  The movement of organisms is subject to a multitude of influences of widely
varying character: from the bio-mechanics of the individual, over the
interaction with the complex environment many animals live in, to evolutionary
pressure and energy constraints. As the number of factors is large, it is very
hard to build comprehensive movement models. Even when movement patterns in
simple environments are analysed, the organisms can display very complex
behaviours. While for largely undirected motion or long observation times the
dynamics can sometimes be described by isotropic random walks, usually the
directional persistence due to a preference to move forward has to be accounted
for, e.g., by a correlated random walk. In this paper we generalise these
descriptions to a model in terms of stochastic differential equations of
Langevin type, which we use to analyse experimental search flight data of
foraging bumblebees. Using parameter estimates we discuss the differences and
similarities to correlated random walks. From simulations we generate
artificial bumblebee trajectories which we use as a validation by comparing the
generated ones to the experimental data.
