Statement of authorship: GIH had primary responsibility for data analysis, writing code, and preparing the initial manuscript; GAF provided support and discussion throughout the project, and contributed substantially to its final form.
Sampling stochasticity leads to overestimation of extinction risk in population viability analysis
Article first published online: 7 JAN 2013
©2013 Wiley Periodicals, Inc.
Volume 6, Issue 4, pages 238–246, July/August 2013
How to Cite
Herrick, G. I. and Fox, G. A. (2013), Sampling stochasticity leads to overestimation of extinction risk in population viability analysis. Conservation Letters, 6: 238–246. doi: 10.1111/j.1755-263X.2012.00305.x
Editor Richard Zabel
- Issue published online: 13 AUG 2013
- Article first published online: 7 JAN 2013
- Accepted manuscript online: 15 NOV 2012 08:10PM EST
- Manuscript Accepted: 30 OCT 2012
- Manuscript Received: 18 MAY 2012
- G. A. Fox. Grant Number: DEB-0614468
- stochastic demography;
- stochastic exponential growth;
- diffusion approximation;
- bias estimation;
- sampling variance;
- extinction risk
Which method should be used for estimating extinction risk? We present four separate estimates of extinction risk for the threatened pine lily (Lilium catesbaei Walter), based on two methods of estimating abundance (direct abundance counts and Jolly–Seber abundance estimates) and two methods of estimating extinction risk (direct simulation of the stochastic exponential growth (SEG) model, and the diffusion approximation). We compare the accuracy of these four combinations with a simulated data set where simulated-true population abundance and extinction risk is known. The Jolly–Seber method of abundance estimation in combination with direct estimation of extinction risk is the least biased combination of the four methods tested. We conclude that Jolly–Seber (or other mark-recapture) estimates should be used in combination with direct simulation of the SEG, when sampling error is expected. For the pine lily, we conclude that risk of extinction is low in the population studied.