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. 2020 Jan 2;7:1. doi: 10.1038/s41597-019-0340-y

The GenTree Dendroecological Collection, tree-ring and wood density data from seven tree species across Europe

Elisabet Martínez-Sancho 1,✉, Lenka Slámová 1, Sandro Morganti 1, Claudio Grefen 2, Barbara Carvalho 3, Benjamin Dauphin 1, Christian Rellstab 1, Felix Gugerli 1, Lars Opgenoorth 2, Katrin Heer 2, Florian Knutzen 4, Georg von Arx 1, Fernando Valladares 3, Stephen Cavers 5, Bruno Fady 6, Ricardo Alía 7, Filippos Aravanopoulos 8, Camilla Avanzi 9, Francesca Bagnoli 9, Evangelos Barbas 8, Catherine Bastien 10, Raquel Benavides 3, Frédéric Bernier 11, Guillaume Bodineau 10, Cristina C Bastias 3, Jean-Paul Charpentier 10, José M Climent 7, Marianne Corréard 6, Florence Courdier 6, Darius Danusevicius 12, Anna-Maria Farsakoglou 8, José M García del Barrio 7, Olivier Gilg 6, Santiago C González-Martínez 11, Alan Gray 5, Christoph Hartleitner 13, Agathe Hurel 11, Arnaud Jouineau 6, Katri Kärkkäinen 14, Sonja T Kujala 14, Mariaceleste Labriola 9, Martin Lascoux 15, Marlène Lefebvre 10, Vincent Lejeune 10, Mirko Liesebach 16, Ermioni Malliarou 8, Nicolas Mariotte 6, Silvia Matesanz 17, Tor Myking 18, Eduardo Notivol 19, Birte Pakull 16, Andrea Piotti 9, Mehdi Pringarbe 6, Tanja Pyhäjärvi 20, Annie Raffin 11, José A Ramírez-Valiente 7, Kurt Ramskogler 13, Juan J Robledo-Arnuncio 7, Outi Savolainen 20, Silvio Schueler 21, Vladimir Semerikov 22, Ilaria Spanu 9, Jean Thévenet 6, Mari Mette Tollefsrud 17, Norbert Turion 6, Dominique Veisse 10, Giovanni Giuseppe Vendramin 9, Marc Villar 10, Johan Westin 23, Patrick Fonti 1
PMCID: PMC6940356  PMID: 31896794

Abstract

The dataset presented here was collected by the GenTree project (EU-Horizon 2020), which aims to improve the use of forest genetic resources across Europe by better understanding how trees adapt to their local environment. This dataset of individual tree-core characteristics including ring-width series and whole-core wood density was collected for seven ecologically and economically important European tree species: silver birch (Betula pendula), European beech (Fagus sylvatica), Norway spruce (Picea abies), European black poplar (Populus nigra), maritime pine (Pinus pinaster), Scots pine (Pinus sylvestris), and sessile oak (Quercus petraea). Tree-ring width measurements were obtained from 3600 trees in 142 populations and whole-core wood density was measured for 3098 trees in 125 populations. This dataset covers most of the geographical and climatic range occupied by the selected species. The potential use of it will be highly valuable for assessing ecological and evolutionary responses to environmental conditions as well as for model development and parameterization, to predict adaptability under climate change scenarios.

Subject terms: Forestry, Forest ecology

Measurement(s) growth ring • wood density
Technology Type(s) measuring table • calculation
Factor Type(s) tree species
Sample Characteristic - Organism Betula pendula • Fagus sylvatica • Picea abies • Populus nigra • Pinus pinaster • Pinus sylvestris • Quercus petraea
Sample Characteristic - Location Europe
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Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11294993

Background & Summary

Tree rings are an important archive of individual life history variation with a large range of applications across both natural and social sciences. In their annual growth rings, trees chronologically record the effect of any factor – ranging from local to global scale – that directly or indirectly affects radial growth processes1. Typical factors influencing annual tree growth are for example growing season temperatures or soil water availability in cold and arid regions, respectively. Large datasets from tree-ring series sampled across wide geographical ranges have been used in a broad range of studies, e.g. to infer climate variability2–4, reconstruct variation in streamflow5, investigate processes affecting forest dynamics6,7, identify the origin of wood used in ancient buildings8, and date historical tools and instruments9,10.

The most important archive of tree-ring data worldwide is the International Tree-Ring Data Bank (ITRDB11). It has more than 4250 centrally-held datasets of 226 tree species from all continents, except Antarctica12. However, most of these tree-ring datasets were obtained following classical dendrochronological protocols, which usually aim to maximize the climatic signals recorded in the ring-width series by sampling climatically stressed and old populations1. Such a sampling design is convenient for climate reconstructions but can lead to bias in terms of climate sensitivity when using these datasets to elucidate ecological and evolutionary processes13,14. This is particularly relevant given that classic site selection criteria predominantly targeted extreme micro-site conditions (e.g., ridge or treeline locations), and selectively excluded measurements with weak common growth signal.

