Table 1.

Posterior summaries of parameters estimated from the SCR Poisson encounter model (null model, M₀) to estimate wolf density.

	Mean	SD	BCI
			2.5%	50%	97.5%
Density ($\hat{\mathrm{D}}$)	2.55	0.42	1.87	2.50	3.51
alpha1 (${\hat{\mathrm{\alpha }}}_0$)	−1.21	0.25	−1.38	−1.21	−0.74
alpha2 (${\hat{\mathrm{\alpha }}}_2$)	0.24	0.13	−0.01	0.24	0.50
psi (ψ)	0.38	0.07	0.27	0.38	0.53
sigma ($\hat{\mathrm{\sigma }}$)	0.33	0.03	0.27	0.32	0.40

Estimates were based on 3 chains of 50,000 iterations, and thin rate = 1, yielding 150,000 total samples from the joint posterior. BCI = Bayesian Credible Intervals. alpha1 (${\hat{\mathrm{\alpha }}}_0$) and alpha2 (${\hat{\mathrm{\alpha }}}_2$) are used to model the baseline encounter rate, being dependent on sampling effort (see main text for details): $\log \left({\mathrm{\lambda }}_0\left[j\right]\right)={\alpha }_0+{\alpha }_1\times \mathrm{L}\left[j\right]$, where L_j is the density of transect in each cell; psi (ψ) is a parameter of the augmented data, and sigma (σ), or movement parameter, is the half-normal scale parameter that describes the rate at which detection probability declines as a function of distance.