Environmental and Earth Sciences

Abstract: Grassland degradation is a global ecological crisis that profoundly alters aboveground vegetation and soil properties, yet its impacts on soil microbial co‑occurrence networks remain poorly understood. Here, we investigated soil bacterial and fungal communities along a well‑defined five‑stage degradation gradient spanning from non‑degraded Leymus chinensis grassland to extremely degraded bare saline patches in the Songnen meadow steppe of northeastern China, using Illumina MiSeq sequencing and co‑occurrence network analysis. Our results revealed that bacterial α‑diversity exhibited a unimodal (hump‑shaped) response peaking at the moderately degraded KA stage, whereas fungal diversity declined monotonically along the gradient, indicating greater sensitivity of fungi to degradation stress. Both bacterial and fungal community compositions shifted directionally with degradation, driven primarily by soil alkalization (pH) and electrical conductivity (EC). Network complexity followed a unimodal pattern for both kingdoms, maximizing at KA and collapsing at AS, suggesting a critical ecological threshold between moderate and severe degradation, while the increased proportion of positive correlations under severe degradation implied enhanced microbial cooperation in response to environmental stress. Structural equation models further revealed distinct regulatory pathways: bacterial networks were governed by both direct environmental filtering and indirect diversity‑mediated effects, whereas fungal networks responded more strongly to direct pH/EC constraints and compositional shifts. Our findings demonstrate that microbial networks exhibit nonlinear threshold responses to grassland degradation, with fungi serving as more sensitive bioindicators than bacteria, and highlight the importance of integrating network‑level properties into degradation monitoring and restoration frameworks.

Abstract: This article presents a design-led environmental media prototype for translating saproxylic beetle evidence into a web-based narrative about slow forest degradation in Crete. The prototype uses five interface scenes to convert ecological constructs, including deadwood continuity, decay-stage diversity, microclimate refuge, drought-stress coupling, pathogen/vector risk, and fragmentation, into comparison, gradient, threshold, and reachability tasks. The contribution is methodological rather than ecological: it proposes an evidence-to-interface translation matrix and an anti-cutesification/anti-spectacle grammar for making proxy variables, uncertainty cues, and versioned sources inspectable in public-facing environmental media. A small mixed-background reader-response session (N = 18) is used as a boundary test of readability. The session suggests that many readers could retell the deadwood-removal inference and distinguish structural loss from simple character appeal, while also revealing recurring misreadings around proxy status, scenario precision, and particle-haze risk cues. The article therefore positions the prototype as a proof-of-concept for auditable environmental-media translation, with broader claims about public use, institutional uptake, and long-term durability reserved for future comparative and longitudinal testing.

Abstract: Northeast India sits within the Indo-Burma and Eastern Himalaya biodiversity hotspots, yet its forests face accelerating loss from extractive industries, infrastructural expansion, and weak governance. Although global conservation discourse increasingly recognises community-based approaches, the contributions of vernacular grassroots organisations from frontier regions remain insufficiently documented in peer-reviewed scholarship. This article addresses that gap. It examines the conservation praxis of Nature's Beckon, an independent activist organisation founded in Dhubri, Assam, in 1982, and the work of its founder-director Soumyadeep Dutta, an Ashoka Fellow and naturalist-writer. The study adopts a qualitative, analytical methodology that integrates historical and ecocritical approaches. Data are drawn from organisational records, Dutta's published Assamese-language corpus, news media, government notifications, and peer-reviewed scientific literature on protected-area outcomes. Four case studies are analysed: the Chakrashila Wildlife Sanctuary movement (1983–1995); the Dehing Patkai rainforest campaign (1992–2021); primate conservation focused on the golden langur (Trachypithecus geei) and western hoolock gibbon (Hoolock hoolock); and the Village Sanctuary institutional model. Findings indicate that Nature’s Beckon has played a documented and substantial role in measurable conservation outcomes, including the gazettement of two protected areas with a combined area of approximately 277.22 km². The article advances an ecology of the margins framework to characterise the organisation's distinctive integration of scientific documentation, vernacular advocacy, and community institution-building. Practical policy implications include formal recognition of community-conserved areas, integration of vernacular knowledge into protected-area management, replication of the eco-emissary training framework, and alignment of conservation funding cycles with the multi-decadal time horizons that durable place-based conservation requires.

