Conclusion
We have seen that ARLAS is particularly well suited for exploring position data of storks. The interactive map navigation provides valuable information on the behaviour of these migratory birds. But ARLAS can also be used to support the production of Machine Learning algorithms by facilitating the creation of training set and the visualisation of classification results. Finally, once the Machine Learning models have been trained, it is possible to apply them to large-scale data with ARLAS PROC/ML and see the results in ARLAS Exploration. All these results are available in a demonstration that is available at demo.
If we have focused here on migration, many other animal behaviour could be the subject of such studies. Obviously, ARLAS can be applied to all kind of geo-traced animals, but also to all geo-referenced data. Feel free to have a look at the other application examples on demo.arlas.io.
Thanks:
We would like to thank the director of the Max Planck Institute of Animal Behavior, Dr M. Wikelski ,and his team for providing this data.
References:
[1] Cheng Y, Fiedler W, Wikelski M, Flack A (2019) “Closer-to-home” strategy benefits juvenile survival in a long-distance migratory bird. Ecology and Evolution. doi:10.1002/ece3.5395
[2] Fiedler W, Flack A, Schäfle W, Keeves B, Quetting M, Eid B, Schmid H, Wikelski M (2019) Data from: Study “LifeTrack White Stork SW Germany” (2013-2019). Movebank Data Repository. doi:10.5441/001/1.ck04mn78