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ctmm: an r package for analyzing animal relocation data as a continuous‐time stochastic process

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Calabrese, Justin M., Fleming, Chris H., Gurarie, Eliezer·2016·Methods in Ecology and Evolution

Abstract

Summary: Movement ecology has developed rapidly over the past decade, driven by advances in tracking technology that have largely removed data limitations. Development of rigorous analytical tools has lagged behind empirical progress, and as a result, relocation data sets have been underutilized. Discrete‐time correlated random walk models (CRW) have long served as the foundation for analyzing relocation data. Unfortunately,CRW s confound the sampling and movement processes.CRW parameter estimates thus depend sensitively on the sampling schedule, which makes it difficult to draw sampling‐independent inferences about the underlying movement process. Furthermore,CRW s cannot accommodate the multiscale autocorrelations that typify modern, finely sampled relocation data sets. Recent developments in modelling movement as a continuous‐time stochastic process (CTSP) solve these problems, but the mathematical difficulty of using CTSP s has limited their adoption in ecology. To remove this roadblock, we introduce the ctmm package for the R statistical computing environment.ctmm implements all of the CTSP s currently in use in the ecological literature and couples them with powerful statistical methods for autocorrelated data adapted from geostatistics and signal processing, including variograms, periodograms and non‐Markovian maximum likelihood estimation. ctmm is built around a standard workflow that begins with visual diagnostics, proceeds to candidate model identification, and then to maximum likelihood fitting and AIC‐based model selection. Once an accurate CTSP for the data has been fitted and selected, analyses that require such a model, such as quantifying home range areas via autocorrelated kernel density estimation or estimating occurrence distributions via time‐series Kriging, can then be performed. We use a case study with African buffalo to demonstrate the capabilities of ctmm and highlight the steps of a typical CTSP movement analysis workflow.

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Metadata

Authors
Calabrese, J. M., Fleming, C. H., & Gurarie, E.
Venue
Methods in Ecology and Evolution
Publisher
Wiley
Published
2016-05-05
Volume
7
Issue
9
Pages
1124-1132
ISSN
2041-210X (ISSN-L)
OpenAlex
W2307442473
Cited by
673
References
1
FWCI
21.29
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: ctmm: an r package for analyzing animal relocation data as a continuous‐time stochastic process. https://app.cochat.ai/papers/10.1111/2041-210x.12559
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Calabrese, J. M., Fleming, C. H., & Gurarie, E. (2016). ctmm: an r package for analyzing animal relocation data as a continuous‐time stochastic process. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.12559

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