DOI: 10.5061/dryad.1s1m8r7
title: Data from: Behavioral classification of low frequency acceleration and temperature data from a free ranging small mammal

description: 1. 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. 2. We conducted 84 hours 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 hours of behavioral observations, not used in the initial
 classification. 3. 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. 4.
 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.
description: axy_data_for_ODBA_window_size8 days of raw acceleration data from 8 different squirrels from which we assessed the window size needed for removing static acceleration from dynamic to calculate ODBA.assess_odba_smoothingwindowr script for assessing the window size needed to calculate odba overrandom_forest_differing_windowsr script for testing how different window sizes influences accuracy of classification using the random forest algorithmvideoExpandAxy2ssquirrel accelerometer and behaviour data for calculating summary stats at 2 seconds for randomForest analysissquirrelAxy_2s.csvsquirrelAxy_4ssquirrel accelerometer and behaviour data for calculating summary stats at 4 seconds for randomForest analysissquirrelAxy_7ssquirrel accelerometer and behaviour data for calculating summary stats at 7 seconds for randomForest analysissquirrelAxy_10ssquirrel accelerometer and behaviour data for calculating summary stats at 10 seconds for randomForest analysissquirrelAxy_14ssquirrel accelerometer and behaviour data for calculating summary stats at 14 seconds for randomForest analysissquirrelAxy_20ssquirrel accelerometer and behaviour data for calculating summary stats at 20 seconds for randomForest analysissquirrelAxy_30ssquirrel accelerometer and behaviour data for calculating summary stats at 30 seconds for randomForest analysissquirrelAxy_decisionTreesquirrel accelerometer and behaviour data used for creating a decision tree for behavioural classificationclassificationAccuracySquirrel behaviour data and associated classification from decision tree for calculating accuracy of method.SquirrelAxyBehavscript_Jan2018R script for translating squirrel accelerometer data into behavioural statesseasonal_squirrel_daily_activityDaily activity values of each behaviour for each north american red squirrel across the different seasons of the year. These were extracted from accelerometer data.