Classifying behavior from short‐interval biologging data: An example with GPS tracking of birds
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-02-01
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| Series: | Ecology and Evolution |
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| Online Access: | https://doi.org/10.1002/ece3.8395 |
| _version_ | 1818113405568942080 |
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| author | Silas Bergen Manuela M. Huso Adam E. Duerr Melissa A. Braham Todd E. Katzner Sara Schmuecker Tricia A. Miller |
| author_facet | Silas Bergen Manuela M. Huso Adam E. Duerr Melissa A. Braham Todd E. Katzner Sara Schmuecker Tricia A. Miller |
| author_sort | Silas Bergen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T03:34:19Z |
| format | Article |
| id | doaj.art-ffba86aba6174dcfae7bfba3583b129a |
| institution | Directory Open Access Journal |
| issn | 2045-7758 |
| language | English |
| last_indexed | 2024-12-11T03:34:19Z |
| publishDate | 2022-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecology and Evolution |
| spelling | doaj.art-ffba86aba6174dcfae7bfba3583b129a2022-12-22T01:22:19ZengWileyEcology and Evolution2045-77582022-02-01122n/an/a10.1002/ece3.8395Classifying behavior from short‐interval biologging data: An example with GPS tracking of birdsSilas Bergen0Manuela M. Huso1Adam E. Duerr2Melissa A. Braham3Todd E. Katzner4Sara Schmuecker5Tricia A. Miller6Department of Mathematics and Statistics Winona State University Winona Minnesota USAU.S. Geological Survey Forest and Rangeland Ecosystem Science Center Corvallis Oregon USABloom Research Inc. Los Angeles California USAConservation Science Global, Inc. West Cape May New Jersey USAU.S. Geological Survey Forest and Rangeland Ecosystem Science Center Boise Idaho USAU.S. Fish and Wildlife Service Illinois‐Iowa Field Office Moline Illinois USAWest Virginia University Morgantown West Virginia USAAbstract 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.https://doi.org/10.1002/ece3.8395bald eaglebehavioral classificationbiologging dataGPS telemetryK‐means clusteringpath segmentation |
| spellingShingle | Silas Bergen Manuela M. Huso Adam E. Duerr Melissa A. Braham Todd E. Katzner Sara Schmuecker Tricia A. Miller Classifying behavior from short‐interval biologging data: An example with GPS tracking of birds Ecology and Evolution bald eagle behavioral classification biologging data GPS telemetry K‐means clustering path segmentation |
| title | Classifying behavior from short‐interval biologging data: An example with GPS tracking of birds |
| title_full | Classifying behavior from short‐interval biologging data: An example with GPS tracking of birds |
| title_fullStr | Classifying behavior from short‐interval biologging data: An example with GPS tracking of birds |
| title_full_unstemmed | Classifying behavior from short‐interval biologging data: An example with GPS tracking of birds |
| title_short | Classifying behavior from short‐interval biologging data: An example with GPS tracking of birds |
| title_sort | classifying behavior from short interval biologging data an example with gps tracking of birds |
| topic | bald eagle behavioral classification biologging data GPS telemetry K‐means clustering path segmentation |
| url | https://doi.org/10.1002/ece3.8395 |
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