The increasing use of dendrochronological techniques in transdisciplinary studies15 is driving demand for improved ecological representativeness in the global tree-ring archives, in particular by adding new datasets from non-stressed populations, which help better representing the full environmental niche of the species14. One example is the recent combination of evolutionary biology and dendrochronology to assess signs of local adaptation in trees by linking phenotypes inferred from tree rings to genomic and environmental information16–18. The advantage that dendrochronology provides in this context is that the outcome of a wide variety of growth-related traits acting over the lifespan of an individual can be inferred from a single sample (wood core). Such kind of integrated phenotypes can then be investigated together with other datasets, although the association analyses should also take in consideration that some external processes such as disturbances or forest dynamics can affect tree growth and limit the potential genetic and climatic information encoded in the tree-ring series. Following this approach, the European project GenTree (http://www.gentree-h2020.eu) aims to provide the first comprehensive pan-European assessment of phenotypic and genomic variation within and among environmentally contrasted populations across multiple tree species. To this end, the GenTree consortium has collected a dataset of tree-core characteristics from 142 sites located across the geographical range of seven ecologically and economically important European tree species. Measurements include the widths of all annual rings dated to the exact calendar year of formation, and whole-core wood density (measured for 125 sites), as well as complementary information at tree level such as tree height and diameter at stem breast height (DBH).

Here, we present this pan-European dataset of tree-ring width series and other fitness-related traits that cover wide geographical ranges and contrasting habitats of the studied species. Despite the limitation given by an underrepresented selection of individuals at each site (only 25), the potential of this dataset goes far beyond the GenTree project goals and will also be of value for assessing and/or modelling forest properties under climate change scenarios.

Methods

Site selection

Sampling covered seven of the most ecologically and economically important tree species in Europe: silver birch (Betula pendula Roth), European beech (Fagus sylvatica L.), Norway spruce (Picea abies (L.) Karst), European black poplar (Populus nigra L.), maritime pine (Pinus pinaster Aiton), Scots pine (Pinus sylvestris L.), and sessile oak (Quercus petraea (Matt.) Liebl.). Sites were selected using the following criteria: i) natural populations with no clear signs of natural or anthropogenic disturbances, ii) within or near to a EUFORGEN Gene Conservation Unit (http://portal.eufgis.org/search/), iii) no infrastructure at close proximity (houses, roads, electric cables, larges pipes), iv) no extreme slope, and v) reasonably accessible. Each sampled tree was georeferenced using hand-held global positioning systems. The centroid of all tree individuals was used to estimate the geographical position of each site. Elevation was extracted for each site using the global multi-resolution terrain elevation data 2010 (GMTED2010)19.

Sites were distributed across most of the geographical range of the species in Europe (Fig. 1). Climate-space diagrams were used to assess the relative climatic positions of each study site based on mean annual temperature and annual precipitation (Fig. 2). The geographical coordinates of tree occurrence in Europe were obtained from a reference publication20, and the corresponding climate data for the period 1979–2013 was extracted from CHELSA21 to plot full climate-space for each species, and overlaid the study sites on this distribution. The resulting plots show that the study sites are located in heterogeneous environmental conditions across a broad span of the climatic spaces occupied by the study species, covering contrasting habitats (Fig. 2).

Fig. 1.

Fig. 1

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Natural distributions of the seven selected tree species and the geographical location of each study site from which tree-ring width measurements were obtained (142 sites in total). Distribution maps were obtained from EUFORGEN (www.euforgen.org).

Fig. 2.

Fig. 2

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Climate-space diagrams for each species based on annual mean temperature and annual precipitation. Grey points represent species occurrences from across their total climate-space and red points show the climatic position of the selected study sites from which tree-ring measurements were obtained.

Sampling and laboratory protocols

Sampling took place from 2016 to 2018. At each study site, 25 dominant or co-dominant trees were selected. All trees were >25 m apart from each other to minimize the risk of sampling closely related individuals. Trees with visual symptoms of decay, infections, scars or abnormally low vigor were avoided. Each sampled tree was permanently labelled and one to three increment cores (depending on owner permission) were extracted at breast height (1.3 m) and perpendicular to the slope direction to avoid sampling reaction wood. Two cores were taken from one side of the stem and a third core from the opposite side. DBH was measured with a tape and height was estimated from ground to top of the tree using a clinometer.