Abstract: Stachys fontqueri is a strict endemic species of the Moroccan Rif that depends on specific ecological conditions. To understand the effect of climate change on the potential evolu-tion of the species' geographic range under current and future climatic scenarios, ecologi-cal niche modelling was performed using MaxEnt algorithm based on five bioclimatic variables. To model the effect of climate change, four climatic scenarios were used, namely CSM2- SSP1-2.6, CSM2-SSP5-8.5, MIROC6-SSP1-2.6, and MIROC6-SSP5-8.5, for the period 2061–2080. The results demonstrated high model performance, with an AUC ranging from 0.921 to 0.930 and a TSS from 0.81 to 0.83. Three bioclimatic variables contributed significantly to determining the suitable potential distribution area of the species, namely Precipitation Seasonality (Bio15), Temperature Annual Range (Bio7), and Annual Mean Temperature (Bio1). The suitable area covered 3,244 km² under the current climate and is projected to decrease by 23.25% to 29.59% under future climate scenarios. This contraction of suitable habitat due to climate change could be exacerbated by human activities, there-by requiring urgent in situ and ex situ conservation measures to ensure the species' resilience.

Abstract: Accurate monitoring of forest carbon stocks represents a critical prerequisite for achieving carbon neutrality. However, conventional remote sensing‑based estimation methods frequently overlook forest heterogeneity, causing systematic overestimation or underestimation. To address this gap, we propose a novel forest carbon stock esti-mation framework that integrates two complementary strategies: (1) forest type‑specific modeling to account for forest heterogeneity, and (2) hyperparameter op-timization to enhance Random Forest model performance. Using ground‑measured carbon stocks and a CCDC‑derived forest vegetation classification map for Hangzhou City, China, we built forest‑type‑specific Random Forest models based on ICESat‑2 canopy height metrics and optimized each model via hyperparameter tuning. The re-sults show that the 70th-90th percentiles and the mean canopy height are relatively highly correlated with carbon stock. Forest‑type‑specific modeling improves estima-tion accuracy, yielding R² gains of 0.10–0.17 and reduced RMSE by 2.28–7.43 Mg C/ha over the non‑stratified model. Integrating forest classification and hyperparameter op-timization strategies improved model R² by 0.16–0.23 and lowered RMSE by 3.05–8.20 Mg C/ha. Overall, this study demonstrates that accounting for forest heterogeneity and applying hyperparameter optimization can significantly enhance the accuracy of for-est carbon stock estimation.

Abstract: Korla fragrant pear diseases and pests detection faces challenges such as significant object scale variation, multi-organ target confusion, and limited computational resources for real-time inference on edge devices. To address these issues, this study proposes a lightweight object detection model, KFP-YOLO, based on YOLO26n. A multi-organ dataset, the Korla fragrant pear diseases and pests dataset (KFP-PDD), was collected, covering pear leaves, fruits, and flowers and containing 11 classes including healthy and diseased samples. To reduce computational cost while maintaining effective feature representation, an ADown lightweight downsampling module is introduced. A C3-PD feature extraction module is designed by integrating Partial convolution and SE attention to reduce redundant computation and enhance feature representation capability. Furthermore, a CFA feature enhancement module is proposed, which incorporates coordinate attention into the multi-scale feature fusion process to improve spatial information modeling and fine-grained feature representation. Experimental results show that, compared with YOLO26n, KFP-YOLO reduces parameters and GFLOPs by 23.0 % and 23.7 %, respectively, while achieving an inference speed of 278.97 FPS. Meanwhile, mAP@0.5 reaches 94.65 %, with only a 0.44 percentage point drop. Ablation studies verify the effectiveness and synergistic optimization of each proposed module. Deployment experiments on the Jetson AGX Orin platform demonstrate strong real-time performance and edge computing adaptability, indicating that the proposed method offers an efficient solution for intelligent orchard diseases and pests monitoring.

