Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jul;91(7):1345-1360.
doi: 10.1111/1365-2656.13695. Epub 2022 Apr 22.

pamlr: A toolbox for analysing animal behaviour using pressure, acceleration, temperature, magnetic or light data in R

Kiran L Dhanjal-Adams  1   2   3 , Astrid S T Willener  1 , Felix Liechti  1
Affiliations

pamlr: A toolbox for analysing animal behaviour using pressure, acceleration, temperature, magnetic or light data in R

Kiran L Dhanjal-Adams et al. J Anim Ecol. 2022 Jul.

Abstract
in English, French

Light-level geolocators have revolutionised the study of animal behaviour. However, lacking spatial precision, their usage has been primary targeted towards the analysis of large-scale movements. Recent technological developments have allowed the integration of magnetometers and accelerometers into geolocator tags in addition to barometers and thermometers, offering new behavioural insights. Here, we introduce an R toolbox for identifying behavioural patterns from multisensor geolocator tags, with functions specifically designed for data visualisation, calibration, classification and error estimation. More specifically, the package allows for the flexible analysis of any combination of sensor data using k-means clustering, expectation maximisation binary clustering, hidden Markov models and changepoint analyses. Furthermore, the package integrates tailored algorithms for identifying periods of prolonged high activity (most commonly used for identifying migratory flapping flight), and pressure changes (most commonly used for identifying dive or flight events). Finally, we highlight some of the limitations, implications and opportunities of using these methods.

Les géolocalisateurs lumineux ont révolutionné l'étude du comportement animal. Toutefois, en raison de leur manque de précision spatiale, leur utilisation a été principalement dirigée vers l'analyse de mouvements à grandes échelles. Les développements technologiques récents ont permis l'intégration de magnétomètres et d'accéléromètres dans les balises de géolocalisation, en plus de baromètres et de thermomètres, permettant de nouvelles analyses du comportement animalier. Nous présentons ici notre R package pour l'identification de modèles comportementaux à partir de balises géolocalisatrices multisensoriels. Le package intègre des fonctions conçues spécifiquement pour la visualisation de données, la calibration des balises, la classification du comportement et l'estimation des erreurs d'analyses. Plus précisément, le package permet l'analyse flexible de n'importe quelle combinaison de capteurs de données en utilisant le k-means clustering, le expectation maximisation binary clustering, les hidden Markov models et les analyses changepoint. En outre, le package intègre des algorithmes adaptés pour identifier les périodes de haute activité prolongée (le plus souvent utilisé pour identifier le vol migratoire d'oiseaux), et les changements de pression (le plus souvent utilisé pour identifier des periodes où l'animal est en plongée ou au vol). Enfin, nous soulignons les limites, les implications et les opportunités d'utilisation de ces méthodes.

Keywords: SOI-GDL3pam; behaviour; classification; clustering; embc; geolocator; hmm; k-means.

PubMed Disclaimer

Conflict of interest statement

The authors have no conflict of interest to declare.

Figures

FIGURE 1
FIGURE 1
Example of a typical workflow in pamlr with available functions for each step to the analyses
FIGURE 2
FIGURE 2
Different visualisations of magnetic field data for alpine swift Tachymarptis melba. To gain an initial impression of the (a) raw data, it can first be plotted as an interactive time series. However, a great deal of insight can also be gleaned from plotting the data as (b) a sensor image. These suggest that resting periods should be easy to distinguish from others using mY as confirmed by (c) histograms and (d) 3D plots. Data can also be visualised without distortions with (e) an m‐sphere
FIGURE 3
FIGURE 3
Schematic representation of the classify_flap algorithm for classifying flapping migratory behaviour. Activity (a) is first divided into inactive and active. Active data are then clustered to define a threshold (thld) between low and high activity. For each high activity event, its duration durA is calculated. If this duration is greater than a user‐defined time t (set to 1 hr by default) then the hoopoe is assumed to be performing migration
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Bäckman, J. , Andersson, A. , Alerstam, T. , Pedersen, L. , Sjöberg, S. , Thorup, K. , & Tøttrup, A. P. (2017). Activity and migratory flights of individual free‐flying songbirds throughout the annual cycle: Method and first case study. Journal of Avian Biology, 48(2), 309–319. 10.1111/jav.01068 - DOI
    1. Bäckman, J. , Andersson, A. , Pedersen, L. , Sjöberg, S. , Tøttrup, A. P. , & Alerstam, T. (2017). Actogram analysis of free‐flying migratory birds: New perspectives based on acceleration logging. Journal of Comparative Physiology A, 203(6–7), 543–564. 10.1007/s00359-017-1165-9 - DOI - PMC - PubMed
    1. Barras, A. G. , Liechti, F. , & Arlettaz, R. (2021). Seasonal and daily movement patterns of an alpine passerine suggest high flexibility in relation to environmental conditions. Journal of Avian Biology, 52(12), 1–10. 10.1111/JAV.02860 - DOI
    1. Bidder, O. R. , Walker, J. S. , Jones, M. W. , Holton, M. D. , Urge, P. , Scantlebury, D. M. , Marks, N. J. , Magowan, E. A. , Maguire, I. E. , & Wilson, R. P. (2015). Step by step: Reconstruction of terrestrial animal movement paths by dead‐reckoning. Movement Ecology, 3(1), 23. 10.1186/s40462-015-0055-4 - DOI - PMC - PubMed
    1. Bridge, E. S. , Thorup, K. , Bowlin, M. S. , Chilson, P. B. , Diehl, R. H. , Fléron, R. W. , Hartl, P. , Roland, K. , Kelly, J. F. , Robinson, W. D. , & Wikelski, M. (2011). Technology on the move: Recent and forthcoming innovations for tracking migratory birds. Bioscience, 61(9), 689–698. 10.1525/BIO.2011.61.9.7 - DOI

Publication types