The best core per tree, i.e. the core that was closest to the pith, without breakage or other obvious defects, was selected to conduct ring width measurements. Cores were air dried, mounted on wooden support beams, and then sanded with progressively finer sanding paper until wood cells were clearly visible under a binocular microscope. For silver birch and European black poplar, cores were surfaced along their cross-section using a core-microtome22 to obtain a clean cut plane surface facilitating the recognition of ring boundaries. Ring widths were measured to an accuracy of 0.01 mm using a binocular microscope connected to a LINTAB measuring device (Rinntech, Heidelberg, Germany). The exact year of formation was assigned to every annual ring through the cross-dating process1,23, by first visually cross-dating the tree-ring width series and then statistically verifying dating quality using the software CooRecorder (Cybis Elektronik & Data AB, Saltsjöbaden, Sweden). Missing rings, i.e. those that were absent within a series, were also actively detected and inserted into the series during the cross-dating process. Tree age at the coring height of 1.3 m was calculated as the length of the cross-dated tree-ring width series plus the estimated number of absent rings in the wood core towards the pith. The latter was estimated by fitting a template of concentric circles with known radii to the curve of the innermost rings and transforming the missing radius length into the number of absent rings. A summary of these parameters per population and species is reported in Online-only Table 1.

Online-only Table 1.

Descriptors and statistics of the ring width series for the 142 measured sites.