Abstract: Microplastic pollution has become an emerging threat to marine biodiversity, yet the developmental-stage-specific responses of endangered marine species remain poorly understood. Tachypleus tridentatus, a threatened marine arthropod of high ecological and conservation value, is exposed to microplastics in coastal habitats. In this study, five-instar-old and six-instar-old juvenile T. tridentatus were exposed to polystyrene microplastics (PS-MPs) with a diameter of 6 μm at environmentally relevant concentrations (0, 102, 104 particles/L) for 21 days. Fluorescence imaging revealed the accumulation of PS-MPs within the intestinal tract, with distinct retention characteristics among developmental stages. Transcriptome analysis revealed significant differential gene expression after exposure, with affected pathways related to energy metabolism, immune regulation, stress response, signal transduction, and development. Younger juveniles exhibited stronger transcriptional disturbances in metabolic and stress-related pathways, whereas older juveniles showed more coordinated regulation of homeostatic and adaptive genes. These findings suggest that juvenile T. tridentatus employs age-dependent molecular response strategies when confronted with microplastic exposure, reflecting developmental differences in stress sensitivity and adaptive capacity. Our study provides new insights into the molecular mechanisms underlying microplastic responses in an endangered marine species and highlights the importance of incorporating developmental-stage variation into ecological risk assessments and conservation strategies for horseshoe crabs in polluted coastal ecosystems.

Abstract: Accurate identification of suitable habitats is essential for the ecological, economic, conservation, and sustainable management of Almaciga (Agathis philippinensis). This study employed a geospatial Maximum Entropy (MaxEnt) species distribution modeling approach to predict the habitat suitability of Almaciga and identify the environmental factors influencing its distribution. Twenty environmental variables were initially evaluated using Pearson correlation analysis, hierarchical clustering, and Variance Inflation Factor (VIF) to minimize multicollinearity. The survey occurrences were split into 80% for training and 20% for testing. The final predictor set consisted of the maximum temperature of the warmest month (BIO5) and elevation (ELEV). Model performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC) with 99% and 98% accuracy for training and testing, respectively. The elevation was the most influential variable, contributing 90.5% to the model and accounting for 93.8% permutation importance. The suitability maps revealed that highly suitable areas (i.e., 331.27 km2) are concentrated in mountainous regions categorized by higher elevations and cooler temperatures. These findings provide valuable spatial information for conservation planning, habitat restoration, sustainable forest management, and climate adaptation strategies for Almaciga across the Philippines.

Abstract: Frequent hydration–desiccation cycles in Antarctica impose strong selective pressures on bryophytes physiology and associated microbiota. Here, we investigated physiological and transcriptional responses of the Antarctic moss Polytrichastrum alpinum and its associated microbial communities under controlled hydration, desiccation and rehydration conditions. Desiccation induced strong but reversible physiological and molecular changes, with most gene expression patterns returning toward hydrated states upon rehydration, indicating efficient recovery of metabolic activity. This response involved coordinated regulation of stress-associated gene groups, including LEA proteins, heat-shock proteins and components of ABA and calcium signalling. Furthermore, field-collected samples exhibited transcriptional profiles similar to desiccated material, suggesting that this species naturally operates in a persistently water-limited state. Associated bacterial and fungal communities showed coordinated shifts in both composition and functional activity during the experiment, particularly in pathways related to carbon and amino acid metabolism. These results highlight the resilience of Antarctic P. alpinum and its microbiome under increasing environmental variability driven by climate change.

