The GenTree Leaf Collection: Inter- and intraspecific leaf variation in seven forest tree species in Europe

Global Ecol Biogeogr. 2020;00:1–8. wileyonlinelibrary.com/journal/geb | 1 Received: 26 June 2020 | Revised: 19 October 2020 | Accepted: 17 November 2020 DOI: 10.1111/geb.13239 DATA PAPER The GenTree Leaf Collection: Interand intraspecific leaf variation in seven forest tree species in Europe Raquel Benavides1 | Bárbara Carvalho1 | Cristina C. Bastias1,2 | David López-Quiroga1 | Antonio Mas1 | Stephen Cavers3 | Alan Gray3 | Audrey Albet4 | Ricardo Alía5 | Olivier Ambrosio6 | Filippos Aravanopoulos7 | Francisco Auñón5 | Camilla Avanzi8 | Evangelia V. Avramidou7 | Francesca Bagnoli8 | Eduardo Ballesteros5 | Evangelos Barbas7 | Catherine Bastien9 | Frédéric Bernier4 | Henry Bignalet4 | Damien Bouic4 | William Brunetto6 | Jurata Buchovska10 | Ana M. Cabanillas-Saldaña11 | Nicolas Cheval4 | José M. Climent5 | Marianne Correard12 | Eva Cremer13 | Darius Danusevičius10 | Benjamin Dauphin14 | Fernando Del Caño5 | Jean-Luc Denou4 | Bernard Dokhelar4 | Rémi Dourthe4 | Anna-Maria Farsakoglou7 | Andreas Fera15 | Patrick Fonti14 | Ioannis Ganopoulos7,16 | José M. García del Barrio5 | Olivier Gilg12 | Santiago C. González-Martínez17 | René Graf14 | Delphine Grivet5 | Felix Gugerli14 | Christoph Hartleitner18 | Katrin Heer19 | Enja Hollenbach19 | Agathe Hurel17 | Bernard Issehuth4 | Florence Jean6 | Veronique Jorge9 | Arnaud Jouineau6 | Jan-Philipp Kappner19 | Katri Kärkkäinen20 | Robert Kesälahti21 | Florian Knutzen13 | Sonja T. Kujala20 | Timo Kumpula21 | Mariaceleste Labriola8 | Celine Lalanne17 | Johannes Lambertz19 | Martin Lascoux22 | Gregoire Le Provost17 | Mirko Liesebach23 | Ermioni Malliarou7 | Jérémy Marchon14 | Nicolas Mariotte6 | Elisabet Martínez-Sancho14 | Silvia Matesanz24 | Helge Meischner19 | Célia Michotey25 | Pascal Milesi22 | Sandro Morganti14 | Tor Myking26 | Anne E. Nilsen26 | Eduardo Notivol27 | Lars Opgenoorth14,19 | Geir Østreng26 | Birte Pakull23 | Andrea Piotti8 | Christophe Plomion17 | Nicolas Poinot4 | Mehdi Pringarbe12 | Luc Puzos4 | Tanja Pyhäjärvi21 | Annie Raffin4 | José A. Ramírez-Valiente5 | Christian Rellstab14 | Sebastian Richter19 | Juan J. Robledo-Arnuncio5 | Sergio San Segundo5 | Outi Savolainen20 | Volker Schneck28 | Silvio Schueler15 | Ivan Scotti6 | Vladimir Semerikov29 | Jørn Henrik Sønstebø26 | Ilaria Spanu8 | Jean Thevenet12 | Mari Mette Tollefsrud26 | Norbert Turion12 | Giovanni Giuseppe Vendramin8 | Marc Villar9 | Johan Westin30 | Bruno Fady6 | Fernando Valladares1,24 1LINCGlobal, Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales, CSIC, Madrid, Spain 2UMR 5175 CEFE, Univ Montpellier, CNRS, EPHE, IRD, Univ Paul Valéry, Montpellier, France This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2020 The Authors. Global Ecology and Biogeography published by John Wiley & Sons Ltd 2 | BENAVIDES Et Al. 3UK Centre for Ecology and Hydrology, Penicuik, United Kingdom 4INRAE, UEFP, Cestas, France 5Department of Forest Ecology and Genetics, Forest Research Centre, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, INIA-CIFOR, Madrid, Spain 6INRAE, URFM, Avignon, France 7Laboratory of Forest Genetics and Tree Breeding, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, Thessaloniki, Greece 8Institute of Biosciences and BioResources, National Research Council (CNR), Sesto Fiorentino, Italy 9INRAE, ONF, BioForA, Orléans, France 10Vytautas Magnus University, Akademija, Lithuania 11Departamento de Agricultura, Ganadería y Medio Ambiente, Gobierno de Aragón, Spain 12INRAE, UEFM, Avignon, France 13Bavarian Office for Forest Seeding and Planting – ASP, Teisendorf, Germany 14Swiss Federal Research Institute WSL, Birmensdorf, Switzerland 15Department of Forest Growth and Silviculture, Austrian Research Centre for Forests BFW, Vienna, Austria 16Institute of Plant Breeding and Genetic Resources, Hellenic Agricultural Organization DEMETER (ex NAGREF), Thermi, Greece 17INRAE, Univ. Bordeaux, BIOGECO, Cestas, France 18LIECO, Kalwang, Austria 19Department of Biology, Philipps-Universität Marburg, Marburg, Germany 20Natural Resources Institute Finland, University of Oulu, Oulu, Finland 21University of Oulu, Oulu, Finland 22Department of Ecology and Genetics, Evolutionary Biology Center, Science for Life Laboratory, Uppsala University, Uppsala, Sweden 23Thünen Institute of Forest Genetics, Grosshansdorf, Germany 24Área de Biodiversidad y Conservación, Universidad