SiteID Species Country N tree MSL Mage (year) MRW (mm) Rbt raw Rbt det Common period EPS EPS (last 25y) Mdensity (g cm−3)
FIBP19 Betula pendula Finland 25 47 48.16 1.93 0.27 0.3 2015-1990 0.923 0.924 NA
FIBP20 Betula pendula Finland 25 43.4 45.56 2.07 0.31 0.25 2015-1998 0.858 0.900 0.556
FRBP03 Betula pendula France 25 29.8 31.25 3.08 0.28 0.23 2013-2004 0.923 0.892 0.549
FRBP04 Betula pendula France 25 52.32 55.04 2.49 0.49 0.24 2015-1985 0.895 0.898 0.57
FRBP21 Betula pendula France 25 46.88 49.08 1.87 0.11 0.09 2007-2005 0.510 0.741 0.54
DEBP09 Betula pendula Germany 25 56.16 61.23 2.28 0.39 0.14 2010-1991 0.809 0.804 0.532
DEBP10 Betula pendula Germany 26 45.77 47.76 1.6 0.56 0.36 2006-1976 0.942 0.932 0.552
ITBP07 Betula pendula Italy 25 54.68 56.13 2.01 0.46 0.05 2012-1979 0.497 0.402 0.579
ITBP08 Betula pendula Italy 25 51.64 54.72 1.36 0.18 0.16 2013-1990 0.741 0.840 0.611
LTBP11 Betula pendula Lithuania 25 27 25.52 4.06 0.4 0.32 2016-2000 0.911 0.831 0.518
LTBP12 Betula pendula Lithuania 25 16.76 17.74 5.15 0.57 0.48 2016-2004 0.953 0.960 0.539
NOBP15 Betula pendula Norway 25 73.24 70.59 1.43 0.27 0.22 2016-1977 0.893 0.889 0.574
NOBP16 Betula pendula Norway 25 26.68 27.24 3.87 0.54 0.32 2016-1996 0.922 0.919 0.578
ESBP01 Betula pendula Spain 25 37 40.16 3.67 0.33 0.26 2012-1994 0.921 0.907 0.536
ESBP02 Betula pendula Spain 25 32.16 34.92 2.87 0.25 0.2 2015-2004 0.845 0.893 0.565
SEBP17 Betula pendula Sweden 25 38.12 40.52 2.73 0.36 0.32 2015-1999 0.930 0.935 0.505
SEBP18 Betula pendula Sweden 25 42.84 43.48 2.56 0.35 0.25 2015-1993 0.887 0.882 0.622
CHBP05 Betula pendula Switzerland 25 46.04 51.09 2.9 0.37 0.19 2015-1992 0.842 0.837 0.561
CHBP06 Betula pendula Switzerland 25 39.04 41.42 2.78 0.18 0.08 2011-1992 0.691 0.779 0.551
GBBP13 Betula pendula United Kingdom 24 58.83 62.41 1.82 0.29 0.15 2010-1986 0.827 0.828 NA
GBBP14 Betula pendula United Kingdom 24 47.21 49.54 2.23 0.25 0.12 2016-2007 0.804 0.815 NA
 Mean 43.46 45.41 2.61 0.35 0.23 0.834 0.848 0.557
ATFS13 Fagus sylvatica Austria 22 147.18 150.81 0.61 0.25 0.16 2015-1960 0.871 0.863 0.584
ATFS14 Fagus sylvatica Austria 25 32.56 33.8 3.11 0.09 0.09 2015-2001 0.760 0.616 0.578
FRFS03 Fagus sylvatica France 25 99.44 103.2 1.17 0.44 0.52 2015-1946 0.970 0.976 0.627
FRFS04 Fagus sylvatica France 25 104.6 105.6 1.03 0.26 0.31 2015-1937 0.913 0.944 0.581
FRFS05 Fagus sylvatica France 25 143.2 148.58 1.7 0.2 0.33 2015-1944 0.953 0.959 0.61
FRFS06 Fagus sylvatica France 25 301.44 369.29 1.01 0.32 0.32 2015-1963 0.951 0.940 0.546
DEFS15 Fagus sylvatica Germany 25 100.36 102.75 1.59 0.27 0.31 2016-1931 0.911 0.884 0.62
DEFS16 Fagus sylvatica Germany 26 93.62 95.38 1.61 0.19 0.32 2015-1938 0.932 0.875 0.634
DEFS17 Fagus sylvatica Germany 25 145.24 170.42 0.89 0.38 0.5 2015-1961 0.968 0.968 0.582
DEFS18 Fagus sylvatica Germany 25 158.24 168.48 1.6 0.46 0.51 2015-1944 0.962 0.958 0.58
GRFS09 Fagus sylvatica Greece 25 101.32 105.2 1.64 0.22 0.3 2008-1965 0.874 0.898 0.713
GRFS10 Fagus sylvatica Greece 25 105.68 110.31 1.48 0.13 0.18 2016-1960 0.877 0.877 0.688
ITFS07 Fagus sylvatica Italy 25 80.64 82.28 2.2 0.21 0.14 2008-1968 0.823 0.853 0.68
ITFS08 Fagus sylvatica Italy 25 97 99.64 1.9 0.3 0.33 2016-1961 0.927 0.945 0.633
NOFS21 Fagus sylvatica Norway 25 98.08 99.38 1.15 0.26 0.36 2015-1971 0.956 0.947 0.606
NOFS22 Fagus sylvatica Norway 25 58.2 59.2 1.94 0.26 0.37 2015-2000 0.956 0.963 0.612
ESFS01 Fagus sylvatica Spain 25 48.08 52 2.6 0.12 0.2 2015-1993 0.858 0.842 0.594
ESFS02 Fagus sylvatica Spain 25 45.88 48.28 2.42 0.16 0.19 2015-1995 0.859 0.885 0.604
SEFS23 Fagus sylvatica Sweden 25 98.44 101.32 2.01 0.25 0.34 2015-1974 0.945 0.943 0.59
SEFS24 Fagus sylvatica Sweden 25 101.16 103.27 1.88 0.18 0.17 2003-1955 0.858 0.898 0.607
CHFS11 Fagus sylvatica Switzerland 25 137.24 144.5 0.94 0.29 0.34 2015-1959 0.921 0.929 0.575
CHFS12 Fagus sylvatica Switzerland 25 77.84 82.64 2.08 0.31 0.21 2015-1963 0.882 0.905 0.612
GBFS19 Fagus sylvatica United Kingdom 30 160.37 166.57 1.61 0.2 0.29 2017-1915 0.948 0.968 NA
GBFS20 Fagus sylvatica United Kingdom 27 140.67 144.74 1.63 0.19 0.15 2017-1955 0.886 0.922 NA
 Mean 111.52 118.65 1.66 0.25 0.29 0.906 0.907 0.612
ATPA05 Picea abies Austria 25 80.92 85 1.6 0.12 0.15 2015-1988 0.893 0.892 NA
ATPA06 Picea abies Austria 25 140.08 151.89 0.68 0.4 0.21 2015-1955 0.764 0.798 NA
FIPA17 Picea abies Finland 25 93.52 94.96 1.2 0.28 0.37 2015-1991 0.917 0.917 NA
FIPA18 Picea abies Finland 25 29.36 30.24 2.26 0.27 0.22 2015-2002 0.860 0.878 NA
