Geographic distribution and genetic structure

Natural populations of a species in a heterogeneous
landscape may have very different patterns of distribution,
which can influence its population genetic characteristics
(Fig. 3) as reviewed by Charlesworth & Charlesworth
(2010). The classical island model assumes populations of
equal finite constant size, with equal migration rates
between them (Wright, 1931). These assumptions can be
relaxed, with variable migration rates and changing
population sizes. Species can also be distributed in large
continuous populations where parts of the range are
connected by symmetric gene flow, as described in the
isolation by distance model by Wright (1943). Populations
located at range margins represent a special case, as they
are at the edge of environmental gradients where carrying
capacity may be limited. In such cases, there is more
migration from the core populations to the margin than
vice versa, resulting in asymmetric gene flow (Kirkpatrick
& Barton, 1997).

Many economically important temperate and boreal
species have large populations covering vast areas, but
other tree species do not fit this distribution model.
<caption>TABLE 1</caption>

Distribution range and genetic estimates for the 27 European conifers

<table><thead><tr><th>Species</th><th>Range</th><th>Distribution</th><th>Mean Q<sub>ST</sub><sup>a</sup></th><th>Q<sub>ST</sub> range<sup>a</sup></th><th>F<sub>ST</sub></th><th>H<sub>e</sub></th><th>Reference</th></tr></thead><tbody><tr><td>Abies nebrodensis</td><td>Sicilia</td><td>south small</td><td></td><td></td><td></td><td>0.201</td><td>Ducci et al. (1999)</td></tr><tr><td>Abies pinsapo</td><td>Andalusia</td><td>south small</td><td></td><td></td><td></td><td>0.056</td><td>Scaltsoyiannes et al. (1999)</td></tr><tr><td>Pinus nigra ssp dalmatica</td><td>South Croatia</td><td>south small</td><td></td><td></td><td>0.091</td><td>0.292</td><td>Nikolic & Tucic (1983)</td></tr><tr><td>Picea omorika</td><td>Croatia Serbia</td><td>south small</td><td></td><td></td><td>0.261</td><td>0.067</td><td>Ballian et al. (2006)</td></tr><tr><td>Pinus nigra ssp laricio</td><td>Corsica Calabria Sicilia</td><td>south small</td><td></td><td></td><td>0.005</td><td>0.182</td><td>Scaltsoyiannes et al. (1994)</td></tr><tr><td>Abies cephalonica</td><td>Balkans</td><td>south small</td><td>0.140</td><td>0.100 - 0.170</td><td>0.048</td><td>0.221</td><td>Fady & Conkle (1993)</td></tr><tr><td>Pinus peuce</td><td>Balkans</td><td>south small</td><td></td><td></td><td>0.083</td><td>0.124</td><td>Zhelev & Tsarska (2009)</td></tr><tr><td>Pinus brutia</td><td>Aegean Sea</td><td>south fragmented</td><td>0.040</td><td></td><td>0.053</td><td>0.196</td><td>Kara et al. (1997)</td></tr><tr><td>Pinus heldreichii</td><td>Balkans</td><td>south fragmented</td><td></td><td></td><td>0.054</td><td>0.177</td><td>Boscherini et al. (1994)</td></tr><tr><td>Abies borisii-regis</td><td>Balkans</td><td>south fragmented</td><td></td><td></td><td></td><td>0.273</td><td>Scaltsoyiannes et al. (1999)</td></tr><tr><td>Pinus nigra ssp pallasiana</td><td>Greece Serbia Bulgaria</td><td>south fragmented</td><td>0.028</td><td>0.020 - 0.040</td><td>0.070</td><td>0.114</td><td>Tolun et al. (1999)</td></tr><tr><td>Pinus nigra ssp salzmannii</td><td>East Spain South France</td><td>south fragmented</td><td></td><td></td><td></td><td>0.216</td><td>Scaltsoyiannes et al. (2009)</td></tr><tr><td>Pinus nigra ssp nigra</td><td>North Italy Croatia Greece</td><td>south large fragmented</td><td></td><td></td><td></td><td>0.264</td><td>Scaltsoyiannes et al. (2009)</td></tr><tr><td>Pinus pinaster</td><td>South West Europe</td><td>south large fragmented</td><td>0.616</td><td>0.441 - 0.791</td><td>0.076</td><td>0.142</td><td>Salvador et al. (2000)</td></tr><tr><td>Pinus pinea</td><td>South Europe</td><td>south large fragmented</td><td></td><td></td><td>0.279</td><td>0.011</td><td>Fallour et al. (1997)</td></tr><tr><td>Pinus halepensis</td><td>South Europe</td><td>south large fragmented</td><td>0.130</td><td></td><td></td><td>0.040</td><td>Schiller et al. (1986)</td></tr><tr><td>16 species with small or fragmented range</td><td></td><td></td><td>0.192</td><td></td><td>0.082<sup>c</sup></td><td>0.171<sup>c</sup></td><td></td></tr><tr><td>Pinus cembra</td><td>Alps Romania</td><td>north large continuous</td><td>0.830</td><td></td><td>0.040</td><td>0.081</td><td>Belokon et al. (2005)</td></tr><tr><td>Pinus uncinata</td><td>Central West Europe</td><td>north large continuous</td><td></td><td></td><td>0.006</td><td>0.260</td><td>Lewandoski et al. (2000)</td></tr><tr><td>Larix decidua</td><td>Central Europe</td><td>north large continuous</td><td></td><td></td><td>0.051</td><td>0.223</td><td>Maier (1992)</td></tr><tr><td>Pinus sibirica</td><td>East Siberia</td><td>north large continuous</td><td></td><td></td><td>0.027</td><td>0.278</td><td>Goncharenko et al. (1992)</td></tr><tr><td>Pinus mugo</td><td>Central East Europe</td><td>north large continuous</td><td></td><td></td><td>0.041</td><td>0.214</td><td>Slavov and Zhelev (2004)</td></tr><tr><td>Abies alba</td><td>Central Europe</td><td>north large continuous</td><td>0.075</td><td>0.000 - 0.150</td><td></td><td>0.252</td><td>Ducci et al. (1999)</td></tr><tr><td>Abies sibirica</td><td>Siberia</td><td>north very large continuous</td><td></td><td></td><td>0.102</td><td>0.083</td><td>Semerikova & Semerikov (2006)</td></tr><tr><td>Larix sibirica</td><td>Siberia</td><td>north very large continuous</td><td></td><td></td><td>0.082</td><td>0.159</td><td>Semerikov et al. (1999)</td></tr><tr><td>Picea abies ssp obovata</td><td>Lapland Siberia</td><td>north very large continuous</td><td></td><td></td><td>0.011</td><td>0.213</td><td>Krutovskii & Bergmann (1995)</td></tr><tr><td>Picea abies ssp abies</td><td>North Central Europe</td><td>north very large continuous</td><td>0.417</td><td>0.106 - 0.727</td><td>0.044</td><td>0.252</td><td>Krutovskii & Bergmann (1995)</td></tr><tr><td>Pinus sylvestris</td><td>Whole Europe</td><td>north very large continuous</td><td>0.519</td><td>0.080 - 0.860</td><td>0.033</td><td>0.286</td><td>Goncharenko et al. (1994)</td></tr><tr><td>11 species with continuous range</td><td></td><td></td><td>0.463</td><td></td><td>0.044</td><td>0.209</td><td></td></tr></tbody></table>

<footnote><sup>a</sup> Mean Q<sub>ST</sub> and Q<sub>ST</sub> range were calculated from estimates only for height increment, bud flush and bud set (for more details and references see Table S1). Q<sub>ST</sub> estimates corresponds to the levels of population differentiation measured either as the proportion of phenotypic variation between populations (V<sub>pop</sub>) or as the proportion of additive genetic variance between populations (Q<sub>ST</sub>) in the provenance trials (for more details see Table S1).</footnote>

<footnote><sup>b</sup> References of the studies using allozyme markers to assess F<sub>ST</sub> and H<sub>e</sub>. See supporting information references for full reference information.</footnote>

<footnote><sup>c</sup> Pinus pinea, which has hardly any within-population variation (Vendramin et al. 2008), was not included in the calculation of mean F<sub>ST</sub> and mean H<sub>e</sub>.</footnote>
We examined the population structure of European conifers in the *Pinaceae* (including pines, spruces and firs), a limited group of species with very good distributional and reasonable population genetics information. A compilation of the distributions of these 27 species (and sometimes subspecies; from (Jalas & Suominen, 1973), allowed us to classify them as having northern or central large, southern large or southern small or fragmented distributions (Table 1). Note that the classification is based on the current distributions, although some currently fragmented species may have had much larger distributions in the past (Soto *et al.* 2010). Species with a predominantly northern distribution, but also occurring in the south (e.g. *Pinus sylvestris*) were classified as northern species. Figure 3 shows examples of distributions of three species (*Picea omorika*, *Pinus pinaster* and *Pinus sylvestris*). There are 11 species with predominantly northern or central, large, continuous distributions, and four southern species with large but somewhat fragmented distributions. About half of the European conifers (12) have southern, small or fragmented distributions. Further, the southern margin of most species seems to consist of fragmented small populations, whereas in the north, the range margin is part of a continuous distribution for several species. This analysis shows that in many tree populations, the threats associated with climate change are accompanied by and likely exacerbated by the effects of fragmentation at southern range margins (see also Lynch, 1996). However, if there is still extensive gene flow among the fragments, the population structure should resemble that of a continuous population.

Consistent with the theoretical predictions, the European conifers with continuous distributions have higher genetic diversity ($H_e$) than the fragmented ones (Table 1). The widespread northern species such as *Picea abies* and *Pinus sylvestris* have low levels of genetic differentiation ($F_{ST}$) in their main range (Heuertz *et al.* 2006, Pyhäjärvi *et al.* 2007). Similar findings have been made in North America for species such as *Pseudotsuga menziesii* (Eckert *et al.* 2010), *Picea sitchensis*, *P. glauca* and *P. mariana* (Chen *et al.* 2009, Holliday *et al.* 2010a, Holliday *et al.* 2010b, Namroud *et al.* 2008). In contrast, the level of population differentiation is almost twice for the southern fragmented species compared to the northern widely-distributed ones (Table 1). Thus, the genetic data available are broadly consistent with the population structure classification based on species distribution and census size. However, current census size may ignore effects of past demographic history such as population size changes or hybridization, and we do not expect the current distributions to account for all variation in patterns of diversity.

Next we examine the patterns of quantitative genetic variation in tree species in general and in these European conifers in particular to evaluate the effects of selection

for local adaptation. We reviewed the literature of provenance trials and found a total of 112 studies on 19 relevant traits related mostly to phenology, growth, cold or drought tolerance or other ecophysiological traits, among which were 36 studies on European conifers (Table S1). Among 59 tree species studied, most were native to Europe and North America (23 and 29 species respectively) while conifers were more studied than angiosperms (36 and 23 species respectively). Only three traits had been measured in a sufficiently large number of experiments (Table 2) to make general comparisons and draw general patterns. We focused on the patterns of genetic variation for height increment and for the timing of bud flush, at the beginning of the growing season in spring, and the timing of bud set, an indication of cessation of growth in fall. Over all studies, these three traits had comparable levels of genetic differentiation between populations (mean value equal to 0.249, 0.324 and 0.392 for bud flush, height increment and bud set, respectively; Table 2).

