Analyzing Encounters using the R package MovementAnalysis and 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

Motivation: Encounters

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

Movement Ecology [Nathan et al. 2008] Why random? understanding movement causes consequences mechanisms patterns of

Movement – from data to paths Why random?

Brownian motion Robert Brown Continuous time random process Position at time, starting at Independent, stationary increments : Diffusion coefficient

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.

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 expected locations location variances

Motivation: Encounters

Demo in R

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 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)

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

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!