FRPA21 Picea abies France 25 122.28 124.46 1.96 0.13 0.2 2015-1978 0.900 0.899 0.335
DEPA09 Picea abies Germany 25 118.08 122.06 2.44 0.22 0.17 2008-1984 0.868 0.872 0.319
DEPA10 Picea abies Germany 25 151.04 159.42 1.51 0.24 0.3 2015-1986 0.935 0.935 0.325
GRPA07 Picea abies Greece 24 57.25 61.24 3.09 0.16 0.19 2016-1998 0.814 0.823 NA
GRPA08 Picea abies Greece 25 54.44 57.21 3.79 0.12 0.17 2016-1998 0.886 0.841 NA
ITPA03 Picea abies Italy 25 105.12 108.7 1.76 0.1 0.23 2013-1967 0.907 0.902 0.336
ITPA04 Picea abies Italy 25 83.44 85.08 1.67 0.19 0.28 2014-1981 0.939 0.950 0.328
LTPA11 Picea abies Lithuania 25 73.96 85.18 2.74 0.25 0.29 2015-1981 0.916 0.934 0.378
LTPA12 Picea abies Lithuania 24 80.54 84.81 2.13 0.17 0.25 2015-1961 0.885 0.870 0.35
NOPA13 Picea abies Norway 25 75.16 78.28 1.93 0.21 0.35 2015-1988 0.948 0.952 0.356
NOPA14 Picea abies Norway 25 123.04 125.16 0.93 0.23 0.37 2015-1942 0.945 0.936 0.367
RUPA19 Picea abies Russia 23 35 39.32 1.45 0.21 0.22 2012-2006 0.860 0.909 0.432
RUPA20 Picea abies Russia 25 71.72 83.44 3.01 0.32 0.3 2015-1982 0.928 0.924 0.303
SEPA15 Picea abies Sweden 25 128.72 138.91 0.91 0.25 0.36 2010-1965 0.942 0.944 0.366
SEPA16 Picea abies Sweden 25 109.56 111.52 1.51 0.36 0.44 2015-1944 0.961 0.971 0.345
CHPA01 Picea abies Switzerland 25 132.52 140.17 1.95 0.28 0.24 2008-1972 0.940 0.958 0.321
CHPA02 Picea abies Switzerland 25 78.08 75.75 3.15 0.17 0.17 2015-1989 0.860 0.867 0.301
 Mean 92.56 97.28 1.98 0.22 0.26 0.898 0.903 0.344
FRPP09 Pinus pinaster France 30 58.5 59.62 2.51 0.66 0.45 2016-1965 0.956 0.971 0.511
FRPP11 Pinus pinaster France 25 31.16 36.2 3.02 0.33 0.2 2016-1999 0.852 0.879 0.483
FRPP12 Pinus pinaster France 25 33.64 39.44 3.4 0.53 0.36 2013-1994 0.940 0.942 0.48
FRPP13 Pinus pinaster France 25 65.16 66.88 3.15 0.53 0.4 2016-1961 0.942 0.933 0.526
FRPP14 Pinus pinaster France 25 75.2 79.4 4.11 0.44 0.23 2016-1983 0.844 0.833 0.565
ITPP15 Pinus pinaster Italy 25 58.4 62.08 3 0.48 0.23 2012-1985 0.870 0.853 0.577
ITPP16 Pinus pinaster Italy 25 40.16 42.46 4.56 0.22 0.11 2013-2009 0.267 0.879 0.546
ITPP17 Pinus pinaster Italy 25 64.24 67.8 2 0.46 0.39 2008-1980 0.958 0.952 0.541
ITPP18 Pinus pinaster Italy 25 33.08 36.44 2.83 0.47 0.36 2015-2003 0.917 0.926 0.495
ITPP19 Pinus pinaster Italy 25 44.36 45.48 2.24 0.39 0.1 2013-2005 0.751 0.761 0.506
ITPP20 Pinus pinaster Italy 25 56.28 57.76 1.53 0.6 0.25 2015-1976 0.908 0.911 0.495
ESPP01 Pinus pinaster Spain 25 22.96 24.04 6.65 0.12 0.13 2015-2010 0.877 0.826 0.433
ESPP02 Pinus pinaster Spain 25 62.44 55.14 3.01 0.14 0.21 2015-2005 0.936 0.906 0.562
ESPP03 Pinus pinaster Spain 25 70.28 72.72 2.57 0.46 0.44 2015-1966 0.952 0.947 0.462
ESPP04 Pinus pinaster Spain 25 80.6 82.67 2.14 0.39 0.34 2013-1979 0.931 0.931 0.527
ESPP05 Pinus pinaster Spain 25 50.8 52.44 2.34 0.67 0.54 2001-1982 0.964 0.977 0.411
ESPP06 Pinus pinaster Spain 25 57.92 61.26 3.11 0.32 0.17 2015-1982 0.851 0.862 0.448
ESPP07 Pinus pinaster Spain 25 50.96 52.28 2.49 0.52 0.57 2013-2002 0.966 0.985 0.464
ESPP08 Pinus pinaster Spain 25 55.08 56.79 2.13 0.44 0.53 2014-1996 0.977 0.973 0.515
 Mean 53.22 55.31 2.99 0.42 0.32 0.876 0.908 0.502
FIPS18 Pinus sylvestris Finland 25 74.16 75.6 1.1 0.58 0.32 2015-1962 0.922 0.918 NA
FIPS19 Pinus sylvestris Finland 25 63.16 65.88 1.96 0.45 0.42 2015-1980 0.954 0.955 0.469
FRPS03 Pinus sylvestris France 26 63.5 64.36 2.61 0.56 0.36 2015-1980 0.937 0.950 0.399
FRPS04 Pinus sylvestris France 25 107.76 111.83 1.53 0.44 0.31 2015-1918 0.923 0.928 0.499
DEPS11 Pinus sylvestris Germany 26 104.58 108 1.42 0.51 0.39 2013-1922 0.945 0.934 0.505
DEPS12 Pinus sylvestris Germany 26 129 126.7 1.57 0.18 0.26 2001-1963 0.883 0.878 0.443
GRPS09 Pinus sylvestris Greece 25 38.48 39.95 4.54 0.46 0.2 2013-2001 0.854 0.874 NA
GRPS10 Pinus sylvestris Greece 25 103.08 114 2.55 0.45 0.2 2009-1954 0.865 0.854 NA
ITPS07 Pinus sylvestris Italy 25 60.32 62.56 1.85 0.51 0.32 2015-1993 0.913 0.914 0.43
ITPS08 Pinus sylvestris Italy 25 81.48 83.16 1.22 0.38 0.23 2015-1973 0.899 0.920 0.445
LTPS20 Pinus sylvestris Lithuania 25 109.36 110.08 1.47 0.3 0.39 2014-1958 0.940 0.955 0.456
LTPS21 Pinus sylvestris Lithuania 21 33.24 34.45 1.05 0.33 0.16 2006-2001 0.672 0.695 0.404
NOPS15 Pinus sylvestris Norway 25 155.72 157.96 0.85 0.2 0.29 2015-1917 0.923 0.877 0.45
NOPS16 Pinus sylvestris Norway 25 135.72 137.48 0.99 0.27 0.31 2015-1946 0.936 0.941 0.419
ESPS01 Pinus sylvestris Spain 25 98.08 100.09 1.79 0.66 0.39 2014-1935 0.939 0.954 0.454
