home range. Further, no measurable covariate information to model r were available, hence an
assumption of constant r is made in this analysis.
Capture histories for sloth bears
Royle & Nichols (2003) suggest building up capture histories by sites based on captures
and recaptures of the species in concern on repeated visits. Since sloth bears move widely (Joshi
et al., 1999), it is not likely that a bear captured at a given camera trap location will be caught at
that same location with the same probability over subsequent camera trap nights. Instead, I
substitute the temporal replicates as suggested in Royle & Nichols (2003) with spatial replicates.
By doing this, I assume that all bears have an equal animal-specific detection probability. In this
arrangement, a camera-trap location is said to have detected bear presence if a bear appears in
that location on any single trap night over all the sampling nights. A capture matrix incorporating
such an arrangement is shown in Table 3-2.
The total number of detections at a site i is w,.. If a bear appeared once at a camera trap
over the period of the entire sampling period, that camera trap is said to have "detected" a bear
and marked as '1', as in the matrix (Table 3-2).
Selection of the mass function to model abundance
The selected study areas are protected areas and are fairly homogenous in habitat structure.
I also know from sloth bear detections observed in 2004 and 2005 (Figures 3-3 and 3-4), that
with the exception of one "hole" in 2005, no other holes or clusters are obvious. With a random
spatial occurrence of detections of this nature, based on the recommendation of Royle and
Nichols (2003) I assume a Poisson model to describe abundance.
Parameters for the prior distribution of 2
From Equation 2-7 in Chapter 2
A ~ gamma[a, b], where a and b are the shape and scale parameters.