On the application of mixed hidden Markov models to multiple behavioural time series

Abstract

Analysing behavioural sequences and quantifying the likelihood of occurrences of different behaviours is a difficult task as motivational states are not observable. Furthermore, it is ecologically highly relevant and yet more complicated to scale an appropriate model for one individual up to the population level. In this manuscript (mixed) hidden Markov models (HMMs) are used to model the feeding behaviour of 54 subadult grey mouse lemurs (Microcebus murinus), small nocturnal primates endemic to Madagascar that forage solitarily. Our primary aim is to introduce ecologists and other users to various HMM methods, many of which have been developed only recently, and which in this form have not previously been synthesized in the ecological literature. Our specific application of mixed HMMs aims at gaining a better understanding of mouse lemur behaviour, in particular concerning sex-specific differences. The model we consider incorporates random effects for accommodating heterogeneity across animals, i.e. accounts for different personalities of the animals. Additional subject-and time-specific covariates in the model describe the influence of sex, body mass and time of night.

An analysis of pilot whale vocalization activity using hidden Markov models

“…HMMs are becoming increasingly popular in ecology, having been applied, inter alia, to animal movement data (see, e.g., Langrock et al 2012), to accelerometer data from animalborne data-loggers (see, e.g., Broekhuis et al 2014) in capture-recapture studies (see, e.g., Johnson et al 2016), within distance sampling, (see, e.g., Borchers et al 2013), to occupancy data (see, e.g., Gimenez et al 2014), to data on feeding behavior (Schliehe-Diecks et al 2012) and to data on diving behavior of whales [DeRuiter et al 2016].…”

Mathematical and theoretical ecology: linking models with ecological processes

“…The basic rationale behind a HMM is that the underlying behavioural or motivational states are 'hidden' and cannot be observed directly, but consequences or outputs of the hidden state can be observed. In the case study of Schliehe-Diecks et al [13], the 'hidden' state is the motivational state relating to hunger-animals are either hungry or satiated, but we cannot observe which state an animal is in directly. However, we can observe the 'output' state-whether the animal is currently feeding or not.…”

“…The use of statistical models to infer properties of behaviour from observed data in individual animals and groups of animals is a topic explored by both Schliehe-Diecks et al [13] and King [14]. SchlieheDiecks et al [13] give a detailed illustration of the application of mixed hidden Markov models (HMMs) in a case study that looks at mouse lemur behaviour.…”

“…SchlieheDiecks et al [13] give a detailed illustration of the application of mixed hidden Markov models (HMMs) in a case study that looks at mouse lemur behaviour. HMMs are powerful and flexible statistical tools that allow the user to infer and quantify sequences of behaviour.…”

Objective classification of latent behavioral states in bio‐logging data using multivariate‐normal hidden Markov models

“…Hidden Markov modeling has had a long history in the field of signal processing, particularly for voice recognition (Gales and Young 2007), but its popularity as a tool for examining ecological data has increased in recent years. Examples of such models applied to ecological problems include the foraging behavior of mouse lemurs (Schliehe-Diecks et al 2012), the spawning success of shovelnose sturgeon (Holan et al 2009), atsea behavior of Manx Shearwater (Dean et al 2012), and diving behavior in Macaroni Penguins (Hart et al 2010). HMMs assume that observations will depend on a finite number of underlying unobservable states (MacDonald and Zucchini 2009).…”