Dylan Irion, Swimming Behaviour of the Common Smoothhound Based on Accelerometer Data
Channel: African Campus
Date: 15/05/2013

Oral Presentation

Dylan Irion1,3, Peter Ryan2
1University of Cape Town, Department of Zoology
2University of Cape Town, Percy FitzPatrick Institute of African Ornithology
3Oceans Research, PO 1767 Mossel Bay 6500, South Africa

A thorough understanding of the behaviour and habitat use of sharks is critical for improving our understanding of the movement ecology and thus the effective conservation of these threatened species. Direct observation of sharks is often difficult to accomplish in the marine environment where access to free-swimming individuals can be restricted by numerous factors. The miniaturisation and reduced costs of producing sensors for bio-logging has provided several solutions to overcome this obstacle. The accelerometer is a sensor that functions by recording changes in acceleration due to the dynamic motion of a body, and the static acceleration caused by gravity.
In this study I demonstrate the potential for utilising tri-axial accelerometry as a method for characterising the movement of sharks. By attaching accelerometers to captive common smoothhound sharks (Mustelus mustelus) and comparing the accelerometer record to visual observations of their behaviour, I was able to detect tail beat frequency, tail beat amplitude, and body posture. A Wilcoxon signed rank test revealed no difference in tail beat frequency between the accelerometer data (0.76 ± 0.29 Hz; mean ± SD) and visual observation (0.77 ± 0.19 Hz; V = 61436.5, p = 0.3286). Steady swimming was readily identified (92.5 ± 6.4%; mean ± S.D.), but detection rates were much lower for resting (36.0 ± 45.0%) and fast start swimming (33.7 ± 32.4%), and especially gliding (4.7 ± 6.4%). Behavioural categorisation was achieved using an autonomous wavelet transformation and k-means clustering algorithm in the Ethographer software package and proved to be more than 100 times faster than manual annotation of the accelerometer record. Calculation of the body angle using the arcsine of acceleration in the surge axis resulted in similarly low detection rates of 25.4 ± 15.5%, 78.8 ± 11.1%, 42.9 ± 26.0% (mean ± S.D.) for inclined, neutral, and declined swimming respectively, when compared with visual observations.
Observational bias introduced by multiple observers may account for some of the discrepancy between the accelerometer and visual record for both the behavioural and postural categorisation. The clustering technique used for behavioural classification also has the potential to erroneously split behaviours that visually appear similar, but may be differentiated by varying tail beat frequencies or amplitudes. Furthermore, my behavioural definitions include transitional behaviours, such as the shift from swimming to gliding, which may not have clearly defined bounds.
Despite low detection rates and high false detection rates for both behavioural and postural categorisation, the method still provides an objective, autonomous, and quick method for handling the large datasets generated by accelerometer data-loggers, warranting further research. Some value is noted in the ability of the technique to perform a coarse discrimination between periods of activity and inactivity, but the method does not yet provide reliable results for free swimming deployments. Recent applications of similar machine learning techniques where models are first trained are providing more accurate detection rates and together, with energetic analysis techniques like overall dynamic body acceleration represent the path forward for accelerometry.