scholarly journals Leg-tracking and automated behavioural classification in Drosophila

2013 ◽  
Vol 4 (1) ◽  
Author(s):  
Jamey Kain ◽  
Chris Stokes ◽  
Quentin Gaudry ◽  
Xiangzhi Song ◽  
James Foley ◽  
...  
Keyword(s):  
Behavioural Classification

scholarly journals Digging into the behaviour of an active hunting predator: arctic fox prey caching events revealed by accelerometry

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jeanne Clermont ◽  
Sasha Woodward-Gagné ◽  
Dominique Berteaux
Keyword(s):  
Foraging Behaviour ◽  
Supervised Machine Learning ◽  
The Arctic ◽  
Arctic Fox ◽  
Arctic Foxes ◽  
Axial Acceleration ◽  
Behavioural Classification ◽  
Spatio Temporal ◽  
Prey Handling ◽  
Bird Colonies

Abstract Background Biologging now allows detailed recording of animal movement, thus informing behavioural ecology in ways unthinkable just a few years ago. In particular, combining GPS and accelerometry allows spatially explicit tracking of various behaviours, including predation events in large terrestrial mammalian predators. Specifically, identification of location clusters resulting from prey handling allows efficient location of killing events. For small predators with short prey handling times, however, identifying predation events through technology remains unresolved. We propose that a promising avenue emerges when specific foraging behaviours generate diagnostic acceleration patterns. One such example is the caching behaviour of the arctic fox (Vulpes lagopus), an active hunting predator strongly relying on food storage when living in proximity to bird colonies. Methods We equipped 16 Arctic foxes from Bylot Island (Nunavut, Canada) with GPS and accelerometers, yielding 23 fox-summers of movement data. Accelerometers recorded tri-axial acceleration at 50 Hz while we obtained a sample of simultaneous video recordings of fox behaviour. Multiple supervised machine learning algorithms were tested to classify accelerometry data into 4 behaviours: motionless, running, walking and digging, the latter being associated with food caching. Finally, we assessed the spatio-temporal concordance of fox digging and greater snow goose (Anser caerulescens antlanticus) nesting, to test the ecological relevance of our behavioural classification in a well-known study system dominated by top-down trophic interactions. Results The random forest model yielded the best behavioural classification, with accuracies for each behaviour over 96%. Overall, arctic foxes spent 49% of the time motionless, 34% running, 9% walking, and 8% digging. The probability of digging increased with goose nest density and this result held during both goose egg incubation and brooding periods. Conclusions Accelerometry combined with GPS allowed us to track across space and time a critical foraging behaviour from a small active hunting predator, informing on spatio-temporal distribution of predation risk in an Arctic vertebrate community. Our study opens new possibilities for assessing the foraging behaviour of terrestrial predators, a key step to disentangle the subtle mechanisms structuring many predator–prey interactions and trophic networks.

A behavioural classification of managerial jobs

Omega ◽  
1973 ◽  
Vol 1 (3) ◽  
pp. 297-303 ◽  
Author(s):  
Rosemary Stewart
Keyword(s):  
Behavioural Classification

scholarly journals Love Thy Neighbour: Automatic Animal Behavioural Classification of Acceleration Data Using the K-Nearest Neighbour Algorithm

PLoS ONE ◽  
2014 ◽  
Vol 9 (2) ◽  
pp. e88609 ◽  
Author(s):  
Owen R. Bidder ◽  
Hamish A. Campbell ◽  
Agustina Gómez-Laich ◽  
Patricia Urgé ◽  
James Walker ◽  
...  
Keyword(s):  
Nearest Neighbour ◽  
Acceleration Data ◽  
Behavioural Classification

An automated sensor-based method of simple behavioural classification of sheep in extensive systems

2008 ◽  
Vol 64 (1) ◽  
pp. 19-26 ◽  
Author(s):  
C. Umstätter ◽  
A. Waterhouse ◽  
J.P. Holland
Keyword(s):  
Behavioural Classification

scholarly journals High accuracy at low frequency: detailed behavioural classification from accelerometer data

2018 ◽  
Vol 221 (23) ◽  
pp. jeb184085 ◽  
Author(s):  
Jack Tatler ◽  
Phillip Cassey ◽  
Thomas A. A. Prowse
Keyword(s):  
Low Frequency ◽  
High Accuracy ◽  
Accelerometer Data ◽  
Behavioural Classification

Dynamic cattle behavioural classification using supervised ensemble classifiers

2015 ◽  
Vol 111 ◽  
pp. 18-28 ◽  
Author(s):  
Ritaban Dutta ◽  
Daniel Smith ◽  
Richard Rawnsley ◽  
Greg Bishop-Hurley ◽  
James Hills ◽  
...  
Keyword(s):  
Ensemble Classifiers ◽  
Behavioural Classification

scholarly journals Creating a behavioural classification module for acceleration data: using a captive surrogate for difficult to observe species

2013 ◽  
Vol 216 (24) ◽  
pp. 4501-4506 ◽  
Author(s):  
H. A. Campbell ◽  
L. Gao ◽  
O. R. Bidder ◽  
J. Hunter ◽  
C. E. Franklin
Keyword(s):  
Acceleration Data ◽  
Behavioural Classification

Assessing the effects of sampling frequency on behavioural classification of accelerometer data

2019 ◽  
Vol 512 ◽  
pp. 22-30 ◽  
Author(s):  
J.L. Hounslow ◽  
L.R. Brewster ◽  
K.O. Lear ◽  
T.L. Guttridge ◽  
R. Daly ◽  
...  
Keyword(s):  
Sampling Frequency ◽  
Accelerometer Data ◽  
Behavioural Classification

Driver behavioural classification from trajectory data

2005 ◽  
Author(s):  
M. Rigolli ◽  
Q. Williams ◽  
M.J. Gooding ◽  
M. Brady
Keyword(s):  
Trajectory Data ◽  
Behavioural Classification
Sign in / Sign up

Export Citation Format

Share Document