The GenTree Dendroecological Collection, tree-ring and wood density data from seven tree species across Europe.
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Martínez-Sancho E, Slámová L, Morganti S, Grefen C, Carvalho B, Dauphin B, Rellstab C, Gugerli F, Opgenoorth L, Heer K, Knutzen F, von Arx G, Valladares F, Cavers S, Fady B, Alía R, Aravanopoulos F, Avanzi C, Bagnoli F, Barbas E, Bastien C, Benavides R, Bernier F, Bodineau G, Bastias CC, Charpentier JP, Climent JM, Corréard M, Courdier F, Danusevicius D, Farsakoglou AM, Del Barrio JMG, Gilg O, González-Martínez SC, Gray A, Hartleitner C, Hurel A, Jouineau A, Kärkkäinen K, Kujala ST, Labriola M, Lascoux M, Lefebvre M, Lejeune V, Le-Provost G, Liesebach M, Malliarou E, Mariotte N, Matesanz S, Michotey C, Milesi P, Myking T, Notivol E, Pakull B, Piotti A, Plomion C, Pringarbe M, Pyhäjärvi T, Raffin A, Ramírez-Valiente JA, Ramskogler K, Robledo-Arnuncio JJ, Savolainen O, Schueler S, Semerikov V, Spanu I, Thévenet J, Mette Tollefsrud M, Turion N, Veisse D, Vendramin GG, Villar M, Westin J, Fonti P
Sci Data 7 (1) 1 [2020-01-02; online 2020-01-02]
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.
PubMed 31896794
DOI 10.1038/s41597-019-0340-y
Crossref 10.1038/s41597-019-0340-y
pii: 10.1038/s41597-019-0340-y
pmc: PMC6940356