Abstract: The rapid expansion of artificial intelligence raises critical questions about its environmental implications in an increasingly globalized world. In this study, we explore the impact of Artificial Intelligence (AI) on environmental sustainability and its role in globalization, which bears significant implications for global sustainability in the digital era. We used the comprehensive evaluation index of the artificial intelligence index established by the Principal Component Analysis (PCA) method on a sample of 62 countries globally, from 2000 to 2022. Our study is based on the estimation of the Autoregressive Distributed Lag (ARDL) model, which distinguishes between short- and long-term dynamics. We noted that the overall level of global AI shows an upward trend. The research results show that AI has a positive and significant inhibitory effect on ecological footprints and greenhouse gas emissions in the long term. In addition, affected by globalization, the effects of AI on environmental sustainability show nonlinear characteristics. AI’s marginal effect on reducing environmental degradation increases. These findings emphasize the important role of AI in environmental governance and provide a new and comprehensive perspective for policymakers. Policymakers should prioritize AI R&D investment, promote cross-sectoral AI integration and strengthen international cooperation to maximize environmental benefits.

Abstract:

Forest ecosystems in Northern Ghana's Guinea Savannah landscape face mounting pressures from illegal logging, charcoal production, agricultural expansion, bushfires, and climate variability, threatening biodiversity, carbon stocks, and the parkland mosaic of shea, dawadawa, neem, and baobab that sustains local livelihoods. The Risk Management Framework (RMF) offers a structured approach to anticipate, assess, and mitigate such environmental risks, yet its operational integration into forest governance in Sub-Saharan Africa remains weak. This study examined the awareness, understanding, and applied knowledge of the RMF among forestry stakeholders in Northern Ghana and analysed the socio-demographic and institutional factors shaping engagement with risk-based environmental governance. Using an explanatory sequential mixed-methods design, a structured survey was administered to 160 stakeholders across five districts (West Mamprusi, Mamprugu Moagduri, North Gonja, Sagnarigu, and Tamale Metropolitan), complemented by five focus group discussions with Community Resource Management Area (CREMA) groups and seven key informant interviews with officers from the Forestry Commission, Environmental Protection Agency, and Ministry of Food and Agriculture. Data were analysed using descriptive statistics, multiple linear regression, a validated three-item Knowledge Scoring Index (Cronbach's α = 0.78), and thematic analysis. Results show that while overall awareness of RMF was high (94%), applied knowledge was substantially weaker, particularly regarding the institution responsible for RMF implementation (mean = 0.32). Education, occupation, and composite knowledge score significantly predicted RMF knowledge, while gender and community-leader status did not. Qualitative findings revealed three structural patterns: symbolic risk governance, a community-leader bottleneck in information transmission, and an awareness–understanding divergence in which stakeholders interpret formal RMF terminology through indigenous and CREMA-based practices. The findings demonstrate that human knowledge systems mediate forest ecosystem outcomes and underscore the need for institutional clarification, targeted capacity-building, and a phased digital tools roadmap, including mobile-based reporting platforms, satellite-derived monitoring dashboards, and integration of indigenous early warning indicators, to strengthen forest sustainability, biodiversity conservation, and climate resilience in dryland Sub-Saharan Africa.

Abstract: Ecological process interruption in shallow lakes often manifests as algal blooms, but multiple daily disturbances may accumulate before bloom occurrence. To characterize ecosystem dynamics, dissolved oxygen (DO), pH, water temperature, and electrical conductivity (EC) were selected as axis variables and monitored at 5-min intervals in a restored shallow urban lake in Wuhan, China. A total of 112,896 observations collected over 392 days were analyzed. Because external inputs were minimal except for rainfall, observed dynamics primarily reflected internal ecological processes. DO was identified as the most sensitive indicator of ecological instability due to its direct coupling with ecosystem metabolism. Piecewise logistic regression revealed a critical DO threshold of 3.62 mg L^-1, below which ecological risk increased sharply. The frequency, duration, and cumulative exposure of threshold exceedance provided effective early-warning signals of ecosystem deterioration. These results demonstrate that high-frequency monitoring of key environmental variables can reveal ecological transitions overlooked by conventional low-frequency assessments and provide a practical framework for early detection of hypoxia-related ecological risk in shallow lakes.

