dian tigers could be explained by higher effective population size, due to (1) high levels of population differentiation between tiger populations within the subcontinent due to habitat variability and past fragmentation and/or (2) high historic abundance of tigers in the Indian subcontinent.
We investigated the impacts of population differentiation on our results. We divided our samples into those roughly from the North, Central and South of the Indian subcontinent. Our results reveal a strong signature of population structure for mitochondrial DNA (Table 2), especially between the North and the Central and Southern region. On the other hand, pairwise Fst values (Table 2) for microsatellite data are low. Although the structure plot (Figure 1B) reveals varied ancestry, there is no clear partition of ancestry between regions.
We estimated historical effective population size for Indian tigers. Our mitochondrial DNA data suggest population expansion within the Indian subcontinent (Fu's F = −26.33 (p = 0.000); LAMARC: MLE of g = 2859.7 (2092.67, 5549.68), indicating growth). In contrast, the microsatellite data from the same populations indicate population decline (M ratio 0.35 (s.d. 0.08), BOTTLENECK : 7 to all of the10 loci with heterozygote excess depending on the mutational model, LAMARC (based on 10 loci): MLE of g = −20.87 (−24.29, −17.09), indicating population decline). Tests for selective neutrality of the tiger mitochondrial genome revealed evidence for negative selection on the cytochrome b gene (dN/dS within species = 1.9, between species = 0.09, p = 0.00002). Because of negative selection, mitochondrial genetic variation may result in underestimates of historical population size.
Given that tigers from Central and Southern India do not reveal strong subdivision (low and non-significant pairwise Fst's for both mitochondrial and microsatellite DNA), we investigate the demographic history of tigers in this region that we refer to as peninsular India (including the states of Madhya Pradesh, Chattisgarh, Maharastra, Karnataka, Tamil Nadu, Andhra Pradesh and Kerala) using coalescent simulations ,. Models including linear and exponential decline (Table S5 and Table S6, Figure S4 and Figure S5) revealed that the current effective size is about one tenth of the historical effective size, indicating that the Indian subcontinent has lost about 90% of its tigers (Figure 2). As these models do not allow us to investigate the absolute historical effective size as well as the timing of decline, we also used the Storz and Beaumont method to explore recent population decline. Results reveal a strong signal of population decline (91.4% decline, Figure 2), which potentially occurred around 200 years before present.
(A) Posterior distributions for population size change based on coalescent simulations for peninsular Indian tigers based on 10 microsatellite loci and the Beaumont method. Red and green curves correspond to the posterior distributions under models of exponential and linear population size change, respectively. The prior distribution is represented by the flat dotted line. Irrespective of various models, there is no support for population increase. For peninsular Indian tigers, the results reveal about 10-fold decrease. (B) Posterior distributions for population size change based on coalescent simulations for peninsular Indian tigers based on 10 microsatellite loci and the Storz and Beaumont method. The posterior distributions for ancestral (red curve) and present (green) effective population size are represented here. The priors are represented by the dotted line (present population) and dashed line (ancestral population). Results confirm that post-decline population size is much smaller than the pre-decline population size. (C) The posterior distribution for the time since the population decline started for Indian tigers (black curve). The priors are shown by the dashed lines. The distribution has a median value at around 200 years. The vertical red and blue lines represent the approximate time since bounty-killing b