ESPS02 Pinus sylvestris Spain 25 85 86.52 1.82 0.66 0.43 2013-1950 0.955 0.948 0.392
SEPS17 Pinus sylvestris Sweden 25 94 95.92 1.35 0.56 0.3 2007-1958 0.926 0.928 0.44
CHPS05 Pinus sylvestris Switzerland 25 122.08 123.04 1.45 0.26 0.27 2015-1981 0.900 0.861 0.422
CHPS06 Pinus sylvestris Switzerland 25 106.6 109.88 1.58 0.41 0.35 1983-1979 0.917 0.907 0.447
GBPS13 Pinus sylvestris United Kingdom 28 166.75 176 1.52 0.21 0.23 2018-1988 0.905 0.886 NA
GBPS14 Pinus sylvestris United Kingdom 25 119.64 125.13 2.29 0.46 0.16 2011-1996 0.893 0.898 NA
 Mean 97.7 100.41 1.74 0.42 0.30 0.905 0.904 0.442
FRPO04 Populus nigra France 29 31.72 34.38 8.46 0.35 0.18 2016-2004 0.910 0.868 0.364
FRPO05 Populus nigra France 25 28.08 26.35 8.47 0.17 0.16 2016-2011 0.274 0.791 0.404
FRPO06 Populus nigra France 28 32.07 37.48 5.16 0.32 0.13 2013-2004 0.529 0.888 0.425
FRPO07 Populus nigra France 29 43.1 46.41 3.6 0.12 0.05 2016-2002 0.676 0.544 0.402
FRPO20 Populus nigra France 29 43.9 31.69 6.02 0.36 0.12 2016-1995 0.773 0.760 0.374
DEPO08 Populus nigra Germany 25 41.84 46.47 6.25 0.43 0.26 2016-1994 0.897 0.891 0.353
DEPO09 Populus nigra Germany 25 28.44 30.2 4.34 0.59 0.44 2016-1995 0.958 0.768 0.357
DEPO10 Populus nigra Germany 25 80.64 86.13 3.77 0.23 0.16 2001-1987 0.834 0.953 0.343
ITPO14 Populus nigra Italy 25 37.16 35.44 9.06 0.22 0.15 2015-2011 0.792 0.848 0.367
ITPO15 Populus nigra Italy 25 57.88 66.5 4.49 0.18 0.17 2010-2000 0.865 0.891 0.405
ITPO16 Populus nigra Italy 25 24.24 27.65 5.61 0.42 0.44 2015-2007 0.959 0.807 0.415
ITPO17 Populus nigra Italy 25 35.68 39.95 4.84 0.39 0.13 1999-1997 0.000 0.967 0.42
ESPO01 Populus nigra Spain 21 40.05 41.8 5 0.18 0.19 2015-2006 0.865 0.846 0.39
ESPO02 Populus nigra Spain 25 15.44 18.29 8.57 0.22 0.23 2016-2013 0.933 0.916 0.424
CHPO12 Populus nigra Switzerland 32 40.59 40.9 4.75 0.36 0.35 2015-2006 0.947 0.945 0.358
CHPO13 Populus nigra Switzerland 27 21.78 23.96 5.62 0.13 0.14 2013-2006 0.745 0.780 0.369
 Mean 37.66 39.6 5.87 0.29 0.21 0.747 0.841 0.386
FRQP03 Quercus petraea France 25 162.8 189.38 1.66 0.25 0.36 1965-1927 0.951 0.880 0.671
FRQP04 Quercus petraea France 25 148.72 155.95 1.22 0.28 0.35 2015-1928 0.916 0.969 0.668
DEQP17 Quercus petraea Germany 26 77.54 79.12 2.03 0.3 0.36 2015-1945 0.930 0.914 0.596
DEQP18 Quercus petraea Germany 27 145.04 147.12 1.19 0.44 0.51 2016-1896 0.961 0.942 0.583
ITQP07 Quercus petraea Italy 25 69.12 70.88 1.84 0.59 0.27 2016-1966 0.920 0.909 0.698
ITQP08 Quercus petraea Italy 25 67.12 70.42 1.05 0.62 0.21 2011-1981 0.793 0.842 0.715
ITQP09 Quercus petraea Italy 25 210.48 257.44 0.75 0.12 0.11 2016-1958 0.870 0.857 0.631
ITQP10 Quercus petraea Italy 25 208.92 272 0.8 0.18 0.14 2014-1972 0.874 0.864 0.624
LTQP20 Quercus petraea Lithuania 25 91.8 104 1.35 0.29 0.35 2015-1968 0.934 0.940 0.586
NOQP13 Quercus petraea Norway 25 132.56 138.18 1.19 0.25 0.43 2015-1975 0.957 0.946 0.632
NOQP14 Quercus petraea Norway 25 111.24 112.72 0.94 0.36 0.51 2015-1968 0.973 0.970 0.62
POQP19 Quercus petraea Poland 25 79.52 79.25 1.56 0.22 0.32 1994-1986 0.924 0.933 0.612
ESQP01 Quercus petraea Spain 25 32.56 34.36 2.51 0.38 0.26 2015-1997 0.918 0.914 0.795
ESQP02 Quercus petraea Spain 25 32.36 33.32 2.43 0.34 0.31 2015-1998 0.935 0.939 0.717
SEQP15 Quercus petraea Sweden 25 82.36 84.16 1.84 0.3 0.43 2015-1945 0.950 0.945 0.605
SEQP16 Quercus petraea Sweden 25 87.72 89.72 1.76 0.26 0.37 2014-1965 0.945 0.954 0.605
CHQP05 Quercus petraea Switzerland 25 115.24 121.63 1.29 0.27 0.38 2015-1968 0.914 0.914 0.589
CHQP06 Quercus petraea Switzerland 26 116.88 120.88 1.51 0.21 0.39 2015-1942 0.948 0.893 0.599
GBQP11 Quercus petraea United Kingdom 21 102.67 101.92 1.94 0.45 0.37 2016-1961 0.909 0.886 NA
GBQP12 Quercus petraea United Kingdom 25 129 127 1.77 0.38 0.32 2016-1999 0.885 0.901 NA
 Mean 110.18 119.47 1.53 0.33 0.34 0.920 0.916 0.641
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Ntree, number of trees; MSL, mean series length; Mage, mean age; MRW, mean ring width; Rbt.raw, mean intercorrelation among raw ring-width series; Rbt.det, mean intercorrelation among detrended ring width series; Common period, time period in common for all the trees from the same site; EPS, expressed population signal for the common period, EPS (last 25 y), expressed population signal calculated for the last 25 years for the maximum pairwise overlap; Mdensity; mean whole-core wood density. Note that EPS and Rbt.det have been calculated after applying a 32-year spline to raw series.

