Back to Search
Start Over
ctmm: an <scp>r</scp> package for analyzing animal relocation data as a continuousâtime stochastic process
- Source :
- Methods in Ecology and Evolution. 7:1124-1132
- Publication Year :
- 2016
- Publisher :
- Wiley, 2016.
-
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, CRWs 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, CRWs 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 CTSPs 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 CTSPs 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.
- Subjects :
- 0106 biological sciences
Continuous-time stochastic process
Computer science
Ecological Modeling
Model selection
Kernel density estimation
System identification
Sampling (statistics)
computer.software_genre
010603 evolutionary biology
01 natural sciences
010601 ecology
Workflow
Kriging
Computational statistics
Data mining
computer
Ecology, Evolution, Behavior and Systematics
Subjects
Details
- ISSN :
- 2041210X
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- Methods in Ecology and Evolution
- Accession number :
- edsair.doi...........59981527eb16acd2f4b4ad3ec73e7cc8
- Full Text :
- https://doi.org/10.1111/2041-210x.12559