Genetic analyses reveal population structure and recent decline in leopards (Panthera pardus fusca) across Indian subcontinent
Cite this dataset
Bhatt, Supriya et al. (2020). Genetic analyses reveal population structure and recent decline in leopards (Panthera pardus fusca) across Indian subcontinent [Dataset]. Dryad. https://doi.org/10.5061/dryad.v6wwpzgrg
Large carnivores maintain the stability and functioning of ecosystems. Currently, many carnivore species face declining population sizes due to natural and anthropogenic pressures. The leopard, Panthera pardus, is probably the most widely distributed and highly adaptable large felid globally, still persisting in most of its historic range. However, we lack subspecies-level data on country or regional scale on population trends, as ecological monitoring approaches are difficult to apply on such wide-ranging species. We used genetic data from leopards sampled across the Indian subcontinent to investigate population structure and patterns of demographic decline.
We collected faecal samples from the Terai-Arc landscape of north India and identified 56 unique individuals using a panel of 13 microsatellite markers. We merged this data with already available 143 leopard individuals and assessed genetic structure at country scale. Subsequently, we investigated the demographic history of each identified subpopulations and compared genetic decline analyses with countrywide local extinction probabilities.
Our genetic analyses revealed four distinct subpopulations corresponding to Western Ghats, Deccan Plateau-Semi Arid, Shivalik and Terai region of the north Indian landscape, each with high genetic variation. Coalescent simulations with microsatellite loci revealed a possibly human-induced 75-90% population decline between ∼120-200 years ago across India. Population-specific estimates of genetic decline are in concordance with ecological estimates of local extinction probabilities in these subpopulations obtained from occupancy modeling of the historic and current distribution of leopards in India.
Our results confirm the population decline of a widely distributed, adaptable large carnivore. We re-iterate the relevance of indirect genetic methods for such species in conjunction with occupancy assessment and recommend that detailed, landscape-level ecological studies on leopard populations are critical to future conservation efforts. Our approaches and inference are relevant to other widely distributed, seemingly unaffected carnivores such as the leopard.
Research permissions and ethical considerations
All required permissions for our field surveys and biological sampling were provided by the Forest Departments of Uttarakhand (Permit no: 90/5-6), Uttar Pradesh (Permit no: 1127/23-2-12(G) and 1891/23-2-12) and Bihar (Permit no: Wildlife-589). Due to non-invasive nature of sampling, no ethical clearance was required for this study.
To detect population structure and past population demography it is important to obtain genetic samples from different leopard habitats all across the study area. In this study, we used leopard genetic data generated from non-invasive samples collected across the Indian subcontinent. We conducted extensive field surveys across the Indian part of Terai-Arc landscape (TAL) covering the north-Indian states of Uttarakhand, Uttar Pradesh and Bihar between 2016-2018. This region has already been studied for large carnivore occupancy using traditional camera trapping as well as field surveys (Johnsingh et al., 2004; Harihar et al., 2009; Jhala et al., 2015; Chanchani et al., 2016). We foot surveyed all existing trails covering the entire region to collect faecal samples. Number of trails walked in a particular area was decided based on existing knowledge of leopard presence by the local people and frontline staff members of the sampling team. We collected a total of 778 fresh large carnivore faecal samples. These samples were collected from both inside (n=469) and outside (n=309) protected areas from different parts of this landscape. In the field, the samples were judged as large carnivores based on several physical characteristics such as scrape marks, tracks, faecal diameter etc. All faecal samples were collected in wax paper and stored individually in sterile zip-lock bags and stored inside dry, dark boxes in the field for a maximum of two weeks period (Biswas et al., 2019). All samples were collected with GPS locations and were transferred to the laboratory and stored in -20°C freezers until further processing.
In addition to the north Indian samples collected in this study, we used genetic data previously described in Mondol et al. (2015), representing mostly the Western Ghats and central Indian landscape. The data was earlier used in forensic analyses to assign seized leopard samples to their potential geographic origins in India (Mondol et al., 2015). Out of the 173 individual leopards described in the earlier study, we removed data from related individuals and samples with insufficient data (n=30) and used the remaining 143 samples for analyses in this study. These samples were collected from the states of Kerala (n=5), Tamil Nadu (n=4), Karnataka (n=53), Andhra Pradesh (n=3), Madhya Pradesh (n=12), Maharashtra (n=46), Gujarat (n=2), Rajasthan (n=5), Himachal Pradesh (n=8), Jharkhand (n=1), West Bengal (n=2) and Assam (n=2), respectively. The sample locations are presented in Figure 1.
