I work primarily on issues in spatial ecology, community assembly, and population viability, all under a conservation biological umbrella. Details can be found on my Website at http://sites.google.com/site/bricesemmens/
by brice.semmens, last updated 4/24/09, sharing set to Public
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.
by eric.ward, last updated 11/20/09, sharing set to Public
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.
by brice.semmens, last updated 8/23/07, sharing set to Public
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.
by brice.semmens, last updated 11/16/11, sharing set to PublicThe tools in this project generate a Bayesian posterior estimate of net salmonid escapement (maximum net upstream count) that compensates for CSOT error and down-time based on daily counts from the DIDSON system. To do this, it employs a parameter driven model with an auto-regressive log linear random walk evolution and a binomial error structure on count observations. See the PDF for full details.
by brice.semmens, last updated 9/29/10, sharing set to PublicThe R code in the project builds GMP SPAG size estimates based on in situ floy tag counts using Bayesian/JAGS methods. Field methods are similar to those outlined in "Evaluation of Aerial Mark-Resighting Estimates of Elk Populations George D. Bear, Gary C. White, Len H. Carpenter, R. Bruce Gill and David J. Essex" -- however, quantative methods are not. I used a (rather simple/effective) Bayesian approach. Note that to run these files, you should update R to the latest version, and download and install JAGS (Just Another Gibbs Sampler) on your machine.
by eric.ward, last updated 5/19/10, sharing set to Public
This project is related to the stable isotope mixing models that include heterogeneity in individual diets and hierarchical nested structures (individuals in groups, groups in regions, etc). All code provided is to be used with R and either JAGS or BUGS (everything was validated using JAGS on a Mac)
by brice.semmens, last updated 8/8/10, sharing set to PublicExample files from the IsoEcol7 mixing model workshop.
by eric.ward, last updated 9/24/08, sharing set to Public
This project includes the WinBUGS files illustrating how to include model selection in MCMC. Variances are modeled as a mixture of source/fractionation and additional residual variance. We include to variations on the MixBUGS model, 1 without residual variance, and 1 modeling variance as a mixture (to calculate posterior probabilities). We also include the R-script illustrating that the SIAR package likely contains a bug (even when data are generated from a 0.7-0.2-0.1 mixture, estimated contributions are nearly equal).
by brice.semmens, last updated 10/2/13, sharing set to Public*Update 8/6/14*
MixSIAR is now being hosted on GitHub at https://github.com/brianstock/MixSIAR/releases. Below the release notes, click the "Source code (zip)" button to download the .zip folder with the code and user manual.
NOTE: I have not yet updated the User Manual, but I thought they were important enough to release now. Stay tuned for the updated manual!
Important changes from the MixSIAR v1.0 code:
- Several bug/error fixes
- New scripted version, check out "mixsiar_script.r"
- Altered the GUI function call to:
- Added option for fixed effects
- Separated OUTPUT button from RUN MODEL button
- Simplified MCMC options (now choose from "test", "short", "long", etc. See "mixsiar_script.r" for details)
by brice.semmens, last updated 8/17/12, sharing set to Public
MixSIR is a Bayesian isotope mixing model that incorporates uncertainty in the estimates of mix and source isotope values. The model also provides the opportunity to incorporate prior information for the proportional contribution of each source to the mix. The programs for this project are written in Matlab. I have provided the source files as well as an .exe file package so that those without Matlab can run the program. Collaborations and improvements are welcome! Prior to installing MixSIR you MUST download the (free) Matlab component runtime library and run it once on your machine. Note also that installing new versions of MixSIR will require a new download and installation of the component runtime library.
***** NOT WORKING ON YOUR NEW 64 BIT MACHINE?? *****
-- You need to install this:
you're runnning a 32-bit compiled Matlab app on a 64-bit Windows machine, and it's not finding the 32-bit Visual C++ runtime it's linked against. The Microsoft software "Microsoft Visual C++ 2005 SP1 Redistributable Package (x86)" will fix the problem. Cut and paste the link above to get the software fix!
by brice.semmens, last updated 3/19/10, sharing set to Public
This program animates the acoustic tagging data based on a system of passive listening stations . The animation can include any number of tags. For instance, you may have conducted a study with 5 tagged fish and an array of 10 listening stations (e.g. VR2 hydrophones). This software will allow you to animate the paths of your 5 tagged fish across 10 listening stations simultaneously. This software shows paths in real-time sped up. In other words, the time-line is preserved, but the user has the option to increase the display speed of that time line. In addition to animating the tag data, the figure darkens and lightens based on daylight (an approximation) and gives lunar phase in the top graph. Prior to installing TagTravel you MUST download the (free) Matlab component runtime library and run it once on your machine. Note also that installing new versions of TagTravel will require a new download and installation of the component runtime library. The folder TagTravel_GoogleMaps includes an updated tagtravel.exe that works automatically with Google Maps. Sweet!
by brice.semmens, last updated 1/18/11, sharing set to PublicThis project includes R scripts and data to estimate daily trap efficiency based on mark recapture data and flow rate data. Efficiency is modeled as an AR1 process with flow influencing transitions between states. Process error is estimated, while observation error is assumed to be Poisson.
by brice.semmens, last updated 6/28/11, sharing set to PublicThese are the files for Eric and Brice's mixing model lab @ Trinity.