y t

10−5 ~N

ey t ∏i=1

Tx i

∞

a

2

1=i

ℝ

L e

AICx iy t ∑i=1

Ti

∫b

109

Using R to study animal movement

Marie Auger-MéthéUniversity of Alberta, Canada

Predicting the impacts of environmental changes

Photos: Dennis Bromage, Wildlife Conservation Society

Monitor behaviours

Photo: Wayne Lynch

Satellite telemetry

Photos: A. Park, C. Franklin, P. Lopez, N. Papathanasopoulou, R. Cameron, S. Anderson, R. Schuckard, L. Thorngren, L. Boehme,C. Jay

Apply models to movement data

Goals my work● 1. Create models for searching strategies

● Basics of creating probability models● Tips for boosting efficiency

● 2. Differentiate between drift and voluntary movement● State-space models● Spatio-temporal data

How do animals search food?

How do animals search food?

Likelihood function

Likelihood● Probability of the data given the model

μ = 0σ = 1

Likelihood● Probability of the data given the model

μ = 0σ = 1

Probability density functions in R● Package: stats

● dnorm(x, mean = 0, sd = 1)

Probability density functions in R● Package: stats

● dnorm(x, mean = 0, sd = 1)● dexp(), dbeta()

● Package: VGAM● dpareto()

● Packages: CircStats● dwrpnorm()

● Packages: Circular● dwrappednormal()

Simulations: R is too slow● R is pretty inefficient in terms of speed● Parallel computing:

● Packages: parallel(), doRNG()● Package: Rcpp

● C++ code in R● http://dirk.eddelbuettel.com/code/rcpp.html● https://github.com/hadley/devtools/wiki/Rcpp

Simulations: error handling● Package: base

> res <- myFx(x)Error ...> res <- tryCatch(myFx(x),

error=function(e) NA)

Simulations: comparing different fx● Package: rbenchmark

> benchmark(myFx1(x), myFx2(x),replications=100)

test replications elapsed ...1 myFx1(x) 100 0.00 ... 2 myFx2(x) 100 70.64 ...

Goals my work● 1. Framework to compare searching strategies

● Basics of creating probability models● Tips for boosting efficiency

● 2. Differentiate between drift and voluntary movement● State-space models● Spatio-temporal data

Differentiating ice drift from bear movement

State-space model

yt = xt + vt + ϵt ϵt~N(µ,σ)yt = xt + vt + ϵt ϵt~N(µ,σ)

xt = ρ xt-1 + ɳt ɳt~N(0,ξ)xt = ρ xt-1 + ɳt ɳt~N(0,ξ)

Observation equation:

Process equation:

Ice movementBear movement

State-space model

State-space model● Package: dlm (with package zoo)

● dlm()● dlmMLE()● dlmFilter(), dlmSmooth()

Spatio-temporal data● Packages: adehabitatLT, adehabitatHR,

adehabitatMA● Packages: raster, sp

Thank you