Biology subjects

Nawaz, M. A.

Publications and source records attributed to Nawaz, M. A..

4 recordsLinked to original sources

bioRxiv2024

Next-generation snow leopard population assessment tool: multiplex-PCR SNP panel for individual identification from feces

Snow leopards, Panthera uncia, are under threat from numerous pressures and are the focus of a great deal of conservation efforts. However, their elusive nature makes it difficult to estimate population sizes. Current methods used to monitor local population sizes include visually identifying individuals from camera trap photos and genetically identifying individuals from fecal samples using microsatellite loci. Here, we present a new method for identifying snow leopard individuals from fecal samples using a multiplex PCR single nucleotide polymorphism (SNP) panel method. The SNP panel we present consists of 144 SNPs and utilizes next-generation sequencing technology, making it cheaper and easier than current microsatellite methods. We validate our SNP panel with paired tissue and fecal samples from zoo individuals, showing a minimum of 96.7% accuracy in allele calls per run. We then generate SNP data from 235 field-collected fecal samples from across Pakistan to show that the panel can reliably identify individuals from low-quality fecal samples of unknown age and is robust to contamination. We also show that our SNP panel has the capability to identify first-order relatives and provides insights into the geographic origin of samples. This SNP panel will empower the snow leopard research community in their efforts to assess local and global snow leopard population sizes. More broadly, we present a method for developing a SNP panel that utilizes open source software for SNP selection and primer design, Illumina sequencing technology, and a streamlined lab and bioinformatics protocol which can be used to create similar SNP panels for any species of interest for which adequate genomic reference data is available.

genetics↗

bioRxiv2020

The response of culturally important plants to experimental warming and grazing in Pakistan Himalayas

The response of wild plants towards climate warming is taxa specific, but overgrazing could also be a determining factor for the alpine ecosystems. Overgrazing and climate warming are important drivers of alpine grassland degradation worldwide. Local indigenous peoples will be the first impacted by such degradation due to impacts on animal production and the availability of local medicinal plants. Studies on plant responses to overgrazing and climate change are rarely performed to assess threats to these biological and cultural systems. Long-term observations or manipulative experiments are promising, but rarely use strategies to evaluate the sensitivity and vulnerability of such ecosystems to climatic change. We studied the combined effects of overgrazing and increased temperatures on culturally important medicinal plants of Khunjerab National Park, Pakistan. Three experimental treatments were used (control, warming through an open-top chamber, and exclusion of grazing animals vs. the control). These experimental plots were installed at different elevations (3352-4969 m) and were monitored routinely. Grazing reduced vegetation cover & biomass by 2.3% and 6.26%, respectively, but that was not significant due to the high variability among study plots. However, warming significantly increased the overall percentage cover and biomass of all target plant species, ranging from 1{+/-}0.6% in Bistorta officinalis to 18.7 {+/-} 4.9% in Poa alpina. Thus, warming may increase the availability of therapeutic plants for indigenous people while overgrazing would have deteriorating effects locally. This research illustrates that vegetation sensitivity to warming and overgrazing is likely to affect man- environment relationships, and traditional knowledge on a regional scale.

ecology↗

bioRxiv2020

Towards optimal sampling design for spatial capture-recapture

Spatial capture-recapture (SCR) has emerged as the industry standard for estimating population density by leveraging information from spatial locations of repeat encounters of individuals. The precision of density estimates depends fundamentally on the number and spatial configuration of traps. Despite this knowledge, existing sampling design recommendations are heuristic and their performance remains untested for most practical applications. To address this issue, we propose a genetic algorithm that minimizes any sensible, criteria-based objective function to produce near-optimal sampling designs. To motivate the idea of optimality, we compare the performance of designs optimized using three model-based criteria related to the probability of capture. We use simulation to show that these designs out-perform those based on existing recommendations in terms of bias, precision, and accuracy in the estimation of population size. Our approach allows conservation practitioners and researchers to generate customized and improved sampling designs for wildlife monitoring.

ecology↗

bioRxiv2020

Identifying Priority Landscapes for Conservation of Snow Leopards in Pakistan

Pakistans total estimated snow leopard habitat is about 80,000 km2 of which about half is considered prime. 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. Current study followed this approach to construct accurate distribution of the species in Pakistan. Moreover, movement corridors, among different landscapes, were also identified through the circuit theory. The habitat suitability map, generated from 384 presence points and 28 environmental variables, scored the snow leopards assumed range in Pakistan, from 0 to 0.97. A large shear of previously 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 contribution in the MaxEnt model were precipitation of 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 validation texts suggest a good model fit, and strong prediction 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 with the population in India. Current study allows for proposing three model landscapes to be considered under GSLEP agenda as regional priority areas, to safeguard safeguard future of the species in the long run. These landsacpes fall in mountain ranges of the Himalaya, Hindu Kush and Karakoram-Pamir, respectively. We also identified gaps in existing protected areas network, and suggest new protected areas in Chitral and Gilgit-Baltistan to protect critical habitats of snow leopard in Pakistan.

zoology↗