Abstract: Increasing atmospheric CO₂ concentration can enhance plant photosynthesis and promote plant growth, thereby affecting the N cycle. To investigate the effects of long-term elevated atmospheric CO₂ concentration (eCO₂) and N deposition on N absorption and distribution in Calamagrostis angustifolia wetlands in the Sanjiang Plain, this study was conducted in open-top chambers (OTC-1) with 15 years of continuous elevated CO₂ concentration and simulated nitrogen deposition treatment.The focus was on exploring the impacts of climate change on N absorption, distribution, and N use efficiency (NUE) in Calamagrostis angustifolia. The results showed that under long-term CO₂ fumigation, N concentration in various plant organs and the whole plant significantly decreased, with greater decreases observed in leaves at the heading stage, stems during the growth stage, and roots at the mature stage. Under eCO₂ concentration and N fertilization treatments, NUE in Calamagrostis angustifolia stems gradually decreased with plant growth and development; NUE in leaves initially increased and then decreased; and the variation pattern of NUE in roots was not obvious. Total N accumulation remained unchanged, but a large amount of N was allocated to leaves, promoting N flow to the upper parts of the plant and enhancing the ability of leaves to acquire N. Moreover, high-N treatments alleviated the negative impact of long-term CO₂ fumigation on biomass, especially during the growth stage, where leaf biomass increased by 87.0% and aboveground biomass increased by 35.2%. However, high-N treatments did not improve NUE in various Calamagrostis angustifolia organs. In addition, long-term CO₂ fumigation led to a significant decrease in N content in leaves and roots, and the interaction between elevated CO₂ concentration and N significantly affected the ability of roots to absorb exogenous N. Therefore, this study indicates that long-term CO₂ concentration fumigation affects plant N absorption and utilization through N availability, providing theoretical support for selecting varieties with higher NUE in agricultural production.

Abstract: Ecological resilience represents a region's fundamental capacity to withstand external disturbances and achieve sustainable development. As a typical ecologically fragile region in China and globally, Southwest China warrants particular attention in terms of understanding the spatiotemporal evolution and driving mechanisms of ecological resilience. Taking 47 cities in Southwest China as the study area,this study constructs an urban ecological resilience evaluation index system based on the "Pressure–State–Response–Innovation" framework. By integrating centroid migration analysis, standard deviation ellipse analysis, kernel density estimation, spatial autocorrelation analysis, the Spatial Durbin Model (SDM), and the XGBoost-SHAP model, the spatiotemporal evolution and driving factors of urban ecological resilience from 2005 to 2024 are systematically examined.The results indicate that: (1) During the study period, disparities in urban ecological resilience across Southwest China gradually widened, accompanied by pronounced regional differentiation. Kernel density estimation further reveals a clear polarization trend in urban ecological resilience. (2) The SDM results demonstrate that urban ecological resilience exhibits strong spatial dependence. For most driving factors, the indirect effects exceed the direct effects, suggesting that their influences are primarily transmitted through spatial spillover mechanisms. (3) The XGBoost-SHAP results reveal significant local threshold effects and interactive relationships among the driving factors of ecological resilience. (4) Significant interprovincial heterogeneity exists in the driving mechanisms of ecological resilience. These findings enrich the analytical framework for urban ecological resilience research and provide important scientific support for differentiated ecological governance and high-quality sustainable development in ecologically fragile regions.

