Figure 1.

Graphical depiction of the random thinning spatial capture–recapture model. Random thinning SCR is hierarchical model with two processes: ecological (population size and location—$s_i$—of individuals) and observation. In this model (like in standard SCR), the detection rate of each individual depends on (i) Euclidean distance between individual's locations and traps (centroids of polygonal grid in the study case); (ii) baseline detection rate (${\lambda }_0$) that here depends on sampling effort (length of transect in each polygon); and (iii) the scale parameter ($\sigma$) from the half‐normal detection function, that describes the animal movement. In the observation process, we obtain two types of data: encounters with identification ($y^{\mathit{ID}}$) and non‐ID data ($y^{\mathit{noID}}$) or counts. Random thinning SCR model uses ID data (in red) like in standard SCR to make inferences about population size and individuals' distribution (including nonobserved individuals, in gray), but also uses the counts (in orange) with a constraint ($y^{\mathit{noID}}=y^{\mathrm{true}}‐y^{\mathit{ID}}$) using a Metropolis–Hastings algorithm—in a mechanistic approach—to make a probabilistic reconstruction of the true encounter frequencies ($y^{\mathrm{true}}$), thus assigning identities to non‐ID samples