## Quantitative variation in fragmented populations

In Europe, small and fragmented conifer populations occur mainly in the southern Mediterranean area. Provided population sizes are sufficiently large, species with greater differences among populations in local phenotypic optimum and higher levels of genetic variance would be expected to have higher equilibrium differentiation. Gene flow in contrast, would reduce differentiation (Hendry *et al.* 2001). Generally, if there is strong differential selection between populations, we would expect that the proportion of total genetic variance found between populations, $Q_{ST}$, should be higher than $F_{ST}$ calculated from neutral markers with appropriate mutation rates (Edelaar *et al.* 2011, Leinonen *et al.* 2008).

In the limited set of provenance trials on European conifers, estimates of quantitative genetic differentiation among populations for species with small or fragmented range were low over all traits (mean $Q_{ST}$ = 0.192, 5 species; Table 1). This average is about twice as high as the neutral $F_{ST}$ (0.082; 9 species; Table 1). Even though sampling across an environmental gradient is clearly not concordant with the assumptions of the island model, data of this kind are frequently analyzed by comparing $Q_{ST}$ and $F_{ST}$ estimates for distinct samples from large and continuous populations. The average $Q_{ST}$ estimate for large populations in northern areas is 0.463 while average $F_{ST}$ is 0.044. Thus, in this small set of studies, the ratio of $Q_{ST}$ to $F_{ST}$ is much lower for species with small or fragmented range than that found in more widespread species. In small populations or fragments, selection for local adaptation is less efficient because of the effects of genetic drift on individual loci, and further, on the associations of alleles at different loci (Le Corre & Kremer, 2012). A review by Leimu & Fisher (2008) found that in plants only about 50% of all population pairs in reciprocal transplantations studies
showed evidence of local adaptation, i.e. each population at its native site had higher fitness than other populations introduced to that site. Local adaptation was much less likely in small than large populations. However, $Q_{ST}$ values could also differ because the studies on species with limited distributions have sampled a smaller range of environmental variation than studies in species with large ranges, or because the scale of fragmentation does not match the scale of environmental variation. Reciprocal transplant experiments are needed to assess the level of local adaptation directly. In the large provenance trial data set over all 19 traits and 59 tree species, 264 of 294 analyses (around 90 %) showed significant differentiation across populations (Table S1), in most cases likely due to climatic selection.

There is also some evidence in the literature for local climatic adaptation in southern European fragmented populations, such as for water use efficiency in *Pinus halepensis* (Voltas et al. 2008). Further, some allelic variants at candidate loci for drought tolerance have also been found to be associated with environmental variables (Grivet et al. 2011). In some of these species, selection may have been strong enough for local adaptation to evolve. Clearly, more studies on the patterns of local adaptation are needed in the species with fragmented southern distributions. Forests at Mediterranean southern limits are threatened by faster changes in precipitations than in the northern range limit. If indeed their adaptive capacity is lower, this could make southern fragmented populations even more vulnerable.

It is also possible that these populations have evolved high adaptive phenotypic plasticity in response to environmental variability instead of genetic differentiation, either for some specific traits or across the genome (Nicotra et al. 2010). This could be likely if there is also a strong temporal component of environmental variation (Hedrick, 2006). In a changing climate, the responses due to phenotypic plasticity may maintain fitness despite climatic changes. More growth chamber or reciprocal transplant experiments will be needed to assess the response functions for these species.

Quantitative variation in continuous populations along environmental gradients

Species present in Central and Northern Europe generally have continuous distributions covering large areas encompassing much heterogeneity in abiotic and biotic environmental factors with large effective population sizes connected by extensive gene flow. If there is differential selection along environmental gradients, we expect to see patterns of clinal variation of traits (Barton, 1999). These patterns can be described by the slope of a regression along an environmental gradient. The proxies for environmental gradients most frequently used are latitude and altitude. For height increment, populations from warmer environments generally grew faster in the provenance trials (see Table S1) but quantitative estimates of the slopes were rarely available. Populations from cold

<figure><img src="image_36.png" /><figcaption>FIGURE 1</figcaption></figure>

Mean silent nucleotide diversity per sites ($\pi_{silent}$) estimates for several tree species.

Average nucleotide diversity at silent sites (for more details and references see Table S2). Angiosperms appear in light grey and conifers in dark.

<figure><img src="image_37.png" /><figcaption>FIGURE 2</figcaption></figure>

Distribution of allelic effect sizes in tree species

Distribution of the percentages of phenotypic variance explained by genotypic classes at SNP loci ($R^2$ marker) detected in significant associations with quantitative traits (for more details and references see Table S3).

environments cease growth earlier, as an adaptation to the approaching winter (see e.g., Savolainen et al. 2004). To compare slopes of clinal variation we focused on the two phenological traits, the timing of bud flush and the timing of bud set, and compared altitudinal and latitudinal clines. To summarize data across species and environments, we considered that one degree of latitude corresponds approximately to a temperature change of 0.6 °C, and correspondingly, 100 m of altitude (Jump et al. 2009). We show examples of an altitudinal cline in bud flush in Quercus petraea (Fig. 4a) and a latitudinal cline in bud set in *Pinus sylvestris* (Fig. 4b).

The results of the summary show that the two phenological traits differ in their patterns. For bud flush, both altitudinal and latitudinal clines showed similar slopes, but the direction of adaptation varied greatly among species (Table 3a). For example, high altitude populations
from the same transect flushed late in *Quercus petraea* (Fig. 4a), whereas in *Fagus sylvatica* they flushed early (Vitasse et al. 2009). This could reflect different compromises in the adaptive tradeoff between maximizing the growing season length and exposing new leaves to late frosts. Bud flush is triggered by the accumulation of cold (or chilling) sums followed by heat (or forcing) sums above a threshold temperature sum. These genetically determined critical temperature sums and thresholds may vary among species, and to a lesser extent among populations of the same species (Hänninen & Tanino, 2011). Bud flush in late successional species is also more influenced by photoperiod than in early successional species (Basler & Körner, 2012, Körner & Basler, 2010). Bud set showed steeper slopes for both gradients and in all species more northern or higher altitude populations had earlier bud set (Table 3b). These data suggest that differential selection on bud set is systematically stronger than on bud flush. Bud flush may display higher phenotypic plasticity as temperatures increase. In contrast, bud set is largely governed by

photoperiods, and modulated by temperatures and drought, which results in a more predictable environmental signal from year to year and location to location (Böhlenius et al. 2006). In a warming climate, spring phenology can likely respond and advance without much genetic change, as has already been seen in many species (Gienapp et al. 2008), provided that the chilling requirement has been met. However, if chilling temperature requirements have not been met, in some cases bud flush may even be delayed (Hänninen & Tanino, 2011), as already seen recently in Tibet (Yu et al. 2010). In the fall, a change in bud set date is more likely to require a genetic change in photoperiodic responses. Some studies suggest that the heritability of bud flush is higher than for bud set (Howe et al. 2003), but estimates of the additive genetic component are rarely available in the literature. The latitudinal slopes were also much steeper than the altitudinal ones (4.91 and 2.35 days/°C, respectively). Sundblad & Andersson (1995) have suggested that along the altitudinal gradients there may be more gene flow so populations do not become as differentiated. The simple

TABLE 2

Genetic differentiation ($Q_{ST}$) estimates for the 19 quantitative traits studied in provenance trials.

<table><thead><tr><th rowspan="2">Trait</th><th rowspan="2">Category</th><th rowspan="2">Mean Q<sub>ST</sub></th><th colspan="2">Q<sub>ST</sub> estimates<sup>a</sup></th><th colspan="2">Qualitative estimation<sup>b</sup></th></tr><tr><th>Q<sub>ST</sub> range</th><th>Nb<sup>c</sup></th><th>trend</th><th>Nb<sup>c</sup></th></tr></thead><tbody><tr><td>Dark respiration</td><td>Ecophysiology</td><td></td><td></td><td>0</td><td>Moderate</td><td>2</td></tr><tr><td>Leaf mass per area</td><td>Ecophysiology</td><td>0.022</td><td>0.000-0.044</td><td>2</td><td>Variable</td><td>6</td></tr><tr><td>Net assimilation</td><td>Ecophysiology</td><td>0.045</td><td>0.015-0.075</td><td>2</td><td>Variable</td><td>8</td></tr><tr><td>Nitrogen leaf content</td><td>Ecophysiology</td><td>0.042</td><td>0.000-0.083</td><td>2</td><td>Variable</td><td>6</td></tr><tr><td>Photosynthetic capacity</td><td>Ecophysiology</td><td>0.101</td><td>0.000-0.201</td><td>2</td><td>Variable</td><td>1</td></tr><tr><td>Stomatal conductance</td><td>Ecophysiology</td><td>0.061</td><td>0.000-0.150</td><td>4</td><td>Variable</td><td>4</td></tr><tr><td>Stomatal density</td><td>Ecophysiology</td><td>0.028</td><td>0.000-0.056</td><td>2</td><td>Low</td><td>5</td></tr><tr><td>Water use efficiency (A/gs)</td><td>Ecophysiology</td><td>0.075</td><td></td><td>1</td><td>Variable</td><td>7</td></tr><tr><td>Water use efficiency (δ<sup>15</sup>C)</td><td>Ecophysiology</td><td></td><td></td><td>0</td><td>Variable</td><td>6</td></tr><tr><td>Fall frost hardiness</td><td>Frost hardiness</td><td>0.581</td><td>0.030-0.890</td><td>9</td><td>High</td><td>10</td></tr><tr><td>Spring frost hardiness</td><td>Frost hardiness</td><td>0.126</td><td>0.000-0.352</td><td>4</td><td>Variable</td><td>3</td></tr><tr><td>Winter frost hardiness</td><td>Frost hardiness</td><td>0.170</td><td>0.000-0.291</td><td>3</td><td></td><td>0</td></tr><tr><td>Growth rate per day</td><td>Growth</td><td>0.284</td><td>0.050-0.710</td><td>8</td><td>Moderate</td><td>3</td></tr><tr><td>Height increment</td><td>Growth</td><td>0.324</td><td>0.040-0.880</td><td>36</td><td>High</td><td>33</td></tr><tr><td>Root allocation</td><td>Growth</td><td>0.340</td><td>0.251-0.430</td><td>2</td><td>Moderate</td><td>4</td></tr><tr><td>Bud flush</td><td>Phenology</td><td>0.249</td><td>0.000-0.700</td><td>24</td><td>Moderate</td><td>37</td></tr><tr><td>Bud set</td><td>Phenology</td><td>0.392</td><td>0.040-0.904</td><td>16</td><td>High</td><td>16</td></tr><tr><td>Germination</td><td>Phenology</td><td>0.521</td><td>0.200-0.940</td><td>6</td><td>High</td><td>3</td></tr><tr><td>Senescence</td><td>Phenology</td><td>0.108</td><td>0.080-0.180</td><td>5</td><td>Low</td><td>3</td></tr></tbody></table>

<footnote><sup>a</sup> Q<sub>ST</sub> estimates corresponds to the levels of population differentiation measured either as the proportion of phenotypic variation between populations (V<sub>pop</sub>) or as the proportion of additive genetic variance between populations (Q<sub>ST</sub>) in the provenance trials (for more details see Table S1).</footnote>

<footnote><sup>b</sup> Qualitative estimation of genetic differentiation between populations corresponds to studies where no Q<sub>ST</sub> estimate was available but significance of genetic differentiation was mentioned in the text.</footnote>

<footnote><sup>c</sup> Nb: number of studies used to calculate mean Q<sub>ST</sub> and Q<sub>ST</sub> range, and the trend of population differentiation.</footnote>
calibration factors we used also may not capture all aspects of the environment.

