In alpine meadows, plants and soil microbes typically engage in competition for nitrogen (N) under N-deficient conditions. However, the acquisition and distribution of N among soil microbes and plants under alpine meadow degradation and climate warming induced by global climate change are still uncharacterized. In this study, we isotope labeled inorganic (NH₄⁺-¹⁵N, NO₃⁻-¹⁵N) and organic (glycine-¹⁵N) N in both degraded and non-degraded plots by using open-top chambers (OTC) to mimic increasing air temperatures. After 6 h, the ¹⁵N contents in soil microbes and plants were measured to investigate the effects of degradation and rising air temperature on N allocations in the ecosystems studied. Results showed that alpine meadow degradation significantly reduced soil microbial N accumulation by 52% compared to those in non-degraded plots. In non-degraded plots, warming significantly lowered the organic N levels of soil microbes by 49%, whereas in degraded ones, it reduced both NH₄⁺-¹⁵N and NO₃⁻-¹⁵N recovery by 80% and 45% on average but increased glycine-¹⁵N recovery by 653%. Meanwhile, warming decreased the plant recovery of NH₄⁺-¹⁵N and NO₃⁻-¹⁵N by 75% and 45% but increased the recovery of glycine-¹⁵N by 45% in non-degraded plots. Conversely, in degraded plots, warming markedly lowered NH₄⁺-¹⁵N recovery by 40% but increased glycine-¹⁵N recovery by 114%. Warming mitigates the effects of alpine meadow degradation on nitrogen allocation among soil microbes and plants. In unwarmed plots, degradation significantly elevated the total ¹⁵N recovery ratio of soil microbes to plants by 60%. However, in warmed plots, the impact of degradation on this ratio was reduced. The responses of the ¹⁵N recovery ratio of soil microbes and plants to rising temperatures were closely related to alpine meadow quality. In non-degraded areas, warming enhanced the recovery ratio for NH₄⁺-¹⁵N by 165% but reduced it for glycine-¹⁵N by 66%. Conversely, in degraded plots, warming decreased the recovery ratio for NH₄⁺-¹⁵N by 66% but increased it for glycine-¹⁵N by 232%. This indicates that warming can increase carbon limitation for soil microbes in degraded alpine meadows, and the restoration of degraded alpine meadows should prioritize restoring carbon accumulation.

Aboveground biomass (AGB) of shrubs and low-statured trees constitutes a substantial portion of the total carbon pool in temperate forest ecosystems, contributing much to local biodiversity, altering tree-regeneration growth rates, and determining above- and belowground food webs. Accurate quantification of AGB at the shrub layer is crucial for ecological modeling and still remains a challenge. Several methods for estimating understory biomass, including inventory and remote sensing-based methods, need to be evaluated against measured datasets. In this study, we acquired 158 individual terrestrial laser scans (TLS) across 45 sites in the Yanshan Mountains and generated metrics including leaf area and stem volume from TLS data using voxel- and non-voxel-based approaches in both leaf-on and leaf-off scenarios. Allometric equations were applied using field-measured parameters as an inventory approach. The results indicated that allometric equations using crown area and height yielded results with higher accuracy than other inventory approach parameters (R² and RMSE ranging from 0.47 to 0.91 and 12.38 to 38.11 g, respectively). The voxel-based approach using TLS data provided results with R² and RMSE ranging from 0.86 to 0.96 and 6.43 to 21.03 g. Additionally, the non-voxel-based approach provided similar or slightly better results compared to the voxel-based approach (R² and RMSE ranging from 0.93 to 0.96 and 4.23 to 11.27 g, respectively) while avoiding the complexity of selecting the optimal voxel size that arises during voxelization.

Complete chloroplast genome sequences are widely used in the analyses of phylogenetic relationships among angiosperms. As a species-rich genus, species diversity centers of Saxifraga L. include mountainous regions of Eurasia, such as the Alps and the Qinghai–Tibetan Plateau (QTP) sensu lato. However, to date, datasets of chloroplast genomes of Saxifraga have been concentrated on the QTP species; those from European Alps are largely unavailable, which hinders comprehensively comparative and evolutionary analyses of chloroplast genomes in this genus. Here, complete chloroplast genomes of 19 Saxifraga species were de novo sequenced, assembled and annotated, and of these 15 species from Alps were reported for the first time. Subsequent comparative analysis and phylogenetic reconstruction were also conducted. Chloroplast genome length of the 19 Saxifraga species range from 149,217 bp to 152,282 bp with a typical quadripartite structure. All individual chloroplast genome included in this study contains 113 unique genes, including 79 protein-coding genes, four rRNAs and 30 tRNAs. The IR boundaries keep relatively conserved with minor expansion in S. consanguinea. mVISTA analysis and identification of polymorphic loci for molecular markers shows that six intergenic regions (ndhC-trnV, psbE-petL, rpl32-trnL, rps16-trnQ, trnF-ndhJ, trnS-trnG) can be selected as the potential DNA barcodes. A total of 1204 SSRs, 433 tandem repeats and 534 Large sequence repeats were identified in the 19 Saxifraga chloroplast genomes. The codon usage analysis revealed that Saxifraga chloroplast genome codon prefers to end in A/T. Phylogenetic reconstruction of 33 species (31 Saxifraga species included) based on 75 common protein coding genes received high bootstrap support values for nearly all identified nodes, and revealed a tree topology similar to previous studies.

Mapping vegetation alliances is essential for understanding ecological patterns and supporting sustainable land management in arid and semi-arid areas. However, traditional remote sensing typically only distinguishes grassland boundaries or broad subclasses, failing to differentiate specific vegetation alliances. Furthermore, while traditional vegetation mapping relies heavily on field surveys, manual interpretation, and expert knowledge, this labor-intensive approach hinders efficient large-scale mapping. This study proposes an efficient method for vegetation mapping by integrating field survey data with multi-source and multi-temporal remote sensing variables using deep learning. A series of deep neural network models was designed to systematically characterize and leverage spectral signatures, climatic factors, and topographic habitat features for fine-grained classification of 32 vegetation alliances in Xinjiang, a typical arid to semi-arid region. The resulting vegetation map achieved an alliance-level classification accuracy of 0.5125 on an independent test set, with the five most dominant alliances: Stipa spp. desert steppe, Stipa spp. steppe, Poa spp. meadow, and Anabasis spp. desert, accounting for over 10.45% of the total area of Xinjiang. Compared with traditional approaches, this method significantly improves mapping efficiency and offers a scalable solution for large-area, updatable vegetation classification. The approach provides a valuable reference for ecological assessment and dynamic vegetation monitoring in arid and semi-arid regions.