Habitat degradation and species range contraction due to land use/land cover changes (LULCC) is a major threat to global biodiversity. The ever-growing human population has trespassed deep into the natural habitat of many species via the expansion of agricultural lands and infrastructural development. Carnivore species are particularly at risk, as they demand conserved and well-connected habitat with minimum to no anthropogenic disturbance. In Pakistan, the snow leopard (Panthera uncia) is found in three mountain ranges—the Himalayas, Hindukush, and Karakoram. Despite this being one of the harshest environments on the planet, a large population of humans reside here and exploit surrounding natural resources to meet their needs. Keeping in view this exponentially growing population and its potential impacts on at-risk species like the snow leopard, we used geographic information systems (GIS) and remote sensing with the aim of identifying and quantifying LULCC across snow leopard range in Pakistan for the years 2000, 2010, and 2020. A massive expansion of 1804.13 km² (163%) was observed in the built-up area during the study period. Similarly, an increase of 3177.74 km² (153%) was observed in agricultural land. Barren mountain land increased by 12,368.39 km² (28%) while forest land decreased by 2478.43 km² (28%) and area with snow cover decreased by 14,799.83 km² (52%). Drivers of these large-scale changes are likely the expanding human population and climate change. The overall quality and quantity of snow leopard habitat in Pakistan has drastically changed in the last 20 years and could be compromised. Swift and direct conservation actions to monitor LULCC are recommended to reduce any associated negative impacts on species preservation efforts. In the future, a series of extensive field surveys and studies should be carried out to monitor key drivers of LULCC across the observed area.

The unchecked and unplanned expansion of urban areas has led to the conversion of millions of green areas to gray areas. The recent urban growth patterns of Pakistan’s metropolitan twin cities, Islamabad and Rawalpindi, is a matter of concern for the surrounding green areas. The present study aimed to categorize and quantify the land-use and land-cover change (LULCC) patterns and the corresponding impacts on the forest carbon dynamics around Islamabad and Rawalpindi. Multispectral satellite images for the year 1990 (Landsat 5 TM) and 2020 (Landsat 8 OLI) were used to determine, quantify, and compare the LULCC inside and around the twin metropolitan cities. Field inventory surveys in the reserved forests of Rawalpindi and Islamabad were also conducted to determine the amount of stored carbon in these forests. Our results showed an accelerated annual urban expansion (i.e., an increase in the built-up area) of 16.49% and 26.72% in Rawalpindi and Islamabad, respectively, during the study period. Similarly, the amount of barren land and agricultural land was reduced at an annual rate of 2.08% and 2.18%, respectively, in Rawalpindi and 0.25% and 1.04% in Islamabad. A reduction in the area of barren mountains also occurred at an annual of 2.26% in Islamabad, while it increased by 4.16% in Rawalpindi. The amount of carbon stored in the reserved forests of Islamabad stood at 139.17 ± 12.15 Mg C/ha while that of Rawalpindi was 110.4 ± 13.79 Mg C/ha. In addition, total stored forest carbon was found to have decreased from 544.70 Gg C to 218.05 Gg C in Rawalpindi, while in Islamabad it increased from 2779.64 Gg C to 3548.16 Gg C. Investment in ecological urban planning, sustainable cities, and appropriate land-use planning is recommended to curb the degradation and conversion of the surrounding green areas of Rawalpindi and Islamabad.

