Tropical deciduous forests face increasing threats of rising temperatures, erratic rainfall patterns, and anthropogenic disturbances, making it more susceptible to forest fires and invasion. To understand the climate sensitivity of tropical deciduous forests, the present study attempts to bridge the significant research gaps by systematically evaluating the long-term trends and patterns of precipitation and temperature over the past five decades (1971-2020) and examining their potential linkages with forest cover and canopy density in highly diverse forests of Pachmarhi Biosphere Reserve (PBR), Central India. The study exhibited pronounced fluctuations in the meteorological variables in recent years with a significant shift in peak precipitation from the second half of the monsoon to post-monsoon periods. However, the region experienced warming trends with the rise in mean temperature during these periods, affecting the forest phenology. The study recorded a decrease in forest cover of 391 km(2) (-13.92 % change), which coincides with the potential variations in these meteorological parameters in PBR. Approximately 30 % of the area recorded considerable changes in vegetation greenness, either declining (browning) or increasing (greening) patterns, as indicated by Sen's slope analysis of annual Normalized Difference Vegetation Index trends. Notably, central PBR (similar to 24 %) exhibited declined Forest Canopy Density (FCD: <40 %), contrast to the improved FCD (>60 %) in western and southern PBR. This study highlighted substantial canopy losses, primarily in the Dry Teak Forest (-41.26 %) followed by the Dry Mixed Deciduous Forest (-19.95 %), thus useful in developing strategic policies to uphold tropical forests under dynamic environmental conditions.

Accurate, spatially consistent estimates of tree density remain elusive at continental scales, limiting our ability to assess forest structure, carbon stocks, and biodiversity. Existing global assessments have relied on simplified statistical models and sparse, heterogeneous ground data that are insufficient to capture nonlinear ecological interactions and spatial variability. To address these limitations, we integrated more than 600,000 harmonized ground-based forest inventory plots with satellite-derived vegetation indices, climate surfaces, soil properties, and topographic covariates to develop a deep learning framework for high-resolution mapping of tree density across North America. We evaluated four modeling approaches—generalized linear models (GLMs), ridge regression (RR), random forest (RF), and a feedforward neural network (FFNN). Among all models tested, the FFNN achieved the highest predictive accuracy (RMSE = 344.8; R2 = 39.53%), and was used to produce a wall-to-wall tree density map at 3 km resolution for the continent. We estimated that the total number of forest trees with diameter at breast height (DBH) ≥ 10 cm across North America ranges from 339 to 514 billion, substantially lower than the widely cited estimate of 603 billion trees reported by Crowther et al. (2015). When smaller stems were included (no DBH threshold), totals more than doubled, reaching 738 billion to 1.12 trillion trees. We quantified uncertainty using Monte Carlo (MC) Dropout, generating pixel-level error estimates and confidence intervals. Spatial patterns reveal high tree densities in boreal and temperate forests, intermediate densities in mixed broadleaf regions, and relatively low densities in deserts, Mediterranean systems, and tundra. Compared to the global GLM-based benchmark by Crowther et al. (2015), our deep learning framework achieves markedly higher predictive accuracy, aligns more closely with national forest inventory statistics, and provides explicit uncertainty quantification, supporting applications in carbon accounting, biodiversity modeling, and ecosystem monitoring at scales through region specific calibration and validation.