Rey Juan Carlos, Móstoles, Spain 25INRAE, URGI, Route de Saint-Cyr, Versailles, France 26Division of Forestry and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), Norway 27Centro de Investigación y Tecnología Agroalimentaria de Aragón - Unidad de Recursos Forestales (CITA), Zaragoza, Spain 28Thünen Institute of Forest Genetics, Waldsieversdorf, Germany 29Institute of Plant and Animal Ecology, Ural Branch of RAS, Ekaterinburg, Russia 30Skogforsk, Sweden Correspondence Raquel Benavides, LINCGlobal, Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales, CSIC, C/ Serrano 115 dpdo, 28006, Madrid, Spain. Email: rbenavidesc[email protected] Funding information Horizon 2020, Grant/Award Number: 676876, 4540-143AP, 6 and 0032; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES (Brazil) Editor: Zhiheng Wang Abstract Motivation: Trait variation within species can reveal plastic and/or genetic responses to environmental gradients, and may indicate where local adaptation has occurred. Here, we present a dataset of rangewide variation in leaf traits from seven of the most ecologically and economically important tree species in Europe. Sample collection and trait assessment are embedded in the GenTree project (EU-Horizon 2020), which aims at characterizing the genetic and phenotypic variability of forest tree species to optimize the management and sustainable use of forest genetic resources. Our dataset captures substantial intraand interspecific leaf phenotypic variability, and provides valuable information for studying the relationship between ecosystem functioning and trait variability of individuals, and the response and resilience of species to environmental changes. Main types of variable contained: We chose morphological and chemical characters linked to trade-offs between acquisition and conservation of resources and water use, namely specific leaf area, leaf size, carbon and nitrogen content and their ratio, and the isotopic signature of stable isotope 13C and 15N in leaves. Spatial location and grain: We surveyed between 18 and 22 populations per species, 141 in total, across Europe. Time period: Leaf sampling took place between 2016 and 2017. | 3 BENAVIDES Et Al. 1 | INTRODUCTION Leaves are the primary site for photosynthesis in terrestrial ecosystems, generating resources and underpinning ecosystem functions. Nitrogen uptake and carbon assimilation by plants are the basis of trophic interactions and, together with leaf decomposability, drive the terrestrial biogeochemical cycling. These essential roles imply the simultaneous performance of multiple functions, for example light interception, water and nutrient transport, gas exchange and heat dissipation, in addition to deploying defence mechanisms against herbivores and pathogens and physical damage. Trade-offs among these functions exist such that under different environmental pressures, shape, anatomy and chemical composition of leaves exhibit great diversity arising from phylogenetic and adaptive processes over evolutionary time (Ackerly & Reich, 1999; Nicotra et al., 2011). Wright et al. (2004) examined 2,548 plant species and found a comprehensive global pattern, suggesting that the tradeoffs span a leaf economics spectrum (LES). This spectrum shows a strong relationship among leaf traits related to the growth potential versus construction costs, going from plants with low photosynthetic rates and slow return on investment of leaf dry matter and nutrients (long life span), to plants with high rates and a rapid return (short life span). The LES concept led to the adoption of leaf trait syndromes being widely used to define species strategies, as they describe how species manage resources (Garnier et al., 2016) from a whole-plant perspective (Poorter et al., 2014). Traits are measured at the individual plant level and can thus reflect the individual resource use and plant–plant interactions beyond the species averages (McGill et al., 2006). It has been shown that 25% of the estimated trait variation in plant communities occurs at the intraspecific level (Siefert et al., 2015), highlighting the importance of this component of variation for ecosystem functioning (Crutsinger et al., 2006; Pérez-Ramos et al., 2019; Siefert et al., 2012) and warning us against neglecting it. In fact, datasets such as LEDA (Kleyer et al., 2008), TRY (Kattge et al., 2011) and BROT (Tavsanoglu & Pausas, 2018), amongst others, that