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Despite the high density of brown bears (Ursus arctos piscator) on the Kamchatka peninsula their genetic variation has not been studied by STR analysis. Our aim was, therefore, to provide population data from the Kamchatka brown bear population applying a validated DNA profiling system. Twelve dinucleotide STRs commonly used in Western-European (WE) populations and four additional ones (G10C, G10J, G10O, G10X), were included. Template input ≥ 0.2 ng was successfully amplified. Measurements of precision, stutter and heterozygous balance showed that markers could be reliably genotyped applying the thresholds used for genotyping WE brown bears. However, locus G10X revealed an ancient allele-specific polymorphism that led to suboptimal amplification of all 174 bp alleles (Kamchatka and WE). Allele frequency estimates and forensic genetic parameters were obtained from 115 individuals successfully identified by genotyping 434 hair samples. All markers met the Hardy-Weinberg and linkage equilibrium expectations, and the power of discrimination ranged from 0.667 to 0.962. The total average probability of identity from the 15 STRs was 1.4 ×10−14 (FST = 0.05) while the total average probability of sibling identity was 6.0 ×10−6. Relationship tests revealed several parent-cub and full sibling pairs demonstrating that the marker set would be valuable for the study of family structures. The population data is the first of its kind from the Kamchatka brown bear population. Population pairwise FST`s revealed moderate genetic differentiation that mirrored the geographic distances to WE populations. The DNA profiling system, providing individual-specific profiles from non-invasive samples, will be useful for future monitoring and conservation purposes

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Global environmental change may lead to changes in community structure and in species interactions, ultimately changing ecosystem functioning. Focusing on spatial variation in fungus–plant interactions across the rapidly changing Arctic, we quantified variation in the identity of interaction partners. We then related interaction turnover to variation in the bioclimatic environment by combining network analyses with general dissimilarity modelling. Overall, we found species associations to be highly plastic, with major rewiring among interaction partners across variable environmental conditions. Of this turnover, a major part was attributed to specific environmental properties which are likely to change with progressing climate change. Our findings suggest that the current structure of plant-root associated interactions may be severely altered by rapidly advancing global warming. Nonetheless, flexibility in partner choice may contribute to the resilience of the system.