Combined Use of GPS and Accelerometry Reveals Fine Scale Three-Dimensional Foraging Behaviour in the Short-Tailed Shearwater

Berlincourt, Maud; Angel, Lauren P.; Arnould, John P. Y.

doi:10.1371/journal.pone.0139351

Cited by 17 publications

(17 citation statements)

References 49 publications

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“…Measuring animal activity with accelerometers overcomes most of these challenges by continuously logging activity wherever the individual goes, and, if small enough, with very little impact on the animal's behavior (Wilmers et al, ). Accelerometers have been used to answer a wide‐range of ecological questions relating to prey capture (Sato et al, ), energetics (Elliott, Chivers, et al, ; Robson, Chauvaud, Wilson, & Halsey, ), physiology (Watanuki, Niizuma, Geir, Sato, & Naito, ), migration strategies (Bishop et al, ; Wiemerskirch, Bishop, Jeanniard‐du‐Dot, Prudor, & Sachs, ); but perhaps the most widespread application of accelerometers is in obtaining time‐activity budgets (Berlincourt, Angel, & Arnould, ; Brown, Kays, Wikelski, Wilson, & Klimley, ).…”

Section: Introductionmentioning

confidence: 99%

A comparison of techniques for classifying behavior from accelerometers for two species of seabird

Patterson

Gilchrist

Chivers³

et al. 2019

Ecology and Evolution

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The behavior of many wild animals remains a mystery, as it is difficult to quantify behavior of species that cannot be easily followed throughout their daily or seasonal movements. Accelerometers can solve some of these mysteries, as they collect activity data at a high temporal resolution (<1 s), can be relatively small (<1 g) so they minimally disrupt behavior, and are increasingly capable of recording data for long periods. Nonetheless, there is a need for increased validation of methods to classify animal behavior from accelerometers to promote widespread adoption of this technology in ecology. We assessed the accuracy of six different behavioral assignment methods for two species of seabird, thick‐billed murres (Uria lomvia) and black‐legged kittiwakes (Rissa tridactyla). We identified three behaviors using tri‐axial accelerometers: standing, swimming, and flying, after classifying diving using a pressure sensor for murres. We evaluated six classification methods relative to independent classifications from concurrent GPS tracking data. We used four variables for classification: depth, wing beat frequency, pitch, and dynamic acceleration. Average accuracy for all methods was >98% for murres, and 89% and 93% for kittiwakes during incubation and chick rearing, respectively. Variable selection showed that classification accuracy did not improve with more than two (kittiwakes) or three (murres) variables. We conclude that simple methods of behavioral classification can be as accurate for classifying basic behaviors as more complex approaches, and that identifying suitable accelerometer metrics is more important than using a particular classification method when the objective is to develop a daily activity or energy budget. Highly accurate daily activity budgets can be generated from accelerometer data using multiple methods and a small number of accelerometer metrics; therefore, identifying a suitable behavioral classification method should not be a barrier to using accelerometers in studies of seabird behavior and ecology.

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Section: Introductionmentioning

confidence: 99%

A comparison of techniques for classifying behavior from accelerometers for two species of seabird

Patterson

Gilchrist

Chivers³

et al. 2019

Ecology and Evolution

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“…The vast majority of published accelerometry studies interpret acceleration data with statistical models developed by experts but without validation to match either modeled predictions with behavioral observations (e.g., [ 7 , 8 , 16 , 17 ]) or to automated segmentation and clustering of accelerometer data [ 18 , 19 ]. The few studies that have used supervised classification to create models and validate predicted behaviors have demonstrated dramatic improvements in classification accuracy as a result of validation [ 9 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds

Sur

Suffredini²,

Wessells

et al. 2017

PLoS ONE

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Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.

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“…Accelerometers were previously used to analyse details of diving behaviour in deep-diving birds (Ropert-Coudert et al 2006;Sakamoto et al 2009;Gómez Laich et al 2011;Berlincourt et al 2015). However, they have not been used in shallow-diving seabirds.…”

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confidence: 99%

A new algorithm for the identification of dives reveals the foraging ecology of a shallow-diving seabird using accelerometer data

et al. 2017

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Island (35°51′33″N; 12°51′34″E) from 12 June to 8 September 2015 by deploying accelerometer data loggers on 60 Scopoli's shearwaters. Four birds were also equipped with TDRs. TDRs recorded only 17.7% of the dives detected by the accelerometers using the algorithm. A total of 82.3% of dives identified by algorithm were too short or shallow to be detected by TDRs. Therefore, TDRs were not accurate enough to detect most of the dives in Scopoli's shearwaters, which foraged mostly close to the sea surface. Our data showed that birds performed shorter foraging trips and dived more frequently in the early chick-rearing period compared with the late chick-rearing and incubation phases. Furthermore, parents dived more frequently during short foraging trips. Our results suggest that Scopoli's shearwaters maximised their foraging effort (e.g. number of dives, short trips) during shorter foraging trips and during early chick-rearing.

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Combined Use of GPS and Accelerometry Reveals Fine Scale Three-Dimensional Foraging Behaviour in the Short-Tailed Shearwater

Cited by 17 publications

References 49 publications

A comparison of techniques for classifying behavior from accelerometers for two species of seabird

A comparison of techniques for classifying behavior from accelerometers for two species of seabird

Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds

A new algorithm for the identification of dives reveals the foraging ecology of a shallow-diving seabird using accelerometer data

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