compile functional traits from different species, biomes and traits incorporate data at the individual level. This variation arises from phenotypic plasticity, genetic diversity and their interaction, driven by adaptation to different local environments (Leimu & Fischer, 2008) and to environmental gradients at different scales (Messier et al., 2010). Nevertheless, a large effort is required to quantify and collate trait variation within species, and accordingly it is frequently under-represented with data from a limited number of individuals per species. Here, we present a dataset of interand intraspecific variation in leaf traits from seven ecologically and economically important tree species, evaluated from materials collected in populations across Europe. These data were collected as part of the European research project GenTree (http://www.gentr ee-h2020.eu), which aims to characterize phenotypes (see also Martínez-Sancho et al., 2020) and genetic variation in a set of European tree species. The final dataset will be of interest to study the relationships between ecosystem functioning and trait variability, to evaluate the relative extents to which genetic variation and plasticity contribute to intraspecific phenotypic variation, to assess the association between genetic and phenotypic variation, and ultimately to estimate the adaptive potential and resilience of species to environmental changes. 2 | METHODS 2.1 | Data source We study leaf traits in seven European tree species in populations across their distributions. The species list comprised the conifers Norway spruce (Picea abies (L.) Karst), maritime pine (Pinus pinaster Aiton) and Scots pine (Pinus sylvestris L.), and the broadleaves silver birch (Betula pendula Roth), European beech (Fagus sylvatica L.), European black poplar (Populus nigra L.) and sessile oak (Quercus petraea (Matt.) Liebl.). Between 18 and 22 populations per species (141 populations in total) were selected in pairs across Europe (Table 1, Supporting Information Table S1; Figure 1) to represent the range of environmental variation experienced by the species but excluding stands disturbed by intense or very recent natural or anthropogenic actions. Locally, population pairs were sampled along an environmental gradient (such as elevation, water availability or day length), but they were close enough to be connected by gene flow. A paired design offers increased statistical power to detect signatures of selection, whilst minimizing the confounding effect of population genetic Major taxa and level of measurement: We sampled at least 25 individuals in each population, 3,569 trees in total, and measured traits in 35,755 leaves from seven European tree species, i.e. the conifers Picea abies, Pinus pinaster and Pinus sylvestris, and the broadleaves Betula pendula, Fagus sylvatica, Populus nigra and Quercus petraea. Software format: The data files are in ASCII text, tab delimited, not compressed. KEYWORDS European forests, intraspecific variability, leaf economics spectrum, leaf functional traits, phenotypic variation, tree species 4 | BENAVIDES Et Al. structure (Lotterhos & Whitlock, 2015). In the north, where it is mostly flat and the gradient was often day length, distance within a pair was sometimes large. Nevertheless, site selection was done considering genetic estimates of gene flow and differentiation of the species there, which showed that distances under c. 100 km do not prevent gene flow and are not sufficient to yield significant differentiation among populations (Robledo-Arnuncio et al., 2004; Rusanen et al., 2003; Tollesfrud et al., 2009). Further details about TABLE 1 Geographical and altitudinal ranges across sampled species, and sample sizes for leaf trait estimation. For each species, the table gives the number of study populations (Npop), surveyed trees (Ntrees), subsampled trees for chemical leaf trait assessment (Ntreech) and leaves measured to assess morphological leaf traits (Nleafmorp) Species Latitudinal range (º N) Longitudinal range (º E) Elevational range (m a.s.l.) Npop Ntrees Ntreech Nleafmorp Betula pendula 41.9689– 66.3663 −3.6645–29.2824 14.6–1,552.1 20 505 280 5,053 Fagus sylvatica 40.5484– 59.3843 −1.0280–23.7195 75.4–1,626.2 22 558 308 5,574 Populus nigra 40.1605– 51.1556 −4.4065–22.8203 5.2–764.9 18 471 279 4,709 Quercus petraea 38.1406– 58.4150 −3.0298–23.7577 10.0–1,619.9 20 500 280 4,999 Picea abies 41.4808– 66.4354 6.4809–58.8777 