UDK 581.19:547.56]:582.736.3 The aim of this study was to determine the total concentrations of some phenolic compounds and antioxidant and antimicrobial activity of methanol extracts of different parts of Illyrian endemic Petteria ramentacea. Concentrations of phenolic compounds were determined with UV/VIS spectrophotometry. The antioxidant activity of plant extracts was determined by 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity. Antimicrobial activity of extracts was evaluated by measuring the inhibition's zone against six selected test bacteria and two fungi. The highest average total phenols concentrations were in seeds (10.78 mg GAE g-1 DW), root (10.51 mg GAE g-1 DW) and bark (10.40 mg GAE g-1 DW), and the lowest in inflorescences (2.99 mg GAE g-1 DW) and leaves (3.12 mg GAE g g-1 DW). The total flavonoids concentrations were determined only in leaves (8.25 mg CE g-1 DW) and in stem (5.66 mg CE g-1 DW). Both flavanols and proanthocyanidins (0.75 mg CE g-1 DW and 3.49 mg CE g-1 DW, respectively) were found only in leaves. Analysis of variance indicated presence of significant differences between total phenols and flavanols concentrations (p<0.05), and Duncan's test confirmed the presence of intraspecies variability according to their concentrations. The highest antioxidant activity (IC50) was observed for the seed's extract (6.86%) and the lowest for the bark’s (51.31%). All methanol extracts showed the most pronounced antibacterial activity against Staphylococcus epidermididis, and the lowest against S. aureus subsp. aureus. Antifungal activity against Candida albicans was moderate. Since Peteria ramentacea methanol extracts are potential natural antioxidant and antimicrobial preparations against selected microorganisms, it is necessary to continue with more detailed analysis.

Forest productive attributes changes over time in native forests has been recognized as crucial challenge for management of uneven aged mixed forests in Bosnia and Herzegovina since middle of the last century. Experimental study has been carried out on set of experimental plots established in mixed stands on mountain Igman in central Bosnia. The most important forest productivity attributes changes based on repeated measures have been monitored over time. The aim of this research was to conduct the post-hoc power analysis for monitored forest attributes: basal area per ha (BA), growing stock per ha (GS) and current annual increment per ha (CAIv). Here are used repeated measures conduced on the 10 experimental plots in two types of mixed stands: fir-spruce and fir-spruce-beech plots (five plots per each type) measured in five (BA and GS) and four (CAIv) occasions in periods between 10–20 years. Analyses of variance (ANOVA) within and within-between repeated measures were applied and power analysis was performed. ANOVA within forest type over time showed highly significant differences for all attributes (α = 0.05, p < 0.001). Here, power analysis for comparison of stand attributes resulted in observed high power values ranged from 82% to 99% (very low risk of Type II errors). Then, ANOVA between two forest types over time showed different significances for forest attributes (α = 0.05, pBA = 0.25, pGS = 0.23 and pCAIv = 0.02). The risks of Type II errors were high for BA and GS (from 66% to 72%) while conclusions for CAIv could be accepted with very low risk (4%). So, the post-hoc power analysis of comparisons of stand attributes between forests types found low power for BA (28%) and GS (34%) and high power for CAIv (96%). These findings confirm importance of proper forest species composition planning in mixed stands related to highest wood productivity and other forest characteristics as biodiversity.

UDK: 630*52/*56:519.8(497.6) The aim of this research was to evaluate estimates of the current annual increment of volume (CAIv) variability considering growing stock (V) as structural variable and topographic conditions and Landsat 8 spectral response as environmental variables on hilly and mountainous mixed forests in the northeast Bosnia using multiple linear regressions based on ordinary least squares (MLR) and geographically weighted regression (GWR). Sample data contains geo-referenced forest inventory data, CAIv (m3/ha/year) and V (m3/ha), extracted values from digital terrain model (altitude, slope and aspect) and derived principal components values from Landsat 8 satellite image for forest stands of the management unit located on hilly and mountain positions in protected area Konjuh, Kladanj. Here are applied MLR and GWR using stepwise procedure. MLR and GWR analyses resulted with global coefficients of significant predictors on hilly position. This was expected due to homogenous vegetation and environmental conditions on hilly position. It was found that growing stock affected CAIv the most. Significant improvement of regression modeling is achieved by GWR appliance on sample from mountainous position. There were obtained local influence of growing stock and the first principle component related to green biomass on CAIv. The highest improvement is found for broadleaves CAIv where quantification of local variability of growing stock increased adjusted coefficient of determination about 11% and reduced relative root mean square error for 6%. Local character of green biomass related to conifers CAIv did not improve regression estimation significantly. The broadleaves root mean square error based on GWR was 1.60 m3/ha/year (coefficient of variation more than 30%) which is still high so further modeling including other structural characteristics (stems number, basal area, mixture) as predictors is required.