# Introduction

Movement methods can serve a variety of purposes from determining mean daily distance moved by an animal to describing the scale at which an animal uses the landscape.  Understanding movements can often shed some light on how an animal uses the landscape based on differences in turning angles and clustering of paths in each defined habitat type. Trajectories can be created from relocations and are also the precursor to several home range estimation methods we will go over later in the course.

Notes from the package AdehabitatLT manual (Calenge 2012): Two types of trajectories can be stored in objects of class ltraj: trajectories of type I correspond to trajectories where the time of relocations is not recorded. It may be because it could not be noted at the time of sampling (e.g. sampling of animalsâ€™ tracks in the snow) or because it was decided that they did not want to take it into account, i.e. to study only its geometrical properties. In this case, the variable date in each burst of the object contains a vector of integer giving the order of the relocations in the trajectory (i.e. 1, 2, 3, ...). Trajectories of type II correspond to trajectories for which the time is available for each relocation. It is stored as a vector of class POSIXct in the column date of each burst of relocations. The type of trajectory should be defined when the object of class ltraj is defined, with the argument type II.

Concerning trajectories of type II, in theory, it is expected that the time lag between two relocations is constant in all the bursts and all the ids of one object of class ltraj (i.e., do not mix animals located every 10 minutes and animals located every day in the same object).  Indeed, some of the descriptive parameters of the trajectory do not have any sense when the time lag varies. For example, the distribution of relative angles (angles between successive moves) depends on a given time scale; the angle between two during 10-min moves of a whitestork does not have the same biological meaning as the angle between two 1-day moves. If the time lag varies, the underlying process varies too. For this reason, most functions of adehabitatLT have been developed for "regular" trajectories, i.e. trajectories with a constant time lag (see help(sett0)).