81.8–2,029.1 20 499 272 5,060 Pinus pinaster 36.8246– 44.9702 −5.1238–11.3479 10.7–1,084.3 20 500 280 5,000 Pinus sylvestris 40.1907– 66.4371 −5.3659–29.3002 18.7–1,857.4 21 536 298 5,360 FIGURE 1 Distribution maps and site locations of the seven study tree species. Distribution information was downloaded from European Forest Genetic Resources Programme (http://www.euforgen.org) | 5 BENAVIDES Et Al. the selection of populations can be found at https://ec.europa.eu/ resea rch/parti cipan ts/docum ents/downl oadPu blic?docum entId s=0 8 0 1 6 6 e 5 b 2 f 1 6 f 3 e & a p p I d =PPGMS 2.2 | Variables The specific leaf area (SLA, the area of an individual fresh leaf divided by its dry mass; mm2/mg) is one of the most frequently used traits in functional ecology. This is partly due to its ease of measurement, but also because it can be used to estimate the position of a species along the acquisition–conservation continuum on the leaf economics spectrum (Garnier et al., 2016). Very sensitive to variation in resource availability, high SLA is common in nutrient rich or shady environments (i.e. acquisitive value) and low SLA in nutrient poor and exposed environments (i.e. conservative value) (PérezHarguindeguy et al., 2013). The area of a leaf (LA; mm2) is linked to the energy balance and water-use during photosynthesis. Small leaves can easily maintain favourable temperature and photosynthetic water-use efficiency under stressful conditions such as low water availability and high solar radiation compared to large leaves (Parkhurst & Loucks, 1972). LA varies at the interspecific and intraspecific levels, and it is influenced by different factors at different scales including phylogeny, climate, geology, altitude, latitude and allometry (Ackerly et al., 2002). Leaf nitrogen and carbon contents (LNC, LCC; %) are indicative of variation in resource acquisition (nitrogen and carbon assimilation). Leaf N constitutes the proteins involved in the photosynthesis, especially RuBisCO, which fixes CO2 inside the leaf (Lambers et al., 2008). Thus, photosynthetic capacity (Amax, the photosynthetic rate per unit leaf mass) correlates linearly with LNC (Field & Mooney, 1986). Nutrient rich environments favour the allocation of an important fraction of leaf N to the photosynthetic machinery, instead of to defensive compounds or supporting tissue, which is more typical in poor environments (Berendsen et al., 2007). LCC is the structural basis and constitutes a rather stable 50% of plant dry mass, and its usefulness lies in its relationship with other nutrients (leaf stoichiometry) that can be indicative of life history strategies, such as responses to stress (Zhang et al., 2017). For instance, an increase in the C : N ratio in plant tissues can reflect a shift from photosynthetic to structural tissue allocation under harsher conditions (Ågren, 2008). The estimation of stable isotope abundance in leaves can provide information about the physical, chemical and metabolic processes (Griffiths, 1991; and references therein). Plant photosynthesis discriminates against the stable 13C isotope (Faquhar et al., 1989) until stomata close and intercellular CO2 concentration drops. Consequently, the isotopic signature δ13C (‰; Equation 1) is linked to plant's water use efficiency (WUE), with higher levels of δ13C achieved under prompt stomatal closure. In this way, higher δ13C is observed in drier sites and during drier years (Marshall et al., 2007). where R is the ratio between the amount of the heavy isotope of an element to the amount of the light isotope in a sample, in reference to a ratio of a given standard material. The isotopic signature of stable isotope 15N in leaves (δ15N; ‰; Equation (1)) varies among species, ecosystems and climatic gradients, and thus it can reveal spatial and temporal patterns of N cycling (Craine et al., 2009, and references therein). For example, at large scales and under cool and wet conditions, there is a trend towards increasing losses of the heavier 15N in soils resulting in 15N-depleted forms of NO3, N2O and other N-containing compounds, and hence lower δ15N in plants (Amundson et al., 2003). Contrastingly, rain events after dry periods cause a larger proportional loss of 14N and increased δ15N in N compounds in ecosystems (Ogaya & Peñuelas, 2008). Hence, δ15N can reflect time-integrated measures of N storage and terrestrial N cycling (Amundson et al., 2003). 