Identifying movement behaviour from 3-dimensional acceleration data for European brown hares (Lepus europaeus) and wild boars (Sus scrofa) J.-P. Wevers1, P.-C. Scherer1, A. Berger2, N. Blaum1, C. Fischer3, 1 3, 4 F. Jeltsch & B. Schröder 1
University of Potsdam, Plant Ecology and Nature Conservation, Maulbeerallee 3, 14469 Potsdam, Germany;
[email protected] 2 Leibniz Institute for Zoo and Wildlife Research (IZW), Alfred-Kowalke-Str. 17, 10315 Berlin, Germany 3 Leibniz-Centre for Agricultural Landscape Research (ZALF) e.V., Eberswalder Straße 84, 15374 Müncheberg, Germany 4 University of Potsdam, Institute of Earth and Environmental Science, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany
Fig. 1 : captive and free living European brown Hare with the applied e-obs collar
Fig. 2: Wild boar with the applied e-obs harness
Introduction Monitoring animal behaviour in dynamic environments such as agricultural landscapes is crucial to understand impacts of current farming practices such as time of mowing, harvest or use of pesticides, and land use changes such as homogenisation of arable crops or energy crops. Continuous recording of animal movement behaviour is challenging since many species are difficult to study because of their low population densities, and often nocturnal and secretive habits. The major goal of this study is to identify and distinguish animal movement behaviour on the basis of the acceleration data (ACC-data), that were recorded with a 3-dimensional acceleration sensor included in a GPS-based telemetry system. Methods We applied 3-dimensional acceleration sensors (neck collars of e-obs) to a captive European brown hare (Lepus europaeus, Fig. 1 (left)) and to a captive wild boar (Sus scrofa, Fig 2). The measurements consist of sequences of ACC-data ('bursts', Fig. 5), that were recorded every 30 seconds for the wild boar (burst length 7.92 sec, 264 samples per axis), and every 12 seconds for the European brown hare (burst length 9.6 sec, 180 samples per axis). To relate the ACC-data to characteristic behaviours, we observed the animals simultaneously to measuring (video and direct observation). We then calculated the standard deviations (SD) of the three ACC-axes of each category to evaluate their use as a parameter for discriminating between basic categories of behaviour. To calculate the characteristic ranges of SD, we randomly chose a part of data of each category. We then tested those ranges with the remaining data. For the wild boar, ten bursts were chosen, for the European brown hare we chose 10% of the data in portions of about one second (18 Samples). More existing data of two captive and several wild living hares have yet to be analysed.
Results (European brown hares)
Results (Wild boar)
Figure 6 shows that no clear differences could be derived from the standard deviations (here shown for the resultant of x, y and z). Three levels of activity were chosen to represent the lowest possible activity (0-10), medium (10-80) and high activity (>80, raw value units). Those were used to categorise the remaining data (Fig.7). From the activity categories, only the behaviour 'sitting' could be identified (81% of all data in activity category '1'). 60% of all 234 seconds of the behaviour 'sitting' fell into activity category '2', though.
The analysis of the SD resulted in five categories which were defined by the minimal and maximal SD of the ten sample-bursts (Fig. 3).
Fig. 3: The SD of the x-axis was used to delimit five categories: 0 Laying/Standing, 1 Foraging/ Walking, 2 Loping, 3 Galloping/Shaking and 4 Shaking.
Fig. 5: Example of one burst of acceleration Data with the behaviuour 3
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Using these intervals of the SD we could categorize the behaviour of the ACCdata for each burst automatically. In comparison with direct observations we found in 176 of 227 bursts matching behaviour categories (77.5%); shorter bursts (132 samples) did not show a higher matching (167 of 227 bursts, 73.6%) (Fig. 4).
Running Sitting Eating or Drinking Smelling Standing on Hind Legs (motionless) Straightening up against the wall
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Range of Standard Deviations (raw-value units from accelerometer)
Fig. 6: Ranges of SDs of the resultant of the three acceleration axes. Borders of three categories of activity intensity were chosen (indicated by the horizontal lines).
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GPS-time (Date: 2011-06-23)
Fig. 7: Example of the data, categorised by intensity of activity, using three ranges of SD.
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Fig. 4: Comparison of the referred behaviour categories which were distinguished by direct observation and by the limits of the SD. We also tested different sample-sizes of the bursts: the matching between direct observations and bursts containing 264 samples was 77.5%, whereas the matching between direct observations and 132 samples was 73.5%.
Discussion For the wild boar, the standard deviations of one axis of ACC-data were sufficient, to distinguish basic behaviours that differed in their activity level, e.g. in their amplitude of forward acceleration. For the European hare using the Standard Deviation of the Resulting of the three axes, did not show satisfactory results in respect to detailed identification of the hares behaviour. Nonetheless, the Standard Deviations provide useful information about the degree of activity. Further, to be able to identify behaviours in more detail, we will use more sophisticated methods to describe patterns in the ACC-data. Therefore, in addition to the standard deviations of all three spacial axes, we will use their arithmetic means and perform Fourier analysis to obtain information about the animals body posture and the frequency composition of each behaviour. We will then use discriminant analysis to reduce the number of these parameters to only those that describe each category of behaviour most effectively. We see a great potential in this new telemetry-system, in that its combination of high resolution information about location and activity level (or even type of behaviour) allows for a whole new range of ecological questions to be posed.
