Insights into the latent multinomial model through mark-resight data on female grizzly bears with cubs-of-the-year
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Abstract
Mark-resight designs for estimation of population abundance are common and attractive to researchers. However, inference from such designs is very limited when faced with sparse data, either from a low number of marked animals, a low probability of detection, or both. In the Greater Yellowstone Ecosystem, yearly mark-resight data are collected for female grizzly bears with cubs-of-the-year (FCOY), and inference suffers from both limitations. To overcome difficulties due to sparseness, we assume homogeneity in sighting probabilities over 16 years of bi-annual aerial surveys. We model counts of marked and unmarked animals as multinomial random variables, using the capture frequencies of marked animals for inference about the latent multinomial frequencies for unmarked animals. We discuss undesirable behavior of the commonly used discrete uniform prior distribution on the population size parameter and provide OpenBUGS code for fitting such models. The application provides valuable insights into subtleties of implementing Bayesian inference for latent multinomial models. We tie the discussion to our application, though the insights are broadly useful for applications of the latent multinomial model.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Insights into the latent multinomial model through mark-resight data on female grizzly bears with cubs-of-the-year |
| Series title | Journal of Agricultural, Biological, and Environmental Statistics |
| DOI | 10.1007/s13253-013-0148-8 |
| Volume | 18 |
| Issue | 4 |
| Year Published | 2013 |
| Language | English |
| Publisher | International Biometric Society |
| Publisher location | Alexandria, VA |
| Contributing office(s) | Northern Rocky Mountain Science Center |
| Description | 22 p. |
| Larger Work Type | Article |
| Larger Work Subtype | Journal Article |
| Larger Work Title | Journal of Agricultural, Biological, and Environmental Statistics |
| First page | 556 |
| Last page | 577 |
| Online Only (Y/N) | N |
| Additional Online Files (Y/N) | N |
| Google Analytic Metrics | Metrics page |