2.3 | Data collection and trait estimation We selected at least 25 healthy adult individuals within each population, totalling 3,569 trees. Ten young but fully expanded leaves were taken from a fully exposed branch – from the top of the crown – without visible herbivory or other damage. They were scanned within 48 hr and LA of each leaf was assessed using WinFOLIA (Regent Instruments Inc., Canada). All samples were then oven-dried at 60 °C for 72 hr and each leaf weighed for dry mass with an electronic balance to the nearest 0.1 mg (Kern ALS 120-4N, Balingen, Germany) to assess SLA. Leaf collection, storage, processing and morphological trait measurement followed Pérez-Harguindeguy et al. (2013). This resulted in 35,755 leaves being used for leaf morphological trait assessment (Supporting Information Table S2, Figures S1, S2). For a subset of 14 trees out of 25 alternatively selected in each population, together with the whole set from two populations (see below for details), a sample composed of several leaves was ovendried and ground. Then, 3–3.5 mg of the ground material was weighed and sealed in tin foil capsules. These samples from 1,997 trees were shipped to the UC Davis Stable Isotope Facility, where the chemical analyses were carried out with an elemental analyser interfaced to a continuous flow isotope ratio mass spectrometer (IRMS; see the website for details https://stabl eisot opefa cility.ucdav is.edu/). The final δ isotope values are expressed relative to international standards Vienna Pee Dee Belemnite (VPDB) and Air for carbon and nitrogen, respectively (Supporting Information Table S2, Figures S3, S4, S5, S6, S7). 2.4 | Technical validation The assessment of morphological leaf traits is relatively simple but sample processing needs to be transparent and repeatable to avoid mistakes. Hence, we generated a sampling protocol followed by all project partners to ensure that samples of each surveyed tree could be tracked (https://ec.europa.eu/resea rch/parti cipan ts/docum (1) 𝛿 isotope = (R sample − R standard R standard ) × 1, 000, 6 | BENAVIDES Et Al. ents/downl oadPu blic?docum entId s=0 8 0 1 6 6 e 5 b 2 f 1 6 f 3 e & a p p I d =PPGMS). Morphological trait assessment and sample preparation for chemical analyses were conducted entirely at the Museo Nacional de Ciencias Naturales (Consejo Superior de Investigaciones Científicas, Madrid). After recording weights and leaf area values, we examined the outliers within each population and checked their credibility by examining the samples and images. We kept these values as long as they were not identified as typos or mixed-up samples. This process was repeated after the estimation of SLA. The validation technique used for the chemical analysis can be found on the UC Davis Stable Isotope Facility website (https://stabl eisot opefa cility.ucdav is.edu/). 3 | DATA STRUCTURE 3.1 | Data tables The dataset comprises three files. The first file (morpho_leaf_traits. csv) provides the morphological traits (i.e. SLA and LA) at leaf level, meaning a dataset with 35,755 observations. The second file (chemical_leaf_traits.csv) contains the chemical composition (LNC, LCC, and isotope signatures) in a subset of 14 trees of each population and the whole batch from two populations of Populus nigra (FR_PO_04 and FR_PO_06), totalling 1,997 observations. In both files, each population has its own code, composed of two letters indicating the country, another two letters indicating the species and a two-digit number (from 01 to 22) to indicate the population. For example, ES_FS_01 is the code of a population located in Spain (ES) of Fagus sylvatica (FS), numbered 01. Moreover, each sampled tree has a unique identifier, which includes the population code with an additional two-digit number indicating the order of survey (from 01 to 25). Country acronyms are: AT: Austria, CH: Switzerland, DE: Germany, ES: Spain, FI: Finland, FR: France, IT: Italy, GB: Great Britain, GR: Greece, LT: Lithuania, NO: Norway, PL: Poland, RU: Russia, SE: Sweden. Species acronyms are: BP: Betula pendula, FS: Fagus sylvatica, PA: Picea abies, PO: Populus nigra, PP: Pinus pinaster, PS: Pinus sylvestris, QP: Quercus petraea. A third file (sites_leaves.csv) contains information about sites (target species, population code, site name, country, geographical coordinates and elevation). 3.2 | Format type Each data file is in ASCII text, semicolon delimited, not compressed. 