Data and code for: Precision and bias of spatial capture-recapture estimates: A multi-site, multi-year Utah black bear case study

See Pederson et al. 2012 (https://doi.org/10.2192/URSUS-D-10-00029.1)
for specific methods about sampling design, data collection, processing, and
genetic analysis. Resulting capture histories and trap locations were formatted
for input into the oSCR package in R:

We formatted the individually identified black bear
capture-recapture data from each site into multi-session (3 - 6 years) capture
histories that retained spatial information on individual (i) detections at
hair collection corrals (j = 16, 25) across sampling occasions (k = 4) such
that yijk ~ Bernoulli(pij). The detection model component
defines probability of detection of an individual at a particular trap (pij)
as a function of distance from the individual’s activity center (si)
to that trap having location xj. We used the half-normal model pij
= p0 * exp(-dist(xj,si)2/2σ2)
where p0 is the baseline encounter probability, x is the location of
trap j, s is the activity center of individual i, and σ is the spatial scale
parameter, sigma, determining the rate of decrease in detection probability in
regards to the distance between xj and si.

We have uploaded a ReadMe document that details each column
in the provided trap data frame (tdf) and encounter data frame (edf) .csv
files.