In the large set of provenance trial studies, clinal variation along environmental gradients was very common. In the 112 studies, 309 analyses of clinal variation of different quantitative traits, 243 (78 %) showed evidence of clinal variation with latitude, altitude, and sometimes longitude (Table S1).

## Adaptation at range margins

An important hypothesis for species range limits is that gene flow constraints adaptation (Haldane, 1932, Mayr, 1963). Many models suggest that gene flow could limit adaptation, and even more so with asymmetrical gene flow towards small peripheral populations (see Lenormand, 2002 for review). In a model of species range involving local adaptation, a strong coupling between fitness and population size favors a feedback effect (a “migration meltdown”) that acts to stabilize a range margin, as exemplified in the now well-known Kirkpatrick and Barton (1997) model. However, there is limited evidence to evaluate this model, and some issues that complicate the predictions. Some models assume that genetic variance is fixed (Kirkpatrick & Barton, 1997, Pease et al. 1989), while gene flow may also increase genetic variance and the response to selection (Barton, 2001, Polechova et al. 2009). Evidence in *Pinus contorta* suggests that gene flow between populations inhabiting heterogeneous environments can increase levels of standing genetic variation (Yeaman & Jarvis, 2006), but it is unclear whether this effect would be important in other species. In single locus models, gene flow can also decrease the genetic variance due to extinction of local low-frequency alleles and the different approaches are thus not fully reconciled (Lenormand, 2002). Genetic drift can also reduce genetic variance and thus adaptation in peripheral populations (Alleaume-Benharira et al. 2006, Bridle et al. 2010, Polechova et al. 2009), but gene flow may replenish genetic variation. Gene flow may even introduce better adapted genes than local ones, especially in a changing climate (Alleaume-Benharira et al. 2006).

Some environments, in particular some polar or arid range margins, are intrinsically less favorable than others, and would sustain only very low population sizes even after a very long history of adaptation. Mainland-island models of local adaptation implicitly address this issue with population sizes, but spatially continuous models are still more informative. In particular, Nagylaki (1975) showed that extrinsic asymmetries in habitat quality strongly modified or could even compensate for asymmetries in selection across habitats. In other words, alleles showing a local advantage can be maintained despite having considerable antagonistic effects in other habitats, provided that the local habitat is of better quality (Nagylaki, 1978). Incorporating differences in carrying capacity in quantitative models could critically affect the potential for population adaptation (Bridle et al. 2010).

<caption>**FIGURE 3**</caption>

### Schemes of the population models used to discuss evolutionary responses

The three different schematic models of population structure encountered in tree species illustrated by the different cases of *Picea omorika* (one limited population), *Pinus pinaster* (several fragmented populations) and *Pinus sylvestris* (large and continuous population). The colour of the circle indicates the environmental condition of the population which is either undefined (in grey) or following a temperature gradient from warm (in red) to cold (in blue).

The arrows represent gene flow connecting populations, with thickness indicating gene flow intensity. For the fragmented populations the brown line symbolizes a physical barrier to gene flow, such as a mountain.

<figure><img src="image_38.png" /></figure>

The leading and the trailing edge of migrating tree distributions face quite different challenges due to the warming climate (Hampe & Petit, 2005). At the southern range edge (in northern hemisphere), the distributions are likely already limited by high temperatures or drought conditions, and associated biotic and abiotic stresses, whereas at the northern margin, many populations have been limited by the cold temperatures (Rehfeldt et al. 2002). For the southern margin, at least at low altitudes, the environment is clearly deteriorating. The risk of extinctions will come from the interplay of multiple factors. In particular, the reduction of water availability and a longer growing season with excessively warm temperatures (IPCC, 2007) could lead to massive diebacks of trees due to drought stress or carbon starvation (Bréda et al. 2006, Sabate et al. 2002) higher mortality due to reduced defense of trees against insects (Rouault et al. 2006), and more frequent forest fires (Mouillot & Field, 2005). Increased mortality due to heat and drought stress has already been observed in many locations globally (Allen et al. 2010). The impact of environmental change will be higher in small populations due to high demographic or environmental stochasticity (Hampe & Jump, 2011).

At the southern margin, there are no populations further south contributing genes conferring necessary adaptation, but gene flow from similar environments could still increase the variance within populations (Barton, 2001).
TABLE 3

Slopes of the linear regressions of a) bud flush and b) bud set along altitudinal and latitudinal gradients

a)

<table><thead><tr><th>Gradient</th><th>Species</th><th>Pop<sup>a</sup></th><th>Cline</th><th>Slopes</th><th>References</th></tr></thead><tbody><tr><td rowspan="15">Altitudinal</td><td>Abies amabilis</td><td>5</td><td>High early</td><td>-1.18</td><td>Worrall (1983)</td></tr><tr><td>Abies lasiocarpa</td><td>2</td><td>High early</td><td>-0.83</td><td>Worrall (1983)</td></tr><tr><td>Fagus sylvatica</td><td>9</td><td>High early</td><td>-0.43</td><td>Vitasse et al. (2009)</td></tr><tr><td>Fagus sylvatica</td><td>158</td><td>High early</td><td>-0.17</td><td>von Wuehlisch et al. (1995)</td></tr><tr><td>Pseudotsuga menziesii</td><td>7</td><td>High early</td><td>-4.38</td><td>Acedevo-Rodriguez et al. (2006)</td></tr><tr><td>Pseudotsuga menziesii</td><td>18</td><td>No cline</td><td>0.00</td><td>Rehfeldt (1978)</td></tr><tr><td>Picea abies</td><td>23</td><td>No cline</td><td>-0.22</td><td>Chmura (2006)</td></tr><tr><td>Picea abies</td><td>8</td><td>No cline</td><td>-0.03</td><td>Skroppa & Magnussen (1993)</td></tr><tr><td>Abies alba</td><td>6</td><td>No cline</td><td>-0.20</td><td>Vitasse et al. (2009)</td></tr><tr><td>Acer pseudoplatanus</td><td>7</td><td>No cline</td><td>-0.20</td><td>Vitasse et al. (2009)</td></tr><tr><td>Fraxinus excelsior</td><td>9</td><td>Low early</td><td>1.90</td><td>Vitasse et al. (2009)</td></tr><tr><td>Larix occidentalis</td><td>82</td><td>Low early</td><td>0.23</td><td>Rehfeldt (1982)</td></tr><tr><td>Quercus petraea</td><td>10</td><td>Low early</td><td>1.15</td><td>Alberto et al. (2011)</td></tr><tr><td>Quercus rubra</td><td>4</td><td>Low early</td><td>1.93</td><td>Mc Gee (1973)</td></tr><tr><td><strong>Total</strong></td><td></td><td></td><td></td><td><strong>-0.17</strong></td><td></td></tr><tr><td rowspan="10">Latitudinal</td><td>Picea abies</td><td>9</td><td>North early</td><td>-2.08</td><td>Sogaard et al. (2008)</td></tr><tr><td>Picea glauca</td><td>63</td><td>No cline</td><td>0.43</td><td>Li et al. (1997a)</td></tr><tr><td>Picea sitchensis</td><td>17</td><td>No cline</td><td>-0.08</td><td>Mimura & Aitken (Mimura & Aitken, 2007)</td></tr><tr><td>Pinus strobus</td><td>66</td><td>No cline</td><td>-0.83</td><td>Li et al. (1997b)</td></tr><tr><td>Populus balsamifera</td><td>4</td><td>No cline</td><td>0.10</td><td>Farmer (1993)</td></tr><tr><td>Fagus sylvatica</td><td>158</td><td>South early</td><td>0.20</td><td>von Wuehlisch et al. (1995)</td></tr><tr><td>Quercus petraea</td><td>16</td><td>South early</td><td>4.17</td><td>Deans & Harvey (1996)</td></tr><tr><td>Tsuga heterophylla</td><td>8</td><td>South early</td><td>2.17</td><td>Hannerz et al. (1999)</td></tr><tr><td><strong>Total</strong></td><td></td><td></td><td></td><td><strong>0.51</strong></td><td></td></tr></tbody></table>

b)

<table><thead><tr><th>Gradient</th><th>Species</th><th>Pop<sup>a</sup></th><th>Cline</th><th>Slopes</th><th>References</th></tr></thead><tbody><tr><td rowspan="10">Altitudinal</td><td>Abies lasiocarpa</td><td>5</td><td>High early</td><td>-3.33</td><td>Green (2005)</td></tr><tr><td>Larix occidentalis</td><td>82</td><td>High early</td><td>-1.28</td><td>Rehfeldt (1982)</td></tr><tr><td>Picea abies</td><td>23</td><td>High early</td><td>-9.07</td><td>Chmura (2006)</td></tr><tr><td>Picea abies</td><td>8</td><td>High early</td><td>-2.63</td><td>Skroppa & Magnussen (1993)</td></tr><tr><td>Picea glauca</td><td>5</td><td>High early</td><td>-1.00</td><td>Green (2005)</td></tr><tr><td>Pinus contorta</td><td>5</td><td>High early</td><td>-1.67</td><td>Green (2005)</td></tr><tr><td>Pinus contorta</td><td>173</td><td>High early</td><td>-0.22</td><td>Rehfeldt (1988)</td></tr><tr><td>Pseudotsuga menziesii</td><td>7</td><td>No cline</td><td>0.37</td><td>Acedevo-Rodriguez et al. (2006)</td></tr><tr><td><strong>Total</strong></td><td></td><td></td><td></td><td><strong>-2.35</strong></td><td></td></tr><tr><td rowspan="10">Latitudinal</td><td>Betula pendula</td><td>7</td><td>North early</td><td>-4.63</td><td>Viherä-Aarnio et al. (2005)</td></tr><tr><td>Picea glauca</td><td>63</td><td>North early</td><td>-3.83</td><td>Li et al. (1997a)</td></tr><tr><td>Picea sitchensis</td><td>17</td><td>North early</td><td>-4.90</td><td>Mimura & Aitken (2007)</td></tr><tr><td>Pinus strobus</td><td>66</td><td>North early</td><td>-3.33</td><td>Li et al. (1997b)</td></tr><tr><td>Pinus sylvestris</td><td>4</td><td>North early</td><td>-5.00</td><td>Hurme et al. (1997)</td></tr><tr><td>Pinus sylvestris</td><td>4</td><td>North early</td><td>-2.35</td><td>Notivol et al. (2007)</td></tr><tr><td>Pinus sylvestris</td><td>2</td><td>North early</td><td>-6.83</td><td>Savolainen et al. (2004)</td></tr><tr><td>Populus balsamifera</td><td>4</td><td>North early</td><td>-5.00</td><td>Farmer (1993)</td></tr><tr><td>Populus tremula</td><td>12</td><td>North early</td><td>-8.33</td><td>Luquez et al. (2008)</td></tr><tr><td><strong>Total</strong></td><td></td><td></td><td></td><td><strong>-4.91</strong></td><td></td></tr></tbody></table>

Slopes of linear regressions are given for each study and expressed as days/°C (for details about the calculation see in the text and for references see Table S1). No cline indicates a non-significant regression.

<footnote>a Number of populations in the provenance trial.</footnote>
Experimental evidence of gene flow from like populations increasing fitness at warm range-edges exists for some plant species (e.g., Mimulus species Sexton *et al.* 2011), and long distance dispersal can be important in fragmented landscapes (Fayard *et al.* 2009, Klein *et al.* 2006, Kremer *et al.* 2012).