DNA extraction, species and individual identification
For all field-collected faecal samples, DNA extraction was performed using protocols described in Biswas et al. (2019). In brief, each frozen faeces was thawed to room temperature and the upper layer was swabbed twice with Phosphate buffer saline (PBS) saturated sterile cotton applicators (HiMedia). The swabs were lysed with 30 µl of Proteinase K (20mg/ml) and 300 µl of ATL buffer (Qiagen Inc., Hilden, Germany) overnight at 56°C, followed by Qiagen DNeasy tissue DNA kit extraction protocol. DNA was eluted twice in 100 µl preheated 1X TE buffer. For every set of samples, extraction negatives were included to monitor possible contaminations.
Species identification was performed using leopard-specific multiplex PCR assay with NADH4 and NADH2 region primers described in Mondol et al., (2014) and cytochrome b primers used in Maroju et al., (2016). PCR reactions were done in 10 µl volumes containing 3.5 µl multiplex buffer mix (Qiagen Inc., Hilden, Germany), 4 µM BSA, 0.2 µM primer mix and 3 µl of scat DNA with conditions including initial denaturation (95°C for 15 min); 40 cycles of denaturation (94°C for 30 s), annealing (Ta for 30 s) and extension (72°C for 35 s); followed by a final extension (72°C for 10 min). Negative controls were included to monitor possible contamination. Leopard faeces were identified by viewing species-specific bands of 130 bp (NADH4) and 190 bp (NADH2) (Mondol et al., 2014) and 277 bp (cytochrome b) (Maroju et al., 2016) in 2% agarose gel.
For individual identification, we used the same panel of 13 microsatellite loci previously used in Mondol et al. (2014) (Table 1). To generate comparable data with the samples used from earlier study by Mondol et al. (2014) we employed stringent laboratory protocols. All PCR amplifications were performed in 10 µl volumes containing 5 µl Qiagen multiplex PCR buffer mix (QIAGEN Inc., Hilden, Germany), 0.2 µM labelled forward primer (Applied Biosystems, Foster City, CA, USA), 0.2 µM unlabelled reverse primer, 4 µM BSA and 3 µl of the faecal DNA extract. The reactions were performed in an ABI thermocycler with conditions including initial denaturation (94°C for 15 min); 45 cycles of denaturation (94°C for 30 sec), annealing (Ta for 30 sec) and extension (72°C for 30 sec); followed by final extension (72°C for 30 min). Multiple primers were multiplexed to reduce cost and save DNA (Table 1). PCR negatives were incorporated in all reaction setups to monitor possible contamination. The PCR products were analyzed using an automated ABI 3500XL Bioanalyzer with LIZ 500 size standard (Applied Biosystems, Foster City, CA, USA) and alleles were scored with GENEMAPPER version 4.0 (Softgenetics Inc., State Collage, PA, USA). During data generation from field-collected samples we used one reference sample (genotyped for all loci) from the earlier study for genotyping. As the entire new data is generated along with the reference sample and the alleles were scored along with the reference genotypes, the new data (allele scores) were comparable with earlier data for analyses.
To ensure high quality multi-locus genotypes from faecal samples, we followed a modified multiple-tube approach in combination with quality index analyses (Miquel et al., 2006) as described previously for leopards by Mondol et al. (2009a, 2014). All faecal samples were amplified and genotyped four independent times for all the loci. Samples producing identical genotypes for at least three independent amplifications (or a quality index of 0.75 or more) for each loci were considered reliable and used for all further analysis, while the rest were discarded.
All microsatellite data used in this paper is provided in two separate excel sheets. Of which earlier data used by Mondol et al. 2015 is named as old data and data generated in this study as new data. Column 1 contains the sample identification number and the remaining columns contains data for thirteen microsatellite loci. For each locus, missing data are indicated by 0.
Bhatt et al_2020_microsatellite data.xls
Department of Science and Technology, Government of India, Award: EMR/2014/000982
Department of Science and Technology INSPIRE Faculty Award, Award: No.IFA12-LSBM-47
Wildlife Conservation Trust-Panthera Global Cat Alliance Grants
Centre for Wildlife Studies
Wildlife Conservation Society- New York and Oracle