The high economic costs of human–wildlife conflicts (HWC) hinder long-term conservation successes, especially in developing countries. We investigated HWC by interviewing 498 respondents from 42 villages in Nowshera district, Pakistan. According to respondents, six species—the common leopard (Panthera pardus), grey wolf (Canis lupus), golden jackal (Canis aureus), red fox (Vulpes vulpes), Indian porcupine (Hystrix indica), and wild boar (Sus scrofa)—were involved in livestock predation and crop-raiding. Livestock predation (N = 670) translated into a total annual economic loss of USD 48,490 across the 42 villages, with the highest economic loss of USD 57.1/household/year attributed to the golden jackal. Crop damage by wild boar and porcupine incurred a total annual economic loss of USD 18,000. Results further showed that livestock predation was highly affected by location, prey type, prey age, and herding practices, while cereals and vegetables were preferred crops for wild boar and Indian porcupine. The grey wolf was declared as the most dangerous carnivore, followed by the golden jackal and common leopard. Negative attitude about golden jackal and wild boar prevails among 90% of the respondents of the study area. We strongly assume that the abundance of apex predators can control the economic impacts of meso-carnivores and wild boar on the community’s livelihood. Keeping relatively smaller herds may reduce carnivore attacks and educating the populous and compensation can minimise negative perceptions of HWC. To reduce HWC in the study area, there should be an incessant and timely coordination between wildlife officials and the local community.

We examined and compared heavy metals bioaccumulation in Cyprinus carpio and Labeo rohita netted from Sardaryab, a tributary of River Kabul. By using atomic absorption spectrometry we assessed different organs including livers, gills, and muscles. Metals studied were chromium, iron, zinc, lead, and copper. Livers of both species showed higher concentrations of metals while muscles showed the least amount. Chromium and iron were the highly concentrated metals in the gills and livers of both species. A quantity of 0.154 ± 0.011, 0.199 ± 0.0079, and 0.024 ± 0.008 μg/g of chromium was found in the gills, livers, and muscles of Cyprinus carpio, respectively. Similarly, the gills, liver, and muscles of Labeo rohita contained 0.133 ± 0.008, 0.165 ± 0.01, and 0.019 ± 0.006 μg/g of Cr, respectively. Iron was highest in carp in the range of 0.086 ± 0.01 in gills and 0.067 ± 0.011 μg/g in muscles, comparatively. All the studied metals were found within the US recommended daily dietary allowances (RDA) limits; hence no immediate risk in their consumption for human was found. The data showed that Cyprinus carpio being omnivorous and bottom feeder stored higher concentrations of metals as compared to Labeo rohita.

Pakistan’s total estimated snow leopard habitat is about 80,000 km2 of which about half is considered prime habitat. However, this preliminary demarcation was not always in close agreement with the actual distribution—the discrepancy may be huge at the local and regional level. Recent technological developments like camera trapping and molecular genetics allow for collecting reliable presence records that could be used to construct realistic species distribution based on empirical data and advanced mathematical approaches like MaxEnt. The current study followed this approach to construct an accurate distribution of the species in Pakistan. Moreover, movement corridors, among different landscapes, were also identified through circuit theory. The probability of habitat suitability, generated from 98 presence points and 11 environmental variables, scored the snow leopard’s assumed range in Pakistan, from 0 to 0.97. A large portion of the known range represented low-quality habitat, including areas in lower Chitral, Swat, Astore, and Kashmir. Conversely, Khunjerab, Misgar, Chapursan, Qurumber, Broghil, and Central Karakoram represented high-quality habitats. Variables with higher contributions in the MaxEnt model were precipitation during the driest month (34%), annual mean temperature (19.5%), mean diurnal range of temperature (9.8%), annual precipitation (9.4%), and river density (9.2). The model was validated through receiver operating characteristic (ROC) plots and defined thresholds. The average test AUC in Maxent for the replicate runs was 0.933 while the value of AUC by ROC curve calculated at 0.15 threshold was 1.00. These validation tests suggested a good model fit and strong predictive power. The connectivity analysis revealed that the population in the Hindukush landscape appears to be more connected with the population in Afghanistan as compared to other populations in Pakistan. Similarly, the Pamir-Karakoram population is better connected with China and Tajikistan, while the Himalayan population was connected with the population in India. Based on our findings we propose three model landscapes to be considered under the Global Snow Leopard Ecosystem Protection Program (GSLEP) agenda as regional priority areas, to safeguard the future of the snow leopard in Pakistan and the region. These landscapes fall within mountain ranges of the Himalaya, Hindu Kush and Karakoram-Pamir, respectively. We also identified gaps in the existing protected areas network and suggest new protected areas in Chitral and Gilgit-Baltistan to protect critical habitats of snow leopard in Pakistan.

Natural wild habitats are either destroyed or shrunk due to human interventions. Therefore, habitat evaluation is crucial for managing wildlife populations and designing robust conservation strategies. Species presence data and geographic information system (GIS) coupled with ground-breaking powerful statistical techniques have made such assessments possible. We used maximum entropy modeling (MaxEnt) to identify suitable habitats for Kashmir markhor (Capra falconeri cashmeriensis) in Malakand Division, Pakistan. MaxEnt was applied to 169 markhor sighting points and topographical and current bioclimatic variables. Results showed that the accuracy of the MaxEnt model was good (AUC = 0.889). Of the total area studied (8407.09 km2), 22.35% (1878.75 km2) was highly suitable and 32.63% (2743.53 km2) was moderately suitable for markhor. Protected areas including Chitral Gol National Park (CGNP), Tooshi-Sasha Conservancy (TSC), and Gehrait-Golain Conservancy (GGC) and their buffers were included in highly suitable habitats. MaxEnt also predicted highly suitable habitats in Kumrat and Kalam valleys. We believe that moderately suitable habitats identified in Jinjeret, Ursoon, Birir valley, and Bumborait valley have the potential to host markhor populations. Based on the results obtained in the current study, we strongly recommend expanding the current protected areas (PAs) network in the study area and strengthening it by inclusive conservation management with local communities.

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