Analyzing Encounters using the R package MovementAnalysis and other usages of MovementAnalysis

1. Analyzing Encounters using the R package MovementAnalysisand other usages of MovementAnalysis Kevin Buchin Joint work with Stef Sijben, Jean Arseneau, Erik Willems, Emiel van Loon, Nir Sapir, Stephanie Mercier September 30, 2013

2. Motivation: Encounters • http://youtu.be/OX6azU3Spq8

3. Motivation: Encounters • data: • 4 groups of vervet monkeys • 1 representative per group • 1 GPS-fix per daytime hour • several month • ecology questions: interaction between groups • general goal: develop algorithmic framework for animal movement analysis • starting point: • Brownian bridge movement model • movement ecology paradigm

4. MovementEcology Why random? • causes • consequences • mechanisms • patterns of understanding movement [Nathan et al. 2008]

5. Movement – from data to paths Why random?

6. Brownian motion Robert Brown 1773-1858 • Continuous time random process • Position at time , starting at • Independent, stationary increments • : Diffusion coefficient 1827

7. Brownian bridge movement model • Brownian bridge: • Brownian motion conditioned under starting and ending position • Brownian bridge movement model: • Each relocation is modeled as a Brownian bridge.

8. Computing with Brownian Bridges • Utilization Distributions [Bullard, 1999, Horne et al. 2007] • Basic Properties and Movement Patterns [Buchin, Sijben, Arseneau, Willems 2012] • Example: Distance • 2 trajectories • positions at time t are bivariate normal • distance is distributed 0 20 40 60 80 100120140 location variances expected locations

9. Motivation: Encounters ?

10. Demo in R

11. Speed and External Factors • Study: European bee-eater migratory flight • link flight mode to atmospheric conditions • compute diffusion coefficients for flight modes separately • flight modes result in significantly differences in diffusion coefficients and speeds

12. Speed and External Factors • Study: European bee-eater migratory flight • link flight mode to atmospheric conditions • compute diffusion coefficients for flight modes separately • flight modes result in significantly differences in diffusion coefficients and speeds Speed (m/s)

13. Speed and External Factors • Study: Vervet monkeys/food availability • linking speed and food availability by NDVI • significant negative correlation between speed and NDVI

14. Summary • Towards a framework for algorithmic movement analysis using Brownian bridges • Basic building blocks for movement patterns • Provided as R package • Case studies: Brownian bridges give insights beyond linear movement Thanks!