Effect of walking speed on the gait of king penguins: An accelerometric approach

How often should dead-reckoned animal movement paths be corrected for drift?

BACKGROUND

Understanding what animals do in time and space is important for a range of ecological questions, however accurate estimates of how animals use space is challenging. Within the use of animal-attached tags, radio telemetry (including the Global Positioning System, 'GPS') is typically used to verify an animal's location periodically. Straight lines are typically drawn between these 'Verified Positions' ('VPs') so the interpolation of space-use is limited by the temporal and spatial resolution of the system's measurement. As such, parameters such as route-taken and distance travelled can be poorly represented when using VP systems alone. Dead-reckoning has been suggested as a technique to improve the accuracy and resolution of reconstructed movement paths, whilst maximising battery life of VP systems. This typically involves deriving travel vectors from motion sensor systems and periodically correcting path dimensions for drift with simultaneously deployed VP systems. How often paths should be corrected for drift, however, has remained unclear.

METHODS AND RESULTS

Here, we review the utility of dead-reckoning across four contrasting model species using different forms of locomotion (the African lion Panthera leo, the red-tailed tropicbird Phaethon rubricauda, the Magellanic penguin Spheniscus magellanicus, and the imperial cormorant Leucocarbo atriceps). Simulations were performed to examine the extent of dead-reckoning error, relative to VPs, as a function of Verified Position correction (VP correction) rate and the effect of this on estimates of distance moved. Dead-reckoning error was greatest for animals travelling within air and water. We demonstrate how sources of measurement error can arise within VP-corrected dead-reckoned tracks and propose advancements to this procedure to maximise dead-reckoning accuracy.

CONCLUSIONS

We review the utility of VP-corrected dead-reckoning according to movement type and consider a range of ecological questions that would benefit from dead-reckoning, primarily concerning animal-barrier interactions and foraging strategies.

Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data

Accelerometers are becoming ever more important sensors in animal-attached technology, providing data that allow determination of body posture and movement and thereby helping to elucidate behaviour in animals that are difficult to observe. We sought to validate the identification of sea turtle behaviours from accelerometer signals by deploying tags on the carapace of a juvenile loggerhead (Caretta caretta), an adult hawksbill (Eretmochelys imbricata) and an adult green turtle (Chelonia mydas) at Aquarium La Rochelle, France. We recorded tri-axial acceleration at 50 Hz for each species for a full day while two fixed cameras recorded their behaviours. We identified behaviours from the acceleration data using two different supervised learning algorithms, Random Forest and Classification And Regression Tree (CART), treating the data from the adult animals as separate from the juvenile data. We achieved a global accuracy of 81.30% for the adult hawksbill and green turtle CART model and 71.63% for the juvenile loggerhead, identifying 10 and 12 different behaviours, respectively. Equivalent figures were 86.96% for the adult hawksbill and green turtle Random Forest model and 79.49% for the juvenile loggerhead, for the same behaviours. The use of Random Forest combined with CART algorithms allowed us to understand the decision rules implicated in behaviour discrimination, and thus remove or group together some 'confused' or under--represented behaviours in order to get the most accurate models. This study is the first to validate accelerometer data to identify turtle behaviours and the approach can now be tested on other captive sea turtle species.

Fat King Penguins Are Less Steady on Their Feet

Returning to the shore after a feeding sojourn at sea, king penguins often undertake a relatively long terrestrial journey to the breeding colony carrying a heavy, mostly frontal, accumulation of fat along with food in the stomach for chick-provisioning. There they must survive a fasting period of up to a month in duration, during which their complete reliance on endogenous energy stores results in a dramatic loss in body mass. Our aim was to determine if the king penguin’s walking gait changes with variations in body mass. We investigated this by walking king penguins on a treadmill while instrumented with an acceleration data logger. The stride frequency, dynamic body acceleration (DBA) and posture of fat (pre-fasting; 13.2 kg) and slim (post fasting; 11 kg) king penguins were assessed while they walked at the same speed (1.4km/h) on a treadmill. Paired statistical tests indicated no evidence for a difference in dynamic body acceleration or stride frequency between the two body masses however there was substantially less variability in both leaning angle and the leaning amplitude of the body when the birds were slimmer. Furthermore, there was some evidence that the slimmer birds exhibited a decrease in waddling amplitude. We suggest the increase in variability of both leaning angle and amplitude, as well as a possibly greater variability in the waddling amplitude, is likely to result from the frontal fat accumulation when the birds are heavier, which may move the centre of mass anteriorly, resulting in a less stable upright posture. This study is the first to use accelerometry to better understand the gait of a species within a specific ecological context: the considerable body mass change exhibited by king penguins.