Whole-core wood density was determined on the second-best core (when available) and, in case the core was broken in several pieces, it was measured using the longest section. Wood volume was determined by the water-displacement method: the sample was immersed in a water-filled tray, which was placed on a balance. Weight of the displaced water was then converted to sample volume. Sample weight was measured on samples that had been dried in an oven at 102 °C for >2 hours (time required to obtain stable weights as reported by previous tests). Finally, wood density was obtained by dividing the sample weight by the sample volume.

The third core was kept as a reserve for any additional analyses. All original cores are stored at the Dendrosciences wood sample archives of the Swiss Federal Research Institute WSL in Birmensdorf (Switzerland) and can be made accessible upon request.

Data records

The dataset is composed of three comma-separated files, and one metadata file, which are freely accessible at Figshare repository24. The first file (site.csv) contains site descriptions including site identifier, geographical coordinates, elevation, and the contact details of the site coordinator. The second file (tree.csv) provides information at tree level, namely geographical coordinates, length of the ring width series, distance to the pith, estimated tree age, stem DBH, tree height, an assessment of dating confidence, and wood core density. The third file (trw_long_format.csv) contains annually-resolved tree ring width measurements of all trees included in the study (3600 trees in total). Missing values in the first two files (site.csv, and tree.csv) are denoted by NA. Missing ring measurements, defined as those actively detected during the cross-dating process, are denoted by 0 in the trw.csv file. The metadata file contains all definitions and unit for each variable. Additionally, rwl files of all sites containing the individual tree-ring width series (exact information than the trw_long_format.csv) are also included.

Both metadata and data files can also be accessed on the GnpIS information system at the following Gnpis Repository25. There, data will be updated as new data on additional GenTree species (Abies alba Mill., Pinus cembra L., Pinus halepensis Mill., Pinus nigra Arn., and Taxus baccata L.) are provided by partners.

