Projects found

This is project involves estimating catastrophes in the presence of observation and process error. It was later expanded into a paper on fur seals (Ward et al. 2007, CJFAS). The model may be run in WinBugs / OpenBugs, or through R. The example has been made to be generic and includes some dummy data. The priors included have been simplified for demonstration purposes.

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.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.

This script writes and runs the data cloning procedure described by Lele et al. (2007, Ecology Letters). The routine is in R, and requires the user to install OpenBUGS before running. The number of clones can (and should) be modified. For this simple example, 100 clones takes ~ 2 minutes on my 2.5 year old laptop (1.66Ghz, 1GB RAM). More details and diagnostics can be found in the multivariate cloning project.

These are function for generating various standard types of stochastic population processes: random walks, Ornstein-Uhlenbeck, discrete Gompertz, etc. Also some random number generators needed by these are here. These matlab files need the Matlat Statistics Toolbox.

A series of modules for estimation of PVA parameters from time series data. Uses kalman filters, REML, and slope methods.