This program allows one to walk through the steps required to conduct a population viability analysis, or PVA, using a population time time series. The model outputs probabilities of extinction as a function of time steps into the future, and importantly, gives confidence intervals for these probabilities.
This tool has two major advantages over traditional PVA techniques:
1) It uses a state-space Kalman filter that allows for both process and non-process error.
So what's the big deal? --Functionally it filters the data, and allows a more accurate fit for population parameters of interest.
2) It uses a Bayesian sampling-importance-resampling algorithm to fully address uncertainty in the parameter estimates given the data.
So what's the big deal? -- Rather than developing a single function that describes the probability of population extinction through time, we can use the uncertainty in parameter estimates to develop 'probabilities of probabilities', or, the uncertainty surrounding the probability of extinction through time.
last updated 11/9/07,
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A series of modules for estimation of PVA parameters from time series data. Uses kalman filters, REML, and slope methods.