Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions

Langrock, Roland; King, Ruth; Matthiopoulos, Jason; Thomas, Len; Fortin, Daniel; Morales, Juan Manuel

doi:10.1890/11-2241.1

Cited by 369 publications

(494 citation statements)

References 23 publications

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“…Thus, SCR has a characterization as a state-space (Patterson et al 2008) or hidden Markov model (HMM; Langrock et al 2012) with specific forms of observation model governed by spatial sampling and an underlying latent process model that describes movement of individuals on…”

Section: Scr For Modeling Movement and Dispersalmentioning

confidence: 99%

Unifying population and landscape ecology with spatial capture–recapture

Royle¹,

Fuller

Sutherland³

2017

Ecography

129

172

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Spatial heterogeneity in the environment induces variation in population demographic rates and dispersal patterns, which result in spatio-temporal variation in density and gene flow. Unfortunately, applying theory to learn about the role of spatial structure on populations has been hindered by the lack of mechanistic spatial models and inability to make precise observations of population state and structure. Spatial capture-recapture (SCR) represents an individual-based analytic framework for overcoming this fundamental obstacle that has limited the utility of ecological theory. SCR methods make explicit use of spatial encounter information on individuals in order to model density and other spatial aspects of animal population structure, and they have been widely adopted in the last decade. We review the historical context and emerging developments in SCR models that enable the integration of explicit ecological hypotheses about landscape connectivity, movement, resource selection, and spatial variation in density, directly with individual encounter history data obtained by new technologies (e.g. camera trapping, non-invasive DNA sampling). We describe ways in which SCR methods stand to advance the study of animal population ecology.

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Section: Scr For Modeling Movement and Dispersalmentioning

confidence: 99%

Unifying population and landscape ecology with spatial capture–recapture

Royle¹,

Fuller

Sutherland³

2017

Ecography

129

172

View full text Add to dashboard Cite

show abstract

“…2004), Hidden Markov Models (Langrock et al. 2012), Markov switching autoregressive models (Pinto and Spezia 2015) and State‐Space Models (Jonsen et al. 2005; Bestley et al.…”

Section: Discussionmentioning

confidence: 99%

The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data

Chimienti

Cornulier

Owen

et al. 2016

Ecology and Evolution

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The recent increase in data accuracy from high resolution accelerometers offers substantial potential for improved understanding and prediction of animal movements. However, current approaches used for analysing these multivariable datasets typically require existing knowledge of the behaviors of the animals to inform the behavioral classification process. These methods are thus not well‐suited for the many cases where limited knowledge of the different behaviors performed exist. Here, we introduce the use of an unsupervised learning algorithm. To illustrate the method's capability we analyse data collected using a combination of GPS and Accelerometers on two seabird species: razorbills (Alca torda) and common guillemots (Uria aalge). We applied the unsupervised learning algorithm Expectation Maximization to characterize latent behavioral states both above and below water at both individual and group level. The application of this flexible approach yielded significant new insights into the foraging strategies of the two study species, both above and below the surface of the water. In addition to general behavioral modes such as flying, floating, as well as descending and ascending phases within the water column, this approach allowed an exploration of previously unstudied and important behaviors such as searching and prey chasing/capture events. We propose that this unsupervised learning approach provides an ideal tool for the systematic analysis of such complex multivariable movement data that are increasingly being obtained with accelerometer tags across species. In particular, we recommend its application in cases where we have limited current knowledge of the behaviors performed and existing supervised learning approaches may have limited utility.

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“…These observations were introduced and modelled as part of a larger set consisting of four elk in the discrete-time 'step and turn' model of Morales et al (2004), and more recently modelled in the vignette of the R package moveHMM (Michelot et al 2016) applying the hidden Markov model of Langrock et al (2012). Observations are shown in Fig.…”

Section: Two-state Switching Movement In Elkmentioning

confidence: 99%

“…Within a behaviour, movement is defined by the straight line 'step length' between two consecutive locations and the 'turning angle' between three consecutive locations, following parametric distributions such as the Weibull and the wrapped Cauchy, respectively (Morales et al 2004;McClintock et al 2014). Popular variants on this include state space models to incorporate observation error (Patterson et al 2010;Jonsen et al 2013), hidden Markov models for efficiency (Langrock et al 2012) and change point analysis rather than Markov chains to identify behavioural switches (Gurarie et al 2009;Nams 2014). Parton et al (2017) introduce a continuous-time movement model based on similar quantities to those of the popular discrete-time 'step and turn' models.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inference for Multistate ‘Step and Turn’ Animal Movement in Continuous Time

Parton

Blackwell

2017

JABES

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Mechanistic modelling of animal movement is often formulated in discrete time despite problems with scale invariance, such as handling irregularly timed observations. A natural solution is to formulate in continuous time, yet uptake of this has been slow. This lack of implementation is often excused by a difficulty in interpretation. Here we aim to bolster usage by developing a continuous-time model with interpretable parameters, similar to those of popular discrete-time models that use turning angles and step lengths. Movement is defined by a joint bearing and speed process, with parameters dependent on a continuous-time behavioural switching process, creating a flexible class of movement models. Methodology is presented for Markov chain Monte Carlo inference given irregular observations, involving augmenting observed locations with a reconstruction of the underlying movement process. This is applied to well-known GPS data from elk (Cervus elaphus), which have previously been modelled in discrete time. We demonstrate the interpretable nature of the continuous-time model, finding clear differences in behaviour over time and insights into short-term behaviour that could not have been obtained in discrete time.

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Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions

Cited by 369 publications

References 23 publications

Unifying population and landscape ecology with spatial capture–recapture

Unifying population and landscape ecology with spatial capture–recapture

The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data

Bayesian Inference for Multistate ‘Step and Turn’ Animal Movement in Continuous Time

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