Estimating nest success and evaluating factors potentially related to the survival rates of nests are key aspects of many studies of avian populations. A strong interest in nest success has led to a rich literature detailing a variety of estimation methods for this vital rate.  In recent years, modeling approaches have undergone especially rapid development.  Despite these advances, most researchers still employ Mayfield’s ad-hoc method (Mayfield 1961) or, in some cases, the maximum-likelihood estimator of Johnson (1979) and Bart and Robson (1982).  Such methods permit analyses of stratified data but do not allow for more complex and realistic models of nest survival rate that include covariates that vary by individual, nest age, time, etc. and that may be continuous or categorical.  Methods that allow researchers to rigorously assess the importance of a variety of biological factors that might affect nest survival rates can now be readily implemented in Program MARK and in SAS’s Proc GENMOD and Proc NLMIXED. To facilitate use of these methods, the following resources are provided.

SAS code – tools for analyzing nest-survival data in SAS & MARK as used in the manuscript’s tables