Figure 1.
Study area map showing sampled sites, genotype locations and habitat connectivity.
The study area of Central India spanning the states of Madhya Pradesh, Maharashtra and Chhattisgarh, showing tiger habitat (forest cover) coded with tiger occupancy probability, protected areas, human habitation (night lights), major roads and least-cost habitat corridors connecting tiger reserves. Individually genotyped tigers (n = 165) are shown as color coded dots at their sampled locations with their colors matching their genetically assigned population.
Table 1.
Area-wise estimates showing population extents, occupied habitats, sampling effort and number of tiger individuals identified.
Table 2.
Summary statistics of mean genetic variation and bottleneck tests across sampled populations.
Figure 2.
Results of individual clustering analyses.
(A) Three dimensional plot showing partitioning between different populations as obtained from PCo analysis based on PhiPT co-dominant genetic distance among individuals. (B) Summary barplot of STRUCTURE run at K = 4 showing population assignments for each individual. Four distinct population clusters are observed. Sampled populations are Melghat (M), Satpura (S), Pench (P), Kanha-Pench corridor (KPC), Kanha (K), Achanakmar (A), Tadoba (T) and Bandhavgarh (B).
Table 3.
Summary of migrant assignments based on STRUCTURE, GENECLASS (migrants based on **α0.01 and *α0.05 type I error levels) and BAYESASS analyses.
Figure 3.
Individual ancestry states of putative migrants.
Posterior distributions of individual assignment to nonimmigrant (gen0), and first (gen1) and second generation immigrant (gen2) ancestry states. Suffixes after indvidual names indicate assignment probabilties as obtained in GENECLASS (G), STRUCTURE (S) and BAYESASS (B).
Table 4.
Locality-wise contemporary migration rates, m, estimated using BAYESASS, showing means (± standard deviation) of the posterior distributions along with the 95% confidence intervals in parentheses.
Table 5.
Cluster-wise contemporary migration rates, m, showing means (± standard deviation) of the posterior distributions along with the 95% confidence intervals in parentheses.
Table 6.
Means of posterior distributions of mutation scaled immigration rate, M, along with the 95% confidence limits (before comma) and mean number of migrants per generation (after comma) estimated from Bayesian runs in MIGRATE.
Table 7.
Principal component (PC) loadings of covariates relevant for modeling tiger occupancy, Eigen values of the components, the percent variation of the original data explained by the PC, their ecological interpretation and a priori effect on tiger occupancy.
Table 8.
Model selection results for estimating tiger occupancy within the Central Indian Landscape incorporating imperfect detections and covariates of landscape characteristics, prey abundance, and human disturbance represented by 10 Principal Components.
Table 9.
Coefficient estimates for the best model selected for estimating tiger occupancy in the Central Indian Landscape.
Figure 4.
CIRCUITSCAPE model of cumulative current flow used to estimate landscape permeability to tiger movement.
Tiger movement modeled as current flow within the Central Indian Landscape using tiger occupancy probability and drainage systems as conductance layers and human settlements as high resistance barriers in CIRCUITSCAPE. Light colors indicate potential habitat corridors. Note the prominent bottlenecks observed in the Kanha-Pench, Kanha-Tadoba, and Tadoba-Melghat habitat corridors.
Figure 5.
Regression of population pair-wise linearized FST values with corridor cost.
The size of each circle is representative of the proportion of migrants shared between each population pair. Depicted corridors are Kanha-Bandhavgarh (KB), Kanha-Achanakmar (KA), Pench-Kanha (PK), Melghat-Kanha (MK), Satpura-Kanha (SK), Satpura-Pench (SP), Melghat-Pench (MP), Melghat-Satpura (MS), Kanha-Tadoba (KT), Pench-Achanakmar (PA), Melghat-Achanakmar (MA), Achanakmar-Bandhavgarh (AB), Pench-Bandhavgarh (PB), Satpura-Bandhavgarh (SB) and Melghat-Bandhavgarh (MB).