Open AccessClassifying behavior from short‐interval biologging data: An example with GPS tracking of birds
Author(s) -
Bergen Silas,
Huso Manuela M.,
Duerr Adam E.,
Braham Melissa A.,
Katzner Todd E.,
Schmuecker Sara,
Miller Tricia A.
Publication year - 2022
Publication title -
ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.17
H-Index - 63
ISSN - 2045-7758
DOI - 10.1002/ece3.8395
Subject(s) - global positioning system , interval (graph theory) , animal behavior , tracking (education) , computer science , geography , fishery , biology , zoology , mathematics , telecommunications , psychology , combinatorics , pedagogy
Abstract Recent advances in digital data collection have spurred accumulation of immense quantities of data that have potential to lead to remarkable ecological insight, but that also present analytic challenges. In the case of biologging data from birds, common analytical approaches to classifying movement behaviors are largely inappropriate for these massive data sets. We apply a framework for using K ‐means clustering to classify bird behavior using points from short time interval GPS tracks. K ‐means clustering is a well‐known and computationally efficient statistical tool that has been used in animal movement studies primarily for clustering segments of consecutive points. To illustrate the utility of our approach, we apply K ‐means clustering to six focal variables derived from GPS data collected at 1–11 s intervals from free‐flying bald eagles ( Haliaeetus leucocephalus ) throughout the state of Iowa, USA. We illustrate how these data can be used to identify behaviors and life‐stage‐ and age‐related variation in behavior. After filtering for data quality, the K ‐means algorithm identified four clusters in >2 million GPS telemetry data points. These four clusters corresponded to three movement states: ascending, flapping, and gliding flight; and one non‐moving state: perching. Mapping these states illustrated how they corresponded tightly to expectations derived from natural history observations; for example, long periods of ascending flight were often followed by long gliding descents, birds alternated between flapping and gliding flight. The K ‐means clustering approach we applied is both an efficient and effective mechanism to classify and interpret short‐interval biologging data to understand movement behaviors. Furthermore, because it can apply to an abundance of very short, irregular, and high‐dimensional movement data, it provides insight into small‐scale variation in behavior that would not be possible with many other analytical approaches.