Negative binomial models for abundance estimation of multiple closed populations

Journal of Wildlife Management
By: , and 

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Abstract

Counts of uniquely identified individuals in a population offer opportunities to estimate abundance. However, for various reasons such counts may be burdened by heterogeneity in the probability of being detected. Theoretical arguments and empirical evidence demonstrate that the negative binomial distribution (NBD) is a useful characterization for counts from biological populations with heterogeneity. We propose a method that focuses on estimating multiple populations by simultaneously using a suite of models derived from the NBD. We used this approach to estimate the number of female grizzly bears (Ursus arctos) with cubs-of-the-year in the Yellowstone ecosystem, for each year, 1986-1998. Akaike's Information Criteria (AIC) indicated that a negative binomial model with a constant level of heterogeneity across all years was best for characterizing the sighting frequencies of female grizzly bears. A lack-of-fit test indicated the model adequately described the collected data. Bootstrap techniques were used to estimate standard errors and 95% confidence intervals. We provide a Monte Carlo technique, which confirms that the Yellowstone ecosystem grizzly bear population increased during the period 1986-1998.

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Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Negative binomial models for abundance estimation of multiple closed populations
Series title Journal of Wildlife Management
DOI 10.2307/3803103
Volume 65
Issue 3
Year Published 2001
Language English
Publisher Wildlife Society
Publisher location Washington
Contributing office(s) Northern Rocky Mountain Science Center
Description 12 p.
First page 498
Last page 509
Country United States
State Idaho, Montana, Wyoming
Other Geospatial Yellowstone National Park
Online Only (Y/N) N
Additional Online Files (Y/N) N