Estimating animal utilization densities using continuous-time Markov chain models

Kenady Wilson, Ephraim Hanks, Devin Johnson

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


A long-standing goal in ecology is to describe an animal's home range or utilization density (UD) without complete knowledge of the animal's movement. There are a number of methods available to calculate a UD from telemetry data, but the most common methods limit the UD to cover areas where the animal was observed during tracking, and do not account for preferential use of different habitats (resource selection). The limiting distribution of a continuous-time Markov chain (CTMC) matches the intuitive understanding of a UD for an animal following a CTMC movement model. By modelling continuous-time movement through discrete-gridded space we can infer environmental effects on animal movement and then predict a UD over the desired spatial area that captures preferential use of space. The r packages crawl and ctmcmove allow ecologists to use telemetry data to predict the UD of an animal using the limiting distribution of a CTMC movement model. We used data collected from Steller sea lions in Alaska to illustrate use of this method for investigating range-wide space use. Our findings show how these packages, and this method, can aid our understanding of space-use by predicting use outside the areas where animals were observed, avoiding barriers to movement, including environmental covariates and removing the release effect of telemetry studies. These results will be important for both conservation and management, particularly when determining critical habitat designation.

Original languageEnglish (US)
Pages (from-to)1232-1240
Number of pages9
JournalMethods in Ecology and Evolution
Issue number5
StatePublished - May 2018

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Ecological Modeling


Dive into the research topics of 'Estimating animal utilization densities using continuous-time Markov chain models'. Together they form a unique fingerprint.

Cite this