Modelling heterogeneity in cetacean surveys

Methods for improving estimation of cetacean abundance from line transect and markrecapture surveys are proposed. Using either generalized linear or generalized additive models (GLMs or GAMs), two approaches are suggested which allow heterogeneity in the spatial distribution of cetaceans to be modelled from standard line transect data. In the rst approach, the transect lines are divided into smaller discrete units, and the expected number of detections in each unit is modelled using explanatory spatial covariates. In the second approach, the response is derived from the observed waiting times (or distances) between detections. Fitting this model within the usual GLM or GAM framework would require restrictive assumptions, therefore an iterative procedure is formulated which enables a realistic model to be tted. Alternatively, it is shown how this approach can be framed as a point process model, and it is suggested how the likelihood for the observed along-trackline distances could be maximized. The methods are illustrated using line transect data from a survey of Antarctic minke whales. A surface representing the variation in density throughout the survey region is obtained, from which abundance may be estimated by numerical integration. It is also shown how the rst approach can be adapted for cue count data, using an example from an aerial survey for minke whales o West Greenland. Heterogeneity in the capture probabilities of individuals is a potential source of bias in mark-recapture estimates. A method is proposed which allows this heterogeneity to be modelled, and accounts for the past capture histories of the individuals. The model may be tted using logistic regression with an o set variable which must be estimated. Results from tting the logistic regression model to photo-identi cation data from a study of bottlenose dolphins in the Moray Firth, Scotland, are presented.