[MARMAM] Inferring Animal Densities from Tracking Data Using Markov Chains

[hwhitehe at DAL.ca]

MARMAM readers may be interested in this paper:

Whitehead H, Jonsen ID (2013) Inferring Animal Densities from Tracking 
Data Using Markov Chains. PLoS ONE 8(4): e60901. 
doi:10.1371/journal.pone.0060901 

Available at: 
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.006
0901

Abstract:
The distributions and relative densities of species are keys to ecology. Large 
amounts of tracking data are being collected on a wide variety of animal 
species using several methods, especially electronic tags that record 
location. These tracking data are effectively used for many purposes, but 
generally provide biased measures of distribution, because the starts of the 
tracks are not randomly distributed among the locations used by the 
animals. We introduce a simple Markov-chain method that produces 
unbiased measures of relative density from tracking data. The density 
estimates can be over a geographical grid, and/or relative to environmental 
measures. The method assumes that the tracked animals are a random 
subset of the population in respect to how they move through the habitat 
cells, and that the movements of the animals among the habitat cells form a 
time-homogenous Markov chain. We illustrate the method using simulated 
data as well as real data on the movements of sperm whales. The 
simulations illustrate the bias introduced when the initial tracking locations 
are not randomly distributed, as well as the lack of bias when the Markov 
method is used. We believe that this method will be important in giving 
unbiased estimates of density from the growing corpus of animal tracking 
data.

Abstract