  We develop an inference framework for spatial capture-recapture data when two
methods are used in which individuality cannot generally be reconciled between
the two methods. A special case occurs in camera trapping when left-side
(method 1) and right-side (method 2) photos are obtained but not
simultaneously. We specify a spatially explicit capture-recapture model for the
latent "perfect" data set which is conditioned on known identity of individuals
between methods. We regard the identity variable which associates individuals
of the two data sets as an unknown in the model and we propose a Bayesian
analysis strategy for the model in which the identity variable is updated using
a Metropolis component algorithm. The work extends previous efforts to deal
with incomplete data by recognizing that there is information about
individuality in the spatial juxtaposition of captures. Thus, individual
records obtained by both sampling methods that are in close proximity are more
likely to be the same individual than individuals that are not in close
proximity. The model proposed here formalizes this trade-off between spatial
proximity and probabilistic determination of individuality using spatially
explicit capture-recapture models.
