ed. 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 program ARLEQUIN . The likelihood-based method used was the population growth rate estimation with program LAMARC . The likelihood-based Bayesian methods used were implemented in the MSVAR programs ,.
The Ewens, Watterson, Cornuet and Luikart approach .
This method uses two summary statistics of the allele frequency spectrum, number of alleles (nA) and expected heterozygosity (He) to achieve the patterns expected for a demographically stable population. Simulations were performed under three mutation models: infinite allele model (IAM), single stepwise model (SMM) and two-phase model (TPM) to obtain the distribution of He and the values are then compared to the real data values. For TPM model, 30% multi-step mutation events were allowed during the simulations. This method can detect departures from mutation-drift equilibrium and neutrality, which can be explained by any departure from the null model, including selection, population growth or decline. More importantly, consistent results from independent loci could be attributed to demographic events over selection. Thus, this approach allows the detection of population size changes across different mutational models.
The Garza-Williamson index/M ratio approach .
This method uses data on the frequency and the total number of alleles and the allelic size range to investigate population decline. In a reducing population, the expectation of the reduction of number of alleles is much higher than the reduction of allelic size range. Thus the ratio between the number of alleles and the allelic size range is expected to be smaller in recently reduced populations than in equilibrium populations.
Likelihood approach (LAMARC) .
This method provides maximum likelihood estimates of growth (or decline) rates using a Metropolis-Hastings Monte Carlo Markov Chain algorithm. As it is a time backward approach, a negative value of g indicates that the population has been shrinking (it was bigger in the past than it is now) and a positive value indicates that it has been growing (it was s