Augmenting Superpopulation Capture-Recapture Models with Population Assignment Data

• Activity Number: 667
• Type: Contributed
• Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): zhi wen*+ and Kenneth Pollock and James Nichols and Peter Waser
• Companies: FDA and North Carolina State University and Patuxent Wildlife Research Center and Purdue University
• Keywords: Assignment procedures ; Capture-Recapture ; Genetic Assignment Test ; Immigration ; Resampling Approach ; Robust Design

Abstract

Here we show how to augment a superpopulation capture-recapture model with such information. We consider a single superpopulation model without age structure, and split each entry probability into separate components due to births in situ and immigration. We show that it is possible to estimate these two probabilities separately. We first consider the case of perfect information about population of origin, where we can distinguish individuals born in situ from immigrants with certainty. Then we consider the more realistic case of imperfect information, where we use genetic or other information to assign probabilities to each individual's origin as in situ or outside the population. We use a resampling approach to impute the true population of origin from imperfect assignment information. The integration of data on population of origin with capture-recapture data allows us to determine the contributions of immigration and in situ reproduction to the growth of the population, an issue of importance to ecologists. We illustrate our new models with capture-recapture and genetic assignment data from a population of banner-tailed kangaroo rats Dipodomys spectabilis in Arizona.