Evaluating Critical Uncertainty Thresholds in a Spatial Model of Forest Pest Invasion Risk

摘要: Pest risk maps can provide useful decision support in invasive species management, but most do not adequately consider the uncertainty associated with predicted values. This study explores how increased a model’s numeric assumptions might affect resultant map. We used spatial stochastic model, integrating components for entry, establishment, and spread, to estimate risks of invasion their variation across twodimensional landscape Sirex noctilio, nonnative woodwasp recently detected United States Canada. Here, we present sensitivity analysis mapped estimates key model parameters. The tested parameter values were sampled from symmetric uniform distributions defined by series nested bounds (±5%, ... , ±40%) around parameters’ initial results suggest that maximum annual spread distance, which governs long-distance dispersal, was far sensitive parameter. At ±15% or larger variability bound increments this parameter, there noteworthy shifts map values, no other had major effect, even at wider variation. methodology presented here is generic be assess impact uncertainties on stability pest as well identify geographic areas management decisions made confidently, regardless uncertainty.