Presentation Abstract

Title: Spatial Design Of Capture-recapture Studies
Session Title: Biometrics and Population Modeling
Session Number: 40
Session Time: Monday, Oct 07, 2013, 1:30 PM - 5:20 PM
Presentation Time: Monday, Oct 07, 2013, 1:50 PM - 2:10 PM
Presentation Number: 2
Author(s): J. Andrew Royle1, Christopher S. Sutherland2, Angela K. Fuller3, 1USGS Patuxent Wildlife Research Center, Laurel, MD, 2New York Cooperative Fish and Wildlife Research Unit, Dept of Natural Resources, Cornell University, Ithaca, NY, 3U.S. Geological Survey, New York Cooperative Fish and Wildlife Research Unit, Dept. of Natural Resources, Cornell University, Ithaca, NY, Contact: aroyle@usgs.gov
Abstract Body:
Design of capture-recapture studies encompasses an array of elements
including choosing the number of occasions to sample, the length of
sample periods, the number of traps, and the spatial organization of
traps. Design choices are most often made using Monte Carlo
simulation of different design scenarios in order to evaluate the
precision with which population size or density is estimated. In our
work, we adapt classical ideas of model-based experimental design to
the design of capture-recapture studies. We identify a number of
suitable criteria related to precision or expected sample size, and we
find the design which is optimal with respect to the identified
criterion. We specifically address choosing the optimal configuration
of traps in the context of spatial capture-recapture models. We find
that the optimal configuration of traps is not regularly spaced, and,
indeed, the configuration and spacing of traps depends on the size and
configuration of the study area and the density of traps available.
We discuss some modifications of the framework to deal with irregular
study areas (such as stream networks), constraints on the sample
locations, clustering of traps, and efficient computation.
Model-based design of capture-recapture studies is more efficient than
Monte Carlo simulation, and, unlike simulation, the method produces
designs that are arbitrarily close to the unique (global) optimal
design.



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