Habitat use models of spatially auto-correlated data: a case study of the common bottlenose dolphin, Tursiops truncatus truncatus, in southeastern Brazil
A common approach to studying habitat use in cetaceans is to conduct line-transect surveys, to investigate their distribution. In developing countries, there are limited resources for data collection. One solution is to employ field surveys to collect a wide range of ecological and behavioural data, for which a haphazard sampling schedule is adopted, to optimize the cost–benefit ratio. As with line-transect surveys, the haphazard sampling may lead to spatial autocorrelation (SAC), an overlooked problem in ecology. Here, we investigated common bottlenose dolphins (Tursiops truncatus truncatus) habitat use on an upwelling area and tested an approach that can improve model-based inference on auto-correlated data. We collected data in Cabo Frio, Rio de Janeiro, photo-identified 429 individuals and compared the predictions and model coefficients of standard generalized linear model (GLM) without correcting for spatial autocorrelation with a spatial eigenvector generalized linear model (SEV-GLM) which compensates for SAC. Our best SEV-GLM predicted dolphins are more likely to occur on cold waters with increased chlorophyll concentration, indicating dolphins are influenced by the upwelling. Moreover, by correcting for SAC, our models had a better fit to data, magnified the relevance of significant variables and showed smaller and less clumped residuals than when not correcting.