Population viability analysis (PVA) is frequently used to quantify the level of risk associated with various populations and also to examine the relative benefits of alternative management actions. Typically, model predictions are extremely sensitive to estimated parameters, resulting in risk estimates that are very imprecise. Therefore, careful consideration of uncertainty is warranted when reporting PVA predictions. Here, we survey some simple and general simulation-based methods for assessing the uncertainty in model-based forecasts. We then illustrate how these methods can be used for incorporating parameter uncertainty when reporting estimates of extinction risk, using a PVA model developed from data on Pacific Chinook salmon stocks. Several researchers have suggested that relative assessments between management options will be more stable than estimates of extinction risk and population growth rate. We illustrate how the uncertainty in relative assessments can be quantified in a rigorous way. Finally, we show how Sobol' indices can be used to ask: "Where is the uncertainty coming from?" These indices are an ANOVA-like decomposition of the effects of parameters on model output that take into account both direct effects (as in standard sensitivity analysis) and effects due to interactions with other parameters. Throughout, we illustrate these methods using models relevant to the life cycle of Pacific salmon, with the intent of assessing the relative influences of habitat, harvest, and hatcheries on the viability of salmon populations. However, the same approaches are applicable to most situations where a model with estimated parameters is being used for forecasting or comparing management options.
|Original language||English (US)|
|Number of pages||11|
|State||Published - Jun 2003|
- Extinction risk
- Population viability analysis
- Sensitivity analysis
- Sobol' indices