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.

Pastoralist–wolf conflict over livestock depredation is the main factor affecting conservation of grey wolf worldwide. Very limited research has been carried out to evaluate the pattern and nature of livestock depredation by wolf. This study aims to determine the status and nature of human–wolf conflict across different villages in the Hind Kush region of Pakistan during the period January 2016–December 2016. For this purpose, a total of 110 local male respondents from all walks of life were interviewed using a semi-structured questionnaire. The grey wolf was declared as a common species in the area by 51.3% of the locals with an annual sighting rate of 0.46 each. During the year (2016), a total of 358 livestock were lost to grey wolf predation and disease. Of the total livestock loss, grey wolf was held responsible for a total 101 livestock losses. Goat and sheep were the most vulnerable prey species as they accounted for 80 (79.2%) of the total reported depredations. Out of the total economic loss (USD 46,736, USD 424.87/household), grey wolf was accountable for USD 11,910 (USD 108.27 per household), while disease contributed 34,826 (USD 316.6 per household). High depredation was observed during the summer season 58.42% (n = 59) followed by spring and autumn. Unattended livestock were more prone to grey wolf attack during free grazing in forests. Most of the respondents (75.45%) showed aggressive and negative attitudes towards grey wolf. The herders shared more negative attitude (z = −3.21, p = 0.001) than businessman towards the species. Herders having larger herd size displayed more deleterious behavior towards wolves than those having smaller herd size. Active herding techniques, vaccinating livestock, educating locals about wildlife importance, and initiating compensating schemes for affected families could be helpful to decrease negative perceptions.

Globally, seagrass meadows provide critical ecosystem services. However, seagrasses are globally degraded at an accelerated rate. The lack of information on seagrass spatial distribution and seagrass health status seriously hinders seagrass conservation and management. Therefore, this study proposes to combine remote sensing big data with a new hybrid machine learning model (RF-SWOA) to predict potential seagrass habitats. The multivariate remote sensing data is used to train the machine learning model, which can improve the prediction accuracy of the model. This study shows that a hybrid machine learning model (RF-SWOA) can predict potential seagrass habitats more accurately and effectively than traditional models. At the same time, it has been shown that the most important factors influencing the potential habitat of seagrass in the Hainan region were the distance from land (38.2%) and the depth of the ocean (25.9%). This paper provides a more accurate machine learning model approach for predicting the distribution of marine species, which can help develop seagrass conservation strategies to restore healthy seagrass ecosystems.

Anthropogenic nutrient pollution from agricultural and urban sources disrupts sediment biogeochemistry. It parallels terrestrial soil erosion by mobilizing organic carbon in coastal ecosystems. We examined nutrient pollution stress (NPS) effects on seagrass productivity, microbial assembly, and carbon fluxes, near Hainan Island, which hosts regionally dominant blue carbon stocks. The sampling sites were exposed to different levels of nutrient pollution pressures. Key analyses included high-throughput sequencing, network metrics, and microbial function prediction. We conclude that NPS creates a complex trade-off, the moderate levels of which may enhance carbon burial, but beyond a threshold, it primes the microbial community for rapid sediment organic carbon mineralization upon sediment resuspension or erosion. Our findings revealed the following: (a) Under moderate NPS, the biomass of seagrass per unit area (686.44 ± 143.93 g/m ² ) markedly increased, followed by that under high NPS (507.75 ± 131.31 g/m ² ). (b) Moderate NPS induced a fundamental shift in the surface sediment microbial community, which was marked by a transition from bacterial to fungal dominance. Furthermore, the development of microbial communities tends to evolve toward neutral communities, with the highest stability of microbial communities at this time. The interaction intensity of microorganisms in sediment is the highest at the lowest NPS. (c) High NPS markedly enhanced the activity of microbial functional genes associated with dissimilatory sulfate reduction. In contrast, low NPS notably amplified community functions linked to aerobic heterotrophic metabolism. Collectively, these findings inform nutrient thresholds for blue carbon protection and provide a science-based foundation for coastal water-quality and land-use policies.