IntroductionTropical dry forests play a critical role in regulating ecological processes and sustaining rich floral and faunal diversity. However, these ecosystems are increasingly threatened by forest fragmentation and the proliferation of invasive plant species (IPS), necessitating urgent conservation measures to maintain ecological balance and support local communities. In this context, the present study evaluates long-term forest fragmentation dynamics in the Palamau Tiger Reserve (PTR), Eastern India, using multi-temporal satellite observations (Landsat TM and Landsat OLI/TIRS) spanning three decades (1993-2023).MethodsFragmentation was assessed using core, interior, edge, and patch metrics and quantified through the Combined Fragmentation Index (CFI). Field-based surveys of IPS across 90 forest grids and spatial analyses were conducted to evaluate IPS dynamics in relation to fragmentation and climatic variables. The analysis provides an empirical basis for identifying threshold levels of forest cover and optimal spatial configurations required to enhance landscape resilience and mitigate fragmentation impacts.ResultsIt reveal a significant decline in forest cover from 1993 to 956.60 km2in 2013, followed by a partial recovery to 1002.22 km² in 2023. Despite the recent gain in forest area, spatial analyses indicate that forest structural integrity remains compromised. The CFI increased substantially from 3.00 in 1993 to 18.92 in 2013, reflecting heightened fragmentation across the landscape.DiscussionThe observed degradation is associated with the widespread expansion of IPS, particularly Lantana camara L., Chromolaena odorata (L.) R.M. King & H. Rob., and Mesosphaerum suaveolens (L.), which are concentrated in the central, northern, and northeastern regions of PTR. Importantly, the relationship between fragmentation and biological invasion appears reciprocal: forest fragmentation facilitates IPS establishment and spread, while invasive species further exacerbate fragmentation by homogenizing plant communities, suppressing native tree regeneration, and altering fire regimes. These findings demonstrate that increases in forest cover alone do not necessarily signify ecological recovery. Therefore, integrating forest cover assessments with fragmentation metrics is essential for developing effective conservation and restoration strategies aimed at improving ecosystem resilience and long-term forest sustainability.

Elwendia persica synonym Bunium persicum, a diploid perennial species of the Apiaceae family, is a high-value spice and medicinal plant endemic to the high-altitude cold desert regions of the Himalayas. Owing to increasing market demand, overexploitation, and propagation constraints, it is considered a species of conservation concern in the Himalayan region. This study presents the first comprehensive population structure analysis of 91 accessions of E. persica collected from the Western Himalayas of India using sequence specific genomic SSR markers, providing novel insights into the genetic diversity, differentiation, and population structure of this important Himalayan species. Population structure analysis classified the accessions into four distinct genetic populations. Population 1, comprising the fewest accessions, exhibited high genetic differentiation with the highest Fst value (0.6328) and low heterozygosity (H = 0.1552), indicating considerable genetic isolation. In contrast, Populations 2 and 3 showed comparatively higher genetic diversity and possessed a greater number of private alleles, while Population 4 displayed moderate diversity (H = 0.228). Overall genetic diversity indices revealed moderate diversity across the studied germplasm, with a mean expected heterozygosity (H) of 0.241 and Shannon’s information index (I) of 0.367. Analysis of molecular variance (AMOVA) indicated that 69% of the total genetic variation existed within populations, whereas 31% was present among populations, suggesting a structured yet interconnected gene pool. Principal Coordinate Analysis (PCoA) explained 30.13% of the total molecular variance through the first three axes. The study highlights the significance of conserving both genetically distinct and genetically diverse populations to preserve the complete genetic spectrum of E. persica for future breeding, climate resilience, and long-term adaptability. The findings provide a valuable molecular foundation for developing targeted conservation, germplasm management, and crop improvement strategies for this threatened but economically important Himalayan species.

The study presents a comprehensive geo-environmental and climatic hazard-risk and resilience in major capital cities of the Himalayas using Fuzzy Analytical Hierarchical Process and geoinformatics. The geo-environmental hazards (based on EM-DAT database) and climatic hazards based on extreme climatic events (ERA-5 land database) and its associated risk was determined. The fuzzy-AHP approach-based study indicated higher urban hazard-risk in the valley cities (Kathmandu, Srinagar and Dehradun) owing to their higher vulnerability as compared to the cities located on mountain ridge (Shimla, Gangtok, Thimphu and Itanagar). The resilience based on climatic and geo-environmental hazard-risk and capacity (based on various socio-economic variables) indicated low resilience in valley cities despite of their moderate to high adaptive and adsorptive capacity owing to their high urban risk and vulnerability. The disaster-risk-resilience study highlighted Shimla as the most resilient city followed by Gangtok (moderate resilience), while Kathmandu, Dehradun, Itanagar, Thimphu and Srinagar are low resilient cities. The dynamic relation between climatic events and increasing urbanisation has been focused in the study as an attempt to highlight the adaptive capacity of the mountainous cities by estimating their resilience towards climate change and increasing frequency of natural hazards. This holistic approach is essential for formulating well-informed disaster risk resilience strategies that can effectively safeguard the high-altitude communities in the Himalayan regions.

