tinent (as presented in Figure 1) are from Luo et al. . We only considered data from tigers of known wild origin. A total of 57 tiger mitochondrial DNA and 68 STR data have been used for comparison in this study.
Marker selection and amplification
We designed tiger-specific mitochondrial DNA primers. Polymorphic regions were ascertained using tiger mitochondrial sequences across all subspecies based on Luo et al.  (Figure S1). These primers were then standardized for fecal DNA samples. A total of nine primer sets were designed, and used to amplify1263 bp from Indian tiger fecal samples. Primers sequences are presented in supplementary information (Table S2).
We selected ten felid-specific microsatellite loci based on PCR success rate, amplicon size, number of alleles and the level of observed heterozygosity (Hobs) in Indian tigers (Table S3). All samples were genotyped at this panel of nine dinucleotide and one tetranucleotide microsatellite locus described initially in the domestic cat .
The mitochondrial regions were amplified in 10 µl volume reactions, cleaned by Exo-Sap mixture (NEB) and sequenced from both ends on an ABI 3100XL capillary sequencer. To monitor possible contamination, PCR blanks were included in all experiments.
Amplification for all the microsatellite loci was done using a multiplex approach.
A modified multiple tube approach, combined with a quality index approval was used for data quality management to account for the varying quality and quantity of DNA obtained from non-invasive sources. The complete genotyping process was repeated three times for all samples, and only those loci with quality index ≥0.75 were included in the analysis .
Genetic diversity statistics, population growth indicators (Fu's F, Tajima's D) and genetic difference (Fst) were calculated using ARLEQUIN 3.1 , assuming two populations of tigers (Indian subspecies and all other subspecies). To avoid the effects of related individuals in our analyses, we used STR data from 39 representative samples from all the areas sampled. For intra-population diversity statistics, samples were divided into Northern India (n = 10), Central India (n = 11) and Southern India (n = 18). Tests for selective neutrality were performed using DNASP 4.0 . A statistical parsimony network based on 1263 bp mtDNA sequences of all the tiger subspecies was created using NETWORK (Fluxus Technology Ltd.). Two-population models were explored to estimate migration rate with the mitochondrial DNA and STR data with coalescent analysis program LAMARC and were based on genetic data from five common microsatellite loci.
Re-sampling to investigate the lower sample size outside India.
If the higher number of haplotypes observed in Indian samples was due to a higher sample size, sampling fewer samples from the Indian haplotype distribution should result in fewer haplotypes. We used the observed haplotypic distribution from Indian tigers, and sampled (with replacement) 57 (total number of samples outside India) individuals for which we tabulated the number of haplotypes. This process is repeated 10,000 times. We then compared the number of haplotypes sampled in the simulation to the observed number of non-Indian haplotypes.
To determine the presence of hidden population structure within India, we performed a Bayesian clustering approach, as implemented in program STRUCTURE. We performed the analysis for K values between one and ten, using 50,000 iterations and a burn-in of 10,000 assuming correlated allele frequencies. The optimal value of K was selected based on Evanno et al. . Ten repetitions for each value of K demonstrated similar results.
Analysis of past demography.
We used five different approaches to detect past population demography. The first two approaches use summary statistics to detect population size changes, whereas the other three are likelihood or Bayesian methods. The summary statistic-based methods used were the Ewens, Watterson, Cornuet and Luikart method implemented in program BOTTLENECK , and the Garza-Williamson index or M ratio implemented in progr