Technical validation

Multiple steps were taken to ensure the technical quality of the measurements. The correct dating and quality of cross-dating was checked statistically with the software COFECHA26. It correlates each individual ring width series with the overall mean site series (after removing the series being tested). This analysis identifies mismatches and mistakes in the ring width measurements. The mean intercorrelation between raw individual series from the same site (Rbt.raw) was calculated and showed good within-site agreement (Online-only Table 1, Fig. S1). The expressed population signal (EPS), a measure of how well the mean series represents the common variability of the entire population if it were infinitely replicated, was also used to check the data quality. Low values of EPS usually indicate that the mean site series is influenced by individual processes rather than a consistent common signal. EPS values were calculated on the high-frequency domain (year-to-year variability) of the measured series. To do so, the low-frequency variability (decadal) was removed from the raw tree-ring series by applying a 32-year spline to each individual series. EPS was calculated in two different ways. To have an overview of the common variability shared by all the trees of a given site, EPS was calculated taking in consideration the common time period. In this case, most of the site chronologies (81%) presented an EPS above 0.85 (accepted threshold for signal strength in dendrochronological studies27) and only 19% showed an EPS lower than 0.85 (Online-only Table 1, Fig. S1). Due to the heterogenous age of the trees included in each site, we also calculated the EPS of the last 25 years but aiming at optimizing the maximum pairwise overlap. Similar percentages of sites presenting EPS above and below 0.85 were obtained (80% and 20%, respectively), but some of the sites that previously showed extremely low EPS values improved their EPS to reasonable values when assessing the maximum pairwise overlap. In general, low EPS values can be caused by a variety of factors such as short series length (not only old trees were selected), or low suitability for dendrochronological studies of some tree species such as silver birch and European black poplar. The sampling design did not specifically aim at selecting climatically limited populations, and consequently, the common signal of some sites might not be as strong as usually expected in dendrochronological studies, resulting in low EPS values. For this reason, and to complement the statistical assessment, the cross-dating confidence level of each dated ring series was classified (A = high confidence, B = possible doubts, C = very questionable). “A” letter was assigned to tree cores that were easily cross-dated and were well correlated with the rest of samples, “B” letter was assigned to cores with intermediate agreement with the rest of samples and/or showing small cracks in the wood, and “C” letter was assigned to tree cores that presented relatively low agreement with the rest of samples. The mean intercorrelation between detrended individual series from the same site was also calculated (Rbt.det, Online-only Table 1), which corroborated the generally good agreement among series in the high-frequency domains.

The average wood density per species was compared to those from a reference dataset28,29 (Fig. 3). As in this dataset, Norway spruce, black poplar, Scots pine, and maritime pine displayed lower mean density values than the ones obtained for silver birch, European beech and sessile oak (Online-only Table 1, Figs. 3 and S2).

Fig. 3.

Fig. 3

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Mean density and mean series length for each tree species and site. Vertical dashed black lines indicated the reference mean wood density values.

Supplementary information

Supplementary File (214.3KB, docx)

Acknowledgements

This publication is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 676876 (GenTree). This work was also supported by the Swiss Secretariat for Education, Research and Innovation (SERI) under contract No. 6.0032. We want to thank to Olivier Ambrosio, Francisco Auñón, Virgilijus Baliuckas, Eduardo Ballesteros, Giorgina Beffa, Sabine Brodbeck, William Brunetto, Jurata Buchovska, Fernando del Caño, Andreas Fera, Rene Graf, Berit Gregorsson, Markus Hartmann, Andreas Helmersson, Enja Hollenbach, Jan Philipp Kappner, Johannes Lambertz, David Lopez-Quiroga, Jérémy Marchon, David Matter, Benjamin Meier, Helge Meischner, Pekka Närhi, Daniel Nievergelt, Juri Nievergelt, Anne Eskild Nilsen, Hans Nyeggen, Geir Østreng, Sebastian Richter, Christoph Rieckmann, Marcus Stefsky, Sergio San Segundo, Ivan Scotti, Jørn Henrik Sønstebø, Arne Steffenrem, Jussi Tiainen, Anne Verstege and Mikael Westerlund for their support during the field campaigns. We are also grateful to all the forest owners and national administrations for providing sampling permissions.

Online-only Table

Author contributions

F.V., L.O., S.C. and B.F. prepared the sampling design. All the authors contributed to the field sampling. L.M. coordinated the core inventory with support of S.M. and performed the tree-ring width measurements, including the cross-dating. E.M.-S. checked data quality with help from F.K., wrote the manuscript and prepared the relative data files. B.C., C.G. and P.F. performed the wood density measurements. B.D. checked the quality of site coordinates. C.R., F.G., B.D., L.O., K.H., G.v.A., E.M.-S. and P.F. coordinated all the activities. All authors commented on earlier versions and approved the current version of the manuscript.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

is available for this paper at 10.1038/s41597-019-0340-y.

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Associated Data

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Data Citations

  1. Martínez-Sancho E, 2019. The GenTree Dendroecological Collection, tree-ring and wood density data from seven tree species across Europe. figshare. [DOI] [PMC free article] [PubMed]

Supplementary Materials

Supplementary File (214.3KB, docx)

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