Visualising uncertainty in radio-telemetry wildlife-tracking data to aid better study design
Thomas R. Etherington A B C , Stéphane Pietravalle A and Dave P. Cowan AA The Food and Environment Research Agency, Sand Hutton, York YO41 1LZ, UK.
B School of Environment, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
C Corresponding author. Email: teth001@aucklanduni.ac.nz
Wildlife Research 37(6) 482-488 https://doi.org/10.1071/WR09151
Submitted: 3 November 2009 Accepted: 29 August 2010 Published: 18 October 2010
Abstract
Context.: Animal movements recorded by radio-telemetry produce a series of spatio-temporal point-location estimates that sample an animal’s continuous track. However, uncertainty in the point locations themselves, and uncertainty of how the animal moved between locations, could be large enough to render data unsuitable for some purposes.
Aims.: Our objective was to develop a method that would visualise these uncertainties by calculating the maximum range of possible movements around the sampled point locations, given different probabilities of point-location error and animal-movement speed. By visualising the uncertainties, we hope to aid better study design.
Methods.: To quantify the probability of different levels of uncertainty, we use a probability density function (PDF) of point-location error and movement speed. By choosing a cut-off probability on each PDF, ellipses can be drawn for consecutive pairs of point-location estimates, which when combined show the maximum range of possible movements at those specific cut-off probabilities of location error and movement speed. We demonstrate how to establish the PDFs and apply the methodology by using an example of grizzly bear (Ursus arctos) radio-telemetry data.
Key results.: By establishing a range of probability cut-offs within each PDF, it is possible to visualise the area within which a grizzly bear could have moved under different combinations of those cut-offs.
Conclusions.: Comparison of the potential maximum range of possible movements, under different combinations of probability cut-offs, enables the relative importance of each source of uncertainty to be evaluated. Acquiring data from intense sampling would be particularly useful in providing robust information on likely movement speeds.
Implications.: This approach could be used during study design and testing to prioritise efforts towards reducing uncertainty in the point-location estimates, and uncertainty of where the animal moved between locations to ensure the radio-telemetry method used is appropriate for the study’s objectives.
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