Year-round behavioural time budgets of common woodpigeons inferred from acceleration data using machine learning

Masello J, Rast W, Schumm YR, Metzger B, Quillfeldt P (2023)
Behavioral Ecology and Sociobiology 77(4): 40.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
Autor*in
Masello, JuanUniBi ; Rast, Wanja; Schumm, Yvonne R.; Metzger, Benjamin; Quillfeldt, Petra
Abstract / Bemerkung
Accelerometers capture rapid changes in animal motion, and the analysis of large quantities of such data using machine learning algorithms enables the inference of broad animal behaviour categories such as foraging, flying, and resting over long periods of time. We deployed GPS-GSM/GPRS trackers with tri-axial acceleration sensors on common woodpigeons (Columba palumbus) from Hesse, Germany (forest and urban birds) and from Lisbon, Portugal (urban park). We used three machine learning algorithms, Random Forest, Support Vector Machine, and Extreme Gradient Boosting, to classify the main behaviours of the birds, namely foraging, flying, and resting and calculated time budgets over the breeding and winter season. Woodpigeon time budgets varied between seasons, with more foraging time during the breeding season than in winter. Also, woodpigeons from different sites showed differences in the time invested in foraging. The proportion of time woodpigeons spent foraging was lowest in the forest habitat from Hesse, higher in the urban habitat of Hesse, and highest in the urban park in Lisbon. The time budgets we recorded contrast to previous findings in woodpigeons and reaffirm the importance of considering different populations to fully understand the behaviour and adaptation of a particular species to a particular environment. Furthermore, the differences in the time budgets of Woodpigeons from this study and previous ones might be related to environmental change and merit further attention and the future investigation of energy budgets. **Significance statement**
In this study we took advantage of accelerometer technology and machine learning methods to investigate year-round behavioural time budgets of wild common woodpigeons (Columba palumbus). Our analysis focuses on identifying coarse-scale behaviours (foraging, flying, resting) using various machine learning algorithms. Woodpigeon time budgets varied between seasons and among sites. Particularly interesting is the result showing that urban woodpigeons spend more time foraging than forest conspecifics. Our study opens an opportunity to further investigate and understand how a successful bird species such as the woodpigeon copes with increasing environmental change and urbanisation. The increase in the proportion of time devoted to foraging might be one of the behavioural mechanisms involved but opens questions about the costs associated to such increase in terms of other important behaviours.
Erscheinungsjahr
2023
Zeitschriftentitel
Behavioral Ecology and Sociobiology
Band
77
Ausgabe
4
Art.-Nr.
40
ISSN
0340-5443
eISSN
1432-0762
Page URI
https://pub.uni-bielefeld.de/record/2984251

Zitieren

Masello J, Rast W, Schumm YR, Metzger B, Quillfeldt P. Year-round behavioural time budgets of common woodpigeons inferred from acceleration data using machine learning. Behavioral Ecology and Sociobiology. 2023;77(4): 40.
Masello, J., Rast, W., Schumm, Y. R., Metzger, B., & Quillfeldt, P. (2023). Year-round behavioural time budgets of common woodpigeons inferred from acceleration data using machine learning. Behavioral Ecology and Sociobiology, 77(4), 40. https://doi.org/10.1007/s00265-023-03306-w
Masello, Juan, Rast, Wanja, Schumm, Yvonne R., Metzger, Benjamin, and Quillfeldt, Petra. 2023. “Year-round behavioural time budgets of common woodpigeons inferred from acceleration data using machine learning”. Behavioral Ecology and Sociobiology 77 (4): 40.
Masello, J., Rast, W., Schumm, Y. R., Metzger, B., and Quillfeldt, P. (2023). Year-round behavioural time budgets of common woodpigeons inferred from acceleration data using machine learning. Behavioral Ecology and Sociobiology 77:40.
Masello, J., et al., 2023. Year-round behavioural time budgets of common woodpigeons inferred from acceleration data using machine learning. Behavioral Ecology and Sociobiology, 77(4): 40.
J. Masello, et al., “Year-round behavioural time budgets of common woodpigeons inferred from acceleration data using machine learning”, Behavioral Ecology and Sociobiology, vol. 77, 2023, : 40.
Masello, J., Rast, W., Schumm, Y.R., Metzger, B., Quillfeldt, P.: Year-round behavioural time budgets of common woodpigeons inferred from acceleration data using machine learning. Behavioral Ecology and Sociobiology. 77, : 40 (2023).
Masello, Juan, Rast, Wanja, Schumm, Yvonne R., Metzger, Benjamin, and Quillfeldt, Petra. “Year-round behavioural time budgets of common woodpigeons inferred from acceleration data using machine learning”. Behavioral Ecology and Sociobiology 77.4 (2023): 40.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Creative Commons Namensnennung 4.0 International Public License (CC-BY 4.0):

Link(s) zu Volltext(en)
Access Level
OA Open Access

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Suchen in

Google Scholar