Stoichiometric ratios of carbon (C), nitrogen (N), and phosphorus (P) are considered indicators of nutrient status and ultimate ecosystem health. A detailed investigation of these elements in the leaves, branches, forest layer vegetation and soil, depending on stand age, was carried out. We investigated the effects of stand age (9-, 18-, 28-, and 48-year) on the aboveground plant parts (leaf, branch, herb, shrub, plant litter) and belowground pools (soil, roots) of P. massoniana plantations. The CNP stoichiometry of trees was affected by stand age. Mean N content in the aboveground parts in the nine-yr stand was greater than the other stands (18-, 28-, 48-yr), which decreased with increasing stand age. As stands aged, the nutrient demands of the plantations increased as well as their N:P ratios in soil. C content in the soil ranged from 30 to 105, the total N was 0.06 to 1.6, and the total P content ranged from 3.3–6.4 g kg⁻¹. Soil C, N and P contents were greatly influenced by both stand age and soil depth, because surface soil sequester C and N more actively compared to deeper horizons, and more nutrients are released to the topsoil by the plant litter layer. Similarly, the ratios of other layers had a similar pattern as CNP because more nutrients were taken up by the plantations, decreasing nutrient supply in the deeper soil horizons. The green leaves N:P ratios (16) indicate limited growth of P. massoniana, as the range for global nutrient limitation for woody plants oscillated between 14–16, indicating N and P limitation. Young stands were observed to have greater P content and P resorption efficiency (56.9%–67.3%), with lower C:P and N:P ratios (704.4; 14.8). We conclude that with stand development, the nutrient demands of the plantations also increase, and soil N:P stoichiometry shows that these improve soil quality.

Instrumental climatological records such as weather stations data of northern areas of Pakistan are not sufficient to assessthe forest extreme eventsreliably. To understand the past climatic variability, tree ring width based climatic reconstruction is the best alternative to trace climate variability that goes back in time.Quercus Incanais the most sensitive species to drought and climatic variation in northern Pakistan. However, very little research quantifies the rate of ongoing climatic changes. A total of 65 tree cores were collected from two sites to understand the radial growth ofQ.Incanato extreme drought events. The radial growth is mainly affected by high temperatures during May-July. In addition, radial growth exhibits a positive correlation with February-June precipitation while it is negatively correlated with the September precipitation. The radial growth decrease, particularly in harsh climatic conditions. The reconstructed tree ring record was strongly coherent with the May-June self-calibrated Palmer drought severity index (scPDSI) and reliable in reconstructing drought variability for the period 1750–2014. During the past 264 years, wet periods were found during 1980–2010, 1812–1836, and 1754–1760, while dry periods were found during 1896–1922, 1864–1876, and 1784–1788. Our reconstruction explains 39.8% of the scPDSI variance. The extreme drought and wet years we arrived at were in close agreement with the drought and wet periods that occurred in northern Pakistan. Wavelet analysis revealed drought variability at periodicities of 2.2–2.5, 3.3, 3–4, 16.7, 16.8, and 68–78.8 years. Hence it is concluded that deforestation and forest degradation rate increased with extreme drought and wet years. Overall, the variation of drought in northern Pakistan seems to have been affected due to El Nino south oscillation, Pacific decadal oscillation, or Atlantic multi-decadal oscillations.

Background: Subtropical pine forests dominated by Pinus roxburghii in northern Pakistan are increasingly vulnerable to forest fires, posing serious ecological and management challenges. This study (i) identifies the primary environmental and anthropogenic drivers of fire risk, (ii) evaluated the performance of four machine learning models Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) for fire susceptibility prediction, and (iii) develops a spatially explicit vulnerability map for the Malakand region using 2001–2023 fire occurrence data . Methods: Spatial modeling was conducted using MODIS fire products (FIRMS and MCD64A1), along with topographic, climatic, vegetation, and human activity variables. Predictor importance and inter-variable relationships were analyzed prior to model training and evaluation. Results: NDVI, skin temperature, and population density emerged as key predictors. Among the models tested, RF and XGBoost outperformed others, with RF achieving 86.2% accuracy and AUC 95.4, and XGBoost showing 87.2% accuracy and AUC 95.2. Multicollinearity analysis confirmed variable independence (Tolerance > 0.1; VIF < 10). Conclusion: The resulting fire vulnerability map delineated distinct spatial patterns, identifying 6.88% of the study area as highly susceptible to fire. These findings provide actionable insights into targeted fire prevention and sustainable forest management in subtropical Himalayan ecosystems.