Wild almond relatives are valuable reservoirs of allelic variation for crop improvement and conservation, yet Kazakhstan’s wild-almond genetic resources remain poorly characterised. We conducted an exploratory SSR assessment of 80 putative individuals from four taxon-locality groups (20 per group), each representing one sampled population: Prunus ledebouriana, P. tenella, P. petunnikowii, and P. spinosissima. Of 22 nuclear simple sequence repeat loci screened for cross-taxon transferability, 15 generated reproducible profiles and were retained; their even genome-wide distribution was not verified. Across the full dataset, the mean number of alleles was 3.55, the effective number of alleles was 2.62, expected heterozygosity (He) was 0.544, and 95.0% of loci were polymorphic. Missing genotypes ranged from 0.0% to 34.7% among groups, and six loci had at least 20% missing data. AMOVA attributed 76.5% of variation to within-group differences and 23.5% to among-group differences (PhiPT = 0.235, p = 0.001). PCoA, unbiased Nei distances, UPGMA, and descriptive Bayesian clustering separated the four sampled groups. A nine-locus sensitivity analysis that excluded the six high-missing loci retained P. spinosissima as the group with the highest mean He (0.699), whereas P. petunnikowii increased from 0.471 to 0.609. Thus, the low full-panel estimate for P. petunnikowii was not robust to missing data. Because taxon identity was fully confounded with locality and the marker panel was limited, the results are interpreted as a regional marker-transferability and methodological baseline rather than as species-wide or genome-wide inference.

Environmental heterogeneity can promote niche differentiation among closely related species, yet ecological segregation may not be mirrored by traits measured during a single field campaign. We tested whether edaphic differentiation exceeded morphological and ecophysiological divergence in Rosa acicularis, R. alberti, and R. spinosissima across the Altai and Tarbagatai Mountains of eastern Kazakhstan. The dataset comprised 40 field records representing 25 geographic coordinates and 30 species–site combinations. Plant height, crown width, chlorophyll fluorescence, chlorophyll-related outputs, and soil pH, electrical conductivity, total nitrogen, and total carbon were analysed using FDR-controlled univariate tests with Dunn post hoc comparisons, PERMANOVA, reduced-variable PCA, partial redundancy analysis, correlation analysis, and small-sample explanatory models with leave-one-out validation. Within Tarbagatai, species identity explained 48.6% of multivariate soil variation but only 9.2% of plant-trait variation. Omnibus differences in pH, nitrogen, and carbon remained significant after correction, and the species–site analysis also supported EC differentiation. The soil block explained no significant independent component of plant-trait variation after species and mountain system were taken into account. Rosa spinosissima was taller in the Altai at the observation level, and a species-plus-region model showed moderate cross-validated performance for height; models for other traits had little predictive value. These results support edaphic niche differentiation accompanied by limited divergence in field-measured traits. Because the study was observational and temporally restricted, the findings demonstrate environmental association rather than adaptation. Still, they identify testable hypotheses and support environmentally stratified conservation of wild Rosa genetic resources.

The mountain systems of Central Asia are important centres of floristic diversity; however, spatially representative and taxonomically verified data on the distribution of many wild taxa remain fragmentary. This study aimed to quantitatively assess the taxonomic struc-ture, spatial distribution, degree of clustering, documented local species richness, and quality of a long-term array of georeferenced observations of the genus Rosa L. in the Tarbagatai mountain system of eastern Kazakhstan. The initial sample included 127 ob-servations from 2019–2026; after taxonomic filtering, the analysis included 118 Rosa rec-ords corresponding to 109 unique coordinates. A total of 61 records (51.7%) were identi-fied to the species level, while 57 records (48.3%) remained at the Rosa sp. level. The da-taset includes R. spinosissima (33 observations), R. laxa (13), R. platyacantha (10), R. alberti (4), and R. canina (1). Ninety-six records were positionally accurate to within 100 m; after duplicate removal, they corresponded to 88 unique locations. Nearest neighbour analysis revealed strong spatial aggregation of high-precision locations: the observed mean nearest neighbour distance was 0.75 km, compared to a mean expected value of 3.02 km across 999 runs of complete spatial randomness (empirical p = 0.001). With grid thin-ning, the 96 high-precision observations corresponded to 32 occupied 1x1 km cells and 26 occupied 2x2 km cells, indicating significant route and observer bias. Species-identified, high-precision records occupied 14 5x5 km cells; the maximum documented richness, equal to four species, was observed in two cells in the central part of the studied spatial cluster. Only 15 records (12.7%) had Research Grade status, while 103 (87.3%) were classi-fied as Needs ID. These results characterise the structure of the documented array of finds, rather than the demographic structure of populations, and provide a reproducible spatial basis for further expert verification, standardised monitoring, and subsequent modelling of species distributions with mandatory control for spatial sampling bias.