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The Role of Provenance for the Projected Growth of Juvenile European Beech under Climate Change

Petrík, Peter 1; Grote, Rüdiger ORCID iD icon 1; Gömöry, Dušan; Kurjak, Daniel ; Petek-Petrik, Anja; Lamarque, Laurent J.; Sliacka Konôpková, Alena; Mukarram, Mohammad; Debta, Harish; Fleischer, Peter, Jr.
1 Institut für Meteorologie und Klimaforschung – Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT)

Abstract:

European beech is one of the most common tree species in Europe and is generally suggested to play even more of a prominent role in forestry in the future. It seems to have the potential to partially replace Norway spruce, as it is less sensitive to expected warmer and drier conditions. It is, however, not well known in which regions these new plantings would be particularly favourable and if specific provenances may be better adapted to the new conditions than others. Therefore, we estimated the potential early height growth under climate conditions in 2040–2060 for 20 beech provenances across a region covering the Czech Republic and Slovakia. This Central European region is expected to experience considerably drier and warmer conditions in the future. For this exercise, we implemented a new neural network model developed from height growth information obtained from the open-access BeechCOSTe52 database. The simulations are driven by past and future climate data obtained from the WorldClim database of historical climate data and future climate projections. Simulations revealed that provenances originating from drier regions performed on average significantly better than those from regions with good water supply. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000154179
Veröffentlicht am 30.12.2022
Originalveröffentlichung
DOI: 10.3390/f14010026
Scopus
Zitationen: 7
Web of Science
Zitationen: 6
Dimensions
Zitationen: 8
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung – Atmosphärische Umweltforschung (IMK-IFU)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2023
Sprache Englisch
Identifikator ISSN: 1999-4907
KITopen-ID: 1000154179
HGF-Programm 12.11.24 (POF IV, LK 01) Adaptation of natural landscapes to climate change
Erschienen in Forests
Verlag MDPI
Band 14
Heft 1
Seiten Art.-Nr.: 26
Vorab online veröffentlicht am 23.12.2022
Schlagwörter Fagus sylvativa, eco distance, phenotypic plasticity, neural network model, common garden, local adaptation
Nachgewiesen in Dimensions
Web of Science
Scopus
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