Hi, Dave. Thanks for pointing out the merits of R-Mark as far as generating AIC tables reflecting the results of nest survival and other data model types. I do indeed use R-Mark for CJS and multistate population modeling, but I prefer the logistic exposure/"Shaffer" nest modeling paradigm for a number of reasons. When you have something of a background in linear models, the GLM approach is perhaps a little more intuitive than Program MARK (but R-Mark circumvents some of that), and data preparation and covariate handling seems to go more quickly and easily. Plots in R come out so nicely, publication quality if you specify them correctly. Also, there's capacity for extending the logistic-exposure models to mixed models (which might not be a wise decision, based on violation of the assumption that the mean of the error distribution is equal to zero, but I digress). I've done nest survival with both Program MARK (not R-Mark) and GLMs in R, and it seems to me (not a biostatistician, but an ecologist who dabbles with statistical tools), that it's ok to just go with whatever suits your particular style. In my case, since I tend to start with (and retain) fairly focused, restricted model suites, it doesn't bother me much to hand construct AIC tables with the "n-effective" calculated AIC values after having run the GLMs. BTW, if anyone needs a script of how to set up the logistic-exposure link function, it's among the examples in help(family). cheers, Jessi Brown
Jessi L. Brown Ph.D. Student, Program in Ecology, Evolution, and Conservation Biology University of Nevada, Reno 1000 Valley Rd. Reno, NV 89512 jlbrown at unr.edu