Old-growth tropical forests store vast amounts of carbon in their aboveground biomass (AGB), yet the relative roles of abiotic factors such as climate, soil, and topography in governing its spatial distribution remain poorly understood. In particular, the degree to which climate acts on AGB through forest structure is still poorly quantified at the pantropical scale. Using a pantropical dataset of more than 2,000 old-growth forest plots and a structure-explicit framework, we assess how climate influences AGB through its effects on four structural attributes: basal area, mean diameter, stem density, and basal area-weighted wood density. We find that climate shapes AGB primarily through its effects on forest structure. However, structural attributes respond to climate in opposite directions, so climate’s net effect on AGB largely cancels out, and no clear climate-AGB relationship emerges across tropical regions. Moreover, only wood density responds consistently, decreasing with annual precipitation and increasing with precipitation seasonality, whereas all other attributes respond to climate differently from one region to another. This geographical variation further obscures any global climatic signal on AGB and points to the role of biogeographic history in shaping forest structure. Our findings highlight the central role of the climate-structure nexus in explaining AGB variation, and call for structure-explicit models to improve carbon stock predictions and inform climate adaptation strategies.

Abstract. Global forest assessments assist climate policy development, ecosystem science, and conservation planning, yet they rely on biomass and canopy data that do not explicitly represent the stand structural attributes derived from tree diameter measurements. This limits the ability to compare size-related structure and within-stand heterogeneity at large spatial scales. Here we present a global, spatially explicit dataset of stand-level tree diameter structure for forest cover in 2020 at 0.027° (~3 km) resolution, based on 1,203,524 georeferenced forest inventory plots comprising 54.6 million trees (≥10 cm DBH) integrated with more than 50 environmental and satellite-derived covariates into machine learning models. The dataset provides the first globally consistent maps of three complementary diameter-based metrics: arithmetic mean diameter (Dmean), quadratic mean diameter (Dqm), and the coefficient of variation of diameter (Dcv), representing average tree size, large-tree dominance, and within-stand size variability, respectively. Model performance of the ecozone-specific Random Forest framework ranged from R² = 0.41–0.82 (RMSE = 3.91–4.63 cm) for Dmean, R² = 0.43–0.83 (RMSE = 4.38–5.27 cm) for Dqm, and R² = 0.47–0.62 with (RMSE = 0.10–0.13) for Dcv across different forest ecozones. By jointly quantifying central tendency and variability in tree size, the dataset revealed spatial patterns of forest structural organization not captured by existing biomass or canopy-height products. It provides a consistent baseline for cross-biome comparison of forest structure, supporting parameterization and evaluation of vegetation and Earth system models, while offering an independent benchmark for remotely sensed structural proxies. Furthermore, it enables spatial assessment of stand structural attributes, including large-tree dominance and structural complexity, facilitating integration of diameter-based structure into global analyses of carbon dynamics and ecosystem functioning.

Forest vegetation is a significant repository of terrestrial carbon, accounting for an estimated 80

In the present study, the impact of urban growth on green spaces in Kolkata Metropolitan City (KMC) was evaluated using the multi-temporal satellite observations spanning the last four decades (1990–2022). The study exhibited a rapid rise in urban areas (178.38

Invasive plant species (IPSs) are highly dominant and spreading frequently due to their rapid growth, reproduction, broad tolerance range, and high dispersal ability. They are the second-most dangerous threat to the world's biodiversity and are of non-native origin. They are widely introduced, either intentionally or unintentionally, through anthropogenic activities throughout the world, which have an impact on a country's biological diversity and economic security. Rising IPSs have a significant negative impact on ecosystem goods and services provided by the agricultural, forestry, and aquatic sectors by disrupting habitat structure and function. These IPSs disrupt the biogeochemical cycle, the cycle of fire, the pattern of plant succession, the process of plant regeneration, and the overall forest dynamics. Remote sensing presents a significant potential for scientists and researchers working on invasive biology, resource executives, and policy planners to create prediction models based on invasive risk assessment and early detection methods. Substantial progress can be made in identifying, modeling, and mapping IPSs across various ecosystems and habitats by combining data from field sampling with remote sensing technologies. Various multispectral remote sensing tools are presently being used for IPS mapping and monitoring purposes, such as Thematic Mapper, Enhanced Thematic Mapper Plus, etc., to record the current status of IPSs in forest ecosystems. Import restrictions, import tariffs, and risk management techniques are the three most commonly used policies for preventing trade-based introductions of IPSs. They can be managed and controlled in a variety of physical, chemical, biological, and cultural ways, but early detection and decisive action with species-specific eradication measures will be the best way to prevent the native plant species loss and to preserve biodiversity.

