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- Title
EFFICACY OF SIMPLE VIABILITY MODELS IN ECOLOGICAL RISK ASSESSMENT: DOES DENSITY DEPENDENCE MATTER?
- Authors
Sabo, John L.; Holmes, Elizabeth E.; Kareiva, Peter
- Abstract
One commonly used PVA (population viability analysis) approach applies a diffusion approximation (DA) of population growth to time series of abundance data to estimate population parameters and various metrics of extinction risk. The simplest versions of this PVA assume density-independent population growth, an assumption that is commonly called into question for populations experiencing self-limitation. Using time series data generated from simulations of populations limited by three commonly used forms of density dependence (ceiling, Beverton-Holt, and Ricker) we asked the question: "When do simple density-independent PVA models provide useful guidelines for prioritizing extinction risk despite density-dependence inherent in the underlying real populations?" Simple DA methods severely underestimated maximum growth rates (μmax) used to generate time series data for all three forms of density dependence. These methods also underestimated the intrinsic environmental variability in growth rates, or process error (σ²). for the ceiling model, but overestimated this parameter for the Beverton-Holt and Ricker models. Despite misestimation of the intrinsic parameters, the estimated probabilities of 50% and 75% declines were highly correlated with the observed probabilities for populations growing with a ceiling (coefficients of correlation, or R² = 0.87-0.93). DA methods were less accurate for populations exhibiting more complex forms of density dependence (R² = 0.6 1-0.79). Although correlations between observed and estimated risks were high, bias (e.g., over- and underestimation) was extensive. Estimated probabilities of 50% declines were typically much lower (overly optimistic) than observed probabilities of the same decline. By contrast, accuracy increased substantially for predictions of 75% decline, and the "optimistic" bias was replaced by conservative bias (overestimates of risk).
- Subjects
BIOLOGICAL extinction; POPULATION viability analysis; TIME series analysis; ECOLOGICAL risk assessment; ECOLOGY methodology; ENVIRONMENTAL impact analysis
- Publication
Ecology, 2004, Vol 85, Issue 2, p328
- ISSN
0012-9658
- Publication type
Article
- DOI
10.1890/03-0035