SpatialRugs: A compact visualization of space and time for analyzing collective movement data

Identification and classification of behavior states in animal movement data can be complex, temporally biased, time-intensive, scale-dependent, and unstandardized across studies and taxa. Large movement datasets are increasingly common and there is a need for efficient methods of data exploration that adjust to the individual variability of each track. We present the Residence in Space and Time (RST) method to classify behavior patterns in movement data based on the concept that behavior states can be partitioned by the amount of space and time occupied in an area of constant scale. Using normalized values of Residence Time and Residence Distance within a constant search radius, RST is able to differentiate behavior patterns that are distance-intensive (e.g., area restricted search), time-intensive (e.g., rest), and transit (short time and distance). We use grey-headed albatross (Thalassarche chrysostoma) GPS tracks to demonstrate RST’s ability to classify behavior patterns and adjust to the inherent scale and individuality of each track. Next, we evaluate RST’s ability to discriminate between behavior states relative to other classical movement metrics. We then sub-sample albatross track data to illustrate RST’s response to less temporally resolved data. Finally, we evaluate RST’s performance using datasets from four taxa with diverse ecology, functional scales, ecosystems, and data-types. We conclude that RST is a robust, rapid, and flexible method for detailed exploratory analysis and meta-analyses of behavioral states in animal movement data based on its ability to integrate distance and time measurements into one descriptive metric of behavior groupings. Given the increasing amount of animal movement data collected, it is timely and useful to implement a consistent metric of behavior classification to enable efficient and comparative analyses. Overall, the application of RST to objectively explore and compare behavior patterns in movement data can enhance our fine- and broad- scale understanding of animal movement ecology.

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A framework for understanding the architecture of collective movements using pairwise analyses of animal movement data

Journal of The Royal Society Interface ◽

10.1098/rsif.2010.0389 ◽

2010 ◽

Vol 8 (56) ◽

pp. 322-333 ◽

Cited By ~ 10

Author(s):

Leo Polansky ◽

George Wittemyer

Keyword(s):

Information Exchange ◽

Animal Movement ◽

Interspecific Interactions ◽

Movement Patterns ◽

African Elephants ◽

Collective Movement ◽

Movement Trajectories ◽

Movement Data ◽

Collective Movements ◽

Free Ranging

The study of collective or group-level movement patterns can provide insight regarding the socio-ecological interface, the evolution of self-organization and mechanisms of inter-individual information exchange. The suite of drivers influencing coordinated movement trajectories occur across scales, resulting from regular annual, seasonal and circadian stimuli and irregular intra- or interspecific interactions and environmental encounters acting on individuals. Here, we promote a conceptual framework with an associated statistical machinery to quantify the type and degree of synchrony, spanning absence to complete, in pairwise movements. The application of this framework offers a foundation for detailed understanding of collective movement patterns and causes. We emphasize the use of Fourier and wavelet approaches of measuring pairwise movement properties and illustrate them with simulations that contain different types of complexity in individual movement, correlation in movement stochasticity, and transience in movement relatedness. Application of this framework to movements of free-ranging African elephants ( Loxodonta africana ) provides unique insight on the separate roles of sociality and ecology in the fission–fusion society of these animals, quantitatively characterizing the types of bonding that occur at different levels of social relatedness in a movement context. We conclude with a discussion about expanding this framework to the context of larger (greater than three) groups towards understanding broader population and interspecific collective movement patterns and their mechanisms.

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From single steps to mass migration: the problem of scale in the movement ecology of the Serengeti wildebeest

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2017.0012 ◽

2018 ◽

Vol 373 (1746) ◽

pp. 20170012 ◽

Cited By ~ 17

Author(s):

Colin J. Torney ◽

J. Grant C. Hopcraft ◽

Thomas A. Morrison ◽

Iain D. Couzin ◽

Simon A. Levin

Keyword(s):

Community Dynamics ◽

Animal Movement ◽

Multiple Scales ◽

Movement Ecology ◽

Population Fluctuations ◽

Ecological Processes ◽

Spatial And Temporal Scales ◽

Collective Movement ◽

Movement Data ◽

Mass Migration

A central question in ecology is how to link processes that occur over different scales. The daily interactions of individual organisms ultimately determine community dynamics, population fluctuations and the functioning of entire ecosystems. Observations of these multiscale ecological processes are constrained by various technological, biological or logistical issues, and there are often vast discrepancies between the scale at which observation is possible and the scale of the question of interest. Animal movement is characterized by processes that act over multiple spatial and temporal scales. Second-by-second decisions accumulate to produce annual movement patterns. Individuals influence, and are influenced by, collective movement decisions, which then govern the spatial distribution of populations and the connectivity of meta-populations. While the field of movement ecology is experiencing unprecedented growth in the availability of movement data, there remain challenges in integrating observations with questions of ecological interest. In this article, we present the major challenges of addressing these issues within the context of the Serengeti wildebeest migration, a keystone ecological phenomena that crosses multiple scales of space, time and biological complexity. This article is part of the theme issue 'Collective movement ecology'.

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Animal movement analysis through residence in space and time

10.7287/peerj.preprints.2480v1 ◽

2016 ◽

Cited By ~ 1

Author(s):

Leigh G Torres ◽

Rachael A. Orben ◽

Irina Tolkova ◽

David R Thompson

Keyword(s):

Animal Movement ◽

Search Time ◽

Movement Ecology ◽

Movement Analysis ◽

Individual Variability ◽

Behavior Patterns ◽

Data Types ◽

Space And Time ◽

Movement Data ◽

The Individual

Identification and classification of behavior states in animal movement data can be complex, temporally biased, time-intensive, scale-dependent, and unstandardized across studies and taxa. Large movement datasets are increasingly common and there is a need for efficient methods of data exploration that adjust to the individual variability of each track. We present the Residence in Space and Time (RST) method to classify behavior patterns in movement data based on the concept that behavior states can be partitioned by the amount of space and time occupied in an area of constant scale. Using normalized values of Residence Time and Residence Distance within a constant search radius, RST is able to differentiate behavior patterns that are distance-intensive (e.g., area restricted search), time-intensive (e.g., rest), and transit (short time and distance). We use grey-headed albatross (Thalassarche chrysostoma) GPS tracks to demonstrate RST’s ability to classify behavior patterns and adjust to the inherent scale and individuality of each track. Next, we evaluate RST’s ability to discriminate between behavior states relative to other classical movement metrics. We then sub-sample albatross track data to illustrate RST’s response to less temporally resolved data. Finally, we evaluate RST’s performance using datasets from four taxa with diverse ecology, functional scales, ecosystems, and data-types. We conclude that RST is a robust, rapid, and flexible method for detailed exploratory analysis and meta-analyses of behavioral states in animal movement data based on its ability to integrate distance and time measurements into one descriptive metric of behavior groupings. Given the increasing amount of animal movement data collected, it is timely and useful to implement a consistent metric of behavior classification to enable efficient and comparative analyses. Overall, the application of RST to objectively explore and compare behavior patterns in movement data can enhance our fine- and broad- scale understanding of animal movement ecology.

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Using Tongue, Lip, and Jaw Movement Data to Improve Diagnostic Accuracy of ALS

10.1044/cred-dsa-bts-004 ◽

2014 ◽

Vol 1 (1) ◽

pp. 1

Author(s):

Jordan Green

Keyword(s):

Diagnostic Accuracy ◽

Jaw Movement ◽

Movement Data

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Languages in Space and Time

10.1017/9781108480659 ◽

2020 ◽

Author(s):

Marco Patriarca ◽

Els Heinsalu ◽

Jean Leó Leonard

Keyword(s):

Space And Time

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On Space and Time

10.1017/cbo9781139644259 ◽

2008 ◽

Cited By ~ 2

Author(s):

Alain Connes ◽

Michael Heller ◽

Roger Penrose ◽

John Polkinghorne ◽

Andrew Taylor

Keyword(s):

Space And Time

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Age Differences in Eye Movements During Reading: Degenerative Problems or Compensatory Strategy?

European Psychologist ◽

10.1027/1016-9040/a000344 ◽

2019 ◽

Vol 24 (4) ◽

pp. 297-311

Author(s):

José David Moreno ◽

José A. León ◽

Lorena A. M. Arnal ◽

Juan Botella

Keyword(s):

Eye Movements ◽

Eye Movement ◽

Meta Analysis ◽

Age Groups ◽

Behavioral Changes ◽

Aging Effects ◽

Movement Data ◽

Sentence Reading ◽

The Difference ◽

Eye Movements During Reading

Abstract. We report the results of a meta-analysis of 22 experiments comparing the eye movement data obtained from young ( Mage = 21 years) and old ( Mage = 73 years) readers. The data included six eye movement measures (mean gaze duration, mean fixation duration, total sentence reading time, mean number of fixations, mean number of regressions, and mean length of progressive saccade eye movements). Estimates were obtained of the typified mean difference, d, between the age groups in all six measures. The results showed positive combined effect size estimates in favor of the young adult group (between 0.54 and 3.66 in all measures), although the difference for the mean number of fixations was not significant. Young adults make in a systematic way, shorter gazes, fewer regressions, and shorter saccadic movements during reading than older adults, and they also read faster. The meta-analysis results confirm statistically the most common patterns observed in previous research; therefore, eye movements seem to be a useful tool to measure behavioral changes due to the aging process. Moreover, these results do not allow us to discard either of the two main hypotheses assessed for explaining the observed aging effects, namely neural degenerative problems and the adoption of compensatory strategies.

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