Urban green spaces (UGS), along with their quality and geographical distribution across urban landscapes, are increasingly recognized for their vital role in mitigating socio-environmental challenges. Therefore, the present study illustrates a methodological framework to comprehensively evaluate the UGS quality and its dynamics in a metropolitan city region (Delhi-NCR), integrating key biochemical and biophysical parameters. The long-term Mann Kendall trend and Sen’s slope (2002–2022) exhibited significant positive trends in LAI, FAPAR, and NDVI, underscoring enhanced vegetation productivity, particularly in winter. The high-resolution Sentinel 2A-based Radiative Transfer Model (RTM) and Light-Use Efficiency (LUE) models exhibited seasonal variations in biophysical properties, quality, and productivity of green spaces in the urban and peri-urban regions during 2016–2022. The core urban areas recorded a decline in GPP (-31.81

Land use and land cover (LULC) changes are essential to air pollution dynamics, affecting atmospheric composition and urban microclimates. Previous research explored air pollution trends, but limited studies examined its spatiotemporal relationship with LULC changes in rapidly growing urban regions. This study assessed the impact of LULC changes on air quality in Asansol, Bardhhaman, and Bankura cities of West Bengal, including their buffer zones, from 1990 to 2023. LULC classification was conducted using Landsat 5 and 8 data, processed in Erdas Imagine, identifying six land-use types: water bodies, vegetation, agricultural land, built-up areas, barren land, and sand deposits. Air pollution parameters (CO, NO2, SO2, CH4, and O3) were extracted from Sentinel-5P satellite data and analysed using Google Earth Engine. The findings revealed a decline in agricultural land and vegetation, with urban expansion leading to increased pollutant concentrations. Between 1990 and 2023, agricultural land declined from 66.86 to 56.81% in Asansol and from 77.92 to 68.31% in Bardhhaman, while Bankura showed a marginal increase. Simultaneously, urban areas expanded significantly, contributing to increased CO, NO2, SO2, and O3 levels, especially in densely populated areas. The findings revealed the direct influence of LULC changes on air quality, with urbanisation increasing pollution due to vehicular emissions, industrial activities, and reducing vegetation cover. The study emphasises the urgent need for sustainable land-use planning, effective emission control strategies, and the development of urban green infrastructure to reduce the environmental degradation and health risks caused by rapid and unplanned urban expansion.

BACKGROUND:The patella plays a crucial role in the extensor mechanism of the knee joint, significantly enhancing the efficiency of the quadriceps muscle. The alignment and height of the patella are critical for maintaining knee joint functionality and preventing knee-related disorders. The Patellotrochlear Index (PTI) is a novel measurement method that provides a more accurate assessment of patellar height and its relationship with the femoral trochlea, particularly in the North Indian population. OBJECTIVE:This study aims to establish normal PTI values in the North Indian population and evaluate the PTI as a reliable method for measuring patellar height. MATERIALS AND METHODS:A cross-sectional study was conducted at King Georges Medical University, Lucknow, involving 80 patients with suspected ligamentous knee injuries. Patients underwent magnetic resonance imaging examinations, and PTI measurements were obtained. Data analysis was performed using SPSS version 22, with statistical significance set at P < 0.05. RESULTS:The mean age of the patients was 31.6 years (±5.6), with 66.3% males and 58.7% having the condition on the right side. The mean PTI was 0.52 (±0.12). PI values increased significantly with age ( P = 0.034) and were higher in females (0.56 ± 0.11) compared to males (0.50 ± 0.11) ( P = 0.024). Significant differences in PI values were also observed between the right (0.48 ± 0.10) and left (0.58 ± 0.11) sides ( P < 0.001). CONCLUSION:The PTI is a dependable and repeatable indicator of patellar height, reflecting the true relationship between the patellar and trochlear articular surfaces. This index can be effectively used to diagnose and manage patellofemoral pain syndrome and other knee disorders, emphasizing its clinical relevance in the North Indian population.