Benford’s Law for Telemetry Data of Wildlife

Benford’s law (<i>BL</i>) specifies the expected digit distributions of data in social sciences, such as demographic or financial data. We focused on the first-digit distribution and hypothesized that it would apply to data on locations of animals freely moving in a natural habitat. We b...

Full description

Bibliographic Details
Main Authors: Lasse Pröger, Paul Griesberger, Klaus Hackländer, Norbert Brunner, Manfred Kühleitner
Format: Article
Language:English
Published: MDPI AG 2021-11-01
Series:Stats
Subjects:
Online Access:https://www.mdpi.com/2571-905X/4/4/55
Description
Summary:Benford’s law (<i>BL</i>) specifies the expected digit distributions of data in social sciences, such as demographic or financial data. We focused on the first-digit distribution and hypothesized that it would apply to data on locations of animals freely moving in a natural habitat. We believe that animal movement in natural habitats may differ with respect to <i>BL</i> from movement in more restricted areas (e.g., game preserve). To verify the <i>BL</i>-hypothesis for natural habitats, during 2015–2018, we collected telemetry data of twenty individuals of wild red deer from an alpine region of Austria. For each animal, we recorded the distances between successive position records. Collecting these data for each animal in weekly logbooks resulted in 1132 samples of size 65 on average. The weekly logbook data displayed a <i>BL</i>-like distribution of the leading digits. However, the data did not follow <i>BL</i> perfectly; for 9% (99) of the 1132 weekly logbooks, the chi-square test refuted the <i>BL</i>-hypothesis. A Monte Carlo simulation confirmed that this deviation from <i>BL</i> could not be explained by spurious tests, where a deviation from <i>BL</i> occurred by chance.
ISSN:2571-905X