Understanding decision making in a food-caching predator using hidden Markov models.

ABSTRACT:

Background

Tackling behavioural questions often requires identifying points in space and time where animals make decisions and linking these to environmental variables. State-space modeling is useful for analysing movement trajectories, particularly with hidden Markov models (HMM). Yet importantly, the ontogeny of underlying (unobservable) behavioural states revealed by the HMMs has rarely been verified in the field.

Methods

Using hidden Markov models of individual movement from animal location, biotelemetry, and environmental data, we explored multistate behaviour and the effect of associated intrinsic and extrinsic drivers across life stages. We also decomposed the activity budgets of different movement states at two general and caching phases. The latter - defined as the

SUBMITTER: Farhadinia MS 

PROVIDER: S-EPMC7011357 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature