Path segmentation for beginners: an overview of current methods for detecting changes in animal movement patterns

Edelhoff, Hendrik; Signer, Johannes; Balkenhol, Niko

doi:10.1186/s40462-016-0086-5

Cited by 175 publications

(203 citation statements)

References 133 publications

(238 reference statements)

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“…Given an animal movement path [2][3][4], as a sequence of time-stamped locations, we focus on how to make inferences about animal movement behavior. Where and when does the animal engage in a specific movement behavior, and how does the movement behavior change over time?…”

Section: Introductionmentioning

confidence: 99%

“…The movement parameters can be good proxies for movement behavioral states along an animal movement path [4].…”

Section: Introductionmentioning

confidence: 99%

“…Recent developments in tracking devices and the increasing availability of movement data provide new opportunities for the inference of movement behavior from animal movement paths [4,[11][12][13]. There are different approaches for distinguishing movement behaviors from animal movement paths, including statistical modelling, data mining techniques, mixtures of random walk models, and movement-derived parameters [3][4][5]9,[14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…There are different approaches for distinguishing movement behaviors from animal movement paths, including statistical modelling, data mining techniques, mixtures of random walk models, and movement-derived parameters [3][4][5]9,[14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Edelhoff et al [4] outline three broad types of research questions that are commonly addressed using path segmentation methods: (1) the quantitative description of movement patterns, (2) the detection of significant change points, and (3) the identification of underlying processes or hidden states.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Deriving Animal Movement Behaviors Using Movement Parameters Extracted from Location Data

Teimouri

Indahl

Sickel

et al. 2018

IJGI

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We present a methodology for distinguishing between three types of animal movement behavior (foraging, resting, and walking) based on high-frequency tracking data. For each animal we quantify an individual movement path. A movement path is a temporal sequence consisting of the steps through space taken by an animal. By selecting a set of appropriate movement parameters, we develop a method to assess movement behavioral states, reflected by changes in the movement parameters. The two fundamental tasks of our study are segmentation and clustering. By segmentation, we mean the partitioning of the trajectory into segments, which are homogeneous in terms of their movement parameters. By clustering, we mean grouping similar segments together according to their estimated movement parameters. The proposed method is evaluated using field observations (done by humans) of movement behavior. We found that on average, our method agreed with the observational data (ground truth) at a level of 80.75% ± 5.9% (SE).

show abstract

Section: Introductionmentioning

confidence: 99%

“…The movement parameters can be good proxies for movement behavioral states along an animal movement path [4].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Deriving Animal Movement Behaviors Using Movement Parameters Extracted from Location Data

Teimouri

Indahl

Sickel

et al. 2018

IJGI

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Movement patterns and habitat use for the sympatric species: Gambelia wislizenii and Aspidoscelis tigris

McAlpine‐Bellis,

Utsumi,

Diamond

et al. 2023

Ecology and Evolution

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Movement is an important characteristic of an animal's ecology, reflecting the perception of and response to environmental conditions. To effectively search for food, movement patterns likely depend on habitat characteristics and the sensory systems used to find prey. We examined movements associated with foraging for two sympatric species of lizards inhabiting the Great Basin Desert of southeastern Oregon. The two species have largely overlapping diets but find prey via different sensory cues, which link to their differing foraging strategies—the long‐nosed leopard lizard, Gambelia wislizenii, is a visually‐oriented predator, while the western whiptail, Aspidoscelis tigris, relies more heavily on chemosensory cues to find prey. Using detailed focal observations, we characterized the habitat use and movement paths of each species. We placed markers at the location of focal animals every minute for the duration of each 30‐min observation. Afterward, we recorded whether each location was in the open or in vegetation, as well as the movement metrics of step length, path length, net displacement, straightness index, and turn angle, and then made statistical comparisons between the two species. The visual forager spent more time in open areas, moved less frequently over shorter distances, and differed in patterns of plant use compared to the chemosensory forager. Path characteristics of step length and turn angle differed between species. The visual predator moved in a way that was consistent with the notion that they require a clear visual path to stalk prey whereas the movement of the chemosensory predator increased their chances of detecting prey by venturing further into vegetation. Sympatric species can partition limited resources through differences in search behavior and habitat use.

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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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Path segmentation for beginners: an overview of current methods for detecting changes in animal movement patterns

Cited by 175 publications

References 133 publications

Deriving Animal Movement Behaviors Using Movement Parameters Extracted from Location Data

Deriving Animal Movement Behaviors Using Movement Parameters Extracted from Location Data

Movement patterns and habitat use for the sympatric species: Gambelia wislizenii and Aspidoscelis tigris

Treed Gaussian processes for animal movement modeling

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