Abstract: Crumb rubber, a recycled tire product used in artificial turf fields, alters soil physical properties and releases chemical contaminants, but few studies have examined its effects on plant physiology and resource allocation. We conducted a greenhouse experiment to evaluate the responses of soybeans (Glycine max) grown in soils containing increasing proportions of crumb rubber (0%, 16.6%, 33.3%, and 50% by weight). Germination, plant growth, chlorophyll content, soil respiration, biomass production, water loss, root allocation, and elemental composition of soils and plant tissues were measured over a 21-day period. Most indicators of plant performance (germination, plant height, chlorophyll content, soil respiration, and total biomass) were not significantly affected by crumb rubber additions. However, crumb rubber significantly reduced cumulative water loss and increased both root biomass and root-to-shoot ratios at moderate and high concentrations. Soil and plant tissue analyses revealed substantial increases in zinc concentrations across the crumb rubber gradient, with leaf zinc concentrations exceeding sufficiency ranges at higher treatments. These results demonstrate that crumb rubber contamination can alter soil water dynamics, plant resource allocation, and trace metal accumulation even when aboveground growth responses remain limited.

Abstract: The Klamath-Siskiyou Ecoregion (KSE) of southwest Oregon-northern California, USA has globally exceptional biodiversity but is experiencing mounting pressures from climate change and land uses. We conducted an ecoregional conservation assessment of the KSE and the Siskiyou Crest subregion (SCS), a proposed climate refugium within the KSE, to integrate protected area priorities with climate change planning and fire risk reduction for communities. Both areas contained very low levels (<30%) of protection (GAP status 1, 2) for nearly all land cover types (n=22), including serpentine substrates where endemic plants are highly concentrated, older forests with potential refugia properties, and habitat for Northern Spotted Owl (Strix occidentalis caurina) and Pacific fisher (Pekania pennanti). At the ecoregional scale, high severity fire levels were proportionately similar across GAP land-use status (“managed” vs. protected). However, high severity fire was lowest for protected areas at the subregional scale reflective of potential refugium properties. Most fuel treatments by federal agencies were >1-km from nearest structures, far removed from effective community fire protection in both locales. The relatively higher-elevation SCS is projected to maintain refugia properties (cooler, wetter) for longer periods than the KSE; however, that function may dissipate toward the end of the century and under a higher emissions scenario. Stepped up protections especially of potential refugia combined with fire risk reduction of the built environment are urgently needed to prevent unprecedented losses.

Abstract: Brasenia schreberi is a nationally protected aquatic macrophyte of substantial ecological value and economic significance, yet its wild populations have declined drastically due to habitat degradation and anthropogenic disturbances. This review systematically synthesizes research progress on the effects of water pH and depth on the growth, ecophysiology, mucilage quality, and community structure of B. schreberi, integrating findings from field surveys and controlled greenhouse experiments to elucidate critical ecological thresholds under combined environmental stressors. Our analysis reveals that natural B. schreberi populations are predominantly distributed in lentic habitats with stable water depths of 0.5-1.5 m (optimally 1.2-1.5 m) and circumneutral to weakly acidic conditions (pH 6.0-7.5). Deviations from these parameters substantially impair plant performance: when water depth exceeds 1.5 m or pH falls below 5.5, photosynthetic efficiency declines, root-to-shoot ratios increase aberrantly, and mucilage thickness decreases significantly. The synergistic critical threshold for population decline was identified at 1.1 m depth × pH 6.3. For artificial propagation, optimal cultivation strategies diverge from wild habitat preferences: maintaining pH at 7.0-7.5 (weakly alkaline) enhances mucilage polysaccharide accumulation and commercial quality, whereas a phenological stage-specific dynamic water-depth management regime (“shallow-deep-shallow-deep”) maximizes vegetative propagation success and yield. This review provides a theoretical framework and parameterized technical guidance for wild population restoration, standardized cultivation, and hydrological regulation in plateau wetland ecosystems. Future research priorities should focus on elucidating the molecular mechanisms underlying pH- and depth-mediated mucilage synthesis, developing precision water quality management systems, and strengthening ex situ germplasm conservation.

