Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence

摘要: Diving behaviour of narwhals is still largely unknown. We build three-state Hidden Markov models (HMM) to describe the diving a narwhal and fit three-dimensional response vector maximum dive depth, duration dives post-dive surface time 8,609 measured in East Greenland over 83 days, an extraordinarily long rich data set. In particular, diurnal patterns for marine mammal being inferred, by using periodic B-splines with boundary knots 0 24 hours. Several HMMs covariates are used characterize patterns. Narwhal have not been analysed like this before, but studies other whale species, variables assumed independent. extend existing allow dependence between state distributions, show that has impact on conclusions drawn about behaviour. It thus paramount relax strong biologically unrealistic assumption obtain trustworthy inferences.