Annoucncing the Dynamic Brownian Bridge Movement Models Workshop

Home range estimations are frequently used to help understand snakes’ activities and habitat use. However, the methods used to estimate snake home ranges have remained largely unchanged for decades despite improvements in tracking technology. Older methods have been found to have potentially critical flaws that can lead to over- and under-estimation of home range size. Errors associated with home range estimation can lead to incorrect assessments of a snakes’ habitat or space requirements.

The dynamic Brownian Bridge Movement Models workshop is designed to introduce participants to a more modern method for home range estimation –dBBMMs. Unlike older methods, dBBMMs use more information gathered during data collection, namely time and location error. By combining locations, location error, times, and estimations of a snake’s speed we are able to produce home range estimations that are more realistic, improving how confidently we can answer questions connected to space use. As dBBMMs explicitly incorporate time, they also enable us to investigate movement patterns over time.

Current work is suggesting that dBBMMs, although designed for use with GPS tracking devices, are suitable for radio-telemetry studies. Dynamic Brownian Bridges Movement Model’s versatility makes them immediately applicable to snake tracking studies, without any dramatic changes to data collection procedure.


  • Presentation: Introduction, why estimate home range? Why dBBMMs?
  • Discussion: Participants, in groups, create hypothetical research questions that could be answered via home range estimation.
  • Presentation: How dBBMMs work?
  • Discussion: Participants refine their questions: how can the added information of dBBMMs improve their question?
  • Code demonstration: How do we run dBBMMs in R (using an example dataset)?
  • Code demonstration: How do we plot the dBBMM outputs in context?
  • Independent activity: Participants explore how dBBMM parameters can affect the output.
  • Independent activity: Participants work independently with an unseen dataset.
  • Presentation: Recap what dBBMMs provide us and why other methods can be problematic.
  • Discussion: Final discussion of hypothetical research questions: how could home range error impact the answers/implications?

Skills for participants

By the end of the workshop participants should understand:

  • the theory behind dBBMMs
  • the justification for using dBBMMs in comparison to other home range estimation methods
  • how to run dBBMMs in program R
  • how to choose the dBBMM parameters
  • how to interpret dBBMM results in the context of a research question
  • how to plot dBBMM ranges in the context of a study region