Until now, the severe climatic conditions at boreal northern range margins have constrained growth, pollen production, seed maturation and dispersal (Sarvas, 1962, Savolainen, 1996), as well as survival (Persson, 1998), and have limited expansion to the north (Chuine & Beaubien, 2001, Morin *et al.* 2007). In the northernmost areas, temperatures are expected to increase by about 4°C (Kattsov & Källen, 2005). Ecophysiologists have used the immediate plastic responses of trees to increased temperature to predict changes in species composition (Kellomäki & Kolström, 1992, Kellomäki *et al.* 2001). However, these predictions have not explicitly taken into account the possibilities of genetic response (Davis & Shaw, 2001, O'Neill *et al.* 2008). The warming in the north will improve survival, increase growth (Rehfeldt *et al.* 2002, Reich & Oleksyn, 2008), increase sexual reproduction (Andalo *et al.* 2005), and increase pollen production (Savolainen *et al.* 2011). Based on modeling studies, pollen and seed are predicted to be dispersed further than before (Kuparinen *et al.* 2009, Kuparinen *et al.* 2010). Production of mature filled seed will likely increase many fold (Kellomäki *et al.* 1997) and the warmer air and soil may result in improved germination and establishment. Northern range margin populations are already colonizing more northern and higher altitude areas (Chen *et al.* 2011, Juntunen *et al.* 2006, Kullman, 2002). The increased survival rates of existing, established trees may however reduce establishment opportunities for better adapted genotypes generated by gene flow and local selection (Kuparinen *et al.* 2010).

At altitudinal range limits, adaptation could be facilitated by the short geographical distance between populations, associated with low climate change velocity (Loarie *et al.* 2009). Gene flow from populations at low altitudes could help the populations at higher altitudes to adapt, as has already been observed, e.g. in oak phenological shifts in *situ* (Alberto *et al.* (2010). Both colonization of new areas at higher altitudes, if available, and local selection aided by gene flow may contribute to adaptation, as many altitudinal gradients show clinal genetic differentiation (see above).

## Conclusions and suggestions for future research

Forest trees are exceptionally well characterized with respect to adaptive quantitative variation, and with respect to responses to different climatic variables. The existing set of provenance trials can be used to extract even more information, for instance on the level of local adaptation, or even on the strength of selection, when the

FIGURE 4

Clines of phenological traits along environmental gradients

Timing of bud flush along an altitudinal gradient in *Quercus petraea*, based on data from Alberto *et al.* (2011).

The timing of bud flush is expressed as the number of days from 1st January to reach the fourth developmental stage of leaf unfolding. Means of populations (large diamonds) are plotted against the altitude of origin. Bars represent standard deviations of the populations. Means of maternal tree progenies (small diamonds) in populations located at 131 m and 1235 m of elevation illustrate high additive genetic variance within populations, slightly decreasing with increasing altitude. Dark colored points represent populations and maternal trees from Luz valley while light colored points represent populations from Ossau valley.

<figure><img src="image_39.png" /></figure>

Timing of bud set along a latitudinal gradient in *Pinus sylvestris*, based on data from Mikola (1982).

The timing of bud set is measured as the number of days from the day of sowing. Means of populations (large diamonds) are plotted against latitude of origin. Bars represent standard deviations of the populations.

<figure><img src="image_40.png" /></figure>
data sets are further analyzed. Long-term estimates of the
strength of selection, in particular in natural conditions,
would be very valuable for providing parameter range
estimates for the prediction models. New reciprocal
transplant experiments are needed for commercially less-
important species, which may be most threatened, but
which are under-represented in existing provenance trials.
Furthermore, the present provenance trials ignore the
likely important early fitness components of germination
and establishment – these components also need to be
studied (as is being done in herbaceous plants (Huang *et al.* 2010, Stanton-Geddes *et al.* 2012). The new experiments
should include field sites at and beyond existing range
margins. Experiments in controlled growth chambers can
also help identify those abiotic aspects of temperature and
moisture regimes to which populations are locally
adapted, and to generate climatic regimes analogous to
those predicted for the coming century.

The role of plasticity and its interaction with natural selection is just starting to be explored in the climate change context (Chevin et al. 2010) - provenance trials can also provide more information on these aspects. The extent and significance of adaptive phenotypic plasticity is still debated (Valladares et al. 2007), and experimental studies on range margins are still few (Angert, 2009, Stanton-Geddes et al. 2012). Wang's (2010) universal response function approach could be used as a mechanistic model to predict population responses.

Commercially less-important species are poorly represented in previously established common gardens, whether they have narrow or wide distributions. The species with smaller ranges are especially vulnerable. Are these species locally adapted to climate? Do these species have limited adaptive potential due to their historically small effective population sizes? While many important boreal and temperate species in the northern hemisphere (and some eucalypts or tropical acacias) have been extensively studied, there is much less information on subtropical or tropical species, which are outside the scope of this review. These species will also be affected by the changing climate, through both abiotic and many complex biotic factors.

Most of the studies on quantitative traits have been
conducted in spaced, reasonably well-tended provenance
trial experiments. Within or between species interactions,
such as competition or diseases have largely been ignored.
Many between-species interactions depend on the
synchronous timing of events in the different species. Even
before any evolutionary responses, phenotypic responses
will affect such biotic interactions (Gilman et al. 2010, Yang
& Rudolf, 2010). During the past decade phenological
shifts have been already observed between trees and pest
populations (Desprez-Loustau et al. 2010, Gordo & Sanz,
2010, van Asch et al. 2007, Visser & Holleman, 2001).

Much of the information on northern trees has been
accumulated through decades of field experiments.
Combining genomic tools with results from the

quantitative and ecological approaches can significantly
aid in predicting selection responses to climate change
(for crop plants, see Morrell et al. 2012). Genomic studies
will allow researchers to examine the geographical pattern
of alleles conferring adaptation – are they globally
occurring alleles with varying frequencies or very localized
ones? Coupled with studies at the quantitative trait level,
genomic surveys will aid in assessing the prospects for
adaptation at the level of the population. Furthermore, the
contribution of epigenetic and maternal effects to
phenotypic variation needs to be assessed.

This review has pointed to several areas where
management and breeding can possibly contribute to
maintenance of populations. An evaluation of such options
is beyond the scope of this review (see e.g. McLachlan et
al. 2007).

In conclusion, the concordant patterns of current local
adaptation among tree populations in numerous northern
species in Europe and North America show that selection
has repeatedly established such patterns. Populations
facing the largest evolutionary challenges are at the range
margins, but the northern and southern (or higher and
lower latitude) margins face quite different limitations.
Better data and models are thus necessary to evaluate
accurately whether natural selection, and migration, may
again allow evolutionary responses for populations to
sufficiently match their new climates.

Acknowledgements

We acknowledge support from the project FoResTTraC
FP7 - 244096 and thank Martin Lascoux and Matti Salmela
for comments on the manuscript. Jouni Mikola kindly
provided access to the original raw data related to Figure
4b. F.A. thanks the University Joseph Fourier of Grenoble
for providing opportunity to work on the manuscript
during the last stages of the redaction. SCG-M and RA
were funded by RTA2010-00120-C02-01 and AdapTree
(funded by Genome Canada and Genome BC) supported
S.N.A. and S.Y. during the preparation and publication of
this manuscript.

REFERENCES

Acevedo-Rodriguez R, Vargas-Hernandez JJ, Lopez-Upton J, Mendoza JV (2006) Effect of geographic origin and nutrition on shoot prenology of Mexican Douglas-Fir (Pseudotsuga sp.) seedlings. *Agrociencia*, **40**, 125-137.

Aitken SN, Yeaman S, Holliday JA, Wang TL, Curtis-McLane S (2008) Adaptation, migration or extirpation: climate change outcomes for tree populations. *Evolutionary Applications*, **1**, 95-111.

Alberto F, Bouffier L, Louvet JM, Lamy JB, Delzon S, Kremer A (2011) Adaptive responses for seed and leaf phenology in natural populations of sessile oak along an altitudinal gradient. *Journal of Evolutionary Biology*, **24**, 1442-1454.
Alberto F, Niort J, Derory J, Lepais O, Vitalis R, Galop D, Kremer A (2010) Population differentiation of sessile oak at the altitudinal front of migration in the French Pyrenees. *Molecular Ecology*, **19**, 2626-2639.

Alleaume-Benharira M, Pen IR, Ronce O (2006) Geographical patterns of adaptation within a species' range: interactions between drift and gene flow. *Journal of Evolutionary Biology*, **19**, 203-215.

Allen CD, Macalady AK, Chenchouni H et al. (2010) A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. *Forest Ecology and Management*, **259**, 660-684.

Andalo C, Beaulieu J, Bousquet J (2005) The impact of climate change on growth of local white spruce populations in Quebec *Forest Ecology and Management*, **205**, 169-182.

Angert AL (2009) The niche, limits to species' distributions, and spatiotemporal variation in demography across the elevation ranges of two monkeyflowers. *Proceedings of the National Academy of Sciences*, **106**, 19693-19698.

Atwell S, Huang YS, Vilhjalmsson BJ et al. (2010) Genome-wide association study of 107 phenotypes in *Arabidopsis thaliana* inbred lines. *Nature*, **465**, 627-631.

Barton N (2001) Adaptation at the edge of a species' range. In: *Integrating genetics and ecology in a spatial context*. (eds Silvertown J, Antonovics J) pp 365-392. London, Blackwell.

Barton NH (1999) Clines in polygenic traits. *Genetical Research*, **74**, 223-236.

Barton NH, Keightley PD (2002) Understanding quantitative genetic variation. *Nature Reviews Genetics*, **3**, 11-21.

Basler D, Körner C (2012) Photoperiod sensitivity of bud burst in 14 temperate forest tree species. *Agricultural and Forest Meteorology*, **165**, 73-81.

Benito-Garzón M, Alia R, Robson TM, Zavala MA (2011) Intra-specific variability and plasticity influence potential tree species distributions under climate change. *Global Ecology and Biogeography*, 766-778.

Beuker E, Valtonen E, Repo T (1998) Seasonal variation in the frost hardiness of Scots pine and Norway spruce in old provenance experiments in Finland. *Forest Ecology and Management*, **107**, 87-98.

Björklund M, Ranta E, Kaitala V, Bach LA, Lundberg P, Stenseth NC (2009) Quantitative trait evolution and environmental change. *PLoS ONE*, **4**, e4521.

Bréda N, Huc R, Granier A, Dreyer E (2006) Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences. *Annals of Forest Science*, **63**, 625-644.

Bridle JR, Polechova J, Kawata M, Butlin RK (2010) Why is adaptation prevented at ecological margins? New insights from individual-based simulations. *Ecology Letters*, **13**, 485-494.

Bräutigam K, Vining KJ, Lafon-Placette C et al. (2013) Epigenetic regulation of adaptive responses of forest tree species to the environment. *Ecology and Evolution*, **3**, n/a/n/a.

Buckler ES, Holland JB, Bradbury PJ et al. (2009) The Genetic Architecture of Maize Flowering Time. *Science*, **325**, 714-718.

Burger R, Lynch M (1995) Evolution and extinction in a changing environments - a quantitative genetic analysis. *Evolution*, **49**, 151-163.

Böhlenius H, Huang T, Charbonnel-Campaa L, Brunner AM, Jansson S, Strauss SH, Nilsson O (2006) CO/FT regulatory module controls timing of flowering and seasonal growth cessation in trees. *Science*, **312**, 1040-1043.

Caffarra A, Donelly A, Chuine I (2011) Modelling the timing of Betula pubescens budburst. II. Integrating the complex effects of photoperiod into process-based models of budburst. *Climate Research*, **46**, 159-170.

