Factors leading to different viability predictions for a grizzly bear data set

L. Scott Mills, Stephen G. Hayes, Calib Baldwin, Michael J. Wisdom, John Citta, David J. Mattson, Kerry Murphy

Research output: Contribution to journalArticlepeer-review

Abstract

Population viability analysis programs are being used increasingly in research and management applications, but there has not been a systematic study of the congruence of different program predictions based on a single data set. We performed such an analysis using four population viability analysis computer programs: GAPPS, INMAT, RAMAS/AGE and VORTEX. The standardized demographic rates used in all programs were generalized from hypothetical increasing and decreasing grizzly bear (Ursus arctos horribilis) populations. Idiosyncracies of input format for each program led to minor differences in intrinsic growth rates that translated into striking differences in estimates of extinction rates and expected population size. In contrast, the addition of demographic stochasticity, environmental stochasticity, and inbreeding costs caused only a small divergence in viability predictions. But, the addition of density dependence caused large deviations between the programs despite our best attempts to use the same density-dependent functions. Population viability programs differ in how density dependence is incorporated, and the necessary functions are difficult to parameterize accurately. Thus we recommend that unless data clearly suggest a particular density-dependent model, predictions based on population viability analysis should include at least one scenario without density dependence. Further, we describe output metrics that may differ between programs, development of future software could benefit from standardized input and output formats across different programs.

Original languageEnglish
Pages (from-to)863-873
Number of pages11
JournalConservation Biology
Volume10
Issue number3
DOIs
StatePublished - Jun 1996

Fingerprint

Dive into the research topics of 'Factors leading to different viability predictions for a grizzly bear data set'. Together they form a unique fingerprint.

Cite this