Uncovering hidden states in African lion movement data using hidden Markov models

Abstract

Context Direct observations of animals are the most reliable way to define their behavioural characteristics; however, to obtain these observations is costly and often logistically challenging. GPS tracking allows finer-scale interpretation of animal responses by measuring movement patterns; however, the true behaviour of the animal during the period of observation is seldom known. Aims The aim of our research was to draw behavioural inferences for a lioness with a hidden Markov model and to validate the predicted latent-state sequence with field observations of the lion pride. Methods We used hidden Markov models to model the movement of a lioness in the Kruger National Park, South Africa. A three-state log-normal model was selected as the most suitable model. The model outputs are related to collected data by using an observational model, such as, for example, a distribution for the average movement rate and/or direction of movement that depends on the underlying model states that are taken to represent behavioural states of the animal. These inferred behavioural states are validated against direct observation of the pride’s behaviour. Key results Average movement rate provided a useful alternative for the application of hidden Markov models to irregularly spaced GPS locations. The movement model predicted resting as the dominant activity throughout the day, with a peak in the afternoon. The local-movement state occurred consistently throughout the day, with a decreased proportion during the afternoon, when more resting takes place, and an increase towards the early evening. The relocating state had three peaks, namely, during mid-morning, early evening and about midnight. Because of the differences in timing of the direct observations and the GPS locations, we had to compare point observations of the true behaviour with an interval prediction of the modelled behavioural state. In 75% of the cases, the model-predicted behaviour and the field-observed behaviour overlapped. Conclusions Our data suggest that the hidden Markov modelling approach is successful at predicting a realistic behaviour of lions on the basis of the GPS location coordinates and the average movement rate between locations. The present study provided a unique opportunity to uncover the hidden states and compare the true behaviour with the inferred behaviour from the predicted state sequence. Implications Our results illustrated the potential of using hidden Markov models with movement rate as an input to understand carnivore behavioural patterns that could inform conservation management practices.

Decision rules for determining terrestrial movement and the consequences for filtering high-resolution global positioning system tracks: a case study using the African lion (Panthera leo)

“…At the very least, we demonstrate the utility of GPS speed to be included as a useful parameter for identifying behaviours and this may be of value to more complex approaches (e.g. machine learning (see [ 98 , 99 – 101 ]), the lowest common denominator (LoCoD) method [ 102 ] and space-state models (e.g. [ 103 , 104 ]) for precluding certain behaviours from movement and screening for location error.…”

Decision rules for determining terrestrial movement and the consequences for filtering high-resolution GPS tracks – A case study using the African Lion (Panthera leo)

“…At the very least, we demonstrate the utility for GPS speed to be included as a useful parameter for identifying behaviours and may be of value to more complex approaches (e.g., machine learning [cf. 96,[97][98][99], the lowest common denominator (LoCoD) method [100]) and space-state models [e.g., 101,102] for precluding certain behaviours from movement and screening for location error. Indeed, applying this method as a validator of movement extent within behaviour-based studies over nely resolved space and time, may facilitate the powers of inference, such as when considering animal responses to human barriers [cf.…”

Hidden Markov movement models reveal diverse seasonal movement patterns in two North American ungulates

“…They are a robust framework for modeling time series of animal movements because they account for the serial autocorrelation in observations and explicitly model the state and observation processes, allowing for the detection of sources of variation in state changes (McClintock et al, 2020). Hidden Markov movement models have been used to identify behavioral states at multiple temporal scales in a variety of taxa, including daily behaviors of African lions ( Leo leo ; Goodall et al, 2019) and migration behaviors of white sharks ( Carcharodon carcharias ; Weng et al, 2007). However, to the best of our knowledge, HMMs have been underutilized as a method to understand seasonal variation in the movement patterns of ungulates, likely due to the technical resources required for their estimation.…”