A new multi-scale measure for analysing animal movement data

Postlethwaite, Claire M.; Brown, Pieta; Dennis, Todd E.

doi:10.1016/j.jtbi.2012.10.007

Cited by 31 publications

(39 citation statements)

References 26 publications

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“…Modern advances in GPS and ARGOS satellite telemetry allow scientists to remotely acquire large quantities of animal movement data at fine spatiotemporal scales [15]. Greater computing power has also enabled researchers to describe and model individual movement patterns and identify behavioral information that is not directly observable [15,16]. To better understand Antarctic minke whale habitat and movement patterns, we attached satellite tags to individual minke whales, tracked their daily movements, and related their behaviors to environmental conditions.…”

Section: Open Accessmentioning

confidence: 99%

“…Here we used the Multi-Scale Straightness Index (MSSI) to analyze the tortuosity and identify different behavioral states throughout each track [16]. The MSSI was chosen over the behavioral states produced by the Bayesian state-space model (SSM) for its ability to detect average behavioral states over different temporal scales as well as its simplicity in implementation and interpretation.…”

Section: Movement Analysismentioning

confidence: 99%

“…The MSSI was chosen over the behavioral states produced by the Bayesian state-space model (SSM) for its ability to detect average behavioral states over different temporal scales as well as its simplicity in implementation and interpretation. The MSSI and SSM perform relatively equally in classifying behaviors for datasets with low to medium spatial noise, but the MSSI outperforms SSMs in the case of noisy data [16]. While it is difficult to assess the relative noise in our current dataset, we chose to use the MSSI as the conservative approach to maximize performance of our behavioral classifications, as even under low noise conditions the MSSI will perform equally as well as SSMs [16].…”

Section: Movement Analysismentioning

confidence: 99%

“…The observational sliding window (w) is the length of time over which we computed the MSSI. The MSSI is then defined as follows [16]:…”

Section: Movement Analysismentioning

confidence: 99%

“…It is recommended to choose the parameters after examining the MSSI over the entire track length [16]. Values should be chosen to represent the temporal scale of the desired behavioral states in order to identify changes in behavior.…”

Section: Movement Analysismentioning

confidence: 99%

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Behavior of satellite-tracked Antarctic minke whales (Balaenoptera bonaerensis) in relation to environmental factors around the western Antarctic Peninsula

et al. 2017

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Section: Open Accessmentioning

confidence: 99%

Section: Movement Analysismentioning

confidence: 99%

Section: Movement Analysismentioning

confidence: 99%

“…The observational sliding window (w) is the length of time over which we computed the MSSI. The MSSI is then defined as follows [16]:…”

Section: Movement Analysismentioning

confidence: 99%

Section: Movement Analysismentioning

confidence: 99%

See 3 more Smart Citations

Behavior of satellite-tracked Antarctic minke whales (Balaenoptera bonaerensis) in relation to environmental factors around the western Antarctic Peninsula

et al. 2017

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Treed Gaussian processes for animal movement modeling

Rieber,

Hefley,

Haukos

2024

Ecology and Evolution

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Wildlife telemetry data may be used to answer a diverse range of questions relevant to wildlife ecology and management. One challenge to modeling telemetry data is that animal movement often varies greatly in pattern over time, and current continuous‐time modeling approaches to handle such nonstationarity require bespoke and often complex models that may pose barriers to practitioner implementation. We demonstrate a novel application of treed Gaussian process (TGP) modeling, a Bayesian machine learning approach that automatically captures the nonstationarity and abrupt transitions present in animal movement. The machine learning formulation of TGPs enables modeling to be nearly automated, while their Bayesian formulation allows for the derivation of movement descriptors with associated uncertainty measures. We demonstrate the use of an existing R package to implement TGPs using the familiar Markov chain Monte Carlo algorithm. We then use estimated movement trajectories to derive movement descriptors that can be compared across individuals and populations. We applied the TGP model to a case study of lesser prairie‐chickens (Tympanuchus pallidicinctus) to demonstrate the benefits of TGP modeling and compared distance traveled and residence times across lesser prairie‐chicken individuals and populations. For broad usability, we outline all steps necessary for practitioners to specify relevant movement descriptors (e.g., turn angles, speed, contact points) and apply TGP modeling and trajectory comparison to their own telemetry datasets. Combining the predictive power of machine learning and the statistical inference of Bayesian methods to model movement trajectories allows for the estimation of statistically comparable movement descriptors from telemetry studies. Our use of an accessible R package allows practitioners to model trajectories and estimate movement descriptors, facilitating the use of telemetry data to answer applied management questions.

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