ALBERT

Description: Ceramics in the system (Bi0.5K0.5)TiO3-BiFeO3 have good electromechanical properties and temperature stability. However, the high conductivity inherent in BiFeO3-based ceramics complicates measurement of the ferroelectric properties. In the present work, doping with niobium (Nb) is carried out to reduce the conductivity of (Bi0.5K0.5)TiO3-BiFeO3. Powders of composition 0.4(K0.5Bi0.5)Ti1−xNbxO3-0.6BiFe1−xNbxO3 (x = 0, 0.01 and 0.03) are prepared by the mixed oxide method and sintered at 1050 °C for 1 h. The effect of Nb doping on the structure is examined by X-ray diffraction. The microstructure is examined by scanning electron microscopy. The variation in relative permittivity with temperature is measured using an impedance analyzer. Ferroelectric properties are measured at room temperature using a Sawyer Tower circuit. Piezoelectric properties are measured using a d33 meter and a contact type displacement sensor. All the samples have high density, a rhombohedral unit cell and equiaxed, micron-sized grains. All the samples show relaxor-like behavior. Nb doping causes a reduction in conductivity by one to two orders of magnitude at 200 °C. The samples have narrow P-E loops reminiscent of a linear dielectric. The samples all possess bipolar butterfly S-E loops characteristic of a classic ferroelectric material. Nb doping causes a decrease in d33 and Smax/Emax.

Description: 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.