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

• http://youtu.be/OX6azU3Spq8

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 1773-1858

1827

• 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

0 20 40 60 80 100 120 140

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

Spe

ed (

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!