3.3 | Header information Header information includes the population code (population), the tree identifier (treeID), the leaf identifier (leafID) in the file with data at the leaf level, and the acronyms of the variables: specific leaf area (SLA), leaf size (LA), leaf carbon content (LCC), leaf nitrogen content (LNC), ratio between carbon and nitrogen (C:N), isotopic signature of stable 13C isotope (δ13C), isotopic signature of stable 15N isotope (δ15N). 3.4 | Row information Each row represents a single measurement. 3.5 | Variable definition All variables measured in the seven species are detailed in the Methods section. ACKNOWLEDGMENTS The authors thank Giorgia Beffa, Guillaume Bodineau, Dieter Boedecker, Sabine Brodbeck, Jean-Paul Charpentier, Nicolas di Gerardi, Patricia Faivre Rampant, Marina Fonti, Vanina Guerin, Kevin Kleeb, Vincent Lejeune, Benjamin Meier, Pekka Närhi, Daniel Nievergelt, Francis Gauvrit, Hans Nyeggen, Gilbert Pichetlt, Patrick Poursat, Antoine Poursat, Frederick Reinig, Vincent Segura, Arne Steffenrem, Jussi Tiainen, Mattias Wellern and also other members of INRAE, UE0570, UEFP (https://doi. org/10.15454/ 1.54832 64699 19372 6E12) and INRAE, UE0995, GBFOR (https://doi.org/10.15454/ 1.55723 08287 50231 7E12) for their support in the field and laboratory; and especially those involved in the leaf weighing and grinding: Aurora Castro Aguilar, Reyes Martín-Toral, Pablo Castro and Pablo Tabarés. We are also grateful to all the forest owners and national administrations for providing sampling permissions. All authors acknowledge support from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 676876 (Project GenTree). Laboratory work was also supported by other projects: COMEDIAS (CGL201783170-R, Spanish Ministry of Science, Innovation and Universities), REMEDINAL IV (TE-CM S2018/EMT-4338, Project of the Autonomous Community of Madrid, Spain), SPONFOREST (BIODIVERSA-3, PCIN-2016-055), and International Laboratory of Global Change (LINCGlobal 4540-143AP). The Swiss contribution was supported by the Swiss Secretariat for Education, Research and Innovation (SERI) under contract no. 6.0032. BC was funded by a Scholarship from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES (Brazil). CONFLICT OF INTEREST The authors declare no conflicts of interest. AUTHOR CONTRIBUTIONS RB, BF, FV, BC and CCB wrote the leaf sampling protocol, coordinated the leaf sampling, received, and stored all images and samples collected by the different groups. | 7 BENAVIDES Et Al. RB, BC, CCB, DLQ and AM measured morphological leaf traits and prepared samples for the chemical analyses. RB checked the data quality, wrote the manuscript and prepared the data files. All the authors contributed to the field sampling, leaf scanning and commented on earlier versions of the manuscript and approved the current version. DATA AVAILABILITY STATEMENT We stored the dataset in a repository (Benavides et al., 2020) and it can be freely downloaded at https://doi.org/10.6084/m9.figsh are.12044370. ORCID Raquel Benavides https://orcid.org/0000-0003-2328-5371 Bárbara Carvalho https://orcid.org/0000-0002-4129-2746 Cristina C. Bastias https://orcid.org/0000-0002-2479-2001 Evangelia V. Avramidou https://orcid.org/0000-0002-5932-1189 Benjamin Dauphin https://orcid.org/0000-0003-0982-4252 Patrick Fonti https://orcid.org/0000-0002-7070-3292 Felix Gugerli https://orcid.org/0000-0003-3878-1845 Mirko Liesebach https://orcid.org/0000-0001-8840-5026 Elisabet Martínez-Sancho https://orcid. org/0000-0003-4413-6818 Silvia Matesanz https://orcid.org/0000-0003-0060-6136 Eduardo Notivol https://orcid.org/0000-0003-4272-4536 Lars Opgenoorth https://orcid.org/0000-0003-0737-047X Andrea Piotti https://orcid.org/0000-0002-3324-5325 Tanja Pyhäjärvi https://orcid.org/0000-0001-6958-5172 Christian Rellstab https://orcid.org/0000-0002-0221-5975 Silvio Schueler https://orcid.org/0000-0003-0155-5692 REFERENCES Ackerly, D. D., Knight, C. A., Weiss, S. B., Barton, K., & Starmer, K. P. (2002). Leaf size, specific leaf area and microhabitat distribution of chaparral woody plants: Contrasting patterns in species level and community level analyses. Oecologia, 130, 449–457. Ackerly, D. D., & Reich, P. B. (1999). Convergence and correlations among leaf size and function in seed plants: A comparative test using independent contrasts. American Journal of Botany, 86, 1272–1281. Ågren, G. I. (2008). Stoichiometry and nutrition of plant growth in natural