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Using Logistic Regression to Analyze the Sensitivity of PVA Models: a Comparison of Methods Based on African Wild Dog Models

Abstract: We used logistic regression as a method of sensitivity analysis for a stochastic population viability analysis 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 that pup survival explained the most variability in the probability of extinction, regardless of whether or not 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 catastrophe were more important during density‐independent simulations. The inclusion of density dependence decreased the probability of extinction, but neither the abruptness nor the inclusion of density dependence were important model parameters. Results of both 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 that pup survival and its variation were more important than other parameters. But both conventional sensitivity analysis of the stochastic model which changed each parameter by 10% of its mean value and elasticity analyses indicated that adult survival was more important than pup survival. We evaluated the advantages and disadvantages of using logistic regression to analyze the sensitivity of stochastic population viability models and conclude that it is a powerful method because it can address interactions among input parameters and can incorporate the range of parameter variability, although the standardized regression coefficients are not comparable between studies. 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.