Innovations in movement and behavioural ecology from camera traps: Day range as model parameter

Abstract

Abstract Camera‐trapping methods have been used to monitor movement and behavioural ecology parameters of wildlife. However, when considering movement behaviours to estimate DR is mandatory to include in the formulation the speed ratio, otherwise DR results will be biased. For instance, some wildlife populations present movement patterns characteristic of each behaviour (e.g. foraging or displacement between habitat patches), and further research is needed to integrate the behaviours in the estimation of movement parameters. In this respect, the day range (average daily distance travelled by an individual, DR) is a model parameter that relies on movement and behaviour. This study aims to provide a step forward concerning the use of camera‐trapping in movement and behavioural ecology. We describe a machine learning procedure to differentiate movement behaviours from camera‐trap data, and revisit the approach to consider different behaviours in the estimation of DR. Second, working within a simulated framework we tested the performance of three approaches to estimate DR: DROB (i.e. estimating DR without behavioural identification), DRTB (i.e. estimating DR by identifying behaviours manually and weighting each behaviour on the basis of the encounter rate obtained) and DRRB (i.e. estimating DR based on the classification of movement behaviours by a machine learning procedure and the ratio between speeds). Finally, we evaluated these approaches for 24 wild mammal species with different behavioural and ecological traits. The machine learning procedure to differentiate behaviours showed high accuracy (mean = 0.97). The DROB approach generated accurate results in scenarios with a speed‐ratio (fast relative to slow behaviours) lower than 10, and for scenarios in which the animals spend most of the activity period on the slow behaviour. However, when considering movement behaviours to estimate DR is mandatory to include in the formulation the speed ratio, otherwise the DR results will be biased. The new approach, DRRB, generated accurate results in all the scenarios. The results obtained from real populations were consistent with the simulations. In conclusion, the integration of behaviours and speed‐ratio in camera‐trap studies makes it possible to obtain unbiased DR. Speed‐ratio should be considered so that fast behaviour is not overrepresented. The procedures described in this work extend the applicability of camera‐trap‐based approaches in both movement and behavioural ecology.