Charlesworth B, Charlesworth D (2010) *Elements of evolutionary genetics*, Greenwood Village, Co., Roberts & Co.

Cheddadi R, Vendramin GG, Litt T et al. (2006) Imprints of glacial refugia in the modern genetic diversity of *Pinus sylvestris*. *Global Ecology and Biogeography*, **15**, 271-282.

Chen IC, Hill JK, Ohlemuller R, Roy DB, Thomas CD (2011) Rapid range shifts of species associated with high levels of climate warming. *Science*, **333**, 1024-1026.

Chen J, Källman T, Gyllenstrand N, Lascoux M (2009) New insights on the speciation history and nucleotide diversity of three boreal spruce species and a Tertiary relict. *Heredity*, **104**, 3-14.

Chevin L-M, Lande R (2010) When do adaptive plasticity and genetic evolution prevent extinction of a density-regulated population? *Evolution*, **64**, 1143-1150.

Chevin L-M, Lande R, Mace GM (2010) Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. *PLoS Biology*, **8**, e1000357.

Chmura DJ (2006) Phenology differs among Norway spruce populations in relation to local variation in altitude of maternal stands in the Beskidy Mountains. *New Forests*, **32**, 21-31.

Chuine I, Beaubien EG (2001) Phenology is a major determinant of tree species range. *Ecology Letters*, **4**, 500-510.

Conner JK, Franks R, Stewart C (2003) Expression for additive genetic variances and covariances for wild radish floral traits: comparison between field and greenhouse environments *Evolution*, **57**, 487-495.

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

Cumbie WP, Eckert AJ, Wegrzyn JL, Whetten R, Neale DB, Goldfarb B (2011) Association genetics of carbon isotope discrimination, height, and foliar nitrogen in a natural population of *Pinus taeda* L. *Heredity*, **107**, 105-114.

Davis MB, Shaw RG (2001) Range shifts and adaptive responses to quaternary climate changes. *Science*, **292**, 673-679.

De Carvalho D, Ingvarsson PK, Joseph J et al. (2010) Admixture facilitates adaptation from standing variation in the European aspen (*Populus tremula* L.), a widespread forest tree. *Molecular Ecology*, **19**, 1638-1650.

Deans JD, Harvey FJ (1996) Frost hardiness of 16 European provenances of sessile oak growing in Scotland. *Forestry*, **69**, 5-11.

Desprez-Loustau ML, Vitasse Y, Delzon S, Capdevielle X, Marcais B, Kremer A (2010) Are plant pathogen populations adapted for encounter with their host? A case
study of phenological synchrony between oak and an
obligate fungal parasite along an altitudinal gradient.
*Journal of Evolutionary Biology*, **23**, 87-97.

Duputie A, Massol F, Chuine I, Kirkpatrick M, Ronce O (2012)
How do genetic correlations affect species range shifts in
a changing environment? *Ecology Letters*, **15**, 251-259.

Eckert AJ, Bower AD, Wegrzyn JL *et al*. (2009)
Association genetics of Coastal Douglas Fir (*Pseudotsuga
menziesii* var. *menziesii*, Pinaceae). I. Cold-fardiness related
traits. *Genetics*, **182**, 1289-1302.

Eckert AJ, van Heerwaarden J, Wegrzyn JL, Nelson CD,
Ross-Ibarra J, Gonzalez-Martinez SC, Neale DB (2010)
Patterns of population structure and environmental
associations to aridity across the range of Loblolly Pine
(Pinus taeda L., Pinaceae). *Genetics*, **185**, 969-982.

Edelaar P, Burraco P, Gomez-Mestre I (2011) Comparisons
between Q(ST) and F(ST)-how wrong have we been?
*Molecular Ecology*, **20**, 4830-4839.

Etterson JR, Shaw RG (2001) Constraint to adaptive
evolution in response to global warming. *Science*, **294**,
151-154.

Falconer DS, Mackay TFC (1996) *Introduction to
quantitative genetics*, Harlow, UK, Longman.

Farmer RE (1993) Latitudinal variation in height and
phenology of balsam poplar. *Silvae Genetica*, **42**, 148-153.

Fayard J, Klein EK, Lefevre F (2009) Long distance
dispersal and the fate of a gene from the colonization
front. *Journal of Evolutionary Biology*, **22**, 2171-2182.

Fisher RA (1918) The correlation between relatives on the
supposition of Mendelian inheritance. *Transactions of the
Royal Society of Edinburgh*, **52**, 399-433.

Fisher RA (1930) *The genetical theory of natural selection*,
Oxford, Oxford Univ. Press.

Franks SJ, Hoffmann AA (2012) Genetics of Climate
Change Adaptation. *Annual Review of Genetics*, **46**,
185-208.

Gienapp P, Teplitsky C, Alho JS, Mills JA, Merilä J (2008)
Climate change and evolution: disentangling
environmental and genetic responses. *Molecular Ecology*,
**17**, 167-178.

Gilman SE, Urban MC, Tewksbury J, Gilchrist GW, Holt RD
(2010) A framework for community interactions under
climate change. *Trends in Ecology & Evolution*, **25**, 325-331.

Goldstein DB, Holsinger KE (1992) Maintenance of
polygenic variation in spatially structured populations:
roles for local mating and genetic redundancy. *Evolution*,
**46**, 412-429.

Gomulkiewicz R, Holt RD (1995) When does evolution by
natural selection prevent extinction. *Evolution*, **49**, 201-207.

Gomulkiewicz R, Holt RD, Barfield M, Nuismer SL (2010)
Genetics, adaptation, and invasion in harsh environments.
*Evolutionary Applications*, **3**, 97-108.

Gomulkiewicz R, Houle D (2009) Demographic and
genetic constraints on evolution. *American Naturalist*, **174**,
218-229.

Gordo O, Sanz JJ (2010) Impact of climate change on
plant phenology in Mediterranean ecosystems. *Global
Change Biology*, **16**, 1082-1106.

Grattapaglia D, Resende MDV (2011) Genomic selection in
forest tree breeding. *Tree Genetics & Genomes*, **7**, 241-255.

Green DS (2005) Adaptive strategies in seedlings of three
co-occurring, ecologically distinct northern coniferous tree
species across an elevational gradient. *Can. J. For. Res.*, **35**,
910-917.

Grivet D, Sebastiani F, Alía R *et al*. (2011) Molecular
footprints of local adaptation in two Mediterranean
conifers. *Molecular Biology and Evolution*, **28**, 101-116.

Haldane JBS (1932) *The causes of evolution*, London,
Longmans.

Hampe A, Jump AS (2011) Climate Relicts: Past, Present,
Future. *Annual Review of Ecology, Evolution, and
Systematics*, **42**, 313-333.

Hampe A, Petit RJ (2005) Conserving biodiversity under
climate change: the rear edge matters. *Ecology Letters*, **8**,
461-467.

Hancock AM, Brachi B, Faure N *et al*. (2011) Adaptation to
climate across the *Arabidopsis thaliana* genome. *Science*,
**334**, 83-86.

Hannerz M, Aitken SN, King JN, Budge S (1999) Effects of
genetic selection for growth on frost hardiness in western
hemlock. *Canadian Journal of Forest Research-Revue
Canadienne De Recherche Forestiere*, **29**, 509-516.

Hedrick PW (2006) Genetic polymorphism in
heterogeneous environments: the age of genomics.
*Annual Review of Ecology Evolution and Systematics*, **37**,
67-93.

Hendry AP, Day T, Taylor EB (2001) Population mixing and
the adaptive divergence of quantitative traits in discrete
populations: A theoretical framework for empirical tests.
*Evolution*, **55**, 459-466.

Hendry AP, Kinnison MT (1999) Perspective: The pace of
modern life: Measuring rates of contemporary
microevolution. *Evolution*, **53**, 1637-1653.

Heuertz M, De Paoli E, Källman T *et al*. (2006) Multilocus
patterns of nucleotide diversity, linkage disequilibrium and
demographic history of Norway spruce (*Picea abies* (L.)
Karst). *Genetics*, **174**, 2095-2105.

Hill JK, Griffiths HM, Thomas CD (2011) Climate change and
evolutionary adaptations at species' range margins. *Annual
Review of Entomology*, **56**, 143-159.

Hill WG, Goddard ME, Visscher PM (2008) Data and theory
point to mainly additive genetic variance for complex
traits. *PLoS Genetics*, **4**, e1000008.

Hoffmann AA, Sgrò CM (2011) Climate change and
evolutionary adaptation. *Nature*, **470**, 479-485.

Holliday JA, Ritland K, Aitken SN (2010a) Widespread,
ecologically relevant genetic markers developed from
association mapping of climate-related traits in Sitka
spruce (*Picea sitchensis*). *New Phytologist*, **188**, 501-514.

Holliday JA, Wang T, Aitken S (2012) Predicting Adaptive
Phenotypes From Multilocus Genotypes in Sitka Spruce
(Picea sitchensis) Using Random Forest. G3:
*Genes|Genomes|Genetics*, **2**, 1085-1093.

Holliday JA, Yuen M, Ritland K, Aitken SN (2010b)
Postglacial history of a widespread conifer produces
inverse clines in selective neutrality tests. *Molecular
Ecology*, **19**, 3857-3864.

Howe GT, Aitken SN, Neale DB, Jermstad KD, Wheeler NC,
Chen THH (2003) From genotype to phenotype:
unraveling the complexities of cold adaptation in forest
trees. *Canadian Journal of Botany*, **81**, 1247-1266.
Huang X, Schmitt J, Dorn L et al. (2010) The earliest stages of adaptation in an experimental plant population: strong selection on QTLS for seed dormancy. *Molecular Ecology*, **19**, 1335-1351.

Hurme P, Repo T, Pääkkönen T, Savolainen O (1997) Climatic adaptation of bud set and frost hardiness in Scots pine (*Pinus sylvestris* L.). *Can. J. For. Res.*, **27**, 716-523.

Hänninen H, Tanino K (2011) Tree seasonality in a warming climate. *Trends in Plant Science*, **16**, 312-416.

Ingvarsson PK, Garcia MV, Luquez V, Hall D, Jansson S (2008) Nucleotide polymorphism and phenotypic associations within and around the phytochrome B2 locus in European aspen (*Populus tremula*, Salicaceae). *Genetics*, **178**, 2217-2226.

IPCC (2001) The Scientific Basis In: *Climate Change 2001*. (eds Houghton JT, Ding Y, Griggs DJ, Noguer M, Van Der Linden PJ, Dai X, Maskell K, Johnsson CA) pp 881. Cambridge, Cambridge University Press.

IPCC (2007) Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. In: *Climate Change 2007: Impacts, Adaptation and Vulnerability*. Canbridge and New York, Cambridge University Press.

Iwata H, Hayashi T, Tsumura Y (2011) Prospects for genomic selection in conifer breeding: a simulation study of *Cryptomeria japonica*. *Tree Genetics Genomes*.

Jalas J, Suominen T (1973) Atlas Floreae Europaea. Coniferales.

Jermstad KD, Bassoni DL, Jech KS, Ritchie GA, Wheeler NC, Neale DB (2003) Mapping of quantitative trait loci controlling adaptive traits in coastal Douglas Fir. *III*. Quantitative trait loci-by-environment interactions. *Genetics*, **165**, 1489-1506.

Johannes F, Porcher E, Teixeira FK *et al*. (2009) Assessing the impact of transgenerational epigenetic variation on complex traits. *PLoS Genetics*, **5**, e1000530.

