Machine learning goes wild: Using data from captive individuals to infer wildlife behaviours.

ABSTRACT: 1. Remotely tracking distinct behaviours of animals using acceleration data and machine learning has been carried out successfully in several species in captive settings. In order to study the ecology of animals in natural habitats, such behaviour classification models need to be transferred to wild individuals. However, at present, the development of those models usually requires direct observation of the target animals. 2. The goal of this study was to infer the behaviour of wild, free-roaming animals from acceleration data by training behaviour classification models on captive individuals, without the necessity to observe their wild conspecifics. We further sought to develop methods to validate the credibility of the resulting behaviour extrapolations. 3. We trained two machine learning

SUBMITTER: Rast W 

PROVIDER: S-EPMC7200095 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature