PARENT SESSION
Oral Session #1: Conservation Ecology: Threatened and Endangered Species. Presiding: W. Bond.
Monday, August 6, 2001. 8:00 AM to 12:15 PM. Madison Ballroom C.
Using logistic regression to analyze sensitivity of PVA models: comparing sensitivity methods using African wild dogs.
Cross, Paul1, Beissinger, Steven1, 1
ABSTRACT- We used logistic regression as a method of sensitivity analysis for a stochastic PVA model of African wild dogs (Lycaon pictus) and compared these results with conventional sensitivity analyses of stochastic and deterministic models. Standardized coefficients from the logistic regression analyses indicated pup survival explained the most variability in the probability of extinction, regardless of whether the model incorporated density dependence. Adult survival and the standard deviation of pup survival were the next most important parameters in density dependent simulations, whereas the severity and probability of catastrophes were more important during density independent simulations. The inclusion of density dependence decreased the probability of extinction, but neither the abruptness nor inclusion of density dependence were important model parameters. Results of relative sensitivity analyses that altered each parameter by 10% of its range and life stage simulation analyses of deterministic matrix models supported the logistic regression results, indicating pup survival and its variation were more important than other parameters. However, conventional sensitivity analysis of the stochastic model that changed each parameter by 10% of its mean value and elasticity analyses indicated adult survival was more important than pup survival. Logistic regression is a powerful method because it can address interactions among input parameters and incorporate the range of parameter variability, although the standardized regression coefficients are not comparable between studies. However, model structure, method of analysis, and parameter uncertainty affect the conclusions of sensitivity analyses. Therefore, rigorous model exploration and analysis should be conducted to understand model behavior and management implications.
KEY WORDS: Modeling, PVA, Elasticity, Lycoan pictus