I was a little slow in getting this up (even though it only took about 2 minutes from start to finish on Figshare.com), but here is my poster presented at the 98th annual meeting of the Ecological Society of America (ESA) in Minneapolis, Minnesota, USA. We used a non-linear, parametric model accounting for detection probability to quantify red-backed salamander (Plethodon cinereus) abundance across clearcut-forest edges. This approach allows for projection across landscapes and prediction given alternative logging plans.
Short Link: shar.es/zoeMD
I recently gave a talk at the Ecological Society of America (ESA) annual meeting in Portland, OR and a poster presentation at the World Congress of Herpetology meeting in Vancouver, BC, Canada. Both presentations were comparing generalized linear mixed models (GLMM) and generalized estimating equations (GEE) for analyzing repeated count data. I advocate for using GEE over the more common GLMM to analyze longitudinal count (or binomial) data when the specific subjects (sites as random effects) are not of special interest. The overall confidence intervals are much smaller in the GEE models and the coefficient estimates are averaged over all subjects (sites). This means the interpretation of coefficients is the log change in Y for each 1 unit change in X on average (averaged across subjects). Below you can see my two presentations for more details.