communities. The Annual Review of Ecology, Evolution, and Systematics, 39, 153–170. Amundson, R., Austin, A. T., Schuur, E. A. G., Yoo, K., Matzek, V., Kendall, C., & Baisden, W. T. (2003). Global patterns of the isotopic composition of soil and plant nitrogen. Global Biogeochemical Cycles, 17, 1031. Benavides, R., Carvalho, B., Bastias, C. C., López-Quiroga, D., Mas, A., Cavers, S., … Valladares, F. (2020). The GenTree Leaf Collection: Interand intraspecific variation of leaf traits in seven forest tree species across Europe. Figshare, https://doi.org/10.6084/m9.figsh are.12044370 Berendsen, F., de Kroon, H., & Braakhekke, W. G. (2007). Acquisition, use and loss of nutrients. In F. I. Pugnaire, & F. Valladares (Eds.), Functional plant ecology (2nd ed., pp. 259–283). CRC Press/Taylor & Francis Group. Craine, J. M., Elmore, A. J., Aidar, M. P. M., Amundson, R. G., Bustamante, M., Coetsee, C., & Wright, I. J. (2009). Nitrogen isotopes in leaves index global patterns of nitrogen availability. Global Change Biology, 183, 980–992. Crutsinger, M., Collins, M. D., & Fordyce, J. A. (2006). Plant genotypic diversity predicts community structure and governs an ecosystem process. Science, 313, 966–968. Farquhar, G. D., Ehleringer, J. R., & Hubick, K. T. (1989). Carbon isotope discrimination and photosynthesis. Annual Review of Plant Physiology and Plant Molecular Biology, 40, 503–537. Field, C. B., & Mooney, H. A. (1986). The photosynthesis-nitrogen relationship in wild plants. In T. J. Givnish (Ed.), On the economy of plant form and function (pp. 25–55). Cambridge University Press. Garnier, E., Navas, M. L., & Grigulis, K. (2016). Plant functional diversity. Organism traits, community structure, and ecosystem properties. Oxford University Press. Griffiths, H. (1991). Applications of stable isotope technology in physiological ecology. Functional Ecology, 5, 254–269. https://doi. org/10.2307/2389263 Kattge, J., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Bönisch, G., Garnier, E., Westoby, M., Reich, P. B., Wright, I. J., Cornelissen, J. H. C., Violle, C., Harrison, S. P., Van Bodegom, P. M., Reichstein, M., Enquist, B. J., Soudzilovskaia, N. A., Ackerly, D. D., Anand, M., … Wirth, C. (2011). TRY – A global database of plant traits. Global Change Biology, 17, 2905–2935. https://doi.org/10.1111/j.1365-2486.2011.02451.x Kleyer, M., Bekker, R. M., Knevel, I. C., Bakker, J. P., Thompson, K., Sonnenschein, M., Poschlod, P., van Groenendael, J. M., Klimeš, L., Klimešová, J., Klotz, S., Rusch, G. M., Hermy, M., Adriaens, D., Boedeltje, G., Bossuyt, B., Dannemann, A., Endels, P., Götzenberger, L., … Peco, B. (2008). The LEDA Traitbase: A database of life-history traits of the Northwest European flora. Journal of Ecology, 96, 1266– 1274. https://doi.org/10.1111/j.1365-2745.2008.01430.x Lambers, H., Chapin, F. S., & Pons, T. L. (2008). Plant physiological ecology (2nd ed.). Springer. Leimu, R., & Fischer, M. (2008). A meta-analysis of local adaptation in plants. PLoS ONE, 3, e4010. https://doi.org/10.1371/journ al.pone.0004010 Lotterhos, K. E., & Whitlock, M. C. (2015). The relative power of genome scans to detect local adaptation depends on sampling design and statistical method. Molecular Ecology, 24, 1031–1046. https://doi. org/10.1111/mec.13100 Marshall, J. D., Brooks, J. R., & Lajtha, K. (2007). Sources of variation in the stable isotopic composition of plants. In K. Lajtha, & R. Michener (Eds.), Stable isotopes in ecology and environmental science (2nd ed., pp. 22–60). Blackwell Scientific Publications. Martínez-Sancho, E., Slámová, L., Morganti, S., Grefen, C., Carvalho, B., Dauphin, B., Rellstab, C., Gugerli, F., Opgenoorth, L., Heer, K., … Fonti, P. (2020). The GenTree Dendroecological Collection, treering and wood density data from seven tree species across Europe. Scientific Data, 7, 1. McGill, B. J., Enquist, B. J., Weiher, E., & Westoby, M. (2006). Rebuilding community ecology from functional traits. Trends in Ecology and Evolution, 21, 178–185. https://doi.org/10.1016/j.tree.2006.02.002 Messier, J., McGill, B. J., & Lechowicz, M. J. (2010). How do traits vary across ecological scales? A case for trait-based ecology. Ecology Letters, 13, 838–848. https://doi.org/10.1111/j.1461-0248.2010.01476.x Nicotra, A. B., Leigh, A., Boyce, C. K., Jones, C. S., Niklas, K. J., Royer, D. L., & Tsukaya, H. (2011). The evolution and functional significance of leaf shape in the angiosperms. Functional Plant