Code

On this page you’ll find GitHub links to code used in my research, organized according to project. I update this material once a project is  complete. You’ll also find course materials developed by myself, as well as others that I have found useful. Please feel free to use and distribute, but cite the source appropriately.

Code used in my research


Evaluating confidence in climate-based predictions of population change

geb-imageObjective: To develop a quantitative method to evaluate the accuracy of climate-based
ecological predictions and to use this approach to assess the extent of spatio-temporal synchrony among distinct regions within the breeding range of a single migratory population of the monarch butterfly.

Code: (1) Learn to manipulate and analyze long-term count data with a negative binomial regression model fit using Bayesian inference (via R and JAGS), and (2) assess temporal predictability of the model using a novel quantitative method that adapts the Bayesian posterior predictive check approach.

Learn more here; code developed by myself.


Dynamic N-occupancy models

Image result for barred owlObjectiveTo develop a model to accurately estimate local abundance, population gains (reproduction/immigration), and apparent survival probabilities while accounting for imperfect detection using only detection/nondetection data. We then apply this model to a case study of barred owls invading an area in Oregon.

Code: (1) Conduct a simulation study to validate the model across a wide range of values and examine the data requirements (number of years and survey sites needed) for unbiased and precise estimation of parameters. (2) Apply the dynamic N-occupancy model to a detection/nondetection dataset on barred owls to estimate spatiotemporal heterogeneity in abundances.

Learn more here; code developed by Dr. Sam Rossman.

 

Course materials from myself and others


Image result for RQuantitative methods in ecology and evolution

Course materials introducing basic statistical concepts and R programming skills. Materials were created by myself, and are part of a semester-long, graduate-level course taught by Dr. Elise Zipkin, Dr. Sam Rossman, and myself at Michigan State University (fall 2016).

Learn more here.


Bayesian hierarchical modeling at SESYNCImage result for bayes rule

Course materials for the SESYNC Bayesian modeling for ecologists and social scientists short course. Materials were created by Dr. Tom Hobbs, Dr. Chris Che-Castaldo, and Dr. Mary Collins.

Learn more here.