Multivariate Regression of Satellite-linked Dive Recorder Data: Simultaneous Analysis of All Bins

Statistical analysis of diving behavior data collected from satellite-linked dive recorders (SDRs) can be challenging because: (1) the data are binned into several depth and time categories, (2) the data from individual animals are often temporally autocorrelated, (3) random variation between individuals is common, and (4) the number of dives can be correlated among depth bins. Previous analyses often have ignored one or more of these statistical issues. In addition, previous SDR studies have focused on univariate analyses of index variables, rather than multivariate analyses of data from all depth bins. We describe multivariate analysis of SDR data using generalized estimating equations (GEE) and demonstrate the method using SDR data from harbor seals (Phoca vitulina) monitored in Prince William Sound, Alaska between 1992 and 1997. Multivariate regression provides greater opportunities for scientific inference than univariate methods, particularly in terms of depth resolution. In addition, empirical variance estimation makes GEE models somewhat easier to implement than other techniques that explicitly model all of the relevant components of variance. However, valid use of empirical variance estimation requires an adequate sample size of individual animals.