Abstract: Naturally formed treefall gaps represent primary sources of environmental heterogeneity in tropical forests, yet their role in driving the components of beta diversity in specialized leaf-litter fauna remains poorly understood. We investigated the influence of natural treefall gaps on harvestmen (Arachnida: Opiliones) community structure and beta diversity partitioning in a well-preserved Atlantic Forest remnant in southern Bahia, Brazil. Using standardized nocturnal searches and leaf-litter sampling, we recorded 845 individuals across 23 species. Coverage-based rarefaction indicated higher estimated richness in gaps, although observed alpha diversity did not differ significantly among habitats. Community composition differed significantly along the gap–forest gradient, driven mainly by litter depth and microclimatic variation. Indicator species analysis identified Protimesius sp. as a robust gap-specialist. Beta diversity partitioning revealed that turnover accounted for 79.5% of total dissimilarity, while nestedness contributed 20.5%. Treefall gaps exhibited the highest internal beta diversity and species exclusivity, supporting their role as dynamic environmental filters that enhance regional diversity. Our findings highlight the ecological importance of natural disturbance and litter structure in maintaining biodiversity patterns in tropical forests.

Abstract: Under global climate change, shifts in the suitable distribution of forest vegetation have become an important issue in ecology and biogeography. Birch forests are widely distributed across cold-temperate, temperate, and montane regions in China, but different birch forest types may vary in their environmental adaptations and spatial responses to climate change. In this study, three representative birch forest vegetation types in China, namely Betula utilis forest, Betula albosinensis forest, and Betula ermanii krummholz, were selected for comparative analysis. Based on vegetation distribution records and environmental variables, an optimized MaxEnt model was constructed using ENMeval to identify current suitable distribution patterns, key environmental drivers, and future habitat changes under climate change scenarios.The results showed that the three birch forest types differed markedly in current suitable distribution patterns. Betula utilis forest was mainly concentrated in the Qinling Mountains, Betula albosinensis forest showed a broader montane distribution pattern, and Betula ermanii krummholz was restricted to high-altitude or high-latitude cold habitats. Climatic factors were the dominant drivers of suitability, but the key environmental variables differed among the three vegetation types, indicating niche differentiation along temperature, precipitation, and elevation gradients. Under future climate scenarios, the suitable habitats of the three types showed type-specific changes in area, spatial stability, and centroid migration. Betula utilis forest and Betula albosinensis forest mainly exhibited regional spatial adjustment and partial expansion, whereas Betula ermanii krummholz showed stronger dependence on high-elevation cold habitats and more limited spatial adjustment capacity. These findings indicate that different birch forest vegetation types in China do not respond uniformly to climate change. The study provides a vegetation-type-specific basis for identifying stable suitable areas, potential expansion areas, and climate-sensitive habitats, and can support adaptive management and conservation planning for montane forest vegetation under future climate change.

Abstract: Monitoring dynamic post-catastrophic landscapes necessitates unsupervised classification approaches capable of incorporating newly emerging landscape-cover states without relying on predefined classes. Within this framework, the temporal matching of independently derived spectral clusters presents a critical methodological challenge. This study compared alternative temporal matching approaches for multi-temporal Sentinel-2 imagery of the post-catastrophic floodplain landscape of Khortytsia Island (Ukraine) from 2021 to 2026. In addition to conventional methods based on centroid distance, Mahalanobis distance, Linear Discriminant Analysis, and Random Forest, geometrically oriented approaches employing the elongation and principal-axis orientation of spectral point clouds were evaluated. A series of tests assessed matching accuracy, robustness to seasonal and interannual drift, graph connectivity, and consensus structure among alternative matching solutions. The results demonstrated that geometrically oriented approaches preserved temporal correspondence among landscape-cover states with high stability despite phenological and interannual variability. In particular, axis-based matching more effectively maintained separation between corresponding and competing clusters amid progressive temporal divergence. Consensus analysis revealed that disagreement among methods was concentrated in ecotonal and actively transforming zones, indicating areas of increased landscape instability. This study shows that the geometry of spectral trajectories contains valuable information for temporal matching and provides a promising foundation for monitoring dynamic post-catastrophic landscape systems.