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

Juntunen V, Neuvonen S, Sutinen R (2006) Männyn puurajan muutokset viimeisen 400 vuoden aikana ja metsänraja-puuraja vaihettumisvyöhykkeen ikärakenne. *Metlan työraportteja*, **25**, 25-32.

Kattsov VM, Källen E (2005) Future climate change: modeling and scenarios for the Arctic. In: *Arctic Climate Impact Assessment - scientific report*. pp 100-150. Cambridge, UK, Cambridge Univ. Press.

Kawecki TJ, Ebert D (2004) Conceptual issues in local adaptation. *Ecology Letters*, **7**, 1225-1241.

Kayihan GC, Nelson CD, Huber DA, Amerson HV, White TL, Davis JM (2010) Clonal evaluation for fusiform rust disease resistance: effects of pathogen virulence and disease escape. *Canadian Journal of Forest Research*, **40**, 1042-1050.

Kellomäki S, Kolström M (1992) Simulation of tree species composition and organic matter accumulation in Finnish boreal forests under changing climatic conditions. *Vegetatio*, **102**, 47-68.

Kellomäki S, Rouvinen I, Peltola H, Strandman H, Steinbrecher R (2001) Impact of global warming on the tree species composition of boreal forests in Finland and effects on emission of isoprenoids. *Global Change Biology*, **7**, 531-544.

Kellomäki S, Väisänen H, Kolström T (1997) Model computatons on the effects of elevating temperature and atmosphere CO2 on the regeneration of Scots pine at te timber line in Finland. *Climatic Change*, **37**, 683-708.

Kingsolver JG, Diamond SE (2011) Phenotypic selection in natural populations: what limits directional selection? *American Naturalist*, **177**, 346-357.

Kingsolver JG, Hoekstra HE, Hoekstra JM *et al*. (2001) The strength of phenotypic selection in natural populations. *American Naturalist*, **157**, 245-261.

Kirkpatrick M, Barton NH (1997) Evolution of a species' range. *American Naturalist*, **150**, 1-23.

Klein EK, Lavigne C, Gouyon PH (2006) Mixing of propagules from discrete sources at long distance: comparing a dispersal tail to an exponential. *BMC Ecology*, **6**.

Kozlowski T, Pallardy S (2002) Acclimation and adaptive responses of woody plants to environmental stresses. *The Botanical Review*, **68**, 270-334.

Kramer K (1995) Phenotypic plasticity of the phenology of 7 European tree species in relation to climate warming. *Plant Cell and Environment*, **18**, 93-104.

Kremer A, Le Corre V (2012) Decoupling of differentiation between traits and their underlying genes in response to divergent selection. *Heredity*, **108**, 375-385.

Kremer A, Ronce O, Robledo-Arnuncio JJ *et al*. (2012) Long-distance gene flow and adaptation of forest trees to rapid climate change. *Ecology Letters*, **15**, 378-392.

Kullman L (2002) Rapid recent range-margin rise of tree and shrub species in the Swedish Scandes. *Journal of Ecology*, **90**, 68-77.

Kuparinen A, Katul G, Nathan R, Schurr FM (2009) Increases in air temperature can promote wind-driven dispersal and spread of plants. *Proceedings Royal Society B*, **276**, 3081-3087.

Kuparinen A, Savolainen O, Schurr FM (2010) Increased mortality can promote evolutionary adaptation of forest trees to climate change. *Forest Ecology and Management*, **259**, 1003-1008.

Körner C, Basler D (2010) Phenology Under Global Warming. *Science*, **327**, 1461-1462.

Lande R (2009) Adaptation to an extraordinary environment by evolution of phenotypic plasticity and genetic assimilation. *Journal of Evolutionary Biology*, **22**, 1435-1446.

Langlet O (1971) Two hundred years of genecology. *Taxon*, **20**, 653-721.

Latta RG (1998) Differentiation of allelic frequencies at quantitative trait loci affecting locally adaptive traits. *American Naturalist*, **151**, 283-292.

Laurie CC, Chasalow SD, LeDeaux JR *et al*. (2004) The genetic architecture of response to long-term artificial selection for oil concentration in the maize kernel. *Genetics*, **168**, 2141-2155.

Le Corre V, Kremer A (2012) The genetic differentiation at quantitative trait loci under local adaptation. *Molecular Ecology*, **21**, 1548-1566.

LeCorre V, Kremer A (2003) Genetic variability at neutral markers, quantitative trait loci and trait in a subdivided population under selection. *Genetics*, **164**, 1205-1219.
Leimu R, Fischer M (2008) A meta-analysis of local adaptation in plants. *PLoS ONE*, **3**, e4010.

Leinonen T, O'Hara RB, Cano JM, Merila J (2008) Comparative studies of quantitative trait and neutral marker divergence: a meta-analysis. *Journal of Evolutionary Biology*, **21**, 1-17.

Lenormand T (2002) Gene flow and the limits to natural selection. *Trends Ecol. Evol.*, **17**, 183-189.

Li P, Beaulieu J, Bousquet J (1997a) Genetic structure and patterns of genetic variation among populations in eastern white spruce. *Can. J. For. Res.*, **27**, 19-198.

Li P, Beaulieu J, Daoust G, Plourde A (1997b) Patterns of adaptive genetic variation in eastern white pine (*Pinus strobus*) from Quebec. *Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere*, **27**, 199-206.

Lindner M, Maroschek M, Netherer S et al. (2010) Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. *Forest Ecology and Management*, **259**, 698-709.

Loarie SR, Duffy PB, Hamilton H, Asner GP, Field CB, Ackerly DD (2009) The velocity of climate change. *Nature*, **462**, 1052-1055.

Luquez V, Hall D, Albrectsen BR, Karlsson J, Ingvarsson P, Jansson S (2008) Natural phenological variation in aspen (Populus tremula): the SwAsp collection. *Tree Genetics & Genomes*, **4**, 279-292.

Lynch M (1996) A quantitative-genetic perspective on conservation issues. In: *Conservation genetics. Case histories from nature*. (eds Avise JC, Hamrick JL) pp 471-501. New York, Chapman and Hall.

Lynch M, Lande R (1993) Evolution and extinction in response to environmental change. In: *Biotic interactions and global change*. (eds Kareiva PM, Kingsolver JG, Huey RB) pp 234-250. Sunderland, Mass., Sinauer.

Magri D, Vendramin GG, Comps B et al. (2006) A new scenario for the Quaternary history of European beech populations: palaeobotanical evidence and genetic consequences. *New Phytologist*, **171**, 199-221.

Mayr E (1963) *Animal species and evolution* Cambridge, Mass., Harvard Univ. Press.

McGee CE (1973) Is variation in budbreak of Red oak the result of heredity or environment? In: *Tree Improvement and Genetics Proceedings - Southern Forest Tree Improvement Conference*. pp. 185-189.

McLachlan JS, Clark JS, Manos PS (2005) Molecular indicators of tree migration capacity under rapid climate change. *Ecology*, **86**, 2088-2098.

McLachlan JS, Hellmann JJ, Schwartz MW (2007) A framework for debate of assisted migration in an era of climate change. *Conservation Biology*, **21**, 297-302.

Menzel A, Fabian P (1999) Growing season extended in Europe. *Nature*, **397**, 659-659.

Menzel A, Sparks TH, Estrella N et al. (2006) European phenological response to climate change matches the warming pattern. *Global Change Biology*, **12**, 1969-1976.

Mikola J (1982) Bud-set phenology as an indicator of climatic adaptation of Scots pine in Finland. *Silva Fennica*, **16**, 178-184.

Mimura M, Aitken SN (2007) Adaptive gradients and isolation-by-distance with postglacial migration in *Picea sitchensis*. *Heredity*, **98**, 224-232.

Morgenstern EK (1978) Range-wide variation of black spruce. *Canadian Journal of Forest Research*, **8**, 463-473.

Morgenstern EK (1996) *Geographic variation in forest trees: genetic basis and application of knowledge in silviculture*, Vancouver, B. C., UBC Press.

Morin X, Augspurger C, Chuine I (2007) Process-based modeling of species' distributions: What limits temperate tree species' range boundaries? *Ecology*, **88**, 2280-2291.

Morin X, Thuiller W (2009) Comparing niche- and process-based models to reduce prediction uncertainty in species range shifts under climate change. *Ecology*, **90**, 1301-1313.

Morrell PL, Buckler ES, Ross-Ibarra J (2012) Crop genomics: advances and applications. *Nature Reviews Genetics*, **13**, 85-96.

Mouillot F, Field CB (2005) Fire history and the global carbon budget: a 1 degrees x 1 degrees fire history reconstruction for the 20th century. *Global Change Biology*, **11**, 398-420.

Nagylaki T (1975) Conditions for existence of clines. *Genetics*, **80**, 595-615.

Nagylaki T (1978) Clines with asymmetric migration *Genetics*, **88**, 813-827.

Namroud MC, Beaulieu J, Juge N, Laroche J, Bousquet J (2008) Scanning the genome for gene single nucleotide polymorphisms involved in adaptive population differentiation in white spruce. *Molecular Ecology*, **17**, 3599-3613.

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

Netherer S, Schopf A (2010) Potential effects of climate change on insect herbivores in European forests-General aspects and the pine processionary moth as specific example. *Forest Ecology and Management*, **259**, 831-838.

Nicotra AB, Atkin OK, Bonser SP et al. (2010) Plant phenotypic plasticity in a changing climate. *Trends in Plant Science*, **15**, 684-692.

Niinemets U (2010) Responses of forest trees to single and multiple environmental stresses from seedlings to mature plants: Past stress history, stress interactions, tolerance and acclimation. *Forest Ecology and Management*, **260**, 1623-1639.

Notivol E, Garcia-Gil MR, Alia R, Savolainen O (2007) Genetic variation of growth rhythm traits in the limits of a latitudinal cline in Scots pine. *Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere*, **37**, 540-551.

O'Neill GA, Hamann A, Wang TL (2008) Accounting for population variation improves estimates of the impact of climate change on species' growth and distribution. *Journal of Applied Ecology*, **45**, 1040-1049.

Olson-Manning CF, Wagner MR, Mitchell-Olds T (2012) Adaptive evolution: evaluating empirical support for theoretical predictions. *Nature Reviews Genetics*, **13**, 867-877.

Orr HA (1998) The population genetics of adaptation: the distribution of factors fixed during adaptive evolution. *Evolution*, **52**, 935-949.

Parducci L, Jorgensen T, Tollefsrud MM et al. (2012) Glacial Survival of Boreal Trees in Northern Scandinavia. *Science*, **335**, 1083-1086.
Parmesan C (2006) Ecological and evolutionary responses to recent climate change. *Annual Review of Ecology, Evolution and Systematics*, **37**, 637-669.

Pease CM, Lande R, Bull JJ (1989) A model of population growth, dispersal and evolution in a changing environment. *Ecology*, **70**, 1657-1664.

Persson B (1998) Will climate change affect the optimal choice of *Pinus sylvestris* provenances. *Silva Fennica*, **32**, 121-128.

Petit RJ, Brewer S, Bordacs S et al. (2002) Identification of refugia and post-glacial colonisation routes of European white oaks based on chloroplast DNA and fossil pollen evidence. *Forest Ecology and Management*, **156**, 49-74.

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

Polechova J, Barton N, Marion G (2009) Species' range: adaptation in space and time. *American Naturalist*, **174**, E186-E204.

Pyhäjärvi T, García-Gil MR, Knürr T, Mikkonen M, Wachowiak W, Savolainen O (2007) Demographic history has influenced nucleotide diversity in European *Pinus sylvestris* populations. *Genetics*, **177**, 1713-1724.

