Testing the Accuracy of Population Viability Analysis

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Abstract
Despite the growing use of population viability analysis ( PVA), the predictions of these models rarely have been tested with field data that were not used in initially developing the model. We review and discuss a suite of methods that may be used to test the predictive ability of models used in PVA. In addition to testing mean predictions, appropriate methods must analyze the probability distribution of the model predictions. The methods we discuss provide tests of the mean predictions, the predicted frequency of events such as extinction and colonization, and the predicted probability distribution of state variables. We discuss visual approaches based on plots of observations versus the predictions and statistical approaches based on determining significant differences between observations and predictions. The advantages and disadvantages of each method are identified. The best methods test the statistical distribution of the predictions; those that ignore variability are meaningless. Although we recognize that the quality of a model is not solely a function of its predictive abilities, tests help reduce inherent model uncertainty. The role of model testing is not to prove the truth of a model, which is impossible because models are never a perfect description of reality. Rather, testing should help identify the weakest aspects of models so they can be improved. We provide a framework for using model testing to improve the predictive performance of PVA models, through an iterative process of model development, testing, subsequent modification and re‐testing.