Projects found

This program (VBA implemented in Excel) animates the path of a tagged animal in a VR2 hydrohpone network. It DOES NOT animate real time sped up. Instead, it animates movement based on relocation events. Thus, whenever a tag is heard at a new hydrophone, the map updates, and provides a summary of how many times and over how many days the animal/tag was heard at that hydrophone. This is a good strategy when you are interested in highlighting migrations as opposed to daily movements, for instance.

BaMM (for Bayesian Metabolic Model) is a Bayesian statistical model of oxygen dynamics which accounts for the dominant physical and biological processes that control dissolved oxygen in aquatic ecosystems. Using this model is it possible to estimate ecosystem metabolic rates (gross primary production, community respiration, gas exchange) from diel data of oxygen concentration and, if available oxygen-18 isotopes. Please see the instructions document for a description on how to work with BaMM.

These routines allow you to take a matrix of MCMC samples and calculate the Bayes factor based on the harmonic mean algorithm proposed by Gelfand and Dey (1994). Caution: Bayes factors tend to be numerically unstable!

This project is a variation of stable isotope mixing models. Previous approaches have fixed source parameters (mean, variance) at their MLE estimates, and proceeded to do a Bayesian analysis of the mixture of consumer diets. This assumption is fine when sample sizes are large, but often in ecology, they are small. Incorporating this uncertainty adds additional parameters, and slightly increases the total variance of mixture estimates, but has the advantage of improving mixing and reducing bias. Often, using the traditional approach may lead to multi-modal estimates of the mixture; we demonstrate that the fully Bayesian mixing model avoids this problem. A final benefit is that it allows prior information about sources to be included. The code folder contains the code to replicate the comparison done with simulated datasets.

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.