Less is more: On‐board lossy compression of accelerometer data increases biologging capacity

• Published in: Journal of Animal Ecology
• Main Authors: Nuijten, Rascha J. M., Gerrits, Theo, Shamoun‐Baranes, Judy, Nolet, Bart A.
• Other Authors: Börger, Luca, Nederlandse Organisatie voor Wetenschappelijk Onderzoek
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2020
• Subjects: Freed; Cygnus columbianus
• Online Access: http://dx.doi.org/10.1111/1365-2656.13164; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2F1365-2656.13164; https://onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2656.13164; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/1365-2656.13164; https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2656.13164

Abstract GPS‐tracking devices have been used in combination with a wide range of additional sensors to study animal behaviour, physiology and interaction with their environment. Tri‐axial accelerometers allow researchers to remotely infer the behaviour of individuals, at all places and times. Collection of accelerometer data is relatively cheap in terms of energy usage, but the amount of raw data collected generally requires much storage space and is particularly demanding in terms of energy needed for data transmission. Here, we propose compressing the raw accelerometer (ACC) data into summary statistics within the tracking device (before transmission) to reduce data size, as a means to overcome limitations in storage and energy capacity. We explored this type of lossy data compression in the accelerometer data of tagged Bewick's swans Cygnus columbianus bewickii collected in spring 2017. Using software settings in which bouts of 2 s of both raw ACC data and summary statistics were collected in parallel but with different bout intervals to keep total data size comparable, we created the opportunity for a direct comparison of time budgets derived by the two data collection methods. We found that the data compression in our case yielded a six times reduction in data size per bout, and concurrent, similar decreases in storage and energy use of the device. We show that with the same accuracy of the behavioural classification, the freed memory and energy of the device can be used to increase the monitoring effort, resulting in a more detailed representation of the individuals’ time budget. Rare and/or short behaviours, such as daily roost flights, were picked up significantly more when collecting summary statistics instead of raw ACC data (but note differences in sampling rate). Such level of detail can be of essential importance, for instance to make a reliable estimate of the energy budgets of individuals. In conclusion, we argue that this type of lossy data compression can be a well‐considered choice in study ...