VALIDATING POPULATION VIABILITY ANALYSIS FOR CORRUPTED DATA SETS

Diffusion approximation (DA) methods provide a powerful tool for population viability analysis (PVA) using simple time series of population counts. These methods have a strong theoretical foundation based on stochastic age‐structured models, but their application to data with high sampling error or age‐structure cycles has been problematic. Recently, a new method was developed for estimating DA parameters from highly corrupted time series. We conducted an extensive cross‐validation of this new method using 189 long‐term time series of salmon counts with very high sampling error and nonstable age‐structure fluctuations. Parameters were estimated from one segment of a time series, and a subsequent segment was used to evaluate the predictions regarding the risk of crossing population thresholds. We also tested the theoretical distributions of the estimated parameters. The distribution of parameter estimates is an essential aspect of a PVA because it allows one to calculate confidence levels for risk metrics. This study is the first data‐based cross‐validation of these theoretical distributions. Our cross‐validation analyses found that, when parameterization methods designed for corrupted data sets are used, DA predictions are very robust even for problematic data. Estimates of the probability of crossing population thresholds were unbiased, and the estimated parameters closely followed the expected theoretical distributions.