Applications of machine learning in animal behaviour studies
© 2017 The Authors. Published by Elsevier Ltd on behalf of The Association for the Study of Animal Behaviour. This is an open access article under the CC BY license (http://creativecommons.org/licenses/ by/4.0/).
In many areas of animal behaviour research, improvements in our ability to collect large and detailed data sets are outstripping our ability to analyse them. These diverse, complex and often high-dimensional data sets exhibit nonlinear dependencies and unknown interactions across multiple variables, and may fail to conform to the assumptions of many classical statistical methods. The field of machine learning provides methodologies that are ideally suited to the task of extracting knowledge from these data. In this review, we aim to introduce animal behaviourists unfamiliar with machine learning (ML) to the promise of these techniques for the analysis of complex behavioural data. We start by describing the rationale behind ML and review a number of animal behaviour studies where ML has been successfully deployed. The ML framework is then introduced by presenting several unsupervised and supervised learning methods. Following this overview, we illustrate key ML approaches by developing data analytical pipelines for three different case studies that exemplify the types of behavioural and ecological questions ML can address. The first uses a large number of spectral and morphological characteristics that describe the appearance of pheasant, Phasianus colchicus, eggs to assign them to putative clutches. The second takes a continuous data stream of feeder visits from PIT (passive integrated transponder)-tagged jackdaws, Corvus monedula, and extracts foraging events from it, which permits the construction of social networks. Our final example uses aerial images to train a classifier that detects the presence of wildebeest, Connochaetes taurinus, to count individuals in a population. With the advent of cheaper sensing and tracking technologies an unprecedented amount of data on animal behaviour is becoming available. We believe that ML will play a central role in translating these data into useful scientific knowledge and become a useful addition to the animal behaviourist’s analytical toolkit.
We thank Paul Gluyas and the staff at Pencoose farm for allowing us to study jackdaws on their land, Mark Whiteside for assisting in the collection of the pheasant egg measurements, and Grant Hopcraft, Felix Borner and Andy Dobson for helpful discussions about the wildebeest count. A.T. and M.K. received funding from a BBSRC David Phillips Fellowship (BB/H021817/1) and a BBSRC SWDTP studentship, respectively. C.J.T. is supported by a James S. McDonnell Foundation Studying Complex Systems Scholar Award. J.R.M. is funded by an ERC consolidator award (616474).
This is the author accepted manuscript. The final version is available from Elsevier Masson via the DOI in this record.
Vol. 124, February 2017, pp. 203–220