Marcin Klisz, Szymon Jastrzębowski, Longina Chojnacka-Ożga and Jan Kowalczyk
The purpose of this study was to determine the growth variability of four provenances of Picea abies on experimental plots in the Wyszków Forest District, central Poland. The experiment was established as a system of random blocks with four repetitions per block. We selected 48 trees from each provenance and the increment cores were colected from sample trees. Standard measurements of the width of annual increments were performed using the WinDendro software. Raw data was then indexed and subject to dendroclimatic analyses based on the average monthly temperatures and precipitation of the period from 1969 to 2012. Furthermore, the COFECHA software was used to check the consistency of the data and to determine the pointer years. High data consistency as well as growth variability of particular provenances in response to climatic conditions was observed. The results obtained here will allow for an improved selection of populations best suited for growing in the climate of central Poland
In the studies on selection and population genetics of forest trees that include the analysis of genotype × environment interaction (GE), the use of biplot graphs is relatively rare. This article describes the models and analytic methods useful in the biplot graphs, which enable the analyses of mega-environments, selection of the testing environment, as well as the evaluation of genotype stability. The main method presented in the paper is the GGE biplot method (G - genotype effect, GE -genotype × environment interaction effect). At the same time, other methods have also been referred to, such as, SVD (singular value decomposition), PCA (principal component analysis), linear-bilinear SREG model (sites regression), linear-bilinear GREG model (genotypes regression) and AMMI (additive main effects multiplicative interaction). The potential of biplot method is presented based on the data on growth height of 20 European beech genotypes (Fagus sylvatica L.), generated from real data concerning selection trials and carried out in 5 different environments. The combined ANOVA was performed using fixed- -effects, as well as mixed-effects models, and significant interaction GE was shown. The GGE biplot graphs were constructed using PCA. The first principal component (GGE1) explained 54%, and the second (GGE2) explained more than 23% of the total variation. The similarity between environments was evaluated by means of the AEC method, which allowed us to determine one mega-environment that comprised of 4 environments. None of the tested environments represented the ideal one for trial on genotype selection. The GGE biplot graphs enabled: (a) the detection of a stable genotype in terms of tree height (high and low), (b) the genotype evaluation by ranking with respect to the height and genotype stability, (c) determination of an ideal genotype, (d) the comparison of genotypes in 2 chosen environments.
Marcin Klisz, Szymon Jastrzębowski, Joanna Ukalska, Paweł Przybylski, Jan Matras and Marcin Mionskowski
The aim of the study was to determine the vulnerability of selected silver fir populations to damage from late frost in the climatic conditions of south-eastern Poland. To determine the vulnerability of apical and lateral shoots to damage caused by late frosts, we observed four test plots in 2009 and 2014, each containing progenies of selected seed stands. Our statistical analyses were based on a model incorporating the following variables: site, year, type of frost damage, population as well as the possible interaction between these variables. Significant differences between the populations were found in terms of their sensitivity to damage from low temperature occurring during the growth period. Furthermore, we indirectly demonstrated differences in the severity of late frost on the experimental plots, as well as the intensity and variability of late frost shoot damage. Based on these results, we divided the studied populations into two groups of low (EF, KRA1 and NAR) and high (LES2 and BAL2) sensitivity to late frost damage.
Vasyl Mohytych, Marcin Klisz, Roman Yatsyk, Yuriy Hayda and Mariana Sishchuk
Current distributions of Swiss stone pine mostly cover the mountain regions of Europe (Alps and Carpathians). Easternmost distribution of this species is located in western Ukraine. Due to environmental fragmentation in Eastern Carpathians and competition with Norway spruce and other species, marginal populations of Swiss stone pine create isolated island, where other species are not able to cope with harsh conditions. Still, Pinus cembra L. play an important role for soil-formation and soil-protection in high elevations. The evidence of recent reduction in the area of Swiss stone pine raises the question whether the introduction of this species at lower altitudes can be successful? According to the studies conducted on reciprocal transplant experiments, Swiss stone pine population from higher elevation are able to profit in low elevation sites. Thus, parallelly with gene conservation activity, the possibilities of assisted migration should be recognized for this species.
Vasyl Mohytych, Małgorzata Sułkowska and Marcin Klisz
Existing knowledge of the Ukrainian foresters related to the historical changes and current state of silver fir forests, as well as on the various methods of restoration of such forests in the Ukrainian Carpathians were discussed. Forest cover of fir stands in this region has been diminishing in the last two centuries. Only in the period from 1947 to 1956, the area of fir stands in Ukrainian Carpathians decreased by 38.8%. Currently, the restoration of fir stands in these areas are crucial for Ukrainian forestry. Therefore, the natural as well as artificial regeneration using seeds obtained from seed orchards are currently used. Thus, improving the forest stands’ conditions mostly composed of single-spruce plantations need to be improved through changing the species compositions. However, the restoration of fir stands is time and labour-intensive, and require a long-term strategy.