Quesada T, Gopal V, Cumbie WP et al. (2010) Association mapping of quantitative disease resistance in a natural population of Loblolly Pine (*Pinus taeda* L.). *Genetics*, **186**, 677-686.

Rehfeldt GE (1978) Genetic differentiation of Douglas fir populations from the northern Rocky Mountains. *Ecology*, **59**, 1264-1270.

Rehfeldt GE (1982) Differentiation of *Larix occidentalis* populations from the northern Rocky Mountains. *Silvae Genetica*, **31**, 13-19.

Rehfeldt GE (1988) Ecological genetics of *Pinus contorta* from the Rocky mountains (USA): a synthesis. *Silvae Genet.*, **37**, 131-135.

Rehfeldt GE, Jaquish BC (2010) Ecological impacts and management strategies for western larch in the face of climate-change. *Mitigation and Adaptation Strategies for Global Change*, **15**, 283-306.

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

Rehfeldt GE, Wykoff WR, Ying CC (2001) Physiologic plasticity, evolution, and impacts of a changing climate on *Pinus contorta*. *Climatic Change*, **50**, 355-376.

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

Reich PB, Oleksyn J (2008) Climate warming will reduce growth and survival of Scots pine except in the far north. *Ecology Letters*, **11**, 588-597.

Resende MFR, Munoz P, Acosta JJ et al. (2012) Accelerating the domestication of trees using genomic selection: accuracy of prediction models across ages and environments. *New Phytologist*, **193**, 617-624.

Richards CL, Bossdorf O, Verhoeven KJF (2010) Understanding natural epigenetic variation. *New Phytologist*, **187**, 562-564.

Ritland K (1996) Marker-based method for inferences about quantitative inheritance in natural populations. *Evolution*, **50**, 1062-1073.

Robertson C, Nelson TA, Jelinski DE, Wulder MA, Boots B (2009) Spatial-temporal analysis of species range expansion: the case of the mountain pine beetle, Dendroctonus ponderosae. *Journal of Biogeography*, **36**, 1446-1458.

Rohde A, Bhalerao RP (2007) Plant dormancy in the perennial context. *Trends Plant Sci.*, **15**, 217-223.

Rouault G, Candau JN, Lieutier F, Nageleisen LM, Martin JC, Warzee N (2006) Effects of drought and heat on forest insect populations in relation to the 2003 drought in Western Europe. *Annals of Forest Science*, **63**, 613-624.

Sabate S, Gracia CA, Sanchez A (2002) Likely effects of climate change on growth of Quercus ilex, Pinus halepensis, Pinus pinaster, Pinus sylvestris and Fagus sylvatica forests in the Mediterranean region. *Forest Ecology and Management*, **162**, 23-37.

Sarvas R (1962) Investigations on the flowering and seed crop of *Pinus sylvestris*. *Communicationes Instituti Forestalia Fennica*, **53.4**, 1-198.

Savolainen O (1996) Pines beyond the polar circle: adaptation to stress conditions. *Euphytica*, **92**, 139-145.

Savolainen O, Bokma F, Garcia-Gil R, Komulainen P, Repo T (2004) Genetic variation in cessation of growth and frost hardiness and consequences for adaptation of Pinus sylvestris to climatic changes. *Forest Ecology and Management*, **197**, 79-89.

Savolainen O, Kujala ST, Sokol C et al. (2011) Adaptive potential of northernmost tree populations to climate change, with emphasis on Scots pine (*Pinus sylvestris* L.). *Journal of Heredity*, **102**, 526-536.

Savolainen O, Pyhajarvi T, Knurr T (2007) Gene flow and local adaptation in trees. *Annual Review of Ecology Evolution and Systematics*, **38**, 595-619.

Savolainen O, Pyhäjärvi T (2007) Genomic diversity in forest trees. *Current Opinion in Plant Biology*, **10**, 162-167.

Sexton JP, Strauss SY, Rice KJ (2011) Gene flow increases fitness at the warm edge of a species' range. *Proceedings of the National Academy of Sciences*, **108**, 11704-11709.

Shaw RG, Etterson JR (2012) Rapid climate change and the rate of adaptation: insight from experimental quantitative genetics. *New Phytologist*, **195**, 752-765.

Shutyav EM, Giertych M (1997) Height growth variation in a comprehensive Eurasian provenance experiment of *Pinus sylvestris* L. *Silvae Genetica*, **46**, 332-349.

Sillanpää MJ (2011) On statistical methods for estimating heritability in wild populations. *Molecular Ecology*, **20**, 1324-1332.

Skroppa T, Magnussen S (1993) Provenance variation in shoot growth components of Norway spruce. *Silvae Genetica*, **42**, 111-120.

Skrøppa T, Kohmann K (1997) Adaptation to local conditions after one generation in Norway spruce. *Forest Genetics*, **4**, 171-177.

Sogaard G, Johnsen O, Nilsen J, Junttila O (2008) Climatic control of bud burst in young seedlings of nine provenances of Norway spruce. *Tree Physiology*, **28**, 311-320.
Soltis DE, Morris AB, McLachlan JS, Manos PS, Soltis PS (2006) Comparative phylogeography of unglaciated eastern North America. *Molecular Ecology*, **15**, 4261-4293.

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

Stanton-Geddes J, Shaw RG, Tiffin P (2012) Interactions between Soil Habitat and Geographic Range Location Affect Plant Fitness. *PLoS ONE*, **7**, e36015.

Sundblad LG, Andersson B (1995) No difference in frost hardiness between high and low altitude Pinus sylvestris (L) offspring. *Scandinavian Journal of Forest Research*, **10**, 22-26.

Tian F, Bradbury PJ, Brown PJ et al. (2011) Genome-wide association study of leaf architecture in the maize nested association mapping population. *Nature Genetics*, **43**, 159-162.

Turchin MC, Chiang CWK, Palmer CD, Sankararaman S, Reich D, Hirschhorn JN, Genetic Invest A (2012) Evidence of widespread selection on standing variation in Europe at height-associated SNPs. *Nature Genetics*, **44**, 1015-1019.

Valladares F, Gianoli E, Gomez JM (2007) Ecological limits to plant phenotypic plasticity. *New Phytologist*, **176**, 749-763.

van Asch M, Tienderen PH, Holleman LJM, Visser ME (2007) Predicting adaptation of phenology in response to climate change, an insect herbivore example. *Global Change Biology*, **13**, 1596-1604.

van Asch M, Visser ME (2007) Phenology of forest caterpillars and their host trees: The importance of synchrony. *Annual Review of Entomology*, **52**, 37-55.

Wang T, Hamann A, Yanchuk A, O'Neill GA, Aitken SN (2006) Use of response functions in selecting lodgepole pine populations for future climates. *Global Change Biology*, **12**, 2404-2416.

Wang TL, O'Neill GA, Aitken SN (2010) Integrating environmental and genetic effects to predict responses of tree populations to climate. *Ecological Applications*, **20**, 153-163.

Vendramin GG, Fady B, Gonzalez-Martinez SC et al. (2008) Genetically depauperate but widespread: The case of an emblematic mediterranean pine. *Evolution*, **62**, 680-688.

Vihera-Aarnio A, Hakkinen R, Partanen J, Luomajoki A, Koski V (2005) Effects of seed origin and sowing time on timing of height growth cessation of Betula pendula seedlings. *Tree Physiology*, **25**, 101-108.

Wilczek AM, Burghardt LT, Cobb AR, Cooper MD, Welch SM, Schmitt J (2010) Genetic and physiological bases for

phenological responses to current and predicted climates. *Philosophical Transactions of the Royal Society B-Biological Sciences*, **365**, 3129-3147.

Visser ME, Holleman LJM (2001) Warmer springs disrupt the synchrony of oak and winter moth phenology. *Proceedings of the Royal Society of London Series B-Biological Sciences*, **268**, 289-294.

Vitasse Y, Bresson CC, Kremer A, Michalet R, Delzon S (2010) Quantifying phenological plasticity to temperature in two temperate tree species. *Functional Ecology*, **24**, 1211-1218.

Vitasse Y, Delzon S, Bresson CC, Michalet R, Kremer A (2009) Altitudinal differentiation in growth and phenology among populations of temperate-zone tree species growing in a common garden. *Canadian Journal of Forest Research*, **39**, 1259-1269.

Voltas J, Chambel M, Prada M, Ferrio J (2008) Climate-related variability in carbon and oxygen stable isotopes among populations of Aleppo pine grown in common-garden tests. *Trees-Structure and Function*, **22**, 759-769.

von Wuehlisch G, Krusche D, Muhs HJ (1995) Variation in temperature sum requirement for flushing of beech provenances. *Silvae Genetica*, **44**, 343-346.

Worrall J (1983) Temperature - bud-burst relationships in Amabilis and Subalpine fir provenance tests replicated at different elevations. *Silvae Genetica*, **32**, 203-209.

Worrell R, Cundall EP, Malcolm DC, Ennos RA (2000) Variation among seed sources of silver birch in Scotland. *Forestry*, **73**, 419-435.

Wright S (1931) Evolution in Mendelian populations. *Genetics*, **16**, 97-159.

Wright S (1943) Isolation-by-distance. *Genetics*, **28**, 114-138.

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

Yang LH, Rudolf VHW (2010) Phenology, ontogeny and the effects of climate change on the timing of species interactions. *Ecology Letters*, **13**, 1-10.

Yeaman S, Jarvis A (2006) Regional heterogeneity and gene flow maintain variance in a quantitative trait within populations of lodgepole pine. *Proceedings of the Royal Society B-Biological Sciences*, **273**, 1587-1593.

Yeaman S, Whitlock MC (2011) The genetic architecture of adaptation under migration-selection balance. *Evolution*, **65**, 1897-1911.

Yu H, Luedeling E, Xu J (2010) Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. *Proceedings of the National Academy of Sciences of the United States of America*, **107**, 22151-22156.
# EPIGENETIC REGULATION OF ADAPTIVE RESPONSES OF FOREST TREE SPECIES TO THE ENVIRONMENT

Bräutigam, Katharina<fnref n="1" />; Vining, Kelly J<fnref n="2" />; Lafon-Placette, Clément<fnref n="3" />; Fossdal, Carl G<fnref n="4" />; Mirouze, Marie<fnref n="5" />; Gutiérrez Marcos, José<fnref n="6" />; Fluch, Silvia<fnref n="7" />; Fernández Fraga, Mario<fnref n="8" />; Guevara, M. Ángeles<fnref n="9" />, <fnref n="10" />; Abarca, Dolores<fnref n="11" />; Johnsen, Øystein<fnref n="12" />; Maury, Stéphane<fnref n="3" />; Strauss, Steven H.<fnref n="2" />; Campbell, Malcolm M.<fnref n="1" />, <fnref n="13" />; Rohde, Antje<fnref n="14" />; Díaz-Sala, Carmen<fnref n="11" />; Cervera, María-Teresa<fnref n="9" />,<fnref n="10" />

## Corresponding Author:

**Key words:** Epigenetics, forest trees, adaptive response, environmental stress, phenotypic plasticity, epigenetic memory of stressful conditions.

