Behavioral classification of low‐frequency acceleration and temperature data from a free‐ranging small mammal. Issue 1 (27th December 2018)

• Record Type: Journal Article
• Authors: Studd, Emily K.; Landry‐Cuerrier, Manuelle; Menzies, Allyson K.; Boutin, Stan; McAdam, Andrew G.; Lane, Jeffrey E.; Humphries, Murray M.
• Is Part Of: Ecology and evolution. Volume 9:Issue 1(2019)
• Page Start: 619
• Page End: 630
• Publication Date: 2018-12-27
• Subjects: accelerometer -- behavioral classification -- decision tree -- methods -- nest -- random forest -- red squirrel; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.4786
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: The miniaturization and affordability of new technology is driving a biologging revolution in wildlife ecology with use of animal‐borne data logging devices. Among many new biologging technologies, accelerometers are emerging as key tools for continuously recording animal behavior. Yet a critical, but under‐acknowledged consideration in biologging is the trade‐off between sampling rate and sampling duration, created by battery‐ (or memory‐) related sampling constraints. This is especially acute among small animals, causing most researchers to sample at high rates for very limited durations. Here, we show that high accuracy in behavioral classification is achievable when pairing low‐frequency acceleration recordings with temperature. We conducted 84 hr of direct behavioral observations on 67 free‐ranging red squirrels (200–300 g) that were fitted with accelerometers (2 g) recording tri‐axial acceleration and temperature at 1 Hz. We then used a random forest algorithm and a manually created decision tree, with variable sampling window lengths, to associate observed behavior with logger recorded acceleration and temperature. Finally, we assessed the accuracy of these different classifications using an additional 60 hr of behavioral observations, not used in the initial classification. The accuracy of the manually created decision tree classification using observational data varied from 70.6% to 91.6% depending on the complexity of the tree, with increasing accuracy as complexity decreased. Short duration behavior like running had lower accuracy than long‐duration behavior like feeding. The random forest algorithm offered similarly high overall accuracy, but the manual decision tree afforded the flexibility to create a hierarchical tree, and to adjust sampling window length for behavioral states with varying durations. Low frequency biologging of acceleration and temperature allows accurate behavioral classification of small animals over multi‐month sampling durations. Nevertheless, low sampling rates impose several important limitations, especially related to assessing the classification accuracy of short duration behavior. Abstract : An under‐acknowledged consideration in biologging is the trade‐off between sampling rate and sampling duration, created by battery‐ (or memory‐) related sampling constraints. Since this is especially acute among small animals, we show that use of low frequency sampling rates on accelerometers can yield high accuracy and provide advice on how to apply this technique to other animals. Using this technique allows for long‐duration deployments that continuous record behaviour revealing seasonal patterns in activity.