Biology, 38, 535–552. https://doi.org/10.1071/FP11057 Ogaya, R., & Peñuelas, J. (2008). Changes in leaf δ13C and δ15N for three Mediterranean tree species in relation to soil water availability. Acta Oecologica, 34, 331–338. https://doi.org/10.1016/j. actao.2008.06.005 8 | BENAVIDES Et Al. Parkhurst, D., & Loucks, O. (1972). Optimal leaf size in relation to environment. Journal of Ecology, 60, 505–537. https://doi. org/10.2307/2258359 Pérez-Harguindeguy, N., Díaz, S., Garnier, E., Lavorel, S., Poorter, H., Jaureguiberry, P., Bret-Harte, M. S., Cornwell, W. K., Craine, J. M., Gurvich, D. E., Urcelay, C., Veneklaas, E. J., Reich, P. B., Poorter, L., Wright, I. J., Ray, P., Enrico, L., Pausas, J. G., de Vos, A. C., … Cornelissen, J. H. C. (2013). New handbook for standardised measurement of plant functional traits worldwide. Australian Journal of Botany, 61, 167–234. https://doi.org/10.1071/BT12225 Pérez-Ramos, I., Matías, L., Gómez-Aparicio, L., & Godoy, O. (2019). Functional traits and phenotypic plasticity modulate species coexistence across contrasting climatic conditions. Nature Communications, 10, 2555. https://doi.org/10.1038/s4146 7-019-10453 -0 Poorter, H., Lambers, H., & Evans, J. R. (2014). Trait correlation networks: A whole-plant perspective on the recently criticized leaf economic spectrum. New Phytologist, 201, 378–382. https://doi.org/10.1111/ nph.12547 Robledo-Arnuncio, J. J., Smouse, P. E., Gil, L., &Alıa, R. (2004). Pollen movement under alternative silvicultural practices in native populations of Scots pine (Pinus sylvestris L) in central Spain. Forest Ecology and Management, 197, 245–255. https://doi.org/10.1016/j. foreco.2004.05.016 Rusanen, M., Vakkari, P., & Blom, A. (2003). Genetic structure of Acer platanoides and Betula pendula in northern Europe. Canadian Journal of Forest Research, 33, 1110–1115. Siefert, A., Violle, C., Chalmandrier, L., Albert, C. H., Taudiere, A., Fajardo, A., & Wardle, D. A. (2012). Incorporating intraspecific variation in tests of trait-based community assembly. Oecologia, 170, 767–775. https://doi.org/10.1007/s0044 2-012-2351-7 Siefert, A., Violle, C., Chalmandrier, L., Albert, C. H., Taudiere, A., Fajardo, A., Aarssen, L. W., Baraloto, C., Carlucci, M. B., Cianciaruso, M. V., de Dantas, V. L., de Bello, F., Duarte, L. D. S., Fonseca, C. R., Freschet, G. T., Gaucherand, S., Gross, N., Hikosaka, K., Jackson, B., … Wardle, D. A. (2015). A global meta-analysis of the relative extent of intraspecific trait variation in plant communities. Ecology Letters, 18, 1406– 1419. https://doi.org/10.1111/ele.12508 Tavsanoglu, Ç., & Pausas, J. G. (2018). A functional trait database for Mediterranean Basin plants. Scientific Data, 5, 180135. Tollefsrud, M. M., Sønstebø, J. H., Brochmann, C., Johnsen, Ø., Skrøppa, T., & Vendramin, G. G. (2009). Combined analysis of nuclear and mitochondrial markers provide new insight into the genetic structure of North European Picea abies. Heredity, 102, 549–562. https://doi. org/10.1038/hdy.2009.16 Wright, I. J., Reich, P. B., Westoby, M., Ackerly, D. D., Baruch, Z., Bongers, F., Cavender-Bares, J., Chapin, T., Cornelissen, J. H. C., Diemer, M., Flexas, J., Garnier, E., Groom, P. K., Gulias, J., Hikosaka, K., Lamont, B. B., Lee, T., Lee, W., Lusk, C., … Villar, R. (2004). The worldwide leaf economics spectrum. Nature, 428, 821–827. https://doi.org/10.1038/ natur e02403 Zhang, H., Yang, X., Wang, J., Wang, G. G., Yu, M., & Wu, T. (2017). Leaf N and P stoichiometry in relation to leaf shape and plant size for Quercus acutissima provenances across China. Scientific Reports, 7, 46133. https://doi.org/10.1038/srep4 6133 BIOSKETCH Raquel Benavides is a forest ecologist working at the Museo Nacional de Ciencias Naturales-CSIC (Madrid). Her main interests lie in the functional diversity of forests and tree species’ responses (performance and traits) to global change. She held a Marie Curie Fellowship at the University of Freiburg (project DIVERFOR) where she started to analyse the role of the intraspecific component of functional diversity in ecosystem functioning. SUPPORTING INFORMATION Additional Supporting Information may be found online in the Supporting Information section. How to cite this article: Benavides R, Carvalho B, Bastias CC, et al. The GenTree Leaf Collection: Interand intraspecific leaf variation in seven forest tree species in Europe. Global Ecol Biogeogr. 2020;00:1–8. https://doi.org/10.1111/geb.13239