A basic machine learning method for identifying individual biological state and ecological context from movement data

Experimental and observational work has demonstrated that the fine time-scale movement behaviour of animals can be affected by both the internal state of the animals, such as their hunger level, and external ecological factors, such as threat of predation or the opportunity to feed. Given that internal state and ecological context have an effect on movement behaviour, from individual to collective level, this leads to the question “can the state and context of an individual be inferred from their movement behaviour”?