Bayesian skyline plot reconstruction of past population size trajectory for the Upper Subansiri population.

<p>The plot is the product of female effective population size (f<i>N_e</i>) and mutation rate (<i>μ</i>) through time, assuming a substitution rate of 0.1643 substitutions per nucleotide per million years <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097061#pone.0097061-Chakraborty2" target="_blank">[29]</a>. The lower and upper 95% confidence interval for the Upper Subansiri TMRCA is also shown. LGM, Last Glacial Maximum, approximately 18 to 20 thousand years before present.</p

Model-checking to measure the discrepancy between the model parameter posterior combination and the real dataset for the three alternative demographic scenarios for the Upper Subansiri population of the Arunachal macaque.

<p>The summary statistics using datasets simulated with the prior distributions of the parameters, the observed data and the datasets from the posterior predictive distributions are represented on the plane of the first two principal components. The model (Scenario 2) fits the data well, as the cloud of datasets simulated from the prior (small green dots), datasets from the posterior predictive distribution (larger green dots) and the observed dataset (yellow circles) overlap completely.</p

Comparison of the three possible alternative demographic scenarios for the Upper Subansiri population.

<p>(A) Estimates of the posterior probability of each scenario and their comparison. Direct estimate: The number of times that a given scenario is found closest to the simulated datasets once the latter, produced under several scenarios, have been sorted by ascending distances to the observed dataset. Logistic regression: A polytomic weighted logistic regression was performed on the first closest datasets with the proportion contributed by each scenario as the dependent variable and the differences between the summary statistics of the observed and simulated datasets as the independent variables. The intercept of the regression, corresponding to an identity between simulated and observed summary statistics, was taken as the point estimate. In addition, 95% confidence intervals were computed <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097061#pone.0097061-Cornuet3" target="_blank">[41]</a>. In all these cases, Scenario 2 explained the observed data best. (B) Principal component analysis: Visual information on how data sets simulated under each scenario are close to the observed data set. Here too, Scenario 2 fits the observed data best <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097061#pone.0097061-Cornuet3" target="_blank">[41]</a>.</p

Model specifications and prior distributions for demographic parameters and locus-specific mutation model parameters.

<p>UN: Uniform distribution, with two parameters – minimum and maximum values; GA: Gamma distribution with three parameters – minimum and maximum values and shape parameter value; LU: Log-Uniform distribution with two parameters – minimum and maximum values. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097061#pone-0097061-g002" target="_blank">Figure 2</a> for the demographic parameters of each model tested. The mutation model parameters for the microsatellite loci were the mutation rate (<i>μ_mic</i>), the parameter determining the shape of the gamma distribution of individual loci mutation rate (<i>P</i>), and the Single Insertion Nucleotide rate (<i>SNI</i>).</p

Possible alternative scenarios of the demographic history of the Upper Subansiri population.

<p>When tested using the ABC approach, Scenario 2 was best fit with the data (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097061#pone-0097061-t002" target="_blank">Table 2</a>). The details of each scenario parameterisation have been given in the Methods. The time-scale is indicated by the arrow on the left. T<i>₁</i> ranges between 20 and 4000 generations. Time has been measured backward in generations before the present. Gen: Generation.</p

Demographic parameters estimated under the best-supported demographic scenario (Scenario 2) of a recent population decline in the Upper Subansiri population of Arunachal macaques.

<p>Population sizes are given in effective number of diploid individuals. Time estimates were calibrated by assuming a generation time of 5 years <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097061#pone.0097061-Harvey1" target="_blank">[44]</a>.</p

Usual Populations, Unusual Individuals: Insights into the Behavior and Management of Asian Elephants in Fragmented Landscapes

<div><h3>Background</h3><p>A dearth in understanding the behavior of Asian elephants (<em>Elephas maximus</em>) at the scale of populations and individuals has left important management issues, particularly related to human-elephant conflict (HEC), unresolved. Evaluation of differences in behavior and decision-making among individual elephants across groups in response to changing local ecological settings is essential to fill this gap in knowledge and to improve our approaches towards the management and conservation of elephants.</p> <h3>Methodology/Principal Findings</h3><p>We hypothesized certain behavioral decisions that would be made by Asian elephants as reflected in their residence time and movement rates, time-activity budgets, social interactions and group dynamics in response to resource availability and human disturbance in their habitat. This study is based on 200 h of behavioral observations on 60 individually identified elephants and a 184-km² grid-based survey of their natural and anthropogenic habitats within and outside the Bannerghatta National Park, southern India during the dry season. At a general population level, the behavioral decisions appeared to be guided by the gender, age and group-type of the elephants. At the individual level, the observed variation could be explained only by the idiosyncratic behaviors of individuals and that of their associating conspecific individuals. Recursive partitioning classification trees for residence time of individual elephants indicated that the primary decisions were taken by individuals, independently of their above-mentioned biological and ecological attributes.</p> <h3>Conclusions/Significance</h3><p>Decision-making by Asian elephants thus appears to be determined at two levels, that of the population and, more importantly, the individual. Models based on decision-making by individual elephants have the potential to predict conflict in fragmented landscapes that, in turn, could aid in mitigating HEC. Thus, we must target individuals, in addition to populations, in our efforts to manage and conserve this threatened species, particularly in human-dominated landscapes.</p> </div

Movement rate (mean ± SE) of elephants in different strata of human disturbance.

<p>Movement rate (mean ± SE) of elephants in different strata of human disturbance.</p

Distribution of land-use types observed in the study area across the zones of low-, medium- and high levels of forage-, water- and shade availability, and human disturbance.

<p>DD: Dry deciduous forest, SC: Scrub forest, RO: Rocky outcrops and hills, PL: Plantations, CF: Cropfields, MI: Sand and granite mines, HH: Human habitations.</p

Classification trees for residence time of female and male elephants in the different strata.

<p>(a) Forage- and (b) water availability. The y-axis of each graph indicates the proportion of groups observed in the different strata. For the trees depicting the influence of shade availability and human disturbance, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042571#pone.0042571.s002" target="_blank">Figure S2</a>.</p