Diagram of the modeled predator-prey dynamics.

<p>Schematic diagram showing the modeled predator-prey interactions of Banff elk (E), Bow Valley elk (N) and Bow Valley wolves (P) for Models 1, 2, 3, 4 and 5. Arrows with solid lines represent interactions present in all years in all models. The Banff elk grow logistically with growth rate, g, and carrying capacity, K. The Bow Valley elk grow exponentially with rate, r, and encounter or interact with wolves at rate, d or d₂. Bow Valley wolves convert some proportion of elk encountered into new wolves with conversion efficiency, c, and have mortality rate, x. The dashed arrow (— —) represents the Banff elk relocation parameter (s) that occurred during the years 1998–2001 in all models. The dashed and double dotted arrow (– ·· –) represents the density-dependent dispersal parameter (m) for Models 2 and 4, the dashed and single dotted arrow (– · –) represents the anti-predator movement parameter (f) for Models 3 and 4, and the dotted arrow (▪▪) represents the short-term, source-sink wolf predation parameter (d₁) for Model 5.</p

Parameter estimates and 95% credibility intervals (CIs) for the Banff elk, Bow Valley elk and Bow Valley wolves from winters of 1985/1986–2010/2011 for Models 1, 2, 3, 4 and 5.

<p>Parameter estimates and 95% credibility intervals (CIs) for the Banff elk, Bow Valley elk and Bow Valley wolves from winters of 1985/1986–2010/2011 for Models 1, 2, 3, 4 and 5.</p

Model 1 fit for all populations.

<p>Model 1 fit for the Banff elk population (a), Bow Valley elk population (b) and Bow Valley wolf population (c) from winter of 1985/1986–2010/2011. Population data shown with dots (•) and model fit shown with a solid line (—).</p

Model selection results for Models 1, 2, 3, 4 and 5 fit to the time-series data of Banff elk, Bow Valley elk and Bow Valley wolves for winters of 1985/1986–2010/2011.

a<p>RSS is the sum of the squared residuals from the model prediction with the median chain value of 100,000 MCMC samples.</p>b<p>AICc is Akaike's information criterion corrected for a small sample computed based upon the RSS.</p>c<p>ΔAICc is the difference between the model with the lowest AICc and a particular model.</p>d<p><i>w_i</i> is the relative model likelihood.</p

Leopard Camera Traps (for dryad)

Camera trap Identification Numbers (cam_id) and UTM easting (x) and Northing (y) coordinates for all camera stations used to detect common leopard in Royal Manas National Park, Bhutan during winter 2010-2011. The UTM coordinates are projected in UTM zone 46 with WGS84 datum

Leopard Camera Trapping Data (for Dryad)

Individual identities, sex, date and location of capture for all leopards included in this study

Bayesian Model Selection Results for Daily Sampling Interval Models for Common Leopards.

<p>Bayesian model selection results for spatially explicit capture-recapture models with daily sampling intervals for common leopards in Royal Manas National Park, Bhutan during 2010–2011. Pr(Model|data) gives the posterior probability of a model given the data among the candidate models. The Bayes factor, <i>B</i>_l0, provides a Bayesian analog to the likelihood ratio of a model compared to the null, distance model, where values greater than one indicate support for the alternative model. log₁₀<i>B</i>_l0 and 2 ln <i>B</i>_l0 describe different transformations of the Bayes factor to the log₁₀ scale as suggested by Jeffreys (1961) and twice the natural logarithmic scale as suggested by Kass & Raftery (1995), respectively. These transformations correspond to the familiar log-odds scale of logistic regression and deviance scale of many information criteria, respectively. For both transformations, values greater than zero favor the alternative hypothesis.</p><p>Bayesian Model Selection Results for Daily Sampling Interval Models for Common Leopards.</p

Examining Temporal Sample Scale and Model Choice with Spatial Capture-Recapture Models in the Common Leopard <i>Panthera pardus</i>

<div><p>Many large carnivores occupy a wide geographic distribution, and face threats from habitat loss and fragmentation, poaching, prey depletion, and human wildlife-conflicts. Conservation requires robust techniques for estimating population densities and trends, but the elusive nature and low densities of many large carnivores make them difficult to detect. Spatial capture-recapture (SCR) models provide a means for handling imperfect detectability, while linking population estimates to individual movement patterns to provide more accurate estimates than standard approaches. Within this framework, we investigate the effect of different sample interval lengths on density estimates, using simulations and a common leopard (<i>Panthera pardus</i>) model system. We apply Bayesian SCR methods to 89 simulated datasets and camera-trapping data from 22 leopards captured 82 times during winter 2010–2011 in Royal Manas National Park, Bhutan. We show that sample interval length from daily, weekly, monthly or quarterly periods did not appreciably affect median abundance or density, but did influence precision. We observed the largest gains in precision when moving from quarterly to shorter intervals. We therefore recommend daily sampling intervals for monitoring rare or elusive species where practicable, but note that monthly or quarterly sample periods can have similar informative value. We further develop a novel application of Bayes factors to select models where multiple ecological factors are integrated into density estimation. Our simulations demonstrate that these methods can help identify the “true” explanatory mechanisms underlying the data. Using this method, we found strong evidence for sex-specific movement distributions in leopards, suggesting that sexual patterns of space-use influence density. This model estimated a density of 10.0 leopards/100 km² (95% credibility interval: 6.25–15.93), comparable to contemporary estimates in Asia. These SCR methods provide a guide to monitor and observe the effect of management interventions on leopards and other species of conservation interest.</p></div

Parameter Estimates and 95% Credibility Intervals from Spatial Capture-Recapture Models with Daily Sampling Intervals for Common Leopards.

<p>Median parameter estimates with 95% credibility intervals in parentheses from spatial capture-recapture models of common leopards in Royal Manas National Park during 2010–2011 with daily sampling intervals. λ₀ gives the baseline capture probability at an individual’s activity center per camera station per day. β_sex denotes the effect of sex on detection probability on the log scale. The σ parameters describe the scale of an individual’s movement distribution in km, which varies by sex in some models. ψ_sex estimates the proportion of the population that is male. θ represents the shape parameter of the individual’s movement distribution, where 0.5 is exponential and 1.0 is Gaussian.</p><p>Parameter Estimates and 95% Credibility Intervals from Spatial Capture-Recapture Models with Daily Sampling Intervals for Common Leopards.</p

Spatial Density Estimate of Common Leopards <i>(Panthera pardus</i>) from the Best-Supported Model with Inset Study Area Map.

<p>The posterior spatial density estimate of common leopards/100 km² from the best-supported spatial capture-recapture model, σ_sex, in the lower foothills of Royal Manas National Park (RMNP), Bhutan for sampling carried out during 2010–2011. The 162 km² sampling area is displayed with the solid black line, the RMNP boundary with broken black line, camera-trapping stations with leopard detections with crosses and camera-trapping stations without leopard detections black circles. Each station represents a pair of cameras. <i>Inset</i>: RMNP (light gray) in Bhutan with the location of the 162 km² gridded study area (black) for common leopards in 2010–2011.</p