Use of a common garden experiment in selecting adapted beech provenances for artificial stand restoration / Sanja Bogunović, Saša Bogdan, Miran Lanšćak, Nevenka Ćelepirović, Mladen Ivanković.
Sažetak

Increased frequency of extreme weather events has seriously affected forestry operations in south-eastern Europe. A precondition for effective artificial restoration of disturbed forest stands is site-adapted forest reproductive material (FRM). Common garden experiments (provenance trials) may assist in selecting such FRM. The main objective of this study was to establish among-provenance variation pattern using data from a beech provenance trial. Usefulness of the results in selecting seed sources for restoration of European beech stands is discussed. The trial was set up in 2007, at a slope of Medvednica mount facing north-west at 730-750 m above sea level. Plant heights were measured and survival scored in 2008 and 2015. Height increments were calculated and processed to determine variance components due to various effects. Highly significant provenance-by-block interaction was revealed, indicating strong microsite effects on provenance performances. Therefore, corrections were made and provenance mean height increments recalculated. Provenance mean height increment multiplied with survival was used as a measure of a provenance’s adaptedness. Regression tree (RT) analysis was used to determine the pattern of among-provenance variations. A set of provenance clus­ters was grown using climatic variables related to the provenance stands of origin as criteria. All analyzed effects were significant (provenance: F=2.07, p<0.05; block: F=5.07, p<0.05; provenance by block interaction: F=7.32, p<0.001). Data corrections reduced the interaction effect, thereby increasing reliability of calculated provenance adaptedness indices (AI). Provenances were grouped into 4 clusters due to elevation, mean July temperature and summer heat-to-moisture index (SHM). Cluster 4, containing provenances from the highest altitudes (>750m), had the highest mean AI (143.9±8.4 cm).