Classifying behavior from short-interval biologging data: An example with GPS tracking of birds
February 22, 2022
- 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.
Citation Information
| Publication Year | 2022 |
|---|---|
| Title | Classifying behavior from short-interval biologging data: An example with GPS tracking of birds |
| DOI | 10.1002/ece3.8395 |
| Authors | Silas Bergen, Manuela Huso, A. Duerr, Missy A Braham, Todd E. Katzner, Sara Schmuecker, Tricia A. Miller |
| Publication Type | Article |
| Publication Subtype | Journal Article |
| Series Title | Ecology and Evolution |
| Index ID | 70229676 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | Forest and Rangeland Ecosystem Science Center |
Related
Data derived from GPS tracking of free-flying bald eagles (Haliaeetus leucocephalus), Iowa, USA
Comma-separated values (.csv) file containing data (and derived data) from GPS tracking of free-flying bald eagles (Haliaeetus leucocephalus), Iowa, USA. NOTE: Data file does not load properly in Microsoft Excel due to the size of data file (2,093,022 rows x 15 columns) that far exceeds Excel's maximum of 1,048,576 rows. Use other program(s) like R or Notepad to view the file.
Manuela M Huso
Scientist Emerita
Scientist Emerita
Email
Phone
Todd E Katzner
Supervisory Research Wildlife Biologist
Supervisory Research Wildlife Biologist
Email
Related
Data derived from GPS tracking of free-flying bald eagles (Haliaeetus leucocephalus), Iowa, USA
Comma-separated values (.csv) file containing data (and derived data) from GPS tracking of free-flying bald eagles (Haliaeetus leucocephalus), Iowa, USA. NOTE: Data file does not load properly in Microsoft Excel due to the size of data file (2,093,022 rows x 15 columns) that far exceeds Excel's maximum of 1,048,576 rows. Use other program(s) like R or Notepad to view the file.
Manuela M Huso
Scientist Emerita
Scientist Emerita
Email
Phone
Todd E Katzner
Supervisory Research Wildlife Biologist
Supervisory Research Wildlife Biologist
Email