Published in *Ecology and Evolution* (2013) 3: 399-415, with doi:10.1002/ece3.461

Epigenetic variation is likely to contribute to the phenotypic plasticity and adaptive capacity of plant species, and may be especially important for long-lived organisms with complex life cycles, including forest trees. Diverse environmental stresses and hybridisation / polyploidisation events can create reversible heritable epigenetic marks that can be transmitted to subsequent generations as a form of molecular "memory". Epigenetic changes might also contribute to the ability of plants to colonise or persist in variable environments. In this review we provide an overview of recent data on epigenetic mechanisms involved in developmental processes and responses to environmental cues in plant, with a focus on forest tree species. We consider the possible role of forest tree epigenetics as a new source of adaptive traits in plant breeding, biotechnology, and ecosystem conservation under rapid climate change.

## Introduction

Epigenetics refers to the study of meiotically or mitotically heritable changes in gene function that do not result from changes in DNA sequence (Bonasio et al. 2010). At the molecular level epigenetic phenomena are mediated by reversible marks such as DNA methylation and histone modifications, and by small RNAs that can alter regulatory states of genes or genomic regions. DNA methylation in plants occurs at cytosines in all sequence contexts (CG, CHG, and CHH where H = A, T, or C), and well-studied post-translational modifications of histone proteins at specific amino acid residues include methylation (Krauss, 2008), acetylation, phosphorylation (Demidov et al. 2009), and ubiquitination (Kouzarides, 2007). Genome-

wide epigenetic patterns, referred to as "epigenomes", are not static; rather, they can undergo precise changes. Epigenome modifications are involved in biological processes including genetic imprinting, transposon silencing, regulation of gene expression, and heterochromatin organization.

The influence of environmental factors on epigenetic marks, and on the resultant changes in gene expression and phenotype, has recently attracted considerable attention (Boyko & Kovalchuk, 2008; Groszmann et al. 2011a; Feil & Fraga, 2011; Mirouze & Paszkowski, 2011; Boyko & Kovalchuk, 2008). Knowledge of the regulatory mechanisms involved in adaptive epigenetic responses may help to guide management of genetic resources and plant breeding, especially in long-lived forest tree species

<footnote n="1">¹ Katharina Bräutigam Centre for the Analysis of Genome Evolution and Function, Department of Cell & Systems Biology, University of Toronto, Toronto, ON M5S 3B2, Canada.</footnote>

<footnote n="2">² Kelly J Vining Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR 97331-5752 USA.</footnote>

<footnote n="3">³ Clément Lafon-Placette University of Orléans, UFR-Faculté des Sciences, UPRES EA 1207 'Laboratoire de Biologie des Ligneux et des Grandes Cultures' (LBLGC), INRA, USC1328 'Arbres et Réponses aux Contraintes Hydrique et Environnementales' (ARCHE). Rue de Chartres, BP 6759, F-45067 Orléans, France.</footnote>

<footnote n="4">⁴ Carl G. Fossdal Department of Biology and Environment, Norwegian Forest and Landscape Institute, PO Box 115, N-1431, Aas, Norway.</footnote>

<footnote n="5">⁵ Marie Mirouze Epigenetic Regulations and Seed Development, Institut de Recherche pour le Développement, UMR232 ERL5300 CNRS-IRD, 911 Av. Agropolis, 34394 Montpellier, France.</footnote>

<footnote n="6">⁶ José Gutiérrez Marcos School of Life Sciences, University of Warwick, Wellesbourne, Warkwick, CV35 9EF, United Kingdom.</footnote>

<footnote n="7">⁷ Silvia Fluch Platform for Integrated Clone Management (PICME), Health & Environment Department, AIT Austrian Institute of Technology GmbH. Konrad-Lorenz-Straße 24, 3430 Tulln, Austria.</footnote>

<footnote n="8">⁸ Mario Fernández Fraga Cancer Epigenetics Laboratory, Institute of Oncology of Asturias (IUOPA HUCA), University of Oviedo, Spain.</footnote>

<footnote n="9">⁹,¹⁰ M. Ángeles Guevara</footnote>

<footnote n="9">⁹ INIA. Forest Research Centre. Dpt. of Forest Ecology and Genetics, Forest Genomics and Ecophysiology group. Crta. La Coruña km 7,5 28040 Madrid. Spain.</footnote>

<footnote n="10">¹⁰ Mixed Unit of Forest Genomics and Ecophysiology, INIA/UPM, Madrid, Spain.</footnote>

<footnote n="11">¹¹ Dolores Abarca Department of Life Sciences, University of Alcalá. Ctra. Madrid-Barcelona Km. 33,600. 28871, Alcalá de Henares, Madrid. Spain.</footnote>

<footnote n="12">¹² Øystein Johnsen Department of Plant and Environmental Sciences, Norwegian University of Life Sciences, P. O. Box 5003, N-1432, Ås, Norway</footnote>

<footnote n="3">³ Stéphane Maury University of Orléans, UFR-Faculté des Sciences, UPRES EA 1207 'Laboratoire de Biologie des Ligneux et des Grandes Cultures' (LBLGC), INRA, USC1328 'Arbres et Réponses aux Contraintes Hydrique et Environnementales' (ARCHE). Rue de Chartres, BP 6759, F-45067 Orléans, France.</footnote>

<footnote n="2">² Steven H. Strauss Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR 97331-5752 USA.</footnote>

<footnote n="1">¹,¹³ Malcolm M. Campbell</footnote>

<footnote n="1">¹ Centre for the Analysis of Genome Evolution and Function, Department of Cell & Systems Biology, University of Toronto, Toronto, ON M5S 3B2, Canada.</footnote>

<footnote n="13">¹³ Department of Biological Sciences, University of Toronto Scarborough, University of Toronto, 1265 Military Trail, Toronto, ON M1C 1A4, Canada.</footnote>

<footnote n="14">¹⁴ Antje Rohde Department Plant Growth & Development, Institute of Agriculture and Fisheries Research, Caritasstraat 21, 9090 Melle, Belgium.</footnote>

<footnote n="11">¹¹ Carmen Díaz-Sala Department of Life Sciences, University of Alcalá. Ctra. Madrid-Barcelona Km. 33,600. 28871, Alcalá de Henares, Madrid. Spain.</footnote>

<footnote n="9">⁹,¹⁰ María-Teresa Cervera</footnote>

<footnote n="9">⁹ INIA. Forest Research Centre. Dpt. of Forest Ecology and Genetics, Forest Genomics and Ecophysiology group. Crta. La Coruña km 7,5. 28040 Madrid. Spain.</footnote>

<footnote n="10">¹⁰ Mixed Unit of Forest Genomics and Ecophysiology, INIA/UPM, Madrid, Spain
E-mail: cervera@inia.es</footnote>
where changes in allele frequency are expected to occur
very slowly. This review provides a brief overview of recent
data on epigenetic mechanisms involved in developmental
processes and responses to environmental cues in forest
species, as well as the implications of forest tree
epigenetics to adaptation as a possible new source of
beneficial traits for plant breeding and conservation in
ecosystems responding to climate change.

Factors driving epigenetic regulation
in plants

Epigenetic regulation in plant development

Epigenetic regulation plays important roles in multiple
aspects of plant development. Two distinct roles of this
regulation can be distinguished, depending on whether
they concern developmentally regulated genes or
transposable elements (TEs). In developmentally
regulated genes, epigenetic marks allow switches in gene
expression in response to developmental transitions
and/or environmental cues. After sexual reproduction,
uniparental expression of parental alleles, imprinting, is
associated with discrete differentially methylated regions
(DMRs) that act in a genome context-independent manner
(Gutierrez-Marcos et al. 2006; Jullien et al. 2006; Haun et
al. 2007).

A well-characterised example of epigenetic control during postembryonic development is vernalisation, the phenomenon of cold temperature-induced competence to flower (Chouard, 1960; Schmitz & Amasino, 2007). In *Arabidopsis thaliana*, regulation of *FLOWERING LOCUS C* (*FLC*) gene expression during vernalisation illustrates how environmental cues are perceived and translated into epigenetic marks that affect plant development (Bastow, 2004; Heo, 2011; Kim & Sung, 2012).

The epigenetic marks of many loci involved in plant
development are normally erased or reset at each
generation following meiosis, thus preventing the
establishment of new "epialleles" (alleles whose expression
is conditioned by their epigenetic status). On the other
hand, stable, i.e. heritable, epialleles can occur naturally
and might confer specific phenotypes. Examples in plants
include floral symmetry in *Linaria* that is influenced by
DNA methylation levels at the *CYCLOIDEA* locus (Cubas
et al. 1999) and absence of ripening in tomato, that is
associated with hypermethylation at the *Colorless non-ripening* locus (Manning et al. 2006). Stable epialleles are
potential targets for selection in evolutionary processes,
or in applied plant breeding. More examples will contribute
to a better understanding of their origin, their stability, and
the role they might play in selection.

In contrast to the transient nature of many developmental
epigenetic marks, those affecting TEs are more stable
(Slotkin & Martienssen, 2007; Bourc'his & Voinnet, 2010;
Lisch & Slotkin, 2011) and the mobility of TEs is observed

when these marks are alleviated in mutants affected in the
epigenetic machinery (Mirouze et al. 2009; Tsukahara et
al. 2009; Ito et al. 2011). However, during development,
transcription of activated TEs in hypomethylated gamete
companion cells are thought to produce small RNAs that
migrate into the germ cell and direct the silencing
machinery to TEs. Hence, at each new generation, the
"immune system" against transposons is perpetuated but
also readjusted to prevent potential genome invasion of
new mobile elements (Lisch & Slotkin, 2011). Given the
abundance of TEs in tree genomes, they should be
considered as potential sources of epigenetic variation
potentially affecting regulation of nearby genes.

The importance of developmentally-related epigenetic
modification has been underscored recently by its
potential involvement in hybrid vigor. Hybrid vigor, also
known as heterosis, describes the superior performance
of hybrid progeny over their parents in traits like biomass
and seed production or stress resistance. Various models
explaining heterotic effects at single loci have been
proposed, including dominance, overdominance, and
pseudo-overdominance, while interactions between genes
(epistasis) have been considered as well (Birchler et al.
2010). The molecular mechanisms causing non-additive
gene expression in hybrids have been the focus of studies
in rice and *A. thaliana*, and epigenetic regulation has
recently been associated with heterosis (Ha et al. 2009;
He, 2010; Groszmann et al. 2011a; Groszmann, 2011b). In
hybrids, a number of short interfering RNAs (siRNAs) were
found to accumulate to non-additive levels which in turn
can lead to changes in DNA methylation and gene
expression, thus contributing to hybrid vigor (Groszmann
et al. 2011a, Groszmann et al. 2011b). Given the
preponderance of hybrids in many plant taxa, including
prominent tree genera like *Populus*, the putative
involvement of epigenetics in heterosis is of great interest.

Epigenetic regulation in plant environmental
responses

Various environmental signals and stresses can induce
persistent changes in epigenetic modifications, thereby
creating a flexible "memory" system for short or
prolonged periods of time (Kvaalen & Johnsen, 2008;
Chinnusamy & Zhu, 2009; Jablonka & Raz, 2009; Whittle
et al. 2009). Environmental conditions have an impact on
a number of different epigenetic marks and mechanisms,
including DNA methylation and histone modifications, or
on frequencies of homologous recombination and
genomic rearrangements (Bond & Finnegan, 2007;
Chinnusamy & Zhu, 2009; Feil & Fraga, 2011; Hauser et al.
2011; Mirouze & Paszkowski, 2011). For example, changes
in genome-wide DNA methylation patterns in response to
biotic and abiotic stress treatments (pathogen, herbivore,
high salt, low nutrients) occur in asexually reproduced
dandelions (Taraxacum officinale). Notably, altered DNA
methylation patterns were transmitted to the non-stressed