Visual Analytics for Cheetah Behaviour Analysis

Recent advances in tracking technology allow biologists to collect large amounts of movement data for a variety of species. Analysis of the collected data supports research on animal behaviour, influence of impact factors such as climate change and human intervention, as well as conservation programs. Analysis of the movement data is difficult, due to the nature of the research questions and the complexity of the data sets. It requires both automated analysis, e.g. for the detection of behavioural patterns, and human inspection, e.g. for interpretation, inclusion of previous knowledge, and for conclusions on future actions and decision making. We present a concept and implementation for the visual analysis of cheetah movement data in a web-based fashion that allows usage both in the field and in office environments.

[1]  Bettina Speckmann,et al.  Analysis and visualisation of movement: an interdisciplinary review , 2015, Movement Ecology.

[2]  H. Hofer,et al.  Queuing, takeovers, and becoming a fat cat: Long-term data reveal two distinct male spatial tactics at different life-history stages in Namibian cheetahs , 2018, Ecosphere.

[3]  T. Caro Cheetahs of the Serengeti Plains: Group Living in an Asocial Species , 1994 .

[4]  John Joseph Valletta,et al.  Applications of machine learning in animal behaviour studies , 2017, Animal Behaviour.

[5]  C. W. Kilpatrick,et al.  Landscape connectivity for bobcat (Lynx rufus) and lynx (Lynx canadensis) in the Northeastern United States , 2018, PloS one.

[6]  L. Hunter,et al.  To Kill, Stay or Flee: The Effects of Lions and Landscape Factors on Habitat and Kill Site Selection of Cheetahs in South Africa , 2015, PloS one.

[7]  Wolfgang Heidrich,et al.  Accelerometer-informed GPS telemetry : Reducing the trade-off between resolution and longevity , 2012 .

[8]  Brian J. Smith,et al.  Analysis of movement recursions to detect reproductive events and estimate their fate in central place foragers , 2019, Movement Ecology.

[9]  Hironobu Fujiyoshi,et al.  Can AI predict animal movements? Filling gaps in animal trajectories using inverse reinforcement learning , 2018, Ecosphere.

[10]  Tobias Isenberg,et al.  Immersive Analytics: An Introduction , 2018, Immersive Analytics.

[11]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[12]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[13]  Iain D Couzin,et al.  From local collective behavior to global migratory patterns in white storks , 2018, Science.

[14]  T. Caro,et al.  Male Cheetah Social Organization and Territoriality , 2010 .

[15]  R. Kays,et al.  Terrestrial animal tracking as an eye on life and planet , 2015, Science.

[16]  Ella Browning,et al.  Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds , 2017 .

[17]  B. Wachter,et al.  Coping with intrasexual behavioral differences: Capture–recapture abundance estimation of male cheetah , 2018, Ecology and evolution.

[18]  S. Pimm,et al.  The distribution and numbers of cheetah (Acinonyx jubatus) in southern Africa , 2017, PeerJ.

[19]  Paul Funston,et al.  The global decline of cheetah Acinonyx jubatus and what it means for conservation , 2016, Proceedings of the National Academy of Sciences.

[20]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[21]  Daniel A. Keim,et al.  Visual Analytics of Movement , 2013, Springer Berlin Heidelberg.

[22]  John Shawe-Taylor,et al.  Movement Activity Based Classification of Animal Behaviour with an Application to Data from Cheetah (Acinonyx jubatus) , 2012, PloS one.

[23]  Chloe Bracis,et al.  Revisitation analysis uncovers spatio‐temporal patterns in animal movement data , 2018 .

[24]  Ying Zhang,et al.  Fly with the flock: immersive solutions for animal movement visualization and analytics , 2019, Journal of the Royal Society Interface.